Disclaimer:
The authors are solely responsible for the opinions expressed herein. These opinions do not
necessarily reflect the position of the COMESA Secretariat, or its member countries or
institutions to which the authors are affliated.
0
ISSUES IN COMESA
MONETARY
HARMONISATION
PROGRAMME
1
Content
List of Contributors
Foreword
Acknowledgements
Executive Summary
1.0
ii
iii
v
vi
Analysis of the Departure of Actual Real Effective Exchange Paths from their
Equilibrium Real Effective Exchange Rate Paths
1
Christopher Kiptoo
2.0
Macroeconomic Convergence
34
Noah Mutoti and David Kihangire
3.0
Sources of Inflation
57
Noah Mutoti and David Kihangire
4.0
Monetary Transmission
90
Noah Mutoti and Michael Etingo-Ego
5.0
Sources of Economic Growth
148
Rojid Sawkut, Seetanah Boopen, Sannassee Vinesh , Fowdur Suraj and Ramessur Taruna
6.0
Monetary and Fiscal Policy Co-ordination
210
Noah Mutoti
7.0
Impact of REER on Trade, Output and Current Account
Christopher Kiptoo
i
242
List of Contributors
Kiptoo Christopher, is Senior Manager, Central Bank of Kenya
Polycarp Musinguzi is Executive Director, Bank of Uganda
Noah Mutoti is an Assistant Director of the Research Division, Bank of Zambia
David Kihangire is the Director of Research, Bank of Uganda
Michael Etingo-Ego is the Executive Director, Bank of Uganda
Rojid Sawkut, Lecturer University of Mauritius
Seetanah Boopen, Lecturer University of Mauritius
Sannassee Vinesh , Lecturer University of Mauritius
Fowdur Suraj,Lecturer University of Mauritius
Ramessur Taruna, Lecturer University of Mauritius
ii
Foreword
The Authority of Heads of State and Government in 1992 adopted a COMESA Monetary
Cooperation Programme towards the establishment of a Monetary Union in the year 2025. The date for
the achievement of the Monetary Union was latter changed to 2018.The primary objectives of the
Programme is to create a common area of monetary and financial system stability which will facilitate
integration of the financial markets in the region in particular and economic integration and economic
growth in general. The achievement of monetary and financial system stability entails the attainment of
economic convergence brought about by the removal of all macro-economic disharmonies which exist
among the member states as a result of the pursuit of divergent macro-economic policies.
To achieve Monetary Union it was considered essential that the member States should first go
through a process of monetary harmonisation with a view to achieving macro-economic convergence. In
order to assess progress being made towards this objective, a number of convergence criteria were
formulated, with a view of gauging the progress being made by the member States in the implementation
of the programme. The COMESA Monetary Integration Programme and the macroeconomic convergence
criteria are consistent with the African Monetary Cooperation Programme. (AMCP).
Under this Programme COMESA made the following significant achievements:
(i) COMESA established the Regional Payment and Settlement System (REPSS) which is aimed at
stimulating economic growth in the region, through an increase in intra-regional trade, by
enabling importers and exporters to pay and receive payment for goods and services through an
efficient and cost effective platform. Local banks access the payment system through their
respective Central Banks. Any participating bank is, therefore, able to make payments directly to,
and receive payments from, any other participating bank. The linkages through Central Banks
thus avoid the complex payment chains that occur in correspondent bank arrangements. The
system, therefore, operates through Member Countries Central Banks and their corresponding
banking systems.
(ii) PTA Bank was established in 1985 as the financial arm of COMESA to provide financing for
trade and development projects of a regional nature. Currently, the bank provides business capital
and trade finance to the private sector.
(iii) In order to retain the insurance business within the region, COMESA set up a re-insurance firm in
1992 called ZEP-RE. ZEP-RE also markets its products both within and outside the COMESA
region. Currently, the company is underwriting business in 29 countries which are both
COMESA and non-COMESA member.
(iv) African Trade Insurance Agency (ATI) was established on 20 th August 2001. ATI is the
continent’s first multilateral export credit and investment insurance agency which is designed to
cover the import risk to the satisfaction of overseas sellers and their financiers.
(v) Member countries adopted an Action Plan for Harmonisation of Bank Supervision and regulation.
There is a high degree of compliance in member countries, to the 25 Core Principles of Bank
Supervision and Regulation which is in line with the adopted Action Plan.
(vi) Member countries have adopted the Financial System Development and Stability Plan for the
Region. The Secretariat is currently developing an assessment framework for Financial System
Development and Stability in the region.
(vii)
The 13th Meeting of the COMESA Committee of Governors of Central Banks which was
held in Cairo, Egypt in October 2008, agreed to establish the COMESA Monetary Institute;
which will be responsible for the preparatory work for achieving Monetary Union in 2018. The
iii
COMESA Monetary Institute will be hosted by the Central Bank of Kenya and expected to be
operational by January 2011.
(viii)
In order to enhance the implementation of the COMESA Monetary Cooperation
Programme ;the COMESA Committee of Governors of Central Banks set up two subcommittees; namely: the Monetary and Exchange Rates Policies Sub-Committee and the
Financial System Development and Stability Sub-Committee. The Monetary and Exchange rates
Policies Sub-Committee is responsible for devising appropriate monetary policy strategies and
appropriate set of monetary policy instruments. This Sub-Committee undertook a number of
studies which are crucial for policy analysis. These studies were considered by the Annual
Meetings of the COMESA Committee of Governors of Central Banks. The studies will be
published in a form of a book in 2009. The Financial System development and stability SubCommittee is responsible to develop strategies for diversifying financial institutions and
instruments at the national and regional level and share experiences on bank supervision and
regulation.
(ix) These two sub-committees were undertaking activities that are necessary for the enhancement of
the COMESA Monetary cooperation programme.
All the above are will significantly contributing for the success of the already established COMESA
FTA, the COMESA Customs Union and for the establishment of the COMESA Common market in
2015.
This book is the product of the commendable activities of the Monetary and Exchange Rates Policies
Sub-Committee. I would like to thank EU for funding all the studies contained in the book. I hope that
the book will stimulate a rich debate and contribute for harmonisation of macroeconomic policies in
the region.
iv
Acknowledgments
The contributors sincerely thank the COMESA Secretariat who on behalf of the
Committee of Central Bank Governors of COMESA selected us to undertake the study. We
especially thank Dr Charles Chanthunya, the COMESA Director of Trade, Customs and
Monetary Affairs and Mr Ibrahim Zeidy, COMESA Senior Monetary Expert, for providing the
logistical support for our Study. We are also grateful to a number of staff in some of the
COMESA central banks for making an effort to provide us with the data required. Finally, we
would not have completed the studies without the contributions of the Research Assistants: John
Atenu (Bank of Uganda), Malimba Mutemwa (Bank of Zambia), Chungu Kapembwa (Bank of
Zambia), Ngosa Mary Kafwembe (Bank of Zambia) and Leonard Kipyegon (Central Bank of
Kenya).
The quality of the manuscript was greatly enhanced bnt ey the comments and suggestions
from COMESA member countries central banks and competent editorial services from Dr. Noah
Mutoti.
v
Executive Summary
Article 1, by Kiptoo and Musinguzi concluded that despite the adoption and implementation of a
liberalized exchange regime in many COMESA countries, success had not been achieved in restoring
equilibrium in the REER. Most countries have experienced more pronounced episodes of REER
overvaluation, implying deterioration in the country’s international competitiveness. The major factors
contributing to the movement in the REER included the degree of openness, TOT movements, levels of
government expenditure and capital flows.
To reduce the extent of exchange rate misalignments COMESA countries should make the
following efforts. First, reduce public expenditure so as to reduce reliance on domestic borrowing which
has tended to increase interest rates. Structural factors within the banking system that kept interest rates
high should therefore be addressed. In particular, governments should streamline the operations of
banking institutions so as to reduce their operational costs. Second, strategies to facilitate diversification
of the country’s export base should be devised. This is meant to avoid a situation in which when one
major export commodity experiences a sharp decline in prices, the country's TOT also significantly
change, leading to significant REER misalignments. Finally, maintain macroeconomic stability through
the pursuit of prudent monetary and fiscal policies. In this regard, governments and central banks should
design and implement monetary policies to attain and maintain low and stable single-digit inflation which
augurs well for export competitiveness.
Relying on a heterogeneous panel econometric framework, Mutoti and Kihangire find, in article 2,
no strong evidence of macroeconomic convergence in COMESA. There was no evidence of inflation
convergence, indicating that COMESA nations had been experiencing uncommon price shocks, driven by
various supply and demand factors. However, excluding nations with triple digit inflation, the evidence of
inflation convergence appeared, validating the remarkable disinflation efforts in a number of COMESA
countries. Fiscal and monetary policy divergence was also recorded. Like inflation, regional monetary
policy convergence did appear in the absence of nations with triple digit money growth. Current account
divergence was also found. Nonetheless, significant real convergence, which is output convergence, to
lower growth rates was established.
They recommended that for COMESA inflation to converge to lower rates, rampant inflation in
DRC, Angola and Zimbabwe needed to be combated. Since inflationary pressures in these countries
seemed to be induced by high money growth, controlling money supply was thus an effective demand
management tool. Output converging to lower growth rates contradicts COMESA’s aspirations. It was
noted that a number of economies are agro-based and agriculture performance are unpredictable, partly
due to unforeseen circumstances, such as drought. Realizing the COMESA growth target of 7 %, in this
context, thus called for the adoption of drought resistant technologies alongside increased investment in
irrigation. Such a policy direction would not only boost output but also help in the fight against inflation,
through increased supply. Accelerated growth could also be propagated by increased domestic
investment. Further, as most of these countries export agricultural produce, there are also benefits to
foreign reserves accumulation and improving the current account position, in the end, convergence of
these respective variables. Improving external viability could also come through reduction in the stock of
external debt. To achieve regional fiscal-policy convergence and thereby fiscal harmonization, increased
revenue, is the major way forward. While revenue consolidation is advocated in performing economies,
more efforts are needed to diversify and broaden the tax base.
Sources of inflation varied from country to country, established Mutoti and Kihangire in article 3.
However, the common phenomenon was that inflation inertia, monetary growth, output, exchange rate
and world prices were important factors in explaining the inflationary process. The following were the
policy implications discerned. First, there was need to pursue policies aimed at containing second-round
effects of inflation arising from exogenous factors. Second, sustained prudent monetary policy stance
was vital to curb inflationary pressures. Thirdly, increased productivity and thus output was an important
vi
factor in slowing down inflation. An exchange rate policy of maintaining a stable exchange rate could
effectively complement monetary policy efforts in fighting inflation.
The channels of monetary policy transmission to output and inflation was the basis of article 5 by
Mutoti and Etingo-Ego. They found that in countries where monetary policy seem to have an impact,
the lag horizon for monetary policy to affect output and inflation differs, which clearly pointed to the
extent of the depth and efficiency of financial markets to mediate changes in the monetary policy stance.
The capability of monetary policy to influence economic activity and inflation is still limited, as important
channels of monetary transmission are not effective. In particular, the interest rate channel remains weak
in almost all the countries. The exchange rate seemed to be the dominant transmission channel of
transimission of monetary impulses. The development of the financial system could be key in enhancing
the monetary policy transmission process in COMESA states.
Article 4 by Sawkut, Boopen, Vines, Suraj and Taruna analyses the sources of growth using the
growth accounting and econometric frameworks. The growth accounting results highlighted the
importance of labour quality in influencing output elasticity. Together with quality of labour, other major
drivers of growth are openness level, capital stock and FDI. Together with investment in capital and
technology, including infrastructure development, there is a need for persistent accumulation in human
capital – re-training, multi-skilling and continuous capacity building.
The results from the econometrics study were also along the same line. Openness was observed to
be an important ingredient of growth. The human capital variable also significantly explained growth,
implying investment in human capital, through increased literacy rate and through technical and
professional formation, would add to the level of growth of COMESA member countries. Financial depth
was also interestingly observed to have a positive impact on growth of COMESA countries. This result
supported the argument that in order to mobilize higher levels of savings for investment, it is important to
have a well-developed capital market. The econometric study also showed the importance of political and
institutional stability and infrastructural availability as important factors for a country’s growth prospects.
Looking at Zambia, Uganda, Kenya and Malawi, Mutoti analysed the extent of monetary and
fiscal policy co-ordinations and the link between budget deficit and inflation in article 5. For Zambia, it
was noted that among the institutions arrangement to enhance monetary-fiscal policy co-ordination, there
was an absence of legal guarantee for the operational independence of the Bank of Zambia. Also the
legislation which limits the central bank direct access to central bank credit did not have a quantitative
measure. Indirectly, the budget deficit growth has the potential of causing expansion in money and
ultimately causing consumer price to rise.
The policy implications are that whereas there is some resemblance of policy coordination as
reflected in improved fiscal performance, lack of monetization of fiscal deficits and lack of evidence of
fiscal deficits being a source of inflationary pressures, there is need to guarantee the central bank’s
operational independence. Also specifying the quantitative limit of government direct access to central
bank credit in the legal framework should be recommended. Otherwise the current legislation is subject to
misinterpretation.
The explicit legal guarantee for the Bank of Uganda’s operational independence and the legal
requirement that government direct access to central bank credit should not exceed 18% of the recurrent
revenue of the Government are strong evidence of the existence of institutional arrangements for
monetary-fiscal co-operations. However, the widening of the fiscal deficits and the heavy reliance on
foreign financing of the budget deficits are areas of concern. Experience teaches that too much
dependence on foreign funding of domestic debt has the exchange rate and/or balance-of-payments risks.
Like in Zambia, an indirect link between budget deficit and inflation was established. Uganda could
enhance its fiscal-monetary co-ordination by including the Ministry of Finance in the MPTC. Further, the
issue of heavy reliance on foreign financing of the deficit needs to be re-examined.
While there is no legal guarantee of the central bank independence, the use of the central bank
policy rate to signal the market could reflect some level of instrument independence of the Central bank
of Kenya. The restriction of Government financing of the budget deficit through the central bank to no
more than 5 % of gross recurrent revenue is further evidence of some level of co-operation between the
vii
monetary and fiscal authority. Nonetheless, the domestic debt, observed to be of above 20% of GDP,
seems problematic. Similar to Zambia, the direct impact of fiscal deficits on consumer price was found to
be marginal. However, there is evidence of the budget deficit indirectly impacting inflation through
money growth. The policy lessons are that while there is some evidence of the existence of institutional
requirement for effective co-ordination reflected in the limiting of government’s direct access to central
bank credit, there is need to guarantee the operational independence of the Central Bank of Kenya.
Further a domestic debt management strategy needs to be put in place.
Like Zambia and Kenya, there is no legal infrastructure that guarantees Reserve Bank of
Malawi’s operational independence. However, there is a limit of direct central bank lending to the
Government. The law stipulates that the central bank could make short-term advances to government not
exceeding 20% of the annual budgeted revenue. This limit could have assisted in reducing the
monetization of budget deficits. The low level of development in the Malawian financial sector should be
a source of concerns to enhancing the monetary –fiscal policy co-ordination which also reflected in the
inclusiveness of the Monetary Policy Committee. Contrary to Zambia, Kenya and Uganda, there is a
strong direct link between budget deficit and consumer price movements. It was recommended that
measures to instill fiscal discipline and develop the financial sector should be put in place in order to get
the maximum benefits of monetary and fiscal co-ordination.
Kiptoo, in article 6, found mixed results on the impact of REER on exports, imports and output.
In Kenya and Malawi, the REER volatility had a negative and highly significant effect on exports. In
Egypt and Rwanda, on the other hand, the results showed that REER volatility had a positive and highly
significant effect on exports. In Burundi, and Mauritius, however, the results showed that REER volatility
did not have a significant effect on exports in the long run. While the results of two countries showed that
REER volatility had a significant impact on imports, the rest showed that it had negative but insignificant
effect on imports. The impulse response functions for most countries showed that REER depreciation had
an expansionary impact on the output in both medium and long terms. The contractionary effects were,
however observed for the short-term horizon.
The article made two policy recommendations. Firstly, efforts should be made in COMESA
countries to develop and deepen the derivatives market to provide for hedging instruments for managing
exchange rate risk. This would involve coming up with a range of trade finance products that make it
possible for risk-averse traders and investors to minimize their exposure to the effect of exchange rate
risky. Secondly, policy makers should design an exchange rate policy that ensures that stability in the
foreign exchange market is maintained. In this respect, the monetary authority should relook at its current
intervention policy in the foreign exchange market with a view to ensure that as much as possible, REER
volatilities are minimized.
viii
1.0
Analysis of the Departure of Actual Real Effective Exchange
Paths from their Equilibrium Real Effective Exchange Rate Paths
Polycarp Musinguzi
and
Christopher K. Kiptoo
1.1
Introduction
T
he importance of real effective exchange rate (REER) adjustments especially in developing
countries, including Common Market for Eastern and Southern Africa (COMESA) states, has
been articulated by many including Edwards (1994, 1995, 1996 and 1997) and Kiptoo
(2004,2007). Instability in the REER and its departure from equilibrium level, which implies
misalignments, influences not only trade and financial flows and hence the balance of payments, but also
the structure and level of production of tradable commodities. Judging the appropriate level of the REER
consistent with a more open and export-oriented economy is therefore of high policy relevance for any
country. This especially is critical for COMESA in that most of its members have undertaken financial
liberalization, and thereby witnessed a rapid pace of internationalisation of their goods and services. The
opening of the current and capital accounts have also led to high capital flows, with serious policy
concerns. The increased capital inflows could have resulted in exchange rate misalignments, and
consequently adversely affected the profitability and growth of the export sector. The need to manage the
monetary and exchange rate consequences of the aid-financed fiscal deficit could have compromised the
central bank’s ability to pursue its inflation objective and support the orderly functioning of the domestic
money and foreign exchange markets.
What has been the magnitude and policy significance of the departure of the REER from their
equilibrium real effective exchange rate (EREER) path in COMESA? Addressing this question is of vital
important in the design of the COMESA exchange rate mechanism under the COMESA monetary
cooperation programme. We provide answers to this broad question by focusing on three specific
objectives. The first one is to establish the determinants of the REER for selected COMESA Countries.
Second, is to establish the extent of REER misalignment. Finally, measures to reduce REER
misalignments as the region moves towards adoption of an exchange rate mechanism stated in the
roadmap of COMESA monetary union are proposed.
The rest of the paper is organized as follows. Section 1.2 provides a brief review of the literature
to inform the choice of the model employed. This is followed by a discussion of methodology employed
in the estimation. Section 1.4 contains the results and analysis from the empirical investigation and
Section 1.5 offers some concluding remarks.
1.2
Literature Review
C
conceptual approaches establishing equilibrium real exchange rates and misalignments could
be grouped into three, namely the purchasing power parity (PPP), partial equilibrium and
general equilibrium approaches. The PPP posits that exchange rates between currencies are
in equilibrium when their purchasing power is the same, meaning that the exchange rate
between two countries should equal the ratio of the two countries' price level of a fixed basket of goods
and services. If a country's domestic price level is rising, the exchange rate must depreciate to return to
1
PPP. Under this approach, a single equilibrium period is chosen to measure the extent of REER
misalignment. That period must be one in which most of the fundamentals are considered to have been
near their ideal levels. Critics of the PPP argue that the equilibrium level changes over time as
fundamentals change. Further, the assumption of perfect commodity arbitrage between the two countries
is invalid and the comparability of general price indices is questionable due to differences in productivity
and weighting procedures.
The partial equilibrium framework, whose key exogenous inputs are medium term capital flows
(i.e. private and public sector net savings behaviour) and the cyclically adjusted level of output, explains
that the primary role of the REER is to influence the resource balance through expenditure switching
mechanisms. In this sense, the equilibrium exchange rate is the rate that produces a current account that
matches the assumption about medium-term capital flows. This approach therefore is related to the notion
of the external sustainability-based equilibrium REER. Some shortcomings of this approach include the
assumption of the law of one price, which in most cases does not hold (Ahlers and Hinkle, 1999). The
other weakness is that it is based on macroeconomic identities and does not involve any theory of
exchange rate determination.
The general equilibrium approach focuses on the dynamic behaviour of the exchange rate,
including short-run movements and deviations. This approach defines the equilibrium REER as a function
of several fundamentals. One model in this category is the behavioural equilibrium exchange rate
(BEER), which empirically comprises four stages. First, is estimating the relationship between the REER,
the fundamentals and short-run variables. Second, calculating the actual misalignment whereby shortterm variables are set to zero and actual values of fundamentals identified substituted into the estimated
relationship. In this case, the actual misalignment is given as the difference between the fitted and the
actual value of the real exchange rate. The third step involves identifying the long-run or sustainable
values for the fundamentals obtained by decomposing the series into permanent and transitory
components (e.g. HP filter, Beveridge-Nelson decomposition). The final stage is the calculation of total
misalignment. Long-term values of fundamentals are substituted into the estimated relationship relating
the REER to the fundamentals, and short-term variables are set to zero again. Thus, total misalignment
depends on the short-term effect and on the departure of fundamentals from their long-term value.
A number of studies identify the terms of trade (TOT), capital flows, trade policy, productivity
growth, government expenditure and trade balance as well as monetary and fiscal policies as the key
determinants of the REER.
1.2.1 Terms of Trade
The direction of the REER, following TOT changes, depends on substitution and income effects.
Improvements in the TOT will lead to a real appreciation if there is a home bias in the tradable goods'
consumption basket and/or the associated positive wealth effect raises demand for, and reduces supply of
non-tradable goods’ (Edwards (1989 and 1994)). In contrast, deterioration in the TOT could lead to a real
depreciation if the substitution effect dominates the income effect for an import price increase. This could
be the case either if imports are competitive or have many domestic substitutes or if imports represent a
low proportion of the production of tradables.
1.2.2
Capital and Financial Flows
The impact of capital inflows on the real exchange rate depends on the composition of the flows.
If capital inflows are composed largely of aid flows and if government’s propensity to import out of the
resource inflow is 100 %, there would be no direct impact on the real exchange rate in the short run since
the aid flow would be self liquidating. In the long-run, however, the real exchange rate may appreciate as
the higher level of aggregate absorption sustained by the aid inflow is likely to be spread across both
tradable and non-tradable goods. It is further argued that capital and financial inflows increases the supply
of tradable goods and as long as the income elasticity of demand for non-tradables is positive, the demand
2
for them will increase and their price will rise, thus leading to an appreciation.
1.2.3
Trade Policy
Trade liberalisation, characterised by a reduction in tariffs and the removal of quantitative
restrictions, results in an increase in the import demand thereby generating a trade deficit. To restore
external balance, the trade liberalisation will need to be accompanied by an increase in the relative price
of tradables or real depreciation (Edwards (1989)) This result may not hold, however, in the presence of
intermediate goods and negative effective protection, for the long run if exportable goods are less capital
intensive than non-tradables or for the short run if substitution effects dominate income effects.
1.2.4
Productivity Growth
Like in the case of TOT, the direction of the impact of productivity growth on the real exchange
rate is unclear. Many theoretical models state that an increase in productivity arising from technological
progress makes the tradable sector more competitive as it leads to reduction in costs. This tends to lead to
exchange rate depreciation. The same models also show that an appreciation will however occur if the
advancement in technology increases income, which in turn leads to increases in the demand for nontradables and thus reduces the relative price of tradables to non-tradables. This is confirmed by the
traditional Balassa-Samuelson model which states that a country's currency will appreciate in real terms if
the productivity of that country's workers in producing manufactured goods relative to their productivity
in producing services grows faster than abroad. Conversely, if the relative productivity growth of
manufacturing goods is lower than abroad, the currency depreciates.
1.2.5
Government Expenditure
Because an increase in government expenditure often leads to a rise in domestic borrowing and
interest rates, an overvalued real exchange rate is the outcome. The overvaluation would even be more
pronounced if the major portion is spent on non-tradable goods and services. This is because such an
increase in government expenditure raises the demand for non-tradables. In the short run, increased
demand bids up their prices, leading to the strengthening of the real exchange rate. Real exchange rate
depreciation will, however, occur if the major portion of government expenditure is spent on the
consumption of goods from the tradable sector rather than on consumption of goods from the nontradables (Chowdhury (1999)).
1.2.6
Trade Balance
Lane and Milesi-Ferreti (2002b) established an inverse relationship between the trade balance and
the real exchange rate. They argued that in spite of the short run feedback between the real exchange rate
and the trade balance, the real exchange rate adjusts in the long run as an equilibrium response to the level
of the trade balance that is associated with the desired long run net foreign asset position.
1.2.7
Monetary and Fiscal Policies
In addition to the fundamental factors, the proxies for the fiscal and monetary policy have been
included in the estimation of the equilibrium real exchange rate. Accordingly, inconsistently expansive
macroeconomic policies are likely to generate a situation of real exchange rate misalignment as explained
in the major works of Edwards (1989, 1994), Musinguzi et al. (2000); and Mongardini (1998).
3
1.3
Methodology
F
ollowing Edwards (1994), the model framework, relied upon, takes the form:
reert  f (tot, prod , cf , g , trader , mon, r , r * ) t
(1.1)
where reer is the real effective exchange rate, tot the terms of trade , p r od denotes
productivity, cf is capital flows , g is government expenditure, trader is the trade balance and mon is
monetary shock, defined the difference between actual base money and programmed base money, while
r and r * is domestic and foreign interest rate, respectively.
The statistical technique employed in the estimation was the cointegation approach. In cases of
fractionally integrated processes the autoregressive distributive lag (ARDL) procedure was employed
instead. The benefit of the ARDL procedure is that it can be applied irrespective of whether the variables
follow an I (0), I (1) process or are fractionally integrated as by Mongardini (1998) in the case of Egypt.
This clearly makes the estimation procedure independent of the assumed order of integration and
therefore provides statistically more reliable estimates. The best model selected based on either the
Akaike Information Criterion (AIC) or the Schwarz Bayesian criteria (SBC).
The econometric procedure not only estimated the statistical long-run relationship between the
REER and its fundamentals but also calculated the actual misalignment whereby short-term variables
were set to zero. We identified the long-run or sustainable values for the fundamentals by decomposing
the series into permanent and transitory components using the Hodrick-Prescott (HP) filter. These longterm values of fundamentals were then substituted into the estimated relationship relating the REER to the
fundamentals, and short-term variables were set to zero again. REER misalignment was then derived as
the difference between the fitted and the actual value of the REER.
1.4
Empirical Approach
1.4.1 Data
Monthly data, for the period 1993-2006 was used. The main sources of data were the IFS and the
DTS. Other included IMF ‘Recent Economic Developments’ reports, ‘International Monetary Fund
Article IV Consultations’ reports, ‘Letters of Intent’, ‘Memorandum of Economic and Financial Policies’
reports, and ‘Regional Economic Outlook’ report on Africa. Further sources of the data were central
banks, Ministries of Finance and Planning and Economic Development.
The data are defined as follows:
(i) The Real Effective Exchange Rate (REER), defined as the rate at which goods and services produced
at home can be exchanged for those produced in another country or group of countries abroad. The
RER is obtained by adjusting the nominal exchange rate (NER) with inflation differential between the
domestic economy and foreign trading partner economies.
4
(ii) The Terms of Trade (TOT), derived as the ratio of an index of export prices to an index of import
prices. In this respect, monthly data required for this variable was that of export prices and import
prices.
(iii) Government Expenditure (g), comprising both recurrent and development expenditures of the central
government financial operations measured in millions of local currency and expressed as a proportion
of real GDP at market prices.
(iv) Net capital and financial inflows (cf), defined as the capital and financial inflows less the capital and
financial outflows for a country as recorded in balance of payments. The capital component of this
data comprises of the net capital official and private transfers as well as the net acquisition or disposal
of non-produced, non-financial assets such land and property rights. The financial account component
on the other hand comprises of the net foreign direct investment (i.e. equity capital, reinvested
earnings and other capital), the net portfolio investment (i.e. equity securities and debt securities) and
the net other investment (i.e. trade credits and loans both to government and private sector).
(v) Trade Policy proxied by Degree of Openness, is a measure of protection of the domestic economy
through use of instruments such as tariffs and quantitative restrictions. Due to scanty information
related to the tariff and non-tariff barriers, which would have been the most appropriate variable of
Kenya's trade policy regime, this study employed the degree of openness of the economy as an
alternative. The degree of economic openness (OPEN) was measured as the ratio of a country’s total
trade (i.e. total imports plus total exports) to real GDP and accordingly used as a proxy of each country’s
trade regime over the study period.
(vi) Productivity Growth/ Technological progress (Prod) - reflects growth in output not attributable to
growth in inputs (such as labour, capital and natural resources). Increases in productivity can be driven by
technological advances through innovation and increases in skills or improvements in efficiency through
making better use of existing technology. Over the long term, productivity improvements are considered
to be the main contributor to rising living standards.
(vii)
Trade balance (Trader), defined as the difference between exports and imports.
1.4.2 Results
Kenya
The trace test (Table 2A, Appendix 2) suggests one cointegrating vector identified as equation
(1.2). While model has the normality problem, there is absence of serial correlation and
heteroscedasticity. Generally, the parameters of the estimated model strongly corroborate to theory as
they have the expected signs. At the 10% level, all the variables have significant influence on the REER
except the TOT (note t-ratios in parentheses).
5
reert  11.41  0.47 opent  0.14 tott  0.79 g t  0.01( r  r * ) t
( 2.01)
( 4.41)
(1.14)
( 3.36)
 0.23 prod t  0.004 trend
( 1.68)
( 2.20)
VEC Normality  (14)  198.11(0.00)
Heteroscedasticity  2829.27(0.34)
2
Serial Correlation LM  60.59(0.12)
Figure 1.1 shows the profile of both the equilibrium REER and the actual REER over the study
period. Average deviations of the fitted values of REER from the actual ones were expected to be zero by
construction. Hence, deviations of actual indices from the fitted values merely showed the short run
misalignment.
In the case of Kenya, the REER was categorized as undervalued (overvalued) when its deviation
from equilibrium was positive (negative). It is evident from the Figure 1.2 that Kenya experienced
relatively more episodes of overvaluation of the REER.
Figure 1.1: Actual and equilibrium RER for Kenya
140
Equilibrium RER
Actual RER
130
120
110
Index 100
90
80
70
60
50
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jul-99
Jul-00
Jul-01
Jul-02
Jul-03
Jul-96
Jul-97
Jul-98
Jul-04
Jul-05
Jul-06
Jul-94
Jul-95
Period
REER**
REER
6
1.2 
Figure 1.2: Profile of Kenya's REER Misalignment
50.00
40.00
Percent
30.00
20.00
10.00
0.00
-10.00
-20.00
-30.00
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Period
RERM
We deduce that Kenya experienced deterioration in its international competitiveness during the following
periods when the RER was generally overvalued.
(i)
September 1994 – June 1995
(ii)
December 1995- January 1996
(iii)
April 1996 – July 1997
(iv)
December 1997 – April 1999
(v)
August 2000 – August 2000
(vi)
March 2005 – May 2006
(vii)
January 1997-July 1997
(viii)
February 1998 – May 1999
(ix)
February 2003 – September 2004
(x)
February 2006 – December 2006
On the other hand, the REER generally remained undervalued, implying improvement in the
country’s international competitiveness, during the following periods.
(i)
January 1993 – August 1994
7
(ii)
July 1995- November 1995
(iii)
February – March 1996
(iv)
August – November 1997
(v)
May 1999 – July 2002
(vi)
June 2006 – October 2006
Uganda
Equation (1.3) depicts the ADL estimated model of REER determinants in Uganda. With the
absence of serial correlation and heteroscedasticity problems, the estimated model could be considered to
fit the data well.
reert  0.10 0.94 reert 1  0.02 tott  0.01tradert  0.003 cft
(1.69)
(43.25)
(2.45)
( 0.55)
(1.84)
 0.003 cft 1  0.02 cft  2  0.001 prodt  0.02 gt
(0.49)
( 3.22)
( 0.20)
(1.3)
(0.50)
Adj R 2  0.95; Se  0.01
Serial Correlation  2 (12)  17.61(0.13)
Heteroscedasticity  2 (1)  0.39(0.53)
It could be noted that the REER movements are significantly influenced by its one- month and
two-month lags; TOT, Trade balance and net capital flows. Contrary to prior beliefs, the TOT has a
positive impact. Figure 1.3 shows the extent of misalignment.
Figure 1.3: Actual and equilibrium REER Misalignment January 93-June 07
2.5
LREER
2.0
1.5
Fitted
1.0
1993M3
1994M6
1995M9
1996M12
1998M3
1999M6
2000M9
2001M12
2003M3
Months
8
2004M6
2005M9
2006M12
2007M6
The statistically insignificant misalignment of 0.25% was tested from residual’s computed t=
(0.0025-0)/0.0121 =0.2066 < 1.96, t-value. It seemed that the residuals (REER misalignments) were
generally well within 2 standard errors or a t-value of 1.96 at the significance level of 5%. However, there
had been both episodes of slight real depreciations and real appreciations well within 0.25% in general as
shown in Figure 1.4. It is worth noting that from October 2006 up to the first week of July 2007 when the
nominal Uganda shilling/US$ experienced sharp appreciation pressures, with Uganda’s REER slightly
overvalued, by only 0.25%.
Figure 1.4: Profile of Uganda’s RER Misalignment (in Decimals), January 1993 – June 2007
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
1993M3 1994M6 1995M9 1996M12 1998M3 1999M6 2000M9 2001M12 2003M3 2004M6 2005M9 2006M12
2007M6
Months
Equation (1.4) is the estimated long –run relationship. It suggests that in the long-run, it is only
the net foreign capital flows, terms of trade and trade balance that exert a significant impact on the REER
at the 10% significance level.
reert  1.67 0.42 tott  0.24 tradert  0.30 cft  0.01 prodt  0.03 gt
(3.60)
(1.57)
(1.53)
( 2.50)
( 0.20)
(0.52)
1.4 
Excluding the insignificant variables from (1.2) yielded equation (1.5), which provides the
following intuitions. A one- month lagged REER, TOT and trade balance exert positive and highly
significant effect on the REER. Two-month lagged net capital flows have a highly significant and
negative impact on the REER.
9
reert  0.09 0.94 reert 1  0.02 tott  0.01tradert  0.003 cf t
(1.71)
(46.68)
(2.59)
( 0.62)
(1.84)
1.5 
 0.003 cf t 1  0.02 cf t  2
( 3.26)
(0.48)
Adj R 2  0.96; Se  0.01
Serial Correlation  2 (12)  17.64(0.13)
Heteroscedasticity  2 (1)  0.49(0.48)
The impact of the departure of the actual base money from the desired base money is depicted in
estimated equation (1.6).
reert  0.06 0.95 reert 1  0.02 tott  0.01tradert  0.01 cf t
(1.11)
(44.48)
(2.04)
( 1.24)
(1.75)
 0.03 mont  0.11 mont 1  0.001 prod t  0.001 g t
( 0.60)
(2.53)
(0.22)
(1.6)
(0.41)
Adj R 2  0.94; Se  0.01
Serial Correlation  2 (12)  14.52(0.27)
Heteroscedasticity  2 (1)  0.65(0.42)
We note (from equation 1.6) that a one-month lagged liquidity overhang exerts a significant and
positive depreciation impact on the REER, while a contemporaneous liquidity overhang exerts an
insignificant and negative or appreciation effect. Net foreign capital flows have economic significance but
insignificant effects when the liquidity overhang variable, as a transitory factor, is included in the shortrun determination of the REER path. This implies that foreign investors ‘eye’ Uganda’s excess liquidity
in the banking system as part of their portfolio behavioural decision for participating in Uganda’s foreign
exchange market and domestic government securities (Treasury Bills and Treasury Bonds market). Figure
1.5 shows the extent of REER misalignment when the liquidity factors are considered.
Figure 1.5: REER with both Fundamentals and Liquidity
2.5
LREER
2.0
1.5
Fitted
1.0
1993M3
1994M6
1995M9
1996M12
1998M3
1999M6
2000M9
2001M12
2003M3
2004M6
Months
10
2005M9
2006M12
2007M6
One of the policy lessons is that the BOU’s monetary policy stance for managing liquidity, using
the RMP has a direct impact on Uganda’s REER path which is insignificant in appreciating the REER
contemporaneously especially as the foreign capital flows are partly attracted by Uganda’s relatively
higher Treasury Bills and bond interest rates compared to foreign interest rates.
Malawi
Equation (1.7) could be deemed a robust long-run relationship of the REER in Malawi as all the
estimated coefficients of the expected signs. Also the estimated coefficients are statistically significant at
the 10% level except government expenditure (see also Appendix for unit roots and cointegration tests)
1.7 
reert  52.99  1.24 opent  0.96 cft  0.43 gt  7.95 prodt
(2.51)
( 2.85)
( 1.16)
(5.42)
Figure 1.6 shows the profile of both the equilibrium REER and the actual REER
Figure 1.6: Actual and Equilibrium RER for Malawi
160
Equilibrium RER
140
120
100
Actual RER
80
60
40
20
0
Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06
REER*
11
REER
Figure 1.7: REER Misalignment for Malawi
30.00
20.00
10.00
0.00
-10.00
-20.00
-30.00
Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06
In this case of Malawi where a decline in the REER implied depreciation, the REER was
categorized as undervalued (overvalued) when it its deviation from equilibrium was negative (positive).
Figure 1.7 below shows that Malawi experienced deterioration in its international competitiveness during
the following periods when the REER was generally overvalued:
(i) January – September 1993
(ii) October 1996- August 2000
(iii) January 2004 – June 2006
On the other hand, the REER generally remained undervalued, implying improvement in the country’s
international competitiveness, during the following periods:
(xi) October 1993 - September 1996
(xii) September 2000 – December 2003
(xiii) July 2006 – December 2006.
Swaziland
The same procedure used in the case of Malawi was employed to derive the equilibrium REER
for Swaziland. The long-run relationship between the REER and its fundamentals are presented as
equation (1.8). All the variables have expected signs and exert significant influence on the long-run
REER.
12
reert  49.07  2.98 cft  6.40 gt  7.53 prodt  0.09 trend
( 5.54)
( 5.47)
(4.86)
(6.13)
(1.8)
Figure 1.8 shows the actual REER versus the equilibrium REER. In the Swaziland case, the
REER was categorized as undervalued when it its deviation from equilibrium was positive and vice versa.
It is evident from Figure 1.9 that Swaziland experienced more episodes of REER overvaluation.
Figure 1.8: Actual and Equilibrium REER
250
Actual RER
200
Equilibrium RER
150
index
100
50
0
Jan-05
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Period
REER* REER
13
Figure 1.9: REER Misalignment for Swaziland
60
50
40
30
20
10
0
-10
-20
-30
-40 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05
We note that Swaziland experienced deterioration in its international competitiveness during the
following periods when the REER was generally overvalued:

April 1994 – December 1998

March 2003- October 2006.
On the other hand, the RER generally remained undervalued, implying improvement in the country’s
international competitiveness, during the following periods:

January – March 1993

January 1999- February 2003

November - December 2006.
Zambia
The max-eigenvalue test rejected the null hypotheses of r=0 at the 5% level of significance (see
also Appendix), in favour of one cointegration relation identified as equation (1.9). This equation argues
that overtime the significant influences of REER in Zambia are openness, TOT and government
14
expenditure. Like in Malawi, high government spending causes an appreciation, suggesting most
expenditure is for tradable goods.
reert  7.69 4.02 opent 0.54 tott  0.76 cft  0.72 gt  0.75 prodt  0.01 trend
( 5.49)
( 3.15)
( 0.76)
( 5.10)
( 0.75)
( 3.15)
The actual REER and Equilibrium REER are shown in Figure 1.11, and in turn used to assess the
extent of misalignment.
Figure 1.11: Actual and Equilibrium REER in Zambia
Equilibrium REER
Actual REER
250.00
200.00
150.00
100.00
50.00
0.00
Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06
REER*
REER
In the Zambia case, the REER was categorized as undervalued (overvalued) when it its deviation from
equilibrium was positive (negative). Figure 1.12 shows that Zambia experienced deterioration in its
international competitiveness during the following periods when the REER was generally overvalued:






January 1993- March 1994;
March 2003- October 2006;
October 1999 – November 1999;
February 2000 –October 2000;
October 2001-April 2002;
February 2005 –December 2006.
The REER generally remained undervalued, implying improvement in the country’s international
competitiveness, during the following periods:
15
1.9 





June 1994 - September 1999;
December 1999 - January 2000;
November 2000 - January 2001;
June 2001 - July 2001; and
May 2002 - January 2005.
Figure 1.12: REER Misalignment in Zambia
80.00
60.00
40.00
20.00
0.00
-20.00
-40.00
-60.00
-80.00 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-
Jan-02 Jan-03 Jan-04 Jan-05 Jan-06
Burundi
Both the trace test and the max-eigenvalue test rejected the null hypotheses of no cointegration at
the 5% level of significance (see Appendix). The cointegrating equation (1.10) suggests that the main
significant determinants of REER are openness, government spending and productivity.
reert  7.69  1.86 opent  0.06 cft  1.34 gt  1.74 prodt  0.01 trend
( 2.83)
(2.00)
( 3.40)
(4.79)
( 3.15)
(1.10)
In the Burundi case, the REER was categorized as undervalued (overvalued) when it its deviation
from equilibrium was positive (negative). Figures 1.13 and 1.14 suggest that Burundi experienced
deterioration in its international competitiveness between the period May 1997 and December 2003 when
the RER was generally overvalued.
16
Figure 1.13: Actual and equilibrium RER for Burundi
350
Equilibrium REER
300
Actual REER
250
200
150
100
50
0
Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06
REER*
REER
It is also shown that Burundi experienced REER depreciation, implying improvement in its
international competitiveness during the following periods.


January 1993 and April 1997
January 2004 and December 2006
17
Figure 1.14: REER Misalignment for Burundi
80.000
60.000
40.000
20.000
0.000
-20.000
-40.000
-60.000
-80.000
Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06
Rwanda
The trace test and the max-eigenvalue test showed that there are two cointegrating vectors (see
also Appendix). Equation (1.11) is the estimated long-run relationship.
reert  6.00  0.08 opent  0.21tott  0.11 cft  0.36 gt  0.29 prodt
(1.03)
(1.92)
( 4.93)
(2.52)
( 1.29)
(1.11)
Like in other cases, the results of the estimated long run parameters were used to calculate the
equilibrium REER and the degree of REER misalignment shown in Figure 1.15. In the Rwanda case, the
REER was categorized as "undervalued" when it its deviation from equilibrium was negative. The RER,
on the other hand, was categorized as ‘overvalued’ when it its deviation from equilibrium was positive.
Figure 1.16 shows that Rwanda experienced deterioration in its international competitiveness:



January 1996 - January 1997
May 1997 – April 2000
December 2000 –January 2003
The results also show that Rwanda experienced RER depreciation, implying improvement in its
international competitiveness during the following periods:




January 1995 - December 1995
February 1997 - April 1997
May 2000- November 2000
February – December 2007
18
Figure 1.15: Actual and Equilibrium REER for Rwanda
130.00
Actual REER
120.00
110.00
100.00
90.00
80.00
70.00
60.00
Equilibrium REER
50.00
40.00
30.00
Jan-95 Jan-96Jan-97Jan-98 Jan-99Jan-00Jan-01 Jan-02Jan-03 Jan-04Jan-05Jan-06 Jan-07
REER*
19
REER
Figure 1.16: REER Misalignment for Rwanda
40.00
30.00
20.00
10.00
0.00
-10.00
-20.00
-30.00
Jan-95 Jan-96Jan-97 Jan-98 Jan-99Jan-00 Jan-01Jan-
Jan-03 Jan-04Jan-05 Jan-06 Jan-07
1.5 Concluding Remarks
T
he objective of this study was to estimate equilibrium REER path for COMESA countries
and determine the degree of REER misalignment between the observed and equilibrium
REER as well as assess the implications of such misalignments on each county's
international competitiveness. This was done for the case of only 8 out of 20 COMESA
countries.
The Permanent Equilibrium Exchange Rate (PEER) approach was employed in this study. The
approach first involved estimating the relationship between the REER and the variables using
cointegration analysis technique. Using the parameters of the long run REER model for each country, the
study constructed a series of equilibrium REER derived to assess the extent of REER misalignment over
the study period which in many countries covered the period 1993-2006. The long-term values of
fundamentals were derived using the HP filter and then substituted into the estimated relationship relating
the REER to the fundamentals. REER misalignments were then derived as the difference between the
fitted and the actual value of the REER.
The results led to the conclusion that despite the adoption and implementation of a liberalized
exchange regime in many COMESA countries, success has not been achieved as expected in restoring
equilibrium in the REER. The results indicated that most countries experienced more pronounced
episodes of REER overvaluation, implying deterioration in the country’s international competitiveness
compared to those of RER undervaluation, implying improvement in the country’s international
competitiveness. This study shows that in spite of the huge depreciation in nominal exchange rates
witnessed in most COMESA countries over the last 10 years, the REER has had several relatively
pronounced episodes of over-valuation. The major factors contributing to the movement in the REER
include the degree of openness, terms of trade movements, levels of government expenditure and capital
flows.
20
COMESA countries should therefore make the following efforts. First, reduce public expenditure
so as to reduce its reliance on domestic borrowing which has tended to put an upward pressure in the level
of interest rates and which in turn have caused overvaluation of REER. Address structural factors within
the banking system that have kept interest rates high should be addressed. In particular, the government
should streamline the operations of banking institutions so as to reduce their operational costs. Second,
devise strategies to facilitate diversification of the country’s export base in order to avoid a situation in
which when one major export commodity experiences sharp decline in prices, then the country's terms of
trade also significantly change, leading to significant REER misalignments. Finally, ensure an entrenched
macroeconomic stability in the country through pursuit of prudent monetary and fiscal policies. A
country's exchange policy is but one component of overall economic policy so that its effectiveness will
in many ways depend on the effectiveness of macroeconomic policy environment. The governments and
Central Banks should therefore endeavour to achieve and maintain a stable macroeconomic environment
through pursuit of prudent monetary and fiscal policies. In particular, Governments and Central Banks
should design and implement monetary policies to attain and maintain low and stable single-digit
inflation which augurs well for export competitiveness.
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26
APPENDIX A
Kenya
Table 1A: Unit Root Tests -Kenya
Variable
Variable
Name
Real
Exchange
Rates
reer
Terms
Trade
ADF
1st
Difference)
PP (Levels)
PP
(1st
Difference)
Order of
Integration
Intercept
Trend
and
Intercept
-1.8489**
-9.836602***
-2.032426***
-9.662421***
I(1)
-3.110662***
-9.643212***
-2.309876***
-9.643212***
I(1)
None
-0.586727
-9.844063
-0.697572
-9.690686
I(1)
Intercept
Trend
and
Intercept
-0.877305***
-3.431381***
-2.203623***
-26.77225***
I(1)
-2.988558***
-3.329852***
-3.329852***
-6.602225***
I(1)
None
-1.401673***
-3.171512***
-3.171512***
-0.982245***
I(1)
Intercept
Trend
and
Intercept
-3.085139***
-9.553897***
-8.166434***
-86.42225***
I(1)
-12.4665***
-9.516154***
-12.4665***
-99.61636***
I(1)
None
1.056817***
-9.4828***
0.463633***
-66.56011***
I(1)
Intercept
Trend
and
Intercept
-1.765308
-14.93998
-1.702962
-14.9201
I(1)
-2.240758***
-14.8946***
-2.10119***
-14.87508***
I(1)
None
0.691297***
-14.9201***
0.735407***
-14.89743***
I(1)
Intercept
Trend
and
Intercept
-1.643258***
-7.283197***
-2.551276***
-1.850567***
I(1)
-5.023723***
-7.268109***
-4.103014***
-7.226949***
I(1)
None
-1.288194
-7.303909
-1.850567
-7.250768
I(1)
Intercept
Trend
and
Intercept
-1.544993***
-12.7632***
-1.565224***
-12.76488***
I(1)
-2.458846***
-12.72563***
-2.593443***
-12.72778***
I(1)
None
0.494801***
-12.74037***
0.467167***
-12.74427***
I(1)
of
tot
Terms
Trade
ADF
(Levels)
Type
of
tot
Degree of
Openness
Capital
flows
(proxied by
Interest rate
differential)
open
cflowa
Productivity
growth
proxied by
total
investment
to
GDP
ratio
prod
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
27
Table 2A: Kenya Trace Test for Cointegaration
Trace
r
0
1
2
3
4
95.28201
47.48000
20.52070
5.925117
0.003183
*
C0.95
69.81889
47.85613
29.79707
15.49471
3.841466
Table 3A: Unit Root Tests Malawi
Variable
Real
Exchange
Rates
Degree
of
openness
Government
expenditure
Net capital
& financial
inflows
Productivity
growth
proxied by
Industrial
Production
Index
Variable
Name
ADF
(Levels)
ADF
1st
Difference)
PP (Levels)
PP
(1st
Difference)
reer
-1.757925***
-12.45004***
-1.886435***
-12.50723***
OPEN
-1.908904***
-8.193436***
-30.70705
-1.411401
g
-1.199119
-7.676671***
-2.604044***
-58.35443***
I(1)
cflows
0.759251***
-5.024480***
1.025538***
-8.960782***
I(1)
prod
-1.269372***
-7.927122***
-15.93460
-4.480093
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
28
Order of
Integration
I(1)
I(1) (I(0)
I(1) (I(0)
Table 4A: Malawi Trace Test for Cointegaration
Trace
r
0
1
2
3
*
C0.95
35.88503
25.72047
18.55818
5.973322
33.87687
27.58434
21.13162
14.26460
Table 5A: Unit Root Tests Swaziland
Variable
Real Exchange
Rates
Degree
openness
Variable
Name
ADF
(Levels)
ADF
1st
Difference)
PP (Levels)
PP
(1st
Difference)
Order of
Integration
Reer
-0.447043**
-4.885403***
-0.142066***
-8.506276***
I(1)
open
-1.908904***
-8.193436***
-30.70705
-1.411401
-0.009863***
-0.977754***
-3.539357***
I(1)
--12.53578***
-2.884567**
-14.24514***
I(1)
-1.053271***
-2.036243*
of
Government
expenditure
Net capital &
financial
inflows
(proxied
by
interest
rate
differential)
Productivity
growth
(proxied
by
GDP Growth )
g
I(1) (I(0)
-3.255864**
cflows
-2.920493**
prod
-2.793700***
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
29
I(1) (I(0)
Table 6A: Unit Root Tests Zambia
Variable
Variable
Name
ADF
(Levels)
ADF
1st
Difference)
PP (Levels)
PP
(1st
Difference)
Order of
Integration
LnREER
-2.573226
-10.12284***
-2.573226
-9.989028***
I(1)
OPEN
1.215390
-1.715483
1.615251
-8.639058***
I(1)
Government
expenditure
Productivity
growth (proxied by
industrial
productivity index)
GEX
-1.583130
-3.886743***
-3.366484
-6.570148***
I(1)
IPI
-0.455727
-3.522733***
-0.861042
-9.555763***
I(1)
Terms of Trade
TOT
1.215390
-1.715483
1.615251
-8.639058***
I(1)
Net
capital
&
financial inflows
NKFI
-0.576342
-4.489907***
--13.05273
-7.840203
I(1)
Real
Rates
Exchange
Degree of openness
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
Table 6A: Zambia Trace Test for Cointegaration
Trace
r
0
1
2
3
4
5
*
C0.95
117.0216
72.28232
42.61564
22.94289
6.690882
0.107945
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
Table 7A: Unit Root Tests for Burundi
The ADF and PP tests showed that all the variables were integrated of order 1, i.e. I (1)
Variable
Variable
ADF
ADF
1st
Name
(Levels)
Difference)
PP (Levels)
Real
Exchange
Rates
Rer
-1.448051***
-13.31671***
-1.340381***
PP
(1st
Difference)
Order of
Integration
-13.44885***
I(1)
-12.86325***
I(1)
Degree of openness
OPEN
-1.019287***
-12.86325***
30
--1.030184***
Government
expenditure
Productivity
growth (proxied by
industrial
productivity index)
Net
capital
&
financial
inflows
(proxied by interest
rate differential)
g
2.575504**
--13.95369***
2.413047***
-30.43538***
I(1)
prod
-0.423894***
-13.43750***
-0.718062***
-18.34475***
I(1)
cflows
-0.722477**
-5.882807***
-13.71118**
-0.781631***
I(1)
PP
(1st
Difference)
Order of
Integration
-5.713981***
I(1)
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
Table 8A: Burundi Trace Test for Cointegaration
Trace
r
0
1
2
3
4
*
C0.95
79.81727
41.41748
20.72788
5.633951
0.004324
69.81889
47.85613
29.79707
15.49471
3.841466
Table 9A: Unit Root Tests for Rwanda
The ADF and PP tests showed that all the variables were integrated of order 1, i.e. I (1)
Variable
Variable
ADF
ADF
1st
Name
(Levels)
Difference)
PP (Levels)
Real
Exchange
Rate
Reer
-1.824017
-4.032588***
-1.730872***
Degree of openness
-2.539815
open
Government
expenditure
Productivity
growth
-9.448322***
-2.519556***
-12.98618***
I(1)
g
-3.135246**
-4.446351***
-5.174049
-10.38094***
I(O) I(1)
Prod
2.406457
-4.077989***
-0.472280***
-6.126325***
I(1)
-3.020707**
-9.025317***
-5.821851
-12.46455***
I(O) I(1)
-0.510771
-3.309486**
-3.397589*
-9.358592***
I(O) I(1)
Terms of Trade
tot
Net
capital
&
financial inflows
cflows
31
*** Significant at 1 % level of significance
** Significant at 5 % level of significance
* Significant at 10 % level of significance
Table 10A: Rwanda Trace Test for Cointegaration
Trace
r
0
1
2
3
4
5
*
C0.95
166.5653
100.0398
55.40683
28.76846
15.38872
3.951725
117.7082
88.80380
63.87610
42.91525
25.87211
12.51798
32
2.0
Macroeconomic Convergence
Noah Mutoti
and
David Kihangire
2.1
Introduction
C
ontributing to the COMESA Monetary Harmonization Program, this paper examines the
degree of diversity in macroeconomic convergence. Specifically, it analyses deviations of
selected macroeconomic variables from the agreed upon convergence targets. Do inflation,
fiscal and current accounts, output, foreign reserves and domestic investment rate, alongside
real interest and exchange rates (of COMESA countries) converge to common levels? Do convergence
processes differ across countries? Whether monetary, fiscal and exchange rate policies are sources of
convergence (divergence) is also of great empirical interest, given that most COMESA members have
completed early stages of financial reforms. What is the necessary policy adjustments required to achieve
convergence?
We address these questions by first inspecting historical trends of relevant macroeconomic
variables in Angola, Burundi, Comoros, DRC, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya,
Madagascar and Malawi, besides Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia and
Zimbabwe. Second, an econometric analysis is undertaken. Contrary to a number of studies on
macroeconomic convergence in Africa, notably by the ECA (2003), the convergence hypothesis is tested
within an heterogeneous panel unit root framework, considering that substantial different steady states are
likely due, in part, to disparities in human capital endowments (Barro and Sala-I-Matin 1992), saving
rates, fertility and available technology (Barro, 1991), along with variations in policies regarding
economic integration (Sachs and Warner (1995). A panel estimation approach advanced by Im, Pesaran,
and Shin (IPS, 2003) is thus relied upon.
As background to the empirical exercise, the next section reviews the literature. This is followed
by an assessment of progress towards convergence. The econometric methodology and empirical results
are respectively in Sections 2.4 and 2.5. Section 2.6 concludes and outlines policy recommendations.
2.2
Literature Review
B
road theoretical and empirical literature underlines various reasons for possibilities of
macroeconomic convergence in COMESA, created in 1994 as a transformation of the PTA
constituted in 1981. The first one arises out of predictions of the neoclassical growth theory
that smaller poorer countries of the convergence club should catch up with richer ones
because they can, for example, take advantage of existing technological advancements and larger markets
in front-runners without having to develop their own (Baumo and Sala-I-Martin (1994)). COMESA
members are thus expected to grow more rapidly.
33
Second, the growing consensus is that openness to trade fosters catch-up convergence as it
involves the flow of capital and goods, serving natural means of coordinating economic development of
the parties involved. Income convergence among countries, while far from being a worldwide
phenomenon, seems to be a prevailing feature among countries trading extensively with one another, BenDavid (1995) argues. Intra COMESA trade is quite heavy, rising by two thirds (COMESA, 2004)
between 1997 and 2003.
Prospects to qualify for the HIPC initiative as well as meeting the MDGs are further inducements
for convergence. The adequate institutional arrangements, along with adjustments in monetary and fiscal
policies, in an attempt to fulfil required conditions are likely to generate similar trends in macroeconomic
fundamentals. For instance, Uganda, Ethiopia, Madagascar and Zambia respectively reached HIPC
completion points in 2000, 2004, 2004 and 2005, gaining substantial debt write offs.
Commitments towards anti-inflationary policies and fiscal stability are also grounds for
(macroeconomic) convergence. Stressed in Sargent and Wallace (1991), a country running expansionary
fiscal policy at some stage monetizes the debt, making restrictive monetary policy impossible. Nations
with low inflation will not want a union with fiscally expansionary economies, adds Ben-David (1995)).
Convergence in macroeconomic stability indicators: inflation, budget and interest rates, is therefore
highly desirable, besides external reserves and current account enforcing external stability.
There are limited studies that have attempted to investigate macroeconomic convergence among
COMESA members over the recent past. Mandevu et al. (1991) examined the extent of macroeconomic
disharmonies among COMESA members, using the means, standard deviations and partial correlation
coefficients of the exchange rate during the period 1971-1990. They concluded that improvements in the
partial correlation coefficients in the 1980s augur well for monetary integration. Harvey et al. (2001)
examined COMESA macroeconomic convergence during 1980-1998 and concluded that COMESA did
not meet the criteria for an optimal currency area. Further examining alternative macroeconomic
convergence along the lines suggested in the Maastricht Treaty for the European Union, it was argued that
progress had been made towards policy convergence, as well as monetary harmonization program.
However, they painted a pessimistic picture for COMESA integration, on the premise that South Africa,
under the SADC, is a major detracting factor for those countries belonging to both SADC and COMESA.
As an alternative, they recommended that COMESA forge macroeconomic linkages with the Euro zone.
From most accounts, the rationale for choosing macroeconomic convergence criteria is to ensure
countries participating into the integration process develop sound and common macroeconomic policy. In
other words, the convergence criteria should be designed in terms of prudent values of key variables
summarizing the overall macroeconomic policy stance. Like a number of regional economic groupings,
including SADC, COMESA convergence criteria are centred on price stability, sustainability of fiscal and
current accounts, limiting of deficit financing by the central bank and maintaining sufficient foreign
reserves. This is in addition to achieving and sustaining high economic growth, supported by high
domestic investment. For COMESA, the specific targets classified into primary and secondary criteria,
achievable during the first stage (2005-2010), the focus of this undertaking is as follows.
2.2.1 Primary Criteria
(i) Overall budget deficit/GDP ratio (excluding grants) of not more than 5%;
(ii) Annual inflation rate not exceeding 5%;
(iii) Minimize central bank financing of budget deficit towards 0% target; and
(iv) External reserves of equal to or more than 4 months of imports of goods and non-factor services.
34
2.2.2 Secondary Criteria
(i) Achieve and maintain stable real exchange rates;
(ii) Achieve and maintain market based positive real interest rates;
(iii) Achieve sustainable growth rates of real GDP of not less than 7%;
(iv) Sustained pursuit of debt reduction initiative on domestic and foreign debt (i.e. reduction of debt
as a ratio of GDP to a sustainable level);
(v) Total domestic revenue to GDP ratio of not less than 20% ;
(vi) Reduction of current account deficit (excluding grants) as a ratio of GDP to a sustainable level;
and
(vii)
Achieve and maintain domestic investment rate of at least 20%.
2.3
Progress towards Convergence
T
his section explores COMESA’s macroeconomic performance vis-à-vis convergence targets
in the first stage (2005-2010). Tables 1-9 report annual data by country grouped according to
exchange rate regimes (based on the IMF classification of exchange rates of June 30, 2004),
for the period 1995-2004. Country and COMESA averages are presented over sub-periods of
5 years, 1995-1999, 2000-2004, the former (period) reflecting initial conditions and the latter assessing
progress towards convergence. Standard deviations (in parentheses), a measure of dispersion, are also
reported as preliminary guide to evaluating the extent of converging or diverging. A decreasing standard
deviation matched by a decreasing average rate of inflation, for example, would indicate nations
converging towards low inflation.
Congo DRC, Madagascar, Malawi and Uganda have adopted independently floating exchange
rate system. This implies that, in these countries, the exchange rate is market determined, with central
bank’s intervention in the foreign exchange market tailored towards moderating exchange rate volatility,
in other words, avoiding undue exchange rate fluctuations, rather than establishing a specific level of
exchange rate. As depicted in Tables 1-9, more than half of COMESA members are under a managed
floating exchange rate with no predetermined path. In this case, the central bank attempts to influence the
exchange rate without having a specific exchange rate path or target. Indicators for managing the rate are
broadly judgmental (e.g. balance of payments positions, international reserves, and parallel market
developments).
Comoros, Eritrea, Libya, Seychelles and Swaziland pursue other conventional fixed peg
arrangements. They peg their currencies at a fixed rate relative to a single currency or a basket of
currencies of major trading, with the weights reflecting the geographical distribution of trade, services or
capital flows. To make sure the exchange rate fluctuates within narrow margins around a central rate, the
monetary authority stands ready to maintain the fixed parity through direct intervention (i.e., via
sale/purchase of foreign exchange in the market) or indirect intervention (e.g., via aggressive use of
interest rate policy, imposition of exchange rate regulations, exercise of moral suasion).
Only Djibouti‘s exchange rate is guided by currency board arrangements. This regime is
characterized by an explicit legislative commitment to exchange domestic currency for a specific foreign
currency at a fixed exchange rate, combined with restrictions on the issuing authority to ensure the
fulfilment of its legal obligation. By implications, the traditional central banks functions, such as
monetary control and lender of last resort, are eliminated, thereby leaving little scope for discretionary
monetary policy.
Unsurprisingly, in all countries under other conventional fixed exchange rate arrangements except
Libya, alongside Djibouti and Zimbabwe, the exchange rate serves as nominal anchor or intermediate
target for monetary policy. The central bank stands ready to buy/sell foreign exchange at its preannounced level or range. In Egypt, Libya, Malawi, Mauritius, Sudan and Zambia, the monetary
authorities use its instruments to achieve a target growth rate for monetary aggregate, such as reserve
money or broad money, the latter considered the nominal anchor and the former the intermediate target.
35
DRC, Ethiopia, Kenya, Madagascar, Rwanda and, Uganda have adopted the Fund-supported or other
monetary program. This involves implementation of monetary and exchange rate policies within the
confines of a framework that establishes floors for international reserves and ceilings for net domestic
asset of the central bank. Indicative targets for reserve money may be appended to this system. Angola
and Burundi do not have explicitly stated nominal anchor but rather monitors various indicators in
conducting monetary policy.
Notwithstanding some countries still struggling to meeting the target, a broad disinflation process
has taken place is the general emerging picture, reflected in the reduced annual average consumer price
(CPI) inflation and standard deviation (Table 2.1). Dispersion of inflation across the region has reduced
such that Uganda, Egypt, Ethiopia, Comoros, Djibouti and Seychelles have managed to keep their
(inflation) rates within the COMESA target. Other single-digit inflation nations (Madagascar, Kenya,
Mauritius, Rwanda, Sudan and Swaziland) are barely above target. Anti-inflation efforts also could not
be missed in five countries. Inflation in DRC at 554 % in 2000 drastically dropped to 3.9 % in 2004. At
the same period, inflation in Burundi declined from close to 100 % to single digits. Though still high
(21.8 %), Zambia had sharply corrected inflationary pressures, which were degenerating early 1990s. The
following observations are, however, unattractive. While Comoros has achieved the target, it is exhibiting
an undesirable convergence pattern (i.e. a decreasing standard error with an increasing average). Over the
last 2 years, there has been a build up of inflationary pressures in Eritrea, Kenya and Rwanda. The case of
Zimbabwe is worrying, ramping inflation driving away from regional trend (see also Figure 2.1).
Several reasons explained the inflation trends among COMESA countries. From the monetarist
viewpoint (Friedman, 1953), high inflation could be associated with monetary expansion (Table 2.2). For
example, in Angola, DRC and Zimbabwe average inflation of 146 %, 191 % and 197 % between 2000
and 2004 could be attributable to the respective triple-digit monetary expansions of 145%, 141% and 217
%, respectively. Rising inflation in Comoros could be in response to higher money growth (see also Table
2). Despite these selected unfavourable monetary developments, regional money growth has been
downward trending. COMESA money growth has been cut by more than half, credited to tightening
exhibited in Malawi, Uganda, Kenya, Rwanda, Sudan, Eritrea, Seychelles and Swaziland. Money growth
has almost remained steady in a number of countries (e.g. Madagascar, Burundi, Egypt, Ethiopia, Libya,
Mauritius and Zambia).
Table 2.3 shows that about 40% of COMESA members have witnessed fiscal improvements,
evidenced by lower average and standard error of the fiscal deficit (excluding grants) as a percentage of
GDP. However, only DRC, Djibouti, Kenya, Sudan and Swaziland have met the prescribed target (20052010) of 5% or less and the regional average has slightly increased. This is plausible as many nations (e.g.
Uganda, Malawi, Zambia and Rwanda) are under the PRGF and HIPC facilities, supported by the World
Bank and IMF. By design, these programs require higher expenditure to accommodate the necessary
donor aid in support of growth and poverty reduction efforts. For example since 2000, expenditure to
GDP has been above 20 % in Uganda and over 30 % in Zambia as well as in Malawi. We further note
fiscal deteriorations in Rwanda and Burundi, with domestic fiscal deficit to GDP ratio more than tripling
between 2002 and 2004.
Few countries (Uganda, Ethiopia, Mauritius and Comoros) have managed to build external
reserves above the COMESA target of 4 months of import cover (Table 2.4). A tendency to run-down
reserves is observed in a number of countries including DRC, Malawi, Burundi, Zimbabwe and
Swaziland. As a group (absent Egypt, Rwanda, Sudan, Djibouti and Eritrea), an average increase is
unnoticeable.
Recent trends suggest economic growth varying markedly, with countries like DRC, Burundi,
Libya, Zambia and Djibouti growing faster (relative to initial period), evidence of great potential (Table
2.5). However, substantial slow downs (i.e. more than 1.4 percentage points) have been observed in most
countries (Egypt, Kenya, Malawi, Rwanda, and Uganda) whereas two countries (Zimbabwe and
Seychelles) posted negative growth rates. In terms of real GDP growth target (7 %), this is only achieved
by Angola. Though growth rates in Uganda, Mauritius, Rwanda and Madagascar occasionally are quite
36
high, their recent performance does not support a tendency to exceed 7 % on a sustainable basis (see also
appendix A). With group standard deviation almost unchanged and average on a declining trend,
convergence to lower (growth) rates is suggested. We argue, increasing production and productivity,
thereby accelerating economic growth above the stated COMESA target requires major drive efforts.
Vis-à-vis the revenue to GDP (excluding grants) target of 20% (or more), 11 countries (Angola,
Burundi, Egypt, Kenya, Libya, Malawi, Zimbabwe, Djibouti, Eritrea, Seychelles and Swaziland) achieved
it (Table 2.6). Hardly below target include Ethiopia, Mauritius and Zambia. Given the flat trend
characterizing most countries, it is unsurprising that the regional average exhibiting little fluctuation. On
the other hand, there is a tendency to greater dispersion. The policy lessons are that while revenue
consolidation is advocated in performing economies, more efforts are needed to diversify and broaden the
tax base among deficient countries (e.g. DRC) to assist in achieving and sustaining macroeconomic
stability, while supporting investments in key sectors (of the economy).
As shown in Table 2.7, domestic investment rate has been on the upswing in a number of
countries, with strong signs of exceeding COMESA target of 20 % in Madagascar, Uganda, Mauritius,
Seychelles and Zambia. Just below target include Egypt, Ethiopia, Sudan, Rwanda, Eritrea and
Swaziland. Yet, the COMESA average has declined while the standard deviation has virtually changed.
COMESA does not attach specific convergence and sustainable target on the current account.
Consistent with the World Bank and IMF programs for least developing countries (LDCs) undergoing
major investment phase supported by external savings, a current account deficit (excluding grants) in
percentage of GDP of 12% may be considered sustainable, given the development and investment needs
for these countries(e.g. Uganda’s 12%). This is particularly so, if such a deficit is perceived to reverse in
subsequent periods, following strong export performance. Mindful of the above, COMESA current
account deficit standing at about 11 % recently may be considered impressive, mainly attributable to
improvements in external positions of DRC, Madagascar, Angola, Egypt, Ethiopia, Kenya, Mauritius,
Sudan, Swaziland, Zimbabwe, Comoros and Swaziland (Table 2.8). While Uganda and Seychelles barely
exceed 12 %, current account deficits are on the rise in Malawi, Burundi, Rwanda, Zambia, Djibouti and
Eritrea.
External debt as a ratio of GDP (Table 9) has been falling in some countries (DRC, Madagascar,
Angola, Egypt, Ethiopia, Kenya, Mauritius, Sudan, Zambia and Zimbabwe) and increasing in others
(Malawi, Uganda, Burundi and Rwanda, Comoros, Djibouti, Eritrea, Seychelles and Swaziland). We
observe unsustainable debt in countries with external debt to GDP ratio exceeding 100 % (Malawi,
Burundi and Sudan), especially if one adopts the European Union (EU) target of 60 % as the upper limit.
Furthermore, debt to GDP has been rising in fixed exchange regimes, a possible scenario since such
countries have to borrow abroad to support the regime in the face of low exports. Due to data limitations,
it is difficult to assess the extent of domestic debt in COMESA. However, we are able to note domestic
debt as a percentage of GDP rising in Burundi, Kenya, Mauritius and Uganda. There are strong signs of
prudent domestic debt management in Rwanda. Zambia’s picture is unclear, domestic debt/GDP has been
up and down (Figure 2.1).
Displayed in Figure 2.2 are nominal interest and inflation rates for selected countries.1 Four basic
features are immediately evident. First, most countries have maintained positive lending rates since 1997.
Second, real deposit rates have been positive in few countries (e.g. Uganda, Egypt, Mauritius and
Madagascar), suggesting disincentives to save in more countries, a discouraging picture for regional
savings mobilization in support of investment. Third, in few countries (e.g. Malawi) real deposit rates
switched from negative to positive around 2000 as a result of lower inflation. Fourth, Zimbabwe’s real
interest rates switched from positive to negative due to escalating inflation. Fifth, the gap between lending
and deposit rates has been rising, indicating financial inefficiencies. Bearing in mind that while positive
real interest rates are beneficial to the economy, high nominal rates distort the allocation of resources (in
financial markets) by increasing the risk faced by borrowers. It is thus preferred to achieve positive real
1
Data on interest rates not available for others.
37
interest rates through reductions in inflation as this allows further cuts in nominal rates. We also argue
curbing interest rate spread, an important element to enhancing financial efficiency, will require
increasing banking sector competition, coupled with appropriate supervisory and regulation efforts.
Figure 2.3 shows the REER appreciating in Angola, Comoros, Kenya, Swaziland and Zambia.
Only Burundi and Uganda exhibit depreciated REER over the period 1997-2004.
Table 2.1: Consumer Price Inflation (%)
1995-1999
Independently Floating
2000-2004
190.5(252.1)
347.0(221.1)
Congo, DR
17.9(18.4)
8.9(6.7)
Madagascar
18.6(9.2)
40.9(27.1)
Malawi
5.4(3.0)
3.8(3.3)
Uganda
Managed floating with no predetermined path
1479.1(1838.1)
145.7((107.5)
Angola
18.6(11.0)
10.2(9.2)
Burundi
6.2(2.1)
2.8(0.4)
Egypt
3.1(7.1)
3.7(8.9)
Ethiopia
7.0(3.8)
7.6(4.0)
Kenya
6.6(1.2)
5.1(0.8)
Mauritius
15.5(19.26)
5.7(4.0)
Rwanda
56.2
(48.1)
7.2(1.4)
Sudan
30.7(8.1)
21.9(2.9)
Zambia
30.6 (16.3)
197.3(158.1)
Zimbabwe
Other Conventional Fixed Peg Arrangements
1.9(3.8)
4.5(0.9)
Comoros
8.8(3.1)
19.2(3.4)
Eritrea
1.6(3.0)
2.5(1.5)
Seychelles
8.0(2.5)
7.5(2.9)
Swaziland
Currency Board Arrangements
3.0(1.2)
1.8(0.7)
Djibouti
109.9(112.9)
34.9(14.7)
COMESA
38
Table 2.2: Money Growth(%)2
1995-1999
Independently Floating
2000-2004
242.5(214.3)
140.6(156.4)
Congo, DR
15.7(4.4)
13.8(9.9)
Madagascar
36.9(23.6)
26.0(11.7)
Malawi
21.1(5.7)
17.6(5.5)
Uganda
Managed floating with no predetermined path
1033.6(1568.8)
145.1(104.6)
Angola
16.0(20.1)
17.3(10.4)
Burundi
11.4(2.4)
13.8(3.4)
Egypt
11.4(8.1)
12.1(2.1)
Ethiopia
10.1(5.5)
4.6(8.5)
Kenya
13.7(9.7)
12.5(3.0)
Mauritius
26.3(33.1)
11.2(2.3)
Rwanda
46.1(22.2)
29.0(3.7)
Sudan
33.1(13.3)
34.0(23.7)
Zambia
43.0(37.4)
216.6(181.6)
Zimbabwe
Other Conventional Fixed Peg Arrangements
2.9(5.5)
13.7(20.1)
Comoros
35.0(11.8)
17.2(5.6)
Eritrea
7.9
8.2
Libya
18.9(6.2)
5.5(9.0)
Seychelles
13.6(6.0)
8.3(8.5)
Swaziland
Currency Board Arrangements
-1.1(5.9)
11.8(7.3)
Djibouti
81.9(76.3)
37.9(8.2)
COMESA
2
For all variable, standard deviations for Libya not computed due to data limitations.
39
Table 2.3: Fiscal Deficit (excluding grants) to GDP(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
-7.2(4.2)
-4.8(2.4)
Madagascar
-8.3(1.3)
-7.9(1.0)
Malawi
-10.8(2.1)
-14.7(1.4)
Uganda
-6.2(0.5)
-12.0(2.4)
Managed floating with no predetermined path
Angola
-21.5(16.6)
-6.1(3.3)
Burundi
-8.9(2.1)
-10.8(7.2)
Egypt
-1.8((1.5)
-7.3(0.8)
Ethiopia
-8.3(2.6)
-13.1(2.8)
Kenya
-1.7(0.9)
-3.6(0.8)
Mauritius
-5.3(2.0)
-5.6(0.9)
Rwanda
-10.8(2.5)
-10.3(1.6)
Sudan
-1.6(1.3)
-0.8(0.1)
Zambia
-9.7(3.4)
-12.1(2.2)
Zimbabwe
-8.5(3.0)
-9.8(7.6)
Other Conventional Fixed Peg Arrangements
Comoros
-12.3(4.1)
-6.9(2.4)
Eritrea
-35.6(19.8)
-44.1(8.1)
Seychelles
-9.9(2.7)
-7.3(8.7)
Swaziland
-0.2(1.4)
-5.0(1.3)
Currency Board Arrangements
Djibouti
-3.6(3.4)
-2.3(0.8)
-9.1(1.8)
-9.6(0.7)
COMESA
Table 2.4: Foreign Reserves (Months of imports)
1995-1999
2000-2004
Independently Floating
Congo, DR
3.4(1.6)
1.8(0.6)
Madagascar
2.5(0.7)
3.0(0.8)
Malawi
3.7(1.4)
2.8(1.2)
Uganda
6.4(0.2)
7.9(0.6)
Managed floating with no predetermined path
Angola
0.8(0.3)
1.5(0.8)
Burundi
6.3(3.0)
3.1(1.5)
Ethiopia
3.6(3.0)
3.9(1.5)
Kenya
2.6(0.2)
3.4(0.5)
Mauritius
2.9(0.4)
5.2(1.5)
Zambia
1.0(0.8)
2.2(1.1)
Zimbabwe
1.0(0.6)
0.6(0.3)
Other Conventional Fixed Peg Arrangements
Comoros
5.8(0.5)
11.0(1.6)
Seychelles
0.6(0.1)
1.0(0.4)
Swaziland
3.2(0.4)
2.4(0.7)
3.2(0.2)
3.7(0.3)
COMESA
40
Table 2.5: Real GDP growth(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
-2.4(2.5)
1.4(5.4)
Madagascar
3.2(1.3)
2.6(8.8)
Malawi
7.0(5.6)
1.5(3.5)
Uganda
7.6(2.9)
5.6(0.7)
Managed floating with no predetermined path
Angola
7.9(3.1)
7.5(5.6)
Burundi
-2.4(5.5)
2.1(2.9)
Egypt
5.2(0.8)
3.6(0.8)
Ethiopia
5.2(4.3)
4.4(5.9)
Kenya
2.7(1.5)
1.3(1.2)
Mauritius
5.2(1.0)
4.3((1.8)
Rwanda
15.6(11.2)
5.4(3.2)
Sudan
6.2(3.0)
6.1(0.5)
Zambia
1.5(4.0)
4.2(0.8)
Zimbabwe
1.6(5.0)
-9.3(3.7)
Other Conventional Fixed Peg Arrangements
Comoros
3.0(3.8)
2.6(1.1)
Djibouti
-1.2(2.6)
2.4(1.2)
Eritrea
4.4(4.0)
1.2(8.7)
Libya
1.7
3.8
Seychelles
5.4(5.8)
-0.8(3.7)
Swaziland
3.7(0.2)
2.5(0.8)
Currency Board Arrangements
Djibouti
-1.2(2.6)
2.4(1.2)
4.3(1.4)
2.7(1.1)
COMESA
41
Table 2.6: Domestic Revenue (excluding grants) to GDP(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
5.1(0.6)
7.3(1.6)
Madagascar
9.8(1.3)
10.4(1.6)
Malawi
16.7(1.5)
19.9(2.8)
Uganda
10.8(0.6)
11.9(0.7)
Managed floating with no predetermined path
Angola
38.7(7.6)
41.3(6.3)
Burundi
16.1(1.5)
20.0(0.5)
Egypt
25.9(1.1)
24.3(0.5)
Ethiopia
18.1(0.4)
19.1(0.7)
Kenya
27.0(1.6)
21.8(0.9)
Mauritius
19.2(1.1)
19.5(1.3)
Rwanda
9.4(1.5)
12.0(1.5)
Sudan
7.2(0.9)
11.8(1.2)
Zambia
19.4(1.2)
18.6(0.7)
Zimbabwe
26.8(1.5)
28.8(2.3)
Other Conventional Fixed Peg Arrangements
Comoros
12.5(1.0)
12.3(1.9)
Eritrea
34.3(4.3)
27.2(4.0)
Libya
42.9
48.4
Seychelles
46.7(1.8)
43.5(3.6)
Swaziland
30.0(0.8)
27.6(1.4)
Currency Board Arrangements
Djibouti
31.5(1.5)
31.4(2.7)
22.5(0.4)
22.8(0.9)
COMESA
42
Table 2.7: Domestic Investment to GDP(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
21.1(9.7)
8.7(4.4)
Madagascar
13.4(1.4)
24.1(6.1)
Malawi
13.8(2.2)
11.3(1.6)
Uganda
17.3(3.3)
21.22(1.2)
Managed floating with no predetermined path
Angola
30.5(4.4)
12.5(1.3)
Burundi
9.4(1.6)
9.8(1.8)
Egypt
19.4(1.4)
17.8(1.5)
Ethiopia
16.7(0.4)
19.5(2.9)
Kenya
18.8(2.2)
14.2(0.5)
Mauritius
27.5(2.3)
23.6((1.5)
Rwanda
14.7(1.5)
18.3(1.6)
Sudan
19.3(2.4)
18.4(0.7)
Zambia
15.5(1.8)
22.6(3.2)
Zimbabwe
20.2(3.5)
4.3(5.3)
Other Conventional Fixed Peg Arrangements
Comoros
14.6(2.9)
11.4(1.00)
Eritrea
28.4(4.4)
19.7(4.8)
Seychelles
33.3(3.7)
26.4(6.3)
Swaziland
20.7(1.3)
21.7(2.3)
Currency Board Arrangements
Djibouti
10.2(2.9)
12.5(3.1)
19.2(0.5)
16.7(0.51)
COMESA
43
Table 2.8: Current account (excluding grants) to GDP(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
-6.6(3.6)
-10.4(0.6)
Madagascar
-7.9(1.4)
-6.9(3.6)
Malawi
-14.6(4.4)
-15.5(2.6)
Uganda
-10.6(2.3)
-12.4(0.8)
Managed floating with no predetermined path
Angola
-20.0(9.8)
-3.0(10.2)
Burundi
-10.1(3.4)
-23.0(11.3)
Egypt
-2.1(1.4)
-0.1(1.6)
Ethiopia
-7.0(2.7)
-11.6(1.7)
Kenya
-3.9(1.5)
-2.8(1.7)
Mauritius
-2.2(2.3)
2.2(2.9)
Rwanda
-18.0(1.1)
-17.6(2.0)
Sudan
-16.6(2.8)
-11.5(3.7)
Zambia
-14.0(2.2)
-17.0(3.5)
Zimbabwe
-4.5(3.4)
-3.5(2.2)
Other Conventional Fixed Peg Arrangements
Comoros
-16.3(7.1)
-3.4(2.2)
Eritrea
-20.8(14.3)
-30.8(3.6)
Seychelles
-16.3(4.2)
-12.6(8.7)
Swaziland
-12.6(3.4)
-2.3(1.1)
Currency Board Arrangements
Djibouti
-14.6(0.8)
-15.0(2.1)
-11.4(1.5)
-10.4(0.6)
COMESA
44
Table 2.9: External Debt to GDP(%)
1995-1999
2000-2004
Independently Floating
Congo, DR
227.6(57.44)
214.7(52.7)
Madagascar
121.69(11.2)
100.8(11.5)
Malawi
120.6(29.8)
156.5(6.9)
Uganda
57.1(1.4)
61.4(4.0)
Managed floating with no predetermined path
Angola
84.6(38.7)
45.7(17.6)
Burundi
125.10.6)
176.8(18.8)
Egypt
38.7(9.6)
25.1(1.4)
Ethiopia
114.0(46.1)
88.7(10.4)
Kenya
51.5(8.8)
39.6(4.5)
Mauritius
15.5(1.9)
10.2(1.4)
Rwanda
69.02(10.2)
80.2(5.4)
Sudan
172.0(30.2)
125.0(9.5)
Zambia
197.8(14.1)
13.4(50.0)
Zimbabwe
45.9(7.6)
42.6(16.4)
Other Conventional Fixed Peg Arrangements
Comoros
94.0(4.3)
96.8(13.3)
Eritrea
15.5(11.6)
60.0(9.9)
Seychelles
20.4(1.9)
32.7(9.0)
Swaziland
21.1(3.2)
25.1(3.4)
Currency Board Arrangements
Djibouti
62.5(3.9)
67.0(1.2)
87.1(7.9)
83.2(5.4)
COMESA
45
Figure 2.1: Domestic Debt/GDP(%)
19
27
18
26
25
17
24
16
23
15
22
14
21
13
20
2000
2001
2002
2003
2004
2000
2001
BUR
2002
2003
2004
2003
2004
2003
2004
KEN
36
11.8
11.6
34
11.4
32
11.2
30
11.0
10.8
28
10.6
26
10.4
24
10.2
2000
2001
2002
2003
2004
2000
2001
MAU
2002
RWA
10
14
9
13
8
12
7
11
6
10
5
9
4
3
8
2000
2001
2002
2003
2004
2000
UGA
2001
2002
ZAM
46
Figure 2.2: Interest rates and Inflation
350
32
Angola
Burundi
28
300
24
20
200
Percent
Percent
250
150
16
12
8
100
4
50
0
1997
0
-4
1998
1999
Deposit
2000
2001
2002
Lending
2003
2004
96
97
Inflation
98
00
Deposit
16
16
14
01
02
Lending
03
04
Inflation
Ethiopia
Egypt
12
12
8
10
Percent
Percent
99
8
4
0
6
-4
4
2
-8
96
97
98
99
Deposit
00
01
Lending
35
02
03
04
96
97
Inflation
98
99
Deposit
01
Lending
02
03
04
Inflation
Madagascar
40
Kenya
00
30
30
20
Percent
Percent
25
15
20
10
10
0
5
0
-10
96
97
98
Deposit
99
00
01
Lending
02
03
04
96
Inflation
97
98
Deposit
47
99
00
01
Lending
02
03
Inflation
04
Figure 2.2 cont…
50
20
40
16
Percent
Percent
24
Malawi
60
30
Mauritius
12
20
8
10
4
0
96
97
98
99
Deposit
16
00
01
Lending
02
03
96
04
97
98
99
Deposit
Inflation
00
01
Lending
02
03
04
Inflation
20
Rwanda
Seychelles
16
12
Percent
Percent
12
8
4
8
4
0
0
-4
-4
96
97
98
99
Deposit
01
Lending
02
03
04
96
97
Inflation
98
99
Deposit
20
20
16
15
12
01
Lending
02
03
04
Inflation
10
8
5
4
0
0
00
Uganda
25
Swaziland
Percent
Percent
24
00
-5
96
97
98
Deposit
99
00
01
Lending
02
03
04
96
Inflation
97
98
Deposit
48
99
00
01
Lending
02
03
Inflation
04
Figure 2 cont…
500
Zambia
50
400
40
300
Percent
Percent
60
30
20
Zimbabwe
200
100
10
0
96
97
98
Deposit
99
00
01
Lending
02
03
04
96
Inflation
97
98
Deposit
49
99
00
01
Lending
02
03
Inflation
04
Figure 2.3: Real Exchange Rate
150
130
120
140
140
120
116
120
112
100
108
80
104
60
100
40
130
110
120
100
110
90
100
80
90
80
70
70
1997
60
1997
1998
1999
2000
2001
2002
2003
2004
1998
1999
ANGOLA
2000
2001
2002
2003
2004
96
1997
1998
1999
BURUNDI
104
2000
2001
2002
2003
2004
106
1998
1999
2000
2001
2002
2003
2004
2002
2003
2004
2002
2003
2004
CONGODR
120
140
104
130
100
110
102
96
120
100
100
98
92
110
100
90
96
90
88
80
94
84
1997
20
1997
COMOROS
1998
1999
2000
2001
2002
2003
2004
92
1997
80
1998
1999
2000
ETHIOPIA
2001
2002
2003
2004
70
1997
1998
1999
KENYA
100
2000
2001
2002
2003
2004
70
1997
1998
1999
MADAGASCAR
120
2000
2001
MALAWI
110
112
99
110
108
98
105
97
100
104
96
90
100
95
100
94
80
95
93
96
70
92
91
1997
1998
1999
2000
2001
2002
2003
2004
60
1997
1998
1999
MAURITIUS
2000
2001
2002
2003
2004
90
1997
1998
1999
RWANDA
2001
2002
2003
2004
SEYCHELLES
130
112
350
120
110
300
108
110
2000
92
1997
1998
1999
2000
2001
SWAZILAND
250
106
100
200
104
90
80
70
1997
150
102
100
100
1998
1999
2000
2001
UGANDA
2002
2003
2004
98
1997
1998
1999
2000
2001
2002
2003
2004
50
1997
ZAMBIA
1998
1999
2000
2001
2002
2003
2004
ZIMBABWE
2.4 Convergence Methodology
anel based unit root tests advanced by Qual (1992, 1994) and Levin and Lin (1993, LL
hereafter) have been widely used to test the convergence hypothesis (e.g. Kočeda(2001 ).
Convergence is inferred if, for example, per capita income disparities (between economies)
follow a mean-stationary process, implying relative per capita income shocks only leads to
transitory deviations from steady state. Both Qual and LL, however, impose homogeneity and thereby
assuming common convergence rate(s), meaning all members of the group sharing the same speed of
adjustment (to steady state) in all variables. For example, Burundi reaching the long run group average at
the same rate as Swaziland is a valid assumption.
Concerns of possible model misspecification, and consequently false inferences due to imposing
identical convergence rates motivated Im, Pesaran and Shin (2003, hereafter IPS) to propose a test that
accommodates heterogeneity across groups, such as individual specific effects and different patterns of
residual serial correlations. This dynamic heterogeneous panel framework is based on the means of
individual unit root statistics. In particular, it proposes a standardized t  bar statistic based on the
augmented Dickey-Fuller (Dickey and Fuller, 1979) statistics averaged across the groups.
P
50
Following IPS, consider a sample of N cross sections (industries, regions or countries) observed
over T time periods. The stochastic process yit is generated within the ADF regressions
k
( yi ,t  y t )   i  i ( yi ,t 1  y t 1 )   i ,k ( yi ,t k  y t  k )   i ,t , i  1,..., N ,t  1,..., T , (2.1)
k 1
where yi ,t  y t is the variable disparity from mean of i  1,..., N at time t . The t  bar statistic is a
simple average of individual ADF statistics for testing the null hypothesis of unit roots, that is, no
convergence:
H 0 : i  0 for all i,
(2.2)
against the alternative of convergence
H1 : i  0,
(2.3)
for at least one i .
Under very general settings, this statistic ( t  bar ) is shown to converge in probability to a
standard normal variate and T   , followed by N   . That is, when T and N are sufficiently large,
it is possible to develop valid t  bar type panel unit root test that are free from nuisance parameters.
In the special case where errors in individual ADF regressions are serially uncorrelated, a
modified version of the (standardized ) t  bar statistic , denoted by Z tbar is shown to be distributed as
standard normal as N   for a fixed T , so long T  5 in the case of ADF with intercepts , and
T  6 in the case of ADF regressions with intercepts and linear trends. An exact fixed N and T test is
also developed using the simple average of the ADF statistics. Based on simulations, it is known that the
standard t  bar statistic provides an excellent approximation to the exact test even for relatively small
values of N .
2.5
Empirical Results
A
nnual data on growth in real GDP, M2 and CPI. In addition, fiscal/GDP, revenue/GDP,
domestic investment rate, current account/GDP, real exchange rate and external debt/GDP3,
over the sample period 1995-2004 were used. The focus is on analysing real, price,
monetary policy, fiscal policy and external convergence. Real convergence is measured by
real GDP. Most COMESA countries are implementing monetary targeting, deeming M2 an appropriate
monetary-policy measure. Price and fiscal convergence are captured by developments in CPI inflation and
fiscal deficit/GDP, respectively. External convergence is investigated using the current account
deficit/GDP and real exchange rate. Data sources include various IFS and the WEO.
The null of inflation divergence for COMESA is not rejected, albeit significant convergence in
Burundi, Djibouti, Egypt, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan and Uganda
(Table 2.10), suggesting nations experiencing uncommon price shocks, driven by various supply and
demand shocks. Excluding nation with triple digit inflation (DRC, Angola and Zimbabwe), the evidence
3
Fiscal/GDP, Revenue/GDP and current account/GDP excluding grants
51
Table 2.10: Estimated Results4
Angola
Burundi
Comoros
Congo, DR
Djibouti
Egypt
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Rwanda
Seychelles
Sudan
Swaziland
Uganda
Zambia
Zimbabwe
t  bar
Inflation
-0.27(-0.55)
-0.50(-2.71)*
-1.28(0.17)
0.06(0.05)
-1.02(-2.48)*
-0.36(-2.47)*
-0.18(-0.34)
-3.31(-0.43)
-1.05(-0.97)
-2.59(-3.46)*
-0.01(-2.83)*
-0.37(-2.75)*
-1.25(-2.62)*
-1.65(-3.59)*
-0.94(-4.70)*
-2..13 (-1.46)
-2.11(-3.22)**
0.04(0.10)
4.58 (1.21)
-1.46
Output
-0.90(-1.36)
-1.61(-4.35)**
-1.47(-1.88)
-0.65(-1.57)
-0.88(-2.25)*
-1.12 (-2.20)*
-1.15(-1.47)
-2.20 (-3.35)**
-0.06(-0.18)
-2.56(-2.75)*
-0.72 (-1.44)
-2.09(-3.46)**
-1.19(-2.45)*
-1.99 (3.65)**
-2.03(-4.39)***
-1.71(-2.44)**
-1.55(-4.64)***
-1.54(-6.03)***
4.35(1.03)
-4.59*
Fiscal
-0.91(-2.92)*
-0.98(-0.56)
-0.82(-2.34)*
-1.21(-1.52)
-1.78(-4.11)**
-0.23(-1.07)
-1.23(-0.21)
-0.53(-1.18)
-0.36(-0.54)
-1.09(-0.99)
-0.39(-0.99)
-1.35(-2.00)
-0.35(-0.33)
-3.93(-0.74)
-1.30(-2.38)*
-0.34(-1.22)
-0.58(0.82)
-0.97(-1.84)
-1.63(-1.31)
-1.53
Revenue
-0.99(-1.13)
-0.41(-2.09)
-1.41 (-1.53)
0.01(0.01)
-2.38(-2.64)*
-0.48(-0.88)
-0.11(-0.36)
-0.55(-0.73)
-0.17(-0.72)
-1.36(-2.02)
0.46(0.62)
-1.98(-1.83)
-0.10(-0.26)
-1.39(-1.67)
-0.17(-0.51)
-1.41(-2.96)*
0.44(0.72)
-0.88(-1.55)
-1.93(-1.04)
-1.08
t-bar critical values: 1 %, -2.21; 5%, -1.99; 10%, -1.89
*, **, ***, statistical significance at 10 %, 5% and 1 % levels.
Table 2.10 cont…
Angola
Burundi
Comoros
Congo, DR
Djibouti
Egypt
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Rwanda
Seychelles
Sudan
Swaziland
Uganda
Zambia
Zimbabwe
t  bar
4
M2
-0.35(-0.71)
-1.58(-1.06)
-1.58(-1.90)
-2.37(-2.31)
-0.16(-0.35)
-1.24(-1.06)
-0.26 (-0.92)
-0.36(-0.69)
-1.31(-2.31)
-3.92(-4.43)**
-1.31(-1.17)
-1.99(-1.45)
-1.66 (-5.29)***
0.21(0.24)
-1.36(-3.83)**
-1.73(-2.07)
-1.96(-3.40)**
-2.59(-2.25)
4.51(1.79)
-1.70
Invest
-0.21(-0.41)
0.20(0.21)
-0.82(-3.32)**
-0.96 (-5.98)***
-2.45(-1.58)
-0.59(-0.54)
0.37 (0.41)
0.97 (0.83)
-0.58(-2.28)
-0.73(-1.19)
-0.83(-0.63)
-0.47(-1.43)
-0.27 (-0.68)
0.64(0.48)
-1.13(-2.98)*
-1.08(-0.27)
-0.04(-0.15)
0.09(0.22)
-0.28(-0.73)
-1.05
t-statistics in parentheses.
52
Current Account
-1.05(-0.73)
1.50(2.39)*
-0.69(-4.35)**
-1.19 (-3.06)*
-3.70(-0.51)
-0.68(-0.81)
-1.17(-0.16)
0.04(0.15)
0.32(0.55)
-1.36(-1.49)
-1.36(-1.04)
-0.64(-1.00)
-1.47(-1.43)
-3.25(-1.16)
0.14(0.19)
-0.24(-0.18)
-0.88(-5.60)**
-1.09(-1.04)
-1.13(-2.08)
-1.57
of group convergence appears as the t  bar statistic falls to -2.02, validating the remarkable disinflation
efforts in a number of economies.
As a region, significant output convergence is observed, mostly induced by the high degree of
output convergence in individual nations (i.e. Burundi, Djibouti, Egypt, Ethiopia, Madagascar, Mauritius,
Rwanda, Sudan, Seychelles, Swaziland, Uganda and Zambia). Caution, this empirical aspect coupled with
the descriptive statistics, suggests convergence to lower rates. Surprisingly, real convergence (i.e. output
convergence) is not supported by developments in domestic investment, as only few countries (Comoros,
DCR and Sudan) showed evidence of convergence. With the coefficient being at the boundary of
statistical significance, whether investment in Kenya is converging or diverging is unclear.
Significant fiscal-policy convergence only recorded in Angola, Comoros, Djibouti and Sudan.
Like the study by ECA (2003), this suggests COMESA exhibiting a statistical fiscal policy divergence.
And with also revenue/GDP (excluding grants) statistically diverging, COMESA is yet to achieve fiscal
harmonization. There is also lack of monetary policy convergence in COMESA, despite strong evidence
of statistical convergence in few countries (Madagascar, Rwanda, Sudan and Uganda). Like investment, it
appears monetary policy is either converging or divergence in Kenya. Similar to inflation, regional
monetary policy convergence does appear in the absence of nations with triple digit money growth
(Angola, DRC, Zimbabwe), as the t  bar statistic of –2.68 is obtained. Significant current account
convergence is recorded in few countries (Burundi, Comoros, DRC and Uganda). However, group
convergence not yet in sight.
2.6
Conclusions and Policy Recommendations
T
his study tested macroeconomic convergence in COMESA using annual macroeconomic
data from 1995 to 2004. Inspecting historical trends of the main variables and employing a
heterogeneous panel unit root framework, we address the following questions: Do inflation,
fiscal and current accounts, output, foreign reserves and domestic investment rate, alongside
real interest and exchange rates of COMESA countries converge to common levels? Do convergence
processes differ across countries? Are monetary, fiscal and exchange rate policies sources of
convergence (divergence)?
We find no strong evidence of inflation convergence, indicating that nations experiencing
uncommon price shocks, driven by various supply and demand shocks. However, excluding nation with
triple digit inflation (DRC, Angola and Zimbabwe), the evidence of convergence appears, validating the
remarkable disinflation efforts in a number of economies. Significant output convergence is observed.
The econometrics aspect coupled with the descriptive statistics, suggests convergence to lower (output)
growth rates. Surprisingly, real convergence (i.e. output convergence) is not supported by domestic
investment. A strong evidence of both fiscal and monetary policy divergence is also recorded. Like
inflation, regional monetary policy convergence does appear in the absence of nations with triple digit
money growth (Angola, DRC, and Zimbabwe). COMESA current account convergence is not yet in sight.
External debt as a ratio of GDP has been falling in a number of countries. However, it has been
rising in fixed exchange regimes, a possible scenario since such countries have to borrow abroad to
support the regime in the face of low exports. Due to data limitations, we are unable to adequately assess
the extent of domestic debt in COMESA. However, we note domestic debt as a percentage of GDP rising
in Burundi, Kenya, Mauritius and Uganda. There are strong signs of prudent domestic debt management
in Rwanda. We are also unable to assess progress towards convergence in central bank financing of the
deficit and real exchanges rate due to data constraints.
Our policy recommendations are that for COMESA inflation to converge to lower rates, rampant
inflation in DRC, Angola and Zimbabwe needs to be combated. Since inflationary pressures in these
countries seem to be induced by high money growth, tight monetary policy is thus an effective demand
53
management tool. Such actions will also contribute to monetary policy (or money supply) convergence.
Lessons from Mutoti(2005) and Kihangire and Mugyenyi (2005) that inflation is also associated with
non-monetary factors, in particular, supply and exchange rate shocks, also guides in prescribing that
policies meant to increasing domestic food supply and stabilizing the exchange rate are vital to achieving
and sustaining COMESA (inflation) target.
Output converging to lower growth rates contradicts COMESA’s aspirations. We find a number
of economies agro-based and agriculture performance (in such countries) unpredictable, partly due to
unforeseen circumstances, such as drought. Realizing the COMESA growth target of 7 %, in this context,
calls for the adoption of drought resistant technologies alongside increased investment in irrigation. Such
a policy direction will not only boost output but also help in the fight against inflation, through increased
supply. Accelerate growth could also be propagated by increased domestic investment. Further, as most
of these countries export agricultural produce, there are also benefits to foreign reserves accumulation and
improving the current account position, in the end, convergence of these respective variables. Improving
external viability will also come through reduction in the stock of external debt, giving support to the
strict adherence to the HIPC requirements.
Regional fiscal-policy convergence and thereby fiscal harmonization has not yet been in sight. As
this may be partially on account of higher expenditures invited under the PRGF, in an effort to reduce
poverty, increased revenue is the major way forward. While revenue consolidation is advocated in
performing economies, more efforts are needed to diversify and broaden the tax base.
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Brainard and G. Perry (eds), Brookings Papers on Economic Activity. Brooking Institute, 1-118.
Sargent, T., and Wallace, N. (1981). ‘Some Unpleasant Monetary Arithmetic’, Quarterly Bulletin of the
Reserve Bank of Minneapolis (5), 1-17.
Tseng, W. and Corker,R. (1991). ‘Financial Liberalization , Money Demand, and Monetary Policy in
Asian Countries’, IMF Occasional Paper, No.84, IMF Washington DC.
Zivot, E., and Andrews, D.W.K. (1992). ‘Further Evidence on the Great Crash, the Oil Price Shock and
the Unit Root Hypothesis,’ Journal of Business and Economic Statistics, Vol 10, pp 251-270.
55
3.0
Sources of Inflation
Noah Mutoti
and
David Kihangire
3.1
Introduction
C
entral bankers almost uniformly agree that sustained low inflation, at a rate not greater than
that defined as price stability plus a cushion to avoid the zero lower bound on nominal
interest rate, is a prerequisite to achieve maximum economic growth. Ghosh and Phillips
(1998) find, for IMF member countries at low inflation rates, a positive inflation-growth
correlation and for higher inflation rates, a negative-growth correlation. Cohen et al. (1999) show that a
decrease in expected future inflation reduces expected future user costs of capital and in turn stimulates
investment spending. A policy of maintaining low, stable inflation also enhances the Government’s
credibility and confidence of households and businesses (in the economy), thereby boosting investment
and growth. Further, there is a monetary cost of inflation that arises by eroding the purchasing power of
money, causing households and firms to incur additional costs to manage money balances.
The foregoing brief discussion suggests that it is every country’s desire to attain price stability,
often defined as an inflation rate that is sufficiently low, stable and predictable so as not to be a factor in
private decisions. COMESA members are not an exception to this desire. In its effort to achieve monetary
integration destined for 2015, one of COMESA’s primary convergence criteria is attaining an inflation
rate not exceeding 5%.
In COMESA members, if not all, monetary targeting is the basis of monetary policy conduct. The
question of the role of money supply in consumer price formation is highly relevant in this context. In an
open economy such as COMESA, inflation could also emanate from external sources, for example, the
depreciation of the exchange rate could raise the cost of imported commodities. What is the importance of
exchange rate in inflation dynamics in COMESA members?
Addressing such questions will not only provide answers to the relevant variables that
significantly influence developments in inflation but will also guide in policies that are necessary for
combating inflation and sustaining it at lower rates in each of the COMESA member country. Overall,
such an undertaking will provide valuable information for designing policies to enable this economic
regional group attain price stability.
The theoretical framework follows. Section 3.3 contains the empirical results of inflation
dynamics in Zambia, Malawi, Swaziland and Madagascar. These are in addition to Uganda, Kenya, Egypt
and Sudan. Concluding remarks are offered in Section 3.4.
3.2
Theoretical Framework
T
hough there are numerous models of inflation, the enduring theoretical representations fall in
three categories (Juselius (1992)). (a) Pure monetarist theories, positing expansion of money
supply at a rate greater than warranted by growth in real productive potential is a necessary
and sufficient condition for there to be inflation. While an initial increase in prices could as
well be due to non-monetary impulses, a substantial and continued price increase can be sustained only if
56
money is accommodative. Similarly, a relative price shock resulting from exogenous policy/preference
changes need not result in a significant increase in the aggregate price level unless there is an increase in
money supply. In this sense, inflation is always and everywhere a monetary phenomenon (Friedman
(1991)). In other words, causes of inflation lies in excess monetary growth, and the desired inflation rate
should be obtained by controlling money supply. (b) Internal theories, usefully divided into (i) labour
market theories and (ii) excess demand theories. The first appeals to the notion that the wage (the price of
labour), which forms a major part of unit cost, is influenced by demand and supply of labour. The second
underscores inflation induced by demand pressures in the goods market. (c) External theories split into (i)
theories involving the transmission of import prices in foreign currency terms into general domestic
inflation and (ii) inflation attributable to exchange rate depreciation.
In a developing economy context, designing a model that incorporates these three categories is a
daunting task, especially in view of the absence of reliable labour statistics. That is why in numerous
previous work on Africa (e.g. Mwansa(1998), Moser (1995) and Ndung’u (1993)), the theoretical
exposition relies on monetarist, external and excess demand theories. It is this approach followed here. A
distinction is made between traded and non-traded goods. Overall domestic consumer price (CPI) level (
pt ) is a weighted average of the prices of traded ( p tT ) and non-traded ( p tN ) goods5.
pt   ptT  (1   ) ptN
(3.1) ,
where 0    1 is the share of imported goods in pt . p tN originates from the domestic sector and it is set
in the money market, where demand for non-traded goods is assumed, for simplicity, to move in line with
aggregate demand. Thus, p tN is determined by money market conditions
ptN   (mts  p )  (mtd  p ) 
(3.2) .
Here (mts  p ) and ( mtd  p ) are real money supply and real money demand, respectively. Demand for
real balances, underlying money market equilibrium, takes the form:
mtd  p  a1 yt  a2 Rt   tmd
(3.3) ,
where yt is domestic output (capturing the transaction motive), Rt nominal interest rate (characterizing
the speculative motive) and  tmd is a money demand shock (e.g. velocity shock).
Adams (1995) and others, however, question whether the inclusion of interest rate makes it
relevant for developing countries. They argue that in such economies, the relatively thin markets for
financial securities make substitution between money and goods, or real assets, quantitatively more
important and that expected inflation (  te ) is a better opportunity cost of holding money in such
economies. We thus modify (3.3) to
mtd  p  a1 yt  a2 te   tmd
(3.4) .
Substituting (3.4) into (3.2) yields
5
All variables are in log except inflation and interest rates. All coefficients are positive
57
ptN   (mts  p  a1 yt  a2 te )   tmd
(3.5) ,
arguing that prices of non-traded goods are influenced by disequilibrium in the money market. Whenever
(m s  p)t exceeds (m d  p )t there is a rise in the public’s cash balances. This, in turn, leads to increases
in consumer spending and if yt does not correspondingly increase, the excess demand induces an
upward pressure on the general price level. To sum up, increases in prices of non-traded goods reflect
money market disequilibria as well as inflation expectation or inertia.
The price of traded goods is determined in world markets where an individual country acts as a
price taker. This follows from the assumption of a small open economy and perfect price arbitrage for
homogenous tradable commodities. By implications, p tT originates from the foreign sector, taking the
form
ptT  c1st  c2 pt*
(3.6) 6
where s t is the nominal exchange rate7 and p t* is foreign CPI. Currency depreciation and an increase in
foreign price leading to higher prices of traded goods, is the argument in (3.6). Substituting (3.5) and (3.6)
into (3.1), and after some simplifications, produces
pt   1mts  2 yt  3 te  4 st  5 pt*   tmd
(3.7)
where 1   , 2  a1 , 3  a2 , 4   c1 1   , 5   c2 1   . This equation predicts consumer price
formation is associated with both monetary and non-monetary factors.
Equilibrium in the goods market is characterized by an open IS equation
yt  1 ( Rt  pt )   2 ( st  pt*  p)   tIS
(3.8)
where  tIS is an IS shock (e.g. fiscal policy shock). It is argued that higher cost of capital, captured by
real interest rate ( Rt  pt ) induces lower output whereas real exchange rate ( st  pt*  pt ) depreciation
leads to higher output.
Another requirement for asset market equilibrium is that domestic assets are perfect substitutes
for securities that pay the (exogenously) world market interest rate ( Rt* ). This is depicted in the capital
market equilibrium condition
st  st 1  ( Rt  Rt* )   ts
(3.9)
also describing the international transmission linkages and fulfilling the uncovered interest rate parity
(UIP) condition if  1 (assuming world financial markets are integrated). The stochastic variable  ts is
a risk premium or exchange rate shock (e.g. shock to current account).
6
It is assumed that the exchange rate pass-through is incomplete
Exchange rate is defined as the number of units of domestic currency, which can purchase one unit of
foreign currency. Therefore, an increase implies a depreciation.
7
58
The statistical model that generates the data, and thereby, directly distinguishes the effects related
to short- and long-run variation, is a reduced-form vector error correction model (VECM) in the sense
=Engle and Granger (1987) and many others:
xt    1xt 1  ...  k xt k 1  xt 1   Dt  vt ,
(3.11)
xt is a n x 1 vector of stochastic variables, Dt are seasonal dummies, 1 ,..., k 1 are short-run dynamics
and vt ,...., vT are Niid p (0, ) . Assuming x t is I (1), (11) contains a mixture of stationary and non-stationary
where
components and
   '
(3.12) ,
has a reduced rank where  and  are n x r . Specifically,  ' gives the cointegration relations and α the
loading or adjustment coefficients (that measure the feedback of the cointegration relations into the
differenced variables, xt ). Given the cointegration rank r  n , the process xt is stationary and x t is
non-stationary, whereas  ' xt is stationary. The last property gives the reason why the relations  ' xt are
potential candidates for economic long-run relations. For these relations to be interpreted in terms of
economically interpretable parameters, testing of structural hypotheses is very crucial part of the analysis,
especially when there is more than one cointegrating vector (Johansen and Juselius (1994)). On the other
hand, if   0 , the model is statistically consistent but there would be no stationary long-run relations in
the data, and (11) would be reduced to the standard VAR in first differences.
3.4
Empirical Analysis8
Zambia
I
nflation dynamics in Zambia is investigated by modelling food, non-food and overall
inflation9. The sample period is 1992-2005. As already noted, all variables are in logarithms
except interest rate. Domestic output is real gross domestic product ( yt )10; Domestic
consumer prices are the Zambia overall CPI( p t ), food CPI ( p tf ) and non-food CPI ( p tnf ) ;Money stock
( m ts ) is broad money defined as the sum of currency, demand and savings deposits;
Domestic interest rate ( Rt ) is the 3-month Zambian Treasury bill rate; nominal exchange rate ( st ) is the
Kwacha/South African Rand; foreign price ( pt* ) is South African CPI; and foreign interest rate ( Rt* ) is
South African Treasury bill rate. Real interest rate ( r int ) and real exchange rate ( rer ) are computed
accordingly. BOZ and IFS publications are the main sources of the data.
The empirical undertaking commenced with the analysis of the long-run structure of the data in
terms of cointegration relations, interpretable as deviations from steady state relations. Using a 5
8
Data series are monthly
Food makes up 57% of the consumer’s basket.
10
Monthly output is interpolated following the Chow-Lin distribution/interpolation procedure (Frain (2004).
Like in Bank of England (1999) and Stock and Watson (1999), potential output is assumed to be constant.
9
59




endogenous variable VECM ptf ( ptnf ), mts , yt , Rt , st and three exogenous variables r int, pt* , Rt* with
a maximum lag of 3 informed by the AIC, a cointegrating rank, r  3 (Table 3.1), was established in both
model 1(with pt f ) and model 2 (with ptnf ). We proceeded by testing whether individual series are
stationary by themselves. Since the number of cointegration relations increases for each stationary
variables included in the cointegration space, the test outcome is useful as a means of identifying the
minimal set of variables needed for cointegration (Hansen and Juselius, 2002). With r  3 , none of the
variables are stationary by themselves (Appendix A, Table 1A).
Tables 3.2 and 3.3 report the 3 identified cointegrated vectors.  1 column denotes the price
equation, arguing long-run food and non-food prices are largely driven by supply factors and exchange
rate. We also find a modest role of money and a minor role of foreign price in non-food prices.  2
(column) characterizes an IS relation, suggesting the real cost of capital the main determinant of long-run
output. Through interest rate differentials influence exchange rate, the UIP hypothesis is not supported (
 2 column).
Table 3.1: Trace Test for Cointegration Rank
r
Model 1
Model 2
*
C0.95
0
190.04
212.15
123.04
1
111.35
112.54
93.92
2
69.10
69.60
68.68
3
40.46
39.46
47.21
4
16.90
15.31
29.34
Table 3.2: Identified Lon-run Structure
(Standard errors in parentheses)
1
2
pf
1
0.005
(0.002)
ms
-0.06
(0.13)
0.38
(0.11)
y
3
1
0.03
(0.01)
1
R
s
r int
p*
-0.34
(0.09)
0
-0.005
(0.002)
0.04
-0.03
(0.01)
-0.005
(0.002)
-0.006
(0.004)
R*
 2 (6)  13.16(0.08)
60
Table 3.3: Identified Long-run Structure
(Standard errors in parentheses)
1
2
pf
1
0.003
(0.001)
ms
-0.17
(0.05)
0.42
(0.10)
y
3
1
0.01
(0.005)
1
R
s
-0.39
(0.15)
-0.003
(0.001)
0.07
(0.03)
-0.003
(0.001)
r int
-0.06
(0.03)
p*
-0.003
(0.001)
R*
 2 (6)  15.21(0.06)
As a starting point, the dynamic models were estimated with a uniform lag structure of 6 for all
the variables except the error-correction term (ecmpt 1 , ecmyt 1 and ecmst 1 ) and seasonal dummies. The
general-to-specific approach, largely credited to Hendry (1995), produces equations (3.13) and (3.14) as
the respective parsimonious estimated food and non-food inflation models11.
ptf  0.07 0.46 ptf1  0.09 st 1  0.09 st 3
(2.36)
(5.47)
(2.96)
(4.02)
 0.15 yt  2  0.02 mar  0.01 apr  0.02 may
( 1.83)
( 4.10)
( 3.05)
( 3.84)
 0.02 dec  0.004 ecmyt 1
( 1.99)
(2.98)
Adj R  0.58; SE  0.02; LM  2 (3)  7.45(0.06);
2
(3.13)
White 2 (10)  12.70(0.07); JB  2 (2)  3.83(0.16); Chow F (10,14)  1.49(0.13)
Ommited var iables : p* ,  2 (7)  11.69(0.11); m s ,  2 (7)  13.10(0.07)
t-values in parentheses; SE =standard error of regression; LM = test for serial correlation; White = White’s
Heteroscedasticity test; JB=Jarque-Beta normality test; and Chow=Chow stability test;
11
61
ptnf  0.14 0.18 ptnf1  0.09 mts 2  0.05 mts3  0.09 st 1  0.09 st  2
(3.41)
(2.54)
(2.14)
(1.84)
(4.58)
(3.44)
 0.07 st 3  0.04 st  4  0.04 st 5  0.03 st 6  0.10 yt 3
(3.00)
(1.89)
 0.002 p
(1.10)
*
t 3
(2.34)
(1.85)
( 1.85)
 0.01 ecmpt 1  0.004 ecmyt 1
( 2.34)
( 2.45)
Adj R  0.62; SE  0.01; LM  2 (6)  6.95(0.06);
2
White 2 (13)  11.61(0.08); JB  2 (2)  4.01(0.12);
(3.14)
Chow , F (13,133)  1.63  0.09
Omitted : p* ,  2 (7)  5.10(0.64)
The diagnostic test statistics reveal both models perform satisfactory, as there is no indication of
residual autocorrelation; the normality and homoscedasticity of the residuals are clearly accepted. The
estimated coefficients are generally highly significant and coefficients have the expected sign. With
standard errors of regression of 0.02 (equation 3.13) and 0.01 (equation (3.14)), within the sample, the
models explain price inflation considerable. The lack of significant evidence of structural breaks in the
data further lends support to these models. The omitted variable F-test supports omission of South
African inflation and money growth in food inflation. Also exclusion of South African inflation from
non-food inflation model is not statistically rejected.
The first lag of food inflation being significant suggests inertia or expectations are important in
food price formations. We note about 0.46 % of the 1 % last month’s food price hikes feedback into
current food inflation. Food inflation is also attributable to exchange rate movements. Sluggishly, food
prices rise by 0.09 % after 1 month and by the same magnitude after 2 months in response to a 1 %
depreciation. With total short-run effects of 0.18 %, partial exchange rate pass-through is implied. What
also underline prices of the food basket are supply side factors. With 2 months lag, food inflation of -0.15
% is recorded following a 1 % increase in output. In conformity with the crop marketing pattern, food
inflation trends downwards between April to May and it edges up in December, plausible reflecting
surged demand due to Christmas activities.
The following are the salient features of non-food inflation model. First, like food inflation, there
is a significant role of inertia or expectations in developments of non-food prices. Second, non-food
inflation does not respond contemporaneously to changes in all relevant variables. Third, developments in
this price do not exhibit seasonal patterns. Third, unlike in food inflation, money is one of its leading
indicators. With nominal money decreasing by 1 %, non-food prices decreases by around 0.09 % after 2
months and by 0.05 % in the 3rd month. Fourth, the Kwacha/Rand appreciation by 1 % initially impacts
non-food prices after 1 month, decreasing by 0.09 %. One and two months thereafter, it declines
respectively by 0.09 % and 0.07 %. In the fourth and fifth months it declines further by 0.04 %. Exchange
rate effects are completed in the 6th month, with non-food prices declining by 0.03 %. Again the passthrough effects are partial, as the total short-run impact is approximately 0.36 %.
The plausibility of the models just discussed was further investigated by modelling overall
inflation. There are 3 identified cointegration relations (Appendix B, Table 1B). The vectors of the
variables are also non-stationary (Appendix B Table 2B). We are able to identify the price, output and
exchange rate equations (Appendix B, Table 3B). Long-run overall consumer price is mainly due to
supply and exchange rate, with money having a modest role. A minor role of foreign prices is also
established. Further, the exchange rate pass-through effects are partial. Like in the food and food prices,
changes in real interest rate dominate long-run output movements. While the UIP specification is not well
captured by Zambia’s data, interest rate differential plays a role in long-run exchange rate developments.
62
The estimated dynamic model (3.15) is also statically valid, as there is no evidence of serial
correlation, heteroscedasticity and structural breaks. The following are the main properties. First, inflation
inertia or expectations is well pronounced. Second, money plays a significant role in overall CPI
movement. Mirroring non-food inflation model (3.14), the impact of money growth on CPI inflation is
not contemporaneous. Money expanding by 1 % fuels inflation pressures, to a tune of 0.07 % two months
thereafter and 0.04 % in the 3rd month. Third, like in previous models, the effects of exchange rate are
sluggish. The Kwacha/Rand depreciating by 1 % results in overall consumer price edging up by a total of
0.2 % in the short-run, accounted for in the first month (0.09 %), third month (0.05 %), firth month (0.03
%) and the sixth month (0.03 %). Again consistent with food and non-food inflation specifications, the
pass-through exchange effects are incomplete. It is generally argued that when significant lags exist in the
transmission of exchange rate changes to domestic prices, exchange-rate depreciation only have limited
impact on the rate of domestic inflation. The supply side variable is also significant, impacting inflation
after 2 months. Specifically, a CPI inflation of 0.09 % is recorded following a 1 % rise in output.
Downward pressures on the overall consumer price are also pronounced in March, April and May, a
familiar seasonal picture, reflecting increased food supply. Monthly overall inflation is significantly up in
December in line with increased demand.
pt  0.07 0.46 pt 1  0.07 mts 2  0.04 mts3  0.09 st 1  0.05 st 3
(4.22)
(5.23)
(2.67)
(1.97)
(4.35)
(3.05)
 0.03 st 5  0.03 st 6  0.09 yt  2  0.01 apr  0.01 may
(1.89)
(1.88)
( 3.78)
( 2.22)
( 2.71)
 0.02 dec  0.002 ecmpt 1  0.01 ecmst 1
( 1.57)
(2.86)
( 4.17)
Adj R  0.70; SE  0.01; LM  2 (6)  11.49(0.07);
2
(3.15)
White 2 (13)  13.72(0.06); JB,  2 (2)  3.62(0.16); Chow, F (2.51)  0.06
Ommited var iable  2 (7)  6.97(0.43)
We draw policy implications from these salient observations. In the short-run, inflation is mainly
driven by exchange rate, followed by money and lastly supply factors. In the long-run, it is mainly on
account of supply factors, followed by exchange rate and lastly money. The implication is that, effective
demand management policies could contribute to recording lower inflation at shorter horizons. Tightened
monetary policy, assisted by exchange-rate stability, is a viable avenue in this regard. With supply side
factors taking a pronounced role in the long-run, sustaining the single digit status should be considered a
serious policy challenge. Supply factors in this economy are most characterized by agricultural activities,
which are subjected to a number of shocks. This is in addition to the energy, whose price is mostly
influenced by external demand and supply shocks. Enhanced production, especially in the agricultural
sector, alongside, steady energy supply, in particular of petroleum products, is necessary to attain such a
goal. Exchange rate stability (in the sense that it is not a source of cost-push pressures), is also a necessary
condition. Long-term exchange rate stability is achievable with increased foreign exchange earning
capacity. The Government’s vision of an export-led growth, driven by the private sector is thus
appropriate. The challenge, however, is implementing a balanced exchange rate policy that ensures
exchange rate stability and external competitiveness as well as effectively assists monetary policy in
combating inflation.
Malawi
Monthly data over the period June 1993 to December 2006 is used. The variables are consumer price ( p ),
s
interpolated real GDP ( y ), broad money( m ), nominal exchange rate and South Africa consumer price ( p* ).
63
Based on a 5-variable VAR with 2 lags, a cointegration rank of one established (Table 3.4). Table
1C (Appendix C) suggests that none of the variables are stationary (See also Figure 1C, Appendix C).
Equation (3.16) depicts the statistically validate long-run relationship, which suggests that output, money
and exchange rate as the main driving force of inflation. Supply factors have the largest influence. A 0.7%
decline in inflation is expected to be observed following a 1% growth in real output. A 1% money growth
and exchange rate depreciation causes CPI to rise by 0.5%.
Table 3.4: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
143.60
68.68
1
92.49
47.21
2
55.94
29.38
3
24.91
15.34
4
8.91
3.84
pt  0.49 mts  0.70 yt  0.50 st  0.003 pt*
(0.15)
(0.33)
(0.09)
(3.16)
(0.002)
 2 (1)  3.68(0.06)
We argue that because the estimated dynamic model (3.17) is statically valid---lack of evidence
of serial correlation, heteroscedasticity and structural breaks—and the expected signs of the parameters
conform to the priors, it is a useful guide in discerning the sources of short-run inflation. First is exchange
rate, with the total impact of 0.18%. Specifically, a 1% currency depreciation would induce CPI to rise by
0.1% immediately and by 0.08% after 6 months. Money ranks second. A 1 % money growth would likely
cause a 0.12% rise in consumer prices, with the first effects (0.06%) felt after one month and the rest after
two quarters. Next are supply-side factors. A 1 % decline in output has the potential of fuelling inflation
to tune of 0.04% after 2 months and 0.05% after 5 months. Fourth are seasonal factors. CPI inflation
tends to pick up at the end of the year, plausible reflecting increased demand of commodities during the
festive periods. Improved food supply explains the significant slowdown in inflation in June. An element
of inflation persistence is also reported.
pt  0.10 0.36 pt 1  0.06 mts1  0.06 mts6  0.10 s  0.08 st 6
(1.71)
(6.78)
(2.16)
(1.77)
(3.81)
(2.59)
 0.04 yt  2  0.05 yt 5  0.05 jan  0.04 jun  0.04 sep
( 2.18)
( 2.71)
(7.09)
( 9.65)
(5.66)
 0.02 dec  0.02 ecmpt 1
(0.0001)
( 1.69)
Adj R 2  0.69; SE  0.02; LM  2 (6)  8.27(0.22);
White (14)  16.93(0.26); JB(2)  0.76(0.68);
Chow, F (14,128)  0.61(0.80)
2
64
(3.17)
The key policy implications are that demand management strategy is cardinal in combating
inflation in the short run. Specifically, the tool of monetary policy should be relied upon, combined with a
stable exchange rate strategy. Sustained output growth, stable exchange rate and non-expansionary
monetary policy have the desired results of attaining and sustaining low inflation over time. With the real
sector largely agricultural driven, concerted efforts to mitigate adverse shocks to this sector needs to be
devised. For example, as most Southern African countries face droughts, well managed irrigation
programs are the right direction in these efforts.
Swaziland
Monthly data over the period June 1994 to December 2006 is used. The variables are consumer price ( p ),
s
interpolated real GDP ( y ), broad money( m ), nominal effective exchange rate( neer ), real effective exchange
rate ( reer )real interest rate( r int ) and South Africa consumer price ( p* ).Table 3.5 suggests that there are
only two valid cointegration relations in the analysed data. The analysis of stationarity of the time series is
provided in Table 1D (Appendix D, see also Figure 1D). None of the variables are stationarity, thus
supporting the cointegration approach as the basis for modelling. A price and IS relations are
identified(Table 3.6). In terms of price formation we note following. First, supply-side factors are the
leading indicators of CPI movement. Over time, close to 0.2% slow down in inflation is envisaged in
response to a 1% output growth. Second in importance is exchange rate, with around 90% pass-through
effects reported. Lastly, money and South African price seems to have modest roles in Swaziland’s
consumer price development (at long horizons). The IS relation suggests that what mostly drive aggregate
demand is the real exchange rate.
.
Table 3.5: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
124.87
93.92
1
75.38
68.68
2
43.70
47.21
3
20.70
29.38
4
7.48
15.34
5
1.81
3.84
65
Table 3.6: Identified Long-run Structure
(Standard errors in parentheses)
1
2
p
1
ms
-0.12
(0.04)
1.97
(0.48)
-0.89
(0.16)
y
neer
1
reer
-0.56
(0.06)
0.002
(0.001)
r int
-0.19
(0.08)
p*
 2 (4)  ()
The parsimonious model (3.18) characterizes inflation dynamics. The direction of short-run CPI
largely depends on supply-side pressures. Like in the case of Malawi and Zambia, a slowdown in inflation
is observed during the harvest season (in this case, April and M ay). The statistical insignificance of the
exchange-rate in inflation is also worth reporting.
pt  0.11 0.10 pt 1  0.01 mts 2  0.03 mts3  1.76 yt 5
(2.82)
(0.93)
(1.82)
( 2.21)
(2.82)
 0.001 p  0.004 apr  0.002 may  0.05 ecmpt 1  0.0001 ecmyt 1
*
t
(1.96)
( 2.61)
( 1.81)
( 2.64)
Adj R  0.52; SE  0.007; LM  (5)  9.36(0.15);
2
2
( 1.60)
(3.18)
White  (9)  3.80(0.58) JB  (2)  1.86(0.39);
Chow , F (10,124)  0.46(0.91)
2
2
Omitted : neer ,  2 (7)  10.52(0.06)
The foregoing results provide the following policy suggestions. Containing inflationary pressures
largely depend on the supply-side strategies in the short-run. For example, Swaziland is likely to witness
low inflation in the short-term following increased food supply. In the long-run, prudent monetary policy
has the benefit of achieving and sustaining low inflation, in addition to high output growth and a stable
exchange rate.
Madagascar
Data is monthly for the period January 1990 to December 2006 is used. The variables are consumer price (
p ), interpolated real GDP ( y ), broad money( m s ), nominal exchange rate( s ) and real interest rate( r int ).
A cointegration rank of 2 (Table 3.7) is established using a 5–variable VAR with 2 lags. Based on
Table 1E and Figure 1E (Appendix E), all the variables of interest are non-stationary. The valid long-run
66
relation is identified as price and IS relations (Table 3.8). Long-run inflation is largely influenced by
developments in output, money and exchange rate. It is estimated that a 1% output growth would induce a
0.83% decline in consumer prices. A monetary action of 1 % money expansion would produce inflation
of 0.63%. About 45% of the exchange rate depreciation is likely to be passed-through to consumer prices.
Table 3.7: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
110.92
68.68
1
50.12
47.21
2
20.36
29.38
3
5.61
15.34
4
0.06
3.84
Table 3.8: Identified Long-run Structure
(Standard errors in parentheses)
1
p
1
ms
-0.63
(0.07)
0.83
(0.24)
-0.45
(0.08)
y
s
2
1
0.0004
(0.0001)
r int
 2 (2)  4.21(0.07)
With absence of serial correlation, heteroscedasticity and structural breaks, combined with
conformity in the signs of the estimated parameters, model (3.19) is deemed to offer some intuition on the
sources of inflation in the short-run. Similar to other country models, a pronounced inflation inertia or
expectation in inflation dynamics is pronounced. Output is the leading indicator of inflation. CPI is likely
to jump by 0.4% following a 1 % drop in output two months ago. Demand or nominal factors, that is,
money and exchange rate influence CPI formation. While exchange rate impacts CPI instantaneously, the
money effects are sluggish. A 1 % monetary contraction has the potential of reducing inflation by 0.2%
after one month. Without delay, a 1% depreciation of the nominal exchange rate would result in a 0.06%
upward adjustment of the CPI. The cost-push effects of exchange rate (on consumer prices) increases to
0.1% after 1 month. The influence of seasonal factors could also not be missed.
67
pt  0.33 0.21 pt 1  0.17 mts1  0.44 yt  2  0.06 st
(2.01)
(2.41)
( 2.21)
(2.29)
(1.99)
 0.10 st 1  0.06 st  4  0.009 jan  0.01 feb
(4.75)
(2.03)
(2.21)
(2.27)
 0.02 mar   0.009 aug  0.01 sep  0.01oct
(3.35)
(2.89)
(4.41)
(3.65)
 0.01 dec  0.02 ecmpt 1  0.003 ecmyt 1
( 2.10)
(3.60)
( 2.18)
Adj R  0.58; SE  0.015; LM  2 (4)  10.21(0.06);
2
(3.19)
White  2 (15)  17.39(0.30)
JB  2 (2)  0.14(0.93); Chow , F (16,167)  1.59(0.07)
What are the policy suggestions? Containing inflation depends on both supply-side and demand –
side strategies. That is increased output and tight monetary policy are critical in sustaining low inflation in
this country.
Uganda, Sudan, Kenya and Egypt
We examined the problem of the sources of inflation for Uganda, Sudan, Kenya and Egypt, by
modelling both headline and underlying inflation rates for 1993-2007. We sourced and compiled monthly
data on key variables headline CPI( p ht ),money supply, ( m2 t ), world prices( p t* ), Shs/US$ exchange rate
( s t ), and index of industrial production ( yt ) from the Uganda Bureau of Statistics, the Bank of Uganda,
and the IFS. We compiled world prices on the basis of the weighted average of Uganda’s major trade
partners, as used in the computation of Uganda’s real effective exchange rate. The data for, Sudan, Kenya
and Egypt was supplied by the respective central banks, and was supplemented by data from IFS where
gaps existed. We first examined the unit-root tests for the data (Table 3.9). The results, based on ADF and
PP tests revealed that all the series were mix of I(0) and I(1). Accordingly, this revealed initial
possibilities of a long-run relationship between the main variables of interest (See also Appendix F).
68
Table 3.9: Unit Root Test
Uganda
ADF
PP
0.742
0.707
p
ht
Sudan
ADF
4.744
PP
5.027
Kenya
ADF
1.0537
PP
2.801
Egypt
ADF
3.621
put
1.437
1.499
---
---
3.457
6.003
---
p t*
1.748
1.751
2.479
2.451
0.294
0.357
0.212
yt
1.349
1.153
0.221
0.492
2.279
2.198
1.410
st
0.565
0.792
1.333
1.333
3.855
3.647
0.070
m2 t
0.575
0.582
3.637
3.316
2.321
2.297
0.154
NEERt 0.653
0.893
---
---
----
----
----
p ht
12.607
12.981
7.776
8.154
5.560
2.591
4.009
p ut
14.211
14.204
---
---
9.198
2.236
---
p t*
10.924
10.929
10.429
10.137
9.975
8.864
11.274
yt
14.557
31.385
13.431
25.724
15.558
15.373
10.709
st
10.781
11.404
12.044
12.044
8.406
8.412
11.159
m2 t
14.559
14.708
10.604
10.872
12.425
12.434
13.610
NEERt 10.268
10.259
---
---
---
---
----
Uganda
The results of cointegration based on trace statistics (Table 3.10), revealed that there was one
cointegrating equation(3.20) for headline inflation. Long-run headline inflation is positively driven
largely by money expansion, exchange rate depreciations, and increases in world prices. On the other
hand, it is s subdued by increases in aggregate supply.
Table 3.10: Trace Test for Cointegration Rank Headline CPI
r
Trace
*
C0.95
0
106.4752
88.80380
1
63.13123
63.87610
3
35.44227
42.91525
4
12.39318
25.87211
5
5.690734
12.51798
69
pht  0.239 yt  0.261m2t  0.200 st  1.254 pt*
(3.20)
Equations (3.21) provide the outcomes of the parsimonious results of the dynamic headline
inflation model.
pht  0.022  0.198 pht 1  0.057 yt  2  0.044 m2t  2 0.101 st  0.375 pt*1  0.301 pt* 4
(0.031)
(0.085)
 0.301 p
(0.162)
*
t 6
(0.016)
(0.027)
(0.053)
(0.159)
(0.161)
 0.008 ecmt 1  0.013 Jan  0.007 Feb  0.004 Mar  0.010 May
(0.009)
(0.003)
(0.003)
(0.003)
(0.003)
 0.019 Jun  0.008 Jul  0.005 Aug ;
(0.003)
(0.004)
(0.003)
Adj. R 2  0.52; SE  0.009;
(3.21)
LM  2 (6)  7.29 (0.296);
JB  2 (2)  2.866 (0.239)
White  2  23  20.95(0.854)
Chow F (8,93) 1.503(0.167)
The results reveal that the dynamic headline inflation model (3.21) for Uganda is satisfactory on
the basis of its diagnostic tests. In addition, the estimated coefficients are significant, and consistent with
the assumptions of the model and economic theory. One of the most important findings is that inflation
inertial explains current inflation. Secondly, increases in the activity variable helps to reduce inflation in
Uganda. The measured elasticity of response is 0.057 with a 2-month lag. Thirdly, monetary expansion
contributes to increases in domestic inflation. The measured elasticity is 0.044 with a lag of two months.
Fourthly, nominal exchange rate depreciations partly explain the rise in inflation in Uganda, with a
measured elasticity of response of 0.101. In addition, the response lag is almost contemporaneous.
Sixth, increases in world prices are the most important factor in explaining increases in domestic
inflation in Uganda. The combined elasticity of response is 0.976, with a response lag of between one
month and persisting up to six months suggests that any increases in the world prices almost feed through
to domestic prices. Seventh, there is very small adjustment lag to the long-run equilibrium path of
inflation. The measured effects of elasticity of response are small, (-0.008), and are insignificant. Finally,
seasonal patterns appear to explain domestic inflation in Uganda. The peculiar months of January,
February, March, May, June, July and August all suggest that inflation tends to decline in those months
on account of seasonality in increased food supply.
In regards to the long-run structure of underlying Inflation, the results of the trace statistics
revealed that there was at least one cointegrating equation (Table 3.11)
70
Table 3.11: Trace Test for Cointegration Rank—Underlying CPI
Trace
r
C*
0.95
0
95.88053
88.80380
1
56.75041
63.87610
3
32.49773
42.91525
4
18.09461
25.87211
5
6.470892
12.51798
The results reveal that Uganda’s underlying inflation is driven largely by increases in money
expansion, exchange rate depreciations, and increases in world prices. On the other hand, underlying
inflation is subdued by increases in aggregate supply.
put  0.122 yt  0.179m2t  0.142 st  0.3151 pt*
(3.22)
Equation (3.23) provides the outcomes of the parsimonious results of the model that is
satisfactory on the basis of its diagnostic tests. The estimated coefficients are significant, and are broadly
consistent with the assumptions of the model and economic theory.
put  0.002  0.123 put 1  0.132 put 3  0.017 yt 1  0.024 yt  2  0.016 yt  4
(0.003)
(0.081)
(0.077)
(0.012)
(0.010)
(0.010)
 0.024 m2t  0.025 m2t  2  0.031 m2t  4  0.034 m2t 5 0.048 st 5 0.063 st 6
(0.015)
(0.120)
 0.165 p
(0.087)
*
t 1
(0.100)
 0.112 p
(0.066)
*
t 3
(0.010)
 0.168 p
(0.070)
*
t 4
(0.026)
(0.030)
 0.011ecmt 1 
(0.726)
 0.005 Feb  0.006 Mar  0.006 Aug
(0.002)
(0.002)
(0.002)
Adj. R 2  0.33; SE  0.005;
(3.23)
LM  (6) 1.965 (0.923);
2
JB  2 (2)  1.763 (0.414)
White  2  33  34.40 (0.400)
Chow F (8,93)  0.433(0.825)
The results reveal that inflation inertial tend to explain current inflation, although the measured
effects tend to clear within three months (e.g. the effects are 0.009). Secondly, increases in the activity
variable helps to reduce inflation in Uganda. The sum of the measured elasticity of response is -0.057
(almost the same as for the headline rate of inflation) with lags of between one and four months, the peak
being observed over 3 months. Thirdly, monetary expansion contributes to increases in domestic inflation
with lags of between 0 and 4 months. The sum of the measured elasticity is very small (-0.004), and at
times relatively insignificant. The tightness of monetary policy to contain inflation over the sampled
period appears to explain this result. Fourthly, nominal exchange rate depreciations tend to subdue
71
underlying inflation overall in Uganda. The sum of the measured elasticity of response is very small and
negative (i.e. -0.015), with response lags of 5 and 6 months.
Sixth, increases in world prices, p t* are again found to be the most important source of
underlying inflation in Uganda. The combined elasticity of response is 0.445, with a response lag of
between one month and four months. In line with the assumptions of a small open economy, these
findings reveal that any increases in the world prices would tend to feed through to domestic prices within
four months. Seventh, we found very small adjustment lags to the long-run equilibrium path of underlying
inflation rate. The measured effects of elasticity of response were small, (-0.011), and again very
insignificant. This suggests that price shocks may take a longer period to revert to their long-run
equilibrium path in Uganda. Finally, seasonal patterns appear to explain underlying inflation in Uganda.
The peculiar months of February, March and August all suggest that inflation tends to decline in those
months on account of seasonality associated with other activities in the economy other than food alone
We discern the following policy implications. First, policy measures aimed minimizing
acceleration of domestic prices might help to slow down inflation. Second, measures aimed at increasing
supply response might assist in stabilizing inflation. Third, monetary policy consolidation could assist to
avert inflation. Fourth, policy measures to stabilize the exchange rate could also be useful in stabilizing
inflation as well. Fifth, policy efforts to increase the supply of domestic goods relative to foreign goods in
the economy could assist to cushion against exogenous price shocks. Sixth, with regards to the seasonal
pattern of inflation, this suggests that other structural policy measures aimed at increasing the supply of
other goods, (in addition to food as stated above) could also assist in stabilizing underlying prices of
goods during seasonal price increases.
Sudan
Like for Uganda, the results of cointegration based on trace statistics (Table 3.12), revealed that
there was one cointegrating equation for inflation in Sudan (see results in equation (3.24)). The results
reveal that Sudan’s headline inflation was largely explained by increases in money expansion, increases
in world prices, and exchange rate depreciations. On the other hand, headline inflation is subdued by
increases in aggregate supply, and private sector credit.
Table 3.12: Trace Test for Cointegration Rank—Underlying CPI
r
Trace
*
C0.95
0
106.0406
95.75366
1
64.56161
69.81889
3
37.86663
47.85613
4
17.35482
29.79707
5
6
7.742560
0.016819
15.49471
3.841466
pht  2.88  0.476 yt  2.253m2t  1.050 psct  0.207 st  0.146 pt*
72
(3.24)
The dynamic model is estimated following a general-to-specific ARDL model based on initial
uniform lags of 6, except for the error correction term. Equation (3.25) provides the outcomes of the
parsimonious results of the model.
pht  0.227 pht 1  0.016 yt 1  0.062 yt  2  0.056 yt 3  0.071 yt  4  0.044 yt 5
(0.085)
(0.012)
(0.016)
(0.018)
(0.019)
(0.017)
 0.279 m2t 6  0.124 psct 1 0.025 st 1  0.024 st  4  0.023 st 6  0.003 pt*5  0.093 ecmt 1
(0.087)
(0.060)
(0.013)
(0.013)
(0.013)
 0.077 ecmt 5  0.050 ecmt 6
(0.026)
(0.006)
(0.020)
(3.25)
(0.020)
Adj. R 2  0.47; SE  0.028;
LM  2 (6)  7.29 (0.296);
JB  2 (2)  2.866 (0.239)
White  2  23  20.95(0.854)
Chow F (8,93) 1.503(0.167)
The results reveal that the headline inflation model for Sudan is satisfactory on the basis of its
diagnostic tests (serial autocorrelation, functional specification, normality, and heteroscedasticity, and
parameter stability tests). In addition, the estimated coefficients are significant, and consistent with the
assumptions of the model and economic theory.
Like for Uganda, an important finding is that inflation inertial explains current inflation. The
elasticity of response is 0.227, with a one period lag, and is highly significant. This suggests that policy
measures aimed minimizing acceleration of domestic prices might help to slow down inflation. Secondly,
increases in the activity variable helps to reduce inflation in Sudan, although persistent efforts to increase
output leads to further inflation. This suggests that the Sudan economy could be operating at the
maximum output potential, such that additional efforts to increase output cannot occur without increasing
inflation. The measured elasticity of response is 0.016 with a 1-month lag; and 0.061, 0.70, and 0.044 for
the second, third, fourth and fifth lags respectively. This again suggests that measures aimed at increasing
supply might not assist in stabilizing inflation in Sudan. Thirdly, monetary expansion contributes to
increases in domestic inflation. The measured elasticity of 0.279 with a lag of six months suggests that
monetary policy restraint might assist in reducing the pace of inflation increases in Sudan. Fourthly,
nominal exchange rate depreciations have a mixed impact in explaining inflation in Sudan. The measured
elasticity of response is 0.025; -0.025 and 0.023 for the one, four and six lags respectively. However,
overall, the results suggest that exchange rate depreciations exacerbate inflationary pressures in Sudan.
This suggests that policy measures to stabilize the exchange rate could be useful in stabilizing inflation as
well.
Sixth, an increase in world prices is the least important factor in explaining increases in domestic
inflation in Sudan. The measured effects are very small (-0.003) and insignificant. This suggests that any
increases in the world prices might not affect domestic inflation in Sudan, a factor that might be explained
by the fact that Sudan has been receiving significant amounts of food aid in support of the refugees.
Finally, there is reasonable and significant adjustment lag to the long-run equilibrium path of inflation in
Sudan. The measured effects of elasticity of response are -0.094; 0.077 and -0.050 for one, five and six
period lags respectively, and are insignificant.
Kenya
The results of cointegration based on trace statistics (Tables 3.13 and 3.14) show that there is one
cointegrating equation for both headline and underlying CPI. We note that long-run headline inflation
(equation 3.26) and underlying inflation (equation 3.27) are driven largely by increases in money
expansion, as well as increases in world prices. On the other hand, they are subdued by increases in
73
aggregate output and exchange rate depreciations. The effect of exchange rate depreciations slowing
down inflation could be explained by increased output as explained by the elasticity approach to the
balance of payments: output tends to increase with depreciations of the currency. The large size of the
manufacturing sector in Kenya’s export-oriented economy assists it to respond to exchange rate
depreciations.
Table 3.13: Trace Test for Cointegration Rank—Headline CPI
Trace
r
C*
0.95
0
94.48845
79.34145
1
53.99756
55.24578
3
30.95843
35.01090
4
13.62477
18.39771
5
2.880239
3.841466
Table 3.14: Trace Test for Cointegration Rank—Underlying CPI
Trace
r
C*
0.95
0
108.3831
88.80380
1
61.47543
63.87610
3
37.24410
42.91525
4
16.66685
25.87211
5
3.255004
12.51798
pht  7.991  1.532 yt  0.671m2t  0.182 st  5.744 pt*
(3.26)
put  2.926  0.226 yt  0.055m0t  0.229 st  0.645 pt*
(3.27)
Equation (3.28) and (3.29) are dynamic headline and underlying inflation model, respectively
74
pht 1.2559 pht 1  0.3837 pht  2  0.0438 pht 6  0.0072 yt  2  0.0101 m2t 3
(0.0727)
(0.0770)
(0.0257)
(0.0044)
0.0115 st 3  0.0099 st 5  0.0087 st 6  0.0814 p
(0.0035)
(0.0035)
(0.00347)
(0.0565)
*
t 4
(0.0015)
 0.00038 ecmt  2
(3.28)
(0.000043)
Adj. R 2  0.967; SE  0.00146;
LM  2 (2) 1.2301 (0.561);
JB  2 (2)  41.006 (0.00)
White  2  55   84.874 (0.178)
Chow F (7,148)  0.152 (0.993)
put 1.047 put 1  0.113 put 3  0.007 yt  2  0.008 yt  4  0.0065 yt 6  0.003 m0t 1
(0.049)
(0.044)
(0.003)
 0.003 m0t  4 0.004 st  0.048 p
(0.0014)
(0.0019)
(0.135)
*
t 4
(0.002)
(0.002)
(0.0015)
 0.011 ecmt 1  0.018 ecmt 3  0.029 ecmt 5
(0.005)
(0.007)
Adj. R  0.988; SE  0.0008;
2
(0.005)
(3.29)
LM  (6)  2.99 (0.861);
2
JB  2 (2)  605.2 (0.00)
White  2  75  158.95(0.180)
Chow F (7,148)  0.152 (0.993)
The corresponding dynamic model of headline inflation for Kenya is satisfactory on the basis of
its diagnostic tests (serial autocorrelation, functional specification, heteroscedasticity, and parameter
stability tests). In addition, the estimated coefficients are significant, and consistent with the assumptions
of the model and economic theory.
Like for Uganda and Sudan, an important finding is that inflation inertial from the past periods
explain most of the current inflation in Kenya. The elasticity of response is 1.259, with a one period lag,
and is highly significant. The effects of lags tend to persist for up to six months. These results suggest that
policy measures aimed minimizing acceleration of domestic prices in Kenya could assist to slow down
inflation. Secondly, increases in the activity variable is associated with increases in inflation in Kenya,
suggesting that efforts to increase output leads to further inflation. This suggests that the Kenya economy
could be operating at the maximum output potential, such that additional efforts to increase output cannot
occur without increasing inflation. The measured elasticity of response is 0.0072 with a 2-month lag. This
again suggests that measures aimed at increasing supply might not assist in stabilizing inflation in Kenya,
unless second generation reforms to increase capacity and productivity are undertaken. Thirdly, monetary
expansion contributes to increases in domestic inflation. The measured elasticity of 0.0101 with a lag of
three months suggests that monetary policy restraint might assist in reducing the pace of inflation
increases in Kenya. Fourthly, nominal exchange rate depreciations are associated with increases in
inflation in Kenya. The measured elasticity of response is 0.0115; 0.0099; and 0.0087 for the three, five
and six months period lags respectively. Overall, the results from the dynamic model suggest that
exchange rate depreciations exacerbate inflationary pressures in Kenya. This suggests that policy
measures to stabilize the exchange rate could be useful in stabilizing inflation in the short-run.
Sixth, increases in world prices is associated with decreases with explaining increases in domestic
inflation in Kenya. The measured elasticity of response is -0.0814 and significant. This suggests that
increases in world prices might assist in reducing domestic inflation in Kenya. Finally, there is very small
75
adjustment lag to the long-run equilibrium path of inflation in Kenya. The measured effects of elasticity
of response are -0.00038 two periods lags.
The corresponding results of the dynamic model for the underlying inflation rate in Kenya
(equation 3.29) follow a similar pattern as that explained above.
Egypt
The results of cointegration based on trace statistics (Table 3.14), reveal one cointegrating
equation for inflation identified as equation (3.30)). These results suggest that Egypt’s headline inflation
is driven largely by increases in world prices and exchange rate depreciations, and increases in world
prices. On the other hand, headline inflation is subdued by increases in aggregate economic activity as
well as money supply.
Table 3.14: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
102.3541
88.80380
1
53.42450
63.87610
3
27.14132
42.91525
4
12.74009
25.87211
5
2.621433
12.51798
pht 1  69.73  0.0514 yt 1  0.999m0t 1 1.268st 1  18.371 pt*1
(3.30)
The results in equation (3.31) provide the corresponding outcomes of the parsimonious dynamic
headline inflation model.
pht  0.2000 pht 1  0.1047 pht  2  0.2047 pht 3  0.1951 pht  4  0.1144 pht 5  0.1082 pht 6
(0.0787)
(0.0793)
(0.0792)
(0.0789)
(0.0792)
(0.0772)
 0.0055 yt 1  0.0425 yt  2  0.0547 yt 3  0.5853 m0t 1 0.7353 st 1  11.0028 pt* 4
(0.0031)
(0.0271)
(0.0263)
(0.5160)
(0.6540)
(9.4845)
 0.5648 ecmt 1  0.5690 ecmt  2
(0.5158)
(0.5157)
Adj. R  0.213; SE  0.0008;
2
(3.31)
LM  (2)  7078 (0.702);
2
JB  2 (2)  3617.2 (0.00)
White  2  75  107.1(0.103)
Chow F (22,134) 1.1311(0.175)
Overall, the results reveal that the headline inflation model for Egypt is satisfactory on the basis
of its diagnostic tests (serial autocorrelation, functional specification, heteroscedasticity, and parameter
76
stability tests). Furthermore, the estimated coefficients are significant, and consistent with the
assumptions of the model and economic theory.
Like for the cases of Uganda, Kenya and Sudan above, the main finding is that inflation inertial,
from the past period explains current inflation in Egypt. The sum of the elasticity of responses was found
to be 0.527, with lags persisting up six periods. This also reveals that policy measures aimed minimizing
acceleration of domestic prices might help to slow down inflation in Egypt. Secondly, increases in the
activity variable, helps to reduce inflation in Egypt. The measured sum of elasticity of responses is 0.0917
with lags of up to three periods. This suggests that measures aimed at increasing supply might assist to
stabilize inflation in Egypt. Thirdly, monetary expansion is strongly associated with increases in domestic
inflation. The measured elasticity of 0.587 with a lag of six months suggests that monetary policy
restraint might assist in stabilizing significantly inflation rates in Egypt. Fourthly, nominal exchange rate
depreciations appear to have a significant impact in reducing inflation in Egypt. The measured elasticity
of response of -0.7353 suggests that policy measures aimed at keeping the exchange rate highly
competitive could assist in stabilizing inflation in Egypt.
Sixth, increases in world prices is not an important factor in explaining slowing domestic
inflation in Egypt. The measured elasticity of response of -11.0028, although very high is very
insignificant, suggesting that any increases in the world prices might not affect domestic inflation in
Egypt. This might be explained by the fact that Egypt produces the bulk of the goods in her basket of
consumer price index. Finally, there is insignificant adjustment lag to the long-run equilibrium path of
inflation in Egypt. The measured effects of elasticity of responses of -0.5648 and -0.5690 for the one and
two period lags respectively, are insignificant.
3.5
Concluding Remarks
T
he results suggest that the sources of inflation vary from country to country. A common
phenomenon is that inflation inertia is a very important factor in explaining current inflation
in all the countries examined. This suggests the need pursue policies aimed at containing
second-round effects of inflation arising from exogenous factors. The effects of monetary
expansion on inflation are also found to be significant, suggesting the need for sustained prudent
monetary policy stance. Thirdly, output is an important factor in slowing down inflation in a number of
countries. This suggests the need for policies to increase output by increasing capacity and productivity.
Fourth, exchange rate is found to be an important factor in explaining inflation, although the effects vary
from country to country. This suggests the need for policies aimed at keeping the exchange rate stable.
Finally, increases in world prices are found to explain inflation in a number of countries, but not all,
suggesting a need for policies to diversify and broaden output of the domestic economies, so as to
mitigate the effects of world prices from feeding into the domestic prices.
3.6.1 References
Adam, C. (1995). ‘Fiscal Adjustments, Financial Liberalization and Dynamics of Inflation Some
Evidence from Zambia’, World Development, Vol. 23. No. 5.
Bank of England (1999). ‘Economic Models at the Bank of England’, Bank of England.
Cohen, D., Hassett, K. and Hubbard, R. (1999). “Inflation and the User Cost of Capital: Does Inflation
Still Matter? In Martin Feldstein, ed., The Costs and Benefits of Price Stability. University of
Chicago Press, for the NBER.
Engle, R.F., and Granger, C.W.J. (1987): ‘Co-integration and Error Correction: Representation,
Estimation and Testing’, Econometrica, 55(2): 251-276.
77
Frain, C.J. (2004). ‘A RATS subroutine to implement the Chow-Lin distribution/interpolation procedure’,
Research Technical paper (2/RT/04), Economic Analysis and Research Department, Central
Bank and Financial Services Authority of Ireland.
Friedman, M. (1991). “Monetarist Economics. Basil Blackwell. Oxford.
Ghosh, A. and Phillips, S. (1998). “Inflation, Disinflation and Growth.” IMF Working Paper May.
Godfrey, L. (1988). ‘Misspecification Tests in Econometrics’, Cambridge University Press, Cambridge.
Hansen, H. and Juselius, K. (2002). ‘CATS IN RATS, Cointegration Analysis of Time Series’, Estima
Evanston, IL.
Henry, D. F. (1995). “Dynamic Economics”, Oxford University Press, New York
Juselius, K. (1992). “Domestic and Foreign Effects on Prices in an Open Economy: The case of Denmark. Journal
of Policy Modelling 1494), 401-428.
Johansen, S. and Juselius, K. (1994). ‘Identification of the Long Run and Short-Run Structure. An
Application to the ISLM Model’, Journal of Econometrics , 63: 7-36.
Moser, G.G. (1995). “The Main Determinants of Inflation in Nigeria”,IMF Staff Working Paper
Vol.42(2).
Mutoti, N. and Kihangire, D(2006). “Macroeconomic Convergence in COMESA.
Mwansa, L (1998). ‘Determinants of Inflation in Zambia’, Ph.D Thesis, Gothenberg University, Sweden.
Ngung’u, N.(1993). ‘Dynamics of Inflationary Process in Kenya’, Ph.D Thesis Gothenberg University,
Sweden.
Stock, J.H. and Watson, W.M.(1999). ‘Forecasting Inflation,’ Journal of Monetary Economics, Vol. 44,
pp. 293-335.
78
Appendix A
ZAMBIA
Table 1B:
Test for stationarity
r
pf
2
ms
y
R
s
r int
p*
1
2
3
4
29.73
16.88
12.83
7.93
25.79
19.69
16.46
9.31
21.84
15.60
12.43
6.15
27.22
19.43
14.29
9.31
20.48
16.65
13.32
7.69
24.42
18.9
11.98
9.21
Test for stationarity
r
p nf
2
ms
y
R
s
r int
p*
1
2
3
4
19.82
15.01
13.10
8.32
23.93
18.39
12.66
9.02
26.55
17.81
13.21
9.44
27.39
20.85
15.98
9.28
21.80
15.11
12.99
8.36
23.53
19.85
12.55
9.30
14.07
12.59
11.07
9.49
14.07
12.59
11.07
9.49
34.40
25.23
17.60
9.32
24.10
15.93
11.93
8.02
79
Appendix B
Table 1B: Trace Test for Cointegration Rank CPI
r
Trace
*
C0.95
0
190.20
123.04
1
108.59
93.92
2
68.92
68.68
3
43.09
47.21
4
16.45
29.34
Table 2B: Test for stationarity
p
r
ms
2
y
R
s
r int
sacpi
1
2
3
4
23.88
18.22
15.57
8.73
20.15
16.48
11.33
8.84
23.89
17.54
15.42
8.52
21.75
15.95
12.41
8.33
20.74
17.82
13.43
8.44
14.07
12.59
11.07
9.49
30.05
22.13
16.11
9.40
22.67
17.11
13.83
8.05
Table 2B: Identified Lon-run Structure
(Standard errors in parentheses)
1
2
p
1
0.002
(0.001)
ms
-0.16
(0.03)
0.40
(0.17)
y
3
1
0.02
(0.01)
1
R
s
-0.38
(0.13)
r int
p*
-0.04
(0.03)
-0.002
(0.001)
0.07
(0.03)
-0.002
(0.001)
-0.003
(0.001)
R*
 2 (8)  12.56(0.13)
80
Appendix C
MALAWI
Table 1C:
r
2
1
2
3
4
Test for stationarity
p
ms
12.59
11.07
9.49
7.81
16.42
10.85
8.33
5.90
24.90
9.31
7.29
3.01
y
R
s
33.96
10.78
9.01
5.29
28.64
9.61
7.32
6.99
37.47
9.32
8.01
2.19
Figure 1C. Variables
LEXCRT
LEVEL
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
DIFFERENCE
0.4
0.3
0.2
0.1
0.0
-0.1
1993
1994
1995
1996
1997
1998
1999
2000
REALRATE
LEVEL
36
24
12
0
-12
-24
-36
-48
-60
1993
1994
1995
1996
1997
1998
1999
2000
DIFFEREN CE
30
20
10
0
-10
-20
1993
1994
1995
1996
1997
1998
1999
2000
81
LCPI
LEVEL
5 .5
5 .0
4 .5
4 .0
3 .5
3 .0
2 .5
2 .0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2004
2005
2006
DIFFEREN CE
0.125
0.100
0.075
0.050
0.025
-0.000
-0.025
-0.050
-0.075
-0.100
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
LMS
LEVEL
1 1 .5
1 1 .0
1 0 .5
1 0 .0
9 .5
9 .0
8 .5
8 .0
7 .5
7 .0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2004
2005
2006
DIFFEREN CE
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
1993
1994
1995
1996
1997
1998
1999
2000
LIPI
LEVEL
5 .1
5 .0
4 .9
4 .8
4 .7
4 .6
4 .5
4 .4
1993
1994
1995
1996
1997
1998
1999
2000
DIFFEREN CE
0.32
0.24
0.16
0.08
0.00
-0.08
-0.16
-0.24
-0.32
1993
1994
1995
1996
1997
1998
1999
2000
82
2001
2002
2003
TB
LEVEL
70
60
50
40
30
20
10
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
DIFFEREN CE
32
24
16
8
0
-8
-16
-24
1993
1994
1995
1996
1997
1998
1999
2000
83
Appendix D
SWAZILAND
Table 1D:
r
2
1
2
3
4
Test for stationarity
p
ms
12.59
11.07
9.49
7.81
37.48
25.37
9.04
3.83
37.60
25.92
9.43
6.87
y
neer
reer
35.30
23.68
8.46
4.50
42.69
30.05
9.30
5.00
43.57
31.09
9.26
5.91
Figure 1D: Variables
TBSA
LEVEL
22
20
18
16
14
12
10
8
6
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2003
2004
2005
DIFFEREN CE
3.2
2.4
1.6
0.8
-0.0
-0.8
-1.6
-2.4
1994
1995
1996
1997
1998
1999
2000
LREER
LEVEL
5 .3
5 .2
5 .1
5 .0
4 .9
4 .8
4 .7
4 .6
1994
1995
1996
1997
1998
1999
2000
DIFFEREN CE
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
1994
1995
1996
1997
1998
1999
2000
84
2001
2002
LNEER
LEVEL
5 .6
5 .4
5 .2
5 .0
4 .8
4 .6
4 .4
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
DIFFEREN CE
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2000
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2003
2004
2005
LM2
LEVEL
1 4 .8
1 4 .6
1 4 .4
1 4 .2
1 4 .0
1 3 .8
1 3 .6
1994
1995
1996
1997
1998
1999
DIFFEREN CE
0.4
0.3
0.2
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
1994
1995
1996
1997
1998
1999
2000
LCPI
LEVEL
5 .4
5 .3
5 .2
5 .1
5 .0
4 .9
4 .8
4 .7
4 .6
4 .5
1994
1995
1996
1997
1998
1999
2000
DIFFEREN CE
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
1994
1995
1996
1997
1998
1999
2000
85
2001
2002
Appendix E
MADAGASCAR
Table 1E:
r
2
1
2
3
4
Test for stationarity
p
ms
9.49
7.81
5.99
3.84
25.63
24.75
5.53
1.94
38.53
30.65
3.37
1.41
y
s
r int
33.68
17.96
4.02
1.93
45.51
32.38
5.07
3.03
29.19
23.32
2.51
2.10
Figure 1E: Variables
LEXCRT
LEVEL
8 .4 0
8 .0 5
7 .7 0
7 .3 5
7 .0 0
6 .6 5
6 .3 0
5 .9 5
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
1999
2000
2001
2002
2003
2004
2005
2006
1999
2000
2001
2002
2003
2004
2005
2006
1999
2000
2001
2002
2003
2004
2005
2006
DIFFEREN CE
0.6
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
1990
1991
1992
1993
1994
1995
1996
1997
1998
REALRATE
LEVEL
100
0
-100
-200
-300
-400
-500
-600
-700
-800
1990
1991
1992
1993
1994
1995
1996
1997
1998
DIFFEREN CE
30
20
10
0
-10
-20
-30
-40
1990
1991
1992
1993
1994
1995
1996
1997
1998
86
LGDP
LEVEL
7 .9 5
7 .9 0
7 .8 5
7 .8 0
7 .7 5
7 .7 0
7 .6 5
7 .6 0
7 .5 5
7 .5 0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2003
2004
2005
2006
DIFFEREN CE
0.036
0.024
0.012
-0.000
-0.012
-0.024
-0.036
-0.048
-0.060
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
LM3
LEVEL
8 .0
7 .5
7 .0
6 .5
6 .0
5 .5
5 .0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2003
2004
2005
2006
2003
2004
2005
2006
2005
2006
DIFFEREN CE
0.12
0.10
0.08
0.06
0.04
0.02
0.00
-0.02
-0.04
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
LCPI
LEVEL
5 .5
5 .0
4 .5
4 .0
3 .5
3 .0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
DIFFEREN CE
0.125
0.100
0.075
0.050
0.025
-0.000
-0.025
-0.050
-0.075
-0.100
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
87
2000
2001
2002
2003
2004
4.0
Transmission Mechanism
of Monetary Policy
Noah Mutoti
and
Michael Etingo-Ego
4.1 Introduction
T
his paper discusses the transmission of monetary policy, i.e. how policy induced changes in
nominal money stock or a short-term interest rate are transmitted through the economy,
affecting aggregate demand, inflation expectation and the rate of inflation in selected
COMESA member states. Argued in Mahadeva and Sinclair (2002), it is vital that central
banks understand the transmission mechanism of monetary policy so that they know which variables
respond to policy instrument changes, such as interest rates or money, when , why and how predictable.
It is also important to understand what monetary policy can do and what it should do to stabilize inflation
and output. Blinder (1998) stresses that a central bank’s interest in the monetary transmission process
arises from its desire to know how to position its policy instrument in order to keep inflation in the future
close to the target, while avoiding any excessive destabilization of output. In sum, to effectively conduct
monetary policy, it essential to understand the transmission channel through which monetary policy
actions affect aggregate demand in an economy and ultimately inflation.
Though the monetary transmission mechanism has been a subject of much research over a
number of years (e.g. Taylor (1995), Lütkepohl and Wolters (2003)), in developing countries, especially
Sub-Saharan Africa, very little is known about issues central to underlying monetary policy. In particular,
how the economy responds to shocks, the relative importance of various transmission channels, the
magnitude and timing of monetary policy effects and the effectiveness of various policy instruments are
less understood. Recently, however, with the deregulation of financial markets and monetary policy more
oriented towards market-based operations, there has been increased interest in understanding the working
of such economies (e.g. Mutoti (2005), Simatele (2004) Etingo-Ego (2000) and Mahadeva and Smidkova
(2000)).
Contributing to this literature, the overall objective of the study is to ascertain the differences and
commonalities of the transmission process of monetary policy in COMESA member countries. The
specific objectives are threefold. First, it is to review the existing literature on monetary transmission
mechanism in each member country. Second is to estimate an empirical model of the transmission process
of monetary policy for selected COMESA member countries. Third is to make policy recommendations.
In doing so, address the following questions: What is impact of money supply on inflation? How long it
takes for monetary policy action to affect inflation? Is the influence of a change in money supply/policy
rate on inflation temporary or permanent? Finally, it is to recommend an appropriate monetary policy
framework to achieve efficient transmission of monetary policy given the targets of price stability in each
member country.
88
The remainder of this article is structured as follows. Section 4.2 reviews the literature of the
transmission process of monetary policy. The econometric framework follows. Section 4.4 provides the
empirical result. Section 4.5 concludes.
4.2 Overview of the Monetary Policy Transmission
T
he monetary transmission mechanism describes how policy-induced changes in nominal
money stock or short-term interest rate impact real gross domestic product (GDP) and
inflation. Many, including Mishkin (1995) argue that the channels of monetary policy
transmission are interest rate or money, exchange rate and credit channels. The basic reason
behind the interest rate (or money) channel is as follows: given some degree of price stickiness, a policy
induced increase in the short-term nominal interest rates initially leads to an increase in longer-term
nominal interest rates, in line with the expectations hypothesis of the term structure. This translates into
an increase in the real interest rate. As firms face increased real cost of borrowing, they cut back on their
investment expenditures. Likewise, households scale back on their purchases of homes, automobiles, and
other durable goods. Consequently, aggregate demand falls and so does inflation. This is precisely the
mechanism embodied in conventional specifications of the “ÏS curve”—whether of the “Old Keynesian
“variety, or the forward-looking equations at the heart of the” New Keynesian” macro models.
In open economies, the effects of a policy-induced change in the short-term interest rate are
transmitted through the exchange rate channel. When the domestic nominal interest rate rises above its
foreign counterpart, the domestic currency is expected to appreciate--this is the condition of uncovered
interest parity. This makes imports more attractive than exports, causing output and as well as inflation to
fall.
The effects of monetary policy to the real sector are also propagated through two distinct credit
channels, bank lending and balance sheet channels. These channels emphasize how asymmetric
information and costly enforcement of contracts creates agency problems in financial markets (Bernanke
and Gertler (1995)). According to the lending view, banks play a special role in the economy not just by
issuing liabilities—bank deposits—that contribute to the broad monetary aggregates, but also by holding
assets—bank loans—for which few close substitutes exist. For especially small banks, deposits represent
the principal source of funds for lending and for many small firms, bank loans represent the principal
source of funds for investment. Suppose an open market operation leads to contraction in the supply of
bank reserves. If banks cannot offset a decline in reserves by adjusting securities holdings or raising funds
through issuing non-reservable liabilities, bank lending must contract. Because small firms have
difficulties obtaining funding from non-bank sources, a contraction in bank lending will force them to
contract their activities. Consequently, employment and output will fall.
Bernanke and Gertler (1995) describe a broader credit channel, the balance sheet channel. This is
based on the theoretical prediction that the external finance premium facing a borrower depends on
borrowers’ financial position. The greater is the borrower’s net worth the lower the external finance
premium should be. In the presence of financial market imperfections, a firm’s cost of credit, whether
from banks or any other external source, rises when the strength of its balance sheet deteriorates. A direct
effect of monetary policy on the firm’s balance sheet comes about when an increase in interest rates
works to increase the payments that the firm must make to service its debt. An indirect effect arises when
the same increase in interest rates works to reduce the capitalized value of the firm’s long-lived assets.
Hence, a policy-induced increase in the short-term interest rate not only acts immediately to depress
spending through the traditional interest rate channel, it also acts, possibly with a lag, to raise each firm’s
cost of capital through the balance sheet channel, deepening and extending the initial decline in output
and employment.
We end this section with a discussion on the demand for money Analysis. The existence of a
well-specified demand for money is very important for the conduct of monetary policy, whether the
89
central banks’ major policy variable is the stock of money or the official interest rate. There is growing
evidence in the literature that disequilibrium in the money market can affect the future output gap and/or
inflation. Goldfeld (1994) considers that the relation between the demand for money and its main
determinants is an important building block in macroeconomic theories and is a crucial component in the
conduct of monetary policy. As a result, the demand for money is one of the topical issues that have
attracted the most attention in the literature both on developed and developing countries. In the context of
developed countries it is argued that disequilibrium in the demand for money (defined as the difference
between the real money stock and the long-term equilibrium real money stock) may affect the efficacy of
interest rate policy in the long run via its impact on output gap and/or inflation. There are a number of
studies that highlight the importance of the demand for money in developed countries because the "real
money gap" (the resulting residuals from the money demand function) helps to forecast future changes in
the output gap and/or inflation (Laidler, 1999, Gerlach and Svensson, 2002).
From standard IS-LM models and their extension to open economies in the Mundell-Fleming
manner to international monetary models and new classical models, money plays a central role. The
demand for money serves as a conduit in the transmission mechanism. In particular, the stability of the
money demand function is critical if monetary is to have predictable effects over time on real output and
the price level. As well as being at the heart of the issue of monetary policy effectiveness, the demand for
money is important in assessing the welfare implications of policy changes and for determining the role of
seigniorage in an economy.
4.3 Econometrics Approach
A
structural vector auto regression (SVAR) model to measure the inflation and output effects
of monetary policy shocks is relied upon because of its wide applicability to test hypotheses
on the monetary transmission mechanism. Consider a set of I(1) domestic variables, xt and
a set of I(1) exogenous world variables, xt* and time periods t  1, 2,...T . Define zt as ( xt, , xt*' ) , a k x 1
vector. Assume that there are r long-run cointegrating equilibrium relationships and define the r x 1 set
of I(0) variables:
 t   zt
(4.1)
For simplicity, assuming the maximum lag length of 1, then the structural form of the system is a
cointegrating vector autoregressive distributed lad (VARDL) model:
0 xt    b t 1  1xt 1  0 xt*  1xt*1  t
(4.2)
where t is the n x 1 vector of unobserved structural disturbance, with E (tt, )   ,
0 , b, 1 , 0 and 1 are structural parameters and  is a vector of constants. Multiplying (4.2) by  01
produces the reduced-form vector error correction model (VECM), in the sense of Engle and Granger
(1987) and many others:
xt     t 1  1xt 1   0 xt*  1xt*1   t
90
(4.3)
where   01 ,   01b, 1  011 ,  0  01 0 , 1  011 and  t  01t is a n1 vector of
VECM residuals with covariance matrix  .
Estimating (4.3) is all what is needed for pure forecasting purposes. However, for policy-making,
one needs to estimate the structural coefficients as well as recover the shocks. This proceeds as follows.
By Granger’s representation theorem (Engle and Granger(1987)), VECM(4.3) has a moving average
(MA) representation:
xt  C ( L) t
(4.4) .
and a structural MA representation (5):
xt  ( L)t
(4.5)
where  ( L)  C ( L)01  ( I  C1 L)01 .
Imposing restrictions on both  01 and the covariance matrix of the structural shocks identifies the
structural model. There are n 2 elements in 0 and n(n  1) 2 unique elements in i , but only
n(n  1) 2 unique elements in  , implying n 2 restrictions are required for model identification. As
usually, assuming the structural shocks are orthogonal and setting the diagonal elements of 0 to unity,
since each structural equation is normalized on a particular endogenous variable, generates n(n  1) 2
additional restrictions, leaving n(n  1) 2 restrictions to achieve complete uniqueness of the structural
shocks (that is, exactly identify the model).
There are a number of ways to generate the remaining restrictions. Bernanke (1986) suggests
imposing contemporaneous restrictions on the  0 matrix. Blanchard and Qual (1989) method achieves
identification by imposing zero restrictions on the long-run effects  1 (1) of the structural shocks on the
level of particular endogenous variables. This formulation requires that the level of the endogenous
variables that such restrictions are imposed on are non-stationary, but not cointegrated so that their first
differences enter the reduced form VAR. The Gali (1992) method uses the Bernanke contemporaneous
restrictions and the Blanchard and Qual long-run restrictions.
The King et al (1991) method, takes into account any cointegrating relationships among the nonstationary variables included in the system. Let k  n  r be the number of common stochastic trends
driving xt . Partitioning t into two components (tP ' ,tT ' ) , tp contains k permanent shocks and tT the
r transitory shocks (Stock and Watson (1988)). Following King et al. (1991), (4.6) and (4.7) guides
recovering tp :
t  0 t
(4.6)
(1)  C(1)01    0
(4.7)
where  is a n  k matrix of long-run multipliers and 0 is a n  (n  k ) matrix of zeros associated
with tT . Since  is determined by the condition that its columns are orthogonal to cointegrating
vectors, tp represents innovations to long-run components of t . Adopting three sets of
91
assumptions exactly identify tp . First, tp and tT are uncorrelated. Second, tp , are mutually
uncorrelated. Third,  is lower triangular, permitting writing   * and thus
 (1)   *  0 
(4.8)
where * is a known n  k with full column rank and  is a k  k lower triangular matrix with full rank
and 1’s on the diagonal.
4.0
Empirical Results
Zambia 12
We use monthly data over the sample period 1992-2003, corresponding to monetary targeting and
the post-liberalization era. Domestic output ( yt ) is real gross domestic product (GDP).13 Domestic price
( ptc ) is Zambia’s consumer price (CPI), whereas the measure of money stock ( mt ) is broad money.
Domestic interest rate ( Rt ) is the 3-month Zambian Treasury bill rate and the nominal exchange rate ( st )
is between Zambian Kwacha and South African Rand. Accordingly, foreign price ( p* ) is South African
CPI and the 3-month South African Treasury bill rate captures foreign interest rate ( Rt f ).14 Bank of
Zambia (BOZ) and IMF International Financial Statistics (IFS) publications were the main data sources.
The empirical undertaking commenced by investigating the long-run structure of the data in terms
of cointegration relations, interpretable as deviations from steady state relations. With the appropriate lag
length chosen using the Schwarz criterion (SC) and Hanna-Quinn (HQ), reported in Table 1A(appendix),
the VECM is estimated with two lags, and the variables ordered as X t  [ p* , y, m, p c , R, s, R f ]t' . Table
2A (appendix) shows the Johansen cointegration test (Johansen (1991)) supporting a cointegration rank
r  4 . Since the sample period is small and r  4 is boundary (accepted), it is highly recommended to
examine additional information, including the characteristic roots to check whether estimating under
alternative r  rank ( ) is plausible (Juselius(2003), Johansen (1995), Johansen and Juselius(1994)).
The three largest roots are quite close to unity, indeed validating r  4 (Table 3A, appendix).
We then proceeded to testing whether individual series are stationary by themselves. Since the
number of cointegration relations increases for each stationary variable included in the cointegration
space, the test outcome is useful as a means of identifying the minimal set of variables needed for
cointegration (Hansen and Juselius, 2002). With r  4 , none of the variables are indeed stationary by
themselves(Table 4A, appendix).
Table 4.1 presents the identified long run structure. The 1 column resembles a money demand
equation, which seems reasonably estimated as all coefficients are significant at the 5 % level and show
expected signs. Similar to Tseng and Corker (1991) and Hubrich (1999) the long-run income elasticity
12
All calculations performed using RATS and CATS Hansen and Juselius (2002))
Monthly output is interpolated by means of the Chow-Lin distribution/interpolation procedure (Frain
(2004), using copper output as indicator variable. Like in the Bank of England(1999) and Stock and
Watson(1999), potential output is assumed to be constant.
14
Foreign influences are assumed to emanate from South Africa as it is by far Zambia’s most important
trading partner--share of trade 60 % of Zambia’s imports come from South Africa.
13
92
exceeds one (i.e., 1.15), suggesting monetary wealth has been growing faster than real GDP.15 There is
strong support for currency substitution effects, making exchange rate an important determinant of money
holdings. The income specification,  2 column, is also well defined. This specification is justified
assuming in the long run nominal interest rate equals real interest rate. The  3 column is an exchange rate
relation.
Like in Dekle et al. (2001), it posits a link between interest differential and nominal exchange
rate, thereby capturing the idea that higher domestic interest rate makes domestic assets more attractive,
causing an appreciation and vice versa. The  4 column gives an impression of South African policy
reaction function.
The loading vectors too look sensible. The parameter 13 depicts money stock equilibrium error
correcting to agents’ demand for money. Inferred from 12 and 14 excess money, measured as
deviations from long-run money demand, significantly increases real aggregate demand and consequently
inflation. The estimated  22 and  24 suggest deviation from long-run goods market equilibrium is error
correcting and has expected effects on inflation. The exchange rate, on the other hand, does not
significantly respond to disequilibrium in the goods market. The negative coefficient  23 is also as
expected. Bank of Zambia (BOZ) reacts to excess demand by monetary tightening. The coefficient  36
shows exchange rate is error correcting and, intuitively, excess demand for exchange rate significantly
increases inflation (  34 ). Finally, South African interest rate insignificantly affects Zambian
macroeconomic variables.
Table 4.2 confirms the statistical validity of VECM specification. The Godfrey (1988)
multivariate Lagrange Multiplier (LM) test indicates lack of evidence of either first or fourth order
residual autocorrelation. Further, the Jarque-Beta multivariate normality test does not rejects the null of
normality. The model ‘s statistical adequacy is finally investigated by recursive stability test. Constancy
of the restricted  cannot be rejected at the 10 % level (Figure 4.1).
15
Tseng and Corker(1991) argues that an income elasticity of money demand exceeding one, reflects higher
savings. This has not been established in this study.
93
Table 4.1: Identified Long Run Structure
Cointegrating vectors:
(Standard errors in parentheses)
1
2
3
4
0
0
0
1
0
m
-1.15
(0.20)
1
-0.15
(0.05)
0
0
0
0
pc
-1
0
0
0
R
0.13
(0.00)
0.07
(0.01)
0
0.52
(0.12)
0
0.61
(0.00)
1
0
-0.18
(0.00)
(t-values in parentheses)
1
p
*
y
s
Rf
p*
y
m
p c
R
s
R f
0
0
1
2
3
4
-0.01
(-1.39)
0.01
(3.40)
-0.06
(-3.51)
0.05
(3.12)
0.15
(2.73)
0.14
(1.75)
0.02
(0.42)
0.02
(1.88)
-0.02
(-2.27)
-0.03
(-0.41)
0.12
(3.79)
0.04
(4.99)
-0.12
(-0.76)
-1.33
(-1.06)
-0.002
(-2.47)
0.001
(2.30)
-0.02
(-1.18)
0.08
(3.88)
0.78
(4.94)
-0.01
(-0.16)
0.19
(0.61)
0.00
(-0.76)
0.00
(1.65)
-0.003
(-2.38)
0.00
(-0.38)
0.54
(2.23)
0.003
(1.08)
0.06
(-2.60)
 2 (9)  7.80
p  value  0.08
94
Table 4.2: Misspecification Tests
Multivariate tests
Residual autocorrelation
LM1 (49)
44.96(p=0.64
)
LM 4 (49)
65.23(p=0.16)
Normality
 2 (14)
20.58(p=0.06)
Univariate tests
Normality(2)
ARCH(2)
Skewness
Kurtosis
p*
4.62
3.08
-0.31
3.38
0.83
y
4.82
1.79
0.16
1.07
0.68
m
3.25
3.01
-0.42
3.19
0.85
p c
0.07
3.24
-0.09
3.53
0.82
R
2.42
2.17
0.25
2.91
0.79
s
4.38
2.35
0.29
3.50
0.75
R f
0.49
2.18
0.47
5.02
0.80
R2
At 5% significance level , critical value for  2 (2)  5.99
Figure 4.1: Recursive Test for Constancy of the Identified 
Test of known beta eq. to beta(t)
4.5
BETA_Z
4.0
BETA_R
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1998
1999
2000
2001
2002
2003
1 is the 5% significance level
What are the implications of the cointegration results for the long-run transmission of Zambia’s
monetary policy? First, a stable money demand exits, satisfying one key precondition for the policy of
monetary targeting by the Bank of Zambia (BOZ). As Juselius (1996) argues, this implies money growth
95
has a predictable impact on economic activity and hence on excess demand in the economy. Second,
demand for money is interest sensitive, suggesting the LM curve is not steep and changes in money
supply have lower effects. Third, inflation is associated with excess demand in the money and goods
market as well as disequilibrium in the exchange rate market, and thus it is not necessarily a monetary
phenomenon. Fourth, although we do not explicitly identify the money-based rule, there is a feedback
between nominal money and inflation. It is observed that higher price leads BOZ to lowering money
supply. Finally, the impact of developments in foreign interest rate on domestic variables is insignificant.
To what extent shocks to money play a role in the transmission process is the topic of the structural
analysis.
In terms of structural Analysis16, the cointegration result suggests the economy is driven by three
permanent shocks (i.e. aggregate supply, monetary policy and foreign price shocks) and four transitory
shocks (i.e. money demand, IS, exchange rate and foreign interest shocks). In the notation of
Section 3, ordering xt  [ p* , y, m, p c , R, s, R f ]t'  in (4.9), the 7  3 matrix of long-run multipliers is a
product of * and  17. From * , the first shock is a foreign price shock. In the long run, it increases
foreign price by 1 % and foreign interest rate by 0.15 basis points. Through an exchange rate relation, it
depreciates domestic currency by 0.03 % and through a money demand relation increases consumer price
marginally. The second shock is a domestic aggregate supply shock, predicted to raise domestic output by
1 % and through an IS relation reduces domestic interest rate by 1.92 basis points. Further, through an
exchange rate relation depreciates nominal exchange rate by 1.17 % and through a money-demand
relation reduces consumer price by 1.32 %. The third shock is a money supply shock. A one-to-one
relation is assumed between money and consumer price (see equation 4.1).
0
 1

1
 0
 0
0

3
   1.9 x10
 1.32
 0
 1.92

1.17
 0.03
 0.15
0

0

0
1   11

1    21
0    31

0
0 
0
 22
 32
0 

0 
 33 
(4.9)
We observe two major assumptions implied in  . First the small country assumption, that is,
foreign price does not react to domestic aggregate supply and money supply shocks in the long run.
Second the neutrality assumption. Output is long run neutral with respect to money supply.
Adopting the following assumptions, which generates r (r 1) 2  6 , exactly identifies the four
transitory shocks.
1). Foreign interest rate does not respond contemporaneously to all domestic transitory shocks.
2). Domestic price only responds contemporaneously to domestic shocks.
3). Domestic interest rate responds on impact to all the shocks, except foreign price shock.
4). Because of expectations, exchange rate responds contemporaneously to all the shocks.
16
Throughout the discussion, domestic consumer price is referred to as consumer price.
17
To obtain
* we re-write money demand in terms of p c and IS in terms of R .
96
Figures 4.2-4.8 plot impulse responses. Whereas dark lines represent point estimates of responses
of the level of each variable to a one standard deviation positive shock, upper and lower dashed lines
enclose the 95 % confidence bands constructed from Monte Carlo integration. With few exceptions, the
responses match theoretical priors and conform to the identification strategy. Absence of puzzle about the
relationship between a monetary policy shock, identified as innovation to money supply, interest rate and
exchange rate responses, suggests the model correctly identifies the policy shock. The results are also
consistent with the small country assumption----foreign variables hardly respond to domestic shocks.
Figure 4.2 shows impulse responses of variables to a monetary policy shock accords with a
number of studies (e.g. Cushman and Zha (1997), Bagliano and Fevero (1998)). The sharp increase (0.7
%) in money stock is accompanied by a significant fall in domestic interest rate that lasts one year. With
foreign interest rate almost unchanged initially, domestic interest rate reduces below foreign interest rate
by almost 0.5 basis points. This induces temporary currency depreciation, reaching the peak value of 1.7
% after 4 months. Though output barely rises, consumer price reacts significantly over 4 months,
contemporaneously by 0.6 % and 4 months later recording the peak increase of roughly 1 %. By
construction, the impact of this shock on real balances is positive and persistent. Whereas output rapidly
returns to its steady state, consumer price persistently rises, though marginally.
Similar to the results for developed economies (e.g. Blanchard and Qual (1989), Gali (1992) and
Astley and Garrant (2000) and Cameroon et al. (2002)), the impact of an aggregate supply shock has the
expected sign, increasing output and reducing consumer price (Figure 4.3). Output is raised by 0.27 % at
first and by 0.5 % over time. Consumer price, which barely falls on impact, records the maximum
response of 3.5 % after 15 months, before settling down to a value of approximately 2 % above the
baseline 3 years later. Nominal money rises significantly over a short period, suggesting in the short run a
beneficial supply shock induces commercial banks to create more loans (Keating (2000)). With lower
domestic interest rate, nominal exchange rate depreciates significantly over 4 months. Finally, this shock
affects real money through its impact on economic activity, interest rate and exchange rate, with a cutback
of around 1.5 % being the overall outcome.
Like in Gavosto and Pellegrini (1999), the effects of an IS shock on output and consumer price
are fast and strong. Output initially rises by 0.3 % and 4 months later attains an utmost response of about
0.4 %, thereafter returning to zero with some fluctuations (Figure 4.4). On impact, consumer price is
raised by 0.11 % and 5 months afterwards by a maximum of 3.5 %. Whereas output gradually returns to
its steady state, the increase in consumer price dies away rapidly. Reflecting higher domestic interest rate,
money supply falls significantly for a short period, nominal exchange rate appreciates significantly for 10
months and real balances fall. Characterizing this shock to have transitory effects is by and large
unfailing.
97
Figure 4.2: Impulse Responses to a Monetary Policy Shock
Output
Consumer Price
0.0100
0.08
0.0075
0.06
0.04
0.0050
0.02
0.0025
0.00
0.0000
-0.02
-0.0025
-0.04
-0.0050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Interest Rate
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
10
Foreign Price
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
Figure 4.3: Impulse Responses to an Aggregate Supply Shock
Output
Consumer Price
0.0100
0.08
0.0075
0.06
0.0050
0.04
0.02
0.0025
0.00
0.0000
-0.02
-0.0025
-0.04
-0.0050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
Interest Rate
15
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
10
Foreign Price
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
98
5
10
15
20
25
30
45
Figure 4.4: Impulse Responses to an IS Shock
Output
Consumer Price
0.0100
0.08
0.0075
0.06
0.04
0.0050
0.02
0.0025
0.00
0.0000
-0.02
-0.0025
-0.04
-0.0050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Interest Rate
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
Foreign Price
10
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
Figure 4.5: Impulse Response to a Money Demand Shock
Output
Consumer Price
0.100
0.08
0.075
0.06
0.04
0.050
0.02
0.025
0.00
0.000
-0.02
-0.025
-0.04
-0.050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
Interest Rate
15
20
25
30
35
40
45
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
Foreign Price
10
15
20
25
30
35
40
45
35
40
45
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
99
5
10
15
20
25
30
Immediately following a money demand shock, real and nominal balances temporarily rise
significantly for about a year (Figure 4.5). Also without delay, output and consumer price rises, the latter
registering the climax response of 3.8 % after 8 months. Unlike the IS shock, the full adjustment of output
and consumer price are rather gradual. With the domestic interest rate going up, a significant currency
appreciation is induced over a short period. Like all domestic shocks, the impact of this shock on foreign
variables is unnoticeable. As expected, the effects of this shock completely diminish over time.
Virtually all impulse responses correspond with the predicted effects of a temporary exchange
rate shock (Figure 4.6). The nominal exchange rate reacts intensively, depreciating by around 6 % on
impact and gradually returns to its pre-shock level. Output rises by a bare margin initially and by 2.5 % 2
quarters later, before slowly falling back to zero after 3 years. Consumer price at first rises by 0.43 %, 4
months later by a maximum of 4 % and 3 years thereafter falls to its pre-shock value. This shock is
followed by temporary lower money supply and higher nominal domestic interest rate, implying monetary
tightening to dampen the anticipated price effects. Transitory lower real balances are also recorded.
Higher foreign price and foreign interest rate accompany a foreign price shock (Figure 4.7),
causing significant currency depreciation over a short period, in turn, raising consumer price by utmost
0.85 % 3 months later and by 0.6 % over time. Since some imports are inputs into production the
resulting higher production costs, reflecting an adverse supply shock, depresses domestic output further
raising consumer price. The recorded monetary policy response is lower money supply, thereby higher
domestic interest rate. By construction, real balances fall. In response to a foreign interest rate shock,
foreign interest rate rises significantly for 6 months, decreasing foreign price (Figure 4.8). A rise in
domestic output is also reported, plausibly, on account of increased net exports associated with lower
import prices. This output response combined with currency depreciation exerts upward pressures on
domestic consumer price.
Figure 4.6: Impulse Responses to an Exchange Rate Shock
Output
Consumer Price
0.0100
0.08
0.0075
0.06
0.04
0.0050
0.02
0.0025
0.00
0.0000
-0.02
-0.0025
-0.04
-0.0050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
Interest Rate
15
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
Foreign Price
10
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
100
5
10
15
20
25
30
Figure 4.7: Impulse Responses to a Foreign Price Shock
Output
Consumer Price
0.0100
0.08
0.0075
0.06
0.04
0.0050
0.02
0.0025
0.00
0.0000
-0.02
-0.0025
-0.04
-0.0050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Interest Rate
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
Foreign Price
10
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
Figure 4.8: Impulse Responses to a Foreign Interest Rate Shock
Output
Consumer Price
0.100
0.08
0.075
0.06
0.04
0.050
0.02
0.025
0.00
0.000
-0.02
-0.025
-0.04
-0.050
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Money
20
25
30
35
40
45
30
35
40
45
35
40
45
35
40
45
Real Money
0.05
0.10
0.04
0.08
0.03
0.06
0.04
0.02
0.02
0.01
0.00
0.00
-0.02
-0.01
-0.04
-0.02
-0.06
0
5
10
15
20
25
30
35
40
45
0
5
10
Interest Rate
15
20
25
Exchange Rate
0.04
0.075
0.03
0.050
0.02
0.025
0.01
0.00
0.000
-0.01
-0.025
-0.02
-0.03
-0.050
0
5
10
15
20
25
30
35
40
45
0
5
Foreign Price
10
15
20
25
30
Foreign Interest Rate
0.0125
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
-0.0075
-0.0100
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
0
5
10
15
20
25
30
35
40
45
0
101
5
10
15
20
25
30
Table 4.3 summarizes variances of output and consumer price explained by each structural shock.
Monetary policy or money supply shocks have relatively little impact on Zambian output, accounting for
less than 10 % of its variance. Money demand shocks only modestly boost short-run output, contributing
utmost 20 % (to its variance). Agreeing with a number of SVAR studies (e.g. Gali (1992), Dhar et al.
(2000) ), output fluctuations are mainly attributable to aggregate supply shocks, underlying 40 % in the
short run (i.e. 12 months) and 82 % in the long run (i.e. beyond 60 months). A key implication (of this
result) is that the business cycle theory arguments that aggregate supply shocks are important in business
cycle frequencies cannot be dismissed. Like in Keating (2000), IS shocks turn out to be the most
important demand factor for the business cycle, responsible for one third of output variability. Unlike
shocks to South African interest rate, South African price shocks have a modest role in domestic output,
especially at longer horizons, accounting for 15 % (of its variance).
Table 4.3: Percentage of Forecast Error Explained by Shocks
Shocks
Variable
Monthsahead
1
Output
6
12
24
36
48
72
Consume
r
Price
1
Aggre.
Supply
45.72
(0.22)
40.85
(0.23)
40.40
(0.24)
42.92
(0.23)
61.75
(0.27)
73.81
(0.27)
82.42
(0.27)
11.03
(0.03)
IS
47.23
(0.10)
25.15
(0.09)
25.25
(0.10)
17.16
(0.11)
4.93
(0.03)
1.77
(0.01)
0.00
(0.00)
11.15
(0.03)
Money
Demand
0.01
(0.04)
12.57
(0.04)
3.37
(0.01)
19.74
(0.04)
20.42
(0.05)
5.90
(0.03)
0.00
(0.00)
46.91
(0.15)
Money
Supply
3.81
(0.01)
1.63
(0.01)
8.42
(0.03)
10.30
(0.04)
0.05
(0.01)
0.02
(0.01)
0.00
(0.00)
17.27
(0.09)
Exch.
Rate
0.15
(0.01)
7.86
(0.03)
8.42
(0.02)
2.58
(0.02)
1.32
(0.01)
1.18
(0.01)
0.00
(0.00)
13.64
(0.04)
Fore.
Price
3.05
(0.10)
8.17
(0.02)
12.12
(0.03)
5.58
(0.03)
9.88
(0.03)
15.75
(0.04)
17.58
(0.06)
0.00
(0.00)
Fore.
Rate
0.03
(0.01)
3.77
(0.01)
2.02
(0.01)
1.72
(0.03)
1.65
(0.01)
1.57
(0.01)
0.00
(0.00)
0.00
(0.00)
6
19.31
8.70
28.99
2.42
33.82
3.86
2.90
(0.05)
(0.03)
(0.14)
(0.08)
(0.05)
(0.01) (0.01)
12
31.05
3.66
28.21
4.63
26.02
3.25
3.18
(0.09)
(0.04)
(0.11)
(0.06)
(0.04)
(0.01) (0.01)
24
37.80
0.15
14.18
4.73
33.09
5.91
4.14
(0.12)
(0.01)
(0.10)
(0.02)
(0.05)
(0.02) (0.02)
36
55.75
0.13
10.22
5.57
11.61
11.15 5.57
(0.11)
(0.01)
(0.07)
(0.02)
(0.04)
(0.02) (0.01)
48
64.57
0.17
1.99
7.45
2.23
17.38 6.21
(0.12)
(0.01)
(0.03)
(0.03)
(0.01)
(0.03) (0.01)
72
71.06
0.00
0.00
7.89
0.00
21.05 0.00
(0.15)
(0.00)
(0.00)
(0.02)
(0.00)
(0.04) (0.00)
Note: Aggre. Supply =Aggregate Supply; Exch. Rate =Exchange Rate; Fore. Price=Foreign Price;
Fore. Rate=Foreign Interest Rate
The contribution of money supply shocks to the variance of Zambia’s consumer price is only
modestly pronounced in the initial period (17 %). At all horizons, more than 30 % of its variance is due to
102
aggregate supply shocks. Specifically, the share of aggregate supply shocks in consumer price variance at
32 % in the short run increases to slightly over 70 % over time. Money demand and exchange rate shocks
have a large role in the short run, the former responsible for 28-47 % and the latter 34 %. Foreign price
shocks have a modest role especially after 24 months, contributing utmost 21 %. Not surprising, shocks to
South African interest rate are the least indicators of developments in Zambia’s consumer price.
We draw policy implications from two key observations. First, though a positive monetary policy
shock leads to a sharp and persistent rise in money supply, a strong consumer price response is only
recorded in the initial period. Second, money demand is stable and yet the role of money demand shocks
in consumer price is only pronounced in the short run. These suggest a weakened link between money and
inflation, giving rise to situations where getting the monetary target does not produce the desired inflation
outcome and where money fails to produce reliable signals of the stance of monetary policy. Since food
price has the largest share in CPI and the dominant role of exchange rate in inflation dynamics
established, sustaining lower inflation in Zambia requires policies meant at boosting domestic food
supply and stabilizing exchange rate.
Malawi
Based on a 5-variable (money, CPI , exchange rate, Tb,GDP) VAR with 2 lags, a cointegration
rank of one is established (Table 1B). Table 2B (Appendix B) suggests that none of the variables are
stationary. The identified cointegration relation (4.10) suggests the desire to hold real balances is mostly
driven by transaction motive. As exchange rate is insignificant, absence of currency substitution is
inferred in the long-run. Figure 4.9 generally confirms stability in the money demand function except
around 2003.
(m s  p)t  0.76 yt  0.014 Rt
(0.29)
(4.10)
(0.002)
 2 (3)  5.68(0.07)
Figure 4.9: Recursive Test for Stability of the Money Demand
Test of known beta eq. to beta(t)
2.00
BETA_Z
BETA_R
1.75
1.50
1.25
1.00
0.75
0.50
2001
2002
2003
2004
2005
2006
1 is the 5% significance level
To undertake structural analysis, we first take a stand on the five shocks underlying the
economic process. Innovations to money are associated with monetary policy shocks, in light of
103
the monetary targeting framework this country is currently implementing. We identify money
demand shocks as innovations to consumer price. Innovations to output are classified as an
aggregate supply shock. As expected, an exchange rate shocks are reflected in the innovation to
the nominal effective exchange rate. Innovations to the nominal interest rate denote IS shocks.
The following contemporaneous assumptions guide our identification strategy18.
(i) Money supply responds immediately only to a monetary policy shock
(ii) Price responds without delay only to money demand and exchange rate shocks
(iii) Output only responds instantaneously to aggregate supply shocks
(iv) Exchange rate responds immediately to monetary policy, money demand and exchange rate
shocks.
(v) Interest rate responds contemporaneous to a monetary policy, money demand and IS shocks
The foregoing generated 12 restrictions, implying the model is over-identified.
Based on the results  2 (5)  9.22(0.06) the structural model’s over-identifying restrictions are valid.
The suitability of the estimated model for policy analysis relied on the model’s dynamics presented as
Figures 4.10-4.14. Generally, the impulse responses do not depart from the theoretical predictions. The
nominal interest rate falls significantly over six months following a 5% increase in the money stock
caused by a positive monetary policy shock (Figure 4.10). In line with the identification strategy, both
output and consumer price responds sluggishly. While output hardly changes, consumer price increases
significantly over 18 months, recording the maximum response (2.5%) after six months. The inflationary
impact of money is, however, not sustained. Further fuelling inflationary pressures are exchange rate costpush effects. Without
Figure 4.10: Impulse Responses to a Money Policy Shock
Money
Consumer Price
0.075
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
45
0
5
Output
10
15
20
25
30
35
30
35
40
40
Exchange Rate
0.020
0.09
0.015
0.06
0.010
0.005
0.03
0.000
0.00
-0.005
-0.010
-0.03
0
5
10
15
20
25
30
35
40
25
30
35
40
45
0
5
Interest
0.050
0.025
0.000
-0.025
0
18
5
10
15
20
Same identifying strategy is used for Swaziland and Madagascar
104
10
15
20
25
45
45
delay, the nominal exchange depreciates significantly during the first five months (in response to a
monetary policy shock). The foregoing suggests the presence of both the money and exchange channels
of transmission of monetary policy to inflation.
What happens after a money demand shock? Money supply increases to meet demand (Figure
4.11). The extent of excess demand for money is reflected in the immediate consumer price response,
which rises significantly over a year, reaching the climax response of close to 4% within the first 3
months. Characterized as a temporary shock, the effect of money demand on consumer price diminishes
in the long run. After a while appreciation in the exchange rate is observed, reflecting high interest rates
(associated with increased money demand).
The macroeconomic impact of an aggregate supply shock, example, favourable weather, are as
follows. First, output increases significantly over a year, registering the climax response of between 1%
and 1.5% after six months. Second, consumer prices declines by a maximum of 2% after 6 months, and
lowers by 1% over a sustained period after 3 years. Third, money supply increases to support increased
production activities. Fourth, despite lower interest rates, a strong domestic currency is witnessed. This
plausible as expanded activities could be export oriented, thereby increasing the supply of foreign
exchange in the country.
Instantly, the Malawian Kwacha vis-à-vis other currencies weakens significantly in light of a
positive shock to the nominal exchange rate. At the same time, consumer price is raised by 2% and after
3 months by a maximum of 3.3%. Over time, consumer price persistently rises by around 0.5%. Contrary
to expectation, output initially drops and then increases after one year. This is plausible as producers face
high import costs, forcing them to cut down on production. After some time, improved international
competitiveness induces them to increase output. With regards to money supply, it is raised suggesting
that monetary policy has been inactive to mitigate the inflation consequences of currency depreciation.
We note the following responses of the variables to an IS shock, notably a fiscal policy shock. First, the
nominal interest rate rises significantly over close to one year, thereby inducing modest currency
appreciation. Benefiting an aggregate demand shock, output is raised and
105
Figure 4.11: Impulse Responses to a Money Demand Shock
Money
Consumer Price
0.075
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Output
20
25
30
35
40
45
Exchange Rate
0.020
0.09
0.015
0.06
0.010
0.005
0.03
0.000
0.00
-0.005
-0.010
-0.03
0
5
10
15
20
25
30
35
40
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
45
Interest
0.050
0.025
0.000
-0.025
0
5
10
15
20
Figure 4.12: Impulse Responses to an Aggregate Supply Shock
Money
0.075
Consumer Price
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
45
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
0
5
10
15
20
25
30
35
40
30
35
40
Output
0.020
Exchange Rate
0.015
0.10
0.010
0.08
0.005
0.06
0.04
0.000
0.02
-0.005
0.00
-0.010
0
5
10
15
20
25
30
35
40
45
-0.02
0
Interest
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
106
5
10
15
20
25
Figure 4.13: Impulse Responses to an Exchange Rate Shock
Money
Consumer Price
0.075
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
30
35
40
45
Exchange Rate
Output
0.09
0.020
0.015
0.06
0.010
0.03
0.005
0.000
0.00
-0.005
-0.03
-0.010
0
5
10
15
20
25
30
35
40
45
35
40
0
5
10
15
20
25
Interest
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
Figure 4.14: Impulse Responses to an IS Shock
Money
Consumer Price
0.075
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
45
0
Output
5
10
15
20
25
30
35
40
Exchange Rate
0.020
0.09
0.015
0.06
0.010
0.005
0.03
0.000
0.00
-0.005
-0.010
-0.03
0
5
10
15
20
25
30
35
40
45
0
Interest
0.050
0.025
0.000
-0.025
0
5
10
15
20
25
30
35
40
107
5
10
15
20
25
30
35
40
45
45
consumer price rises briefly. The significant lower money supply recorded over a short time, reflect high
interest rate.
Mirroring impulse responses, the variance decompositions (Table 4.4) suggests that output
growth is largely driven by aggregate supply, money demand and IS shocks both in the short and long
run. Sources of inflation in the short-run (i.e. 1 year) are money demand, followed by monetary policy,
exchange rate and aggregate supply shocks. In the long run, it is largely attributable to aggregate supply
and exchange rate shocks.
Table 4.4: Percentage of Forecast Error Explained by Shocks
Variable
Mont
hsahead
1
Monetar
y Policy
Money
Demand
Aggreg.
Supply
Exch.
Rate
0.00
0.00
100.00
0.00
(0.00)
(0.00)
(0.30)
(0.00)
6
0.19
26.08
50.74
0.01
(0.04)
(0.07)
(0.12)
(0.001)
12
0.12
35.39
46.42
0.03
(0.030)
(0.09)
(0.13)
(0.02)
24
0.06
32.06
35.02
0.11
(0.03)
(0.10)
(0.11)
(0.03)
36
0.03
30.23
35.95
0.12
(0.01)
(0.06)
(0.10)
(0.04)
45
0.05
29.01
35.85
0.11
(0.02)
(0.05)
(0.09)
(0.03)
Consumer
1
0.00
52.86
0.00
47.14
Price
(0.00)
(0.16)
(0.00)
(0.11)
6
19.34
28.04
15.28
25.31
(0.06)
(0.14)
(0.06)
(0.10)
12
22.44
23.95
17.26
20.21
(0.05)
(0.11)
(0.05)
(0.09)
24
8.01
6.51
42.94
40.11
(0.04)
(0.04)
(0.08)
(0.11)
36
5.14
4.47
45.33
41.78
(0.03)
(0.05)
(0.08)
(0.13)
45
5.05
4.00
44.77
42.90
(0.02)
(0.03)
(0.07)
(0.15)
Note: Aggre. Supply =Aggregate Supply; Exch. Rate =Exchange Rate
Output
IS
0.00
(0.00)
22.98
(0.11)
18.04
(0.10)
32.75
(0.09)
33.67
(0.08)
33.81
(0.08)
0.00
(0.00)
12.03
(0.04)
16.14
(0.03)
2.43
(0.03)
3.28
(0.03)
3.28
(0.02)
Swaziland
Using similar variables like in Malawi case, one cointegration rank is noted (Table 4.5), identified
as a money demand relation (4.11). It is suggested that the transaction and speculative motives determines
real money holdings in the long-run. The stability test reviews an unstable demand for money function,
especially after 2003 (Figure 4.15)
.
108
Table 4.5: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
82.54
68.68
1
43.70
47.21
2
20.70
29.38
3
7.48
15.34
4
1.81
3.84
(m s  p)t  0.16 yt  0.09 Rt
(0.29)
(4.11)
(0.002)
 2 (3)  3.21(0.08)
Figure 4.15: Recursive Test for Stability of the Money Demand
Test of known beta eq. to beta(t)
5.0
BETA_Z
BETA_R
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
2000
2001
2002
2003
2004
2005
2006
1 is the 5% significance level
Generally, the impulses responses are in accordance with the narrative evidence. With few
exceptions, the characterization of the transmission process accords with theory. The sharp increase (5.5
%) in the money stock is accompanied by an unnoticeable fall in the nominal interest rate. Unsurprisingly
output barely rises (Figure 4.16). Sluggishly, consumer price modestly responds significantly over 12
months, reaching the peak increase of 2% after 6 months. Surprise monetary policy significantly
depreciates the nominal exchange rate by a maximum of about 0.5%.
Figure 4.17 depicts the impact of a money demand shock to be in line with the literature (e.g.
Levin et al. (1999)). Higher nominal interest rate follows a one standard deviation positive shock to
money demand. As this is considered a demand shock, output also rises, attaining the peak response of
around 2% after 7 months. A significant consumer price reaction is also recorded. On impact, it rises by
0.8% to 1% and after that the effects decline and settles at around 0.3%t. Domestic currency after
sometime appreciates, in part, due to higher interest rate.
A one standard deviation positive aggregate supply shock significantly raises output and reduces
consumer price (Figure 4.18). Output, which barely rises on impact, attains the maximum response of
0.6% 10 months later. Consumer price falls significantly over 35 months. In the long run, output rises by
0.5% and consumer price falls by 0.4%. In response to this shock, money supply falls and thereafter rises,
suggesting that it takes time for commercial banks to accordingly react to a beneficial supply shock by
109
creating more loans. In support of increased output, currency depreciation is significantly registered over
time.
The main consequences of a positive exchange rate shock are summarized in Figure 4.19. First,
nominal exchange rate strongly depreciates. Second, consumer price rises on impact and sustains a rise
from the 20th month. Third, output modestly rises. Fourth, following high money supply interest rate falls
eventually. A positive IS shock causes higher nominal interest rate, lower money supply and appreciated
domestic currency (Figure 4.20). Output increases by a maximum of 0.1% after 6 months. With a delay,
consumer price barely rises (in light of an IS shock).
Figure 4.16: Impulse Responses to a Monetary Policy Shock
Money
0.06
0.05
Consumer Price
0.04
0.010
0.03
0.008
0.02
0.006
0.004
0.01
0.002
0.00
0.000
-0.01
-0.002
-0.004
-0.02
0
5
10
15
20
25
30
35
40
-0.006
45
0
5
10
15
Output
20
25
30
35
40
45
Interest Rate
0.0100
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
0
5
10
15
20
25
30
35
40
0
45
Exchange Rate
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
0
5
10
15
20
25
30
35
40
45
110
5
10
15
20
25
30
35
40
45
Figure 4.17: Impulse Responses to a Money Demand Shock
Money
Consumer Price
0.06
0.010
0.05
0.008
0.04
0.006
0.03
0.004
0.02
0.002
0.01
0.000
0.00
-0.002
-0.01
-0.004
-0.02
-0.006
0
5
10
15
20
25
30
35
40
45
0
5
10
Output
15
20
25
30
35
40
45
35
40
45
Interest Rate
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.100
0.075
0.050
0.025
0.000
-0.025
-0.050
0
5
10
15
20
25
30
35
40
45
30
35
40
45
0
5
Exchange Rate
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
0
5
10
15
20
25
111
10
15
20
25
30
Figure 4.18: Impulse Responses to an Aggregate Supply Shock
Money
Consumer Price
0.06
0.010
0.05
0.008
0.04
0.006
0.03
0.004
0.02
0.002
0.01
0.000
0.00
-0.002
-0.01
-0.004
-0.02
-0.006
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
Interest Rate
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
Output
0.0100
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
0
5
10
15
20
25
30
35
40
45
0
Exchange Rate
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
0
5
10
15
20
25
30
35
40
45
112
5
10
15
20
25
30
35
40
45
Figure 4.19: Impulse Responses to an IS Shock
Money
0.06
Consumer Price
0.05
0.010
0.04
0.008
0.03
0.006
0.02
0.004
0.002
0.01
0.000
0.00
-0.002
-0.01
-0.004
-0.02
-0.006
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
Output
0.0100
Interest Rate
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.0075
0.0050
0.0025
0.0000
-0.0025
-0.0050
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
Exchange Rate
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
0
5
10
15
20
25
30
35
40
45
Figure 4.20: Impulse Responses to an Exchange Rate Shock
Consumer Price
Money
0.06
0.010
0.05
0.008
0.04
0.006
0.03
0.004
0.02
0.002
0.01
0.000
0.00
-0.002
-0.01
-0.004
-0.006
-0.02
0
5
10
15
20
25
30
35
40
0.0050
0.0025
0.0000
-0.0025
-0.0050
10
15
20
25
30
35
40
45
0
5
Exchange Rate
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
0
5
10
15
20
10
15
20
25
30
35
40
45
0.6
0.5
0.4
0.3
0.2
0.1
-0.0
-0.1
-0.2
-0.3
0.0075
5
5
Interest
Output
0.0100
0
0
45
25
30
35
40
45
113
10
15
20
25
30
35
40
45
45
It is suggested in Table 4.6 that growth in output is largely attributed to aggregate supply shocks.
However, in the short run, it is also modestly on account of money demand and IS shocks. Sources of
inflation are money demand and aggregate supply shocks in the short run. Over time, there are aggregate
supply shocks, followed by IS shock, money demand and to a less extent monetary policy shocks.
Table 4.6: Percentage of Forecast Error Explained by Shocks
Variable
Mont
hsahead
1
Monetar
y Policy
Money
Demand
Aggreg.
Supply
Exch.
Rate
0.00
0.00
100.0
0.00
0.0)
(0.00)
(0.01)
(0.01)
6
5.01
17.98
60.92
0.19
(0.02)
(0.03)
(0.23)
(0.001)
12
0.95
4.55
75.22
6.20
(0.002)
(0.003)
(0.25)
(0.02)
24
0.06
6.02
82.71
9.14
(0.003)
(0.02)
(0.28)
(0.03)
36
0.07
6.12
90.58
2.15
(0.002)
(0.02)
(0.31)
(0.01)
45
0.07
5.34
91.36
2.15
(0.003)
(0.03)
(0.31)
(0.01)
Consumer
1
0.00
87.51
0.00
12.49
Price
(0.00)
(0.25)
(0.00)
(0.04)
6
9.19
60.06
23.30
3.42
(0.04)
(0.19)
(0.09)
(0.05)
12
8.03
53.81
28.02
4.07
(0.04)
(0.16)
(0.12)
(0.03)
24
7.05
36.38
48.51
3.04
(0.03)
(0.10)
(0.15)
(0.01)
36
8.07
10.27
61.20
9.34
(0.03)
(0.07)
(0.15)
(0.04)
45
8.04
6.86
64.87
9.36
(0.02)
(0.03)
(0.17)
(0.01)
Note: Aggre. Supply =Aggregate Supply; Exch. Rate =Exchange Rate
Output
IS
0.00
(0.00)
15.90
(0.05)
13.08
(0.04)
2.07
(0.01)
1.08
(0.01)
1.08
(0.01)
0.00
(0.00)
4.03
(0.01)
6.07
(0.01)
5.02
(0.02)
11.12
(0.02)
10.87
(0.03)
Madagascar
We found one cointegration relation among money, consumer price, interest rate, output and
exchange (Table 4.7), identified as equation (4.12). This equation suggests that transaction and currency
substitution are strong factors in household money holding decisions. Based on Figure 4.21, the
hypothesis of a stable money demand could not be supported.
Table 4.7: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
71.17
68.68
1
34.22
47.21
2
15.43
29.38
3
6.52
15.34
4
0.78
3.84
114
(m s  p)t  1.36 yt  0.01 Rt  0.17 st
(0.31)
(0.003)
(4.12)
(0.061)
 2 (1)  4.19(0.06)
Figure 4.21: Recursive Test for Stability of the Money Demand
Test of known beta eq. to beta(t)
2.72
BETA_Z
BETA_R
2.56
2.40
2.24
2.08
1.92
1.76
1.60
1.44
2001
2002
2003
2004
2005
2006
1 is the 5% significance level
With few exceptions, the impulse responses are line with theory. A 2 % rise in money stock,
following surprise monetary policy has the following consequences. First, the high liquidity is reflected in
a decline in the nominal interest rate (Figure 4.22). Second, despite the barely rise in output, consumer
price responds strongly, recording the peak increase of around 1.5% within the first 12 months. Third, a
weak exchange rate is recorded over time, reaching the climax depreciation of about 1%. The foregoing
suggests evidence of money and exchange channels of transmission of monetary policy to inflation.
A shock to money demand is immediately followed by a sharp increase in consumer price,
registering a sustained rise of slightly over 1.5% (Figure 4.23). A modest rise in output over a short period
is also witnessed. The nominal interest rate rises briefly and declines latter, in part reflecting increased
money supply. Despite lower interest rates, a strong domestic currency is experienced after a money
demand shock.
Shown in Figure 4.24, an aggregate supply shock is followed by a strong and significant increase
in output and a decline in consumer prices. The response of monetary policy (to this shock) is mute--money stock and interest rates barely change. On the other hand, the exchange rate initially depreciates to
support increased output.
A 4% surprise exchange rate depreciation has the potential of causing a maximum of 2% increase
in consumer price (Figure 4.25). We infer that the monetary authority is very aware of the inflation
consequences of exchange rate movements---monetary policy is tightened in responses to exchange rate
depreciation. As a result of such action, a rise in interest rate is observed after sometime. A significant
and sustained rise in output is further reported reflecting improved international competitiveness. Lastly, a
significant increase in interest rate is registered following an IS shock (Figure 25). And with increased
aggregate demand, both output and consumer prices rises. The exchange rate response to this shock (IS
shock) is unnoticeable.
115
Figure 4.22: Impulse Responses to a Monetary Policy shock
Money
Consumer Price
0.04
0.025
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0
5
10
15
20
25
30
35
40
45
0
5
10
Output
15
20
25
30
35
40
35
40
45
Exchange Rate
0.020
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
45
Interest
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
30
35
40
45
Figure 4.23: Impulse Responses to a Money Demand Shock
Money
Consumer Price
0.04
0.025
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0
5
10
15
20
25
30
35
40
45
0
0.010
0.005
0.000
-0.005
-0.010
10
15
20
25
30
35
40
45
30
35
40
45
0
Interest
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
15
20
25
30
35
40
35
40
45
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.015
5
10
Exchange Rate
Output
0.020
0
5
116
5
10
15
20
25
30
45
Figure 4.24: Impulse Responses to an Aggregate Supply Shock
Money
0.04
Consumer Price
0.025
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
35
40
Exchange Rate
0.06
Output
0.020
0.05
0.015
0.04
0.03
0.010
0.02
0.005
0.01
0.000
0.00
-0.005
-0.01
-0.02
-0.010
0
5
10
15
20
25
30
35
40
45
30
35
40
45
0
5
10
15
20
25
30
Interest
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
Figure 4.25: Impulse Responses to an Exchange Rate Shock
Money
0.04
Consumer Price
0.025
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
35
40
45
Output
0.020
Exchange Rate
0.015
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
30
35
40
45
35
40
0
Interest
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
30
45
117
5
10
15
20
25
30
45
Figure 4.26: Impulse Responses to an IS Shock
Money
Consumer Price
0.04
0.025
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0
5
10
15
20
25
30
35
40
45
0
5
10
15
Output
20
25
30
35
40
45
Exchange Rate
0.020
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
30
30
35
35
40
45
0
Interest
0.020
0.015
0.010
0.005
0.000
-0.005
-0.010
0
5
10
15
20
25
40
45
118
5
10
15
20
25
30
35
40
45
Table 4.8 argues that output fluctuations are largely explained by aggregate supply and exchange
rate shocks in both the short- and long-run. We find that monetary policy has a significant role in inflation
formation both in the short and long run. Other major sources of inflation are exchange rate, money
demand and aggregate supply shocks.
Table 4.8: Percentage of Forecast Error Explained by Shocks
Variable
Mont
hsahead
1
Monetar
y Policy
Money
Demand
Aggreg.
Supply
Exch.
Rate
0.00
0.00
100.0
0.00
0.0)
(0.00)
(0.30)
(0.00)
6
0.06
9.82
64.86
15.19
(0.00)
(0.05)
(0.31)
(0.05)
12
0.15
8.55
63.92
19.17
(0.00)
(0.04)
(0.32)
(0.05)
24
0.07
7.02
65.75
20.14
(0.00)
(0.02)
(0.30)
(0.06)
36
0.01
4.12
42.41
48.15
(0.00)
(0.05)
(0.28)
(0.08)
45
0.07
1.34
41.38
51.90
(0.00)
(0.01)
(0.28)
(0.08)
Consumer
1
0.00
86.26
0.00
13.74
Price
(0.00)
(0.15)
(0.09)
(0.06)
6
22.14
18.84
26.13
30.94
(0.07)
(0.14)
(0.08)
(0.07)
12
26.94
17.40
23.33
31.21
(0.08)
(0.11)
(0.06)
(0.09)
24
29.42
17.73
20.84
31.92
(0.10)
(0.10)
(0.05)
(0.09)
36
32.68
17.36
19.61
30.30
(0.012)
(0.07)
(0.04)
(0.10)
45
33.12
17.23
19.62
29.98
(0.13)
(0.03)
(0.05)
(0.12)
Note: Aggre. Supply =Aggregate Supply; Exch. Rate =Exchange Rate
Output
IS
0.00
(0.00)
10.07
(0.04)
8.21
(0.03)
7.02
(0.03)
5.31
(0.03)
5.23
(0.04)
0.00
(0.00)
1.95
(0.01)
1.12
(0.01)
0.09
(0.02)
0.05
(0.02)
0.05
(0.03)
Uganda
For Uganda, Kenya, Burundi, Rwanda, Sudan, Ethiopia, Eritrea, Djibouti, Egypt and Libya, the
estimation is done using seasonally adjusted monthly data spanning January 1992 to October 2006. Data
sources are the IMF International Financial Statistics (IFS) publications and country publications,
especially central banks’ publications. The following variables, in logarithm except interest rate, were
used: real gross domestic product (LRGDPB); consumer price (LGP); broad money (LGM2) ; 91
Treasury bill rate (TB91); nominal effective exchange rate (LGNEER); base money (LGBM), and Net
Domestic Credit (LGNDC).
It is worth noting that the variable that is used to gauge the stance of monetary policy is assumed
to be base money for all countries except Egypt. Among the group of intermediate variables, the nominal
exchange rate is included to study the importance of the exchange rate channel. Net domestic credit is
included to gauge the importance of credit channel. Finally, the two goal variables output and price level
are also included. The standard information criteria are used to select the lag length of the VARs.
The real gross domestic product series were interpolated using Quadratic average match. The
procedure is to fit a local quadratic polynomial for each observation of the GDP frequency, then use this
119
polynomial to fill in all observations for the monthly frequency series associated with the period. The
quadratic polynomial is formed by taking sets of three adjacent points from the GDP annual series and
fitting a quadratic so that the average of the high monthly frequency points matches the low annual
frequency data observed.
According to Lukepohl and Wolters (2001), to model the monetary policy transmission
mechanism, there should be at least one cointegration relation in the system, which could be identified as
a money demand equation. Ignoring the relation may lead to producing an inaccurate results. We start the
analysis therefore by investigating cointegration relationship.
For the impulse response functions to have meaningful interpretations as responses to exogenous
structural shocks, the retrieval of the corresponding structural form is important, and also an
orthogonalization process is crucial to render the error terms structurally independent and thus an
innovation to any equation in the VAR can be rightly interpreted as an independent structural shock. The
structural identification assumption used is described as follows. The first two equations represent
sluggish response of prices and real GDP with respect to shocks to the nominal variables, money stock,
short-term interest rates, and the nominal exchange rate. The third equation suggests that the nominal
exchange rate responds immediately to price level, money supply and monetary policy stance. The fourth
equation can be interpreted as a short-run money demand equation, with money demand allowed to
respond contemporaneously to shocks in output, prices, and short-term interest rates. The fifth equation is
private sector credit flow which assumes that credit responds to shocks in demand, money supply and
monetary policy stance. The last equation can be interpreted as the monetary policy reaction function,
which responds contemporaneously to money demand and the exchange rate, but does not respond
immediately to contemporaneous output and price shocks because data on output and prices is usually
only available with a lag.
Given the short sample, we will not consider an explicit analysis of the long-run behaviour of the
economy. By estimating the VAR in levels, implicit cointegrating relationships are allowed in the data.
Money Demand Analysis
A VAR of 5 endogenous variables ( LGP, LGRGDPB LGNEER, LGM tb91) is used in the
cointegration analysis for Uganda. Table 4.9 shows that there is one cointegration vector.
Table 4.9 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
4
89.37280
51.99963
30.37529
18.76871
8.379820
*
C0.95
76.97277
54.07904
35.19275
20.26184
9.164546
We thereafter impose a unity restriction on money to identify a money demand function. All the
coefficients have the expected signs. The demand for money is positively related to income and negative
to the Treasury bill rate (equation (4.13)). Demand for nominal money balances rise with income,
exchange rate and price level but fall with interest rate. The income elasticity of M2 is to close unity and
it is significantly different from zero. The finding of unitary income elasticity suggests that, over the
sample period, changes in real income have been inducing on average a proportionate increase in the
demand for M2. The rise in demand for nominal balance due to increase in the price level is significantly
different from 1 which could suggest money illusion.
120
LGM 2  11.62 0.60 LGP  0.97 LGRGDP  0.12 LNEER  0.22 TB91
t
t (0.01)
t (0.08)
t (0.05)
t
(1.71) (0.10)
(4.13)
To test for the stability of the demand function, we specify an error correction model for money
demand and then use recursive coefficients approach to examine whether the function has been stable
over the sample period. Visual inspection of Figure 4.27 suggests no evidence of any structural break.
This indeed confirms the stability identified through convergence of the VARs and also the statistically
significant error correction term in the model.
Figure 4.27: Stability Test Uganda
.03
0
.02
-1
.01
.00
-2
-.01
-3
-.02
-.03
-4
97
98
99
00
01
02
03
04
05
06
97
98
99
Recurs ive C(1) Es tim ates
± 2 S.E.
00
01
02
03
04
05
06
05
06
05
06
Recurs ive C(2) Es tim ates
± 2 S.E.
1.0
.6
0.8
.4
0.6
.2
0.4
.0
0.2
-.2
0.0
-.4
-0.2
-.6
97
98
99
00
01
02
03
04
05
06
97
98
99
Recurs ive C(3) Es tim ates
± 2 S.E.
00
01
02
03
04
Recurs ive C(4) Es tim ates
± 2 S.E.
2
.2
.1
1
.0
0
-.1
-1
-.2
-.3
-2
-.4
-3
-.5
97
98
99
00
01
02
03
04
05
06
97
Recurs ive C(5) Es tim ates
± 2 S.E.
98
99
00
01
02
03
04
Recurs ive C(6) Es tim ates
± 2 S.E.
With regard to the M2 velocity, it is integrated of order one. Thus it properties have changed over time as
indeed can be seen from the Figure 4.28. Since its properties have changed over time, this points to its
instability over the sample period.
Figure 4.28: Velocity Uganda
121
Velocity (GDP/M2)
13
12
11
10
9
8
7
6
1996
1998
2000
2002
2004
2006
2008
Impulse Responses and Variance Decompositions
The impulse response (Figure 4.29) indicates that an unexpected rise in base money fades away
over 7 months. The rate of inflation starts to accelerate in response to a positive monetary shock after
about 2 months, afterwards follows a zigzagged course and in the long run it asymptotes to its pre-shock
level, which is consistent with the widely accepted premise of the long-run neutrality of money. A onetime monetary shock (i.e. a temporary impulse) is translated into a long-run increase in the price level,
with a return to the pre-shock rate of inflation, given that expectations did not change. Only recurring
impulses in reserve money could cause a permanent increase in inflation as expectations adjust to the new
permanent monetary policy stance.
A monetary stimuli in the base money tends to be followed by a slight reduction in GDP but later
causes a slight rise in real output. However, overall it seems to exhibit marginal effect on real income.
This is not a surprising result given the slew of structural weaknesses in the financial sector, which are
likely to hamper the transmission mechanism of monetary policy. The impact on log of CPI seems to be
mild but is noticeable within the first 3 months and then gradually fazes out within 7 months.
Looking at variance decomposition (Table 4.10), underlying CPI is driven by its own innovations,
as well as by domestic credit and exchange rate shocks. For instance after 12 months, 24% of variation in
LGCPI would be attributed to domestic credit, 50% to own innovation, 6.8% to exchange rate, 11.2 % to
M2, 2.4% to output and 5.3% to monetary shock. Domestic credit seems to explain a larger share of the
variation of the LGCPI than reserve money. This seems to suggest that monetary shocks does not account
significantly to the fluctuations in prices. Overall, both the reserve money and interest rate seem to some
have an identifiable transmission mechanism on consumer prices.
122
Figure 4.29:Impulse Responses with innovation to Base Money as Monetary Policy Shock.
Table 4.10: Variance Decomposition of Price, Base Money as Policy as the Monetary Policy Shock
Period
LGP
LGRGDPB
LNEER
LGM2
LGNDC
LGBM
1
6
12
24
100.00
68.57
50.23
45.72
0.00
2.89
2.42
6.59
0.00
8.43
6.79
4.22
0.00
9.16
11.25
8.82
0.00
6.92
24.04
28.28
0.00
4.04
5.29
6.37
As mentioned earlier we also use the interest rate on the 91 days treasury bills as a measure of
monetary policy stance (Figure 4.30). The impulse response indicates slight reduction in the price level as
a result of a shock in the Treasury bill rate. In particular, aggregate demand in Uganda responds very little
to changes in bank lending rates due to low levels of monetization and financial intermediation , which
are among the lowest in the developing countries. Moreover, Uganda’s banking system has been
experiencing related-party lending which may contribute to a low interest elasticity of credit demand.
Credit, which reacts systematically to changes in the monetary policy stance seems to have
positive impact on the price level but not impact on output. In terms of variance decomposition, for
instance after 12 months, almost a half of the fluctuations in the price level would be still attributed its
own fluctuation, 20 % to credit, 8.6% to exchange rate, 7.3% to monetary instrument, while around a 13
% of the fluctuations in price can be attributed to the money stock (Tables 4.11, 4.12 and 4.13). Hence,
consistent with the impulse response analysis, a monetary shock has a slight impact on prices. However,
over the longer horizon, say 24 months, the monetary policy shock seems to have a stronger impact.
Given the weak link between output and monetary stance amid relatively stronger link between price
stability and monetary stance, there seems little scope for balancing the two competing goals of output
stabilization and price stability. This seems to support BoU focus on price stability.
123
Figure 4.30: Impulse Responses with innovation to 91Tb as monetary policy shock
Table 4.11: Variance Decomposition of LGP
Period
1
6
12
24
LGP
100.00
70.63
47.55
31.23
LGRGDPB
0.00
3.76
3.05
17.31
LNEER
0.00
2.53
8.59
6.026
LGM2
0.00
2.04
13.16
10.37
LGNDC
0.00
13.19
20.39
20.85
LGTB91
0.00
7.86
7.26
14.22
Table 4.12: Variance Decomposition of LGRGDPB
Period
LGP
LGRGDPB
LNEER
58.69
41.31
0.00
1
34.76
55.47
1.77
6
31.08
53.07
4.24
12
24
30.81
40.92
10.66
LGM2
0.00
1.69
3.71
5.46
LGNDC
0.00
3.01
4.44
6.12
LGTB91
0.00
3.28
3.46
6.03
LGM2
0.00
0.76
6.57
7.09
LGNDC
0.00
43.42
37.80
32.15
LGTB91
0.00
2.08
2.53
11.15
Table 4.13 Variance Decomposition of LGNEER
Period
1
6
12
24
LGP
0.48
3.60
12.94
20.10
LGRGDPB
3.39
3.77
7.465
6.70
LNEER
96.13
46.36
32.70
22.80
124
Kenya
Data was seasonally adjusted monthly data spanning January 1992 to October 2006 for
the following variables which are in log form except interest rates: broad money (LGM2):
consumer price(LGCPI), real GDP(LGRGDP) and exchange rate(LGEXCH), policy rate(PR),
private sector credit (LGPSC).
Money Demand
Table 4.14 shows that there are 2 cointegrating vectors. The identified long run demand for
money in Kenya is significantly influenced by the scale variable, exchange rate, interest rate and the price
level (Equation 4.14). As in the case of Uganda, the coefficient on the price variable is significantly
different from 1 suggesting some degree of money illusion. However, the coefficient on GDP seems to
indicate unity elasticity. The results also suggest that there is opportunity cost of holding money in
relation to returns of other financial assets as measure by short term interest rate. Kenya shilling
depreciation seems to cause a decrease in the demand nominal money.
Table 4.14 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
4
147.3717
65.77826
33.08779
12.98619
2.399460
*
C0.95
76.97277
54.07904
35.19275
20.26184
9.164546
LGM 2t  0.68 LGCPIt  1.22 LGRGDPt  0.12 LGEXCHt  0.22 PRt
(0.69)
(0.45)
(0.91)
(0.01)
(4.14)
To ascertain whether the demand for money has been stable in Kenya, we use vector error
correction model since cointegration tests between real money balances, real GDP, exchange rate and real
interest rate indicates at most two cointegrating relationships we normalise in relation to demand for real
balances (money market equilibrium) and the output (IS curve). The focus here is on whether once the
two long run relationships are in disequilibrium there mechanism to return them to equilibrium which
would be reflection of stability. The Vector error correction estimates indicate a statistically significant
error correction term on the demand for money equation which indicates a stable money function.
In terms of evolution of velocity, as shown in the Figure 4.31, velocity has declined substantially
for the period 1992-2006.
125
Figure 4.31: Velocity
VEL
10
9
8
7
6
5
4
3
2
1
1992
1994
1996
1998
2000
2002
2004
2006
Impulse Response and Varience Decompositions
A key concern is the choice of the variable that accurately gauges the stance of monetary policy
and that is also invariant during the period of estimation. Kenya targets monetary aggregates and uses
reserve money program as a monetary policy framework. We therefore use both the base money and the
bank rate as an indicators of the monetary policy stance. The caveat in using base money as an indicator
of monetary policy stance is that sometimes base money growth reflect changes in monetary base supply
due to factors outside the control of the central bank as as well as due to monetary policy actions.
The results seem to suggest that CPI and output responses to monetary policy rate seem to be
quite faint (Figure 4.32). A shock in the policy rate seems to have no significant impact on goal variables
but particularly so on the output. However, the exchange rate seems to have a prolonged positive effect on
real output. Credit impact on price level is noticeable within 5 months and money supply shock seems to
have an impact on price level but not output.
Variance decompositions are also reported below over 24 months (Tables 4.15 and 4.16).
Structural decomposition of the LGCPI variance suggests that bulk of variation can be attributed to
exchange rate, credit, output and its own innovations. In the case of reserve money as the monetary
instrument, the impulse response still indicates a weak response from the monetary shock, however, a
shock in the reserve money seems to have a persistent negative effect on the real output. Also the
exchange rate seems to have a persistent positive effect on real output while price level has a persistent
negative effect.
The protracted output response to monetary base innovations is puzzlingly (Figure 4.33). This
could be explained by the fact that that a positive monetary shock affects expectations about the future
stance of monetary policy where market agents expect a continued monetary expansion. This change in
expectations could cause a protracted decrease in output since persistent increase in monetary base could
cause inflation. Credit to the private sector seem to have stronger impact on both inflation and output
(Tables 4.17 and 4.18).
126
Figure 4.32: Impulse Responses with policy rate as an indicator of monetary policy stance.
Table 4.15 Variance Decomposition of LGCPI:
Period
S.E.
LGCPI
LGRGDP
1
0.293291
19.45
16.38
6
0.991663
19.05
16.28
12
1.247169
19.11
16.05
24
1.352500
18.91
15.83
Table 4.16 Variance Decomposition of LGRGDP:
Period
S.E.
LGCPI
LGRGDP
1
0.013226
30.29
35.32
6
0.071248
26.61
32.52
12
0.191986
24.91
28.04
24
0.519912
21.54
21.81
LGEXCH
47.37
47.83
47.82
48.04
LGEXCH
26.32
30.91
35.87
44.59
127
LGM2
0.45
0.42
0.51
0.64
LGM2
0.54
0.31
0.12
0.03
LGPSC
16.30
16.41
16.51
16.56
PR
0.04
0.01
0.01
0.02
LGPSC
7.50
9.63
11.05
12.04
PR
0.04
0.03
0.01
0.01
Figure 4.33: Impulse Responses with base money as an indicator of monetary policy stance.
4.17
Period
1
6
12
24
4.18
Period
1
6
12
24
Variance Decomposition of LGCPI:
S.E.
0.05
0.50
0.58
0.63
LGCPI
3.97
15.76
15.81
15.42
LGRGDP
49.97
37.46
37.73
37.62
LGEXCH
16.68
23.10
23.11
22.40
LGM2
0.49
0.38
0.40
0.79
LGPSC
26.34
23.03
22.20
21.53
LGBM
2.55
0.27
0.74
2.26
LGEXCH
37.59
10.71
14.99
19.90
LGM2
0.49
1.39
1.46
2.06
LGPSC
5.49
15.36
14.69
11.55
LGBM
15.74
5.17
3.12
2.25
Variance Decomposition of LGRGDP:
S.E.
0.006676
0.056830
0.155352
0.343365
LGCPI
33.45
8.12
11.58
15.32
LGRGDP
7.24
59.25
54.17
48.91
128
Egypt
Seasonally adjusted monthly data, over the period January 1992 to October 2006 is used. The
specific variables in logarithmic form, except interest rates, are as follows: broad money (LGM2):
consumer price(LGCPI), real GDP(LGRGDP) and exchange rate(LGEXC), policy rate(Prate), private
sector credit (LGCP).
Money Demand
As shown in Table 4.19, one cointegration vector was established and identified as a money
demand function (4.15). It is suggested that demand for money rises with exchange rate depreciation,
increase in income and the rise in price level and fall with a rise in interest rate. Elasticity of money
demand with respect to income is less than unity. Similarly elasticity of demand for money with respect
to price level is different from one suggesting money illusion as in the case of Uganda and Kenya. The
influence of opportunity cost on demand for money is much stronger in comparison to Uganda and
Kenya, in part suggesting a more developed financial system.
Table 4.14 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
4
86.72522
45.53225
23.18148
7.311930
0.574806
*
C0.95
69.81889
47.85613
29.79707
15.49471
3.841466
LGM 2t  0.51 LGCPIt  0.56 LGRGDPt  0.85 LGEXCt  0.62Pr atet
(0.19)
(0.19)
(0.13)
(0.16)
(4.15)
In relation to the stability of demand for money function, since the cointegration test indicated
one cointegrating vector, we specify an error correction model and test for coefficients stability using
recursive coefficients approach. The parsimonious results are reported below and the recursive estimates
indicates a stable demand for money function (Figure 3.34).
129
Figure 4.34: Stability Test
.
.02
8
2
6
.016
.014
1
.01
4
.012
0
.00
2
.010
-1
0
.008
-.01
-2
-2
-.02
1994
1996
1998
2000
2002
2004
2006
-4
1994
1996
Recursive C(1) Estimates
± 2 S.E.
1998
2000
2002
2004
2006
.006
-3
1994
1996
Recursive C(2) Estimates
± 2 S.E.
1.2
1998
2000
2002
2004
2006
.004
1994
1996
Recursive C(3) Estimates
± 2 S.E.
2
2
0
1
-2
0
-4
-1
1998
2000
2002
2004
2006
Recursive C(4) Estimates
± 2 S.E.
.008
1.0
.006
0.8
.004
0.6
.002
0.4
.000
0.2
-.002
0.0
1994
1996
1998
2000
2002
2004
2006
-6
1994
1996
Recursive C(5) Estimates
± 2 S.E.
1998
2000
2002
2004
2006
-2
1994
1996
Recursive C(6) Estimates
± 2 S.E.
.6
1998
2000
2002
2004
2006
-.004
1994
Recursive C(7) Estimates
± 2 S.E.
.012
1996
1998
2000
2002
2004
2006
Recursive C(8) Estimates
± 2 S.E.
.8
.6
.4
.008
.4
.2
.004
.2
.0
.0
.000
-.2
-.2
-.004
-.4
-.4
-.6
1994
1996
1998
2000
2002
2004
2006
Recursive C(9) Estimates
± 2 S.E.
-.008
1994
1996
1998
2000
2002
2004
2006
-.6
1994
1996
Recursive C(10) Estimates
± 2 S.E.
1998
2000
2002
2004
2006
Recursive C(11) Estimates
± 2 S.E.
Regarding the trend in velocity, Figure 3.35 indicates a systematic decline in velocity over the sample
period.
Figure 3.35: Velocity
VELOCITY
22
20
18
16
14
12
10
8
6
4
1992
1994
1996
1998
2000
2002
2004
2006
Impulse Responses and Variance Decomposition
Unlike other COMESA countries in this group of study that are under a floating exchange rate,
Egypt uses a policy rate as an instrument of monetary policy. The increase in policy rate seems to have no
effect on goal variables, price level and GDP (Figure 3.36). Looking at variance decomposition, log of
underlying CPI is driven by the exchange rate. Money stock, monetary policy shock and own innovations.
For instance after 12 months, 72% of variation in LGCPI would be attributed to exchange rate, 10% to
own innovation, 10% to M2 and 5% to monetary policy shock (Tables 4.20, 4.21 and 4.22). Similarly,
GDP variations seem to be attributable largely to the exchange rate, M2, price level, own innovations and
to some extent monetary policy shocks. The exchange rate shocks seem to be more predominant in Egypt
largely because of the pegged exchange rate regime in part of the sample period under the review.
130
Figure 3.36: Impulse Responses
Response to Structural One S.D. Innov ations ± 2 S.E.
Response of LG CPI t o LG CPI
Response of LG CPI t o LG RG DP
Response of LG CPI t o LG EXC
Response of
LG CPI
t o LG M2
Response of
LG CPI
t o LG CP
Response of LG CPI t o PRAT E
.06
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.02
.00
.00
- .02
.00
- .02
- .04
10
15
Response of
10
15
10
15
- .02
- .04
5
20
Response of LG RG DP t o LG EXC
.00
- .02
- .04
5
20
LG RG DP t o LG RG DP
.00
- .02
- .04
5
20
.00
- .02
- .04
5
Response of LG RG DP t o LG CPI
10
15
- .04
5
20
Response of LG RG DP t o LG M2
10
15
5
20
Response of LG RG DP t o LG CP
Response of
.008
.008
.008
.008
.008
.008
.004
.004
.004
.004
.004
.004
.000
.000
- .004
.000
- .004
- .008
10
15
Response of LG EXC t o LG CPI
10
15
Response of LG EXC t o LG RG DP
10
Response of
15
LG EXC t o LG EXC
10
Response of
15
- .008
5
20
LG EXC t o LG M2
10
Response of
15
5
20
LG EXC t o LG CP
.02
.02
.02
.02
.02
.01
.01
.01
.01
.01
.01
.00
.00
.00
- .01
- .02
10
Response of
15
LG M2 t o LG CPI
10
15
Response of LG M2 t o LG RG DP
10
Response of
15
LG M2 t o LG EXC
10
15
- .02
5
20
Response of LG M2 t o LG M2
10
15
5
20
Response of LG M2 t o LG CP
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.02
.00
.00
.00
- .02
- .04
10
Response of
15
LG CP t o LG CPI
10
15
Response of LG CP t o LG RG DP
10
Response of
15
LG CP t o LG EXC
10
15
- .04
5
20
Response of LG CP t o LG M2
10
15
5
20
Response of LG CP t o LG CP
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.02
.00
.00
.00
- .02
- .04
10
15
Response of PRAT E t o LG CPI
Response of
10
15
PRAT E t o LG RG DP
10
15
Response of PRAT E t o LG EXC
10
15
- .04
5
20
Response of PRAT E t o LG M2
10
15
5
20
Response of PRAT E t o LG CP
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.1
.0
.0
.0
- .1
- .2
10
15
20
.0
- .1
- .2
5
10
15
20
.0
- .1
- .2
5
10
15
20
10
15
20
20
- .1
- .2
5
15
.0
- .1
- .2
5
10
Response of PRAT E t o PRAT E
.2
- .1
20
- .02
- .04
5
20
15
.00
- .02
- .04
5
20
.00
- .02
- .04
5
20
.00
- .02
- .04
5
10
Response of LG CP t o PRAT E
.04
- .02
20
- .02
- .04
5
20
15
.00
- .02
- .04
5
20
.00
- .02
- .04
5
20
.00
- .02
- .04
5
10
Response of LG M2 t o PRAT E
.04
- .02
20
- .01
- .02
5
20
15
.00
- .01
- .02
5
20
.00
- .01
- .02
5
20
.00
- .01
- .02
5
10
Response of LG EXC t o PRAT E
.02
- .01
20
- .004
- .008
5
20
15
LG RG DP t o PRAT E
.000
- .004
- .008
5
20
.000
- .004
- .008
5
20
.000
- .004
- .008
5
10
- .2
5
10
15
20
5
10
15
20
Table 4.20:
Variance Decomposition:LGCPI
Period
S.E.
LGCPI
LGRGDP
1
5.243965
7.38
4.60
6
8.053041
8.26
2.16
12
9.787098
8.58
2.96
24
15.42471
9.05
8.52
LGEXC
73.37
73.76
72.73201
67.56
LGM2
9.87
10.65
10.72
10.42
LGCP
0.05
0.06
0.05
0.11
Prate
4.73
5.12
4.96
4.34
Table 4.21:Variance Decomposition of LGRGDP:
Period
S.E.
LGCPI
LGRGDP
1
0.45
5.28
72.29
6
1.06
8.21
14.48
12
1.60
8.85
9.84
24
2.51
8.82
6.85
LGEXC
16.52
63.40
66.92
69.33
LGM2
3.72
9.48
10.06
10.36
LGCP
1.31
0.26
0.17
0.12
Prate
0.87
4.15
4.15
4.53
Table 4.22:Variance Decomposition of LGEXC:
Period
S.E.
LGCPI
LGRGDP
1
1.908073
9.56
2.888
6
3.340065
8.93
3.928
12
3.691461
8.92
4.30
24
5.725545
8.22
9.00
LGEXC
72.19
71.96
71.76
68.26
LGM2
10.82
10.54
10.50
9.90
LGCP
0.04
0.09
0.09
0.18
Prate
4.50
4.56
4.44
4.44
Rwanda
Data was seasonally adjusted monthly data spanning January 1992 to October 2006 for the
following variables which are in log form except interest rates: broad money (LGM2): consumer
price(LGCPI), real GDP(LGRGDP) and exchange rate(LGEXCH), reserve money (LGBM), private
sector credit (LGCP) and interest rate (TD).
131
Money Demand Analysis
Cointegration test LGM2, LGCPI, LGRGDP, TD and exchange rate indicates two cointegrating
vectors (Table 4.23) which we normalise in terms of demand for money depicted in equation (4.16). It is
suggested that the demand for money in Rwanda rises with exchange rate depreciation, increase in
income and the rise in price level and falls with a rise in interest rate. Elasticity of money demand with
respect to income is less than unity. Similarly the elasticity of demand for money with respect to price
level is no different from unity suggesting absence of money illusion. The influence of opportunity cost
on demand for money is much weaker in comparison to Uganda, Kenya, and Egypt in part suggesting a
weaker financial system.
Table 4.23 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
66.43
30.29
9.80
1.46
*
C0.95
40.17
24.28
12.32
4.13
LGM 2t  0.90 LGCPIt  0.63 LGRGDPt  0.36 LEXCHt  0.01TDt
(0.13)
(0.08)
(0.12)
(4.16)
(0.008)
As already pointed out, the interest is to test for stability of demand for money. Looking at the
vector error correction estimates, there is an indication that once the demand for money function is out of
its long run equilibrium, it adjusts and the speed of adjustment is about 4%. Moreover, this speed of
adjustment is statistically different from zero thus suggesting a stable demand for money function. In
relation to the evolution of velocity, there is an indication of general decline for the period under review
with exception of the period immediately after the 1994 genocide (Figure 4.37).
Figure 4.37: Velocity Rwanda
VELOCITY
18
16
14
12
10
8
6
4
2
1992
1994
1996
1998
2000
2002
2004
2006
Impulse Responses and Variance Decompositions
The impulse response functions in the Rwandan case are presented in Figures 4.38 and 3.39. They
allow us to see the monetary transmission mechanism unfolding by illustrating the response of the system
to a shock in our measures of monetary policy (Tb rate and change in monetary base).The impulse
response graph indicate that a shock in monetary base seems to increase output but the impact fazes out
after one year, however its effect seem to be faint. Monetary base innovations cause a rise in price level
132
quite fast, within 6 months. Base money shocks seems to have a marginal effect on the exchange rate and
credit to private sector where both act as transmission channels to mediate the effect of changes in the
monetary policy stance on output and inflation.
The impact of credit shock on price level is sluggish in the early periods and appears to be at
maximum after 20 months. It also impacts negatively on the output in the first periods after the shock but
it turns positive after 10 months. Money supply shock seems to have no effect on both output and price.
The exchange rate shock cause a rise in the price level in the initial months but the impact becomes
negative after 6 months. This can be attributed to the response of output to exchange rate depreciation.
Overall, it appears credit seems to be playing a crucial role as intermediary variable. We also use 91-day
Treasury bill rate as an alternative indicator of the monetary policy stance and the impulse response
functions indicate no significant effect. A plausible explanation for is that the Rwandan financial system
is plagued with structural weaknesses, thereby hampering the monetary transmission to the rest of the
economy. In relation to the variance decomposition Tables 4.24 and 4.25, CPI is driven by its own
innovations, as well as by output, money stock, and credit. After 12 months, 29% of variation in CPI
would be attributed to the output, 12% to own innovation, 31% to exchange rate, 26% to credit, 2% to M2
and 0.8% to base money. Output variation is largely due to own innovations, the exchange rate and credit.
Figure 4.38: Impulse response functions (base money for policy instrument)
Table 4.24:
Period
1
6
12
24
Variance Decompositions of LGCPI
S.E.
0.24
2.59
2.85
3.56
LGCPI
13.66
10.77
12.28
12.04
LGRGDP
27.80
31.72
28.68
29.08
LGEXCH
32.60
27.43
31.05
31.57
133
LGM2
0.73
1.88
2.09
2.05
LGCP
25.20
27.59
25.12
24.50
LGBM
0.00
0.61
0.78
0.77
Table 4.25:
Variance Decompositions of LGRGDP
Period
S.E.
LGCPI
LGRGDP
LGEXCH
1
2.188
12.24
29.17
33.31
6
2.238
12.25
29.05
33.20
12
2.288
12.44
28.71
33.43
24
2.478
12.88
27.87
34.22
LGM2
2.21
2.19
2.16
2.26
LGCP
22.30
22.52
22.50
21.92
LGBM
0.78
0.78
0.76
0.85
Figure 4.39:Impulse response functions (policy rate indicator of monetary policy stance)
Ethiopia
Data was seasonally adjusted monthly data spanning January 1992 to October 2006 for the
following variables which are in log form except interest rates: broad money (LGM2): consumer
price(LGCPI), real GDP(LGRGDP) and exchange rate(LGEXCH), reserve money (LGBM), private
sector credit (LGCP) and treasury bill rate (TB).
Money Demand Analysis
One cointegrating relation was established (Table 4.26) and identified as a money demand
equation (4.17). Ethiopia’s long run demand for money function rises with exchange rate depreciation,
increase in income and the rise in price level and falls with a rise in interest rate. Elasticity of money
demand with respect to income is less than unity. The elasticity of demand for money with respect to
price level is different from unity implying money illusion as in the previous cases whereas with respect
134
to the exchange rate it is unity. The magnitude of the influence of opportunity cost on demand for money
is quick small although statistically different from zero.
Table 4.26 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
4
69.23876
31.72035
13.52188
3.416221
0.224069
*
C0.95
60.06141
40.17493
24.27596
12.32090
4.129906
LGM 2t  0.74 LGCPIt  0.47 LGRGDPt  0.99 LGEXCHt  0.01TBt
(0.20)
0.17
(0.42)
(4.17)
(0.02)
Using the error correction framework, we investigate whether the parsimonious model derived
through general-to-specific approach is stable and also where the error correction term is negative and
statistically significant from zero. The results appear to indicate a stable demand for money function
(Figure 4.40).
Figure 4.40: Stability Test.
.04
0.4
.03
0.2
.012
1.2
0.8
.008
0.0
.02
0.4
-0.2
.01
.004
-0.4
.00
0.0
-0.6
-.01
.000
-0.4
-0.8
-.02
-1.0
98
99
00
01
02
03
04
05
06
-.004
98
99
Recursive C(1) Estimates
± 2 S.E.
00
01
02
03
04
05
06
-0.8
98
99
Recursive C(2) Estimates
± 2 S.E.
01
02
03
04
05
06
98
99
Recursive C(3) Estimates
± 2 S.E.
3
1.0
.010
2
0.8
.008
0.6
1
00
00
01
02
03
04
05
06
05
06
05
06
Recursive C(4) Estimates
± 2 S.E.
0.8
0.4
.006
0.0
0.4
0
.004
0.2
-1
-0.4
.002
0.0
-2
-3
-0.4
98
99
00
01
02
03
04
05
06
-0.8
.000
-0.2
-.002
98
99
Recursive C(5) Estimates
± 2 S.E.
00
01
02
03
04
05
06
-1.2
98
99
Recursive C(6) Estimates
± 2 S.E.
0.4
0.0
00
01
02
03
04
05
06
98
99
Recursive C(7) Estimates
± 2 S.E.
.010
.008
.008
.006
.006
00
01
02
03
04
Recursive C(8) Estimates
± 2 S.E.
.4
.2
.004
.0
.002
-.2
.000
-.4
.004
-0.4
.002
.000
-0.8
-.002
-.002
-1.2
-.004
98
99
00
01
02
03
04
05
06
Recursive C(9) Estimates
± 2 S.E.
-.6
-.004
98
99
00
01
02
03
04
Recursive C(10) Estimates
± 2 S.E.
05
06
-.8
98
99
00
.2
.1
.0
-.1
-.2
-.3
98
99
00
01
02
03
04
05
01
02
03
04
Recursive C(11) Estimates
± 2 S.E.
06
Recursive C(13) Estimates
± 2 S.E.
135
05
06
98
99
00
01
02
03
04
Recursive C(12) Estimates
± 2 S.E.
In relation to velocity, the Figure 4.41 indictates decline over time of the velocity
Figure 4.41: Velocity
VELOCITY
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1992
1994
1996
1998
2000
2002
2004
2006
Impulse Responses and Variance Decompositions
The impulse response indicates that a monetary shock has no impact on both goals but seems to
cause exchange rate depreciation (Figure 4.42). It seems that credit innovations have a larger effect on
inflation where the rate of inflation increases (relative to the pre-shock rate) and the impact seems to be at
maximum after 12 months. This could indicate that the credit channel is more influential relative to say
the exchange rate channel in transmitting the effect of monetary shocks to both output and inflation.
Credit and M2 shocks seem to have a prolonged effect on output but the impact dies out after 24 months.
Variance decomposition indicates that CPI in Ethiopia is driven largely by monetary aggregates, as well
as by own innovations. For instance over the after 12 months horizon, 18% of the variation in CPI would
be attributed to monetary policy shocks and 12% to own innovation (Tables 4.27 and 4.28).
136
Figure 4.42:
Impulse responses.
Table 4.27: Variance Decomposition of LGCPI
Period
S.E.
LGCPI
LGRGDP
1
0.02
100.00
0.00
6
0.03
54.92
5.67
12
0.08
12.23
4.15
24
0.09
12.26
5.32
LGEXCH
0.00
0.38
0.35
0.46
LGM2
0.00
8.70
23.17
22.11
LGCP
0.00
24.89
41.59
40.15
LGBM
0.00
5.44
18.50
19.71
Table 4.28 Variance Decomposition of LGRGDP:
Period
S.E.
LGCPI
LGRGDP
LGEXCH
1
0.01
10.31
89.699
0.00
6
0.02
13.21
41.26
0.21
12
0.03
7.71
22.10
2.43
24
0.05
6.57
14.18
3.19
LGM2
0.00
30.11
45.27
46.04
LGCP
0.00
13.56
21.80
26.72
LGBM
0.00
1.66
0.70
3.30
Burundi
Data was seasonally adjusted monthly data spanning January 1992 to October 2006 for the
following variables which are in log form except interest rate: broad money (LGM2): consumer
price(LGCPI), real GDP(LGRGDP), exchange rate(LGEXCH), base money (DBM) and net domestic
credit (LGCNDC) and treasury bill rate (TB).
137
Money Demand
Table 4.29 suggests one cointegrating vector identified as a money demand relation (equation
4.18). Evidence points to demand for money in Burundi as positively being influenced by the exchange
rate, income and price level and negatively by the opportunity cost of holding money. The influence of
price level is statistically no different from one. The income elasticity is substantially weak and
homogeneity with respect to income is rejected.
Table 4.29 :Trace Test for Cointegration Rank
Trace
r
0
1
2
3
4
144.12
45.50
24.86
10.49
3.03
*
C0.95
69.82
47.86
29.80
15.49
3.84
LGM 2t  0.94 LGCPIt  0.19 LGRGDPt  0.18 LGEXCHt  0.05TBt
(0.18)
(0.11)
(0.09)
(4.18)
(0.01)
As in the previous cases, we specify an error correction model, focusing on establishing whether
the demand for money is stable in Burundi. The parsimonious model obtained is reported below. The
recursive estimates indicate that the function has largely been stable over the sample period (Figure 4.43).
However, there are noticeable large fluctuations in the mid 1990s. The error correction term does confirm
a demand for money function that adjusts when it is out of equilibrium confirming a stable demand for
money function.
Figure 4.43: Stability Test
.20
5
.15
0
.10
-5
.05
-10
.00
-15
.020
.016
.012
.008
.004
-.05
.000
-20
1994
1996
1998
2000
2002
2004
2006
-.004
1994
1996
Recursive C(1) Estimates
± 2 S.E.
1998
2000
2002
2004
2006
1994
10
.012
0.8
5
.008
0.6
0
.004
0.4
-5
.000
0.2
-10
-.004
0.0
-15
1996
1998
2000
2002
2004
2006
Recursive C(4) Estimates
± 2 S.E.
1996
1998
2000
2002
2004
Recursive C(5) Estimates
± 2 S.E.
.0
-.2
-.4
-.6
1996
1998
2000
2002
2004
2000
2002
2004
2006
-.008
1994
.2
1994
1998
Recursive C(3) Estimates
± 2 S.E.
1.0
1994
1996
Recursive C(2) Estimates
± 2 S.E.
2006
Recursive C(7) Estimates
± 2 S.E.
138
2006
1994
1996
1998
2000
2002
2004
Recursive C(6) Estimates
± 2 S.E.
2006
The trend in velocity appears to indicate zigzag behaviour in large part reflecting the turbulent
environment Burundi has been going through. Overall, there is an indication of a downward trend (Figure
4.44).
Figure 4.44: Velocity
VELOCITY
14
13
12
11
10
9
8
7
1992
1994
1996
1998
2000
2002
2004
2006
Impulse Responses and Variance Decompositions
In the addition to the variables mentioned earlier in the section, we include in the VAR and
cointegration test a dummy for the period 2000-2007 to capture the shift impact that could have impacted
the economy by relative stability and removal of the trade embargo. The SVAR results indicated no
response from monetary policy instruments we therefore tried the Cholesky decomposition procedure
which gave relatively plausible. As in the previous cases, we use both monetary base and 91-day Treasury
bill rate as monetary policy instruments. The impulse response graph indicates that an unexpected rise in
base money results in an increase in the price level after 10 months and the impact is persistent (Figures
4.45 and 4.46). Although not statistically significant, a shock in base money results in a slight reduction in
output perhaps due to the effect on inflation. An innovation in credit has a strong positive and significant
impact on output but has no effect on the price level. The money supply shock seems to cause the
exchange rate to depreciation but has no significant effect on output and prices. In terms of variance
decomposition of underlying CPI is driven by several factors (Table 4.30 and 4.31). For instance, after 12
months, 5% of underlying CPI variation would be explained by output, 14 by changes in base money, 6%
by the exchange rate, 2% by monetary supply, 5% by credit and 67% by own innovation. In terms of 91day Treasury bill rate as a monetary policy instrument, innovations in the TB rate seem to have no effect
the goal variables and intermediate variables.
139
Figure 4.45:Impulse responses- change in base money as a monetary policy instrument.
Table 4.26: Variance Decomposition of LGCPI:
Period
1
6
12
24
S.E.
0.04
0.05
0.06
0.09
DBM
0.02
3.83
14.01
28.48
LGNDC
3.95
3.95
5.44
2.70
LGM2
1.06
2.43
2.31
1.44
LGEXCH
1.37
5.08
6.21
15.51
LGRGDP
5.80
4.92
5.15
22.47
LGM2
0.50
0.14
0.45
5.57
LGEXCH
24.06
14.58
7.82
5.07
LGRGDP
68.42
68.69
72.88
64.58
LGCPI
87.79
79.79
66.88
29.45
Table 4.27: Variance Decomposition of LGRGDP
Period
1
6
12
24
S.E.
0.00
0.02
0.03
0.05
DBM
0.07
4.39
4.98
7.46
LGNDC
6.96
9.97
12.10
16.59
140
LGCPI
0.00
2.24
1.77
0.72
Figure 4.46:Impulse responses- 91 day Treasury bill rate as a monetary policy instrument.
4.5
Conclusion.
The preceding analysis raises a number of implications for the conduct of monetary policy. The
empirical results on MTM obtained from the estimated VAR models suggest that monetary policy
instruments are not similar in the countries. In some countries, reserve money qualifies better than policy
rate in capturing the stance of monetary policy. In countries where monetary policy seem to have an
impact, the lag horizon for monetary policy to affect output and inflation differs which clearly points to
the extent on the depth and efficiency of financial markets to mediate changes in the monetary policy
stance.
The empirical results indicate that the capability of monetary policy to influence economic
activity and inflation are still limited, as important channels of monetary transmission are not effective. In
particular, the interest rate channel remains weak in almost contries, even though there is some evidence
in some countries for a transmission of policy rate rate changes to CPI.
Most empirics indictate a tendency of monetary impact to affect inflation before output or even not to
affect output at all. Assuming that the central banks focus primarily on minimizing the deviations of
actual inflation from a declared target (with an explicit role for output stabilization to address
unemployment concerns), the finding that changing the stance of monetary policy would affect inflation
before it affects output is a dilemma for the monetary authority.
141
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143
Appendices
Appendix A
Table 1A : Lag Length Selection
p 1
-5.13
-6.03
SC
Hanna-Quinn
p2
p3
p4
-4.86
-6.59
-6.23
-7.22
-4.62
-6.95
Table 2A: Trace Test for Cointegration Rank
r
i
Trace(i)
*
C0.95
0
0.32
177.51
123.04
1
0.27
122.49
93.92
2
0.19
78.65
68.68
3
0.15
48.41
47.21
4
0.11
26.41
29.34
5
0.06
10.00
15.34
6
0.01
1.98
3.84
Table 3A: Eigenvalues of the Companion Matrix
Real
Imaginary
Modulus
0.93
0.89
0.87
0.68
0.61
0.61
0.31
0.01
0.00
-0.13
-0.00
0.22
-0.22
0.12
0.93
0.89
0.88
0.69
0.64
0.64
0.34
Table 4A:
r
2
1
2
3
4
5
6
14.07
12.59
11.07
9.49
7.81
5.99
Test for stationarity
y
m
p*
20.74
17.82
13.43
9.54
6.18
4.08
23.88
18.22
15.57
9.73
6.59
3.85
22.67
17.11
13.83
10.05
7.84
3.78
pc
30.05
22.13
16.11
9.60
7.15
5.06
144
R
s
Rf
20.15
16.48
11.33
8.84
6.08
5.57
23.89
17.54
15.42
9.56
6.72
4.22
21.75
15.95
12.41
10.33
7.94
5.87
APPENDIX B
MALAWI
Table 1B: Trace Test for Cointegration Rank
r
Trace
*
C0.95
0
143.60
68.68
1
92.49
47.21
2
55.94
29.38
3
24.91
15.34
4
8.91
3.84
Table 2B:
r
2
1
2
3
4
12.59
11.07
9.49
7.81
Test for stationarity
p
ms
16.42
10.85
8.33
5.90
24.90
9.31
7.29
3.01
y
R
s
33.96
10.78
9.01
5.29
28.64
9.61
7.32
6.99
37.47
9.32
8.01
2.19
145
5.0
Sources of Economic Growth
Rojid Sawkut
Seetanah Boopen
Sannassee Vinesh
Fowdur Suraj
Ramessur Taruna
5.1 Introduction
A
n econometric analysis of the sources of growth in selected COMESA countries is the broad
objective of this undertaking. At a more specific level, the aim of this study is to take stock
of the current situation in selected COMESA member countries, by making use of existing
data and theoretical and econometrics models and to compare the results with those of newly
industrialised economies in an attempt to propose realistic policies to increased the rate of growth of those
selected COMESA Members under study.
The rest of the paper is organized as follows. Section 5.2 reviews existing literature on sources of
growth. This is followed by a discussion on the recent economic performance of selected COMESA
countries. In this same section, we analyse developments in the stock market, facilities and incentives to
investors, capital formation, the sectoral composition of GDP and selected trade indicators. Section 5.4
decomposes of sources of growth using a standard growth accounting framework, based on a standard
Cobb-Douglas production function. An econometric evidence of the sources of growth is contained in
Section 5.5. Section 5.6 determines the optimal economic ratios. We conclude and provide policy
recommendations in Section 5.7.
5.2
Literature Review
T
here are two main categories of growth theories namely the Solow model and the
endogenous growth model. The Solow model puts emphasis on capital accumulation,
technological innovation and exogenous rates of change in population. According to this
model, free-market economies having the same rate of technological progress and population
growth will eventually grow at the same constant rate. Romer (1986), however, stated that the traditional
theory failed to reconcile empirical observations with its own prediction. Plosser (1992) also argued that
the Solow model cannot properly describe changes in growth rate across countries.
Endogenous growth models attempt to better explain observed trends. Such models stipulate that
long-run growth is determined by economic incentives. In this case, inventions are intentional and
generate technological spill over that lower the cost of future innovations. Endogenous models also
maintain that an educated labour is crucial in determining the rate of technological innovation and the
long-run growth. Further, greater accumulation of human knowledge will not only result in higher income
but also in a permanent higher growth rate.
A number of studies, including, OECD (2003) summarize the following as the factors that
influence economic growth: political and economic stability; government intervention; the political
economy; international trade; capital investment; human capital; trade barriers; population; banking
system; risk management; information.
146
5.2.1 Political and Economic Stability
Stability is associated with lower level of uncertainties. In a stable society, business people can
take sound decisions and lack of uncertainty also promotes business and consumer optimism. The
medium and long-term benefits are highly conducive to growth. Barro (1991) estimated that political
instability, measured by the number of revolutions and political assassinations in a given country,
decreases GDP growth per capita. For examine, South Korea would have experienced a growth rate of
6.25% instead of 5.25 % on an annual basis from 1960 to 1985 had there been a higher degree of political
stability. El Salvador lost a potential growth of 7 % per year in per capita growth because of the high level
of political instability.
5.2.2 Government Intervention
Government intervention distorts market signals and as such leads to miss-allocation of resources.
It also gives the wrong signal to the market where private investors feel insecure. This may eventually
lead to a lower level of investment in productive opportunities. The size of government also affects
economic growth. This is in part because Government spending often creates distortions from taxation
and reduces private savings leading to lower growth. Barro (1989,1990,1991) concluded that the larger
the ratio of government spending (excluding education and defence) to GDP, the lower the growth rate
and investment.
5.2.3
The Political Economy
Acemoglu et al. (2005) confirmed that good institutions ensure property rights and access to
economic resources for the population at large. Governance consists of voice and accountability; political
stability and physical security; government effectiveness; regulatory quality rule of law, and control of
corruption. Hazama (2008) discussed the direct and indirect effects on economic growth of institutions,
governance and democracy. He found that institutions and governance have a positive effect on growth.
While democracy neither promotes nor hampers growth directly, its indirect effects on growth cannot be
ignored. Mild and short-term institutional changes have often led to rapid economic growth. For example,
China is reaping the benefits of institutional reforms. Corrales (2003) concluded that growth has been
higher in countries that have adopted extensive reforms.
Claessens and Laeren (2001) argue that the types of institutions in a country affect growth
performance. Acemoglu et al. (2003) showed that institutions accounted for Botswana’s success.
Similarly, Subramanian and Roy (2003) explained that Mauritius success story has been influenced by
economic and institutional factors. In addition, they found that the uniqueness in Mauritius lies in its
ethnic fragmentation. For instance, the economic elite minority (the French) avoided nationalization and
heavy taxation by sharing their rent with the political elite majority (Indians).
Kaufmann and Kraay (2002) concluded that governance has a positive effect on growth. In
practice, democracy, legitimacy and culture determine the quality of governance. Przeworski et al. (2000)
showed that democracy does not affect growth. Feng (2003) argued that political instability and political
uncertainty significantly reduces economic growth. However, while democracy does not affect growth
rates, it has indirect influences on individual aspects of economic growth. Moreover, Rodrik (2007) stated
that growth rates (both short term and long term) were more stable in a democracy. In addition,
democratic regimes recovered more quickly from economic shocks.
Democracy attracts FDI flows and promotes human capital development. FDI by increasing the
inflow of investment and hence technology, leads to transfer of technology in favour of the recipient
country. Borensztein et al. (1998) suggested that FDI is an important vehicle for the transfer of
technology. However, FDI contributes to growth only if the economy can absorb advanced technologies
otherwise the transfer of technology generated by inflows of FDI would have no effect on the domestic
production.
147
5.2.4 International Trade
International trade promotes growth since countries are expected to produce goods and services in
which they have comparative advantages. Trade provides new unexplored market, enabling firms to
produce on a larger scale. However, often when a government tampers with international trade through
tariffs, subsidies, quotas and foreign exchange control, this reduces the benefits derived from international
trade. Poor infrastructure implies that a country will not be reaping the full benefits of international trade.
Hopkins (1973) inferred that the primary engine growth of African countries during the colonial era as the
exploitation of Africa’s comparative advantage in the production of certain primary products.
Nonetheless, many countries failed to further expand primary commodity exports after independence. In
parallel, price distortions resulting from government intervention brings inefficiency, which in turn slows
growth.
Barro (1991) compared relatively closed economies (Africa and Latin America) with opened
economies (Singapore, Hong Kong, South Korea and Taiwan). He found that relatively closed economies
had a lower growth rate (less than 5%) as opposed to the opened economies (8-10%). In a similar study,
Ben-David (1991) argued that new members of the European Union experienced an increased in incomes
and approached those of the wealthier nations since they had to drop their trade barriers. Roubini and
Sali-i-Martin (1991) inferred that if a country shifts from complete openness to a closed economy, growth
rate would fall by 2.5%. Gould and Ruffin (1993) found that if human capital, measured by the literacy
rate, is relatively high in open economies, then growth rate is 1-2% higher than in a closed economy.
5.2.5 Capital Investment
De Long and Summers (1991) stated that new technologies are embodied in new machines.
Assuming a constant relative labour productivity, growth of the labour force, school enrolment rates and
investment other than in machinery and equipment, they found that for every 1% of total output devoted
to investment in machinery, there is an additional economic growth of 0.26%. Kim and Law (1996)
studied the sources of Asian Pacific economic growth and they concluded that high level of investment
and savings are important elements for rapid economic growth. They also recommended that the countries
needed to devote a higher proportion of their resources to research and development so as to increase the
contribution of technical progress to economic growth.
Accumulation of machinery and equipment (also called physical capital) leads to economic
growth. The industrialisation process has been triggered by the accumulation of capital. As the number of
machinery per worker goes up, productivity increases. In practice, it is rare to find new capital equipment
or consumer product embedding all existing technological innovation. Public or private knowledge is also
embedded in capital equipment. Private knowledge (patents, know-how), in fact, belongs to firms and
other institutions on developed economies. Public knowledge can be non-excludable; for example,
scientific knowledge, which is regularly published in research journals, can be accessed by anyone.
However, Fafchamps (2000b) argued that few Africans have the required level of technical expertise so as
to use this information efficiently with a view to diffuse technological innovation in Africa. There is an
acute need of equipped laboratories and research institutes together with a qualified labour. In addition, ,
for a proper implementation of technological innovations, there is a need for equipment. For example,
internet cannot be used without computers.
However, capital accumulation alone cannot account for long lasting growth because too many
machinery per worker will lead to saturation. Solow (1965) stipulated that if an economy is driven only
by accumulation of physical capital, the economy will stop growing at some point in time. In fact, it will
reach a point where it will be no longer profitable to increase output because of diminishing returns.
Schultz (1961), Barro (1991) and Mankion et al. (1992) argued that education and skills are
complementary to physical capital.
148
Developing countries will have to tap into the same stock of knowledge as developed nations. It
may be argued that poor countries can grow simply by applying existing technology and know-how and
adapting them to the local context. Developing countries can improve technological transfer by
subsidizing local research, sending scientists and students in developed countries and focusing on the
adaptation of technologies or research to the local context. Furthermore, these countries can also attract
foreign firms possessing the technical know-how via incentives or joint-venture agreements. Technology
thus has an important role to play in promoting growth. Aghion and Howitt (1992) concluded that firms
needed to innovate constantly so as to remain competitive. This is the process of creative destruction
proposed by Shumpeter as the driving force behind growth. Schumpeterian economics also stress on the
important role played by entrepreneurs. African government may nurture local business talents by
providing a host of incentives to promote new businesses.
Economic development can also be seen as a modernization process. Hence, technological
innovation is an important source of economic growth. Developing countries can boost growth by
adopting new technologies. Removing obstacles in technological transfer boosts growth. Hence, the
government has an important role to play in the technological transfer process. Technology is embedded
in physical capital. Hence, by accumulation capital, an economy can acquire new technologies.
Technology transfer increases the productivity of both labour and capital. It also increases the standard of
living of the population. For example, mobile phones and cars have improved the quality of life of many
individuals.
5.2.6
The Human Capital
Many including Levine and Renelt (1992) argue that educating the population is a key to
economic growth. The more educated a population is, the faster will be the technological progress since
individuals will build on ideas of others or will simply use technology of other countries in a better way
leading to increased productivity. Barro (1991) concluded that if the Guatemalan government had
invested in education so as to achieve a 50% attendance at schools in 1960, the growth rate per capita of
the country might have increased by 1.3% per year from 1960 to 1985.
5.2.7
Removal of Barriers
Removal of barriers to economic efficiency promotes growth. Idle resources exist when an
economy is underdeveloped. By putting idle resources to work, the economy moves nearer to its
production possibility frontier. For instance, clearing unproductive vegetation or draining marshy land
increases the land available for productive uses. Introducing new production techniques or variety of
crops enables better use of labour. Additionally, improving transport network and reducing transport costs
make isolated resources more employable.
However, once all idle resources have been used efficiently, there is a need to look for other
sources of growth namely allocative efficiency. Producing goods and services desired by the population
can enhance welfare. Hence, allocative efficiency is important in promoting growth. It focuses on
eliminating all price distortions and minimising the role of the government. It may be argued though that
growth stops once allocative efficiency has been achieved. Moreover, Dervis, de Melo and Robinson
(1982) concluded that welfare gains from allocative efficiency rarely exceed a one-off rise of a few
percent in the GDP. In a dynamic perspective, growth is the result of an accumulation process. Given that
accumulation leads to more productive resources being available for production, using all resources will
eventually enhance growth prospects of an economy.
5.2.8
Population Growth
Population growth affects the long-term economic growth. The Malthusian view that an increase
in population would lead to negative per capita growth had strong influence on policy. However, fast
149
rising population has not been accompanied by a decrease in food availability. Boserup (1965) argued that
the Malthusian view ignored the fact population growth often triggers investment in infrastructure,
education and the technological innovations. This is beneficial in the long run.
5.2.9
Efficient Banking System
There is a direct link between banks and economic activity. By economizing on the costs of
acquiring and processing information about the firms and managers, banks can influence resource
allocation. Better banks are lower cost producers of information with consequent implication for capital
allocation and productivity growth (Diamond (1984); Greenwood and Javanovic (1990); and King and
Levine (1993b)) .Schumpeter (1961) stressed out that financial services are paramount in promoting
economic growth. In this view production requires credit to materialise, and one can only become an
entrepreneur by previously becoming a debtor. That is, what the entrepreneur first wants is credit.
Levine (1997) identified five basic functions of the banking sector, which helps to explain its
critical contribution to a country’s economic development. These are savings mobilisation; risk
management; acquiring information about investment opportunities; monitoring borrowers and exerting
corporate control; facilitating the exchange of goods and services. The mobilization of savings is perhaps
the most obvious and important function of the banking sector. The provision of savings facilities or
transaction bank accounts enables households to store their money in a secure place, and allows this
money to be put to productive use. That is, channelling funds through lending to individuals or enterprises
to finance their investment projects, thus encouraging capital accumulation and promoting private sector
development. However, lack of access to secure savings facilities leads people to save in physical assets
such as jewellery, or store their savings at home. As such, bringing these savings instead into the banking
sector where they can be utilised productively, would by itself make a significant contribution to the
home country’s economic development.
In addition, the returns on investment can create positive expected returns for the savers, which
may in turn increase savings. It can also facilitate the development and adoption of better technologies.
Adoption of a better technology can be best explained with McKinnon (1973) illustration of a farmer who
cannot afford a particular investment out of his own savings, as such, he needs to borrow in order to buy
some piece of equipment, that is, invest in “new technology” which would increase his productivity and
enable the farmer to earn a higher income thereafter. Thus, by mobilizing savings and hence increasing
the availability of credit, the banking sector facilities investment in new technologies across the economy,
increasing overall productivity. De Gregorio (1996) argues that credit may also be made available to
finance investment in education or health, and can thus promote the accumulation of human capital. Thus,
savings mobilization can have a significant impact on growth by increasing investment, productivity and
human capital.
5.2.10 Risk Management
Many projects or enterprises require a medium to long-term commitment of capital, whereas most
savers prefer to have the option to draw on their savings, or move them into another investment
opportunity, should the need arise, that is, they like their savings to be liquid. Levine (1991) argued that
banks combine many households’ savings, and because savers usually do not want to withdraw their
money at the same time, this allows the banks to simultaneously provide medium to long-term for
investment and liquidity for savers.
Bencivenga and Smith (1991) explained that banks help to ensure that capital is allocated to the
best projects, even if they require a long-term financial commitment. They can also affect the rate of
technological change in long-term commitments of resources to research and development promotes
technological innovation. As these factors serves to increase the return on savings, they may also increase
savings and capital inflows.
150
Obsfeld (1992) inferred that the banking sector also promoted the concept of risk diversification.
Investing in an individual project is riskier than investing in a wide range of projects whose expected
returns are unrelated. As savers generally dislike risk, banks allow investments to be made in riskier
projects with higher expected returns in aggregate. This again increases overall investment returns and
improves capital allocation, with a subsequent impact on growth.
Risk diversification can also increase technological change. While it is well known that
innovation is risky and may well result in failure, the ability to diversify risk by investing in many
different innovation-based enterprises may make investment in otherwise prohibitively risky enterprises
possible. So by making more capital available to innovators, banks that facilitate diversification may also
increase technological change and thus promote economic development.
5.2.11 Acquiring Information about Investment Projects
Individual savers are unlikely to have the time or capacity to collect, process and compare
information on many different enterprises, managers and market conditions before choosing where to
invest. As such high information costs may prevent capital from flowing to its highest value use. In
addition, they will be less keen to invest in activities about which they have little information.
5.2.12 Monitoring Borrowers and Exerting Corporate Control
The ability of banks to monitor the performance of enterprises on behalf of many investors, who
cannot gather critical information about performance of enterprises because of lack of resources
individually, and to exercise corporate control (for example, lenders holding meetings with borrowers to
discuss business strategy), helps to ensure that investors receive returns that properly reflect the
enterprise’s performance. Further, this ensures that investors are not being defrauded by the firm’s
managers as a result of their lack of information, and thereby creates the right incentives for the managers
of the borrowing enterprises to perform well. Bencivenga and Smith (1991) argue that financial
arrangements that improve corporate control tend to promote faster capital accumulation and growth by
improving the allocation of capital.
5.2.13 Facilitating Exchange
The banking sector facilitates transactions in the economy, both physically by providing the
mechanisms to make and receive payments, and by reducing information costs in the ways outlined
above. So by providing financial intermediation in this way, the banking sector reduces transactions costs
and facilitates the trading of goods and services between businesses and households. In doing this, the
banking sector allows greater specialization which in turn facilitates productivity gains, technological
innovation and growth. As such, anything that reduces transaction costs and better facilitates exchange of
goods and services, whether it is faster payment systems, more bank branches, or improved remittance
services, will help to promote economic development.
5.3
Economic Performance
M
ost of the countries in COMESA have experienced an annual GDP constant growth
rate of at least 5 % in 2006 (Table 5.1). And for most of the countries, this growth rate
is much better than the rate of growth in 1990 and 1995. The only two countries that
seem to be performing worse than their performance in the 1990’s are Comoros and Mauritius. Their
annual rate of growth has declined over the years. For Comoros, this result is mainly attributed to the recurrent political crisis, tensions and civil war in the country, which is hindering the economic progress
and pushing the country further towards a debt trap. For Mauritius, the lower annual rate of growth of real
151
GDP is mainly attributed to the erosion of sugar trade preferences, the dismantling of the Multi Fiber
Agreement and soaring oil prices. Ethiopia has experienced the highest annual growth rate, 11.5 %,
mainly attributable to good weather conditions.
The inflation rate at the end of 2006 is a one-digit rate for most of the selected countries. There
are some countries, which are still facing a two-digit inflation rate, but it is important to note that these
countries were facing even higher inflation rate at the end of 1990 or 1995. This means that even if the
inflation rate is double-digit, these countries are in the process of reducing the level of price increase.
Table 5.1 GDP and Inflation for Selected COMESA Countries, 1990 - 2006 (%)
GDP
Inflation
1990
1995
2006
1990
1995
2006
Burundi
3.4
-7.9
5.1
11.0
19.0
9.0
Comoros
5.0
3.6
1.2
N/A
-7.0
2.0
Congo
-6.5
0.6
5.6
265.0
370.0
18.0
Ethiopia
2.6
6.1
11.5
5.0
15.0
12.0
Kenya
4.1
4.2
6.1
41.0
7.0
16.0
Madagascar
3.1
1.6
5.0
9.0
37.0
11.0
Malawi
5.6
13.8
7.9
23.0
75.0
10.0
Mauritius
4.9
4.4
3.6
10.0
6.0
8.0
Rwanda
0.4
35.2
5.4
6.0
38.0
12.0
Seychelles
7.4
0.5
5.3
4.0
1.0
1.0
Uganda
6.5
11.3
5.0
27.0
11.0
7.0
Zambia
-3.4
-2.8
6.2
110.0
46.0
8.2
Source: World Development Indicators 2007, World Bank and World Economic Outlook 2008, IMF
Table 5.2 shows liquidity aspects (development) of the stock market of selected COMESA
members. Market capitalization as a percentage of GDP can be used as a proxy for economic performance
in the financial sector of an economy. The higher the ratio, the more developed is the financial market. It
is clear that the importance of the stock market in the three countries has increased over the years.
However, when compared with the average of other regions of the world, they have a long way to go.
Table 5.2: Market capitalization of listed companies (% of GDP) 1990-2006
1990
1995
2006
Kenya
5
21
50
Mauritius
11
35
57
Zambia
1
11
Europe and Central Asia
67
High Income Non-OECD
251
High Income OECD
122
Latin America and
52
Caribbean
Low and Middle Income
73
Source: World Development Indicators 2007, World Bank
152
The economic performance of a country depends to a large extent on the facilities and incentives
available to the private sector. At the very outset, the main factors hindering growth include red tape and
corruption level. Table 5.3 gives an overview of red tape in selected COMESA countries for the year
2006. The number of days required to start a business in countries like Angola and Congo are too many
compared to other COMESA member countries. Since the procedures take very long, the effects can be
multiple in an economy. For instance, small entrepreneurs would not register their firms and as such
operate in the informal sector leading to tax revenue forgone. Also, foreign investors would be very
reluctant to start business in such countries and thus the actual level of FDI would be less than the
potential FDI level. Besides Congo, the number of days taken to start a business in the selected COMESA
member countries is directly comparable with average number of days of other regions of the world.
Table 5.3: Number of days required to start Business(2006)
Number of Days required
43
23
155
16
54
21
37
46
16
38
28
35
31
33
16
77
54
Burundi
Comoros
Congo
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Rwanda
Seychelles
Uganda
Zambia
Europe & Central Asia
High-Income non-OECD
High-Income OECD
Latin America & Caribbean
Low & Middle Income
Source: World Development Indicators 2007, World Bank
With the exception of Burundi, Comoros and Congo, the average of other regions of the world,
for the year 2006, shows that the ratio of gross capital formation (GCF) to GDP of the COMESA
countries are more or less the same as those other regions, approximately 20% (Table 5.4).
The level of schooling of the citizens of a country is an indicator of the level of skills existing in
the country. The more advance an economy is, the higher is the level of schooling for the citizens of the
country, both in terms of number of students at schools and the level of education reached. For the
purpose of analysis, the primary completion rate as a percentage of the relevant age group for the
COMESA members is reported in Table 5.4. Over the years under study, primary completion rate as a
percentage of relevant age group has been increasing in the COMESA countries, implying that people in
are becoming more educated and therefore the productive capacity is increasing.
153
Table 5.4: Gross Capital Formation (% of GDP) & Primary Completion Rate, 1990-2006
Gross Capital
Formation (%
GDP)
1990
1995
2006
Primary Completion rate,
total (% of relevant age
group)
1990
1995
2006
Burundi
15
6
17
41
Comoros
20
19
10
-
36
-
-
Congo
9
9
16
-
-
-
Ethiopia
13
18
20
-
18
49
Kenya
24
22
19
-
-
-
Madagascar
17
11
25
35
28
57
Malawi
23
17
24
29
53
55
Mauritius
31
29
25
64
98
92
Rwanda
15
13
21
45
Seychelles
25
30
33
-
-
-
Uganda
13
12
23
-
-
-
Zambia
17
16
24
-
-
84
Europe & Central Asia
-
-
23
-
-
High-Income non-OECD
-
-
20
-
-
-
Latin America & Caribbean
-
-
21
-
-
-
Low & Middle Income
-
-
27
-
-
-
Source: World Development Indicators 2007, World Bank
Another way of assessing whether an economy has performed well over the years is to consider
the change in quality of life of its citizens. Table 5.5 shows different indicators related to quality of life
for the period 1990 to 2006. The data on electric power consumption shows the spending capacity of the
citizens of a country on electricity. The higher the expenditure capacity, the more mechanized apparatus
are used by household and the more the electricity power consumption per head. For the COMESA
members, over the years 1990 to 1995, it seems that electricity power consumption per head has
increased. This shows that the expenditure capacity of the citizens has improved. The number of fixed
line and mobile phone subscribers per hundred people has increased over the years in almost all countries.
This variable also shows increased consumption capacity. The more an economy performed, the more
revenue the government receives and the more is spent on medical care facilities by the government and
the better the medical facilities and ability for citizens to use proper medical care, the higher is the life
expectancy at birth. For the COMESA countries selected, life expectancy has improved over the years,
although still very low in absolute terms.
Table 5.5: Quality of Life, 1990-2006
Electricity power
consumption (kWh
per capita)
Fixed line and mobile phone
subscribers (per 100 people)
Life expectancy at
birth, total (years)
1990
1995
2006
1990
1995
2006
1990
1995
2006
Burundi
-
-
-
0
0
-
46
45
49
Comoros
-
-
-
1
1
-
56
59
63
119
100
-
0
0
7
46
44
46
Congo
154
Ethiopia
21
24
-
0
0
2
48
49
52
Kenya
116
131
-
1
1
19
59
56
53
Madagascar
-
-
-
0
0
6
51
54
59
Malawi
-
-
-
0
0
49
48
48
Mauritius
-
-
-
5
14
90
69
70
73
Rwanda
-
-
-
0
0
3
32
31
46
Seychelles
-
-
-
12
17
100
70
71
72
Uganda
-
-
-
0
0
7
50
46
51
Zambia
754
48
43
42
698
1
1
15
Source: World Development Indicators 2007, World Bank
Table 5.6 highlights the composition of GDP. During the 10 years under study, the structure of
the COMESA members did not change much. Seychelles, which had a low agricultural sector in 1996,
continued to be the same in 2006. In Madagascar, the service sector as a share of GDP has fallen to the
benefit of the manufacturing sector.
Table 5.6: Sectoral Composition of GDP, Value Added (% of GDP) 1995-2006
Agriculture
1995
2002
Burundi
48
41
Comoros
41
50
DR Congo
57
Ethiopia
Industry
2006
1995
2002
19
19
45
12
12
51
46
17
57
42
47
Kenya
31
29
Madagascar
27
Malawi
30
Mauritius
10
Rwanda
44
Seychelles
4
Uganda
Services
2006
1995
2002
33
41
12
47
38
33
21
28
26
27
27
10
15
13
33
43
39
27
16
17
19
53
53
54
32
28
9
14
15
64
54
57
38
34
20
17
20
50
45
46
7
6
32
31
27
58
62
68
41
41
16
21
21
40
37
38
3
3
23
30
26
73
67
71
49
31
32
14
22
18
36
47
49
Zambia
18
22
22
36
26
33
46
52
45
Zimbabwe
15
14
29
21
56
65
Source: World Development Indicators 2007, World Bank
In what follows we discuss specific countries.
Burundi
The Burundi’s economy is made up of mainly the agricultural sector with more than 90% of the
population dependent on subsistence agriculture. This sector, as is well known, depends to a large extent
on the weather conditions. The poor rains of 2003 and 2005 negatively hit the agriculture sector very hard
(GDP growth was -1.3 percent and 0.9 percent, respectively) which eventually resulted in a yearly GDP
growth rate of only 2.7% between 2001 and 2006. Burundi is with an underdeveloped manufacturing
sector. Reforms and parallel political progress have resulted in an increase in agricultural production and
construction, as private confidence has returned and donors have re-engaged. The main agricultural
155
2006
production includes coffee, cotton, tea and corn and the main industrial production includes light
consumer goods such as blankets, shoes and soap.
Comoros
Agriculture, which mainly includes fishing, hunting, and forestry for Comoros, contributes about
45% to GDP and employs 80% of the labour force. This is also the most important sector in terms of
exports revenue. Comoros has limited resource endowments, a small domestic market as well as a narrow
export base. The main agricultural production includes vanilla, cloves and ylang-ylang and the main
industrial production includes fishing.
Democratic Republic of Congo (DRC)
The DRC is endowed with vast potential wealth. She is endowed with the second largest rain
forest in the world, with fertile soils, ample rainfall, and considerable and varied mineral resources – but
with a GDP per capita, which is lowest in the world. Mining (copper, cobalt, diamonds, gold, zinc, and
other base metals) and petroleum extraction used to account for about 25 percent of the country's GDP but
the economy is now, due to the impact of the conflict, centred on subsistence agriculture and informal
activities, with a collapse of export and value-adding activities. The main agricultural production includes
coffee, sugar and palm oil and the main industrial production includes mining (diamonds, gold, copper,
cobalt, coltan zinc) and mineral processing.
Egypt
The highly fertile Nile valley, where most economic activity takes place, bisects Egypt. The
Egyptian economy experienced quite a high rate of GDP growth, about 5% per year in 2005-06, and 7%
in 2007. This was the result of the favourable taxation and privatization policies that has been initiated in
the economy since 2005. Agriculture, Industry and services accounted for 14%, 41% and 45% of GDP in
2007, respectively. The main agricultural production includes cotton, rice and corn and the main
industrial production includes textiles, food processing and chemicals.
Egypt ranks 63 among the world’s exporters in goods (Table 5.7), with an annual growth in
exports value of 33% per annum over the period five year 2002 – 2006 and 48% per annum over the
period 2005-2006. Given that growth of the world’s exports is around 17% per annum over the period
2002-2006, Egypt exporters are therefore increasing market share.
The sectors in which Egypt is gaining market may be attractive sectors for investments (foreign
or domestic). At HS 2-digit level, Table 7 highlights these sectors. They are, amongst others, mineral
fuels, oils and distillation products (HS 27), iron and steel (HS72), Salt, sulphur, earth, stone, plaster, lime
and cement (HS 25), aluminium and articles thereof (HS 76), plastic and articles thereof (HS 39) or
Edible fruit, nuts, peel of citrus fruit, melons (HS 08) and these sectors are experiencing high growth in
value, 48%, 51%, 50%, 28%, 42% and 37% respectively.
In 2006, mineral fuels, oils and distillation products (HS 27), out of which petroleum gases (HS
2711), petroleum oils, not crude (HS 2710) and petroleum oils, crude (HS 2709) represent 45%, 27% and
26% respectively, were mainly exported to India (16%), Italy (16%) Spain (15%) and USA (10%). At a
more detailed level, the main exported products are Natural gas, liquefied (HS 271111) mainly to Spain
and USA, Petroleum oils and oils obtained from bituminous minerals, other than crude (HS 271000)
mainly to India and Petroleum oils and oils obtained from bituminous minerals, crude (HS 270900)
mainly to Italy and India. Iron and steel (HS72), were mainly exported to USA (27%), Saudi Arabia
(16%), Italy (12%) and Spain (12%).
Tables 5.8 and 5.9 show Egypt’s top products exported at the HS 6-digit level. While Table 5.9
gives a time series exports value over the 4-year, 2003-2006, period, Table 5.8 gives trade indicators for
the year 2006. It can be seen from Table 5.9 that over the years, exports of almost all the products listed,
156
with the exception of cotton, has increased more than two-fold. Therefore, all these sectors be interesting
sectors to focus trade promotion efforts.
NO REFERENCE TO 5.10 ETC
Table 5.8: List of products at 2 digits level exported by Egypt in 2006 (Mirror)
Annual
Annual
Annual
growth
growth
growth
in value
in value
of world
Trade
between between imports
balance
20022005between
Exported value
2006, US$
2006
2006
20022006, US$ ‘000’ ‘000’
(%)
(%)
2006 (%)
Ranking
in world
exports
All products
Mineral fuels,
oils, distillation
products, etc
20,049,000
-13,412,640
33
48
17
63
9,581,498
6,218,987
48
81
31
36
Iron and steel
Salt, sulphur,
earth, stone,
plaster, lime and
cement
Articles of
apparel,
accessories, knit
or crochet
Articles of
apparel,
accessories, not
knit or crochet
Aluminium and
articles thereof
Plastics and
articles thereof
1,382,936
- 330,899
51
39
27
36
762,920
682,603
50
3
15
12
669,067
425,257
16
35
10
39
577,904
370,161
16
28
9
37
486,709
329,908
28
35
19
47
447,693
- 740,607
42
48
17
53
Fertilizers
444,606
390,985
23
95
18
16
Cotton
Edible fruit, nuts,
peel of citrus fruit,
melons
Edible vegetables
and certain roots
and tubers
400,661
- 154,045
0
-5
6
21
392,999
277,869
37
-2
13
27
374,470
176,023
21
11
12
20
157
Table 5.9: List of products exported by Egypt 2006
All products
Natural gas, liquefied
Petroleum oils and oils
obtained from
bituminous minerals,
crude
Exported Value in
2003, US$’000’
Exported Value in
2004, US$’000’
Exported
Value in 2005,
US$’0000’
Exported Value in
2006, US$’0000’
8,125,014
11,061,343
14,006,000
20,049,006
1,614,502
3,846,936
0
979,450
1,427,174
1,101,509
2,544,668
810,262
1,181,623
1,485,712
1,937,173
348,293
420,755
576,719
634,962
132,511
298,989
297,727
391,600
183,861
122,445
172,858
367,558
Portland cement nes
91,956
199,953
248,724
288,207
Propane, liquefied
81,937
99,263
170,010
261,531
Oranges, fresh or dried
Cotton, not carded or
combed
Mens/boys trousers and
shorts, of cotton, not
knitted
108,429
145,901
277,894
236,576
259,079
184,665
251,508
211,037
124,312
132,820
150,070
200,959
Aviation spirit
Light petroleum
distillates nes
Hot roll iron/steel nes,
coil >600mm x <3mm
Urea in aqueous
solution in packages
weighing more than 10
kg
158
Table 5.10: List of products at 6 digits level exported by Egypt in 2006 (Mirror)
Annual
growth in
value
between
20052006, %,
p.a.
Annual
growth
of world
imports
between
20022006, %,
p.a.
Share in
world
exports,
%
Ranking
in world
exports
48
17
0.17
63
138
30
6.45
6
Exported
value 2006,
US$’000’
Trade balance
2006 , US$’000’
Annual
growth in
value
between
20022006, %,
p.a.
All products
Natural gas,
liquefied
Petroleum oils
obtained from
bituminous
minerals
Petroleum oils and
oils obtained from
bituminous
minerals, crude
Hot roll iron/steel
nes, coil >600mm
x <3mm
Urea in aqueous
solution in
packages
weighing more
than 10 kg
Portland cement
nes
20,049,000
- 13,412,640
33
3,846,936
3,829,381
2,576,487
1,469,833
30
2
25
37
0.57
40
2,544,668
1,510,505
27
2
138
31
0.27
35
391,600
362,964
50
26
39
24
3.44
9
367,551
364,412
24
-
113
23
5.83
6
288,207
286,800
107
100
29
19
5.21
3
Propane, liquefied
Oranges, fresh or
dried
Cotton, not carded
or combed
Mens/boys
trousers and
shorts, of cotton,
not knitted
261,531
77,249
43
17
26
1.33
15
236,575
236,352
38
9
7.93
4
211,036
87,673
-
24
6
54
11
16
15
1.95
8
200,906
175,830
20
17
34
9
1.06
27
Annual
growth in
quantity
between
2002-2006,
%, p.a.
Figure 5.1 plots Egypt’s annual export growth (horizontal axis) and the annual growth of
international demand (vertical axis) both over the 4-year period. The vertical red line (reference line)
indicates the average nominal growth of total exports of Egypt for the period 2002-2006 and the
horizontal red line indicates the average nominal growth of world imports over the same period, which is
17%. These reference lines divide the chart into four quadrants – loses in growing sectors, losers in
growing sectors, losers in declining sector and winners in declining sectors. The criterion for
distinguishing growing and declining products in this chart is the average nominal growth rate of total
world imports from 2002 to 2006 that is 17%.
159
Products, whose world imports have grown below this rate, are classified as declining products as
their share in the world trade is declining, while products located in the upper quadrants are growing
products, as they are growing faster than the world market. Winners in the growth market are those
products for which the world exports are growing and for which Egypt exporters have increased their
share, i.e. exporters of these products have proven their international competitiveness over the period.
These are sectors that would normally attract investors easily as the products forms part of national
success stories. A number of iron products appear to be in this segment. Examples include bars and rods
(HS 721420), hot roll iron and steel (HS 720839) and wire of refined copper (HS 740811).
Losers in growth markets refer to products for which international demand has been growing at
above-average rates, while the demand for these products from Egypt has been falling, i.e. Egypt has been
losing international market share in sectors, which is growing in the world market. This means that
although markets for these products exist; yet Egypt cannot take full advantage, may be because of supply
capacity or any other factor that is limiting the product to reach the markets. We find that petroleum oils
and oils obtained from bituminous minerals, other than crude (HS 271000) and oils obtained from
bituminous minerals, crude (HS 270900) appear to be in this segment.
Winners in declining markets refer to cases where world demands for the products are below
average but the Mauritius exporters are gaining market share. This may mean that competing country
producers’ countries are moving away from the production of these sectors but Egypt continues to trade in
these products because either it is more competitive or Egypt is facing difficulty in shifting production
towards other sectors. Normally, a niche-marketing strategy is adopted in this segment to isolate positive
trade performance from the overall decline in these markets. In terms of value, the exports rice (HS
100630) and trousers and shorts, of cotton (HS 620342 and HS 620462) from Egypt fall under this
category.
Losers in declining markets are exports of products for which demand in the world is decreasing
and at the same time Egypt exporters’ share of the market is declining. Cotton exported by Egypt (HS
520100) appears in this segment.
Figure 5.1: Growth of National Supply and International Demand for the twenty leading Exports in 2006.
160
Ethiopia
Ethiopia's economy is based on agriculture, accounting for almost 50% GDP, 60% of exports, and
80% of total employment. With an annual growth rate of 11% during 2003-2007, Ethiopia is experiencing
161
an unprecedented spell of economic growth. Given that there are doubts about indefinitely lasting good
weather conditions, this high growth rate in GDP has to be analysed with care. The main agricultural
production includes cereals, pulses and coffee and the main industrial production includes food
processing and beverages.
Table 5.11: List of products at 2 digits level exported by Ethiopia in 2006
Trade
balance 2006
US$’000’
Annual
growth in
value
20022006(%)
Annual
growth
in value
20052006
(%)
1,042,963
- 4,164,357
28
13
435,734
345,103
29
173,252
152,242
85,948
Annual
growth of
world
imports
20022006( %)
Share in
world
exports,
%
Ranking
in world
exports
17
0
139
25
16
2
15
50
13
10
1
21
68,737
12
2
8
15
1
77,693
76,184
9
16
5
0
45
64,518
63,594
118
45
15
0
84
56,365
32,596
13
47
12
0
51
36,906
2,396
417
180
10
0
28
Live animals
33,311
28,956
217
38
12
0
37
Meat and edible
meat offal
17,400
17,321
77
5
12
0
63
Cotton
10,375
8,395
-9
77
6
0
95
Exported
value 2006,
US$’000’
All products
Coffee, tea, mate
and spices
Oil seed, oleagic
fruits, grain, seed
and fruit
Vegetable plaiting
materials, vegetable
products nes
Raw hides and skins
(other than fur
skins) and leather
Pearls, precious
stones, metals,
coins, etc
Edible vegetables
and certain roots and
tubers
Live trees, plants,
bulbs, roots, cut
flowers etc
Ethiopia ranks 139 among the world’s exporters in goods (Table 5.11), with an annual growth in
exports value of 28% per annum over the period five year 2002 – 2006 and 13% per annum over the
period 2005-2006. Given that growth of the world’s exports is around 17% per annum over the period
2002-2006, Ethiopia exports are therefore gaining market share. The sectors in which Ethiopia is gaining
market may be attractive sectors for investments (foreign or domestic). At HS 2-digit level, Table 5.11
highlights these sectors. They are, amongst others, Coffee, tea, mate and spices (HS 09), Oil seed, oleagic
fruits, grain, seed, fruit, etc, nes (HS 12) and Pearls, precious stones, metals, coins, etc (HS 71). These
sectors are experiencing high growth in value, 29%, 50% and 118% respectively.
In 2006, at HS 4-digit level, out of coffee, tea, mate and spices (HS 09), the main exporting
product group was coffee (0901). It represented 81% of total exports under HS 09. At a more detailed
162
level, the main exported products are Coffee husks and skins, coffee substitutes (HS 090190) and are
mainly exported to Germany (25%), Japan (21%), Saudi Arabia (12%) and USA (9%). At HS 4-digit
level, out of Oil seed, oleagic fruits, grain, seed, fruit, etc, nes (HS 12), the main exporting product group
is oil seed (1207). It represented 92% of total exports under HS 12. At a more detailed level, the main
exported products are Sesame seeds, whether or not broken (HS 120740) and are mainly exported to
China (45%) and USA (10%).
Tables 5.11 and 5.12 show Ethiopia’s top products exported at the HS 6-digit level. It can be seen
that over the years, exports of sesamum seeds, whether or not broken (HS 120740) has out-placed exports
of Vegetable products nes (HS 140490). These two sector may be interesting sector to focus trade
promotion efforts. Also, the sectors cut flowers & flower buds for bouquets or ornamental purposes,
fresh (HS 060310) and Kidney beans and white pea beans dried shelled, whether or not skinned o split
(HS 071333) seem to be picking up quite well.
Table 5.12: List of products exported by Ethiopia 2003-2006
All products
Coffee, not roasted, not
decaffeinated
Sesamum seeds, whether or
not broken
Vegetable products nes
Cut flowers & flower buds for
bouquets or ornamental
purposes, fresh
Antiques of an age exceeding
one hundred years
Kidney beans and white pea
beans
Gold in other semimanufactured form nonmonetary(including gold
plated)
Chickpeas, dried, shelled,
whether or not skinned or split
Silver in unwrought forms
Bovine, live except pure-bred
breeding
Sheep or lamb skins, pickled,
without wool on
Exported
Value in
2003, US$
thousand
Exported
Value in
2004, US$
thousand
Exported
Value in
2005, US$
thousand
Exported
Value in
2006, US$
thousand
Exported
Value in
2007, US$
thousand
512,689
614,701
926,204
1,042,963
1,277,145
181,214
185,663
32,543
72,257
413,221
47,939
110,192
61,914
3,808
173,307
275
160,619
10,961
133,029
106,120
305
1,907
12,082
25,039
68,816
-
-
-
-
37,507
9,715
9,946
3,366
5,683
35,171
-
71,212
44,405
64,420
34,917
777
-
12,850
-
28,417
-
36,475
-
25,227
24,796
333
3,372
23,375
31,578
24,365
479
1,636
6,922
14,327
21,602
163
Table 5.13: List of products at 6 digits level exported by Ethiopia in 2006
All products
Coffee husks and
skins, coffee
substitutes
Sesamum seeds,
whether or not broken
Vegetable materials
nes, used primarily
for plaiting
Coffee, not roasted,
not decaffeinated
Gold in other semimanufactured form
nonmonetary(including
gold plated)
Chickpeas, dried,
shelled, whether or
not skinned or split
Bovine, live except
pure-bred breeding
Cut flowers & flower
buds for bouquets or
ornamental purposes,
fresh
Sheep or lamb skin
leather, nes
Sheep or lamb skins,
pickled, without wool
on
Annual
growth in
quantity
between
20022006, %,
p.a.
Annual
growth in
value
between
20052006, %,
p.a.
Annual
growth
of world
imports
between
20022006, %,
p.a.
Share in
world
exports,
%
Ranking
in world
exports
13
17
0.01
139
Exported
value
2006,
US$’000’
Trade
balance
2006 in
US$’000’
Annual
growth in
value
between
20022006, %,
p.a.
1,042,963
- 4,164,357
28
353,666
353,647
909
815
17
22
89.93
1
160,619
148,654
58
37
-7
22
17.84
2
74,917
74,917
-14
39
55.71
1
72,257
-15,585
-28
122
21
0.67
21
64,420
64,420
423
45
40
0.37
25
36,475
36,005
72
64
28
15
6.36
6
31,578
29,709
467
460
35
9
0.58
20
25,039
10,670
537
758
107
10
0.41
21
15,228
15,073
48
33
77
-6
1.58
12
14,327
14,327
121
178
107
-11
8.22
4
- 39
Figure 5.2 plots Ethiopia annual export growth (horizontal axis) and the annual growth of
international demand (vertical axis) both over the 5-year period. The vertical red line (reference line)
indicates the average nominal growth of total exports of Ethiopia for the period 2002-2006 and the
horizontal red line indicates the average nominal growth of world imports over the same period, which is
17%. These reference lines divide the chart into four quadrants – loses in growing sectors, losers in
growing sectors, losers in declining sector and winners in declining sectors. The criterion for
distinguishing growing and declining products in this chart is the average nominal growth rate of total
world imports from 2002 to 2006 that is 17%.
Products, whose world imports have grown below this rate, are classified as declining products as
their share in the world trade is declining, while products located in the upper quadrants are growing
164
products, as they are growing faster than the world market. Winners in the growth market are those
products for which the world exports are growing and for which Ethiopia exporters have increased their
share, i.e. exporters of these products have proven their international competitiveness over the period.
These are sectors that would normally attract investors easily as the products forms part of national
success stories. The products at HS-6 level in this category are Gold in semi-manufactured form (HS
710813), sesamum seeds (HS 120740) and coffee husks and skins (HS 090190). Exports for these
products grew at an annual rate of 423%, 58% and 909% respectively, over the five-year period.
Losers in growth markets refer to products for which international demand has been growing at
above-average rates, while the demand for these products from Ethiopia has been falling, i.e. Ethiopia has
been losing international market share in sectors, which is growing in the world market. This means that
although markets for these products exist; yet Ethiopia cannot take full advantage, may be because of
supply capacity or any other factor that is limiting the product to reach the markets. We find that coffee
not roasted not decaffeinated appears to be in this segment. In fact, Ethiopia is a net importer of this
product.
Winners in declining markets refer to cases where world demands for the products are below
average but the Ethiopia exporters are gaining market share. This may mean that competing country
producers’ countries are moving away from the production of these sectors but Ethiopia continues to trade
in these products because either it is more competitive or Ethiopia is facing difficulty in shifting
production towards other sectors. Normally, a niche-marketing strategy is adopted in this segment to
isolate positive trade performance from the overall decline in these markets. In terms of value, the exports
of Chickpeas, dried, shelled, whether or not skinned or split (HS 071320), Bovine, live except pure-bred
breeding HS (010290) and Cut flowers & flower buds for bouquets or ornamental purposes, fresh (HS
060310) from Ethiopia fall under this category.
Losers in declining markets are exports of products for which demand in the world is decreasing
and at the same time Ethiopia exporters’ share of the market is declining. Cotton, carded or combed (HS
520300) appears in this sector.
Figure 5.2: Growth of National Supply and International Demand for the twenty leading Exports in
2006
165
166
Kenya
GDP in 2007 grew at about 6% and this was well better that some previous years when GDP
grew only at 1% to 2% per annum following droughts which affected agricultural production, fall in
commodity prices which affected revenue from exports and high level of corruption which affected
investors confidence. The main agricultural production includes tea, coffee and corn and the main
industrial production includes small-scale consumer goods, agricultural products and horticulture.
Kenya ranks 110 among the world’s exporters in goods (Table 5.14), with an annual growth in
exports value of 13% per annum over the period five year 2002–2006 and 2% per annum over the period
2005-2006. Given that growth of the world’s exports is around 17% per annum over the period 20022006, Kenya exporters are therefore losing market share in the world market. Although, in total, the share
of Kenya exports is below that of world’s exports, yet there are some sectors in which Kenya is gaining
market share and these sectors may be attractive sectors for investments (foreign or domestic). The HS 2digit level sectors are highlighted as follows: live trees, plants, bulbs, roots, cut flowers etc (HS 06) and
Articles of apparel, accessories, not knit or crochet (HS 62). These sectors are experiencing high growth
in value, 20% and 18% respectively. However, in 2005-2006, HS 62 grew at a negative rate of –1%.
In 2006, at HS 4-digit level, out of coffee, tea, mate and spices (HS 09), the main exporting
products group were tea (0902) and coffee (0901). They represented 73% and 26%, respectively, of total
exports under HS 09. At a more detailed level, the main exported products are Black tea (fermented) &
partly fermented tea in packages exceeding 3 kg (HS 090240), representing 97% of Kenya exports of tea
(0902) and are mainly exported to UK (33%) and Pakistan (32%). Kenya's exports represent 25.34% of
world exports for this product, its ranking in world exports is 1. At HS 4-digit level, out of coffee (0901),
the main exporting product is Coffee, not roasted, not decaffeinated (HS 090111) with an annual growth
rate of 14% during the period 2002-2006 and represented 98% of total coffee exported by Kenya. Coffee,
not roasted, not decaffeinated (HS 090111) is mainly exported to Germany (25%), USA (15%) and
Sweden (11%).
Tables 5.15 and 5.16 show Kenya’s top products exported at the HS 6-digit level. It is shown that
light petroleum distillates nes (HS 271019) has not only suffered a fall in the value of exports, but also
weigh less in importance compared to 2002 when it used to be the main exported product for Kenya. At
HS 6-digit level, products which would probably attract investment (i.e. those sectors for which the
annual growth rate in export supersede the annual world exported growth rate for this sector) are Cut
flowers & flower buds for bouquets or ornamental purposes, fresh (HS 060310), Beans, shelled or
unshelled, fresh or chilled (HS 070820), Women’s/girls trousers and shorts, of cotton, not knitted (HS
620462). These sectors may be interesting sector to focus trade promotion efforts.
Table 5.14: List of products at 2 digits level exported by Kenya in 2006 (Mirror)
Coffee, tea, mate and
Annual
growth
in value
20052006(
%)
Annual
growth
of world
imports
20022006(
%)
Share in
world
exports,
(%)
Ranking
in world
exports
Trade balance
2006
US$’000’
Annual
growth
in value
20022006(%)
3,014,898
- 3,733,133
13
2
17
0.03
110
600,455
514,719
9
3
16
2.73
11
Exported
value
2006,
US$’000’
All products
Annual
growth
in
quantity
20022006(
%)
167
spices
Live trees, plants, bulbs,
roots, cut flowers etc
Edible vegetables and
certain roots and tubers
Articles of apparel,
accessories, not knit or
crochet
Mineral fuels, oils,
distillation products, etc
Vegetable, fruit, nut, etc
food preparations
Fish, crustaceans,
molluscs, aquatic
invertebrates nes
Articles of apparel,
accessories, knit or
crochet
Salt, sulphur, earth,
stone, plaster, lime and
cement
Iron and steel
469,871
453,762
20
17
10
3.18
7
243,528
222,607
13
17
12
0.64
26
212,248
176,364
18
-1
9
0.13
56
173,169
- 1,341,076
1
- 50
31
0.01
106
102,322
96,371
9
-1
12
0.29
42
82,745
61,797
-
-3
10
0.13
73
74,286
47,161
27
-9
10
0.05
78
69,456
52,914
10
-4
15
0.24
56
67,366
- 238,202
25
-14
27
0.02
85
168
Table 5.15: List of products exported by Kenya 2003-2006
Exported
Exported
Exported
Value in
Value in
Value in
2002, US$
2003, US$
2004, US$
thousand
thousand
thousand
All products
1,400,372
Black tea (fermented) & partly
fermented tea in packages
exceeding 3 kg
139,997
Cut flowers & flower buds for
bouquets or ornamental purposes,
fresh
99,386
Coffee, not roasted, not
decaffeinated
35,070
Beans, shelled or unshelled, fresh or
chilled
38,177
Light petroleum distillates nes
175,746
Women/girls trousers and shorts, of
cotton, not knitted
1
Peas, shelled or unshelled, fresh or
chilled
5,535
Tiles, cubes and sim nes, glazed
ceramics
87
Mens/boys trousers and shorts, of
cotton, not knitted
17
Pineapples preserved,
sugared,sweetened, spirited or not
44,132
Exported
Value in
2005, US$
thousand
Exported
Value in
2006, US$
thousand
2,551,073
2,683,206
3,040,980
3,014,926
478,961
458,423
427,171
429,934
175,446
231,370
355,569
414,915
89,934
93,080
145,410
154,678
61,500
363,879
88,970
464,383
111,320
234,231
137,851
129,473
2
970
145,853
129,241
7,905
6,084
55,027
59,587
392
11
580
58,982
24
134
39,403
55,304
51,193
45,366
54,823
52,566
169
Table 5.16: List of products at 6 digits level exported by Kenya in 2006 (Mirror)
All products
Black tea (fermented) &
partly fermented tea in
packages exceeding 3 kg
Cut flowers & flower
buds for bouquets or
ornamental purposes,
fresh
Petroleum oils obtained
from bituminous
minerals, other than
crude
Coffee, not roasted, not
decaffeinated
Beans, shelled or
unshelled, fresh or
chilled
Women/girls trousers
and shorts, of cotton, not
knitted
Peas, shelled or
unshelled, fresh or
chilled
Tiles, cubes and sim
nes, glazed ceramics
Mens/boys trousers and
shorts, of cotton, not
knitted
Pineapples preserved,
sugared,sweetened,
spirited or not
Annual
growth
in
quantity
between
20022006,
%, p.a.
Annual
growth
in
value
betwee
n 20052006,
%, p.a.
2
Annual
growth
of
world
imports
betwee
n 20022006,
%, p.a.
17
Share
in
world
exports
,%
0
Ranking
in world
exports
110
3
2
7
25
1
18
10
7
4
Exported
value
2006,
USD
thousand
3,014,898
Trade
balance
2006 in
USD
thousand
-3,733,133
Annual
growth
in value
between
20022006,
%, p.a.
13
429,927
357,657
7
414,895
412,867
21
161,345
-1,287,239
1
- 15
-52
37
0
88
154,652
153,760
14
1
7
21
1
16
137,843
137,813
23
12
24
19
24
1
129,232
127,827
25
23
-11
11
1
29
59,549
59,454
42
32
8
15
30
1
58,976
52,695
209
7
10,247
12
1
16
55,282
50,843
11
15
40
9
0
40
52,551
52,539
3
-2
-4
9
6
4
Figure 5.3 plots Kenya’s annual export growth (horizontal axis) and the annual growth of
international demand (vertical axis) both over the 5-year period. The vertical red line (reference line)
indicates the average nominal growth of total exports of Kenya for the period 2002-2006 and the
horizontal red line indicates the average nominal growth of world imports over the same period, which is
17%. These reference lines divide the chart into four quadrants – loses in growing sectors, losers in
170
growing sectors, losers in declining sector and winners in declining sectors. The criterion for
distinguishing growing and declining products in this chart is the average nominal growth rate of total
world imports from 2002 to 2006 that is 17%. Products, whose world imports have grown below this rate,
are classified as declining products as their share in the world trade is declining, while products located in
the upper quadrants are growing products, as they are growing faster than the world market.
Winners in the growth market are those products for which the world exports are growing and for
which Kenya exporters have increased their share, i.e. exporters of these products have proven their
international competitiveness over the period. These are sectors that would normally attract investors
easily as the products forms part of national success stories. The products at HS-6 level in this category
are negligible for Kenya.
Losers in growth markets refer to products for which international demand has been growing at
above-average rates, while the demand for these products from Kenya has been falling, i.e. Kenya has
been losing international market share in sectors, which is growing in the world market. This means that
although markets for these products exist; yet Kenya cannot take full advantage, may be because of
supply capacity or any other factor that is limiting the product to reach the markets. We find that coffee
neither roasted nor decaffeinated (HS 090111) appears to be in this segment.
Winners in declining markets refer to cases where world demands for the products are below
average but the Kenya exporters are gaining market share. This may mean that competing country
producers’ countries are moving away from the production of these sectors but Kenya continues to trade
in these products because either it is more competitive or Kenya is facing difficulty in shifting production
towards other sectors. Normally, a niche-marketing strategy is adopted in this segment to isolate positive
trade performance from the overall decline in these markets. In terms of value, the exports of Cut flowers
& flower buds for bouquets or ornamental purposes, fresh (060310); men’s and women’s trousers (HS
620342 and 620462)
Losers in declining markets are exports of products for which demand in the world is decreasing
and at the same time Ethiopia exporters’ share of the market is declining. Portland cement (HS 252329)
appears in this sector.
171
Figure 5.3: National Supply and International Demand for the twenty leading Kenya Exports in 2006.
Mauritius
After having reached a low level of 1.2% in 2005, GDP growth rebounded to 3.9% in 2006. The
economy continued to recover in 2007 growing by 5.6%. Interestingly, excluding sugar, the economy has
performed even better as the growth rate was 6.4% compared to 5.3% in the previous year. The renewed
good times of the Mauritius economy in 2007 has been in large part driven by a boom in the tourism
sector, which in turn led to strong growth in the construction sector, and a better performing textile
industry. Tourist arrivals reached 900,000 which translated in a growth rate of 13.1% in 2007 compared
with 3.5% in 2006. The construction sector grew at an impressive 15%, compared to 5.2% growth rate in
2006, mainly due to the construction and renovation of hotels, Integrated Resort Scheme (IRS) projects
and expansion of textile and wearing apparel industries. The manufacturing sector grew marginally
slower in 2007 at 3.7% compared to 4.0% in 2006. Textile and food processing industries performed quite
well as they grew respectively at 7.2% and 3.9%. Mauritius has been transformed from a mono-crop
agrarian economy during the 70’s to a manufacturing economy in the 1980’s and 1990’s and now much
more into services (68% in 2006).
Mauritius ranks 119 among the world’s exporters in goods (Table 5.17), with an annual growth in
exports value of 5% per annum over the period five year 2002 – 2006 and 8% per annum over the period
2005-2006. Given that growth of the world’s exports is around 17% per annum over the period 20022006, Mauritius exports are therefore losing market share.
172
Although, in total, the share of Mauritius exports is below that of world’s exports, yet there are
some sectors in which Mauritius is gaining market share and these sectors may be attractive sectors for
investments (foreign or domestic). At HS 2-digit level, table 5.11 highlights these sectors. They are,
amongst others, meat, fish and seafood preparations (HS 16) or live animals (HS 01) and these sectors are
experiencing high growth in value, 23% and 28% respectively. In 2005, 2006 and 2007, Meat, fish and
seafood preparations, particularly prepared or preserved tuna, skipjack and Atlantic bonito (HS
16041400), were mainly exported to UK (52%), Spain (27%), Italy and France. Live animals, in
particular live primates (HS 01061100) were mainly exported to US (60%), UK (24%), France and Spain.
Tables 5.17 and 5.18 show Mauritius’s top products exported at the HS 6-digit level. While table
5.12 gives a time series exports value over the 5-year, 2003-2007, period, table 5.13 gives trade indicators
for the year 2006. It can be seen from table 5.12 that over the years, exports of tuna (HS 160414) has outplaced exports of men/boys shirt of cotton not knitted (HS 620520). The sea food sector may be an
interesting sector to focus trade promotion efforts.
Table 5.17: List of products at 2 digits level exported by Mauritius in 2006 (extract)
Annual
Annual
Annual
growth in Annual
growth
Exported
Trade
growth in quantity
growth in of world
value
balance
value
2002value
imports
2006,
2006
20022006 (
20052002US$’000’
US$’000’
2006( %) %)
2006( %) 2006( %)
All products
Articles of apparel,
accessories, knit or
crochet
Sugars and sugar
confectionery
Electrical, electronic
equipment
Articles of apparel,
accessories, not knit or
crochet
Meat, fish and seafood
food preparations nes
Pearls, precious stones,
metals, coins, etc
Fish, crustaceans,
molluscs, aquatic
invertebrates nes
Nuclear reactors, boilers,
machinery, etc
Aircraft, spacecraft, and
parts thereof
Share in
world
exports,
(%)
Ranking
in world
exports
2,173,838
- 1,469,492
5
8
17
0.02
119
553,732
546,542
-
4
10
0.38
41
357,020
332,125
6
2
15
1.21
19
293,391
-139,306
95
4
17
0.02
66
212,096
186,719
-18
3
9
0.13
57
158,131
141,854
23
46
13
0.54
30
86,119
-13,578
6
-4
15
0.04
76
65,917
-136,766
2
19
10
0.11
77
54,537
- 273,491
14
65
14
-
93
39,663
-184,730
26
1,019
7
0.02
60
Cotton
32,173
-156,898
-7
3
6
0.06
75
Live animals
Optical, photo, technical,
medical, etc apparatus
25,892
15,096
28
-13
12
0.18
41
21,509
-17,773
5
-1
17
0.01
75
Sources: ITC calculations based on COMTRADE statistics.
Table 5.18: List of leading products exported by Mauritius 2003-2007, HS-6 digit level (extract)
173
Exported
Value in
2003,
US$’000’
Exported
Value in
2004,
US$’000’
Exported
Value in
2005,
US$’000’
Exported
Value in
2006,
US$’000’
Exported
Value in
2007,
US$’000’
All products
T-shirts, singlets and other vests, of
cotton, knitted
Raw sugar, cane
Tunas, bonito, prepared/preserved,
whole/in pieces, ex mincd
Mens/boys shirts, of cotton, not
knitted
Transmission apparatus, for radio,
telephone incorporating reception
apparatus
Mens/boys trousers and shorts, of
cotton, not knitted
1,862,056
1,925,291
2,004,352
2,173,838
2,054,082
377,809
301,882
402,020
354,860
331,699
344,429
340,528
351,245
357,136
297,142
71,630
80,608
107,398
156,936
196,936
97,157
129,858
114,797
113,144
130,137
13,835
34,947
263,821
262,559
85,390
106,187
59,890
40,836
51,161
71,208
Mens/boys shirts, of cotton, knitted
Fish nes, frozen, excluding heading
No 03.04, livers and roes
T-shirts, singlets and other vests, of
other textile materials, knitted
Diamonds non-industrial nes
excluding mounted or set diamonds
Pullovers, cardigans and similar
articles of cotton, knitted
26,785
31,078
26,269
34,576
65,125
35,484
37,943
51,435
59,897
57,389
17,523
35,772
46,203
50,159
45,589
37,258
41,013
42,978
44,217
40,143
47,800
41,693
34,615
32,919
29,483
Live primates
Women/girls trousers and shorts, of
cotton, not knitted
15,338
21,822
29,065
25,379
25,496
103,026
57,417
13,241
8,728
21,044
Sources: ITC calculations based on COMTRADE statistics.
174
Table 5.19: List of leading products at 6 digits level exported by Mauritius in 2006 (extract)
Product Label
All products
Raw sugar, cane
T-shirts, singlets and
other vests, of cotton,
knitted
Transmission apparatus,
for radio telephone
incorporating reception
apparatus
Tunas, bonito,
prepared/preserved,
whole/in pieces, ex
mincd
Mens/boys shirts, of
cotton, not knitted
Fish nes, frozen,
excluding heading No
03.04, livers and roes
Mens/boys trousers and
shorts, of cotton, not
knitted
T-shirts, singlets and
other vests, of other
textile materials, knitted
Diamonds non-industrial
nes excluding mounted
or set diamonds
Aircraft of weight
exceeding 15,000 kg
Mens/boys shirts, of
cotton, knitted
Pullovers, cardigans and
similar articles of cotton,
knitted
Exported
value 2006,
US$’000’
Trade
balance
2006
US$’000’
Annual
growth in
value
20022006(%)
Annual
growth in
quantity
20022006 (%)
Annual
growth in
value
20052006( %)
Annual
growth
of world
imports
20022006
(%)
Share in
world
exports(
%)
Ranking
in world
exports
8
17
0.02
119
2
16
4.62
3
2,173,838
-1,469,492
5
351,245
350,542
6
340,528
339,818
-
3
15
1.48
17
262,559
-20,245
162
-
27
0.14
29
156,936
156,682
23
46
12
4.80
5
113,144
112,533
5
-1
9
1.27
19
59,897
-17,438
2
16
11
1.55
18
51,161
50,742
-26
25
9
0.27
41
50,159
49,966
44
9
13
0.66
27
44,217
31,493
7
3
13
0.09
23
36,179
-178,899
10
0.04
31
34,576
34,173
7
32
12
0.71
27
32,919
32,535
-6
-5
8
0.20
45
5
15
11
Sources: ITC calculations based on COMTRADE statistics.
Figure 5.4 plots Mauritius annual export growth (horizontal axis) and the annual growth of
international demand (vertical axis) both over the 5-year period. The vertical red line (reference line)
indicates the average nominal growth of total exports of Mauritius for the period 2002-2006 and the
horizontal red line indicates the average nominal growth of world imports over the same period, which is
17%. These reference lines divide the chart into four quadrants – winners in growing sectors, losers in
growing sectors, losers in declining sector and winners in declining sectors. The criterion for
175
distinguishing growing and declining products in this chart is the average nominal growth rate of total
world imports from 2002 to 2006 that is 17%.
Products, whose world imports have grown below this rate, are classified as declining products as
their share in the world trade is declining, while products located in the upper quadrants are growing
products, as they are growing faster than the world market.
Winners in the growth market are those products for which the world exports are growing and for
which Mauritius exporters have increased their share, i.e. exporters of these products have proven their
international competitiveness over the period. These are sectors that would normally attract investors
easily as the products forms part of national success stories. The only product at HS-6 level in this
category is transmission apparatus for which exports grew at an annual rate of 162% over the five-year
period.
Losers in growth markets refer to products for which international demand has been growing at
above-average rates, while the demand for these products from Mauritius has been falling, i.e. Mauritius
has been losing international market share in sectors, which is growing in the world market. This means
that although markets for these products exist; yet Mauritius cannot take full advantage, may be because
of supply capacity or any other factor that is limiting the product to reach the markets. We find that raw
sugar, cane appears to be in this segment. Sugar appears in this segment because sugar from Mauritius
had a guaranteed access at a guaranteed price in the EU market but because of the drastic cut arising from
the EU Sugar Reform, the value of Mauritius sugar exports has declined. However, Mauritius has chosen
to engage into a profound restructuring of its sugar sector to turn it into a sugar cane industry.
Accordingly, the Multi-Annual Adaptation Strategy Action Plan 2006-2015 (MAAS), which was
prepared in consultation with all the stakeholders of the industry, defines the strategic orientations and
key measures that need to be undertaken. The EU will be providing grants for restructuring the sugar
industry.
Winners in declining markets refer to cases where world demands for the products are below
average but the Mauritius exporters are gaining market share. This may mean that competing country
producers’ countries are moving away from the production of these sectors but Mauritius continues to
trade in these products because either it is more competitive or Mauritius is facing difficulty in shifting
production towards other sectors. Normally, a niche-marketing strategy is adopted in this segment to
isolate positive trade performance from the overall decline in these markets. In terms of value, the exports
t-shirts (HS 610990) and tunas (HS 160414) from Mauritius fall under this category.
Losers in declining markets are exports of products for which demand in the world is decreasing
and at the same time Mauritius exporters’ share of the market is declining. A number of leading products
exported by Mauritius at HS-6 digit level appear in this segment, most of them belong to the wearing
apparel cluster.
176
Figure 5.4: Growth of National Supply and International Demand for the twenty leading Mauritian Exports
in 2006.
177
Zambia
The Zambian economy continues to perform better than in the 1990’s.Over the period 2005-2007,
real GDP growth is around 6%. Strong macro economic performance continues. Zambian production and
exports is largely dominated by the copper industry. The Zambian Government is pursuing an economic
diversification program to reduce the economy's reliance on the copper industry. This initiative seeks to
exploit other components of Zambia's rich resource base by promoting agriculture, tourism, gemstone
mining, and hydropower.
Zambia ranks 108 among the world’s exporters in goods (Table 5.20), with an annual growth in
exports value of 40% per annum over the period 2002 – 2006 and 108% per annum over the period 20052006. Given that growth of the world’s exports is around 17% per annum over the period 2002-2006,
Zambia exporters are therefore gaining market share in the world market. The sectors in which Zambia is
gaining market may be attractive sectors for investments (foreign or domestic). At HS 2-digit level, Table
5.20 highlights these sectors. They are, amongst others, Ores, slag and ash (HS 26), Copper and articles
thereof (HS 74) and tobacco and manufactured tobacco substitutes (HS 24). These sectors are
experiencing high growth in value, 194%, 49% and 50% respectively. In 2006, at HS 4-digit level, out of
Ores, slag and ash (HS 26), the main exporting products group were Copper ores and concentrates (HS
2603). They represented 97% of total exports under HS 26. At HS 4-digit level, out of copper and articles
thereof (HS 74), the main exporting product are copper cathodes and sections of cathodes unwrought (HS
740311) and Plate, sheet & strip of refined copper, not in coil, exceeding 0.15mm thick (HS 740919) with
an annual growth rate of 32% and 3739%, respectively during the period 2002-2006 and represented 56%
and 34%, respectively of total Copper and articles thereof (HS 74) by Zambia. Copper cathodes and
sections of cathodes unwrought (HS 740311) are mainly exported to Switzerland (72%), and Thailand
(8%). Plate, sheet & strip of refined copper, not in coil, exceeding 0.15mm thick (HS 740919) are mainly
exported to Switzerland (39%), Thailand (17%) and Egypt (13%). Zambia is in fact the number one
exporter of this product on the world market. It can be seen that Switzerland is the first importer of both
HS 740311 and HS 740919 and this makes Switzerland the number one importer of Zambia’s products
(40%) followed by South Africa (11%).
Tables 5.21 and 5.22 show Zambia’s top products exported at the HS 6-digit level. Exports of
Plate, sheet & strip of refined copper, not in coil, exceeding 0.15mm thick (HS 740919), has been
increasing over the period 2002-2006 and in terms of value of exports, it even exceeded Copper ores and
concentrates (HS 260300) and Cobalt and articles thereof, nes (HS 810590). At HS 6-digit level,
products that would probably attract investment (i.e. those sectors for which the annual growth rate in
export supersede the annual world exported growth rate for this sector) are Plate, sheet & strip of refined
copper, not in coil, exceeding 0.15mm thick (HS 740919), Copper ores and concentrates (HS 260300) and
Wire of refined copper of which the max cross sectional dimension > 6mm (740811). These sectors may
be interesting sector to focus trade promotion efforts.
178
Table 5.20: List of products at 2 digits level exported by Zambia in 2006
Annual
growth
Annual
Annual
in
growth
Exported
growth in quantity in value
value
Trade balance value
200220052006,
2006
20022006,
2006,
US$’000’
US$’000’
2006( %) (%)
(%)
Annual
growth
of world
imports
20022006(%)
Share in
world
exports,
(%)
Ranking
in world
exports
All products
3,770,370
696,109
40
108
17
0.03
102
Copper and articles thereof
2,613,420
2,605,244
49
160
37
1.94
17
Ores, slag and ash
Other base metals,
cermets, articles thereof
431,909
402,162
194
242
41
0.45
28
146,895
146,078
4
-9
29
0.96
21
Cotton
Tobacco and manufactured
tobacco substitutes
Sugars and sugar
confectionery
Nuclear reactors, boilers,
machinery, etc
Electrical, electronic
equipment
Live trees, plants, bulbs,
roots, cut flowers etc
Edible vegetables and
certain roots and tubers
82,238
77,637
16
4
6
0.17
47
76,361
72,065
50
18
6
0.30
47
66,978
59,890
23
- 11
15
0.23
63
60,432
- 587,732
220
1,160
14
-
89
40,248
- 214,193
57
131
17
-
95
32,178
31,371
15
69
10
0.22
32
27,600
13,983
31
16
12
0.07
69
Table 5.21: List of products exported by Zambia 2003-2006
Exported
Exported
Exported
Value in
Value in
Value in
2003,
2004,
2005,
US$’000’
US$’000’
US$’000’
Exported
Value in
2006,
US$’000’
Exported
Value in
2007,
US$’000’
All products
Copper cathodes and sections
of cathodes unwrought
Plate, sheet & strip of refined
copper, not in coil, exceeding
0.15mm thick
980,445
1,575,627
1,809,763
3,770,370
4,618,619
394,224
497,777
629,223
1,483,419
2,106,181
0
9,091
134,731
909,209
825,136
Copper ores and concentrates
11,010
25,604
77,533
422,715
255,853
Cobalt and articles thereof, nes
Copper unrefined, copper
anodes for electrolytic refining
Wire of refined copper of
which the max cross sectional
dimension > 6mm
48,907
258,420
161,417
143,657
247,939
0
24
8,846
49,299
151,766
19,386
43,687
73,712
149,514
149,428
Raw sugar, cane
Tobacco, unmanufactured, not
stemmed or stripped
31,135
34,031
72,767
60,287
80,785
8,577
29,629
38,964
41,488
54,539
Cotton, not carded or combed
Powders, copper, of nonlamellar structure
24,734
122,222
57,320
61,705
41,174
0
5
4
0
38,781
179
Table 5.22: List of products at 6 digits level exported by Zambia in 2006
Annual
growth in
value
20022006( %)
Annual
growth in
quantity
20022006( %)
Annual
growth
in value
20052006,
(%)
Annual
growth
of world
imports
20022006,
(%)
Share in
world
exports,
%
Ranking
in world
exports
108
17
0.03
102
136
43
3.36
10
575
22
52.02
1
Exported
value 2006,
US$’000’
Trade
balance
US$’000’
All products
Copper cathodes and
sections of cathodes
unwrought
Plate, sheet & strip of
refined copper, not in coil,
exceeding 0.15mm thick
Copper ores and
concentrates
Wire of refined copper of
which the max cross
sectional dimension > 6mm
Cobalt and articles thereof,
nes
Cotton, not carded or
combed
3,770,370
696,109
40
1,483,419
1,482,840
32
909,209
909,130
3,739
422,715
395,929
252
203
445
53
1.36
12
149,514
149,486
78
38
103
46
0.90
23
143,657
143,256
17
8
-11
36
22.09
2
61,705
58,822
41
43
8
15
0.57
21
Raw sugar, cane
Copper unrefined, copper
anodes for electrolytic
refining
Tobacco, unmanufactured,
not stemmed or stripped
Electric conductors, for a
voltage >80V but not
exceeding 1,000 V, nes
60,287
59,449
23
8
-17
16
0.79
20
49,299
48,990
508
379
457
46
0.79
15
41,488
41,488
48
45
6
2
2.03
11
33,097
30,414
70
38
109
27
0.24
51
Product Label
-4
Figure 5.5 plots Zambia’s annual export growth (horizontal axis) and the annual growth of
international demand (vertical axis) both over the 5-year period. The vertical red line (reference line)
indicates the average nominal growth of total exports of Kenya for the period 2002-2006 and the
horizontal red line indicates the average nominal growth of world imports over the same period, which is
17%. These reference lines divide the chart into four quadrants – loses in growing sectors, losers in
growing sectors, losers in declining sector and winners in declining sectors. The criterion for
distinguishing growing and declining products in this chart is the average nominal growth rate of total
world imports from 2002 to 2006 that is 17%. Products, whose world imports have grown below this rate,
are classified as declining products as their share in the world trade is declining, while products located in
the upper quadrants are growing products, as they are growing faster than the world market.
Winners in the growth market are those products for which the world exports are growing and for
which Zambia exporters have increased their share, i.e. exporters of these products have proven their
international competitiveness over the period. These are sectors that would normally attract investors
easily as the products forms part of national success stories. The products at HS-6 level in this category
are Plate, sheet & strip of refined copper, not in coil, exceeding 0.15mm thick (HS 740919), Copper ores
and concentrates (HS 260300) and Wire of refined copper of which the max cross sectional dimension >
6mm (740811).
180
Losers in growth markets refer to products for which international demand has been growing at
above-average rates, while the demand for these products from Zambia has been falling, i.e. Zambia has
been losing international market share in sectors, which is growing in the world market. This means that
although markets for these products exist; yet Zambia cannot take full advantage, may be because of
supply capacity or any other factor that is limiting the product to reach the markets. We find that Copper
cathodes and sections of cathodes unwrought (HS 740311) (number 10 exporter in the world market) and
Cobalt and articles thereof, nes (HS 810590) (number 2 exporter in the world market) appear to be in this
segment.
Winners in declining markets refer to cases where world demands for the products are below
average but the Zambia exporters are gaining market share. This may mean that competing country
producers’ countries are moving away from the production of these sectors but Zambia continues to trade
in these products because either it is more competitive or Zambia is facing difficulty in shifting
production towards other sectors. Normally, a niche-marketing strategy is adopted in this segment to
isolate positive trade performance from the overall decline in these markets. In terms of value, the exports
of Cut flowers & flower buds for bouquets or ornamental purposes, fresh (060310) and cotton not carded
not combed appear in this segment (HS 520100)
Losers in declining markets are exports of products for which demand in the world is decreasing
and at the same time Zambia exporters’ share of the market is declining. Almost none of the main
exporting products by Zambia appear in this segment.
Figure 5.5: National Supply and International Demand for the twenty leading Zambian Exports in 2006.
5.4
Growth Accounting
181
G
rowth accounting allows the decomposition of the economic growth rate of a country
into contributions from different factors. Specifically, GDP growth can be decomposed
into three shares: the capital, the labour and the total factor productivity (TFP) shares.
The latter is computed as the residual of GDP growth once capital and labour
contributions were removed. The framework typically adopts a constant return to scale Cobb-Douglas
production function and shows that the parameter of the function is equal to the share of the remuneration
of physical capital in aggregate output. The most basic version of growth accounting, this perspective is
provided by an aggregate production function:
 1
Y AK L
(5.1)
t
t t t
Where Yt is output (i.e. GDP), K t is the capital stock, Lt is labour force and At is the total
factor productivity, At . Since there are three factors affecting GDP in this production function, this
framework will allow us to decompose observed growth rates into contributions from capital, labour, and
productivity. Only capital and labour are actually observable in the data and productivity serves as a
catch-all for anything else that is left unexplained by the other two factors. If GDP goes up by more than
can be explained through capital and labour alone, the increase is interpreted by productivity. Since
productivity serves as a residual in this sense, it is computed from the production function (5.1), as
opposed to being inferred from some other source. Solving (5.1) for At we get:
Yt
A 
(5.2)
t K  L1
t t
By computing productivity in this way, for any values of Yt , K t and Lt , we can find the “right”
productivity values such that all output is accounted for in (5.1). An additional step that needs to be taken
before we can use 5.2 in practice to compute values for At is to estimate parameter α that enters the
computations, and needs to be set in some fashion. Again, there is no obvious way how α should be
chosen. It turns out, however, that α has a simple interpretation under the additional assumption that there
is perfect competition among firms in the economy. To see this, consider the profit maximization problem
of a firm using production function (5.1) that rents capital at rental rate rt and hires labour at wage wt

max At Kt L1t   rt Kt  wt Lt
Kt , Lt

The first-order conditions for this problem require that the marginal product of each factor equals
its price:
1
 Lt 
r   A  
(5.3)
t
t Kt
 
182
L
w  (1   ) A  t
t
t Kt





(5.4)
Given these expressions, we can compute the capital and labour shares for the economy (the
fraction of output used to pay capital and labour, respectively) as:
rt Kt

Yt
(5.5)
wt Lt
 1
Yt
(5.6)
Therefore, under the assumption of perfect competition, the capital share is a measure of the
parameter α. Once the capital and labour shares have been found, equation (5.2) could be used to compute
productivity values for any given year.
To proceed to the goal of decomposing economic growth into separate contributions from capital,
labour, and productivity, we argue that growth rates can be computed as (natural) log-differences. Thus
taking logs of the production function (5.1) gives:
log Y  log A   log K  (1   ) log L
t
t
t
t
(5.7)
The growth rate of output can therefore be expressed as:
log Y
 log Y  log A
 log A   (log K
 log K )
t 1
t
t 1
t
t 1
t
 (1   )(log L
 log L )
t 1
t
(5.8)
The growth rate of output equals the growth rate of productivity, plus α times the growth rate of
the capital stock, plus 1 − α the growth rate of the labour force. Intuitively, the impact of any given factor
of production on output growth is proportional to its share in output. Assume, for example, that the capital
share is one-third and the labour share is two-thirds. If the capital stock increased by 3% in a given year,
this growth would translate into just 1% growth in output. If instead the labour force were to grow by 3%,
the resulting output growth would be 2%. Changes in the labour force have a bigger impact on output,
since labour is more important than capital in production.
A central element in the above process is to estimate the appropriate share of capital and labour.
The early literature drew on the national accounts of some industrial countries and set the parameter for
the capital share between 0.3 and 0.4 (with the labour share varying correspondingly between 0.6 and
0.7). Thus the parameter, and hence technology, is assumed to be the same across countries and this is
questionable. In general, authors conduct some sensitivity analysis on the value of the parameter but none
of them tests the assumption of identical technologies across countries. It is only recently that Senhadji
(2000) relaxed the assumption of identical technologies across countries by estimating separate
production functions for 88 countries and found significant differences across countries.
In fact estimating the capital shares assumes the traditional constant return to scale Cobb-Douglas
production function in per capita form and to measure the relative contribution of factor accumulation and
productivity the following regression can be estimated (see Senhadji, 2000).
183
 log(Y / L )      log( K / L )  
it it
i
i
it it
it
(5.9)
The slope α represents the capital share in output, Y represents real output, K the capital stock
and L labour. Thus, instead of assuming a common value for the capital share (α), which are in fact
mostly based on estimates of industrialized countries or assumed to be constant for all countries, we used
the above regression model based to estimate each country’s share of capital individually (and also for the
sample) and these are presented in Table 5.23. The results confirm the expectation that capital shares
differ significantly across countries. On average the α of our the sample is 0.5 (although varying across
countries) and this is consistent with the ones obtained by Senhadji (2000) (refer to table 1 in Appendix).
This confirms that applying the same share to all countries may be misleading. The computed estimates
are then used to compute the contributions of capital, labour and TFP to the growth rate of GDP in 12
COMESA countries. The choice of countries is imposed by the availability of the data. The contribution
of the various factors to GDP growth is computed for the three sub-periods namely 1980-1990; 19912000 and 2001-2007. Output is measured by the GDP at constant prices and the labour is approximated
by total labour force and both proxies are from the World Bank World Development Indicators 2008 and
from the IMF World Economic Outlook (WEO). The capital stock series are constructed using the
perpetual inventory methodology (PIM). It is assumed that the initial capital-output ratio in 1980 was 3
and the depreciation rate was set at 5 percent19 (following Connell and Ndulu, 2000 and Tahari, Ghura,
Akitoby, and Aka, 2004). The results from growth accounting exercises for individual countries and for
COMESA as a group over the period 1980-2007 are provided in Table 5.23.
19
Refer to appendix for an overview of the PIM methodology.
184
Table 5.23: Contribution to Growth: COMESA countries (α specific Contribution to Growth)
Cou
Growth
Capi
Labo
ntries
Rate
tal
ur
TFP
1980-1990
Burundi
3.5
2.65
1.45
-0.6
1991-2000
-1.0
-1.25
1.5
-1.25
2001-2007
3.1
2.0
1.0
0.1
1980-2007
1.9
1.3
1.3
-0.6
1980-1990
Congo DR
0.8
1.0
0.5
-0.7
1991-2000
1.8
1.2
0.8
-0.2
2001-2007
4.6
2.6
1.5
0.5
1980-2007
2.4
1.6
0.9
-0.1
1980-1990
Egypt
4.9
4.7
1.2
-2
1991-2000
4.4
1.7
1.5
1.1
2001-2007
4.5
1.6
1.9
1
1980-2007
4.6
2.8
1.5
0.3
1980-1990
Ethiopia
2.6
-0.2
2.2
0.6
1991-2000
3.2
1.55
2.25
-0.6
2001-2007
5.2
2.3
2.7
0.2
1980-2007
3.6
1.2
2.3
0.1
1980-1990
Kenya
4.6
2.0
1.5
1.1
1991-2000
1.75
1.3
1.3
-0.85
2001-2007
4.3
2.2
1.4
0.7
1980-2007
3.7
1.9
1.5
0.3
1980-1990
Madagascar
0.45
1.9
2.2
-2.65
1991-2000
1.4
-0.9
2.45
-0.15
2001-2007
3.3
2.0
1.5
-0.2
1980-2007
1.9
1.1
2.0
-1.1
1980-1990
Malawi
1.8
1.75
1.5
-1.45
1991-2000
3.0
0.2
1.75
1.05
2001-2007
2.5
1.1
1
0.3
1980-2007
2.7
1.4
1.3
0.0
1980-1990
Mauritius
6.35
3
2
1.35
1991-2000
5.6
2.5
2.1
1.0
2001-2007
4.1
2.5
0.9
0.7
1980-2007
5.3
2.6
1.7
1
1980-1990
Rwanda
2.2
1
2.2
-1
1991-2000
0.8
-0.25
2
-0.9
2001-2007
5.9
3.7
2
0.2
1980-2007
3.0
1.5
2
-0.5
1980-1990
Uganda
3.5
-1.7
2.7
2.5
1991-2000
6.2
2.5
2.5
1.2
2001-2007
5.8
2.2
1.7
1.9
1980-2007
5.0
0.9
1.9
1.9
1980-1990
Zambia
1.3
1.3
1.5
-1.5
1991-2000
0.5
1.2
1.3
-2
2001-2007
5.2
2.4
2.2
0.6
185
α
specific
0.6
0.55
0.58
0.4
0.5
0.38
0.45
0.6
0.4
0.6
0.53
Table 5.23Cont..: Contribution to Growth: COMESA countries (α specific Contribution to Growth)
Growth
Capital
Labour
α specific
Rate
TFP
1980-1990
Zimbabwe
5.0
2.7
1.8
0.4
0.5
1991-2000
1.6
1.3
1.4
-1.1
2001-2007
-5.8
-1
-1
-3.8
1980-2007
0.5
1
0.8
-1.3
1980-1990
COMESA
3.1
1.53
1.73
-0.16
0.5
1991-2000
2.43
0.73
1.64
0.06
2001-2007
4.53
2.13
1.45
0.95
1980-2007
3.35
1.5
1.6
0.28
An evaluation of economic growth during 1980-2007 shows that average growth in the COMESA
region rose from 3.1 percent during 1980-90 to 4.53 percent during 2001-2007, although a slower rate of
2.34 is registered for the period 1990-2000. Over the period 1980-2007, COMESA member countries
have grown approximately by around 3.3 percent, with members like Egypt, Kenya, Uganda and
Mauritius registering the highest growth (> 4% growth) and Zimbabwe the lowest one. Growth in the
region remained however among the slowest of all other regions in the world (with Asian economies and
China growing at an average rate of about 6% in real terms over the period), driven largely by the
insufficient record of Total Factor Productivity growth (TFP).
Initially during the period 1980-1990, labour growth, with a higher contribution value of 1.73,
was the major driver of growth as compared to capital and it is should be observed that the region
registered a negative TFP growth during that period (same was observed by Tahari et al for the same
period for the case of SSA). During the subsequent decades it is interesting to note the improvement in
TFP, especially during the period 2001-2007, and overall a positive value of TFP for the aggregate period
of 0.28 is calculated reflecting that overall the region has gained in productivity and that the latter is also a
driver of growth, although to the same extent as observed in other regions and countries of the world. An
interesting comparison is that of Tahari et al.(2004) work on a sample of around 40 Sub Saharan
Countries for the period 1960-2000 where the author reported similar average growth rate for the SSA but
with no TFP growth for the region during the period and even negative productivity for the period 19802000. Collins and Bosworth (1996) also reported negative growth rate (-0.42) of TFP for sample of SSA
over the period 1960-1994 and Bosworth and Collins (2003)20 confirmed a negative or no TFP growth
for a study period 1960-2000.
More than half of the countries in the sample experienced declines or no increase in TFP on
average during 1980-2007. Mauritius and Uganda experienced an average TFP growth of more than 1%
during the period. The increased TFP growth during the latter period are those countries which benefited
from the efficiency gains from the shifts in the policy regime beginning around 1980, the ongoing process
of liberalization and opening up of the economy and from the implementation of macroeconomic and
structural reforms they embarked on. The associated increases in reliance on markets and reductions in
the role of government would be expected to result in improved economic efficiency.
Although COMESA members appears on the average to outperform fellow SSA countries at
large in TFP, the average TFP growth for COMESA remains below the world average and could explain
the relatively poor overall growth performance of the region. It is noteworthy that nearly the East Asian
countries and China which are among the best performers worldwide have TFP contribution of around 1.2
with China alone reporting around 2.5 (for the period 1960-2005) Bosworth, et al.(2006) reported TFP of
1.7 for the case of India over the period 1980-2004.
It should be stressed that real GDP growth was driven largely by factor accumulation, and in fact
nearly all of the output growth during the first period is associated with increases in factor inputs,
20
Bosworth and Collins (2003) provide a comparison of the growth performance of various sub-regions in the world
during 1960-2000 (Table 10).
186
particularly labour. With time capital is also observed to establish itself as an important driver of growth
outpacing the contribution of during the last decade. This is reflected probably by the massive investment
in capital infrastructure from the part of government coupled by an overall increase in domestic and
foreign direct investment. Over the 1980-2007 period the contribution of labour and capital appears to be
the same.
As a robustness check the contribution of the various factors to GDP growth is computed using
the same alpha coefficient (i.e. the sample alpha 0f 0.5) for all countries and the findings were found
robust to that alternative value (the latter corresponds to the COMESA average)
The growth accounting framework can be extended to assess the role of human capital for the
determination of output and economic growth. Up to this point, potential human capital differences across
countries or over time were subsumed in the productivity term If information on the stock of human
capital is available, we can incorporate human capital into the production function in order to measure its
contribution to growth. Thus extending the Cobb Douglas function with human capital term takes the
following form:
  1  
Y AK H L
t
t t t t
where
H
t
(5.10),
is human capital. To proceed to the goal of decomposing economic growth into separate
contributions from capital, labour, and productivity, we argue that growth rates can be computed as
(natural) log-differences; thus the growth rate of output in a given year, for example, can be computed as
logYt+1 − log Yt. The growth rate of output can therefore be expressed as:
log Y
 log Y  log A
 log A   (log K
 log K )
t 1
t
t 1
t
t 1
t
  (log H
t 1
 log H )  (1     )(log L
 log L )
t
t 1
t
(5.11)
Thus, the growth rate of output equals the growth rate of productivity, plus α times the growth
rate of the capital stock, plus β times the growth rate of human capital, plus 1 − α − β the growth rate of
the labour force. The measurement of data are as above and human capital is measured here as the
average years of schooling. However given that such information is available for very few countries as
reported in Table 4.2. Including human capital reveals interesting results and shows that this factor is
interestingly explaining part of the growth as well (maybe captured in labour mainly from the previously
study). This is particularly true for the case of Mauritius and Kenya.
Bosworth and Collins (2003) have argued that the growth accounting framework is a useful tool
to understand growth experiences across countries. The same authors have, however, noted the limitations
of this methodology. A key weakness relates to the interpretation that the measured residual from the
growth accounting exercise represents TFP growth. In practice, in addition to providing a measure of
gains in economic efficiency, the residual may also reflect a number of other factors, including political
disturbances and conflicts, institutional changes, droughts, external shocks, changes in government
policies, and measurement errors. This limitation is particularly important for sub-Saharan African
countries mired in conflicts and subject to significant drought-related and external shocks. Also, the
results from growth accounting exercise should not be misconstrued as providing the fundamental causes
of growth (rather than the proximate sources of growth).
Table 5.24: Contribution to Growth :COMESA countries (with Human Capital)
187
Periods
Growth Rate
1980-1990
1991-2000
2001-2007
1980-2007
1980-1990
1991-2000
2001-2007
1980-2007
1980-1990
1991-2000
2001-2007
1980-2007
1980-1990
1991-2000
2001-2007
1980-2007
Kenya
Mauritius
Uganda
Zambia
5.5
4.6
1.75
4.3
3.7
6.35
5.6
4.1
5.3
3.5
6.2
5.8
5
1.3
0.5
5.2
2.4
Capital
Labour
2.0
1.1
2.1
1.9
2.9
2.4
2.3
2.5
-1.7
2.5
2.1
0.9
1.3
1.2
2.3
1.6
Human
Capital
1.3
1.1
1.2
1.3
1.9
1.8
0.7
1.5
2.7
2.2
1.6
1.7
1.4
1.1
2.1
1.6
0.2
0.3
0.4
0.3
0.3
0.4
0.5
0.4
0.3
0.3
0.3
0.3
0.1
0.2
0.3
0.2
TFP
1.1
-0.85
0.6
0.2
1.25
0.9
0.6
0.9
2.5
1.1
1.8
1.8
-1.5
-2.0
0.5
-1.0
Econometric Approach
T
his section attempts to investigate the classical determinants of growth for the COMESA
case by i) using panel data analysis for the whole sample. We proceed firstly by
estimating a Cobb Douglas production function to consolidate results from the growth
accounting framework and then in the second instance we examine the relative
contribution of some selected policy variables to growth using an augmented Solow growth model. We
shall also attempt to estimate the optimum level of these policy variables proxies to help in assisting
policy decisions.
5.5.1
The Econometric Models
Following the literature, we alternatively adopt similar economic framework and selected the
classical explanatory variables of model based on the work of Barro (1991, 1998); Mankiw, et al. (1992),
Levine and Renelt (1992); Islam (1995); Krueger and Lindahl, (2001) and Easterly (2001) whereby a
standard production function is derived from the augmented Solow-type model. Fosu (1999), Tahari,
Ghura, Akitoby, and Aka (2004) and Khadaroo and Seetanah (2007, 2008) recently used such
specification for their respective works based on Africa.
The panel model specifications take the form:
yit  f (k , l )it
(5.12)
yit  f (k , l , open, edu, pdgdp, fdi,inf, govtconsump, pol )it
188
(i  1, 2,....n)
(5.13)
where
y is total output, k
open proxies the level of trade openness,
countries debt level, fd is financial development, inf is
is the capital stock,
l
is labour,
edu is the level of education. pdgdp is a
inflation, fdi represents foreign direct investment, govtconsump government consumption and
pol is political situation. Note that equations (5.12) and (5.13) are the Solow and augmented Solow
growth models, respectively.
In the growth literature there exist a unanimous consensus (see Delong and Summers, 1990,
1994; Reinhart, 1989 and more recently Sala-i-Martin, 1997 and Arin 2004) of the role of capital
investment in promoting economic performance, possibly because technological change is embodied in
recent vintages of capital. This is measured by the level of capital stock of the respective countries (refer
to previous section).
Openness of the country is included in the model following the work of Dollar (1992), Sachs and
Warner (1995) and Edwards (1998). These authors supported the idea that increased trade openness raised
economic growth through access for a country to the advances of technological knowledge of its trade
partners, access bigger markets and encouraging the development of R&D through increasing returns to
innovation and also through providing developing countries with access to investment and intermediate
goods that are vital to their development processes. In addition, they are likely to have a greater division
of labour and production processes that are more consistent with their comparative advantages, which
enable them to grow faster. Openness is captured by the ratio of total trade to GDP.
Following the arguments and empirical evidences of Mankiw, Romer, and Weil (1992), Barro
(1998) and more recently Temple (2001), education is to account for the quality of labour. This is because
human capital could be thought of as affecting economic growth in the sense that workers with higher
levels of education or skills should, ceteris paribus, be more productive and more inventive and
innovative. Higher levels of human capital may also encourage capital accumulation, or may raise the rate
of technological catch-up for follower countries (Temple, 2001). The secondary enrolment ratio is used to
proxy for the above.
Total external debt is the sum of public, publicly guaranteed, and private non guaranteed longterm debt, use of IMF credit, and short-term debt. Theoretically inadequate debt management and a
permanent growth of debt level to GDP ratio may result in negative macroeconomic performance, like
crowding out of investment, financial system instability, inflationary pressures, exchange rate fluctuations
and more importantly adverse effects on economic growth. The theoretical underpinnings (see Krugman,
1988; Savvides 1992; Agénor and Montiel, 1996; Serven, 1997 and Moss et al, 2003) are linked namely
to debt overhang, liquidity constraint, fiscal effect, productivity suppression and reduction in human
capital accumulation along which external debts affects negatively growth. There are also certain social
and political implications of unsustainable debt burden. Persistent and high public debt calls for a large
piece of budgetary resources for debt servicing. As a result, the government finds itself with no other
alternative but to cut allocations for other public services.
The relationship between inflation and GDP growth is twofold. In the short run, high inflation can
be associated with high growth in high activity cycles. In the long run, however, high inflation is
associated with macroeconomic policy miss-management and is expected to have a negative impact on
growth. Fischer (1993) show that growth is negatively associated with inflation, huge budget deficits and
distorted foreign exchange markets. Fischer and Modigliani (1978) argued that inflation uncertainty
reduces efficiency by discouraging long-term contracts and increasing relative price variability. A high
and unpredictable rate of inflation generally results in poor performance of businesses and households.
While most authors find growth and inflation to be inversely related (see Fischer 1993, Levine and
Renelt, 1992, Cozier and Selody, 1992; Levine and Zervos, 1993 and Barro, 1995) with the implication
that inflation is quite costly, there are exceptions (Sala-I-Martin, 1991).
The financial system is also known to affect the level of economic growth in a country.
According to Levine and Zervos (1993), economies with more developed and more efficient financial
189
systems will be able to more effectively allocate savings to the best investments, which in turn leads to
increased productivity, potentially higher savings rates, and faster growth. Financial development is
directly linked with money supply growth. Financial development is measured as the ratio of M3 to the
country’s Gross Domestic Product (GDP). It a typical measure of ‘Financial Depth’ and has been widely
used (King and Levine, 1993).
Most theoretical and empirical findings imply that foreign direct investment has a strong positive
growth impact on the recipient economy (Dunning and Narula(1996); Borenstein, et al.(1998), Markusen
and Venables 1999, Balasubramanyam, et al. (1996); Bende-Nabende and Ford 1998; Soto 2000, Li and
Liu 2005). Nyatepe-Coo (1998) and Bosworth and Collins (1999) in their studies including African states
reported positive contribution of FDI. Obwona (1999) also found similar results for Uganda and Akinlo
(2004) for the case of Nigeria. However, Cockcroft and Riddell (1991) results suggested that FDI made a
negligible contribution to productivity in most African countries during the 1980s. Assanie and
Singletone (2002) confirmed such results. We use the ratio of the country’s FDI inflows to GDP to
capture FDI flows in the COMESA states.
Other alternative policy variables have also been included in the model specification. Barro
(1995) argued that government policies play a very crucial role in determining where an economy will go
in the long run. For example, favourable public policies including better maintenance of law, fewer
distortions of private markets, less non-productive government consumption and greater public
investment in high-return areas – lead, in the long run, to higher levels of real per capita GDP. Hall and
Jones (1997) believe that differences in levels of economic success across countries are driven mainly by
the institutions and government policies and political stability that frame the economic environment. We
thus include pol , namely, the political risk rating as provided by the International Country Risk Guide
(ICRG 2003). The rating awards the highest value to the lowest risk and the lowest value to the highest
risk and provides a mean of assessing the political and institutional framework of the countries5 (see
ICRG 1999).
Higher government saving is likely to support aggregate economic growth through two ways: (1)
countries which have higher government saving rates also tend to have greater overall savings and
investment, and therefore grow faster; and higher government saving indicates sound overall
macroeconomic management, which lowers risks for investors and increases investment (Barro, 1991).
Thus prudent government fiscal policies appear to be associated with faster overall economic growth.
Many studies examine the role of government fiscal surpluses and deficits in affecting economic growth.
The general view is that high levels of government deficits are bad for growth (Fischer, 1993). Fischer
emphasises the importance of a stable and sustainable fiscal policy, to achieve a stable macroeconomic
framework. Easterly and Rebelo (1992) find a consistent negative relationship between growth and
budget deficits. Levine and Zervos (1993) attempt to measure the role of government in economic activity
by using the ratio of government consumption to GDP. They find a negative relationship between
government consumption to GDP and growth, though insignificant. Barro (1991) also states that growth
is inversely related to the share of government consumption in GDP. We thus adopt a similar measure of
Barro (1991) and Levine and Zervos (1993), that is, the ratio of government consumption to GDP, in an
attempt to capture the above.
5.5.2
Empirical Results
Data
Equations(5.12) and (5.13) were estimated by OLS and dynamic panel frameworks. The main
source of the data was the IFS, World Bank World Development Indicators (various issues) and the Penn
World Table 6.1 (updated). The number ( n ) of countries in the study is 12 over the period 1980-2007.
All the variables are form except,
inf
and
pol .
190
We commenced the empirical analysis by carrying out panel unit root tests following the
approach of Im, Pesaran, and Shin (IPS) (1995) who developed a panel unit root test for the joint null
hypothesis that every time series in the panel is non stationary, we could reject a unit root in favour of
stationarity (the results were also confirmed by the Fisher-ADF and Fisher-PP panel unit root tests) at the
5 percent significance level and it was deemed safe to continue with the panel data estimates of the above
econometric specification.
Results of Panel Analysis
Table 5.25 presents the results of OLS panel estimation. From column 2 (the Cobb Douglas
specification), labour appears to have had a relatively greater influence on growth with an output
elasticity of 0.61 (that is a one percent change in labour is likely to bring a 0.61% change in the output of
the countries in the sample. It is interesting to note that this reconcile to a large extent the results obtained
from the growth accounting exercise in section above.
The augmented Solow Growth Model (column 3) reveals that the capital stock of the country
remains the major driver of growth with openness level, education and FDI playing promising roles as
well. The key role of capital stock and investment in promoting growth is in line with earlier studies of
Beddies (1999) for the case of Gambia, Ghura and Hadjimichael (1996), Rodrik (1998), Calamitsis, Basu
Variable
Table 5.25: OLS (fixed) panel estimates, dependent variable, y (t-values in parentheses).
Cobb Douglas
Augmented Solow(2) Augmented Growth
(1)
Model (3)
k
0.53
0.45
( 5.24)***
l
0.61
( 5.66)***
0.39
( 6.25)***
( 4.34)***
0.39
0.25
( 2.11)*
( 2.43)*
open
0.25
0.23
( 2.11)**
edu
( 2.31)**
0.28
0.21
( 4.51)***
( 2.55)***
 0.17
pdgdp
( 3.06)***
fd
0.14
0.11
( 3.07)***
( 2.77)***
 0.16
inf
 0.18
( 9.28)***
fdi
( 5.45)***
0.18
0.21
( 3.35)***
( 2.33)**
Govtconsump
 0.15
pol
 0.17
( 2.34)**
( 2.14)*
2
R
Observations
Hausman Test
0.46
336
0.51
336
prob   2  0.00
0.54
336
prob   2  0.01
Note
*significant at 10%, ** significant at 5%, ***significant at 1%
-The quantities in brackets are the heteroscedastic robust t/z-values.
-No serial correlation was detected according to Bhargava, Franzini and Narendranathan (BFN) (1982).
-Note that in specification 2 and 3, the k variable was replaced by the ratio of savings to GDP
(savings/GDP) and coefficients of 0.4 and 0.34 were respectively obtained. The coefficients of the other explanatory
variables did not change significantly.
191
and Ghura (1999) for a sample of Sub Saharan countries and Fosu (2001) for a larger sample of African
states. More recently Makdisi, Fattah and Limam estimated a coefficient of 0.2 for the case of the MENA
region and O’Connell and Ndulu (2000) an output elasticity of 0.4. It is noteworthy that Aisen (2007)
estimated a value of 0.33 for the Emerging Asia case.
Openness is also observed to be another important ingredient of growth in COMESA states. This
confirms the results of Sachs and Warner (1997), Sacerdoti,et al. (1998), Calamitsis, Basu and Ghura
(1999) and Fosu (2001) and dirk de Willem for sample of African countries. The coefficient obtained is
lower as compared to most other region of the world and may imply a lower impact of openness on
growth in the COMESA region. This may be explained by the prolonged application of inward-looking
strategies based on import substitution, by many countries during the 1960’s and 1970’s. This can also be
explained by the fact that the region continues to be among the least integrated regions in the World.
O’Connell and Ndulu (2000) also reported an elasticity of 0.15. However Kim and Hanh (xxx) for the
East and South Asia case observed an elasticity of 0.74 while Aisen observed 0.4.
As expected, a significant and positive relationship is derived from education. The implied
elasticity of output with respect to human capital varies between 0.21-0.27, suggesting its vital role for
COMESA. Knight, et al. (1992) and Barro (1991, 1995) also obtained an elasticity of around 0.2
education for large panel data sets. Beddies (1999), for the Gambian case, Khadaroo and Seetanah (2007)
for Mauritius and Calamitsis, et al. (1999), Sacerdoti, et al. (1998) and Rodrik (1998) also confirm such
results for the African context.
The positive and non negligible impact of FDI on COMESA performance concur with the
findings with that of Nyatepe-Coo (1998), Bosworth and Collins (1999) and Assanie and Singletone
(2002) for the case of developing countries which include African panel data set. For countries case
Obwona (1999) for Uganda, Akinlo (2004) for Nigeria and Subramanian and Roy (2001) for Mauritius
obtained similar positive effects. Our results are however not in line with Cockcroft and Riddell (1991)
who suggested that FDI made a negligible contribution to productivity in most African countries during
the 1980s. The relative lower impact of FDI for African states in general (including COMESA) may be
due to the fact the these countries have not really attracted that level of FDI which could have important
threshold effects.
Financial depth (as proxied by the ratio of money supply to GDP) is also interestingly observed to
have a positive, although a relatively lower impact on growth of COMESA countries. Allen and
Ndikumana (1998) for the case of the Southern African Development Community (SADC) found some
evidence of a positive correlation between financial development and growth of real per capita GDP.
O’Connell and Ndulu (2000) recently reported a coefficient of 0.16 for a sample of African countries and
Seetanah and Padachi (2007) an elasticity of 0.05 for the case of 40 Sub Saharan African countries.
As per theoretical prediction, inflation and public debt are seen to have negative influence on
economic performance. The adverse effect of inflation is consistent with pioneering work from Barro
(1995). Te velde (2000) and Ghura and Hadjimichael (1996) also reported negative association of
inflation for SSA region. O’Connell and Ndulu (2000) additionally reported an output elasticity of -0.1
for a sample of African country. Recently Aisen (2007) showed that inflation has also be country growth
enhancing for the case of Asian countries.
The reported negative and significant coefficient of external debt (-0.17) from specification 2
tallies with the findings of the debt overhang hypothesis features supported by research from Iyoha
(1996), Fosu (1999), Mbanga and Sikod (2001) and Clements, et al. (2003) for developing countries. The
estimated coefficient is close to Seetanah and Durbarry (2007) who estimated an elasticity of -0.16 for a
sample of SSA while Makdisi, Fattah and Limam obtained -0.1 for Mena Region and O’Connell and
Ndulu (2000) -0.05 for a sample of African states. Kasibhatla and Stewart (2007) also recently showed
that that debt overhang impeded growth in Latin American economies severely and the impact was
moderately negative in the Asian region.
As far as the third econometric equation is concerned (refer to column 3) the variable pol, which
is a proxy the political and institutional framework of the countries turned out to be negative and
192
insignificant in line with theoretical predictions and empirical works of Gynimah-Brempong and Traynor
(1999) and Rodrik (1998) for Africa. Our estimated coefficient of -0.17 is not too far from that obtained
from O’Connell and Ndulu (2000) with a reported elasticity of -0.14
Government consumption which comes as another alternative policy variables in specification 3
is also confirmed to have a negative impact on growth with the estimated parameter more or less in the
range of that obtained by Te velte (2005) and O’Connell and Ndulu (2000) (-0.1 to -0.2). Sacerdoti,
Brunschwig and Tang (1998) earlier found similar results and Aisen (2007) lately reported elasticity of 0.2 from a cross country of Asian economies.
We also included the level of infrastructural availability (here in lieu of government
consumption), measured as the level number of kilometers of paved roads and also additionally by the
number of telephone lines per 1000 inhabitants (data available from the WDI) in the above specification
for still further analysis. Although we have not reported the entire set of results here, but a positive and
significant correlation coefficient of 0.13 and 0.07 respectively were estimated suggesting that
infrastructural support also enhances growth. These results are in line with Khadaroo and Seetanah (2007,
2008) for the African case.
Finally, we expand our analysis towards determining whether some determinants, if available and
combined simultaneously and together would also benefit growth in the COMESA states. We do so by
interacting some pairs of explanatory variables (each in turn) namely trade openness and education, trade
openness and FDI, FDI and capital stock and also capital stock and financial development. The results
while including the above interacting terms yields notable findings as they all turned out to be positive
and significant indicating that the growth effects are still better with the simultaneous
presence of some ingredients. The other results obtained previously did not change significantly and
confirms robustness.
Table 5.26 presents the results of the dynamic panel regression using GMM estimators (Arellano
and Bond’s 1991). The estimated equation passes the diagnosis test related to Sargan Test, which is a test
for over-identifying restrictions. The reported p − values for the Sargan test on over-identification suggest
no invalid over-identifying restrictions.
The results from the dynamic panel analysis validate and consolidate the main findings from the
previous analysis and gives short run estimates. Interestingly the positive and significant coefficient of
yt-1 from the table suggests that lagged income of the countries in the sample contributes positively
towards the current level of y confirming the existence of dynamism and endogeineity in the modelling
framework. This is consistent with recent works from Bende-Nabende, Ford, Sen and Slater (2000) and
Choe (2003) and Li and Liu (2005). In fact the value of the coefficient of the lagged income is 0.41
implying a coefficient of partial adjustment α of 0.59. This means that y in one year is 59 percent of the
difference between the optimal and the current level of y. The other explanatory variables are also
confirmed to be important ingredients in explaining growth pattern in these countries.
Table 5.26: GMM estimated Dynamic Panel Model
Cob-Douglas(1)
Augmented Solow
Growth(2)
Augmented Solow
Growth(3)
yt 1
0.66
(3.3)***
0.41
(7.87)***
0.35
(5.84)***
kt
0.20
(1.89)*
0.24
(5.37)***
0.15
(5.65)***
lt
0.50
(1.81)*
0.30
(2.21)**
0.27
(1.99)*
0.13
(3.3)***
0.20
(3.24)***
opent
193
educt
0.12
(1.65)*
0.09
(1.34)
0.07
( 3.06)***
pdgdpt
fdt
0.06
(3.62)***
0.06
(3.23)***
inf t
0.11
( 7.19)***
0.11
( 6.14)***
fdit
0.12
(2.34)**
0.13
(2.23)**
govtconsumpt
0.06
( 1.99)*
pol
0.11
( 2.12)*
Sargan Test of Over
identifying restrictions
prob   2  0.21
prob   2  0.23
-*significant at 10%, ** significant at 5%, ***significant at 1%
-The small letters denotes variables in natural logarithmic, d denotes variables in first difference
and the heteroscedastic-robust z-values are in parentheses
-Note that in specification 2 and 3, the k variable was replaced by the ratio of savings to GDP
(savings/GDP) and coefficients of 0.23 and 0.15 were respectively obtained. The coefficients of the other
explanatory variables did not change significantly.
5.6
0ptimal Economic Ratios Based on the Experience of NIEs
The growth performance of the COMESA region has always been less on average as compared to
most region of the world with an average real growth rate of around 3% (and 4% during the recent years).
The Newly Industrialized Countries (South Africa, Mexico, Brazil, China, India, Malaysia, Philipiness,
Thailand and Turkey) recorded an average of 6.5 percent real growth late in 2007. Taking the above
categories of countries as a useful benchmark, in this section we attempt to determine optimal economic
ratios with respect to savings/investment, and export and import to GDP ratio. It is hoped that this may
help in assisting COMESA member countries in economic planning based on the experience of Newly
Industrialised Countries (NICs).
5.6.1 The Incremental Capital-Output Ratio
In recent years, the use of incremental capital output ratios and the Harrod-Domar model as a
suitable framework for estimating development finance requirements has come under criticism. However,
no alternative methodology is available, so this approach is used here.
The growth of output (Y/Y) can be expressed as the product of the ratio of investment to
national output (I/Y) and the productivity if investment (Y/I), that is (Y/Y)= (I/Y) (Y/I)
This is in line to Harrod’s model of growth (1939) where g=s/c where g is the growth rate
((Y/Y); s is the savings ratio(S/Y), and c is the incremental capital output ratio ((Y/I). The latter is the
194
amount of investment or increase in the capital stock required to increase the flow of output by one unit
(this is the reciprocal of the productivity of investment, (Y/I))
The simple Harrod growth framework has been very useful for planning and forecasting purposes
and in assisting target plans and ratios for many developing countries. Thus for a given capital output
ratio for a country, the required rate of savings and investment can be determined to achieved a targeted
growth rate. Thus if a country wishes to growth at 5 per cent per annum, from the above, and given a
capital-output ratio of 3, then it must have a saving and investment ratio of 15%. If less savings is
achieved, the growth will be lower (unless the incremental capital-output ratio is reduced or alternatively
the productivity of investment is raised.)
Taking the above economic framework into account, the optimal savings and investment rates of
the COMESA states is estimated, using the growth rates of the NICs as a benchmark. An important
element is the incremental capital output ratio. Nehru and Dhareshwar (1994), Amoako (1999), McQuinn
and Whelan (2007) and Khadaroo and Seetanah (2007) suggested that such ratio for the case of Africa
and developing countries is around 4. Lighhart (2000), Pedroni and Canning (1999), Canning and
Bennathan (2000) used similar ratio for a wider sample of countries.
From Table 5.31, the average incremental capital output ratio for COMESA countries is 4.1. Thus
for the case of COMESA as a whole, investment and savings of 4.1 x 6.5 = 26.7 per cent of GDP would
be needed to reach the NIC actual annum growth. The current domestic investment rate in the COMESA
is around an average of 19 per cent of GDP for 2007 and is below those regions such as Central Asia
(23%), and Latin America 21% and East Asia around 35%. Thus a further 7% would be needed from
external sources to finance the gap. It should be noted that some countries such as Mauritius, Malawi,
Uganda and Zambia are not far from these target ratios.
Table 5.31 gives a country wise comparative analysis of the optimum savings/investment ratios.
The respective incremental capital output ratio has been estimated as above with the optimum ration
subsequently calculated taking NIC growth as a benchmark. As confirmed some countries are very close
to the optimal while other faces some savings-investment gaps that need to be closed by external
financing.
5.6.2 Optimal Import Ratios
As discussed in the Harrod model of growth previously, the relation between growth and saving
is determined by the incremental capital-output ratio (c), (g = s/c), where g is the growth rate and s is the
saving ratio. Ahmad (2001) likewise argued that the growth rate can be expressed as the product of the
incremental output-import ratio ( Y/M = m) and the ratio of investment goods imports to income (M/Y =
i), i.e. g=im.
Thus if domestic saving is calculated to be less than the level required to achieve the target rate of
growth, there is said to exist an investment-saving gap. Similarly, if minimum import requirements to
achieve the growth target are calculated to be greater than the maximum level of export earnings available
for investment purposes, there is said to exist an import-export gap. If the target growth rate is to be
achieved, foreign capital flows must fill both of the two gaps.
The incremental output-import ratio ( Y/M = m) has been generated for each selected countries
and are reported in Table 5.27. The average for the COMESA comes to 0.21. Thus based on the fact g =
im and with a targeted growth rate of 6.5, the optimum import to output ration amounts to 30. Country
specific optimum ratios have been subsequently generated and are reported below. In general the optimal
import requirements to achieve the growth target are seen to be greater than the actual level of export
earnings available for investment purposes and thus an import-export gap exists with the exceptions of
few COMESA states.
Table 5.27: Comparative optimal Ratios of Savings/Investments and Import/Exports
Countries Ethiopia
Mauritius Zambia
Uganda
Kenya Egypt
Malawi
195
Burundi
NIC =6.5%
c  I / Y
m  Y / M
I*
M*
Actual I
Actual m
Actual x
3.7
0.18
24
36
20
42
16
5.7
4
0. 14
26
47
25
67
60
3.8
0.24
25
27
24
30
38
4.5
0.24
29
27
23
29
15
3.9
0.18
26
36
19
36
26
4
0.24
26
27
25
30
17
4.3
0.23
28
28
24
29
17
4.8
0.16
31
40
17
48
11
Conclusion and Policy Recommendations
T
he broad objective of this paper was to undertake a study on the sources of growth in
some selected COMESA Member countries, making use of accounting frameworks and
econometrics analysis. The extended growth accounting frameworks (both Cobb-Douglas
and Solow) we used shed light on the importance of labour quality in influencing output
elasticity. Together with quality of labour, other major drivers of growth are openness level, capital stock
and FDI. Together with investment in capital and technology, including infrastructure development, there
is a need for persistent accumulation in human capital – re-training, multi-skilling and continuous
capacity building.
The results from the econometrics study are also along the same line. Openness is observed to be
an important ingredient of growth. Therefore, removal of especially NTBs as far as possible to facilitate
and reduce costs of international trade would enhance growth. The human capital variable significantly
explains growth. Investment in human capital, through increased literacy rate and through technical and
professional formation, would add to the level of growth of COMESA member countries. As it is
currently, in some countries, there are (remote) areas where the level of absenteeism and failure rates are
quite high.
Financial depth is also interestingly observed to have a positive impact on growth of COMESA
countries. In order to mobilize higher levels of savings for investment, it is important to have a welldeveloped capital market. Mauritius and Egypt already have a quite developed capital market and have
plans to further develop the markets to induce investment. The econometric study has shown the
importance of some other variables like political and institutional stability and infrastructural availability
as important factors
In view of the aggregate nature of our study and in light of the above findings, several relevant
policy issues deserve further analysis within a country specific context. This policy recommendation
section is based on the analysis on COMESA mainly as an aggregated group. However, for practical
policy recommendation, the members within COMESA have to be studied more in details. In the study,
there have been some attempts to deal with country cases but still these country cases can be studied
deeper and then formulate policies regarding the growth of each member state. The following points
should however be noted.
To achieve sustainable growth in the future, COMESA countries must take policy measures that
should substantially enlarge and diversify their economic base. Industrial policies, while changing the
structure of the economy should attempt to create more value added products. More value added products
could mean a shift from agricultural sector to manufacturing sector or from an agricultural based
economy to an agro-industry based economy. Growth requires non-marginal change that market forces
alone may not generate. Governments need to coordinate investment decisions to promote a ‘Big Push’.
The strategy involves changing the incentive structure to redirect investment towards more value added
industries. There is a need to investigate and assess each country’s resource availability and past industrial
and trade policies before coming up with new improved policies.
196
The importance of capital and technology has been highlighted in the analysis of NICs section. It
has also been highlighted that the level of savings, capital and investment for some COMESA members
are much lower than the required rates to reach the growth levels of NICs. In order to mobilize higher
levels of savings, it is important to have a well-developed capital market. However, many COMESA
members do not have such a capital market, although the banking sector is well developed in many
COMESA members. Financial investors do not therefore have the opportunity to invest in a portfolio of
well diversified financial assets which could help in further mobilize savings. As a medium-term strategy,
the COMESA member countries should consider developing their capital markets and also further
developed the money markets by developing new financial instruments to mobilize savings. Some
COMESA members have already started developing their capital markets. For example, the Egyptian
stock market is equipped with one of the most advanced trading floor and a highly sophisticated
interactive web site, with E – Business solutions. The stock exchange of Mauritius also constitutes a stateof-the-art electronic trading system built on third generation technology. Trading in securities is
conducted through dedicated trading workstations located at intermediate dealers and linked by
communication lines to the stock exchange of Mauritius trading engine. The stock exchange of Mauritius
has also introduced the trading of treasury bills, i.e. an active secondary market for government
instruments. In an attempt to help increase domestic savings to finance investments and increase
economic growth rates, in 1998, Egypt enacted an insurance legislation which permits private sector entry
into the capital of Egypt’s three state-owned insurance companies. The new law also removed all
restrictions on minority foreign ownership of insurance firms and abolished the ban on service by foreign
nationals as corporate officers. As from 2008, in Mauritius, government employees contribute 6% of their
salary in a pension scheme. This is an attempt to further mobilize savings in the country. Another
example is the establishment of Micro Finance Bank in Pakistan, which is a major private sector
investment and will encourage other domestic and international entrepreneurs to invest in Micro Finance
sector. However, parallel to initiatives and policies designed to mobilizing domestic savings, which is a
medium term strategy, in order not to stick to the low-savings low-investment trap in the short term, there
is a need to promote FDI. Of course in an attempt to further increase FDI, image building and investor
targeting is important.
In order to attract FDI it is also important is to remove all unnecessary obstacles in doing business
in the country. In this context, the world Bank Doing Business index and ranking can be used as an
indicator. Egypt, with a rank of 114 (out of 181 countries) in 2009 is the only COMESA member out of
the top ten reformers in doing business in 2007/2008 (ranked 125 in 2008). The only COMESA member
that ranks among the top 25 countries is Mauritius. Most of the COMESA members are well after the
100th. Measures to reduce the cost of doing business in Mauritius started with the 2006 Business
Facilitation Act which provided a new legal framework whereby businesses operated on the basis of selfadherence to comprehensive and clear guidelines and the authorities checked for compliance by
exercising ex post control, facilitate doing of business and acquisition of properties by foreigners and
enable small enterprises to start their business activities within 3 working days.
It should be noted that well devised industrial policies as proposed in point 1 above do not always
lead to the intended results. A good example is the case of Nigeria. A case study on Nigerian
manufacturing highlighted that in 2000 Nigeria was one of the most unindustrialised economies in the
world with manufacturing value-added accounting for only 5% of GDP (lower than levels at
independence in 1960). This was the case in spite of government’s huge investment in the industrial
sector, active industrial policies as well as a restrictive trade policy with effective rate of protection higher
than the developing country average. This is also in spite of four different national development plans
(1962–1985), and industrialization as the number one priority of successive governments in Nigeria.21
There is a need for the industrial policies to go in tandem with measures that is needed to enhance their
capacity to withstand adverse domestic and external shocks and lessen their exposure to the volatility that
the region as a whole has experienced. For example, due to the concentrated nature of production,
21
ODI (2007) Creating Country Trade Negotiations Strategies
197
principally due to trade preferences, COMESA countries have suffered a lot during the soaring oil price
increase and some of the COMESA countries seriously faced the adverse effects of trade liberalization
like the end of MFA agreement and reduction in sugar prices imported by the EU.
There is a further need for private-public partnerships (PPPs) and corporate social responsibility
(CSR). Given that the state normally intervenes in the market to correct all sorts of market inefficiencies,
investment by the state should have been enhancing private investment. However, due to the high level of
corruption in some of the COMESA countries and the long procedures to start business, PPPs and CSR
should be well developed so that the private sector participates to a larger extent in the development of the
economies. For example, in Mauritius, the Minister of Finance, in his Budget Speech 2007/08, appealed
to the private sector to dedicate at least 1 % of profits to social activities in guise of solidarity with the
vulnerable and poor. In fact, it has become difficult for Government alone to address the major social
concerns of the country especially given its well-documented weak fiscal position. The private sector has
responded positively to his appeal.
Countries should look for opportunities in the changing international economic environment.
There is however a need for capacity building of the staffs of the governments of member countries so
that they understand the changing global business framework – WTO, Technical Barriers to Trade,
Sanitory and Phytosanitary measures etc – and the tools available to make analysis and take decisions
thereafter. Policies of greater openness and integration in the world economy should be vigorously
pursued simultaneously with appropriate domestic economic and institutional reforms.
The role of the state is important. Removal of internal barriers to doing business is not all. The
state has the essential role to make it cheap to do business. This means that the state should invest
massively in infrastructure – road networks, high-speed Internet connection not only in main cities, make
it cheaper to make international calls and send faxes, set up organizations that will help investors in the
process of setting up their firms, set up specialized organizations to guide domestic investors in
understanding the international markets and conduct market profiling for them, make information cheap
and reliable. Governments may also provide broad-based tax incentives for R&D. Governments should
encourage a supportive environment for the private sector, including active competition policies, the
application of the rule of law, an open framework for trade and investment and sound fiscal and monetary
policies. In the area of finance, policies need both to promote domestic savings and to attract external
resources for productive investment.
tors that may affect growth.
5.8
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Appendix
Table A1:Capital Shares in various Regions and countries
Table A2: Sub-Saharan Africa: Sources of Real GDP Growth by Sub-Group of Countries, 19602002
Source: Tahari, Ghura, Akitoby, and Aka, 2004
203
Table 4: SSA: Sources of Real GDP Growth, by Subgroup of Countries and by Decades, 19602000
Source: Bosworth and Collins (2003). Table 10. Sources of Growth in the World by Region,
1960-2000
Table 5:Sub-Saharan Africa: Sources of Real GDP Growth, by Subgroup of Countries and by
Decades, 1960-2000
204
6.0
Co-ordination of Monetary and Fiscal Policies
Noah Mutoti
6.1
Introduction
E
ffective monetary-fiscal co-operation is the only framework within which policy may be
successfully conducted in small open economies to promote internal equilibrium (price
stability) at the same time external equilibrium, that is, sustainable balance of payments
according to Fry (1995)). Sargent and Wallace(1981) as well as Lambertini, et al. (2001)
emphasize fiscal discipline as a pre-requisite for monetary stability.Laurens and Enrique (1998) add that
co-ordination of monetary and fiscal policies ensures credibility since the appearance of conflict (between
the central bank and the treasury) tends, in general, to undermine macroeconomic policy and in particular,
increases uncertainty in financial markets as well as price and output volatility, besides provoking capital
flights. On the whole, without the monetary and fiscal authorities taking into account each other’s
objectives and actions, financial instability could ensue, leading to high interest rates, exchange rate
pressures, rapid inflation and adverse impact on economic growth.
Why fiscal policy matters for monetary policy and vice versa is because there is little standing to
lowering inflation by monetary measures alone in the presence of soaring budget deficits and public debt
(Dahan (1998)). Further, not only does public debt management affect monetary policy through interest
rates, it too complicates the central bank’s task of maintaining an orderly behaviour of monetary
aggregates. On the other hand, the degree of monetary policy credibility is vital for effective fiscal
management. Because credible monetary policy, to some extent, implies an independent central bank, it
prevents or minimizes the monetization of fiscal deficits. The actions of the monetary authority also have
a bearing on public debt. While relaxed monetary policy would initially facilitate government borrowing
at lower cost, the inflation that may follow would cause upward adjustments in nominal interest rates to
cover investors’ perceived risks, in the end raising the cost of debt service.
This paper discusses the Common Market for Eastern and Southern Africa (COMESA)
perspective on the interaction of monetary and fiscal policies in Zambia, Uganda, Kenya and Malawi. The
goal is not to break any new theoretical ground but rather to delineate the procedures in these selected
COMESA member countries for bringing about co-ordination between central banks and fiscal authorities
in pursuit of common objectives of price stability and sustained economic growth. To this end, Section
6.2 provides a survey of the literature on the rationale for co-ordination. The focus is on the institutional
arrangements, which is highly relevant as in recent years, most COMESA countries, if not all, in line with
the worldwide trend, have set up institutional and operational mechanisms to ensure more efficient policy
conduct. Section 6.3 outlines the theoretical framework. This is followed by empirical analyses of the
relation between fiscal deficits, money supply and inflation. Concluding comments and necessary policy
adjustments in cases of non-coordination to avoid negative consequences of macro and financial sector
instability are contained in Section 6.5.
6.2
Rationale for Co-ordination
T
he rationale for the monetary and fiscal policy coordination is derived from the following
interrelated objectives. First, it is to set internally consistent and mutually agreed targets of
205
monetary and fiscal policies with a view to achieve non-inflationary stable growth. Second, it is to
facilitate effective implementation of policy decisions by setting targets of monetary and fiscal policies
efficiently through a mutually supportive information sharing. Third, to compel the central bank and
government adopt sustainable policy. Overall, without efficient policy coordination, financial instability
could ensue, leading to high interest rates, exchange rate pressures, rapid inflation, and adverse impact on
economic growth.
Dahan(1998), Laurens and Enrique (1998) as well as Hilbers(2005) add that the main sphere of
interaction between monetary and fiscal policies relates to the financing of the budget deficit and
monetary management. The particular stance of monetary policy affects the capacity of the government to
finance the budget deficit by changing the cost of debt service and by limiting or expanding the available
resources of financing. At the same time, the financing needs of the government and its funding strategies
place constraints on the operational independence of the monetary authority.
Not only does efficient coordination make it easier for policy makers to achieve their policy
objectives, it also ensures the commitment of decisions markers responsible for the two policy areas to
mutually agreed objectives. Equally important for the overall policy framework is achieving credibility.
The stabilization of expectations through monetary policy can only be successful if public finances do not
give rise to destabilizing expectations, as the pursuit of price stability could lead to high interest rates
owing to an unfavourable perception of the fiscal stance.
Public debt management is an additional area that requires the fiscal and monetary authorities
closely working together. The capacity of the government to place debt at low financial cost depends, to a
large degree, on the stance of monetary policy. An expansionary monetary policy would initially permit
the placement of public debt in the market at low interest rates. If inflation follows the implementation of
relaxed monetary policy or if the budget deficit grows rapidly, given the prevailing low financial costs,
investors would demand high interest rates to cover the perceived risks, in the end raising the cost of debt
service. On the other hand, a restrictive monetary policy could initially increase the cost of debt service,
but if applied in a co-ordinated fashion with fiscal policy, it would help to build up credibility.
In carrying out open market operation (OMO), the central bank must decide whether to intervene
by transacting in its own paper (deposit) or in government securities. The choice between these
instruments depends on the characteristics of the financial market and on the ability of the monetary and
fiscal authorities to co-ordinate policy targets and operational procedures. The ideal choice is to have the
central bank conduct OMO in the secondary market, while the treasury places debt in the primary market.
This permits the fiscal and monetary authorities to pursue their own objectives simultaneously, while
government securities stand to gain in liquidity. Even in the absence of a well-developed secondary
market, whereby both authorities operate in the primary market, the importance of policy co-ordination
cannot be overstated if the goals of both authorities are to be accomplished.
Where the central bank issues its own securities for monetary policy purposes while the treasury
issues government paper to support debt management objectives, co-ordination is necessary not only to
achieve efficient market segmentation but also to avoid competition between the fiscal authorities---who
try to reduce the costs of debt service---and the monetary authorities—would try to maintain interest rates
high enough to prevent excessive build up of liquidity.
Co-ordination is also inevitable during liberalization of the financial sector (World Bank(1989)).
Such reforms could only proceed within a framework of a supportive fiscal policy that provides
macroeconomic stability. If high fiscal deficits persist while the authorities are undertaking such reforms,
interest rates would rise to unprecedented levels or, if the interest rates are kept at artificially low levels, either
inflation would surge or the demand for credit and distortions in resource allocations would grow significantly. In
either case, the financial reform program more than likely will be unsuccessful.
6.2.1
Monetary Effects of Fiscal Policy
206
B
ecause fiscal policy influences demand pressures, via both direct spending by government
and changes to private disposable income (through taxation and the benefit system), it
impacts on inflation. In particular, through the monetization of fiscal deficits, fiscal policy
undermines monetary policy as it fuels inflationary pressures. Even in the absence of
monetization by the central bank, higher deficit policies may cause inflation, as the government’s
borrowing requirements will increase the net credit demands, drive up interest rates and crowds out
private investment (Miller (1983). The resulting reduction in economic growth would lead to a decrease
in the amount of goods available for a given level of cash balances, causing higher price levels. The other
channel, through which deficits fuel inflation, when central banks do not monetise the debt, is the private
monetization of the debt. This occurs when high interest rates induce the financial sector to develop new
interest bearing assets that are almost as liquid as money and are risk free. Hence, the government debt
not monetized by the central bank is monetized by the private sector and the inflation effects of higher
deficit policies prevail.
There is another, more direct channel of fiscal policy affecting central bankers, and that is, the
impact of indirect taxes on price levels (Akcay, et al (1996). If governments resort to substantial increases
in indirect taxes---sales taxes, value added taxes--- this would have a direct impact on prices, through the
wage-price spiral. Alongside these direct relationships, there is a more indirect channel through
expectations. Perceptions and expectations of huge budget deficits and resulting large borrowing
requirements are likely to trigger a lack of confidence in the economic prospects, posing risks to financial
system stability. On the external side, there are risks of too much dependence on foreign funding of
domestic debt, arising from unsustainable fiscal deficits. This may result in exchange rate and/or balanceof-payments crises, which are worrisome to central banks.
Fiscal policy as well affects monetary policy through public debt. The modality of public debt
management influence interest rates, while the financial operations of the government usually complicate
the central bank’s task of controlling money supply. Related, the sustainability of the public debt has
serious implications for monetary policy. If market participants perceive the growth in public debt as
unsustainable, the credibility of the overall policy mix is reduced, and interest rates will rise. And with a
liberalized capital account, high interest rates would attract foreign capital that would require monetary
sterilization operations by the central bank.
6.2.2
Fiscal Effects of Monetary Policy
The degree of monetary policy credibility is an important factor that determines the fiscal
position. As credible monetary policy implies an independent central bank, it prevents monetization of
government debt to a certain degree. Dahan (1998) summarizes the impact of monetary policy on the
fiscal stance. The first one is the revenue effect. In the short-run, tight monetary policy may lead to
lower output growth and for that reason, tax revenues might reduce, causing a rise in the budget
deficit. Second, is the effect on public debt. A tight monetary policy results in higher interest rates,
making servicing public debt expensive. With caution, the overall impact would depend on how
economic agents formulate their expectations and the degree of credibility of monetary policy. There
are two possibilities: (i) the public expects that the monetary authority will fail to achieve the inflation
target and eventually abandon the tight policy (ii) the tight policy will bring down inflation (and
inflation expectations) once the policy is announced. In the first scenario, tight policy may lead to
higher inflation and higher nominal interest rates. In the second scenario, the expected reaction in
inflation tends to decrease the nominal interest rate and thus the debt effect is ambiguous. Further the
sign of the debt effect is positive if the government is a net borrower and negative if the government is
a net lender. The magnitude of the debt effects depends on the level of public debt, the maturity of
government bonds and the share of flexible interest rate on bonds and the sensitivity of various interest
rates.
207
The third is on seigniorage. A deceleration in the rate of money creation (through OMO) leads to
an increase in debt creation, resulting in higher budget deficits in subsequent periods. This effect is at
work depending on the government–central bank relationship. If the central bank is nothing but an
agent of the government, such arrangements allows the government to borrow from the central bank
(e.g. government has an unlimited access to base money). Thus the central bank sales of government
bonds to the private sector have to be replaced by other sources of financing of the budget deficit. This
is also consistent with the government not allowed to borrow from the central bank, and yet the central
bank has to transfer usually, at the end of the period, the net gains to the treasury.
6.2.3
Essential Features of Effective Monetary –Fiscal Co-ordination
To reduce the appearance of conflict between the central bank and the treasury that could
undermine the credibility of macroeconomic policy, demands effective co-ordination. This requires
appropriate supporting institutions and operational arrangements, the main ones discussed below.
Independence of the central bank
By central bank independence, we do not mean absolute independence, which in any event does
not exist. What is advocated is central bank independence from the political power. Assigning monetary
policy responsibilities to an independent central bank insulated from political pressures and having a large
degree of operational autonomy is seen as an effective way of anchoring monetary policy to long-run
considerations and to resist pressures to trade price stability for temporary increases in output.
Specifically, the central bank should be empowered by law to apply the tools of monetary policy without
prior approval of the treasury, prohibited by law from lending to government or quasi government on
non-market terms, and accountable to parliament. Nonetheless, the fact that a central bank enjoys some
form of independence does not mean that it no longer needs to co-ordinate its operations with the fiscal
authority. In fact, a crucial element of a successful monetary policy framework based on central bank
independence depends on developing efficient means of policy co-ordination between the central bank
and the fiscal authority.
The theoretical justification for central bank independence, however, weakens in the case of small
open economies. The reasoning is that monetary and exchange rate policies are opposite side of the same
coin. Central banks in open economies are not independent, in fact, unless they have final authority over
the exchange rate. Moreover, because fiscal policy changes affect both monetary and exchange rate
targets, the former (targets) are always conditional on fiscal policy .
Limiting direct central bank credit to the government
An issue closely related to the operational autonomy of the central bank is the extent to which the
government can receive direct credit from the central bank. When securities markets are underdeveloped,
direct central bank credit is the main source of domestic government financing. Excessive central bank
credit is likely to pose a threat to macroeconomic stability. Institutional arrangements to limit direct
central bank credit to the government are thus critical to enhance central bank independence and constrain
the risk of inflation.
Fiscal Discipline
208
When the public sector borrowing requirement arises, the central bank sells government paper,
bidding up interest rates and choking off expenditure until the balance of supply and demand of loanable
funds is restored. In an open economy, rising interest rates may attract capital inflows, which may directly
or indirectly finance the increased government deficit. On the other hand, taking into account information
asymmetry, expectations and credibility effects, fiscal indiscipline is costly. If financial markets come to
believe that the public sector borrowing requirement is too high, expectations may arise of an acceleration
of inflation and /or a depreciation of the exchange rate, meaning that very high interest rates may be
required to avoid capital flight.
It is for these reasons fiscal discipline is essential to maintain macroeconomic stability. Lack of
fiscal discipline generally stems from the use of policy discretion. While some of the benefits of
discretion could be seen in terms of the ability of policymakers to respond to unexpected shocks,
discretion could be misused, resulting in persistent deficits, rising debt levels, and over time, loss in
overall policy credibility.
One of the mechanisms that could instil fiscal discipline and improve transparency and
accountability is the Fiscal Responsibility Law(FRL). In 1994 New Zealand approved a FRL. Brazil
enacted a FRL in 2000 to, inter-alia, establish a broad framework of fiscal planning, execution, and
transparency at the federal, state, and municipal levels. But FRL by itself is not enough to guarantee fiscal
discipline. Other factors such as existence of sound budget institutions, political consensus for prudent
fiscal policy, political commitment to observe the rules, existence of a sound public financial management
system, quality of fiscal information and overall transparency of budget execution and reporting are
essential elements for fiscal discipline.
Developed Financial Markets
Since the transmission of monetary and fiscal impulses is by way of financial markets, particularly
through interest rates, exchange rate and futures prices, it is unclear how monetary-fiscal co-ordination is
to be achieved in the absence of well-developed financial markets. The domestic financial market
provides the least distortion source of financing of the fiscal deficit, while the need to pay marketdetermined debt service costs act as a deterrent to large fiscal deficits. At the same time, these markets
allow the central bank to conduct monetary policy more efficiently through indirect, market-based
instruments. Finally, domestic markets impose discipline on the monetary and fiscal authorities given
their responsibilities in ensuring a stable financial environment.
Public Debt Management Strategy
Already noted, the actions of the monetary authority affect the management of public debt through
a variety of channels, including the policy stance, the choice and design of central bank instruments, and
the measures taken to promote the development of domestic financial markets. Likewise, the financing
strategy of the government to place debt in the market affects monetary policy and puts constrains on the
operational autonomy of the central bank. Institutional arrangements may be in the form of a debt and
monetary management committee. It can play an important role in coordinating the volume of debt
issuance in the primary market with monetary policy goals and help resolve conflicts concerning the
stance of policy.
6.3
Government Budget Constraint
T
he link between fiscal and monetary policy is established through the budget constraints of
the fiscal authority and the central bank. That is the accounting identity that insists that every
budget deficit must be financed by selling bonds either to the public or to the central bank.
209
This identity points out that today's fiscal-monetary decisions have implications for the number of bonds
that will have to be sold to the public today, and thus for the feasible set of fiscal-monetary combinations
in future periods.
The fiscal budget identity takes the form
Gt  it 1 BtT1  Tt  ( BtT  BtT1 )  RCBt
(6.1) ,
where Gt is government expenditure on goods, services and transfers, it 1 BtT1 is interest on the
outstanding debt (where BtT is the total debt and it is the interest rate)22, Tt is tax revenue, RCBt denotes
direct receipts from the central bank.
The monetary authority or central bank also has a budget identity that links changes in its assets
and liabilities expressed as
( BtM  BtM1 )  RCBt  it 1 BtM1  ( H t  H t 1 )
(6.2) ,
where BtM  BtM1 is central bank purchase of government debt, it 1 BtM1 is the central bank‘s receipt of
interest payments from the Treasury and ( H t  H t 1 ) is the change in the high-powered money (or
monetary base).
Let B  BtT  BtM be the stock of government interest-bearing debt held by the public, and
combing (6.1) and (6.2) yields the consolidated budget constraint
Gt  it 1Bt 1  Tt  ( Bt  Bt 1 )  ( Ht  Ht 1 )
(6.3)
that captures that government spending plus interest payments on outstanding debt must be funded by tax
receipts and an increase in public debt as well as high-powered money. The revenue the government gets
from an increase in high-powered money is referred to as seigniorage.
Define the real interest factor as
(1  r ) 
1 i
( pt pt 1 )
(6.4)
and dividing (6.3) through by the price level pt yields the budget constraint in inflation adjusted or real
terms (with lower case denoting real terms), which after re-arranging yields
(1  r )bt  gt  tt  bt  st
22
(6.5) ,
Total debt includes foreign debt, which is affected by foreign interest and exchange rate movements
210
where st is the real increase in high-powered money or seigniorage. i.e., the increase in high-powered
money adjusted for the level of prices. Iterating (6.5) forward produces
T
(1  r )bt  
i 0
T
tt i  gt i T st i
bt i




i
i
i
(1  i)
i  0 (1  i )
i  0 (1  i )
(6.6) .
Relationship (6.6) is the intertemporal budget constraint, showing how the government resources and
spending are connected over time and further indicating that the government must plan to raise enough
revenue (in the present value terms) through taxation and seigniorage to pay for its existing debt and
planned expenditures.
A key implication of the intertemporal budget constraint is that any government with a current
outstanding debt must run, in the present value terms, future surpluses (Walsh, 2003). One way to
generate a surplus is to increase revenues from seigniorage, which brings in the implications of budget
deficits for future money growth. Suppose the government decides that for a set path of future spending, it
will lower current and future taxes permanently. This will decrease the present discounted value of future
surpluses. So to find the path of future spending, the government would need to increase the present
discounted value of seigniorage. Since seigniorage is related to high-powered money, money growth must
rise in the future. The reverse is true if taxes are increased permanently.
Equation(6.6) also provides intuitions on the link between deficits and inflation. If the monetary
authority must act to ensure that the government intertemporal budget is balanced, then fiscal policy is set
independently, so that the monetary authority generates enough seigniorage to satisfy the intertemporal
budget condition. Leeper (1991) describes such a situation, as one in which there is an active fiscal policy
and a passive monetary policy. It is also described as a situation of fiscal dominance (or Non-Ricardian
fiscal policy). When monetary policy is dependent, it responds to fiscal policy so that seigniorage revenue
becomes an important component of government finance. In this case, the treasury might decide to run
permanent deficits, a situation that may require seigniorage to make up the gap between the value of the
public debt and the present discounted value of budget surpluses. One would expect to see a link between
deficits and inflation since monetary policy makers respond to deficit spending.
Suppose that whenever there is a change in the present discounted value of seigniorage, fiscal
policy adjust so that the intertemporal budget constraint holds. The monetary policy is therefore
independent in the sense that monetary policymakers take action without regard to fiscal policy, and then
fiscal policy adjusts to maintain a balanced budget (i.e., a Richardian fiscal policy). This is a situation of
active monetary policy and passive fiscal policy. With monetary independence, policymakers are free to
purse goals such as low and stable inflation and not to worry about money growth to finance treasury
budget deficits. In this case, we would not expect a tight link between government budget deficits and
inflation, because current budget deficits would be largely offset by future government surpluses.
To further this discussion, specifically how does unsustainable fiscal policy affect price stability?
The classical view (Sargent and Wallace (1981), which is rooted in the quantity theory of money (QTM),
is that fiscal deficits cause inflation because governments that run persistent fiscal deficits tend, over time,
to resort to money creation to finance the deficits.
However, according to more recent studies leading to a fiscal theory of price level (FTPL)
developed by Woodford (1994, and 1998), Leeper (1991), Sims (1994), and Cochrane (1998, and 2000)
and extended to an open economy by Daniel (2001), money creation may not be the only channel through
which fiscal policy becomes dominant and budget deficits cause inflation. This theory argues that a fiscal
dominant (non-Ricardian) regime may arise when fiscal policy is not sustainable and government bonds
are considered net wealth (Woodford, 1998). These wealth effects could jeopardize the objective of price
stability, irrespective of central bank commitment to low inflation. Thus in a non-Ricardian regimes, it is
fiscal, not monetary, policy that determines the price level and becomes the nominal anchor.
The FTPL therefore challenges the conventional wisdom dictated by the QTM, which implies that
Ricardian regimes are the norm and that sooner or later fiscal policy will have to adjust to guarantee the
211
solvency of the government intertemporal budget constraint. These competing views of the interaction
between monetary and fiscal policy and their effects on price stability are very relevant for policy makers.
In Ricardian regimes, it is the demand for liquidity and its evolution over time that determines prices.
6.4
Empirical Approach
I
n the process of examining the existence, in a country, of the essential features for effective
fiscal-monetary co-ordination, a number of questions are addressed. What is the extent of
monetary and fiscal co-coordinating policy targets and operational procedures? Is there too
much dependence on foreign borrowing arising from fiscal difficulties? Has financial
operations of the government complicated the ability of the central bank to control money supply? We
then proceeded to modelling the link between budget deficits, money and inflation.
6.4.1
Zambia
Legal and Institutional Framework
Currently, there is no legislation that guarantees an autonomous Bank of Zambia (BOZ). However,
BOZ, especially in recent times, has been conducting monetary policy without due influence of the
Treasury, entailing some degree of operational independence. The macroeconomic policy framework,
which depicts co-ordination between the monetary and fiscal authorities, included the Medium Term
Expenditure Framework (MTEF) and the annual budget speech, which clearly reflect the inflation target.
Further, the Monetary Policy Committee includes officials from the Ministry of Finance. This suggests
some of level co-operations to set policy targets.
Fiscal Discipline
Table 6.1 and Figure 6.1 suggest that there have been improvements in the fiscal position over the
years. Between 2005 and 2007, the domestic budget deficit was cut by more than half to slightly over 2%
from slightly over 5% of gross domestic product (GDP), recorded between 1999 and 2001. At the same
time, a primary surplus had been registered, partly attributed to favourable economic performance (see
Figure 6.2). Following the consistent pursuance of comprehensive market and structural reforms over the
past 15 years, there has been a boost in foreign direct investment (FDI), especially in the mining sector,
and consequently the economy has witnessed remarkable growth. For eight consecutive years, starting in
1999, positive real GDP growth has been registered and since 2003, it has averaged around 5% annually.
The recent commodity boom, including the price of copper, the country’s main stay has assisted in the
growth of the economy and government revenue. Since 1992, domestic revenue has exceeded 20 % of
GDP. Also there had been expenditure restraints, reflected in total government spending being below 30
% of GDP since 2004.
Since 2002, there has been a shift towards heavy reliance on domestic source of deficit financing,
such that between 2005 and 2007, foreign financing (of the deficit) declined to below 1% of GDP (Table
6.1).
Table 6.1: Fiscal Budget
(% of GDP)
Revenue
Expenditure
Interest Payments
Primary balance
1990-92
1993-95
1996-98
1999-01
2002-04
2005-07
19.7
23.6
1.2
-2.7
28.7
36.1
2.4
-5.0
25.7
30.1
2.6
-1.8
25.2
30.6
2.8
-2.6
25.0
29.6
3.8
-0.8
22.9
25.0
2.1
0.0
212
Overall balance
-3.9
-7.4
-4.4
-5.4
-4.6
-2.1
1.4
2.5
0.7
6.7
-0.1
4.5
1.2
4.2
2.4
2.2
1.4
0.7
Financing
Domestic
Foreign
Figure 6.1: Revenue and Expenditure Relative to GDP (%)
40
35
(%)
30
25
20
15
10
90
92
94
96
98
00
Revenue
02
04
06
Expenditure
Figure 6.2: Budget deficit and Economic Growth
8
4
(%)
0
-4
-8
-12
90
92
94
96
98
Real GDP
00
02
04
06
Budget/GDP
Limiting Central bank Credit to Government
There has been no direct central bank financing of government deficit since 2006, with the legal
framework assisting in this effort (see also Box I). The domestic sources of deficit financing have been
through the securities market. Figure 6.3 suggests the role of BOZ in the Treasury bills market has been
unnoticeable since 2004, with commercial banks taking up about 80% of the market share and the
participation of non-bank financial institutions on the rise.
213
Box I
THE BANK OF ZAMBIA ACT
Advances to Government
Section 49. The Bank shall not advance funds to the Government except in special circumstances and on such
terms and conditions as may be agreed upon between the Bank and the Minister
Limitation of lending to Government
1) Except as provided for in Section 49, the bank shall not directly or indirectly , at any time, give
credit to the Government by way of short tern advances , purchases or securities in a primary use, or
any other form or extension of credit that exceeds fifteen percent of ordinary revenue of the
Government in the previous year.
2) If in the opinion of the Bank the limitation provided for in Sub-section((1), is likely to be exceeded ,
the Bank shall submit to the Minister stating
a)
The details of the amounts then outstanding of the funds advanced and credit facilities extended
by the Bank and the Bank’s holdings of securities referred to in sub section (1);
b) The causes which are likely to lead to such limitation being exceeded; and
c) Its recommendation to forestall or otherwise remedy the situation.
3) The Bank shall continue to make further reports and recommendations on the matters referred to in
subsection(2), at intervals of not more than six months until such time, as , in its opinion, the
situation has been rectified.
4) Where the limitation provided for in subsection (1) is exceeded, the Bank shall forthwith advise the
Minister of that fact and shall not allow any further increase , whether directly or indirectly , in the
aggregate amount of the funds advanced and credit facility extended by the Bank and the Bank’s
holding of securities referred to in subsection(1)
Though for long-term debt instruments, the central bank has a dominant role (40%), there has been an
encouraging participation by the non-banks (Figure 6.4).
Figure 6.3: Treasury bill Holdings
214
90
Bank of Zambia
Commercial Banks
Non-bank financial institutions
80
70
(%)
60
50
40
30
20
10
0
04:01
04:07
05:01
05:07
06:01
06:07
Figure 6.4: Bond Holdings
70
Non-bank financial institutions
Commercial banks
Bank of Zambia
60
(%)
50
40
30
20
10
04:01
04:07
05:01
05:07
06:01
06:07
We argue that such developments have assisted in letting market signals guide the conduct of
monetary policy and finance the budget deficit with market-based instruments, which are vital
elements of effective policy co-ordination. This has further led to, by and large, fiscal operations not
been a problem in the central bank‘s reserve management. In fact, between 2005 and 2007, fiscal
operations have assisted in the slowdown in reserve money growth to just below 10% in 2007 from
27% in 2005 (Table 6.2), suggesting a low extent of monetisation of the fiscal deficits. It is further
suggested that the contribution of fiscal deficit to money growth, as reflected in the NCG, has been
low especially in recent times. In 2006, annual broad money growth would have exceeded 45%
without good fiscal performance, which contributed –27.3% to money growth (Table 6.3). Overall,
one could argue that fiscal operations have not complicated the central bank’s control of money
supply.
215
Table 6.2:
Reserve
Money
NFA
NDA
NCG
Table 6.3:
M2
NFA
NDA
NCG
Contributions to Reserve Money Growth(%)
1999
2000
2001
2002
2003
2004
2005
2006
2007
28.64
-5.89
34.53
29.41
53.35
-394.11
447.47
116.60
45.75
148.72
-102.97
14.54
44.81
-97.05
141.85
-24.08
11.88
70.39
-58.50
11.39
33.83
2.60
31.23
14.61
27.13
124.26
-97.12
-11.10
34.01
159.07
-125.05
-9.38
9.20
31.01
-21.81
-26.56
2005
0.15
28.88
-28.73
-2.17
2006
45.02
72.28
-27.26
1.51
Contributions to Broad Money Growth(%)
2000
2001
2002
2003
2004
71.32
12.30
31.26
22.56
31.27
-78.56
32.84
-17.77
18.33
11.21
149.87
-20.53
49.02
4.23
20.06
45.97
17.97
-2.68
27.54
-4.44
2007
26.2
13.5
12.7
-7.3
Financial Sector Development
In an effort to improve the performance of the financial sector, financial liberalization had been
undertaken starting early 1990s. This move reflected, in part, the underpinning view of the new
Government that excessive controls and regulations were inappropriate for efficient resource allocation
and economic growth. Negative real interest rates, coupled with weak and negative (economic) growth,
suggested that an overly regulated financial system discouraged savings, created distortions in investment
decisions, and generally failed to intermediate between savers and investors. It was also clear combating
triple-digit inflation required more effective means of absorbing liquidity, limiting access to central bank
credit, and financing the government from less inflationary sources.
In this regard, interest rates were liberalized supported by the introduction of a government
securities auctioning system aimed at financing fiscal needs, especially financing maturing securities,
without resorting to central bank credit. In 2005, working with BOZ, the government introduced the 364days Treasury bill as well as the 3- and 5-year bonds. Liberalizing domestic financial markets was
accompanied by the removal of exchange controls and the shift towards flexible exchange rates. Financial
sector developments efforts further involved facilitating the emergence of a competitive banking system,
a prudent supervisory system and an efficient payments system. These reforms contributed to positive
developments in the financial sector, as reflected in broad money supply (M2) rising to around 24% of
GDP in 2006 from 14% in 1990 (Figure 6.5).
216
Figure 6.5: M2 as a ratio of GDP
26
24
22
20
18
16
14
90
92
94
96
98
00
02
04
06
Public Debt Management
The improved fiscal position is also reflected in the domestic debt, which has reduced to 18% in
2007 from 32% in 2000 (Figure 6.6).
Figure 6.6: Domestic Debt/GDP
36
32
(%)
28
24
20
16
00
01
02
03
04
05
06
07
Private sector credit and Interest Rates
Unsurprisingly, there is a strong positive relation between commercial bank lending rates and the
Treasury bill rate, which reflects fiscal operations (Figure 6.7). With improved fiscal performance, the
lending rates have trended downwards and private sector credit has increased. As already alluded to,
positive real growth has also been witnessed during the period.
217
Figure 6.7: Interest rates and private sector credit
60
50
(%)
40
30
20
10
0
99
00
01
02
03
04
05
06
07
08
Private Sector Credit
Lending Rate
91TB rate
Empirical Model
This section addresses the question of whether there is a link between fiscal deficits, money and
inflation using econometric models. While the theoretical framework guides the choice of the variables in
the empirical model, we also benefit from other empirical studies. A two-model approach is adopted. The
first model depicts the fiscal view of prices, and therefore, a standard model of inflation is adjusted by
replacing money with fiscal factors. The second model underpins the money supply and the inflation
processes. The cointegration technique is the estimation strategy employed.
We considered a vector xt  (T , g , p, y, s, m, psc, res, p* )t , where Tt is domestic revenue, gt is
total government expenditure, pt denotes domestic consumer price index (CPI), yt is output, st
represents the nominal exchange rate (between the Kwacha and the Rand) and mt is broad money supply.
psct is credit to the private sector, rest represents international reserves and pt* is South Africa CPI. The
sample period is 1998-2008. All variables are monthly and in logarithms23. The main source of data is
various BOZ and Ministry of Finance publications.
Examining the data properties commenced with cointegration analysis. In the first model, using a
1-lag Vector Error Correction Model (guided by the HQ criteria) of 5 endogenous variables
(T , g , p, y, s)t and pt* as an exogenous variable, there was 1 cointegrating vector (Appendix 1, Table
1A). Following Hansen and Juselius (2002), with a cointegration rank ( r )  1 , none of the variables are
stationary by themselves (Appendix A, Table 2A).
The long-run relation, lacking serial correlation and non-normality problems, was identified as
equation (7), which resembles a price relation.24 Though the fiscal variable is correctly signed and
significant, it has unnoticeable direct impact on consumer price. The main sources of long-run consumer
price movements are output, followed by exchange rate and foreign price.
23
24
Monthly output is interpolated following the Chow-Lin distribution/interpolation procedure (Frain (2004).
t-ratios in parentheses
218
pt  1.41 yt  0.35 st  0.04( g  T )t  0.17 pt*
( 3.78)
(2.20)
(2.06)
(1.86)
 2 (1)  0.37(0.54)
Serial Correlation :  2 (25)  25.77(0.26)
(6.7)
Normality :  2 (10)  12.43(0.26)
The second model is a 1-lag VECM of 6 endogenous variables (m, T , g , p, y, s)t while pt* , psct
and rest are considered exogenous. The trace test supports the existence of 2 cointegrating vectors
(Appendix A, Table 3A). Similarly, with a cointegration rank ( r )  2 , none of the variables are
stationary by themselves (Appendix A, Table 4A). The identified structure, which passes the diagnostic
tests, is reported as equation (8) (see also Appendix 1, Table 6A). We deduce the following. First, sources
of consumer price movements are domestic supply, followed by exchange rate, foreign price and money
growth. Second, money growth is significant induced by the budget deficit and private sector credit.
Third, a budget deficit growth of 10% has the potential of causing a 14 % expansion in money. In
response, consumer price will rise by 1.8% over time.
pt  1.42 yt  0.13 mt  0.26 st   0.19 pt*
(2.19)
(6.21)
(2.63)
(3.11)
mt  1.43( g  T )t  0.75 psct  0.02 reservest
(5.22)
(6.81)
(0.92)
 2 (7)  0.20(0.65)
Serial Correlation :  2 (36)  37.30(0.41)
(6.8)
Normality :  2 (12)  13.69(0.09)
6.4.2 Uganda
The Legal and Institutional Framework
The roles and functions of the Bank of Uganda (BOU) are enshrined in the BOU Act. Cap. 51.
Article 162(2) of the 1995 Constitution of the Republic of Uganda specifically guarantees the
independence of the central bank. Before 1993, responsibility for monetary policy formulation in Uganda
was vested in the Ministry of Finance.
Like many central banks, the objective of monetary Policy is price stability in form of low and
stable inflation and stability in financial markets. Government sets annual inflation performance target
conducive for sustainable economic growth. In the conduct of monetary policy, the central bank (Bank of
Uganda) coordinates with the government using a number of strategies in managing liquidity. The
strategy used to manage the excess liquidity injections resulting from government expenditure is a
cautious instrument mix to minimize instability in the financial markets. Government Securities Treasury
bills and Treasury bonds sold in the domestic financial markets as well as sales of foreign exchange help
in sterilizing liquidity. Repurchase Agreements are also used to manage intra-auction liquidity variations.
Policy rates, bank rate and rediscount rate are also used to supplement the quantity based instruments.
Fiscal discipline
219
The overall budget deficit has been widening between 2005/6 and 2007/8 financial year. The
major source of deficit financing is external (Figure 6.8).
Figure 6.8: Deficit Financing
800
400
0
-400
-800
-1200
1998
2000
2002
2004
2006
Overall Deficit
Domestic Financing
External Financing
220
2008
Minimize central bank financing of budget deficit
Monetization of the fiscal deficit by the central Bank was stopped as part of the macroeconomic
stabilization program (see also Box II). Further, Figure 6.9 suggests the role of BOU in holding of
government securities is minimal. About 50% of Government Treasury bills are held by commercial
banks and the role of insurance companies has growth from a share of less than 10% in 2006 to around
25% in 2008.
Box II
Bank of Uganda Statute 1993
PART VI—BANK RELATIONSHIP WITH THE GOVERNMENT.
34.
Temporary advances.
1)
The bank may make temporary advances to the Government and local governments in respect of
temporary deficiencies of recurrent revenue.
2)
The Treasury shall, at the beginning of each financial year, identify and submit to the bank all its
requirements for temporary advances for that year; and the bank shall, subject to subsection (3), operate
within that requirement.
3)
The total amount of advances made under subsection (1) shall not at any time exceed 18% of the
recurrent revenue of the Government.
40
The bank shall charge market rates of interest on any advance to the Government or local
government unless the board determines otherwise.
35. Report on advances.
(1) Where in the opinion of the bank the limitations on bank credit prescribed under section 33(3) or the
holding of securities is exceeded, the bank shall make a report on the bank’s outstanding advances or holding
of securities in terms of those sections and the causes that have led to the breach
of the limitations, together with any recommendation or remedy; and the bank shall make further reports and
recommendations to the Minister at intervals not exceeding six months until the situation has been rectified.
(2) At any time when the limitations on bank credits or the submitted requirement is exceeded, the powers of
the bank to grant additional financing shall cease until the situation has been rectified.
221
Figure 6.9: Holdings of Government Securities
70
60
50
(%)
40
30
20
10
0
06:01
06:03
07:01
07:03
08:01
08:03
Insurance
Commercial banks
Bank of Uganda
Figure 6.10 suggests growth in reserve money has been kept below 20% and Government
operations, reflected by the NCG, and has assisted in controlling reserve money.
Figure 6.10: Sources of Reserve Money
120
80
%
40
0
-40
-80
1994
1996
1998
2000
2002
2004
2006
Reserve Money
Net Foreign Assets
Net Claims on Government
Since 2001, net claims on government contribution to broad money (M3) have been negative,
suggesting fiscal operations have greatly assisted BOU in controlling money supply (Figure 6.11).
222
Figure 6.11: Sources of Broad Money
120
80
(%)
40
0
-40
-80
97
98
99
00
01
02
03
04
05
06
07
Broad Money
Net Foreign Assets
Net Claims on Government
Empirical Model
We relied on monthly data, over the 1997-2006 period, xt  (T , g , p, y, s, m, p* , psc, reserves )t ,
where Tt is domestic revenue, gt is total government expenditure, pt denotes underlying consumer price
index (UCPI), yt is output25, st represents the nominal effective exchange rate , mt is broad money and
pt* is World CPI while psct and reservest are private sector credit and gross international reserves,
respectively. Bank of Uganda is the main source of the data.
One cointegration vector is established (Appendix B, Table 1B) and all the series are not
stationary by themselves (see Appendix B, Table 2B). The cointegrating relation is identified as equation
(6.9). Though the fiscal variable is correctly signed, it has no significant effect on underlying inflation.
The significant factors are world price, followed by domestic supply and exchange rate.
The second model is a 1-lag VECM of 6 endogenous variables (m, T , g , p, y, s)t with pt* , psct
and rest considered exogenous. The trace test supports the existence of 2 cointegrating vectors (see
Appendix B, Table 3B). Similarly, with a cointegration rank ( r )  2 , none of the variables are stationary
by themselves (Appendix B, Table 4B). The identified structure, which passes the diagnostic tests, is
reported as equations (6.10) and suggesting the following. A 10% growth in the budget deficit would
eventually result in 13% growth in broad money. This in turn will induce an inflation rate of 1.6%, other
things equal.
pt  0.23 yt  0.14 st   0.39( g  T )t 1.33 pt*
(4.50)
(2.12)
(0.61)
(9.47)
 (1)  2.25(0.13)
2
25
(6.9)
Output is proxied by the Industrial Production index (IPI)
223
pt  0.14 yt  0.12 mt  0.13 st 0.17 pt*
(10.00)
(5.20)
(3.32)
(1.58)
mt  1.30( g  T )t  0.37 psct  0.74 reservest
(10.18)
(6.51)
(6.10)
(5.69)
 2 (7)  10.05(0.19)
6.4.3 Kenya
Central Bank Independence
While there is no legal guarantee of the central bank independence, the use of the Central bank policy rate
to signal the market could reflect some level of operational independence.
Fiscal Discipline
Between 1990 and 2004, relative good fiscal performance was observed especially after 1995
when the overall fiscal deficit averaged below 1% of GDP (Table 6.4). This was largely due to improved
revenue performance and a drop in expenditure (Figure 6.12). Revenue increased to about 28% in 1993
from 22% of GDP in 1990. At the same time, expenditure declined to 30% from 32%. The 1999-2004
periods generally witnessed a decline in both revenue and expenditure (relative to GDP).
Table 6.4: Fiscal Budget
1990-92
1993-95
1996-98
1999-01
2002-04
(% of GDP)
Revenue
Expenditure
Overall balance
24.7
28.2
-3.5
27.1
31.0
-3.9
26.8
27.4
-0.6
22.5
22.8
-0.3
20.5
22.0
-1.5
Financing
Domestic
Foreign
2.3
1.2
3.9
0.0
1.4
-0.8
0.9
-0.6
2.0
-0.7
224
Figure 6.12: Domestic Revenue and Expenditure (in % of GDP)
36
32
(%)
28
24
20
16
90
92
94
96
98
Revenue
00
02
04
06
Expenditure
Limiting Central Bank Credit to Government
The amendment of Central Bank of Kenya (CBK) Act of 1996 limited the Kenya Government
financing of its budget deficit through Central Bank to no more than 5 % of the latest audited
Government gross recurrent revenue (see also Box III)
What has been the impact of fiscal operations on reserve money and broad monetary aggregates?
By and large, claims on government (net) have been moderating the liquidity effects of net foreign assets
(Figures 6.13 and 6.14). This suggests, in line with the limiting of central bank credit to government, the
deficit has not been largely monetized.
Figure 6.13: Contributions to Reserve Money
35
30
25
(%)
20
15
10
5
0
-5
2001
2002
2003
2004
2005
Reserve money
Net Claims on Government
Net Foreign Assets
225
2006
Box III
CHAPTER 491
THE CENTRAL BANK OF KENYA ACT
Advances to Government no.9 of 1996 s.18
1) Subject to the provisions of this section, the Bank may make direct
advances to the Government for the purpose of offsetting
fluctuations between receipts from the budgeted revenue and
payments of the Government
2) Each advance made by the Government under this section shalla) Be secured with negotiable securities issued by the Government
which mature not later than twelve months;
b) Bear interest at market rate; and
c) Be made solely for the purpose of providing temporary
accommodation to the Government
3) The total amount outstanding at any time of advances made under
this section shall not exceed five per centum of the gross recurrent
revenue of the Government as shown in the Appropriation accounts
for the latest for which those Accounts have been audited by the
Controller and
Auditor
General
Figure
6.14:
Contributions to Broad Money
20
16
(%)
12
8
4
0
-4
2001
2002
2003
2004
2005
2006
Broad Money
Net Claims on Government
Net Foreign Assets
Domestic Debt Management
Despite improved fiscal performance, domestic debt has generally been above 20% of GDP (6.15).
Figure 6.15: Domestic Debt
226
26
24
(%)
22
20
18
16
97
98
99
00
01
02
03
04
05
Empirical Model
Monthly data, over the 1997-2006 period, xt  (T , g , p, y, s, m, p* )t , where Tt is domestic
revenue, gt is total government expenditure, pt denotes headline consumer price index (CPI), yt is
output26, st represents the nominal effective exchange rate and mt is broad money and pt* is World CPI.
CBK is the main source of the data.
One cointegration vector is established (Appendix C, Table 1C) and all the series are not
stationary by themselves (Appendix C, Table 2C). The cointegrating relation is identified as equation
(6.11) (see also Appendix B, Table 3B), suggesting that world price has a dominant feature in headline
inflationary process in the long run, followed by domestic supply and exchange rate. Similar to Zambia,
the direct impact of fiscal developments on consumer price is marginal.
pt  0.26 yt  0.20 st  0.08( g  T )t 1.18 pt*
( 3.25)
(4.74)
(3.77)
(8.43)
 (1)  2.25(0.13)
2
(6.11)
The second model is a 1-lag VECM of 6 endogenous variables (m, T , g , p, y, s)t with psct and
rest considered exogenous. The trace test supports the existence of 2 cointegrating vectors (see Appendix
C, Table 3C). Similarly, with a cointegration rank ( r )  2 , none of the variables are stationary by
themselves (Appendix C, Table 4C). The identified structure, which passes the diagnostic tests, is
reported as equations (6.12) and suggesting the following (see also Appendix 1, Table 6A). A 10%
growth in the budget deficit would eventually result in 12% growth in broad money. This in turn will lead
to consumer price rising by 2.6%.
26
Output is proxied by the Industrial Production index (IPI)
227
pt   0.07 yt 0.22 mt  0.30 st 0.63 pt*
(0.96)
(6.91)
(8.63)
(4.91)
mt  1.21( g  T )t
(6.12)
(14.96)
 2 (5)  8.40(0.14)
6.4.4 Malawi
Legal and Institutional Framework
There is no legal infrastructure that guarantees central bank independence. This has been
provided for in the draft Reserve Bank of Malawi Bill which has been drafted on the basis of the SADC
Model. The Secretary to the Treasury and the Director of Economic Affairs in the Ministry of Finance are
ex-officio members of the monetary Policy Committee (MPC). However, they only sit as non-voting
members of the committee. The Governor appoints members of the MPC. In addition, the Head of the
Economics Department at the University of Malawi-Chancellor College, the Secretary to the Treasury,
Principle Secretary of the Ministry of Economic Planning and Development and the Director of Economic
Affairs at Ministry of Finance are ex-officio members. The Governor chairs the MPC.
Fiscal Discipline
The domestic budget deficit, by and large, has been below the COMESA convergence criteria of
no more than 5% of GDP (Table 6.5). Recently, revenue has been rising while expenditure has been
falling relative to GDP (Figure 6.16).
Table 6.5: Fiscal Budget
1990-92
(% of GDP)
Revenue
20.7
Expenditure
26.1
Overall balance
-5.4
Financing
Domestic
2.0
Foreign
1.5
1993-95
1996-98
1999-01
2002-04
2005-07
24.9
30.7
-5.8
19.8
23.1
-3.2
25.1
28.5
-3.5
28.5
38.9
-7.4
41.9
44.4
-1.9
3.3
4.3
-0.6
3.1
2.5
1.5
7.4
-0.3
0.11
0.35
Figure 6.16: Revenue and Expenditure (in % of GDP)
228
50
45
40
35
30
(%)
25
20
15
10
90
92
94
96
98
00
Domestic Revenue
02
04
06
Expenditure
Limiting Central Bank Credit to Government
There is a limit of central direct bank lending to the Government. The law stipulates that the central
bank could make short-term advances to government not exceeding 20% of the annual budgeted revenue
(see also Box IV).
BOX IV
PART VIII POWERS AND FUNCTIONS OF THE BANK
LAWS OF MALAWI (CHAPTER 44:02)
40. (1) The Bank may make short-term advances to the Government in respect of temporary shortfalls in budget
revenues on such terms and conditions as the Bank may determine.
(2) The total amount of advances outstanding at any time made by the Bank under this section shall not exceed
twenty per cent of the annual budgeted revenues of the Government as defined in subsection (3).
(3) For the purposes of this section, the annual budgeted revenues of the Government shall be those revenues
derived from sources within Malawi as estimated for the Government's financial year in which such advances
are made.
(4) All advances made under subsection (I) shall be repaid as soon as possible and, in any event, shall be
repayable within four months of the end of the Government's financial year in which they are made, and if after
the end of the financial year such advances remain outstanding, the power of the Bank to grant further such
advances shall not be exercisable unless and until the outstanding advances have been repaid.
(5) If at any time the Bank has any Government loans and advances outstanding, irrespective of maturity, the
Bank may require the Government to issue to it treasury bills or promissory notes and other instruments as the
Bank may deem fit for open market policy purposes, and the terms and conditions shall be agreed upon
between the Bank and the Minister.
Prior to 2003, fiscal performance, as reflected in net claims on government was a major source of
reserve money, contributing more than 80%. This suggests monetization of fiscal deficits (Figure
6.16). During this time, fiscal deficits also contributed largely to money supply accounting for between
20% and 40% of broad money growth (Figure 6.18).
Figure 6.17: Contributions to Reserve Money
229
100
(%)
50
0
-50
-100
2001
2002
2003
2004
2005
2006
Reserve Money
Net Claims on Government
Net Foreign Assets
Figure 6.18: Contributions to Broad Money
40
30
(%)
20
10
0
-10
-20
2001
2002
2003
2004
2005
2006
Broad Money
Net Claims on Government
Net Foreign Assets
Financial Sector Development
Figure 6.19 depicts the low level of development in the Malawian financial sector.
Figure 6.19: M2 as a ratio of GDP
230
14
13
12
(%)
11
10
9
8
7
91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
M2/GDP
Empirical Model
Monthly data, over the 1997-2007 period, xt  (T , g , p, y, s, m, p* , psc, reserves )t , where Tt is
domestic revenue, gt is total government expenditure, pt denotes headline consumer price index (CPI),
yt is output27, st represents the nominal exchange rate and mt is broad money and pt* is World CPI
whereas psct and reservest are private sector credit and gross international reserves, respectively.
Reserve Bank of Malawi is the main source of the data.
One cointegration vector is established (Appendix B, Table 1B) and all the series are not
stationary by themselves (see Appendix B, Table 2B). The cointegrating relation is identified as equation
(6.13) (see also Appendix B, Table 3B). Contrary to Zambia, Kenya and Uganda, there is a strong direct
link between budget deficit and consumer price movements. For example, a 10% growth in budget deficit
will ultimately cause consumer price to rise by around 5%. Within this framework, consumer price is also
induced from foreign sources, domestic supply side as well as exchange rate movements.
pt  0.70 yt  0.52 st  0.47( g  T )t  1.65 pt*
3.11
7.09
7.22
3.68
 (1)  1.31(0.24)
2
(6.13)
The second model is a 1-lag VECM of 6 endogenous variables (m, T , g , p, y, s)t with psct and
rest considered exogenous. The trace test supports the existence of 2 cointegrating vectors (see Appendix
1, Table 4A). Similarly, with a cointegration rank ( r )  2 , none of the variables are stationary by
themselves (Appendix 1, Table 5A). The identified structure, which passes the diagnostic tests, is
reported as equations (6.14). Money supply rises by about 5% in response to a 10% growth in the budget
deficit, consequently inducing consumer price rising by about 0.9%.
27
Output is proxied by the Industrial Production index (IPI)
231
pt   0.65 yt 0.17 mt  0.64 st 0.61 pt*
( 0.95)
(2.45)
(6.87)
(0.89)
mt  0.55( g  T )t  0.83 psct  0.07 reservest
(7.91)
(15.09)
0.99
(6.14)
 2 (7)  7.21(0.19)
6.5
Concluding and Policy Implications
O
verall, this study delineates the procedures in selected COMESA member states (Kenya,
Malawi, Uganda and Zambia) for bringing about co-ordination between central banks and
fiscal authorities in pursuit of common objectives of price stability and sustained economic
growth. Specifically, it analyses the extent of coordination; review the essential institutional
arrangements for effective co-ordination; econometrically establish the relation between fiscal deficits,
money supply and inflation; and recommend necessary policy adjustments in case of non-coordination to
avoid negative consequences of macro and financial sector instability.
The rationale for the monetary and fiscal policy coordination stems from the following
interrelated objectives. First, it is to set internally consistent and mutually agreed targets of monetary and
fiscal policies. Second, it is to ensure policy credibility. Third, it is for effective management of the
domestic debt. Fourth, this is necessary to support the development of domestic financial markets. The
need for policy coordination also arises during financial sector liberalization. On the whole, without the
monetary and fiscal authorities taking into account each other’s objectives and actions, financial
instability could ensue, leading to high interest rates, exchange rate pressures, rapid inflation and adverse
impact on economic growth.
The success of the co-ordination of fiscal and monetary policies depends on institutional and
operating arrangements, which include instrument independence of the central bank; central bank credit to
the government should be restricted; fiscal discipline; and developed financial markets.
The theoretical link between fiscal and monetary policy is established through the budget
constraints of the fiscal authority and the central bank. That is the accounting identity that insists that
every budget deficit must be financed by selling bonds either to the public or to the central bank. This
identity points out that today's fiscal-monetary decisions have implications for the number of bonds that
will have to be sold to the public today, and thus for the feasible set of fiscal-monetary combinations in
future periods.
According to the fiscal theory of price level, money creation may not be the only channel through
which fiscal policy becomes dominant and budget deficits cause inflation. This theory argues that a fiscal
dominant (non-Ricardian) regime may arise when fiscal policy is not sustainable and government bonds
are considered net wealth. These wealth effects could jeopardize the objective of price stability,
irrespective of central bank commitment to low inflation. Thus in a non-Ricardian regimes, it is fiscal, not
monetary, policy that determines the price level and becomes the nominal anchor.
Empirical results suggest the following. Among the institutions arrangement to enhance
monetary-fiscal policy co-ordination, we observe an absence of legal guarantee for the operational
independence of the Bank of Zambia. The legislation which limits the central bank direct access to central
bank credit does not have a quantitative measure. While a direct impact of the budget on inflation is
unnoticeable, a budget deficit growth of 10% has the potential of causing a 14 % expansion in money and
ultimately causing consumer price to rise by 1.8%. The policy implications are that whereas there is some
resemblance of policy coordination as reflected in improved fiscal performance (that is fiscal discipline),
lack of monetization of fiscal deficits and lack of evidence of fiscal deficits being a source of inflationary
pressures, to enhance monetary-fiscal co-operation co-ordination, there is need to legislate the central
232
bank operation independence. Also specifying the quantitative limit of government direct access to central
bank credit in the legal framework should be recommended. Otherwise the current legislation is subject to
miss-interpretation.
The explicit legal guarantee for the Bank of Uganda’s operational independence and the legal
requirement that government direct access to central bank credit should not exceed 18% of the recurrent
revenue of the Government are strong evidence of the existence of institutional arrangements for
monetary-fiscal co-operations. However, the widening of the fiscal deficits recently should be a source of
concerns. Also there is a heavy reliance on foreign financing of the budget deficits. Experience teaches
that too much dependence on foreign funding of domestic debt has the exchange rate and/or balance-ofpayments risks. There is no significant link between budget deficits and underlying inflation. However, a
10% growth in the budget deficit would eventually result in 13% growth in broad money. This in turn will
induce an inflation rate of 1.6%, other things equal. We argue that Uganda could enhance its fiscalmonetary co-ordination by including the Ministry of Finance in the MPTC. Further the issue of heavy
reliance on foreign financing of the deficit needs to be re-examined.
While there is no legal guarantee of the central bank independence, the use of the central bank
policy rate to signal the market could reflect some level of instrument independence of the Central bank
of Kenya. The restriction Government financing of its budget deficit through Central Bank to no more
than 5 % of gross recurrent revenue is further evidence of some level of co-operation between the
monetary and fiscal authority. Nonetheless, the domestic debt, observed to be of above 20% of GDP,
seems problematic. Similar to Zambia, the direct impact of fiscal developments on consumer price is
marginal. A 10% growth in the budget deficit would eventually result in 12% growth in broad money.
This in turn cause consumer price to rise by 2.6%. While there is evidence some evidence of the
existence of institutional requirement for effective co-ordination reflected in the limiting of government’
direct access to central bank credit, there is needed to guarantee the operational independence of the
Central Bank of Kenya. Further a domestic debt management strategy needs to be put in place.
Like Zambia and Kenya, there is no legal infrastructure that guarantees Reserve Bank of
Malawi’s operational independence. However, there is a limit of central direct bank lending to the
Government. The law stipulates that the central bank could make short-term advances to government not
exceeding 20% of the annual budgeted revenue. This limit could have assisted in reducing the
monetization of budget deficits. Prior to 2003, fiscal performance, as reflected in net claims on
government was a major source of reserve money, contributing more than 80%. During this time, fiscal
deficits also contributed largely to money supply accounting for between 20% and 40% of broad money
growth. The low level of development in the Malawian financial sector should be a source of concerns to
enhancing the monetary –fiscal policy co-ordination which also reflected in the inclusiveness of the
Monetary Policy Committee. Contrary to Zambia, Kenya and Uganda, there is a strong direct link
between budget deficit and consumer price movements. A 10% growth in budget deficit will ultimately
cause consumer price to rise by around 5%. Further, money supply rises by about 5% in response to a
10% growth in the budget deficit, consequently inducing consumer price rising by about 0.9%. We
recommend that the recent evidence of fiscal discipline should be consolidated. Measures to develop the
financial sector should put in place in order to get the maximum benefits of monetary and fiscal coordination.
We conclude by arguing that that there is room for improvements in all countries examined and
countries should learn from each other. For example, Zambia and Uganda should learn from Malawi by
having a Monetary Policy Committee which includes academia. Malawi should learn from others in terms
what need to be done to develop its financial sector. Except for Malawi, the main channels through which
budget deficits affect inflation is through the indirect money channel.
233
4.6
References
Akcay, E. and Alper, E.C. and Ozmucur, S. (1996)” Budget Deficit, Money Supply and Inflation” Evidence From
Low and High frequency Data from Turkey, Bogazici University Research paper
Baldini, A. and Marcos, P. R. (2008). “:Fiscal and Monetary Anchors for Price Stability: Evidence from
Sub-Saharan Africa.” IMF Working Paper WP/08/121, Washing DC.
Cochrane, J. (2000), “Money as Stock: Price Level Determination with no Money Demand,” NBER Working Paper
No. W7498 (Cambridge, MA: MIT Press).
________1998, “A Frictionless View of US Inflation,” in NBER Macroeconomics
Annual, ed. by Ben Bernanke and Julio Rotemberg (Cambridge, MA: MIT Press)
pp. 323–84.
Dahan, M(1998), “The Fiscal Effects of Monetary Policy”, IMF Working Paper , 98/66 May.
Daniel,B.C (2001), “The Fiscal Theory of the Price Level in an Open Economy,” Journal
of Monetary Economics, Vol. 48, pp. 293–308.
Frain, C.J. (2004). ‘A RATS subroutine to implement the Chow-Lin distribution/interpolation procedure’,
Research Technical paper (2/RT/04), Economic Analysis and Research Department, Central
Bank and Financial Services Authority of Ireland.
Fry, M.(1995),”Money, Interest and Banking in Economic Development’, Second Edition, Johns Hopkins
University Press
Hansen, H. and Juselius, K. (2002). ‘CATS IN RATS, Cointegration Analysis of Time Series’, Estima
Evanston, IL.
Hilbers, P(2005). “Interaction of Monetary and Fiscal Policies: Why Central Bankers Worry about
Government Budgets”, Paper Presented at an IMF Seminar on Current Developments in
Monetary and Financial Law, Washington DC.
King, R.G. and Plosser, C.I. (1985). “Money, Deficits and Inflation”, Carnegie-Rochester Conference Series on
Public Policy (22), 147-196
Lambertini, Luca and Rovelli (2001), “Independent or Coordinated? Monetary
and Fiscal Policy in EMU” An unpublished paper of Universita di Bologna
Laurens, B. and Enrique de la Piedra (1998) “Co-ordination of Monetary and Fiscal Policies”, IMF
Working paper WP/98/25, Washington, D.C.
Leeper, E.M(1991) “Equilibria Under Active and Passive Monetary and Fiscal Policies,” Journal of
Monetary Economics , 27(1), 129-147.
Miller, P.J. (1983). “Higher Deficit Policies Lead to Higher Inflation” Federal Reserve Bank of
Minneapolis, Quarterly Review , Vol. 7, 8-19.
Mutoti, N and Kihangire, D(2008). “Sources of Inflation in Selected COMESA Member States”,
COMESA
Sargent, T., and Wallace,N (1981)“Some Unpleasant Monetarist Arithmetic,”
Federal Reserve Bank of Minneapolis Quarterly Review, pp. 1–18.
Sims, C.A(1994). “Ä Simple Model for Study of the Determination of the Price Level and the Interaction
of Monetary and Fiscal Policy”, Economic Theory (4), 381-399
Walsh, E. C. (2003) “Monetary Theory and Policy”, The MIT Press, Cambridge Massachusetts
Woodford, M. (1998) “Public Debt and the Price Level,” June 18–19 (unpublished; prepared for the Bank of
England Conference on Government Debt and Monetary Policy).
Woodford, M(1995). “Price Level Determinacy Without Control of Monetary Aggregates”, CarnegieRochester Conference Series on Public Policy (43), 1-46,
________1994, “Monetary Policy and Price level Determinacy in a Cash-inAdvance Economy,” Economic Theory, No. 4, pp. 345–80.
234
Appendix A
ZAMBIA
Table 1A: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
137.24
68.68
1
36.53
47.21
2
18.53
29.34
3
8.38
15.34
4
1.00
3.84
Table 2A:
r

1
2
3
4
11.07
9.49
7.81
5.99
2
Test for stationarity
p
y
s
16.31
9.21
6.24
0.24
17.92
8.51
7.12
1.52
12.10
9.03
7.08
4.71
T
g
13.09
8.11
6.12
1.20
17.41
8.71
5.01
1.54
Table 3A: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
267.12
93.92
1
164.11
68.68
2
43.27
47.21
3
28.11
29.34
4
11.12
15.34
5
1.07
3.84
Table 4A:
r
2
1
2
3
4
5
14.07
12.59
11.07
9.49
7.81
Test for stationarity
p
m
26.12
19.00
10.90
8.16
4.11
36.63
22.01
11.00
7.98
4.01
y
s
T
g
30.11
18.76
8.33
7.93
6.90
28.82
19.23
8.13
7.35
4.78
35.04
22.29
8.32
7.12
2.62
37.38
28.33
10.87
8.79
6.87
235
APPENDIX B
UGANDA
Table 1B: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
123.34
68.68
1
39.34
47.21
2
27.10
29.34
3
12.11
15.34
4
1.20
3.84
Table 2B:
r

1
2
3
4
11.07
9.49
7.81
5.99
2
Test for stationarity
p
y
s
19.12
8.06
5.91
2.70
18.50
7.11
6.21
1.70
16.45
8.16
7.01
3.14
T
g
19.39
8.80
6.01
4.21
17.71
9.13
6.71
3.21
Table 3B: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
128.50
93.92
1
70.24
68.68
2
42.56
47.21
3
20.01
29.34
4
13.73
15.34
5
2.50
3.84
Table 4A:
r
2
1
2
3
4
5
15.51
14.07
12.59
11.07
9.49
Test for stationarity
p
m
23.66
16.98
10.51
8.33
5.21
34.50
17.3
12.01
10.15
8.39
y
s
T
g
34.14
19.89
11.98
9.15
8..53
23.35
14.07
12.59
11.07
9.49
31.94
25.83
11.74
10.46
8.67
32.14
25.18
9.86
5.25
2.10
236
APPENDIX C
KENYA
Table 1C: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
217.62
68.68
1
40.14
47.21
2
28.10
29.34
3
10.21
15.34
4
2.12
3.84
Table 2C:
r
2
1
2
3
4
11.07
9.49
7.81
5.99
Test for stationarity
p
y
s
18.20
8.19
6.86
3.51
16.37
5.77
3.86
2.15
12.01
6.27
3.43
2.10
T
g
12.08
7.09
3.43
1.67
16.91
8.97
7.09
5.88
Table 3C Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
129.90
93.92
1
76.77
68.68
2
40.84
47.21
3
27.70
29.34
4
12.30
15.34
5
1.70
3.84
237
Table 4C
r
2
1
2
3
4
5
12.59
11.07
9.49
7.81
5.99
Test for stationarity
p
m
26.12
19.00
8.90
7.16
4.11
36.63
22.01
9.00
7.1
4.01
y
s
T
g
30.11
18.76
8.33
7.63
4.90
28.82
19.23
8.13
7.35
4.78
35.04
22.29
8.32
7.12
2.62
37.38
28.33
8.87
6.79
4.87
APPENDIX D
MALAWI
Table 1D: Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
102.84
68.68
1
43.01
47.21
2
22.06
29.34
3
9.79
15.34
4
1.70
3.84
Table 2D:
r
2
1
2
3
4
11.07
9.49
7.81
5.99
Test for stationarity
p
y
s
19.33
7.20
5.11
1.20
17.22
8.09
5.91
3.45
13.98
8.86
5.71
2.93
T
g
14.87
9.19
6.11
4.29
18.33
8.87
6.29
.4.10
Table 3D Trace Test for Cointegration Rank
r
Trace(i)
*
C0.95
0
130.89
93.92
1
89.56
68.68
2
44.84
47.21
3
23.89
29.34
4
10.20
15.34
5
2.98
3.84
Table 4D
r
2
1
2
3
15.51
14.07
12.59
Test for stationarity
p
m
23.66
16.98
10.51
34.50
17.63
12.01
y
s
T
g
34.14
27.36
12.01
23.35
17.58
12.38
41.94
25.82
11.74
32.14
25.18
9.18
238
4
5
11.07
9.49
8.33
5.21
10.15
8.39
8.33
6.53
9.64
7.75
239
10.46
8.67
5.15
2.10
7.0 Impact of REER on Trade, Output and Current account
Christopher Kiptoo
7.1 Introduction
This study seeks to explore the role played by the REER in influencing exports, output and
current account in selected COMESA countries. The specific objectives are three. The first is to
analyse the structure and major characteristics of the current account. The second is to empirically
investigate the impact of RER volatility on the current account and output. To do this, long-run and
short-run export and import functions are estimated. The third objective is to make recommendations
on what policies should be pursued to avoid negative effects of REER volatility.
The remainder of the paper is structured as follows. Section 7.2 presents some of the stylised
facts on the structure of current accounts of COMESA countries. This is followed by the review of the
literature and the model framework. The empirical approach is presented in Section 7.5. Section 7.6
offers some concluding and policy recommendations.
7.2
Trends in the Current Account and GDP
Table 7.1 shows that most countries in the COMESA region had been experiencing
deteriorating current account deficits. This had been due to the widening of the trade deficit caused by
relatively faster growth in imports compared to exports (Table 7.2).
Table 7.1: Average Current Account (% of GDP)
Burundi
Egypt
Ethiopia
Kenya
Libya
Madagascar
Rwanda
Mauritius
Rwanda
Seychelles
Sudan
Swaziland
Uganda
Zambia
Source: IFS
1993-1997
-1.2
0.4
0.5
-0.9
8.4
-4.5
-2.3
-1.6
-0.4
-3.8
-2.6
-1.2
-4.1
-4.9
240
1998-2002
-13.3
-1.1
-3.9
-1.7
24.7
-7.8
-6.0
1.8
-5.1
-17.8
-5.7
-1.2
-9.1
-18.3
2003-2007
-37.4
4.5
-11.1
-2.8
12.5
-16.0
-5.5
-8.5
-18.7
-13.2
4.7
-5.9
-18.7
Table 7.2: Average Export and Imports (% of GDP)
Exports
1993-1997
1998-2002
2003-2007
Burundi
3.7
5.9
9.0
Egypt
5.9
6.9
20.4
Ethiopia
6.0
7.2
9.9
Kenya
12.9
14.4
25.1
Libya
155.6
88.4
29.2
Madagascar
7.9
18.6
26.0
Rwanda
6.2
24.0
Mauritius
29.3
37.1
43.2
Rwanda
2.4
4.1
7.0
Seychelles
12.3
28.9
54.7
Sudan
2.1
10.9
25.8
Swaziland
35.5
72.3
105.9
Uganda
7.0
7.9
14.0
Zambia
14.3
25.8
81.1
1993-1997
6.3
15.5
14.3
16.6
128.4
10.1
7.7
35.6
8.9
40.3
4.5
41.1
12.1
13.6
Imports
1998-2002
14.3
16.0
20.5
22.8
50.8
22.2
28.4
44.3
13.5
57.3
12.8
77.3
16.8
32.2
2003-2007
33.2
29.9
39.8
44.2
12.9
39.1
60.4
22.3
88.3
27.1
102.0
26.0
70.8
What follows is a brief account of each country’s performance with regards tithe current
account, real gross domestic product (GDP) and REER. Burundi’s economy is predominantly
agricultural with more than 90% of the population dependent on subsistence agriculture. Economic
growth depends on coffee and tea exports, which account for 90% of foreign exchange earnings. The
other export commodities are sugar, cotton and hides. Thus, the country’s ability to pay for imports
rests primarily on weather conditions and international coffee and tea prices. Burundi’s main import
items include petroleum products, capital goods (iron and steel) and cement, glass as well as glass and
glass wares.
In 2006, Burundi’s major export destinations were as follows: Germany (25.3%), Switzerland
(20.5%), Pakistan (5.5%) and Belgium (4.6%). During the same year, the main import partners were
Saudi Arabia (15.4%), Kenya (10.4%), Belgium (7.8%), France (5.5%), Uganda (4.9%), Germany
(4.9%), India (4.3%) and Russia (4.2%).
As shown in Figure 7.1, Burundi’s REER depreciated in 1993 to 1997 and appreciated
thereafter until 2004. Real GDP declined generally until 2004 when it registered an upward trend.
Current account deficit generally remained stable during the first half of 1990s and deteriorated
generally thereafter. Current account deficits were due to trade deficit attributed to higher imports
compared to exports.
Figure 7.1: Burundi Current Account, Real GDP and REER
241
4500
140
4000
120
Current a/c & GDP (US$ million)
3500
REER Index (2000=100)
3000
100
2500
80
2000
1500
60
1000
40
500
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
20
2007
-500
-1000
0
Current Account
Real GDP
REER
Egypt has a fully diversified economy with enormous potential for sustained high growth
across all sectors. Oil is a major source of foreign exchange. Egypt’s main exports of goods are fuel
products, manufactured goods, and agricultural products, mainly cotton. Egypt has considerable
earnings from its tourism, attracting around 7 million tourists annually. Egypt imports the vast
majority of its consumer goods and capital equipment (machinery and transport equipment) as well as
chemicals. About 14% of imports are purchased for activities at Egypt’s free trade zone. The
European Union is the main supplier (40% of total imports), followed by the US, (14%). The EU’s
main imports from Egypt are energy (42%), textiles and clothing (16%), agricultural products (10%)
and chemicals (6%). Trade with Arab countries has been growing, up from 10% to 13% of the total.
Figure 7.2 shows the evolution of GDP and current account balance of Egypt. The real GDP
improved from 1993 until 2000 when it declined and thereafter picked up in 2004. Egypt generally
registered current account deficit since 1995 to 2001 and thereafter recorded surplus current account
mainly due to surpluses in the services account (on account of increased tourism revenue) and net
transfers (due to increased remittances), which help to make up for the rise in trade deficit.
Figure 7.2: Egypt Current Account
5000
120000
Current Account (US$ million)
4000
100000
GDP (US$ million)
3000
80000
2000
1000
60000
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
40000
-1000
20000
-2000
-3000
0
Current Account
GDP
Kenya's economy could be divided into three sectors, namely: the primary sector, the
secondary sector and the tertiary sector. The primary sector is composed of agriculture,
forestry, mining and quarrying activities. Kenya’s economy has traditionally been based on
242
the performance of the primary sector where the agriculture activity plays a prominent role.
The primary sector’s contribution to GDP has, however, been on a declining trend over the
decades. Kenya’s secondary sector has similarly declined in performance as a share of GDP.
This development has been mainly in the utilities and construction sub-sectors as the
manufacturing activity has grown, albeit marginally. Kenya’s tertiary sector has, however,
registered significant growth as a share of GDP since independence. This sector is mainly
composed of trade, restaurants, and hotels; transport, storage, communications, financial
institutions and ownership of dwellings.
Kenya’s current account balance has been experiencing increasing deficits over the
recent past mainly due to widening trade, reflecting faster increase in imports relative to
exports. The increased merchandise imported reflects increased imports of oil, machinery and
transport equipment, manufactured goods, chemicals and other miscellaneous imports
particularly palm oil, raw materials, cereals, beverages and tobacco. Increased oil imports bill
was mainly in crude oil, diesel oil and jet fuel. The increased value of manufactured goods
imported reflected increased imports of iron, steel, paper products, textiles, rubber products
and other metal manufactures. The increased imports of machinery and transport equipment
was mainly in road vehicles, electrical machinery, industrial machinery and equipment,
telecommunication and sound recording equipment and power generating machinery while
increased chemicals imported was mainly in industrial chemicals, medicinal and
pharmaceutical products, fertilizers and plastics.
Merchandise exports have also been increasing albeit less fast than imports. The
increase has mainly in horticulture, coffee, manufactured goods, raw materials and other
miscellaneous exports, particularly chemical products, electrical machinery, sugar, tobacco,
animal and vegetable oils. Increased exports of coffee, tea, horticulture and oil products
benefited from improved export prices while the increase in manufactured goods exports,
mainly of processed leather, cement and metal manufactures, was attributed to an increase in
both export volume and prices.
As at May 2008, coffee, tea and horticulture made up 35.9% of the total exports while
manufactured goods and oil, respectively, contributed 12.4%. The main export destinations
were Uganda (12.4%), United Kingdom (11.2%), the Netherlands (8.0%), Tanzania (7.8%),
United States of America (6.3%), Sudan (4.3%), Somalia (3.8%), Pakistan (3.7%) and Egypt
(3.2%). Overall, African countries absorbed 45.2% of Kenya’s merchandise exports. Imports
mainly originated from the United Arab Emirates (17.0%), India (10.4%), China (8.0%),
Japan (6.7%), South Africa (6.1%), United Kingdom (4.3%), United States of America
(4.0%), Germany (3.7%), Indonesia (3.2%), Saudi Arabia (2.9%) and France (2.6%).
Shown in Figure 7.3, Kenya’s REER appreciated from 1993 until 1998 when it
remained fairly stable. Since 2005 it has generally appreciated. Real GDP rose generally from
1993 until 1996 and thereafter declined until 2001 when it registered positive trend. Kenya’s
current account balance has been in deficit in most of the period and recorded consistent
deterioration since 2004 mainly due to rising trade deficit.
Figure 7.3: Kenya Current account, REER and GDP
243
18000
160
Current a/c & GDP, US$ million
16000
140
14000
120
REER (2000=100)
12000
100
10000
8000
80
6000
60
4000
40
2000
20
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
-2000
0
Current account
GDP
REER
The Libyan economy depends primarily upon revenues from the oil sector, which
contribute about 95% of export earnings, about one-quarter of GDP and 60% of public sector
wages. The non-oil manufacturing and construction sectors, which account for more than
20% of GDP, have expanded from processing mostly agricultural products to include the
production of petrochemicals, iron, steel, and aluminium. Climatic conditions and poor soils
severely limit agricultural output, and thus Libya imports about 75% of food. The main
exports are crude oil, refined petroleum products, natural gas and chemicals. As at end 2006,
Libya’s main destination of its exports were Italy (37.1%), Germany (14.6%), Spain (7.7%),
US (6.1%), France (5.6%) and Turkey (5.4%). In 2006 also, Libya’s sources of imports were:
Italy (18.9%), Germany (7.8%), China (7.6%), Tunisia (6.3%), France (5.8%), Turkey
(5.3%), US (4.7%), South Korea (4.3%) and UK (4%). Libya’s current account surpluses
have generally improved in tandem with expansion in real GDP (Figure 7.4)
Figure 7.4: Libya's Current Account and Real GDP
30000
25000
20000
US$ million
15000
10000
5000
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
-5000
Current account
GDP
Ethiopia's economy is based on agriculture, which accounts for almost half of GDP,
60% of exports, and 80% of total employment. Coffee is the main export commodity. The
other export commodities are at, gold, leather products, live animals and oilseeds. In 2006, the
main destinations of these exports were China (11%), Germany (9.1%), Japan (7.8%), US
(7.1%), Saudi Arabia (6.1%), Djibouti (6%) and Italy (5.2%). The main sources of Ethiopia’s
imports in 2006 were Saudi Arabia (18.1%), China (11.4%), India (8.1%) and Italy (5.1%).
244
During the first half of 1990s, Ethiopia current account was in surplus but generally
recorded deficit since 1997 (Figure 7.5). Real GDP has been generally on upward trend over
the study period.
Figure 7.5: Ethiopia Current account and GDP
500
12000
Current account (US$
10000
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
GDP (US$ million)
8000
-500
6000
-1000
4000
-1500
2000
-2000
0
Current Account
GDP
Since gaining independence in 1968, Mauritius has developed from a low-income,
agriculturally based economy to a middle-income diversified economy with growing
industrial, financial and tourist sectors. For most of the period, annual growth has been in the
order of 5% to 6%. Sugarcane is grown on about 90% of the cultivated land area and accounts
for 25% of export earnings. Expansion of local financial institutions and domestic information
telecommunications industry, have also been responsible for the rise in the growth of the
economy. Mauritius has attracted more than 9,000 offshore entities, many aimed at commerce
in India and South Africa, and investment in the banking sector alone has reached over $1
billion. Mauritius also has a strong textile sector that has been well poised to take advantage
of the Africa Growth and Opportunity Act (AGOA). Figure 7.6 shows a sizeable current
account deficit, due to heightened import demand against stagnating exports.
Figure 7.6: Mauritius Current account, GDP and REER
20000
160
Current a/c & GDP, US$ million
140
REER Index (2000=100)
15000
120
100
10000
80
5000
60
40
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
20
-5000
0
Current account
GDP
REER
Agriculture is the most important sector of Uganda’s economy, employing over 80%
of the work force. Coffee accounts for the bulk of export revenues. Since 1986, the
245
government, with the support of foreign countries and international agencies, has acted to
rehabilitate and stabilize the economy by, among others, undertaking macroeconomic and
structural reforms. In 2000, Uganda qualified for enhanced HIPC debt relief worth $1.3
billion and Paris Club debt relief worth $145 million. These amounts combined with the
original HIPC debt relief added up to about $2 billion. Growth for 2001-02 was solid, despite
continued decline in the price of coffee, Uganda's principal export.
Uganda’s REER depreciated generally from 1993 to 1997, it thereafter appreciated
until 2004 when it recorded depreciation (Figure 7.7). Real GDP improved from 1993 to
1997. It then declined up to 2001 and thereafter expanded generally. Deterioration in current
account deficit throughout most of the period under review was registered. Worsening current
account deficit due to unfavourable trade balance as imports grew at faster rate than exports.
Rising import bill was mainly on account of growth in private sector imports for capital and
consumer goods such as petroleum products, iron and steel, mineral fuels, electrical
machinery, pharmaceutical products and sugars. The petroleum products’ import bill
increased on account of the increase in local demand for oil arising from hydro electricity
power shortage and rising international oil prices.
Figure 7.7: Uganda Current Account, GDP and REER
9000
140
8000
120
Current
a/c and GDP, US$ million
7000
6000
100
REER (2000=100)
5000
80
4000
3000
60
2000
40
1000
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
20
-1000
-2000
0
Current account
GDP
REER
Agriculture in Zambia accounts for 80% of total employment and 20% of total GDP.
Important crops in the country are maize, soybean, cotton, sugar and sunflower. Both the services
sectors as well as industrial sector make the highest contribution to the country's total GDP. Copper
and agricultural exports are the major export earnings. Following the privatization of governmentowned copper mines, copper output has increased steadily since 2004, due to higher copper prices and
the opening of new mines.
Figure 7.8 below shows the evolution of REER, GDP and current account balance of Zambia.
The REER generally depreciated in 2006 and thereafter recorded an appreciation. Real GDP
deteriorated since 1993 to 2003 and thereafter generally improved. Zambia recorded widening current
account deficit throughout most of the period. The current account balance which was in surplus in
2006 turned into deficit in 2007 due to narrowing trade surplus and deficit in the service account. The
favourable trade balance was due to increase exports earning from copper due to high global copper
price and increase in non-metal exports.
246
Figure 7.8: Zambia Current Account, GDP and REER
Current a/c and Real GDP (US$ million)
30000
200
180
25000
REER (2000=100)
160
20000
140
120
15000
100
10000
80
60
5000
40
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
-5000
2004
2005
2006
2007
20
0
Current Account
GDP
REER
Rwanda economy is predominately agricultural, with about 85% of the population living in
rural areas. Agriculture accounts for about one-third of GDP and four-fifth of export revenues.
Tobacco accounts for more than half of the export earnings. Rwanda’s other main exports are cotton,
rice, coffee and maize. The main imports include petroleum products, iron and steel, cement,
machinery and transport equipments. The country has experienced both trade deficit as well as
balance of payment deficit over years.
In 2007, levels of exports and imports of the country were as follows. Exports amounted to
$145 million mainly from exports of tea, coffee, coltan, cassiterite, hides, iron ore, and tin.
Agribusiness accounted for 36.2% of Rwanda's GDP ($2.8 billion) and 40.2% of exports. Its major
exports partners are Spain, Belgium, Germany, China and Brazil. Imports were worth $488 million
with foodstuffs, machinery and equipment, steel, petroleum products, cement, and construction
material being the main products. Kenya, Germany, Belgium, US, France, Uganda, and Israel are the
major suppliers of imports to Burundi.
The agriculture sector that includes fishing and forestry is the mainstay of the Madagascar
economy, accounting for 34% of GDP and contributing more than 70% to export earnings.
Madagascar’s main export commodities are coffee (45%) and vanilla (20%). The others are cloves,
shellfish, sugar, petroleum products. The main destinations of Madagascar’s exports are France
(40%), United States (9%), Germany (8%), Japan (6%) and United Kingdom (6%). Madagascar’s
main import commodities are: intermediate manufactures (30%), capital goods (28%), petroleum
(15%), consumer goods (14%) and food (13%). The main sources of these imports are France (39%),
Hong Kong (5%) and Japan (5%0. The others are the People's Republic of China and Singapore.
Shown in Figure 7.9, Madagascar’s real GDP generally deteriorated since 1993. Madagascar’s
current account deficit generally remained stable since 1993 to 2000 with improvements thereafter.
Figure 7.9: Madagascar’s Current Account and GDP
247
Current account (US$ million)
12000
0
-100
10000
GDP (US$
million)
-200
8000
-300
6000
-400
4000
-500
2000
-600
0
-700
1993
1994
1995
1996
1997
1998
GDP
1999
2000
2001
2002
2003
2004
2005
Current account
Swaziland is a small, landlocked economy whose subsistence agriculture occupies
more than 80% of the population. It has a diversified manufacturing sector. Mining has
declined in importance in recent years with only coal and quarry stone mines remaining
active. The main exports commodities are soft drink concentrates, sugar, wood pulp, cotton
yarn, refrigerators, citrus and canned fruit. The main destinations of these exports are South
Africa (60%), EU (9%), US (9%) and Mozambique (6%). The main imports commodities are
motor vehicles, machinery, transport equipment, foodstuffs, petroleum products and
chemicals. Its main sources of imports are: South Africa (95%), EU (1%), Japan (1%) and
Singapore (0.3%).
Agricultural production remains Sudan's most important sector, employing 80% of
the work force and contributing 35% of GDP. Sudan's economy is booming on the back of
increases in oil production, high oil prices, and large inflows of foreign direct investment.
GDP growth registered more than 10% per year in 2006 and 2007.
The main exports commodities are oil and petroleum products; cotton, sesame,
livestock, groundnuts, gum Arabic and sugar with the main destinations being China (67.8%),
Japan (19%) and South Korea (2%). The main imports commodities are foodstuffs,
manufactured goods, refinery and transport equipment, medicines and chemicals, textiles and
wheat. The main sources of these imports are China (27.9%), Saudi Arabia (7.5%), India
(6.3%), Egypt (5.6%), UAE (5.5%) and Japan (4.2%).
7.3
Literature Review
What is commonly used in the literature to explain the impact of exchange rate changes on
the current account are the elasticity and absorption approaches to balance of payments.
7.3.1
Elasticity Approach
According to the elasticity approach, the exchange rate is determined in the process of
balancing the value of the imports and exports. Assuming flexible exchange rate, a current account deficit
will lead to the depreciation in the exchange rate. Because depreciation in the currency makes imports
more expensive and exports cheaper, exports and imports will respectively rise until the current account
is balanced. The speed of adjustment, however, depends on how responsive exports and imports are to
exchange rate. Since this approach stresses trade or the flow of goods in the determination of exchange
rates, it is more useful in explaining exchange rate in the long run than in the short run.
7.3.2
Absorption Approach
The Absorption Approach is based on the idea that the current account is equivalent to the
difference between national income and domestic absorption arising from private and public
consumption and investment. The approach states that devaluation affects the current account directly
through its effects on real income and absorption and indirectly on the income elasticity of absorption.
248
In contrast to the elasticity approach, the absorption approach views external balance via national
income accounting
What is the impact of devaluation according to this approach? When unemployed resources
exist, devaluation increases exports and decreases imports. This in turn causes an increase in
production (income) through the multiplier mechanism. If total expenditure rises by a smaller amount,
there will be an improvement in the balance of trade. In this sense, devaluation not only aids the
balance of payments, but also helps the economy move towards full employment. However, under
conditions of full employment, devaluation cannot be expected to produce, by itself, the desired extent
of change in the overall balance. Appendix 1 provides an illustration of the effects of exchange rate
depreciation on the current account of the Balance of Payments.
7.3.3 Effect of RER Volatility on Trade and Investment
Given the erratic pattern of the exchange rates in most developing countries following
devaluation and subsequent adoption of floating exchange rate regimes, there has been increasing
concern about the possible effects of exchange rate volatility on trade. Consequently, two general
theoretical schools of thought have come up that attempt to explain the effect of exchange rate
volatility on international trade and investment. The first is the traditional school, while the second is
the risk-portfolio school.
The traditional school holds the position that higher volatility increases risk. It considers risk
averse exporters/investors who consider the REER as the source of uncertainty. Thus, a rise in
exchange rate volatility increases the uncertainty of profits on contracts denominated in a foreign
currency because this risk leads risk-averse to redirect their activity from higher risk foreign markets
to the lower risk home market. Two assumptions are crucial for the volatility of the REER to affect
the exporting/investing decision. One is that there is no perfect hedging, meaning that access to
exchange rate forward market would reduce the effect. The other is that exporters have to be very risk
averse. Thus, the school focuses on firm behaviour and presumes that increased REER volatility
would increase the uncertainty of profits on contracts denominated in a foreign currency and would
therefore reduce international trade to levels lower than would otherwise exist if uncertainty were
removed. This uncertainty of profits, or risk, would lead risk-averse and risk-neutral agents to redirect
their activity from higher-risk foreign markets to the lower risk home market. Examples of theoretical
studies that belong to this school include Côté (1994), Clark (1973), Hooper and Kohlhagen (1978).
The risk-portfolio school maintains the belief that higher risk presents greater opportunity for
profit, and should increase trade and investment. This school is not a unified body of thought, but
rather comprises multiple theories, varying in complexity, but united in the opinion of the traditional
school as unrealistic. The school argues that one reason why the traditional school holds the view that
trade may be adversely affected by exchange rate volatility stems from the fact that they assume that
the firm cannot alter factor inputs in order to adjust optimally to take account of movements in
exchange rates. When this assumption is relaxed and firms can adjust one or more factors of
production in response to movements in exchange rates, increased variability can in fact create profitmaking opportunities. Thus, higher exchange rate volatility and thus higher risk represents greater
opportunity for profit and might increase trade according to this risk-portfolio school of thought. Two
important theoretical papers from the risk-portfolio school are those of Broll and Eckwert (1999) and
Dellas and Zilberfarb (1993). Both studies argued that due to the convexity of the profit function,
exporters’ return from favourable exchange rate movements and the accompanying increased output
outstrip the decreased profits associated with adverse exchange rates and decreased output. The
studies pointed out that although the convexity of the profit function may imply a positive correlation
between trade and exchange rate risk, the more prominent tenet of the risk-portfolio school examines
exchange rate risk in light of modern portfolio diversification theory.
Farrell et al. (1983), Canzoneri et al. (1984) and Gros (1987) are other examples of studies that
are classified as those belonging to the risk portfolio school. The studies observe that the effect of
REER volatility depends on the interaction of two forces at work. First, if the firm can adjust inputs to
both high and low prices, its profits will be larger with greater exchange rate variability. This is
249
because the firm will sell more when the price is high, and sells less when the price is low. Second, to
the extent that there is risk aversion, the higher variance of profits has an adverse effect on the firm
and constitutes a disincentive to produce and to export. If risk aversion is relatively low, the positive
effect of greater price variability on expected profits outweighs the negative impact of the higher
variability of profits, and the firm will raise the average capital stock and the level of output and
exports. It also argued that under certain conditions, increased price variability can result in increased
average investment and output as the firm adjusts to take advantage of high prices and to minimize the
impact of low prices.
7.3.4
Empirical Studies on Effects of REER Volatility on Trade
The empirical evidence from studies undertaken in developed economies especially that of
the USA and other major industrial countries, generally supports the view that REER volatility has
significant and negative effect on the volume of trade. These studies include Cushman (1983, 1986,
1988,), Maskus (1986), De Grauwe and De Bellefroid (1986), Bini-Smaghi (1991), Feenstra and
Kendall (1991), Kumar (1992), Kroner and Lastrapes (1993), Arize, (1995, 1997), Hassan and Tufte
(1998), Fountas and Aristotelous (2000), Anderton and Skudelny (2001), Siregar and Rajan (2004)
and Égert and Morales-Zumaquero (2005). However, a number of studies on developed countries did
not find any significant relationship between REER volatility and trade. These studies are Hooper and
Kohlhagen (1978), Perée and Steinherr (1989), Koray and Lastrapes (1989), Kroner and Lastrapes
(1993), Sukar and Hassan (2001), Clark et al. (2004) and Hondroyiannis et al. (2005). However, few
studies found a positive and statistically significant relationship between exchange rate volatility and
trade. These studies for developed countries are Asseery and Peel (1991), and Sercu and Vanhulle
(1992).
Some studies for developing economies found a negative and significant relationship between
REER volatility and trade. These include Coes (1981); Brada and Mendez (1988); Caballero and
Corbo (1989); Bah and Amusa (2003); Arize et al. (2004); Kihangire (2004) and Cameron et al.
(2005). A summary of each of these studies is provided in Appendix 4. These studies were mainly
carried out in Latin America and Africa.
Brada and Mendez (1988) is among the few studies that have analysed the relationship
between exchange rate variability and foreign trade for Latin American countries in spite of the fact
that many Latin American countries moved to a flexible exchange rate regime at some point in the
late 1970s and early 1980s. The other study is that by Arize et.al (2004) who investigated empirically
the impact of RER volatility on the export flows of eight Latin American countries over the quarterly
period 1973-1997 drew similar results.
Bah and Amusa (2003) Cameron et al. (2005) and Kihangire (2004) are those carried out in
Africa. Bah and Amusa (2003) used the Auto-Regressive Conditional Order (ARCH) and Generalized
ARCH (GARCH) models to investigate the impact of the REER volatility on South Africa's exports
to its largest, single-nation trading partner, the USA. The results indicated that volatility of the Rand's
REER exerted a significant and negative effect on exports in both the long and short-run. Cameron et
al. (2005) investigated empirically the effects of monthly exchange rate variability on Uganda’s
exports of coffee over the period 1988-2001. The results suggest that the effects of exchange rate
variability on Uganda’s exports coffee were more severe under the floating exchange rate regime,
than for the fixed exchange rate regime period. Similar results were drawn by Kihangire (2004) who
investigated the effects of exchange rate variability on Uganda’s aggregate export growth under the
floating exchange rate policy regime (1994-2001), benchmarked on the fixed exchange rate regime
(1988-1993). Studies for developing economies that have conclude that exchange rate volatility plays
no significant role in explaining trade in developing economies include Gonzaga and Terra (1997) and
Kihangire et al. (2005).
7.4
Model Framework
250
7.4.1 Export Function
Following the two-country models of international trade developed by Goldstein and Khan
(1978), the main determinants of the demand for a country’s exports are external relative prices of
exports and foreign income levels expressed as:
xdt  f (reer , y * )t
(7.1)
where xdt is the world demand for a country’s exports, reert refers to the relative price of
country’s exports defined in equation form as; reert 
pt
where p ft is the foreign
 neer t * p ft )
price, pt is the domestic price level and neer is the nominal exchange rate defined as the amount of
local currency per unit of foreign currency. yt* represents the real foreign income of a country’s
major trading partner. When the measure of exchange rate volatility (rervolt ) is added to equation
(7.1), the following function is obtained
xdt  f (reer , y * , rervol )t
(7.2).
A reduced export demand model could also be obtained from the export supply function, derived from
the assumption of profit maximization. The main determinants of structural export supplies are
domestic relative prices of exports, and domestic capacity utilization proxied by real GDP ( yt ).
Consistent with literature, we extend this theoretical framework to include exchange rate variability,
yielding a structural export supply function:
xst  f (reer , y, rervol )t
(7.3)
where xst denotes the supply of exports. Assuming equilibrium in the export markets, the
following relationship holds:
xdt  xst  xt  f (reer , y, y * , rervol )t
7.4.2
(7.4)
Import Function
There are two primary determinants of imports (Hooper and Marquezz, 1988). The first is the
domestic income variable, which measures the economic activity, and hence the purchasing
power of each trading partner. This is called the income effect. The second is the price effect,
which takes into account the relative price or terms of trade variable. There is, however, an
additional factor called the volatility effect, which captures the effect of RER volatility on the
demand for imports. When all these factors are incorporated, then we get the following importdemand function:
mt  f (reer , y, rervol )t
(7.5)
251
where mt denotes real imports, and
reer
t
the relative price serves as an indicator of external
competitiveness and is measured as the terms of trade. Model (7.4) and (7.5) were estimated
within a cointegration framework.
7.5 Empirical Approach
7.5.1
Data
To estimate the export and import functions, the following variables were needed: real
exports, real imports, real domestic income, domestic relative prices proxied by REER, real foreign
income (proxied by real GDP of respective COMESA country major trading partners) and REER
volatility.
Real exports refer to a country’s export values adjusted for inflation. Real foreign income is
defined as the sum of all the incomes (proxied by real GDP) of the major trading partners of each
COMESA country. The DTS and IFS Databases of the IMF were the source of data for real GDP of
each COMESA country trading partners. Since all the data were on an annual basis, interpolation had
to be done in order to get the monthly data using the Chow and Lin (1971) approach. In this respect,
the industrial production index published by OECD was used as a proxy for the trend of monthly
GDP. Real foreign income, which was initially measured in millions of US dollars, was converted to the
respective domestic currency using the relevant exchange rates.
The relative price, proxied by the real exchange rate, is defined as the ratio of respective
country’s exports prices (i.e. index of unit value of exports) to the world export prices (i.e. index of
unit value of world exports) all expressed in domestic currency. It serves as an indicator of the
country’s external competitiveness. Due to lack of monthly data not only on the unit value of exports
but also the unit values of world exports, the REER was used as a proxy for relative prices.
Real imports refer to imports measured in real terms (i.e. import values measured in millions of
domestic currency and adjusted for inflation). Real income of domestic economy is proxied by real
GDP. The source of real GDP data was obtained from the Surveys produced by the National Statistics
Offices of respective COMESA Countries in addition to the IFS of the IMF. Since the data obtained
was on annual basis, EViews package was used interpolate i.e. convert the annual to monthly real
GDP data.
The Terms of Trade (TOT) defined as the ratio of import prices to domestic prices of import
substitutes. Monthly data on import prices and the import substitute prices was unavailable. The TOT was
therefore used as a proxy variable. In this respect, monthly data required for this variable was that of
export prices and import prices. All the TOT data was measured as an index.
REER volatility is defined as short-term fluctuations of the REER about their longer-term
trends. In the context of this study, it was assumed that the REER uncertainty (i.e. volatility) is
generated by first order autoregressive process (See appendix 2). All the variables were in logarithmic
form.
7.5.2
Empirical Results
Burundi
Equations (7.6) and (7.7) are the estimated long-run and dynamic export and import
functions, respectively
xt  1.11 yt*  1.33 reert
(1.85)
(0.95)
 0.47 rervol
t
(0.47)
(7.6)
252
xt  0.13 xt  2  0.23 xt 3  2.05 reert  4  10.65 rervolt 5  7.63 rervolt 8
(1.69)
( 1.50)
(3.03)
( 2.40)
(1.71)
 0.17 ecmt 1  0.20
( 4.35)
(2.76)
Adj R  0.14; Se  0.75;
2
(7.7)
BG LM Test  (2)  0.00(1.00)
2
White HeteroscedasticTest  2 (12)  88.57(0.00)
JB Normality  1301.98(0.00)
Generally, the statistical fit of the model to the data was poor, as indicated by the values of adjusted
R2, which were 0.14. Also the statistical appropriateness of the dynamic models was not supported by
most of the diagnostic tests. In particular, the estimated short run model did not fulfil the conditions of
no serial correlation and homoscedasticity.
Equations (7.8) and (7.9) are the estimated long-run and dynamic import function,
respectively.
mt  2.02 yt  0.62 neert  0.97 tott  0.51 rervolt
(10.28)
(0.92)
(2.20)
(7.8)
( 0.18)
mt   0.62 mt 1  0.09 mt  4  0.66 yt  0.46 yt 1  0.07 yt  2
( 9.17)
( 1.77)
(14.82)
(7.10)
( 1.40)
0.77 reert 3  2.42 rervolt  2  1.20 tott 8  0.03 ecmt 1  0.77
(2.24)
(2.18)
(1.80)
( 4.35)
(1.01)
Adj R  0.63; Se  0.18;
2
(7.9)
BG LM Test  (2)  0.00(1.00)
2
White HeteroscedasticTest  2 (18)  88.93(0.00)
JB Normality  29.79(0.00)
The empirical results suggested that the statistical fit of the model to the data was satisfactory, as
indicated by the values of adjusted R2, which was 0.63. However, the diagnostic tests performed
showed that estimated model had mixed performance in respect of tests such as normality and other
stability tests of residuals.
Bivariate Granger causality tests were done for the REER and real GDP. The results indicate
that REER Granger causes real GDP at 5% significance level while there is no statistically significant
effect in the other direction (Table 7.3).
Table 7.3 Granger Causality Tests Test between REER and real GDP in Burundi
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
178
F-Statistic
3.11929
0.95089
Prob.
0.0467
0.3884
.
Egypt
Equations (7.10) and (7.11) describe the estimated cointegrated and dynamic models,
respectively.
253
xt  1.20 yt*  0.14 reert  1.01 rervolt
( 0.65)
(6.56)
(7.10)
(6.28)
xt  0.38 xt 1  0.17 xt  2  0.27 xt 3  0.32 xt  4  0.25 xt 5  0.12 xt 6
( 4.83)
( 2.21)
( 3.55)
( 4.28)
( 3.43)
( 1.84)
0.21 xt 7  0.17 xt 8  1.61 yt*1  1.55 yt*7  0.01 reert  0.01 reert 1
( 3.49)
( 2.96)
2.23
( 2.17)
( 2.03)
(2.06)
 0.01 reert  2  0.01 reert 5  0.02 reert 8  1.73 rervolt 1  2.05 rervolt 7
(1.64)
( 3.35)
(1.35)
( 2.91)
(7.11)
(3.53)
 0.32 ecmt 1  0.04
( 5.02)
(2.84)
Adj R 2  0.45; Se  0.15;
BG LM Test  2 (2)  1.41(0.49)
White HeteroscedasticTest  2 (18)  17.95(0.46)
JB Normality  12.77(0.002)
It should be noted that the residual diagnostic tests for Egypt’s short run export demand
model fulfilled the conditions of no serial correlation and homoscedasticity.
Egypt’s long-run and dynamic import functions are estimated as equations (7.12) and (7.13),
respectively. The statistical fit of the dynamic model was satisfactory, as indicated by the value of
adjusted R2, which was 0.45. Moreover, the statistical appropriateness of the models was supported
by most of the diagnostic tests. In particular, the conditions of no serial correlation, homoscedasticity
and normality.
mt  0.68 yt  0.29 reert  0.45 rervolt  2.94
(2.48)
(1.18)
(7.12)
(2.08)
mt  0.67 mt 1  0.65 mt  2  0.32 mt 3  0.13 mt  4  1.52 yt 1  0.62 yt  2  0.37 yt 7
( 5.75)
(6.74)
( 3.22)
( 1.69)
( 2.20)
(0.89)
( 0.56)
 0.01 reert 5  0.01 reert 6  0.01 reert 8  0.85 rervolt  0.53 rervolt 1  1.20 rervolt  4
( 2.44)
(1.68)
( 1.72)
( 1.09)
(1.81)
(2.40)
 1.21 rervolt 5  0.56 rervolt 7  0.17 ecmt 1  0.04
( 2.41)
(1.10)
( 2.32)
(2.48)
Adj R  0.51; Se  0.13;
2
(7.13)
BG LM Test  (2)  3.51(0.17)
2
White HeteroscedasticTest  2 (16)  14.85(0.56)
JB Normality  2.27(0.32)
The bivariate Granger causality tests indicate that REER Granger causes real GDP at 5%
significance level. The results also indicate that GDP Granger causes REER at 5% significance level
(Table 7.4).
Table 7.4: Causality Tests Test Between REER and real GDP in Egypt
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
166
254
F-Statistic
1.01526
0.32258
Prob.
0.3646
0.7247
Kenya
Equation(7.14) is the estimated long-run export function.
xt  1.61 yt*  1.66 neert  3.34 rervolt  15.36
(12.74)
(7.14)
( 2.59)
(4.31)
Kenya’s short run export demand model is presented as equation(7.15). The estimated model fulfilled
the conditions of no serial correlation and homoscedasticity.
xt  0.23 xt 1  0.15 xt  2  0.10 xt 7  0.09 xt 10  0.16 xt 11  0.17 xt 12
( 3.53)
( 2.28)
( 1.76)
( 1.41)
(2.42)
(2.54)
 0.23 y  0.68 reert  0.74 reert 3  0.56 reert 9  0.43 reert 12  1.64 rervolt  4
*
t
(3.84)
( 3.09)
(2.53)
( 2.55)
( 1.85)
(1.12)
 2.06 rervolt 10  1.94 rervolt 11  0.96 rervolt 12  0.20 ecmt 1  9.41
( 2.07)
( 1.10)
(1.80)
( 6.17)
(0.84)
Adj R  0.42; Se  0.09;
2
BG LM Test  2 (2)  0.00(1.00)
White HeteroscedasticTest  2 (32)  37.64(0.23)
JB Normality  10.87(0.004)
The respective import functions are equations (7.16) and (7.17).
mt  1.61 yt  0.74 reert  1.45 rervolt  0.47 tott 11.58
(3.33)
( 1.95)
( 1.54)
(7.16)
( 3.62)
mt  0.20 mt  2  0.09 mt 5  0.10 mt 6  0.13 mt 7  0.05 mt 8  0.93 yt 3
( 1.16)
(3.14)
( 3.22)
( 1.40)
(0.64)
( 2.11)
 0.38 yt 7  0.30 reert 1  0.31 reert  2  0.62 reert 6  0.53 tott 6  1.09 rervolt
(0.92)
(0.83)
(0.86)
(1.83)
(3.41)
( 0.47)
 4.64 rervolt 5  4.55 rervolt 6  0.46 ecmt 1  10.04
( 2.66)
(3.26)
( 7.22)
(0.14)
Adj R 2  0.41; Se  0.14;
(7.17)
BG LM Test  (2)  0,.00(1.00)
2
White HeteroscedasticTest  2 (34)  28.04(0.75)
JB Normality  49.16(0.00)
The results indicate that a satisfactory statistical fit of the dynamic model as indicated by the value of
adjusted R2, which were 0.41. The estimated model also fulfilled the conditions of no serial
correlation, homoscedasticity. Table 7.5 suggests that REER in Kenya Granger causes real GDP at
5% significance level. The results however indicate that GDP does not Granger cause REER at 5%
significance level.
Table 7.5 Granger Causality Tests Test between REER and real GDP in Kenya
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
178
255
F-Statistic
0.21927
5.58849
Prob.
0.8033
0.0044
(7.15)
Malawi
Equation (7.18) is the long-run export function.
xt  0.16 yt*  0.70 reert  0.12 rervolt  3.78
( 2.03)
(2.05)
(7.18)
( 0.17)
The dynamic behaviour of the export function is presented in equation (7.19)
xt  0.11 xt 1  0.13 xt 3  1.86 yt*1  3.23 yt*3  1.95 yt*5  3.23 yt*6
(1.74)
(2;02)
(2.35)
(3.76)
( 3.89)
(2.15)
 1.63 reert 1  2.51 reert 3  1.72 reert 5  1.97 reert 12
(2.21)
(3.19)
(7.19)
( 2.23)
(1.95)
 3.66 rervolt 5  0.66 ecmt 1  1.21
( 3.02)
( 9.63)
( 0.14)
Adj R  0.428; Se  0.37
2
BG LM Test  2 (2)  2.05(0.36)
White HeteroscedasticTest  2 (12)  14.14(0.29)
JB Normality  40.50(0.00)
The estimated import functions (7.20) and (7.21) are for the long-run and short-run,
respectively.
mt  1.80 yt  1.05 reert  1.17 rervolt  0.92 tott 10.81
( 1.94)
(1.93)
(0.87)
(7.20)
(1.02)
mt  0.17 mt 3  0.25 mt  4  1.31 reert 1  1.78 reert  4  4.32 tott 6
(2.53)
(3.78)
(2.24)
(2.89)
(1.93)
(7.21)
 2.99 rervolt  4  0.63 ecmt 1  13.79
( 2.45)
( 9.74)
( 9.74)
Adj R  0.36; Se  0.42;
2
BG LM Test  2 (2)  3.50(0.17)
White HeteroscedasticTest  2 (11)  8.60(0.66)
JB Normality  1.90(0.39)
The results indicate a satisfactory statistical fit of the dynamic model. The estimated model also
fulfilled the conditions of no serial correlation and homoscedasticity.
The bivariate Granger causality tests were done for the REER and real GDP. The results
indicate that REER in Malawi Granger causes real GDP at 5% significance level. The results,
however, indicate that GDP does not Granger cause REER at 10% significance level (Table 7.6).
Table 7.6: Pairwise Granger Causality Tests Test between REER and real GDP in Malawi
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
178
Mauritius
256
F-Statistic
1.75757
2.50168
Prob.
0.1755
0.0849
Equations (7.22) and (7.23) are the estimated export functions.
xt  1.51 yt*  1.52 reert  0.25 rervolt  14.87
(2.62)
(2.70)
(7.22)
(0.44)
xt  0.26 xt 6 0.21 xt 7  0.27 xt 8  3.30 yt*6  2.17 reert  2  2.87 reert 5
( 3.77)
( 2.71)
( 3.95)
( 1.28)
( 2.87)
( 3.09)
 4.84 reert  4  4.20 rervolt 5  4.66 rervolt 6  1.98 rervolt 7  0.60 ecmt 1  0.03
( 2.00)
( 1.93)
(1.67)
( 9.13)
(1.02)
(2.04)
Adj R  0.46; Se  0.14;
2
(7.23)
BG LM Test  2 (2)  3.50(0.17)
White HeteroscedasticTest  2 (11)  8.61(0.65)
JB Normality  1.90(0.39)
The estimated long and short run import functions are (7.24) and (7.25), respectively.
mt  1.02 yt  0.71 reert  2.54 rervolt  1.30 tott  4.03
(4.64)
( 1.52)
( 3.51)
(7.24)
( 2.02)
mt   0.41 mt 1  0.27 mt  2  0.26 mt  4  0.25 mt  4  1.55 yt 1  1.72 yt  4
( 5.27)
( 3.30)
( 3.19)
( 3.37)
(3.04)
(3.57)
 1.06 reert 5  1.03 reert 7  1.23 reert 8  3.84 tott 3  0.08 ecmt 1  1.08
( 1.60)
( 1.44)
(1.69)
( 2.38)
( 2.66)
( 10.11)
Adj R  0.32; Se  0.13;
2
(7.25)
BG LM Test  2 (2)  0.00(0.00)
White HeteroscedasticTest  2 (22)  68.03(0.76)
JB Normality  17.80(0.00)
The results of residual diagnostics for Mauritius dynamic import demand function suggested that the
statistical fit of the model to the data was satisfactory. Moreover, except for the normality, the
diagnostic tests the estimated model fulfilled the conditions of no serial correlation and
homoscedasticity.
Table 7.7 indicates that REER in Mauritius Granger causes real GDP at 5% significance
level. The results, also, indicate that the reverse is through i.e. GDP does Granger cause REER at 5%
significance level.
Table 7.7: Granger Causality Tests Test between RER and Real GDP in Mauritius
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
166
F-Statistic
0.36570
0.77993
Prob.
0.6943
0.4602
Rwanda
Equations (7.26) and (7.27) are the respective estimated long and short-run export functions.
xt  1.21 yt*  1.08 reert  2.84 rervolt  5.84
(5.11)
( 2.03)
(3.71)
257
(7.26)
xt  0.21 xt  2  0.17 xt 3  6.18 yt*8  2.83 reert  2  4.71 reert 5
(2.94)
(2.31)
( 2.84)
(1.86)
(3.11)
15.98 rervolt 3  0.69 ecmt 1  20.11
( 2.46)
( 9.13)
(0.84)
Adj R 2  0.36; Se  0.41;
(7.27)
BG LM Test  (2)  0.87(0.65)
2
White HeteroscedasticTest  2 (7)  10.24(0.17)
JB Normality  18.51(0.00)
The respective long-run and short-run import functions are equations (7.28) and (7.29).
mt  2.86 yt  0.54 reert  0.11rervolt  0.32 tott  5.84
(5.76)
(0.76)
(0.03)
(7.27)
(1.63)
mt  0.20 mt 1  0.38 mt  2  0.23 mt 3  10.05 yt  2  6.51 yt 8
(2.15)
(4.33)
(3.14)
( 3.10)
( 2.62)
 2.65 reert  2  4.88 reert 5  2.24 reert 7  1.88 tott 3
(1.52)
(3.02)
( 1.30)
(1.83)
 2.17 tott 7  10.76 revolt 1  14.63 revolt 3  0.90 ecmt 1  3.09
( 1.59)
(1.94)
( 2.21)
( 8.57)
( 1.22)
Adj R  0.46; Se  0.39;
2
(7.29)
BG LM Test  2 (2)  0.25(0.88)
White HeteroscedasticTest  2 (13)  9.45(0.73)
JB Normality  50.84(0.00)
The causality results are shown in Table 7.8 suggests that REER in Rwanda Granger causes
real GDP at 5% significance level. The results, also, indicate that the reverse is through i.e. GDP does
Granger cause REER at 5% significance level but not at 10% significance level.
Table 7.8: Causality Tests Test Between REER and real GDP in Rwanda
Null Hypothesis:
y does not Granger Cause reer
reer does not Granger Cause y
Obs
141
258
F-Statistic
1.66123
2.28698
Prob.
0.1784
0.0815
7.6
Conclusion and Policy Recommendations
This study estimated export and import demand functions for selected COMESA countries
over the period 1993 to 2007. There was mixed performance in respect of the effect of REER
volatility, which was the variable of primary interest in this study, on exports in the long run. While
some results were negative and significant at the 5% level, others were positive and also significant at
the 5% level. In Kenya and Malawi, for instance, the results showed that REER volatility had a
negative and highly significant effect on exports. In Egypt and Rwanda, on the other hand, the results
showed that RER volatility had a positive and highly significant effect on exports. In Burundi, and
Mauritius, however, the results showed that REER volatility did not have a significant effect on
exports in the long run. The exchange rate risk reflected in REER volatility, however, seemed to be an
important factor in explaining export demand in all in the short run. This is because the results
showed that increases in the exchange-rate volatility exerted a highly significant and negative effect
upon export demand in the in the short-run in all the countries.
For most countries, import-demand was largely explained by real GDP (which relates to the
general level of economic activity in the country). As in the case of export demand models also, there
was mixed performance in respect of the effect of REER volatility on imports in the long run. While
the results of two countries showed that RER volatility had a significant at the 5% level on imports,
the rest showed that RER volatility had negative but insignificant effect at the 5% level of
significance in imports.
Evidence from the study also revealed mixed results on the Bivariate Granger causality and
impulse response functions in respect of effect of RER depreciation on output (real GDP). The
impulse response functions for most countries showed that RER depreciation had an expansionary
impact on the output in both medium and long terms. The opposite (contractionary impact), was,
however observed for the short-term horizon.
Based on the above results and conclusion, this study makes two policy recommendations that
would lead to minimal volatility and consequently boost trade and thus read to improved economic
growth and reduction in poverty levels. Firstly, efforts should be made in COMESA countries to
develop and deepen the derivatives market to provide for hedging instruments for managing exchange
rate risk. This would involve coming up with a range of trade finance products that make it possible
for risk-averse traders and investors to minimize their exposure to the effect of exchange rate risky.
The unpredictability of exchange rates creates uncertainty about the profits to be made and, hence,
reduces the benefits of trade and investment. Hedging instruments for managing exchange rate risk
should therefore be broadened in Kenya.
Secondly, policy makers should design an exchange rate policy that ensures that stability in
the foreign exchange market is maintained. This is because the results have shown that RER volatility
has a significant detrimental effect on trade and private investment. Thus, if trade and investment
were to significantly promote the country’s economic growth and thus help reduce poverty, it is
necessary that a stable exchange rate policy be pursued. In this respect, the monetary authority should
relook at its current intervention policy in the foreign exchange market with a view to ensure that as
much as possible, REER volatilities are minimized. This entails continued maintenance of the policy
of allowing the forces of supply and demand to determine foreign exchange rate in the market and
only intervening to deal with major instabilities but not to target a particular exchange rate.
Additionally, prudent coordination and management of fiscal and monetary policies should be
pursued as a means of promoting the achievement and maintenance of a stable macroeconomic
environment and hence reduced uncertainties for traders and investments.
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Appendix 1
Appendix 1
To illustrate the elasticity approach of exchange rate determination, we consider the effects of
depreciation of the exchange rate on the current account of the balance of payments. First, we
assume that prices of goods and services are fixed such that changes in the NER imply
corresponding changes in the RER. In other words, we assume that the supply elasticities for the
domestic export good and foreign import good are perfectly elastic, implying changes in demand
volumes have no effect on price. Thus, the current account is expressed as:
CAt  NERt * X t  M t …………………………………………………………….. (1)
Where CAt is the current account, NERt is the nominal exchange rate defined as amount of
foreign currency per unit of domestic currency, X is the value of domestic exports and M is the
value of domestic imports. When the exchange rate depreciates, implying decline in the absolute
value of NER, foreign residents find domestic goods and services less expensive, implying that X
negatively depends on the exchange rate as shown in equation 2 below:
 dX t


 0  …………………………………………………………………………. (2)
 dNERt

Thus, the price elasticity of demand for exports is defined as the percentage change in exports
over the percentage change in prices, which in this case are represented by the nominal exchange
rate, that is:
x  
dX t
Xt
dNERt
……………………………………………………..………. (3)
NERt
Similarly, when the exchange rate depreciates, domestic residents find foreign goods more
expensive, implying M depends positively on the exchange rate, that is:
 dM t


 0  …………………………..………………………….……………………(4)
 dNERt

It follows from the above also that the price elasticity of demand for imports is defined as the
percentage change in imports over the percentage change in prices, which in this case are also
represented by the NER, that is:
m 
dM t
Mt
dNERt
…………………………………………..….………………..…..(5)
NERt
Thus, the effect of the exchange rate depreciation on the current account is expressed as follows:
 dX   dM
dCAt
 X  NERt 
  
dNERt
 dNERt   dNERt
 dX   NERt
dCAt
 X  NERt 
 
dNERt
 dNERt   X

 …………….……………….…..…..(6)

 X

 NERt
  dM
  
  dNERt
  NERt
 
  M
 M

 NERt

 ..(7)

dCAt
M
 X  x X m
……………………………………………….……(8)
dNERt
NERt
If we assume that we were initially in a balanced current account where M=NER*X, then
dCAt
 X   x X   m X ……………………………….……....…………….…..(9)
dNERt
264
Dividing equation (9) by X yields the Marshall-Lerner Condition28:
dCAt 1
 (1   x   m ) ………………………………………………………..(10)
dNERt X
Thus
dCAt
 0 if  x   m  1
dNERt
…………………………………...……………………….……..………..(11)
The Marshall-Lerner Condition states that starting from equilibrium in the current account, a
depreciation of the exchange rate will improve the current account only if the sum of elasticities of
exports and imports is greater than unity (Williamson, 1985). In this exchange rate elasticity model,
price effects contribute to a worsening of the current account because imports become expensive.
Volume effects, however, tend to contribute to improvement of current account because exports
become cheaper from the foreign country’s perspective whenever the exchange rate depreciates.
Thus, in the short run, the Marshall-Lerner condition might not hold since export and import volumes
do not substantially change, hence price effects tend to dominate, thus leading to worsening of the
current account position following exchange rate depreciation. The reverse is true in the long run.
28
Named after English political economist Alfred Marshall (1842-1924) and Romanian-born
economist Abba Lerner (1905-1982). In its simplest form, Marshall-Lerner principle states that
for a currency devaluation to have a positive impact in trade balance and hence balance of
payments, the price elasticity of demand for imports and exports must be greater than unity. As
a devaluation (revaluation) of the exchange rate means a reduction (increase) on price of
exports, demand for these will increase (decrease). At the same time, price of imports will rise
(decline) and their demand diminishes (increases). The net effect on the trade balance will
depend on price elasticities. If goods exported are elastic to price, their demand will increase
proportionately more than the decrease in price, and total export revenue will increase.
Similarly, if goods imported are elastic, total import expenditure will decrease. Both will
improve the trade balance.
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Appendix 2
In the context of this study, the model assumes that the RER uncertainty (i.e. volatility) is generated
by first order autoregressive process that is specified as:
reert   0  1reert   t
where reer is the natural logarithm of REER,  0 and  1 are the parameters to be estimated and  t is
 
an error that is normally distributed with zero (0) mean and constant variance  2 . The variance of
the error term depends upon time (t). The ARCH model characterizes the way this dependence can be
captured by an autoregressive process of the form:
 2 t   0   1 2 t 1   2  2 t  2  ....   m  2 t  m
where  2 is the conditional variance of the RER,  2 t i for i=1,2,3….m denotes the squared
residuals derived from equation 19 and  i for i=0,1,…m are the parameters to be estimated. The
restriction  i  0 is meant to ensure that the predicted variance is always not a negative value. The
term  2 t i represents the ARCH and is therefore a measure of information about the RER volatility
in the previous period. This study, however, employed the GARCH which is an extension of the
ARCH model in which the variance is given by:
 2 t   0  1 2 t 1   2 2 t 2  ....   k  2 t k  1 2 t 1   2 2 t 2  ...   m 2 t m
where  2 t  j for j=1, 2…k is the GARCH term representing the last period’s forecast
variance. GARCH (1,1) is the simplest specification in this class and is the most widely used
specification. Thus, the GARCH (1, 1) model is given by:
 2 t   0   1 2 t 1   1 2 t 1
This equation was employed as the GARCH process to capture the RER volatility in the respective
selected COMESA countries.
266