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IMPACT OF THE HIPC DEBT INITIATIVE ON MACRO ECONOMIC
VARIABLES: THE CASE OF UGANDA
Michael Atingi-Ego
Bank of Uganda
Ego@bou.or.ug
ABSTRACT
Following its consistency in pursuing sound macro-economic policies and
commitment to structural reforms, Uganda become the first country to qualify for
debt relief under the HIPC Initiative in 1998. As a result, the government deficit
as a percentage of GDP increased from 6.7 to 12.8 in 1997/98 and 2001/2
respectively as the government expenditure on poverty reducing sectors more than
doubled. As a result of the additional liquidity injection created from this process,
the conduct of monetary policy became complicated with real interest rate rising
and real exchange rate appreciating. From our empirical analysis, we see that the
increase of government expenditure and foreign inflows by one percent saw the
real exchange rate appreciate by 0.24 and 0.2 percent respectively. On the other
hand, a 1% appreciation of real exchange rate has led to deterioration of noncoffee export performance by 4 percent. As developing countries therefore look
forward to accessing Millennium Development Goals resources, the other side of
this assistance should carefully be looked into as it could have several implications
for the long-run sustainability of the private and external sectors of their
economies.
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IMPACT OF THE HIPC DEBT INITIATIVE ON MACRO ECONOMIC
VARIABLES: THE CASE OF UGANDA.
1.0
Introduction
In 1996, the World Bank and IMF conceived a multilateral mechanism for extending
debt relief to the Heavily Indebted Poor Countries (HIPC). The primary objective of
this initiative is to reduce the poor countries debt burdens to sustainable levels in order
to enable them have more resources to implement polices required to overcome
constraints to sustainable economic growth. A key condition for obtaining the HIPC
debt relief is that a country has to sustain strong macroeconomic performance and must
have put together a comprehensive framework for structural and social reforms. In
particular a country qualifying for debt relief must have comprehensive policies towards
poverty alleviation through social sector reforms for which the saving made available
from debt relief will be used. Therefore Uganda’s consistency in pursuing sound
macroeconomic policies and its commitment to structural reforms enabled it to become
the first country to qualify for debt relief under the HIPC Initiative in 1998.
Prior to the HIPC debt initiative, Uganda implemented several strategies to reduce its
debt burden. These strategies have included among others, borrowing on concessional
terms, rescheduling with the Paris Club creditors during which a total of US$ 184 million
was rescheduled. Between 1981 and 1987, the Uganda government negotiated debt
rescheduling with the Paris Club creditors during which a total of US$ 184 million was
rescheduled. Uganda also gained access to the Enhanced Toronto terms in January 1989,
under which about 30% of the eligible maturities were written off, and the maturity of
the remaining debt was reduced to 14 years with an 8 year grace period. A further write
off of 50% of the outstanding maturities was obtained under the June 1992 Paris Club
agreement. In July 1993, using the debt reduction facility of the World Bank, Uganda also
bought back US$ 153million of commercial debt at substantial discount. Uganda has
also benefited from other write-off and cancellations granted by the Paris Club countries,
which led to bulk of its debt to the OECD being written-off. In 1995, Uganda also
received additional debt relief from the bilateral donors, via the Multilateral Debt Fund
(MDF) to help service multilateral debt obligations.
In spite of the above efforts, the external debt indicators as indicated in appendix table 2
continued to deteriorate as the country accumulated more debt to finance development
programmes in the 1990s. By the end of 1995/96, Uganda remained heavily indebted
with the external debt stock amounting to US$ 3.5 billion while the stock arrears of
neared US$ 250 million. Multilateral debt as a percentage of debt stock was high
averaging 77% in 1997/98. The ratio of debt to export of goods also remained high
although it declined from 1526.6% in 1992/93 to 535.5% in 1996/97. The Multilateral
Development Fund and HIPC were set up to substantially address the issue of
multilateral debt in order to give substantial relief to countries that were pursuing
prudent macroeconomic policies and to further sustain growth in these economies.
Although it is still in its early stages, the HIPC debt initiative has helped bridge the
resource gap in Uganda and has thus helped increase government expenditure on poverty
reducing programmes. HIPC has therefore released resources that would have otherwise
been used for debt service for investment in poverty reducing social sector programmes.
To date, these resources, which are channeled through the Poverty Action Fund (PAF),
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are an integral part of the Medium Term Expenditure Framework (MTEF), which
comprises a rolling three year plan for the Government of Uganda.
In light of the above, this paper attempts to qualitatively and quantitatively assess the
extent to which the HIPC debt initiative through increased budgetary provisions has
impacted on macro economic variables in Uganda. The paper is structured as follows:
Section 2 discusses HIPC Initiative in Uganda, use of freed resources and mechanism
used to disburse these resources. Section 3 then discusses the impact of increased
budgetary provisions as a result of HIPC on macro economic variables. Section 4 gives
empirical results of the impact of increased government expenditure on some
macroeconomic variables such as the exchange rate and interest rates and their impact on
the private sector activities.
2.0
The HIPC Debt Relief Initiative
As earlier mentioned, in 1996 the World Bank and the IMF conceived the HIPC
initiative as a multi-lateral mechanism for extending debt relief to the heavily indebted
poor countries that were pursuing prudent macro economic polices. The key guiding
principles of the initiative included additionality of debt relief, maintenance of the
financial integrity of the multilateral financial institutions and burden sharing on a fair
and equitable basis. Uganda’s consistency in pursuing sound polices and its commitment
to structural reforms enabled her to become the first country to qualify for debt relief
under the HIPC initiative in April 1998. Relief was based on the Net Present Value
(NPV) of debt to exports with the sustainable level of debt assessed at 202% of exports.
To reach this level, the amount of relief required was US$ 347 million in NPV terms,
which is equivalent to total saving of US$ 650 million, spread over a thirty year period. In
the first year, 1998/99, Uganda received US$ 45 million in debt relief and US$ 40 million
was expected to be received annually for three successive years.
The first HIPC was expected to provide an “exit strategy” from the debt rescheduling
process or, leave beneficiary countries with sustainable debt levels. Post-HIPC, a country
would be expected to service its debt and still be able to direct sufficient resources to
poverty reduction. However, because of some unforeseeable factors such as the El Nino
weather phenomenon, changes in global interest rates and a decline in international
commodity prices, it was difficult for Uganda to have post-HIPC sustainable debt levels.
Other heavily indebted poor countries also faced varying calamities, which prevented
them from achieving debt reductions to sustainable debt levels. The HIPC Debt relief
Initiative was then reformed (hence the Enhanced HIPC) to enable it benefit more
countries and deliver deeper debt relief more quickly. The key elements of the Enhanced
HIPC initiative are:
•
•
•
•
Lowering the debt sustainability thresholds to a Net Present Value (NPV) of Debt at
no more that 150% of exports of goods and services. This is to provide greater safety
cushion and increased prospects for permanent exit from unsustainable debt.
Faster debt relief starting from decision points
Floating completion points; and
A strong link between debt relief and poverty reduction
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In order to meet the last element of the Enhanced HIPC package, countries are expected
to prepare national poverty reduction strategies. Uganda did accomplish this, and the
international financial institutions and the donor community, accepted her strategy as a
viable and comprehensive one. Uganda then became the first country to benefit from the
enhanced HIPC initiative in April 2000. Annual debt relief is estimated at US$ 55 million
in addition to the US$ 40 million from the original initiative. Table 1 summarises the
Debt relief under the first HIPC and the enhanced HIPC initiative.
Table 1
A. HIPC Initiative
Multilateral
Bilateral
B.
Enhanced
HIPC
Initiative
Multilateral
Bilateral
Total Debt relief
Debt Relief Under the HIPC Initiative
Nominal Debt
Service Relief
Million US$
650
1,300
HIPC Debt Relief,
NPV Terms (Million
of US$)
347
274
73
656
1,950
546
110
1,003
Completion
Point
April 1998
May 2000
Use of Debt Relief
Resources from both the HIPC and the Enhanced HIPC are channeled to poverty
reduction programmes through the Poverty Action Fund (PAF). At the advent of HIPC
debt initiative, the Uganda Government established a Poverty Action Fund (PAF)
through which savings made from debt relief would be channeled to finance poverty
reduction programmes in accordance with the Poverty Eradication Action Plan (PEAP)
and Poverty Reduction Strategy Paper (PRSP) priorities. Although the original intention
was to create transparent mechanisms for ensuring that the resources saved from the
HIPC initiative are channeled fully to poverty eradication programmes, the PAF has
attracted additional donor support for poverty reducing programmes over and above the
regular programmes of some donors. HIPC debt relief funds one third of the total
resources in PAF.
The PAF resources are spent on PEAP/PRSP priority programmes such as primary
education, primary health care, agriculture extension, rural road programmes, rural water
supply and sanitation services and enhanced accountability. The share of the PAF in total
spending rose rapidly from 17% in 1997/98 to 31% in 2000/2001. Government has
pledged to continue increasing this share over time.
All PAF expenditures are an integral part if the governments budget, as envisaged by the
PEAP/PRSP, which provides a framework for sector wide plans and investment
programmes, which in turn are translated into actual budgets as contained in the Medium
Term Expenditure Framework (MTEF). The contribution of savings from HIPC debt
relief to the PEAP/PRSP priority spending on the provision of social services and
support to activities aimed at enhancing the ability of the poor to increase their incomes,
channeled through PAF are shown in Table 2
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Table 2:
PEAP Expenditures (Billions of Ushs.)
Total Expenditure (Excluding arrears)
Of which:
PAF
HIPC
% Contribution of PAF to Total
expenditure
% Contribution of HIPC to total
expenditure
% Contribution of HIPC to PAF
resources
Measures to increase incomes
Rural Roads
Implementation of the Land Act
Agriculture Extension
Micro
Finance/
Restocking
Programmes
Measures to Improve Quality of Life
Primary Health Care
Water and Sanitation
Primary Education
Other
District Grants
Monitoring and Accountability
Exchange rate UShs per US$
1997/98
141.38
1998/99
231.84
1999/00
302.69
2000/01
439.8
2001/02
624.61
97.84
44.64
42.2
193.98
65.55
64.1
298.8
159.16
67.9
400.4
127.3
64.1
19.3
21.7
36.2
20.4
45.6
33.8
53.3
31.8
8.96
8.42
20.39
20.18
0.54
0.47
0.21
0.49
31.78
24.58
2.7
4.49
7.29
35.79
28.67
3.0
4.12
11.64
65.63
42.7
7.93
15.6
12.54
128.25
4.16
3.86
120.23
203.28
20.24
12.34
169.83
0.87
3.7
1150
7.68
1362
251.06
21.42
17.57
211.57
0.5
2.0
10.57
1512
349.96
57.17
35.33
254.66
2.8
24.71
17.67
1762
459.46
104.43
53.13
293.7
8.2
53.28
26.01
1773
Source: Ministry of Finance, Planning and Economic Development
On average HIPC relief constituted 25% of total expenditure over the period 1998/99 to
2001/02 and 42% of PAF resources over the same period as indicated in Table 2.
It is therefore clearly evident that the savings from the HIPC debt relief have significantly
supplemented budgetary resources and have enabled the Ugandan Government to
increase expenditure in poverty reducing sectors and this has contributed significantly to
the poverty reduction efforts. Over time the PAF expenditures have been rising and
estimates for 2001/02 are put at UShs.609 billion (approximately US$ 330 million).
3.0
Increased Government Expenditure and Impact on Macro Economic Variables
The increase in government expenditure and the associated fiscal deficit has become a
contentious issue in Uganda. The increased expenditure as a result of the HIPC initiative
has been associated to an increasing fiscal deficit. In a paper “Is Budget Deficit
Reduction Necessary”1 six measures of the deficit were highlighted including:
• Overall deficit (including grants), defined as total revenue plus grants minus
government expenditure which rose rapidly from 1.4% of GDP in 1997/98 to 5.7%
of GDP in 2001/02
• Primary deficit similar to overall deficit (including grants) but excludes domestic and
external interest payments from the computation of government expenditure which
rose from 0.26% in 1998/99 of GDP to 4.6% in 2001/2002
1
Martin Brownbridge (2000)
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•
•
•
•
Overall deficit (excluding grants) is defined as domestic revenue minus expenditures
and is a key deficit measure for Uganda rose from 6-7% of GDP but began to rise
rapidly in 1998/99 and stood at 13% of GDP in 2001/02
Current deficit is current revenue minus current expenditure this deficit increased to
1.7% of GDP in 2001/2 which indicates that the domestic revenues could not even
cover governments current expenditures.
GOU Budget Deficit excludes the externally financed development expenditures and
is defined as domestic revenue minus the GOU budget. This was less around 1 % of
GDP between 1994/95 and 1997/98, but then increased to 7.3% of GDP in 2001/2
Domestic deficit is defined as the GOU deficit excluding the interest payments on
external debt and government imports financed directly through the BOU but
including arrears and PN payments. This deficit was less than 1% of GDP between
1993/94 and 1998/99. but then began to rise rapidly to 6.2% of GDP in 2001/2.
An analysis of all six measures revealed that the fiscal deficit in Uganda has enlarged and the
increase is largely attributable to the increased availability of donor aid, mainly in form of
budget support, including debt relief, which has been used to expand the GOU budget.
4.0 Implications of Increased Government Spending on Macro economic variables
The increase in Government spending since HIPC became operational in the late 1990’s and the
associated rise in the fiscal deficit, certainly generated some important benefits in that debt relief
had enabled GOU to increase expenditure in poverty reducing sectors and this has contributed
significantly to the poverty reduction efforts. However it also created macro economic problems.
This was because higher Government spending, even if funded by donor aid, creates liquidity in
the domestic economy. This liquidity must be mopped up – the technical term is sterilized – by
the Central Bank if it is not to lead to large inflationary money supply increases.
As shown in Appendix table 1, the fiscal deficit almost doubled between 1997/98 and 2001/02.
The widening of the fiscal deficit led to tenfold increase in the liquidity created by Government’s
fiscal operations – that is Government expenditures in the domestic economy minus domestic
revenues. Liquidity created by programmed fiscal operations increased from Ushs.68 billion in
1998/9 to Ushs.664 billion in 2001/2. This liquidity had to be mopped up by BOU if it was not
to cause inflation using a combination of foreign exchange sales to the Inter-bank Foreign
Exchange Market (IFEM) and net issued of treasury bills.
Net foreign exchange sales also increased very rapidly over the last five years rising from US$ 5
million in 1998/99 to the programmed level of almost $254 million in 2001/2. However, actual
foreign exchange sales were much lower at US$198 million, partly because of concerns by the
BOU over the level of foreign reserves in the face of sharply lower than programmed
disbursements of budget support, and because of concerns for the stability of the exchange rate
and potential exchange rate appreciation. With foreign exchange sales being lower than
programmed, TB sales had to increase to control the rise in base money. At the margin almost all
additional TB issues have to be held by the commercial banks because non-bank demand for
TB’s is very limited. The net issuance of Treasury Bills climbed very quickly from the late 1990’s
from Ushs.37 billion in 1997/98 and by 2001/2, the net issuance of Treasury Bills increased to a
massive Shs.268 billion leading to volatile treasury bill interest rates.
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Sales of foreign exchange and treasury bills in order to mop up excess liquidity caused by
increased government expenditure have implications on macroeconomic variables and these are
as follows;
1. Increased foreign exchange sales have inevitably appreciated the real exchange rate. This
in turn has damaged the competitiveness of private sector exporters, which is crucial for
boosting economic growth. As shown in appendix Table 1, the RER appreciated as sales
of foreign exchange in the market increased. In addition, examining the trends in the
price indices for the major components of GDP shows that prices for non-traded goods
in Uganda have grown much faster than prices for traded goods as depicted by appendix
Figures 1 (a) and (b). This implies that price incentives within the domestic economy
have shifted away from traded goods production and towards non-traded goods
production in the last few years.
The increased fiscal deficits since 1997/98 took place at the same time that Uganda ‘s
external terms of trade fell sharply, by 35% between 1997/98 and 2001/2, largely
because of a fall in world market coffee prices. Compared to 1997/98 prices for exports
and imports, the terms of trade shock cost Uganda around 6% of GDP in 2001/2, which
is of roughly the same magnitude as the increase in government spending funded from
increased donor aid. Therefore in terms of aggregate spending in the economy, the
external terms of trade shock acted to offset the impact of the fiscal expansion, although
the terms of trade shock and the fiscal expansion did not have symmetrical effects at the
sectoral level. The aid funded fiscal expansion led to an increased trade deficit from
10.1% of GDP in 1997/98 to 15.6% of GDP in 2001/2. Because of the terms of trade
loss however, the trade deficit measured in constant prices actually declined marginally
between 1997/98 and 2001/2.
We cannot ignore the fact that a shift in relative prices from tradable to non-tradable will
undermine the GOU objective of creating a dynamic export led economy. Private sector
led-export promotion is central to the Medium Term Competitiveness Strategy (MTCS)
and this objective should not be compromised by an excessive fiscal deficit.
Sterilization of the fiscal deficit through the sales of foreign exchange is not a sustainable
policy since it would mean that the whole economy, and not just the Government
budget, would inevitably become over-dependent on a continuous large inflow of donor
funds intermediated through the Central Bank. This will affect the structure of both
production and demand in the economy. Yet from experience, actual disbursements of
donor aid usually fall short of what has been programmed – the reality over the last few
years is that disbursements in each year have averaged less than 60% of what had been
programmed at the start of that year. The reliance on donor funds to finance activities in
Medium Term Budget Framework will be setting the economy up for a serious crisis if,
for any reason, these donor funds did not materialize in full. Moreover, this crisis would
not be confined to the Government budget, but it would spill over into the foreign
exchange markets and into the wider economy.
2. The huge increase in Treasury Bill sales has also squeezed the funds available in the
banking system for lending to the private sector – hence the fiscal deficit is directly
crowding out private sector borrowing.
Commercial Bank holdings of TB increased from 23% to 32% of their asset portfolio
between June 2000 and June 2002. This was a period in which Private Sector Credit
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(PSC) slowed down over the period 1999/00 and 2001/2 combined while bank loans to
the private sector fell from 30% to 23% of the banks asset portfolios. They may have
been other reasons independent of the widening fiscal deficit, for the stagnation of PSC
over the last two years such as weak demand for credit from credit worthy borrowers,
but it is difficult to escape the conclusion that even if demand for private sector credit
had been strong enough to support the programmed PSC growth, the banks would not
have had the resources to supply this credit because of their increased holding of TB’s
and therefore growth in PSC would have still been chocked off.
Given the difficulty in selling the programmed volume of forex to the IFEM, the only
way in which room could have been created for faster growth in PSC would be through a
tighter fiscal stance, which would have allowed net TB issues to be lower, thereby filling
up resources in the banks asset portfolios for more lending to the private sector.
In the next section, empirical results showing how increased Government expenditure has
affected export performance, the price of non-tradables and private sector credit is reviewed.
5.0
Increased Government Expenditure, Real Exchange Rate Appreciation and
Export Performance.
5.1
Empirical Results
The assessment of the impact of increased Government expenditure on export performance and
the price of non tradables versus price of tradable goods, was done in a study by Atingi Ego and
Sebudde(2000)2. An approach based on Edwards (1989) and El Badawi (1994) model of the real
and equilibrium exchange rate was used to assess whether developments in the Real Effective
Exchange Rate (REER) were in line with economic fundamentals and hence consistent with
external viability of the economy. This required computing an exchange rate that evolves with
the permanent component of fundamentals in the economy, which results in the simultaneous
attainment of both internal and external equilibrium. REER was defined as the nominal
exchange rate adjusted for the price differential between Uganda and her major trading partners.
The fundamental variables that determine Uganda’s real exchange rate movements were defined
as terms of trade, the degree of openness, government expenditure and sustainability of capital
flows.
Estimation
A three-phase approach towards estimating the equilibrium exchange rate (ERER) was adopted.
(i) estimation of the REER by cointegration and the Error Correction Method (ECM) approach,
(ii) filtering transitory factors from the fundamentals and (iii) estimating the ERER using the
permanent components of the fundamentals. The two filter methodologies employed are
Hodrick Prescott, (HP 1984) approach and the Moving Average methodology used by El
Badawi, 1994. The deviation is what constitutes the magnitude of misalignment in the exchange
rate.
Evidence provided by the time series of both the real exchange rate and the nominal effective
exchange rate for the period 1970-1999, depicts the shilling generally depreciating except in the
1987/88 financial year and the 3 year period between 1992 and 1994. The real exchange rate
(REER) has closely followed a similar trend. This evidence could, however, be misleading since a
2
Uganda’s Equilibrium Real Exchange Rate And Its Implications for Non-Traditional Export Performance
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real appreciation following a successful liberalization could either arise from changes in the
economic fundamentals or from other non fundamental factors. However changes arising out of
the latter lead to misalignment of the RER from its equilibrium. The real effective exchange rate
has also been increasing showing the prices of non tradable has been increasing.
The time series properties of the variables were investigated and order of integration determined
using the Dickey Fuller(DF) and the Augmented Dickey Fuller (ADF) unit root test. And these
are summarized in table 2 below.
Table 2:
Variable
Time Series Properties of the Variables Sample Period: 1970-1999
Variable name ADF (X)
ADF
Order of
X
(∆X) Integration
Real Effective Exchange LREER
-2.6951
-4.1549
I (1)
Rate
Openness
LOPEN
-1.7911
-3.5776
I (1)
Real
Government LGE
-2.8732
-3.5776
I (1)
Expenditure
Terms of trade
LTT
-1.6172
-4.1248
I (1)
Foreign Exchange Inflows LFLOW
-1.4011
-4.0543
I (1)
Bank Credit Growth
DLCREDIT
-2.1581
-3.7777
I (1)
Critical value at 5% level of significance
-3.5796
-3.58671
Where: LREER, LOPEN, LGE, LTT, LFLOW and LCREDIT are respectively the logarithmic notions
of REER, OPEN, GE, TT, FLOW and CREDIT and the letter L denotes the logarithmic notion of the
variable as defined earlier
Having established the order of stationarity, the I(1) variables were entered into the co
integrating vector to establish the existence of estimating long-term relationship using the
Johansen (1988) procedure. Using a VAR of 2 a long run relationship for REER was established
as:
LREER= 0.503*LOPEN – 0.238*LGE -0.201*LFLOW-0.83*LTT …………… (1)
The results show that a 1% increase in openness will depreciate the REER by 0.503% while a
1% increase in foreign exchange inflows, terms of trade or government expenditure will exert
appreciative pressure on the REER amounting to 0.201%, 0.83% and 0.238% respectively. (See
appendix table 2.3 and 2.4 for details of other results)
Going by appendix figure 2 we see that the level of misalignment in the REER reduced
significantly as the Ugandan government undertook several trade reforms in the 1990’s.
However, the appreciative pressures observed in the REER in the period 1977 to 1981, 1984 to
1988 and 1991 to 1996, could, to a large extent, be attributed to the significant improvements in
the terms of trade arising from the improved unit export prices over this period. For the latter
part of the 1990s the increased government expenditure and foreign exchange flows appear to
have exerted appreciative pressures on the exchange rate. To the extent that increases in
government expenditure exert appreciative pressures on the REER, it would appear that the
increased government expenditure during the, 1990’s impacted appreciative pressures on the
REER.
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The appreciation pressures on the REER emanating from increased government expenditure
could be on account of the fact that the bulk of this expenditure is spent on non-tradables such
education, health, water and sanitation, rural roads and including construction and the supply of
some of these are fixed in the short to medium term. Consequently, their prices have arisen
leading to appreciation pressures on the REER as shown in appendix figures 1 (a) and (b). On
the other hand, the donor financing of this expenditures through increased flows has appreciated
the exchange rate via increased foreign sales. As earlier mentioned, this results from the
monetary authorities actions to sterilize the shilling liquidity created by increased expenditures
through increased sales of foreign exchange.
These results confirm that an increase in government expenditure as a result of HIPC relief
funded by the donor community led to an increase in foreign flows have indeed exerted
appreciative pressure on REER thereby indicating an increase in price of non-tradable goods
relative to price of tradable goods.
The levels of misalignments in the exchange rate3 shown in appendix figure 2 and the
performance impact on non-traditional exports are summarized in appendix tables 2.8 (A-H).
We perform a Granger causation test on these two variables and find that there is a unidirectional causation from misalignment in the exchange rate to non-traditional exports.
Table 3:
Cointegration for the non-traditional exports (NTE) and the level of the ERER
(Moving Average) – VAR=4.
A. Johansen Maximum Likelihood Test for cointegartion based on Maximal
Eigenvalue
Null Alternative Statistic
r=0 r=1
18.5809
r<= 1
r=2
95% Critical Value
14.9000
1.8704
90% Critical Value
12.9120
8.1760
6.5030
B. Test for cointegration based on Trace of the Stochastic Matrix
Null Alternative
r = 0 r>= 1
Statistic 95% Critical Value
20.4513
17.9530
90% Critical Value
15.6630
r<= 1 r = 2
1.8704
8.1760
6.5030
C. Normalized Co-integrated Vectors (β-vector) and Adjustment Matrices (αvectors)
(i)
LNTE
LREER
-1.00000
0.53904
4.1114
-0.08797
Further, given that the Ugandan economy has experienced huge foreign exchange inflows, which
our analysis has treated as a fundamental factor driving the equilibrium exchange rate, one would
wonder whether such inflows have not had a negative impact on the export sector performance.
To ascertain this, and whether the level of the equilibrium rate itself has any impact on the
performance of non-traditional exports, an analysis of the performance of the export sector in
relation to the movement in the equilibrium exchange rate itself has been done through cointegration and error correction.
3
measured as the deviation of the REER from the ERER
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As depicted by table 3, the single co-integrating relationship found confirms the fear that
appreciation of the ERER negatively affects the export sector performance. The long run
relation would thus be given as:
LNTE =
……………………….(2)
4.1114*LREER
This suggests that a 1% deprecation in the LREER increases non-traditional exports by 4.1%.
From the results in equation (1) above, which depicts capital flows (FLOWS) to be negatively
related to the REER, we can argue further that the increased foreign exchange flows which have
appreciative pressures on REER, have not augured well with the non-traditional export sector.
However, our short run analysis presented in Table 3, suggests that the changes in the REER has
no significant policy implications on changes in non-traditional exports. It is current changes in
non-traditional exports depend largely on their past values and the impact of the long run
presented by the ECM term.
Table 12:
Ordinary Least Squares Estimation with Changes in ERER(HP method)
Dependent variable is DNTE
16 observations used for estimation from 1984 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.40844
.15498
2.6354[.023]
DNTE(-1)
-.24606
.17685
-1.3913[.192]
DNTE(-2)
-.51526
.17935
-2.8729[.015]
DNTE(-3)
-.34472
.20343
-1.6946[.118]
ECM(-4)
-.40252
.18100
-2.2239[.048]
R-Squared
.46019
F-statistic F( 4, 11)
2.3444[.119]
R-Bar-Squared
.26389
S.E. of Regression
.41301
Residual Sum of Squares
1.8764
Mean of Dependent Variable .16364
S.D. of Dependent Variable .48139
Maximum of Log-likelihood -5.5571
DW-statistic
2.4252
CONCLUSION
This paper empirically suggests that the HIPC Debt initiative has had an impact on macro
economic variables in Uganda. Increased government expenditure financed by increased donor
flows has appreciated the REER rate by magnitudes of 0.24 and 0.20 percent respectively for
every one percent increase in them. On the other hand, for every one percent appreciation on
the real exchange rate has seen the performance of non-coffee exports deteriorate by 4 percent.
There are some lessons for poor countries to learn as they look forward to accessing the
millennium development goals (MDG’s) resources. Increased government expenditure on
account of MDG resources are likely to create and complicate macro-economic management of
the resultant higher interest rates and appreciating exchange rate, thereby crowing out the private
sector which could compromise the governments policy of the private sector export led growth
in reducing poverty. Developing countries hoping to access the MDG resources should
therefore do so carefully by accessing only those quantities that will not fully crowd out their
private sectors and compromise country efforts to generate sustainable external sectors while
ensuring increased absorption for the resources. The latter will lead to lowering the cost of doing
business while maintaining the export competitiveness of their countries.
11
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References
Adlung and Carzaniga (2000), Trade in Health Services under GATS- Past and Future
Atingi Ego (2002), The Role of the HIPC Debt Relief Initiative in bridging the Resource Gap
for the Financing of Poverty Reduction Programmes and the Role of SMES in PovertY
Reduction: The Case of Uganda
Atingi Ego and Sebudde (2000), Uganda’s Equilibrium Real Exchange Rate and ITS
Implications for Non-Traditional Export Performance
Bank of Uganda (2002), Private Capital Flows Survey 2001
Bank of Uganda (2002, 2001), Annual Supervision Report
Bank of Uganda (2002, 2001,2000), Annual Report
Edward S (1989). Real Exchange Rates, Devaluation and Adjustment: Exchange Rate Policy in
Developing Countries. Cambridge, MA: MIT
El Badawi I(1994) Estimating long run Equilibrium Exchange Rates published in Williamson J.
(1994) Ch. 5 Estimating Equilibrium Exchange Rates
Muwanga Zake and Ndhaye (2001), The HIPC Debt Relief Initiative Uganda’s Experience
Nanyonjo J (2001). The HIPC Debt Initiative: Uganda’s Social Sector Reforms and Outcomes
UNU/WIDER Discussion Paper no. 2001/138
Republic of Uganda (2000). Poverty Eradication Action Plan, PEAP.
Republic of Uganda(1996/97-2002/2003). Background to the Budget
Republic of Uganda(2002). Poverty Reduction Strategy Paper (PRSP) Progress Report
Republic of Uganda(2002). Statistical Abstract
12
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Appendix 1.
TABLE 1:
Government Deficit, Liquidity Creation and Sterilisation, Real Exchange Rate and Terms
of Trade
Fiscal Deficit (excluding Grants)
(as % of GDP)
Liquidity Created by Fiscal Operations
(Billions of Shillings)
Foreign Exchange Sales by BOU
US$ Millions
Net Treasury Bill Issues
(Billions of Shillings)
Stock of TB’s
(Billions of Shillings)
Growth in Private Sector Credit (%)
Base Money-start of the year
(Billions of Shillings)
Interest Cost of Domestic
(Billions of Shillings)
Terms of Trade*
(% change)
Real Exchange Rate
(% change)
1997/98
6.7
1998/99
7.8
1999/00
11.2
2000/01
11.5
63
58
212
418
664
5
25
117
174
198
37
36
133
168
268
112
148
281
449
717
24.8
332
33.6
373
6.2
433
9.4
442
4.2
550
29.1
21.4
29.6
57
91.5
98.2
-2.6
140.4
21.0
115.2
17.4
129.4
-7.8
114.5
-0.6
100
-22.7
121.3
5.9
74.3
-25.7
121.0
-0.3
61
-17.9
*- Terms of trade define as the relative prices of non-trade versus traded goods.
13
2001/02
12.8
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Table 2:
Composition of External Debt and Debt Indicators
Total External Debt Stock (millions of US$)
Multilateral Debt (millions of US$)
Bilateral Paris Club (millions of US$)
Bilateral Non-Paris Club (millions of US$)
Commercial Banks (millions of US$)
Commercial Non Banks (millions of US$)
Other Loans (millions of US$)
Debt Service (millions of US$)**
Accumulation of arrears out of current maturities (millions of
US$)
Exports of Goods (millions of US$)
Exports of Goods and Non-factor Services (millions of US$)
Debt Indicators (Percentage)
Multilateral debt as a % of total debt stock
Bilateral Paris Club as a % of total debt stock
Bilateral Non-Paris Club as a % of total debt stock
Commercial Banks as a % of total debt stock
Commercial Non Banks as a % of total debt stock
Other Loans as a % of total debt stock
Total External debt as a % of export of goods & services
Total External debt as a % of export of goods
Total external debt as a % of GDP
Total debt service as a % of export of goods and services
Total debt service as a % of GDP
1992/93
2637.2
1993/94
2999.3
1994/95
3386.9
1995/96
3515.8
1996/97
3660.2
1997/98
3664.4
1998/99
3499.6
1815.9
281.8
415.6
17.1
62.0
44.8
169.7
46.2
2156.1
332.0
398.4
1.3
38.2
73.4
167.8
17.1
2487.9
380.2
407.8
7.7
27.2
76.2
128.1
19.9
2655.2
350.6
404.1
3.0
26.2
75.9
142.2
35.3
2727.6
346.0
417.3
1.0
26.2
74.7
155.9
20.8
2826.9
324.5
461.8
2.5
26.7
22.0
166.5
27.4
2782.6
288.3
361.7
5.4
35.3
26.3
152.9
52.8
172.8
253.5
264.7
200.3
593.0
664.8
588.0
723.3
683.5
837.5
458.4
633.9
549.2
734.4
68.9
10.7
15.8
0.6
2.4
1.7
1040.3
1526.6
87.4
71.2
5.2
71.9
11.1
13.3
0.0
1.3
2.4
1497.4
1133.2
80.5
42.8
3.6
73.5
11.2
12.0
0.2
0.8
2.2
509.5
571.2
64.2
26.4
2.7
75.5
10.0
11.5
0.1
0.7
2.2
486.1
597.9
64.0
23.4
2.6
75.9
9.6
11.6
0.0
0.7
2.1
437.0
535.5
64.3
19.8
2.6
77.1
8.86
12.6
0.07
0.73
0.6
578.1
792.1
58.8
27.4
2.7
79.5
8.2
10.3
0.2
1.0
0.8
476.0
636.6
60.4
24.2
2.0
*Projection
Source: Bank of Uganda and Ministry of Finance Planning and Economic Development
14
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15
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Figure.1(a)
180.0
170.0
160.0
150.0
140.0
130.0
120.0
public construction
private construction
110.0
100.0
90.0
1997/98
1998/99
1999/2000
2000/01
2001/02
2002/03
Figure 1(b)
140.0
130.0
120.0
Non traded/traded goods
(prod)
non traded/traded goods (exp)
110.0
100.0
90.0
1997/98
1998/99
1999/2000
16
2000/01
2001/02
2002/03
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Appendix Table 2.2: Uganda’s Nominal and Real Effective Exchange Rates
Year
NEER
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
REER
0.16
0.16
0.16
0.19
0.22
0.26
0.28
0.50
0.73
1.26
2.30
4.18
5.12
6.13
7.62
Year
134.17
177.03
179.74
161.60
144.13
120.82
97.39
125.34
94.60
94.20
125.95
193.79
187.41
179.97
143.18
NEER
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
14.45
31.44
100.00
234.57
471.69
923.39
1525.73
2111.29
2031.16
1836.05
1838.84
1855.93
1940.08
2417.42
REER
114.96
91.01
100.00
83.97
103.73
183.96
252.33
258.66
290.78
276.70
257.30
255.67
266.84
337.93
AppendixTable 2.3: Johansen and Juselius Cointegration Procedure Results
VAR = 2; Sample Period: 1970-1999
Additional I(0) variables in VAR: DDLCREDIT
Null Alternative -T.ln(1-µi) 5%c.v 10% c.v Σ -T.ln(1-µI) 5%c.v. 10% c.v
r=0
r≤1
r≤2
r≤3
r≤4
r =1
r =2
r =3
r =4
r =5
30.41
22.83
14.72
7.27
1.67
33.46
27.07
20.67
14.07
3.76
30.90
24.73
18.59
12.07
2.69
76.90
46.50
23.66
8.94
1.67
68.52
47.21
29.68
15.41
3.76
64.83
43.94
26.79
13.33
2.69
Appendix Table 2.4: Estimated Normalized Co-integrated Vectors (β-vectors) and
Adjustment Matrices (α-vectors) in the Johansen Estimation
Lreer
(i)
(ii)
-1.000
0.030
-1.000
0.536
lopen
-27.164
0.057
0.503
0.166
lge
-1.707
0.00345
-0.238
0.192
lflow
ltt
24.797
-0.917
-0.048
0.056
-0.201 -0.831
0.181 -0.042
The long-run relationship for REER is consequently derived from vector (ii) and is expressed as:
lreer = 0.503*lopen - 0.238*lge - 0 .201*lflow – 0.831* ltt…..…(14)
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Appendix Table 2.5: Over-parameterized Model by Ordinary Least Squares Estimation
Ordinary Least Squares Estimation
Dependent variable is DREER
28 observations used for estimation from 1972 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT ( C )
1.1159
.28512
3.9136[.001]
DREER(-1)
-.32737
.20670
-1.5838[.133]
DOPEN
.69081
.30527
2.2629[.038]
DOPEN(-1)
.39689
.35056
1.1322[.274]
DGE
-.13547
.18216
-.74369[.468]
DGE(-1)
-.022452
.14249
-.15756[.877]
DTT
-.068505
.13207
-.51869[.611]
DTT(-1)
-.21167
.13140
-1.6109[.127]
DFLOW
-.16009
.25799
-.62053[.544]
DFLOW(-1)
-.016842
.25252
-.066698[.948]
DDLCREDIT
.12331
.093630
1.3170[.206]
ERROR(-2)
-.44979
.11462
-3.9243[.001]
R-Squared
.76953
F-statistic F(11, 16)
4.8566[.002]
R-Bar-Squared
.61108
S.E. of Regression
.16034
Residual Sum of Squares
.41133
Mean of Dependent Variable
.028457
S.D. of Dependent Variable .25710
Maximum of Log-likelihood 19.3576
DW-statistic
1.5246
Durbin's h-statistic
*NONE*
* Test Statistics
* A:Serial Correlation
* B:Functional Form
* C:Normality
* D:Heteroscedasticity
Diagnostic Tests
*
LM Version
*CHI-SQ( 1)= 3.2207[.073]
*CHI-SQ( 1)= 5.4258[.020]
*CHI-SQ( 2)= 2.7136[.257]
*CHI-SQ( 1)= 1.0650[.302]
*
F Version
*F( 1, 15)= 1.9496[.183]*
*F( 1, 15)= 3.6053[.077]*
*
Not applicable *
*F( 1, 26)= 1.0281[.320]*
Appendix Table 2.6: Variable Deletion Test (OLS case)
Dependent variable is DREER
List of the variables deleted from the regression:
DTT
DGE
DGE(-1)
DFLOW
28 observations used for estimation from 1972 to 1999
Regressor
Coefficient
Standard Error
INPT ( C )
1.0969
0.25855
DREER(-1)
-0.36152
0.18157
DOPEN
0.46081
0.11820
DOPEN(-1)
0.37497
0.15672
DTT(-1)
-0.18870
0.10590
DDLCREDIT
0.15544
0.078986
ERROR(-2)
-0.43861
0.10379
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic CHI-SQ( 5)= 3.9695[.554]
Likelihood Ratio Statistic
CHI-SQ( 5)= 4.2807[.510]
F Statistic
F( 5, 16)= .52860[.751]
18
*
DFLOW(-1)
T-Ratio[Prob]
4.2425[.000]
-1.9911[.060]
3.8987[.001]
2.3926[.026]
-1.7818[.089]
1.9679[.062]
-4.2262[.000]
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Figure 2: Uganda's Equilibrium Exchange Rate(ERER) and Real
Exchange Rate(RER)
1975-1999
6
5.5
5
4.5
4
Year
reer
erer(MA)
19
erer(HP)
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
3.5
1975
Exchange Rate (in logs)
6.5
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Appendix Table 2.7:
Year
Non-Traditional Exports and Exchange Rate Misalignments
Total
Exports
(mil US$)
Coffee Exports NonDegree of Misalignment(%)
Traditional
Moving
Hedrick(mil US $)
Exports
average
Prescott Filter
(mil US $)
1980
319.4
312.2
2.5
57.3
45.4
1981
229.3
224.2
2.5
15.5
22.2
1982
347.3
341.7
1.4
11.2
8.9
1983
367.7
340.2
14.2
12.6
1.5
1984
407.3
375.6
14.8
38.5
26.9
1985
347.8
325.8
6.62
78.8
47.9
1986
394.9
382.2
4.5
90.4
72.2
1987
333.6
321.0
6.6
73.0
61.2
1988
266.3
259.2
1.016
79.9
67.7
1989
277.7
262.8
7.1
48.6
32.9
1990
177.8
140.4
25.23
19.4
-5.1
1991
173.2
117.6
32.5
8.7
-26.7
1992
151.2
95.4
35.6
3.7
-36.9
1993
200.0
114.5
61.7
11.7
-29.7
1994
462.9
356.9
85.2
4.7
-24.9
1995
560.3
419.0
115.2
-11.0
-17.5
1996
639.3
396.1
211.1
-14.6
-12.2
1997
575.6
310.4
209.6
-5.4
4.2
1998
510.2
295.0
161.9
-14.5
-0.2
1999
536.9
285.5
194.7
-18.7
-1.9
Source: Bank of Uganda Annual Reports and computations by authors
Note: Degree of misalignment ((+) is over-valuation and (-) is under-valuation.
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Table 2.8(A – H):
Results of the Co-integration and , error correction analysis for Nontraditional Exports and Exchange Rate Misalignments
2.8A. Time Series Properties of Over-valuation and Non-traditional exports
Variable
Variable name
ADF(x)
ADF(∆x)
Over-valuation by MA
Over-valuation by HP
Non-Traditional Exports/Total Exports ratio
Critical Value at 5%
OVER(ma)
OVER(hp)
NTE
-1.3396
-2.4049
-3.6386
-3.1046
2.9970
2.9970
Order of cointegration
I(1)
I(1)
I(1)
2.8B.
Johansen and Joselius Cointegration with restricted intercepts and no trends in the VAR
Cointegration LR Test Based on Maximal Eigenvalue of the Stochastic Matrix
17 observations from 1983 to 1999. Order of VAR = 3
List of variables included in the cointegrating vector:
List of variables included in the cointegrating vector:
LNT
LOVER(ma)
Intercept
LNT
LOVER(hp)
Intercept
List of eigenvalues: 0.56132 0.32141 0.0000
List of eigenvalues : 0.60391 0.34111 0.0000
Null
Alternative Statistic
R=0
R<= 1
r=1
r=2
95% C.V.
90% C.V.
Null
14.0078
15.8700
13.8100
R=0
6.5917
9.1600
7.5300
Alternative Statistic
R<= 1
r=1
r=2
95% C.V.
90% C.V.
15.7438
15.8700
13.8100
7.0923
9.1600
7.5300
2.8C.
Johansen and Joselius Cointegration with restricted intercepts and no trends in the VAR
Cointegration LR Test Based on Trace of the Stochastic Matrix
17 observations from 1983 to 1999. Order of VAR = 3.
17 observations from 1983 to 1999. Order of VAR =3.
List of variables included in the cointegrating vector:
List of variables included in the cointegrating vector:
LNT
LOVER(hp)
Intercept
LNT
LOVER(ma)
Intercept
Null Alternative Statistic
95% C.V 90% C.V
Null Alternative Statistic 95% C.V 90% C.V
R=0
r>= 1
22.8361
20.1800 17.8800
R=0
R<= 1
r=2
7.0923
9.1600
R<= 1
2.8D.
7.5300
r>= 1
20.5994
r=2
6.5917
20.1800 17.8800
9.1600
Estimated Cointegrated Vectors in Johansen Estimation (Normalized in Brackets)
Cointegration with restricted intercepts and no trends in the VAR
Vector
β
α
LNTE
-1.000000
β
α
-1.0000
0.102
LOVER(ma)
-5.6711
LOVER(hp)
-7.2596
-
Intercept
-1.1938
Intercept
-1.6526
-
21
7.5300
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2.8E. Ordinary Least Squares Estimation of Over-parameterized Model(HP)
Dependent variable is DNT
17 observations used for estimation from 1983 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.52487
.23213
2.2612[.045]
DNT(-1)
-.46660
.23773
-1.9627[.075]
DNT(-2)
-.55844
.32363
-1.7256[.112]
DHP(-1)
-4.8434
2.5958
-1.8658[.089]
DHP(-2)
2.3976
1.7727
1.3525[.203]
ECM2(-3)
.10208
.23152
.44091[.668]
R-Squared
.46272
R-Bar-Squared
0.21851
S.E. of Regression
.85372
F-stat. F( 5, 11)
1.8947[.175]
Mean of Dependent Variable .26467
S.D. of Dependent Variable .96572
Residual Sum of Squares
8.0172
Equation Log-likelihood
-17.7332
Akaike Info. Criterion
-23.7332
Schwarz Bayesian Criterion -26.2328
DW-statistic
1.6885
Diagnostic Tests
* Test Statistics *
LM Version
*
* A:Serial Correlation*CHSQ( 1)= .36405[.546]*F( 1, 10)= .21883[.650]
* B:Functional Form *CHSQ( 1)= 3.3530[.067]*F( 1, 10)= 2.4570[.148]
* C:Normality
*CHSQ( 2)= 1.7471[.417]*
Not applicable
* D:Heteroscedasticity*CHSQ( 1)= .23669[.627]*F( 1, 15)= .21180[.652]
F Version
2.8F.
Variable Deletion Test (OLS case) and Ordinary Least Squares Estimation - HP
Dependent variable is DNT
List of the variables deleted from the regression:
DHP(-2)
ECM2(-3)
17 observations used for estimation from 1983 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.43709
.22058
1.9815[.069]
DNT(-1)
-.47696
.23388
-2.0393[.062]
DNT(-2)
-.57230
.26610
-2.1507[.051]
DHP(-1)
-3.9094
1.6270
-2.4028[.032]
Joint test of zero restrictions on the coefficients of deleted variables:
Lagrange Multiplier Statistic CHSQ( 2)= 2.4908[.288]
Likelihood Ratio Statistic
CHSQ( 2)= 2.6933[.260]
F Statistic
F( 2, 11)= .94418[.418]
R-Squared
.45323
R-Bar-Squared
.27097
S.E. of Regression
.82457
F-stat. F( 4, 12) 2.4868[.099]
Mean of Dependent Variable .26467
S.D. of Dependent Variable .96572
Residual Sum of Squares
8.1589
Equation Log-likelihood
-17.8821
Akaike Info. Criterion
-22.8821
Schwarz Bayesian Criterion -24.9651
DW-statistic
1.7094
22
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2.8G. Ordinary Least Squares Estimation with Over-valuation under MA method
Dependent variable is DNT
17 observations used for estimation from 1983 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
0.35079
.24706
1.4198[.181]
DNT(-1)
-0.37425
.24447
-1.5309[.152]
DNT(-2)
-0.42294
.26471
-1.5977[.136]
DMA(-1)
-3.7375
2.0915
-1.7870[.099]
DMA(-2)
-0.38659
1.7986
-.21494[.833]
Diagnostic Tests
* Test Statistics
* LM Version
* A:Serial Correlation*CHSQ( 1)= 2.7485[.097]
* B:Functional Form *CHSQ( 1)= .85294[.356]
* C:Normality
*CHSQ( 2)= .12499[.939]
* D:Heteroscedasticity*CHSQ( 1)= .10728[.743]
*
F Version
*F( 1, 11)= 2.1214[.173]
*F( 1, 11)= .58106[.462]
*
Not applicable
*F( 1, 15)= .095261[.762]
2.8H. Variable Deletion Test (OLS case) and Ordinary Least Squares Estimation - MA
Dependent variable is DNT
List of the variables deleted from the regression: DMA(-2)
17 observations used for estimation from 1983 to 1999
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.36394
.23043
1.5794[.138]
DNT(-1)
-.36946
.23435
-1.5765[.139]
DNT(-2)
-.42165
.25475
-1.6551[.122]
DMA(-1)
-3.8415
1.9587
-1.9612[.072]
Joint test of zero restrictions on the coefficients of deleted variables:
Lagrange Multiplier Statistic CHSQ( 1)= .065199[.798]
Likelihood Ratio Statistic
CHSQ( 1)= .065324[.798]
F Statistic
F( 1, 12)= .046200[.833]
----------------------------------------------------------------------------------------------------------------------------------------------R-Squared
.29848
R-Bar-Squared
.13660
S.E. of Regression
.89735
F-stat. F( 3, 13) 1.8438[.189]
Mean of Dependent Variable .26467
S.D. of Dependent Variable .96572
Residual Sum of Squares 10.4680
Equation Log-likelihood
-20.0004
Akaike Info. Criterion -24.0004
Schwarz Bayesian Criterion -25.6668
DW-statistic
1.4041
For all diagonistics, the following signs stand for :
A: Lagrange multiplier test of residual serial correlation
B: Ramsey's RESET test using the square of the fitted values
C: Based on a test of skewness and kurtosis of residuals
D: Based on the regression of squared residuals on squared fitted values
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