THE IMPACT OF INTEREST RATE LIBERALISATION ON PRIVATE SECTOR CREDIT
IN MALAWI
MASTER OF ARTS (ECONOMICS) THESIS
BY
MAXWELL CHIMPHANDA
MA/ECO/07/18
Dissertation submitted to the Department of Economics in partial fulfilment of the requirements
for the Master of Arts (Economics).degree
UNIVERSITY OF MALAWI
CHANCELLOR COLLEGE
2020
i
DECLARATION
I undersigned declare that this thesis is my own original work which has not been submitted in any
other institution for similar purposes. Where other peoples documentation have been used
acknowledgements have been made.
________________________________
Full legal name
________________________________
Signature
________________________________
Date
ii
CERTIFICATE OF APPROVAL
We declare that this dissertation is from the candidate’s own work and effort. Where he has used
other sources of information, it has been acknowledged. This dissertation is submitted with our
approval.
Signature________________________________ Date: ________________________________
Dr. Exley B.D Sulumbu
Senior Lecturer in Economics
Main Supervisor
Signature________________________________ Date: ________________________________
Associate Professor Levison Chiwaula
Head of Economics Department
Main Supervisor
iii
DEDICATION
I dedicate this work to each and every single member of my family. You have stood by me all the
way to this level and I am confident you will do that countless times throughout the course of my
life.
iv
ACKNOWLEDGEMENTS
Thanks should be to God for guiding and keeping me focused and motivated to complete this piece
of work. His name be praised forever.
My sincere and supreme gratitude goes to Associate Professor Exley B.D Sulumbu and Associate
Professor Levison Chiwaula whose guidance and supervision led me to come up with this work.
I also say thank you to all the Lecturers and all members of staff of the Economics Department
who assisted and supported me in various ways in the course of my postgraduate studies.
My gratitude should also go to my family and friends and indeed anyone who actively assisted me
in various ways with this thesis
.
v
ABSTRACT
This study examined the impact of interest rate liberalisation on private sector credit in Malawi
using the Vector Error Correction Model. The study was motivated by the conflicting results that
have been reported in the literature in recent years on the efficacy of interest rate liberalisation in
developing countries. The empirical results of this study reveals that interest rates have a negative
and significant impact on private sector credit in the long-run, but the impact is not significant in
the short run. Besides, the study reveal that there is no differential impact of interest rate
liberalisation on private sector credit in Malawi. Consequently, the study recommends that interest
rate policy interventions should be intensified in Malawi with long term objectives if the private
sector is to benefit. Furthermore, monetary authorities should keep an eye on the interest rates in
order to ensure that they remain within the acceptable threshold. As regards to liberalising interest
rates, it is recommended that interest rates should not be liberalised to boost access to credit since
it has been found to be insignificant in this role rather interest rates should be liberalised because
its role is significant in affecting other important economic factors.
vi
LIST OF TABLES
Table 4. 1 Augmented Dickey Fuller Test .................................................................................................. 24
Table 4. 2 Stationarity test using Phillip Perron Test.................................................................................. 24
Table 4. 3 Stationarity Test using DF-GLS ................................................................................................ 25
Table 4. 4 Lag Order Selection Test ........................................................................................................... 26
Table 4. 5 Johansen Test for Cointegration ................................................................................................ 26
Table 4. 6 Breusch Godfrey LM test for Autocorrelation ........................................................................... 28
Table 4. 7 Jacque Bera Test ........................................................................................................................ 29
Table 4. 8 Short Run Vector Error Correction Model Results .................................................................... 35
Table 4. 9 Variance Decomposition of Real Private Sector Credit Results ................................................ 38
Table 4. 10 Variance Decomposition of Interest rate results ...................................................................... 38
vii
LIST OF FIGURES
Figure 1 the relationship between Interest rate and private sector credit in Malawi..................................... 3
Figure 2 Lending Rates before Liberalisation............................................................................................... 8
Figure 3 Interest Rates after Liberalisation ................................................................................................... 9
Figure 4 Private sector credit trends in Malawi .......................................................................................... 10
Figure 7 Impulse Response Function .......................................................................................................... 39
Figure 8 Forecasting with the Cointegrating VECM .................................................................................. 41
viii
TABLE OF CONTENTS
DECLARATION ............................................................................................................................................... ii
CERTIFICATE OF APPROVAL ......................................................................................................................... iii
DEDICATION ................................................................................................................................................. iv
ACKNOWLEDGEMENTS ................................................................................................................................. v
ABSTRACT..................................................................................................................................................... vi
LIST OF TABLES ............................................................................................................................................ vii
LIST OF FIGURES ......................................................................................................................................... viii
TABLE OF CONTENTS.................................................................................................................................... ix
CHAPTER ONE ............................................................................................................................................... 1
INTRODUCTION ............................................................................................................................................. 1
1.1
Background ................................................................................................................................... 1
1.1.1
Interest Rates and Financial Liberalization in Malawi.......................................................... 2
1.1.2
Private Sector credit and interest rates in Malawi ................................................................. 3
1.2
Problem statement ......................................................................................................................... 4
1.3
Relevance of the Study ................................................................................................................. 5
1.4
Objectives ..................................................................................................................................... 6
1.5
Study hypotheses .......................................................................................................................... 6
1.6
Outline of the Thesis ..................................................................................................................... 6
CHAPTER TWO .............................................................................................................................................. 7
POLICY CONTEXT OF INTEREST RATE LIBERALIZATION IN MALAWI ............................................................. 7
2.1. Introduction ........................................................................................................................................ 7
2.2 Management of Interest Rate before Liberalisation ............................................................................ 7
2.3 Management of Interest Rates after Liberalisation ............................................................................. 8
2.3 Private Sector Credit trends in Malawi ............................................................................................... 9
CHAPTER THREE .......................................................................................................................................... 12
LITERATURE REVIEW ................................................................................................................................... 12
3.1. Introduction ...................................................................................................................................... 12
3.2. Theoretical review ........................................................................................................................... 12
ix
3.2.1 Classical Theory of Interest Rate ............................................................................................... 12
3.2.2 Neoclassical Theory of Interest Rate or Loanable funds Theory ............................................... 13
3.2.3 The Liquidity Preference Theory ............................................................................................... 13
3.2.4 Neo-Keynesian Theory of Interest or Hicks IS – LM Curve or Modern Theory of Interest ..... 14
3.2.5 McKinnon-Shaw Hypothesis ..................................................................................................... 15
3.3Empirical Review............................................................................................................................... 15
CHAPTER FOUR ........................................................................................................................................... 19
METHODOLOGY .......................................................................................................................................... 19
4.1.
Introduction ` .............................................................................................................................. 19
4.2.
Model Specification .................................................................................................................... 20
4.3.
Definition of Variables................................................................................................................ 21
4.3.1.
Real Private Sector Credit ................................................................................................... 21
4.3.2.
Real Interest rates ................................................................................................................ 21
4.3.3.
Real Inflation Rate .............................................................................................................. 22
4.3.4.
Real Government Expenditure ............................................................................................ 22
4.3.5.
Real Exchange Rate ............................................................................................................ 22
4.3.6.
Dummy (DUM)................................................................................................................... 22
4.4.
Data Sources ............................................................................................................................... 22
4.5.
Stationarity .................................................................................................................................. 22
4.5.1.
The Dickey-Fuller Test for Unit Root ................................................................................. 23
4.5.2.
The Augmented Dickey Fuller Test .................................................................................... 23
4.5.3.
Phillip Perron Test ............................................................................................................ 24
4.5.4.
The Dickey Fuller- Generalized Least Squares Test ( DF-GLS Test) .......................... 24
4.6.
Cointegration............................................................................................................................... 25
4.7.
Vector Error Correction Model ................................................................................................... 27
4.8.
Diagnostics Tests ........................................................................................................................ 28
4.8.1.
Breusch Godfrey LM Test for Autocorrelation .................................................................. 28
4.8.2.
The Normality Test ............................................................................................................. 29
4.8.3.
Stability test ........................................................................................................................ 29
CHAPTER FIVE ............................................................................................................................................. 32
EMPIRICAL RESULTS AND INTERPRETATION............................................................................................... 32
5.1.
Introduction ................................................................................................................................. 32
x
5.2.
VECTOR ERROR CORRECTION MODEL LONG RUN RESULTS ..................................... 32
5.3.
THE VECTOR ERROR CORRECTION MODEL SHORT RUN RESULTS .......................... 35
5.4.
INNOVATION ACCOUNTING ........................................................................................................ 37
5.4.1.
VARIANCE DECOMPOSITION ................................................................................................... 37
5.4.2.
IMPULSE RESPONSE FUNCTIONS ................................................................................ 38
5.5.
Forecasting with VECMs Cointegrating VECMs.................................................................. 41
CHAPTER 6 .................................................................................................................................................. 43
CONCLUSION AND RECOMMENDATION .................................................................................................... 43
4.1.
Conclusion .................................................................................................................................. 43
4.2.
Recommendation ........................................................................................................................ 44
REFERENCES ................................................................................................................................................ 46
APPENDIX A: DATA ...................................................................................................................................... 51
xi
CHAPTER ONE
INTRODUCTION
1.1 Background
Most African countries, Malawi inclusive, became independent from their colonial masters in the
early 1960s. Immediately after independence, the countries were geared to achieve rapid economic
growth and development which was prevailing at very low growth rates at the time. This feeling
of optimism, was shared by their donors and other interested organizations who extended massive
financial and policy support. A socialistic approach, which entails massive government control,
was adopted. As a consequence, there was an enactment of a comprehensive legal framework that
enabled the direct control of economic variables for instance prices and interest rates in Africa
(Heidhues & Obare, 2011). Moreover, governments put together trade restrictions and engaged in
direct control of credit and foreign exchange allocation to various sectors of their economies.
At the outset, the approach produced significant positive results. This was evinced by the extensive
surge in economic growth rates, significant improvement in infrastructure development,
telecommunication, power generation, health and education (Heidhules etal, 2004). However,
from the mid-1970s, the situation started reversing and most African economies started going
through a slump. Among the possible causes, Heidhues (2011) pointed out the oil shocks of 1973.
The argument was, the shocks led to the negligible growth of most economies productive sectors,
declining investment levels and falling export returns. Subsequently, high budget and balance of
payments deficits and significant public debts characterized many African countries economies.
By 1980, the situation was getting worse. Most countries were going through a series of declining
annual levels of output and low levels of investment and saving. Following this, the World Bank
released a report titled “Towards Accelerated Development in Sub Saharan Africa” targeting the
Sub-Saharan African countries in 1981. Among other things, the report aimed at providing possible
solutions and policy recommendations directed at eradicating the hardships that the region was
facing because of the extensive government control of the key macro-economic variables ( World
Bank, 1981).
1
To say the least, the World Bank suggested economic liberalization as the plausible solution.
Specifically, it advocated for minimal foreign and domestic trade control of macroeconomic
variables. To facilitate this, it extended the structural adjustment loans and neo liberal policies
which advocated for the minimal control of budget deficits and financial liberalization (involving
multilateral objective of market determined interest rates) among others (Williamson, 1994). Since
the situation was delicate, governments in the region were quick to embrace this and started taking
efforts to liberalize their economies marking the genesis of financial liberalization.
1.1.1 Interest Rates and Financial Liberalization in Malawi
For financial liberalization to take place there are variables that economists suggest should be used.
Among these variables are interest rates and foreign exchange rates. Interest rates are defined as
the percent charged for the use of other people’s money (Mishkin, 2004). It is charged when the
money is borrowed, and paid when it is loaned. The interest rate that the lender charges is a percent
of the total amount loaned. Similarly, the interest rate that the institution such as a bank pays for
holding ones money is a percentage of the total amount deposited (Aftab etal, 2016). Interest rates
therefore, act as the channel through which the funds flow from the savers to the borrowers.
Usually the funds are generated from financial intermediaries for instance banks, microfinance
institutions, mutual funds and insurance companies among others
In economics and finance, several theories have argued that financial liberalization should be done
using interest rates (Appelt, 2016). The reasoning is that interest rate liberalisation influences
investment hence it should be promoted. Among the theories, is the McKinnon and Shaw
hypotheses which argues that interest rate liberalization is key in affecting investment since it
influences credit allocation, affects imperative macro-economic variables, improves the flexibility
and efficiency in the financial sectors, inspires growth and development of market economies
(Shaw, 1973) and (McKinnon, 1973). Empirical research in Malawi and other sub-Saharan
countries agrees with the theory. Studies by Mwanamveka (1994), Chnnkono (1997) and Chirwa
(2001) are some of the studies conducted in Malawi supporting the theory. The underlying notion
in most research papers has been that, financial liberalization through interest rate liberalizations
is the key to investment because it positively affects credit allocation to the productive sectors of
the economy including the private sector.
2
1.1.2 Private Sector credit and interest rates in Malawi
There are a number of key factors that determine the development of the private sector in any
economy. Private Sector development of a country refers to such elements, policy making
decisions and institutions that lead to an efficient private sector and easy access to capital and
financial services (World Economic Forum, 2008). Among the key factors that determine private
sector development is private sector credit. Private sector credit is defined as the domestic credit
allocated to the private sector for investment (Aftab etal, 2016). A strong private sector credit base
makes an economy to prosper, while a developed economy is an indicator of strong private sector
credit system. A developed economy will raise demand for credit and hence lead to credit growth.
A developed private sector gives birth to successful businesses which drives growth, create jobs,
and pay taxes that sustains financial services and investment among other advantages. The
moderate the monetary policies and the vigorous the private sector of an economy the increase the
demand for credit. Strict monetary policies discourages investors and lowers credit and economic
growth rates.
As alluded to in the in the preceding section, interest rate affects private sector credit. Below is
figure 1 showing the relationship between interest rate and private sector credit in Malawi.
Figure 1 the relationship between Interest rate and private sector credit in Malawi
60
50
40
30
LENDING INTEREST RATE
20
PRIVATE SECTOR CREDIT
10
2018
2015
2012
2009
2006
2003
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
0
Figure 1 presents the relationship between private sector credit and interest rates in Malawi. In the
figure, private sector credit is expressed as a percentage of Gross Domestic Product and interest
rates are expressed as lending interest rates. From the figure 1 above, it is clear that private sector
credit is related to interest rates. That is, from 1970 to somewhere around 1987 private sector credit
3
and interest rate trends move together. This means that, during the period the two variables are
positively related. Above this, the level of private sector credit as a percentage of gross domestic
product is higher in all years for the period between 1970 and 1987 while the rate of interest rate
for the period is lower relative to the period after it. From 1988 to 2018, the relationship is negative.
That is, as the interest rate is declining the level of credit allocated to the private sector is
increasing. The level of private sector credit as a percentage of gross domestic product is lower
relative to the period before it. Similarly, the interest rates during this period are higher than in the
preceding period. This change in relationship and level of private sector credit and interest rates is
interesting to look into. It forms the basis for research aimed at understanding the reasons making
the direction of the relationship to change overtime. Specifically, one would like to understand
whether there was policy changes or changes in the taste to credit by the private sector among
other reasons. An immediate thought would be that the year 1987 is the year that Malawi
liberalized its interest rates and probably the liberalisation played a role in influencing private
sector credit.
1.2 Problem statement
Following the relevance that interest rates and its liberalisation have been given so far, reviewing
the literature, one would expect to come across overwhelming empirical research on it. However,
there is not much that has been done.
Besides, from the studies reviewed, there are contradictory results on the impact of interest rates
and its liberalisation on different variables. One of the most controversial issues among researchers
is the impact of interest rate liberalization on private sector credit. According to Naimy and Dame
(2005) in their study conducted in Lebanon for example, interest rate liberalization is negatively
related to private sector credit in Lebanon. Similar results were found in Lesotho according to a
study done by Molapo and Damane (2017). However, the results were challenged by Liu etal
(2019) in their study conducted in China.
Joining the debate on different fronts researchers have tested other important factors which
according to early research by McKinnon (1973) and Shaw (1973) are crucial in explaining private
sector credit. These factors include private sector investment, economic growth, Savings, and
financial intermediaries’ performance among others. On these factors, Inedu (2015) found a
negative impact of interest rate liberalization on private investment coinciding with the results
4
found in Lesotho and Lebanon. One would tend to argue that the positive result was only found in
China, a developed economy. But a study by Mendoza (2003) on private investment in Venezuela
showed evidence of positive impacts as well. Similarly, a study on the impact of interest rate
liberalization on economic growth through Savings and Investment in the Southern Africa
Development Community (SADC) region by Moyo and Le Roux (2018) found that the impact is
positive. However, this contradicts the study by Naude (1995) on selected African countries.
The lack of a strong literature base and the controversy surrounding research on interest rate
liberalization also surfaces on research conducted in Malawi. Odhiambo (2016) for example, in a
study examining the impact of interest rates liberalization on private investment in Malawi, found
a positive and significant impact in the short-run, but a negative effect in the long-run. This
contracts a study by Chirwa (2001), in a study investigating the impact of interest rate liberalisation
on the performance of commercial banking system in Malawi. The finding was however similar
to what Chinkono (1997) found in a study testing the impact of interest rate liberalization on the
demand for small and medium scaled enterprises in Malawi and Mwanamveka (1994) in his study
testing the impact of interest rate liberalisation on saving in Malawi.
It is therefore evident that, there is not enough literature on interest rate and its liberalisation.
Besides this, there exist a controversy on research done assessing the impact of interest rates and
its liberalisation on private sector credit and even other factors which are important factors in
explaining the same variable. Congruent to the debates, is the Interest rate Capping Bill which was
presented in the Malawi Parliament for debate. It is therefore true that there is an eminent debate
on interest rate and its liberalisation. As such, this study is examining the impact of interest rate
liberalisation on private sector credit in Malawi as a way of contributing to the existing lean body
of knowledge on interest rate liberalisation which if made adequate would become a fountain in
which policy makers can tap from as the debate on the impact of interest rate and its liberalisation
continues..
1.3
Relevance of the Study
The findings of this study would be useful to various stakeholders in Malawi to take a scientifically
proven position on the relevance of interest rate and its liberalisation on private sector credit in
Malawi and provide a reference point to the legislature in its ever existing sub role of debating,
amending and enacting financial laws in favor or against interest rates liberalization in Malawi.
5
1.4
Objectives
The main objective of the study is to examine the impact of interest rate liberalisation on private
sector credit in Malawi. The specific objectives are: a) To examine the short run and long run impacts of interest rates on private sector credit in
Malawi.
b) To determine the differential impact of the pre and post interest rate liberalisation regimes
on private sector credit in Malawi.
1.5
Study hypotheses
Based on the objective of the study, the following are the hypotheses.
i.
Interest rate has no significant short run and long run impacts on private sector credit in
Malawi.
ii.
There is no differential impact of interest rate liberalization on private sector credit in
Malawi during the pre and post-liberalization regimes.
1.6 Outline of the Thesis
The remainder of the thesis is structured as follow; Chapter two is a discussion on the policy
context of interest rate liberalisation in Malawi, Chapter three is the literature review with
theoretical and empirical evidence, Chapter four is the methodology that was followed in data
collection and analysis and modeling while Chapter five is the results and interpretation and
chapter six presents the conclusion and recommendations.
6
CHAPTER TWO
POLICY CONTEXT OF INTEREST RATE LIBERALIZATION IN MALAWI
2.1. Introduction
From 1964, the year Malawi got independence, a number of policies have been adopted with the
aim of ensuring price stability, a sustainable external position and faster economic growth and
development among other key objectives (Chimkono, 1997). Specifically, a number of policy
instruments including exchange rate policies and interest rate adjustments were adopted. From the
1970s Malawi adopted administered interest rates, credit ceilings, segmented capital markets and
excessive intermediation costs policies. With time, the structural adjustment programs supported
by the World Bank and the International Monetary Fund influenced Malawi to embark on a journey
of massive structural reforms in the financial sector which have been underway for three decades
now. The key aspects of the reforms have been to achieve efficient resources mobilization and
optimal resource allocation (Heidhues & Obare, 2011). Among the important aspects of the
financial reforms was the interest rate liberalization adopted in 1987. Below is a discussion of what
transpired in Malawi before and after the interest rate liberalisation.
2.2 Management of Interest Rate before Liberalisation
Before liberalisation, the Malawi’s interest rate structure was directly controlled by the Malawi’s
Central Bank, the Reserve Bank of Malawi (RBM). Consequently, interest rates were sticky. There
was minimal consideration diverted towards the underlying macroeconomic conditions, especially
the rate of inflation as well as the demand and supply of loanable funds (Mwanamveka, 1994).
The gist behind this was an attempt to keep the interest rates low with the aim of reducing
government expenditures and promoting private investment. During this period, the Reserve Bank
of Malawi administered deposit rates and the Commercial Bank were mandated to administer the
lending rates subject to a predetermined ceiling rate.
Due to this, during the pre liberalisation era the lending rates hovered around 13 percent and 18.5
percent and below the 19 percent ceiling interest rate set by the RBM. The bank rate and the deposit
rate hovered around 10.0 percent with hardly any changes throughout the period (Odhiambo N.
M., 2016). Further to that, in March 1980, a preferential interest rate favoring the agricultural
sector was introduced in Malawi. This rate was 1 to 2 percentage points below the prime rate
7
prevailing at that time. This was done as a way of encouraging farmers to access loanable funds
from the financial sector and boost agriculture which is considered the key sector in Malawi.
Figure 2 below, shows how the Lending rates determined by the commercial banks behaved from
1970 to 1986 a period before the liberalisation
Figure 2 Lending Rates before Liberalisation
LENDING INTEREST RATES BEFORE
LIBERALISATION
20
15
10
LENDING INTEREST RATE BEFORE
LIBERALISATION
5
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
0
2.3 Management of Interest Rates after Liberalisation
The interest rate deregulation or liberalization started in July 1987. The deregulation was
advocated by the theoretical works of McKinnon (1973) and Shaw (1973) with the aim of
achieving financial development and as a way of adopting growth enhancing economic policies in
developing countries. The hypothesis argued that, considering administered interest rates as an
important tool for promoting investment by keeping interest costs low was faulty as it led to
nothing but financial repression. Financial repression is defined as a distortion in financial prices
for example interest rates which reduces the real value of financial assets (Odhiambo L. A., 2013).
Consequently, the overall volume of savings decreases and negatively affects investment. The
policy prescription advocated by the McKinnon and Shaw for these financially repressed
economies is to raise institutional interest rates and reduce inflation.
As a way forward, there was a need to gradually deregulate interest rates in order to support the
adjustment program. This started in July 1987 by allowing commercial banks to set their own
lending interest rates. This was followed by the deregulation of interest rates in April 1988 and the
abolition of the preferential interest rates, offered to the agricultural sector in August 1988. By
May 1990 all interest rates became fully liberalized. After complete interest rate liberalization was
8
launched in 1990, the Bank rate, the rate at which commercial banks borrow from the central bank,
played a more important role in the financial system. This role was boosted by the enhancement
in the development of the money market and the frequent use of open market operations as a tool
for monetary policy (Odhiambo N. M., 2016). The bank rate was adjusted at least seven times by
monetary authorities mainly to reduce excess demand for cash, particularly during the period
between 1994 and 1995. This is also the period when Malawi experienced high levels of inflation.
From end 1994, to curb inflationary expectations, the bank rate was raised to 50.0 percent by May
1995. This was also in line with Treasury Bill (TB) yield which soared during this period. From
1996, however, the bank rate has been steadily adjusted downwards. This downward adjustment
resulted from developments in the economy as both TB yield and inflation followed a downward
trend.
Up to date, the Reserve Bank of Malawi has continuously used the Bank Rate as an indicator of
monetary policy stance (Moyo & Le Roux, 2018). Every time the RBM adjusts the Bank Rate
what follows is the adjustment of interest rates in the financial system. The Bank rate is set on the
basis of interest rates on the Treasury bill market as well as developments in inflation rates. Below
is a figure showing the behavior of nominal interest rate after the liberalisation.
Figure 3 Interest Rates after Liberalisation
NOMINAL INTEREST RATE AFTER LIBERALISATION
60
50
40
30
20
10
0
LENDING INTEREST RATE
2.3 Private Sector Credit trends in Malawi
Interest rate Liberalisation was earmarked with the primary purpose of improving the private
Sector by ensuring the availability of credit. With reference to the theoretical underpinning
proposed by the McKinnon and Shaw hypothesis as briefly discussed above one would expect to
see the growing deposit rates improving savings, lowering lending rates making credit cheaper
9
inevitably increasing the amount allotted towards private sector credit, stimulating investment and
hence positively affecting financial development. However, as is the case with many African
countries the Malawi private sector has not been growing as anticipated.
This has given birth to the trending debate of whether financial liberalization through interest rate
reforms is helping Malawi or not. Underlining this assertion is the Interest rate capping issue whose
principles are against financial liberalization principles. The interest rate Capping Bill was tabled
as a private members Bill in the parliament of Malawi sorting to amend the Financial services Act
and make provisions regulating interest rates, setting maximum recovery from any loaned amount,
setting the policy rate, setting Treasury Bills rate among other amendments with the hope that the
same will make accessibility of loans cheap, reduce the closure of businesses due to exorbitant
interest rates and loans, enable a significant return to depositors of credit among other benefits (
Human Rights Defenders Coalition, 2019).
Below, is graphical representation of private sector credit trends in Malawi from 1970 to 2018.
Figure 4 Private sector credit trends in Malawi
PRIVATE SECTOR CREDIT
25
20
15
10
PRIVATE SECTOR CREDIT
5
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
0
As depicted in the figure 4 above, from 1970 to 1986 there is an almost constant level of credit to
the private sector. This represents a period of stiff government control of interest rates before the
interest rates were liberalised. From 1987 domestic private sector credit starts declining as the
interest rate were now liberalised and were increasing entailing a negative relationship between
the two variables.
10
Following the liberalization of the lending rates in 1987, deposit rates deregulation in 1988, credit
interest rate controls in 1989. The interlinking of the bank rate to the official auction-bill rate in
1990, removal of credit ceilings in 1991 to 1994.we see the Lending rate from 19.5 percent in 1987
to 31 percent in 1994 with a further decline in the domestic private sector credit particular lower
between 1991 1994 partially because the government was borrowing to finance the referendum in
1993 and the general elections in 1994. This shows a persistent negative relationship between the
two variables.
From 1995 to 2001 interest rates are seen rising mainly due to expansionary fiscal policies which
perpetrated massive government borrowing aimed at sponsoring the Free Primary Education
Programme implemented soon after the 1994 general elections, sponsoring the 1999 general
election and the 2001 famine. From there we see the same declining with an extensive slowdowns
noticed between 2005 and 2011 which the period under the late Professor Bingu Wa Muthalika
government is. Reasons for the same would be due to the bumper harvest realized during the same
period which depressed the food Inflation which is highly correlated with the interest rate in
Malawi, the Fixed Exchange rate regime adopted within the same period apart from other
contributing factors.
From 2012 we see the interest rate rising again due to lower harvest realized during the same period
which affected the food Inflation which is highly correlated with the interest rate in Malawi, the
Flexible Exchange rate regime adopted which saw the infamous massive currency devaluation,
expansionary fiscal policy to sponsor the 2014 General elections and the infamous Cash Gate,
freezing of the Donor Funding among others. This has continued up to 2018.
Now, from the graph it is clear that the liberalisation of interest rate has negatively affected the
amount of credit allocated to the private sector hence affecting private sector development and
investment.
11
CHAPTER THREE
LITERATURE REVIEW
3.1. Introduction
This chapter reviews related literature on theories of interest rate and private sector credit and their
implications on the study. The chapter also summarizes the information from other research on
similar research topics and their empirical findings. To do this, it specifies objectives, theories and
the methodologies used to derive those findings. At the end of the chapter it summarizes theoretical
and empirical relationships and the gap that is being researched on.
3.2. Theoretical review
There are a number of theories on interest rates. Most of these, explain the role that interest rates
play in stimulating the availability of loanable funds. This study reviews five theories of interest
rate and derail a detailed discussion on the impact of interest rate on loanable funds which are used
to finance private sector credit in Malawi in the subsequent subsections.
3.2.1 Classical Theory of Interest Rate
The classical theory of interest rates is the first theory to be reviewed. This theory is perceived as
the conventional theory of interest rates and is associated with prominent names in economics’
namely David Ricardo, Marshall, A.C. Pigou, Cassels, Walras, Taussing and Knight (Pal, 2018).
The theory is famously referred to as the “real theory of interest rate” because it focusses on real
factors of production and not monetary factors as is the case with other interest rate theories. The
theory puts forward two arguments. On one hand, it argues that there is a negative relationship
between investment and interest rate (Lending rate) and on the other hand it argues that there exist
a positive relationship between interest rate (deposit rate) and savings (Laubach, 2003).
To determine the market interest rate the theory therefore posits that economists should equilibrate
the rate that determine amount of funds that market players demand for investment in the market
to the rate that determine the amount of funds that is supplied by market players to the market
through savings. That is, for the private sector to benefit from the loanable funds that are available
in the market the sector should borrow or lend at the prevailing equilibrium interest rate.
12
3.2.2 Neoclassical Theory of Interest Rate or Loanable funds Theory
The Loanable funds which is also called the Neoclassical Theory of Interest Rate is perceived as
an improvement of the classical theory of interest rates since it incorporates monetary and real
factors of production (Pal, 2018). These factors include savings, bank credit, dishoarding and
disinvestment. It is referred to as the loanable funds theory because its main argument is that what
determines the rate of interest in the market is the demand for and supply of loanable funds.
The demand for loanable funds has primarily three sources. These are the government, the private
sector and consumers who need the funds for purposes of investment, hoarding and consumption.
The private sector borrows funds for the purchase of capital goods and for investment projects.
Such borrowings are interest elastic and depend mostly on the expected rate of profit as compared
with the interest rates (Belke & Polleit, 2009). The demand of loanable funds on the part of
consumers is for the purchase of durable consumer goods like scooters, houses and many others.
Individuals’ borrowings are also interest elastic. The tendency to borrow is more at a lower rate of
interest than at a higher rate. In other words there is a negative relationship between interest rate
and the demand for loanable funds. As such since the private sector is a player in the market the
theory explains that its tendency to borrow is more at a lower rate of interest and this can be used
to understand the behavior of the private sector when it comes to demanding loanable funds.
3.2.3 The Liquidity Preference Theory
The real factors which were considerably emphasized by both the classical and neo-classical
economists in their theories of Interest rate are vehemently argued against by the Liquidity
Preference Theory developed by John Maynard Keynes. Keynes articulated that, what determines
the rate of interest is the demand for and supply of money in the money market. In his classic work,
“The General Theory of Employment, Interest and Money (1936),” Keynes argued that interest
rate adjusts to balance the supply and demand for the economy’s most liquid asset, money (Keynes,
1936). Keynes defined the rate of interest as the reward for not holding but rather a reward for
parting with liquidity for a specified period. It is not the price which brings into equilibrium the
demand for resources to invest with the readiness to abstain from consumption. It is the price which
equilibrates the desire to hold wealth in the form of cash with the available quantity of cash. In
other words, the rate of interest in the Keynesian sense is determined by the demand for and the
supply of money (Pal, 2018).
13
Supply of money refers to the total quantity of money in the country for all purpose at any time.
Though the supply of money is a function of the rate of interest to a degree, yet it is considered to
be fixed by the monetary authorities. The demand for money according to Keynes is the liquidity
preference by which his theory of interest rate is commonly known. The liquidity preference is the
desire to hold cash. The rate of interest in Keynes word is the premium which has to be offered to
induce people to hold the wealth in some form other than hoarded money. The higher the liquidity
preference, the higher will be rate of interest that will have to be paid to the holders of cash to
induce them to part with their liquid assets. The lower the liquidity preference, the lower will be
the rate of interest that will be paid to cash-holders (Appelt, 2016).
Keynes gives three reasons the private sector and all players in the market hold money. These are
for current transactions, as a precaution and for speculation. He holds that the transactions and
precautionary motive of holding money has nothing to do with the rate of interest. But the
speculative motive of holding money is to make gain by investing in bonds. Money held for
speculative purposes is a liquid store of value which can be invested at an opportune moment in
interest-bearing bonds or securities (Keynes, 1936). Bonds and the rate of interest are inversely
related to each other low bond price are indicative of high interest rates, and high bond price reflect
low into other low bond price are indicative of high interest rates, and high bond price reflect low
interest rates. This theory therefore explains
3.2.4 Neo-Keynesian Theory of Interest or Hicks IS – LM Curve or Modern Theory of
Interest
After the Keynesian theory, Hicks and Learner synthesized the classical theory, the neo classical
theory and the Keynesian theory. Their synthesis formulated a new theory which is known as
Hicks’ Investment and Saving (IS)-Liquidity Market (LM) model (Belke & Polleit, 2009). The
new theory is also termed as the determinate theory of interest rate for succeeding in relating the
rate of interest with income. The theory took saving, and investment from the classical theory of
interest, liquidity preference or demand for money and the supply of money from the liquidity
preference theory to determine the rate of interest and real income (Spahija, 2016). This is
successful by using Investment Saving (IS) and Liquidity Money (LM) curves in the commodity
and money market to find an equilibrium interest rate.
14
3.2.5 McKinnon-Shaw Hypothesis
The theoretical relationship between financial sector reforms and private sector development can
be linked to the theoretical works of McKinnon (1973) and Shaw (1973) also known as the
McKinnon-Shaw hypothesis. According to these authors, a low or negative real rate of interest
discourages savings, and hence reduces the availability of loanable funds, which ultimately
constrains private sector development. The hypothesis argues that low interest rates discourage
people from holding money or other financial assets because of the low incentives for holding such
assets (McKinnon, 1973). As such a rise in the rate of interest increases the volume of financial
savings through financial intermediaries, which, in turn, raises investment funds – a phenomenon
he called the ‘conduit effect’ (Odhiambo N. M., 2016). The realized investment in this case
increases because of the greater availability of funds through increased savings, which is caused
by a rise in interest rates. In other words, investment in this theory is constrained by the availability
of loanable fund savings, rather than the demand for investment funds.
This study uses the McKinnon-Shaw hypothesis to explain the behavior of private sector
development before and after the liberalization. This is because the hypothesis is considered as the
conventional theory explaining the relevance of financial reforms, interest rate liberalization
inclusive, in affecting imperative macro-economic variables, flexibility and efficiency in the
financial sectors, growth and development of market economies (Shaw, 1973). As a matter of fact,
the hypothesis has been used to explain the market structure, liberalization and performance in the
Malawian banking industry (Wadonda, 2001) economic growth, savings and investment (Moyo
& Le Roux, 2018), Financial sector reforms and private investment (Odhiambo N. M., 2016) on
demand for credit in Medium and Small enterprises (Chimkono, 1997) among others.
3.3Empirical Review
On the empirical front, one of the most controversial issues among the economist and finance
researchers is the relationship between interest rate reforms and private sector development using
private sector credit as a proxy. The debate is still eminent and not resolved.
According to Naimy and Dame, (2005) in their study conducted in Lebanon found a negative
relationship between interest rate reforms and private sector credit and stressed that interest rate
reforms widens crowding out gap of credit for private sector development by the Lebanese
government. Similar results were found in Lesotho in a research paper investigating the effect of
15
various factors on the supply of credit to the private sector in Lesotho using the autoregressive
distributed lag cointegration approach (Molapo & Damane, 2017). However, in China contrary
results were observed in a study examining the consequences of interest-rate liberalization in a
two-sector general equilibrium model of China found a positive effect between interest-rate
liberalization and private sector credit retracting findings of crowding out effect found in other
studies (Liu, Wang, & Xu, 2019).
Joining the debate on different fronts researchers have tested other important factors which
according to early research hinting on private sector development by McKinnon (1973) and Shaw
(1973) are important in explaining private sector development. These factors include private sector
investment, economic growth, Savings, and financial intermediaries’ performance among others.
Inedu (2015) investigated the impact of interest rate liberalization on private investment in Nigeria
using the Vector Error Correction Model (VECM) and found the existence of no differential
impact of interest rate liberalization on investment in Nigeria during the pre and post-liberalization
regimes coinciding with the results found in Lesotho and Lebanon. One would tend to argue that
the positive result was only found in China, a developed economy but a study by Mendoza (2003)
on private investment in Venezuela using the Logistic Smooth Transition Vector Error Correction
(LSTVEC) model showed evidence of positive growth on private investment after interest rate
liberalisation.
Similarly, when testing the impact of interest rate liberalisation on economic growth through
Savings and Investment in the Southern Africa Development Community (SADC) region, Moyo
and Le Roux (2018) using the ARDL bounds also found a positive impact of interest rate reforms
on economic growth through savings and investments. However, this contracts what Naude (1995)
found in his study on the impact of interest rate reforms on private sector development and
economic growth of Africa in general, which found that interest rate liberalisation negatively
affects African banks performance, reduces the supply of credit and negatively affect economic
growth in Africa.
Nonetheless, Asare etal (2000), in his study examining the impact of interest rate reforms on bank
performance in Ghana argued that the reforms have largely succeeded in terms of enhancing
financial development and the expansion in size and diversity of the banking sector. Due to the
16
reforms Ghana has witnessed enactment of the new Banking Law which brought more discipline
to the banking sector. The Central Bank of Ghana has improved its regulatory activities as
compared with the pre-interest reform period. Since the introduction of interest rate reforms, banks
have derived considerable profit from their investments in government securities and Bank of
Ghana securities. As such, the study recommended the need for further reforms.
The controversial surrounding research on interest rate reforms also surfaces on research
conducted in Malawi. Odhiambo (2016) for example, in a study examining the impact of reforms
on private investment in Malawi, using the Auto Regressive Distributed Lag (ARDL) bounds
testing approach found a positive and significant impact on private investment in the short-run,
but a negative effect in the long-run.
This was refuted by Chirwa (2001), in a study investigating the impact of interest rate reforms on
the performance of the commercial banking system in Malawi who found a positive impact both
in the short and long run using panel regression analysis. The finding was similar to what Chinkono
(1997) found in a study testing the impact of interest rate reforms on the demand for small and
medium scaled enterprises. Moreover, Mwanamveka (1994) agreed with the finding in his study
testing the impact of interest reforms on saving in Malawi
Nevertheless, Mwabutwa etal (2012), in a study testing the impact of interest rate reforms on
Consumption, Savings and investment found a negative effect and attributed the same to the notion
that liberalisation was implemented on the background of weak institutions and unstable
macroeconomic environment.
It is therefore evident that, there is controversy regarding research on interest rate reforms on
private sector development and even other factors which are important factors which according to
early research hinting on private sector development by McKinnon (1973) and Shaw (1973) are
important in explaining private sector development. Congruent to the debates, is the Interest rate
Capping Bill which was brought in the Malawi Parliament for debate. It should be mentioned that,
this was done without adequate scientific information to refer to as shown in the review. As such,
this study is examining the impact of interest rate reforms on private sector development in Malawi
as a way of contributing to the existing lean body of knowledge on interest rate reforms which if
17
made adequate would become a fountain in which policy makers can tap from as the debate of
determining whether Malawi needs liberalized interest rates or not continues.
18
CHAPTER FOUR
METHODOLOGY
4.1. Introduction `
It can then be argued that, private sector credit is constrained as a consequence of depressed levels
of savings resulting from the repressed financial system. Conversely by removing regulation on
interest rate the expectation is that real interest rates would rise in effect increasing the level of
savings and private sector credit. It should be mentioned however that, despite both Mackinnon
and Shaw advocating for interest rates deregulation as a solution to the hurdles put forward by
financial repressive policies used by developing countries they further articulated that the
underlining implication of the theory is that the demand for real money balances (M/P) has a direct
positive relationship with real income, Y, on the real rate of interest on bank deposits, R, and the
real average return on capital, r. Critically, the positive association between the average real return
on capital and the demand for money balances represents the complementarity between capital and
money. This, however, is only one leg of the complementarity hypothesis.
According to McKinnon, the Private sector credit ratio, PSC/Y must also be positively related,
inter alia, to the real rate of return on money balances. This is because a rise in the real return on
bank deposits, R, if it raises the demand for money and real money balances are complementary
to Private Sector Credit, it must also lead to a rise in the private sector credit ratio. The researcher
hereby adopts this theory as the main theoretical framework of this research.
The McKinnon-Shaw theory therefore gives a demand for money function and a demand for
private sector credit function as.
𝑚
𝑝
𝑃𝑆𝐶
= 𝑓 (𝑦, 𝑦 , (𝑑 − 𝜋 𝛼 )) …………………………………………………………………1
𝑃𝑆𝐶
Equation (1) is the standard long run real money demand function with Y= Real Income, 𝑌 is the
ratio of private sector credit, (𝑑 − 𝜋 𝛼 ) is the Real rate of interest, d is the nominal interest rate and
𝑀
𝜕( )
𝑃
𝜋 𝛼 is the anticipated inflation rate. 𝜕(𝑑−𝜋
𝛼 ) .A positive real interest rate allows a greater money
19
demand. However, McKinnon complementary theory appears in the following private Sector
credit function:
𝑃𝑆𝐶
𝑦
= 𝑓(𝑟, (𝑑 − 𝜋 𝛼 ))………………………………………………………………………2
Equation (2) is the Private Sector credit function
𝑟 = the physical capital average current rate.
𝜕(
𝑃𝑆𝐶
)
𝑌
𝜕(𝑟)
And
𝑃𝑆𝐶
)
𝑌
𝛼
𝜕(𝑑−𝜋 )
𝜕(
Thus the complementarity theory seen in partial derivative following
𝑀
𝑌
𝑃𝑆𝐶
𝜕(
)
𝑌
𝜕( )
………………………………………………………………………………………....3
𝑃𝑆𝐶
)
𝑌
𝜕(𝑑−𝜋𝛼 )
𝜕(
………………………………………………………………………………………..4
For Shaw, the Private Sector Credit (PSC) is an increasing function of real interest rate (r). The
above private sector credit and saving functions are express as below (clarify)
𝜕(𝑃𝑆𝐶)
𝑃𝑆𝐶 = 𝑃𝑆𝐶(𝑟) …………. 𝜕(𝑟) < 0………………………………………………..5
𝜕(𝑆)
𝑆 = 𝑆 (𝑟. 𝑔) ………….. 𝜕(𝑟) > 0
𝜕(𝑆)
𝜕(𝑔)
> 0 ………………….……………………6
4.2. Model Specification
Following the theoretical framework discussed above, the study adopts a Private Sector credit
model by Aftab etal (2016) whose functional form is specified as follows:
𝑃𝑆𝐶 = 𝑓(𝐶𝑃𝐼, 𝑅𝐸𝑋𝑅, 𝑅𝐼𝑁𝑇𝑅, 𝐺𝑂𝑉𝐸𝑋𝑃)………………………………………………………7
Where;
PSC = Real Private sector credit
REXR= Real Exchange Rate
RINTR= Real Interest Rates
GOVEXP=Real Government Expenditure
CPI = Consumer price index.
20
The equation (7) above can be transformed into equation (8) as follows;
𝑙𝑛𝑃𝑆𝐶𝑡 = 𝛽0 + 𝛽1 𝑅𝐼𝑁𝑇𝑅𝑡 + 𝛽2 𝑅𝐸𝑋𝑅𝑡 + 𝛽3 𝑙𝑛𝐶𝑃𝐼𝑡 + 𝛽4 𝑙𝑛𝐺𝑂𝑉𝐸𝑋𝑃𝑡 + 𝐵5 𝐷𝑈𝑀𝑡 + 𝜀1𝑡 …….8
However, in the structural equation approach (structural modeling), the equation of model is
basically using economic theory to model the behavioral relationship among the variables of
interest. Unfortunately, economic theory is not often rich enough to provide a dynamic
specification that identifies all of these relationships. Furthermore, estimation and inference are
complicated by the fact that endogenous variables may appear on both the left and right sides of
the equations in the model.
These problems lead to alternative, non-structural approaches to modeling the relationship among
several variables. One such alternative is the Error Correction Model whose details will be
discussed in one of the subsequent paragraphs.
4.3. Definition of Variables
Below is a presentation of the definition of the variables used in this study.
4.3.1. Real Private Sector Credit
Real Private sector credit is the dependent variable of the study. The variable represents the
domestic credit provided to the private sector for investment by the financial institutions in
Malawi. Since the study needed the variable to be expressed in real terms the study presents it as
a percentage of Malawi’s Gross Domestic Product.
4.3.2. Real Interest rates
The variable, Real Interest Rate (RINTR), is an independent variable meant to capture the impact
of financial liberalisation on the level of Real Private Sector Credit in Malawi. The coefficient of
this variable is expected to be negative. That is, as real interest rates increases we expect a decrease
in private sector credit in Malawi.
21
4.3.3. Real Inflation Rate
This is the average rate at which prices of consumer goods increases in a specified period.
Consumer price index (CPI) is a widely used indicator of inflation. CPI is measured as the mean
change in the price of goods and services in a specific period. It has been included in the model
largely as an indicator of instability and a country’s ability to control macroeconomic policy. Its
coefficient is expected to be negatively related to the private sector credit.
4.3.4. Real Government Expenditure
Real Government Expenditure represents the amount of funds that the government of Malawi
spends in a particular year as a percentage of the total Gross Domestic Product. The coefficient of
the real government expenditure is expected to be negatively related to private sector credit
because an increase in the government expenditure may be possible due to the increase in domestic
borrowing by the government which poses a burden on the amount of funds available to the private
sector for borrowing.
4.3.5. Real Exchange Rate
Exchange Rate represents the Malawi Kwacha Units expressed as a rate to the United States
dollars. The coefficient of real exchange rate is expected to be negatively related to private sector
credit because an appreciation of the currency will make credit cheaper and spur demand for credit
hence increasing private sector credit.
4.3.6. Dummy (DUM)
The variable Dummy (DUM), is a variable representing financial liberalization. It takes the value
of 0 before liberalization and 1 thereafter.
4.4. Data Sources
The study examines the impact of interest rate liberalisation on private sector credit in Malawi
using data for the period between 1975 and 2018. The data is collected from the World Bank
Indicators, the Reserve Bank of Malawi and the International Financial Statistics. The data analysis
is done using Stata 14.
4.5. Stationarity
The assumption of the classical regression model necessitates that both the dependent variable and
the regressors be stationary. That is, the errors should have a zero mean and a finite variance. In
22
the existence of non stationarity there might be what Granger (1974) called a spurious regression.
In a spurious regression the results obtained shows statistically significant relationships between
the variables in the regression model when in fact all that is obtained is evidence of a
contemporaneous correlation rather than meaningful causal relationship. For that reason, before
estimating a regression, variables must be stationary or if all variables are non-stationary, they
must be cointergrated. To test for stationarity the study uses the Augmented Dickey Fuller, the
Phillip Perron and the Dickey Fuller Generalised Least Squares Unit root tests discussed below.
4.5.1. The Dickey-Fuller Test for Unit Root
Dickey and Fuller (1979) developed a procedure for testing non-stationarity. The key intuition to
their test is that testing for non stationarity is corresponding to testing for the presence of a unit
root. The Dickey- Fuller test is based on the first order autoregressive model of the form:
𝑌𝑖 = 𝛼𝑌𝑡−1 + µ𝑖
What the test examines here is whether α is equal to 1 (Unity and hence “Unit root”). The null
hypothesis is𝐻0 : 𝛼 = 1, and the alternative hypothesis is 𝐻1 : 𝛼 < 1.
A different but more convenient test follows after subtracting 𝑦𝑡−1 from both sides of the equation
above as below;
𝑌𝑡 − 𝑌𝑡−1 = 𝛼𝑌𝑡−1 − 𝑌𝑡−1 + µ𝑖
∆𝑌𝑖 = (𝛼 − 1)𝑌𝑡−1 + µ𝑖
∆Yi = λYt−1 + µi
Where λ = (α − 1). After that, the null hypothesis becomess H0 : λ = 0 and that the altenative
hypothesis H1 : λ < 1 where if λ = 0 , then Yt follows a pure random walk
4.5.2. The Augmented Dickey Fuller Test
As the error term is unlikely to be white noise, Dickey and Fuller extended their test procedure
suggesting an augmented version of the test which includes extra lagged terms of the dependent
variables in order to eliminate autocorrelation. The lag length on these extra terms is either
determined by the Alkaike Information Criteria (AIC) or Schwartz Bayesian criterion (SBC), or
more usefully by the lag length necessary to whiten the residuals. In our case, the variables used
23
in the study were subjected to Unit Root test using the Augmented Dickey Fuller as shown in the
table 4.1 below.
4.5.3. Phillip Perron Test
The Phillip Perron Test controls for higher order autocorrelation in a series. It is similar to Dickey
Fuller Test except that it relaxes assumptions about autocorrelation and Heteroskedasticity. This
test is based on the following first order auto-regressive [AR (1)] process
∆𝑌𝑖 = 𝛼 + 𝛽𝑌𝑡−1 + µ𝑖
Where 𝑌𝑡 the variable of interest α is is the constant term and β is the slope coefficient. Stationary
test is a one-tailed test and skewed to the left. It is a negative test. The results of the Phillip Perron
are presented in the Table 4.2 below.
4.5.4. The Dickey Fuller- Generalized Least Squares Test ( DF-GLS Test)
Although common practice in time series econometrics has involved the application of the
Augmented Dickey–Fuller and the Phillip Perron Test in their Econometrica article, Elliot,
Rothenberg and stock proposed an efficient test modifying the Dickey Fuller Test using the
Generalised Least Squares. They demonstrated that the modified test has the best overall
performance in terms of small sample size and power. The results of the DF-GLS test are presented
in the Table 4.3 below
Table 4. 1 Augmented Dickey Fuller Test
Variable
Augmented Dickey Fuller(ADF) Test
Order of Integration
At Level
After Differencing
No Trend Trend No Trend Trend
ln PSC
-2.281
-2.126
-3.957** -3.989***
I(1)
ln CPI
0.085
-2.058 -4.012*** -3.965**
I(1)
REXR
-0.114
-2.500 -4.093***
-4.027**
I(1)
RINTR
-0.727
0.748 -5.276***
-5.249 *** I(1)
lnGOVEXP -1.161
-1.135 -6.374 ***
-6.304 *** I(1)
NB: The *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively
Table 4. 2 Stationarity test using Phillip Perron Test
Variable
Phillip Perron (PP) Test
At Level
After Differencing
No Trend Trend No Trend Trend
24
Order of Integration
lnPSC
-2.302
-2.021 -5.846*** -5.849*** I(1)
lnCPI
-0.620
-1.428 -4.957*** -4.882*** I(1)
REXR
0.318
-2.386 -4.084*** -4.019**
I(1)
RINTR
-1.934
-2.127 -5.451*** -5.412 *** I(1)
lnGOVEXP -4.390
-4.390 -10.215** -10.062*** I(1)
NB: The *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively
Table 4. 3 Stationarity Test using DF-GLS
Variable
DF-GLS Test
Order of Integration
At Level
After Differencing
No Trend Trend No Trend
Trend
lnPSC
-1.854
-1.459 -3.699*** -3.721 ** I(1)
lnCPI
-1.694
-0.532 -2.107**
-4.110*** I(1)
REXR
-1.797
-0.191 -3.168**
-3.664** I(1)
RINTR
-2.333
-1.691 -4.777***
-4.823*** I(1)
lnGOVEXP -1.934
-2.972
-3.041*** -4.522*** I(1)
NB: The *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively
The test results presented above shows that all the variables are non-stationary at level but they are
stationary after differencing once. As such it is concluded that all the variables are integrated of
order one, [I (1)]. Consequently, the results prompts us to proceed to test for Co-integration using
the Johansen cointegration test.
4.6. Cointegration
Econometrically it is a known fact that non stationary data leads to spurious regressions. One way
of resolving this is to difference the series successively until stationarity is achieved and then use
the stationary series for research analysis. However, the solution is not ideal. Applying first
differences of the data leads to loss of long run properties, since the model in differences has no
long run properties. Apart from that, there is a desire to have models which combines both the
long run and short run properties and at the same time maintaining stationarity in all of the
variables. This has led to consideration of the problem of regression using variables that are
measured at their levels. The basic idea is that there are economic time series that are integrated
and of the same order (which means they are non-stationary) which we know are related (mainly
through a theoretical framework). It is possible to combine them together into a single series which
is itself is non-stationary. The series that exhibit the properties are said to be Cointergrated.
25
To test for Cointegration the study uses the Bounds Test of cointegration. To do this, the test first
requires doing a Lag Exclusion Test aimed at obtaining the order of lags on the first differenced
variables from the unrestricted models. To do this, the study uses the Akanke-Information
Criterion (AIC) that has proven to be preferable to other lag selection approaches, especially when
the sample is small as the case is in this study. The table 4.4 shows the results of the Lag Order
Selection Test.
Table 4. 4 Lag Order Selection Test
Lag
0
1
2
3
4
LL
-236.249
-27.785
9.4496
20.456
40.0164
LR
Df P
416.93
74.469
22.013
39.121*
16
16
16
16
0.000
0.000
0.143
0.001
FPE
1.44484
.000121
.000044*
.00006
.000057
AIC
11.7195
2.33097
1.29514*
1.53873
1.36505
HQIC
11.7803
2.63536
1.84303*
2.33013
2.39996
SBIC
11.8866
3.16686
2.79974*
3.71204
4.20707
As discussed above the selection of a suitable lag value is based on the minimum value of AlkaikeInformation Criterion (AIC) on a specific lag order. The results above indicates that AlkaikeInformation Criterion (AIC) is minimum at lag 2, which posits the study to select 2 lags in its
estimations. The next step is the test for co-integration relationship among the variables. Below is
the Johansen Test for Cointegration results.
Table 4. 5 Johansen Test for Cointegration
maximum Rank Parms
0
30
1
39
2
46
3
51
4
54
5
55
HO: No Cointegration
LL
39.490642
55.80306
65.37998
70.669772
75.660068
78.159827
Eigen Values Trace Statistics
77.3384
0.54875
44.7135*
0.37322
25.5597
0.22743
14.9801
0.21606
4.9995
0.11480
5% Critical Values
68.52
47.21
29.68
15.41
3.76
The results in the table above indicates that we reject the null hypothesis of no cointegration among
the variable at Rank 0 since the trace statistic 77.3384 is greater than the 5% critical value 68.52.
However, we fail to reject the null hypothesis at Rank 1 which indicates the existence of one
cointegrating equation since the trace statistic 44.7135 is less than the 5% critical value 47.21. The
26
Johansen results therefore shows the presence of one cointegrating vector in the equation which
call for the estimation of the Vector Error Correction Model discussed below
4.7.Vector Error Correction Model
When we regress an equation whose variables have been made stationary by differencing, the
model will give us correct estimates of the parameters and the spurious equation problems would
have been resolved. However, what we would have from such an equation is only the short run
relationships between the variables. Knowing that economists are generally interested in long run
relationships, this constitutes a big problem. In order to resolve this the concept of Cointegration
and Error Correction Model (ECM) are very useful. According to Granger (1986) representation
theorem, a system of Cointergrated variables can be represented by a dynamic error correction
model. Specifically, to the model containing stationary variables, we add the residuals (lagged
once) that are obtained from the underlying Cointegrating (long run relationships). These residuals
are called the error correction whose coefficient reflects the process by which the dependent
variable adjusts in the short run to its long run equilibrium path. Below is the specification of the
Vector Error Correction Model;
𝑙𝑛𝑃𝑆𝐶𝑡 = 𝜑0 + 𝜑1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝜑2 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝜑3 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝜑4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝜑5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝜑6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝜑7 𝐸𝐶𝑇𝑡−𝑖 + 𝜇𝑡 …10
𝑅𝐸𝑋𝑅𝑡 = 𝛿0 + 𝛿1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝛿2 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝛿3 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝛿4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝛿5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝛿6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝛿7 𝐸𝐶𝑇𝑡−𝑖 + 𝜀𝑡 …….11
𝑅𝐼𝑁𝑇𝑅𝑡 = 𝜃0 + 𝜃1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝜃2 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝜃3 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝜃4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝜃5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝜃6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝜃7 𝐸𝐶𝑇 + 𝜋𝑡 ……..12
𝑙𝑛𝐶𝑃𝐼𝑡 = 𝛾0 + 𝛾1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝛾2 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝛾3 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝛾4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝛾5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝛾6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝛾7 𝐸𝐶𝑇𝑡−𝑙 + 𝜏𝑡 ……..13
𝑙𝑛𝐺𝑂𝑉𝐸𝑋𝑃𝑡 = 𝛼0 + 𝛼1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝛼2 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝛼3 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝛼4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝛼5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝛼6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝛼7 𝐸𝐶𝑇𝑡−𝑙 + 𝜏𝑡 ……14
𝐷𝑈𝑀𝑡 = 𝜆0 + 𝜆1 ∑𝑘𝑗=𝑖 𝑙𝑛𝑃 𝑆𝐶𝑡−𝑖 + 𝜆2 ∑𝑘𝑗=𝑖 𝑅𝐸𝑋 𝑅𝑡−𝑖 + 𝜆3 ∑𝑘𝑗=𝑖 𝑅𝐼𝑁𝑇 𝑅𝑡−𝑖 + 𝜆4 ∑𝑘𝑗=𝑖 𝑙𝑛𝐶 𝑃𝐼𝑡−𝑙 +
𝜆5 ∑𝑘𝑗=𝑖 𝑙𝑛𝐺𝑂𝑉 𝐸𝑋𝑃𝑡−𝑙 + 𝜆6 ∑𝑘𝑗=𝑖 𝐷𝑈 𝑀𝑡−𝑙 + 𝜆7 𝐸𝐶𝑇𝑡−𝑙 + 𝜎𝑡 …….….15
27
Where:
All variables are as defined above
𝑘 = lag length,
𝜑, 𝛼, 𝛿 , 𝜃, 𝛾 , 𝜆 = The coefficient parameters
𝜇𝑡 , 𝜀𝑡 , 𝜎𝑡 𝜏𝑡 , 𝜋𝑡 ,……. = are structural innovation (error term)
The ECM is important and popular for various reasons. Firstly it is a convenient model for
measuring the correction from disequilibrium. Secondly, since ECMs are formulated in terms of
first differences, which typically trends from the variables involved, they resolve the problem of
spurious regression. Thirdly, ECMs can fit into the general to specific approach of econometric
Modelling, which is in fact a search for the most parsimonious ECM model that best fit the given
sets. Finally the most important features of the ECM come from the fact that the disequilibrium
error term is a stationary variable (by definition of Cointegration). Because of this, the ECM has
important implications, the fact that the two variables are Cointergrated implies that there is some
adjustment process, which prevents the error in the long run relationship becoming larger and
larger.
4.8.Diagnostics Tests
The following Diagnostics tests were carried out to verify the appropriateness of the model.
4.8.1. Breusch Godfrey LM Test for Autocorrelation
This test is generally used for higher order serial correlation. In this test, the order of lags thought
to be determining the disturbances is specified. This study tested the presence of second order
autocorrelation. The null hypothesis of the test is that there is no serial correlation in the residuals
up to the specified order and the results are presented in the table below.
Table 4. 6 Breusch Godfrey LM test for Autocorrelation
Lag Chi2
df Prob>chi2
1
15.8353 36 0.87545
2
40.5562 36 0.27635
28
H0: no autocorrelation at lag order
From the table above, the probability value is higher than the significant values at lag 2. Hence,
the study fails to reject the null of no serial correlation implying that the model employed in the
study does not suffer from serial correlation.
4.8.2. The Normality Test
The assumption that the errors are independently, identically, and normally distributed with zero
mean and finite variance allows us to derive the likelihood function. If the errors do not come from
a normal distribution but are just independently and identically distributed with zero mean and
finite variance, the parameter estimates are still consistent, but they are not efficient. To do this we
use the Jacque Bera Test whose results are presented below
Table 4. 7 Jacque Bera Test
Jarque Bera Test
Equation
D.lnRPSC
D_lnGOVEXP
D_lnCPI
D_RINTR
D_REXR
D_Dummy
ALL
Chi2
2.167
0.882
6.878
0.226
46.172
114.024
170.348
Df
2
2
2
2
2
2
12
Pro>Chi2
0.33847
0.64323
0.03209
0.89310
0.00000
0.00000
0.00000
Ho: normality
The results presented in the study indicates failure to reject the null hypothesis of normally
distributed errors. Since the significant levels are lower than the probability of the natural log of
real private sector credit equation. Hence the model employed in the study is normally distributed.
4.8.3. Stability test
The stability test is used to check whether we have correctly specified the number of cointegrating
equations. The companion matrix of a VECM with K endogenous variables and r Cointegrating
equations has K−r unit eigenvalues. If the process is stable, the moduli of the remaining r
29
eigenvalues are strictly less than one. Because there is no general distribution theory for the moduli
of the eigenvalues, ascertaining whether the moduli are too close to one can be difficult
Eigenvalue stability condition
Eigenvalue
1
1
1
1
1
.9352521
-.8685469
.6740828
.6740828
-.3120779
-.3120779
-.5676553
.2643864
.2643864
.1500093
.1500093
-.2813857
-.2813857
Modulus
+
+
-
.4518004i
.4518004i
.6895805i
.6895805i
+
+
+
-
.4385488i
.4385488i
.3438732i
.3438732i
.1588576i
.1588576i
1
1
1
1
1
.935252
.868547
.811487
.811487
.756911
.756911
.567655
.512079
.512079
.375169
.375169
.323131
.323131
The VECM specification imposes 5 unit moduli.
1
Roots of the companion matrix
.5
0.243
0.189
0.677
0
Imaginary
0.488
0.625
0.131
0.432
0.065
0.000
0.677
-.5
0.625
0.488
0.189
-1
0.243
-1
-.5
0
Real
.5
The VECM specification imposes 5 unit moduli
Points labeled with their distances from the unit circle
30
1
The table above presenting the eigenvalues of the companion matrix and the graph of the
eigenvalues shows that none of the remaining eigenvalues are not within the unit circle hence the
stability check does not indicate that our model is misspecified. Hence the model is stable
31
CHAPTER FIVE
EMPIRICAL RESULTS AND INTERPRETATION
5.1. Introduction
In the preceding chapter, it was established that there exists a long run relationship between the
dependent variable and the explanatory variables. This entailed that, the variables are cointergrated
which called for the estimation of the Vector Error Correction Model .This chapter presents the
results of the Vector Error correction Model starting with the long run outcomes followed by the
short run outcomes and their interpretations. At the end, it presents the impulse response functions
and forecast of the Vector Correction Model results and their interpretation.
5.2. VECTOR ERROR CORRECTION MODEL LONG RUN RESULTS
The study begins the empirical results presentation and interpretation by reporting the regression
results of the estimated long run equations of the Vector Error Correction Model. The results are
presented below.
Table 4. Vector Error Correction Model Long Run Results
Variable
lnRPSC
lnGOVEXP
lnCPI
RINTR
REXR
Dummy
Cons
Obs
Chi2
Prob(Chi2)
Coefficient
1
-1.828328***
.180165***
.0093707**
-.0016307**
-.1589682
-11.8493
42
140.5744
0.0000
Std.Error
Z-Statistics
P>|z|
.6767371
-2.70
0.007
.0369896
4.87
0.000
.0037463
2.50
0.012
.000677
-2.41
0.016
.8730577
-2.18
0.021
- .1274372
-1.25
0.212
Alkaike Info criterion
10.98653
Schwarz Criterion
Log Likelihood
14.74429
-130.7307
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
From the table 4.8 above, the study presents the Johansen normalization restriction imposed model
which is also called the Vector Error Correction Long run Model from where the Error Correction
term is generated. The error correction term is presented in the short run model that is to follow in
the next subsection. The variable natural log of real Private Sector Credit (lnRPSC) in the table
32
above, has a coefficient of 1 because it’s where the restriction is placed as it is the study’s target
variable. One thing that has to be noted is that, to interpret the output of the Johansen normalization
restriction imposed or the long run Vector Error Correction Model the signs of the coefficients are
reversed. Below is a detailed interpretation and discussion of the results.
To begin with, we start by looking at the coefficient of the natural log of Government Expenditure
(lnGOVEXP) which is given as -1.828328. Since when interpreting the long run outcomes we
have to reverse the sign it means in the long run a one unit increase in the government expenditure
as a percentage of the Gross National Product of Malawi will lead to approximately a 1.82
percentage increase in the real private sector credit. This is an unusual outcome as it contradicts
with the crowding out effect theory which argues that a rising government expenditure drives down
and even eliminates private sector spending. This is because most of the government increase in
expenditure is inspired by increasing government borrowing both locally and internationally. Since
the government is a low risk borrower, as it is unlikely for the government to default, the financial
market lenders prefers to lend funds to the government over the private sector. The results are
however not very new in the literature as they were also found by studies conducted by Ahmed
and Sehrish (2004) in Pakistan. As a policy prescription, it is therefore true that, expansionary
fiscal policies in Malawi have a positive impact on private sector credit in the long run as such any
policy interventions on the fiscal variables should be made with long term objectives if the private
sector is to be benefit in as far as access to credit is concerned.
In addition, the results shows that in the long run the natural log of the Consumer Price index
(lnCPI) has a coefficient value 0.180165. Since the coefficient is positive it means that, in the long
run the natural log of Consumer price index negatively affects the natural log of Real Private Sector
credit in Malawi. Precisely, a one unit increase in the Consumer Price Index will lead to a 0.18
percent decrease in the value of the price sector credit expressed as the percentage of gross national
product. Theoretically, this finding is supported by the three theories of inflation which are the
market power theory of inflation, Conventional Demand Pull inflation theory and the structural
theory of inflation. On the empirical front, the result is not new. This is the case because the
findings were also found in the literature in different countries and using different types of
econometric approaches. For instance, studies conducted by Inedu (2015) in Nigeria using the
Vector Error Correction Model (VECM) and Le Roux (2018) using the Autoregressive Distributed
33
Lag bounds test in Africa found similar findings. The results found entails that any policy that
increases consumer price index will negatively affect the access to credit by the private sector in
the long run which is not desirable. This means policy interventions on consumer price index
should be made not to increase the index in the long run to avoid negatively affecting access to
credit by the private sector.
From the results presented, in the long run Real Interest Rates (RINTR) has a negative impact on
the natural log of Real Private Sector Credit (lnPSC) in Malawi. This follows from the fact that,
the coefficient of real interest rate is .0093707. Specifically, a one percent increase in the value of
real interest rate decreases the amount of real private sector credit as a percentage of the gross
national product 0.9 percent. Theoretically, the negative relationship between the variables can be
explained by using the Keynesian theory of interest rates determination. The Keynesian theory in
a nutshell argues that the relationship between interest rate and private sector credit is negative
(Keynes, 1936). The reasoning is that, since interest rates are a cost of capital any increase in it
will increase the cost that one has to pay to access capital. This means the increase in interest rate
inevitably decreases the demand for capital entailing a negative relationship. On the empirical
front, the negative long run relationship between interest rate and private sector credit is not a new
thing as it was also found before from studies conducted by Naimy and Dame, (2005) in Lebanon.
The same results were found in Lesotho in a research paper investigating the effect of various
factors on the supply of credit to the private sector in Lesotho using the autoregressive distributed
lag cointegration approach by Molapo and Damane (2017). This means policy interventions on
interest rates should be made with long term objectives if the private sector is to be benefit in as
far as private sector access to credit is concerned.
From the table, it is clear that in the long run Real Exchange Rates (REXR) has a positive impact
on the natural log of Real Private Sector Credit in Malawi. Precisely, the coefficient is -.0016307
which entails that a one kwacha increase (depreciation) in the exchange rate will positively affect
access to credit by the private sector in the long run. The results are supported by studies conducted
by Asare etal (2000), in a conducted in Ghana. This is also supported by a study by Odhiambo
(2016) in a study conducted in Malawi. Not forgetting studies by Mwanamveka (1994) and
Mwabutwa etal (2012). The results found therefore entails that, any policy that increases the
exchange rate will positively affect the access to credit by the private sector in the long run which
34
is desirable. This means policy interventions on exchange rates should be made with long term
objectives if the private sector is to be benefit in as far as private sector access to credit is
concerned.
The Dummy variable in the table shows the differential Impacts of Interest rate liberalisation on
Private Sector Credit in Malawi. The coefficient of the dummy variable is -0.15 and the standard
error of -2.18 and it is not statistically significant. Since the coefficient is not significant it implies
that the impact of interest rate liberalization on private sector credit is not justified by the pre or
post liberalization regimes. Hence, there is no differential impact of interest rate liberalization on
private sector in Malawi during the pre and post-liberalization regimes. This means that whether
the interest rate are liberalised or not the behavior of private sector credit does not depend on the
liberalisation. As such liberalizing interest rate is not a commendable policy direction if the aim is
to affect private sector credit in Malawi.
5.3.THE VECTOR ERROR CORRECTION MODEL SHORT RUN RESULTS
The study then proceeds to present the short run empirical results interpretation of the Vector Error
Correction Model. The results are presented in the table below.
Table 4. 8 Short Run Vector Error Correction Model Results
Variable
ADJ
lnRPSC(-1)
lnRPSC(-2)
lnGOVEXP(-1)
lnGOVEXP(-2)
LnCPI(-1)
LnCPI(-2)
RINTR(-1)
RINTR(-2)
EXR(-1)
EXR(-2)
Cons
Obs
Chi2
Prob(Chi2)
Coefficient
-.6721383***
.2837092**
.4183705**
-1.354497*
-.524033
-1.604166***
-.0291386
.0107919
.0069666
-.0000997
.001776
.057744
42
140.5744
0.0000
Standard Error
Z-statistic
P>|z|
.1802327
-3.73
0.000
.1816258
1.56
0.018
.1717485
2.44
0.015
.7273837
-1.86
0.063
.7173578
-0.73
0.465
.5783018
-2.77
0.006
.4315513
-0.07
0.946
.009786
1.10
0.270
.0086538
0.81
0.421
.0012708
-0.08
0.937
.0021082
0.84
0.400
.0884776
0.65
0.514
Alkaike Info criterion
10.98653
Schwarz Criterion
Log Likelihood
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
35
14.74429
-130.7307
From the short run results presented in the table above, the results shows that the error-correction
term is negative and statistically significant, as was expected. The results of the error-correction
term shows that about 67 percent of the discrepancy between the actual and equilibrium values of
private sector credit are corrected in each period. This means that the model has indeed an error
correction mechanism and long run characteristics and the study was right to estimate an error
correction model.
Moreover, from the table 4.9 above, the coefficient of the natural log of Government Expenditure
lagged one period (lnGOVEXP (-1)) is -1.354497. The coefficient shows that in the short run a
one unit increase in the government expenditure as a percentage of the Gross National Product of
Malawi will lead to approximately a 1.35 decrease in the value of real private sector credit in
Malawi. This is an expected outcome as it is in line with the crowding out effect theory which
argues that a rising government expenditure drives down and even eliminates private sector
spending. This is because most of the government increase in expenditure is inspired by increasing
government borrowing both locally and internationally. Since the government is a low risk
borrower, as it is not likely to default, the financial market lenders prefers to lend loanable funds
the government over the private sector. As a policy prescription, expansionary fiscal policies in
Malawi have a negative impact on private sector credit as such any of its policy interventions on
the fiscal variables should be made with long term objectives if the private sector is to be benefit
in as far as access to credit is concerned.
Moreover, the results shows that in the short run the natural log of the Consumer Price index lagged
one period (lnCPI (-1)) has a coefficient -1.604. Since the coefficient is negative, the result means
that in the short run the natural log of Consumer price index negatively affects the natural log of
Real Private Sector credit in Malawi. Precisely, a one unit increase in the Consumer Price Index
will lead to a 1.604 percent decrease in the value of the price sector credit expressed as the
percentage of gross national product. Theoretically, this finding is supported by the three theories
of inflation which are the market power theory of inflation, Conventional Demand Pull inflation
theory and the structural theory of inflation. On the empirical front, the result is not new. This is
the case because the findings were also found in the literature in different countries and using
36
different types of econometric approaches. For instance, studies conducted by Inedu (2015) in
Nigeria using the Vector Error Correction Model (VECM) and Le Roux (2018) using the
Autoregressive Distributed Lag bounds test in Africa found a similar findings. The results found
entails that any policy that increases consumer price index will negatively affect the access to
credit by the private sector in the short run which is not desirable. This means policy interventions
on consumer price index should be made not to increase the index, in the short run if the objective
is to improve the private sector’s access to credit.
From the results presented, results of lagged values of Interest rates and Exchange rates are not
significant. This means that real interest rates and exchanges rates have no significant effect on
real private sector credit in the short run. On the empirical front, the insignificant short run impacts
of exchange rate and interest rates are not new as they were also found in the studies conducted by
Naimy and Dame, (2005) in Lebanon. The same results were found in Lesotho in a research paper
investigating the effect of various factors on the supply of credit to the private sector in Lesotho
using the autoregressive distributed lag cointegration approach by Molapo and Damane (2017).
This means policy interventions on interest rates and exchange rates must be made with long term
objectives.
5.4.
INNOVATION ACCOUNTING
This section presents the results of the impulse response function and the variance decomposition
that will aid the interpretation of the structural responses of private sector credit changes to changes
in interest rate in Malawi overtime.
5.4.1. VARIANCE DECOMPOSITION
The results of the variance decomposition of the variables considered in this study, using the
cholesky - dof ordering are presented below. Explicitly the result shows the effect of one standard
deviation shock or innovation on self and other variables. Specifically, period 1 of the table
indicates that a shock to real private sector credit causes a positive standard deviation value of
100.00 on real private sector credit (own shock). The table also shows that real private sector credit
does not respond to innovations from Interest rate, Consumer price index, government expenditure
and exchange rates in period 1. Period 2 shows a standard deviation value of 97.23 in real private
sector credit resulting from own shock, 2.44 to a response to a shock from real interest rates, 0.0186
37
to a response from real exchange rates, 0.205900 to a response to government expenditure and
0.101933 to response to consumer price index.
In period 10, private sector credit responds positively with a standard deviation of 18.77 originated
from own shock and standard deviation values of 8.05, 7.94, 12.43 and 15.59 arising from a shock
from interest rates, exchange rates, government expenditure and consumer price index
respectively. Furthermore, real interest rate responds positively to shocks from real private sector
credit and own shock in period 1 with standard deviation values of 4.591 and 4.591 respectively.
The standard deviation values of 4.903 and 8.623 indicate positive responses in real interest rates
due to shock from real private sector credit and own shock in Period 2. In the long run, real interest
rate respond positively to innovations from real private sector credit, own shock, exchange rates,
government expenditure and consumer price index in period 10 with standard deviations of
15.2359, 15.4114, 5.03372, 6.88330 and9.98708 respectively. .
Table 4. 9 Variance Decomposition of Real Private Sector Credit Results
PERIOD
1
2
10
RPSC
100.00 (0.00)
97.23 (6.14)
62.60 (18.70)
RINTR
0.00 (0.00)
2.44 (5.27)
2.84 (8.05)
REXR
0.00 (0.00)
0.02 (1.94)
8.30(7.94)
GOVEXP
0.00 (0.00)
0.21(1.52)
11.11 (12.43)
CPI
0.00 (0.00)
0.10 (1.82)
15.16 (15.59)
GOVEXP
0.00 (0.00)
5.57 (6.64)
6.45 (6.88)
CPI
0.00 (0.00)
0.42 (2.73)
(9.99)
Table 4. 10 Variance Decomposition of Interest rate results
PERIOD
1
2
10
RPSC
2.25 (4.59)
2.93 (4.90)
32.97 (15.24)
RINTR
97.75(4.59)
90.74 (8.62)
58.27 (8.05)
REXR
0.00 (0.00)
0.34(3.23)
1.07 (5.03)
5.4.2. IMPULSE RESPONSE FUNCTIONS
The study then proceeds to compute, estimate and interpret the Impulse Response Functions. An
Impulse response (IRF) is the reaction of any dynamic system in response to some external change.
Whereas Impulse Response Functions from a stationary Vector Autoregressive model die out over
time, Impulse Response Functions from a cointegration Vector Error Correction Model do not
always die out. Because each variable in a stationary Vector Autoregressive Model has a time
38
invariant mean and finite, time-invariant variance, the effect of a shock to any one of these
variables must die out so that the variable can revert to its mean. In contrast, the [I (1)] variables
modeled in a cointegrating Vector Error Correction Model are not mean reverting, and the unit
moduli in the companion matrix imply that the effects of some shocks will not die out over time.
These two possibilities gave rise to new terms. When the effect of a shock dies out over time, the
shock is said to be transitory. When the effect of a shock does not die out over time, the shock is
said to be permanent. Below we present the Impulse Response Functions of the Error Correction
Model earlier presented..
Figure 5 Impulse Response Function
39
vec1, Dummy, Dummy
vec1, Dummy, REXR
vec1, Dummy, RINTR
vec1, Dummy, lnCPI
vec1, Dummy, lnPSC
vec1, REXR, Dummy
vec1, REXR, REXR
vec1, REXR, RINTR
vec1, REXR, lnCPI
vec1, REXR, lnPSC
vec1, RINTR, Dummy
vec1, RINTR, REXR
vec1, RINTR, RINTR
vec1, RINTR, lnCPI
vec1, RINTR, lnPSC
vec1, lnCPI, Dummy
vec1, lnCPI, REXR
vec1, lnCPI, RINTR
vec1, lnCPI, lnCPI
vec1, lnCPI, lnPSC
vec1, lnPSC, Dummy
vec1, lnPSC, REXR
vec1, lnPSC, RINTR
vec1, lnPSC, lnCPI
vec1, lnPSC, lnPSC
15
10
5
0
-5
15
10
5
0
-5
15
10
5
0
-5
15
10
5
0
-5
15
10
5
0
-5
0
10
20
30
0
10
20
30
0
10
20
30
0
10
20
30
0
10
20
30
step
Graphs by irfname, impulse variable, and response variable
The figure 7 above shows Impulse response function of the vector correction Model used in the
analysis above. From the figure, the iorthogonalized shock to the Real Private Sector Credit by
real Interest rate has a permanent effect, Similarly the iorthogonalized shock to the Real Exchange
40
rate by the consumer price index, the real private sector credit, and itself Sector Credit has a
permanent effect, However, some the orthogonalized shock to some of the variables have a
transitory effect. This means the variables modeled in this cointegrating Vector Error Correction
Model are not mean reverting since the effects of some shocks do not die out over time.
5.5.
Forecasting with VECMs Cointegrating VECMs
The results presented in this chapter are also used to produce forecasts of both the first-differenced
variables and variables at level. Comparing the variances of the forecast errors of stationary Vector
Autoregressive Model with those from a Cointegrating Vector Error Correction Model reveals a
fundamental difference between the two models. Whereas the variances of the forecast errors for
a stationary VAR converge to a constant as the prediction horizon grows, the variances of the
forecast errors for the levels of a cointegrating VECM diverge with the forecast horizon. Because
all the variables in the model for the first differences are stationary, the forecast errors for the
dynamic forecasts of the first differences remain finite. In contrast, the forecast errors for the
dynamic forecasts of the levels diverge to infinity.
Figure 6 Forecasting with the Cointegrating VECM
Forecast for lnGOVEXP
9
Forecast for lnCPI
4.8
7
6
2
3
4.6
4
5
8
5
6
5.2
Forecast for lnRPSC
2015
Forecast for REXR
500
0
20
40
60
1000 1500 2000
Forecast for RINTR
2015
2020
2025
2030
2015
2020
2025
95% CI
observed
2030
forecast
41
2020
2025
2030
From the Figure 8 it is clear, the 10 year forecast of the results presented in this chapter shows a
widths of the confidence intervals growing with the forecast horizon of the model. This is an
expected outcome for a Vector Error Correction Model.
42
CHAPTER 6
CONCLUSION AND RECOMMENDATION
4.1.Conclusion
This study has examined the dynamic relationship between interest-rate and its liberalisation on
private sector credit in Malawi. The study uses the Vector Error Correction Model to examine this
linkage. The study was motivated by the painful experience that some developing countries have
had with the liberalization of interest rates, as well as the conflicting results that have emerged in
the literature in recent years on the efficacy of interest-rate liberalization in developing countries.
From the studies reviewed, this may be the first study of its kind to empirical examine the impact
of interest rate liberalisation on private sector in Malawi credit using the Vector Error Correction
Model.
The empirical results of the study reveals that, interest rates have a negative and significant impact
on private sector credit in Malawi in the long-run but it is not significant in the short run. The longrun results are supported by the coefficient of the interest rate which has been found to be negative
and statistically significant; while the short run results are supported by the short run coefficient
of the interest rate which was found to be statistically insignificant. The results of the Consumer
Price Index shows that, it has a negative effect on private sector credit both in the short-run and in
the long-run. This is confirmed by the coefficient of the Consumer Price Index variable, which is
negative and statistically significant both in the short and long run.
Furthermore, the results of the exchange rate show that a there is a long-run positive relationship
between the real exchange rate and private sector credit in Malawi. This is supported by the
coefficient of the real exchange rate variable in the long run which is positive and statistically
significant. However, in the short-run, the exchange rate has been found to be statistically
insignificant. In addition, the results of the real Government expenditure coefficient shows that
there is a long-run positive relationship between the real government expenditure and private
sector credit in Malawi but a negative relationship in the short run. As regards to interest rate
liberalisation the study has found that there is no differential impact of interest rate liberalisation
on private sector credit in Malawi The study also reveals that the error-correction term is negative
43
and statistically significant, as was expected. The results of the error-correction term shows that
about 67 percent of the discrepancy between the actual and equilibrium values of private sector
credit is corrected in each period which is desirable.
4.2.Recommendation
In light of the study’s findings, the following recommendations are made in order to effect policy
interventions to achieve short term and long run objectives.



The study has tested the impact of interest rate liberalisation on private sector credit in
Malawi using the Vector Error Correction Model. As such it recommends further study on
the same subject using other equally lightening approaches to see if similar results can be
drawn and to beef up the lean body of literature on the tested subject matter..
The study also recommends that interest rate policy interventions should be intensified in
Malawi but with long-run objectives since they are insignificant in the short in as far as
influencing real private sector credit is concerned, Besides the monetary authorities should
keep an eye on the interest rates, in order to ensure that they remain within the acceptable
threshold.
Any policy that increases real government expenditure will negatively affect the access to
credit by the private sector in the short run which is not desirable. However if the policy is
maintained for long periods the level of private sector credit will increase and the policy
becomes desirable. As such policy interventions on real government expenditure should be
made with long term objectives if the private sector is to be benefit in as far as private
sector credit is concerned.

Any policy that increases consumer price index will negatively affect the access to credit
by the private sector in both the short run and long run which is not desirable. This means
policy interventions on consumer price index should be made not to increase the index both
in the short and long run if the objective is to improve the private sector’s access to credit.


Policy interventions on exchange rates should be made with long term objectives if the
private sector is to be benefit in as far as private sector access to credit is concerned.
There is no differential impact of interest rate liberalization on private sector in Malawi
during the pre and post-liberalization regimes. This means that whether the interest rate are
liberalised or not the behavior of private sector credit does not depend on the liberalisation.
As such liberalizing interest rate is not a commendable policy direction if the aim is to
affect private sector credit.
44
According to the study it is clear that, interest rate liberalisation has no significant effect on
private sector credit in Malawi. Nevertheless the study has found it significant on other
economic factors. As such, it is important for policy makers not to liberalize interest rate for
the sake of affecting private sector credit but they can do the same to affect other economic
factors. In addition, there is need for further research on interest rate liberalisation in Malawi
to build on the existing lean body of literature.
45
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48
49
50
APPENDIX A: DATA
Country
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
CPI
RINTR
0.2134
0.3478
0.3684
0.38756
0.470536756
0.521
0.53
0.579053637
0.657240415
0.788861374
0.871841512
0.994307938
1.244427049
1.666439203
1.873850259
2.095405657
2.35974769
2.920219694
3.585232863
4.827503005
8.850057285
12.17785981
13.29059378
17.24436618
24.97056033
32.35722375
39.70231354
45.5562746
49.91910694
55.62476406
64.19673192
73.16777221
78.98622704
85.86798253
93.09982204
100
107.6228226
130.5155584
166.1245533
17.345
17.597534
18.001
18.00567
18.0133457
18.345
18.45678
18.5
18.33333333
16.5
18.375
19
19.5
22.25
23
21
20
22
29.5
31
47.33333333
45.33333333
28.25
37.66666667
53.58333333
53.125
56.16666667
50.54166667
48.91666667
36.83333333
33.08333333
32.25
27.71527778
25.27777778
25.25
24.625
23.75
32.32986111
46.01117424
REXR
1.000001
1.00009
1.0023
1.003
1.001
1.09
1.07
1.055509
1.174763
1.41338
1.719097
1.861073
2.208743
2.561301
2.759524
2.728882
2.803313
3.603275
4.402778
8.736405
15.28374
15.30847
16.44418
31.07268
44.08814
59.54381
72.19733
76.68661
97.43248
108.8975
118.42
136.0125
139.9575
140.5217
141.1683
150.4858
156.5158
249.1067
364.4058
51
RPSC
19.58
19.5
19.01
18.88
18.08
17.58437701
17.58437701
17.58437701
17.72359205
13.37466401
10.9331071
10.72676926
7.840840381
8.152784065
9.561916265
10.94941731
11.55162514
14.69475054
9.393337119
12.56020005
6.496234223
4.509537744
4.289617436
7.108802294
7.960645825
9.075886547
8.420598231
4.423683756
4.125786351
4.562613915
5.962738803
6.885669054
8.484207118
9.113463138
10.87477962
13.82961932
13.93238573
14.5742627
12.45304171
GOVEXP
Dummy
116.7642062
0
108.4313725
0
104.6291209
0
117.9218184
0
117.6286871
0
113.9787086
0
105.7937009
0
106.3262685
0
107.5713436
0
98.05552876
0
105.7020927
0
102.1380907
0
102.3573495
0
109.2208041
0
115.737665
1
109.6386912
1
106.0357216
1
119.2496086
1
116.0916641
1
132.1123097
1
117.7199312
1
109.0509158
1
112.228895
1
105.334856
1
115.2714197
1
109.7313266
1
111.1359543
1
110.1795634
1
110.5015363
1
113.7697431
1
121.2056734
1
119.0922982
1
109.8212746
1
116.6522516
1
111.6257655
1
112.0734105
1
107.2334814
1
115.4171875
1
106.6314526
1
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
2014
2015
2016
2017
2018
2019
205.6490149
250.6189997
305.0311745
340.2421245
382.5008024
418.3443255
44.28958333
44.38721591
44.11208333
38.59291667
32.29
31.78
424.8967
499.6058
718.005
730.2725
732.3333
735.67
52
11.40363665
12.25859373
10.46680829
11.0363716
12.15859373
10.76680829
105.5517668
107.6130043
112.5327478
107.2199133
1
1
1
1
1