1
THE DETERMINANTS OF PRIVATE SECTOR CREDIT: CASE STUDY OF
UGANDA
BY
ORISHABA JUDITH
REGISTRATION NUMBER: 2016/HD06/1132U
A RESEARCH REPORT SUBMITTED TO THE GRADUATE SCHOOL IN
PARTIAL FULFILLMENT FOR THE AWARD OF MASTER OF ARTS IN
ECONOMIC POLICY AND MANAGEMNET DEGREE OF MAKERERE
UNIVERSITY
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DEDICATION
To my mother Mrs. Lucy Byabayi and my beloved son, Tumwebaze Lucas.
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ACKNOWLEDGEMENT
First of all I thank God for the gift of life and knowledge as well as the strength which has enabled
me to come this far. And more so that I got chance to do this course. Without Him nothing would
have been accomplished!
Secondly, I am deeply obliged to my university supervisor Dr. Bbale John Mayanja for his
exemplary guidance and support without whose help; this report would not have been a success.
In the same regard, I commend all lecturers that trained me; I must say it was a job well done.
Am also deeply thankful to the African Capacity Building Foundation for awarding me a
scholarship to undertake this Masters course. Without them, this would have been far from
achievable. In addition, I extend my heartfelt gratitude to the Commissioner, Water Resources
Planning and Regulation Department; Dr. Callist Tindimugaya as well as my work supervisors for
the time and the enabling environment given to me during my studies. To all my colleagues at
office especially David, Martha, Simon, Charity and Grace, thank you so much for the moral and
courage, you kept me moving until the end. I will always be grateful to my friend Nabukera Juliet
who informed me about this Masters Programme and even helped me drop my applications.
I can’t forget to thank my fellow graduate students who we put effort together to finish the course
and without whom this journey would have proved tougher. Lastly, I would like to thank my family
members and friends for their spiritual, moral, social and financial support throughout my
education.
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TABLE OF CONTENTS DECLARATION ........................................................................................................................................... i
APPROVAL ................................................................................................................................................. ii
DEDICATION ............................................................................................................................................. iii
ACKNOWLEDGEMENT ........................................................................................................................... iv
ABSTRACT ................................................................................................................................................ vii
CHAPTER ONE ........................................................................................................................................... 1
INTRODUCTION ........................................................................................................................................ 1
1.1 Background of the Study .................................................................................................................. 1
1.2 Problem Statement ............................................................................................................................ 3
1.3 Objectives of the Study ..................................................................................................................... 4
1.3.1 Main objective ............................................................................................................................ 4
1.3.2 Specific Objectives ..................................................................................................................... 5
1.4 Research questions ............................................................................................................................ 5
1.5 significance of the study .................................................................................................................... 5
1.6 Structure of the Report..................................................................................................................... 6
1.7 Scope of the Study ............................................................................................................................. 6
2.0 Introduction ....................................................................................................................................... 7
2.1 Theoretical literature ........................................................................................................................ 7
2.1.1 James Tobin’s Theory of the role of Money ............................................................................ 7
2.1.3 The Neoclassical Growth Theory and Credit .......................................................................... 9
2.2 Empirical Review ............................................................................................................................ 10
2.2.1 Private Sector Credit (PSC) and Gross Domestic Product (GDP) ...................................... 10
2.2.3 Private Sector Credit (PSC) and Broad Money .................................................................... 13
2.2.5 Private Sector Credit (PSC) and Official Exchange Rate .................................................... 16
CHAPTER THREE .................................................................................................................................... 17
MODEL SPECIFICATION AND METHODOLOGY .............................................................................. 17
3.0 Introduction ..................................................................................................................................... 17
3.1Data sources and variable specifications ....................................................................................... 17
3.2 Model specification ......................................................................................................................... 20
3.3 Method of Data Analysis and Estimation Techniques ................................................................. 21
3.3.1Unit Root Test ........................................................................................................................... 21
3.3.2 Determination of Optimal Lag Length .................................................................................. 23
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3.3.3 Johansen Cointegration Test ................................................................................................... 24
3.3.4 Vector Error Correction Model (VECM) .............................................................................. 25
3.3.5 Testing for Causality ................................................................................................................ 26
CHAPTER FOUR ....................................................................................................................................... 28
ESTIMATION AND DISCUSSION OF RESULTS .................................................................................. 28
4.0 Introduction ......................................................................................................................................... 28
4.1 Descriptive Statistics ....................................................................................................................... 28
4.2 Unit Root Test ................................................................................................................................. 29
4.3 Lag Length Selection and Estimation of Long Run Growth Model ........................................... 39
4.4 Cointegration analysis; applying the Johansen Procedure ......................................................... 40
CHAPTER FIVE ........................................................................................................................................ 48
SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS ..................................... 48
5.1Summary of findings and Conclusions........................................................................................... 48
5.3 Limitations of the study .................................................................................................................. 51
5.4 Areas for further Research ............................................................ Error! Bookmark not defined.
REFERENCES ........................................................................................................................................... 53
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ABSTRACT
The main objective of the study was to investigate the determinants of Private Sector Credit in
Uganda. The study used Cointegration analysis applying the Johansen Procedure and Vector Error
Correction Model (VECM) approach for empirical analysis. The model involved private sector
credit (LnPSC) as the dependent variable and the explanatory variables as Gross Domestic Product
(lnGDP), Lending Rate (lnLR), Bank Credit to Government (LnBCG), Broad Money (LnBM),
and Official Exchange Rate (OER). The period considered was 1980 to 2015.
From the Cointegration analysis; Gross Domestic Product and lending Rate have a long run
negative relationship with Private Sector Credit in Uganda. However, bank credits to government,
broad money, and official exchange rate have appositive relationship with Private sector credit
although OER is not significant. The short run results indicate that broad money has a positive and
significant effect on private sector credit while GDP, LR, BCG and OER are not significant in the
short run.
Granger Causality Test shows the evidence of unidirectional causal relationship from GDP to
private sector credit , same from PSC to broad money and bidirectional casuals relationship
between lending rate and private sector credit which implies that GDP ,lending rate and broad
money are key determinants of private sector . Therefore commercial banks should pay attention
to the overall macro-economic situation of the country, factors that influence lending rate and their
liquidity ratio while taking lending decision. Government should put in place policies that
encourage and support access to credit. Also the issues of lending rate should not be left to be
determined by the forces of demand and supply.
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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Private sector investment is a critical element to promote economic advancement and finance is
one factor that influences private sector investment because it allows the firm to procure the much
needed factor inputs. Any growing firm needs a source of finance to assist its operational and non-
operational activities.
The Government of Uganda recognizes the importance of private sector credit in boosting
economic growth. In the journey to foster economic growth and development, Uganda started the
liberalization of her financial sector as part of her broad structural reforms, implemented in
the early 1990s. These reforms included; reduction in the overvaluation of the exchange rate,
liberalization of the foreign exchange market and introduction of a market for government
securities. In addition, it has tried to boost private investments through privatization of government
parastatals and have formed the private sector institutions. For example, Uganda Manufacturers
Association (UMA), Uganda Investment Authority (UIA) and Uganda Private Sector Foundation
(UPSF), among others.
Banks are a major source of credit for many households and economic enterprises across the
different sectors. Commercial banks provide a lending service (grant loans and advances) to
individuals, firms and government which may be in the form of short, medium or long term basis
bearing in mind, the three principles guiding their operations which are; profitability, liquidity and
solvency, Olokoyo, (2011). The banks mobilize funds from surplus economic units in the form of
deposit (savings) and provide it to the deficit economic units (borrowers) in the form of credit, a
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process that leads to the introduction of credit system. This means deposits are aggregated from
domestic savings by financial institutions like commercial banks for lending it back to the deficit
economic units.
According to the Global economy report 2015, for a country to be said to have a well-developed
financial system, its banking credit to the private sector as percentage of GDP must be accounting
to 70% and above. In some very advanced economies it is even higher than 200%. However, in
some poor countries, the amount of credit could be lower than 15% of GDP.
The Bank of Uganda state of Economy report of 2013, revealed that changes in the willingness
and ability of banks to extend credit have implications to aggregate economic activity and Credit
demand, as judged by loan applications, has grown faster than supply, as judged by loan approvals,
in recent years although both have followed an upward trend. Important to note is that Increased
credit demand, may indicate improved economic activity; however this may be hindered by low
credit supply, which could be attributed to more risk averse behavior of commercial banks as they
realize increased loan defaults on their balance sheets.
The ratio of Private Sector Credit to GDP in Uganda has more than doubled since the
implementation of the financial sector reforms in early 1990s andprivate sector institutions. Basing
on the data from world development indicators, it has increased from 3.9 % in 1980 to 15.14% in
2011 as indicated in the figure below although more recently from 2012 to 2015 there was a small
fall.
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Figure 1. 1: Graph showing the trend of private sector credit
Source: Author’s Computations
Figure 1.1 shows that Uganda hasexhibited growth of private sector credit although it keeps
fluctuating. Therefore, this study is aimed at investigating the factors that determine private sector
credit in Uganda.
1.2 Problem Statement
A number of Studies such as Oshikoya, (1994), Husain et al, (2006), Majeed & Khan, (2008) and
Shijaku & Kalluci, (2014) have been conducted and confirmed that private investment is a key
driver of economic growth and they further emphasize private sector credit as one of the major
determinants of private investment; for example Husain et al, (2006) in his study to evaluate the
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Trend of Private Sestor Credit in uganda
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determinants of private investment, he used the Johansen multiple Cointegration and Error
Correction Model to estimate long run and short run relationships and found out that credit to
private sector has a positive relationship with household investment both in the long run and short
run. However, access to credit by private sector in Uganda remains low due to lack of collateral,
the cumbersome loan application procedures, limited access to financial institutions to mention,
but a few.
Private sector investment plays a vital role in economic growth via promoting innovations, job
creations, and generating more revenues and improving the wellbeing of the poor. Moreover,
considering the long run growth of countries and analyzing the convergence rate of per-capita
income among countries, aggregate investments were emphasized by Barro, (1991) and Mankiw
et al. (1992). Thus, investment determines productivity in the long run through the accumulation
of capital stock.
Having realized the significant contribution ofprivate investmentto economic growth in Uganda,
this consequently calls for an effective and supportive macroeconomic environment to enable
private firms’ access creditwhich is the main factor for investment. Hence, identifying main
determinants of private sector credit should be given more emphasis so as to stimulate investment
leading to economic growth. It’s upon this back ground that this study aims at investigating the
determinants of private sector credit in Uganda.
1.3 Objectives of the Study
1.3.1 Main objective
The main objective of this study is to investigate the determinants of private sector credit in
Uganda.
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1.3.2 Specific Objectives
The study was guided by the following objectives:
i. To evaluate the effect of GDP on private sector credit in Uganda.
ii. To determine the effect of lending rate on the private sector credit in Uganda.
iii. To investigate the effect of Broad money on private sector credit in Uganda.
1.4 Research questions
The study was guided by the following objectives:
i. Does GDP affect private sector credit in Uganda?
ii. To what extent does lending rate influence private sector credit in Uganda?
iii. How does broad money affect private sector credit in Uganda?
1.5 significance of the study
While most previous studies have been emphasing on the micro factors,limited studies in Uganda
have so far looked at the effect of Macroeconomic factors on private sector credit. The findings of
this study will therefore help in addressing the existing knowledge gap in literature on the
macroeconomic determinants of private sector credit in Uganda. Furthermore, findings from
different studies show that there is contradiction among relationship of different variables to
private sector credit; take an example of Nkusu, (2011) and Ali & Daly, (2010) who found that
GDP per capita had an inverse relationship to credit while Beck, et al., (2013), found a positive
relationship between GDP and private sector credit. Additionally, Warue, (2013), found a positive
relationship between lending interest rates and private sector credit in his study while Jebra.N et
al, (2016) found that lending rates had an inverse relationship to credit. This study therefore seeks
to find out the relationship of these factors with private sector credit in Uganda.
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Serven & Solimano (1993) argue that there are many factors that affect private sector credit
accessin developing countries, key among them being GDP growth, real exchange rate, public
investment, real interest rates, public debt and uncertainties however there is still limited evidence
for the case of Uganda and this study will fill this gap by analyzing annual data from 1980 to 2015.
The study will therefore be of great importance to the students and other scholars as it will be a
basis for literature review and further research in this area.
1.6 Scope of the Study
Mainly, this study uses the annual data from 1980 to 2015 to investigate the relationship of the
selected variables and the literature reviewed is in the same range.The choice of the period was
based on the following considerations; availability of the economic data on some of the variables
and coverage of the period after Uganda’s financial sector had undergone major reforms.
1.7 Structure of the Report
The subsequent part of the report is organized as follows; chapter two; the literature review
includes sub themes under which both theoretical and empirical literature about the different
aspects of the variables under study is captured, chapter three; the methodology which presents
data sources and types, model specification, and estimation procedures, chapter four; presents the
empirical results and discussion of study findings while chapter five presents the summary,
conclusions and policy recommendations from the study.
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CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter highlights on what other scholars have found out about private sector credit and its
determinants. The theoretical and empirical literature on the matter is covered. The literature
covers both the independent and dependent variables of the study and explains the nature of the
relationship among these variables as established by earlier theories and empirical research.
Literature on both developed and developing countries is also covered.
2.1 Theoretical literature
For the past few decades, a number of theories have been developed to explain how private sector
credit relates with its determinants. Some of these theories are discussed below.
2.1.1 Tobin’s Theory of the role of Money
Tobin’s theory (1969) emphasizes the role of money in determining the steady state equilibrium
growth of the economy. According to his theory, the steady state equilibrium growth would be
lower if people hold money. Holding money has a negative impact on the commercial bank’s
ability to extend credit to the private sector because it reduces the commercial bank’s excess
reserves (Excess Reserves are reserves available to commercial banks to lend to the public). On
the other hand increase in savings from the public increases commercial bank’s excess reserves
and hence their ability to extend loans to the private sector. This would ultimately boost economic
activity through facilitating capital accumulation.
Tobin also postulates that, increase in demand deposits (savings) stimulates the ability of
commercial banks to create money hence increases money supply. If the money supply grows,
people realize that they are holding more money balances than required. Hence they try to “get rid
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of it” (spend). Some people use the money to buy shares, thereby the demand for this type of
security grows, and similarly does their value (price). The growth in share prices (PA) increases
the market value of firms and thus leads to a growth in the coefficient q and a growth in investment
expenditures hence growth in income. The transmission mechanism of monetary policy then looks
as follows: 𝑀 ↑→ 𝑃𝐴 ↑ → 𝑞 ↑ → 𝐼 ↑ → 𝑌 ↑.
2.1.2 Neoclassical model/ flexible accelerator model
The neoclassicalmodel; Jorgenson (1967) explains total investment as a function of the expansion
and replacement investment at a time t. investment function in flexible accelerator model the takes
the form:
𝐼𝑡 = 𝐾𝑡 − 𝐾𝑡−1 = 𝜶(𝐾∗𝑡 − 𝐾𝑡−1
Where 𝐾𝑡 is actual capital at time t; 𝐾𝑡−1 previous period capital stock; K* is the desired capital;
and 𝑰𝒕 investment at time t, α denotes adjustment coefficient.
This illustrates that investment is a function of the gap between the desired and the existing capital
stock which calls for demand of credit. The rate of investment activity rises when the gap between
the desired and the existing capital stock increases and this explains the need for private sector
credit forinvestment.
Desired capital stock (K*) is the amount of capital that the sector would like to have in the future
and the existing capital is accumulated value at the time (t). The desired capital (𝑲∗𝒕) isnegatively
associated with the rental cost and positively with the level of output growth. The increment rate
between the desired and the existing capital stock is given by the flexible accelerator model
𝑰𝒕 = 𝜶(𝑲∗𝒕 − 𝑲𝒕−𝟏)
Thus, parameters that affect the desired capital level in this case private sector credit tend to
influence the privateinvestment level.
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2.1.3 The Neoclassical Growth Theory and Credit
The usual two factor neoclassical growth model developed by Solow, (1957) has incorporated the
role of credit in determining economic growth. According to Solow’s model, savings translate
directly into investment and thus through this linkage, credit affects growth through capital
investment. More so, the neo-classical growth theory states that labor and capital are the major
factors of production, that is to say: 𝑌 = 𝑓(𝐾, 𝐿) where 𝑌 represents aggregate output, 𝐾
represents aggregate capital stock, and 𝐿 is the labor force. Credit facilitates means to acquire more
capital in this production function. When a new technology is available, the labor and capital need
to be adjusted to maintain growth equilibrium. Hence credit allows the acquisition of this new
technology which eventually increases total factor productivity and finally fosters economic
growth. This theory is supported by Trew, (2006), in his review of the finance-growth literature;
he noted that, financial sector services such as credit availability influence economic growth
through their impact on capital accumulation and technological innovation. That is to say the credit
facilitates growth through the following capital accumulation model:
𝐾𝑡 = 𝐼𝑡 + (1 − 𝜎)𝐾𝑡−1
Where 𝐾𝑡 represents new capital acquired, 𝐼𝑡 is investment and 𝜎 depreciation of capital stock.
The above model implies that a certain proportion of the new capital (credit) is used for investment
purposes and the remaining proportion is used for servicing warn out capital.
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2.2 Empirical Review
2.2.1 Private Sector Credit (PSC) and Gross Domestic Product (GDP)
With the main objective to test and confirm the effectiveness of the determinants of commercial
bank lending behavior in Nepal, a study by Neelam , (2014) period; 1975 – 2014 using time series
Ordinary Least Square regression approach for empirical analysis was done. From the regression
analysis, it was found that Gross Domestic Product has the greatest impacts on their lending
behavior. Granger Causality Test shows the evidence of unidirectional causal relationship from
GDP to private sector credit. Hofmann, (2001) through a cointegrating VAR for 16 industrialized
countries, finds significant positive relations of real credit to real GDP.
Pham, (2015), empirically investigated the determinants of bank credit by using a large data set
covering 146 countries at different levels of economic development over the period 1990-2013
and found evidence of the country specific effect of economic growth on bank credit.
Egert et al, (2006) investigated the determinants of the domestic bank credit to the private sector
as a percentage of GDP in 11 CEE countries. They used three alternative techniques for estimation:
fixed-effect ordinary least squares; panel dynamic OLS and the mean group estimator, for 43
countries, which are then grouped into other small panels and GDP was found to have a positive
effect on the dependent variable.
Calza, M, & J, (2003), used VECM for the euro area data to model the factors that affect the
demand for credit and finds that in the long run, the demand for credit is positively related to real
GDP growth.
An attempt by Million, (2014), to examine the short and long-run impact of bank-specific,
monetary policy and Macroeconomic variables on bank credit to private sector in Ethiopia, using
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supply-side approach over the period 1978/79-2010/11 employed methodology based on the
ARDL econometric approach, findings indicates GDP has significant impact on banks credit to
the private sector in the long-run. However, in the short-run economic growth does not influence
commercial banks credit to private sector.
Ivanović, (2016), focused on identification and estimation of determinants of credit growth in
Montenegro, exploring both demand and supply side factors, and particularly paying attention to
supply factors and the findings confirm that positive economic developments and an increase in
banks’ deposit potential lead to higher credit growth. In addition the results provide evident that
the weakening of banks` balance sheets, in terms of high non-performing loans and low solvency
ratio, has a negative effect on credit supply.
ARDL (Autoregressive Distributed Lag) approach was used to analyze long run relationship and
error correction mechanism (ECM) for short run relationship of private investment determinants
and the analysis concluded that GDP has a positive and significant relationship with private
investment (Verma, 2007).
2.2.2 Private Sector Credit and Lending Rate
Lending rate is the cost of borrowing money by the borrower. It is also return to the depositor in
his/her account in bank, or return on investments such as government bonds. It is the channel
through which the funds flow from savers to borrower. Usually these funds are generated from
financial intermediaries like scheduled banks, development banks, mutual funds and insurance
companies etc. It is an indicator that determines the flow of funds from savers to borrowers
directly, or through financial intermediation. If the supply of loanable fund is more than the
demand of loanable fund, interest rate falls, and if the demand is more than the supply, interest rate
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rises. Fluctuation in interest rate and changes in the quantity of loanable funds affect the economic
indicators (Jebra. N, et al, 2016).
In the study by Jebra.N, et al, (2016), the long and short term effect of interest rate on private sector
credit on Pakistan for the period of 1975 to 2011were explored. The Stationarity of data was
analyzed by Augmented Dickey Fuller and Phillips Peron test and Auto Regressive Distribution
Lag (ARDL) model for the purpose of analyzing long and short term relationship. The results
revealed a significant negative effect of interest rate on private sector credit in the long run, and
also in the short run. However, exchange rate was found to have no effect on private sector credit.
Also study by Hofmann, (2001)through a cointegrating VAR for 16 industrialized countries, finds
significant negative correlation of real private credit with real interest rates.
Chizea, (1994) in his study emphasized that increase in interest rates would increase inflation rates
which discourage the investment. Hence an inverse relationship between private sector credit and
interest rate. Gupta (1987) studied the significance of two important factors, that is, financial
intermediation and real interest rate. Using pooled time series data, a model of savings was
anticipated for Latin American and Asian countries. Findings show that there is no clear support
for the effect of each of the two factors on Latin America countries, but showed some robustness
for Asian countries.
Also Million, (2014), examined the short and long-run impact of bank-specific, monetary policy
and Macroeconomic variables on bank credit to private sector in Ethiopia, using supply-side
approach over the period 1978/79-2010/11 and employed methodology based on the ARDL
econometric approach, findings indicates that real lending interest rate, has significant impact on
banks credit to the private sector in the long-run.
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Catao, (1997) analyses both demand and supply indicators of private sector credit in Argentina
from 1991 to 1996. On the demand side, he identified that changes in interest rates, coupled with
expected changes in the economy may contribute to the weakening of private sector credit.
Cointegration, vector autoregressive (VAR) and error correction techniques were blended to
estimate the long run and short run impact of macroeconomic policies on private investment and
was revealed that devaluation policies also contributed to discouraging private sector capital
expansion, Verma & Wilson (2005).
Calza, et al. (2001), using VECM for the euro area data modelled the factors that affect the demand
for credit and found out that in the long run, the demand for credit is negatively related to short
term and long term real interest rates.
In addition, Barder & Malawi (2010) examined the effect of interest rate on investment in Jordan,
by using co-integration analysis. The results indicated that investment was negatively affected by
real interest rate. The results highlighted that one percent increase in rate of interest reduced the
investment by 44 percent.
2.2.3 Private Sector Credit (PSC) and Broad Money
Million, (2014), examined the short and long-run impact of bank-specific, monetary policy and
Macroeconomic variables on bank credit to private sector in Ethiopia, using supply-side approach
over the period 1978/79-2010/11 and employed methodology based on the ARDL econometric
approach, findings indicates that M2 as percentage of NGDP has significant impact on banks credit
to the private sector in the long-run.
According to Guo& V, (2011) both demand-side and supply-side factors of credit growth were
investigated with a focus on supply side for 38 emerging market economies covering both pre-
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crisis and post-crisis periods (2002-2010). Findings show that domestic deposits and non-residents
liabilities positively contribute to credit growth and that they symmetrically serve as funds for
credit growth, whether domestic or foreign sources.
Albulescu, (2009) evaluates the equation through OLS for the growth rate of credit granted in
domestic currency, for Romania. He finds that credit growth rate is linked positively with deposits
in domestic currency growth, economic growth, but negatively with interest rates
Through the GMM method,Vika, (2009) identifies several factors that affect total credit to private
sector and credit denominated in domestic currency ‘Albanian lek’(during 2004-2006), finding
indicate positive correlation of the dependent variable with liquidity of the banking system and the
interaction term between monetary policy indicator and liquidity
With the main objective to test and confirm the effectiveness of the determinants of commercial
bank lending behavior in Nepal, a study by Neelam , (2014) for period; 1975 – 2014 using time
series Ordinary Least Square regression approach for empirical analysis was done. From the
regression analysis, it was found that liquidity ratio of banks and Gross Domestic Product have the
greatest impacts on their lending behavior.
2.2.4 Private Sector Credit (PSC) and Bank Credit to Government
Most literature establishes credit to the public sector as an important supply-side determinant of
private sector credit and empirical evidence suggests that public borrowing crowds-out credit to
the private sector as discussed below;
Robust evidence that there is a significant crowding out effect of government borrowing from the
banking sector on private credit using panel data on 25 developing countries was provided by
Emran & Farazi (2008). The potential endogienity of government was addressed using appropriate
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estimators (Systems –GMM and Pooled Mean Group (PMG)). It results reveal that point estimates
keep varying depending on the estimator or set of control variables used.
In an attempt to identify and evaluate the long run determinants of bank credit to the private sector
in the case of Albania, Gerti & Irini (2017) employed a Vector Error Correction Mechanism
(VECM) approach based on demand and supply indicators. Estimations show that diminishing
government domestic borrowing, lower cost of lending, and a more qualitative bank credit would
create further lending incentives.
Using a VEC model, Shijaku & Kalluci (2014) find a significant negative relation between the
stock of public debt and bank credit. Another study to examine the crowding out effect of
government domestic borrowing carried out by Anthony, (2016) used a panel data model for 28
oil-dependent countries over the period 1990-2012. The model was estimated using both fixed-
effects and Generalized Method of Moments estimators and found that a one percent increase in
government borrowing from domestic banks significantly decreases private sector credit by 0.22
percent and has no significant impact on the lending rate banks charge to the private sector. The
finding suggested that government domestic borrowing resulted in the shrinking of private credit
and works through the credit channel and not the interest rate channel.
Söğüt, (2008) uses panel cross-sectional fixed effects to investigate financial developments and
private sector credit for 85 developing and industrial countries using annual data spanning 1980 –
2006. He finds that increases in public sector credit and central government debt reduce private
sector credit in low-income and lower-middle income counties
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Determinants of credit growth to the private sector in 15 Central and Eastern European economies
were empirically estimated by Cottarelli, et al. (2003) and found a significant inverse relation
between private sector credit and the ratio public debt.
2.2.5 Private Sector Credit (PSC) and Official Exchange Rate
Sajid & Sarfraz (2008) investigated causal relationship between private investment and exchange
rate using co integration technique and vector error correction model to examine causality between
investment and exchange rate. The result showed that there is long run as well as a short run
equilibrium relationship between them.
Similarlythe study Shijaku & Kalluci (2014) employ a VEC framework to examine demand and
supply for bank credit in Albania. Their results reveal a significant positive long run relationship
between exchange rates and bank credit.
In their investigation of the credit cycle, Evaraert, et al.,(2015) included the exchange rate in their
panel estimation to reflect that 400 banks in 20 Central and Southern European countries held
significant quantities of loans denominated in foreign currencies. However, they find no
significance for the exchange rate, which they attribute to a high correlation (0.5) between it and
the inflation rate which exerted a negative and significant effect on credit growth.
Taiwo & Adesola (2013), also finds a significant negative relationship between fluctuations in the
exchange rate and the ratio of loan losses to total advances. They interpret their findings as an
indication that exchange rate volatility affects lenders’ ability to manage loans.
Study by Jebra.N. et al. (2016) on private sector credit in Pakistan for the period of 1975 to 2011
usingAuto Regressive Distribution Lag (ARDL) model for the purpose of analyzing long and short
term relationship revealed that exchange rate has no effect on private sector credit.
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CHAPTER THREE
MODEL SPECIFICATION AND METHODOLOGY
3.0 Introduction
This chapter discusses the research methodology that was used in the study. It comprises of the
research design, data sources and variable specifications, model specification, method of data
analysis and estimation techniques.
3.1 Data sources and variable specifications
The study used annual time series data for Uganda on Private sector credit (PSC), Gross Domestic
Product (GDP), Lending Rate (LR), Bank Credit to Government (BCG), Broad Money (BM) and
official exchange rate (OER) covering a period of 35 years (1980 to 2015) which meets the
minimum of 30 observations required for time series analysis techniques adopted for the study.
The data on PSC, GDP, LR, and OER was obtained from World Development Indicators and data
for BCG was from The Global Economy data base. These sources were used because the data from
these sites is credible and covers longer period and so many variables which enabled me to get the
variables of interest for this study.Sourcing data from official websites also ensures validity and
reliability of the results.
The variables in the study include:
Private Sector Credit (PSC) measured as % of GDP as the dependent variable. This refers to
financial resources provided to the private sector by other depository corporations (deposit taking
corporations except central banks), such as through loans, purchases of non-equity securities, and
trade credits and other accounts receivable, that establish a claim for repayment.Private sector
credit from commercial banks is an important avenue for private investment in developing
18
countries such as Uganda. This variables has been used as dependent variables in many studies
such as; Shijaku & Kalluci (2014) in Albania; Million , (2014) in Ethiopia and Dorothy,et al. (
2016) in Ugnada.
Explanatory variables which include;
Gross Domestic Product (GDP); (constantUS$).This measures the overall health of the economy.
GDP is expected to have a positive effect on private sector credit.The Gross Domestic Product
which captures the aggregate demand conditions in the economy is expected to exert a positive
effect on private investment hence increase in demand for private sector credit. The same GDP
was used by; Neelam, (2014), Hofmann, (2001), Imran ,(2006) for Pakistan in their studies to
investigate the determinants of private sector credit. The sign of GDP is expected to be positive.
Lending rate (LR) measured in percentage. Lending rate is the bank rate that usually meets the
short- and medium-term financing needs of the private sector. This rate is normally differentiated
according to creditworthiness of borrowers and objectives of financing. The terms and conditions
attached to these rates differ by country. It’s expected to affect PSC negatively but there are some
contradictions in the work that has been done by some scholars who have used it in their studies
for example; Warue (2013) and Beck, et al., (2013) found a positive relationship between lending
interest rates and PSC while Jebra .N. et al, (2016), found that lending interest rates had an inverse
relationship to credit. The sign of this coefficient can be ambiguous at times.
Bank credit to government (BCG); measured as % of GDP. This refers to the credit by domestic
banks that is given to the public sector. Empirical study done by; Söğüt (2008), Cottarelli et al.
(2003), Emran & Farazi (2008) and Gerti & Irini (2017) used this variable in their studies and
19
found a negative relationship with private sector credit. The expected sign for this variable is
Ambigous although most studies have have out a negative relationship to private sector credit.
Broad money (BM); measured as a percentage of GDP is an indicator of financial sector
development and liquidity. A well-developed financial sector ensures efficient allocation of
resources at acceptable and affordable interest rates. The growth in broad money (M2) reflects a
rise in the level of intermediation given a wide array of financial assets and hence resulting into
financial development and improved banking efficiency. Therefore, broad money is expected to
be positively related to private sector credit. A number of studies have used it as one of he
determinants of private sectors, these include studies by; Million , (2014), Neelam , (2014) aand
Vika, (2009).
Official exchange rate (OER); measured as amount of local currency per US$, period average.
Exchange rate is considered as one of the determinants of banks' lending behavior. Increase in
exchange rate means depreciation of Ugandan currency and exchange rate depreciation makes
export demand higher and thereby increasing production in the country however exchange rate
volatility can also affect lenders’ ability to manage loans. Taiwo & Adesola, (2013), Evaraert, et
al., (2015), and Sajid & Sarfraz (2008) used it in their studies while investigating the determinants
of private sector credit and private investment respectively. Therefore the effect of exchange rate
on private sector credit can be ambiguous depending on how it affects the private investment
section.
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3.2 Model specification
According to the theoretical and empirical frameworks, most of the factors relevant to private
sector credit are incorporated in the model and this study employed E-views 7.1 statistical package
software to do the analysis.
Imran & Nishat (2012) conducted a study on “Determinants of bank credit in Pakistan: A supply
side Approach” for the period between 1971 and 2010 using ARDL model. The study concluded
that in long-run foreign liabilities, domestic deposits, economic growth, exchange rate, and the
monetary conditions (proxy by M2 as percentage of GDP) have significant and positive association
with private credit, while the inflation and money market rate do not affect the private credit.
Likewise, in short-run all the variables are significant and positively associated with private credit
except domestic deposit and inflation which do not influence the private credit in Pakistan.
Gerti & Irini, (2017), in their study to identify and evaluate the long run determinants of bank
credit to the private sector in the case of Albania Vector Error Correction Mechanism (VECM)
approach was employed based on demand and supply indicators. Estimations show that an
adjustment mechanism exists bringing bank credit back to equilibrium. The results imply that
lending is positively linked toeconomic growth. In addition, lower cost of lending, diminishing
government domestic borrowing and a more qualitative bank credit would create further lending
incentives. At the same time, the exchange rate is found to pick up some demand valuation and
consumption smoothing effects.
Therefore the functional form of this model is represented as follows;
LnPSC = f (lnGDP, lnLR, lnBCG, lnBM, OER, u)
Where u, the error term, contains other variables not explicitly included in the model.
21
The econometric form of equation above is represented as follows:
𝒍𝒏𝑷𝑺𝑪𝒕 = 𝜷𝟎 + 𝜷𝟏𝒍𝒏𝑮𝑫𝑷𝒕 + 𝜷𝟐𝒍𝒏𝑳𝑹𝒕 + 𝜷𝟑𝒍𝒏𝑩𝑪𝑮𝒕 + 𝜷𝟒𝒍𝒏𝑩𝑴𝒕 + 𝜷𝟓𝑶𝑬𝑹𝒕 + 𝒖𝒕
Where;
LnPSC: Log private sector credit
LnGDP: Log gross domestic product
LnLR: Log Lending rate
LnBCG: Log bank credit to the public sector or government.
LnBM: Log broad money
OER: Official exchange rate
u is the stochastic error term that captures other effects.
3.3 Method of Data Analysis and Estimation Techniques
The data collected was analyzed quantitavely and went through a test of the unit root on each
variable, test of Cointegration to assess long run relationship of private sector credit and its
determinants; Vector Error Correction Model (VECM) was used to estimate the short dynamics of
the equation and finally thegranger causality to establish the causal relationship of the variables.
All analysis and estimations were carried out using econometric software package, E-views 7.1.
3.3.1Unit Root Test
A stochastic process is said to be stationary if its mean and variance are constant over time and the
value of the covariance between the two time periods depends only on the distance or gap or lag
between the two time periods and not the actual time at which the covariance is computed. If a
22
time series is not stationary in this sense, it is called a nonstationary time series. In other words, a
nonstationary time series will have a time varying mean or a time varying variance or both,
Gujarati, (2004).
A study on the stationarity of variables is relevant for the reason that it incorporates important
behavior for these variables since making analysis with nonstationary variables may result in
spurious correlation. A stationary time series is superior or more important than a nonstationary in
economic analysis as it makes easier the study of the behavior of variables in the long run, Gujarati,
(2004). Hence, owing to the fact that most financial data is non-stationary and yet use of non-
stationary data may generate spurious results and poor forecasts, prior to estimation, study
variables were tested to ascertain their stationarity.
To test for stationarity, unit root test was carried out using theAugmented Dickey Fuller (ADF)
and Phillips Peron test methods. Once the variables were found to be non-stationary at their levels,
the traditional approach of differencing the series until stationarity is achieved was adopted. That
is to say, the following Augmented Dickey fuller model was fit;
∆𝑌𝑡 = 𝛼 + 𝛿𝑡 + 𝛽𝑌𝑡−1 + ∑ 𝛾𝑖
𝑛
𝑖=1
𝑌𝑡−𝑖 + 휀𝑡
Where휀𝑡 is a pure white noise error term and ∆𝑌𝑡−1 = 𝑌𝑡−1 − 𝑌𝑡−2 ,∆𝑌𝑡−2 = 𝑌𝑡−2 − 𝑌𝑡−3 , and so
on. The number of lagged difference terms to include is often determined empirically, the idea
being to include enough terms so that the error term is serially uncorrelated, Gujarati, (2003).
The hypotheses of this test:
H0: = 0, i.e., there is a unit root – the time series is non-stationary.
23
H1:< 0, i.e., there is no unit root – the time series is stationary.
Rejection rule: the null hypothesis is rejected if the absolute|𝐴𝐷𝐹𝑐| > |𝐴𝐷𝐹∗| , where 𝐴𝐷𝐹𝑐 is
the computed value of the statistic and 𝐴𝐷𝐹∗ is the critical value.
Undertaking unit root tests also enables the researcher to determine the order of integration of each
variable employed in the study. The determination of the order of each series was necessary for
co-integration and thus for error correction mechanism, Engle & Granger, (1987).
Phillips & Perron, (1988), on the other hand, proposed a nonparametric method of controlling for
serial correlation when testing for a unit root. The PP method estimates the non-augmented DF
test equation and modifies the t-ratio of theα coefficient so that serial correlation does not affect
the asymptotic distribution of the test statistic and a test of unit root using the Phillips-Perron
approach does not require a lag length determination, Waheed et al, (2006).
The test regression for the PP tests is given by the following equation, Phillips,(1998):
∆𝑌𝑡 = 𝐶 + 𝛼𝑌𝑡−1 + 𝑢𝑡
Where𝑢𝑡 is I (0) and may be heteroskedastic. The PP tests correct for any serial correlation and
hetroskedasticity in the errors 𝑢𝑡of the test regression by directly modifying the test statistics.
These tests are known as Phillips Zα and 𝑍𝑡tests. The Z -tests allow for a wide class of time series
with heterogeneously and serially correlated errors.
3.3.2 Determination of Optimal Lag Length
Determination of the optimal lag length is critical in attainment of serially uncorrelated and precise
results. Too many lags may lead to a loss of degrees of freedom hence less precise results, while
very few lags may not deal with the problem of serial correlation occurrence that leads to
24
inconsistent parameter estimates, Gujarati, (2004). Information criteria was used in selecting the
optimal lag length because they take into account both goodness of fit and parsimony of the model.
The optimal lag length were therefore selected using the Schwarz Information Criterion (SIC) and
the Akaike Information Criteria (AIC), based on the following formulas.
𝑆𝐼𝐶(𝐾) = 𝑙𝑛 (𝑅𝑆𝑆(𝐾)
𝑇) + 𝐾
𝑙𝑛𝑇
𝑇And(𝐾) = 𝑙𝑛 (
𝑅𝑆𝑆(𝐾)
𝑇) + 𝐾
2
𝑇 .
Where, 𝑇 is the sample size, 𝑅𝑆𝑆 is the Sum of Squared Residuals and 𝐾 is the number of
coefficients including the intercept in the estimated model. The number of lags that minimizes the
information criterion were chosen as a consistent estimator of the true model lag length.
3.3.3 Johansen Cointegration Test
Owing to the fact that the Engle-Granger method for co-integration has some challenge like
allowing only for a single Cointegration equation. And therefore in case more than two variables
are involved, there is a possibility that more than one equation may depict the long run
relationships among the various variables.
An alternative approach that does not suffer from these drawbacks was proposed by Johansen,
(1988), who developed a maximum likelihood estimation procedure, which also allows one to test
for the number of Cointegration relations. The procedure suggested by Johansen, (1988) basically
depends on direct investigation of Cointegration in the vector autoregressive (VAR)
representation. This analysis yields maximum likelihood estimators of the unconstrained
Cointegration vectors, but it allows one to explicitly test for number of Cointegration vectorsso
that the weakness of Engle-Granger, (1987) two step procedure are overcome. Moreover, Johansen
test enables estimating and testing for the presence of multiple Cointegration relationships in a
25
single-step procedure and does not require a priori endogenous-exogenous distinction among
variables, it can also identify multiple Cointegration vectors. The Johansen procedure setsout a
maximum likelihood procedure for the estimation and determining the presence of Cointegration
in VAR system.
VAR is one form of multivariate modeling where no variablein the system is assumed to be
exogenous a priori. Based up on this procedure, the variablesof the model are represented by
defining a vector of potentially endogenous variables.
In identifying the number of Cointegration vectors, the Johansen procedure provides n eigen
values denoted by λ (also called characteristic roots) whose magnitude measures the extent of
correlation of the Cointegration relations with the stationary elements in the model.
Hence, to identify the number of Cointegration vectors in the system, the Johansen procedureuses
two test statistics: the Maximal Eigen values (λ max statistics) and the Trace Statistics
(λtrace).These statistics are used to test the null hypothesis that there are at most “r‟
Cointegrationvectors against the alternative that there are “r + 1‟ Cointegration vectors, Enders,
(1995).
3.3.4 Vector Error Correction Model (VECM)
If two variables are not cointegrated or proved to have no long run relationship, the testing
procedure will stop there and one will not go for the construction of an error correction model. But
if they are cointegrated or proved to have a long run relationship one needs to go for an error
correction mechanism. The error correction mechanism (ECM) is a mechanism used to correct any
short run deviation of the variables from their long run equilibrium.
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In the previous section we have discussed how the long run relationship between the variables of
interest is determined. However, economic variables have short run behavior that can be captured
through dynamic modeling. A class of models that represents the concept of correction has been
developed and is referred as the Error Correction Model (ECM). A vector error correction model
is a restricted VAR designed for use with non-stationary series that are known to be cointegrated.
The VEC has Cointegration relations built in to the specification so that it restricts the long-
runbehavior of the long-run variables to converge to their cointegrating relationships while
allowing for short-run adjustment dynamics. To do this, the lagged value of first difference
of,GDP, lending rate, bank credit to government, broad money and official exchange rate were the
explanatory variable of PSC with error correction variable at first difference as follows
∆𝒍𝒏𝑷𝑺𝑪𝒕 = 𝜷𝟎 + ∑ 𝜷𝟐
𝒑
𝒊=𝟏
∆𝒍𝒏𝑮𝑫𝑷𝒕−𝟏 + ∑ 𝜷𝟑
𝒑
𝒊=𝟏
∆𝒍𝒏𝑳𝑹𝒕−𝟏 + ∑ 𝜷𝟒∆𝒍𝒏𝑩𝑪𝑮𝒕−𝟏
𝒑
𝒊=𝟏
+ ∑ 𝜷𝟓∆𝒍𝒏𝑩𝑴𝒕−𝟏
𝒑
𝒊=𝟏
+ ∑ 𝜷𝟓∆𝑶𝑬𝑹𝒕−𝟏
𝒑
𝒊=𝟏
+ 𝑬𝑪𝑻𝒕−𝟏
3.3.5 Testing for Causality
According to Granger, (1969), definition of causality states that “if 𝑋𝑡 Granger causes 𝑌𝑡 , then the
past values of 𝑋𝑡 should contain information that helps to predict 𝑌𝑡 above and beyond the
information contained in the past values of 𝑋𝑡 alone.To test the direction of the causality
relationship between the variables of interest; PSC and its determinants under study i.e.GDP, LR,
BCG, BM and OER, the study performed the pairwise Granger causality tests.
In this study, granger causality was implemented using the F-statistic for the normal Wald test on
coefficient restrictions given by:
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𝐹 =(𝑅𝑆𝑆𝑅 − 𝑅𝑅𝑆𝑈) 𝑚⁄
𝑅𝑆𝑆𝑈 (𝑛 − (2𝑚 + 1))⁄
Where: 𝑅𝑆𝑆𝑅 and 𝑅𝑆𝑆𝑈 are the sum of squared residuals for the restricted and unrestricted
regression models respectively, 𝑚 is equal to the number of lagged terms and 𝑛 is the number of
observations used to estimate the model. The term (2𝑚 + 1) is the number of parameters in the
unrestricted regression such that m is divided into components when the lags of respective
variables are different.
The Null Hypothesis (𝐻0) states that 𝑋𝑡 does not granger cause𝑌𝑡. If the 𝑝-value of the 𝐹 statistic
is sufficiently low, the null hypothesis can be rejected. The test for granger causality can yield four
possible outcomes namely: no granger causality; one-way granger causality in either direction;
feedback, and Granger causality running both ways.
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CHAPTER FOUR
ESTIMATION AND DISCUSSION OF RESULTS
4.0 Introduction
This chapter presents the study findings of the study which include;the descriptive summary and
graphical analysis of the variables; the diagnostic tests such as unit root test; lag structure,
Cointegration test; vector error correction model estimation and Granger causality analysis of the
relationship between Private Sector Credit and the determinants under investigation .
4.1 Descriptive Statistics
This gives the summary statistics of the variables and helps to understand the nature of variables
under investigation. The results are represented in table 4.1.
Table 4. 1: Descriptive statistics
LNPSC LNGDP LNLR LNBCG LNBM OER
Mean 1.773341 22.91484 3.108264 2.512169 2.683880 1232.061
Median 1.694294 22.88436 3.069835 2.367305 2.724008 1217.661
Maximum 2.717551 23.99092 3.688879 3.578879 3.161995 3240.645
Minimum 0.972903 22.02028 2.379546 1.716477 1.986200 0.074170
Std. Dev. 0.570723 0.641633 0.287360 0.480401 0.339203 937.0710
Skewness 0.282616 0.206304 -0.012291 0.771692 -0.525612 0.089056
Kurtosis 1.703442 1.677024 3.440722 2.679654 2.247624 1.972330
Jarque-Bera 3.000827 2.880764 0.292261 3.726984 2.506714 1.631744
Probability 0.223038 0.236837 0.864045 0.155130 0.285545 0.442254
Sum 63.84026 824.9344 111.8975 90.43808 96.61966 44354.19
Sum Sq. Dev. 11.40038 14.40927 2.890145 8.077465 4.027062 30733575
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Observations 36 36 36 36 36 36
Source: Author’s Computations
From table 4.1, it shows that the average of efficiency for the commercial banks for the thirty five
years was 1.7733 with a standard deviation of 0.5707, GDP growth rate 22.91484 with a standard
deviation of 0.4804the bank lending rate was 3.1083 on average with a standard deviation of
0.2874, bank credit to government was2.5122 with a standard deviation of 0.4804, broad money
was 2.6839 with a standard deviation of 0.3392 andofficial exchange rate of USHS against US
dollar is1232.061 with a standard deviation of 937.0710. In addition, the results also indicate that
all the variables are normally distributed because the probabilities of their Jarque-Bera statistics
are greater than zero. The distribution of the variable are symmetrically skewed since the mean
and median are almost equal for all the variables.
4.2 Unit Root Test
It’s very essential to test the existence of unit root in the variables before any meaningful regression
is performed with time series. This also important in establishing the order of integration of
variables.In order to produce meaning full relationship from the regression, Variables used in
analysis should be stationary and cointegrated. This is mainly because working with such non
stationary variables direct leads to spurious regression (seemingly related variables) results, from
which further inference is more meaningless. In order to avoid problems of spurious correlation
normally associated with the inclusion of non-stationary series in regression models, appropriate tests
of stationarity on variables of interest should be employed.
Two types of formal tests are conducted to examine whether the data series is stationary or not. These
tests are the conventional Augmented Dickey-Fuller test (ADF) and the Phillips-Perron test (PP). These
30
two tests allow for three options of output in conducting the tests; without intercept and trend, with
only intercept and with both intercept and trend. The null hypothesis for the test claims that the data
series under investigation has unit root. Conversely, the alternative hypothesis claims that the series is
stationary.
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The result of the test for the variables at level and at their first difference is presented in Table 4.2 and 4.3 respectively.
Table 4. 2: Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) Unit Root Tests at Level.
Variables
Specification
ADF Unit Root Test
PP Unit Root Test Order of
Integration
ADF test statistic 1%
critical
Value
5%
critical
value
P-value PP test
statistic
1%
critical
Value
5%
Critical
Value
P- value
test
statistic
Lag
length
Variables at Level
lnPSC
Without C
and T
2.803 2 -2.637 -1.951 0.9982 2.209 -2.633 -1.951 0.9922 I(1)
With C 0.432 2 -3.646 -2.954 0.9814 0.418 -3.632 -2.948 0.9810
With C and T -3.973 0 -4.244 -3.544 0.0191** -4.160 -4.244 -3.544 0.0122**
lnGDP
Without C
and T
11.312 0 -2.633 -1.951 1.0000 8.273 -2.633 -1.951 1.0000
With C 1.585 0 -3.633 -2.948 0.9992 1.199 -3.633 -2.948 0.9975
With C and T -2.249 0 -4.244 -3.544 0.4494 -2.249 -4.244 -3.544 0.4494
lnLR
Without C
and T
-0.806 6 -2.647 -1.953 0.3583 0.426 -2.632 -1.951 0.8004 I(1)
With C -3.658 3 -3.654 -2.957 0.0099 -2.763 -3.632 -2.948 0.0740*
With C and T -4.413 3 -4.273 -3.558 0.0071*** -2.8104 -4.244 -3.544 0.2032
lnBCG
Without C
and T
-0.001 0 -2.633 -1.951 0.6755 -0.001 -2.633 -1.951 0.6755
With C -2.029 0 -3.633 -2.948 0.2736 -2.111 -3.633 -2.948 0.2416
33
Variables
Specification
ADF Unit Root Test
PP Unit Root Test Order of
Integration
ADF test statistic 1%
critical
Value
5%
critical
value
P-value PP test
statistic
1%
critical
Value
5%
Critical
Value
P- value
test
statistic
Lag
length
With C and T -2.369 0 -4.244 -3.544 0.3883 -2.369 -4.243 -3.544 0.3883
lnBM
Without C
and T
0.270 1 -2.635 -1.951 0.7585 0.423 -2.633 -1.951 0.7996 I(1)
With C -1.585 1 -3.639 -2.951 0.4790 -1.426 -3.633 -2.948 0.5583
With C and T -3.161 0 -4.244 -3.544 0.1088 -3.272 -4.244 -3.544 0.0876*
OER
Without C
and T
3.253 0 -2.633 -1.951 0.9995 2.897 -2.637 -1.951 0.9986
With C 0.991 0 -3.633 -2.948 0.9956 0.832 -3.633 -2.948 0.9932
With C and T -1.895 0 -4.244 -3.544 0.6357 -2.149 -2.244 -3.544 0.5015
*, ** and *** indicates the rejection of the null hypothesis (unit root) at 10%, 5% and 1% respectively. Where C and T are constant
and T trend respectively.
Source: Author’s Computations
34
From the results above in table 4.2, both the ADF (adjusted for lag length by Akaike information
criteria) and the PP class of tests show that lnPSC is non stationary in levels for two specifications,
i.e., without constant and trend and with constant and is stationary at 5% level significance with
constant and trend specification. This is because the null hypothesis of unit root is not rejected at
1% and 5% levels of significance at the mentioned specifications (without constant and trend and
with constant). In addition according to the ADF (adjusted for lag length by Akaike information
criteria) test, the variable lnLR is non stationary at 1% and 5% level of significance at levels with
specifications of without constant and trend and with constant but is stationary at 1% level
significance with the specification of with constant and trend. However with the PP class test, the
variable is non stationary throughout all the levels of significance and specifications.
The tests also revealed that lnGDP, lnBCG, lnBM and OER are all non-stationary at levels in all
specifications both at1% and 5% level of significance. In order to make the variables stationary
the first deference was undertaken for all the variables and the results are shown in table 4.3 below.
35
Table 4. 3: Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) Unit Root Tests at first difference
Variable at First Difference
Variables
Specification
ADF Unit Root Test PP Unit Root Test Order of
Integration
ADF test statistic 1%
critical
Value
5%
critical
value
P-value PP test
statistic
1%
critical
Value
5% Critical
Value
P- value
DlnPSC
Without C
and T
-8.222 0 -2.635 -1.951 0.0000*** -7.819 -2.635 -1.951 0.0000***
With C -5.636 1 -3.646 -2.954 0.0000*** -9.035 -3.639 -2.951 0.0000***
With C and T -5.704 1 -4.263 -3.553 0.0003*** -9.316 -4.253 -3.548 0.0000***
DlnGDP
Without C
and T
-0.885 1 -2.637 -1.951 0.3252 -1.556 -2.635 -1.951 0.0111** I(1)
With C -3.681 0 -3.639 -2.951 0.0090*** -3.822 -3.639 -2.951 0.0063***
With C and T -4.102 0 -4.253 -3.548 0.0144** -4.175 -4.253 -3.548 0.0121**
DlnLR
Without C
and T
-1.976 2 -2.639 -1.952 0.0474** -3.690 -2.635 -1.951 0.0006***
With C -5.259 5 -3.679 -2.968 0.0002*** -3.687 -3.639 -2.951 0.0089***
With C and T -2.730 1 -4.263 -3.553 0.2317 -3.8.5 -4.253 -3.548 0.0285**
DlnBCG
Without C
and T
-5.436 0 -2.635 -1.951 0.0000*** -5.436 -2.645 -1.951 0.0000*** I(1)
With C -5.370 0 -3.639 -2.951 0.0001*** -5.371 -3.639 -2.951 0.0001***
With C and T -5.329 0 -4.253 -3.548 0.0006*** -5.331 -4.253 -3.548 0.0006***
DlnBM Without C
and T
-5.214 0 -2.633 -1.951 0.0000*** -5.669 -2.635 -1.951 0.0000***
36
Variable at First Difference
Variables
Specification
ADF Unit Root Test PP Unit Root Test Order of
Integration
ADF test statistic 1%
critical
Value
5%
critical
value
P-value PP test
statistic
1%
critical
Value
5% Critical
Value
P- value
With C -5.268 0 -3.639 -2.951 0.0001*** -5.792 -3.639 -2.951 0.0000***
With C and T -4.785 1 -4.263 -3.553 0.0027*** -5.716 -4.253 -3.548 0.0002***
DOER
Without C
and T
-2.482 0 -2.635 -1.951 0.0147** -2.494 -2.345 -1.951 0.0142**
With C -3.423 0 -3.639 -2.951 0.0170** -2.981 -3.639 -2.951 0.0468**
With C and T -3.549 0 -4..253 -3.548 0.0499** -3.123 -4.253 -3.548 0.0173**
*, ** and *** indicates the rejection of the null hypothesis (unit root) at 10%, 5% and 1% respectively. Where C and T are constant
and T trend respectively.
Source: Author’s Computations
35
From the table 4.3, all the variables become stationary after the first difference is done because the
null hypothesis is rejected at the levels of 1% and 5% level of significance for both ADF and PP
class tests
The test result is also confirmed by the graphical representation of plot of variables at level and
their first differences. Accordingly, from figure 4.1, plot of the variables (in levels) shows that all
the variables are not stationary. Alternatively, in figure 4.2 the variables in first difference suggest
the presence of stationarity as shown in the figures below
37
Figure 4. 1: Graph showing variables at level
Source: Author’s Computations
0.8
1.2
1.6
2.0
2.4
2.8
1980 1985 1990 1995 2000 2005 2010 2015
LNPSC
22.0
22.5
23.0
23.5
24.0
24.5
1980 1985 1990 1995 2000 2005 2010 2015
LNGDP
2.0
2.4
2.8
3.2
3.6
4.0
1980 1985 1990 1995 2000 2005 2010 2015
LNLR
1.6
2.0
2.4
2.8
3.2
3.6
1980 1985 1990 1995 2000 2005 2010 2015
LNBCG
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
1980 1985 1990 1995 2000 2005 2010 2015
LNBM
0
1,000
2,000
3,000
4,000
1980 1985 1990 1995 2000 2005 2010 2015
OER
38
Figure 4. 2: Graph showing variables in the first difference
Source: Author’s Computations
-.4
-.2
.0
.2
.4
1980 1985 1990 1995 2000 2005 2010 2015
D(LNPSC)
-.04
.00
.04
.08
.12
1980 1985 1990 1995 2000 2005 2010 2015
D(LNGDP)
-.2
-.1
.0
.1
.2
.3
.4
1980 1985 1990 1995 2000 2005 2010 2015
D(LNLR)
-1.2
-0.8
-0.4
0.0
0.4
0.8
1980 1985 1990 1995 2000 2005 2010 2015
D(LNBCG)
-.4
-.2
.0
.2
.4
.6
1980 1985 1990 1995 2000 2005 2010 2015
D(LNBM)
-400
-200
0
200
400
600
800
1980 1985 1990 1995 2000 2005 2010 2015
D(OER)
39
Generally, from the above the ADF and the PP tests provide identical results for all variables and
the variables are integrated of the same order (i.e. they are all integrated of order one, I (1))
according to these two tests. As a result, the determination of cointegrating relationships doesn’t
suffer from mixed order of integration and hence Cointegration analysis is reasonable in carrying
out the specified growth model estimation in the following section.
4.3 Lag Length Selection and Estimation of Long Run Growth Model
There are many tests that can be used to choose appropriate lag length. These are the Log
Likelihood (LL), the Akaike information criteria (AIC), the Schwarz information criteria (SIC)
and the Hannan-Quinn information criteria (HIC). The optimal lag length for this study is
determined by using the Akaike Information Criteria (AIC) as this method has been proven in most
empirical papers to be superior to other tests. According to the Akaike Information Criteria, the
lag length with the lowest AIC in absolute value is the most efficient one. In addition, the optimal
lag length that is obtained from the AIC is also confirmed by the VAR estimates considering
successive lags. This is shown in the table 4.4, below;
Table 4. 4: Lag Order Selection Criteria
Lag length Information Criteria
LL AIC SC HQ
0 -211.7535 12.80903 13.07839 12.90089
1 -19.30746 3.606321 5.491826* 4.249332*
2 25.60221 3.082223* 6.583874 4.276386
* indicates lag order selected by the criterion.
Source: Author’s Computation
Accordingly, from table 4.4, the optimal lag length used in the equation is two and therefore VAR
(1) is appropriate to carry the Cointegration test.
40
4.4 Cointegration analysis; applying the Johansen Procedure
The Johansen method does not require a priori endogenous-exogenous distinction among
Variables and it can also identify multiple Cointegration vectors. The Johansen procedure sets out
a maximum likelihood procedure for the estimation and determining the presence of cointegrating
in VAR system. The unit root test results as reported in table 4.3shows that all the variables
included in the equation are stationary at first difference and this (1) stationary condition allows
to conduct the test for Cointegration among the variables of interest.
To determine the number of cointegrating vectors two test statistics called the maximum
eigenvalue (λmax) and trace statistics (λtrace) are computed. For k-endogenous variables each
with a single unit root, there is a possibility to find from zero to k-1 linearly independent
cointegrating relations.
For this study therefore, two types of test statistics are used to determine the rank of the model in
this study; namely the trace test and the maximum Eigen/likelihood ratio test. The trace test
(λtrace) tests the null hypothesis of r cointegrating vectors against the alternative hypothesis of k
cointegrating vectors, where k is the number of endogenous variables, for r=0,1,2…,k-1. The
maximum eigen-value test, on the other hand, tests the null hypothesis of r cointegrating vectors
against the alternative hypothesis of r+1 cointegrating vectors. Both the trace statistics and the
maximum eigen/likelihood ratio test results in one cointegrating equations at 5% level of
significance for this study as shown in the results below.
41
Table 4. 5: Johansen Cointegration Test
Null
Hypothesis
Alternative
Hypothesis
Eigen
Value
Statistic 5%
Critical
value
Prob.** Hypothesized
No. CE(s)
Trace test (λ trace)
r = 0 r ≥ 0 0.743575 128.6402 95.75366 0.0001 None *
r ≤ 1 r ≥ 1 0.600273 82.36893 69.81889 0.0036 At most 1 *
r ≤ 2 r ≥ 2 0.554285 51.19186 47.85613 0.0235 At most 2 *
r ≤ 3 r ≥ 3 0.372205 23.71726 29.79707 0.2127 At most 3
r ≤ 4 r ≥ 4 0.160100 7.888828 15.49471 0.4773 At most 4
r ≤ 5 𝑟 ≥ 5
0.055927 1.956748 3.841466 0.1619 At most 5
Max Eigenvalue test (λ max)
𝑟 = 0 𝑟 = 1 0.743575 46.27131 40.07757 0.0089 None *
𝑟 = 1 𝑟 = 2 0.600273 31.17707 33.87687 0.1016 At most 1
𝑟 = 2 𝑟 = 3 0.554285 27.47460 27.58434 0.0516 At most 2
𝑟 = 3 𝑟 = 4 0.372205 15.82843 21.13162 0.2350 At most 3
𝑟 = 4 𝑟 = 5 0.160100 5.932080 14.26460 0.6219 At most 4
𝑟 = 5 𝑟 = 6 0.055927 1.956748 3.841466 0.1619 At most 5
* denotes rejection of the hypothesis at the 0.05 level
Source: Author’s Computations
As pointed out in table 4.5, the trace statistic test confirms that there are three cointegrating
equations at 5% level of significance. The null hypothesis of no Cointegration (r = 0) is rejected at
5% level when tested against the alternative hypothesis of 𝑟 ≥ 0 cointegrating vectors because
λtrace = 128.6402 exceeds the respective critical value of 95.75366
Similarly, the null hypothesis of one Cointegration (𝑟 ≤ 1) and two Cointegration (𝑟 ≤ 2) is
rejected in favor of the alternative hypothesis of 𝑟 ≥ 1 and 𝑟 ≥ 2 cointegrating vector
respectivelysinceλtrace = 82.36893and λtrace = 51.19186 are greater than their respective critical
value of 69.81889and 47.85613 respectively.
42
However, the null hypothesis of three or fewer cointegrating vectors cannot be rejected against the
alternative hypothesis of more than three cointegrating vectors (3 ˂ 𝑟 ≤ 6).
The maximum eigenvalue (λmax)confirms existence of one Cointegration equation at 5% level of
significance because null hypothesis of no Cointegration (r = 0) is rejected at 5% level when tested
against the alternative hypothesis of 𝑟 = 1cointegrating vectors sinceλmax = 46.27131exceeds
the respective critical value of 40.07757.
Since the maximum Eigen/likelihood ratio test confirms only one cointegrating equation at 5%,
then it is possible to conclude thatthere is existence of one Cointegrating vector in the estimated
model hence thereexists a linear combination of I (1) variables that cointegrates them in a stable
long run relationship.
Table 4. 6: Normalized Cointegrating Coefficient One (1) Co Integrating Equation
LNPSC LNGDP LNLR LNBCG LNBM OER
1.000000 -3.764431 -2.447645 1.685000 4.212859 0.000619
(0.69505) (0.75273) (0.52476) (0.57770) (0.00049)
Log likelihood -15.58226 Standard errors in parenthesis
Source: Author’s Computations
The long run equation is;
𝐥𝐧𝐏𝐒𝐂 = − 𝟑. 𝟕𝟔𝟒𝟒𝟑𝟏𝐥𝐧𝐆𝐃𝐏 − 𝟐. 𝟒𝟒𝟕𝟔𝟒𝟓𝐥𝐧𝐋𝐑 + 𝟏. 𝟔𝟖𝟓𝐥𝐧𝐁𝐂𝐆 + 𝟒. 𝟐𝟏𝟐𝟖𝟓𝟗𝐥𝐧𝐁𝐌 + 𝟎. 𝟎𝟎𝟎𝟔𝟏𝟗𝐎𝐄𝐑
(0.69505) (0.75273) (0.52476) (0.57770) (0.00049)
From the Cointegrating equation above, Gross Domestic Product and lending Rate have a long run
negative relationship with Private Sector Credit. The result implies that for a 1 percent increase in
LnGDP and LnLR, will lead to a 3.76%, and 2.45%, reduction in private sector credit
respectivelyin Uganda.
However, bank credit to government (LnBCG), broad money (LnBM), and official exchange rate
(OER) have appositive relationship with Private sector credit. That is, a 1% increase inLnBCG,
43
LnBM and OER will respectively bring about 1.69%, 4.21%, 0.00062% increasein Private sector
credit in Uganda. This positive sign of OER is attributed to the fact that devaluation of the shilling
stimulates Private sector credit in Uganda because it will make Ugandan goods attractive (low
priced) to foreign countries which fosters investment to meet the increased demand while other
countries’ goods (Imported goods) will be expensive and less attractive to Ugandans, thereby
boosting private investment in Uganda and eventuallyincreasing private sector credit.
A look at their standard errors indicates that all the variables are statistically significant except the
official exchange rate.
44
Table 4. 7: The Short Run Dynamic Modelling (Vector Error Correction Model)
Dependent Variable: D(LNPSC)
Method: Least Squares
Date: 10/19/17 Time: 16:42
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments
Coefficient Std. Error t-Statistic Prob.
ECT_1 -0.07582 0.03023 -2.5083 0.0187
D(LNPSC(-1)) -0.53476 0.17832 -2.9989 0.0059
D(LNGDP(-1)) -0.84166 0.89604 -0.9393 0.3562
D(LNLR(-1)) -0.29901 0.28997 -1.0312 0.312
D(LNBCG(-1)) 0.026178 0.1048 0.24979 0.8047
D(LNBM(-1)) 0.252257 0.139 1.81485 0.0811
D(OER(-1)) -2.75E-05 0.00018 -0.1543 0.8786
C 0.109773 0.05835 1.88131 0.0712
R-squared 0.469269 Mean dependent var 0.03746
Adjusted R-squared 0.32638 S.D. dependent var 0.14995
S.E. of regression 0.123069 Akaike info criterion -1.1498
Sum squared resid 0.393798 Schwarz criterion -0.7907
Log likelihood 27.54679 Hannan-Quinn criter. -1.0273
F-statistic 3.284148 Durbin-Watson stat 1.95773
Prob(F-statistic) 0.012291
Source: Author’s Computations
From table 4.7 above, the lagged error correction term (ECT-1) that is included in the model
captures the short run dynamics between the cointegrating series and is correctly signed (negative)
and significant implying that private sector credit responds to changes in the selected variables
with a lag. The value of the coefficient implies that when there is an exogenous shock in the
economy which distorts the equilibrium, then about 7.58% of the errors from the lags are
absorbed/adjusted in one period.
45
The variables lnPSC and lnBM are significant implying that the previous period of one lag has an
effect on the present year values of PSC. The results also show that 46.9% of the variations in the
model are due to the explanatory variable in the model. The Durban Watson (DW) test results also
confirm that there is no autocorrelation problem.
The result indicate that the short run changes in private sector credit (PSC) is affected negatively
and significantly by the one period lagged private sector credit and is affected positively and
significantly by the one period lagged Broad money.
However changes in Gross Domestic Product, bank credit to the government, lending rate
andofficialexchange rate have a negative short run effect on PSC though they are not
significant.Implying that there is no immediate multiplier effect from these variables to private
sector credit.
46
Table 4. 8: Pairwise Granger Causality Test between Private Sector Credit and the selected
determinants
Null Hypothesis: Obs F-Statistic Prob. conclusion
LNGDP does not Granger Cause LNPSC 34 16.4949 2.E-05 Reject
LNPSC does not Granger Cause LNGDP 34 1.88560 0.1699 Fail to reject
LNLR does not Granger Cause LNPSC 34 3.56762 0.0412 Reject
LNPSC does not Granger Cause LNLR 34 3.97636 0.0298 Reject
LNBCG does not Granger Cause LNPSC 34 0.98819 0.3844 Fail to reject
LNPSC does not Granger Cause LNBCG 34 1.39007 0.2652 Fail to reject
LNBM does not Granger Cause LNPSC 34 0.76064 0.4765 Fail to reject
LNPSC does not Granger Cause LNBM 34 4.57011 0.0188 Reject
OER does not Granger Cause LNPSC 34 4.68627 0.0172 Reject
LNPSC does not Granger Cause OER 34 0.61447 0.5478 Fail to reject
Source: Author’s Computations
From the table above, the first result reveals a long-run unidirectional causation from gross
domestic product to private sector credit. The second result reveals bi-directional causation from
lending rates to private sector credit, confirming their long-run relationship. The third result
indicates no causality between private sector credit and bank credits to government. The fourth
result reveals a long-run unidirectional causation from private sector credit to broad money. The
fifth result reveals a long-run unidirectional causation from official exchange rate toprivate sector
credit.
47
Finally, to ensure and confirm the fitness of the model, the diagnostic and stability tests are also
conducted; thediagnostic tests examine the serial correlation, functional form, normality and
heteroscedasticity associated with the selected model were carried out. Cumulative sum (CUSUM)
and cumulative sum of squares recursive residuals (CUSUMSQ) tests are conducted for testing
the stability of the model.
Findings; The serial auto correlations test indicated that the model is free from auto correlation
and also the Heteroskedasticity test still indicated that there is no problem of Hetro in the residual.
More so, the test for normality results showed that the residual is normally distributed.The tests on
stability of parameters that were carried out on the ECM reflect that the short-run model does not
depict any sign of instability. This is evident from the results generated from the CUSM CUSUMQ
stability test that reflect stability of the private sector credit model in the short run.
The recursive estimation of the coefficients and the residuals of the model to test Parameter
constancy were also undertaken and the results also indicate stability of the model. The recursive
residual tests also affirm parameter stability as the recursive residuals are within the band. Thus,
the model exhibits parameter constancy, implying that that there is evidence of stability over the
sample period in the short run.
In general, from the outputs of the diagnostic teststhere is enough evidence to conclude that this
model is econometricallywell specified.
All the above explanation is illustrated in the APPEDIX.
48
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS
5.1 Summary of findings and conclusions
This study empirically investigates the determinants of private sector in Uganda by employing;
Vector Error Correction Model, Johansen co-integration approach and ADF approach using annual
time series data from 1980 to 2015. Five variables were used; Private sector credit which is the
dependent variable and; gross domestic product, broad money, lending rate, bank credit to
government and official exchange rate as the independent variables.
The results from the Cointegrating long run equation indicate that, Gross Domestic Product and
lending Rate have a long run negative relationship with Private Sector Credit in Uganda.
While most studies such as; Pham, (2015), Egert et al, (2006), Calza. M, & J, (2003), have been
done and found a positive relationship between gross domestic product and private sector credit,
the findings of this study find an inverse long run relationship. In other words, as GDP increases,
private sector credit keeps reducing. For developing countries like Uganda this can be attributed
to the fact that as the economy develops and people’s income increases, they will no longer opt to
borrow money for investment but instead use their own income. Also GDP could be increasing
due to other factors like foreign direct investment which eventually affects domestic private
investment there by reducing private sector credit, (see Apergis et al. (2006) and Agosin &
Machado (2005)).
The negative sign of lending rate coefficient is the expected sign theoretically and it also agrees
with the results by Jebra, N. et al, (2016) and Hofmann, (2001) who found a significant negative
effect of interest rate on private sector credit in the long run, and also in the short run. It implies
49
that as lending rate increases, the cost of borrowing goes high which lends to a reduction in private
sector credit.
Bank credit to government, broad money and official exchange rate have a positive relationship
with Private sector credit. Although, official exchange rate is not significant both in the long run
and short run. These results agree with study findings by Evaraert, et al. (2015) that found no
significance for the exchange rate to credit.
Broad money is positive and significant in the long run and short run and these results are similar
to those of Vika, (2009) whose findings indicate positive correlation of private sector credit with
liquidity of the banking system and the interaction term between monetary policy indicator and
liquidity. Therefore, it can be right to conclude that broad money is a key determinant of private
sector credit in Uganda
The sign of bank credit to the government is positive and significant which contradicts with most
empirical studies that found a negative relationship between private sector credit and bank credit
to the private sector. The results can be due to the fact government borrowing will increase the
expenditure of government inform of providing infrastructure such as roads which leads to increase
in economic growth that stimulates private investment hence to increase in private sector credit.
Precisely, if the borrowed funds by government are used optimally, then there is no crowding out
effect of government borrowing on private sector credit.
Changes in; Gross domestic product, bank credit to the government, lending rate and official
exchange rate do not have a significant effect on private sector credit in the short run. Implying,
there is no immediate multiplier effect from these variables to private sector credit.
50
The ECT is statistically significant in the equations for, private sector credit, and the variables
understudy implying that any deviations of the variables from the equilibrium in the long run are
corrected in the short run.
Analysis of the long-run Granger causality reveals unidirectional causality from GDP to private
sector credit; from PSC to Broad money, and from official exchange rate to private sector credit.
During the same period studied, there was a causal feedback effect from core BCG to private sector
credit and vice versa.
5.2 Recommendations
The government should not leave Lending rate to be determined by the forces of demand and
supply but rather fix it at a very reasonable rate in order to encourage private sector credit in
Uganda. It is also strongly recommended that government should ensure that Ugandan economy
has conducive environment for accessing private sector credit by putting in place policies through
practical strategies that will ensure consistent, moderate and acceptable levels of inflation rate,
interest rate, exchange rate and credit to private sector in the economy of Uganda. This also calls
government to ensure that the resources borrowed from commercial banks are allocated and
exhausted efficiently. In addition, Policies should be put in place to ensure that as the economy is
growing, private investment should be enhanced but not negatively affected.
The government should aim at having a well-developed financial sector to ensure efficient
allocation of resources at acceptable and affordable interest rates and Commercial banks also need
to the improve the level of intermediation given a wide array of financial assets and hence resulting
51
into financial development and improved banking efficiency. This leads to an increase in private
sector credit.
5.3 Limitations of the study
One of the limitations of this study was the time engaged in the collection, analysis and
interpretation of data. This is because the required data was not available in one file, format or
location and had to be gathered from several different sources and compared to check what gives
good results thus required plenty of time to organize and check for quality.
The time taken to carry out this study was not sufficient for the amount of detail and analysis
involved therefore with more time, detailed tests could be conducted to determine whether the
same conclusions would be derived with more variables included in the research model.
Limited data. In Uganda, it’s still a challenge to get detailed data on some variables especially
when one needs data for so many years back. This limited the researcher on the variables to be
used for the study.
Further research on determinants of private sector credit can be undertaken using panel data
approach on East African Countries.
Further studies can try to investigate more deeply on supply and demand side factors, clearly
pointing out the effects macro-economic and institutional factors like political stability.
53
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57
APPENDIX
A. Normality test
B. Stability test
0
1
2
3
4
5
6
7
8
9
-0.2 -0.1 0.0 0.1 0.2 0.3
Series: ResidualsSample 1982 2015Observations 34
Mean -9.47e-17Median 0.003960Maximum 0.252993Minimum -0.228934Std. Dev. 0.112900Skewness -0.057902Kurtosis 2.536560
Jarque-Bera 0.323265Probability 0.850754
-16
-12
-8
-4
0
4
8
12
16
86 88 90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Significance
58
C. Stability using cumulative sum of squares
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
86 88 90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM of Squares 5% Significance
59
D. Results for serial correlation test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.505234 Prob. F(2,28) 0.6088
Obs*R-squared 1.184260 Prob. Chi-Square(2) 0.5531
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 10/28/17 Time: 16:43
Sample: 1982 2015
Included observations: 34
Presample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
C(1) -0.001124 0.026113 -0.043027 0.9660
C(2) 0.002011 0.324720 0.006194 0.9951
C(6) -0.020863 0.140534 -0.148452 0.8831
C(8) 0.001042 0.023665 0.044034 0.9652
RESID(-1) -0.101145 0.365767 -0.276530 0.7842
RESID(-2) -0.173974 0.262343 -0.663156 0.5127
R-squared 0.034831 Mean dependent var -9.47E-17
Adjusted R-squared -0.137520 S.D. dependent var 0.112900
S.E. of regression 0.120413 Akaike info criterion -1.236988
Sum squared resid 0.405982 Schwarz criterion -0.967630
Log likelihood 27.02880 Hannan-Quinn criter. -1.145129
F-statistic 0.202094 Durbin-Watson stat 1.883516
Prob(F-statistic) 0.958886
60
E. Results for Heteroskedasticity test
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.785849 Prob. F(8,25) 0.1277
Obs*R-squared 12.36423 Prob. Chi-Square(8) 0.1357
Scaled explained SS 7.395564 Prob. Chi-Square(8) 0.4946
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 10/28/17 Time: 16:50
Sample: 1982 2015
Included observations: 34
Variable Coefficient Std. Error t-Statistic Prob.
C -0.752950 0.812860 -0.926298 0.3631
LNPSC(-1) 0.047255 0.029205 1.618026 0.1182
LNGDP(-1) 0.036492 0.039503 0.923763 0.3644
LNLR(-1) -0.007375 0.026647 -0.276771 0.7842
LNBCG(-1) 0.001918 0.014221 0.134848 0.8938
LNBM(-1) -0.013793 0.016335 -0.844394 0.4065
OER(-1) -3.40E-05 1.21E-05 -2.819294 0.0093
LNPSC(-2) -0.025097 0.026621 -0.942761 0.3548
LNBM(-2) -0.005408 0.017751 -0.304675 0.7631
R-squared 0.363654 Mean dependent var 0.012372
Adjusted R-squared 0.160023 S.D. dependent var 0.015566
S.E. of regression 0.014266 Akaike info criterion -5.439886
Sum squared resid 0.005088 Schwarz criterion -5.035849
Log likelihood 101.4781 Hannan-Quinn criter. -5.302098
F-statistic 1.785849 Durbin-Watson stat 2.258010
Prob(F-statistic) 0.127722