the determinants of private sector credit: case study …

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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.

17

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.

20

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.

26

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:

27

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.

28

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

29

Observations 36 36 36 36 36 36


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.

32

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


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.


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


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


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.


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


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


-.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


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


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


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


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

REFERENCES

Agosin, M., & Machado , R. (149-162). Foreign Investment in Developing Countries: Does it Crowd in

Domestic Investment?”. Oxford Development Studies, 33 (2),, 2005.

Albulescu , C. T. (2009). Forecastin credit growth in Romania: From credit to credit crunch? MPRA Paper

No. 16740.

Ali , A., & Daly, K. (2010). Macroeconomic determinants of credit risk: Recent evidence from a cross

country study. International Review of Financial Analysis, 165-171.

Anthony , A. (2016). Government Domestic Debt, Private Sector Credit,and Economic Growth in Oil-

Dependent Countries:. NEW ZEALAND: Lincoln University Digital Thesis.

Apergis, N., Katrakilidis , C., & Tabakis, N. (2006). Dynamic Linkages Between FDI Inflows and Domestic

Investment: A Panel Cointegration Approach. Atlantic Economic Journal 34, 385-394.

Bank Of Uganda. (2013). state of the economy.

Barro, R. J. (1991). Economic Growth in a Cross Section of Countries. The Quarterly Journal of Economics,

407-443.

Beck, T., Adolfo, B., Era, D.-N., & Reza , Y. (2013). Too Cold, Too Hot, or Just Right?Assessing Financial

Sector Development Across the Globe. International Monetary Fund , 1-30.

Barder, M., & Malawi, A. I. (2010). Impact of interest rate on investment. Eco &Adm, 199-209.

Calza, A., M, M., & J, S. (2003). Aggregate loans to the Euro Area private sector. ECB Working paper

series, No. 202.

Catão, L. (1997). Bank Credit in Argentina in the Aftermath of the Mexicanrisis: suply or demand

constrained? IMF Working Paper, No.97/32.

Chizea, B. (1994). Finance for farming:A guide to lending Banker. Lagos:.

Cottarelli, C., Dell'Ariccia, G., & Vladkova-Hollar, I. (2003). ank redit growth to the private sector in

Central and Eastern europe and in the Balkans. Journal of banking and finance No. 29, 83-104.

Dorothy, N. G., A. Tinyinondi, & Duncan R. K. (2016). Determinants of Private Sector Credit in Uganda:

The Role of Mobile Money. WP No. 04/2016, 1-25.

Egert, B., P, B., & T, Z. (2006). Credit groth in cetral and eastern Europe new(over) shooting stars? ECB

working paper series No 687/October 2006.

54

Emran, S. M., & Farazi, S. (2008). Government borrowing and private sector credit; Evindence fron

developing countries. Washington.

Enders, W. (2015). Applied Econometric Time Series (4th ed.).

Engle, R., & Granger, C. (1987). Cointegration and error correction: Representation, estimation and

testing. conometrica.55, 251-276.

Evaraert, G., Che, N., Geng, N., Gruss, B., Impavido, G., Lu, Y., . . . Zeng, L. (2015). Does sullpy or demand

drive the credit cycle? Evindence form Central, Esatern and Southern Europe. IMF working paper

WP/15/15.

Gerti, S., & Irini, K. ( 2017, May). Determinant of bank credit to private sector; case study of ALBANIA.

Munich Personal RePEc Archive, 6-37.

Granger, C. (1969). Investigating causal relationship by econometric models and cross-spectral methods.

econometrica 37, 424-438.

Gujarati, D. (2004). Basic Econometrics (4th ed.). McGraw−Hill Companies.

Guo, K., & V, S. (2011). Determinants of bank credit in emrging market economies. IMF working paper

WP/11/51.

Gupta, K. L. (1987). Aggregate Saving Financial Intermediation and interest rate(vol.69). the review of

economics ant statistics, 303-311.

Hofmann, B. (2001). The detrminants of private sector credit in industrialsed countries: Do property

prices matter? BIS Working paper No. 108.

Hussain, J., Millman, C., & Matlay, H. (2006). SME financing in UK and China: a comparative perspective.

Journal of Small Business and Enterprise Development, 584-599.

Ivanović, M. (2016). Determinants of Credit Growth: The Case of Montenegro. Journal of Central

Banking Theory and Practice, 101-118.

Johansen, S. (1988). Statistical analysis of cointegration vectors. journal of economic dynamics and

control, 231-254.

Jorgenson, D. (1967). The Theory of Investment Behavior. National Bureau of Economic Research, 129-

175.

Majeed , M. T., & Khan, , S. (2008). The Determinants of Private Investment and the Relationship

between Public and Private Investment in Pakistan. NUST Journal of Business and Economics, 41-

48.

Mankiw, N. G., David eil, R., & David N. W, N. W. (1992). A Contribution to the Empirics of Economic

Growth. The Quarterly Journal of Economics, 407-437.

55

Million , A. ( 2014). Determinants of Growth in Bank Credit to the Private Sector in Ethiopia: A Supply

Side Approach. Research Journal of Finance and Accounting, 90-102.

Neelam , T. (2014). Determinants of Bank Lending in Nepal. NRB Economic Review, 20-44.

Nkusu, M. (2011). Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies.

nternational Monetary Fund, 1-28.

Odedokun, M. O. (1989). Casualities between financial aggregates and economic activities in Nigeria.

Savings and development, 23(1), 101-111.

Olokoyo, F. O. (2011). Determinants of Commercial Banks’ Lending Behavior in Nigeria. International

Journal of Financial Research, 2.

Oluitan, R. O. (2012). Bank credit and economic growth, evindence from Nigeria. Journal if international

business and management, 102-110.

Oshikoya, W. T. (1994). Macroeconomic Determinants of Domestic Private Investiment in Africa: An

Emperical Analysis. Economic Development and Cutural Change, 573-596.

Perotti, R. (1999). An Empirical Characterization of the Dynamic Effects of Changes in Government

Spending and Taxes on Output. National Bureau of Economic Research, 617-868.

Pham, T. H. (2015). Determinants of Bank Lending. HAL Id: hal-01158241, 1-26.

Phillips, P., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika.75, 335-346.

Sajid, G., & Sarfraz, M. (2008). Saving and economic growth in Pakistan: An issue of casuality. Pakistan

economic and social review, 17-36.

Serven, l., & Solimano, A. (1992). Private Investment and Macro economic Adjustments. The World Bank

Research Observer, 95-114.

Shijaku, G., & Kalluci, I. (2014). Determinates of bank credit to the private sector: The case of Albania.

Bank of Albania working paper, No.09(48).

Söğüt, E. (2008). The determinants of financial development and private sectorr credit. Approval of the

Graduate School of Social Sciences, 1-35.

Solow, M. R. (1957). Techinical Changes and Aggregate Production Function. Review of Economics and

Statistics,39, 312-320.

Taiwo, O., & Adesola, A. O. (2013). Exchaange rate volatility and bank performance in Nigeria. Asian

economic and financial review, 178-185.

Timsina, N. (2014). Imapct of bank credit on economic growth in Nepal. N.R.B Working paper.

Tobin, J. (1969). A General Equilibrium Approach to Monetary Theory. Journal of Money,, 15-29.

56

Trew, A. (2006). Finance and Growth: A Critical Survey. Economic Record, Volume 82(Issue 259), 481-

490.

Verma, R. (2007). Savings, investment and growth in India. South Asia economic journal, 87-98.

Verma, R., & Wilson, E. (2005). A multivariate analysis of savings, investment and gowth in India.

Economic working paper, 1-23.

Vika, I. (2009). Role of banks in the transmission of monetary policy. bnak of Albania, working paper

series 2009.

Waheed, M., Tasneem, A., & Ghauri, S. (2006). Structural breaks and unit root: Evidence from Pakistani

macroeconomic time series. State Bank of Pakistan.

Warue, B. N. (2013). The Effects of Bank Specific and Macroeconomic Factors on Nonperforming Loans

in Commercial Banks in Kenya: A Comparative Panel Data Analysis. Advances in Management

and Applied Economics, 3.

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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


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






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


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






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