Graduate School

Master Degree Project in Economics

The Effect of Budget Support on Private Sector

Credit in Malawi

Author: Magdalena Andersson Paul, 19830617-5665

Supervisor: Professor Dick Durevall

Abstract

In order to study the impact of budget support disbursement on credit to the private sector in

Malawi, I use an error correction framework and apply it to monthly data. I first identify a

cointegrating vector including real Private Sector Credit, real GDP, the Interest Rate Spread and the

real Treasury Bill rate. I then specify the model based on observations from 2002 to 2014, in which

the error correction term is found statistically significant suggesting that the real Treasury bill rate

have a significant negative impact on credit to the private sector. The results also show that there is

a crowding out effect caused by government borrowing. Adding budget support to the error

correction models shows the significance of the cointegration vector, and find that has a statistically

significant negative impact on credit to the private sector in Malawi.

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Acknowledgements

This thesis would not have been possible without the encouragement, patience and support I have

received through the process.

To Elof Hansson Foundation, for awarding me a travel grant which allowed me to travel to Malawi

and interview key stakeholders on the ground. A sincere Thank You!

To Ann Veiderpass, for your mentorship along the way. It is easy to mentor students at their best but

it is when they are at their worst it takes someone with a huge heart, incredible knowledge and the

wisdom only life can bring, to bring someone out from the woods, even at a business school.

To Per-Åke Andersson, for the moral support and guidance in finishing the actual paper, and the

many chats about life in Africa. Thank you!

To Dick Durevall, from econometric approach and discussions about Malawi and its economy to

having the patience to see this paper to the end. Thank you for your continuous support!

To my husband David, for pushing and pulling, for endless discussions about the private sector in

Malawi, for not losing faith and for being there, thank you!

Finally, to Mamma och Pappa, who will never read this paper, yet to whom I owe this work. You laid

the foundation for it many years ago by giving me the confidence to go out into the world with open

eyes and open mind. You taught me not to be discouraged by difficulties but always remember “the

kite needs upwind to rise” - something particularly applicable in development economics. This paper

is devoted to you – the Thank You I never got to say.

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Table of Contents

Abstract ........................................................................................................................... 1

Acknowledgements .......................................................................................................... 2

1. Introduction .............................................................................................................. 4

2. Background ............................................................................................................... 6

3. Literature Review .................................................................................................... 10

3.1. Economic Development and Credit to the Private Sector ........................................................ 10

3.2. Economic Development and Budget Support ........................................................................... 13

4. Theoretical Framework ............................................................................................ 14

4.1. Determinants of Credit to the Private Sector ........................................................................... 14

4.2. Determinants of Crowding Out and the Impact of Budget Support ......................................... 14

5. Data and Descriptive Statistics ................................................................................. 15

5.1. Descriptive Statistics ................................................................................................................. 16

6. Empirical Framework ............................................................................................... 18

6.1. Co-Integration Analysis ............................................................................................................. 20

6.1.1. Engle-Granger Cointegration Test .................................................................................... 20

6.1.2. Johansen Test for Cointegration ....................................................................................... 20

6.2. The Error Correction Model ...................................................................................................... 20

7. Empirical Analysis .................................................................................................... 21

7.1. Unit-root Testing - Results ........................................................................................................ 21

7.2. Engle-Granger - Results ............................................................................................................. 21

7.3. Johansen Test - Results ............................................................................................................. 23

7.4. Error Correction Model – Results ............................................................................................. 24

8. Discussion ............................................................................................................... 27

8.1. Results ....................................................................................................................................... 27

8.2. Limitations................................................................................................................................. 28

8.3. Further Research ....................................................................................................................... 28

9. Conclusion ............................................................................................................... 29

10. References ........................................................................................................... 30

Appendix ........................................................................................................................ 32

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1. Introduction

The seventeen Sustainable Development Goals set up by the United Nations have as their main

objective to reduce poverty and enable long term sustainable development through food security,

education, health and gender equality by a global partnership for development.1 To achieve this,

economic growth in general and private sector growth in particular are fundamental, as a

sustainable economy depends on production to provide employment and raise taxes for

investments in infrastructure and for redistribution. For private sector growth, access to credit is an

essential financial requirement as short term working capital for operational needs as well as for

long term investment. The demand for credit however, is not only limited to the private sector but

the public sector also turns to the domestic market for financing. This aspect of demand for credit

increases when other sources of government funding fall short. For many governments in poor

countries budget support is a significant source of revenue aimed at supporting economic and social

development. However, the conditionalities for continuance tend to make the assistance volatile

over time, as a breach in agreements can lead to suspension of the support and a negative cash flow

for the government. This increases the government’s demand for domestic credit which in turn

increases the total demand for credit in the domestic market causing interest rates to rise and

subsequently causes an increase in the cost of borrowing for private companies and restricts private

sector development.

The environment for the private sector in Malawi has been extremely adverse over the last two

decades. Faced with strict foreign exchange controls and high inflation rates as well as high nominal

interest rates, foreign as well as local investments have been heavily inhibited. The nominal lending

rates over the period 2000 to 2015 have been marked by high volatility and have fluctuated between

a low of 6.28 percent in December 2010 and a peak of 37.91 percent in February 2013 implying

significant fluctuations in the cost of borrowing for the private sector. This volatility is also

recognised in the budget support disbursements data. For Malawi, budget support is a significant

part of government revenue. For the budget year 2000/2001, total grants in which budget support

grants are included, corresponded to 33 percent of the total revenue that year. It peaked in

2008/2009 at 43 percent only to drop to 13 percent for 2014/20152.

Economists have detected a link between budget support, interest rates, domestic borrowing and

hence credit to the private sector. In 2001, the IMF suspended financial assistance to Malawi

1 https://sustainabledevelopment.un.org/?menu=1300

2 IMF article IV 2002, 2008 and 2015. Table 2. Malawi: Central Government Operations. (respective figures

from the respective versions).

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following failure to meet IMF programme targets. Following IMF’s actions and recommendations,

bilateral donors also stopped budget support disbursements leaving the Government of Malawi with

an increased budget deficit. The joint staff assessment by IMF/IDA concludes “The suspension of

budgetary assistance by donors did not result in reduced overall expenditure but instead in increased

domestic borrowing, which contributed to high interest rates and a rise in domestic debt” (IMF/IDA,

2003).

Previous research has focused on credit to the private sector, its determinants, and the impact of

budget support separately. There is therefore a gap in the literature with regards to Malawi where,

as far as I am aware, no research with this focus has been conducted. The working hypothesis is that

there is a positive relationship between credit to the private sector and budget support

disbursements which implies that an increase in budget support corresponds to an increase in

private sector credit while withdrawal of budget support will result in a decrease in credit. The chain

of events starts with the budget process in which the Government allocates funds for the budget

year to come. Budget support donors then agree to provide general budget support3, which is

included in the financial management system, given certain conditions. A breach in the conditions

for the support leads to suspension of disbursements. While the donors expect the suspension in

budget support to lead to a reduction in government expenditure observations suggest that the

government tends to seek alternative funding instead. By issuing treasury bills to the domestic

market as well as direct borrowing from local banks, the government meets the shortfall in the short

term. This increases government borrowing which causes interest rates to rise and crowds out the

private sector. The result is that the credit to the private sector is reduced, implying an increase in

the cost of working capital and a reduction in investment.

The aim of this thesis is to analyse the link described above and answer the question: How is credit

to the private sector in Malawi affected by budget support disbursements?

I do this by testing the following three hypotheses:

1. Economic activity measured in real GDP has a significant positive relationship to

Private Sector Credit while the cost of borrowing measured by the Interest Rate

Spread, has a significant negative impact.

2. Government borrowing causes crowding out of Private Sector Credit.

3. Budget Support has a significant impact on credit to the private sector.

3 Budget support normally includes general budget support as well as project budget support however the

latter is outside the scope for this paper.

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I estimate an error correction model based on time series data from the Reserve Bank of Malawi, the

IMF and the World Bank data bases respectively. The sample is based on 150 monthly observations

from May 2002 to October 2014. Due to a limited number of observations for budget support, the

regressions which include budget support are based on 76 observations, starting in May 2008 to

October 2014.

I find that there is a statistical significant long-run relationship indicated by the estimated

cointegration vector which includes credit to the private sector, economic activity, measured in GDP,

the cost of borrowing, measured in the Interest Rate Spread, and the real Treasury Bill rate. The

estimated cointegration vector suggest that an increase in economic activity has a significant

positive impact on private sector credit while an increase in the cost of borrowing as well as in the

Treasury Bill rate, has a significant negative impact. The first difference results for Treasury Bill rate

as well as for real Government Borrowing are also statistically significant which confirms the

hypotheses that an increase in government borrowing has caused a crowding out effect on the

private sector. Applying the model on the short time period, 2008 to 2014, I find that real Budget

Support is statistically significant and negative which implies that suspension of budget support

leads to an increase in demand for credit from the private sector.

This paper is structured accordingly: A section on Background describes the circumstances in Malawi

and the economic and political context on which this paper is based. The Literature Review provides

a summary of previous general literature on Credit to the Private Sector and the Impact of Budget

Support respectively to be followed by a section on the respective areas with focus on Malawi. In the

Theoretical Framework, I lay out the foundation on which my model is based and describe the model

derived for the analysis. The Data section contains the data sources and descriptive statistics. The

Empirical Framework presents the empirical method applied and the Empirical Analysis takes the

reader through the econometric testing, from a preliminary graphical analysis, on to co-integration

analysis to the final regressions. The main findings are examined in Discussion in the context of the

Background and previous literature. It also discusses the limitations of the study and ideas for

further research. Finally, the Conclusion summarizes the paper and comments on the implications of

the results.

2. Background

In order to understand the mechanisms at play within the Malawi economy is important to be aware

of the major political and economic events which have occurred during the time period of interest.

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While political leadership and foreign exchange policy in itself is not the scope of this paper, they do

play a role when trying to comprehend the political turbulence which affected both donors and

economic indicators such as the inflation rate and demand for credit.

The period starts with Bakili Muluzi as the sitting president (2000) and a floating exchange rate

regime with broad money as the intermediate target. In 2002 Malawi experienced a food shortage

caused by a combination of poor harvest due to drought in 2001 and food reserve mismanagement

by the National Food Reserve Agency. The mismanagement led to suspension of donor funds while

suspected corruption was investigated. In 2004, Bingu wa Mutharika won the elections. Under his

leadership the exchange rate was pegged to the US dollar in an attempt to keep inflation rates under

control and interest rates down. In cooperation with donors he also introduced the Fertilizer Subsidy

Programme which had a significant positive impact on agricultural production boosting the

economic growth in the country. During Mutharika’s second term, pressure from the IMF to devalue

the currency and criticism of the leadership of the country, led to infected international relationships

culminating in the British High Commissioner being asked to leave the country. Once again, this

resulted in the suspension of aid from several donors following the IMF recommendations. The

unexpected death of Bingu wa Mutharika in April 2012, led to a change in governance and vice

president Joyce Banda took over the leadership and managed to improve the relationship with

donors. The most significant policy change was to allow for a devaluation of the currency followed

by an exchange rate regime change, from a fixed exchange rate to a floating exchange rate. The

discovery of substantial fraud and theft of public means, also referred to as the “cashgate scandal” in

2013 was again a setback for the improved relationships and the investigation that followed

revealed that top leaders and politicians were involved. Subsequently budget support donors

suspended their funding with immediate effect. In May 2014 Peter wa Mutharika, the younger

brother of the late Bingu wa Mutharika, won the elections and is the sitting president at present.

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Table 1: Time line Political Leadership, Exchange Rate Policy and Economics Events

Year President Foreign Exchange

Policy

Key Economic Events

1994 – 2004

Bakili Muluzi (UDF) Fixed exchange rate 2001/2002 – Food crisis

2004 – 2009

Bingu wa Mutharika

(UDF/DPP)

Exchange rate pegged to

the US$

2007 – 2011 - Fertilizer subsidy

programme

2011/2012 – Foreign exchange and

fuel crisis

2012 - 2014 Joyce Banda (DPP) Floating exchange rate

(introduced May 2012)

Sep 2013 – “Cash gate” scandal

2014 - present Peter wa Mutharika (DPP) Floating exchange rate 2015 – Severe floods affecting the

country

Source: http://africanelections.tripod.com/mw.html, and IMF Article IV papers.

The political events described above affected the private sector both directly and indirectly. Private

firms are closely linked to the government budget in many areas; banks, insurance companies and

pension funds, construction companies and medical suppliers are only to mention a few sectors,

which are directly linked to the government through contracts and/or indirectly by dependency on

fiscal as well as monetary policies such as the exchange rate policy. The private sector and public

sector are also competitors in the demand for credit on the domestic financial market. As budget

support is a significant source of revenue for the country and the annual budget, suspension of

budget support creates a significant shortfall in the budget. The government can either reduce

government spending or find alternative funding. While the suspension is aimed to target the

administration of the country and/or the individuals responsible for such breaches in agreements,

the rational choice for the decision makers is rarely what would make them unpopular among their

voters, hence they seek alternative funding where possible and delay payments where possible.

From the recent history of Malawi as described above, a pattern occurs which enables a chain of

events to be derived. The chain starts with the budget process in which the Government allocates

funds for the budget year to come. Budget support donors agree to provide general budget

support4, which is included in the financial management system, given certain conditions. A political

or economic event takes place, such as the food crisis in 2001 or the Cashgate scandal in 2013 which

triggers a response by the donors and the breach in the conditions leads to suspension of

disbursements. The government must then decide between reducing government expenditure or

4 Budget support normally includes general budget support as well as project budget support however the

latter is outside the scope for this paper.

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seeking alternative funding. The literature suggests that the government tends to seek alternative

funding, at least on a short-term basis. By issuing treasury bills to the domestic market as well as

direct borrowing from local banks, the government meets the shortfall in the short term. Over time,

the increased government borrowing causes interest rates to rise and crowds out the private sector.

The result is that credit to the private sector is reduced, implying an increase in the cost of working

capital and a reduction in investment.

The purpose of Figure 1 below is to present a hypothetical and simplified chain of events and show

how shocks in one sector affect other players. It is important to highlight that this chain of events

takes the variables included in this paper into account and omits important factors which are beyond

the scope of this paper. The figure presents the events according to observations during the work on

this thesis suggesting that important factors can have been omitted, misinterpreted or accentuated.

occur divided onto the three players, Government of Malawi, Donors and the Private Sector.

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Table 2: Hypothetical Chain of Events

Time Government of Malawi Donors Interest Rates Private Sector

t=1 Annual government budget is developed

in agreement with development partners.

Conditions x,y,z are agreed upon and

the budget is approved in Parliament.

Donors agree to

provide general

budget support given

a number of

conditions x,y,z.

t=2 Annual budget is enrolled; wages of

public servants are set procurement

contracts are signed etc.

General budget

support in

accordance with

agreement is

disbursed.

Contracts signed.

Projects enrolled.

Expectations for the budget year set

on which companies decide their

need for financing and investments.

t=3 Breach in agreement due to unexpected

circumstances, ie:

- Food crisis (2001)

- British High Commissioner

requested to leave the country

(2011)

- Cash Gate Scandal (2013

General budget

support is

suspended.

t=4 Negative cash flow as a result of

suspended budget support:

The government choose between:

- Postponement of

payments/wages

- Increase domestic borrowing -

give out Treasury Bills to finance

shortfall

- Increase in taxes

Awaiting

investigation.

- Postponement of payments

leads to a negative cash flow

for companies. Leading to

increased borrowing.

- Increase in taxes affects

companies and employed

people

t=5 - Increased demand

for credit leads to an

increase in interest

rates.

Increase in interest rates leads to an

increase in cost of borrowing which

leads to reduced private sector

demand for credit

t=6

When the cost of borrowing is

increased, the private sector reduces

investments. Together with a

negative cash flow due to postponed

payments, private sector

development inhibited.

t=7 As a result of an unprofitable private

sector, real tax revenue is reduced and

which further aggravates the situation.

Source: Compiled by the author based on theory and interviews with key stakeholders within the Private Sector in Malawi.

3. Literature Review

Previous literature has been divided into research on credit to the private sector and the impact of

budget support respectively.

3.1. Economic Development and Credit to the Private Sector

One of the early economists to develop a theory on development and credit was Joseph Schumpeter

who in 1911 presented his arguments that financial intermediaries, such as banks and other financial

institutions play an essential role for technical innovations and economic growth. King and Levine

(1993) examines if this theory still holds in modern times when analysing the impact of financial

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development on long term economic growth using the four indicators; liquid liabilities as share of

GDP, deposit banks versus central banks importance when allocating domestic credit, credit (to non-

financial firms) as a share of total credit and credit (to non-financial firms) as share of GDP. They find

that there is a significant positive relationship between financial development and economic growth

as well as the accumulation of physical capital and economic efficiency. They also find that financial

development has a significantly positive impact on the following 10-30 years of economic

development and that higher financial development is positively correlated with future

accumulation of capital and its efficient use.

Calderón and Liu (2002) tests the causality of the relationship between financial development and

economic growth on data from 109 countries and confirms that financial development in general

leads to economic development. They find that capital accumulation and technological innovation

are catalysed by financial development. They also find the co-existence of Granger causality that

goes from financial development to economic growth as well as from economic growth to financial

development. This supports the theory that financial development is important for economic

development and one part of this is to understand what drives demand for private sector credit in

general and especially so in poor countries where economic growth is crucial for development.

Most studies on credit to the private sector include a measure for economic activity, often

represented by GDP, as well as variables to explain the cost of credit, such as the lending rate. The

alternative cost in defined as the interest rate spread complements the lending rate in some studies

and replaces it in other. In addition to this, in countries with limited credit available, government

lending has been included in order to capture potential crowding out.

Calza et al (2010) applies the Vector Error Correction Model (VECM) framework when studying the

demand for credit to the private sector in the euro area. In this study, more than sixty percent of

loans to the private sector are to be considered long-term credit, hence have a maturity longer than

five years. They find that there exists a long run relationship between credit and real GDP, real short-

term and long-term interest rates. Real GDP is found to be positively related to credit while interest

rates in the short as well as in the long run are found to have a negative relationship. Abouka and

Egesa (2007) also find a long-term positive relationship between credit to the private sector and GDP

per capita.

Brzoza-Brzezina (2005), estimate private sector credit in Poland, Hungary and the Czech Republic by

using a VECM framework. They find that a decline in lending rates have a significant positive effect

on loans as this reduces the total cost of lending, which enables companies to increase borrowing at

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the same nominal cost. Abouka and Egesa (2007) model credit to the private sector in East Africa,

using credit to the private sector as share of GDP as the dependent variable. The study includes GDP

per capita, inflation, interest rate spread as well as infrastructure, urbanisation and bank credit to

the government as explanatory variables. They find that the nominal lending rate has a significant

and positive impact on private sector credit. The authors consider this finding counter intuitive as

one would expect that an increase in interest results in an increased cost for the individual company

which according the fundamental supply and demand theory would lead to a reduction in the

demand for credit. The results are therefore interpreted as the influence of high inflation rates in

some of the included countries.

Other variables included in models in previous literature are credit to government and the interest

rate spread. Sacerdoti, 2007, finds that credit to government has a significant negative impact on

private sector credit. As the government face a budget deficit it must seek financing in the domestic

banking system resulting in an increase in interest rates and decrease in available funds for the

private sector. The dependent variable used is credit to the government as part of GDP and while

this ratio was low for a large number of HIPC countries5 Nigeria, Kenya and Zambia all exceed 18%

with the Gambia reaching as high as 31%. Abouka and Egesa (2007) use the interest rate spread as

an indicator for financial sector liberalization in East Africa but does not find this variable significant

in explaining the demand for credit.

There appears to be a gap in the literature when it comes to specific research on credit to the

private sector in Malawi, however there are a few papers which are touching the subject. Chirwa

and Mlachila (2004) looks at the commercial banking sector in Malawi with focus on interest rate

spreads and financial reforms between 1990 and 2000. They use an alternative definition for interest

spreads in the analysis and find that the regardless of definition, the spreads have increased from

approximately 10 percent in the beginning of the period to close to 40 percent at the end. These

findings indicate that the liberalization of the financial markets in Malawi resulted in a significant

increase in the spread and that this has happened at the expense of the commercial bank

customers. Their statistical analysis also indicates that the high interest rate spreads are a result of

high central bank discount rates.

A later paper by Chirwa, Kaluwa and Chirwa (2015), on competition and banking industry regulation

in Malawi, addresses the issue of high market concentration in Malawi as a potential explanation for

the high lending rates averaging between 27 percent and 46.6 percent between the years of 2005

and 2014. They find that the high margins on credit have been sustained by a combination of factors,

5 HIPC=Heavily-Indebted-Poor-Countries

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among these lending to the government which is considered a comparatively safe and profitable

client causing a crowding out effect on private sector credit.

3.2. Economic Development and Budget Support

Budget support is one of the tools employed by donors to assist poor countries. Molenaers,

Cepinskas and Jacobs (2010), describe the use of budget support as a long-term channel to increase

the levels of human development, consolidate democratic institutions and improve socio-economic

standards for people in low-income countries. Koeberle et al (2006) describe budget support as a

financial instrument used by donors to assist countries with poverty reduction by targeting

improvements in financial management as well as the specific socio economics needs in the country

at stake, for example within education and health. The support is designed in accordance with the

specific strategic plans agreed between donors and recipients and is part of a country’s budget

procedure and included in the financial management system.

As a mean of providing aid has budget support received increased attention lately as an alternative

to traditional project support, Koeberle, S. et al (2006). Where project support suffers from low

disbursements rates and short-term thinking, general budget support is considered a more strategic,

medium term form of economic assistance. Budget support also provides a partnership-based

alternative to traditional conditionality and enables improvements in mutual accountability between

donors and recipients. Molenaers, Cepinskas and Jacobs (2010) find that budget support is

considered flexible and encourages ownership. It is also political and often directly linked to specific

leadership and can be considered a symbol of trust between the receiving government and the

donor organisation. As the assistance is part of a receiving government’s overall budget it

emphasizes the importance of local ownership and use of country specific structures and budget

processes.

While there are many positives related to budget support, Molenaer et al. (2010) highlight the

weaknesses and the impact of suspension of support due to a breach in the agreement. They

consider budget support as high risk due to its fungible nature where funds can easily be relocated

within a budget or displaced and where this can trigger a breach in trust between donor and

recipient. Koeberle, S. et al (2006) recognizes that budget support can have a disruptive role when

the disbursements are unpredictable and volatile. Delays in disbursements or sudden surges can

have a negative impact on the macroeconomic stability in the recipient country.

Nilsson (2004) analyses the volatility of budget support in a number of Sub-Saharan countries

between 1994 and 2002, including Malawi and finds that there is significant volatility in budget

14

support over the time period. The authors suggest that it is the unpredicted volatility that is most

harmful to the economy.

4. Theoretical Framework

As far as I am aware, no previous research with specific focus on credit to the private sector using

budget support as an explanatory variable has been conducted. Modelling of private sector credit

however, has been done frequently in Europe as well as Africa.

4.1. Determinants of Credit to the Private Sector

Calza et al (2010), examines the long-run demand for credit to the private sector in the Euro area

and includes GDP and interest rates according to the following relationship presented below:

𝐿𝑂𝐴𝑁𝑆 = 𝛼 + 𝛽1 ∗ 𝐺𝐷𝑃 + 𝛽2 ∗ 𝑆𝑇 + 𝛽3 ∗ 𝐿𝑇 (1)

The variable 𝐿𝑂𝐴𝑁𝑆 denotes real loans to the private sector, 𝐺𝐷𝑃 denotes real GDP and 𝑆𝑇 and 𝐿𝑇

are the real interest rates on short-term and long-term basis respectively6. The general conclusion in

the literature suggests that an increase in economic activity leads to an increase in private sector

borrowing, hence 𝛽1 > 0 and that an increase in the interest rate results in an increased cost of

borrowing leading to a reduction in demand for private sector credit, 𝛽2 < 0.

Adjusting the relationship above to the Malawi context, which involves low financial development

reflected in no differentiation between long term and short-term interest rates, the long-term

lending rate 𝐿𝑇, can be omitted and the model include a “cost of borrowing”-variable which gives

me the following theoretical relationship:

C = α + β1 ∗ GDP + β2 ∗ CB (2)

In the equation above, C denotes real credit to the private sector in Malawi, GDP denotes real GDP

in Malawi and 𝐶𝐵 denotes the cost of borrowing, which is defined as the real lending rate or the

interest rate spread alternatively.

4.2. Determinants of Crowding Out and the Impact of Budget Support

Credit to the government is found to be a determinant to private sector credit. Given a small

financial sector and a limited supply of credit, an increase in government borrowing leads to an

increase in interest rates and a reduction of credit available as well as an increased cost of lending

for the private sector, hence a crowding out of private sector credit.

6 The notations using capital letters are taken directly from Calza et al (2010).

15

As per the discussion in Background, budget support disbursements (and the suspension of the

same) appear to have both an indirect impact on private sector credit through the channel of

increased government borrowing and potentially a direct impact as well.

In order to test if the findings in previous literature hold for Malawi, I include the variables of

interest in the fundamental model above in order to establish if government borrowing has caused

crowding out of private sector credit and determine the impact of budget support on credit to the

private sector. Including government borrowing, the Treasury bill rate and budget support

disbursements into the model, gives me the following relationship:

C = α + β1 ∗ GDP + β2 ∗ CB + βi ∗ Χ 𝑖 (3)

In the equation above, Χ 𝑖, denotes a vector of explanatory variables including real government

borrowing, GB, the 91 days Treasury Bill rate, TBR, and real budget support disbursements, BS,

respectively.

5. Data and Descriptive Statistics

The primary data used for the empirical analysis is collected from three main sources; the Reserve

Bank of Malawi, the IMF and the World Bank Group.

The data set consists of monthly observations starting January 2002 to October 2014. The

econometrical long-term analysis to allow for a long-run cointegrating vector is based on 150

observations starting in May 2002 while the short-term analysis which include budget support

disbursements is based on 78 observations due to the lack of available data for budget support

disbursements. All data is based on current local currency, thus Malawi Kwacha.

The dependent variable, Credit to the Private Sector and the explanatory variable Credit to

Government are retrieved from the “Commercial Bank Assets and Liabilities” survey downloaded

from the Reserve Bank of Malawi’s (RBM) online database7. Bank Rate, Deposit rate, Lending rate

and Treasure Bill Rate (91 days) are retrieved from the data set “Interest Rates” while the variable

Inflation comes from the data set “Inflation” and indicates overall inflation, also from the RBM

online data base.

The data for Budget Support is collected from the International Monetary Fund (IMF) and the

International Financial Statistics (IFS) online data base where it is defined under “Budgetary Central

7 www.rbm.mw

16

Government, Revenue” as Grants8. The variable for economic activity is approximated by

interpolated annual GDP collected from the World Bank Group9 online data base.

In addition to the variables above the Consumer Price Index (CPI) is used to calculate real values and

the figures are retrieved from the IMF and the IFS online data base10. CPI was normalized and set to

100 on 1st January 2010.

5.1. Descriptive Statistics

The data set consists of 150 monthly observations starting in May 2002 to October 2014.

Table 3: Descriptive Statistics – Nominal values

Variable: Unit: Mean: Standard Dev: Min: Max: No. Obs:

Credit to the Private Sector (C) MWK billions 95.97 86.28 9.40 288.04 150

Gross Domestic Product (GDP) MWK billions 80.33 54.98 22.28 265.37 150

Credit to the Government (GB)

MWK billions 27.65 19.55 3.332 97.23 150

Budget Support (BS) MWK billions 7.03 6.94 0.358 34.42 76

Interest Rate Spread (IRS) Percentage units

19.83 4.93 14.00 29.86 150

Deposit Rate (DR) Percent (%) 9.88 7.35 3.50 30.5 150

Lending Rate (LR) Percent (%) 29.72 10.92 17.75 52 150

Treasury Bill Rate 91 days (TBR)

Percent (%) 19.75 11.20 5.66 45.5 150

Inflation Rate (IR) Percent (%) 13.57 7.39 6.28 37.91 150

Source: IMF, the World Bank, the Reserve Bank of Malawi

The dependent variable, Credit to the Private Sector is measured in Malawi Kwacha (MWK) billions,

with a mean of MWK 95.97 billion. The figures are nominal values and the increase from MWK 9.40

billion (minimum) to MWK 288.04 billion (maximum) suggest that there has been a significant

growth in credit in nominal terms, throughout the period, most likely driven by inflation and

economic growth.

GDP is included as a proxy for economic activity and the data reveals that a significant growth in

nominal terms has taken place with an increase in GDP from the minimum MKW22.28 billion to the

maximum of MWK 265.37 billion. The original data was on annual basis and was interpolated to

monthly figures to enable analysis.

Figure 2 shows the real figures of the variables plotted over time and suggest that also in real terms,

credit to the private sector as well as government borrowing and GDP has seen a positive growth

8 http://data.imf.org/?sk=5dabaff2-c5ad-4d27-a175-1253419c02d1

9 http://data.worldbank.org/

10 http://data.imf.org/?sk=5dabaff2-c5ad-4d27-a175-1253419c02d1

17

during the time period. The data for budget support disbursements reveal a trend that payments

seemed to be made on a quarterly or half year basis rather than evenly distributed on a monthly

basis. They were therefore converted to indicating a three-month moving average. Despite the

conversion the graph shows significant fluctuations during the time period with a maximum MWK

34.42 billion and a minimum of MWK 0.358 billion.

Figure 1: Private Sector Credit, GDP, Government Credit and Budget Support

Deposit rate, Lending rate and Treasure Bill Rate (91 days) are measured in percentage and are the

monthly averages for a given time period. The Deposit rate, Lending rate and Treasure Bill Rate (91

days) have a mean of 9.88 percent, 29.27 percent and 19.75 percent respectively. Figure 3 shows the

interest rates over time. It reveals high levels of inflation, starting from 2012, which corresponds to

the devaluation of the currency and the change to a floating exchange rate policy. As the graph

shows this increase had a significant impact on the real lending rate, defined as the nominal lending

rate minus inflation. The interest rate spread, measured as the lending rate minus the deposit rate is

therefore found to be an alternative proxy for the cost of borrowing since the change in exchange

rate regime had a subtler impact on deposit rates than on inflation rates.

050

10

015

020

0

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Billions kwacha

real Private Sector Credit

40

60

80

10

012

014

0

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Billions kwacha

real GDP

10

20

30

40

50

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Billions kwacha

real Government Credit

05

10

15

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

3 month moving average (Billions kwacha)

real Budget Support

Source: Authors calculations

18

Figure 2: Interest Rates

6. Empirical Framework

Different applications of error correction frameworks are employed in previous literature to capture

cointegration properties. The specific models vary due to availability of data and country-specific

context however the general approach, as described in the literature review, include explanatory

variables for economic activity, most commonly measured as GDP, the cost of credit, measured as

interest rates. In addition to this, credit to the government and the treasury bill rate are included

separately to measure crowding out. My addition to the literature is to include budget support in the

model.

Following previous literature, I develop an error correction model, able to capture co-integration

properties, to analyse the relationship between Credit to the Private Sector and the explanatory

variables:

C = α + β1 ∗ GDP + β2 ∗ CB + βi ∗ Χ 𝑖 (4)

01020304050

2008m1 2012m1 2014m12006m12002m12002m1 2004m12004m1 2010m12010m1Date

real Lending Rate nominal Lending Rate

real and nominal

Lending Rate

15

20

25

30

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Lending Rate minus Deposit Rate

Interest Rate Spread

-20

020

40

60

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

real Treasury Bill Rate nominal Treasury Bill rate

real and nominal

Treasury Bill Rate (91 days)

010

20

30

40

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Inflation Rate

Source: Authors calculations

19

In the equation above, Χ 𝑖 denotes the Treasury Bill rate, Government borrowing and Budget

support respectively.

I wish to test the following hypotheses:

1. The impact of the fundamental determinants to credit to the private sector in

Malawi, that is economic activity measure in real GDP and the cost of borrowing, are

consistent with previous studies, hence:

𝛽1 > 0 and 𝛽2 < 0

2. Government borrowing cause crowding out of private sector credit, hence:

𝛽3 < 0 or 𝛽4 < 0

3. Budget support disbursement have a significant impact on credit to the private

sector, hence:

𝛽5 ≠ 0

The variables are transformed to their natural logarithms of the real values with exception for the

lending rates which continue to be presented in real terms and the following time series model is

fitted to the data:

𝑙𝑜𝑔 (C𝑡

𝑐𝑝𝑖𝑡) = α0 + β1 ∗ 𝑙𝑜𝑔 (

GDP𝑡

𝑐𝑝𝑖𝑡) + β2 ∗ 𝐶𝐵𝑡 + βi ∗

Χ 𝑖𝑡

𝑐𝑝𝑖𝑡+ 휀𝑡 (5)

Denoting the log of real values with lower case letters in italics we get the following equations:

𝑐𝑡 = 𝛼0 + 𝛽1 ∗ 𝑔𝑑𝑝𝑡 + 𝛽2 ∗ 𝑐𝑏𝑡 + βi ∗ x 𝑖𝑡 (6)

The dependent variable, c𝑡, is the natural logarithm of real credit to the private sector where 𝑡

denotes time. 𝑔𝑑𝑝𝑡 is the natural logarithm of real GDP, 𝑐𝑏𝑡is the real cost of borrowing and Χ 𝑖𝑡

denotes the vector which includes the natural logarithm of real government borrowing, gb𝑡, the real

treasury bill rate, 𝑡𝑏𝑟𝑡, and the natural logarithm of real budget support disbursements, 𝑏𝑠𝑡,and 휀𝑡

is the error term, all at a point in time, 𝑡.

Time series analysis is based on the assumption that the variables are stationary or that a

combination of variables are cointegrated. The first step is to test for unit roots, which I do by

applying the Augmented Dickey Fuller. The null hypothesis in the test is that a unit root exists,

denoted I(1), i.e. the variable is integrated of order one. Based on specific variable characteristics a

deterministic trend is included in the test where applicable.

20

6.1. Co-Integration Analysis

If the variables are non-stationary, there is still a possibility that a linear combination of them is

stationary, hence that the variables are co-integrated and contains a long-run equilibrium

relationship. To test for co-integration the Engle-Granger test is performed followed by the Johansen

test. The econometric theory and methodology follow the chapters on co-integration and error

correction models in Enders, 2015.

6.1.1. Engle-Granger Cointegration Test

The Engle-Granger test is performed in three steps. The first step is to estimate the long-run

equilibrium relationship. An Ordinary Least Square (OLS) regression is run, including the non-

stationary variables. In the case of co-integration, the OLS regression results are “super-consistent”

indicated by high R values. The residuals are the estimated values of the deviation from the long-run

relationship wherefore the second step is to save and plot the residuals over time. If the residuals

move around a mean of zero, this suggests that the residuals are stationary and that a long-run co-

integration relationship exists. The third step is to perform the Augmented Dickey Fuller test on the

residuals using adjusted critical values. If the residuals are found stationary, one can conclude that a

cointegrating vector exist between the variables. As there might be several variables that are found

non-stationary in the initial testing procedure, different combinations are tested in order to assess

the number of cointegrating vectors.

6.1.2. Johansen Test for Cointegration

The Johansen maximum likelihood test for cointegration provides a similar test to Engle-Granger

with the difference that it allows for the existence of several cointegrated vectors. The cointegrating

vector is interpreted as the long-run relationship between the variables included in vector. The null

hypothesis in the test is that there are p cointegrating vectors.

6.2. The Error Correction Model

Based on the existence of one or more cointegrating vectors, one or more cointegration vectors are

introduced in the model to capture the long-run relationship. The coefficient before the co-

integrating vector is interpreted as the speed of adjustment and indicates the time period it takes to

get back to equilibrium after a shock in the system. In the case no cointegrating vector is identified

or the error correction term is statistically insignificant, the model is reduced to a difference-in-

difference model.

21

7. Empirical Analysis

7.1. Unit-root Testing - Results

Table 4 presents the Augmented Dickey Fuller test. Based on a graphical analysis of the real

variables, the decision to include a trend and/or a constant has been made and the variables are

tested with 2 lags.

Table 4: Augmented Dickey Fuller test results

*= 10% level of significance, **=5% level of significance, ***=1% level of significance

Note 2: Number of observations. # =Data only available from 2008, number of observations are therefore 75.

The results suggest that I cannot reject the null hypothesis of a unit root for Private Sector Credit,

GDP, Loans, Interest Rate Spread and Treasury Bills however when testing the first differences they

are all found to be stationary. For Government Credit and Budget Support the results show that I can

reject the null hypothesis of a unit root for hence conclude these variables as stationary within the

sample.

7.2. Engle-Granger - Results

The results from the Augmented Dickey Fuller test on the individual variables showed that Private

Sector Credit, GDP, the Lending rate, the Interest Rate Spread and the Treasury Bill rate are non-

stationary. Using the log of the real variables, the real lending rate, the interest rate spread and the

real Treasury Bill rate, different combinations are tested for co-integrations. All combinations include

Augmented Dickey Fuller

Variable: Trend/Constant: ADF t-statistic: 5 % critical value:

c (t) ∆ c (t)

Constant -1.116 -11.659***

-2.887 -2.887

gdp (t) ∆ gdp (t)

Trend and Constant -2.930 -7.930***

-3.443 -3.444

gb (t) ∆ gb (t)

Constant -2.792* -14.097***

-2.887 -2.887

bs (t) ∆ bs (t)

Constant -7.343*** -13.004***

-2.910 -2.911

tbr (t) ∆ tbr (t)

Trend and Constant -2.872 -10.301***

-3.443 -2.887

irs (t) ∆ irs (t)

Constant -0.662 -10.324***

-2.887 -2.887

lr (t) ∆ lr (t)

Constant -1.956 -10.625***

-2.887 -2.887

Number of observations 150

22

Private Sector Credit as this is our independent variable of interest. Table 3, presents the most

significant combination which includes Private Sector Credit, GDP, the Interest Rate Spread and the

Treasury Bill Rate.

Table 5: Engle-Granger – OLS regression

Dependent variable: Private Sector Credit (c (t)) Cointegrating Vector

Variable: Coefficient: t-value: (P>t):

gdp(t) 2.45 34.91

irs (t) -0.017 -7.51

tbr (t) -0.010 -6.04

Constant -5.983 -19.79

Number of observations 150

R-squared 0.9701

Source: Authors Calculations

The results from the initial OLS regressions on the potential co-integrated vectors show that all three

combinations are super-consistent with R-square values of 0.9701, which indicate that there is

cointegration.

Figure 3 plots the residuals from the regression. The plot suggests that the residuals are stationary

though they deviate from the mean of zero towards the end of the period.

Figure 3: Engle-GrangerGraphical Analysis

Table 6 reports the results from the Augmented Dickey Fuller test on the residuals.

-.6

-.4

-.2

0.2

.4

Re

sid

ua

ls

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1Date

Source: Authors Calculations

Private Sector Credit, GDP, IRS and TBR

Residuals

23

Table 6: Engle-Granger –Augmented Dickey Fuller on residuals

Engle-Granger Regression: Test Statistics: Observations:

Cointegrating Vector: - c(t) - gdp (t) - irs (t) - tbr (t)

2 lags

-3.232** 147

Critical values: (n=3) 1% = -3.6215, 5% = -3.0048, 10% = -2.6744 Source: Authors calculations.

Since the test statistic in the Augmented Dickey Fuller test on the residuals is greater than

MacKinnon critical value, I can conclude that the residuals are stationary and co-integration exists.

The results show that the residuals from the cointegration vector is significant at a five percent level.

7.3. Johansen Test - Results

The following semi log-linear relationship is tested for co-integration where the cost of borrowing,

𝑐𝑏𝑡, is defined as the Interest Rate Spread in Regression and the alternative cost is defined by the

real Treasury Bill rate.

Table 7: Johansen test for cointegration

Cointegrating Vector: - c(t), gdp (t), irs (t) and tbr(t)

Null hypothesis:

Rank = p

Eigen-value: Trace Statistics: 5 % crit value:

p = 0 -

40.84* 47.21

p ≤ 1

0.134 19.51 29.68

p ≤ 2

0.088 5.88 15.41

p ≤ 3

0.024 2.29 3.76

p ≤ 4

0.015 - -

Number of observations: 148

Source: Authors calculations.

In the cointegrating vector, the trace statistic is 40.84 is smaller than the critical value. However, it is

close enough to the 10 percent level of significance so I could likely conclude that there is a co-

integrating vector.

The estimates of the coefficients in the cointegrating vector are presented in Table 8.

24

Table 8: Cointegrating Vector

Estimation of Cointegrating Vector:

Variable: Coefficient: z-statistics: (P>Z):

c (t) 1

- - gdp(t) -1.306 -3.66 0.000

irs (t) 0.037 3.24 0.001

tbr (t) 0.031 3.72 0.000

Constant 0.603 - -

Number of observations: 148

Source: Authors calculations.

In the estimations, the co-integrating equation is statistically significant as well as the coefficients for

GDP, Interest Rate Spread and Treasury Bill Rate. As per the definition of the equation, Private

Sector Credit is normalised to 1. GDP is estimated to -1.306, which is interpreted as a positive

relationship suggesting that in the long run, a 1 percent increase in GDP corresponds to a 1.306

percent increase in credit to the private sector. The coefficient for the Interest Rate Spread is 0.037,

which suggests a negative relationship and that a one unit increase in the Interest Rate Spread

corresponds to a 0.037 percent reduction in private sector credit in the long-run respectively. The

coefficient for the Treasury Bill rate is 0.031 implying that a one percent increase in the Treasury Bill

rate leads to a 0.031 percent decrease in credit to the private sector.

7.4. Error Correction Model – Results

The results from the error correction model which includes the cointegration vector are presented in

the Table 9. Different combinations of lags are tested for significance and the most significant results

are presented. Robust standard errors are used in order to adjust for heteroscedasticity.

The basic error correction model is described by the following equation:

∆c𝑡 = 𝛿0 + 𝛾𝐴2 ∗ (c𝑡−1 − 1.306 ∗ 𝑔𝑑𝑝𝑡−1 + 0.037 ∗ irs𝑡−1 + 0.031 ∗ tbr𝑡−1) + 𝛿1 ∗ ∆c𝑡−1 + 𝛿2 ∗ ∆irs𝑡 + 휀𝑡 (7)

Specification 1 includes the cointegrating vector, (1 ∗ c𝑡−1 − 1.306 ∗ 𝑔𝑑𝑝𝑡−1 + 0.037 ∗ irs𝑡−1 +

0.031 ∗ tbr𝑡−1) which is interpreted as the long-run relationship and where 𝛾𝐴 denotes the speed

of adjustment parameter. It also includes the first difference of the interest rate spread. The first

difference of real GDP is excluded in the specification as the monthly observations are interpolated

hence the first differences are the function of the interpolation key, not observed economic activity.

25

In Specification 2, real Treasury Bills Rate (tbrt) is added to the model as per Specification 1 and in

Specification 3, real Government Borrowing (gbt) is included.

Table 9: Error Correction Model Results (2002 - 2014)

Specification 1: Specification 2: Specification 3:

Variable: Coefficient: Standard

Error:

(robust):

Coefficient: Standard

Error:

(robust):

Coefficient: Standard

Error:

∆ c (t)

ect_1 (t-1) -0.054*** 0.017 -0.048*** 0.017 -0.050*** 0.017

∆ c (t-1) 0.024 0.075 0.029 0.073 0.049 0.077

∆ irs (t) 0.003 0.004 0.002 0.004 0.001 0.004

∆tbr (t) - - 0.004** 0.002 - -

∆gb (t-5) - - - - -0.088** 0.037

Constant -0.017* 0.010 -0.013** 0.010 -0.138** 0.010

Test:

Number of observations: 148 148 144

F-test (Prob>F) 3.60

(0.0152)

4.72 (0.0013) 4.22 (0.0029)

R-squared 0.063 0.013 0.089

Cointegration Vector A2: (ect_1) c GDP irs tbr Constant

1 -1.306*** 0.037*** 0.031*** 0.603

*=significant at 10%. **=significant at 5%. ***=significant at 1%.

Source: Authors Calculations

In all three specifications, the first lag of the error correction term, ect_1, which denotes the

cointegrating vector, is statistically significant indicating a long-run relationship between Private

Sector Credit and real GDP, the Interest Rate Spread and the Treasury Bill rate. A one percent

increase in real GDP results in a 1.306 percent increase in credit to the private sector, a one

percentage increase in the interest rate spread results in a 0.037 percentage reduction in credit and

a one percent increase in the treasury bill rate in a 0.031 percent. The speed of adjustment

parameter is estimated to -0.054, -0.048 and -0.50 in Specification 1, 2 and 3 respectively, which

implicates the time of adjustment per month it takes to get back to the long-run equilibrium after a

shock in the system. The first difference of the interest rate spread is statistically insignificant.

When adding the first difference of Treasury Bills Rate (tbr𝑡) in Specification 2 the coefficient is

statistically significant and suggest that a one percent increase in the real Treasury Bill rate increase,

results in a 0.004 percent increase in real Private Sector Credit.

The results when including real Government Borrowing (gb𝑡) in Specification 3, suggest that there is

a delay in the effect of Government Borrowing indicated by a statistical significant fifth lag. The

26

results imply that a one percent increase in Government Borrowing leads to a 0.099 percent

reduction in Private Sector Credit five months later. The full results of 1-6 lags are included in the

Appendix.

Given the limited number of budget support data, the second part of the analysis is based on the

shorter time period (2008 – 2014) with a total of 76 observations. Specification 4 corresponds to

Regression 1 above but based on the shorter time period. Specification 5 includes real Budget

Support (three months moving average).

Table 10: Error Correction Model Results (2008 - 2014)

Specification 4: Specification 5:

Variable: Coefficient: Standard Error

(robust):

Coefficient: Standard

Error

(robust): ∆ c (t)

ect_1 (t-1) -0.039* 0.023 -0.040* 0.023

∆ c (t-1) 0.056 0.094 0.109 0.106

∆ irs (t) 0.005 0.004 0.004 0.003

∆ bs (t-2) - - -0.026** 0.013

Constant -0.009 0.011 -0.010 0.011

Test:

Number of observations: 76 73

F-test (Prob>F) 1.55 (0.2095) 2.36 (0.062)

R-squared 0.046 0.104

Cointegration Vector A2:

(ect_1)

Private Sector Credit

(logREPrivSecCred)

GDP

(logreGDP)

Interest Rate

Spread (IRS)

Treasury Bill rate Constant

1 -1.306*** 0.037*** 0.031*** 0.603

*=significant at 10%.

**=significant at 5%.

***=significant at 1%.

**=significant at 5% ***=significant at 1%

The results from Specification 4 and 5, based on the shorter time period, confirms that the long-run

relationship defined by the cointegration vector and reflected in the error correction term ect_1 is

statistically significant and negative. The results in Specification 5 also reveals that real budget

support is statistically significant at a five percent level and that a one percent increase in budget

support leads to a 0.026 percent decrease in private sector credit two months later, as indicated by

the second lag.

27

8. Discussion

8.1. Results

The paper aimed to test if the fundamental determinants to private sector credit are applicable in

the Malawi case study, if government borrowing has crowded out private sector credit and if budget

support has any impact on credit to the private sector.

The results show that the standard determinants for credit to the private sector are applicable to

Malawi. The statistically significant cointegrating vector infers that an increase in GDP results in an

increase in Private Sector Credit, that an increase in the cost of borrowing as indicated by the

Interest Rate Spread reduces Private Sector Credit and that an increase in the alternative cost

indicated by the Treasury Bill rate also leads to a reduction in Private Sector Credit. As per previous

literature this is a positive finding given that Malawi has seen significant economic growth the last 15

years and that this has had a significant impact on the development of the financial sector which

according to previous literature, then promotes further economic growth.

The finding that the real lending rate is not a significant determinant to private sector credit

goes against previous literature. One plausible explanation is that the problem lies within Inflation.

As Malawi went from a fixed exchange rate regime to a floating in May 2012, the inflation rates shot

up to 37 percent after a long period of steady inflation rates of under 10 percent. While the real

lending rate remained at acceptable levels, the nominal lending rates to shoot up and peak at over

50 percent, increasing the cost of borrowing significantly for the private sector. The finding that the

Interest Rate Spread has a significant negative impact on credit, suggests that the spread reflects the

reality on the financial market better than the real lending rate. In addition to this, the interest rate

spread has been interpreted in previous literature to reflect risk. Using this interpretation, the

results would suggest that a higher risk has a significantly negative impact on the demand for credit

as the increased risk is reflected in the increased cost of borrowing.

The empirical results also support the hypothesis of crowding out, measured as the indirect

impact of the Treasury Bill rate or direct impact in Government Borrowing as an increase in both

variables has a statistically significant negative effect on credit to the private sector. It implies that

the private sector in Malawi and the Government of Malawi are competitors in the domestic credit

market and that an increased demand for domestic credit from the government’s side has a

statistically significant negative effect on credit to the private sector.

Finally, the results show that Budget Support, defined as Grants as per the data available, has a

statistically significant negative impact on Private Sector Credit. One plausible explanation to this

28

relationship is indicated in the Chain of Events in the Background section. When budget support

become suspended this leads to a negative cash flow for private companies as the government

postpone payments owed to the private sector. This forces companies to increase their domestic

debt at a higher cost of borrowing through the channel of interest rates.

8.2. Limitations

This study was faced with a number of limitations which had a significant impact on the work

process and the results:

As far as I am aware of, no previous study has been done on private sector credit and budget

support or on credit and aid in general in Malawi. Most budget support evaluations are done from a

qualitative perspective based on interviews and public financial data on how the money was spent

as opposed to analysing the impact of budget support on the financial institutions and the private

sector. The hypotheses in this paper were therefore based on the more developed framework

looking at demand for private sector credit.

The second limitation was availability of data. Requests were made to directly to the Reserve

Bank of Malawi and the National Statistics Office (NSO). While both were willing to assist, it proved

to be impossible to retrieve consistent data for the time period of interest. This resulted in data

collection, in particular with regards to budget support disbursements on a monthly basis, taking a

significant amount of time and eventually I was forced to work with available information and the

specifications of the model which includes budget support, is based on observations from 2008 to

2014 only. Attempts to update the data to include observations for 2015 also failed as the online

data base on the RBM webpage was no longer accessible as of February 2017.

The challenges related to data also include the nature of data in developing countries where

poor data management together with policy changes such as the foreign exchange rate regime shift

and political crises cause the data to be inconsistent and/or unavailable. The shocks in the economic

systems are therefore difficult to analyse and as a result it is hard to distinguish the cause and effect

within the events that occur, such as the suspension of budget support.

8.3. Further Research

In order to fully understand the implications of the relationship between Private Sector Credit and

Budget Support further research is required. As budget support is one tool used to assist poor

countries and reduce poverty, it is important to ensure that the assistance is sustainable and does

not cause unintended consequences which have a negative effect on other parts of the economy.

This study is based on a limited number of observations, it would therefore be interesting to do the

29

same study over a longer time period. Given the lack of previous research on the link between credit

to the private sector and budget support, it would also be very valuable to study a number of

countries with similar characteristics; hence do a study using panel data to establish if there is

significant relationship between budget and private sector credit outside of Malawi.

9. Conclusion

This paper analyses the determinants to private sector credit in Malawi and the impact of budget

support.

The results for the period 2002 – 2014 show that real GDP, the Interest Rate Spread and the real

Treasury Bill rate are significant long-run determinants to real Private Sector Credit as indicated by

the significant cointegration vector. The results also suggest that government borrowing causes a

crowding out effect on the private sector which is indicated by statistically significant negative

relationships between Private Sector Credit and real Treasury Bill rate and real Government Credit

respectively.

Based on the short time period, 2008-2014, the results confirm the significance of the long-run

relationship. The results also show that real Budget Support is a statistically significant determinant

to real Private Sector Credit in Malawi and that a decrease in budget support leads to an increase in

private sector credit.

In order to fully comprehend the effects of budget support and promote sustainable economic

development in Malawi, further research on the effects of budget support on the private sector in

Malawi is recommended.

30

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Akaike, H., 1981. “Likelihood of a model and information criteria”. Journal of econometrics, 16(1),

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Backé, P., Égert, B., O. and Zumer, T., 2007. “Private-Sector Credit in Central and Eastern Europe:

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Brzoza-Brzezina, M. 2005. “Lending booms in Europe’s periphery: South-Western lessons for Central-

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the euro area”. Applied economics, 35(1), pp.107-117.

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32

Appendix

Error Correction Model – Additional Results including Cointegrating Vector.

Table 11: Error Correction Model (2002 – 2014) –results specifying lags on Specification 3

Specification 3b:

Variable: Coefficient: Standard Error: (robust):

∆ c (t)

ect_1 (t-1) -0.051*** 0.018

∆ c (t-1) 0.037 0.082

∆ irs (t) -0.001 0.005

∆ tbr (t) - -

∆ gb (t) -0.001 0.052

∆ gb (t-1) 0.028 0.041

∆ gb (t-2) 0.020 0.050

∆ gb (t-3) 0.031 0.044

∆ gb(t-4) 0.051 0.053

∆ gb(t-5) -0.080** 0.038

∆ gb (t-6) -0.015 0.045

Constant -0.015 0.010

Test:

Number of observations: 143

F-test (Prob>F) 2.15 (0.0249)

R-squared 0.1043

Cointegration Vector : (ect_1) c GDP irs tbr Constant

1 -1.306*** 0.037*** 0.031*** 0.603

*=significant at 10%.

**=significant at 5%.

***=significant at 1%.

**=significant at 5% ***=significant at 1%

33

Table 12: Error Correction Model Results (2008 - 2014) – results specifying lags on Specification 5

Specification 5b:

Variable: Coefficient: Standard Error (robust):

∆ c(t)

ect_1 (t-1) -0.029* 0.016

∆ c (t-1) 0.018 0.127

∆ cb (t) 0.001 0.006

∆ bs (t) -0.002 0.014

∆ bs (t-1) 0.010 0.014

∆ bs (t-2) -0.029** 0.013

∆ bs (t-3) -0.022 0.014

∆ bs (t-4) -0.026* 0.014

∆ bs (t-5) -0.012 0.015

∆ bs (t-6) -0.020 0.013

Constant -0.107 0.066

Test:

Number of observations: 69

F-test (Prob>F) 2.24 (0.0272)

R-squared 0.2291

Cointegrating Vector: (ect_1) Private Sector Credit

(logREPrivSecCred)

GDP

(logreGDP)

Interest Rate

Spread (IRS)

Constant

1 -2.234*** 0.0648*** 4.4294

*=significant at 10%. **=significant at 5% ***=significant at 1%

Source: Authors Calculations