Determinants of Rural Households' Participation in Formal Microfinance Programs in Malawi: An Instrumental Variables Approach
The study aimed at investigating the major determinants of rural households" participation in formal microfinance programs in Malawi. A probit model was used on secondary data on "credit and loans" collected by the National Statistical Office (NSO) between March 2004 and April 2005 under the World Bank funded Integrated Household Surveys program to answer the study hypotheses. To control for the effect of omitted variable bias and endogeneity, we use exogenous age and land size as instrumental variables for credit constraint, and estimate that credit constraint is markedly negatively related to participation in formal microfinance programs. Further, the findings reveal that households with smaller dependency ratio, without contact with extension agents and those residing far away from trading centers/bomas are less likely to participate in formal microfinance programs. The empirical model results further show that households headed by younger and less educated heads, male headed households, households with larger dependency ratio and smaller land size are more likely to be credit constrained. The findings suggest important policy lessons which can help improve the participation of rural households particularly women in formal credit programs. The binding land constraint in most rural households suggests a change of focus in the delivery of microfinance services to include both agricultural and non-farm loans which will give the rural poor an opportunity to make trade-offs between the two as per their convenience. Additionally, an improvement in the delivery of extension services on issues of credit would help create awareness about the potential sources of credit to the rural populace.
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Academic year: 2009 2010
Determinants of Rural Households Participation in Formal
Microfinance Programs in Malawi: An Instrumental
Variables Approach
Nyoni, Abel Mauluka
Dr. ir. Marijke DHaese
Dr. ir. Jeroen Buysse
Thesis submitted in partial fulfilment of the requirements
for the joint academic degree of International Master of Science in Rural Development (Agricultural
Economics) from Ghent University (Belgium), Agrocampus Rennes (France), Humboldt University of
Berlin (Germany) and University of Cordoba (Spain) in collaboration with Wageningen University (The
Netherlands), Slovak University of Agriculture in Nitra (Slovakia) and the University of Pisa (Italy).
This thesis was elaborated and defended at Ghent University (Belgium) within the framework of the
European Erasmus Mundus Programme Erasmus Mundus International Master of Science in Rural
Development (Course N 2004-0018/001- FRAME MUNB123)
This is an unpublished Master of Science thesis and is not prepared for further distribution. The
author and the promoters give the permission to use this thesis for consultation and to copy parts
of it for personal use. Every other use is subject to copyright laws; more specifically the source
must be extensively specified when using results from this thesis.
Place of Defence: Ghent University, Belgium.
The Promoters,
The Author,
Nyoni, Mauluka Abel
The study aimed at investigating the major determinants of rural households participation in
formal microfinance programs in Malawi. A probit model was used on secondary data on credit
and loans collected by the National Statistical Office (NSO) between March 2004 and April
2005 under the World Bank funded Integrated Household Surveys program to answer the study
hypotheses. To control for the effect of omitted variable bias and endogeneity, we use exogenous
age and land size as instrumental variables for credit constraint, and estimate that credit
constraint is markedly negatively related to participation in formal microfinance programs.
Further, the findings reveal that households with smaller dependency ratio, without contact with
extension agents and those residing far away from trading centers/bomas are less likely to
participate in formal microfinance programs. The empirical model results further show that
households headed by younger and less educated heads, male headed households, households
with larger dependency ratio and smaller land size are more likely to be credit constrained.
The findings suggest important policy lessons which can help improve the participation of rural
households particularly women in formal credit programs. The binding land constraint in most
rural households suggests a change of focus in the delivery of microfinance services to include
both agricultural and non-farm loans which will give the rural poor an opportunity to make tradeoffs between the two as per their convenience. Additionally, an improvement in the delivery of
extension services on issues of credit would help create awareness about the potential sources of
credit to the rural populace.
In the preamble, let me give special thanks to the Almighty God for His everlasting mercies and
blessings. Special thanks to my parents, brothers and sisters for their unceasing love, moral support and
encouragement in my life. God bless you all.
I owe my heartfelt appreciation to my promoters: Dr. ir. Marijke DHaese and Dr. ir. Jeroen Buysse, for
their constant guidance, advice and valuable inputs in every single step of my thesis execution. I say
without you this piece of work would not have been possible.
In a special way I would like to thank Ghent University for awarding me a master grant which enabled me
to pursue this MSc. program. Furthermore, I extend my gratitude to the IMRD programme coordinator,
Prof. Dr. Ir. Guido Van Huylenbroeck, the entire Management Board and all the members of staff at the
IMRD secretariat for their kind hospitality and tireless efforts to see to the needs of students. I also extend
my appreciation to Judis Renate (local coordinator) at Humboldt University of Berlin (Germany) for her
motherly support and care throughout my stay in Berlin.
My heartfelt appreciation to all the lecturers in the Departments of Agricultural Economics both at Ghent
University (Belgium) and Humboldt University of Berlin (Germany) for the valuable knowledge imparted
on me. Let me also express my sincere gratitude to the members of staff in the Faculty of European
Studies and Regional Development and the Faculty of Economics and Management at the Slovak
University of Agriculture in Nitra (Slovakia) particularly Prof. Anna Bandlerova, Jela Tvrdoov,
Barbora Milotov, Loreta, Lucia and Ivan for the wonderful organisation, guidance, support and reception
in Nitra.
I also would like to thank the entire IMRD students family in Gent (Belgium) and Berlin (Germany) for
the social and moral support as well as constructive criticisms rendered to me in the course of my studies.
I would say this has helped to shape me in one way or the other and prepare me for future challenges. It
was really an experience worthy going through. Lastly but not least, I give my sincere thanks to my dear
one, Ms. Maria Chinoko Ngulube, for her patience, love and encouragement. To you, I say thank you for
being by my side through those difficult times. God bless you.
Acquired Immune Deficiency Syndrome
APIP
Agricultural Productivity Investment Programme
CUMO
Concern Universal Microfinance Organization
Development of Malawian Enterprises Trust
ECLOF
Ecumenical Church Loan Fund
Extension Planning Area
Finance for International Community Assistance
FITSE
Finance Trust for the Self Employed
GTZ
Gesellschaft fur Technische Zusammenarbeit (German Technical Cooperation Agency)
Integrated Household Survey
Government of Malawi
MAMN
Malawi Microfinance Network
Malawi Rural Development Fund
Malawi Growth and Development Strategy
MPRSP
Malawi Poverty Reduction Strategy Paper
MRFC
Malawi Rural Finance Company
Micro and Small Enterprise
Malawi Savings Bank
MUSCCO
Malawi Kwacha
NABW
National Association of Business Women
NEAP
National Environmental Action Plan
Non-Governmental Organisation
National Statistical Office
OIBM
Opportunity International Bank of Malawi
PMERW
Promotion of Microenterprises for Rural Women
PRIDE Malawi
Promotion of Rural Initiatives and Development Enterprises
Rotating Savings and Credit Association
Reserve Bank of Malawi
Smallholder Agricultural Credit Association
Savings and Credit Cooperative
Structural Adjustment Programme
SEDOM
Small Enterprise Development Organization of Malawi
Statistical Package for Social Sciences
Touching Lives Fund
WSBI
World Savings Banks Institute
Abstract ........................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Abbreviations ..................................................................................................................... iv
Table of Contents ........................................................................................................................... vi
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Chapter 1: Introduction ................................................................................................................... 1
1.1 Background of the Study ....................................................................................................... 1
1.2 The Genesis of the Microfinance Concept ............................................................................ 2
1.3 Motivation of the Study......................................................................................................... 4
1.4 Objectives of the Study ......................................................................................................... 6
1.5 Hypotheses of the Study........................................................................................................ 7
1.6 Structure of the Report .......................................................................................................... 7
Chapter 2: Theoretical and Conceptual Framework ....................................................................... 9
2.1 The Potential Role of Microfinance in Poverty Alleviation ................................................. 9
2.2 Group Lending Theory ........................................................................................................ 16
2.3 The Microfinance Sector in Malawi.................................................................................... 21
2.3.1 The Structure of the Financial Sector in Malawi .......................................................... 21
2.3.2 Microfinance Institutions in Malawi ............................................................................ 24
2.4 Demand and Supply of Microfinance Services in Malawi ................................................. 26
2.5 Participation in Formal Microfinance Programs ................................................................. 27
2.5.1 Malawi Rural Finance Company (MRFC) ................................................................... 27
2.5.2 Conceptual Framework................................................................................................. 29
Chapter 3: Methodology ............................................................................................................... 31
3.1 Facts and Figures about Malawi.......................................................................................... 31
3.1.1 Geographic and Climatic Information .......................................................................... 31
3.1.2 General Economic Indicators ....................................................................................... 32
3.2 Study Area ........................................................................................................................... 34
3.3 Data for the Study ............................................................................................................... 35
3.4 Data Analysis ...................................................................................................................... 36
3.5 Analytical Model ................................................................................................................. 37
3.5.1 Theoretical Description of Credit Constraint ............................................................... 37
3.5.2 Empirical Model Specification ..................................................................................... 38
3.5.3 Variables and Hypothesized Effects ............................................................................. 39
Chapter 4: Descriptive Analysis of the Socio-economic Characteristics According to
Participation and Credit Constraint Status .................................................................. 42
4.1 General Socio-Economic Characteristics of the Respondents ............................................ 42
4.2 Household Loan Characteristics.......................................................................................... 47
4.2.1 Loan Size and Repayment Period ................................................................................. 47
4.2.2 Reasons for Obtaining a Formal Loan .......................................................................... 49
4.2.3 Group Membership and Loan Purpose ......................................................................... 50
4.2.4 Reasons for not attempting to Borrow from Formal Sources ....................................... 51
4.3 Household Characteristics According to Participation Status............................................. 52
4.3.1 Household Demographic Characteristics ..................................................................... 53
4.3.2 Wealth Status Indicators ............................................................................................... 54
4.3.3 Micro-credit Related Factors ........................................................................................ 55
4.4 Effect of Main Occupation on Household Wealth Status ................................................... 57
4.5 Household Characteristics According to Credit Constraint Status ..................................... 57
4.6 Participation and Credit Constraint Status of Head According to Expenditure Deciles ..... 60
Chapter 5: Quantitative Analysis of the Determinants of Participation in Formal Microfinance
Programs ..................................................................................................................... 62
5.1 Overview of Study Objectives and Hypotheses .................................................................. 62
5.2 Empirical Model Results ..................................................................................................... 63
5.2.1 Determinants of Credit Constraint Condition ............................................................... 63
5.2.2 Determinants of Participation in Formal Microfinance Programs ............................... 67
Chapter 6: Discussion, Conclusion and Policy Implications ........................................................ 70
6.1 Discussion and Conclusion ................................................................................................. 70
6.2 Policy Implications .............................................................................................................. 75
References ..................................................................................................................................... 76
Table 2.1: Legal and Ownership Structures of MFIs in Malawi .................................................. 25
Table 3.1: Changes in Selected Economic Indicators (period averages): 1963- 2008 ................. 34
Table 3.2: Description of the Empirical Model Variables ............................................................ 41
Table 4.1: Distribution of Respondents According to Age........................................................... 43
Table 4.2: Distribution of Respondents According to Educational Status ................................... 44
Table 4.3: Distribution of Respondents According to Main Occupation ..................................... 44
Table 4.4: Loan Size and Repayment Period ................................................................................ 47
Table 4.5: Pearson Correlation Results for Loan Size and Household Characteristics ................ 48
Table 4.6: Effect of Loan Purpose on Loan Size .......................................................................... 51
Table 4.7: Reasons for not obtaining a Formal Loan.................................................................... 52
Table 4.8: Household Characteristics According to Participation Status ..................................... 55
Table 4.9: Effect of Main Occupation on Wealth Status Indicators ............................................. 57
Table 4.10: Household Characteristics According to Credit Constraint Status ............................ 59
Table 5.1: Probit Model Results of Determinants of Credit Constraint ....................................... 66
Table 5.2: Probit Model Results for the Determinants of Participation ....................................... 69
Figure 1.1: Growth of Microfinance Coverage: 1997-2007 ........................................................... 3
Figure 1.2: Growth of MFIs in Malawi: 1990-2010 ....................................................................... 5
Figure 2.1: Transmission Mechanism of Microfinance to Poverty Alleviation ........................... 15
Figure 2.2: A Dynamic Presentation of Problems and Solutions in a Multi-stage Joint Liability
Loan ........................................................................................................................... 20
Figure 2.3: Structure of the Financial Sector in Malawi ............................................................... 23
Figure 2.4: Conceptual Framework .............................................................................................. 30
Figure 3.1: Map of Malawi ........................................................................................................... 32
Figure 3.2: Map of Malawi Locating Kasungu District ................................................................ 35
Figure 4.1: Distribution of Respondents According to Gender (n=166) ...................................... 42
Figure 4.2: Distribution of Household Income According to Source (n=166) ............................. 45
Figure 4.3: Distribution of Respondents According to Participation Status (n=166) ................... 46
Figure 4.4: Distribution of Households Participation Status According to Credit Constraint
Status (n=166) ............................................................................................................. 46
Figure 4.5: Distribution of Borrowers According to Loan Size ................................................... 48
Figure 4.6: Reasons for obtaining a Formal Loan ........................................................................ 49
Figure 4.7: Group membership and Purpose of the Loan ............................................................. 50
Figure 4.8: Participation and Credit Constraint Status of Household Head According to
Expenditure Deciles (n=166)..................................................................................... 61
"The key to ending extreme poverty is to enable the poorest of the poor to get their foot on the ladder of development. The
ladder of development hovers overhead, and the poorest of the poor are stuck beneath it. They lack the minimum amount of
capital necessary to get a foothold, and therefore need a boost up to the first rung." -- Jeffrey Sachs1
The need to reduce the scale and depth of poverty among the growing population has been a
major development issue facing many developing countries for decades (Adjei et al., 2009).
Currently, about 1.4 billion people live under very precarious conditions and lack basic
necessities to sustain their livelihoods; of which over 75 per cent live in remote rural areas and
earn their living through subsistence farming (IFAD, 2009). Nonetheless, low agricultural
productivity emanating from erratic weather patterns, declining land holding sizes and
skyrocketing input prices coupled with low output prices has made it difficult for rural
households to continually depend on agriculture for survival. This has since generated so much
pressure on policy makers in the developing world to explore viable ways that would ensure that
the poor have access to the tools they need to build better lives (Clover and Eriksen, 2008).
One notable challenge the poor face in their daily struggle to raise their living standards is lack
of access to financial services which would enable them to diversify their economic activities by
undertaking investments in new agricultural activities, setting up small businesses or expanding
existing ones. Accordingly, it has been argued that improving the poors access to credit and
other financial services is a positive step towards improving their livelihoods (IFAD, 2009;
Hishigsuren, 2007; Burritt, 2006; Diagne and Zeller, 2001). Microfinance has thus been singled
out as an effective tool in the fight against poverty and consequently, attaining the Millennium
Development Goal of halving the proportion of people living in extreme poverty by 2015 (IFAD,
Microfinance is basically understood as the provision of small-scale financial services to low
income segments of the population (WSBI, 2008; Burritt, 2006). It is undoubtedly the most
visible innovation in anti-poverty policy in the last half century. Microfinance is unique among
American economist and Director of the United Nations Millennium Project Millennium Development Goals
economic development initiatives because it has the ability to contribute directly to peoples
economic and social progress, by allowing them to invest and multiply their scarce assets
(Schreiner, 2003). The UN General Assembly has recognized the positive impact of
microfinance on poverty reduction and has since pledged its full support for programs that aim at
bringing financial services to the rural poor. This was affirmed by the Assemblys decision to
dedicate 2005 for the commemoration of the International Year of Microcredit which served as
an opportunity to highlight and support microfinance programs throughout the world (IFAD,
2009). Furthermore, the Nobel Peace Prize awarded to the microcredit icon, Professor
Mohammad Yunus in 2006 brought microfinance into the spotlight in developing countries but
also in the developed world as a major tool for empowering vulnerable people, addressing social
and economic exclusion, and alleviating poverty (Dworkin and Blankenship, 2009 and WSBI,
1.2 The Genesis of the Microfinance Concept
Informal savings and credit groups have operated in different forms around the world for
centuries, but the field of microfinance took-off in the early 1970s in Bangladesh when
Mohammed Yunus lent $0.64 to a bamboo weaver (Schreiner, 2003). Thereafter a number of
experimental development programs began lending tiny amounts of money to groups of poor
women and by 1976 the Grameen (village) bank of Bangladesh was born. Grameen had only
36,000 members when it was formalized as a bank in 1983, but by 2003, the bank had 3.1
million members, most of them rural poor women. Today, Microfinance has become a global
institution and the concept of Grameen has been replicated both in low and high income
countries (IFAD, 2009; Armendriz and Morduch, 2005; Schreiner, 2003).
As shown by Figure 1.1 below, it is estimated that between 1997 and 2007, the total number of
microfinance clients worldwide grew from 13.5 million to 155 million while the number of MFIs
went up from 618 to 3,552, respectively. Similarly, the number of the worlds poorest families
accessing microfinance services increased from 7.6 million in 1997 to 107 million by end of
2007, representing an average growth rate of about 40 per cent per year (Microcredit Summit
Campaign Report, 2009). Of the 107 million poorest clients, 83.4 per cent were women. This
demonstrates the pace at which the microfinance phenomenon is gaining ground as a potential
Number of clients (millions)
tool for poverty reduction and social change (Microcredit Campaign Summit Report, 2009).
Total clients reached
Total poorest clients reached
Figure 1.1: Growth of Microfinance Coverage: 1997-2007
(Source: Microcredit Summit Campaign Report, 2009)
During the early years after the birth of the microfinance concept, most microfinance programs
focused on the provision of credit to the poor. However, in the past few decades, the
microfinance sector has undergone several transformations and has moved beyond credit to
include savings, business development services, money transfers and a variety of insurance
products that cover life, health and agriculture. Currently, a wide range of institutions ranging
from grass-roots savings and credit associations, financial cooperatives, microfinance
institutions, rural banks, specialized NGOs, agricultural development banks and government
sponsored programs are all involved in the delivery of microfinance services in rural
communities all over the world. Notwithstanding, the major challenge still remains to extend
microfinance coverage to the poorest families in the developing world where millions lack
access to even basic savings and credit services (IFAD, 2009; Microcredit Campaign Summit
Report, 2009; WSBI, 2008). IFAD (2009) reported that only about 10 per cent of the worlds 1.4
billion poorest people have access to the most basic financial services. This just illustrates the
magnitude of the task the world is faced with not only in extending financial services to the
poorest but also in eradicating abject poverty and consequently attaining the MDGs.
1.3 Motivation of the Study
In Malawi, studies have shown that lack of access to credit is impacting negatively on various
aggregate and household-level outcomes, including technology adoption, agricultural
productivity, food security, nutrition, health, MSE development, and overall household welfare
(USAID, 2006: 2004; Diagne and Zeller, 2001). Recognizing this challenge, the government and
its development partners have undertaken numerous initiatives to ensure that the poor have
access to basic financial services. This has resulted into the proliferation of the microfinance
sector in recent years. The microfinance concept is thus increasingly gaining importance in
Malawi as an effective tool in the fight against HIV/AIDS, social mobilization and poverty
alleviation (Burritt, 2006; GOM, 2002).
Malawi embraced the microfinance concept around mid-1990s, following the introduction of the
Structural Adjustment Programs (SAPs) which resulted into the gradual liberalization of the
financial sector (USAID, 2004). This facilitated the entry of new players into both the formal and
semi-formal financial systems, which has fostered competition and innovation in the sector.
Since then the microfinance sector has received tremendous political and financial support from
the government. To underscore its commitments towards supporting the microfinance sector, in
2002, the government developed the microfinance policy and action plan to create an enabling
legal and regulatory environment for the operation of MFIs (Burritt, 2006; GOM, 2002). This
was augmented by the formulation of the national development strategy; the Malawi Growth and
Development Strategy (MGDS) in 2005 which is the governments medium term operational
strategy (2006/07 2010/2011) for the attainment of the nations Vision 2020. The overriding
philosophy of the MGDS is poverty reduction through sustainable economic growth and
infrastructure development. The MGDS recognizes the role of economic empowerment in
poverty alleviation and has since placed emphasis on programs that support the development of
enterprises in rural areas; of which microfinance is one of them (GOM, 2006a).
Currently, several government, private sector and NGO sponsored programs and schemes are
involved in the provision of financial services to rural households. Most of the microfinance
programs target a wide range of clientele ranging from smallholder farmers and microentrepreneurs to large-scale farmers and entrepreneurs (USAID, 2008; Burritt, 2006). As
presented by Figure 1.2 below, the number of formally registered MFIs has significantly
increased over the past two decades from about 6 at the beginning of the 1990s to around 10 in
early 2000. Presently, there are over 20 MFIs registered by the Malawi Microfinance Network
(MAMN) representing a period average growth of about 37 per cent (AFMIN, 2010; MAMN,
2008; Burritt, 2006; USAID, 2004; Diagne and Zeller, 2001).
Number of MFIs
Figure 1.2: Growth of MFIs in Malawi: 1990-2010 (Source: Various2)
According to the Malawi National Gemini MSE Baseline Study 2000, about 19.8 per cent of
rural households and 10.5 per cent of urban households reported acquiring a loan from some
source, both formal and informal (NSO, 2001). Surprisingly, despite a remarkable increase in the
number of MFIs in the past decade, Simtowe and Phiri (2010) observes that the proportion of
households with access to any form of credit still remains less than 20 and 10 per cent for rural
and urban households, respectively. Burritt (2006) further estimates that only about 3 per cent of
the population in Malawi has access to deposit services. This suggests that the majority of the
population in Malawi still do not have access even to basic financial services despite a
proliferation in the number of MFIs operating in the country.
This may prompt one to wonder as to why most rural households do not participate in formal
microfinance programs in an environment of the booming rural microfinance sector. Is the
problem with program targeting, transaction costs, interest rates charged or there are some
AFMIN, 2010; MAMN, 2008; Burritt, 2006; USAID, 2004 and Diagne and Zeller, 2001
household or community level factors that hinder rural households from utilizing such credit
schemes or programs to alleviate their capital problems? It is under this motivation that this
study attempts to empirically determine the factors that influence rural households to participate
in formal microfinance programs.
A number of studies have been carried out in the field of microfinance in Malawi with the
majority of them focusing on the impact of credit on technology adoption (Gin and Yang, 2009;
Simtowe et al, 2008); impact of credit on general household welfare (Diagne and Zeller, 2001);
impact of credit program participation on child schooling (Shimamura and Cornhiel, 2009);
effectiveness of group lending approaches (Simtowe and Zeller, 2006); and credit demand and
supply (USAID, 2006). However, none of these studies has attempted to (holistically) understand
the determinants of rural households participation in formal microfinance programs. This is
important as it will help one to understand why the majority of rural households still do not
participate in formal credit markets despite the proliferation of the rural microfinance sector.
The underlying objective of the study was to analyze the determinants of rural households
participation in formal microfinance programs. Specifically, the study aimed to look at the
1. To examine if there are any differences in socio-economic characteristics between
participants and non-participants; and credit constrained and unconstrained households;
2. To establish the factors that influence households credit constraint condition;
3. To find out if credit constraint has an influence on households decisions to participate in
formal microfinance programs; and
4. To ascertain the major determinants of rural households participation in formal
microfinance programs.
1.5 Hypotheses of the Study
Basing on the above objectives, the following were the major study hypotheses:
H0: There are no significant differences in socio-economic characteristics between
H0: There are no factors that influence households credit constraint condition;
H0: Credit constraint has no influence on participation in formal microfinance programs; and
H0: No factors influence rural households to participate in formal microfinance programs.
1.6 Structure of the Report
The rest of the report is organised as follows: Chapter 2 reviews the theoretical and empirical
literature on microfinance. The chapter begins by giving an insight on the role of microfinance in
economic development and poverty alleviation, followed by an overview of the group lending
theory and the microfinance sector in Malawi. The final section of the chapter provides a
description of Malawi Rural Finance Company (MRFC), the focus MFI for the study, as well as
a conceptual framework of the determinant of participation in formal microfinance programs.
Chapter 3 provides a methodological framework which was used to answer the above stated
study hypotheses. It starts by giving a brief background of Malawis geographic, climatic and
economic situations followed by a description of the selected study district, the data used in the
study and the methods used for analysis. The chapter concludes by providing the analytical
framework of the empirical model used to analyse the determinants of participation in formal
Chapter 4 presents the descriptive analysis results of the study. The chapter begins by giving a
general overview of the socio-economic characteristics of the respondents followed by a
discussion on household loan characteristics in section two. The third section focuses on the
comparison of socio-economic and micro-credit related characteristics between participants and
non-participants while the final part of the chapter draws a comparison between credit
constrained and unconstrained households with respect to household socio-economic and microcredit related characteristics.
Chapter 5 presents the empirical model results in line with objectives 2, 3 and 4 of the study. The
chapter begins by giving an overview of the study hypotheses followed by the empirical model
results in sections two and three.
Chapter 6 provides a discussion of the results, conclusion and draws some recommendations
basing on the findings. Additionally, the chapter also provides some suggestions for further
Chapter 2: Theoretical and Conceptual Framework
Since the birth of the microfinance concept in the 1970s much has been documented about its
potential role in economic development and poverty alleviation. Over time a diversity of models
under which microfinance programs are implemented have emerged responding to the changing
economic, social, political, cultural and technological environments. This has necessitated some
improvements to the existing products as well as the development of new products that serve the
poor better while satisfying the triangle of microfinance vis--vis: financial sustainability,
outreach and welfare impact (Armendriz and Morduch, 2005).
In recent years, much of the economic development literature on microfinance has been centered
on the group lending theory pioneered by, among other, Stiglitz (1990), Besley and Coate
(1995), Ghatak (1999), and Van Tassel (1999). Group lending with joint liability is celebrated as
a major breakthrough in microfinance as it defeats the popular notion by traditional commercial
banks that believe that the poor are not trusted borrowers. In his pioneer work on group lending
theory, Stiglitz (1990) argues that joint liability in groups can help to mitigate adverse selection
and moral hazard which are common problems experienced in credit and insurance markets.
This chapter gives an overview of the economic development literature on microfinance. It
begins by giving an insight on the role of microfinance in economic development and poverty
alleviation, followed by an overview of the group lending theory and the microfinance sector in
Malawi. At the end, a conceptual framework of the determinants of participation in formal
microfinance programs is developed.
2.1 The Potential Role of Microfinance in Poverty Alleviation
It is argued that, if targeted at the poorest segments of the society, microfinance has the potential
to significantly contribute towards the fight against poverty as it makes available the financial
services to the poor who often lack access to or are neglected by commercial banks and other
formal financial institutions (IFAD, 2009; Todaro and Smith, 2009; Burritt, 2006; Armendriz
and Morduch, 2005; State Bank of Pakistan, 2005). Most formal lenders require collateral such
as land or houses as a prerequisite to issue out loans but often times poor people lack such
physical assets. As a result, they are denied the opportunity to access credit and other financial
services that they need to best manage their assets and generate income to improve their
livelihoods (Adjei et al., 2009). This constrain them from improving productivity from their
small landholdings and diversifying into other food and cash crops as well as non-farm activities
which are key to poverty alleviation (Diagne and Zeller, 2001). However, the growth in the rural
microfinance sector, which offer relatively flexible lending terms, has reduced the credit
constraint among the poor (Armendriz and Morduch, 2005; Schreiner, 2003).
Microfinance offers a wide range of benefits to the poor and the notable ones include; increase in
savings and effects in production, increase and diversification of incomes, changes in
consumption patterns, promotion of new non-agricultural activities, and improvement of
methods of production (Hidalgo-Celari et al., 2005). Thus, it has multiplier effects on people's
standards of living through enhancing basic household welfare, such as food security, nutrition,
shelter, sanitation, health and education services (IFAD, 2009; UN, undated). Apart from
addressing material poverty, at individual level, microfinance can effectively address issues
associated with non-material poverty, which include social and psychological effects that prevent
people from realizing their potential. It can help to build individual self-esteem and confidence
which eventually empowers the poor to actively participate in the decision making process of the
society. Additionally, when properly guided, the material benefits of microfinance can extend
beyond the household into the community (UN, undated).
It is argued that microfinance plays a crucial role in household food security either through own
production or market access. Access to credit enables farmers to purchase inputs such as
fertilizer, seeds, tools and machinery and hire extra labour which ultimately increases the
productivity of their small plots. This is essential in ensuring household food security and better
nutrition (IFAD, 2009; Todaro and Smith, 2009; Armendriz and Morduch, 2005; Diagne and
Zeller, 2001). Further, the small loans the poor obtain from MFIs enable them to engage in a
variety of income generating activities that continually generate income for them which help to
reduce their income variability and increase their spending power. Ultimately, this enables them
to meet their basic needs and smooth consumption throughout the year. Microfinance programs
thus have the potential to provide a fallback position for the households in case of production
failure or other adverse shocks as it may help them to avoid distress through sales of assets while
at the same time enable them to replace productive assets destroyed in natural disasters (Burritt,
2006; Martin et al., 2002; Vonderlack and Schreiner, 2002; Zeller, 1999).
In recent years, economic development literature on microfinance has also given much attention
on micro-savings and micro-insurance which are equally important to the poor as microcredit but
often times not provided to them (Burritt, 2006; Armendriz and Morduch, 2005). It is argued
that if the small loans the poor get from MFIs are to make any significant impact, then they
should be given an opportunity to access micro-savings facilities which they need in order for
them to deposit the surplus income they generate from their farms or non-farm activities. Later
on they can withdraw it in times of need or use it as collateral for new loans. Such basic microsaving facilities play a crucial role in income and consumption smoothing over the year besides
making the poor less vulnerable to unexpected events. Further, a deposit account can help the
rural poor to take out a loan when they need it or obtain a life or health insurance which can
provide them with security from unexpected events and income shocks (IFAD, 2009; Martin et
al., 2002; Zeller, 1999).
It is also argued that extending micro-savings services to the poor would help the countries in the
mobilization of the much needed savings which play a significant role in generating investments
required for accelerating economic growth. This is on the assumption that the middle class and
even the poor generally save and invest a significant proportion of their income compared to the
rich who spend much of their incomes on imported luxuries or they invest it in foreign banks
which offer relatively higher returns which leads to capital flight (Todaro and Smith, 2009;
Morduch, 2000; Tinker, 2000). Nevertheless, it is also observed that most of the loans obtained
by the poor are mainly used for consumption other than for investment purposes as highlighted
by most development agencies (Hidalgo-Celari et al., 2005; NSO, 2001).
Microfinance is also argued to be the major source of employment for the majority of the
unemployed rural and urban residents that dominate the informal sector in the developing world.
With the increasing failure of the rural and urban formal sectors to absorb the additions to the
labour force emanating from high population growth, the informal sector is seen as a panacea for
the growing unemployment problem (Todaro and Smith, 2009; Burritt, 2006; Woller and
Woodworth, 2001). It is estimated that over 500 million of the worlds poor people are
economically active and are either self-employed or are working in microenterprises.
Consequently, the informal non-farm sector, in developing countries, is argued to provide refuge
to the extremely poor who are either landless or near landless (Haggblade, 2005; World Bank,
2000; Ellis, 1999).
It is observed that a highly heterogeneous collection of microenterprises, supported by both
formal and informal MFIs, operate in the rural areas of the developing world. These provide the
backbone of the rural economy and their contribution to aggregate household income has been
increasing over time. For instance, non-farm activities currently account for over 40 per cent of
average household incomes in Africa; a continent whose rural population is believed to be
predominantly subsistence (Bandiera, 2007; Haggblade, 2005; and Barrett et al., 2001). Halder
(2003) points out that the expansion of microfinance has thus been rooted in the expectation that
it can help generate income and self-employment, which can eventually solve both the problems
of unemployment and poverty.
Besides increased employment and income, microfinance has also been commended as a
powerful weapon in the fight against gender inequality and its role in ensuring women
empowerment (Dworkin and Blankenship, 2009; Kim et al., 2007; Armendriz and Morduch,
2005; Hidalgo-Celari et al., 2005; Schreiner, 2003). Most microfinance programs target rural
women who are often excluded from educational opportunities, highly valued job skills, and
traditional financial institutions and services. Women are perceived to be a better credit-risk than
men and more responsible managers of meager resources. They are more committed to using
their loans for the benefit of their households rather than self-gratifying consumption as most
men do (Dworkin and Blankenship, 2009; Corsi, 2006; Tinker, 2000; Goetz and Gufta, 1996). It
has been argued that the multiplier effects from microfinance programs are higher when the
recipients are women compared to men. A small loan obtained by a woman is said to have farreaching impacts for the household as it is invested in child health and education apart from
improving the general household welfare (IFAD, 2009; Shimamura and Cornhiel, 2009; Todaro
and Smith, 2009; Hidalgo-Celari et al., 2005; UN, undated).
The advocates of credit programs targeting women point out that microfinance increases
womens participation in the labour force through the creation of self-employment opportunities
and this promotes their economic empowerment. Ultimately, this promotes gender-equality,
increases womens intra-household bargaining power, authority, autonomy and participation in
household decision making (Pronyk et al., 2007; Halder, 2003; Vonderlack and Schreiner, 2002;
Goetz and Gufta, 1996). Studies have shown that womens participation in microfinance
programs leads to increased reproductive health decision making power which in turn plays a
great role in reducing fertility rates (Kim et al., 2007; Pronyk et al., 2008: 2007; Hidalgo-Celari
et al., 2005). Further, the status of women, in their homes and in their communities, improves
when they are responsible for loans and manage their household savings (IFAD, 2009).
Microfinance has therefore been recognized as a powerful instrument of social change,
institution-building at the grassroots level and a step towards achieving the MDGs on gender
equality and women empowerment (Kim et al., 2007; Goetz and Gufta, 1996).
Nonetheless, empirical studies on the role of microfinance in women empowerment and gender
equality have shown mixed results. While some studies have confirmed womens economic and
social empowerment others have argued that despite that making credit available to women is a
positive contribution towards efforts to challenge gendered terms of access to productive
resources and opportunities, a significant proportion of womens loans are directly invested by
their male relatives, while women borrowers bear the liability for repayment. It is observed that a
significant proportion of women transfer the power and control over the loans to men within
their households who eventually play a pivotal role in making investment decisions, general
management of the loans, controlling accounts and marketing of products apart from providing
labour inputs and deciding on any use of income generated (Corsi et al., 2006; Hunt and
Kasynathan, 2001; Goetz and Gufta, 1996).
This eventually leaves women with very limited or no direct control over credit defeating the
whole objective of women empowerment and gender equality in terms of control and use of
productive resources. Consequently, development literature argue that the link between targeted
access to credit for women and the transformation in gender relations required for empowerment
and equality is not as automatic as depicted by most development agencies committed to the
empowerment of women and gender equality (Corsi et al., 2006; Hunt and Kasynathan, 2001;
Morduch, 2000).
In recent years, microfinance has also gained so much attention as an important instrument in the
fight against HIV/AIDS. The number of microfinance programs that are currently integrating
poverty reduction and HIV/AIDS prevention in their initiatives is increasing globally (Dworkin
and Blankenship, 2009; Kim et al., 2007). Studies have shown that poverty and gender inequality
are the two main structural determinants of HIV/AIDS. Economic empowerment can thus greatly
assist in the prevention and mitigation of the disease, particularly for women (Dworkin and
Blankenship, 2009; Pronyk et al., 2008; Kim et al., 2007).
Generally, poverty affects women and girls more negatively than men and boys (Todaro and
Smith, 2009). As a result, women and girls are more likely to be constrained into sexually risky
situations which increase their risk of contracting HIV (Hallman, 2004). Tan Minh et al. (2004)
as cited by Dworkin and Blankenship (2009) argue that many women who do sex work are first
induced and partly trapped into it for economic reasons. Consequently, it has been argued that
microfinance programs that target women do not only help to empower them economically by
providing alternative sources of income but to a greater extent promote HIV/AIDS prevention.
Thus, by combining HIV/AIDS prevention and microfinance, important synergies may be
produced that extend beyond the economic realm to provide more enduring structural
protection from HIV/AIDS risks than HIV/AIDS prevention can do alone (Dworkin and
Blankenship, 2009).
Finally, empirical studies have also established a strong link between microfinance and child
labour as fewer cases of child labour and school absenteeism have been reported in households
which participate in microfinance programs. It has been argued that access to credit expands a
households income earning opportunities thereby reducing its reliance on casual labour as a
major source of livelihood. In turn, this reduces childrens participation in the labour market and
in the process improves their school attendance (Shimamura and Cornhiel, 2009). However,
another line of literature argue that microcredit has the potential to decrease the schooling levels
of children as it increases the labour demand on children either for household production or
chores as the working-age adults get more involved in income generating activities financed by
the loans (Morduch, 1999; Wydick, 1999).
The flow chart in Figure 2.1 below shows the transmission mechanism of microfinance to
poverty alleviation. The chart illustrates that microfinance, if targeted at the poorest segment of
the society, leads to increased employment and women participation in economic activities
which translate into increased household income. Eventually, this results into an increase in per
capita income, increased consumption of goods and services and better standards of living. This
ignites an increase in aggregate demand and supply of goods and services which eventually spur
a growth in the economy and results into a reduction in poverty levels (State Bank of Pakistan,
empowermen
Target poorest
Segment of society
Increased women participation in
Increased role of women in
family decision making
Reduced fertility rates
Improved family well being
Reduced risk of contracting
Rise in income level
Improved food security & nutrition
Increased health opportunities
Access to education improved
Reduced child labour
Increase in productivity
Increase in per capita income
Small population size
Improved standards of living
Better provision of health &
The economy grows
& poverty declines
Increase in consumption of goods
Aggregate demand increased
Increase in investment &
Aggregate supply increased
Figure 2.1: Transmission Mechanism of Microfinance to Poverty Alleviation
(Source: Adopted with some modification from The State Bank of Pakistan, 2005)
2.2 Group Lending Theory
This section provides an overview of the merits and demerits of group lending theory which
MRFC, the target MFI for the study, uses when providing loans to rural households which do not
have physical collateral. The discovery of group lending in microfinance is seen as the major
breakthrough in the delivery of financial services to the poor and ultimately in the fight against
poverty. Empirical evidence suggests that the success of most microfinance programs
implemented across the globe, as reflected by high repayment rates, is to a greater extent
attributed to the group lending approaches used by most MFIs. It is argued that group lending
has shown how unconventional contracts can work where true banking practices failed to
achieve tangible results. Accordingly, the discovery of group lending contracts has opened up
new possibilities for microfinance and it is by far the most celebrated microfinance innovation
(Guttman, 2008; Cassar et al., 2007; Armendriz and Morduch, 2005; Chowdhury, 2005;
Kritikos and Vigenina, 2005; Schreiner 2003).
Basically group lending relies on joint liability or group responsibility contracts in which,
instead of collateral, poor borrowers act as guarantors for each other (Besley and Coate, 1995;
Ghatak, 1999). The group forms voluntarily and while loans are made to individuals within
groups, all members are expected to support each other when difficulties arise. Unlike
commercial banks which demand physical collateral as a precondition for lending, most MFIs
implement group-based lending programs with joint liability contracts and peer pressure as
substitutes for collateral, along with community-based delivery systems that seek to exploit the
social capital and information advantages of local communities in screening and monitoring
borrowers (Cassar et al., 2007; Armendriz and Morduch, 2005; Kritikos and Vigenina, 2005;
Rankin, 2002). Nevertheless, Schreiner (2003) argues that joint liability by itself cannot result
into higher repayment rates but a combination of a set of incentives, such as progressive lending,
targeting of women, weekly or monthly repayment schedules, public repayments, routine
monitoring by loan officers from MFIs and other dynamic incentives also come into play.
Credit theorists argue that joint liability contracts in group lending can help to circumvent the
principal-agent (agency) problems that arise in credit and insurance markets due to information
asymmetry such as adverse selection, moral hazard, and lack of enforcement (Ghatak, 1999;
Besley and Coate, 1995; Stiglitz, 1990). Firstly, joint liability contracts are argued to help in
overcoming the problem of adverse selection which arises due to lack of information about
borrowers on the part of the principal (lender). This makes it costly and difficult for MFIs to
determine the creditworthiness of borrowers and the process of screening prospective borrowers
has the potential to escalate their transaction costs. As such they are unable to discriminate
against risky borrowers and in the process they charge high interest rates to compensate for the
possibility of having very risky borrowers. However, potential borrowers are able to easily
screen each other and through assortative matching they are able to choose amongst themselves
best partners and for them this is a sunk cost. The screening is based on the knowledge they have
about each other from informal relationships. It is argued that the voluntary formation of groups
induces borrowers to self-select into homogenous groups of low risks before the loan contract is
concluded and it induces several peer measures within the group of borrowers if anybody
defaults (Cassar et al., 2007; Armendriz and Morduch, 2005; Kritikos and Vigenina, 2005).
Secondly, when loans are issued out, most outside financial institutions encounter moral hazard
problems which emanate from difficulties in monitoring the actions of borrowers which
eventually raises the monitoring costs for the banks. However, the advocates of group lending
argue that joint liability contracts can help to mitigate this problem (Armendriz and Morduch,
2005; Ghatak and Guinnane, 1999; Stiglitz, 1990). Stiglitz (1990) argues that joint liability helps
to circumvent ex-ante moral hazard by inducing members to monitor each others choice of
investments and to inflict penalties upon borrowers who have chosen excessively risky projects.
In this regard, group members faced with the joint responsibility for the loans are induced to
monitor each others investment decisions and this ensures that members do not squander their
loans or invest in projects that are not viable. Thus borrowers are given tasks of both managing
their loans and monitoring peers to ensure that they take safe decisions that would protect them
from falling into repayment problems (Simtowe and Zeller, 2006). Nonetheless, empirical
studies elsewhere have shown that members in credit groups rarely experience any form of
excessive monitoring and borrowers do not receive any pressure from peers regarding the types
of projects to invest in (see for example Kritikos and Vigenina, 2005; Diagne et al., 2000 as cited
by Simtowe and Zeller, 2006).
Thirdly, after undertaking an investment, the project might fail due to some external factors
beyond the control of the borrower, thereby making it difficult for the member to repay the loan.
The proponents of joint liability lending argue that such circumstances are taken care of by peer
support as other members without repayment problems can come in and repay the loan on behalf
of the defaulters. This provides a form of intra-group insurance which is argued to help reduce
default rates in joint liability lending (Simtowe and Zeller, 2006; Ghatak, 1999). But under
certain circumstances, borrowers may be unwilling to repay their loans even when their
investments have been able to yield higher returns. This results into the ex-post moral hazard
problem which is argued to raise enforcement costs for the banks as they try to recover their
money. On the contrary, under joint liability contracts peer monitoring and enforcement as well
as peer pressure and social sanctions if needed, ensures that members abide by the agreed
contracts and repay their loans (Armendriz and Morduch, 2005; Ghatak, 1999; Besley and
Coate, 1995).
Conversely, Diagne et al. (2000) as cited by Simtowe and Zeller (2006) point out that peer
monitoring rarely occurs in credit groups in Malawi and when it occurs it does not lead to
improvements in repayment rates as unwillingness to repay (moral hazard) and not the inability
to repay was reported to be the main reason behind high default rates in Malawi Rural Finance
Company (MRFC) credit programs. Additionally, Che (2002) argues that in certain cases joint
liability contracts create a free-riding problem as some group members may deliberately decide
not to repay their loans knowing that it is the responsibility of the group to take care of such
situations thereby increasing the burden for other members. As a result, this makes group lending
unattractive. Wydick (1999) also observes that while peer monitoring appears to have a positive
effect on group loan repayment, strong social ties within the group make it difficult to pressure
fellow members to repay their loans. This demonstrates that much as joint liability contracts have
produced positive results elsewhere, the model has failed to achieve high repayment rates in
other programs. At times, joint liability has yielded mix results even for groups under the same
MFIs.
Finally, apart from the advantages of group lending highlighted above, it is also argued that
offering loans through groups helps MFIs to cut on operational costs as they are able to reach out
to many clients at a cheaper cost than when targeting individual lenders. MFIs also utilize these
groups to provide various forms of trainings to the clients at a lower cost (Armendriz and
Figure 2.2 illustrates the different stages of a loan transaction; problems encountered at each
stage and corresponding theoretical solutions as suggested by credit theorists. At the beginning
of the process there is a pool of potential borrowers unknown by the lender (safe and risky)
trying to access loans. But for them to qualify for a loan they need to be affiliated to a group and
bearing in mind that each group will need to sign a joint liability contract with the principal
(lender). Each borrower, through peer selection tries to match with members of similar risk type.
It is this self-selection process among the peers that is argued to reduce incidences of adverse
selection (Cassar et al., 2007; Simtowe and Zeller, 2006).
Thereafter each group member undertakes an investment. At this stage the lender is faced with
the ex-ante moral hazard problem which occur because the borrower either misuses the loan;
invests in a risky project or does not apply enough effort to manage the project. However, credit
theory argues that peer monitoring can help circumvent this problem. The third stage is about
investment outcomes. Under this stage, there are two possible outcomes: the project may fail due
to factors beyond the borrowers control which results into a limited liability problem or the
borrowers project may succeed but the borrower might be unwilling to repay the loan which
results into the ex-post moral hazard problem. As a remedy, group lending theory argues that
peer support can help address the former case as other members may repay for the defaulters
while peer pressure and social sanctions can help to address the latter situation (Simtowe and
Zeller, 2006).
Repay
Ex-ante moral hazard
(project choice)
(Peer monitoring)
Unwillingness to repay
Intra-group insurance
(Peer support)
Ex-post moral hazard
(limited enforcement)
Enforcement through
(Peer pressure)
Figure 2.2: A Dynamic Presentation of Problems and Solutions in a Multi-stage Joint Liability Loan
(Source: Adopted from Simtowe and Zeller (2006); modified after Sadoulet (2004))
A number of distinctive group lending models with joint liability contracts exist reflecting the
fact that microfinance has evolved differently in different environments and the types of models
used differ widely across countries. Some of the popular and widely used group lending models
relying on joint liability include: the Grameen model (see: Armendriz and Morduch, 2005;
Schreiner, 2003; Zeller, 2003); cooperatives and credit union model (see: Armendriz and
Morduch, 2005; Zeller, 2003); the village bank model (see: Zeller, 2003; Buckley, 1997);
Rotating Savings and Credit Association (ROSCA) and Self-Help Groups models (see:
Armendriz and Morduch, 2005; Zeller, 2003; Vonderlack and Schreiner, 2002).
In recent years, new lending models which do not necessarily rely on joint liability are emerging.
These models basically target individuals with relatively flexible lending terms as opposed to
traditional commercial banks. Currently, a wide range of MFIs worldwide also offer individual
loans to the poor with flexible collateral terms. It has been noted that these new forms of lending
contracts are very helpful in areas with low population densities or highly diverse populations
and in situations where more established clients seek greater flexibility. In such circumstances,
peer monitoring costs are high and the social punishments for non-compliance are more difficult
to implement (Armendriz and Morduch, 2005).
2.3 The Microfinance Sector in Malawi
Malawi, just like other developing countries, has not been spared by the microfinance revolution
and the concept is gaining importance as an effective tool for social mobilization and poverty
alleviation. Initially, microfinance was seen as a development tool and its activities were often
isolated from the wider financial system. However, over time with the success of credit-led
NGOs in many countries, their transformation into licensed banking institutions, and the entry of
mainstream and specialized commercial banks into the business of micro-lending and savings
collection, the microfinance industry has evolved into a powerful sub-sector of the formal
financial system (Burritt, 2006; USAID, 2004).
2.3.1 The Structure of the Financial Sector in Malawi
The formal financial sector in Malawi is fairly small and limited in scope. The sector is regulated
by the Reserve Bank of Malawi (RBM) which was established in 1965 under an Act of
Parliament. Through the Banking Act of 1989, the RBM is mandated to regulate and supervise
the activities of most financial institutions. The bank is also responsible for licensing and
registering banks and financial institutions (USAID, 2004).
The central bank basically categorizes formal financial institutions under two broad headings and
these are: commercial banks (providing all types of savings, lending and investment products)
and other financial institutions (non-banks). Currently, there are about 11 commercial banks in
the formal financial sector. Other financial institutions include discount houses, leasing
companies, a building society, a finance company, development finance institutions, savings
institutions and an insurance industry made up of several insurance companies. There are also
many semi-formal actors providing financial services in Malawi. These include NGOs,
parastatals, Savings and Credit Cooperatives (SACCOs), private companies, and projects of
international development agencies and donors. It is to this category that most microfinance
institutions operating in Malawi belong. Generally, the regulations do not allow most of these
actors to mobilize savings for on-lending with the exception of SACCOs. Their focus is mainly
to provide credit, both in-kind and cash. As such, none of these semi-formal institutions is
subject to any kind of prudential regulation from the central bank as is the case with formal
financial institutions (Burritt; 2006; USAID, 2004).
Malawi also has a vibrant informal financial sector which caters for lower-income clients and is
operated through Katapila (moneylenders), Rotating Savings and Credit Associations
(ROSCAs), and networks of families or friends. These informal credit providers operate beyond
any regulatory or supervisory framework. It is estimated that informal sources account for over
26 per cent of the money used to start-up micro and small businesses compared to 2 per cent
from formal sources (USAID, 2004). Figure 2.3 shows the structure of the financial sector in
Malawi.
Financial Sector in Malawi
Formal Sector
(Non-banks)
(Parastatals)
, relatives,
friends, etc
SEDOM, DEMAT, MRFC, MSB
and MARDEF
ECLOF, FITSE,
Microloan, CUMO,
NABW and TLF
Figure 2.3: Structure of the Financial Sector in Malawi (Source: Author)
FINCA, PRIDE Malawi,
MUSCCO and OIBM
Savings and
(ROSCAs)
2.3.2 Microfinance Institutions in Malawi
The MFIs operating in Malawi are basically defined as semi-formal financial institutions and can
operate under several legal forms and ownership structures (USAID, 2004). In terms of both
legal form and ownership structure the institutions are categorized into three groups as described
a. Cooperative Associations.
These include both financial and non-financial cooperatives registered under the Cooperative
Societies Act of 1946. Examples of institutions registered under this act are the Savings and
Credit Cooperatives (SACCOs) and Malawi Union of Savings and Credit Cooperatives
(MUSCCO). MUSCCO is an affiliated network of 66 SACCOs dispersed throughout the country
but both MUSCCO and its member SACCOs are registered independently as cooperative
associations. SACCOs can mobilize and on-lend savings of members (USAID, 2008: 2004).
Non-Profit Institutions
All NGO microcredit institutions without a profit-making motive registered under the Trustees
Incorporation Act of 1962 belong to this category. NGOs are generally engaged in multi-sector
development activities with microcredit being one of many. Ownership structure is not always
clear. Microcredit operations are usually started as donor projects or initiated by the government.
Notable examples include microcredit providers that operate as donor-funded projects within
international NGOs such as Ecumenical Church Loan Fund (ECLOF), Microloan, TLF and
Finance Trust for the Self Employed (FITSE). This category also include parastatals such as
Small Enterprise Development Organization of Malawi (SEDOM) and Development of
Malawian Enterprises Trust (DEMAT). The National Association of Business Women (NABW)
is also a microcredit institution registered under this act (MAMN, 2008; Burritt, 2006; USAID,
Profit-Oriented Institutions
This category includes all profit-making institutions limited by guarantee or shares that are
registered under the Companies Act of 1986. Institutions like Finance for International
Community Assistance (FINCA), Concern Universal Microfinance Organization (CUMO),
PRIDE Malawi and the government owned Malawi Rural Finance Company (MRFC) are all
registered under this Act. The government owned Malawi Savings Bank (MSB) and the
Opportunity International Bank of Malawi (OIBM) registered under the banking act also belong
to this group. MSB is mainly responsible for the disbursement of funds under the Malawi Rural
Development Fund (MARDEF) which was created by the government to provide small loans to
rural people mainly targeting non-farm activities (MANM, 2008; Burritt, 2006; USAID, 2004).
The table below summarizes all the MFIs discussed above in terms of their legal forms and
ownership structures.
Table 2.1: Legal and Ownership Structures of MFIs in Malawi
MFIs
Registering Act
Government owned
Microloan
Private sector owned
Source: Adopted with some Modification from USAID (2004)
2.4 Demand and Supply of Microfinance Services in Malawi
The demand for microfinance services both by households and microenterprises is quite large in
Malawi and the larger part of it is still unmet despite the ever increasing number of players in the
sector. It is estimated that the sector has the potential clientele base of around 622,000; mostly
comprising of microenterprises, of which over 80 per cent are located in rural areas. The demand
for loans is highly seasonal as MSEs mostly depend on agriculture (Burritt, 2006). The supply of
microfinance services is basically a mixture of agricultural credit and small loans for the
microenterprises which account for 91 per cent of all the MSEs (USAID, 2004; GOM, 2002;
NSO, 2001). Generally, agricultural-related credit dominates the sector and is mostly issued out
as in-kind inputs of fertilizer and seed to smallholder farmers in groups (USAID, 2004).
The number of players in the microfinance sector has been increasing over time but the
government still remains the major actor; controlling a higher share of the market. By 2004,
Government owned parastatals (MRFC and MSB) controlled over 80 percent of the microsavings market and around 63 per cent of the micro-credit market. APIP, a bilateral agricultural
credit project funded by the EU accounted for about 18 per cent of the credit market (USAID,
2004). Private sector institutions and NGOs control a small share of the market and most of them
target urban and semi-urban residents with few penetrating into the remote areas of the country
(MAMN, 2008; USAID, 2004). However, between 2004 and 2008, the sector experienced a
dramatic shift in MFI market share among previous players as well as the entry of new entities
that has led to an enormous deepening and broadening of the microfinance sector (USAID,
In terms of national coverage, MRFC, MARDEF and MUSCCO are the main actors reaching out
to rural areas in almost all the 28 districts of the country (MAMN, 2008). With regard to microsavings, a few MFIs are licensed to accept savings in Malawi, thus this service is very limited.
The only formal savings institutions broadly available to rural residents are the MSB and MRFC.
These are also the major providers of microcredit used for agricultural production and non-farm
microenterprises. Additionally, credit cooperatives, such as SACCO and MUSCCO, service both
rural and urban areas and can mobilize and on-lend savings of members (USAID, 2004; Diagne
and Zeller, 2001).
2.5 Participation in Formal Microfinance Programs
Participation in formal microfinance programs in Malawi is still very low despite the increase in
the number of players in the microfinance sector. Just like in other developing countries, the
majority of households in Malawi rely on a combination of both formal and informal sources of
finance and they make trade-offs as per their convenience. It is argued that much as informal
sources tend to be better positioned in the rural areas and offer relatively flexible lending terms,
the formal sector still remains the major and stable provider of a wide variety of services to the
rural poor. Just like elsewhere, in Malawi the informal sector (especially moneylenders) charge
extremely high interest rates but the absence of formal intermediaries leaves the poor with no
choice but to access deposit, credit and transfer services from informal sources at higher costs
(USAID, 2004; Zeller, 2003).
In this study, the focus will be on households that participated in credit programs under Malawi
Rural Finance Company (MRFC) which is a good representative of the spectrum of formal
microfinance programs as it is the major provider of both credit and savings services in the rural
areas of Malawi. As such, the following section will give a brief description of the institution.
2.5.1 Malawi Rural Finance Company (MRFC)
MRFC is wholly owned by the government and is registered under the companies Act. Apart
from issuing out loans, the company is also given a special exemption by the RBM to accept
deposits from the public. It is the largest provider of both micro-savings and microcredit services
in rural areas and operates nationwide. It provides agricultural loans, group loans, individual
loans and consumer loans in cash or in-kind. Its client base is mainly composed of small and
medium scale farmers, women and employees (MAMN, 2008; Burritt, 2006; USAID, 2004).
MRFC was created with financial and technical assistance from the World Bank, to replace the
bankrupt Smallholder Agriculture Credit Administration (SACA) which was disbanded in 1993.
It begun its operation in 1994 and inherited much of SACAs infrastructure consisting of rural
extension service offices in the Extension Planning Areas (EPAs). SACA was a department of
the Ministry of Agriculture established in 1987 and was mandated to provide seasonal
agricultural loans to smallholder farmers (Diagne and Zeller, 2001). Since 1995, MRFC has
absorbed other rural financial projects such as the Promotion of Microenterprises for Rural
Women (PMERW supported by GTZ), a microcredit program targeted at women in support of
non-farm income-generating activities; and the Malawi Mudzi project, a Food Security Program
(Shimamura and Cornhiel, 2009; Diagne and Zeller, 2001). The Mudzi project is one of the
earliest microfinance programs in Malawi introduced in the late 1980s with funding from IFAD
and was based on the Grameen model (Chirwa, 1998 and Hulme, 1991 as cited by Shimamura
and Cornhiel, 2009).
The company has 7 branch offices, 23 satellite offices, and 122 field offices throughout the
country. MRFC is the major provider of agricultural credit with 80 per cent of its loan portfolio
being agricultural in nature. According to the MRFC annual report for 2004 as cited by
Shimamura and Cornhiel (2009), the companys loans for agricultural production totaled MK
1,001.7 million (US$ 9, 668, 918.9) while the loans for microenterprise activities were pegged at
MK 328.2 million (US$ 3, 167, 953.7); with an average loan size of Mk 7,252 (US$70). Burritt
(2006) observes that while MRFC accepts savings deposits, about 70 per cent of these are forced
savings to collateralize the loans.
According to the MRFC annual report for 2006 as cited by MAMN (2008), the company has
over 200,000 clients countrywide, of which the majority are smallholder farmers. Women
account for about 40 per cent of the total clients and are the major borrowers of small loans for
microenterprises while men dominate in the agricultural credit category. Agricultural loans are
provided mostly in-kind, in form of seeds and fertilizer, while microenterprise loans are
distributed in cash. After its establishment in the mid-1990s, MRFC was mainly providing
agricultural credit for the production of hybrid maize; however, of late the focus has changed as
it is mainly targeting smallholder farmers who produce cash crops such as tobacco, groundnuts
and cotton which are deemed more profitable than maize (Shimamura and Cornhiel, 2009).
MRFC provides agricultural credit in two major forms: individual credit (targeting estates) and
group lending (through clubs); mainly targeting small farmers. Basically, it is the agricultural
credit targeted at small farmers that is considered as micro-credit. In both cases, collateral, in
form of cash or other types is required. Generally, smallholder farmers without land titles are
required to belong to joint liability credit groups of 5 to 10 members for them to participate in the
program, and to assume collective liability for the total amount of loan that the group borrows
from MRFC (Shimamura and Cornhiel, 2009; Diagne and Zeller, 2001).
2.5.2 Conceptual Framework
This section presents the conceptual framework which illustrates the various factors that
influence households decision to participate in formal microfinance programs. However, before
describing the conceptual framework it is important to provide some definitions and distinctions
between terms that will be used quite often in the sections that follow. Firstly, participation in
formal microfinance programs is distinguished from access to credit. Diagne and Zeller (2001)
argue that if one is to satisfactorily analyze the socioeconomic determinants of both access to
credit and participation in formal credit programs and assess their respective impacts on
household welfare outcomes, then there is a need to make a clear distinction between the two
terms though quite often the two terms are used interchangeably. Basically, a household is said
to be participating in credit programs when it is borrowing from that source of credit while it is
said to have access to a particular source of credit if it is able to borrow from that source but due
to other reasons the household may choose not to. The extent of access to credit is measured by
the maximum amount a household can borrow (its credit limit). If this amount is positive then
the household is said to have access. A second important term is credit constraint and a
household is said to be credit constrained when it lacks access to credit or cannot borrow as
much as it wants (Diagne and Zeller, 2001).
Figure 2.3 presents the conceptual framework for the study. All the factors hypothesized to be
determinants of participation in formal microfinance programs have been grouped into three
major categories and these include: socio-economic factors, social capital and micro-credit
related factors. Socio-economic factors have been further sub-divided into household
demographic characteristics and wealth status indicators. The major socio-economic factors that
influence credit constraint condition and households decision to participate in formal
microfinance programs include: age, gender, marital status, main occupation, education, family
size, number of household member active in the labour force, dependency ratio, value of
household assets, income and farm size. On the other hand, interest rates charged, loan
repayment period, availability of collateral, residing in program area and contact with extension
agent are the major micro-credit related factors influencing participation while membership to
local institutions is the only factor under social capital that affect both participation and credit
constraint condition. The study further wanted to establish if credit constraint condition
influences household decisions to participate in formal microfinance programs. These factors are
explained in chapter 3 when motivating the independent variables of the empirical models.
Socio-economic Factors
Demographic Characteristics
-Age of head (IV)a
-Gender of head
-Education of head
-Main occupation of head
-Marital status of head
-Spouse education
-Household size
-Number of household
members employed
-Dependency ratio
Micro-credit Related Factors
Membership to local
Credit constraint
Contact with
extension agent
Formal Microfinance
Availability of
Wealth Status Indicators
-Value of household assets
Living in a
-Landholding size (IV)
-Household income
Figure 2.4: Conceptual Framework (Source: Author)
Note: IV refers to instrumental variables
Chapter 3: Methodology
3.1 Facts and Figures about Malawi
3.1.1 Geographic and Climatic Information
Malawi is a land-locked country situated in the south-eastern part of Africa. The country is
bordered by the United Republic of Tanzania to the north and northeast, Mozambique to the east,
south and southwest, and Zambia to the west (Figure 3.1). It covers about 118,484 km 2 with a
total length of about 900 km and a maximum width of about 250 km. About 20 per cent of the
total land area is covered by surface water bodies while 34 per cent is arable land. Permanent
pastures, forests and woodlands account for 20 and 39 per cent of the total land area,
respectively. The countrys topography is characterized by extremely diverse physical features. It
is divided into four major ecological zones and these include; the highlands of Mulanje, Zomba
and Dedza in the southern and central parts of the country; the plateau of the central and northern
regions; the rift valley escarpment; and the rift valley plains along the lakeshores of Lake
Malawi, the Upper Shire and Lower Shire Valleys (GOM, 1994).
The country has a sub-tropical climate, which is relatively dry and strongly seasonal. The warmwet season stretches from November to April, during which 95 per cent of the annual
precipitation takes place. Annual average rainfall varies from 725mm to 2,500mm. A cool, dry
winter season is evident from May to August with mean temperatures varying between 17 and 27
degrees Celsius, with temperatures falling between 4 and 10 degrees Celsius. A hot, dry season
lasts from September to October with average temperatures varying between 25 and 37 degrees
Celsius (GOM, 2006b).
Figure 3.1: Map of Malawi (Source: http://www.unmalawi.org/images/malawi-map.gif)
3.1.2 General Economic Indicators
Malawi has a population of about 13.1 million people of which 84.7 per cent live in rural areas
and are engaged in agriculture (NSO, 2009; GOM, 2007). About 52.4 per cent of the population
lives below the poverty line with 22.4 per cent being extremely poor (NSO, 2005). Population
growth has stagnated at around 3 per cent since independence while the GDP per capita is still
among the lowest in the world and is currently at 250 US dollar (World Bank, 2009).
The countrys economy is predominantly based on agriculture, which accounts for 39 per cent of
GDP; 80 per cent of foreign exchange earnings and employs nearly 80 per cent of the population.
The sector contributes about 63.7 per cent of total income for the rural population and 65 per
cent of raw materials for the manufacturing sector (GOM, 2007). Despite its contribution to the
GDP being lower than the services sector, which accounts for 46 per cent of GDP, agriculture
still remains the major driver of Malawis economy. The performance of other sectors,
particularly the industrial sector, which contribute only about 20 per cent of GDP, is heavily
dependent on agriculture which is the major supplier of its raw materials. Tobacco, tea and sugar
are the major export commodities. Other export crops include; cotton, coffee, peanuts, wood
products, pigeon pea, sunflower, chilli, macadamia and rice (World Bank, 2009; GOM, 2007;
Ngongola, 1996).
Malawis agricultural sector is characterized by a dualistic structure which includes the
smallholder sub-sector and the estate sub-sector. The smallholder sub-sector accounts for more
than 70 per cent of GDP compared to the estate sub-sector which contributes less than 30 per
cent (GOM, 2007). The estate sub-sector contributes only about 20 per cent of total national
agricultural production but accounts for over 80 per cent of total agricultural exports mainly from
tobacco, sugar and tea and to a lesser extent from coffee and macadamia nuts. On the other hand,
maize, tobacco, cassava, groundnuts, pulses, sweet potatoes, cotton, sorghum and millet are the
major crops grown by smallholder farmers (GOM, 2007; Mangisoni et al., 2007; Chirwa et al.,
The smallholder sub-sector is characterized by small and fragmented land holdings, low
productivity, low level of input and technology use, low capital investments and high
dependency on rain-fed agriculture. Over 70 per cent of the smallholder land is taken up by
maize which is the main staple grain and the sub-sector accounts for 80 per cent of Malawi's total
food production and 10 per cent of exports (GOM, 2007; Mangisoni et al., 2007; Chirwa et al.,
2006). Table 3.1 provides a summary of the changes in the economic indicators between 1963
Table 3.1: Changes in Selected Economic Indicators (period averages): 1963- 2008
GDP growth (average, %)
Services, etc., value added (% of GDP)
GDP/Capita (US$)
Population growth (%)
Source: World Bank (2009)
3.2 Study Area
The study targeted rural households who participated in MRFC programs in Kasungu district in
the central region of Malawi3. Kasungu was singled out mainly because it is regarded as one of
the major agricultural districts in Malawi with a high concentration of both small and large-scale
farmers. Just like other districts, Kasungu also has a heterogeneous collection of non-farm
activities that are directly or indirectly influenced by the agricultural sector. Most of the Micro
and Small Enterprises (MSEs) operating in the district are financed by both formal and informal
sources of credit. Comparatively, Kasungu has a vibrant rural non-farm economy which is
heavily supported by its strong agricultural base. According to the Integrated Household Survey
2004/05, Kasungu district recorded the highest mean annual household agricultural and nonagricultural incomes at national level of MK 65,679 (US$534) and MK 68,367 (US$556) 4 ,
respectively (NSO, 2005).
Furthermore, Kasungu district registered the highest proportion of households (64 per cent)
engaged in tobacco production (the countrys major cash crop) and about 67.1 per cent of the
rural households which participated in formal microfinance programs indicated that they
obtained loans mainly for the purchase of tobacco inputs; the major cash crop supported by
MRFC. At the time of the study, a number of MFIs operated in the district mainly providing
micro-credit to rural households but MRFC was the major provider of both micro-credit and
micro-savings services followed by SACCOs, banks and NGOs in that order (NSO, 2005). It is
Administratively, Malawi is divided into three regions namely: northern, central and southern regions. The
northern region is composed of six districts while the central and southern regions have 9 and 13 districts,
In 2004, 1 US$ was equivalent to 123 Malawi Kwacha.
under this motivation that this study wanted to look at the determinants of rural households
participation in formal microfinance programs provided by MRFC in the district. Figure 3.2
below situates Kasungu district on the map of Malawi.
Kasungu District
Figure 3.2: Map of Malawi Locating Kasungu District
(Source: http://www.unmalawi.org/images/malawi-map.gif)
3.3 Data for the Study
The study made use of secondary data on credit and loans collected by the National Statistical
Office (NSO) under the World Bank funded Integrated Household Surveys programme which
was conducted between March 2004 and April 2005. The aim of the second Integrated
Household Survey (IHS2) was to provide up to date information for the monitoring of the
countrys poverty reduction strategy (MPRS) and related development goals such as Millennium
Development Goals (MDGs). The survey also provided information for setting benchmarks for
the countrys new development strategy; the Malawi Growth and Development Strategy
(MGDS) (NSO, 2005).
The IHS 2 was a comprehensive survey conducted in all the 27 districts of Malawi with a total
sample size of 11,280 households which were drawn using a two-stage stratified sampling
procedure from a sample frame using the 1998 Population and Housing Census enumeration
areas. Each of the twenty-seven districts was considered as a separate sub-stratum of the main
rural stratum while the urban stratum included the four major urban areas of Lilongwe, Blantyre,
Mzuzu, and Zomba. The primary sampling units (PSU) were the Enumeration areas. These were
selected for each stratum on the basis of probability proportional to size (PPS). The second stage
involved randomly selecting 20 households in each EA. Every listed household in an EA had an
equal chance of being selected to be enumerated (NSO, 2005).
The survey had two main questionnaires which were the household and community
questionnaires. The household questionnaire captured data on household demographic and socioeconomic characteristics while the community questionnaire was designed to collect information
that was common to all households in a given area such as basic physical and demographic
characteristics of the community; access to basic services; economic activities; agriculture; how
conditions have changed over the last five years, etc. (NSO, 2005). For more information on the
data, sampling procedure, coverage and survey questionnaires visit the National Statistical Office
(NSO) website (www.nso.malawi.net).
A total of 480 households were sampled from Kasungu district of which 143 participated in various
formal credit programs while the rest did not attempt to borrow from any formal credit sources. The study
only targeted 96 households that borrowed from MRFC (participants) and 70 households which did not
borrow from any formal MFI (non-participants).
3.4 Data Analysis
The data for the study were analysed using the Statistical Package for Social Sciences (SPSS)
and STATA. SPSS was used to analyse some descriptive statistics such as frequencies,
descriptives and cross-tabulations for household demographic and socio-economic characteristics
as well as the independent samples t-tests and ANOVA tests to draw a comparison on various
characteristics between participants and non-participants; credit constrained and unconstrained
households as per objective 1 of the study. STATA was used to carry out the probit regression
analysis as per objectives 2, 3 and 4 of the study.
3.5 Analytical Model
3.5.1 Theoretical Description of Credit Constraint
A credit constraint condition is hypothesized to play a crucial role in households decisions to
participate in credit programs (Omonona et al., 2008; Diagne and Zeller, 2001). Credit constraint
refers to a situation where a household lacks access to credit or cannot borrow as much as it
wants. Theoretically, according to Simtowe et al. (2009), credit constraint is defined as follows:
let C be the households actual consumption of some amount of goods in a given period of time
and C* represents the optimal consumption in the absence of credit constraints (cc). If C* C,
then the credit constraint is not binding; and if C* > C, then the credit constraint is binding.
The gap (H*) between optimal consumption (C*) and the actual consumption (C) measures the
existence or not of a credit constraint. It is assumed that the consumption gap is defined as H* =
C C*. H* can take both negative and positive values. A household is said to have a binding
credit constraint if H* < 0 and if H* 0 then the credit constraint is not binding. In case of this
study, as illustrated by equation 1 below, the variable credit constraint (cc) assumed a value of 1
if a household had a binding credit constraint and 0 when otherwise:
Where H* = C C*
However, it should be noted that practically it is complicated and very subjective to measure a
households credit constraint condition using the theoretical approach given above. Nonetheless,
some empirical studies have employed a relatively simpler method of capturing this variable (see
for example Simtowe et al., 2009). In this method, credit constraint is not directly observed but is
constructed from the responses given by households. Households are asked if they attempted to
get a loan from formal MFIs. If the respondents did not make any attempt, then they are asked to
give reasons, which in the case of this study were as follows: 1) No need for loan; 2) Believe
would be refused; 3) Too expensive; 4) Too much trouble for what it is worth; 5) Inadequate
collateral; 6) Do not like to be in debt; 7) Do not know any lender; and 8) Others. Households
which indicate responses 2 to 7 as reasons for not trying to get a loan from formal sources are
categorized as discouraged borrowers and are put together with households which did not get as
much credit as they wanted. These households altogether are classified as credit constrained
(Simtowe et al., 2009).
3.5.2 Empirical Model Specification
To establish the influence of credit constraint and other factors on households decisions to
participate in MRFC programs, a probit regression model was used (Gujarati, 2003). However,
to control for omitted variable bias and endogeneity, a two-step instrumental variables approach
was used. The exogenous variables- age and land size were used as instrumental variables for
credit constraint. Age and land size were found to be highly correlated with credit constraint but
not participation. Thus, land as a production factor and age as an indicator of access to physical
assets such as land and as a proxy for experience age are plausible instruments for credit
constraint especially under MFIs that provide loans that are primarily agricultural in nature.
Arguably, land size and age influence the households credit constraint condition but not
participation in MRFC programs directly.
Landless or near landless households stand a lower chance of accessing in-kind loans from
agricultural credit schemes as their binding land constraint indirectly prevents them from doing
so thereby making them rely on unattractive off-farm and non-farm activities. Further, in rural
communities, age of household head has an influence on both the stability of household income
sources and access to land with households headed by younger people being strongly and
negatively affected by both. The instrumental variable method applied in this study tries to
ascertain that the relationship between credit constraint, age of household head and land size is
not just a simple correlation, but rather a causal relationship.
The study used a two-stage equation probit estimation method (Kono and Takahashi, 2009;
Acemoglu et at., 2001). In the first stage equation, the dependent variable, credit constraint (cci)
takes the value of 1 if a households credit constraint is binding and 0 when otherwise. X is a
vector of exogenous variables that influence a households credit constraint condition (see Table
3.2) while age (Ai) and land size (Li) are instrumental variables for credit constraint. 0 is a
constant while 1, 2 and 3 are the coefficients and e is a random disturbance term (Gujarati,
The second-stage equation estimates the influence of credit constraint on households decisions
to participate in formal microfinance programs. The decision for ith household to participate in
formal microfinance programs (Participationi) is used as a binary variable taking the value of 1 if
the household participated and 0 when otherwise. X is a vector of explanatory variables related
to participation while cc is the credit constraint. is a constant while i and i are coefficients
for Xi and cci, respectively. e is a random disturbance term. The probit model was estimated
using the Maximum-Likelihood (ML) method of estimation in Stata.
3.5.3 Variables and Hypothesized Effects
The explanatory variables have been categorised into two broad groups which are socioeconomic characteristics and micro-credit related factors. The socio-economic characteristics
have been further sub-divided into household demographics (age, gender, education, marital
status, household size, dependency ratio and household members active in labour force); and
wealth status indicators (non- consumable household assets5 and livestock value). Micro-credit
related factors include contact with the extension agent on issues of access to credit and distance
to the nearest trading centre/boma6.
Household demographics (gender, education, marital status, household size, dependency ratio
and household members active in labour force) normally influence household decisions to
participate in formal microfinance programs (see for example: Hoque and Itohara, 2009;
Non-consumption durables refer to the value of all other household assets excluding land and livestock. Examples
include: house, car, bed, table, radio, television, video cassette recorder, bicycle, ox-cart, hoes, axes, sickle and beer
brewing (NSO, 2005).
6 Boma refers to a major administrative or commercial centre of a district.
Shimamura and Cornhiel, 2009; Omonona et al., 2008; Diagne and Zeller, 2001; Morduch, 1999;
Wydick, 1999). For instance, household heads with more years of schooling are more likely to
participate in formal microfinance programs as they have the ability to clearly understand the
requirements and the processes involved in loan transactions compared to those with fewer years
of schooling (Hoque and Itohara, 2009; Diagne and Zeller, 2001).
Wealth status indicators (non- consumable household assets and livestock value) play a crucial
role for households to decide whether to borrow from formal sources or not. Generally,
households with a higher value of assets are more likely to be less credit constrained hence
reducing their probability of participating in formal microfinance programs (Simtowe, et al.,
2009; Diagne and Zeller, 2001).
Micro-credit related factors (contact with extension agent and travel time) are the other variables
that determine a households participation in formal microfinance programs. Households which
had an opportunity to receive advice on access to credit from an extension agent are more likely
to participate than those that did not. On the other hand, households which live closer to main
trading centres (less travel time) have better access to information on various credit source
thereby increasing their probability of participating in formal programs (see for example:
Omonona et al., 2008; Diagne and Zeller, 2001).
Table 3.2 below provides a summary of the explanatory variables and their expected signs as per
the findings of other empirical studies.
Table 3.2: Description of the Empirical Model Variables
Variable description
Hypothesized
Socio-economic Variables
Household Demographics
(1=male,
0=female)
Household head marital status (1=married)
Years of schooling of household head and
Household labour7
Household members active in labour force
Dependency ratio8
Dependency ratio
Value of household assets (non-consumables
Household assets
and livestock value)
Contact with extension Binary
extension agent on access to credit, 0
Standardized travel time to nearest trading
center/boma in minutes
Instrumental variables
Age of household head (years)
Land holding size in hectares
Household labour measures the number of household members who are employed (active in the labour force) as
per the International Labour Organisation (ILO) definition.
Dependency ratio refers to the proportion of young people under the age of 15 and the elderly above 64 years of
age to the economically productive population aged between 15 to 64 (Todaro and Smith, 2009).
Chapter 4: Descriptive Analysis of the Socio-economic Characteristics
According to Participation and Credit Constraint Status
This chapter discusses the descriptive analysis of household characteristics according to
participation and credit constraint status of respondents. The chapter begins by giving a general
overview of the socio-economic characteristics of the respondents. The second part discusses the
loan characteristics of the households while the third section focuses on the comparison of socioeconomic and micro-credit related characteristics between participants and non-participants. The
section also looks at the relationship between main occupation and household wealth status
indicators. Finally, the fourth part draws a comparison between credit constrained and
unconstrained households with respect to household socio-economic and micro-credit related
characteristics.
4.1 General Socio-Economic Characteristics of the Respondents
Figure 4.1 shows the distribution of respondents by gender of household head. Of the 166
households (participants and non-participants), about 143 were male-headed while only 23 were
female-headed representing 86 per cent and 14 per cent, respectively. Considering that the
selected district is one of the major producers of tobacco, a cash crop mainly targeted by the MFI
understudy, the gender distribution is not a big surprise. This reflects the dominance of men in
the production of cash crops particularly tobacco which is the major cash earner for smallholder
farmers.
Figure 4.1: Distribution of Respondents According to Gender (n=166)
(Source: IHS2 survey data)
Table 4.1 presents the distribution of respondents according to the age of the household head.
The majority of the respondents (50 per cent) were within the age range between 25 and 39 years
followed by the age range of 40 to 55 years which accounted for 29.5 per cent. Household heads
aged over 55 accounted for 14.5 per cent while those with less than 25 years accounted for only
6 per cent. The average age for all the respondents was found to be 40.16 years with a standard
deviation of 13.9 years. This shows that the majority of the respondents are still within the
economically active age group.
Table 4.1: Distribution of Respondents According to Age
Source: IHS2 survey data
Table 4.2 shows the distribution of respondents by education status of household head. The
majority of household heads (62.7 per cent) at least attended some formal education up to
primary school compared to 14.5 per cent who have not done any formal schooling. Household
heads with at least secondary and tertiary education accounted for 20.5 per cent and 2.4 per cent,
respectively. The relatively lower education background of most household heads is likely to
affect their understanding of the processes involved in the loan transactions. This might result
into too many discouraged borrowers as most of them might look at the loan transaction
processes as too much trouble for what the loan is worth considering that the loans given to them
by MFIs are usually very small. The low level of schooling may also have a bearing on the use of
the actual loans when obtained.
On the other hand, the lower education level of most household heads reflects the situation
prevalent in most developing countries where individuals with relatively higher education level
(secondary school and above) migrate to urban and peri-urban areas seeking better opportunities
in the urban formal and informal sectors (Todaro and Smith, 2009). In Malawi, the poor
performance of the agricultural sector coupled with the underdeveloped nature of the rural non43
farm economy make the rural areas very unattractive for the educated few thereby promoting
rural-urban migration (GOM, 2006a).
Table 4.2: Distribution of Respondents According to Educational Status
No formal Education
Table 4.3 presents the distribution of respondents according to the main occupation of the
household head. The results show that a larger proportion of the respondents (88 per cent) rely
on farming as their primary occupation while only 6 per cent reported wage employment as their
major source of livelihood. Household heads employed as labourers within and outside
agriculture accounted for 1.2 per cent each. On the other hand, only 3.6 per cent of the household
heads were engaged in trade or other forms of self-employment. This is particularly so owing to
the fact that the majority of rural households mainly take agriculture as their major source of
livelihood but they just get involved in non-farm activities during the off-season period when
their labour is idle.
Table 4.3: Distribution of Respondents According to Main Occupation
Per cent
Wage employment
Trade/self-employment
Labour (within agriculture)
Labour (outside agriculture)
Figure 4.2 summarizes the distribution of household income by source mainly categorized into
five groups which include: incomes from tobacco; other agricultural activities (crops, fruits and
trees); livestock; remittances; and salary, wages, pension and rentals. The results show that
agriculture is the major source of livelihood for the respondent households as about 78 per cent
of their incomes come from agriculture. Of these, tobacco alone accounted for 33 per cent while
other agricultural activities and livestock contributed 35 per cent and 10 per cent, respectively.
This underscores the significance of agriculture in Malawi not only in supporting rural
livelihoods but also its potential contribution to poverty alleviation. On the other hand, salary,
wages, pension and rental incomes accounted for 19 per cent of total household income whereas
remittances contributed only 3 per cent.
Other agricultural
incomes,
Livestock income,
Salary,wages,pens
ion & rental
income,
Remittances,
Tobacco income,
Figure 4.2: Distribution of Household Income According to Source (n=166)
Figure 4.3 shows the distribution of respondents according to their participation status. About 58
per cent of the respondents indicated to have participated in formal microfinance programs under
MRFC in the last 12 months period prior to the survey while 42 per cent reported not to have
participated in any formal microfinance programs.
Non-participants,
Figure 4.3: Distribution of Respondents According to Participation Status (n=166)
Figure 4.4 presents the distribution of household heads participation status differentiated
according to their credit constraint status. The results show that of the total 166 respondents
(borrowers and non-borrowers), 77.7 per cent reported to have a binding credit constraint
condition while the remaining 22.3 per cent were found not to be credit constrained. With regard
to participation status, participants reported a higher proportion of credit constrained households
(80.2 per cent) compared to non-participants (74.3 per cent). This suggests that most of the
households that participated in credit programs under MRFC were not able to obtain as much
credit as they had wished to satisfy their demand.
Non-participants
Credit constrained
Credit Unconstrained
Status (n=166) (Source: IHS2 survey data)
4.2 Household Loan Characteristics
4.2.1 Loan Size and Repayment Period
Table 4.4 presents a summary of the amount borrowed, paid back and the repayment period. The
results show that the average size of the loan obtained was MK 23,104 (US$188) with a
minimum amount of Mk 1,885 (US$15) and a maximum amount of MK 200,000 (US$1,626).
This suggests that the size of the loans obtained by most participants were too small to have any
long term impact on income levels. With regard to loans from the previous cycle, the results
show that an average amount of MK 29,735 (US$242) was repaid back with a mean repayment
period of 7.3 months.
Table 4.4: Loan Size and Repayment Period (n=96)
Amount borrowed (MK)
Amount repaid back (MK)a
Repayment period (Months)b
Note: a and b represent amount repaid back and repayment period for loans from previous cycle.
Figure 4.5 below shows the density function of the distribution of borrowers according to loan
size sub-groups. The density function shows a high concentration of borrowers on the loan size
within the range of MK 5,000 (US$41) to MK 10,000 (US$82) with about 77 per cent of the
participants obtaining loan sizes below the average (US$188). Overall, the results show that 83
per cent of the borrowers obtained loans of less than MK 30,000 (US$244) while only 16
individuals obtained loans greater than MK 30,000.
Number of borrowers
Loan size (MK)
Figure 4.5: Distribution of Borrowers According to Loan Size (n=96) (Source: IHS2 survey data)
Table 4.5 presents the Pearson correlation results between loan size and some selected household
characteristics. The results show a positive and significant correlation between loan size and land
size (P-value =0.06). This suggests that the size of the loan increases with an increase in farm
size. However, it should be noted that despite land size being correlated with loan size, it does
not influence household decisions to participate in MRFC credit programs. The results also show
a positive and significant relationship between loan size and education of household head (Pvalue = 0.02) though the correlation is relatively weaker. This indicates that borrowers with
relatively more years of schooling are more likely to obtain a larger loan amount that their
counterparts with lower education.
Table 4.5: Pearson Correlation Results for Loan Size and Household Characteristics (n = 96)
Loan size
Education of head Education of spouse
Land size (hectares)
Education of head
Education of spouse
0.22**
Note: *, ** significant at 90% and 95% confidence levels, respectively
4.2.2 Reasons for Obtaining a Formal Loan
Figure 4.6 summarizes the major reasons participants indicated as the purpose of obtaining the
loan from MRFC. About 61 per cent of the households reported to have participated in MRFC
credit programs to obtain a loan for purchasing tobacco inputs. On the contrary, only around 10
per cent of the participants indicated that they used the loan to purchase inputs for other cash
crops such as paprika or hybrid maize for commercial purposes. The lower percentage of
respondents who indicated that they used the loan to purchase inputs for food crops (5 per cent)
is a clear reflection of a change in focus of MRFCs credit programs over time which initially
mainly targeted smallholder farmers producing hybrid maize, the major staple crop but now
focuses on cash crop production (Shimamura and Cornhiel, 2009).
Similarly, the lower
percentage of participants who reported to have used the loan as start-up capital for businesses
(15 per cent) and for the purchase of non-farm inputs (9 per cent) justifies MRFCs lower
emphasis in non-farm activities.
Puchase non-farm
inputs,
capital,
Puchase
inputs for
other cash
crops,
agricultural inputs
for food crops,
Purchase inputs
for tobacco,
Figure 4.6: Reasons for obtaining a Formal Loan (Source: IHS2 survey data)
4.2.3 Group Membership and Loan Purpose
The results show that 84 per cent of the participants belonged to groups while 16 per cent took
individual loans. As shown by Figure 4.7 below, the majority of those who belonged to groups
obtained the loans to purchase tobacco inputs (69 per cent) followed by 14 per cent for business
start-up capital. Group members who took loans for purchasing inputs for other cash and food
crops accounted for 8 and 6 per cent, respectively. Only 3 per cent of group-based loans were
used to buy non-farm inputs. On the other hand, the majority took individual loans to start-up
small businesses (56 per cent) while 28 per cent took the loans to purchase non-farm inputs. The
remaining (17 per cent) were middle-scale farmers who obtained the loans to purchase tobacco
inputs for their estates.
The higher proportion of group-based loans suggests that MRFC utilizes social capital in rural
communities to circumvent the principal-agent problem. As argued under group lending theory,
this helps to address problems of adverse selection, moral hazard and enforcement. By
implementing group-based credit programs, MRFC is able to lend to the rural poor who do not
have physical collateral but use joint liability contracts and peer pressure as substitutes for
collateral. This helps to reduce cases of default. However, due to data limitations this study was
not able to fully examine the success of joint liability contracts in MRFC credit programs.
Purchase non-farm inputss
Business start-up capital
Purchase inputs for other crops
Purchase inputs for food crops
Purchase tobacco inputs
Belonged to a group
Did not belong to a
Figure 4.7: Group membership and Loan Purpose (n=96) (Source: IHS2 survey data)
Table 4.6 presents the ANOVA results of the effect of the purpose of obtaining a loan on the
volume of the loan itself. The results show that there are no marked differences in terms of the
effect of the purpose of the loan on the loan size. However, in volume terms, the loan obtained
for the purchase of tobacco inputs has a relatively higher contribution (MK 27,120) on the
average loan size followed by a loan obtained for the purchase of inputs for food crops (MK
23,200)9. Relatively smaller loans are obtained for the purchase of inputs for other cash crops
other than tobacco (MK 11,090).
Table 4.6: Effect of Loan Purpose on Loan Size
(MK)
non-farm
(53,14.96)
(27,959.35) (33,609.55)
In terms of micro-savings, the IHS2 survey data did not capture the extent to which rural
households use MRFC savings facilities. However, some empirical studies have shown that
while MRFC accepts savings deposits, about 70 per cent of these are forced savings to
collateralize the loans. This means that voluntary savings by participants represent a small
proportion of the total savings that MFI registers (Shimamura and Cornhiel, 2009; Burritt, 2006).
4.2.4 Reasons for not attempting to Borrow from Formal Sources
Table 4.7 summarizes the reasons non-participants indicated for not obtaining any formal loan.
About 25.7 per cent of the non-participants indicated that there was no need for them to obtain a
loan while 22.9 per cent complained about the exorbitant interest rates charged by MFIs as the
hindering factor for them to access the loans. Slightly over 17 per cent of them indicated that
they do not have any information about MFIs operating in the area while those who reported the
fear of being refused by MFIs as the reason for not attempting to borrow from formal sources
accounted for 12.9 per cent. Likewise, around 13 per cent indicated that the loan transaction
MK stand for Malawi Kwacha: In 2004, 1 US$ was equivalent to 123 Malawi Kwacha
processes are too involving and the troubles encountered are not worthy considering the small
sizes of the loans involved. About 7 per cent reported lack of collateral as the limiting factor
while only 1.4 per cent indicated not to have attempted to borrow from formal sources because
of the dislike of being in debt.
Table 4.7: Reasons for not obtaining a Formal Loan
No need for a loan
Loan too expensive
Do not know any lender
Believe would be refused
Too much trouble for what it is worth
Inadequate collateral
Do not like to be in debt
4.3 Household Characteristics According to Participation Status
This section presents the results of the comparisons between participants and non-participants
with respect to socio-economic and micro-credit related characteristics. As mentioned above, the
socio-economic characteristics have been sub-divided into two categories which include:
household demographic characteristics and wealth status indicators. Household demographic
characteristics include age, gender, education level, marital status, main occupation, education
level of spouse, household size, number of household members active in the labour force and
dependency ratio. On the other hand, wealth status indicators capture variables such as value of
household assets, land size, income and expenditure. Contact with extension agent on issues of
credit and travel time to the nearest trading centre/boma are the only variables under micro-credit
related factors. The results for all the variables under the two categories are presented in Table
4.8 below.
4.3.1 Household Demographic Characteristics
The results show that 89.6 per cent of the participating households were male headed compared
to 81.4 per cent for non-participants though statistically the two are not different. Of particular
interest is the low representation of women in the group of participants who account for only
10.4 per cent. This could be attributed to the fact that since MRFC mainly targeted tobacco
farmers in the area and tobacco being a major cash crop in most rural households; it is very
unlikely for women to participate in such credit programs as tobacco is mostly dominated by
men while women are mainly given the responsibility to manage food crops.
Further, the relatively lower level of access to and control over land by women compared to men
due to cultural and traditional beliefs affects their participation in cash crop production. This is in
line with the MRFC annual report for 2006 as cited by Shimamura and Cornhiel (2009), which
states that women account for about 40 per cent of the total clients but are mainly the major
borrowers of small loans for microenterprises while men dominate in the agricultural credit
The average age for participants (40.5 years) was found to be relatively larger than for nonparticipants (39.7 years) but statistically the difference is insignificant. Participants have a
significantly (P-value = 0.004) larger household size (6.4 persons) compared to non-participants
(5.2 persons). The results further show that the years of schooling of household heads are
statistically different (P-value = 0.027) between participating households (7.2 years) and nonparticipants (5.7 years). Nonetheless, the education levels of spouses are statistically not different
though spouses for participants have a relatively higher education level (5.6 years) than nonparticipants (5 years).
The results show no marked differences in terms of main occupation between participants and
non-participants. About 90.6 per cent of the participants reported farming as their primary
occupation compared to 84.3 per cent for non-participants. None of the household heads
employed as labourers within and outside agriculture reported to be participating in MRFC credit
programs while the two accounted for 5.7 per cent and 2.8 per cent of non-participating
households, respectively. Household heads who reported wage employment as the major source
of livelihood accounted for 7.3 per cent of participants compared to 2.1 per cent for those in
trade or self-employment.
Similarly, there were no significant differences in terms of marital status between participating
and non-participating households. About 72.9 per cent and 20.8 per cent of the participating
household heads were married monogamous and married polygamous, respectively compared to
62.9 per cent and 24.3 per cent for non-participants. Likewise, household heads in the divorced
and widowed or widower category accounted for 2.1 per cent and 4.2 per cent for participants
and 4.3 per cent and 8.6 per cent for non-participants, correspondingly.
There were also no marked differences in terms of the average number of household members
employed between participants and non-participants. However, participating households have a
comparatively larger number of members active in the labour force (3.6 persons) compared to
non-participants (3.2 persons). Likewise, despite participating households registering a relatively
larger dependency ratio (1.3) compared to non-participants (1.1), statistically the difference is
insignificant.
4.3.2 Wealth Status Indicators
In terms of wealth status indicators, results show marked statistical differences between
participating and non-participating households with respect to total value of household assets,
value of assets held as livestock, expenditure and income. This might partly reflect the impact of
the loans on these indicators in the participating households. On the other hand, statistically,
there are no differences in terms of land size and household non-consumable assets (excluding
land and livestock).
In terms of household assets, the results suggest that participants are wealthier than nonparticipants. Participants reported a significantly (P-value = 0.045) higher value of total
household assets (MK 42,263) than non-participants (MK 23,927). Similarly, participants
reported a markedly (P-value = 0.021) higher values of household assets held as livestock (MK
27,558) compared to non-participants (MK 12,581). Further, participants recorded a significantly
higher household expenditure (MK 146,162) and income (MK 79,452) compared to nonparticipants who reported an average expenditure of Mk 101,873 and income of Mk 38,310 (Pvalues of 0.000 and 0.061, respectively).
With regard to the insignificant variables under this category, results still show that participants
have a comparatively larger land holding size (2.5 hectares) than non-participants (2.1 hectares).
This was expected as households with larger farm sizes have the flexibility to venture into cash
crop production particularly tobacco which might not be the case with those with small farm
sizes. Additionally, one of the conditionality of accessing agricultural credit from MRFC is to
provide assurance about the availability of land to be used for the production of the selected
crops. Participants also reported a relatively higher value of non-consumable assets (MK 14,116)
than non-participants (MK 10,747)
4.3.3 Micro-credit Related Factors
With regard to contact with extension agents on issues of access to credit, participants reported a
significantly (P-value = 0.04) higher proportion (34.4 per cent) than non-participants (20 per
cent). This suggests that participation in formal microfinance programs is significantly associated
with getting in contact with extension agents. Extension agents make households aware of the
various sources of formal credit available to them as well as getting advice on which MFIs offer
relatively better services. The results further show that participants reported a significantly lower
(P-value = 0.006) travel time (53.7 minutes) to the nearest trading centre than non-participants
(71.2 minutes). This suggests that individuals who stay far away from trading centres/bomas are
less likely to obtain loans from MRFC. This is mainly because such households do not have easy
access to information about the various services offered by MRFC.
Table 4.8: Household Characteristics According to Participation Status
Continuous Variables
t-valuea
Household Demographic Characteristics
Age of head
-2.23**
-2.95***
Household members
(61,983.9)
(152,778.5)
Total value of household
assetsb
Value of household assetsc MK
Value of assets held as
Total annual household
Travel time to nearest
trading centre/boma
Contact with Extension
% yes
-1.89*
2.795***
Chi-square
Categorical Variables
Gender of head (1=male, 0
otherwise)
Main occupation of head
Labour (within
agriculture)
Labour (outside
Marital Status of head
Married (monogamous)
Married (polygamous)
Widowed or Widower
Source: Own calculations from IHS 2 survey data
Note: Figures in parenthesis are standard deviations.
*, **, ***: Significant at 90%, 95% and 99% confidence levels, respectively.
Value of household assets ( non-consumables, livestock and land)
Value of household non-consumables (excluding livestock and land)
4.4 Effect of Main Occupation on Household Wealth Status
Table 4.9 presents the results of the potential effect of the main occupation on household wealth
status. The ANOVA results show that there are no noticeable differences in terms of the effect of
the different categories of main occupation on wealth status indicators. Nevertheless, farming has
the highest influence on all household wealth status indicators followed by wage employment.
On the other hand, labour from outside agriculture has the least contribution reflecting the
underdeveloped nature of the rural non-farm labour markets which offer relatively lower wages
for unskilled labour.
Table 4.9: Effect of Main Occupation on Wealth Status Indicators
Total value of
(MK)a
Value of household
assetsb (MK)
Value of assets held
as livestock (MK)
Total annual
expenditure (MK)
Annual household
income (MK)
(outside
Trade/Selfemployment
Fstat
(within
MK refer to Malawi Kwacha: In 2004, 1 US$ was equivalent to 123 Malawi Kwacha
4.5 Household Characteristics According to Credit Constraint Status
This section presents socio-economic and micro-credit related characteristics divided according
to the credit constraint status. Just as in section 4.3 above, in this section all the variables have
been categorized into 3 groups which include: household demographic characteristics, wealth
status indicators and micro-credit related factors. The results are presented in table 4.10 below.
The results show no marked differences between constrained and unconstrained households for
all the variables under this category as reflected by the t-values and chi-square values.
Surprisingly, the average age for unconstrained households is relatively larger (43.1 years) than
for constrained households (39.3 years). This is contrary to what was hypothesized earlier on that
older individual are more likely to be credit constrained than younger ones. In terms of
education, unconstrained households reported a relatively higher education level for both the
head (7.2 years) and the spouse (5.4 years) compared to 6.3 years (head) and 5.3 years (spouse)
for constrained households. Expectedly, constrained households reported comparatively higher
household size (6.0 years) and dependency ratio (1.2) compared to a household size of 5.4
persons and a dependency ratio of 1.1 for unconstrained households.
Unconstrained and constrained households reported almost the same proportion of household
heads that rely on farming as their primary occupation (89.2 and 87.6 per cent, respectively). In
terms of marital status, about 72.1 per cent of constrained household heads are married
(monogamous) compared to 56.8 per cent for unconstrained households. The proportion of
household heads in the divorced and widowed or widower categories are almost the same for the
two groups.
The results show that unconstrained households have a significantly higher income, expenditure
and land size than constrained households. Nonetheless, there were no marked differences in
terms of household assets between the two groups. Unconstrained households reported a
significantly higher income (MK 118,450) and expenditure (MK 160,107) than constrained
households that reported an averaged income of MK 45,941 and an annual expenditure of MK
118,129 (P-values of 0.01 and 0.02, respectively). Similarly, unconstrained households reported
a markedly (P-value = 0.04) higher land size (2.9 hectares) than constrained households (2.2
hectares).
On the other hand, though not statistically significant, unconstrained households were found to
be wealthier than constrained households. The former reported a comparatively higher value of
total household assets (Mk 43,192) compared to the latter (Mk 32,047). Similarly, unconstrained
households reported higher values of non-consumable assets (Mk 13,190) and assets held as
livestock (MK 27,941) compared to constrained households values of MK 12,553 and MK
19,321 for non-consumable assets and assets held as livestock, respectively.
In terms of contact with extension agents, there were no significant differences between the two
groups. However, constrained households reported a slightly higher proportion of households
(28.7 per cent) that had received some form of advice from extension agents on issues of access
to credit than unconstrained households (27 per cent). Similarly, there were no marked
differences in terms of travel time to the nearest trading centre between credit constrained and
unconstrained households despite unconstrained households reporting a higher travel time (68
minutes) than constrained households (59 minutes).
Table 4.10: Household Characteristics According to Credit Constraint Status
Unconstrained
assetsc
2.59***
Wage labour
Source: Own calculations from Malawi-IHS 2 Data
Note: Figures in parenthesis are standard deviations;
Value of household non-consumables, livestock and land
4.6 Participation and Credit Constraint Status of Head According to Expenditure Deciles
Figure 4.8 shows the distribution of household heads according to participation and credit
constraint status and the relative expenditure levels. Expenditure is taken as a proxy for income
and as such as an indicator of poverty. Thus households in the lower expenditure deciles are
considered to be poor. Figure 4.8 shows that a higher proportion of participants (59 per cent)
belonged to the expenditure categories defined as non-poor (7th to 10th deciles) while the
remainder came from the lowest deciles. On the other hand, the proportion of credit constrained
households were almost the same for the expenditure categories defined as poor (1st to 6th
deciles) and non-poor categories. This suggests that participation in MRFC credit programs is
dominated by the rural rich despite a slightly higher percentage (51) of households in the
expenditure category defined as poor indicating to be credit constrained.
Expenditure deciles
Expenditure Deciles (n=166)
Chapter 5: Quantitative Analysis of the Determinants of Participation in
Formal Microfinance Programs
This chapter presents the results of two-stage instrumental variables estimation, with a probit
model, of the determinants of participation in formal microfinance programs as per objectives 2,
3 and 4 of the study. The chapter begins by giving an overview of the study objectives and
hypotheses while section two presents the empirical model results. The first part of section two
quantifies the factors that influence households credit constraint condition as per the first-stage
equation of the instrumental variables probit model. The second part quantifies the influence of a
household being credit constrained on participation in formal programs in addition to other
socio-economic and micro-credit related factors as per the second-stage equation.
5.1 Overview of Study Objectives and Hypotheses
This section gives an overview of the study hypotheses that the empirical model was attempting
to answer. In line with objectives 2, 3 and 4 of the study, the following hypotheses were
formulated:
To test the above hypotheses, a two-stage equation probit estimation approach was used and the
results are discussed below in section 5.2. The probit model was estimated using the maximum
likelihood method of estimation in STATA. To control for omitted variable bias and
endogeneity, age of household head and land size were used as instrumental variables for a
household being credit constrained.
5.2 Empirical Model Results
5.2.1 Determinants of Credit Constraint Condition
Table 5.1 presents the empirical results of the determinants of credit constraint. The first-stage
equation probit model results show a significant negative influence of both instrumental
variables (age and land size) on credit constraint. Further, the findings show that households
headed by men, households with less educated heads and higher dependency ratio are more
likely to be credit constrained.
The negative and significant sign on age suggests that households headed by younger people are
more likely to be credit constrained (p-value = 0.014). Mostly households headed by younger
people do not have stable, reliable and diversified income sources which make their actual
consumption to fall short of the optimal one. In such agriculture dependent communities, most
households headed by younger heads are landless or near landless as they lack customary land
ownership rights which are often times held by older members of the family. This renders it
difficult for them to undertake diversification into food and cash crops particularly tobacco
which is the major source of income for rural households. Further, in terms of exploiting social
capital, younger household heads are less likely to fully make use of the informal social networks
to address their credit constraint condition compared to older heads who can take advantage of
their positions in society to utilise the informal networks to their advantage. Thus households
with younger heads are more likely to have a lower level of income diversification, less
diversified assets and less access to opportunities. This is likely to make them have a binding
credit constraint in comparison to their older colleagues.
The significant negative sign on land size (log land size) suggests that households with smaller
farm sizes are more likely to be credit constrained (p-value = 0.006). Just like elsewhere, in
Malawi land is considered as a principal source of natural capital, a source of livelihoods and a
vital asset for the rural poor who earn their living through subsistence farming (Clover and
Eriksen, 2008; Ellis, 1999). Households with larger farm sizes have the opportunity to widen
their income base by diversifying their agricultural activities while at the same time undertaking
non-farm investments that require land which is not the case with those with smaller farm sizes.
Additionally, land rich households have the chance to use income generated from agricultural
activities to finance non-farm activities thereby enabling them to stabilize their incomes,
accumulate and diversify their assets. As such, the likelihood of land rich households
experiencing a binding credit constraint is much lower than those with small farm sizes.
Further, Diagne and Zeller (2001) argue that in Malawi the level of diversification of household
assets plays a much greater role when MFIs assess the credit worthiness of households as this is
assumed to help increase and stabilize repayment rates. This therefore suggests that households
with larger farm sizes, which are more likely to have diversified assets, stand a better chance of
accessing credit from MFIs compared to those with less land. Diagne and Zeller (2001) further
noted that the binding land constraint limits most rural households from participating in MRFC
credit programs as the opportunity cost of them allocating their little land to tobacco at the
expense of food crops is quite high considering the volatile nature of tobacco markets and the
erratic weather patterns. Thus the over-reliance on farming in such land constrained communities
leaves households with less diversified income sources, assets and with no capital to embark on
non-farm income generating activities. The lower level of income and asset diversification
among land constrained households explains why such households are more likely to be credit
constrained than their counterparts with larger farm sizes.
Surprisingly, gender was found to have a positive significant influence on credit constraint which
suggests that men are more likely to be credit constrained than women (p-value = 0.079). This
can be explained by the fact that mostly respondents were household heads, the majority of
whom were men (86 per cent) who control the production of tobacco, the major cash crop
supported by MRFCs agricultural credit programs. Further, most participants (80 per cent) still
indicated to be credit constrained despite obtaining loans particularly because they got much less
credit than what they wished. This partly explains why gender had a positive sign rather than a
negative one as earlier hypothesized.
The negative and significant sign on education of head indicates that households with educated
heads are less likely to be credit constrained (p-value = 0.043). Thus, credit constraint condition
is more likely to worsen with less number of years of schooling of the household head. This
result was corroborated by the findings of Omonona et al. (2008) who found that the lower
education level of farmers in Oyo State in Nigeria significantly contributed to their binding credit
constraint condition.
The positive and significant sign on dependency ratio implied that households with higher
dependency ratio were more likely to be credit constrained (p-value = 0.04). This is particularly
the case as households with higher dependency ratio are more likely to have an actual household
consumption level that is much lower than the optimal one thereby making their credit constraint
more binding than those with lower dependency ratio. This is substantiated by the findings of
Omonona et al. (2008) who also found that dependency ratio positively influence the credit
constraint condition of farm households in Oyo State in Nigeria.
The results also indicate that both the wealth status indicators were not significant. This could
partly be attributed to the reason that the value of the assets was quite small in most households
(mean of US $281 with a standard deviation of US $504) while at the same time the composition
of both livestock and non-consumables assets was somehow uniform in almost all the
households making it difficult to observe any significant differences in terms of its influence on
credit constraint.
Table 5.3: Probit Model Results of Determinants of Credit Constraint (1=credit constraint )
Parameter estimate
Standard Error
Z-Valueb
Gender (1=Male)
Education of head (years)
Spouse education (years)
Log value of assetsc
Log livestock value
Contact with extension agent (yes=1)
Travel time (minutes)
Log land size
Log likelihood
Number of obs
Wald chi2 (12)
Prob>chi2
Model prediction level (%)
Credit constrained households (n=129) and unconstrained households (n=37)
Non-consumable assets (excluding land and livestock)
5.2.2 Determinants of Participation in Formal Microfinance Programs
As shown by Table 5.2 below, the second-stage equation probit model results show a significant
and negative correlation between credit constraint and participation in formal microfinance
programs which suggests that credit constraint significantly influences the likelihood of
participating in MRFC programs. The empirical model results further show that households with
lower dependency ratio, households without contact with extension agent on issues of credit and
households residing further away from trading centres/bomas are less likely to participate in
MRFC programs.
Unexpectedly, credit constraint had a negative significant sign which indicates that credit
constrained households are less likely to participate in formal credit programs (p-value = 0.046).
This could be explained by the fact that being credit constrained is strongly and negatively
correlated with land size which suggests that households with smaller farm sizes are more likely
to have a binding credit constraint compared to those with larger farm sizes. As explained in the
previous section, households with smaller land sizes are also more likely to have less diversified
household assets as observed by Diagne and Zeller (2001). Further, this study concluded that
MFIs in Malawi mainly look at the level of diversification of household assets when assessing
the credit worthiness of potential borrowers. Thus it can be argued that the likelihood of credit
constrained households, which are more likely to have less diversified assets, to participate in
MRFC programs is much lower. This is particularly the case as credit constrained households are
considered as risky borrowers as their lower level of asset diversification may render it difficult
for MFIs to recover their money in case of defaults. Additionally, since credit constrained
households are more likely to have smaller farm sizes, it would be risky for them to participate in
agricultural credit programs that target cash crops as doing so at the expense of food crops would
be costly.
The positive significant sign on dependency ratio suggests that households with more children
(below 15 years) and the elderly (over 64 years) are more likely to seek for opportunities to raise
income to support the large number of dependants and this increases the likelihood of them
participating in credit programs (p-value = 0.02). On the contrary, Diagne and Zeller (2001) in
their study of MFIs in Malawi found that dependency ratio negatively significantly influence the
probability of participating in MRFC programs. However, it was found that the dependency ratio
positively influences the decision to join other credit programs particularly those offering nonagricultural loans. This could partly be attributed to the binding land constraint which limits most
households from participating in agricultural credit schemes and instead they prefer applying for
non-farm loans.
Expectedly, contact with extension agents on issues of access to credit was also found to have a
significant and positive influence on participation (p-value = 0.098). This indicates that
participation in MRFC credit programs is closely associated with having prior information on
issues of credit. Consequently, household heads that at some point had received some advice on
credit issues from extension agents were more likely to participate in MRFC programs than their
counterparts who did not. Such contacts enable individuals to become aware of the various credit
sources as well as which MFIs offer relatively better services.
Travel time to the nearest trading centre/boma was found to have a marked negative influence on
participation (p-value = 0.012). This suggests that households living close to trading centres were
more likely to participate in MRFC credit programs compared to their counterparts residing in
the remote rural areas. This is mainly because these households have access to information on
credit markets as most MFIs or rural banks operate from the trading centres/bomas from where
they expand their coverage deep into rural areas. On the other hand, lack of exposure to such
information limits the participation of rural households far away from such centres/bomas.
Additionally, since most loan transactions take place at such centres, households from further
away places find it worthless to travel such long distances considering the small sizes of loans
With regard to wealth status indicators, none of the two was found to have any influence on
participation. However, basing on the findings of Diagne and Zeller (2001) such a result is not
surprising. As mentioned above, Diagne and Zeller (2001) observed that in Malawi the
composition of household assets is a more important determinant of household access to formal
credit than the total value of household assets or landholding size. Thus formal lenders in Malawi
prefer to lend to households with more diversified asset portfolios and therefore more diversified
incomes, presumably to increase and stabilize repayment rates. Consequently, households whose
assets consist of mostly land and livestock but who wish to diversify into non-farm incomegenerating activities may be constrained by a lack of capital, as both sectors of the market do not
grant them access to credit. They may thus be forced to rely on farming as their sole source of
income, and one that can be unreliable because of the frequency of drought in Malawi.
Table 5.4: Probit Model Results for the Determinants of Participation (1=participationa)
Z-Value
Education head (years)
Log household assetsc
Credit constraint (yes=1)
Participants (n=96) and Non-participants (n=70)
Logarithm of the value of non-consumable assets excluding land and livestock
Chapter 6: Discussion, Conclusion and Policy Implications
6.1 Discussion and Conclusion
This section provides some discussion and conclusions on the descriptive analysis and empirical
model results presented in the previous two chapters. The section begins by discussing the
descriptive analysis results in chapter four followed by empirical model results in chapter five.
The section concludes by briefly looking at other general factors that may limit participation in
formal microfinance programs but were not covered in this study due to data limitations. Further,
the section also gives an insight of some factors that might hinder MFIs from achieving the three
pillars of microfinance vis--vis financial sustainability, outreach and welfare impact.
The descriptive analysis results reveal that the degree of participation in MRFC credit programs
is much lower for women compared to men. Lack of control over the use of productive resources
by women due to cultural and traditional beliefs renders it difficult for them to actively
participate in credit programs that promote the production of lucrative cash crops such as tobacco
which is the major income earner for the majority of rural households. This defeats the whole
notion that microfinance is a potential tool in ensuring gender equality and women
empowerment as propagated by development agencies committed to the empowerment of
women and gender equality. Considering that MRFC is the major MFI in Malawi providing both
credit and savings facilities, the low women representation in its agricultural credit programs has
negative repercussions in the fight against poverty, gender inequality and the attainment of the
MDGs.
The findings further show that farming is the primary occupation of the majority of rural
households despite most of them also being involved in non-farm activities particularly during
off-seasons. Agriculture accounts for over 75 per cent of total household incomes of which
almost half comes from tobacco alone while non-farm sources including pensions account for
less than a quarter of total incomes. This demonstrates the lower degree of cash crop and general
income diversification among rural households as most of them rely only on agriculture
particularly tobacco11. This increases their vulnerability to various forms of shocks in case of
production and market failures for tobacco and this has direct negative consequences on their
livelihoods as well as in the fight against poverty.
The results reveal that a higher proportion of respondents (78 per cent) reported a binding credit
constraint while unexpectedly, about 80 per cent of the participants also indicated to be credit
constrained despite borrowing from MRFC. This indicates that the loans offered by MRFC were
very small (mean of US$188 with a standard deviation of US$227) to satisfy the credit demands
of the households. However, this could partly be attributed to the reason that most households
had small farms (average of 2.3 hectares) which made it difficult for them to apply for larger
loan sizes as the loans were mainly provided in-kind in form of farm inputs. Further, MRFC just
like any other MFI puts a limit on the loans applied mainly basing on the credit worthiness of
borrowers. This helps to reduces losses on its part in case of defaults. The findings further reveal
that a higher proportion of participants obtained their loans through groups (84 per cent) of
which the majority used the loans to buy inputs for tobacco (69 per cent). This reflects the level
to which MRFC is exploiting social capital in the rural communities to enable the poor access
credit while at the same time reducing the operational and transaction costs.
An analysis of interest rates from previous loans (30.2 per cent) revealed that the interests
charged were much higher suggesting that participating households benefited less from the loans
as most of their earnings were directed towards loan repayments especially considering the
volatile nature of the tobacco markets and the erratic rainfall pattern in Malawi. The interest rates
charged by most MFIs in Malawi are much higher (at times double) than commercial banks
lending rates which are currently at around 19.6 per cent (minimum). This is one of the major
factors hindering participation in credit programs as the poor are not able to fully benefit from
such programs. Thus if MFIs are to attract the interest of rural poor and expand their coverage
they need to revisit their exorbitant interest rates.
The results also show that despite land being the major household asset and source of livelihood
in the study area, most households (73 per cent) cultivate less than 3 hectares of land. This partly
This is particularly the case because the study district is the major producer of tobacco at national level and most
households consider tobacco production as their major economic activity.
reflects the major challenge the poor face in their quest to undertake diversification into food and
cash crops as well as other investments that require land. Thus in such land constrained rural
communities blending agricultural credit with non-farm loans would significantly help
households to diversify their income sources.
The independent samples t-test and chi-square test results show marked differences in terms of
education of household head, household size, total value of household assets, value of household
assets held as livestock, total household expenditure, annual household income and contact with
extension agents between participants and non-participants. Participating household heads
reported higher education level than non-participants. Results also show that participants had
larger household sizes than non-participants. Similarly, participants were found to be wealthier
than non-participants as they reported significantly higher values of total household assets,
household assets held as livestock, income and expenditure. However, it should be noted that the
observed differences between participants and non-participants under the independent samples ttest with regard to wealth status indicators are only partial as the indicators are not significant in
the probit model when other variables are taken into account.
Further, most participating households indicated that at some point they had some contact with
extension agents on issues of credit. This was very useful as it made them aware of the possible
credit sources as some respondents reported not to be aware of any formal lender operating in
their area. On the other hand, results show no significant differences between constrained and
unconstrained households with regard to the socio-economic characteristics and micro-credit
related factors except for income, expenditure and land size. Unconstrained households reported
significantly higher income, expenditure and land size than constrained households. Further, the
findings show that the highest proportion of participants emanated from the higher expenditure
deciles (7th-10th) while the majority of respondents in the expenditure categories classified as
poor (1st 6th deciles) reported to be credit constrained. This suggests that MRFC programs do
not target households in the lowest expenditure deciles which are considered the poorest. Such
households are in most cases landless or near landless and have less diversified household assets
making it difficult for them to participate in agricultural credit programs. Thus their livelihoods
depend much on the provision of cheap off-farm and non-farm labour. This poses a challenge in
the fight against poverty and if microfinance programs are to yield any impact in poverty
reduction and economic development, they have to aim at extending basic financial services to
this marginalized group.
The empirical model results from the first-stage equation show that credit constraint condition is
strongly associated with age, gender, education level of head, dependency ratio and land size.
The results show that households with older and more educated heads, smaller dependency ratio
and larger farm sizes are less likely to be credit constrained. The results also suggest that male
headed households have a more binding credit constraint than their female counterparts. Further,
the findings reveal that participation is negatively and significantly influenced by credit
constraint while dependency ratio, contact with extension agents and travel time to the nearest
trading centre are its other determinants. Households with higher dependency ratio are more
likely to participate in MRFC credit programs as they seek for opportunities to raise income to
support the large number of dependants. Nevertheless, this also raises fears of child labour as
tobacco is more labour demanding which partly suggests that most of these households utilize
child labour to meet the labour demands.
The results further show that the delivery of extension services on issues of credit remains a
major challenge in rural areas thereby limiting participation only to those staying closer to
trading centres as they can easily access information about credit sources as most MFIs operate
from these centres. Thus improving the delivery of extension services on issues of credit is very
critical as it helps rural households to become aware of the various credit sources available in
their area. This would significantly help improve the participation of households in remote rural
Due to data limitations, the study was not exhaustive when looking at the determinants of
participation in formal credit programs. Thus, it is also important to briefly discuss some factors
external to the households such as community characteristics and social institutions or networks,
which were not covered in this study, but may greatly influence participation. Local social
institutions and networks play a crucial role in influencing its members to actively participate in
various development initiatives. IFAD (2009) argues that the informal networks and interactions
in local communities provide an entry point for development programs apart from holding the
key to their success and sustainability. MFIs can thus utilize such local networks to create
awareness about the various microfinance products they offer as well as to influence community
members to patronise their services. This would help make the communities aware of their
products but also help achieve higher participation levels and repayment rates.
Additionally, to unlock the puzzle of lower participation levels in rural formal credit markets, it
would be important to understand the institutional environment in which MFIs operate.
Sometimes the regulatory and policy environment renders it difficult for MFIs to achieve
microfinance deepening and widening in a sustainable manner. For instance, the regulatory
framework in the formal financial sector in Malawi restricts most of the MFIs to mobilize
savings thereby limiting their services to mere credit provision. This has implications on the
number of products offered, financial gains for the MFIs, coverage and expected impact on rural
households. As pointed out previously, IFAD (2009) argue that the small loans the poor get from
MFIs can only make a significant impact on their lives if they are allowed to save the surplus
income and withdraw it during times of need. However, this is not the case in Malawi where the
rural poor lack micro-savings, micro-insurance and other services offered by MFIs elsewhere. It
is therefore evident that unless MFIs consider deepening their services, their efforts will not
produce tangible results in rural communities.
Further, higher transaction costs limit the level of credit widening of most MFIs in rural areas as
the higher operational, screening, monitoring and enforcement costs threaten their financial
sustainability. Thus most MFIs are forced to limit their services to areas and communities where
they can provide them at a lower cost. Rural infrastructure is another factor that limits the
coverage of most MFIs. Poor road, communication and energy infrastructure make it difficult for
MFIs to penetrate deep into rural areas while at the same time limit the use of modern
technology when carrying out loan transactions. This exacerbates transaction and operational
costs for the MFIs. Thus, to a larger extent, improvements in credit uptake by rural poor depend
much on factors external to them. As such, increasing the number of microfinance products
offered as well as improving the regulatory environment and rural infrastructure would help to
improve coverage of rural microfinance programs.
6.2 Policy Implications
households particularly women in formal credit programs. Firstly, the binding land constraint in
most rural households and communities calls for a policy shift in the delivery of financial
services to the poor. MFIs need to expand their credit products by blending agricultural and nonfarm loans to give the rural poor the choice to access either micro-credit for non-farm or
agricultural activities depending on which one fits their situation. This will enable them to
diversify their income sources and move away from their dependence only on farming as a
source of livelihoods.
Secondly, by supporting credit programs that target non-farm activities, MFIs will play a
significant role in addressing gender inequalities as they will directly extend financial services to
women who dominate most non-farm small businesses in rural areas. Further, participation in
formal credit programs is positively associated with contact with extension agents on issues of
credit. As such, major players in the rural microfinance sector need to intensify or improve on
the delivery of extension services in rural areas to ensure that many people become aware of
their services instead of limiting uptake to areas close to trading centers/bomas.
Due to data limitations, this study was not able to clearly isolate the factors that limit credit
uptake in rural communities despite the proliferation of the rural microfinance sector. As such, it
would be interesting to conduct a similar but comprehensive research based on primary data that
would focus on the institutional analysis of MFIs which will help to unveil the institutional factor
that hinder the smooth operation of MFIs which results into lower levels of credit deepening and
widening. Further, studies can explore the role that social capital plays in the rural formal and
informal financial sector to address issues of access to credit while at the same time exploring the
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