investigating the effects of access to microfinance on household investment in malawi

7/24/2019 Investigating the Effects of Access to Microfinance on Household Investment in Malawi

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Abstract

Microfinance has been widely recognised as a tool for poverty reduction. This paper seeks to

expand the scope of effects of microfinance to economic growth through household investment.

The study investigated the effects of access to microfinance services on household investment in

productivity enhancing activities in Malawi. It used Finscope 2008 data and a linear probability

model to determine the extent at which financial services are used for investment purposes as

opposed to consumption. It also investigated the types of financial services, loan or savings and

how this varied with different demographics, mainly location. The econometric results showed

that formal financial services are more likely used for investment purposes. People who borrow

specifically to invest are 16% more likely to use formal financial services. Similarly, people who

save specifically to invest are 25% more likely to use formal financial services. More specific to

institution, people who save and/or borrow to invest have a 13% higher incidence of using

Microfinance Institutions. The findings from the study can help change the focus of financial

inclusion policies as well as poverty reduction strategies. From the results, we see that Malawian

households borrow and save for investment purposes, thus economic growth can be promoted by

increasing access to financial services by maximising the investment potential of households.

Table of Contents

Declaration of originality ...............................................................Error! Bookmark not defined.

Certificate of Approval ..................................................................Error! Bookmark not defined.

Dedication ......................................................................................Error! Bookmark not defined.

ACKNOWLEDGEMENT .............................................................Error! Bookmark not defined.

ABSTRACT ..................................................................................................................................... i


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List of Tables ................................................................................................................................. iv

Abbreviations .................................................................................................................................. v

Chapter 1 Background .................................................................................................................... 1

1.1 Background ............................................................................................................................... 1

1.1.1

Microfinance ................................................................................................................. 2

1.2 Research Problem ..................................................................................................................... 3

1.3 Study Objectives ....................................................................................................................... 5

1.4 Hypothesis Being Tested .......................................................................................................... 6

1.5 Significance (Rationale of Study) ............................................................................................. 6

Chapter 2 Literature Review ........................................................................................................... 7

2.1 Theoretical Literature Review .................................................................................................. 7

2.1.1 Theoretical Basis of Microfinance ..................................................................................... 7

A. Relationship between Microfinance and Economic Growth............................................ 7

B. Relationship between Microfinance and Poverty ............................................................. 8

C. Alternative views of Microfinance ................................................................................... 9

2.2 Empirical Literature Review ................................................................................................... 10

A.

Microfinance and Poverty Reduction ............................................................................. 10

B. Microfinance Impact in Human Capital (Education) ..................................................... 11

C. Microfinance Impact in Physical Capital (Business, Agriculture) ................................. 13

D. Concluding Remarks Literature Review ........................................................................ 13

CHAPTER 3 Methodology ........................................................................................................... 15

3.0 Introduction ............................................................................................................................. 15

3.1 Sources of Data ................................................................................................................... 15

3.2 Descriptive Statistics ........................................................................................................... 15

3.3 Model Specification ............................................................................................................ 16

3.2.1 The Dependent Variable ............................................................................................... 16


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3.3.2 Independent variables ................................................................................................... 17

3.3.3 Rationale for Using LPM ............................................................................................. 20

Chapter Four Empirical Results and Interpretation ...................................................................... 21

4.1 Variable Analysis .................................................................................................................... 21

4.1.2 Normality of the Error Term ............................................................................................ 21

4.1.3 Heteroscedasticity ............................................................................................................ 21

4.1.4 Multicollinearity Test ....................................................................................................... 21

4.2 Empirical Estimation and Interpretation: Linear Probability Models..................................... 22

Interpretation of Results ................................................................................................................ 22

Chapter Five Recommendation and Conclusion........................................................................... 25

5.1 Conclusion .............................................................................................................................. 25

5.2 Policy Implications ................................................................................................................. 26

5.3 Limitations of the Study.......................................................................................................... 26

5.4 Direction for Future Research ................................................................................................. 26

APPENDIX 1 Diagnostic Tests .................................................................................................... 31

APPENDIX 2 Regression Results ................................................................................................ 32


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List of Tables

Table 1 Descriptive Statistics. (All Variables Are Dummy Variables Except For Income And

Age)............................................................................................................................................... 16

Table 2: Investment Vs. Consumption Reasons To Save Or Borrow ........................................... 18

Table 3: Relationship Between Usage Of Formal Financial Services And Investment Using

Finscope 2008 Survey Results. ..................................................................................................... 22

Table 4 Diagnostic Test Results ................................................................................................... 31

Table 5 Regression Results ........................................................................................................... 32


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Abbreviations

A&M Armendriz de Aghion and Morduch 2005

BIDS Bangladesh Institute of Development Studies

BRAC Bangladesh Rural Advancement Committee

BRDB Bangladesh Rural Development Board

GB Grameen bank

GDP Gross domestic product

GoM Government of Malawi

IHS Integrated Household Survey

IV Instrumental variable

IKP Indhira Kranthi Patham

JLG Joint liability group

MF Microfinance

MFI Microfinance institution

NGO Non-governmental organisation

NR Nitya Rao

OLS Ordinary least square

PnK Pitt and Khandker 1998

PSM Propensity score matching

RCT Randomised controlled trial

ROSCA Rotating savings and credit associations

WB World Bank


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Chapter 1 Background

1.1 Background

Malawi is one of the least developed countries in the world ranking 160

th

out of 182 countries onthe Human Development Index (World bank, 2013).The country has a population of

approximately 15 million people with 85% living in rural areas and 15% urban areas (IHS 2011).

According to the integrated household survey of 2010/2011, 17% of the urban population is

living in ultra-poverty compared to 57% of the rural population. At the national level the poverty

rate is at 50.7%. Agriculture is the main source of income earning activity with 85% of the

population engaging in subsistence farming or related farming activities which are seasonal and

very volatile to shocks. These poor households and individuals do not have access to

sustainable financial services; the percentage of poor families without access to financial

services in rural areas is even greater. Those groups with the least access to finance

include smallholder farmers whose production of maize and tobacco make up the bulk of

the countrys supply of staple foods and contribute to the majority of the countrys export

earnings (UNCDF, 2007).A major contributor to the lack of access is the fact that the financial

sector in Malawi is underdeveloped, the financial system is small in comparison to other African

countries (African Development Bank, 2012). In order to increase access to financial services

there is need to create an enabling environment for all income groups to be able to have access to

both credit and savings facilities. This is where microfinance comes into play, realization that

people on low incomes are often excluded from access to financial services.

Even though microfinance institutions date from 1992, the development of microfinance

institutions began after the democratic process in 1994. By the year 2013, Malawi had

approximately 26 institutions offering microfinance services targeting women, the rural poor and

low income self-employed (MAMN, 2014). Until the year 2010, microfinance institutions in

Malawi remained unregulated until the enforcement of the Microfinance Act of 2010. The

Reserve Bank of Malawi (RBM) now has the mandate to regulate these institutions (Finscope,

2012). Thus even the legal framework necessary to create an enabling financial sector is in its

early stages.


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This is despite the fact that increased access to credit, savings opportunity and other financial

activities is very crucial in moving Malawis economy from being predominantly agro-based to a

manufacturing and service industry(GoM, 2002).The Government's Policy Framework for the

Poverty Alleviation Program developed in 1995 recognizes this role and one of its strategies is

the improvement of access to credit facilities by deepening and broadening the financial sector to

assist the poor to diversify their sources of income. This strategy has reappeared in numerous

policy documents in different forms such as strengthening the policy environment for micro-

finance as well as increasing advocacy for microfinance (GoM, 2006). In these documents, the

strategy aimed to improve resilience and quality of life for the poor to move out of poverty and

vulnerability. In addition, increased access to microfinance to women has also been recognized

as a strategy to increase gender equality and women empowerment as shown in the Malawi

Growth and Development strategy (GoM, 2010).

In the National Export Strategy (GoM,2013),a Financial sector development policy was

formulated focusing on ensuring the provision of affordable access to finance for micro, small

and medium enterprises (MSMEs), large businesses, smallholder farmers, cooperatives, youth

entrepreneurs and womens groups.This policy is to be prioritised by central government.

This dissertation was written to examine the extent to which access to microfinance services

affects poverty reduction through one area of impact that is household investment in

productivity-enhancing activities. Thus the study will investigate the effects of access to

microfinance services on household investment in productivity enhancing activities in Malawi.

Productivity-enhancing activities are defined as activities which may be expected to contribute to

a higher income in business, or investment in agricultural inputs or equipment (Ellis, Lemma, &

Rud, 2010). It is proposed that if better access to financial services can facilitate greater

household level investment (as opposed to household consumption); this could contribute

directly to income growth and therefore reduce poverty.

1.1.1 Microfinance

Microfinance is the provision of financial services to low income poor and very poor self-

employed (Otero, 1999). These financial services include savings and credit but can also include

other financial services such as insurance and payment services(Ledgerwood,1999).With this

definition in mind, microfinance Institutions (MFI) are organisations providing microfinance

services whether regulated or unregulated. In addition to financial intermediation, many MFIs


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provide social intermediation services such as group formation, development of self-confidence,

and training in financial literacy and management capabilities among members of a group

(Ledgerwood, 1990).

According to Von (1991), Microfinance came into existence from the appreciation that micro

entrepreneurs and some poorer clients can be bankable, that is they can repay both the

principal and interest on time and also make savings provided financial services are tailored to

suit their needs. After Grameen Bank proved successful other MFIs followed like Accion

international and now more than 7000 micro-lending institutions are serving over 25 million

poor clients (Chemin, 2008).Microfinance has since become a mainstream activity attracting

funding and promotion from NGOs, bilateral and multilateral donors. The major providers of

microfinance services in Malawi include Katapila1, nongovernmental organizations (NGOs),

savings and loans cooperatives, credit unions, government banks, commercial banks and non-

financial institutions. For purposes of this study, we will distinguish between formal and

informal sources of finance and financial services. All institutions registered under MAMN or

under the reserve bank supervision will be regarded as formal institutions while as the rest

(Katapila, Family and friends and Religious organisations) will be grouped as informal sources.

The target group of MFIs are self-employed low income entrepreneurs who are; traders, street

vendors, small farmers, artisans and blacksmith (Ledgerwood, 1999).

Microfinance has grown out of realization that people on low incomes are often excluded from

access to financial services and the more an individual or a community is marginalized from

financial services, the more likely it is that they will be socially excluded and this will lead to

their overall civic marginalization.

1.2 Research Problem

It has been shown that there is a high demand for financial services such as savings, credit and

insurance services in Malawi (Finscope, 2008). This lack of adequate access to financial services

is believed to have negative consequences for various aggregate and household level outcomes

including food security, nutrition, health and household investment in productivity enhancing

activities (education, business investment) (Diagne, Zeller, & Mataya, 1996).

1Loan sharks


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According to Chirwa (2002), the contribution of existing microfinance programmes in the

reduction of poverty in Malawi was not known with certainty. It had not been established which

microfinance programme design best suits the conditions in Malawi. Therefore there was need to

conduct a study that showed the effect of microfinance on poverty reduction in Malawi. This gap

in literature was partly field by Kwataine (2002) in a study which was aimed at showing the

effect of formal credit on household food security in Zomba. The study used primary data

obtained from clients who obtained loans from FINCA and PRIDE Malawi and resided in

Malosa, Thondwe, Chingale, Namadzi and Domasi in Zomba district. The model specification

used was adopted from Diagne et al and a Tobit model was used. The results rejected the

hypothesis that it does not matter who gets a loan whether male or female in order to improve

household welfare in terms of food security. It showed that most women who had access to loans

were more food secure than men. However this study still left a considerable gap in the literature.

Its sample was biased towards women (100 females versus 31 males) and thus its results were

still inconclusive. Furthermore, it did not indicate whether such changes were observed across all

regions or districts. In addition, it left the gap that it did not address all sources of microfinance

that can be available to households, thus formal, semi-formal and informal but instead only

looked at two formal institutions.

Oliver (2010) conducted a study on the impact of microfinance on poverty reduction. Oliver used

a causal model of the effects of Microfinance that measured the effect on socioeconomic status

(SES) using a reflective multi-dimensional construct. The model applied variables in measuring

SES that are viewed as reflections of their origin the level of poverty and drew upon factor

analytical techniques. Oliver transformed an existing World Bank data set on household income

and poverty in Malawi into a causal model using structural equation modelling (SEM). The

analysis showed that Microfinance has a significant interaction with income in its impact on

SES, thus demonstrating its instrumental value in reducing poverty. Contrary to Kwataines

results, they found no differences across gender or income levels for the impacts of

Microfinance. The effect was also found to be different between urban and rural households.

This study managed to remove most of the uncertainty regarding the impact of microfinance on

poverty reduction. However, the study left some ground to cover. The study used IHS data set

and World Bank indicators which only capture formal financial service providers for the most

part although it is clear that informal and semi-formal providers reach a much greater proportion

of the population in Malawi than banks (Finscope, 2009). There is little research focusing


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explicitly at characterizing the demand-side of microfinance and the relevant financial usage

patterns (Martinez, 2011). Therefore developing a greater understanding of the role that access to

and usage of financial services as a whole (including formal, semi-formal and informal financial

services) through a demand side approach is an important but nevertheless under-researched area

of investigation.

This study aims to address this gap in the literature, by utilising Finscope household survey

results on the usage of financial services in Malawi. This is an extremely rich dataset, which

includes a great deal of information which is not available from any other source. The dataset

includes nationally representative information about which financial services and financial

service providers are being used, for what purposes, and what barriers to financial access are

being faced. This can be broken down in many different ways using the detailed information that

has been collected on individual characteristics (gender, wealth, family position, location,

attitudes etc.). Despite the richness of this new dataset, it has been under-utilised for the

purposes of economic research so far.

1.3 Study Objectives

The main objective of the study is to find the extent to which access to microfinance services

facilitates household investment in productivity-enhancing activities.

The specific research objectives will be to find:

The extent to which financial services are used for investment purposes rather than for

consumption.

The types of financial services and financial providers (formal and informal) that are used

and how this varies within different demographics.

What the policy implications are in terms of how best to promote productivity-enhancing

investment at the household level.


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1.4 Hypothesis Being Tested

Formal borrowing is not used for investment purposes.

Formal saving is not used for investment purposes.

There is no difference between rural and urban in the usage of financial services.

1.5 Significance (Rationale of Study)

Credit is an important source of additional finance for households. The interest in understanding

the characteristics of demand for credit for investment in both agricultural and non-agricultural

enterprises is becoming more important for the Malawi government. This is a result of the

increasing role placed on small scale economic activities as tools for poverty alleviation (GoM,

2010).In the MGDS strategy promotion of village savings and loans programmes as well as

promoting longer term skills oriented and asset enhancing interventions was a strategy to

improving resilience and quality of life for the poor to move out of poverty and vulnerability.

Furthermore, under theme 6, Gender and Capacity building, promoting equal access to

appropriate technologies and micro-finance schemes was one of the strategies to reduce gender

inequality. Additionally, The National Export Strategy (GoM, 2013) puts creating a conducive

environment through access to financial services (savings and credit) to women, farmers, micro-

enterprises and the youth as key priority for achieving its overall goal of economic development.

As a result, a comprehensive study on the effects of access to microfinance on poverty reductionthrough its effects on investments can change the implementation of policies by encouraging

more people to save and borrow, not only for catering for unforeseen circumstances such as

funerals, but to also invest in capital enhancing activities. Looking at the types of financial

services that are used in different demographics will shed light on how to meet both urban and

rural demand for microfinance services. The study will measure the effects of microfinance on

investment as opposed to consumption which has been shown to directly influence economic

growth and in the long run, poverty reduction.


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Chapter 2 Literature Review

2.1 Theoretical Literature Review

The literature on microfinance is extensive, with a large amount assessing the efficiency of

microfinance institutions. However, the focus here is on the impact of microfinance on poverty

reduction and social welfare through productivity enhancing investments. The theoretical

literature review will look at the theoretical basis of microfinance. It will also cover the

relationship between microfinance and investment and how this relates to poverty and economic

growth. And lastly, It will examine what different scholars have written under microfinance,

however, the methods used and types of programmes microfinance institutions use to deliver

Microfinance services will not be discussed.

2.1.1 Theoretical Basis of MicrofinanceA. Relationship between Microfinance and Economic Growth

One of the underlying ideas microfinance impact is based on is the Separation Theorem, which

states that the availability of credit allows consumption to be separate from investment decisions

(Angioloni et al, 2011). It also assumes that access to financial services facilitates greater

household level investment in productivity-enhancing assets, and that this increases household

income in future.

According to growth theory (Solow (1956), Romer (1990)), growth depends on the stock of

human and physical capital in the economy, as well as technological progress. Investment at the

level of the firm or the individual can contribute to both human and physical capital(National

Export Strategy recognises this by putting access to finance as gateway to research and

development and thus technological progress and skill accumulation), and thus plays an

important role in facilitating long run economic growth. In many developing economies, lack of

savings and capital make it difficult to engage in self-employment and undertake productive

enhancing activities(Khandker, 1998).The argument in developing economics literature is thatformal markets tend to fail the poor due to the collateral requirements that the poor cannot satisfy

and due to the belief that the incentives to repay for the poor are limited given the asymmetric

information and high monitoring costs of micro-individual borrowers(Hulme and Mosley, 1996)


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From the above basis we see that microfinance services aim at providing capital constrained,

resource poor households and firms an avenue to effectively contribute to economic growth and

development through income generation. In the case of Malawi, households act as both firm and

households, that is investment and consumption decisions of households are not separate from

those of their enterprise (be it farming or SMEs). The goal of microfinance in this respect is to

ease the constraints of households through either savings or credit in order to smoothen

consumption during lean times and allow for investment in either human or physical capital in

essence increasing their income and consumption and at national level, facilitating economic

growth.

B. Relationship between Microfinance and Poverty

The idea of a positive effect of microfinance is based on a variant of resource theory (Alvarez &

Barney, 2002). It provides the theoretical foundation of our understanding of how it impacts

poverty; as a result of access to microfinance the poor will possess the capacity to improve their

income generating activities, which are mainly limited by the lack of financial resources,

whether from savings, credit or insurance (Arun, Imai, &Sinha, 2006). Microfinance is also

based on the assumption that in the long term a microfinance loan will overcome this lack in

order to improve the households welfare (Oliver, 2011). Yet, there can be a great cost to

overcoming the credit barrier with interest rates for microfinance averaging 26 per cent, and

ranging to 85 per cent or higher globally (Rosenberg, Gonzalez, & Narain, 2009).

Otero (1999) states that microfinance creates access to productive capital for the poor, which

together with human capital, addressed through education and training, and social capital,

achieved through local organisation building, enables people to move out of poverty (Otero

1999). By providing material capital to a poor person, their sense of dignity is strengthened and

this can help to empower the person to participate in the economy and society (Otero, 1999). The

author notes that the aim of microfinance is not just about providing capital to the poor to combat

poverty on an individual level, it also has a role at an institutional level (Otero, 1999). It seeks to

create institutions that deliver financial services to the poor, who are continuously ignored by the

formal banking sector.

Littlefield and Rosenberg (2004) state that the poor are generally excluded from the financial

services sector of the economy, so MFIs have emerged to address this market failure. By

addressing this gap in the market in a financially sustainable manner, an MFI can become part of


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the formal financial system of a country and so can access capital markets to fund their lending

portfolios, allowing them to dramatically increase the number of poor people they can reach

(Otero, 1999). More recently, commentators such as Littlefield, Murdoch and Hashemi (2003),

Simanowitz and Brody (2004) and the IMF (2005) have commented on the critical role of

microfinance in achieving the Millennium Development Goals. Simanowitz and Brody (2004,

p.1) state, Microfinance is a key strategy in reaching the MDGs and in building global financial

systems that meet the needs of the most poor people. Littlefield, Murdoch and Hashemi (2003,

p1) state microfinance is a critical contextual factor with strong impact on the achievements of

the MDGsmicrofinance is unique among development interventions: it can deliver social

benefits on an on-going, permanent basis and on a large scale. Referringto various case studies,

they show how microfinance has played a role in eradicating poverty, promoting education;

improving health and empowering women. These case studies will be covered in the empirical

literature review.

C. Alternative views of Microfinance

However, other scholars hold differing views on the supposed impacts of microfinance on

household welfare and economic development. Hulme and Mosley (1996), while acknowledging

the role microfinance can have in helping to reduce poverty, concluded from their research on

microfinance that most contemporary schemes are less effective than they might be (1996).

They state that microfinance is not a cure-all for poverty-alleviation and that in some cases the

poorest people have been made worse-off by microfinance. Rogaly (1996) finds five major faultswith MFIs. He argues that:

They encourage a single-sector approach to the allocation of resources to fight poverty,

Microcredit is irrelevant to the poorest people,

An over-simplistic notion of poverty is used,

There is an over-emphasis on scale,

There is inadequate learning and change taking place.

Wright (2000) states that much of the scepticism of MFIs stems from the argument that

Microfinance projects fail to reach the poorest, generally have a limited effect on

incomedrive women into greater dependence on their husbands and fail to provide additional


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services desperately needed by the poor. In addition, Wright (2006) says that many

development practitioners not only find microfinance inadequate, but that it actually diverts

funding from more pressing or important interventions such as health and education as argued

by Navajas et al (2000), there is a danger that microfinance may siphon funds from other projects

that might help the poor more. They state that governments and donors should know whether the

poor gain more from microfinance, than from more health care or food aid for example.

Therefore, there is a need for all involved in microfinance and development to ascertain what

exactly has been the impact of microfinance in combating poverty. A brief empirical literature

review will discuss what different researchers have found and their conclusions.

2.2 Empirical Literature Review

There has been a wide body of research on microfinance and its impacts. However, relatively

few rigorous impact studies with longitudinal design have been completed, and the evidence onstatistical impacts has been mixed so far. There is not yet a widely acclaimed study that robustly

shows strong impacts, but many studies suggest the possibility (Armendriz and Murdoch 2010).

A. Microfinance and Poverty Reduction

Pitt and Khandker (1998) found that microcredit has a significant effect on the well-being of

poor families and they argued that this effect is greater when women are the program

participants. They contend that group lending schemes may have an informational advantage

compared with individual credits, obtaining information about the actions of each member of a

group in a low-cost way. Additionally, group members can monitor as well as train and assist

each other more economically than MFIs could do on an individual basis. The method used in

the Pitt-Khandker study is based on cross-section data. The study used a quasi -experimental

survey design to resolve the problems of endogeneity both at the village and household level.

The study used Bangladesh Institute of Development Studies and World Bank data of 1,798

households drawn from 87 villages in 29 thanas2 during 1991/92.They also used data from a

BIDS-WB follow up study of 1998/9.They provided separate estimates of the influence of

2A thana is an administrative unit that is smaller than a district and consists of a number of

villages. In Bangladesh, you have Divisions and under those divisions you have Zilas.

Thanas are under the umbrella of zilas


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borrowing by both men and women on six behaviours (family expenditure, non-land assets held

by women, male and female labour supply and boys and girls schooling), finding that credit is a

determining factor in many of these outcomes, and that credit provided to women was more

likely to influence these behaviours than credit provided to men. Additionally they found that the

flow of consumption expenditure increased 18 cents for every dollar borrowed by women, but

only 11 cents for every dollar borrowed by men.

However, a study done by Murdoch (1999), using the same BIDS-WB survey but employing

different methodology finds the program estimates non-existent or very small. The study

investigated a 1991-92 cross-sectional survey of nearly 1800 households in Bangladesh served

by microfinance programs of the Grameen Bank, the Bangladesh Rural Advancement Committee

(BRAC), and the Bangladesh Rural Development Board (BRDB). The sample also included a

control group of households in areas not served by any microfinance programs. Murdoch argues

that the Pitt and Khandker results are overestimates and flagship programs such as the Grameen

Banks do little to help the poor .He poses the question whether the benefits of microfinance can

indeed justify the high costs. However, both studies agree that access to microfinance is

associated to reduction of variability in consumption across seasons. Thus microfinance helps

smoothen consumption and thus reduces vulnerability of the poor (Khandker et al 1998,

Murdoch, 1999).

In support of the positive effects of microfinance a study by Imai et al. (2010) assessed the

impact of microfinance on poverty reduction in India. Cross-sectional data on 5,260 client (from

20 different MFIs) and non-client households was collected across India. They used a treatment

effects model and (Propensity score matching) PSM to account for selection bias. Impact was

assessed on an index-based poverty ranking indicator that contains information on landholdings,

income, assets, housing and sanitation. The authors found that microfinance had significantly

positive impacts on poverty reduction.

B. Microfinance Impact in Human Capital (Education)

Studies conducted in other countries show such contradictory results in other impact

determinants such as education. An example is a study by Shimamura and Lastarria-Cornhiel

(2010) who investigated the trade-offs between child labour and schooling. They investigated a

microfinance programme in Malawi and its impact on childrens school attendance and the


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likelihood of being involved in other productive activities. The authors conducted a paired -site

sampling survey to account for sample site variations and applied a two stage instrumental

variable (IV) approach. They found that microfinance participation decreased school attendance

by girls in particular, and that the programme did not reach the poorest people. Doubts are raised

about the validity of the instrument and hence their findings; no identification tests were run to

assess the validity of the instrument and no specification tests, i.e. a Hausman test, was

conducted to gauge whether OLS estimates would have been as useful as the IV estimates

presented in the paper(Duvendak et al,2011).

Nevertheless, supporting Shimamura et al findings is a study in Uganda where client households

were significantly more likely than non-client households to be unable to pay school charges for

one or more household members for at least one term during previous two years, hence children

had to drop out of school (Barnes, Gaile, et al 2001). Further, data suggest that the length of time

within the credit program fails to increase positive impacts on expenditure on education (Adjeiet

al, 2009), and worse still, decreases childrens enrolment: one study found that on-going

borrowing reduced childrens enrolment in school, with the proportion of the households girls

aged six to sixteen in school decreasing greater for continuing clients, than for departing clients

and non-clients (Barnes, Keogh et al, 2001).

In contrast, a number of studies although not specifically measuring the effect of microfinance

services on education showed that access to microfinance services(borrowing and/or savings) has

positive effects on education, mainly reduced dropout rates and an increase in girls

enrolment(Pitt and Khandker 1998;Chen and Snodgrass).In addition, a book which examined

microfinance projects concluded that microfinance contributes to improvements in childrens

welfare through increased incomes and thus: improved nutrition, housing, health and school

attendance, and reductions in harmful child labour (Marcus, Porter, & Harper, 1999).It stated that

a positive effect almost always outweighs the negative. However, it is important to note that

most microfinance impact studies that reported a positive correlation between access to

microfinance and education used microfinance institutions whose program design included theenrolment of children in school as a requirement for obtaining a loan or other services such as

Grameen banks.


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C. Microfinance Impact in Physical Capital (Business, Agriculture)

According to Hulme and Mosley (1996), the effects of failed investments have received little

attention. They suggest that a significant proportion of enterprises financed by Microfinance

failfor example 10-15% of those supported by BancoSol in Bolivia and 25% of the early

activities financed by the Malawi Mudzi Fund (Hulme and Mosley, 1996). Kondo et al (2008) in

the Philippines found that there was reduced reliance on higher priced loans, and some

consumption smoothing, positive impacts on employment and number of enterprises, but no

significant impacts on assets or human capital, although the length of time in which impact could

occur on these variables might be considered short.

In terms of agriculture, it was found that farmers receiving microcredit diversify the crops they

grow (Barnes, Gaile, & Kibombo, 2001; Barnes, Keogh, et al, 2001), only one of these studies

found that this increase in the number of crops grown translated into greater business income

(Barnes, Gaile et al., 2001). A study focused on a combined agricultural business development

and credit program in Kenya showed increased farmers income from export crops, but this

could not be attributed to the micro-credit element of the intervention (Ashrafet al., 2008). The

available evidence regarding impact on saving levels seems to be more positive, though only

four studies looked at savings levels; it suggests that both micro-credit and micro-savings have

positive impacts on the levels of poor peoples savings (Adjei, Arun, & Hossain, 2009; Barnes,

Gaile, et al, 2001).Similarly, the evidence shows that micro-credit and micro savings increase

both expenditure and the accumulation of assets.

D. Concluding Remarks Literature Review

From the above literature review it is important to note that many studies only looked at one

aspect of microfinance of credit, however a consideration of both savings and credit may offer

more insight into the effects of microfinance. Furthermore, the methodology of most of the

studies reviewed used a supply side approach, whereby microfinance institutions were first

identified and the effects tested on their clients. This approach brings across selective bias and

ignores the informal microfinance sector, thus not portraying the effects of access to

microfinance. Only one study by Ellis et al (2010) used a demand side approach linking

microfinance access, investments and economic growth. It used finaccess data survey from

Kenya and finscope data survey results from Tanzania and used Linear Probability Model to

measure the effect of microfinance household investment. The results showed that access to


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financial services enables households to make investments in education(which contributes to

human capital), starting or expanding a business, or investing in agricultural inputs or new

equipment (which contributes to physical capital and technological progress). It also showed that

supply side barriers to access to microfinance services reduce household investment (Elis et al

2010).


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CHAPTER 3 Methodology

3.0 Introduction

This chapter will outline the data used in the study, the model used to estimate the data and

definitions of the variables included in the model.

3.1 Sources of Data

The study used data from the Finscope data survey (2008) focusing on households that had

access to microfinance services at that time. The data has 4993 observations collected through

face to face interviews captured between November 2008 and January 2009.The majority of

sample respondents consisted of Malawian residents of the age of 18 and above. This data set is

preferred because it includes information about a larger number of institutions that offer

microfinance than the 2010 Integrated Household Survey data set. It offers more demand side

variables on access to microfinance.

3.2 Descriptive Statistics

Table 1 shows descriptive statistics for the finscope data survey of 2008.There were 4993

observations in total. Approximately 22% of respondents obtained a loan and 75% saved during

the period 2008 to 2009.This shows that Malawians are generally a nation of savers. Out of the

people who saved and borrowed, 10.9% borrowed or saved for the purpose of investment while

as 10.7% wasforconsumption purposes.

The mean age of head of Household was 41.7 with the majority of respondents being between

the age of 25 and 44. Approximately 80% of the respondents were married with 82.5 %of the

respondents being female. This shows that the data overrepresented women as opposed to men.

Another interesting characteristic is that majority of the respondents, 80% were from rural areas.

Furthermore, only 3.2 % of the population reached tertiary education.

In occupation, 14.8% were employed in wage employment, with the majority engaged in

farming.


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Table 1 Descriptive Statistics. (All variables are Dummy variables except for Income and

Age)

VARIABLE Mean

Borrowing 22%

Savings 75%

Investment 10.9%

Consumption 10.7%Others uses 01%

Household head age(between age of 25 and 44) 50%

Household head marital status 80%

Whether own business 14%

Farming 47.8%

Employed 14.8%

Transfers 4.9%

Highest level of education attained 3.2%

Household Head Sex 82.5%

Outside transfers 0.14%

Within transfers 0.53%

Reside 81%

Utility 13%

Owned 79.8%

Income (less than MK5000) 55%

3.3 Model Specification

A Linear Probability Model of the form similar to the one used by Ellis et al (2010) discussed in

the literature review will be used:

Pihd= d + B1F + B2Xihd +B3Hhd +ihd

Equation1

3.2.1 The Dependent Variable

ihP is a discrete variable equal to 1 if the person in household h uses formal borrowing or loan

facilities, and a value of 0 if they use informal borrowing or loan facilities. I will regress this

variable against a dummy variable F with a value of 1 if that individual has borrowed for

investment purposes, or equal to 0 if the person has only borrowed for consumption purposes. In

the Finscope data set, whereby formal refers to all individuals who are users of financial products


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which are governed by a legal precedence however, this is not exclusive usage as they may also

use informal mechanisms such as borrowing or saving with family and friends(Finscope,2008).

The same regression will be run for savings, for example using a sample of all those people who

have saved, whereih

Pis a discrete variable equal to 1 if the person in household h uses formal

savings facilities, and a value of 0 if they used informal savings facilities, and then will regress

this variable against a dummy variable F with a value of 1 if that individual has saved for

investment purposes, and equal to 0 if the person has saved only for consumption purposes.

Using the same model, a more specific regression will be run focusing on only those who have

access to Microfinance services, thus the sample will consist of both savers and borrowers. Thus

ihP

will be a discreet variable equal to 1 if the person in household h uses microfinance services

whether for savings or loan facilities and a value of 0 if they use informal borrowing or loan

facilities.

3.3.2 Independent variables

These include:

Investment

Household investment is measured by all a categorisation of responses deemed for investment

purposes. The finscope survey asked respondents to specify the purpose for which they saved or

borrowed. We used this information to categorise savers and borrowers according to whether

they were saving for investment or consumption purposes. Reasons to borrow or save are

classified as investment reasons if they could contribute to increasing the income of the

household in the future through human or capital accumulation (Ellis, Lemma, & Rud, 2010).

The categorisation of what are deemed investment and consumption purposes are set out in Table

1 overleaf:


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Table2: Investment vs. consumption reasons to save or borrow

Consumption reasons to save or borrow

For meeting household needs For meeting day to day expenses

For an emergency For old age

Holiday or Travel To pay off own debts

For Someone else to use To repay for someone else

To Improve a house To buy a house for your family to live in

Acquire household goods Purchase a building or house

To buy a car or motorbike Personal purchases

To leave something to your children Purchase land

Investment reasons to save or borrow

Agricultural improvements For education

Agricultural implements Fishing equipment

Agricultural inputs To purchase shares/stocks/bond/T Bills

To start a new business To buy a building/house to rent out

To invest In someone else's business Purchase livestock

To expand own business

Household investment is expected to give a positive sign on the basis that access to financial

services such as microfinance leads to investment in human capital as well as physical capital.

The data also included a set of control variables. The section below discusses these control

variables and how they may relate to the explanatory variable.

Household Head Sex

This is captured by a dummy variable HH Sex where by the base is female with value zero and

male is equal to 1.The expected sign is that it will give a positive sign.

Household Head Age

This is used as is in the data set. It is a continuous variable. It is expected to give a positive effect

to using formal loans. This is because such facilities as microcredit and usage of banking

facilities such as borrowing a loan are attached to age, thus people above the age of 18 will have

positive correlation. Furthermore, this may also be indicative of the increasing responsibilities as

one grows older, and thus more needs that need to be satisfied by extra finance for example

borrowing. To capture the influence of aging further, mainly old age, age2

(given by HHAgesq)

will be included in the regression; this is expected to give a negative value.


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Household Head Marital Status

This is given by a dummy variable HH Marital Status where by the base value is zero for all

those who are not married and if married, it is equal to 1.This is expected to give a positive sign

as married people are expected to seek help from formal mechanisms than from family and

friends i.e. less likely to rely on hand-outs.

Household Head Education level

This has been measured by a set of dummies for primary school level, secondary school and

tertiary level with no education as the base level. The expectation is that it will give positive

coefficients as education level increases.

Household Head Occupation

This is given by a proxy for occupation which is largest source of income. It has been

categorised into six different occupations included in the model as dummies. Employed covered

all those who receive wages or salary. Own business covered all income from own business

(such as trading, providing service, making/manufacturing, including buying and selling crops,

produce or fish) as well as Rental income (renting out rooms, houses, land, equipment, vehicle

or other property) and lending money. Farming covered income from fishing and crop produce.

As well as dummies for transfers divided into those who receive transfers from Malawi, from

outside Malawi, and those that receive transfers from friends, family and Aid organisations. All

variables on occupation are expected to give positive signs for example; own business is

expected to be positively correlated with usage of formal financial services for both savings and

investment as businesses need to safe keep their earnings as well as obtain loans etc. Farming is

also expected to give positive sign for the same reason. However, people who rely on transfers

from friends, family and Aid organisations are expected to give a negative sign.

Household Characteristics

This captured all factors that may influence one accessing formal or informal financial services.

In Malawi, prerequisites for holding a bank account include giving physical address which is

proxied by giving in a utility bill either (electricity or water).This has been captured by a dummy

variable whereby households that have electricity or piped water have a value of 1, and those that

dont have a value of zero. Household characteristics also includes a dummy for location, thus

rural and urban with rural as base. The household characteristics utility and location are expected

to be positively correlated with usage of formal services.


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3.3.3 Rationale for Using LPM

The linear probability model (LPM) was chosen for the econometric analysis because the data

provided by the finscope questionnaire, as noted in Elis et al is mostly categorical and variables

have to be transformed into dummies. This allows for the interpretation of coefficients to be

straightforward because the linear model gives the conditional average effect of moving from a

value 0 to a value 1 in a given category (e.g. the conditional average difference in formal savings

between households with a female head and households with a male head). In non-linear models

such as probit and logit, the coefficients are assumed to change at different points in the

distribution, making the estimates harder to interpret when the explanatory variables are discrete.

Similarly, when using interaction terms, the LPM allows for a clear interpretation of the

coefficients that is not possible in probit or logit.


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Chapter Four Empirical Results and Interpretation

4.1 Variable Analysis

Empirical investigation of cross section properties of the variables is the first step undertaken in

this study before regression analysis for conventional cross section regression analysis

4.1.2 Normality of the Error Term

The LPM follows the Bernoulli distribution, thus the assumption of normality for the disturbance

term is not tenable for the LPM because they also follow the Bernoulli distribution

(Gujarati).However, this is not a major problem since OLS estimators are still unbiased.

Furthermore, as the sample size increases, OLS estimators tend to be normally distributed

generally. As a result, in large samples the statistical inference of the LPM will follow the usual

OLS procedure under the normality assumption. In our case, n>30 which is large and as such F

and t tests can be computed.

4.1.3 Heteroscedasticity

The variance of the LPM s given by Var (Ui) =Pi(1-Pi)

This variance is heteroscedasticity which violates the CLRM assumption of homoscedastic

variances. In the presence of heteroscedasticity OLS estimators are not efficient such that they do

not have minimum variance. However, unbiased and consistent estimates of the standard errors

can be obtained by using white robust standard errors in STATA. See results in Appendix 2.

4.1.4 Multicollinearity Test

Another assumption of the CLRM is that there are no perfect linear relationships among the

explanatory variables. The Presence of perfect or near perfect linear relationships among some or

all the regressors may lead to indeterminate coefficients and highly inflated standard errors.

However, even if there is very high Multicollinearity the OLS estimates are still BLUE.

Nevertheless variance inflation factors were calculated to check for Multicollinearity, there was

no serious Multicollinearity.


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4.2 Empirical Estimation and Interpretation: Linear Probability Models

Table 3 shows us the results from the three regressions. In the first column, it shows the

relationship between usage of formal financial services with investment through savings. In the

second column, it shows the relationship between usage of formal loan facilities with investment.

In the last column, it shows the relationship between microfinance access and usage of savings or

loans to invest. Finally, the table shows the relationship between location of a household and

usage of formal loan, formal savings and microfinance institution.

Table 3: Relationship between usage of formal financial services and investment using

Finscope 2008 survey results.

Standard errors are in ( );* significant at 5%

Interpretation of Results

Access to Financial Services (Borrowing) and Investment

There is a positive relationship between access to formal financial services and investment. This

has been shown in both savings and borrowing. In the regression for borrowing i.e. taking loans,

the econometric results show that on average, other things being constant, households that use

loans to invest are 16.2 percentage points more likely to use formal financial services than

households that take loans to consume, even after controlling for individual and household

If use formal

Loan

If use Formal

saving

If use MFIfor loan or

saving

Use of loan to Invest .1623557

( 1069 )*

Use of Savings

to Invest

.245252

(.0223214)*

Use of Savings and

loans to invest

.1339835

.0178282*

Location; Rural .0370116

.0429922

-.0096098

.016388

-.01001

.0165505

Controls Included Yes Yes Yes

Number of Observations 1069 3722 3933


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characteristics. This is a significant result at the 5% level of significance. We therefore reject the

null hypothesis that Malawians do not use formal borrowing facilities for investment purposes.

This is in line with previous studies that found that micro credit has significantly promoted the

incidence of borrowing for investment. It implies that the effect of usage of formal financial

services and investment is strong.

Access to Financial Services (Saving) and Investment

The results from running the regression using the 2008 survey show there is a positive

relationship between using savings to invest and using formal financial services. On average, a

1% increase in using savings to invest increases the probability of using formal financial services

by 24.5% than for those who use savings for consumption, even after controlling for individual

and household characteristics. This is a significant result at the 5% level of significance. We

reject the null hypothesis that Malawians do not use formal saving facilities for investment

purposes. This is in line with available evidence regarding financial services impact on saving

levels, though only four studies looked at savings levels; it suggests that both micro-credit and

micro-savings have positive impacts on the levels of poor peoples savings (Adjei,Arun, &

Hossain, 2009; Barnes, Gaile, & Kibombo, 2001). Similarly, the evidence shows that micro-

credit and micro savings increase both expenditure and the accumulation of assets.

Access to Microfinance and Household Investment

The results from running the regression using the 2008 survey show there is a positive

relationship between using savings or borrowing to invest and usage of microfinance services.

People that borrow or save for investment purposes are 13.4 percentage points more likely to be

members or clients of a microfinance institution than those that borrow or save for consumption,

even after controlling for individual and household characteristics. This is a significant result at

the 5% level of significance. We reject the null hypothesis that Malawians do not use

microfinance facilities for investment purposes. This is in line with theoretical basis of

microfinance that households that have access to microfinance can invest in human capital as

well as physical capital (Otero 1999).

Relationship between Access to Financial Services and Place of Residence

The results from running the regression using the 2008 survey show there are mixed results in

the relationship between location and usage of formal financial services. When looking at loan

attainment, on average, households that reside in rural areas are 4 per cent more likely to use


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formal financial services than household that reside in urban areas; this result is statistically

insignificant at 10%, thus we fail to reject our null hypothesis that there is no difference between

rural and urban in terms of usage of formal loan facilities. It is also in contrast with a priory

expectations and empirical work such as that of Ellis et al (2010).The expectation would be that

rural households would be less likely to use formal loan facilities due to access barriers such as

availability of formal services. The same result is observed in the case usage of formal savings

facilities, access of microfinance services and living in rural areas. Thus there is no significant

difference in access whether one lives in rural or urban areas.

The results discussed above establish a link between access to formal financial services and

investment, and hence growth, and shows that formal financial services are used by

Malawian households, both rural and urban.

Control Variables and Access to Formal Facilities

It is important to also note the effects of individual and household characteristics on usage of

formal financial services. We see that in formal usage of savings facilities, household head sex,

location of household, all transfers, farming as occupation and marital status of head of

household do not significantly affect the usage of formal financial services. However, this is

similar for usage of loan facilities apart from Household head Sex and Transfers (outside of

Malawi and from friends, family and pensions.)


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Chapter Five Recommendation and Conclusion

5.1 Conclusion

The study investigated the effects of access to microfinance services on household investment in

productivity enhancing activities in Malawi. It used Finscope 2008 data and a linear probability

model to determine the extent at which financial services are used for investment purposes as

opposed to consumption. It also investigated the types of financial services, loan or savings and

how this varied with different demographics, mainly location.

The econometric results showed that formal financial services are more likely used for

investment purposes. People who borrow specifically to invest are 16.2 % more likely to use

formal financial services. Similarly, people who save specifically to invest are 25 % more likely

to use formal financial services. More specific to institution, people who save and/or borrow to

invest have a 13% higher incidence of using Microfinance Institutions.

This implies that formal financial services either; 1) promote saving or borrowing for investment

purposes perhaps because they offer more income or due to institutional structure and conditions.

Or 2) they attract people who specifically save or borrow for investment purposes thus prefer

formal financial services because of security in terms of savings or due to stigma attached to

informal mechanism such as Katapila. Nevertheless we are able to establish an important link

between access to financial services and investment, and thus poverty reduction at the household

level, in terms of higher incomes from investments. At the national level, we see the link

between access to financial services and economic growth; this will eventually lead to higher

incomes and standards of living, reducing poverty at the national level.

The data on demographics such as the individual characteristics show that many of the

characteristics do not significantly influence a households decision to use formal financial

services. Of particular interest was location of household. It was found that it does not matter

where a household resides, whether rural or urban in accessing financial services. This finding

warrants further investigation.


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5.2 Policy Implications

The findings from the study can help change the focus of financial inclusion policies as well as

poverty reduction strategies. The current focus of investment and savings is mainly on Small and

Medium Sized Enterprises. From the results, we see that Malawian households borrow and save

for investment purposes, thus economic growth can be promoted by increasing access to

financial services by maximising the investment potential of households. This can be done by in

cooperating investment promoting policies together with increased access.This can be achieved

through offering savings and loan coaching services to households or by promoting village

savings and loans services through institutionalising them. Policy makers can take a step further

by introducing insurance services in the same way. This could be more effective if it is backed

by policies that increase householdsdisposable income for example reduction in Tax. The fact

that location is not a significant factor in determining savings or borrowing means that banks and

microfinance institutions can focus on capturing this demand for financial services.

5.3 Limitations of the Study

The study used finscope 2008 survey data set, a more recent data set the finscope Consumer

survey 2014 would have yielded more recent results and offer more insight. However, it was not

possible to obtain the data. Another limitation of the study was in terms of the data set was the

higher number of rural households interviewed, this may have influenced the nature of the

results.

5.4 Direction for Future Research

To provide further insight and explore all the positive benefits of microfinance and financial

services in general in Malawi, a comprehensive study on barriers to access to financial services

and how these affect household investment would be worth investigating.


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APPENDIX 1 Diagnostic Tests

Table 4 Diagnostic Test Results

Model Ramsey Reset Test Breusch-Pagan Test

for heteroscedasticity

Multicollinearity Test

Mean Variance

Inflation Factor

If Use Formal

Borrowing(2.40)

0.0662

{20.49}

0.000

5.17

If Use Formal Saving (7.82)

0.000

{287.16}

0.000

5.07

If use MFI (3.34)

0.0184

{490.03}

0.000

5.05

Note: numbers in ,{} denotes Chi square values,() denote F values


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APPENDIX 2 Regression Results

Table 5 Regression Results

(1) (2) (3)FORMAL FORMAL MFI

INVESTMEN

T

0.162 0.245 0.134

(6.35) (10.99) (7.52)

HHAge 0.0126 0.00521 0.00165

(2.55) (2.54) (1.01)

HHAgesq -0.000137 -0.0000446 -0.0000159(-2.66) (-2.09) (-0.94)

HHMaritalStatus

0.0134 -0.00140 0.00526

(0.96) (-0.21) (1.08)

OWNBUSINESS

0.226 0.0548 0.0385

(4.40) (2.74) (2.27)

FARMING 0.0525 0.00248 -0.00474

(1.60) (0.18) (-0.39)

EMPLOYED 0.273 0.220 0.0394

(5.34) (8.02) (1.93)

TRANSFERS 0.228 0.0530 0.0483(2.34) (1.60) (1.81)

HIGHEREDUCATION 0.219 0.365 -0.0512

(3.54) (9.60) (-2.17)

HHSEX -0.107 -0.0109 -0.0222(-2.57) (-0.51) (-1.43)

OTHERUSE 0.0548 0.0775 0.0472


38/38

(0.94) (1.27) (1.03)

OutsideTransf

ers

0.694 0.303 0.177

(8.40) (1.56) (1.00)

WithinTransfe

rs

0.0357 -0.0788 -0.0849

(0.30) (-0.84) (-3.97)

RESIDE 0.0370 -0.0284 -0.0100(0.79) (-1.31) (-0.60)

Utility 0.241 0.192 -0.0179

(4.44) (7.01) (-0.93)

Owned -0.114 -0.0609 -0.00829

(-2.78) (-2.97) (-0.57)

Income 0.00171 0.00125 -0.000291(1.25) (2.35) (-0.83)

_cons -0.117 -0.00910 0.0280

(-1.02) (-0.18) (0.71)

N 1069 3722 3933tstatistics in parentheses*p< 0.05, **p< 0.01, ***p< 0.001

investigating the effects of access to microfinance on household investment in malawi

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