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Impact of Microfinance on Poverty By Umara Noreen FUIEMS Foundation University Islamabad 2010

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Page 1: Poverty and microfinance in Pakistan

Impact of Microfinance on Poverty

By

Umara Noreen

FUIEMS

Foundation University Islamabad

2010

Page 2: Poverty and microfinance in Pakistan

Impact of Microfinance on Poverty

By

Umara Noreen

A Dissertation Submitted to

The Faculty of Management Sciences

Foundation University, Islamabad

In partial fulfillment of the requirements for the

Degree of Doctor of Philosophy

In

Management Sciences

2010

Page 3: Poverty and microfinance in Pakistan

“I start with the name of ALLAH,

The most Compassionate, The most Merciful

All praises be to Allah, Lord of the Universe,

The most Compassionate, The most Merciful,

Sovereign of the Day of Judgment,

Thee Alone we worship; and no Thee Alone we turn for help.

Guide us to the straight path;

The path of those on whom Thou has bestowed Thy grace;

Not of those who have incurred thy anger,

And nor of those who have gone astray.”

(Holy Qur’an 1:1-7)

Page 4: Poverty and microfinance in Pakistan

Supervision, Evaluation and Defense

Supervision Prof. Dr. M. Iqbal Saif Supervisor FUIEMS

[email protected] Foundation University

Islamabad, Pakistan

Evaluation Prof. Dr. Amin Sarkar Foreign School of Business Alabama

[email protected] Evaluator A & M University USA

Economic Development and

Development Issues in

Business

Prof. Dr. Syed Azizi Wafa Foreign Labuan International Campus

[email protected] Evaluator Universiti Malaysia, Sabah,

Malyasia

Development Issues in

Business

Prof. Dr. Mushtaq A. Sajid Local University College of

[email protected] Evaluator Administrative Sciences,

University of Azad Jammu &

Kashmir, Kotli Campus

Defense Prof. Dr. Mushtaq A. Sajid Local University College of

[email protected] Examiner Administrative Sciences,

University of Azad Jammu &

Kashmir, Kotli Campus

Dr. H Humayun Naeem Local FUIEMS

[email protected] Examiner Foundation University

Islamabad, Pakistan

Dr. Syed Hassan Raza Local Allama Iqbal

[email protected] Examiner Open University

Business Administration

Islamabad

Page 5: Poverty and microfinance in Pakistan

TABLE OF CONTENTS

Declaration i

Acknowledgement ii

Abstract iii

List of Acronyms iv

List of Tables v

List of Figures ix

List of Appendices x

1 Chapter One. INTRODUCTION

1.1 Background of Research 1

1.1.1 Defining Microfinance 3

1.1.2 Microfinance and Micro credit 4

1.1.3 Microfinance in Developing Countries 5

1.1.4 Defining Poverty 8

1.1.5 Microfinance and Poverty 8

1.1.6 Poverty in the Global Perspective 11

1.2 Microfinance in Pakistan 12

1.3 Identification of the Knowledge Gap 14

1.4 Research Objectives 15

1.5 Significance of the Study 16

1.6 Statement of the Problem 17

1.7 Expected Contribution of the Study 17

2 Chapter Two. REVIEW OF LITERATURE

2.1 Theoretical Underpinnings 20

2.1.1 Types of Impact Assessment 22

2.1.2 Challenges of Impact Evaluation 22

2.2 Conclusion of Impact Assessment Studies 31

2.3 Social Impact of Microfinance 32

2.4 Generic model of Social Impact Assessment 34

3 Chapter Three. CONCEPTUAL FRAME WORK

3.1 Research Design 36

Page 6: Poverty and microfinance in Pakistan

3.2 Formulation of Hypotheses and Operationalization of Variables 38

3.2.1 Family/Household-Level 38

3.2.2 Micro-enterprise Level 47

3.2.3 Demographic Characteristics of the Clients 50

3.3 Proposed Research Model 51

4 Chapter Four. METHODOLOGY

4.1 Small Enterprise Education and Promotion (network) SEEP 52

4.2 Client Assessment Continuum 53

4.3 Unit of Assessment 53

4.4 Longitudinal vs. Cross-sectional Design in Impact Assessment 54

4.5 Survey Method 57

4.6 Instrumentation: Development of Interviewee Data Form 57

4.7 Sampling Procedures 59

4.7.1 Cluster Sampling 60

4.7.2 Sample Size 61

4.8 Selection of Microfinance Providers 62

4.9 Pretest 63

5 Chapter Five. DATA ANAYSIS

5.1 Response Rate and Non-Response Bias 65

5.2 Overall Sample Demographic Profile 65

5.3 Descriptive Analysis of Responses 68

5.4 Reliability 75

5.5 Validity 78

5.6 Chi Square Test 80

5.6.1. Basic Computational Equation 80

5.7 Econometric Evidence: Logistic Regression 102

6 Chapter Six. INTERPRETATION

6.1 Household Level 113

6.2 Micro-enterprise Level 120

7 Chapter Seven. CONCLUSION and RECOMMENDATIONS

7.1 Salient Findings 124

Page 7: Poverty and microfinance in Pakistan

7.2 Conclusion 125

7.3 Practical Implications 125

7.4 Limitations 126

7.5 Recommendations 127

7.6 Future Research 129

REFERENCES

APPENDICES

Page 8: Poverty and microfinance in Pakistan

i

DECLARATION

I, Umara Noreen, PhD scholar in the Department of Management Sciences, Foundation

University, Islamabad, hereby declare that the matter printed in the thesis titled “Impact

of Microfinance on Poverty” is my own work and has not been submitted as research

work to any university or institution in Pakistan or abroad.

Umara Noreen

Page 9: Poverty and microfinance in Pakistan

ii

ACKNOWLEDGEMENT

I am grateful to almighty Allah for His constant mercy, guidance and support for the

completion of present manuscript.

I owe considerable thanks to my supervisor Professor Dr. M. Iqbal Saif, Director,

Management Sciences, Foundation University, Islamabad for his constant

encouragement, invaluable insight, and unconditional support with material and

resources. I am indebted to Dr. Qaisar Abbas, Dr. Shumaila Yakub Khan Yousafzai,

Arshad Zaheer and Manshoor Shaukat for their reassurance and enthusiasm. Special

thanks are due to the management of NRSP, Khushali Bank, First Microfianace Bank,

Pak Oman Microfinance Bank and Pakistan Microfinance Networks for their kind

cooperation during data collection. I am thankful to my Baji, Mamoon, Zeeshan and

Nouman from the bottom of my heart for their continuous support, encouragement and

motivation.

I am also indebted to all my friends for their constant support and motivation, Atiya,

Hanniya and Sabeen to whom I can count for anything.

Credit goes to my parents for their help that held me up in the hour of need. I

appreciate the many prayers and hopes they have whispered for my success. I wish to

thank my children, Fatima and Taha for their patience and understanding through out my

research phase. At last I owe a great deal to my husband for understanding, encouraging

and being a constant emotional and moral support during the work.

Umara Noreen

Page 10: Poverty and microfinance in Pakistan

iii

ABSTRACT

The primary objective of this thesis is to analyze the impact of microfinancing on poverty

through a sample survey of four microfinance institutions, using concepts: like household

income/expenditure, asset holdings and diversity, education and various measures of

vulnerability at household and enterprise level.

The study employed the tool developed in collaboration by Assessing the Impact of

Microenterprise Services (AIMS) and Small Enterprise Education and Promotion

network (SEEP). The tool has been modified in the local context. Face to face structured

interviewing was used to collect primary data.

Chi square test is used to analyze the difference between incoming (Less than one year)

and established clients (2-5 years) on the basis of poverty indicators established at

household and enterprise level. Role of demographic and other independent variable is

analyzed with the help of multinomial regression analysis.

The evidence turns out to be mixed one like (i) strong positive impact on children

education and enterprise financial performance. (ii) mixed evidence on food security,

household expenditures and household assets and no impact has been observed on

housing and income smoothening of enterprise.

Present study can be very useful to IA practitioners and policy makers. This research has

made a significant contribution in unraveling some of the myths of microfinance hence

advancing literature and research on this important issue.

Keywords: Microfinance, Poverty Alleviation, Household Welfare, Enterprise

Management, Impact Assessment.

Page 11: Poverty and microfinance in Pakistan

iv

LIST OF ACRONYMS

AIMS Assessing the Impact of Microenterprise Services (project)

ASHI Ahon Sa Hirap

BRAC Bangladesh Rural Advancement Committee

BRI Bank Raykat Indonesia’s Unit Desa in Indonesia

CGAP Consultative Group to Assist the Poor

FMFB The First Microfinance Bank Ltd.

HEPM Household Economic Portfolio Model

Imp-Act Improving the Impact of Microfinance on Poverty: Action Research Programme

KB Khushhali Bank

MDG Millennium Development Goals

MFB Microfinance Banks

MFI Microfinance Institution

MTDF Medium Term Development Framework

NRSP National Rural support Programmme

POMFB Pak-Oman Microfinance Bank Ltd.

PRSP Poverty Reduction Strategy Paper

RSP Rural Support Programme

SEEP Small Enterprise Education and Promotion (network)

SPI Social performance indicator

SPM Social performance management

USAID United States Agency for International Development

Page 12: Poverty and microfinance in Pakistan

v

LIST OF TABLES

TABLE TITLE PAGE

1. Challenges Faced By Microfinance Industry in Pakistan 13

2. Summary of Impact Assessments (1995-2007) 26

3. Operationalization of Education 39

4. Operationalization of Housing 40

5. Operationalization of Food Security 42

6. Operationalization of Household’s Income/expenditure 44

7. Operationalization of Household Assets 46

8. Operationalization of Financial Performance 48

9. Operationalization of Enterprise Resource Base 49

10. Operationalization of Income Smoothening 50

11. Cost and Benefit Analysis of Poverty Assessment Approaches 52

12. Units of Assessment and their Advantages and Disadvantages 54

13. Impact Assessment Studies using Different Study Designs 55

14. Overview of the Employed Research Methods 57

15. Minimal Sample Size for Two Groups of Clients Disaggregated

by one subcategory

61

16. Minimal Sample Size for Two Client Groups Disaggregated by

two sub-categories

61

17.

18.

Summary Statistics of Microfinance Institutes

Demographic Profile of the Respondents

63

65

19. Household Profile of the Respondents 66

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vi

20. Organizational and Client Profile 67

21. Descriptive Statistics of Children’s Education 69

22. Descriptive Statistics of Housing 70

23. Descriptive Statistics of Food Security 70

24. Descriptive Statistics of Household Expenditure 71

25. Descriptive Statistics of Household Assets 72

26. Descriptive Statistics of Enterprise of Financial Performance

and Enterprise Resource Base

73

27. Descriptive Statistics of Income Smoothening 75

28. Reliability Statistics 78

29. Rationale for choosing Chi-Square Test 79

30. Chi-Square Tests for EDU 1 80

31. Chi-Square Tests for EDU 2 81

32. Chi-Square Tests for EDU 3 81

33. Chi-Square Tests for HUS 1 82

34. Chi-Square Tests for HUS 2 82

35. Chi-Square Tests for FD1 83

36. Chi-Square Tests for FD 2 83

37. Chi-Square Tests for FD 3 84

38. Chi-Square Tests for FD 4 84

39.

40.

Chi-Square Tests for FD 5

Chi-Square Tests for HSIN 1

85

85

41. Chi-Square Tests for HSIN 2 86

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vii

42. Chi-Square Tests for HSIN 3 86

43. Chi-Square Tests for HSIN 4 87

44. Chi-Square Tests for HSIN 5 87

45. Chi-Square Tests for HSIN 6 88

46. Chi-Square Tests for HSIN 7 88

47. Chi-Square Tests for HSIN 8 89

48. Chi-Square Tests for HSIN 9 89

49. Chi-Square Tests for HSAS 1 90

50. Chi-Square Tests for HSAS 2 90

51. Chi-Square Tests for HSAS 3 91

52. Chi-Square Tests for HSAS 4 91

53. Chi-Square Tests for HSAS 5 92

54. Chi-Square Tests for HSAS 6 92

55. Chi-Square Tests for HSAS 7 93

56. Chi-Square Tests for HSAS 8 93

57. Chi-Square Tests for ENT 1 94

58. Chi-Square Tests for ENT 2 94

59. Chi-Square Tests for ENT 3 95

60. Chi-Square Tests for ENT 4 95

61. Chi-Square Tests for ENT 5 96

62.

63.

Chi-Square Tests for ENT 6

Chi-Square Tests for ENT 7

96

97

64. Chi-Square Tests for ENT 8 97

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viii

65. Chi-Square Tests for ENT 9 98

66. Chi-Square Tests for ENT 10 98

67. Chi-Square Tests for ENT 11 99

68. Chi-Square Tests for ENR 12 99

69. Chi-Square Tests for ENR13 100

70. Chi-Square Tests for INS 1 100

71. Chi-Square Tests for INS 2 101

72. Chi Square Results at a Glance: Summary Table 101

73. Summary of Multinomial Logistic Regression for Education,

Housing and Food

105

74. Summary of Multinomial Logistic Regression for Household

Expenditures

107

75. Summary of Multinomial Logistic Regression for Household

Assets

108

76. Summary of Multinomial Logistic Regression for Enterprise

Management

110

77. Multinomial Regression Results at a Glance: Summary Table 111

Page 16: Poverty and microfinance in Pakistan

ix

LIST OF FIGURES

FIGURE TITLE PAGE

1. Matrix of Microfinance (and related) Institutions in Bangladesh,

2006

5

2. Microfinance Can Reach the Lower Income Levels 10

3. A Simple Impact Assessment Model 21

4. Categories of Social Impact 33

5. The Generic Social Impact Assessment Model 35

6. Proposed Research Model 51

7. Client Assessment Continuum 53

8. Choice Points in Sampling Design 60

Page 17: Poverty and microfinance in Pakistan

x

LIST OF APPENDICES

1. Interviewee Data Form English & Urdu

2. Calculation for KR-20 Formula and the Cronbach’s Alpha

Page 18: Poverty and microfinance in Pakistan

Microfinance & Poverty 1

Chapter 1

Introduction

1.1 Research Background

A loan to poor used to be an absurd concept. Millions of poor, vulnerable non-poor

and unbanked households want financial services. They seek a diverse range of

services including loans, savings, insurance, and facilities for sending and receiving

remittances. Households use financial services to build incomes, mitigate risk, and

protect against vulnerability often exacerbated by economic crises, illness, and natural

disaster. They invest in micro and small businesses, purchase assets, improve their

homes, and access health and education services. Yet formal financial intermediaries

like commercial banks generally do not serve these households.

Conventional banks have failed to serve this market for a variety of reasons. Firstly,

their business models are generally unsuitable for managing a microfinance business,

characterized by high-volume, low-value transactions. Secondly, they employ

traditional lending technologies based on collateral requirements (to which the

unbanked generally don‘t have access). And in many cases conventional banks

believe, unjustly, that the unbanked are unwilling and unable to repay loans and save

money.

In developing countries a large number of people do not have access to credit which is

perhaps main cause of poverty. Mani reasons behind that is inability to provide

collateral and high processing cost. (Hermes & Lensink, 2007).

Donaghue (2004) explains the contribution of financial services as a development

tool, gone through the evolution from subsidized loan to poor farmers, micro financial

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Microfinance & Poverty 2

institutions (MFI) providing loans to female borrowers and offering a range of

services including credit, savings and insurance to reduce poverty.

Microfinance emerged as a poverty fighting tool and its origin is often dated as late as

the 1970s. However, this concept was accomplishing some of the objectives in the

cooperative movements in the nineteenth century, in the rural finance experience post-

World War II and in the microenterprise development sector starting in the 1970s.

These diverse roots can be categorized in to five common objectives which include

microenterprise development, innovation/investment promotion, consumption-

smoothing, women‘s empowerment and financial systems development. The

wonderful feature of microfinance is that it can achieve all of these objectives

(Dunford, 2006).

The precedence for microfinance lies in the many traditional and informal systems of

credit that has existed in developing economies for centuries. Many of the current

practices are derived from community-based mutual credit transactions that were

based on trust, peer-based, non-collateral, borrowing and repayment (Hassan, 2002).

Hermes and Lensink (2007) identified that the idea of microfinance in terms of being

a tool for poor people has gained considerable importance during the last decade. The

evidence lies in the high accomplishment of Grameen Bank (Bangladesh) and Bank

Rakayat (Indonesia).

In 1983, Yunus formed Grameen (meaning "Village") Bank. Its business focused

entirely on providing very small loans to impoverished people, mostly women, who

organized themselves into small groups of five to help, reinforce, and supervise one

another. Loans were typically less than $80 for first-time borrowers, in contrast to

commercial loans that would typically be larger than $800. Before Grameen Bank,

moneylenders were the only source of finance for the poor, and they charged very

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Microfinance & Poverty 3

high interest rates. That not in absolute poverty could join credit cooperatives, but the

amount they could borrow was often too small to meet their needs. Conventional

banks were rarely an option since they required collateral, which most poor people did

not have, and they required a great deal of paperwork, which the poor often found

intimidating.

Bhutt & Tang (2001) have reported that;

―The last decade has seen the evolution of microfinance institutions that have created

significant income and employment opportunities for the poor in developing

countries. In addition to reaching out to many disadvantaged micro entrepreneurs,

some programs have made significant strides in moving toward operational and

financial self-sufficiency‖.

Academicians and policy makers have given a lot of consideration to microfinance

recently. According to a statistic, there was 19.7% increase in microfinance

institutions and 119% increase in the number of borrowers only during the period of

1997-2005. Interestingly, more than 80% of the borrowers are women (Daley-Harris,

2006).

1.1.1 Defining Microfinance

There has been tremendous research and literature available on microfinance but still

there is no universally agreed definition of microfinance. To most, microfinance is the

provision of a broad range of financial services to those excluded from the formal

financial system. Systems of exclusion are not based just on lack of wealth but also on

social, cultural, and gender barriers.

Yunus (2006) looks at the concept from Grameen‘s standpoint and uses a more lender

oriented approach by saying ―Microcredit is the extension of small loans to

entrepreneurs too poor to qualify for traditional bank loans‖. It has proven to be an

effective and popular measure in the ongoing struggle against poverty.

Page 21: Poverty and microfinance in Pakistan

Microfinance & Poverty 4

Delgado (2005) defines micro lending as;

―Micro lending is the practice of extending small loans to the poor (also known as

micro-loans) for income-generating activities often coupled with other financial

services such as savings and insurance‖.

The provision of small loans (microcredit) to poor people to help them engage in

productive activities or grow very small businesses. The term may also include a

broader range of services, including credit, savings, and insurance.

While conventional banking offers loans to the poor, microfinance focuses lending to

the poor with no collateral requirement. Unlike government credit programs and

traditional bank credit programs that emphasizes large loans for long repayment

periods, microfinance provides small loans that are repaid within short periods of

time.

1.1.2 Microfinance and Micro credit

Microfinance is a broad concept of providing a web of different financial products

like loans, insurance, savings, home loans and funds transfer whereas, microcredit is

providing loans only. Hence, it can be stated that microcredit is just one ingredient in

the bowl of microfinance. Also, microfinance institution (MFI) may include different

activities like skill training, entrepreneurial education counseling on importance of

children education, improving nutrition choice and health (Grameen, 2008).

The importance of microcredit is also noted in the United Nations World Summit

Outcome Document, 2005, (The United Nations, 2005) which states that

―We recognize the need for access to financial services, in particular for the poor,

including through microfinance and microcredit‖

The document stipulates that microcredit will help member countries achieve the

millennium development goals (MDGs) of reducing poverty rates by 50% by 2015.

Indeed, the year 2005 was declared the Year of Microcredit by the General Assembly

of United Nations.

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Microfinance & Poverty 5

Microfinance institutions provide a broad range of services including insurance, credit

finance and many more. In order to represent programme diversity, examples of

Bangladeshi MFIs can be placed on a matrix (Figure 1). On the horizontal axis is the

continuum between pure credit provision and broader financial service provision; on

the vertical access we find the continuum between only credit or finance, credit or

finance plus business-related services, and credit or finance plus social programmes.

Figure 1: Matrix of Microfinance (and related) Institutions in Bangladesh, 2006

Source: Based on Hulme and Moore 2006

1.1.3 Microfinance in Developing Countries

The last decade has seen the evolution of micro finance institutions that have created

significant income and employment opportunities for the poor in developing

countries. In addition to reaching out to many disadvantaged microentrepreneurs,

some programs have made significant strides in moving toward operational and

financial self-sufficiency. The superior performance of such programs as ACCION's

BancoSol in Bolivia, Bank Rakyat Indonesia's (BRI) Unit Desa program in Indonesia,

and the Grameen Bank in Bangladesh are frequently cited as evidence that it is

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Microfinance & Poverty 6

possible for microfinance institutions to make small loans to large numbers of poor

people in a sustainable manner (Bhutt & Tang, 2001).

It is important to state, that the term ‗microfinance‘ has been used interchangeably

with ‗microcredit‘ in Pakistan, largely because other services and products in the

sector have been far less developed than credit. Savings and insurance, for example,

are still in their infancy as far as their provision by microfinance institutions is

concerned, and even some microfinance banks have been slow to evolve their savings

instruments and potential (Zaidi, Jamal, Javed & Zaka, 2007).

The experiences of these and other prominent microfinance programs have triggered

replication efforts in one form or another worldwide--in countries ranging from

Bolivia, Peru, Mexico, and Costa Rica to Nigeria, Mali, Malawi, Togu, Chile,

Malaysia, Indonesia, Sri Lanka, Nepal, and India. The performance of most such

programs, however, has not been encouraging. Many have been plagued with such

problems as high default rates, inability to reach sufficient numbers of borrowers, and

a seemingly unending dependence on subsidies. Few of them have lived up to their

original objective of "including the excluded" (Bhutt, 2001).

There are both macro and micro factors that explain the low level of financial sector

development and performance in poorer countries. These include distortion in

macroeconomic policies, weak institutions, and inefficient markets characterized by

poor business practice. High rates of inflation, government deficits which ―crowd out‖

private borrowers, weak governance and institutional capacity, and an inadequate

contracting and information environment lead to a lack of resilience, balance and

variety, and discourage serving nontraditional segments. All these contribute to

―broken‖ financial sectors in many poorer developing countries, and they are at the

very root of why informality is still dominant there (Barthe, 2006).

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Microfinance & Poverty 7

In the long run, access to adequate and appropriate financial resources is critical in

solving societal problems such as illiteracy, poverty, lack of skill, inaccessible

markets etc. and the real issues that lie behind these issues: lack of political will and

leadership, lack of transparency, high graft and corruption, lopsided developmental

policies ... etc. Microfinance is indeed an essential ingredient in the development

process but not the only ingredient.

In Bangladesh Grameen Bank's positive impact on its poor and formerly poor

borrowers has been documented in many independent studies carried out by external

agencies including the World Bank, the International Food Policy Research Institute

(IFPRI) and the Bangladesh Institute of Development Studies (BIDS).

In Nepal, operations of existing MFIs have encountered certain problems on their way

in provision of rural financial services and specifically micro financial services.

Supremacy of government and its agencies in micro-credit, limited outreach in the

hilly areas sustainability and interest rate are the main reasons.

In India, microfinance does not directly address some structural problems facing

Indian society and the economy, and it is not yet as efficient as it will be when

economies of scale are realized and a more supportive policy environment is created.

MiBanco, Peru Accion Communitaria del Peru (ACP) was established as a non-profit

NGO in Peru with the major objective of community development. Initially, in 1980s

ACP was focusing on microcredit in the city of Lima only. But lately in 1985, ACP

was transformed in to a for-profit financial institution. Reason quoted for this

transformation was lack of access to capital and expansion of the institute in terms of

services.

As in much of the world, microfinance programmes are also growing in number and

size in transition economies. Since 1994, programmes have been operating in

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Microfinance & Poverty 8

economies as diverse as China, Albania and Russia. The principal objectives of the

programmes is to raise incomes and broaden financial markets by providing financial

services (principally credit) to small scale entrepreneurs who otherwise lack access to

capital markets (Morduch & Aghion, 2000).

1.1.4 Defining Poverty

While there is world-wide agreement on poverty reduction as an overriding goal of

development policy, there is little agreement on the definition of poverty and

definition of poverty does matter for poverty eradication strategies (Laderchi, Saith &

Stewart, 2003).

The condition of poverty has been interpreted conventionally as ―one of lack of access

by poor households to the assets necessary for a higher standard of income or welfare,

whether assets are thought of as human (access to education), natural (access to land),

physical (access to infrastructure), social (access to networks of obligations) or

financial (access to credit) (World Bank 2000)‖.

Poverty is said to ―exist in a given society when one or more persons do not attain a

level of material well-being deemed to constitute a reasonable minimum by the

standards of the society‖ (Ravallion, 1992).

Poverty is usually measured in absolute terms, providing a measurement of income or

expenditures in current terms to determine whether the household is poor.

Collectively, the poverty line in a country is the cutoff annual income below which

households are considered poor.

1.1.5 Microfinance and Poverty

Sometimes referred to as banking for the poor, Microfinance is not a magical silver

bullet that is going to change the world by itself but inside it, lies a solution to combat

poverty. Microfinance is providing a stable income which may be repaid in six

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Microfinance & Poverty 9

months and then taking another small loan which turns into the social uplift. This

improvement can be depicted in the improvement of housing conditions like ―moving

from a house made of mud to one made of wood‖ or advancement in nutrition and

children education.

Rubana (2008) explains that microfinance has surfaced out as an efficient

development strategy and has significant policy implications in terms of eradicating

poverty and accomplishing MDGs.

Experience shows that microfinance can help the poor to increase income, build

viable businesses, and reduce their vulnerability to external shocks. It can also be a

powerful instrument for self-empowerment by enabling the poor, especially women,

to become economic agents of change. Surely, one cannot deny the role of

microfinance in poverty reduction as it raises income and consumption of poor

households (Khandker, 2005; Copestake, Dawson, Fanning, McKay, & Wright-

Revolledo, 2005).

Poverty is multi-dimensional. By providing access to financial services, microfinance

plays an important role in the fight against the many aspects of poverty. For instance,

income generation from a business helps not only the business activity expand but

also contributes to household income and its attendant benefits on food security,

children's education, etc. Moreover, for women, who, in many contexts, are secluded

from public space, transacting with formal institutions can also build confidence and

empowerment.

Recent research has revealed the extent to which individuals around the poverty line

are vulnerable to shocks such as illness of a wage earner, weather, theft, or other such

events. These shocks produce a huge claim on the limited financial resources of the

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Microfinance & Poverty 10

family unit, and, absent effective financial services, can drive a family so much

deeper into poverty that it can take years to recover.

Having said that poverty is a multidimensional and multifaceted concept, it is

important to specify those dimensions which a particular research is targeting.

Indexed Based Ranking (IBR) Indicator (Arun, Imai & Sinha, 2006) a composite

indicator that includes various aspects of welfare, possession of land, number of

salaried persons, livestock, assets, housing, and sanitation facility has been taken for

this research for measuring poverty.

Following figure shows the wealth pyramid, numbers of people and their annual per

capita expenditures are taken from VISA International and The World Bank. The

solid horizontal line approximates an international poverty line.

Figure 2: Microfinance Can Reach the Lower Income Levels

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Microfinance & Poverty 11

As it is obvious from the figure, commercial banks reach the top pyramid. Credit

unions through their cooperative principles and low cost have been successful in

serving the lower strata of pyramid but were unable to reach the international poverty

line. It is generally agreed that financially sustainable microfinance operations reach

the ―near poor‖ and the ―upper poor. Further down the pyramid, there is The Question

(symbolized by the dotted-line arrows from ―microfinance‖ on Figure 2) about the

sustainability and impact of microfinance when offered to large numbers of the

―poor,‖ especially those living on the borderline of destitution; that is, those living on

a dollar a day or less (Dunford, 2006).

1.1.6 Poverty in the Global Perspective

Millinium Development Goals (MDGs), stated by the World Bank that all the

countries have agreed to achieve by 2015 are:

―To eradicate extreme poverty and hunger; to achieve universal primary education; to

promote gender equality and empower women; to reduce child mortality; to improve

maternal health; to combat HIV/AIDS, malaria and other diseases, to ensure

environmental sustainability and develop global partnership for development‖.

Speakers at the meeting insisted for provision of education to women and more

emphasis on fund allocation especially in health sector so that each one, particularly

those living in the remote rural areas could have access to the proper health care

facilities. They were disappointed about the proper utilization of funds allocated for

the social sector development (Dawn, 2005).

Donor agencies are orienting their programming around the attainment of the

millinium development goal and are mobilizing new resources to reduce hunger and

poverty, eliminate HIV/AIDS and infectious diseases, empower women and improve

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Microfinance & Poverty 12

their health, educate all children, and lower child mortality (Littlefield, Morduch &

Hashemi, 2003).

1.2 Microfinance in Pakistan

Recognizing the importance of microfinance as a tool of poverty reduction and social

mobilization, the Government of Pakistan has accelerated its efforts to establish

strong foundations of microfinance in the banking sector; The Khushhali Bank was

established as the first specialized microfinance bank (MFB) in 2000.

Less than a year later, a wholly separate regulatory framework for State Bank-

licensed microfinance institutions was promulgated –the Microfinance Institutions

Ordinance, (MFI)2001. As a result, during the last six years, six MFBs have started

operations. Besides Microfinance Banks, other types of institutions such as

specialized microfinance institutions, NGOs rural support programs and commercial

financial institutions are also actively involved in the provision of microfinance in

Pakistan. In order to facilitate these non-bank microfinance providers, the Pakistan

Poverty Alleviation Fund (PPAF) was established in 1999 as a distributor/wholesaler

of credit to non SBP-regulated micro financial providers.

The Pakistan Microfinance Network (PMN) is an association of organizations

engaged in microfinance and dedicated to improving the outreach and sustainability

of microfinance services in Pakistan. PMN and its members are working together to

broaden access to financial services for all and create opportunities for the progress

and prosperity of poor people.

Recently, SBP through an amendment has allowed micro-finance banks to issue term-

finance certificates for meeting the 15% capital adequacy requirement. World Bank

reported that Poverty Alleviation Fund (PPAF) Program in Pakistan, initiated seven

years ago is achieving the desired objectives. Number of micro-finance provided has

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Microfinance & Poverty 13

increased from 60,000 borrowers to 1.5 million. The average size of a loan is $ 150

and has benefited 9 million people in 111 districts across the country (Saleem, 2008).

The government‘s Poverty Reduction Strategy Paper (PRSP) articulated in 2003, and

its Medium Term Development Framework 2005-10 (MTDF), both address the key

problem of poverty in the country and consider microfinance as a critical tool to make

progress in all three areas mentioned above – for excellent background studies on the

microfinance sector in Pakistan in recent years, see in particular, Hussein & Hussain

(2003).

Besides the enormous growth and growing potential for the microfinance industry in

Pakistan, there are number of challenges which practitioners are facing. The outreach

of these institutions has grown, albeit at a slow rate, and the numbers and

beneficiaries remain much below our requirements (Choudry, 2008). Following table

shows list of some of the challenges:

Table 1. Challenges Faced By Microfinance Industry in Pakistan

S. No Challenges

1 Limited Market Outreach

2 Lack of Government Commitment for Microfinance Development

3 Legislation and Regulation

4 Absence of Savings Deposit

5 Limited Products and Services

6 High Cost Factor and high interest rates

7 Weak Capacity and Inefficiency of NGO MFIs

8 Obstacles for Microfinance Outreach to Women

9 Absence of Risk Mitigation Measures

Source: Based on Choudry 2008

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Salient features of the microfinance industry in Pakistan are a massive upfront

investment, investment initiating growth, globally competitive credit delivery costs,

solid loan repayment for most institutions, insufficient revenue generation to cover

costs, lack of movement towards sustainability (Burki, 2006).

1.3 Identification of the Knowledge Gap

Three issues are of importance when impact of microfinance is discussed:

―First, which contribution is seen as the most important (improvement of income,

accumulation of assets, empowerment of women, etc); second, does microcredit reach

the core of the poor or does it predominantly improve income of the better-off poor;

and third, do the benefits outweigh the costs of microfinance schemes (Dunford,

2006)‖.

Small amounts of microfinance loans are not enough to produce significant income to

meet interest expense and this problem magnifies further when we talk about rural

population (Scully, 2004).

The problem magnifies further when researchers have the limitations about the

literature on Impact Assessment Methodologies. Most of this literature underscores

the pitfalls of undertaking studies in which an attempt is made to observe, leave alone

quantify, the ‗impact‘ of any intervention in order to address poverty. Impact

Assessment (IA) experts caution researchers about making grand statements and

reaching final conclusions which are based on quantification of too many measurable

(Zaidi et al, 2007).

It has been observed that amongst the academic development community there is

recognition that perhaps we know much less about the impact of these programs than

might be expected given the enthusiasm for these activities in donor and policy-

making circles (Weiss & Montgomery, 2005).

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Microfinance & Poverty 15

There has been a discussion about the transaction cost of microfinance which is a big

question mark on the sustainability of microfinancial institution. Latest research

reports that most microfinance programmes are lacking financial sustainability and

they are sill dependent on the donor agencies in order to bear the high cost (Cull, Kunt

& Morduch, 2007).

Despite the fact that donor‘s eagerness about microfinance program, there is a huge

gap for thorough research on the impact of microfinance, cost effectiveness and

outreach. Research is greatly required to tackle the issues of selection and placement

biases (Weiss & Montgomery, 2005).

1.4 Research Objectives

The objectives of this study are:

1. To assess and analyze the impact of microfinance at household level such as

household welfare (children education, housing and food security), household‘s

expenditure and household assets.

2. To assess and analyze the impact of microfinance at enterprise level, such as

financial performance, enterprise resource base and income smoothening.

3. To identify the degree to which demographic (age, education, gender) and other

independent variables such as, number of households, number of salaried persons

and type of area can affect the impact of microfinance at household level.

4. To identify the degree to which demographic (age, education, gender) and other

independent variables such as, number of households, number of salaried persons

and type of area can affect the impact of microfinance at enterprise level.

5. To propose and test a model of impact of microfinance at household and

enterprise level to see impact of poverty.

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Microfinance & Poverty 16

1.5 Significance of the Study

An impact evaluation perform dual tasks, first is that it helps to identify whether the

program has any significant positive impact on clients or not; secondly, impact

evaluations offer vital information about the prospective products and services that

could satisfy the needs of clients.

In addition to this impact evaluations provide grounds for donors and government

whether to promote the existing program or not (Snodgrass & Sebstad 2002).

Results of best practicing MFIs can become the benchmark for rest of other MFIs plus

it will give light to the policy makers for decision making for the future (Karlan &

Goldberg, 2007).

However, amongst the academic development community there is recognition that

perhaps we know much less about the impact of these programs than might be

expected given the enthusiasm for these activities in donor and policy-making circles

(Kurmanalieva, Montgomery, & Weiss, 2003).

To quote a recent authoritative volume on micro finance.

MFI field operations have far surpassed the research capacity to analyze them, so

excitement about the use of micro-finance for poverty alleviation is not backed up

with sound facts derived from rigorous research. Given the current state of

knowledge, it is difficult to allocate confidently public resources to micro-finance

development (Zeller & Meyer, 2002).

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Microfinance & Poverty 17

1.6 Statement of the Problem

The focus of this study is primarily on impact of microfinance at household and

enterprise levels. This research addresses the following questions.

1. Is there a difference between new clients and established clients of

microfinance at household level, such as household expenditure, household

assets, housing, children education and food security?

2. Is there any difference between new clients and established clients of

microfinance at enterprise level, such as financial performance, income

smoothening and enterprise resource base?

3. Is there any significant difference between new clients and established clients

with respect to gender, age, education, number of households, number of

salaried persons and type of area?

1.7 Expected Contribution of the Study

Present study identifies a gap in existing research and contributes to existing literature

as follows:

1. Although microfinance was a buzz word in terms of a tool for poverty reduction,

prior literature about microfinance and poverty is lacking empirical research.

Major dilemma is how to measure the involvement of microfinance (Dunford,

2006).

This study is first of its kind of empirical research which is highlighting the

ground realties in Pakistan, offering important contribution for underscoring these

impacts at household and enterprise levels, therefore contributing to the body of

knowledge.

2. There is need of high quality impact assessment studies to understand the role of

microfinance in eradicating poverty especially in different environments to give

direction to microfinance institutions (Kurmanalieva et al, 2003).

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Microfinance & Poverty 18

Despite the fact that microfinance has proven as an anti-poverty tool, but extant

research conclude that there is need of rigorous research to understand the

overoptimistic views of impact of microfinance in the right direction (Hermes &

Lensink, 2007).

3. At evaluation level, many questions remain to be answered concerning the impact

of microfinance on the different dimensions of poverty and the trade-offs between

these dimensions as stressed by Morduch (2000) and World Bank (2000). As long

as these questions remain unanswered the appropriate design technology for

microfinance and other instruments of poverty reduction themselves remain open

questions. These questions can best be answered only when some dimensions of

the poverty are chosen and other than the program participation, role of other

independent variables which have an impact on well being of the clients is studied

in-depth, hence this study offers substantial contribution as it addresses the role of

other independent variables.

4. Rigorous research is needed in neighborhood of developed countries such as

United Sattes, where there is high income inequality. It would be beneficial to

know about the contribution of microfinance in terms of improving the life styles

and eradicating poverty in those poor countries that are stuck in the vicious circle

of poor systems of education, high unemployment rates, malnutrition and the

worst living conditions (Gibb, 2008).

5. Despite a multitude of studies devoted to the topic, it is interesting to note that

literature on impacts of microfinance on the poor in developing countries is

debatable, since it is presenting mixed evidence. Finding of some studies are

positive and extremely convincing (Hossain, 1988; Wahid, 1993; Yaron, 1994)

whereas some studies find negative impacts (Morduch, 2000, Weiss &

Montgomery 2005). Again, few other studies have shown positive impacts in

some areas and no impacts in other areas (Sebstad & Chen, 1996).This research

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Microfinance & Poverty 19

attempts to uncover these impacts at household and enterprise levels with

reference to the Pakistan and would try highlighting important implications for

rest of the developing countries.

6. A review of the micro-finance sector in Pakistan reveals that the interest in

assessing the widespread impact of micro-credit on poverty is a relatively recent

phenomenon and that almost all the major studies in this regard were undertaken

after 2000. (Hussein & Hussain, 2003). It was therefore imperative to study this

area that has a distinguished contribution for achieving MDGs.

7. There has been a lack of empirical research to truly support the claims of overly

optimistic opinions about the wonders of microfinance. Present study is trying to

cover the gap. This is the first study of its kind and scale in Pakistan that attempts

to quantify and demonstrate some of the outcomes from microfinance

interventions. Present study has adopted a tool developed by Assessing the Impact

of Microenterprise Services (AIMS) and Small Enterprise Education and

Promotion network (SEEP). However, it was developed further to incorporate

cultural diversity by adding few items where necessary.

8. In particular the model is extended to incorporate both household and enterprise

level outcomes as a means to alleviate abject poverty. The evidence on the impact

that how does it affects the welfare of household (in terms of poverty) and

enterprise has been explored. Further, the study underscores the issues of role of

other independent variables i-e gender, age, education, number of households,

number of salaried persons and type of area on the microfinance intervention.

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Microfinance & Poverty 20

Chapter 2

Review of Literature

2.1 Theoretical Underpinnings

This chapter presents an overview of extant research on impact assessment studies.

The objective of this chapter is to review important impact assessment studies being

conducted in various parts of the world and its significance for the microfinance

institutions, practitioners, policy makers donors etc. Second objective is to investigate

the methodological flaws that come in the way of conducting impact assessments.

Third objective is to build up a strong base for theoretical framework for this research.

Microenterprise development and impact assessment (IA) can not be separated

nowadays. All the stakeholders which include practitioners, industry, donor agencies,

policy makers and of course researchers have interest in such studies. It becomes the

obligation for MFIs to prove that funds have been spent to attain the primary objective

for which they have been working. Also, practitioners would like to get feed back

about their product and services whether they are meeting the client‘s needs

satisfactorily. (Nelson, 2000).

Microfinance programs typically target the poor. Barnes (2001) found that MFI

programs are targeting and giving benefits to low-middle income micro entrepreneurs.

To quote the director of a large Asian microfinance institution that has received

substantial amounts of aid financed impact assessment (IA) consultancy and internal

IA-capacity building

‘...impact assessment studies keep donors happy... we don’t use them very much’.

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Microfinance & Poverty 21

That is why it is becoming important to study the impacts of these microfinance

programs on the lives of the poor. It is important from the point of view of

stakeholders who are also getting benefit from it. For this matter impact studies are

conducted. Several studies in the developing countries have been conducted and the

purpose remain the same i-e what impact these studies have on poor in terms of their

social and economic well being. The main goal of IA methods is to investigate the

attribution of changes in target client well being to microfinance intervention (Pawlak

& Matul, 2004).

Monitoring tools like CGAP‘s Poverty Assessment Tool, Cashpore Housing index,

SEF‘s Participatory Wealth Ranking, and USAID‘s AIMS Tools have been successful

to reveal the impact on clients and outreach in the past few years.

These tools have been found to be practical in terms of ease of use and yield useful

information to donors (Morduch & Haley, 2001). A simple impact assessment model

has been shown in the Figure 3.

Figure 3. A Simple Impact Assessment Model

Source: Learning from Clients: Assessment Tools for Microfinance Practitioners

Mediating variables explains all those factors that are not directly associated with the

program but can improve or limit the change. Examples are gender of the client,

number of households etc. External factors might cause certain change exclusive of

Impacts

Mediating

variables

External Factors Agent or

Program

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Microfinance & Poverty 22

the program itself. Examples are change in income of the household not due to the

client‘s activities.

2.1.1Types of Impact Assessment

Sharma (2000) explains two kinds of impact assessment are investment led and

insurance led are usually conducted. Investment led seeks to determine the return on

investment in terms of income, consumption and wealth. Insurance led target to

explore whether access to credit has provided any stability in the events of unexpected

income (bad harvest) and expenditure shocks (health emergencies).

Karlan & Goldberg (2007) explained three types of impact evaluation. First, and

perhaps most importantly, ―program‖ evaluation refers to examining whether a

particular microfinance institution is effective or not in improving the welfare of its

clients. Second, ―product or process‖ evaluation refers to evaluating the relative

effectiveness for a particular microfinance institution in implementing one product

versus another, or one process versus another. Third, ―policy‖ evaluation refers to

macro level policies for example banking regulations etc.

2.1.2Challenges of Impact Evaluation

Abundant studies have demonstrated the effectiveness of microfinance to alleviate

poverty in various regions of the world. An important reason put forward is that its

impact goes far beyond business loans since access to financial services is a

fundamental basis for the other interventions to alleviate poverty. Improvements in

health care, nutritional advice and education can be sustained only when households

have increased earnings and greater control over financial resources (Kulik &

Molinari, 2004).

Impact evaluations can be used either to estimate the impact of an entire program or

to evaluate the effect of a new product or policy. In either case, the fundamental

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Microfinance & Poverty 23

evaluation question is the same: ―How are the lives of the participants different

relative to how they would have been, if the program, product, service or policy was

not implemented?‖ The first part of that question, how the lives of the participants are

different, is the easy part. The second part, however, is not. It requires measuring the

counterfactual, how their lives would have been had, if the policy was not

implemented. This is the evaluation challenge (Karlan & Goldberg, 2007).

Despite the impressive impacts of microfinance services on poverty, health, and

empowerment, the development community realizes other services and strategies

besides credit must be made available to create a web of support to help families lift

themselves out of poverty (Watson & Dunford, 2006).

Bowen (2007) clarifies that MFIs should go beyond providing micro-credits only,

since effectiveness largely depends on the broad range of services including

insurance, savings and home loans etc.

Hulme (1997) describes that besides impact assessment design, its implementation is

even more challenging. Main problems are interviewer quality, interviewee‘s

motivations and cost.

“Self Selection” Bias

True researcher on the impact assessment looks at the research design chosen to

conduct the study. Although number of studies have been conducted on the impact

assessment but the problem of selectivity bias pops out as one of the major problems.

Perhaps the most difficult issue a researcher has to address in an impact study is to

sort out whether wealth is created due to the program participation or participants

were already relatively wealthy when they joined the program, known as selection

bias. Hulme (1997) explains that location of the program plays a significant role in the

success of a program and can be one of the selection biases.

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Selection bias may occur because of the following reasons:

a. Difficulties in finding a location at which the control group‘s economic, physical

and social environment match that of the treatment group;

b. The treatment group systematically possessing an ‗invisible‘ attributes which the

control group lacks (most commonly identified as entrepreneurial drive and ability);

c. Receiving any form of intervention may result in a short-term positive response

from the treatment group (the Hawthorne effect);

d. The control group becoming contaminated by contact with the treatment group

(Though this could be a long term program goal!); and

e. The fungibility of the treatment (e.g.; when a loan is transferred from a borrower to

someone else or when the loan is not used in the planned way).

By ensuring that control and treatment groups are far apart. Problems (b) and (c) are

more challenging but can be handled by using the control group of prospective clients.

(Hulme & Mosley 1996).

―One of the most widely cited impact assessments, by Pitt and Khandker (1998), use

the World Bank‘s data set from three MFIs in Bangladesh and a combination of

survey design and intricate econometric techniques (Weighted exogenous sampling

maximum likelihood, Limited information maximum likelihood and village fixed

effects, or WESML-LIML-FE) to tease out gender differentials in program impact.

They do find that credit to women has a larger impact than credit provided to men for

a number of impact variables, including labor supply, children‘s education and

household expenditure (Alexander, 2006)‖.

Hermes & Lensink (2007) quotes that

―The main problem with lending to the poor is that information costs are high as

compared to the size of the loan. It is generally known that information costs of

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Microfinance & Poverty 25

lending may be high since lenders are not able to distinguish projects with respect to

their risk profiles when allocating credit (adverse selection problem) and borrowers

may be able to apply the funds to different uses than those agreed upon with the

lender‖.

Program Placement

In order to confront the program placement problem, idea is to do randomized

selection from large number of possible sites. It is assumed that program sites are very

likely to be similar to the non-program sites. Difference in the wellbeing of two

groups over time can be considered the contribution of program.

(Dunford, 2006).

Not will be out of place recording a summary of different impact studies conducted in

different countries during the decade of 1995-2007 in table 2.

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Microfinance & Poverty 26

Table 2. Summary of Impact Assessments (1995-2007)

Year MFI/Country Title Authors Study Setting Results Sample size Variables

tested

Subject

type/Respondents

1995 BRAC

Bangladesh

―Do Poverty Alleviation

Programmes Reduce Inequity in

Health: Lessons from Bangladesh,‖

A.M.R.

Chowdhury and A.

Bhuiya

Longitudinal research

(1992-1995) basic

competency in reading, writing, and arithmetic

among children 11-14

years old was studied to compare the impact of

program

Positive impact was

experienced as the member

joined and stayed with the microfinance program

1996 15 countries from Asia, 10

from Africa, 3

from Latin America, 4

Overviews of studies on the impact of micro enterprise-credit

Jennefer Sebstad and Gregory Chen

Mixed-method approach, using surveys

and Case study

Positive effects on household production and

incomes, asset

accumulation and consumption

Household, enterprise and

individual level

indicators

1996

Thailand

Mk Nelly et al

―Non participants in non

program villages used

as control‖

―Positive benefits, but no

statistical tests for

difference reported‖

1997 CARD,

Philippines

―Reaching the Poor with

Effective Microcredit: Evaluation of a Grameen Bank

Replication

in the Philippines‖

Mahabub Hossain

and Catalina P. Diaz,

Older borrowers were

compared with new borrowers

Income from older

borrowers‘ micro enterprises was 3.5 times

higher than newer

borrowers‘enterprises, and older borrowers also

increased income from

other sources.

1998 Grameen Bank

Bangladesh

Fighting Poverty with

Microcredit, a study conducted

by World Bank

Relationship between

microfinance and

children‘s schooling was studied for

participants and non-

participants of the program.

Higher levels of schooling

for children of all credit

program participants and statistically significant

higher rates of schooling

for girls in Grameen households was found

1998 Grameen Bank

Bangladesh

―Does microfinance really help

the poor? Evidence from

flagship programs in Bangladesh‖

Jonath Morduch Cross-sectional design Households having access

and eligible to borrow

donot have notably higher consumption and impact

on education than control

households

1800

households

Household

consumption

and Education

Control and treatment

group

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Microfinance & Poverty 27

1998

BRAC,

Bangladesh

―Poverty Alleviation and

Empowerment: The Second Impact Assessment Study of

BRAC‘s Rural Development

Programme‖

M. Muazzam

Husain

Compared participants

of microfinance programs with the

nonparticipants

Participants changed in a

positive direction, being the longitudinal study,

provided pretty good

evidence for impact as compared to the first

impact.

1999 CRECER Bolivia

―Impact of Credit with Education on Mothers and Their

Young Children‘s Nutrition:

CRECER Credit with Education Program in Bolivia‖

Barbara MkNelly and Christopher

Dunford

Before and after comparison

Eighty-six percent of clients said their savings

had increased; 78 percent

did not have any savings prior to program

participation.

1999 Peru

Lima

Microfinance clients in Lima,

Peru: Baseline Report for AIMS core impact assessment

Elizabeth Dunn

Longitudinal Households receiving

program credit have incomes above the poverty

line, and are building

themselves through entrepreneurship

701

entrepreneurial households

Household and

entrepreneurial level

Clients vs non-clients

1999 Indonesia Bank

Rakayat(Island of Lombok)

―Gender, self-employment and

literate programs an Indonesian case study‖

Rosintan D.M.

Panjaitan-Drioadisuryo,

Kathleen Cloud

Compared treatment

group and control group

Positive impact on

household income, involvement in decision

making, nutrition and

children‘s education

215, 121

treatment and 94 control

group

Household

income, involvement in

decision

making nutrition and

children‘s

education, participation in

household work

Treatment and control

group

1999 Thialand The impact of group lending in northeast Thailand

Brett Coleman Comparison between participants and non-

participants households

and villages where programs introduced

and villages where not

yet introduced

No evidence of program impact on assets or income

variables

2001 Bangladesh, BRAC

―Assessing the poverty and Vulnerability Impact of Micro-

Credit in Bangladesh: A case

study of BRAC‖

Hassan Zaman Before and after comparisons

Results show that microcredit can diminish

vulnerability by women

empowerment, asset accumulation and crisis

coping mechanisms.

1072 Education, awareness to

social issues,

control over assets

Treatment and control group

2001 SHARE, India

(Andhra

Pradesh)

―Paths out of Poverty: The

Impact of SHARE Micro fin

Limited in Andhra Pradesh, India‖

Helen Todd

Todd created a poverty

index composed of four

elements: sources of income; productive

assets; housing quality,

Dramatic differences

between mature and

incoming clients boys, the study found no relationship

between poverty status and

229

respondents

125 share clients 104 new

clients

Income, assets,

education,

housing

Mature and incoming

clients

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Microfinance & Poverty 28

as measured by the

SML House Index and household dependency

burden

school attendance, but for

girls there was a negative relationship—poor clients

were more likely to send

their girls to school.

2001 Bangladesh Does Micro-finance Really

Benefit the Poor? Evidence from

Bangladesh. Reforming Policies

and Institutions for Poverty

Reduction held by the Asian

Development Bank. Manila.

Khandker, Shahid. A longitudinal study

was conducted in 91/92

and 98/99 to check the impact.

Results clearly reveal that

there was a positive change

the poverty during 91/92 and 98/99. There was an

improvement in income,

expenditure and household net worth.

87 villages with

2599

respondents

Household

Income and net

worth

Program participants,

target non-participants

and nontarget group

2001 Foundation for

Credit and Community

Assistance

Uganda

Uganda 95% of clients improved

health and nutrition practices for their children,

as compared to non-clients.

Also, 32% of clients had tried at least one AIDS

prevention practice,

compared to 18% for non-clients

2001 India Managing Resources, Activities,

and Risk in Urban India: The Impact of SEWA Bank

Martha A. Chen

and Donald Snodgrass

Longitudinal research

(1997-99)

Positive impact on housing

and business food, fixed assets, self esteem,

confidence, economic

decisions and personal savings

900 Housing,

business, food, medical, fixed

assets, personal

savings

Poor women

2001 Zimbabwe

(Harare, Chitungwiza,

Bulawayo and

Mutare)

―Microfinance Program Clients

and Impact: An Assessment of Zambuko Trust, Zimbabwe‖

Carolyn Barnes, Impact of microfinance

program on nutrition choice was observed

1997-1999

It led to a positive impact

on the consumption of high protein foods (meat, fish,

chicken, and milk) for

extremely poor client households.

579

respondents: 338 clients and

241 non clients

2001 Peru (Lima) ―The Impacts of Microcredit: A

Case Study from Peru‖

Elizabeth Dunn

and J. Gordon

Arbuckle Jr.,

1997-1999

701 respondents in 1997

and 529 in 1999

found Mibanco clients

earned $266 more per

household member per year than non-participants

Income,

education.

Food,savings

Mostly female, and

married having at least

one enterprise

2002 Thialand

(Village Northeast

Thialand)

Microfinance in Northeast

Thialand: Who benefits and how much?

Brett E. Coleman ―It controls for

endogenous self-selection and program

placement, using data

from a unique survey conducted in 1995-1996

Cross sectional‖

―Results demonstrate that

microfinance loans positively affect many

measures of household

welfare for the wealthy committee members, but

the impact is largely

insignificant for Poorer rank and file

members‖

445 households

(14 villages)

Wealth,

employment, household

savings,

household expenses,

health care and

education

Household participants

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Microfinance & Poverty 29

2003 The Activists

for Social Alternatives

(ASA), India

―ASA-GV Microfinance Impact

Report 2003,‖

Helzi Neponen

The Internal

Learning System (ILS). Was used in which

literate clients can keep

track of the changes in their own

living situations, and

members crosscheck each other‘s reports for

accuracy.

Older member improved

on their housing, food, nutrition and education as

compared to the new

members

2004 India Small Industries Development Bank of India

(SIDBI), India a study

conducted by EDA Rural Systems Private Ltd., Gurgaon,

India in collaboration with

Institute of Development Policy and Management (IDPM), U.K,

The two-stage longitudinal socio-

economic research from

April 2001 – April 2004

Positive impacts were found on education,women

empowerment, income

generating activities, decision making and

increased mobility.

2004 Kashf, Pakistan

―Impact Assessment of Kashf‘s

Microfinance and Karvaan

Enterprise Development Programme‖

Arjumand and

Associates,

consultants

Clients vs non-clients

were compared for

poverty longitudinal was followed

Clients increased their

income as compared to non

client The poverty rate was decreased by 20 percentage

points

2004 India Impact of Microfinance

Programs on Children‘s

Education Do the Gender of the Borrower

and

the Delivery Model Matter?

Nathalie Holvoet

Interviews from clients. Analysis indicates that

combined financial and

social-group intermediation leads

to higher educational

inputs and outputs, mainly for girls.

2005 Khushhali Bank, Paksitan

Meeting the Double Bottom Line –The Impact of Khushhali

Bank‘s

Microfinance Program in Pakistan

Montgomery, H. Both access to and participation in the

program had strong

positive impacts on all variables tested for income

generation. She showed

that as the number of loan cycles increased, assets in

terms of amount of land

cultivated, value of farm

equipment, and hours of

tractor use increased

significantly

2005 Moris Rasik, Timor Leste,

Indonesia

―Moris Rasik: An Interim Impact Assessment‖

David Gibbons

It keeps records of the poverty status of each

client at the time of

Results shows that 54% of Very Poor clients

experienced a decline of at

Page 47: Poverty and microfinance in Pakistan

Microfinance & Poverty 30

Source: Developed

entry into the program;

thus progress, in terms of poverty can be

measured

least one category of

poverty since joining

2005 Local Initiatives

Project, Bosnia

and Herzegovina

―Impacts of Microcredit on Clients in Bosnia and

Herzegovina‖

Elizabeth Dunn,

Clients and non-client entrepreneurs were

interviewed using the

longitudinal research design

Client households increased their income

more than non-client

households. New clients did even better. Increase

in the employment and

wages of non-household employees, but only

among the newest clients

2000 clients and 1,200 no-

clients

Annual per capita income,

poverty level of

household, improvements

in business

premises and investment in

equipments

Clients, nonclients and new clients

2005-2006 Japan (Laos) Impact of microfinance on

Household welfare

Longitudinal/cross

sectional

251 households

in six villages

Income,

expenditure,

savings, health, empowerment,

education,

assets

Old saving groups, new

savings groups, and

control savings groups

2007 Philippines

(Barangay)

―Impact of Microfinance on

Rural Households in the

Philippines; A Case Study from the special Evaluation study on

the effects of microfinance

operations on poor rural households and the status of

women‖

Toshio Kondo Longitudinal

Quasi-experiment

Positive impact on per

capita income, savings,

food, employment, fixed assets, livestock and

poultry and household

appliances

2200 household

& 28 MFIs

Per capita

income,

expenditures, savings, and

expenditure on

food, education, household

appliances &

assets

Households participants

and nonparticipants

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Microfinance & Poverty 31

2.2 Conclusion of Impact Assessment Studies

As it is evident from the table 2, evidence is mixed; most of the studies conducted before

2000 reveals a significant positive impact in terms of ―increase in household savings,

income, and improved choice on nutrition, education, health and schooling‖ for those

who participated in the program. Similarly, strong positive impacts on women both

interms of economic and social empowerment (in terms of ability to access loans, own

productive resources, engagement in income generation activities, decision making and

increased mobility) have been observed through participation in the program. However,

some of the studies report negative or no impact. Interesting evidence came from one

study conducted at India, which reported a negative relationship for enrollment. Poor

clients were more likely to send their girls to school.

Many MFIs around the world such as Grameen Bank, Bangladesh Rural Advancement

Committee (BRAC) in Bangladesh, Bank Raykat Indonesia (BRI) and the BancoSol in

Bolivia have reported significant positive impacts on the lives of poor in terms of

household expenditures, children education and health, accumulation of household assets

(Chaves & Gonzalez-Vega, 1996; Hashemi & Schuler, 1994; Hulme & Mosley, 1996;

Khandker, 1998). Few more positive impacts were found in the impact assessment

studies (Goetz & Sen Gupta, 1996; Meyer, Gonzalez-Vega, & Rodriguez-Meza, 2000;

Mosley, 2001; Navajas, Schreiner & Rahman, 1998).

Results of a study by U.S Agency for International Development of three microfinance

organizations in Uganda shows that microcredit has helped to improve the lives of the

poor (Baido, 2008).

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Microfinance & Poverty 32

There has been found a positive change in the literacy rates of children of BRAC clients

from 1992 to 1995. Statistics show that improvement was 12 to 27 percent in three years

(Chowdhury & Bhuiya, 2001).

One study from Indonesia shows that credit contributes to increased expenditure on

education; another from Kenya shows that program borrowers are more likely than a

control group to spend a portion of their enterprise profits for school fees (Sutoro 1989;

Buckley 1996). But the findings from other studies are less positive. Pitt and Khandkar‘s

Bangladesh study shows that, overall, credit has an impact on boys schooling but not

girls.

While the huge potential of microfinance is always acknowledged, studies on the impact

of microfinance conclude that it is unclear whether microfinance contributes to a

reduction in poverty or is the most efficient method to reduce poverty without additional

measures in areas such as education, health and infrastructure. Moreover, it is recognized

that ‗impact‘ takes some years to work its way through into the lives of beneficiaries, and

contradictory or ‗mixed‘ results are not uncommon (Zaidi et al, 2007).

2.3 Social Impact of Microfinance

Utilization of microfinance loans lead to a higher and better diversified income situation

and ability to survive in the periods of reduced income levels. These impacts can be

experienced at personal/household level, local community level and regional level.

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Microfinance & Poverty 33

Figure 4. Categories of Social Impact

Source: Adapted from Social Investment Services AG: www.responsAbility.ch

Personal/Household level

At this level the following effects can take place:

• Empowerment of women, who are often, preferred clients of MFIs. This can lead to a

higher social status, better education and more independence of women.

• Better education in acquiring basic skills and financial knowledge.

• Ability to cope with economic shocks by means of savings, credit, micro insurance

products.

• Better access to education, healthcare, sanitary infrastructure, food supply etc.

All the above mentioned impacts have been found as poverty indicators which is very

essential part of this research. Other two impacts are wider impacts at local community

and regional levels. It includes creation of jobs, higher and more stable income increased

trade with neighboring communities and regions improving the economic base and

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Microfinance & Poverty 34

resilience at local levels. Strengthening of the microenterprise and Strengthening of the

financial sector as a whole, widening its scope and outreach are some of the impact at

regional level.

Social performance is not equal to social impact, i.e. social performance is about

investigating the structure of an organization which include mission, and management

practices etc. its conduct in the market and local and wider community. Thus, social (and

economic) performance precedes social (and economic) impact (Zeller, Cécile, & Martin

2003).

This can be better understood with the help of following:

Structure Conduct Performance Impact (on

clients/non-clients, communities etc. in many dimensions)

2.4 Generic model of Social Impact Assessment

Ghalib (2007) explains the social impact on lives of the poor by means of a standard

model. This is sort of an experimental design which consists of a control group and a

treatment group. Treatment group is exposed to microfinance intervention whereas

control group is not, assuming that both the groups are living in the identical economic

and social conditions. The difference in the quality of lives, in terms of social indicators

is considered the impact of microfinance. Since social impact is a complex process and

number of other factors will contribute to the model.

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Microfinance & Poverty 35

Figure 5 graphically explains the social impact of microfinance.

Figure 5. The Generic Social Impact Assessment Model

Source: Ghalib, 2007

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Microfinance & Poverty 36

Chapter 3

Conceptual Framework

The previous two chapters discussed the existing studies in order to establish a

conceptual framework and to build a foundation for hypothesis development. The first

objective of this chapter is to propose hypotheses based on the previous studies of impact

assessment in order to find out how microfinance is helping to improve lives of clients at

enterprise and household level. The second objective is to develop a model to assess the

impact of microfinance using household welfare, household assets and household income

at household level and by taking income smoothening, financial performance and

enterprise resource base at enterprise level. Demographic variables such as gender, age,

education, number of households and number of salaried persons were taken for analysis

purpose.

3.1 Research Design

The first step in designing a quantitative survey is to conceptualize the impact chain to be

examined. It should specify the unit(s) of analysis to be assessed (e.g., household,

individual, enterprise, community) and the types of impacts to be studied (Hulme, 1997).

Although there has been abundant literature and research available on impact of

microfinance, practitioners have been experiencing the methodological flaws in the

impact studies since long. The most difficult issue in the impact studies has been to find

out that whether the impact was due to microfinance alone or there were certain other

economic level activities in the area which were ostensible in poverty reduction.

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Microfinance & Poverty 37

Even when clear evidence of impacts is obtained, it is difficult to understand what

happens in the ―black box‖ that transforms inputs/intervention into outputs/impact, and

thereby to know why we get the impacts we get (Dunford, 2006).

Armendáriz & Morduch (2005) explains that biases in such research designs can

overstate or understate the actual impacts

Even if the researcher has chosen the appropriate design, it is not possible to exclude all

the validity threats. The best technique is to use multiple designs in order to minimize the

uncertainty about the size of treatment effects (Reichardt & Mark, 1998).

Among three methods for supply of Impact Assessment Information, positivist method

was chosen since it is more rigorous and possibility of quantitative estimates of impact is

available. Further, it is more convincing to skeptical outsiders. (See appendix 1 for supply

of impact assessment information). Positivistic ideal calls for deductive approach. In

deductive approach, Hypotheses are generated from the theory followed by empirical

research to test the hypothesis. (Bryman & Bell, 2003).

Graziano & Raulin (2004) mentioned that the deductive approach emphasizes on

deductions from constructs. The deductions are started as hypotheses and then

empirically tested for the research.

Arun & Hulme (2003) suggest the need of range of product and services to tackle the

heterogeneity of the demand structure of poor clients.

In recent years microfinance projects and institutions have been subjected to a vast

amount of impact assessment study. The initial emphasis on ‗scientific‘ sample surveys

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Microfinance & Poverty 38

and statistical analyses has shifted as multi-method IAs and most recently participatory

approaches have been utilized (Hulme, 1999).

The researcher is addressing the selection biases in choosing the program sites and

sample selection.

3.2 Formulation of Hypotheses and Operationalization of Variables

Based on the literature review presented in chapter 2 and establishing the relationships

among variables through logical reasoning in the theoretical framework, next step is to

formulate the hypotheses and operationally define the variables. Purpose of

operationalization is rendering the constructs measurable. This is done by looking at the

dimensions which is then translated into observable and measurable elements. These

hypotheses are established at family/household and enterprise level.

3.2.1 Family/Household-Level

Present study has taken three impact domains; household welfare (education, housing and

food security), household expenditure and household assets.

Children Education

Education has been recognized as an effective and expedient change agent. It helps to

broaden the mental horizon of the people hence, motivate them to participate actively in

the social and economic development of the family and the country at large. Schultz

(1961) and Becker (1975) treated education and training as a form of investment

producing future benefits in the form of higher income for both educated individuals and

for society as a whole. Income generated through household enterprise is used for

children schooling, social investment and consumption (Balkenhol, 2006). Education has

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Microfinance & Poverty 39

a positive influence on eradicating poverty, since increased education leads to increase

income, health and nutrition (Psacharopoulos & Woodhall 1985).

There is a growing body of literature focusing on the determinants of children‘s

education and that of girls in particular (King & Hill, 1993 & World Bank, 2001). As it is

clearly depicted in table 2, that microfinance participation leads to improve children

education. So, one can hypothesize the relationship of participation in the microfinance

program and children education.

H 1a: Participation in the program leads to increase in percentage of school going

children.

H 1b: Participation in the program leads to more expenditure on children’s education.

H 1c: Participation in the program leads to increase the highest level of children

education.

Operationalization of children education is based on three dimensions, number of school

going children, highest educational level attained and school expenditure as explained in

table 3.

Table 3. Operationalization of Children Education

Dimension Scale Item Source

Number of school going

children

How many children (5-17) go to school?

a)1-3 b) 4-6 c) 6 & above d)None

(Pitt and Khandker

1998)

Highest educational level

attained

The highest level of schooling that any of your children has completed.

a. Primary b. Secondary c. Matriculation d. FA

e. Diploma/technical education If Any other

(Todd,2001)

School expenditure How much did your household spend on school fees and other

education expenses for school going children? (for current year only)

a) Rs 1,000-10,000 b) Rs11,000-20,000 c) Rs 21,000-30,000

New item

Source: Developed

Housing

Ahon Sa Hirap (ASHI) a microfinance institution of Philippines has used its own housing

index for it impact assessment as that measures size, structure and roof of the house as

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Microfinance & Poverty 40

the eligibility criteria for clients to enter in the program, originally developed by

CASHPOR.

Value of house, access to clean drinking water and sanitation are important indicators of

housing which have been found in previous studies (Mustafa 1996; Copestake, Dawson,

Fanning, McKay, & Wright-Revolledo, 2005). Positive relationship has been reported in

the previous studies (Chen & Snodgrass, 2001; Neponen, 2003, & Todd 2001).

Following hypotheses can be formulated.

H 2a: Participation in the program leads to improve the housing conditions.

H 2b: Participation in the program leads to improve drinking water source.

Following the literature, the operationalization of housing is based on dimensions of

construction material, house repair, improvements or additions, structural conditions.

ownership status, drinking water and electricity . Rational behind is to observe the

improvement in housing which is an indicator of up gradation in the living standard of

the clients. (See table 4)

Table 4. Operationalization of Housing

Dimension Scale Item Source

Construction material 1. What type of roofing material is used in the main

house?

Thatched roof (branches, twigs, leaves, grass)

__________ Country clay tiles

(Herny et al, 2000; Morris et al,

2006)

House repair,

improvements or

additions

Were there any repairs or improvements made to your

house during the last two years?

Yes /No *

Did you use funds generated through enterprise for

improvements?

Yes /No *

During the time when you were using loan have you

made any expansion of house (e.g built new room

etc)

Yes/No

Any Improvement in water or sanitation system (e.g

new wash basin etc)

Yes/No

(Nelson, 2000)

(Nelson, 2000)

(Nelson, 2000)

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Microfinance & Poverty 41

Ownership status 1. What is the ownership status of this

house/apartment?

Built on squatter land ______ Rented

(Herny et al, 2000; Morris et al,

2006)

Structural condition 3. What is the observed structural condition of the

main dwelling?

Seriously dilapidated________Sound structure

(Herny et al, 2000; Morris et al,

2006)

Electricity 2. What is household electrical supply?

No connection _______ Own connection

(Herny et al, 2000; Morris et al,

2006)

Drinking water What is the main source of drinking water for

members of your household? Piped water

______Tanker Truck

(Copestake, Dawson, Fanning et

al. 2005; Herny et al, 2000;

Morris et al, 2006)

Source: Developed

NOTE: * Item dropped from final scale.

Food Security

Basic purpose to study this indicator is to see how household diet pattern is changing.

Husain (1998) defines nutrition as ―instances per week/month of consumption of specific

nutritious foods (e.g., meat, fish, dairy, vegetables)‖.

Developing countries often face a ―hungry‖ season when there is a bad harvest compared

to the previous year leading to high prices of food commodity and fear of food shortage.

These indicators capture food insecurity during these periods (Nelson, 2000).

Drawback of this indicator is that concept of ―improvement‖ or ―eat less‖ varies from

respondent to respondent. To overcome this problem of subjectivity, few more indicators

were added, developed by USAID.

Significant positive relationship between food security and microfinance was observed in

previous studies (Banes, 2001; Chen & Snodgrass 2003; Neponen, 2003; Rosintan,

Drioadisuryo & Cloud 1999). Following hypotheses can be formulated.

H 3a: Participation in the program leads to increase the consumption of nutritious

food item (cereals)

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Microfinance & Poverty 42

H 3b: Participation in the program leads to increase the consumption of nutritious

food item (milk)

H 3c: Participation in the program leads to increase the consumption of nutritious

food item (eggs)

H 3d: Participation in the program leads to increase the consumption of nutritious

food item (meat)

H 3e: Participation in the program leads to increase the consumption of nutritious

food item (fruit)

The operationalization of food security in this study is based on five dimensions. Number

of eating occasions, Diet diversity i-e number of different foods or food groups

consumed, expenditure on food, hunger episodes and coping strategies in dealing with

financial shocks (hunger episodes) as explained in table 5.

Table 5. Operationalization of Food Security

Dimension Scale Item Source Number of meals

taken per day

Yesterday, did you or anyone in your household consume? Yes/No *

Any food before a morning meal

A morning meal

Any food between morning and midday meals

A midday meal

Any food between midday and evening meals

An evening meal

Any food after the evening meal

USAID food security

indicators (used by

PL 480 Title II-

funded programs)

Diet diversity

Yesterday, did you or anyone in your household consume? Yes/No

Cereals

Vegetables

milk/milk products

eggs

Meat (chicken, fish, mutton, beef)

sugar/honey

fruits

USAID food security

indicators (used by

PL 480 Title II-

funded programs)

Expenditure on food Did you use the income earned from your business to purchase food?

1. Yes 2. No

(Nelson, 2000)

Hunger episodes If worsened, was there ever a time when it was necessary for your

household to eat less during the last 12 months?*

1. Yes 2. No

(Nelson 2000)

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Microfinance & Poverty 43

Coping strategies in

dealing with

financial shocks

What was the reason of less eating? *

1. Lack of money 2. Lack of food 3. Other: specify________

What did you do to get rid of this difficult situation? Yes/no *

Borrowed money from family/friends

Borrowed food from family/friends

Sold personal property

Left area to seek employment

Family member left and seek employment

Got local employment

Family member got local employment

(Nelson 2000)

Source: Developed

NOTE: * Item dropped from final scale.

Household’s Income/Expenditure

Two widely used approaches are income approach and expenditure approach. Income

approach can be measured by taking sources and levels of income whereas; expenditure

approach counts all household expenditures. Accuracy and less time consumption in

using expenditure approach are found to be more practical (Meyer, Nagarajan & Dunn,

2000). Mahjabeen (2008) explains the role of MFIs to raise income and consumption,

reduce income inequality and improve welfare. Significant positive impacts on income

and asset levels were observed in the previous study (Mosley, 2001).

Following hypotheses can be formulated.

H 4a: Participation in the program leads to increase household expenditure in clothes

and household items.

H 4b: Participation in the program leads to increase the likelihood to provide it to

spouse.

H 4c: Participation in the program leads to improve expenditure on house repair.

H 4d: Participation in the program leads to increase spending in food items.

H 4e: Participation in the program leads to increase the loaning activity to relatives.

H 4f: Participation in the program leads to increase the expenditure in celebrations.

H 4g: Participation in the program leads to purchase of land.

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Microfinance & Poverty 44

H 4h: Participation in the program leads to repay old loans.

H 4i: Participation in the program leads to repay existing loans.

This study is using the income and expenditure approach. It takes 7 dimensions of

household income, out of which, 5 items are on expenditures and 2 items on income (see

table 6). Two dimensions of income are sources of income and levels of income. Five

items of expenditures include consumables, loans, repayments, occasions and

investments.

Table 6. Operationalization of Household’s Income/expenditure

Dimension Scale Item Source

Source of income 1. Major source of family income? *

Wage

Pension

Social assistance

Income from business

Income from agriculture

Income from rent

Other (specify) _____________

2. No of salaried persons *

None

One

Two or more

(Aleskerov, 2007)

New item

Change in income

Over the last 12 months, has your overall household income?

Decreased greatly ___ Increased greatly *

If increased, why did your income increase? *

If decreased, why did your income decrease? *

(Nelson, 2000)

(Nelson, 2000)

Consumables Buy food for your household? Yes/No

Buy clothes or other household items? Yes/No

(Nelson, 2000)

Loans Give or loan the money to your spouse? Yes/No

Give or loan the money to some relatives/friends? Yes/No

(Nelson, 2000)

Repayments To repay microfinance loan Yes/No

To repay other debt Yes/No

(Nelson, 2000)

Occasions For house/land improvement Yes/No

To spend on a celebration or death etc Yes/No

(Nelson, 2000)

Investments For purchasing new house/land Yes/No (Nelson, 2000)

Source: Developed

NOTE: * Item dropped from final scale.

Household Assets

Poor households have little money to spend even in basic need therefore they do not have

enough to spend in household assets. That‘s the main reason that one can associate

accretion of assets with household income levels. Complete valuation of all household

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Microfinance & Poverty 45

assets is not compulsory hence measuring specific types of household assets can single

out those who are better as compared to others (Henry et al, 2000). Present research has

taken three major categories, livestock, transportation and appliances. Perception about

value of livestock varies from one culture to another.

For example, in Kenya, most households were reluctant to count livestock because it is

thought to bring bad luck. Similarly in India, keeping cattle does not mean to increase the

monetary worth of the household because of their religious affiliation. Transportation is

another household asset which can be a relative measure of poverty. Bikes, motorcycles,

and other motorized vehicles vary in degree of ownership from country to country.

People in mountainous regions may own fewer bicycles; people in urbanized areas,

relatively more. All major appliances and electronics are considered good for

differentiating relative poverty levels. In India, ownership of electric fans was a

significant measure for signaling relative wealth. In the Kenyan highlands, few

households own fans but many own televisions. In Nicaragua, most surveyed households

owned at least one television, and its value was a significant determinant of the

household‘s relative wealth. Significant positive relationship between microfinance

participation and household assets was observed in various studies (Kondo, 2007;

Sebstad & Chen, 1996 & Sengsourivong, 2006).

Following hypotheses can be formulated;

H 5a: Participation in the program leads to increase the ownership of household asset

(Refrigerator).

H 5b: Participation in the program leads to increase the ownership of household asset

(CD player).

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Microfinance & Poverty 46

H 5c: Participation in the program leads to increase the ownership of household asset

(motorcycle).

H 5d: Participation in the program leads to increase the ownership of household asset

(washing machine).

H 5e: Participation in the program leads to increase the ownership of household asset

(sewing machine).

H 5f: Participation in the program leads to increase the ownership of household asset

(bed with foam).

H 5g: Participation in the program leads to increase the ownership of household asset

(cell phone).

H 5h: Participation in the program leads to increase the ownership of household asset

(television).

Operationalization of household assets consist of 1 dimension ownership, which is

comprised of three categories i-e ownership of livestock, transportation and appliances as

depicted in table 7.

Table 7. Operationalization of Household Assets

Dimension Scale Item Source

Ownership How many of the assets are owned? Yes/No Categories

(1) Livestock

(Buffaloes, Cows, Sheep, Goats, Hens, Horses, Donkeys)

(2) Transportation

(Cycle, Motor cycle, Tractor, Trolley, Cart)

(3) Appliances

(Refrigerator, Television, CD player, Washing Machine,

Sewing Machine, Cell-Phone, Others)

(Zaidi et al. 2007)

Source: Developed

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3.2.2 Micro-enterprise Level

There has been found an enormous importance of small and medium enterprises sector to

the national economy with regards to job creation and the alleviation of abject poverty

(Bekele & Muchie, 2003).

Hazelhurst (2006) explains the significance of small businesses and enterprises and refers

it as a prerequisite for overall economic growth and the alleviation of poverty and

microfinance is playing a very vital role in poverty reduction that is why it is important to

study enterprise development. Three impact domains have been taken i-e financial

performance, enterprise resource base and income-smoothening effect.

Financial Performance

Indicators like expansion of business, additional workers hired and adding new products

to the enterprise may be used as substitute (proxy) indicators for measuring increased

revenues and profitability. Researchers advocate that growth is more accurate and easily

accessible performance indicator than accounting measure hence superior to indicators of

financial performance.

Studies have shown that impacts on enterprise profits may occur early and then taper off

within the first year or two of microfinance programme participation (Zaidi et al, 2007).

Kondo (2007) that explains positive impact on employment through participation of

microfinance program. Morris & Barnes (2005) found positive impact of microfinance on

addition of new products in the enterprise, improve desirability of products, reduce costs

by purchasing in bulk and improved knowledge of most profitable product hence

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Microfinance & Poverty 48

contributing towards the improvement of financial performance overall. Following

hypotheses can be formulated:

H 6a: Participation in the microfinance program increase expansion of enterprise.

H 6b: Participation in program leads to addition of new products in the enterprise.

H 6c: Participation in the program leads to hiring more workers in the enterprise.

H 6d: Participation in the program leads to improve the product quality of enterprise.

H 6e: Participation in the program leads to improve desirability of products.

H 6f: Participation in the program leads to reduce costs by purchasing in bulk.

H 6g: Participation in the program leads to keep enterprise money separate from

household and personal use.

H 6h: Participation in the program leads to make profit calculation based on cost and

earnings.

H 6i: Participation in the program leads to improve on the knowledge of most

profitable product.

H 6j: Participation in the program leads to have a fixed location for production.

H 6k: Participation in the program leads to have a fixed location for storing.

This study measures the financial performance on five dimensions, expanded size,

exploiting new opportunities, profits, and fixed location.

Table 8. Operationalization of Financial Performance

Dimension Scale Item Source

Expanded Size 1. During the last 12 months did you add new products? (Nelson, 2000)

Cost effectiveness 1. During the last 12 months did you reduce costs by purchasing at

wholesale prices and in bulk?

2. During the last 12 months, did you reduce costs with cheaper source

of credit?

(Nelson, 2000)

(Nelson, 2000)

Exploiting new

opportunities

1. During the last 12 months did you develop a new enterprise?

2. During the last 12 months did you sell in new markets/locations?

(Nelson, 2000)

(Nelson, 2000)

Profits

1. Do you keep your enterprise money separate from the money you

have for personal and household expenses?

2. Do you calculate your profit based on records of your costs and

(Nelson, 2000)

(Nelson, 2000)

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Microfinance & Poverty 49

earnings?

3. Do you know which product(s) bring you the most profit?

4. Do you pay yourself a wage for your work in your enterprise

(Nelson, 2000)

(Nelson, 2000)

Fixed location

1. Do you have a fixed location with protection from the sun and rain

for selling your products, such as a store, stall, or kiosk?

2. Do you have a fixed location for producing or storing your

products that is different from the location where your family lives?

(Nelson, 2000)

(Nelson, 2000)

Source: Developed

Enterprise Resource Base

To assess the value of net worth of enterprise (total assets) is difficult, hence capturing

indicators that determine the resource base of the business is a smart choice (Nelson,

2000).

H 7a: Participation in the program leads to increase the major investment in the

enterprise.

H 7b: Participation in the program leads to increase the minor investment in the

enterprise.

Following the literature, enterprise resource base is based on one item, investment further

categorized to major and minor investment as shown in the table below.

Table 9. Operationalization of Enterprise Resource Base

Dimension Scale Item Source

Investment Have you made a major investment in your enterprise? (shop

stall etc)

Have you made a minor investment in your enterprise?(chair,

table, etc)

Nelson, 2000

Nelson, 2000

Source: Developed

Income Smoothening

The objective of this indicator is to assess the income-smoothening effect, especially

poorer entrepreneurs who are more sensitive to income/expenditure shocks such as bad

harvest etc. and they do not have access to any other source of credits and savings.

Following hypotheses can be formulated;

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H 8a: Participation in the program assists clients to survive periods of reduced cash

flow.

H 8b: Participation in the program leads to reduce the repayment problems.

Operationalization is based on two items, sufficient money and repayment as shown in the

table below. Basically, through this indicator researcher is measuring the sufficiency of

money for smooth running of enterprise.

Table 10. Operationalization of Income Smoothening

Dimension Scale Item Source

Sufficient money 1. During the last 12 months, did you feel that money you had

was not enough to conduct your enterprise?

2. If yes, how long did this period last?

Nelson, 2000

Repayments 1. Did you have a problem in repayment in the last loan cycle?

2. If yes, what caused your repayment problems?

Nelson, 2000

Source: Developed

3.2.3. Demographic Characteristics of the Clients

Including other independent variables (personal and demographic characteristics) has

been supported by the existing literature (Coleman, 1999; Kondo et al., 2008; Marr,

2002; Montgomery, 2005). Age is expected to be a factor because it is well-known that

age-earning profile is not flat. Education, of course, is a known determinant of both

earning capacity and productivity in non market (home) production (Kondo et al, 2008).

Demographic characteristics of the respondents (gender, age and education) and number

of households, number of salaried persons and type of area rural vs. urban will serve as

other independent variables for impact on household and enterprise level.

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3.3 Proposed Research Model

On the basis of discussion made so far, and theoretical underpinnings explained in table

2, the proposed model would be like as shown below in Figure 6. The present research

model is a unified framework that sheds light on the impact of microfinance both at

household and enterprise level. In addition to this demographic and other independent

variables have been added.

Figure 6. Proposed Research Model

(Proposed)

Participation in the

microfinance

program

Demographics

Gender of the

owner

Age

Education

Other independent

variables

No of salaried

persons

No of households

Area

Children‘s education

Housing

Food Security

Household Expenditure

Household Assets

Financial Performance

Enterprise resource base

Income smoothing effect

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Chapter 4

Methodology

4.1 Small Enterprise Education and Promotion (network) SEEP

SEEP includes set of five tools that addresses different aspects from the point of users.

This set of tools can be used individually or in any combination. Significance of these

tools is that they are helpful to assess how the microenterprise development programs are

contributing towards community development in terms of household welfare/security,

well being of individuals and enterprise stability (Sebstad & Chen, 1996).

Table 11. Cost and Benefit Analysis of Poverty Assessment Approaches

Tools Cost Benefits

1. Detailed Household

Expenditure Surveys and

Living Standard Measurement

Survey

Large samples, time consuming, analytically too

demanding Accuracy, rigorous

2. Rapid Appraisals and

Participatory Appraisals

Too subjective

reliability

Best to get the fast

information on local level

economic conditions

a. Participatory Wealth

Ranking1

Tool has the limitation to be used on larger populations

or determine the poor(est) in a large geographical area.

Can identify the poor at

community level

Holistic,

People-centric

determination of poverty

Reliabilty

3. Indicator Based Methods

a. Housing Index

b. Human Development Index

(UNDP 1999)

Tool has the limitation of generalizibity across rural

and urban areas across regions and countries.

Neglecting other dimensions of poverty such as food

security and human resources

Uses three indicators out of which 2 i-e life expectancy

at birth, and per capita income are costly and cannot be

operationalized.

Simple, observable and

verifiable

4. Consultative Groups to

Assist the Poorest (CGAP)

Practical,

accurate, and relatively

simple mean of

assessment

Source: Developed

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4.2 Client Assessment Continuum

Academicians argue that there is a need of rigorous market research to assess the client

for microfinance intervention which is too costly and time consuming. However, on the

other hand impact evaluations call for longitudinal approach, large sample sizes and

require complex analysis. Since AIMS-SEEP tool fits in between market research at one

end and impact evaluation on the other as shown in figure 7.

Figure 7. Client Assessment Continuum

Source: Based on Nelson 2000

4.3 Unit of Assessment

Following the design and impact path model, next step is the choice of the unit(s) of

assessment (or levels of assessment). Assessment at all the levels i-e household,

enterprise, individual and community level is made which gives the fullest picture of over

all impact by household economic portfolio model (HEMP), a project AIMS (Chen &

Dunn, 1996).

Common units of assessment are the household, the enterprise or the institutional

environment within which agents operate (Hulme, 1999). The relative advantages and

disadvantages of different units of assessment are summarized in the following Table 12.

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Table 12. Units of Assessment and their Advantages and Disadvantages

Unit Advantages Disadvantages

Individual

• Easily defined and

identified

• Most interventions have

impacts beyond the

individual

• Difficulties of

disaggregating group

impacts and impacts on

‗relations‘

Enterprise

• Availability of analytical

tools (profitability,

return on investment

etc)

• Definition and

identification is difficult

in microenterprises

• Much microfinance is

used for other enterprises and/or

consumption

• Links between

enterprise performance

and livelihoods need

careful validation

Household

• Relatively easily defined

and identified

• Permits an appreciation

of livelihood impacts

• Permits an appreciation

of interlinkages of

different enterprises and

consumption

• Sometimes exact

membership difficult to

gauge

• The assumption that

what is good for a

household in aggregate

is good for all of its

members individually is

often invalid

Community

• Permits major

externalities of

interventions to be

captured

• Quantitative data is

difficult to gather

• Definition of its

boundary is arbitrary

Institutional Impacts

• Availability of data

• Availability of analytical

tools (profitability, SDIs,

transaction costs)

• How valid are inferences about the

outcomes produced by institutional

activity?

Household Economic

Portfolio (ie household,

enterprise, individual and

community)

• Comprehensive

coverage of impacts

• Appreciation of linkages

between different units

• Complexity

• High costs

• Demands sophisticated

analytical skills

• Time consuming

Source: Based on Hulme 1999

4.4 Longitudinal vs. Cross-sectional Design in Impact Assessment

Many USAID projects have tradition of working with the longitudinal designs. This

include two studies after a specified interval, pre-test followed by post-test.

Hulme (1997a) states that in longitudinal data collection clients may not show their

interest in second and third interview as they had in the first interview. In such

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circumstances interviewees can be rewarded at few places to enhance the data quality.

This can be in the form of social reward such as small gifts, like snacks, soda water

bottles etc. This practice was quite successful in East Africa where the interviewee was

paid cash for surrendering his/her time.

This research uses a cross-sectional design with many advantages when used in field

settings. Distinctive advantages are less expensive in terms of time and resources. It also

provides more timely information useful to program a manager which is an edge over the

longitudinal data. Field studies mostly use cross-sectional designs because it saves time,

cost and effort (Sekaran, 2003). Following table shows the study design chosen in the

previous studies.

Table 13. Impact Assessment Studies using Different Study Designs

Name of the Study Design

―Bangladesh Institute of Development Studies(BIDS) and

World Bank (WB)‖ joint study in Bangladesh

Cross-sectional

Managing Resources, Activities, and Risk in Urban India:

The Impact of SEWA Bank

Longitudinal (1997-99)

―The Impacts of Micro credit: A Case Study from Peru‖ Longitudinal

―Impact of microfinance on rural households in the

Philippines; A case study from the special evaluation study

on the effects of microfinance operations on poor rural

households and the status of women‖

Longitudinal

Quasi-experiment

Impact of microfinance on Household welfare, Japan Longitudinal/cross sectional

Small Industries Development Bank of India

(SIDBI), India

Longitudinal (2001-2004)

Source: Developed

There were three options available to the researcher;

Option 1, ―Clients only‖ gives a ―quick dirty‖ assessment of MFI clients. Since there is

no comparison group, it is difficult to attribute the ―change‖ as microfinance success.

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Option 2, ―Clients and non clients‖ is the most common and popular cross-sectional

design, which has been used tremendously in previous studies (see literature review

Table 2). It gives a comparison with/without microfinance intervention. However, results

can be misleading because clients and non-clients are different in having the

entrepreneurial drive.

Option 3, ―Mature clients and incoming clients‖ is considered appropriate because these

two groups comprise of same ―type‖ of people who choose to join the program. Incoming

clients serve as the comparison group and are considered as proxy for non clients. The

assumption is that incoming clients have same to existing clients have same exposure of

social environment and characteristics like motivation, business experience, and

entrepreneurial drive. Hence it offers more appropriate and well identified comparison

group. This helps to reduce self-selection bias reason being they also opted to join the

program (Nelson, 2000).

Regardless of the chosen design and the elaborateness of comparisons, however, some

uncertainty about the size of treatment effects will always remain. It is impossible to rule

out completely all threats to validity. Ultimately, researchers must rely on accumulating

evidence across multiple designs and the corresponding multiple estimates of effects

(Reichardt & Mark, 1998).

The cross-sectional approach claims to overcome the problem of experiencing the

difference in the entrepreneurial spirit, since both its control and treatment group consist

of individuals who have opted to participate in the MFI. The new entrants are the control

group, whereas the veteran participants with two or more years experience with the MFI

are the treatment group (Karlan, 2001 & Marr, 2002).

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4.5 Survey Method

Different impact assessments have used different tools in the previous studies to tackle

the cultural diversity. Present research has used AIMS-SEEP tool as a baseline though it

was adapted further according to the culture. New clients (incoming clients) are

compared with the established clients. Difference in the lives of two groups based on the

indicators under study can be attributed to the program impact. Selection bias was

controlled by comparing new and established client; rational is that two groups will not

have the difference in their entrepreneurial spirit.

4.6 Instrumentation: Development of Interviewee Data Form

Based on AIMS-SEEP tool, new scale items were added by conducting focus groups and

to make it suitable for local environment. Following table explains the employed research

methods.

Table 14. Overview of the Employed Research Method.

Method Type Number Year

Literature Review

Analysis of books, academic

magazines and journals, newspapers

and company reports, conferences and

workshops proceedings.

………… April 2007-April

2009

Focus Groups

a. Focus group consisting of four

members with NRSP executives.

b. Focus group consisting of three

members with of Pakistan

Microfinance Networks (PMN)

c. Focus group consisting of three

members of PakOman Microfinance

bank

3 Focus groups

May-September

2007

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Interviews

Personal interviews with microfinance

clients and practitioners. The main aim

was to check that the questionnaire

captures all the facets of the constructs

mentioned in main conceptual model.

4 Interviews March 2008

Sorting Rounds

Review of local survey instruments

(tools used by NRSP in conducting

local surveys and data forms by banks

used for verification of clients) Sorting

of items for questionnaire for

questionnaire by a group of 3 judges

(1 Professor, 2 doctoral students)

4 Rounds May 2008

First Pilot Study

Field tests of draft instrument with

client

12 usable responses October 2008

Second Pilot Study Field interviews with NRSP and KB

clients

48 usable replies December 2009

Final Survey Final field survey to the clients of

NRSP, KB, POMF and FMFB

384 usable replies January- April 2009

Source: Developed

Questions on enterprise development has been added, similar approach has been followed

by Kondo et al, 2008 in order to establish a household survey questionnaire which was

adopted from the Annual Poverty Indicators by adding questions on loan accounts,

enterprises, and gender-related matters.

Initially eight clients were interviewed to sort and resolve measurement issues. Since it

was a field study it was important to explore issues like interview length, question format,

sensitivity issues, recall ability and information accuracy. Based on these interviews, the

household-level and enterprise-level questionnaires for the baseline study were

constructed. Sequence of questions was changed and questions were reworded where

necessary. After this, interview form was field tested and revised four times. Pilot test

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was run on a sample of 100 households. Based on this, questions on household and

enterprise levels were finalized.

Different impact assessments have used different tools in the previous studies to tackle

the cultural diversity. Present research has used AIMS-SEEP tool as a baseline though it

was adapted further according to the culture. New clients (incoming clients) are

compared with the established clients. Difference in the lives of two groups based on the

indicators under study can be attributed to the program impact. Selection bias was

controlled by comparing new and established client; rational is that two groups will not

have the difference in their entrepreneurial spirit.

To ensure consistency in the questionnaire wording, researcher has translated it into Urdu

language. However, depending on the literacy level and exposure of the respondents,

interviews were conducted in local languages as well.

(Both English and Urdu version of Interview forms are attached as appendix I).

4.7 Sampling Procedures

There has been a much debate on the appropriate size of sample. Two approaches are

followed which are entirely reverse of each other. A maximalist approach is usually used

by a statistician who is more concerned about the quality also he wants to ensure that all

the assumptions are met to apply a certain statistical test. On the other hand, field

researcher adopts minimalist approach but still provides credible results. Reality is that

field research is much more expensive in terms of money and time

Maximalist takes at least 500 whereas minimalist recommends at least 35 to 50 for each

subgroup we want to compare and analyze ((Nelson et al, 2000 & USAID, 2008).

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4.7.1 Cluster Sampling

Cluster sampling is mostly used in those situations when population is geographically

dispersed and it is not possible to reach every respondent when time is limited.

Since the four microfinance institutions chosen for analysis have geographically

dispersed clients so cluster sampling was thought to be the most appropriate. One way to

group or cluster the clients in on the basis of time spent in the microfinance program (e.g.

1-3years vs. more than 2 years etc). This type of clustering is more practical when good

records are available. The major significance of such a procedure is that it counters any

possibility of bias in the samples to a significant extent.

Figure 8 shows the choice points in sampling design.

Figure 8. Choice Points in Sampling Design

Source: Based on Sekaran, 2003

Cluster sampling would work best for this research because the clients for four different

microfinance institutions were geographically dispersed and it involved huge cost in

reaching them especially in rural areas where they were away several kilometers.

Representativeness of sample is

critical for the study so chosen

PROBABILITY Sampling

Purpose is generalizability

Cluster sampling Two heterogeneous groups by time i-e

New and established clients (Simple

random sampling of clusters)

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4.7.2 Sample Size

Collection of data from survey sample is constrained by time and budget. This cost

includes transportation, photocopies, filed team compensations, and other related costs of

data cleaning and data verifications. Time is also a big cost which is reflected in training

the interviewers, facilitations in the field etc. Generally, rural survey samples are much

more expensive than the urban surveys. For most cross-sectional impact assessments

using the two categories of clients suggested in this research design, sample sizes of

either 170 (for the sampling design in Table 15) or 340 (for the sampling design in Table

16) would seem reasonable. These figures include an additional 20% over the sum of all

the cells—including ―extras‖ to replace those who are not available to be interviewed.

Interestingly, these sample sizes will also likely produce requisite numbers for achieving

statistical significance in many cases.

Table 15. Minimal Sample Size for Two Groups of Clients

Disaggregated by one subcategory

Established Clients Incoming Clients

Male 35 35

Female 35 35

Source: Based on Nelson 2000

Table 16. Minimal Sample Size for Two Client Groups

Disaggregated by two sub-categories

Established Clients Incoming Clients

Male Female Male Female

35 35 35 35

35 35 35 35

Source: Based on Nelson 2000

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Based on the above guidelines and as proposed by Sekaran (2003), the present study has

taken sample size of 384.

4.8 Selection of Microfinance Providers

Based on the spectrum of services, microfinance institutes have been categorized to in

four major classes (Pakistan Microfinance Review, 2006) as under:

Rural Support Programme (RSP)

RSP‘s are running microfinance operation as part of multi-dimensional rural development

programme.

Microfinance Banks (MFB)

MFB‘s are licensed and prudentially regulated by the State Bank of Pakistan to

exclusively service microfinance market.

Microfinance Institution (MFI)

MFI‘s providing specialized microfinance services.

Others

All institutions that do not fall in the above three categories.

Four different microfinance institutes have been selected for this study, based on the

statistics presented in the Table 17 consisting of one RSP and three MFBs.

National Rural Support Programme has been taken as RSP and Khushhali Bank (KB),

The First Microfinance Bank Ltd. (FMFB), Pak-Oman Microfinance Bank Ltd.

(POMFB) as MFBs. Moving from oldest to the latest, this study has taken NRSP as the

oldest and Pak-Oman Bank as the latest establishment in Pakistan as microfinance service

providers.

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Two categories i-e Rural Support Program and Microfinance Banks were taken to get the

true opinion and diversity. Similar pattern has been found in the previous study by Kondo

et al, 2008.

Table 17. Summary Statistics of Microfinance Institutes

RSP MFB’s

NRSP KB

POMFBL

FMFBL

Age 14 7 2 6

Total Assets 3,673,667 6,703,280 495,587 2,807,162

Average Gross Loan

Portfolio 2,619,282 2,400,264 89,393 954,234

Number of Active

Borrowers 292,456 283,965 14,397 101,394

Number of Active

Savers

704,318

-

12,249

79,827

Total Number of Staff 2,469

1,865

201

1,045

Total Number of Loan

Officers

1,968

616

70

651

Borrowers per staff

118

152

72

97

Total Assets

3,673,667

6,703,280

495,587

2,807,162

Equity-to-Asset ratio

11.8%

27.0%

91.8%

23.9%

Debt-to-Equity ratio 7.5

2.7

0.1

3.2

Average Loan Balance

per Active

Borrower/per capita

income 11 16 11 21

Average Number of

Active

Loans/(Deposits) 241,651 260,441 18,532 136,191

Adjusted Cost per

Borrower (Rs. In 000) 2 3 4 2

Source: Adapted from Micro WATCH. A Quarterly Update on Microfinance outreach in

Pakistan: Jan-Mar 2008.

4.9 Pretest

A pretest was carried out before conducting the actual survey to test and measure the tool

in the local settings because this tool was tested first time in Pakistan. The population of

the study consists of all the active borrowers of the microfinance institutions. Face-to-

face interviews were designed and interview form was translated into Urdu language. An

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initial 100 interviews were conducted from two different microfinance institutions as a

pretest.

Like client of any other bank, confidentiality of client personal data is maintained by the

microfinance institution. One has to get written permission from the Head Office for

reaching clients. That is why a numbers of steps were involved in the process.

1. Writing letters/e-mails/telephone to the Head office seeking permission.

2. Head office approves and directs to regional branch

3. Regional branch refers it to the service centre (locality where microfinance clients

are being served)

4. A customer representative officer (CRO) at service centre was assigned to help

access the client.

5. Appointments for visiting clients (at their homes or visiting branch office on

recovery days)

The credibility of research findings is a very important element of a research. For this

reason, it is important to do a good research design from the beginning (Saunders, Lewis,

& Tronhill, 2007) Credibility includes validity, reliability, generalization and

transferability, discussed in chapter 5.

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Chapter 5

Data Analysis

5.1 Response Rate and Non-Response Bias

The final data collection process was conducted over a period of 12 weeks, commencing in

January 2009 until April 2009. 400 face-to-face interviews with the clients of 4 different

microfinance institutions were conducted. 384 usable interviews which make a response rate of

96%, was more than double of initial anticipation. Since a large percentage of clients were

illiterate i-e 30.2% the researcher had to read out questions to them in order to get the response.

This was the main reason for being such a high response rate.

5.2 Overall Sample Demographic Profile

Demographic data allows one to demonstrate that the clients in the sample are similar to and

representative of the entire client population. Grouping and analyzing the clients based on age,

education, marital status, length of time with the program, amount of micro credit, type of

business, or loan size can provide insight into how program services could be tailored to better

meet the needs of particular client subgroups. In data analysis, demographic characteristics are

often used for cross tabulating with the results of a survey question.

Table 18. Demographic Profile of the Respondents

Variable Category

Research Sample

(n = 384)

Frequency Percentage

Sex Male 196 51.0

Female 188 49.0

Total 384 100.0

Age 18-25 39 10.2

26-35 145 37.8

36-45 131 34.1

46-55 62 16.1

56 and above 7 1.8

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Total 384 100.0

Marital Status Single 49 12.8

Married 326 84.9

Widow 9 2.3

Total 384 100.0

Educational

Achievement/level

Illiterate 116 30.2

Primary 59 15.4

Middle 65 16.9

Secondary 104 27.1

Higher

Secondary/Graduation 40 10.4

Total 384 100.0

Source: Field Data

The demographic profile of the survey respondents presented in Table 18 shows that 51%

of the respondents were male and 49% were female. The largest group consisted of those

aged 26-35(37.8%) followed by the age group 36-45 (34.1%). 1.8 % of the respondents

were above age of 56years and 10.2% were aged 18-25 years.

About the educational level of the respondents, it was very interesting to find out that

30% of the respondents were illiterate followed by 27% having matriculation, 17%

middle and last group of respondents having higher secondary and graduation.

Table 19. Household Profile of the Respondents

Variable Category Frequency Percentage

No of Households 1-4 members 83 21.6

5-8 members 236 61.5

8 and above 65 16.9

Total 384 100.0

Area Rural 148 38.5

Urban 236 61.5

Total 384 100.0

No of salaried persons in the household None 71 18.5

1 member 168 43.8

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2 and more members 145 37.8

Total 384 100.0

No of Children (5-17yrs) No 126 32.8

Yes 258 67.2

Total 384 100.0

Source: Field Data

The above table briefly explains the household profile of the respondents. It was found

that 61.5% of the respondents had number of household ranging between 5-8 members.

Only 16.5% of the respondents had household members 8 and above. 38.5% of the

respondents belonged to rural area and 61.5% to urban areas.

Table 20. Organizational and Client Profile

Variable Category Frequency Percentage

Microfinance Institution NRSP 104 27.1

KB 97 25.3

FMFBL 82 21.4

POMFBL 101 26.3

Total 384 100.0

Category of Client

New Clients

(Less than 1 and 1year) 258 67.2

Established Clients

(2-5 years) 126 32.8

Total 384 100.0

Amount of Microcredit 1,000-10,000 77 20.1

11,000-20,000 206 53.6

21,000-30,000 54 14.1

31,000 &above 47 12.2

Total 384 100.0

No of Loan Cycles One 296 77.1

Two 64 16.7

three 16 4.2

four and above 8 2.1

Type of Business before

Microcredit

Agriculture 9 2.3

Livestock 46 12.0

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Retail 53 13.8

Others 276 71.9

Total 384 100.0

Source: Field Data

The above table presents brief profile of the clients and the organization to which they

belonged to. The largest group of 27.1 % belonged to NRSP, 25.3% to KB, 21.4% to

FMFBL, 26.3% to POMFBL.

The table shows that 67.2% of the clients are new(less than 1 and 1 year) and 32.8% are

established clients (2-5 years).

About the amount of Microcredit, largest group of 53.6% of the respondents had the

amount of Rs11, 000-20,000 and only 12.2% of the respondents had 31,000 and above.

It was interesting to discover that largest group of client i-e 71.9% belonged to varied

kind of businesses before they have taken microcredit. These businesses include,

tailoring, spare parts of automobiles, beauty parlors, and bakery, just to name a

few.13.8% of the respondents belonged to retail business, 12% to the livestock, and only

2.3% to agriculture. In microfinance field most of the business had sole proprietors. Khan

& Rahaman (2007) observed that family members contribute to this small-scale business

as additional workers, similar was observed in the present research.

5.3 Descriptive Analysis of Responses

After identifying the demographic characteristics of the survey respondents and their

household and organizational profile, attention turned to how they answered the survey

questions related to the latent constructs in the conceptual model. The table reports the

percentage frequencies for all the items and their central tendency (mean) and dispersion

(Standard deviation).

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Table 21. Descriptive Statistics of Children’s Education

Variable Category Percentage Mean

SD E

DU

1

1-3 50.5

2.20

1.36 4-6 13.3

6 and above 1.6

None 34.6

ED

U2

1000-10,000 39.6

2.38

1.31 11,000-20,000 17.2

21,000-30,000 8.9

NA 34.4

ED

U3

Primary 18.5

3.29

1.62

Middle 18.5

Matric 19.8

Intermediate 5.7

Graduation & Masters 4.7

NA 32.8

Source: Field Data

The respondents were first asked about the children education. The findings show:

1. 50.5% of the respondents had the children of 5-17 years between 1-3. 34.6% had

no children of aged 5-17 years. (EDU1: mean = 2.20; SD = 1.36).

2. 39.6% of the respondents spent on children education an amount of Rs 1000-

10,000 and only 8.9% spent Rs 21,000-30,000 annually. (EDU2: mean = 2.38; SD

= 1.31).

3. 19.8% of the respondents had matriculation as the highest children educational

achievement and only 4.7% were graduation and masters. (EDU3: mean = 3.29;

SD = 1.62).

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Table 22. Descriptive Statistics of Housing

Variable Category Percentage

Mean

SD

Hou

sin

g

HUS1 Yes 35.2

1.64

0.47 No 64.8

HUS2

Hand pump, well water, canal,etc 41.1

1.58 0.49 Piped water, motor pump, etc 58.9

Source: Field Data

Housing condition of the clients was measured on categorical scale. The findings show

that:

1. 64.8% of the respondents did not spent on housing improvement and repair

whereas 35.2% has done so. (HUS1: mean = 1.64; SD = 0.47).

2. 58.9% of the respondents had piped water as source of drinking water whereas

41.1% had hand pump, well water etc. (HUS2: mean = 1.58; SD = 0.49).

Table 23. Descriptive Statistics of Food Security

Variable Response Scale (%)

(1) No (2)Y Mean

SD

Food

Sec

uri

ty

FDI 49.0 51.0 1.51 0.50

FD2 36.2 63.8 1.63 0.48

FD3 49.5 50.5 1.50 0.50

FD4 61.7 38.3 1.38 0.48

FD5 58.9 41.1 1.41 0.49

Source: Field Data

A five-item scale measured client‘s food security.

1. 51% have consumed food item (cereals) yesterday and 49% have not consumed it.

(FD1: mean = 1.51; SD = 0.50).

2. 63.8% have consumed food item (milk) yesterday and 36.2% have not used it.

(FD2: mean = 1.63; SD = 0.48).

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3. 50.5% have used food item (eggs) yesterday and 49.5% have not consumed it.

(FD3: mean = 1.50; SD = 0.50).

4. Only 38.3% have used food item (meat) yesterday, whereas 61.7% have not used

it. (FD4: mean = 1.38; SD = 0.48).

5. 41.1% of the respondents have used food item (fruit) yesterday, whereas 58.9%

have not used it. (FD5: mean = 1.41; SD = 0.49).

Table 24. Descriptive Statistics of Household Expenditure

Variable Response Scale (%)

(1) No (2)Y Mean

SD

Hou

seh

old

Exp

end

itu

re HSIN1 99 1 1.01 0.10

HSIN2 97.7 2.3 1.02 0.15

HSIN3 98.4 1.6 1.01 0.12

HSIN4 99.2 0.8 1.00 0.08

HSIN5 98.4 1.6 1.01 0.12

HSIN6 99.0 1.0 1.01 0.10

HSIN7 98.7 1.3 1.01 0.11

HSIN8 98.7 1.3 1.01 0.11

HSIN9 98.2 1.6 1.02 0.16

Source: Field Data

A nine-item scale measured the respondent‘s household expenditure.

1. 99% of the respondents had not increased their spending in clothes and household

item. (HSIN1: mean = 1.01; SD = 0.10).

2. 97.7% had not given the microcredit to their spouses whereas only 2.3% have

done so. (HSIN2: mean = 1.02; SD = 0.15).

3. 98.4% have not spent on house repair whereas 1.6% has not done so. (HSIN3:

mean = 1.01; SD = 0.12).

4. 99.2% have not participated in food items, whereas 0.8% has done so. (HSIN4:

mean = 1.00; SD = 0.08).

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5. 98.4% have not increased their loaning activity to their relatives, whereas 1.6%

has loaned it. (HSIN5: mean = 1.01; SD = 0.12).

6. 99.0 % have not spent in celebration, whereas 1.0% has spent. (HSIN6: mean =

1.01; SD = 0.10)

7. 98.7% have not done purchase of land, whereas 1.3% has done so. (HSIN7: mean

= 1.01; SD = 0.11)

8. 98.7% have not repaid old loans, whereas 1.3% has done so. (HSIN8: mean =

1.01; SD = 0.11)

9. 98.2% have not repaid existing loans, whereas 1.6% has done so. (HSIN9: mean =

1.02; SD = 0.16)

Table 25. Descriptive Statistics of Household Assets

Variable Response Scale (%)

(1) No (2)Y Mean

SD

Hou

seh

old

Ass

ets

HSAS1 26.8 73.2 1.73 0.44

HSAS2 73.2 26.8 1.26 0.44

HSAS3 74.2 25.8 1.25 0.43

HSAS4 22.1 77.9 1.77 0.41

HSAS5 19.0 81.0 1.80 0.39

HSAS6 37.8 62.2 1.62 0.48

HSAS7 14.6 85.4 1.85 0.35

HSAS8 25.8 74.0 1.75 0.48

Source: Field Data

An eight-item scale measured the respondent‘s household assets.

1. 73.2% of the respondent owned refrigerator whereas, 26.3% don‘t have this asset.

(HSAS1: mean = 1.73; SD = 0.44)

2. 26.3% of the respondents owned CD player whereas, 73.2% don‘t have this asset.

(HSAS2: mean = 1.26; SD = 0.44)

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3. 25.8% of the respondents owned motorcycle whereas, 74.2% don‘t have this asset

(HSAS3: mean = 1.25; SD = 0.43)

4. 77.9% of the respondents owned washing machine whereas, 22.1% don‘t have

this asset (HSAS4: mean = 1.77; SD = 0.41)

5. 81.0% of the respondents owned sewing machine whereas, 19.0% don‘t have this

asset (HSAS5: mean = 1.80; SD = 0.39)

6. 62.2% of the respondents owned bed with foam whereas 37.8% don‘t have this

asset. (HSAS6: mean = 1.62; SD = 0.48)

7. 85.4% of the respondents owned cell phone whereas 14.6% don‘t have this asset.

(HSAS7: mean = 1.85; SD = 0.35)

8. 74.0% of the respondents owned television whereas 25.8% don‘t have this asset.

(HSAS8: mean = 1.75; SD = 0.84)

Table 26. Descriptive Statistics of Enterprise Financial Performance and Enterprise

Resource Base

Variable Response Scale (%)

(1) No (2)Y Mean

SD

En

terp

rise

Fin

an

cial

Per

form

an

ce

ENT1 19.5 80.5 1.80 0.39

ENT2 59.4 40.6 1.40 0.49

ENT3 91.4 8.6 1.08 0.28

ENT4 78.6 21.4 1.21 0.41

ENT5 80.7 19.3 1.19 0.39

ENT6 83.3 16.7 1.16 0.37

ENT7 56.0 44.0 1.44 0.49

ENT8 31.5 68.5 1.68 0.46

ENT9 28.1 71.9 1.71 0.45

ENT10 33.1 66.9 1.66 0.47

ENT11 67.7 32.3 1.32 0.46

Enterprise

Resource

Base

ENR12 87.5 12.5 1.12 0.33

ENR13 68.5 31.5 1.31

0.46

Source: Field Data

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An eleven-item scale measured client‘s enterprise financial performance.

1. 19.5% of the respondents had expanded the enterprise whereas 80.5% didn‘t expand

size of the enterprise by participating in the program. (ENT1: mean = 1.80; SD = 0.39)

2. 40.6% of the respondents had added new products in the enterprise whereas,

59.4% didn‘t do. (ENT2: mean = 1.40; SD = 0.49)

3. 8.6% had hired new workers whereas, 91.4% didn‘t do. (ENT3: mean = 1.08; SD

= 0.28)

4. 21.4% had improved the product quality of enterprise whereas, 78.6% didn‘t do.

(ENT4: mean = 1.21; SD = 0.41)

5. 19.3% had improved the desirability of products whereas, 80.7% didn‘t do.

(ENT5: mean = 1.19; SD = 0.39)

6. 16.7% of the respondents had reduced costs by purchasing in bulk whereas,

83.3% didn‘t do. (ENT6: mean = 1.16; SD = 0.37)

7. 44.0% had kept money separate from household and personal use whereas, 56.0%

didn‘t do. (ENT7: mean = 1.44; SD = 0.49)

8. 68.5% of the respondents had done profit calculation based on cost and earnings

whereas, 31.5% didn‘t do. (ENT8: mean = 1.68; SD = 0.46)

9. 71.9% of the respondents had the knowledge of most profitable product whereas

28.1% didn‘t have this knowledge. (ENT9: mean = 1.71; SD = 0.45)

10. 66.9% of the respondents had fixed location for production whereas, 33.1% didn‘t

have this. (ENT10: mean = 1.66; SD = 0.47)

11. 32.3% of the respondents had fixed location for storing whereas, 67.7% didn‘t

have this. (ENT11: mean = 1.32; SD = 0.46)

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A two-item scale measured client‘s enterprise resource base.

1. 12.5% of the respondents had done major investment in the enterprise whereas,

87.5% had not done so. (ENR12: mean = 1.12; SD = 0.33)

2. 31.5% of the respondents have done minor investment in the enterprise whereas,

68.5% had not done so. (ENR13: mean = 1.31; SD = 0.46)

Table 27. Descriptive Statistics of Income Smoothening

Variable Response Scale (%)

(1) No (2)Y Mean

SD

Income

Smoothening

INS1 45.8 54.2 1.45 0.49

INS2 71.6 28.4 1.71 0.45

Source: Field Data

A two-item scale measured enterprise income smoothening.

1. 54.2% of the respondents experienced to survive periods of reduced cash flow,

whereas 45.8% didn‘t have the same experience. (INS1: mean = 1.45; SD = 0.49)

2. 28.4% of the respondents felt reduced repayment problems whereas 71.65 did not

have the same experience. (INS2: mean = 1.71; SD = 0.45)

5.4 Reliability

The reliability of a measure is an indication of the stability and consistency with which

the instrument measures the concept and helps to assess the goodness of a measure

(Sekaran, 2003).

Reliability refers to the extent to which the data collection techniques or analysis

procedures will yield consistent findings. It can be assessed by posing the following three

questions (Easterby-Smith, Thorpe, & Lowe, 2002).

1. Will the measures yield the same result on the other occasions?

2. Will similar observations be reached by other observations?

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Microfinance & Poverty 76

3. Is there a transparency in how raw data have been used to draw conclusions?

Since the tool was taken as a baseline measure and was developed according to local

context, it was mandatory to measure its reliability.

There are three perspectives by which reliabilities can be measured (Cooper & Emory,

1995).

Stability

A measurement is said to be stable if you can secure consistent results with repeated

measurements of the same person with the same instrument. Stability measurement in

survey situations is more difficult and less attractive than for observation studies.

Equivalence

While stability is concerned with personal and situational fluctuations from one time to

another, equivalence is concerned with variations at one point in time among observers

and samples of items.

Internal Consistency

Internal consistency means degree to which instrument items are homogeneous and

reflect the same underlying construct(s).

Among the academic researchers community, the most popular method for measuring

reliabilities is the internal consistency methods, Cronbach‘s alpha (Koufteros, 1999).

SPSS was used to measure the internal consistency of constructs. Sekaran (2003)

proposes Kuder-Richardson KR-20 for the dichotomous scale however; the most popular

reliability statistics in use today is Cronbach's alpha (Cronbach, 1951) reason being that

the KR-20 is mathematically equivalent to the formula for coefficient alpha (Barrett,

2007). (See Appendix II for calculations) KR-20 is just a convenient way of simplifying

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the calculations of reliability for binary-response items. Nunnally & Bernstein (1994)

define alpha and KR-20 in terms of population variances. Crocker & Algina (1986) also

define the same relationship between alpha and KR-20.

Field Data measures the Cronbach‘s alpha and Guttman Split-Half Coefficient in order

to measure internal consistency. Cronbach's alpha is an index of reliability associated

with the variation accounted for by the true score of the "underlying construct." Construct

is the hypothetical variable that is being measured (Hatcher, 1994). Alpha coefficient

ranges in value from 0 to 1 and may be used to describe the reliability of factors extracted

from dichotomous (that is, questions with two possible answers) and/or multi-point

formatted questionnaires or scales (i.e., rating scale: 1 = poor, 5 = excellent). The higher

the score, the more reliable the generated scale is. Nunnaly (1978) has indicated 0.7 to be

an acceptable reliability coefficient but lower thresholds are sometimes used in the

literature. This study uses a newly developed scale and reliability coefficients that lie

between 0.5-0.9 which shows not only the evidence of internal consistency and reliability

of scales but has also the literature support. A reliability coefficient value of 0.5-0.6 has

also been reported as sufficient for preliminary research (Nunnally, 1978).

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Table 28. Reliability Statistics

Variables Number of

Items

Cronbach's Alpha

Coefficient

Guttman Split-Half

Coefficient

Household Income 9 0.968 0.705

Asset Ownership

8 0.777 0.667

Children Education

3 0.985 0.815

Food Security

6 0.507 0.556

Housing

2 0.551 0.551

Enterprise Management

13 0.823 0.729

Income Smoothening

2 0.560 0.560

Overall 42 0.778 0.767

Source: Field Data

5.5. Validity

Validity is concern with whether the findings are really about what they appear to be

about. Thus, validity is highly linked with the credibility of a research (Silverman, 1997).

It also refers to how well the result of a research can give the right answer to the research

question (Remenyi, Williams, Money, & Swartz, 1998). The difficulty in meeting this

test is that usually one does not know what the true differences are; if one did, one would

not do the measuring. (Cooper & Emory, 1995). Field Data has explored the literature

thoroughly, building the conceptual framework, choice of tool, sampling design and data

collection methods appropriately in order to answer the research question. Also the

choice of statistical test makes Field Data valid.

In general, one can do the research about microfinance in different contexts, like as from

the client context or from the MFIs context or from both contexts. Field Data is mainly

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Microfinance & Poverty 79

focused on the clients‘ perspective of microfinance. Use of chi-square for the present

study is consistent with the previous research (Amin, Rai, & Ropa, 2003). Rationale for

choosing chi square test for this study can best be judged through the following table.

Table 29. Rationale for choosing Chi-Square Test

No of IVs No of DVs Measurement scale of

variables

Objective

1 IV with 2 or

more levels

(independent

groups)

2 or more Categorical

To see the

difference between

clients and

nonclients

Source: Developed

Parametric statistics test hypotheses based on the assumption that the samples come from

populations that are normally distributed. Also, parametric statistical tests assume that

there is homogeneity of variance (variances within groups are the same). The level of

measurement for parametric tests is assumed to be interval or at least ordinal. There are

several hypothesis-testing techniques that provide alternatives to parametric tests. These

tests are nonparametric tests.

Nonparametric statistical procedures test hypotheses that do not require normal

distribution or variance assumptions about the populations from which the samples were

drawn and are designed for ordinal or nominal data. One of the advantages of

nonparametric techniques (such as Chi Square) is that it is much easier to compute.

Another unique value of nonparametric procedures is that they can be used to treat data

which have been measured on nominal (classificatory) scales. Such data cannot, on any

logical basis, be ordered numerically, hence there is no possibility of using parametric

statistical tests which require numerical data. Gamston (2006) suggests chi square test to

determine the difference between two groups. This study has chosen Chi square test to

determine the difference between established clients and incoming clients. Use of chi

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Microfinance & Poverty 80

square has been found consistent with the previous studies (Effa, Herring, 2005; Marr,

2002; Morris & Barnes, 2005; Schmidt, C.M., Kolodinsky, M, J., Flint, C., & Whitney,

B, 2006).

5.6. Chi Square Test

The Chi Square test is undoubtedly the most important and most used member of the

nonparametric family of statistical tests. Chi Square is employed to test the difference

between an actual sample and another hypothetical or previously established distribution

such as that which may be expected due to chance or probability. Chi Square can also be

used to test differences between two or more actual samples.

5.6.1. Basic Computational Equation

The level of confidence for all analysis in this study is 95% or p<0.05 as it is a rule of

thumb for social science studies.

Microfinance and Children’s Education

Table 30. Chi-Square Tests for EDU 1

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.730 3 .033

Likelihood Ratio 8.833 3 .032

Linear-by-Linear

Association 4.810 1 .028

N of Valid Cases 384

Source: Field Data

The significant value 0.033 shows that there is a strong relationship between

microfinance participation and children‘s education. χ2

(3, n = 384) = .033, p<.05. It

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Microfinance & Poverty 81

means that established clients have sent higher percentage to the school as compared to

new clients.

Table 31. Chi-Square Tests for EDU 2

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 11.081 3 .011

Continuity Correction

Likelihood Ratio 10.867 3 .012

Linear-by-Linear

Association 1.144 1 .285

N of Valid Cases 384

Source: Field Data

Significant value of .011 shows that participation in microfinance has led to more

expenditure on children‘s education as compared to new clients. i-e χ2(3, n = 384) = .011,

p<.05.

Table 32. Chi-Square Tests for EDU 3

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 11.054 5 .050

Continuity Correction

Likelihood Ratio 10.864 5 .054

Linear-by-Linear

Association

.048 1 .827

N of Valid Cases 384

Source: Field Data

Results of chi square clearly reveal that significant value is equal to .05 which means that

microfinance new clients and established clients had difference in the children‘s highest

education. χ2(5, n = 384) = .050, p =.05.

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Microfinance & Poverty 82

Microfinance and Housing

Table 33. Chi-Square Tests for HUS 1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .087 1 .768

Continuity

Correction(a) .033 1 .856

Likelihood Ratio .087 1 .768

Fisher's Exact Test .820 .430

Linear-by-Linear

Association .087 1 .768

N of Valid Cases 384 .

Source: Field Data

No significant difference with respect to repair and improvement in the housing

conditions was found when compared new clients with established clients because

significant value is greater than 0.05. χ2(1, n = 384) = 0.768, p >.05.

Table 34. Chi-Square Tests for HUS 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 1.297 1 .255

Continuity

Correction(a) 1.058 1 .304

Likelihood Ratio 1.292 1 .256

Fisher's Exact Test .271 .152

Linear-by-Linear

Association 1.294 1 .255

N of Valid Cases 384

Source: Field Data

Results of chi square clearly disclose that no significant difference in the source of

drinking water when compared new clients with the established clients has been found. χ2

(1, n = 384) = 0.255, p >.05.

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Microfinance and Food Security

Table 35. Chi-Square Tests for FD1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .135 1 .714

Continuity

Correction(a) .067 1 .796

Likelihood Ratio .135 1 .714

Fisher's Exact Test .745 .398

Linear-by-Linear

Association .134 1 .714

N of Valid Cases 384

Source: Field Data

Significant value is grater than 0.05 so it can be said safely that there has been found no

significant difference between new clients and established clients when compared for the

usage of food item (cereal) was found. χ2 (1, n = 384) = 0.714, p >.05.

Table 36. Chi-Square Tests for FD 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .348 1 .555

Continuity

Correction(a) .228 1 .633

Likelihood Ratio .350 1 .554

Fisher's Exact Test .574 .318

Linear-by-Linear

Association .347 1 .556

N of Valid Cases 384

Source: Field Data

χ2 (1, n = 384) = 0.555, p >.05 Chi-square test shows that no significant difference

between new clients and established clients with respect to usage of food item milk was

found. The significant value is 0.555.

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Table 37. Chi-Square Tests for FD 3

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .892 1 .345

Continuity

Correction(a) .698 1 .403

Likelihood Ratio .892 1 .345

Fisher's Exact Test .385 .202

Linear-by-Linear

Association .889 1 .346

N of Valid Cases 384

Source: Field Data

Results clearly indicate that no significant difference between new clients and established

clients with respect to the usage of food item eggs was found. χ2 (1, n = 384) = 0.345, p

>.05.

Table 38. Chi-Square Tests for FD 4

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .250(b) 1 .617

Continuity

Correction(a) .150 1 .698

Likelihood Ratio .250 1 .617

Fisher's Exact Test .656 .350

Linear-by-Linear

Association .249 1 .618

N of Valid Cases 384

Source: Field Data

Significant value is greater than 0.05 so it can be safely said that there was no difference

in usage of food item between new clients and established clients when asked about the

usage of food item meat. χ2 (1, n = 384) = 0.617, p >.05.

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Table 39. Chi-Square Tests for FD 5

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 3.245(b) 1 .072

Continuity

Correction(a) 2.859 1 .091

Likelihood Ratio 3.226 1 .072

Fisher's Exact Test .078 .046

Linear-by-Linear

Association 3.237 1 .072

N of Valid Cases 384

Source: Field Data

No significant difference in the usage of food item (fruit) between new and established

clients was found, since the significant value is greater than 0.05. χ2 (1, n = 384) = 0.072,

p >.05.

Microfinance and Household Expenditure

Table 40. Chi-Square Tests for HSIN 1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 3.263 1 .071

Continuity

Correction(a) 1.616 1 .204

Likelihood Ratio 3.017 1 .082

Fisher's Exact Test .105 .105

Linear-by-Linear

Association 3.255 1 .071

N of Valid Cases 384

Source: Field Data

No significant difference between new clients and established clients with respect to the

household expenditure in clothes and household items has been found. Significant value

is greater than 0.05. χ2 (1, n = 384) = 0.71, p >.05.

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Table 41. Chi-Square Tests for HSIN 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 2.162(b) 1 .141

Continuity

Correction(a) 1.235 1 .266

Likelihood Ratio 2.011 1 .156

Fisher's Exact Test .161 .134

Linear-by-Linear

Association 2.157 1 .142

N of Valid Cases 384

Source: Field Data

Table shows the result of chi square for use of microcredit given to spouse, no significant

difference between new clients and established clients in this expenditure has been found.

Since χ2 (1, n = 384) = 0.141, p >.05.

Table 42. Chi-Square Tests for HSIN 3

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .817 1 .366

Continuity

Correction(a) .217 1 .642

Likelihood Ratio .767 1 .381

Fisher's Exact Test .399 .308

Linear-by-Linear

Association .815 1 .367

N of Valid Cases 384

Source: Field Data

Again, no significant difference between new clients and established clients when

observed with respect to the expenditure spent on house repair has been found. χ2 (1, n =

384) = 0.366, p >.05.

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Table 43. Chi-Square Tests for HSIN 4

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 6.191 1 .013

Continuity

Correction(a) 3.501 1 .061

Likelihood Ratio 6.735 1 .009

Fisher's Exact Test .035 .035

Linear-by-Linear

Association 6.175 1 .013

N of Valid Cases 384

Source: Field Data

A significant difference between new clients and established clients when asked about

spending on food items has been found since the significant value is less than 0.05. χ2 (1,

n = 384) = 0.013, p <.05.

Table 44. Chi-Square Tests for HSIN 5

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 7.057 1 .008

Continuity

Correction(a) 4.921 1 .027

Likelihood Ratio 6.643 1 .010

Fisher's Exact Test .016 .016

Linear-by-Linear

Association 7.038 1 .008

N of Valid Cases 384

Source: Field Data

The table shows that a significant difference between new clients and established clients

when asked about the use of microcredit to loan it to relative has been found. χ2 (1, n =

384) = 0.008, p <.05.

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Table 45. Chi-Square Tests for HSIN 6

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 8.277 1 .004

Continuity

Correction(a) 5.483 1 .019

Likelihood Ratio 9.001 1 .003

Fisher's Exact Test .011 .011

Linear-by-Linear

Association 8.255 1 .004

N of Valid Cases 384

Source: Field Data

A significant difference has been found between new clients and established clients when

asked about expenditure in celebrations, since the significant value is less than 0.05. χ2

(1, n = 384) = 0.008, p <.05.

Table 46. Chi-Square Tests for HSIN 7

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 1.699 1 .192

Continuity

Correction .679 1 .410

Likelihood Ratio 1.569 1 .210

Fisher's Exact Test .336 .201

Linear-by-Linear

Association 1.694 1 .193

N of Valid Cases 384

Source: Field Data

Result of chi-square reveals that no significant difference has been found as the value is

greater than 0.05 with respect to spending on purchase of land, when compared to new

clients with established clients. χ2 (1, n = 384) = 0.192, p >.05.

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Table 47. Chi-Square Tests for HSIN 8

Value Df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 5.117 1 .024

Continuity

Correction(a) 3.178 1 .075

Likelihood Ratio 4.773 1 .029

Fisher's Exact Test .042 .042

Linear-by-Linear

Association 5.103 1 .024

N of Valid Cases 384

Source: Field Data

The significant value is less than 0.05 it can be said that there is a significant difference

between new clients and established clients when asked about their repaying their old

debt. χ2 (1, n = 384) = 0.024, p <.05.

Table 48. Chi-Square Tests for HSIN 9

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.298 2 .522

Continuity

Correction

Likelihood Ratio 1.556 2 .459

Linear-by-Linear

Association .065 1 .799

N of Valid Cases 384

Source: Field Data

The above table shows that no significant difference has been found with respect to

spending in repaying the existing loan between new clients and established clients since

the significant value is greater than 0.05. χ2 (2, n = 384) = 0.522, p >.05.

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Microfinance and Household Assets

Table 49. Chi-Square Tests for HSAS 1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 1.385(b) 1 .239

Continuity

Correction(a) 1.111 1 .292

Likelihood Ratio 1.409 1 .235

Fisher's Exact Test .270 .146

Linear-by-Linear

Association 1.381 1 .240

N of Valid Cases 384

Source: Field Data

Results of chi square clearly reveal that significant value is equal to 0.239 which shows

that microfinance new clients and established clients had no difference in the ownership

of household asset refrigerator.

χ2 (1, n = 384) = 0.239, p > 0.05.

Table 50. Chi-Square Tests for HSAS 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 1.629 1 .202

Continuity

Correction(a) 1.331 1 .249

Likelihood Ratio 1.606 1 .205

Fisher's Exact Test .221 .125

Linear-by-Linear

Association 1.625 1 .202

N of Valid Cases 384

Source: Field Data

Value of p = .202, shows no significant difference has been found between new clients

and established clients in the ownership of CD player since χ2 (1, n = 384) = 0.239, p >

0.05.

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Table 51. Chi-Square Tests for HSAS 3

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .142 1 .706

Continuity

Correction(a) .064 1 .801

Likelihood Ratio .141 1 .707

Fisher's Exact Test .711 .398

Linear-by-Linear

Association .141 1 .707

N of Valid Cases 384

Source: Field Data

No significant difference has been found between new clients and established clients with

respect to household asset (motor cycle) as the significant value is 0.706 and χ2 (1, n =

384) = 0.706, p > 0.05.

Table 52. Chi-Square Tests for HSAS 4

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .245(b) 1 .621

Continuity

Correction(a) .133 1 .716

Likelihood Ratio .247 1 .619

Fisher's Exact Test .695 .361

Linear-by-Linear

Association .244 1 .621

N of Valid Cases 384

Source: Field Data

Results of Chi square clearly show no significant difference has been found in the

ownership of household asset (washing machine) since value of p statistics is 0.621. χ2 (1,

n = 384) = 0.621, p > 0.05.

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Table 53. Chi-Square Tests for HSAS 5

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .070 1 .792

Continuity

Correction(a) .016 1 .900

Likelihood Ratio .070 1 .791

Fisher's Exact Test .890 .454

Linear-by-Linear

Association .070 1 .792

N of Valid Cases 384

Source: Field Data

Results of chi square clearly indicate that significant value is less than 0.792 which

means that microfinance new clients and established clients had no difference in the

ownership of household asset (sewing machine) χ2 (5, n = 384) = 0.792, p >.05.

Table 54. Chi-Square Tests for HSAS 6

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 6.738(b) 1 .009

Continuity

Correction(a) 6.168 1 .013

Likelihood Ratio 6.890 1 .009

Fisher's Exact Test .010 .006

Linear-by-Linear

Association 6.720 1 .010

N of Valid Cases 384

Source: Field Data

The significant value of p<.05, helps safely saying that there is a significant difference in

the ownership of household assets (Bed with foam) between new clients and established

clients. χ2 (1, n = 384) = 0.009, p <.05.

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Table 55. Chi-Square Tests for HSAS 7

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .179 1 .672

Continuity

Correction(a) .073 1 .788

Likelihood Ratio .181 1 .670

Fisher's Exact Test .759 .398

Linear-by-Linear

Association .179 1 .672

N of Valid Cases 384

Source: Field Data

Chi square test clearly reveals that no significant difference has been found in the

ownership of asset (cell-phone) between new clients and established clients. χ2 (1, n =

384) = 0.672, p >.05.

Table 56. Chi-Square Tests for HSAS 8

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.217 2 .544

Continuity

Correction

Likelihood Ratio 1.515 2 .469

Linear-by-Linear

Association 1.153 1 .283

N of Valid Cases 384

Source: Field Data

It was revealed from chi square test that there is no significant difference between new

clients and established clients with respect to ownership of household assets (Television).

χ2 (1, n = 384) = 0.544, p >.05.

There was no indication of impact on household assets. Results of this study are consistent with

Dunn & Arbuckle, 2001 but contradict with the findings of Chen & Snodgrass, 2001 &

Barnes, 2001.

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Microfinance and Enterprise

Table 57. Chi-Square Tests for ENT 1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .145 1 .703

Continuity

Correction(a) .060 1 .807

Likelihood Ratio .144 1 .704

Fisher's Exact Test .784 .400

Linear-by-Linear

Association .145 1 .703

N of Valid Cases 384

Source: Field Data

Chi square test clearly indicates that no significant difference between new and

established clients with respect to the expansion of enterprise has been found since the

significant value is greater than 0.703. χ2 (1, n = 384) = 0.703, p >.05.

Table 58. Chi-Square Tests for ENT 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 8.039 1 .005

Continuity

Correction(a) 7.424 1 .006

Likelihood Ratio 7.977 1 .005

Fisher's Exact Test .006 .003

Linear-by-Linear

Association 8.018 1 .005

N of Valid Cases 384

Source: Field Data

A very low significant value shows that there is a significant difference between new and

established clients about the addition of new products in the enterprise. It means

microfinance leads to addition of new products in the enterprise. χ2 (1, n = 384) = 0.005,

p <.05.

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Table 59. Chi-Square Tests for ENT 3

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 5.728(b) 1 .017

Continuity

Correction(a) 4.838 1 .028

Likelihood Ratio 5.394 1 .020

Fisher's Exact Test .020 .016

Linear-by-Linear

Association 5.713 1 .017

N of Valid Cases 384

Source: Field Data

Again a low significant value of 0.017 shows that there is a significant difference

between new and established clients when asked about hiring of more workers. χ2 (1, n =

384) = 0.017, p <.05.

Table 60. Chi-Square Tests for ENT 4

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact Sig.

(1-sided)

Pearson Chi-Square 7.166 1 .007

Continuity

Correction(a) 6.474 1 .011

Likelihood Ratio 6.919 1 .009

Fisher's Exact Test .011 .006

Linear-by-Linear

Association 7.148 1 .008

N of Valid Cases 384

Source: Field Data

A significant difference between new and established clients has been found when asked

about improvement in the product quality as expressed by a very low significant value. χ2

(1, n = 384) = 0.007, p < 0.05. This shows that microfinance leads to the improvement in

quality of product for enterprise development.

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Table 61. Chi-Square Tests for ENT 5

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 10.427 1 .001

Continuity

Correction(a) 9.557 1 .002

Likelihood Ratio 9.979 1 .002

Fisher's Exact Test .002 .001

Linear-by-Linear

Association 10.400 1 .001

N of Valid Cases 384

Source: Field Data

Again low significant value of 0.001 indicates that there is a significant difference

between new and established clients with respect to improved desirability of products. χ2

(1, n = 384) = 0.001, p < 0.05.

Table 62. Chi-Square Tests for ENT 6

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 6.889 1 .009

Continuity

Correction(a) 6.145 1 .013

Likelihood Ratio 6.597 1 .010

Fisher's Exact Test .013 .007

Linear-by-Linear

Association 6.871 1 .009

N of Valid Cases 384

Source: Field Data

A significant difference between new and established clients with respect to reduced

costs by purchasing in bulk has been found. χ2 (1, n = 384) = 0.009, p < 0.05.

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Table 63. Chi-Square Tests for ENT 7

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .288 1 .591

Continuity

Correction(a) .183 1 .669

Likelihood Ratio .289 1 .591

Fisher's Exact Test .662 .335

Linear-by-Linear

Association .288 1 .592

N of Valid Cases 384

Source: Field Data

A very high significant value of 0.591 shows that no significant difference between new

and established clients has been found, when asked about the enterprise money they keep

separate from household and personal use. χ2 (1, n = 384) = 0.591, p > 0.05.

Table 64. Chi-Square Tests for ENT 8

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .159 1 .690

Continuity

Correction(a) .079 1 .778

Likelihood Ratio .159 1 .690

Fisher's Exact Test .727 .391

Linear-by-Linear

Association .158 1 .691

N of Valid Cases 384

Source: Field Data

The above table shows a very high significant value of 0.690 indicating no significant

difference between new and established clients was found with respect to profit

calculation of the enterprise. χ2 (1, n = 384) = 0.690, p >0.05.

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Table 65. Chi-Square Tests for ENT 9

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 5.205 1 .023

Continuity

Correction(a) 4.668 1 .031

Likelihood Ratio 5.389 1 .020

Fisher's Exact Test .029 .014

Linear-by-Linear

Association 5.191 1 .023

N of Valid Cases 384

Source: Field Data

Chi-square test result shows that there has been found a significant difference between

new and established clients about the knowledge of most profitable product. χ2 (1, n =

384) = 0.023, p <0.05.

Table 66. Chi-Square Tests for ENT 10

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 4.992(b) 1 .025

Continuity

Correction(a) 4.489 1 .034

Likelihood Ratio 5.120 1 .024

Fisher's Exact Test .028 .016

Linear-by-Linear

Association 4.979 1 .026

N of Valid Cases 384

Source: Field Data

A very low significant value of 0.025 shows that there is a significant difference between

new and established clients when asked about the fixed location for production with

protection from the sun and rain for selling χ2 (1, n = 384) = 0.025, p <0.05.

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Table 67. Chi-Square Tests for ENT 11

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 6.914 1 .009

Continuity

Correction(a) 6.316 1 .012

Likelihood Ratio 6.788 1 .009

Fisher's Exact Test .010 .006

Linear-by-Linear

Association 6.896 1 .009

N of Valid Cases 384

Source: Field Data

The results show that there is a significant difference between new and established clients

when asked about the presence of a fixed location for storing purpose. χ2 (1, n = 384) =

0.009, p <0.05.

Table 68. Chi-Square Tests for ENR 12

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 9.241 1 .002

Continuity

Correction 8.269 1 .004

Likelihood Ratio 8.725 1 .003

Fisher's Exact Test .005 .003

Linear-by-Linear

Association 9.217 1 .002

N of Valid Cases 384

Source: Field Data

There is a significant difference between the two groups of established and new clients

about the major investment in the enterprise. The significance value is 0.002, which is

very low. χ2 (1, n = 384) = 0.002, p <0.05.

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Table 69. Chi-Square Tests for ENR 13

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .005(b) 1 .945

Continuity

Correction(a) .000 1 1.000

Likelihood Ratio .005 1 .945

Fisher's Exact Test 1.000 .517

Linear-by-Linear

Association .005 1 .945

N of Valid Cases 384

Source: Field Data

Results show a very high significant value which means that no significant difference

between new and established clients has been found when asked about the minor

investment in the enterprise χ2 (1, n = 384) = 0.945, p >0.05.

Table 70. Chi-Square Tests for INS 1

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square .360(b) 1 .549

Continuity

Correction(a) .241 1 .624

Likelihood Ratio .360 1 .548

Fisher's Exact Test .586 .312

Linear-by-Linear

Association .359 1 .549

N of Valid Cases 384

Source: Field Data

High significant value reveals that no significant difference has been found between new

and established clients when asked about their view point about money not enough for

the enterprise.

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Table 71. Chi-Square Tests for INS 2

Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Pearson Chi-Square 2.660 1 .103

Continuity

Correction 2.281 1 .131

Likelihood Ratio 2.722 1 .099

Fisher's Exact Test .117 .064

Linear-by-Linear

Association 2.653 1 .103

N of Valid Cases 384

Source: Field Data

Again, significant value of 0.103 reveals that no significant difference has been found

between new and established clients when asked about the repayment problems of

microcredit. χ2 (1, n = 384) = 0.103, p >0.05.

Following table shows the result of chi square at glance.

Table 72. Chi Square Results at a Glance: Summary Table

Hypotheses Variables Tested at

Significance Level

Status

Upheld/Rejected

H1a EDU 1 0.05 Supported

H1b EDU 2 0.05 Supported

H1c EDU 3 0.05 Supported

H2a HUS 1 0.05 Rejected

H2b HUS 2 0.05 Rejected

H3a FD 1 0.05 Rejected

H3b FD 2 0.05 Rejected

H3c FD 3 0.05 Rejected

H3d FD 4 0.05 Rejected

H3e FD 5 0.05 Rejected

H4a HSIN 1 0.05 Rejected

H4b HSIN 2 0.05 Rejected

H4c HSIN 3 0.05 Rejected

H4d HSIN 4 0.05 Supported

H4e HSIN 5 0.05 Supported

H4f HSIN 6 0.05 Supported

H4g HSIN 7 0.05 Rejected

H4h HSIN 8 0.05 Supported

H4i HSIN 9 0.05 Rejected

H5a HSAS 1 0.05 Rejected

H5b HSAS 2 0.05 Rejected

H5c HSAS 3 0.05 Rejected

H5d HSAS 4 0.05 Rejected

H5e HSAS 5 0.05 Rejected

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H5f HSAS 6 0.05 Supported

H5g HSAS 7 0.05 Rejected

H5h HSAS 8 0.05 Rejected

H6a ENT 1 0.05 Rejected

H6b ENT 2 0.05 Supported

H6c ENT 3 0.05 Supported

H6d ENT 4 0.05 Supported

H6e ENT 5 0.05 Supported

H6f ENT 6 0.05 Supported

H6g ENT 7 0.05 Rejected

H6h ENT 8 0.05 Rejected

H6i ENT 9 0.05 Supported

H6j ENT 10 0.05 Supported

H6k ENT 11 0.05 Supported

H7a ENR 12 0.05 Supported

H7b ENR 13 0.05 Rejected

H8a INS 1 0.05 Rejected

H8b INS 2 0.05 Rejected

Source: Field Data

5.7 Econometric Evidence: Logistic Regression

Since the probability of an event must lie between 0 and 1, it is impractical to model

probabilities with linear regression techniques, because the linear regression model

allows the dependent variable to take values greater than 1 or less than 0. The logistic

regression model is a type of generalized linear model that extends the linear regression

model by linking the range of real numbers to the 0-1 range. Multinomial logistic

regression does not make any assumptions of normality, linearity, and homogeneity of

variance for the independent variables.

In certain aspects, logistic regression is similar to chi-square as like chi-square it

calculates likelihood ratio chi-square and Pearson chi-square; however, one can add

additional predictor variables. These additional predictors can be either categorical or

continuous and this can not be done with a simple Pearson Chi-Square. Use of logistic

regression (Cramer, 2003) was found to be consistent with previous studies. (Bekele &

Muchie; Fuster, Mutonyi, Houser & Coates, 2008; Kamal & Haider, 2008 & Mersland &

Page 120: Poverty and microfinance in Pakistan

Microfinance & Poverty 103

Strom, 2008; Mohindra, Haddad & Narayana, 2008; Pagura, Graham & Meyer,

2001;Seiber & Miller, 2004).

The advantage of using the regression framework is that it can account for the differences

in household and community characteristics which can happen even with a well-designed

sampling scheme in a quasi-experimental design. (Kondo et al, 2008).

Before getting into the estimation procedure, it is imperative to describe the nature of the

treatment variables and the outcome variables considered in the study.

Outcome variables: Several outcome variables are considered in this study, namely: (a)

at household level, children education, housing, nutrition choice, household expenditure,

household assets, (e) at enterprise level, financial performance, enterprise resource base

and income smoothening.

Treatment variables: There are four possible treatment variables that can be used to

assess the impact of microfinance. These are: (1) number of years the clients spent in the

program (2) amount/value of loans availed (3) number of loan cycles. Treatment variable

1 and 2 are deemed better in representing program availability (Coleman, 1999). Present

study has taken (1) as the treatment variable to assess the impact of microfinance.

Other Independent Variables.

Other independent variables are included in the control function, such as sex, age,

education, type of area, number of households and number of salaried persons.

This study chooses to model the relationship between, participation in the program

(incoming clients) for less number of years vs. more number of years (established

clients). Difference in the two groups with respect to variables children‘s education,

housing, food security, household expenditure, household assets, financial performance,

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Microfinance & Poverty 104

enterprise resource base and income smoothening can be attributed to the impact of

microfinance.

Following expression explains the effect of treatment variable on variable of interest by

taking into account certain additional independent variables such as sex, age, education,

number of households, number of salaried persons and type of area.

Y = α + β1sex + β2age + β3education + β4number of households + β5number of salaried

persons+ β6type of area + βTi

Where,

Y= household/enterprise outcome of interest

Ti = treatment variable1 if membership was 2-5 years

0 if membership was less than 1or 1 year

Similar expressions have been formulated in previous studies (Coleman, 1999; Kondo et

al., 2008; Montgomery, 2005) in and had employed nearly identical evaluation strategy.

Present study analyses the data by using SPSS16.

Multicollinearity in the multinomial logistic regression solution is detected by examining

the standard errors for the b coefficients. A standard error larger than 2.0 indicates

numerical problems, such as multicollinearity among the independent variables, zero

cells for a dummy-coded independent variable because all of the subjects have the same

value for the variable, and 'complete separation' whereby the two groups in the dependent

event variable can be perfectly separated by scores on one of the independent variables.

None of the independent variables in this analysis had a standard error larger than 2.0.

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Household Level

Following table provides summary of results for multinomial logistic regression for

household welfare (children education, housing and food). The level of confidence for all

analysis in this study is 95% or p<0.05 as it is a rule of thumb for social science studies

Table 73. Summary of Multinomial Logistic Regression for Education, Housing and Food

IVS/DVS EDU 1 EDU 2 EDU 3 HUS 1 HUS 2 FD 1 FD 2 FD 3 FD 4 FD 5

Category .024 .069 .308 .666 .790 .792 .909 .319 .941 .121

Sex .033 .321 .462 .665 .020 .352 .608 .381 .733 .193

Age .138 .008 .010 .602 .560 .660 .120 .052 .116 .865

Education .221 .000 .042 .755 .466 .067 .574 .335 .704 .009

No of Household .000 .000 .000 .104 .047 .154 .860 .735 .862 .034

No of salaried Persons .001 .000 .009 .019 .004 .007 .025 .073 .331 .557

Type of Area .692 .544 .340 .528 .000 .918 .523 .915 .108 .027

Source: Developed

Results clearly reveal that significant difference was observed about the percentage of

school going children between new and established clients. Among other independent

variables, sex, number of households and number of salaried persons contributed to the

model.

Results of expenditure on children education clearly indicate that age, education, number

of household and number of salaried persons contributes to the model however, and sex

of the respondent and type of area does not have any relationship with children education

expenditure.

Age, education, number of household and number of salaried persons have significant

relationship with the children highest education, however, sex and type of area does not

contribute to the model.

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Microfinance & Poverty 106

To summarize the results of education variable, it can be stated that number of household

and number of salaried persons were the most important variable however type of area

has no relationship with education

For HUS 1, It is very obvious from the statistics given in the table that only number of

salaried persons has a significant relationship and rests of all other variables do not

contribute to the model.

Results of source of drinking water clearly indicate that sex, number of household,

number of salaried persons and type of area does contribute to the model. However, Age

and education does not contribute to the model.

To summarize the housing, it can be stated that number of salaried persons has the most

significant importance; however, type of area has the most significant relationship for

source of drinking water. Age and education has no relationship with housing.

For FD 1, it can be clearly stated that only number of salaried persons contributed to the

model. Age and education of the respondents, number of household, number of salaried

persons and type of area do not contribute to the model.

Again only the number of salaried persons contributed to the model. Rest of all other

variables has no relationship for FD 2.

Results clearly indicate that none of the variables for food item eggs and meat has

contributed to the model.

Statistics in the table clearly indicate that education has the most significant contribution

to the model, however, number of households and type of area also has the significant

relationship for FD 5.

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In summary, it can be safely said that for food security, number of salaried persons and

education were found to be the most important variables contributing to the model.

Table 74. Summary of Multinomial Logistic Regression for Household Expenditures

IVS/DVS HSIN 1 HSIN 2 HSIN 3 HSIN 4 HSIN 5 HSIN 6 HSIN 7 HSIN 8 HSIN 9

Category .351 .289 .573 .096 .008 .096 .657 .061 .231

Sex .033 .096 .424 .999 .531 .007 .098 .159 .829

Age .123 .630 .435 .287 .657 .003 .202 .108 .990

Education .053 .029 .217 .029 .076 .003 .492 .277 .499

No of Household .127 .623 .115 1.000 .729 .250 .171 .075 .176

No of salaried Persons .073 .153 .372 .021 .042 .005 .200 .314 .754

Type of Area .158 .525 .317 .999 .595 .999 .083 .103 1.000

Source: Developed

Results clearly indicate that for HSIN 1 only sex has contributed to the model whereas

rest of the other variables have no significant contribution to the model.

It is very clear from the statistics for HSIN 2 given in the table that only education has

significantly contributed to the model, rest of other variables have no relationship.

None of the variables has significantly contributed to the model for HSIN 3.

For HSIN 4 number of salaried persons and education has contributed to the model; rest

of other variables has no relationship.

Number of salaried persons has contributed to the model, whereas, sex, age, education,

number of households and type of area has no relationship for HSIN 5.

For HSIN 6 sex, age, education and number of salaried persons has contributed to the

model, number of household and type of area don‘t have any relationship.

None of the variables for HSIN 8 purchase of land, HSIN 9 repayment of old loans and

HSIN 10 repayment of existing loan has contributed to the model.

In summary, it can be stated that for household expenditure; number of household and

education has significantly contributed to the model.

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Table 75. Summary of Multinomial Logistic Regression for Household Assets

IVS/DVS HSAS 1 HSAS 2 HSAS 3 HSAS 4 HSAS 5 HSAS 6 HSAS 7 HSAS 8

Category .246 .266 .433 .801 .343 .005 .629 .623

Sex .334 .868 .704 .707 .427 .184 .268 .320

Age .753 .650 .024 .013 .039 .202 .548 .829

Education .000 .634 .699 .000 .020 .000 .003 .122

No of Household .221 .147 .498 .161 .131 .026 .242 .232

No of salaried Persons .116 .541 .760 .177 .436 .883 .650 .015

Type of Area .129 .419 .010 .006 .000 .098 .001 .010

Source: Developed

Results clearly indicate that education has significantly contributed to the model for

HSAS 1 none of the variable has significantly contributed to the model for HSAS 2.

Age and type of area have significantly contributed to the model; whereas rest of other

variables has no relationship for HSAS 3.

For HSAS 4 age, education and type of area have significantly contributed to the model

as can be seen in the table.

Age, education and type of area significantly contributed to the model for HSAS 5.

Results clearly indicate that for HSAS 6 education and numbers of households have the

most significant contribution to the model.

Education and type of area contribute to the model given in the table for HSAS 7.

Results clearly indicate that number of salaried persons and type of area contributed to

the model for HSAS 8.

In summary it can be stated that for household assets, type of area, education and age

significantly contribute to the model.

Micro Enterprise Level

Results clearly reveal that education contributes to the model whereas rests of other

variables do not have any contribution for ENT 1.

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For ENT 2 participation in the microfinance program has contributed to the model. None

of the variables has any contribution to the model.

Participation in the program and number of salaried persons has contributed to the model

whereas rest of all other variables has no contribution for ENT 3.

Participation in the program has a significant value of 0.014 hence contributed to the

model for ENT 4. Rest of all other variables has no contribution.

The table shows that participation in the program and number of salaried persons has

significantly contributed the model for ENT 5.

Results clearly reveal that participation in the program, age, number of households,

number of salaried persons and type of area significantly contribute to the model for ENT

6. Number of salaried persons significantly contributed to the model for ENT 7.

For ENT 8 none of the variables has significantly contributed to the model.

Participation in the program, number of households and type of area significantly

contribute to model rest of all other variables has no relationship for ENT 9.

None of the variables has significantly contributed to the model for ENT 10.

Results clearly reveal that participation in the program and sexes have contributed to the

model for ENT 11. Rest of all other variables have no relationship.

In summary it can be stated that for enterprise development number of household and

number of salaried persons have significantly contributed to the model. The table

indicates that participation in the program, education and type of area significantly

contribute to the model for ENR 12. For ENR 13 sex, education and type of area

significantly contribute to the model. Rest of all other variables has no relationship. In

summary, for enterprise resource base, education and type of area significantly

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contributed to the model. For INS 1 number of salaried persons and type of area

significantly contribute to the model. None of the variables have significantly contributed

to model for INS 2. To summarize, the income smoothening effect, numbers of salaried

persons and type of area have significantly contributed to the model.

Table 76. Summary of Multinomial Logistic Regression for Enterprise Management

IVS/DVS

ENT

1

ENT

2

ENT

3

ENT

4

ENT

5

ENT

6

ENT

7

ENT

8

ENT

9

ENT

10

ENT

11

ENR

12

ENR

13

INS

1

INS

2

Category .506 .012 .043 .014 .001 .007 .407 .396 .014 .061 .035 .024 .916 .321 .180

Sex .498 .390 .085 .904 .632 .919 .432 .887 .392 .114 .001 .672 .029 .088 .252

Age .182 .121 .761 .391 .422 .044 .801 .103 .070 .060 .893 .196 .123 .691 .559

Education .002 .097 .148 .120 .944 .526 .525 .511 .142 .639 .514 .023 .011 .099 .923

No of Household .213 .227 .051 .875 .162 .032 .478 .387 .000 .268 .511 .377 .379 .480 .068

No of salaried

Persons .349 .295 .007 .776 .001 .049 .007 .099 .129 .342 .452 .302 .302 .000 .660

Type of Area .880 .657 .655 .818 .068 .015 .234 .334 .000 .401 .549 .025 .029 .005 .138

Source: Developed

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Following table presents the summary of multinomial logistics regression.

Table 77. Multinomial Regression Results at a Glance: Summary Table

Variable

Participation

in the

Microcredit

Sex Age Education No of

household

No of salaried

persons Type of area

EDU1 S S R R S S R

EDU2 R R S S S S R

EDU3 R R S S S S R

HUS1 R R R R R S R

HUS2 R S R R S S S

FD1 R R R S R S R

FD2 R R R R R S R

FD3 R R S R R S R

FD4 R R R R R R R

FD5 R R R S S R S

HSIN1 R S R S R S R

HSIN2 R S R S R R R

HSIN3 R R R R R R R

HSIN4 S R R S R S R

HSIN5 S R R R R S R

HSIN6 R S S S R S R

HSIN7 R R R R R R R

HSIN8 R R R R R R R

HSIN9 R R R R R R R

HSAS1 R R R S R R R

HSAS2 R R R R R R R

HSAS3 R R S R R R S

HSAS4 R R S S R R S

HSAS5 R R S S R R S

HSAS6 S R R S S R R

HSAS7 R R R S R R S

HSAS8 R R R R R S S

ENT1 R R R S R R R

ENT2 S R R R R R R

ENT3 S R R R S S R

ENT4 S R R R R R R

ENT5 S R R R R S R

ENT6 S R S R S S S

ENT7 R R R R R S R

ENT8 R R R R R R R

ENT9 S R R R S R S

ENT10 R R R R R R R

ENT11 S S R R R R R

ENR12 S R R S R R S

ENR13 R S R S R R S

INS1 R R R R R S S

INS2 R R R R R R R

Source: Developed

R = Rejected

S = Supported

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Chapter 6

Interpretation

Interestingly, microfinance impact assessment studies produce mixed evidence, it is clear

that there is no clear-cut or definite answer regarding the impact of microfinance

schemes. Conclusions might differ because of different methodologies used, because of

diverse subjective interpretations given to the same research findings, or because of the

particular features of the program one is studying (Holvoet, 2004).

The present study also provides mixed results: while no negative effects are identified,

positive and significant loadings are found in several, but not all cases. This result is

similar to other studies on the provision of microcredit in Bangladesh, India, Indonesia,

Sri Lanka, and northeastern Thailand.

The findings suggest that targeting microfinance on the poorest households may not be

the most appropriate way to help them escape poverty. The projects selected by the

poorest households to finance with microcredit loans did not generate sufficient profit to

increase household income (ADB, 2007).

There are examples of many other studies that are either inconclusive or provide less

convincing results. Coleman (1999) and MkNelly, Watetip, & Dunford (1996) both focus

on experiences with village banking in Thailand.

Maldonado (2005) presents ambiguous results on Bolivia, where the availability of credit

in rural activities has seemingly driven parents to use their children‘s labor supply in new

productive projects.

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Microfinance & Poverty 113

6.1 Household Level

Starting from household level, interpretation of the results will be moved to enterprise

level and demographic characteristics.

Education

Following hypotheses were developed to measure the impact of microfinance on children

education as stated earlier in chapter 3.

H 1a: Participation in the program leads to increase in percentage of school going children

H 1b: Participation in the program leads to more expenditure on children’s education.

H 1c: Participation in the program leads to increase the highest level of children education.

It is evident from the data analysis that there is a strong relationship between

microfinance participation and percentage of school going children. Participation in

microfinance has led to more expenditure on children‘s education as compared to new

clients. However, microfinance new clients and established clients had difference in the

children‘s highest education. These results are consistent with several studies.

(Chowdhury & Bhuiya 2001; Effa & Herring, 2005; Holvoet, 2004; Neponen, 2003;

Rosintan, D.M., Drioadisuryo P., & Cloud, K. 1999; Sengsourivong, 2006). However,

these results contradict with the findings of previous studies (Coleman, 1999; Kondo, et

al. 2008).

For each hypothesis, role of other independent variables such as sex, age, education,

number of households, number of salaried persons and type of area were modeled by

applying multinomial regression. These variables used in the control functions are similar

to those used in existing literature. (Coleman 1999; Kondo et al, 2008; Montgomery

2005).

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Results of multinomial regression for clearly H 1a indicate that participation in the

microfinance program, sex, number of households and number of salaried persons

contributed to the model, since p<0.05. So it can be said that these variables have a

contribution for percentage of school going children. For EDU 1, age, education, number

of household and number of salaried persons contributes to the model since significant

values are less than 0.05. However, participation in the microfinance has no significant

impact on expenditure on children‘s education (EDU 2). Age, education, number of

household and number of salaried persons have significant relationship with the children

highest education (EDU 3) having significant values less than 0.05, however,

participation in the microfinance program has no impact on children highest education.

Housing

Following hypotheses were developed to measure the impact of microfinance on housing

as stated earlier in chapter 3.

H 2a: Participation in the program leads to improve the housing conditions.

H 2b: Participation in the program leads to improve drinking water source.

As it is evident from the table no significant difference with respect to repair and

improvement in the housing conditions when compared new clients with established

clients has been found. The significant value is 0.768. For hypothesis H 2b, no significant

difference in the source of drinking water was found. These results are supported by

Kondo et al, 2007 and contradict with some of the studies (Chen, A, M., & Snodgrass, D.,

2001; Neponen, 2003). It was observed that most of the microfinance clients used to

build their houses illegally. That was the main reason for not finding out any difference in

their housing conditions. Mosley (2001) found similar observations in a study conducted

in Bolivia where clients used to live in illegal housing. Some of those were seriously

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Microfinance & Poverty 115

dangerous because they were situated in areas of slide risk on the near-vertical

escarpments surrounding the city. Actually these people constitute a major market for

microfinance in developing countries. Hence, housing cannot be considered a very strong

indicator for poverty.

Multinomial logistic regression results show that for HUS 1, only number of salaried

persons contributed to the model, as the value of p=0.019 indicates. Results for HUS 2,

source of drinking water clearly indicate that sex, number of household, number of

salaried persons and type of area does contribute to the model. However, participation in

the microfinance program has no contribution to housing.

Food Security

Following hypotheses were developed to measure the impact of microfinance on

consumption of nutritious food items as stated earlier in chapter 3.

H 3a: Participation in the program leads to increase the consumption of food item (cereals).

H 3b: Participation in the program leads to increase the consumption of food item (milk).

H 3c: Participation in the program leads to increase the consumption of food item (eggs).

H 3d: Participation in the program leads to increase the consumption of food item (meat).

H 3e: Participation in the program leads to increase the consumption of food item (fruit).

Results of chi square clearly indicate that no significant difference between new and

established clients with respect to usage of nutritious food item was found. As it is

evident from table, for hypothesis H 3a, p = 0.71, H 3b, p = 0.55, H 3c, p = 0.34, H 3d, p =

0.61 and H 3e, p = 0.07. So it can be safely said that program participation has not

influenced the choice of nutritious food items. These results are supported by the

previous study of Barnes, 2005. Only mildly significant positive impacts were observed

by Kondo et al, 2008. However, findings of this study contradict with some of the studies

(Barnes, 2001; Effa & Herring, 2005; Kondo, 2007; Neponen, 2003; Rosintan et al,

1999). Reason for rejection of these hypotheses is that clients do not prefer to spend in

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food items when they have priorities of other household expenses and they try to meet the

minimum requirement for living. Another reason is that clients were sensitive when

asked about usage of certain food items like meat and fruits.

Role of other independent variables can be explained with the help of multinomial

logistic regression. For FD 1, number of salaried persons contributed to the model, since

p=0.007. However, Participation in the microfinance program, age and education of the

respondents, number of household, number of salaried persons and type of area do not

contribute to the model. For FD 2, p=0.025 only number of salaried persons has

significant value. It can be said that consumption of food items, cereals and milk do not

increase with the participation of microfinance program but only with the increased

number of salaried persons. None of the variables has contributed for FD 3 and FD 4. For

FD 5 education, number of households and type of area has contributed to the model

since significant values are 0.009, 0.034 and 0.027 respectively. Hence it can be said that

participation in the microfinance program has not significantly improved nutrition choice.

Household’s Expenditure

Following hypotheses were developed to measure the impact of microfinance on

household expenditure as stated earlier in chapter 3.

H 4a: Participation in the program leads to increase household expenditure in clothes and

household items.

H 4b: Participation in the program leads to increase the likelihood to provide it to spouse.

H 4c: Participation in the program leads to improve expenditure on house repair.

H 4d: Participation in the program leads to increase spending in food items.

H 4e: Participation in the program leads to increase the loaning activity to relatives.

H 4f: Participation in the program leads to increase the expenditure in celebrations.

H 4g: Participation in the program leads to purchase of land.

H 4h: Participation in the program leads to repay old loans.

H 4i: Participation in the program leads to repay existing loans.

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Results of chi square show that significant value is 0.07, being greater than 0.05, which

does not support H 4a. Only H 4d, H 4e and H 4f are supported having significant values

of 0.01, 0.008 and 0.004 respectively. H 4b is also rejected as significant value is 0.14.

The significance value is 0.36, which does not support H 4c. Similarly H 4g is rejected

since significance value is 0.19. Further H 4h is also supported having significance value

of 0.02. H 4i is rejected having significance value of 0.52.

These findings are supported by the previous studies, Alexander, 2006 and Effa &

Herring, 2005 who have shown a positive impact of microfinance on household

expenditure. Morduch (1998), however, argued that the eligible households that

participated in the microfinance programs have strikingly less consumption levels than

the eligible households living in villages without the programs. Same has been observed

in the present study for H4b H4c, H4g and H 4i.

Analysis of multinomial logistic regression revealed that for HSIN 1, only sex has

contributed to the model having p = 0.033. For HSIN 2, only education has significantly

contributed to the model having p = 0.029. For HSIN 3, none of the variables has

contributed to the model. Number of salaried persons and education having significant

values of 0.029 and 0.021 has contributed for HSIN 4. Participation in the microfinance

program and number of salaried persons has contributed to the model for HSIN 5, having

significant values of 0.008 and 0.042 respectively. Sex, age, education and number of

salaried persons has contributed to the model, since p<0.05, for HSIN 6. None of the

variables for HSIN 7, purchase of land, HSIN 8, repayment of old loans and HSIN 9,

repayment of existing loan has contributed to the model.

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So it can be safely said that microfinance participation has no impact on household

expenditure with only exception to one household expenditure i-e loaned to relatives.

Household Assets

Following hypotheses were developed to measure the impact of microfinance on

household assets as stated earlier in chapter 3.

H 5a: Participation in the program leads to the ownership of household asset (Refrigerator).

H 5b: Participation in the program leads to the ownership of household asset (CD player).

H 5c: Participation in the program leads to the ownership of household asset (motorcycle)

H 5d: Participation in the program leads to the ownership of household asset (washing

machine)

H 5e: Participation in the program leads to the ownership of household asset (sewing

machine)

H 5f: Participation in the program leads to the ownership of household asset (bed with foam)

H 5g: Participation in the program leads to the ownership of household asset (cell phone)

H 5h: Participation in the program leads to the ownership of household asset (television)

All the above hypotheses except for H 5f are rejected. Significance value for H 5f is

0.009, which permits safely saying that there is a significant difference between new and

established clients about the ownership of asset i-e bed with foam. Significance value for

H 5a is 0.239; hence it is rejected. Similarly values for H 5b, H 5c, H 5e, and H 5f are

0.202, 0.706, 0.672 and 0.544 respectively. Since p> 0.05 hence all these hypotheses are

rejected. Overall, it can be concluded that there is no significant impact of microfinance

on household assets, since only one of the hypothesis was accepted. Main reason behind

rejection of these hypotheses is that most of the clients had these household assets already

in their ownership. This was possible because of dowry which is the tradition of our

culture. Hence participation in the program does not lead to purchase of such household

assets. These results are supported by Kondo et al, 2008, who found no significant impact

of the microfinance program on any of the four classes of household assets. Mckernan

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(2002) found an inverse relationship between participation in program and household

assets. However these results contradict with Kondo, 2007; Sebstad, J. & Chen, G. 1996

& Sengsourivong, 2006.

A survey conducted on Khushali Bank by Setboonsarng & Parpiev, 2008 revealed that

borrowers owned significantly more household assets. These findings contradict with the

present study reason being that this study has controlled for selection bias by taking new

clients as the control group.

Results of multinomial regression for HSAS 1 reveal that only education has significantly

contributed to the model having significant value of 0.000. For HSAS 2 none of the

variables in the equation has contributed to the model. Age and type of area has

significantly contributed, having significance values of 0.024 and 0.010 respectively. Age

and type of area has contributed to the model having significant values of 0.024 and

0.010 respectively. Again for HSAS 4, age, p = 0.013, education, p = 0.000 and type of

area, p = 0.006 has significantly contributed to the model. Age, education and type of

area have significantly contributed to the model having values of 0.039, 0.020 and 0.000

for HSAS 5. For HSAS 6, participation in the program, education and number of

households has significantly contributed to the model, having values of 0.005, 0.000 and

0.026 respectively. Education and type of area have significant values of 0.003 and 0.001

respectively for HSAS 7. Number of salaried persons and type of area has significant

values 0.015 and 0.010 respectively for HSAS 8, hence contributed to the model.

It can be concluded that participation in the microfinance program has led to the

ownership of only one asset i-e HSAS 5, bed with foam. However, significant

relationships were found for other variables.

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6.2 Micro-enterprise Level

Financial Performance

Following hypotheses were developed to measure the impact of microfinance on

enterprise for financial performance as stated earlier in chapter 3.

H 6a: Participation in the program increase expansion of enterprise.

H 6b: Participation in program leads to addition of new products in the enterprise.

H 6c: Participation in the program leads to hiring more workers in the enterprise.

H 6d: Participation in the program leads to improve the product quality of enterprise.

H 6e: Participation in the program leads to improve desirability of products.

H 6f: Participation in the program leads to reduce costs by purchasing in bulk.

H 6g: Participation in the program leads to keep enterprise money separate from household

and personal use. H 6h: Participation in the program leads to make profit calculation based on cost and

earnings.

H 6i: Participation in the program leads to improve on the knowledge of most profitable

product. 0.023

H 6j: Participation in the program leads to have a fixed location for production.

H 6k: Participation in the program leads to have a fixed location for storing.

Significance values for H 6a, H 6g and H 6h are 0.703, 0.591 and 0.690 respectively,

which do not support the hypotheses. These hypotheses are related to expansion of

enterprise, keeping enterprise money separate from personal use and profit calculation

based on cost and revenues. These findings are consistent with Shetty, 2008, who

explains that microfinance has failed in unleashing the micro entrepreneurship among the

poor. Reason for rejection of these hypotheses is clients do not have the guidance about

management of enterprise money and decisions pertaining to reinvestment. Clients are

unable to segregate enterprise money from personal use and do not know how to

calculate profits. However, rest of all other hypotheses were supported since p<0.05.

Significance value for H 6b is 0.005 which supports this hypothesis about addition of

new products between new and established clients.

Since the significance value is 0.017 which means H 6c is accepted. H 6d is also

supported since value is 0.007 i-e p <0.05. For H 6e, significance value is 0.001 hence,

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Microfinance & Poverty 121

this hypothesis is also supported. Similarly, H 6f, H 6j and H 6k are also supported since

the significance values are 0.009, 0.025 and 0.009 respectively. Results of hypothesis 6b,

6e, 6f and 6i are consistent with Morris & Barnes (2005).

Out of eleven hypotheses only three are rejected. Overall, it can be concluded that

participation in the microfinance has led to increase the financial performance of the

enterprise which ultimately transfers to increase the well being of household that

alleviates poverty. These results are supported by previous study by Kondo (2007) that

explains positive impact on employment, which is supported by this research as well.

Multinomial regression results report that for ENT 1, expansion of enterprise only

education has significantly contributed to the model, p=0.002. For ENT 2, participation

in the microfinance program has significantly contributed to the model, p=0.012.

Participation in the program and number of salaried persons has significantly contributed

to the model for ENT 3, having significant values of 0.043 and 0.007 respectively. For

ENT 4, participation in the program has a significant value of 0.014 hence contributed to

the model. Results show that participation in the program and number of salaried persons

has significantly contributed to the model for ENT 5 having values of 0.001 and 0.001

respectively. For ENT 6, participation in the program, age, number of households,

number of salaried persons and type of area significantly contribute to the model having

significant values of 0.007, 0.044, 0.032, 0.049 and 0.015 respectively. For ENT 7,

Number of salaried persons having significant value of 0.007 shows contribution to the

model. Participation in the program, number of households and type of area significantly

contributed to the model for ENT 9, since significant values are 0.014, 0.000 and 0.000

respectively. None of the variables have significantly contributed to the model for ENT 8

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and ENT 10. For ENT 11, Participation in the program and sex has significantly

contributed to the model.

It can be concluded that there has been found significant difference between new and

established clients for addition of new products, to improve the product quality, to

improve desirability of products, reduced costs by purchasing in bulk, kept money

separate from household and personal use and fixed location for storing.

Enterprise Resource Base

Following hypotheses were developed to measure the impact of microfinance on

enterprise resource base as stated earlier in chapter 3.

H 7a: Participation in the program leads to increase the major investment in the enterprise.

H 7b: Participation in the program leads to increase the minor investment in the enterprise.

Results of chi square clearly demonstrate that significance value for H 7a is 0.002, which

means that there was found significance difference between new and established clients

with respect to major investment. However, for H 7b significance value is 0.945, hence it

is rejected.

For ENR 12, Participation in the program, education and type of area has significantly

contributed to the model, having values of .024, .023 and .025 respectively. It means that

participation in the program has led the clients to improve on their major investment in

the enterprise. Sex, education and type of area having significant values of 0.029, 0.011

and 0.029 respectively has contributed to the model for minor investment in the

enterprise.

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Income-Smoothening Effect

Following hypotheses were developed to measure the impact of microfinance on Income-

smoothening effect as stated earlier in chapter 3.

H 8a: Participation in the program assists clients to survive periods of reduced cash flow.

H 8b: Participation in the program leads to reduce the repayment problems.

Results of chi square clearly show that participation in microfinance did not have any

effect on income smoothening, since value for H 8a is 0.549, hence it is rejected.

Similarly, H 8b is rejected, since significance value is 0.103.

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Microfinance & Poverty 124

Chapter 7

Conclusion and Recommendations

7.1. Salient Findings

Results of chi square test are summarized as below:

Strong positive impact on children education and enterprise financial performance.

Mixed evidence on food security, household expenditures and household assets.

No impact has been observed on housing and income smoothening of enterprise.

Results of multinomial logistic regression are given below:

Number of household and number of salaried persons were the most important

variable however type of area has no relationship with children education.

For housing, it can be stated that number of salaried persons has the most

significant importance; however, type of area has the most significant relationship

for source of drinking water. Age and education has no relationship with housing.

Number of salaried persons and education were found to be the most important

variables contributing to the model for food security.

For household expenditure; number of household and education has significantly

contributed to the model.

Household assets, type of area, education and age significantly contribute to the

model.

For enterprise financial performance, number of household and number of

salaried persons have significantly contributed to the model.

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Education and type of area significantly contributed to the model for enterprise

resource base.

For income smoothening effect, number of salaried persons and type of area has

significantly contributed to the model.

7.2. Conclusion

Present study was designed to gauge the impact of microfinance on poverty. Indicators

were taken at household and enterprise levels, the improvement of which could alleviate

poverty. Additionally, some other independent variables were taken into the analysis. The

study adapted AIMS Seep tool as a baseline and modified in the cultural context. Data

was collected from four different microfinance institutions. Overall it comes out with

mixed evidence on household and enterprise levels.

Results show that microfinance has a strong positive impact on children education and

enterprise financial performance. However, there is mixed evidence found on food

security, household expenditures and household assets. No impact has been observed on

housing and income smoothening of enterprise. Among other independent variables, it

was revealed that number of salaried persons was found to be very important variable

contributing to the wellbeing of the microfinance clients.

7.3. Practical Implications

Present study has specifically looked into the impact of variables that might have a

relationship on wellbeing of clients at household and enterprise level as such impact

studies are source of valuable information for MFIs targeting the microfinance. MFIs will

be able to refine their eligibility criteria before getting into the loaning procedure. Present

study finds very strong implication for children‘s education and at enterprise level for

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Microfinance & Poverty 126

financial performance and enterprise resource base. Present study can be very useful to

IA practitioners and policy makers. Despite the limitations, the study makes a meaningful

contribution in the literature of impact assessment of microfinance in general and

Pakistan in particular.

7.4. Limitations

Microfinance clients are geographically dispersed and it incurs heavy costs while

visiting them. Even within a locality where the clients reside the distance between the

clients is 1-5 kilometers. Comparatively, cost of conducting interviews in rural area is

very high where clients are residing at a considerable distance as compared to urban

area. It includes all the related costs such as cost of visiting the field, costs of

transport, photocopying the survey forms etc. Rural survey samples are generally

more expensive per survey than urban survey samples. Since present study has also

taken interviews from rural area, the cost was one of the biggest limitations.

At certain places presence of representative of microfinance institution was

necessary, which makes the clients opinion biased.

Clients became emotional when asked questions regarding indicators of food in

particular hence the response was biased

There is a deep rooted nationwide network of microfinance institutions. Data for the

present study was collected from the twin cities of Rawalpindi and Islamabad due to

resource constraints. Future researches can be extended by taking sample from all

major cities of Pakistan.

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Clients recommended that amount of loan was too small to start up with new

establishment. If the microfinance clients could be given a substantial amount of

loans then they would have explored much better investment opportunity.

Regarding the ownership of house, it was revealed that most of the clients had their

own residence; however, their houses were built in places which were unauthorized

encroachments and can be demolished by Capital Development Authority (CDA) at

anytime without notification.

Clients complained that microfinance institutions were charging very high interest

rate specially charges for opening of account with the respective bank, insurance

charges, penalty for late payments and certain other service charges. It poses high

financial burden on microfinance clients. The transaction cost is substantial and

programs have been relying on donors for sustaining their operations.

7.5. Recommendations

Selection of businesses

Mostly clients are involved in small businesses like stitching, spare parts of

automobiles, beauty parlors, bakery, video shop, clothing, frozen foods, fruit and

vegetable shop etc. There is a need to assist the clients in selecting appropriate

business, guiding them about the availability of raw material/services needed to

startup and how to go about it.

Training and development

Microfinance banks do not have separate department which can guide the clients for

establishment of possible businesses at small scale. Given the fact that clients are

given a very small amount of loan it is recommended that they should be given proper

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direction how to use this money. Exclusive business development centre can be set up

in microfinance institutions to help out clients for this purpose.

Monitoring and follow up of loans

Although MFIs have proper system to ensure repayments but they are lacking in

monitoring the loan usage activities. Since the loan usage was not actually transferred

to investment, it is imperative that there should be periodic monitoring of loans usage.

For example if loan is provided for one year at least quarterly monitoring should be

done to see what are the problems being faced by the clients while progressing with

their new businesses. Most of the clients have used the loan in consumables.

Majority of the clients are illiterate and need to be guided throughout their loan cycle

about the enhancing sales and decisions pertaining to reinvestment.

Web of services

Focus of microfinance banks should be on provision of web of support services in

addition to microcredit. For example, provisions of microcredit along with the

direction of choice of appropriate business coupled with training and periodic

monitoring of usage of loan can produce positive results. This is the only way to

eradicate poverty.

Introduction of new programs

Since the present study has found a positive relationship between program

participation and children education. New program like education insurance can be

introduced. This program will call for adding a small amount of premium in their

installments (established clients) hence facilitating them in getting higher levels of

education.

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Enterprise Management Training

Clients are unable to segregate enterprise money from personal use and do not know

how to calculate profits. A training program can be introduced about the calculation

of profits and reinvestment plus guidance about percentage of reinvestment and

profits should be given.

7.6. Future Research

Future research needs to determine the extent to which the findings of the present

study can be extended to include other persons, settings, and time, (Cook & Campbell

1979). One way could be to extend this research in to cross cultural settings. The

results might be different because of having difference in the macroeconomic

environment and people‘s entrepreneurship spirit.

Present study has taken microfinance participation, on the basis of time period spent

in the program. Future researches can be extended to take number of loan cycles and

amount of microcredit as a variable for microfinance participation.

Coleman (2001) has a useful non-technical explanation of the difficulties of applying

this approach and eliminating ‗selection‘ and ‗placement‘ bias in micro credit studies.

The present study is not an exception to it. Few steps have been taken as

recommended by Nelson (2000) but still more research is needed in this area.

Further research can be done on improvement and refinement scale used in this study.

Present study has used non parametric test (chi square) to differentiate between the

new and established client (which has certain limitations) and also multinomial

logistic regression. Future researches can be extended to use econometrics for

analytical purpose for in-depth analysis.

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Role of moderating variables between microfinance and its impact on household and

enterprise level can also be one of the very interesting areas to explore.

Self-selection and placement biases can further be controlled and studied in future

researches so that unbiased findings form a base for more sustainable microfinance

industry.

Longitudinal researches have been a tradition in the field of microfinance sector.

Same study can be replicated by taking this as a base line after 5 years to compare the

impact.

Few more areas, difference among MFIS, (such as banks, NGO‘s etc) rural vs. urban

contrast can be extensively studied for impact of microfinance at household and

enterprise levels.

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APPENDIX I

Interviewee Data Form

Name of the place:_______________

Area Type of roofing material is used in the main

house. (Observe)

Structural condition of the

house (Observe)

Category of client Sex

Urban 1 Thatched roof (branches, twigs, etc) 1 Seriously dilapidated One year client Male 1

Rural 2 Tarpuaulin, plastic sheets 2 Needs major repair 2-4 client Female 2

Wooden roof 3 Needs minor repair 5 & above

Concrete 4 Sound structure

Clay tiles 5

Other (specify) ___________________

Age Marital status Educational

achievement/level

No of Households Major source of

family income?

No of salaried

persons

Children (5 – 17

years)

18-25 Single Primary 1-4 Wage None Yes

26-35 Married Secondary 5-8 Pension 1 No

36-45 Separated/divorced Matriculation 9 & above Social assistance 2 or more

46-55 Widowed FA Income from business

56 & above Diploma/technical

education

Income from

agriculture

Any other

(specify)_______

Income from rent

Other (specify)

Page 167: Poverty and microfinance in Pakistan

How many children (5-

17) go to school?

For the current school year, how much did your

household spend on school fees and other education

expenses for school going children?

Last highest educational achievement

among children

Ownership status of the house

1-3 RS 1000-10,000 Primary Built on squatter land

4-6 RS 11000-20,000 Secondary Owned

6 & above RS 21,000-30,000 Matriculation Given by relative or other to use

None FA Provided by government

Diploma/technical education Rented

Any other (specify)_______ Other (specify) ______________

Ownership of assets (All land,

rural, urban, agriculture etc)

Different household items in use, (if yes

put and No then

Type of cooking fuel primarily used Main source of drinking water

Yes Livestock, (buffaloes, cows, sheep, goats,

hens, horses, donkeys)

Dung 1 Piped water 1

No Refrigerator Collected wood 2 Ground water 2

CD player Purchased wood or sawdust 3 Well water 3

Cycle Kerosene 4 Surface/pond water 4

Motor cycle Gas 5 Rainwater 5

Colored TV Electricity 6 Other (specify)____________

Tractor, trolley, cart

Washing Machine

Sewing Machine

Bed with Foam

Cell-Phone

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Others

Usage of Loan Yes No Have no idea Have you made any improvement/repair in the structure of house from the profits of enterprise?

An income-generating activity? Yes

Buy food for your household? No

Buy clothes or other household items? Do not remember exactly

Give or loan the money to your spouse In the last week, was any income that you earned in your business used to purchase food?

Give or loan the money to some one

(relative)

Yes

Keep money on hand in case of an

emergency

No

To repay microfinance loan Do not remember exactly

To repay other debt

For house/land improvement or purchase? From profits of your enterprise, have you improved sanitation system?(for example, electric motor,

shower, latrine etc)

To spend on a celebration or death etc Yes

No

Do not remember exactly

Change in the household's diet compared to last year

Improved greatly 1

Improved slightly 2

Remained constant 3

Slightly bad 4

Worsened 5

Yesterday, did you or anyone in your household consume

Yes No

Page 169: Poverty and microfinance in Pakistan

cereals

vegetables

milk/milk products

eggs

Meat (chicken, fish, mutton, beef)

sugar/honey

fruits

During the last 12 months, was there ever a time when it

was necessary for your household to eat less?

What did your household do to get through this difficult situation?

Yes 1 Borrowed money from family/friends 1

No 2 Borrowed food from family/friends 2

Don’t remember exactly 3 Sold personal property 3

Left area to seek employment 4

Family member left and seek employment 5

Got local employment 6

Family member got local employment 7

Have no idea 8

Other (specify)_____________________________

During the last 12 months, did you make any of the following changes to your enterprise activity? Yes

No Have no idea

Expanded size of enterprise/business facility

Added new products

Hired more workers

Improved quality of product

Improved desirability of products

Reduced costs by purchasing in bulk

Page 170: Poverty and microfinance in Pakistan

In managing your enterprise activity

Do you keep your enterprise money separate from the money you have for personal and household expenses?

Do you calculate your profit based on records of your costs and earnings?

Do you know which product(s) bring you the most profit?

Do you get a wage for your own work in the enterprise?

Do you have a fixed location with protection from the sun and rain for selling your products, such as a store, stall etc?

Do you have a fixed location for production other than your residence?

Do you have a fixed location for storing other than your residence?

Have you made a major investment in your enterprise? (shop stall etc)

Have you made a minor investment in your enterprise?(chair, table, etc)

During the last 12 months, was there ever a time

when you did not have enough money to conduct

your enterprise?

If yes, how long

did this period

last?

Did you face any difficulty

repaying your loan to the

program in the last loan cycle?

If yes, what caused your repayment problems?

Yes 1 1-3 months 1 Yes 1 Loan activity was not profitable 1

No 2 4-7 months 2 No 2 You were sick 2

Have no idea 3 Have no idea 3 Have no idea 3 Your family member had been sick 3

Are you going to take the credit again?

Lack of sales 4

Yes 1 Death in family 5

No 2 Family celebration (wedding, birth, etc.) 6

Have no idea 3 Disaster (natural, theft, fire, etc.) 7

Page 171: Poverty and microfinance in Pakistan

Appendix II Calculation for KR-20 Formula and the Cronbach’s Alpha

Page 172: Poverty and microfinance in Pakistan
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