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POLITECNICO DI MILANO

POLO TERRITORIALE DI COMO

Loan Repayment Performance of

Microcredit Programs- Evidence from India

[Laurea Magistrale in Ingegneria Gestionale]

Supervisor: Prof. Paolo Landoni

Assistant Supervisors: Prof. A. Caragliu Ing. Giorgio Di Maio Dott. Emanuele Rusinà

Master Graduation Thesis by: Arianna Molino

Student ID. Number: 787250

Academic Year 2013-2014

Tesi di Laurea Magistrale in Ingegneria Gestionale A.Y. 2013-2014

Arianna Molino Pagina 1

TABLE OF CONTENTS

TABLE OF CONTENTS ........................................................................................................................ 1

ABSTRACT ............................................................................................................................................. 5

CHAPTER 0 - STRUCTURE OF THE THESIS ............................................................................................. 6

CHAPTER 1 - INTRODUCTION TO MICROFINANCE ............................................................................... 8

1.1 DEFINITIONS ............................................................................................................................... 9

1.2 MICROFINANCE PURPOSES AND REAL IMPACT ....................................................................... 11

1.2.1 POVERTY ALLEVIATION ..................................................................................................... 11

1.2.2 WOMAN EMPOWERMENT................................................................................................ 12

1.2.3 FINANCIAL SUSTAINABILITY .............................................................................................. 12

1.2.4 DRAW BACKS ..................................................................................................................... 13

1.3 ACTORS ..................................................................................................................................... 15

1.3.1 MFIs ................................................................................................................................... 15

TRENDS ....................................................................................................................................... 18

1.3.2 CUSTOMERS ...................................................................................................................... 19

1.4 SERVICES AND PRODUCTS........................................................................................................ 22

1.4.1 FOCUS ON FINANCIAL SERVICES ....................................................................................... 22

SAVINGS ..................................................................................................................................... 23

INSURANCE ................................................................................................................................ 24

CREDIT ........................................................................................................................................ 25

1.5 MICROFINANCE MECHANISMS ................................................................................................ 29

1.5.1 PEER SELECTION ................................................................................................................ 29

1.5.2 PEER MONITORING ........................................................................................................... 29

1.5.3 DYNAMIC INCENTIVES ....................................................................................................... 30

1.5.4 REGULAR REPAYMENT SCHEDULES .................................................................................. 30



CHAPTER 2 - IIMC - INSTITUTE FOR INDIAN MOTHER AND CHILD .................................................... 31

2.1 ECONOMIC ENVIRONMENT – WEST BENGALI ......................................................................... 31

2.2 INTRODUCTION TO IIMC .......................................................................................................... 33

2.2.1 HISTORY ............................................................................................................................. 34

2.2.1 ORGANIZATIONAL STRUCTURE ......................................................................................... 34

2.3 PROGRAMS............................................................................................................................... 36

2.3.1 MEDICAL PROGRAMME .................................................................................................... 36

2.3.2 EDUCATION PROGRAMME ............................................................................................... 37

2.3.3 WOMEN EMPOWERMENT PROGRAMS ............................................................................ 38

2.3.4 RURAL DEVELOPMENT PROJECT ....................................................................................... 38

2.3.5 MICROFINANCE PROGRAMS ............................................................................................. 39

2.4 PURE MICROCREDIT PROGRAM ............................................................................................... 40

2.5 MOTHER’S BANK ...................................................................................................................... 44

CHAPTER 3 - RESEARCH QUESTIONS AND ANALYSIS APPROACH ...................................................... 48

3.1 LITERATURE REVIEW ................................................................................................................ 48

3.1.1 REPAYMENT RATE ............................................................................................................. 48

3.1.2 INSTALLMENT FREQUENCY ............................................................................................... 49

3.1.3 GROUP SOCIAL INTERACTION FACTOR ............................................................................. 53

3.1.4 REGULAR REPAYMENT SCHEDULE .................................................................................... 55

3.2 RESEARCH QUESTIONS ............................................................................................................. 57

3.2.1 MICROFINANCE PROGRAMS COMPARISON: HYPOTHESIS ............................................... 57

3.2.2 RESEARCH QUESTIONS ...................................................................................................... 59

CHAPTER 4 – DATA COLLECTION AND DATABASE ............................................................................. 61

4.1 IIMC MICROCREDIT PROGRAMS – PHOTO COLLECTION AND ORGANIZATION ...................... 62

4.1.1 COLLECTED PHOTO - MICROCREDIT PROGRAM ............................................................... 63

4.1.2 COLLECTED PHOTO – MOTHER’S BANK PROGRAM .......................................................... 69



4.2 DATABASE EXPLANATION OF THE IIMC MICROCREDIT PROGRAMS ....................................... 74

4.2.1 EXPLANATION OF THE COLUMNS ..................................................................................... 76

4.2.2 ADDITIONAL VARIABLES IN THE Mother’sBankSPSS SHEET ............................................. 87

4.2.3 EXPECTED RESULT ............................................................................................................. 90

CHAPTER 5 - PERFORMANCE COMPARISON MODEL OF 2 MICROCREDIT PROGRAMS .................... 93

5.1 OUTLIERS’ EXCLUSION ............................................................................................................. 93

5.2 GENERAL STATISTIC ANALYSIS ................................................................................................. 99

5.2.1 LOAN RELATED VARIABLES ANALYSIS ............................................................................... 99

5.2.2 SAVINGS RELATED VARIABLES ANALYSIS ........................................................................ 117

5.2.3 MONTHLY VARIABLES ANALYSIS ..................................................................................... 124

5.3 CORRELATION ANALYSIS ........................................................................................................ 129

5.3. MODEL REGRESSION ............................................................................................................. 140

5.3.1 THEORETICAL INTRODUCTION ........................................................................................ 140

5.3.2 MODEL APPLICATION ...................................................................................................... 141

5.4 REGRESSION RESULTS ............................................................................................................ 146

5.4.1 MODEL PERFORMANCE STATISTIC PARAMETERS .......................................................... 146

5.4.2 BETA COEFFICIENTS TABLE ............................................................................................. 149

5.4.3 ADDITIONAL EVALUTION ON THE REGRESSION RESULTS .............................................. 155

CHAPTER 6 - CONCLUSIONS ............................................................................................................. 164

6.1 REPAYMENT PERIOD PERFORMANCES IN THE TWO MICROCREDIT PROGRAMS. ................ 164

6.2 LOAN SIZE CATEGORIES AND THE REPAYMENT PERIOD ....................................................... 166

6.3 REGULAR REPAYMENT CASH FLOW AND REPAYMENT PERFORMANCE ............................... 166

6.4 RESPECT OF THE POLICY IN TERMS OF CASH FLOW AND REPAYMENT PERIOD ................... 167

6.5 SAVINGS ................................................................................................................................. 167

6.6 ADDITIONAL CONSIDERATIONS ............................................................................................. 168

ANNEXES .......................................................................................................................................... 170



TABLES .......................................................................................................................................... 194

CHARTERS ..................................................................................................................................... 196

BIBLIOGRAPHY ............................................................................................................................. 200



ABSTRACT

Microfinance Institutions select loan repayment schedule in order to fill the needs of the poor but

it is important to evaluate how this program design affect the client’s performance in the

repayment.

In the research the two microfinance programs developed by an Indian MFI, IIMC (Institute for

Indian Mother and Child, Kolkata) are compared: one is called Microcredit Program and it is

developed in the area near Kolkata, providing microfinance services to groups of women through

weekly meeting. The second, Mother’s Bank, is dedicated to the mothers of children sponsored in

the IIMC educational program: they also have access to microfinance services but not in group and

through monthly visit of IIMC headquarter.

Consequently the factors considered in this work are the installment frequency and the respect of

the policy in terms of loan installments amount. Indeed the analysis is based on the cash flows of

both loan installments and savings deposits, considering the performance in terms of repayment

period for completing the loan reimbursement.

The results suggest an overall better performance of the weekly frequency schedule with

individual lending but weekly group meetings. On the other hand the comparison performance

model demonstrates that the last part of the loan is repaid faster by the other microfinance

program the one dedicated to the mothers of children sponsored in the IIMC education program.

In this last case the loan installments are monthly and the borrowers come to the headquarter

individually.

We deduce that a regular repayment schedule with frequent group meeting for installments

collection secures higher repayment rate thanks to the involvement of the client in the programs.

In addition the strictly respect of the policy in terms of variance in loan installments does not

necessarily lower down the overall repayment period.

In addition, by analyzing the cash flows, we find that the savings of the client have low significant

effect on the repayment

Key words: Microfinance, Regular Repayment Schedule, India, Frequency , Repayment

Performance



CHAPTER 0 - STRUCTURE OF THE THESIS The thesis is divided into 7 charters, starting from a general description of microfinance, moving to

the specific case of the institution on which the research is based, then, after the literature review

on the research question the data collection and digitalization are explain. The thesis continues

with the model description, a general statistic analysis and regression result and the work ends

with a summary of the conclusions we arrived at.

Chapter one introduces the microfinance topic, describing the main purposes of this financial

innovation, along with its actual impact on the society. The actors (institutions and clients) are

explained in their main features, followed by the services and products main characteristics.

Finally some basic microfinance mechanisms are briefly delineated.

Chapter two portrays the IIMC, the institute that deliver the microfinance services on which the

research is based. After having concisely reported the economic environment of West Bengali, the

organization is illustrated through a historical picture, its organizational structure and finally its

multiple humanitarian programs are drawn, from the educational one to the health care services.

The last sections have a specific focus on the microfinance programs, dedicating one section for

each.

Chapter three zooms on the research questions, by focusing the attention on those papers of the

literature related to the repayment rate first, second the installment frequency characteristic of

microcredit program, then the social interaction factor and finally the regular repayment schedule.

With this acquired knowledge, the research questions are articulated, along with the hypothesis

on the programs differences, and the expected results.

Chapter four explains the data collection in both the Microcredit Program and in Mother’s Bank

program, describing the collection books on the one side and the database ERP on the other. The

second part is dedicated to the database created for the models, by describing step by step each

variable.

Chapter five is related to the econometric model and statistic results. It starts from the outliers

exclusion process in order to depurate the sample, then the general statistic analysis is computed

on the selected database with special focus on the most important variables as the repayment

period, the program specific predictor and the loan size. Thanks to the correlation analysis the



initial result can be explained. Then the chapter continues with the regression analysis, from a

theoretical introduction, to the application of the method. In this last section the expected values

are compared with the actual results of the research, both in terms of model preciseness and beta

coefficients. Finally a specific section is dedicated to the interaction effect, a method applied for

evaluating possible interactions between predictors and then the conclusions are drawn..

Chapter six conclude the thesis by summarizing the results, providing advices for the IIMC

microfinance management and suggesting possible future researches.



CHAPTER 1 - INTRODUCTION TO

MICROFINANCE Microfinance is a set of financial practices designed to serve the unbanked poor (Armendàriz &

Labie, 2011). Even if during the first years of its development and diffusion it was seen as a brilliant

solution against poverty, the numerous case studies and rigorous academic research suggest that

this powerful tool should be improved and refined according to the real situation and environment

in which it is applied. For this reason our research focuses on Microcredit programs of a specific

Indian No-profit organization in order to provide suggestions for a better performance.

Briefly the main characteristics of microfinance activities can be summarized into the following

points:

Small loans, typically for working capital

Informal appraisal of borrowers and investments

Collateral substitutes such as group guarantees or compulsory savings

Progressive lending (Goto, 2012), in other words access to repeat and larger loans based on

repayment performance

Streamlined loan disbursement and monitoring

Secure savings products

As it could be seen reading the literature, in reality there are a lot of nuances in all the previous

dots, then this chapter tries to synthetically give a wide picture of the microfinance services,

without going into details.

In the following paragraphs, the concept and the characteristics of this poverty alleviation tool are

described, starting from some definitions and the evolution of Microfinance, then moving to the

main purposes of this innovative financial tool, with a focus on its real impact. Indeed the

estimates of the number of poor potential micro-entrepreneurs to be served differs between

researches, but they converged to the order of more than five hundred million economically active

poor people in the world operating small business (Women’s World Banking, 1995). After a brief

picture of the found gaps between theoretical purposes and real influence, the analysis moves to

the different types of Microfinance Institutions , from the nongovernmental organizations (NGOs),



commercial or government bank, to savings and loan cooperatives or credit unions. Finally the

focus arrives to the characteristics of the target clients, generally self-employed in low-income

activity.

Finally this chapter ends with a synthetic description of the products and services most commonly

developed by the institutions: poor households are typically excluded from the formal banking

system for lack of collateral, but the micro-finance movement exploits new contractual structures

and organizational forms that reduce the riskiness and costs of making small, un-collateralized

loans.

1.1 DEFINITIONS

“Microfinance has evolved as an economic development approach intended to benefit low-income

women and men” (Ledgerwood, 1999). This definition indirectly includes not only the concept of

financial intermediation, but also the social intermediation, as for example group formation

support and indirect self-confidence development.

In fact it is important to divide the concept of “microfinance” from “microcredit”. The former

takes into account the fact that the unbanked poor need a package of various financial services

other than just credit, as for example savings, insurance, remittances and many more; while the

latter consists only in the loan disbursement activity.

The roots of its development rely also on the main idea that the lack of finance is generally

acknowledged as being an important impediment to economic activity. Especially in less

developed economies, many investment projects of micro- and small-scale entrepreneurs may

therefore remain unrealized because there is no finance available. There are many reasons why

poor do not have enough access to financial services, but among them the mains are identified in

the lack of traditional financial services conditions, as lack of education, lack of collateral and high

cost of money transaction (Hermes, 2011)

Historically, Microfinance concept was born in the 1970s as a response to credit needs from the

poor farmers: the international donors assumed that the poor required cheap credit and saw this

as a way of promoting agricultural production by small landholders. In the 1980s the frequent

required recapitalization and accumulated large loan losses pushed the institutions towards a new

long-terms, more sustainable approach, with the help of the community development concept.



Today the focus is not only on credit provision while on a broader integrated package of financial

services and training, as it is described in the next paragraphs. (Ledgerwood, 1999)

Nowadays, five important trends are pointed out in this financial service field (Armendariz & Labie,

2011):

1) Fundamental variation in financial priorities: from a self-sustainability focus, the challenge

moved from the attention on the financial sustainability of the programs to the method for

sharing the derived profit and benefit among different stakeholders, as the operational

staff and the client themselves.

2) Radical transformation in supervision and regulation: generally local authorities are trying

to prevent from monopolistic practices, fostering competition and increasing supervision

for fully regulated suppliers.

3) Larger and more diverse pool of suppliers: not only NGOs and cooperatives but also local

commercial banks.

4) A variation in the supply of financial products: from an exclusive attention on microcredit

service, the vision evolved to a larger concept of microfinance word, where the array of

financial services becomes broader, as savings accounts and insurance.

5) A change in lending methodology: from an initial approach of solidary groups and joint

liability, today the individual lending seems to be the most common policy, as also it will be

seen in the programs of the Indian NGOs analyzed.

Microfinance has been developed all around the world, gaining country-specific peculiarities that

this analysis will not highlight, while the focus is concentrated on India, the country were the study

took place.



1.2 MICROFINANCE PURPOSES AND REAL IMPACT

As already anticipated, the purpose of Microfinance practices consists in the poverty alleviation if

it is considered only the financial point of view. But Littlefield, Morduch and Hashemi (CGAP, 2003)

have argued that Microfinance impact goes beyond just economic results: the financial services

are not only used for business investment but also to invest in health and education, to manage

household emergencies and to meet a wide variety of other cash needs that they might

encounter.

The following paragraphs put in evidence three main important objectives for which this

development tool is implemented.

1.2.1 POVERTY ALLEVIATION

The foremost objective of Microfinance is the poverty reduction. World Bank defines extreme

poor that part of the population (1.2 billion people) that live on less than $1.25 a day at 2005

international prices. Each country has its own national poverty line.

An issue is not only how poverty is measured but what poverty means. This concept has been

changing along the years: during the early decades of microfinance development (1950s, 1960s)

the bulk of the poor was identified in the rural small farmer’s families, with consequent objective

of raise incomes through agricultural credit subsidies. Then in the early 1980s the dominant

thinking moved to the women as representatives of poor, coping with their situation by running

microenterprises, adding in addition the broader scope of social empowerment (Matin et al.,

2002).

So nowadays the meaning of poverty alleviation takes a broader definition: the needs the poor

face are not only economical and linked to the income, but they involve also social factors and

survival perspective. In fact not only the material needs should be considered in order to evaluate

the poverty level. Thus even if on the one hand the income and consumption variables allow to

compare diverse groups across countries and to analyze the capacity of different people to meet

their immediate material needs, on the other hand the final aims of financial services in this

nontraditional sector is the impact on the secondary factors as livelihood level. In fact, as the well-

known theory of Maslow hierarchy of needs suggests, when people have satisfied their



physiological needs, they are able to look at the safety and belonging needs towards the self-

actualization.

Microfinance, therefore, acts not only as an economic stimulator for small enterprises but also has

far reaching social impacts.

1.2.2 WOMAN EMPOWERMENT

Among poor, women cover the higher percentage: in its 1995 Human Development Report, the

UNDP reported that 70% of the 1.3 billion people living on less than 1$ per day are women, and in

addition this category has higher unemployment rate and receives in average lower salary than

men.

Although access to credit alone will not automatically lead to women empowerment, it is hoped

that putting capital into women's hands gives them more independence and confidence,

contributing to the family income and gaining importance within it. Moreover, targeting the

woman means improving the welfare of the family because they invest a higher percentage of

their income in children education and households’ expenses than what the men do (UNCDF,

2002).

Furthermore, since many micro-finance programs have targeted women as clients, they

demonstrate as empowered women appear more responsible and show a better repayment

performance (Hashemi & Morshed 1997; Littlefield et al. 2003; Cheston & Kuhn 2002).

1.2.3 FINANCIAL SUSTAINABILITY

This last objective is a complex topic and in this research it will be only partially explained.

“”In general financial sustainability describes the ability to cover all costs on adjusted basis

and indicates the institution’s ability to operate without ongoing subsidy (i.e. including soft

loans and grants) or losses.” (Guntz, 2011)

First, financial sustainability depends on the type of MFIs that deliver the services, as some of

them rely on public funds, thus they are not required to be self-sustainable. Unsustainable

microfinance organizations tend to inflict costs on the poor in the future far greater than the gains

enjoyed by the poor in the present. In microfinance sector the type of MFIs and their level of

sustainability varies a lot: on the one side the small unstructured organization that operates in few



locations and serves a small and particular type of clients, is often unable to mobilize enough

savings to acquire sustainability (Zeller& Meyer 2002; Morduch 1999); on the other side there are

examples of other institutions that have grown over time serving millions and their institutional

innovations have made it possible to make them sustainable.

In conclusion, although all these three objectives are complementary and depend on one another,

MFIs usually emphasize on one of them, contributing partially to the others. The tendency

depends on the type of MFI and its mission and vision. For example a formal institution as private

bank normally would push for the application of cost reducing information systems, introduced to

improve financial sustainability, while an NGO is more focused on designing demand oriented and

low cost complimentary services for the poor, aiming to alleviate poverty.

1.2.4 DRAW BACKS

Studies on the impact of microfinance in the poverty alleviation present different results. Recent

estimates suggest that the service touches one hundred and fifty million individual worldwide, out

of two and half billion of unbanked poor (Daley-Harris, 2009), underling the failure in reaching the

lowest level of society. There are mainly three reasons for this unutilized potentiality: first the

poor sometimes prefers to turn to informal sources of finance as friends, family-members and

moneylenders; second there is a low attractiveness of microfinance service for the majority of

entrepreneurial poor because they cannot afford to pay back the microfinance loans on time, as a

result if they need money they go directly to other sources of funding instead of microfinance

banks. Indeed, some small businesses do not allow to have a steady income in order to pay

constantly the loan back. And last cause is considered to be the forced self sustainability of donors

and responsible investors, not easy to find (Armendàriz & Labie, 2011).

Considering the results on another perspective, Morduch (1998) survey summarizes the impact of

the loans on the poor, showing that access to credit does not result in the alleviation of (income)

poverty as is popularly believed but rather has an impact on the reduction of vulnerability

(Morduch 1998, pp. 29-31). The study however is limiting in that it takes only a single country,

namely Bangladesh, into consideration.



In addition the need of subsidies and the rare self-sustainability of the programs were highly

criticized by researchers because they limit the overall impact of Microfinance in the poverty

alleviation: if from one hand the idea that allures most of the supporters of microfinance is based

on the fact that the institutions that adopt the rationale of good banking will automatically also

serve the task of poverty reduction or alleviation, this win-win proposition (Morduch, 2000) does

not convince a part of the practitioners. The critics argue that this logic is more complicated to

happen than it seems, where the success depends on aspects that have been mostly not

considered, like the occupations of the borrowers or the use of the loans. For example, the

research developed by Mehrdad Mirpourian (2013)on the performance of the Microcredit

program of IIMC highlights the dependency between the client’s performance in terms of

repayment period with the type of occupations the borrower has, despite the small loan size.

Finally in most cases the financial sustainability of an MFI is reached with the increase of the

interest rates: financially sustainable institutions have high interest rates that serve as an

automatic screener for borrowers with projects with low rates of return (Hulme & Mosley, 1996).



1.3 ACTORS

In the next paragraphs the main two main actors’ categories of Microfinance are considered,

starting from the suppliers of the services and then continuing with the beneficiaries.

1.3.1 MFIs

“An [financial] institution is a collection of assets – human, financial and others – combined to

perform activities such as granting loans and taking deposits overtime” (Schmidt & Zeitinger,

1994). In the specific case of microfinance, the establishment of microfinance institutions (MFIs)

world-wide is focused on the provision of collateral free loans to the poor through mechanisms

and instruments not known to normal commercial banks. This very broad definition includes a

wide range of providers that varies in the legal structure, mission, methodology, and sustainability.

However, all share the common characteristic of providing financial services to a clientele poorer

and more vulnerable than traditional bank clients (CGAP 2003; www.cgap.org)

Microfinance institutions are under attention in the last 20 years as they represent a powerful

development policy strategy for unbankable, with a resulted rapidly expansion: statistics show

that nearly 150 million people use the microfinance institutions services (Balkenhol, 2011).

In fact MFIs serve the relevant target poor groups with appropriate and permanent services,

tailoring the offer in terms of type of product, timing, source availability and level of product

customization. The different combinations of these factors depend also on the scope and objective

of the institution, paying attention to remain stable both on the financial side and on

organizational side. It is also true that the requirement to be subsidy-independent is not always

applicable, because it requires that all the operation costs are covered by revenue, including

expenses as loans losses, opportunity cost of equity and inflation-adjusted cost of debt. For this

objective it is fundamental to reach a significant scale, standardizing the practices at proper level.

Commercial banks recognized this market as a profitable one, therefore they started to lend

microfinance organizations more money, and in most cases they could completely receive their

money back with a profit. The attractiveness of this huge market motivates the players to enter

the market through different methods like venture capital funds, that recently are coming into this

market, in particular, both from the Indian and off-shore side, and all in all contribute to

encourage the development of microfinance in India (Allen, 2007).



ISTITUTIONAL TYPE

There are three types of MFIs: the formal, the semiformal and the informal financial institutions.

INSTITUTION TYPE

DESCRIPTION EXAMPLE

FORMAL Subject BOTH to general regulation AND to specific banking regulation and supervision

Essential goal of financial sustainability, equity building and profit delivery

Public development banks, private development banks, savings banks and postal savings banks, commercial banks, nonbank financial intermediaries

SEMIFORMAL

Registered entities subject ONLY to bank general law

Not expected to generate profit but to deliver financial services to unbankable target, although they are expected to operate efficiently and to cover as much of their costs as possible

Credit unions, multipurpose cooperatives, NGOs

INFORMAL Operations are so informal that often they go beyond the legal system

Pure moneylenders, traders, landlords, rotating savings and credit associations, families and friends

Table 1.1 Types of Microfinance institutions

Going in deep with the analysis, the different subcategories of financial institutions type are briefly

considered.

Firstly the formal financial institutions are bigger in size and serve strategic sectors such as

agriculture or industry. While the public development banks rely on international and foreign

support, the savings and postal bank are typically not government owned but the equity is a

mixture of public and private ownership. The strength of the public development bank is the

centralization of resources and power in promoting programs but the necessary condition for a

successful result is the willingness to constantly improve and the political external environment.

One example of state owned commercial bank is the Bank Rakyat, Indonesia that set up mainly for

the provision of financial services to the non-urban and remote areas along with a special aim to

encourage the farmers and support the agricultural sector (Maurer 1999, p. 6).

The results of a MFIs inventory compiled by the Sustainable Banking with Poor Project (Bennett &

Cuevas, 1996) highlight that the commercial and savings banks are responsible for the largest

share of the outstanding loan balance deposit balance, while only 11% of it the total loan is

covered by credit union and 9% by the NGOs.



On the other hand the semi-informal institutions differs from previous type of providers in terms

of financial success (boasting the repayment rate above 95%), level of innovation (group lending

contracts) high degree of autonomy from bureaucrats and politicians and finally the acceptance of

household need to access not to cheap credit but credit itself (Matin et al., 2002). In general terms

NGOs offer the smallest loan sizes and relatively more social services than the other MFIs.

Informal providers are heterogeneous in terms of types of offers and intermediation: information

about them is not very precise, maintained in few records and in such temporary arrangements

that knowledge of informal services remains not well defined. In general terms they finance

mainly consumption smoothing and working capital needs, going from lending by individuals on a

non-profit basis to regular for-profit lenders as traders. Analyzing the credit services, the financial

market for small enterprises with intermittent and reciprocal lending between households eases

longer-term credit constraints.

There are examples on informal institution that evolved into a formal one: the Grameen Bank

started as a small pilot project with NGO-features in Chittagong (Bangladesh) in 1976, and it

arrived to serve over six million clients with hundreds of replications worldwide. Yunus started a

project giving out collateral free loans from his own pocket to the poor villagers for income

generating activities like weaving bamboo stools and making pots (Morduch, 1999). The

microfinance approach selected was the group lending, where five people voluntarily create a

group in the rural village. Aided by high repayment rates, the project grew to near areas and today

has 1,195 branches, working in 43,681 villages, with the number of borrowers totaling to 3.12

million, 95 per cent of whom are women (Yunus, 2004).

Finally an example of informal microcredit association is the ROSCAs, Rotating Savings and Credit

Associations: they are basically groups of people who decide to pool their money, make regular

contributions, giving then money to members on a rotating basis. Mostly women are participants

of ROSCAs, however, also men take part in it. Structures of these informal organizations are

extremely diverse as are the aims of the members. In general however, each woman has another

member "guarantee" her loan, so that if the first woman is not able to pay, her guarantor assumes

the debt (Mayoux, 1995).

INDIA MARKET

In specific context of India economy, MFIs belongs to one of the fast growing sectors in national

financial market. Although Microfinance market experienced fluctuations, in this Asian country it is



one of the sustainable sectors: considering the fact that over 40% of the Indians do not have a

bank account and 75% of India‘s Population live below 2$ a day, the influential role that MFIs can

play is evident. Moreover historically Microcredit in the form of small loans to poor is not a new

topic in India: moneylenders by tradition used to provide credits to the rural poor, usually charging

their clients at high interest rate, in some cases up to 30%, (Karmakar, 1999) which cause hardship

and difficulties of repayment.

This numbers and data could help us to feel the importance and necessity of the institutions which

can provide money and financial services to poor people, and they can be replaced instead of

money lenders. Indian government started its policies to encourage rural development since

1960s, and during these years it has tried to reach to this goal through different tools and

methods. One of the main milestones in this area was the establishment of the National Bank for

Agriculture and Rural Development (NABARD) in 1982; after this episode, additional other players

has entered in the MFIs market such as The Small Industrial Development Bank of India (SIDBI),

Rashtriya Mahila Kosh (RMK), commercial (both private and state-owned) banks ; regional rural

banks; cooperative banks as well as non-banking financial companies (NBFCs), many non-profit

organizations (NGOs) which try to contribute to this goal.

TRENDS

As already highlighted previously, added to the traditional suppliers as NGOs and cooperatives,

there is a new trend towards commercialization: the local commercial banks more and more are

responding to demand for microfinance products such as consumer credit. In addition socially

responsible investors are also contributing to an increased supply of funds available for financial

intermediation.

This work is focused on microfinance in developing country, while for developed economies these

not traditional financial services reach only a small percentage of the population. This limited

impact depends on two facts: on the one hand the difficulty of starting income-generating

activities with microloans in such economic environments, and on the other hand the alternative

no risk remuneration obtainable in the labor market (reserve wage) (Canale, 2010). These topics

are not considered in this work, while the attention is concentrated on the results in countries as

India, were the finance for the poor represents an instrument to exploit unutilized resources, and

not only as an instrument to fill the gap left by the absence of social safety nets and the welfare

reduction (Canale, 2010).



Looking at the relationship between MFIs, many of them started as an experimental venture in the

1970s, like the Grameen Bank of Bangladesh (Hashemi and Morshed, 1997), or as part of initiative

entities set up by NGOs and leaders of business communities in the middle 1980s, like the

PRODEM of Bolivia that gave rise to BancoSol, the first private commercial bank in the world

dedicated exclusively to microenterprise (ACCION, 2003).

1.3.2 CUSTOMERS

As already mentioned before, it is quite impossible to estimate precisely the number of

unbankable potential client that this poverty alleviation tool could serve: according to the Asian

Development Bank, alone in the Asian and Pacific region, more than 670 million over 900 million

poor people (i.e., those who earn less than $1.00 a day) live in the rural areas where they rely on

secondary micro-business occupations as agriculture alone is not enough to provide for their

growing needs (Sharma, 2001).

As evidence, the statistics show that in developing countries 40% to 80 % of people lack access to

formal banking services. The World Bank data of 2008 show that more than 50 percent of the

populations in most developing countries do not have bank account (Galema, 2011), with 75

percent of the poor people living in rural areas, highly dependent on agriculture. In this context,

considering the important environmental factors that cause business vulnerability, and the fact

that the level of profit margin is in average low, the role of accessing to finance and financial

services become more dominant (Morvant-Roux, 2011).

For example, zooming in the Asian pacific region, over 900 million poor people, more than 670

million live in rural areas based on the researches of Khawari (2004) and on the Asian

Development Bank (ADB) database. Since the income of agriculture activities is not sufficient to

run a family, most of them have a second occupation for which they need to access to financial

services, not available in the traditional banking system (Khawari, 2004).

Even if the general thought is that the provision of financial services to the poor who live in the

rural and remote areas often without basic institutional infrastructure, case studies demonstrate

as also that the demand comes from urban area, where microfinance services answer to the

needs from low-income entrepreneurs as street vendors, small traders, hairdressers, rickshaw

drivers, owner of small street food restaurants. Although the income of these people is low, the

main important characteristic in order to be able to pay back the loan is the stable and continuous



profit. Consequently they are not considered as poorest of the poor (Karlan & Goldberg,, 2011).

Indeed all the works involved in the microfinance investment normally belong to the informal

sector, with closely interlinked household and business activities and earning low income (Central

Bank of Philippines 2002; www.bps.gov.ph).

Stopping on the loans motivation, the needs for which the clients ask for loans are diverse, naming

only a part they consists in the life-cycle and emergency needs, sponsorship for education,

marriage, homebuilding, old age support, funeral expenses, and finally business opportunities as

buying land or productive assets.

Looking at the service effect, one important research of Morduch (1998) defended the assertion

that higher income borrowers experience a greater income impact. This is because clients above

the poverty line are more willing to take risks and invest in technology for the efficiency or

advancement of their activities that would in turn most probably increase income flows. On the

other hand, very poor borrowers tend to take out small, subsistence protecting loans and rarely

invest in new technology, fixed capital or hiring of labor.

WOMEN

A special paragraph is dedicated to women, clients of the microcredit programs studied in Kolkata.

According to the data of Microcredit summit campaign in 2006, 69 million out of 82 million of the

microfinance clients are women and this trend with inclination towards women is increasing. Just

from 1999 to 2005 the number of women clients has increased 570 percent, revealing the MFIs‘

tendency towards giving loan to this client category. (Armendariz, 2011).

The inclination to prefer to have female clients has two reasons, the first is to create equality and

empowerment for women, and the other reason is the fact that women are more punctual and

pay back the loan installments better compared to men (Guérin, 2011).

Indeed, a survey of Grameen, BRAC and BRDB by Pitt and Khandker (1998) in Bangladesh used a

sample of 1800 households, in 87 villages. Among other findings, the most profound results of the

survey showed that the increase in household consumption was more when the borrowers were

women and not men and schooling of girls particularly increased when the borrowers were

women and lending from Grameen (Pitt & Khandker, 1998). Banks have had lesser problems to

attain repayments in their rural programs than in their urban ones and this is yet another reason

why microfinancing is less popular with men than women, who are tied to one geographical area



(Morduch, 1999; Sumar, 2002) and in addition, according to Morduch, dynamic incentives are

strengthened for women since they have fewer borrowing alternatives than men.

The other important issue in the area of gender is a concept which is known as gender of finance.

Financial providers are somehow gender biased: they use criteria and policies which sometimes

make differences between men and women in the access to the financial resources and services..

Some of these restrictions are formally and explicitly defined, and in some cases the restrictions is

implicitly defined and take indirect routes (Fletschne & Kenney, 2011). In the specific case of IIMC

microcredit programs, as it will be explained in the next chapters, one requirement for the clients

is the condition to be either woman of mother of a sponsored child.

This issue leads to lower rates of job market involvement, becoming restricted to traditional

sectors which usually have low profit margin, fewer growth opportunities and more difficult

competitions. All these obstacles in front of women show that the common sources of finance for

women is not fair (Guerin, 2011). Another important issue about microfinance and women is

about repayment rates. Microfinance programs such as Grameen bank and some affiliates of Finca

and Accion International in 1990s started to increasingly target women. This time their goal was

not just poverty alleviation and empowering women, but in contrast they found out that female

repayment rate is significantly higher than those of men (Mayoux, 2011).



1.4 SERVICES AND PRODUCTS

As already outlined in the previous chapters, the MFIs can offer their clients a variety of products

and services, but the effective provision of financial services to low income men usually requires

social intermediation. Actually MFIs have -or are trying- to create special mechanism in order to

bridge the gaps between poverty, remoteness and illiteracy through a process of social capital

creation as a support to sustainable financial intermediation (Bennett, 1997).

Consequently some MFIs provide enterprise development services (skills and basic business

training) and social services (health care, education) depending on factors as the institution’s

objective, the demands of the target market, the existence of other service providers or finally an

accurate calculation of the costs and feasibility of the delivery of additional services (Joanna

Ledgerwood, 1999). In the specific case of the IIMC, the NGOs analyzed in this research, the

microcredit programs are only one small part of the project which include the Education

sponsorship of children, the building and management of primary schools, the medical assistance

in rural area and additional mother’s dedicated services that will be described in the IIMC chapter.

Four broad categories of services may be provided to microfinance clients (Ledgerwood, 1999):

Financial Intermediation such as savings, credit, insurance and payment systems.

Social Intermediation such as group formation, leadership training and cooperative

learning Enterprise development services such as business training, marketing and

technology services, skills development and subsector analysis. Social services such as

education, health and nutrition program and literacy training.

All the nonfinancial services described above aim to improve the ability of the clients to utilize

financial services themselves. In this context MFIs can have minimalist approach if they focus only

the first categories practices, or integrated if offer more types of services.

1.4.1 FOCUS ON FINANCIAL SERVICES

This is the primary role for MFI that should respond effectively to the client demand for liquidity

and design product easily understandable for the clients and easily manageable for the institution.

In the following paragraph three subgroups of products are considered, but it is important to



know that there are additional subcategories as credit cards and payment services not analyzed in

this work.

In general terms it is demonstrated that the poor strata of population might be better reached if a

broader range of financial services is provided (Matin et al, 2002): for example in Sri Lanka,

SANASA’s poorest clients use savings services more than credit services (Hulme & Mosley 1996)

and small, high-cost emergency loans more than larger lower cost investment loans. What may

make the difference is the availability of the following three types of financial services. Availability

of financial services does not mean that all poor households need to be in debt or save at a certain

point in time. However, all households, including the poor will benefit from the availability of

financial services that allows them to save when they want, cope with a crisis when it occurs and

borrow to take advantage of opportunities when they arise.

SAVINGS

The largest and most sustainable MFIs rely on savings mobilization, according to the World Bank’s

(2001): in fact low income clients can and do save but they seldom have reliable place to store

their money. It is also true that the amount of deposits is influenced by the macroeconomic and

legal environment, as for example the level of population density and the average growth in per

capita GNP of the country have a positive correlation with it (Paxton, 1996).

Within this category of service, another distinction can be done between the compulsory and the

voluntary savings: in the first case they represent funds provided before the loan disbursement

and they can be considered part of a loan product rather than an actual savings service. In fact the

client perceives them as a “fee” she/he must pay to participate and gain access to credit. Normally

compulsory savings serve as additional guarantee and also they demonstrate the ability of clients

to manage cash flow that of course is important for loan repayment. As it happen in the IIMC

microfinance programs, the compulsory savings cannot be withdrawn by members during the loan

repayment.

The second subcategory is the voluntary savings, as mentioned before. They are provided to both

borrowers and no borrowers who can deposit and withdraw at any time. This approach assumes

that the working poor already save and they do not need to learn financial discipline but the role

of MFIs is to answer to the client need. Moreover the environment (legal and regulatory

frameworks) should enable a reasonable level of political stability so that the MFI can safely take

the option of voluntary savings. (CGAP, 1997). Additional requirements are for example the high



level of client confidence in the institution with an easy access to deposits and to the MFI, the

flexibility and diversity of savings instruments and a positive real deposit interest rate (Yaron et al,

1997).

In general terms the provision of savings services by an MFI enhances the clients’ perception of

“ownership” of a MFI, increasing their commitment to repay the loans, and it encourages the MFI

to intensify efforts to collect loans due to market pressures from deposits (Yaron et al, 1997).

Finally not all the MFIs are able to manage this additional service, because the administrative

complexities and costs associated may be too high for the institution, thus caution must be taken

when deciding to introduce savings mobilization

Savings deposit and withdrawal behavior can be a useful proxy for debt capacity (Matin et al,

2002).

INSURANCE

Many MFIs are beginning to offer an insurance or guarantee scheme, following the example of

Grameen Bank that requires the contribution of 1% of the loan amount to an insurance fund: in

case of the death of a client, the loan is repaid thanks to this fund and in addition the deceased

client’s family have the possibility to cover burial costs.

MFIs try to increase their level of services, and somehow there is a competition between them in

this aspect. Ledgerwood (1999) mentions that there are many MFIs that provide credit cards,

payment services and insurances.

INSURANCE TYPES

PROPERTY INSURANCE: This type of insurance covers the losses which are caused by a

damage or failure of an asset such as: tools, vehicles, workshop, etc.

HEALTH INSURANCE: This insurance covers partially or completely cost of hospital, cost of

operations, and cost of visiting a doctor, etc.

DISABILITY INSURANCE: This type of insurance is related to health insurance, but it

compensates losses or reduction of the income which are because of an injury, illness,

accident, etc. (Miller & Nothrip, 2001)

LIFE INSURANCE:



o Term Life Insurance provides coverage in case of death of the insured person,

guaranteeing a predetermined amount if the fact happens. It is the most common

insurance offered by micro insurers.

outstanding balance life insurance (the most limited coverage, but the

cheapest type of insurance) or credit life insurance which will pay off a loan

balance if a borrower passes away.

Debt cancellation with additional benefit is an elaborated version of

outstanding balance life insurance. This version provides debt cancellation

with benefit which may pay a fixed payment to the family, or an overall

fixed payment of the same amount of the original loan which results a

higher benefit in case that the loan has completely paid off.

Loan default insurance repays the loan when the loan goes into default. This

type of insurance is accompanied by to major issues. This type of insurance

is highly in the exposure of moral hazard, and it may cause a weak credit

methodology.

o Permanent Life Insurance provides similar coverage as term life insurance provides,

but it has not a particular term. Also this insurance has a cash value that the insured

person can use completely or use the cash flow for borrowing against like a saving

account.

o Live Savings Insurance: This type of Insurance is the most popular insurance offered

by credit unions.

CREDIT

There are different characteristics that describe a loan: the maturity, the maximum loan size

available, the requirement of a of business purpose, the interest rate, the method of credit

delivery, the amount of the loan installment and its frequency, the group or individual lending

approach. Some of these parameters are analyzed in the following paragraphs.

MATURITY

This parameter varies a lot depending on the credit programs: looking at MFIs as Grameen Bank

and IIMC the loans are required to be completely paid within 1 years. On the other hand FINCA



Village Banks asks for 4 months repayment period, while Bank Rakyat Indonesia Unit Desa offers

different products whose typical loan term goes from 4 to 24 months.

Table 1.2 Characteristics of selected leading Microfinance programs

PURPOSE

Generally loans have restriction in terms of purpose, as the policy of one IIMC programs requires

the business activity aim; but on the other hand MFI can make loans for consumption or housing.

For example it is demonstrated that the engagement in non-agricultural activities has a negative

impact on repayment performance, Mokhtar & al. (2012) suggested that the business of

agriculture which is sensitive to weather conditions needs to be taken care of, and the institution

should consider the flexibility for farmers‘ repayments. Consequently MFIs programs should be

designed in order to differentiate the requirements in terms of repayment period according to the

income-generating activity the client has.

INTEREST RATE



One of the most discussed issues in the microfinance field is the interest rate. Having small loan

sizes the costs per loan require interest rates significantly higher than commercial bank rates. In

fact the clients operate in mini-economy in terms of small amounts in consumption, production,

savings, borrowing and income earning; in addition the level of insecurity and risk is higher

because of household specific factors (loss of earnings through sickness, urgent medical expenses,

premature death, theft, insecure conditions of employment) and because of broader

environmental factors (natural hazards, harvest failure for flooding, national economic crisis). All

these elements have important implications in the level of interest rate to cover transaction costs

and uncertainty. Actually Grameen Bank asks for 20% nominal interest rate, but FINCA arrives to

55%.

Indeed, Microfinance activities are still labor-intensive operations; therefore the personal costs of

these activities become high. Also most MFIs send their staff to the field to collect loan

installments which is costly, mainly because the transportation cost on uncomfortable roads.

(Gonzalez, 2011).

Comparing this parameter across the financial markets, in other words between the MFIs and the

traditional formal banks, it is important to highlight that the scales are not comparable: actually,

commercial banks often give large loans, and their transaction cost per loan is much lower than

microcredit banks. So it makes the comparison meaningless, and it is obvious that interest rate of

microfinance organizations become higher than formal banks.

Moreover , also a comparison between MFIs should be evaluated on the same model for interest

rate: for example if on the one side the institution is based on donation, receives subsidy, or is

governmental, then it is not correct to compare it with a private institution, not based on

donations but relying on loan interest or additional financial services (Fernando, 2006).

METHODS OF CREDIT DELIVERY

In general terms there are two approaches for the loan delivery: the individual lending and the

group based lending.

In the first one the clients are required to be able to provide the MFI with some form of collateral

or cosigner (a person who agrees to be legally responsible for the loan but who usually has not

personally received a loan from the MFI). The institution can tailor the loan size and the term to

business needs, as the staff develops close relationships with clients so that each client represents

a significant investment of staff time and energy (Waterfield & Duval, 1996). The IIMC programs



analyzed in this research belong to this type, thus it was possible to see that the program manager

or the installments collector personally knew the lender and could provide information by heart

on the loan repayment situation, the family pattern, the loan purpose and the reason for some

delay in the complete repayment.

In the second category, the group-based approach involves the formation of groups of people who

can access to financial services. The number of member changes a lot from institution to

institution: for example Grameen Bank create small group (of 5 to 10 people) and make individual

loans to group members, while the Foundation for International Community Assistance (FINCA)

village banking model utilizes larger group size (of 30 to 100 members) and lend to the group

itself. As it is easy to imagine, one advantage of this method is the peer pressure as a substitute

for collateral: the default of one member generally means that further lending to other members

of the group is stopped until the loan is repaid. In addition it may reduce certain institutional

transaction costs by shifting the monitoring costs to the group because the members of the same

community generally have excellent knowledge about who is creditworthy. Moreover the

transaction costs decrease for the reason that the loan officer, most of the time, does not

personally collect installments but deal with the group representative, responsible for group

installment collection.

Opposite, there are disadvantages as the one demonstrated by Bratton (1986) who showed that

the group performance is better in terms of repayment rates not always but only in good years,

while during crisis it performs worse than individual lending programs. In fact the generated

domino effect causes the group collapse.

From a study conducted by Sharma & Zeller (1997) the most common threads that weave around

the institutional structures of most nongovernment organization NGO are, first, the not strictly

targeted services for a well-defined set of clients, as the most common criterion used being the

amount of land owned. Second, credit is in general provided to small groups of borrowers on the

basis of joint liability and without any physical collateral. However, even though loans are

individual, the entire group is denied further credit when outstanding arrears exist for any one of

the members.



1.5 MICROFINANCE MECHANISMS

This subsection briefly explains the methods and approach for the microfinance programs

managements, from the selection of the clients to the policies definition.

1.5.1 PEER SELECTION

The peer selection is a fundamental phase for keeping the transaction cost low and for gaining

symmetric information about the client. In these terms the self-selection serves as a screening and

monitoring mechanism replacing the need for collateral, at low expenses. Group lending

mechanism is an effective substitute for collateral, where it is possible to separate good borrowers

from bad borrowers bringing similar types of groups together. In addition it becomes easier to

gather indirect information on borrowers from the local networks (Ghatak & Guinnane, 1999).

Ghatak (1999), in his elaborate econometric work, has shown that the grouping process is a

helpful means in raising repayment rates, lowering interest rates and fostering social wellbeing.

One of the methods which MFIs in India broadly use is the Self-Help Group (SHG) lending model

which helps their members to be linked to the banks: they are small groups of ten to twenty

women that save money and use them as loan fund. Funds may then be lent back to the members

or to others for any purpose. In India, many SHG's are 'linked' to banks for the delivery of micro-

credit.

Today, it is estimated that there are at least over 2 million SHGs in India. In many Indian states,

SHGs are networking themselves into federations to achieve institutional and financial

sustainability. Cumulatively, 1.6 million SHGs have been bank-linked with cumulative loans of Rs.

69 billion in 2004-05 (Reddy & Manak, 2005). Indeed the SHG Bank Linkage Program (SBLP) which

is the dominant microfinance model is growing fast, increasing from 2001 to 2006 it has increased

nine folds. (Guérin, 2011)

1.5.2 PEER MONITORING

A common low cost monitoring instrument implemented in microfinance programs is the group

lending model that tends to harness social collateral: in fact, while defaulter borrowers with

individual lending contracts have to fear only the penalties imposed by the bank, in contrast in the



group lending, in case of default, the borrower will also be confronted with the wrath of his or her

peers. The implicit social costs for the defaulting member may be very high, especially in

communities with a high social cohesion (Besley & Coate, 1995). The 'strategic defaults' (that

happen when the borrowers are unwilling, rather than unable to pay their loans) can be ruled out

because of the geographical proximity, trade link between peers and the execution of social

sanctions (Armendàriz, 1999).

1.5.3 DYNAMIC INCENTIVES

Normally the Microfinance programs follow the dynamic incentive of progressive lending, in which

the loans have small amounts for the first time borrowers and, upon satisfactory repayment, they

gradually increase in size. This tactic allows the lenders to develop relationships in time and so sort

out potential defaulters before the loan scale is expanded (Ghosh & Ray, 1997).

Dynamic incentives tend to function much better in areas where mobility is low, thus it works

better in rural areas then in urban, where defaulters would then try to establish a credit line with a

different agent or program in the community by moving away (Sumar, 2002).

1.5.4 REGULAR REPAYMENT SCHEDULES

In contrast to commercial bank's standard loan contracts, MFIs have established a new way of loan

repayments by adding up the principal and the interest due in total, then, depending on the

frequency established by the policy, the loan installments amount is calculated dividing the total

by the installments number. In the case of IIMC, the NGO studied in this research, one program

asks for weekly installments, so the loan size is divided by 44, considering 4 meetings per month

and with the first month of no repayment requirement.

In the chapter 3 this topic is well explained, as it refers to the research question of this thesis.



CHAPTER 2 - IIMC - INSTITUTE FOR

INDIAN MOTHER AND CHILD This chapter is focused on the no-profit organization that provides the two microcredit programs

studied in this research. The first part introduces IIMC institute, analyzing the different

humanitarian services it offers, and describing its evolution and features. Then the attention

moves to the microfinance programs, describing firstly the pure Microcredit Program delivered on

the territory and secondly the Mother’ Bank project. The chapter ends with the two programs

comparisons in terms of microcredit policy and service delivery.

2.1 ECONOMIC ENVIRONMENT – WEST BENGALI

IIMC focuses most of its activities in the South 24 Parganas District, in West Bengal. For this

section the data refers to the “Minority Concentration District Project - South 24 Parganas, West

Bengal”, sponsored by the Ministry of Minority Affairs Government of India (2008)

West Bengal is the fourth most populous state in the Eastern Region of India accounting for 2.7%

of India’s total area, more than 80 million inhabitants in 2002, the 7.8% of the country’s

population (Bagchi, 2005), and ranks first in terms of density of population which is 904 per square

km. By considering the religion pattern, Hinduism is the main one (65.86%), while Muslims

amount to 33.24% of the total population and Sikhism, Christianity and other religions make up

the remainder (Census 2001).

Concentrating the attention only on socioeconomic indicators, the comparison between the

national and the district situation can be observed in the table.

Table 2.1 Socio-economic indicators in West-Bengali and Parganas District



In average the literacy percentages indicators are higher in the district than in West Bengal, but

the work participation, especially for women, has lower values than the common trend in the

country, suggesting the need to promote woman dedicated programs in business field, as

microfinance projects for example. The report points out as, on an average, all the four classes in a

primary school cannot be held, mainly because of lack of teachers rather than lack of

infrastructures. This last point is on the other hand important for the secondary level of

educations, where the district performance results very poor. Indeed the IIMC interventions are

also focused on education programs in rural area as it will be explained later.

Moreover, going in deep with the found results, the research discovers the urgent need for health

service public infrastructure, as vaccination or institutional delivery is inadequate. A mere 11.11%

of villages have government hospitals in its neighborhood, 37.30 % of villages have primary health

centers or sub-centers situated within the village, while in average the distance of primary health

center or sub-centers is 2.16 Km. (for government hospital it is 8.02 Km).

With 72% of people living in rural areas, the State of West Bengal is primarily an agrarian state

with the main produce being rice and jute. In these areas, the means of transport and

communication are not well developed, with all the attendant consequences. Inhabitants of these

zones hardly ever have access to sanitation structures; this condition, coupled with lack of hygiene

services and drinkable water, contributes to the spread of diseases.

The proportion of people living below the poverty line is around 32%; main activities are

agriculture (mostly rice and vegetables), farming, fish culture and basic trade.

The literacy rate is 79.2 % for men and 59 % for women, with a big difference between the literacy

rate of the urban population (85.4% for males and 73.7% for females) and the one of rural

population (77.9% for males and 56.1% for females). However, the rate for higher education is

much lower, especially in rural areas, where the schools are often difficult to be reached and, in

any case, most of the families cannot afford tuition and school equipment for children.

Another major problem is the condition of women: they suffer for discrimination, because of

cultural prejudices, and they are often victims of exploitation, violence and abuse, both in a family

and in a social context. Even more, though prohibited by law in 1961, the extraction of dowry from

the bride's family prior to marriage still occurs and this is one of the main reasons for sex-selective

abortions and female infanticides in India. In many rural families girls and women have to face

nutritional discrimination within the family, and often, especially pregnant mothers, are anemic



and malnourished. Further, indigence and illiteracy prevent them from having access to loans and

legal support.

2.2 INTRODUCTION TO IIMC

VISION

“The Institute for Indian Mother & Child (IIMC) is a social empowerment organization, conceived to improve the lives of the poorest amongst the poor people of India in an educational, medical and economical way”. (IIMC 2011-2012 Overview Report)

It is a non-governmental voluntary organization, committed to promote child and maternal health,

literacy and in most general terms it aims to contribute to the acceleration of International

solidarity. Indeed this institute wish to create a prosperous, peaceful and successful civil society

free from illness, illiteracy, injustice and ignorance.

MISSION

The priorities and targets of the mission can be summed up in three principal activities: education,

medical assistance and woman social empowerment. These three set of programs will be analyzed

in the following paragraphs. But, according to IIMC annual report of 2011, IIMC missions in details

are:

- Conventional education of the rural population, in order to allow all children to get the

chance to go to school, both in terms of schools building in remote villages, and also

providing sponsorship programs for valuable children.

- Giving more priority to rural women in economic, social, cultural and intellectual

development.

- Economic empowerment of women through microfinance coverage and creation of

women‘s groups to give them the possibility to take social responsibilities .

- IIMC network extensions to encourage other authorities to take analogous initiatives.



It is important to precise that the purpose of the organization is not to do charity activities, but to

provide help to people for “walk with their feet”, teaching them to be independent and to believe

in their strengths.

2.2.1 HISTORY

IIMC was founded by Dr.Sujit Kumar Brahmochary. He started working on his own mission in

Tegharia in 1989, a poor and remote area 30 km south of Kolkata, without good roads, and not

provided by medical facilities and health care for the population.

The project started with medical help and the building of a clinic, but soon it was implemented

with the education program, in which donors could support distance adoption of students. The

following step was the creation of the first IIMC School in Chakberia village, and then the women

empowerment program begun in 1996. Two years after, the rural development program started in

order to support rural development project, with then the support of the new-born adventure in

the microfinance field services: microcredit bank started initially for the mothers of the sponsored

children, that had the opportunity to ask for a small loan. But from 1999 IIMC created the pure

microcredit banks as a separated section in the institution, adopting the Grameen bank model.

After the first bank, Hogolkuria, the next year the organization went beyond the city borders, in

order to give services to much more remote areas like islands close to the border of Bangladesh. In

parallel the institution began transferring its knowledge to small NGOs which were working in the

mentioned areas. Finally, in 2008, IIMC stated its most innovative project of women peace council,

that will be described in the next pages.

2.2.1 ORGANIZATIONAL STRUCTURE

IIMC organizational structure consists in the Board of Trustees and 5 Sub-Committees, supported

on the one side by the local volunteers and on the other side by the international volunteers.

The Board of Trustees is composed by 7 members that decide the organizational policy;

each year, during 3 meetings that take place every 4 months, they review the financial

transactions and make plans for the next year/months. It delegates then a Sub-Committee

for each Unit to look after the activities.

5 Sub-Committees are: Medical & Health; Finance & Administration; Education & Woman

Empowerment; Agricultural, Microcredit & Rural Development and finally Women Peace



Council. Each Sub-Committee organizes a meeting every 6 months, where suggestions and

proposals for future plans are evaluated and discussed.

700 Indian volunteers: the Indian local human resources, mainly composed by women

(72%), receive only a reimbursement of expenses as wage. Their roles are mainly school

teachers, medical staff (such as nurses and doctors), office staff, cleaning and kitchen staff,

and people who work in the Agriculture Unit or in the Microcredit one.

Foreign Volunteers: Each month IIMC host from 10 to 20 foreign volunteers who come to

Kolkata to participate in the IIMC mission and encourage the voluntary spirit of the project.

Most of them are students of Medicine, Economics or Engineering, but also teachers,

doctors and social activists participate to the Cooperation and Solidarity project. Indeed

they are involved in all the IIMC activities, trying to allocate each one in his favorite fields

while helping out also with the general plan. This mix of cultures, coming from 25 different

countries, stay in a Guest House in Kolkata and reach each day the Indoor Clinic in

Sonarpur in order to provide assistance in the medical projects, study the Microfinance

services, support energy and architecture initiative, and help improving the English

language level at school.

In the following paragraphs, the different projects and programs are briefly explained in order to

have an idea of the huge organization and impact of IIMC at social, educational, economic and

medical level.



2.3 PROGRAMS

2.3.1 MEDICAL PROGRAMME

Medical Program aims to provide primary health care and medical facilities to the poor and needy

people both of the surrounding villages near Tegharia and of the rural areas: indeed, after the first

indoor clinic that offers 17 beds, IIMC decided to spread the service over the years: in 2008

another Indoor clinic, Dhaki, was constructed in an isolated area, with a Maternity Center that

offers a home-delivery service; then, an Outdoor clinic in Tegharia and four Sub-Centers in the

remote villages of the district offer primary health care to poor people in rural areas for a symbolic

amount of money. Where totally medical treatment is provided to about 130.000 patients per

year and involving 10 Indian volunteer doctors.

ADDITIONAL HEALTH RELATED PROGRAMS:

Health education and health promotion unit (he&hp): This project aims to improve

the knowledge of health, hygiene and nourishment in the villages of West Bengal, with

the belief that when people become educated, they are therefore able to promote

their health by themselves and be, in part, independent from some basic health

services. As it can be imagined, the illiteracy of the audience requires not traditional

teaching method but designs, mime and drama are preferable.

Reproductive child health programme: the target of this special program are the

particularly poor pregnant mothers, that receive support in terms of food (for example

twice a month IIMC gives them 2 kilos of rice, 500 gr. of lentils, 1 kilo of potatoes).

Moreover IIMC pays charges for all examinations, for hospital delivery charges and, for

newly born babies, as the program continues up to three months after birth, following

them into the postnatal care.

Intensive care programme: Nutritional Diet Food are prepared for malnourished

patients and pregnant mothers, for preventing maternal and child malnutrition.

Cancer detection camp: collaborating with the Chittaranjan National Cancer Institute,

this program allows women to do free test for cervical cancer.



2.3.2 EDUCATION PROGRAMME

Being education not compulsory in India, being private school too expensive, being government

schools overcrowded and too far away from the villages, IIMC put in place projects and programs

in order to spur primary and secondary education. 26 schools were built in rural areas of the 24

South Parganas District.

During the school year 2011-12, 5.081 children attended IIMC’s schools: 984 in the pre-primary,

3.191 in the primary and 906 in the secondary school.

THE SPONSORSHIP PROGRAMME

As another project for increasing literacy and avoiding child labor, in 1994 IIMC launched its

Sponsorship Program to allow people worldwide to support young student and enabling in this

way to have in each family one member that can read and write. To support this program is

sufficient a monthly contribution of 20 euros: donations come from 23 different Countries and

they reach more than 2.500 children.

IIMC is linked with these Countries through local coordinators; in particular in the nations where child

sponsorship is very active, these volunteers founded local associations (like Project for People in Italy) that

carry on fundraising and sponsorship promotion.

The amount the family receives is used for school fees (they can choose between IIMC schools or

government schools), private tuition fees, study materials, uniforms, shoes, school bags, books, and also

free medical assistance in case of sickness.

ADDITIONAL CHILDREN CARE SERVICES:

Children day care: this center provides to women from poor families that work every day

(as house cleaners or farm laborers) the possibility to leave their youngest children in a

safe structure.

IIMC center for the handicapped: Due to deeply rooted gender discrimination, when a girl

is physically unfit to walk or to talk, then she is just a burden for her family. From 2007,

IIMC offer a home, food, education and also good guidance to them, through a center near

the Indoor Clinic in Sonarpur.



2.3.3 WOMEN EMPOWERMENT PROGRAMS

IIMC tries to improve women’s conditions through different programs that give women the

opportunity to learn a job, to manage a shop, be aware of their rights and spread support for any

family troubles, giving the mother the opportunity to live a dignified life.

Professional training programme: it is composed by 5 Units that offer different kind of job

training to women as sewing, knitting, bag, needle, and handloom units. In addition in

these structures some member not only train but actually work and produces products to

be sold in the IIMC shops.

Women cooperative: it is a shop near the Indoor Clinic and from July 2012 also in Dhaki,

where people can buy stationery, bed sheets, sarees, shirts, pants, school and house

materials, but also, for examples, bottles of water and toilet paper. The aim of the project

was to answer the demand of products that are hardly available at the local market, and to

reinforce the role of local women by involving them in this activity.

Women peace council: project born between 2000 and 2001 under the suggestion of a

Canadian volunteer, aims to improve women’s rights in order to avoid substantial

discrimination and promote gender equality and women emancipation. Women from rural

villages meet in groups two hours, 5 days a week, and they receive valuable lessons for

their everyday life by reading newspapers, women and health magazines. They also discuss

specific problems, receive motivation becoming therefore aware of their conditions and of

their rights, as well as socialize, exchange information. In addition once a week they visit

village houses to get acquainted with the families, and if they discover any problem they

discuss it among themselves and try to solve it; if needed, the Unit may ask for help from

IIMC’s Coordinators. The number of groups is constantly increasing; now we have 35

groups for a total of 340 women. Members of the WPC receive a monthly compensation

for their participation, depending on the effort they put in the project.

2.3.4 RURAL DEVELOPMENT PROJECT

IIMC’s Rural Development Project includes several activities, such as

Agricultural and Fishponds, in 3 different villages (Dukerpole, Purbajata and Dhaki) where 80% of

people depend on cultivation and 10% live from fishing.



Water Sanitation: in Indian villages people use the same pond to wash themselves, to drink, to

wash clothes and dishes and also to provide drinking water to animals, but the main point is the

bacteriologically contaminated groundwater that causes 80% of diseases in rural areas. With this

project IIMC creates safe water supplies and proper latrines that give medical safety and social

dignity, and also privacy for females.

Housing: IIMC builds houses to give homeless people a permanent shelter and a complete

rehabilitation in their birthplace.

Hogolkuria Mozzarella Cheese Unit: this project started in 2005 thanks to the collaboration

between IIMC, the Italian General Consulate and Fire&Ice, a famous Kolkata Restaurant where you

can eat a delicious Italian pizza, that now buys every day our cheese made in Hogolkuria. In

addition the milk is bought to women involved in IIMC Microcredit Bank: they buy cows with the

money provided by a micro-loan and then refund the loan selling milk to our Cheese Production

Unit.

2.3.5 MICROFINANCE PROGRAMS

IIMC developed two programs which provide microsavings and microcredit services for women:

Microcredit program (Mahila Udyog) and Mother‘s Bank (Matree Udyog). In the next pages we will

explain each program and then show their main differences. The responsible for the two

microcredit programs is Mr. Apurba Chakroborty, that manages and supervises the projects from

the IIMC headquarter.

In both cases the method selected is the INDIVIDUAL LENDING that consists in the provision of

credit to individuals who are not members of a group that is jointly responsible for the loan

repayment. This type of Microfinance approach requires frequent and close contact between the

client and the credit officers that usually manage a relatively small number of clients (between 60

and 140) (Ledgerwood, 1999).

In the next paragraphs the two programs will be explained separately.



2.4 PURE MICROCREDIT PROGRAM

The first Microfinance program of IIMC was born in 1999 and named Mahila Udyog that means

“Women Bank” in Bengali language. The initial service consisted in the possibility of save

microdeposit at Hogolkuria office, the first branch founded, and was successively followed by the

creation and diffusion on the territory of 6 additional branches (Chakberia, Hatgacha, Kalyanpur,

Dhaki, Amoragori, Prasadpur) and three microcredit networks ( Bithari Disha, Barasat Bharpara,

Nagandrapur).

Dr. Sujit aimed to design an effective Socioeconomic Development Program focused on women

and poor people, that had the ability to serve the poorest of the poor and at the same time

achieved self-sustainability, thus the program was amplified with a microcredit system based on

Grameen Bank policy and the provision of life insurance. Indeed, the bank encourages financial

habits by providing poor women with the chance to get loans at a very low interest rate (10%) in

order to start a business project by which they will be able to improve their condition, to trust in

themselves and thus gain their husbands’ respect of and of the society.

DISTRIBUTION ON THE TERRITORY

Going into details, each branch operates within a radius of 10 km and an average of 46 villages,

and the service’s rules and policies are generally the same in all the 7 branches.

MICROFINANCE POLICY

Indeed IIMC decided to apply a customized and slightly modified Grameen banks‘ model that is

described in the following lines.

CONDITION TO ACCESS TO THE PROGRAM SERVICES: As already explained, Mahila Udyog

program is dedicated just to women, who belong to a microcredit group. In addition only

married client are eligible to ask for loan, while the other women can make savings.

GROUP FORMATION: The first approach consist in visiting the villages and introducing

microcredit program concept to women of rural areas. The IIMC field officers then start to

select those women interested in the program, creating groups of maximum 25 members.



The group attends weekly meeting, whose place and time is fixed based on the agreement

between all group members with the field officer, called Community Organizer (C.O.).

HUMAN RESOURCES: the responsible of this program is Alim Sarder that works in the

headquarter and periodically visits the branches. Locally, for each branch IIMC employs

one Manager, one Assistant Manager (AM), Cashier Accountants (they must be women),

one Community Development Manager (C.D.M.) and Community Organizers (C.O.). The

Community Organizers go to the villages to create groups and offer them support.

SAVINGS

o AMOUNT: the savings deposit amount is minimum 10 IRS to maximum 50 IRS per

week for this type of program.

o INTEREST RATE: the annual interest rate applied on the minimum balance of the

monthly savings, is 4% and it is paid in April. The minimum balances of the monthly

savings are summed and then divided by 300 to determine the payable amount of

interests on savings.

o DURING LOAN REPAYMENT: while the client is repaying the loan she is highly

recommended to continue to make savings, but it is not mandatory

o PROCEDURE: During weekly meeting, in the morning, the field officer ( C.O) gathers

both the loans installments and the savings deposits in the villages, writing down

the sum on the group registers.

LOAN:

o TYPE: the method selected is the individual lending, so the loan is given to a single

client who is responsible for its repayment.

o DISBURSEMENT CONDITION: the client should have in her savings account, an

amount of 1/10 of the loan size she asks.



o FIRST LOAN: After making three months savings and having at least 120 IRS a

married member becomes eligible for receiving the first loan, with a maximum

amount of 2000-3000 IRS.

o PURPOSE: The loan has to be used only for business purposes, in other words for

income generation activities.

o REPAYMENT PERIOD: The clients have to give back the loan within ONE year. After

the disbursement date the client is expected to start the repayment from the

second month, with a total number of loan installments equals to 44.

o INTEREST RATE: The interest rate applied is the 10% of the loan amount which they

call service charge. Till 5-6 years ago there were two different interest rates for the

loans. Loans which were up to 10,000 IRS had 10% interest rate, and loans from

10,000-15,000 IRS had 15% interest rate, but at present the policy is homogeneous

for all the loans. For example if the client receives 4000 IRP she is required to pay

back 4400 IRP, within the year.

o IF DELAY OR DEFAULT: If a client cannot give the loan back in one year, IIMC gives

her 3 months extra time, and after the extra time the borrower will be considered

as a defaulter. When one is a defaulter she should not pay fine, but IIMC tries to

push her, through her husband, and through other group members to complete the

repayment. In addition in the future she cannot ask for a new loan.

o LOAN SIZE: The amount of loan that IIMC gives each time becomes 1000, 2000, or

3000 larger compare to previous loan that a client has asked, provided that she was

a responsible client and paid back her loan on time, without delay. So each year she

can have larger loan and after 6-7 years she can reach to the maximum level of loan

that is available in the bank that she belongs to (10000, 15000).

o LOAN DISBURSEMENT PROCEDURE: The client is required to go to the branch,

usually maximum 10 km distant, in order to fill a loan request module. After the

manager evaluation of the conditions and the possibility to provide her the desired

amount of money, a deal is signed and eventually the loan disbursed.



LIFE INSURANCE: this is a type of social security (S.c.) for which each client has to pay 1% of

the loan amount which is withheld when the loan is disbursed. It is useful in the unlucky event

of the client death, thus her family has not to payback the outstanding loan.

WITHDRAWALS:

o DURING LOAN REPAYMENT: the client is not allowed to withdraw from the savings

account while she has a loan; only in order to complete the repayment, she can use

the savings on her account.

o AMOUNT: if the client has no loan to be repaid, she can withdraw as much as she

has in the savings account.

o PROCEDURE: the client should go to the branch in order to withdraw money, the

service is available all the afternoon during the working days.



2.5 MOTHER’S BANK

“Mothers’ Bank” program, Matree Udyog in Bengali language, provides micro-financial services in

the IIMC headquarter in Sonarpur and is mainly dedicated to the mothers of the sponsored

children.

Indeed, as already explained previously, IIMC supports 3,000 studious and hardworking students

who face financial problems that would not permit them in most cases to continue their studies.

While the child benefits from this educational program, parents go to IIMC headquarter for

receiving the monthly help and, during this visit, the mother can both ask for a loan or make

savings in Mothers‘ bank.

DISTRIBUTION ON THE TERRITORY

The service has no distribution on the territory, since it is provided only by the IIMC headquarter in

Sonarpur.

MICROFINANCE POLICY

The following points are those that differ from the pure microcredit program.

CONDITION TO ACCESS TO THE PROGRAM SERVICES: As already explained, Matree Udyog

program is dedicated just to the mothers of sponsored children.

NO GROUP MEETING: The mother does not belong to a group, but she comes and asks

individually for the loan, having a personal loan account and another one for the savings. In

this way, as it is easy to see, the program responsible and desk- employee knows

personally each client situation.

HUMAN RESOURCES: the responsible of the program is Mr. Debashish that works in the

headquarter and takes the accountancy documentation. He refers to Barnali for the loans

disbursement approval, but he is the only IIMC representative that the client meets in this

program.



SAVINGS

o AMOUNT: monthly installments amount goes from a minimum size of 20 R.s to a

maximum of 200 R.S.

o INTEREST RATE: the annual interest rate is 4%, determined on the monthly savings

deposits and credited in April. In the past the interests were not paid in a specific

date, but from 2013 the procedure became more standardized, thus in the savings

account data the yearly interest amount can be seen in the deposit of the 31th of

March.

o BEFORE LOAN DISBURSEMENT: the savings are mandatory minimum for 3 months

before receiving the first loan.

o PROCEDURE: when the mother goes to the headquarter in order to receive the

financial support for the her child, she visits the Mother’s Bank office and provides

the monthly savings deposit, whose amount will be inserted in the savings account

software and written down on her personal book.

LOAN:

o DISBURSEMENT CONDITION: as in the pure microcredit program, the client should

have in her savings account an amount equal to 1/10 of the loan size she asks.

o FIRST LOAN: After making three months savings and having at least 120 IRS a

married member becomes eligible for receiving the first loan, with a maximum

amount of 2000-3000 IRS.

o PURPOSE: there is no condition referring to the purpose of the loan, it can be used

either for business activity, for household expenditure or education.

o REPAYMENT PERIOD: also in this bank the loan should be completely paid back

within ONE year. After the disbursement date the client is expected to start the

repayment from the second month, with a total number of loan installments equals

to 11.

o INCENTIVE: the Mother’s Bank program has an additional repayment incentive

compared to the pure Microcredit Program: while both of them give the possibility



to access to a larger repeated loans (progressive lending), the Mother’s Bank

program gives a discount on the interest payable if the client pay back the entire

loan amount within 5 months from the disbursement date.

o DELAY OR DEFAULT: If a client does not give the loan back in one year, she is

considered a defaulter, and she should pay a fine of 10% of the outstanding debt.

For example, if after a year a client has already paid 2,300 out of 3,300 IRP loans,

she has an outstanding debt of 1,000 IRP, and consequently she would be charged

by 100 IRP more, with a resulting debt of 1,100 IRP. It is important to highlight that

this rule is not strictly followed and therefore this information will not considered

reliable and useful for the analysis

o LOAN SIZE: the maximum size reachable in the Mother’s Bank program is lower

than the one of the pure microcredit program, indeed it consists in 8,000 IRP, and a

maximum loan must be approved also by the education program‘s coordinator. For

low sizes the program’s responsible can takes decisions in autonomy. As in the pure

microcredit program, in the mothers‘ bank program one client can ask for a first

loan of around 1,000-2,000 IRP. Subsequent loans may be gradually higher if she is

responsible and punctual according to the client’s need and past performance

o LOAN DISBURSEMENT PROCEDURE: The client is required to go to the bank, in order

to fill a loan request module where she write the loan amount desired and a letter

of motivation of the Education Program Responsible, Ms. Barlali. The program

responsible submits the application and waits for the response. During the next visit

to the headquarter the mother receive the request answer and signs the contract.

LIFE INSURANCE: as in the pure microcredit program, it is provided this type of social security

(S.c.) for which each client has to pay 1% of the loan amount at the disbursement date. If the

client died before the completion of the loan repayment, this insurance covers the

outstanding amount, without any charge to the client’s family.



WITHDRAWALS: very similar to the Pure Microcredit program.

o DURING LOAN REPAYMENT: the client is not allowed to withdraw from the savings

account while she has a loan; only in order to complete the repayment, for the last

loan installment, she can withdraw.

o AMOUNT: if the client has no loan to be repaid, she can withdraw as much as she

has in the savings account

o PROCEDURE: the client should go to the bank in order to ask for withdrawing

money, where the service is available during the working days.

In the following paragraph the two programs will be compared in order to highlight the most

important differences.



CHAPTER 3 - RESEARCH QUESTIONS AND

ANALYSIS APPROACH The research focus the attention on the client repayment performance and the different results

depending on the repayment frequency policy and the group lending factor. Before a brief

literature review on previous researches is conducted for studying the factors influencing the

repayment performance. Then the attention moves to the IIMC case: after the analysis of the

difference between the two programs, the research questions and the expected results are

explained .

3.1 LITERATURE REVIEW

3.1.1 REPAYMENT RATE

Delinquency tends to be more volatile in MFIs than in commercial banks: according to Rosenberg

(1999) one of the reason is the lack of tangible assets for securing microloans. The clients’ main

motivation to repay is their expectation that the MFI will continue providing them with valued

services in the future if they pay promptly today, along with peer pressure, especially in group

lending programs.

High repayment rates are the base for fundamental improvement in the services, both from the

client and the MFI point of view: on the one hand it may allow to lower the interest rate thus

reducing the financial cost of credit and enabling more borrowers to have access to credit. On the

other hand it would be possible to reduce the dependence on subsidies leading to a better

sustainability level and moreover this type of study aims to evaluate the adequacy of MFI's

services to clients’ needs. (Godquin, 2004)

The main reasons for high default rate are associated with information asymmetries or low

performance of institutions because of politics, environment and education issues: gaining

information on the characteristics or on the behavior of the borrower is costly for the MFI, with a

consequent difficulty in a client’s reliable selection or with the risk of loans’ allocation to

borrowers with high level of default probability or moral hazard. From the managerial point of



view, the MFI should design appropriate processes and establish customized policy and

appropriate credit schemes. (Godquin, 2004)

In the literature, researchers tried to understand the factors influencing repayment either

considering variables related to group lending, social intermediation or dynamic incentives. For

example, a study conducted by Godquin (2004) draws a model with dependent variable the

consistency between the repayment period the client declared and the real one, creating a default

dummy variable. Firstly the research looks at predictors tied with social relations, finding that the

age in the group showed a significant negative impact on the reimbursement; secondly the

variables related to the accessibility to non-financial services have a negative impact attributed to

correlation with unobservable variables like the level of risk of the project of the borrower (for

professional training) or idiosyncratic shocks (for the access to health).

In our sample we consider that all the woman have the possibility to beneficiate from all IIMC

services as they are delivered in the same area of microcredit program: in most of the cases the

branch building is devoted not only to financial services but part of the structure host the medical

and educational programs.

3.1.2 INSTALLMENT FREQUENCY

The literature has paid scant attention to a central feature of the typical credit contract offered

by microfinance institutions: frequency of the repayment in a group setting (Armendariz &

Morduch, 2005).

The IIMC Pure Microcredit Program repayment schedule is the typical one offered by a MFI,

consisting of weekly repayment where the installment amount is usually calculated as the

principal and interest due divided by the number of weeks until the end of term. Indeed weekly

payment collection during group meeting by bank personnel is one of the key features of

microfinance that is believed to reduce default risk in the absence of collateral and make lending

to the poor viable. On the other hand, the drawback faced by the MFI is the high level of

transactions costs.

Thus this tradeoff pushes to the important question of whether reduced repayment frequency

actually impacts on the likelihood that a client defaults on her loan.

Synthetically two important observations can be done by analyzing the literature:



1) Contrasting results on the repayment performance when the repayment schedule changes.

2) No evidence of difference between the two frequency pattern.

1) CONTRAST RESULTS WHEN THERE IS A CHANGE IN THE FREQUENCY REPAYMENT

SCHEDULE

The research to consider is the one conducted by Feigenberg et a.(2013) in which Microfinance

clients were randomly assigned to repayment groups that met either weekly or monthly during

their first loan cycle, and then graduated to identical meeting frequency for their second loan.

Thus comparing to our case the main differences are 3: first here there is a change in the policy,

then the clients do not decide the type of program, and finally the focus is on the first loan while

the IIMC clients enter in different period in the programs.

The research reveals that the clients initially assigned to weekly groups were also three times less

likely to default on their second loan. In contrast, also in the experiment of Mcintosh (2008), the

variation in the repayment schedule contracts offered by FINCA in Uganda predicts that the

fortnightly repayment schedule performs better comparing to the weekly one in the case of a

change of the policy. The clients had the option to elect (by a unanimous vote) to move from the

standard weekly repayment practice to repaying the loan every other week. Relative to weekly

repayment schedule, groups which opted for the fortnightly weekly schedule saw lower drop-out

and increased repayment. While supportive of the predictions from economic theory, the fact that

clients chose their repayment schedule makes it possible that “better" clients self-selected into

the fortnightly repayment schedule (Field & Pande, 2008).

Empirical evidence on the effect of repayment frequency is both limited and mixed, and considers

change in the policy but not different policy at the same moment. Indeed BRAC, one of the largest

MFIs with nearly six million clients, abandoned a move to biweekly repayment when an

experiment showed increased delinquencies (Armendariz & Morduch, 2005). Satin Credit Care, an

urban MFI targeting trading enterprises, saw delinquencies increase from less than 1% to nearly

50% when it tested a move from daily to weekly repayment (Fisher & Gathak, 2010).

2) INDEPENDENCY OF THE REPAYMENT PERFORMANCE FROM THE FREQUENCY SCHEDULE

Historically the most common frequency schedule implemented by MFIs is the weekly installment

repayment, as in the Grameen Bank and in Caja Los Andes model. Armendariz and Murdoch



(2005) evaluated that another 42 percent made repayments every other week (i.e., biweekly), and

the remaining 6 percent made monthly installments.

The following table, from the Economics of Microfinance (Armendariz & Murdoch, 2005) shows

that the weekly repayment schedules are demanded on smaller-sized loans, while the larger loans

carried biweekly or monthly installments.

Table 3.1 Performance of programs with different installments frequency

The study demonstrated that frequent installments schedule are preferable: the authors

suggested that, by meeting weekly, credit officers are able to get more information and thus they

have the possibility to detect early warnings about emerging problems, with a consequent

activation of protocol by which to get to know borrowers more effectively.

Moreover, another peculiarity of microfinance contract is the requirement of starting the

repayment nearly immediately after loan disbursement and occur weekly thereafter. Even though

economic theory suggests that a more flexible repayment schedule would benefit clients and

potentially improve their repayment capacity, microfinance practitioners argue that the fiscal

discipline imposed by frequent repayment is critical to preventing loan default.



The research conducted by Feigenberg et al. (2013) reveals that, if any change in the policy is

implemented, holding meeting frequency fixed, the pattern is insensitive to repayment frequency

during the first loan cycle.

In particular, in terms of late payments within the year, the experiment detected 1.4% of weekly

repayment clients, 2.9% of monthly repayment-weekly meeting clients. Although monthly

meetings are on average 3 minutes longer (and the difference is statistically significant), loan

officers do not rank monthly clients' ability to repay at the group meeting as worse than weekly

clients. One criticism to this experiment was written by Fischer and Ghatak (2010): they suggest

that the incentive compatibility constraints may not have been binding for either group and is

consistent with the relatively small loan sizes involved.

Finding no significant effect of type of repayment schedule on client delinquency or default, on the

other hand, a more flexible schedule can significantly lower transaction costs without increasing

client default among microfinance clients who are willing to borrow at either weekly or monthly

repayment schedules. In addition the lower expenses could allow MFIs to invest in a service

operations expansion and thus reach up to four times as many clients without hiring additional

collection officers and without incurring a loss.

Moreover it is important to underline that frequent repayment increases transaction costs

incurred by both borrowers and lenders: from the MFI side, activity based costing exercises

suggest that weekly collection meetings account for as much as one-third of direct operating

expenses (Shankar, 2006; Karduck & Seibel, 2004). In addition, from the client point of view,

Women’s World Banking (2003) found that meeting frequency was a factor in the drop-out

decision of 28% of their clients in Bangladesh and 11% in Uganda.

Looking at the flexibility of the repayment schedule, Fischer and Ghatak (2010) assess that

classically rational individuals should benefit from more flexible loan installment pattern, and less

frequent repayment should increase neither default nor delinquency. Indeed they argue that

more frequent repayment can increase the maximum incentive compatible loan size but lead to

over-borrowing; consequently the welfare effects are ambiguous, as it has a negative correlation

with the loan sizes if over-borrowing.

Micro finance practitioners believe that more frequent repayment schedules improve client

repayment rates, for the following reason:



1) it provides clients a credible commitment device, enabling them to form the habit of saving

regularly;

2) frequent meetings with a loan officer may improve client trust in loan officers and their

willingness to stay on track with repayments.

Most lending contracts require weekly repayment, and there is a pervasive sense among

practitioners that frequent repayment is critical to achieving high repayment rates. (Fischer &

Ghatak, 2010)

3.1.3 GROUP SOCIAL INTERACTION FACTOR

A fundamental pillar of microfinance is the social interactions that encourage norms of reciprocity

and trust, and thus economic returns. Arguably, the improvements in risk-sharing are even more

striking because they were obtained in the absence of joint-liability contracts, and provide a

rationale for the current trend among MFIs of maintaining repayment in group meetings despite

the transition from joint to individual liability contracts (Gine & Karlan, 2011).

Actually social capital is considered particularly valuable in low-income countries where formal

insurance is largely unavailable and institutions for contract enforcement are weak. Indeed,

numerous development assistance programs emphasize social contact among community

members under the assumption of significant economic returns to regular interaction.

Group homogeneity and social ties are also expected to increase the repayment performance not

per se but because they allow a better efficiency of group dynamics. Group homogeneity as a

result of effective peer selection group homogeneity in terms of risks (Ghatak, 1999) and as a

mean to increase peer monitoring, group homogeneity in terms of interest, economic power, etc.,

(Stiglitz, 1990) should go together with higher repayment rate. High level of social ties should have

the same impact as they facilitate peer monitoring and increase the potential social sanction of

peer pressure (Besley & Coates, 1995). Dynamic incentives and social intermediation, which are

extra group microfinance financial innovations, are also expected to increase the repayment

performance.

The client performance was also analyzed in the agricultural context, by considering the difference

in the repayment behavior of group loans compared to individual loans: using data from

Zimbabwe, Bratton (1986) states that group loans perform better than individual loans in years of

good harvest and worse in drought years when peers are expected to default. Paxton (1996)



analyzes credit groups in Burkina Faso raising the attention on what she calls the domino effect

that can outweigh the positive effects of group lending. Zeller (1998) provides evidence in favor of

group lending, with a sample of 146 credit groups in Madagascar, showing indeed that the group

generates insurance which leads to a better repayment performance. (Zeller,1998)

The success of Bangladesh's Grameen Bank in using small groups of borrowers in servicing the

poor and achieving high rates of repayment is now well known (Hossein,1989). So are the

experiences of SANSA in Sri Lanka (Montgomery, 1996) and Credit Solidaire in Burkina Faso

(Gurgand et al., 1994). In Thailand, the Bank for Agriculture and Agricultural Cooperatives

achieved high repayment rates even though it sometimes used groups consisting of as many as 30

members (Huppi & Feder, 1990; Yaron, 1994).

Moreover this social interaction method is especially good in terms of repayment rates for

relatively remote communities, and even in communities that are likely to have higher than

average rates of poverty. According to Sharma & Zeller (1997), the reason for the good program

performance does not lie just in the cost reduction of screening, monitoring, and enforcing loan

contracts, but also in the successful and not transitory demonstration of microcredit benefit at

financial level in small rural communities.

In conclusion repayment rates are not uniformly high, however, for all institutions or across

groups within an institution. In Nepal, the repayment performance of groups formed under the

Small Farmers Development Program (SFDP) exhibit a mixed result (Sharma, 1993; Desai & Mellor,

1993).

Opposite trend is put in evidence by Armendariz and Morduch (2000). The authors of the research

started the evaluation of new innovative incentive and effective mechanisms of incentive in terms

of repayment rate because they sustained that group lending model tends to impose limits on

wealthier borrowers: indeed both the pioneers of this method, Grameen Bank and BancoSol, have

abandoned it for the individual lending contracts. In particular the experiences point out that the

group lending poorly fits the area already relatively industrialized, as eastern Europe and Russia

countries (Churchill, 1999)

In IIMC programs the social interactions should be considered only for the pure microcredit

program, in which the clients meet in groups, while for mother’s bank this factor is not present as

the loan installments are not provided during meeting but through an individual visit of the

mother to the branch. But in both cases the loans are disbursed with individual lending, never with



joint-liabilities. Indeed Armendariz and Morduch (2000) suggested that it is not obvious that join

liability drives the good results in group lending, but other factors should be taken into

consideration as public repayment, facilitation of education and participation alongside

neighbours.

Finally we remind that, in general term the women selected are Mother’ s of sponsored children

that can freely decide either to ask a loan in the pure Microcredit Program or in the Mother’s

Bank.

3.1.4 REGULAR REPAYMENT SCHEDULE

The regular repayment schedule links the topic of repayment rate, frequency installment and

group lending: it is one of the mechanisms for allowing the microcredit programmes to generate

high repayment rates from low income borrowers without requiring collateral and without using

group lending contracts that feature joint liability (Armendariz & Morduch 2000).

The weekly frequency is more likely to hold in poorer households, where the opportunity cost of

time is relatively low and where mechanisms to enforce financial discipline are relatively limited

(Rutherford, 2000): indeed frequent collection is desirable for small-scale business as they

generate a flow of revenue on a daily or weekly basis.

As it can be easily imagined, regular repayment schedules have the great advantage of constant

screening of the borrowers, from which the institution steadily monitors the client behavior and

thanks to which the loan officers can timely activate protocols when necessary. In addition by

being able to commit to making small, regular instalments to the microfinance institution, the

clients get a usefully large amount of money at their disposal much as would happen through a

regular saving plan. (Armendariz & Morduch, 2000)

Finally, also Godquin (2004) declares that an increase in the duration along with irregular

repayment schedules may also increase his probability of default.

However, the regular repayment schedule requires to start repaying the loans soon after loan

disbursement. An alternative rationale for this loan repayment structure lies in the difficulty of

monitoring borrowers’ actions, so the potential for moral hazard leads MFIs to use innovative

mechanisms, such as regularly scheduled repayments, which indirectly coopt the better-informed

informal lenders (Jain & Mansuri, 2003).



Finally, the provisioning of microfinance loans with inflexible (standard loans) and flexible (flex

loans) repayment schedules was analyzed by looking at the loan delinquencies of agricultural

borrowers. Based on a Madagascar MFI, flexible repayment schedules result more adequate for

redistribution of principal payments during periods with low agricultural returns (grace periods) to

periods when agricultural returns are high. Moreover, the results reveal on the one side no

significant delinquency differences between farmers and non-farmers who received standard

loans, while on the other side they demonstrate that farmers with flex loans but without grace

periods show significantly higher delinquencies than non-farmers with standard loans. (Weber &

Musshoff, 2013).



3.2 RESEARCH QUESTIONS

In this section the research questions are explained, along with the method by which they are

approached in this thesis.

Is it better the more traditional weekly group-meeting repayment schedule comparing

to the monthly individual installment requirement? Is the type of the loan installment

frequency (monthly or weekly) important for the client performance?

Does the respect of the program policy permit an on -time repayment? Are those clients

who are consistent with the frequency in the loan installments, better performer than

those with a less regular behavior? If the client follows the repayment policy, has she a

lower repayment period than a more heterogeneous behavior?

The study is based on the comparison of the two microcredit programs developed by IIMC.

Consequently, before analyzing one by one each topic, the features of the two programs are

considered separately in order to evaluate in which characteristics they differs.

3.2.1 MICROFINANCE PROGRAMS COMPARISON: HYPOTHESIS

Table 3.2 Characteristics’ comparison of the two Microfinance programs in IIMC

MICROCREDIT PROGRAMS COMPARISON

CHARACTERIST PURE MICROCREDIT PROGRAM MOTHER’S BANK

TERRITORY DISTRIBUTION

7 branches 1 bank

ACCESS CONDITION

Be a woman Be a mother of a sponsored child

SAVINGS 4% interest

Weekly deposits

Amount [10; 50]

4% interest

Monthly deposits

Amount [20;200]

LOAN Disbursement condition: savings amount equal to minimum 1/10 of loan size

1st loan: 1000-3000 IRP

Purpose: business activities

Repayment period: 1 year

Weekly installments

GROUP MEETING

Incentive: larger loan

Fee if default: no

Disbursement condition: savings amount equal to minimum 1/10 of loan size

1st loan: 1000-3000 IRP

Purpose: any

Repayment period: 1 year

Monthly installments

Incentive: larger loan + discount

Fee if default: yes (1% outstanding loan)

LIFE INSURANCE

Social security (1% of the loan size) Social security (1% of the loan size)

WITHDRAWALS Only if null outstanding debt Only if null outstanding debt



First of all, the political, economic and managerial environment in which the IIMC programs are

developed is the same. In this sense the sample is homogenous, as the clients of both projects live

in the same livelihood conditions, with similar literacy and education level, and can access to the

same financial services.

Considering the microfinance policies of the Pure Microcredit Program and Mother’s Bank

Program separately, the following table resumes the main similarities and differences between

these two credit programs.

TERRITORY DISTRIBUTION: at first look it seems a huge difference that highly influences the

behavior of the client, but the sample is designed in order to null it. Indeed the considered

women of the PMP are mothers of sponsored children in IIMC Education Program. This

characteristic requires that one parents goes to the IIMC headquarter each month in order

to have the money from the sponsorship office, and normally is the mother who goes to

the Sonarpur for this task. This means that she has the opportunity to ask for a loan in the

other program, the Mother’s Bank Program, with no additional cost: the monthly loan

installment can be provided the date when she comes to receive the monetary help for the

child. This consideration leads to the following conclusions:

o the clients of Mother’s Bank Program should not cover additional distance in order

to provide the monthly installments because this tasks can be done in the occasion

of the monthly help withdrawal.

o The clients of Pure Microcredit Program should go to weekly meetings that take

place in the same village of their house, so the distance they should cover does not

influence the decision to apply for the program participation since it does not imply

transportation costs.

ACCESS CONDITION: as explained in the previous point, all the clients in the sample are

mothers of sponsored children, but one part of them decided to participate to the Pure

Microcredit Program while the others applied for the Mother’s Bank Program. For this

reason there is not this difference in the sample analysis that can impact on the results.

PURPOSE: for Pure Microcredit Program the client should justify the loan request with a

business purpose activity that helps her generate income; on the other side, for Mother’s

Bank Program it is not required. This difference is not relevant because IIMC is not rigid in

verifying the actual utilization of the money disbursed, being impossible to really test the

client words IIMC relies on trust. It is possible that most of the time the mother uses the

loan for household expenses or other no income-generating activities. For this reason it

can be considered that this difference in the programs’ policy does not impact on the client

performance.



INCENTIVES: the Mother’s Bank Program gives discount if the repayment is completed

within 5 months but this is quite an exception. So being very rare, it seems that it is not an

effective incentive method for a fast repayment, thus it can be ignored.

FEE: this rule is not strictly applied. Recently the responsible takes more attention on it, but

for the period considered the data are quite inconsistent, thus it cannot be considered as a

deterrent for delay behavior.

In conclusion the main difference between the two policies is the frequency of the installments

and the fact that in Microcredit program the clients have the possibilities to establish social

relationship during the group meeting, even if it is important to underline that both programs give

individual loan.

These two differences, the higher frequency of the installments and the greater ease of

establishing social relations of the Pure Microcredit Program compared to the Mother’s Bank

Program, can not be divided.

Even if the literature review suggested contradictory findings, in general terms we can expect that

the Pure Microcredit Program’s clients perform with a faster repayment both because they meet

with higher frequency and in group meeting, so that the social interaction improves both the

repayment rate and the time for loan complete repayment. As already mention, the results can be

affected by the possibility to choose the program by the clients.

3.2.2 RESEARCH QUESTIONS

Microfinance programs are designed in order to enable the poor people to easily repay the loans

by asking them frequent installments: in this way the client learns to regularly preserve a certain

amount of money and to decrease step by step the outstanding debt. But which is the best

frequency to ask for the loan installments? Does the client with weekly meeting repay the loan

faster than the women that provide less frequent installment? In addition, is a regular behavior a

pre-requisite or a condition for an on-time repayment?

In order to find out evidence about the first point, the research focuses on two microcredit

programs with different frequency in the loan installment: in the pure microcredit program the

client provides weekly amount of IRP, while in the mother’s bank the policy asks for monthly

deposits. Again, this difference in the loan repayment frequency can not be separated from the

difference in the social relations involved in the loan repayment because both programs disburse



individual loans but the Microcredit’s clients go for regular meeting where they can tie social

relationships important for motivation and support in the repayment.

It is expected to see a dependency of the performance to the level of coherence in the client

behavior with the program policy. In other words, the expected results between the repayment

period (in weeks) are:

Negative correlation with the Distance from Loan Repayment Regularity: as much as it

tends to 1 (regular repayment), as much it tends to have a regular repayment;

Negative correlation with the percentage of Standard Loan Installment: the higher the

percentage of loan installments that respects the policy, the lower the repayment period;

Negative correlation with the variances: the higher the variance, the poorer the

performance;

Negative correlation with savings: if a client can save a significant amount of money, then

she is able to repay on time because she does not face liquidity problems and she is

efficient in managing money.

The following chapters focus the attention on a description of the data collected, then on the

design of the database and finally on the statistic model implemented for the analysis.



CHAPTER 4 – DATA COLLECTION AND

DATABASE In this chapter the on-field research will be described, first exposing the collection method and the

collected documents, then the deduced information and their digitalization in an ad hoc database,

where the data were structured into variables useful for the regression analysis.

Before starting the data description it is fundamental to describe the selection process of the

clients involved in the research.

How we selected the banks:

1) Microcredit Program: we focus the attention on 2 branches out of 7 according to

logistic considerations (if the branch was easy to reach and at which frequency the

volunteers went there) and considering also communication issues (if the manager

spoke English or not). With the staff help we conducted a research for identifying

the clients that are mothers of a child sponsored by IIMC. For this purpose the CEOs

investigated for more than one week the groups, asking if any clients have this

characteristic and writing down the name if it was the case.

2) Mother’s bank: this program has no branches, but the responsible works in the IIMC

headquarter. With his help the needed data from the period selected were

collected.

How we selected the clients of each sample:

1) Microcredit Program: we selected those women who have a child in the

sponsorship program and asked for loans to the microcredit program instead of

mothers’ bank. In the beginning we did not have a list to be able to find them, so

during the four weeks on-field research the CEOs were asked to support the

information collection and we visited more than 100 groups of Chakberia and

Hatgacha Banks, asking members of each group if she was sponsored.

2) Mother’s bank: we inserted all the loans disbursed in the time period of April 2010-

July 2012, because the loans given after July 2012 can not be completely repaid at

July 2013.



4.1 IIMC MICROCREDIT PROGRAMS – PHOTO COLLECTION AND ORGANIZATION

During the one month’s mission in India (July 2013) we collected data and information on the

Microcredit programs of IIMC. The research was developed in two directions.

On one hand, the mission consisted in completing the previous work Mehrdad Mirpourian started

in November 2012: photos of the cash flow collection registers were taken for the two branches

already studied, Chakberia and Hatgacha, for the period November 2012-March 2013 and partially

for May-July 2013. Indeed, at now, the weekly savings and installments at single client level can be

examined only from the paper registers: a software is under implementation, hopefully ready to

be installed for the end of 2014. As a matter of fact, at the headquarter, the microcredit programs’

managers are responsible for checking and inserting the data of all the branches at group level,

not individual. Consequently the only option was to take photos and later digitalize them into an

excel file, creating a customer based database.

On the other hand, I dedicated time in understanding the microcredit service for the mothers of

sponsored children. For this purpose, according to the responsible’ s availability, I dedicated an

average two hours per day to collect the loans and savings data of the program, that are inserted

separately in two different ERP Tally9 software. In fact the procedure followed was the following

one: first take the customer loan installments from the dedicated ERP, then check in the loan

module requests register the linked number of client’s savings account and finally enter in the

other ERP and collect the data.



4.1.1 COLLECTED PHOTO - MICROCREDIT PROGRAM

The structure of the photos’ organization is the same defined in the previous thesis work: the

photos are organized into 2 main folders according to the 2 branches selected, Chakberia and

Hatgacha.

Then the criteria for the second level of division is the

following one: if in a group there is a mother whose child

receives scholarship from IIMC, this is classified as ‘sponsored’

and consequently inserted in the Sponsored folder;

otherwise, if none of the client has a sponsored child

according to the information of the CEO, the group’s photos

are collected in the Non-Sponsored folder.

In addition, for each group the photos are organized by register of one year of cash flow: for

example the folder 2009 contains the pictures of the sheets related to the period April 2009 –

March 2010.

For the branch of Hatgacha it was not possible to collect the data for the 2013 registers because of

the political situation of that part of the region: in fact the Microcredit Program Manager

suggested to postpone the visit after the local elections due to parties manifestations and

intimidation of the bank’s employees. Then the student was allowed to reach the branch in safe

conditions only the last week of the research period, with a consequent partial completion of the

data collection.

REGISTER DESCRIPTION (collection books)

As already explained, the time windows in the register starts from the first week of April and

finishes the last week of March; the collection book has more or less 30 pages, as it is shown in the

following lines.

Chart 4.1 Collection photo organization for Microcredit program



1ST PAGE

Image 4.1: 1st page of Microcredit Program Collection Book

Available Information on it:

Branch name: branch to which the group belongs; in our case it can be Hatgacha or

Chakberia branch;

Group name: the identification name which each group that belongs to a branch should

have;

Group number: the identification number which each group that belongs to a branch

should have and its value goes from 1 to 250;

Meeting place: the fixed place in which all the group members meet each other;

Day and Time of the weekly meeting;

Coordinator’s name: Name of the man or women who is responsible to visit the group each

week, and collect loan installments and savings.



2ND PAGE

Image 4.2: 2nd page of Microcredit Program Collection Book

It includes all the information described in the first page and also a table with the attributes of

status, name, signature and name of the C.O (field officer who collects installments and savings),

and name and signature of the four coordinators who help the C.O including president, secretary,

cashier, and one member of the group as a member representative.



3RD-4TH PAGES

Image 4.3: 3rd and 4th page of Microcredit Program Collection Book

Includes the ‘LOAN DISRBURSMENT STATUS’ that summarizes the group’s loans, writing down

Member’s name, husband-father’s name;

How many times: it indicates the number of loans the client has already received, including

her current loan;

Date of disbursement: the data in which the bank gave her the loan;

Loan size: By considering the interest rate (10%) of the loan; the value interval is between

2200 - 11000 for Chakberia while for Hatgacha is 2200 -16500;

Disburse number: the code, which identifies the loan, is unique within that year of that

branch. Each year (1st April) the code starts again from 1;

Business purpose: short description of the purpose in terms of business activity of the loan.



MAIN PAGES of the GROUP COLLECTION BOOK

Image 4.4: Main page of Microcredit Program Collection Book

Includes:

General personal information of the clients: the first 3 columns report the member’s

number in the group, her name and her husband/father’s name;

Opening balance: it is the monthly opening balance. It is divided into two

columns, Savings and Loan. Savings are the overall savings at the beginning of the

month, Loan is the outstanding loan;

Time information: the upper labels of the table represent the month, year, week and date

of meetings;

Meeting cash flow data: it is the cash flow registered in the meeting, with 3 main columns

that represent Savings installment, loan repayment installment and Withdrawals.

Closing balance: it is the meeting closing balance. It is divided into two

columns, Savings and Loan. Savings are the overall savings after the meeting, Loan is the

outstanding loan



FINAL PAGE with the STATEMENT OF INTEREST ON SAVINGS

Image 4.5: Final page of Microcredit Program Collection Book

For each client they write down

Min.Bal of “Month”: the table has one column for each month where there is the minimum

balance of the client;

Total: yearly total amount of savings in the register period, sum of the 12 monthly balances

(1st April -31st March);

Interest payable: it is the 4% of interest paid to the client (total amount of minimum

balance of the client/300)

IMPORTANT: for the year 2013 the register is uncompleted, because the images were collected in

the month of July 2013.



4.1.2 COLLECTED PHOTO – MOTHER’S BANK PROGRAM

During the experience in IIMC, we dedicated in average, according to the responsible availability,

one or two hours per day to collect data from the microcredit program designed for a precise

target of customer: mothers of sponsored children, IIMC volunteers, students. The attention was

focused only on the first type of clients in order to have the possibility to compare the two

microcredit programs. As specified previously, the mother's bank database is divided into two

software, managed only by one IIMC volunteer and by the program’s responsible, that kindly

supported the research. Even if the data were already digitalized, I decided to take pictures of the

screen for the following reason: first, the organization does not allow external people to use the

software, so I was depending on the program responsible; in addition, linked to the previous issue,

it is important to say that it seems he did not know how to export data in excel or maybe this last

point probably is also due to communication problem. Secondly the fact that I could not work

independently, resulted in an uncertainty related to the time the manager could dedicate to the

research. A third point is related to the database: the intermediation of the manager was helpful

also to identify the mothers among the clients and to discard the volunteers or the students.

Indeed this information is not available from the software but the manager selected the client one

by one knowing them personally.

The pictures are organized according to the two database of the program, one for savings account

(client base) and one for loans account (loan base), linked by the excel file

Loans_Savings_Accounts.

Charter 4.2: Collection photo organization for Mother’s Bank program

1) Folder DB Structure: photos that can help understanding the structure of the Mother’s

Bank Loan ERP, having taken the pictures of the main folders in which the data are

organized. It is important to notice that this division is finalized to see the debt allocation

across categories. For our analysis we focus the attention on the group of the Mothers, but

DB Structure Photo Folder

Loans_Savings_ Accounts Excel file

Loan_ module Photo Folder

Loan_ Installments Photo Folder

Loan with savings

Loan without savings

Savings Photo Folder



it is not possible to open the loan’s related data from this folders’ division which gives only

the aggregate data . In fact the loan’s accounts should be queried by number code, so it is

not possible to understand at which subgroup they belong.

2) Folder Loan_module: in order to match the loan account to the savings client account the

request module should be examined; in this folder you can see the photos of one example.

During this phase there were some problems due first to the language (some modules are

written in Bengali), second to the writing (the clearness of the document words was

sometimes so low that the help of the managers, author of the module, was request), third

the data incompleteness (in some cases the savings account was not pointed out) and

finally the correctness (in few occasions the number of the savings account reported in the

module was matched with a different name in the database, with a consequent lack of

consistency).

Image 4.6 Request model for Mother’s Bank program

3) Excel file Loans_Savings_Accounts: provides the correspondences between the two

numbers of the loans and the savings accounts. It is important to highlight that the name

of the client, most of the times, is not written in a univocal way because the Bengali

language has a different alphabet so the translation of the sounds varies between the

savings and the loan accounts. It is organized into four sheets. The first with the list of the

loans with the correspondent savings account collected, specifying also if it is defaulter or

Loan Code

Number

Savings Account

Number



not; in the third one you can see the loans accounts numbers collected but of whom I did

not have time to take the account number of savings and take the corresponding picture.

Maybe they can be deduced from the name of the client but it is difficult because there are

client with the same name. Notice that the first loan from the period considered (April

2010) has the account number 823. Finally the last sheet has only some notes about the

structure of the DB and corresponding interest rate for each category.

Table 4.1: Matching savings and loan account in Mother’s Bank program

4) Folder Loan_installments: photos of the loan accounts. They are organized into two

folders: one for the loans that do not have a the corresponding savings account

information due to a lack of time during the mission or because they started after July 2012

so they are not completely repaid at July 2013; finally the last one contains the loans with

the correspondent savings account collected.

Image 4.7: ERP database for Mother’s Bank program’s loan account

This picture provides the following information:

Loan Code: 962. It is the loan account number, sequential over the time in Tally9 software, independently to which category the client belongs.



Client Name: Anita Mondal. Because of a different alphabet in the Bengali language, the translation of the name is not unique. Consequently in a lot of cases the names on the savings account and on the loan accounts or also of 2 loans accounts are not the same. On the other hand there are clients with the same name, so it is important to check the request module in order to match the two accounts.

Sponsor country name: it (Italy). Name of the country from where the money to sponsor the mother child come from.

Sponsor Individual Code Number: 80. It is related to the person, from the sponsor country that sent the money.

Date: in the first column there are the dates related to the loan disbursement (first date, with a negative cash flow), loan installments (from the second date) and the date of complete repayment (the last one).

Particulars: this column gives the information of the nature of the installments. The client repays only in cash but in case of complete repayment within 5 months a discount is allowed, so it goes not under the category of cash but of discount.

Vch type – Vch No.: accountings details for the installments, registered as Vouchers.

Debit – Credit: it specifies the nature of the cash flow, if it is negative or positive for the loan account.

Current total: the sum of the debit values and the credit values. If they are the same

it means that the loan is completely repaid.

5) Folder savings: photos of the savings accounts. It is important to highlight that for one

account there can be more than one photo, according to the number of savings

installments the client has provided during the period April 2010- July 2013.

Image 4.8: ERP database for Mother’s Bank program’s savings account



This picture provides the following information:

Client ID: 3134. Savings account number, sequential over the time in Tally9 software looking at the moment the client enter in the program, independently to which category the client belongs.

Client Name: Maduri Chatterjee. As defined in the previous photo.

Date: in the first column there are the dates related to the withdrawals, savings installments and paid interests.

Particulars: this column gives the information of the nature of the installments. The client provides savings only in cash as also withdrawal’s case, while when IIMC adds the interest on the total amount of the account of the fiscal year; it goes not under the category of cash but of Interest (Today).

Vch type – Vch No.: accountings details for the installments, registered as Vouchers.

Debit – Credit: it specifies the nature of the cash flow, if it is negative (amount of the withdrawals) or positive for the loan account (Amount of each savings installments in cash and of the interest)

Opening Balance: 0. If the savings account was opened before the 1st of April 2010 (date from which we start the analysis) the opening balance is positive. While if the client enters the program after this date, the opening balance is zero.

Current total: 1547. The sum of the debit values and the credit values. If they are the same it means that the savings are zero.

Closing Balance: 1547. It is the amount of rupees in the accounts at July 2013.



4.2 DATABASE EXPLANATION OF THE IIMC MICROCREDIT PROGRAMS

The information collected in the photos described above were digitalized into an excel file. The

last version is an optimized evolution of different previous files, where step by step variables were

added in order to detect more accurately the programs characteristics evaluation. The actual file

comes from the merge of the database focused on Microcredit program and the one related to

Mother’s Bank: indeed the first one was developed with Mehrdad Mirpourian as he concentrated

the attention on the evaluation of that program. Then the data related to the second microcredit

program were digitalized and finally the two databases were merged.

The excel file BothDB contains the complete set of data for the microcredit programs’ comparison.

In all of them the first rows lines contains the data related to the Microcredit Program, while the

second set of rows, after a light blue line, refer to the Mother’s Bank Program.

Its structure consists of six sheets:

1) BothDB: the first sheet is the main one, containing both the data found in the IIMC’s ERP

and registers along with the derived computed variables, with the exception of the

monthly indicators (LRBi, LCRi, CSi) that can be checked in the second sheet, BothDB_SPSS.

It is client-base organized, as each row is filled with the customer loan data and also, if

available, the personal savings account data. In the following paragraph all the columns will

be explained.

2) BothDB_SPSS: the second sheet is designed in order to be easily uploaded in the SPSS

econometrics tool, depurated from those variables that are not relevant for the analysis.

Consequently it is loan based: each row contains the derived variables and information

related to one loan. As the main sheet also this page can be divided into 2 parts: the first

columns describe statistic indicators, loan characteristics and performance index, while the

second part is made by the weekly cash flows of the loan necessary for the variables

computation.

3) PreparVariance: this third sheet is functional to the variance’s and other indexes’

calculation. In fact it separately takes the loan installments, then the savings and finally the

withdrawals from the second sheet through a formula. As it can be noticed, the empty cells



are reported as zero, but this implies that the variance is computed considering null values

(no data available). For this reason an additional sheet was needed.

4) Variance&others: this fourth sheet allows the correct variance calculation, taking the data

from the PreparVariance and putting an X where the value is zero. In addition in this table

others variables are computed and then linked to the first and second pages. Finally the

first columns are designed in order to check that all the data inserted are consistent one

with the another.

5) Variance&others(2): it is similar to the previous one but here the loan installments and

savings deposits are aggregated by month in order to allow the computation of variables as

the monthly deposit median and its variance.

6) LoanInstVARs: this fifth sheet is necessary in order to compute variables related to the

loan installments, in particular the amount of loan repaid within the year or the number of

weeks necessary for giving back 70% of the loan.

7) LRegularity: this last sheet considers the loan installments, depurated from the null and

the ‘X’ values in order to compute the indicators related to the loan repayment regularity

and barycenter. In addition also the savings cash flows were reported for the computation

of the monthly cumulative savings indicators.



4.2.1 EXPLANATION OF THE COLUMNS

As already highlighted, the first page contains most of the variables computed in the excel file.

Consequently the description starts with its columns that will be reported one by one, while then

an additional subchapter is dedicated to the data reported in the second sheet.

In the following labels description, the string ‘xx-yy’ shows the time period code in the database:

the values go from 10-11 to 13-14. In particular 10-11 means that the data refer to the period

2010-2011. The reason why the annual period starts on April, 1st and ending on March 30th is the

Indian fiscal year that has this structure.

In addition the columns’ labels are in different colors in order to highlight if the information is

taken from the Group collection book (dark blue) or if it is a derived information not directly

reported in it (light blue).

Moreover some variables are related to one specific program and not to the other, so the

availability is specified with the following code:

{MB} when the information is available only for Mother’s Bank Program

{MP} when the information is available only for Microcredit Program

If not specified, the information is available for both the programs

CRITERIA: The main criteria for inserting the loans in the time period windows consists in looking

at the Date of Complete Repayment: if it falls between 01/04/2010 and 31/03/2011 (it means that

the information can be checked in the register 2010-2011) we put the loan data in the period

2010-2011 (label 10-11). There are two cases in which this criteria cannot be strictly followed

because the date of complete repayment is not available: when either it is too early for the loan to

be repaid at present or there is a default. In this case we consider the repayment due date, adding

one year to the disbursement date, and the loan is inserted in the corresponding time period.



Table 4.2: Excel columns shotscreen 1 (client info)

Client ID: Code that identifies the client in the database

o Mother’s Bank: if available, it is the savings account of the client because each mother

has her own savings account, while there could be more than one loan disbursed to

her. For the cases in which we have only the loan data, but not the savings account, in

this variable we inserted the loan code, anticipated by an ‘L’. In fact without savings

account we cannot know if the clients with the same name are or not the same person.

Consequently, in this last case, for each line there are data only for one loan.

o Microcredit Program: it is designed with the codes of the branch, the group and the ID

of the client in the group with the following excel function:

CONCATENATE(Branch Code;"_";Branch Group Number;"_";Client ID in the Group).

Program Code: dummy variable that takes value 1 if the woman asks loans in the Microcredit

Program, while it is 0 for those clients that repay the loans in the Mother’s Bank Program.

Client Name: name and surname of the client. In the Bengali alphabet, the name translation is

not unique. Consequently in several cases the names of the savings and loan accounts or also

of 2 loans accounts are not the same. On the other hand there are clients with the same name

but different savings accounts, so it is important to check the request module in order to

match the data.

Branch Name {MP}: branch to which the client belongs; only Hatgacha and Chakberia branch

were considered.

Branch Code {MP}: number linked to the Branch: the value 0 represents Chakberia, the 1

Hatgacha.


Branch Group Number {MP}: number of the group, which value goes from 1 to 250.

Client ID in the Group {MP}: personal number that identifies the client in the group; value

from 1 to 25 because in each group there are maximum 25 members.



Sponsor Country Name {MB}: name of the country that sponsors the child whose mother

received the loan.

Sponsor Individual Code Number {MB}: it is related to the person, from the sponsor country

that sent the money. Each state has its own list, so accordingly there can be two persons

associated to the same Sponsor Individual Code number but they are of different countries.

Table 4.4: Excel columns shotscreen 3 (Loan and repayment general data)

Loan Code xx-yy: it is the loan account number, sequential over the time in Tally9 software,

independently to which category (student, employee, mother, etc.) the client belongs.

Loan Size xx-yy: Amount of the loan, considering the interest rate (10%). The value interval is

[1100, 7700] for Mother’s Bank.

Loan Size without interest xx-yy: Loan size amount depurated from the interest of the 10%.

The value interval is [1000, 7000] for Mother’s Bank. The following formula was applied:

Loan Size without interest = (Loan Size)/1,1.

Number of times she received the loan xx-yy {MP}: it indicates the number of the loans that

the client received in the microcredit program until the period xx-yy. Consequently the year xx-

yy loan is the loan the client receives. It answers to the question: is it her 4th time, 5th time, or

it is her first time she receives a loan in the period xx-yy? The values were inserted looking at

the registers’ data. The range is [1; 11].

Disbursement date xx-yy: date on which the woman received the loan; the loans considered

were disbursed after April 2010. Only few loans disbursed after July 2012 are considered, in

order to have completely repaid loans.

Date of complete repayment xx-yy: date on which the client finishes all of her loans’

installments. In mother’s bank it is the date of the last individual installment while for the

Microcredit Program it is the date of the group meeting in which the client gives the last

installment.

Repayment period in weeks xx-yy: Repayment period between the disbursement date and the

repayment completion. Because the collection sheets in the Microcredit Program are based on

weekly data, we show the repayment period in number of weeks. Since we have in our

database the loan disbursement date and the loan complete repayment date, we calculate the

repayment period applying the following excel function, with D that gives the input data to

count Days:



RP = DATEDIF(Disbursement date; Date of complete repayment; "D")/7

The defaulters should be treated in a different way: when the loan is not completely repaid at

July 2013, the variable takes the following standard: DEFk where ‘k’ can take the values 0, 1, 2

according to the years since the date of disbursement of the loan. Indeed, if the client has

received the loan before July 2011 (therefore minimum 2 years have already passed) the index

is 2, while if the date of disbursement is between July 2012 and July 2011 (accordingly the

client has not already repaid after more than 1 year but less than 2 years) k is 1. Finally the

value 0 is for the loans that have been disbursed after July 2012, so that one year has not

already passed and consequently the clients can’t be considered defaulter because there is the

possibility that they completely repay the loan in the following months, within one year from

the disbursement.

Table 4.5: Excel columns shotscreen 4 (Number Loan Installments and Default Indicator data)

Number Loan Installments xx-yy: monthly number of installments that the client supplied in

order to completely repay the loan.

o {MB} Simply the number of installments provided by the client

NLI = COUNT(first loan installment : last loan installment)

o {MP} the real number of installment is adjusted in order to be comparable with the

data of the other program; the MB requires 11 monthly installments, while the MP asks

for 44 weekly installments, thus the mother’s bank value should be divided by 4

NLI = COUNT(first loan installment : last loan installment) /4

Percentage NLI xx-yy: number of installments that the client supplied in order to completely

repay the loan compared to the policy requirement of 11 installments.

NLI = NLI/11

Abs (NLI-11)/11 xx-yy: indicator of the distance from the policy requirement in terms of

number of installments that the client should supply in order to completely repay the loan.

=( NLI—11)/11



Table 4.6: Excel columns shotscreen 5 (Savings variables)

Defaulter Indicator xx-yy: this variable assumes the value 0 when the client repaid the loan

within the year (Repayment period shorter than 52.14 weeks), while the value 1 is assigned

when the payment completion arrived later than 365 days or in the case of a default.

IF(RP<52.143, 1 , 0)

Number of savings Deposits xx-yy: number of savings deposits that the client supplied during

the repayment period. It is calculated taking into consideration the third sheet

(‘Variance&other’) considering only the time window of the repayment. Consequently the with

the formula:

COUNT(first-last savings deposit)

Savings Amount in the RP xx-yy: total amount of rupees deposited in the savings account

during the repayment period. It is the sum of the single savings installments, computed in the

third sheet and reported both in the second and in the first page of the database.

SUM(first-last savings deposits)

Savings Deposits Median xx-yy: median of the savings deposits supplied in the savings

account, taking into consideration the time window of the repayment period and only the

savings installments whose value is not null.

MEDIAN(first-last savings deposits)

Variance of Savings Deposits xx-yy: statistic measure of the variability of the deposits in the

repayment period; variance of the savings installments computed taking the related not null

values in the repayment period. It is calculated with the excel formula:

VAR.POP(value1; … ; valueN).

Savings Mean per month in RP xx-yy: average monthly amount of the rupees saved in the

repayment period.

[(Savings amount in the RP)*365]/(RP*12*7)

Monthly Savings Deposits Median xx-yy: median of the aggregate monthly savings deposits

supplied in the savings account, taking for each deposit the total sum of savings provided in

one month. The deposits are computed in the sheet Variance&others(2).

MEDIAN(first-last monthly savings deposits)



Variance of Monthly Savings Deposits xx-yy: statistic measure of the variability of the monthly

savings deposits in the repayment period; as in the previous variable, the computation is done

considering as monthly deposits the total amount of rupees supplied in one month. The

deposits are computed in the sheet Variance&others(2).

VAR.POP(value1; … ; valueN).

Table 4.7: Excel columns shotscreen 6 (Loan installments variables)

Loan Installments mean per month xx-yy: average monthly amount of the loan installments in

the repayment period.

[(Loan Size)*365]/(RP*12*7)

Standard Loan Installments Amount xx-yy: amount of rupees that the client should pay for

each installment, monthly or weekly, equal to the

o loan size/11 for Mother’s Bank

o loan size/44 for Microcredit Program

Percentage Number Standard Loan Installments xx-yy: number of installments that the client

paid with an amount equal to the standard loan installment over the total number of loan

installments. It is an index of the degree of the client’s compliance to the program policy.

COUNTIF(1st installment : last installment; SLI)/ NLI

Loan Installments’ Median xx-yy: median amount of the installments supplied in order to

repay the loan.

MEDIAN (first week installment amount: last week installment)

Variance of Loan Repayment Installments xx-yy: statistic measure of the variability of the

installments in the repayment period. Variance of the loan repayment installments computed

taking the related not null values in the repayment period. It is calculated with the excel

formula:

VAR.POP(value1; … ; valueN)



Table 4.8: Excel columns shotscreen 7 (Regularity variables and outsstanding balance)

Period to repay 70% loan xx-yy: number of weeks the client takes in order to repay the

amount of rupees equal to the 70% of the loan. The number is manually identified in the

LoanInstVARs sheet, having first inserted a row that represents the cumulative amount of loan

repaid, and then a second functional row with the following formula

IF(CumulativeRepaidLoan>=(0,7*LoanSize), COUNT(first week : this week), 0).

Consequently in this row the values were null until reaching the 70% of the loan size.

In some cases this variable can not be computed because the client is a defaulter and she has

never reached the 70% of the loan amount in the repayment.

Taking the first number not null it is necessary to adjust it because the register has 5 weeks per

month. So this value should be modified dividing by 5 and multiplying by 52/12 (weeks per

month).

Period to repay 50% loan xx-yy: the same concept of the previous variable but considering the

50% of the loan amount repaid thus inserting 0,5 in the formula instead of 0,7.





LRB xx-yy: Loan Repayment Barycenter is an indicator of the distribution in time of the loan

installments repayment. From the last sheet this index is computed with the following

formula: 1 1

1

T T

t t

t t

T

t

t

i t i t

LRBL

i

if regular repayment:

11 11

1 1

1

1 11(11 1)11 116

11 2

( 1)

2

t tOnTime

N

n

L Lt t

LRBL L

N Nn



it : loan installments repaid in month t; ∑ it= L

t : index of the month that goes from 1 to

T : time instant of the last loan installment and of the complete repayment;

L : Loan amount, with interest; it’s the amount due by the client;

L/11 : regular loan installment.

Charter 434: Loan Repayment Regularity codification along the months

LRR xx-yy: Loan regularity index, calculated from the previous index, highlights how regularly

the client repaid the loan, if in delay or not. If the client is a defaulter this variable can not be

computed. The formula applied is the following:

1

6

T

t

t

i t

LRRL

= LRB/6

o If the client repays the loan earlier (greater repayments earlier) LRR<1

o if she repays later (greater repayments later) LRR>1

Table 4.9: Excel columns shotscreen 8 (Monthly loan installments variables)

Median Monthly loan installments: median of the loan installment calculated in each month.

The MB program has already monthly data but for the MP the weekly information is summed

up in the page Variance&Others(2).



MEDIAN (first: last monthly installment amount)

Median (Loan installment-SLI): median of the difference between the monthly loan

installment and the Standard loan installment amount, taken in absolute value. In the

Variance&other sheet, from each monthly loan installment the SLI amount is subtracted and

the absolute value registered. Then the values are used for the computation of this variable

and of its variance.

MEDIAN (first: last ABS(monthly installment amount- Standard loan Installment amount))

Var Monthly loan Installments: statistic measure of the variability of the monthly loan

installments in the repayment period.


Var(Loan Installment-SLI): statistic measure of the variability of the differences between the

loan installments and the standard loan installment amount.


Business purpose xx-yy {MP}: in order to receive a loan, the client should motivate it with a

business aim. This variable is codified in 6 categories that in a second step will become 6

dummy variables. They represent the following activities:

o 1= fishing business

o 2= paddy and rice culture

o 3= vegetables culture

o 4 = clothes’ business

o 5= different shops categories

o 6 = remaining categories.

Outstanding Savings: amount of rupees in the savings account at April 2010. If the savings

account was opened before the 1st of April 2010 (date from which we start the analysis) the

opening balance is positive. While if the client enters the program after this date, the opening

balance is zero.

Outstanding Debt: amount of rupees of the outstanding debt at April 2010.

Closing Balance xx-yy: amount of rupees in the savings account at July 2013.



Table 4.10: Excel columns shotscreen 9 (Cash flow digitalization)

Weekly data: starting from an analysis of the data available, we decided to design the

database with a weekly structure in order to better and more precisely represent the cash

flows available. There are totally 501 columns, corresponding to the number of the columns of

the Group collection books that can have been filled in to describe the meetings cash-flow.

For the Mother’s Bank Program the installments are monthly but it happens that in one month

the clients withdraws or puts savings in the account more than one time. In addition the cash

flows also include the interest paid by IIMC.

In the Microcredit program it happens that some groups meet 5 times in a months, while

others 4 or 3: in order to indicate that the meeting did not take place, in the cell of the

corresponding weeks there are not values, but an ‘x’ to show that there was not any meeting

in those dates. The value 0 indicates when there was a meeting but the client did not withdraw

or save or pay any amount of money. For Microcredit program the interests in the past were

not paid at fix dates, while for the last year we can notice that all the savings account have the

interests paid at the 31st of March.

For each column you can see also a code composed by 3 numbers, [A_B_C code] which help us

to detect the data and tell us if it shows a saving, a withdrawal or an installment. It tells us in

which week, and which month of each year the cash flow occurred. The codification is

explained clearly in the following lines:

o A:

If A=1 then the values in that column represent savings.

If A=2 then the values in that column represent withdrawals. As the Microcredit

policy states, clients can withdraw only if they do not have an outstanding loan

or if the withdrawal is used to complete the loan repayment.

If A=3 then the values in that column represent loan installments. Negative

values in that column show the loan disbursement, while positive values are the



amount of money that the client gives back to the bank in order to pay back the

loan. Moreover if a cell in that column is highlighted in yellow, this means that a

loan has been disbursed or fully repaid in the week.

If A=4 then the column represents the outstanding loan at the end of the

month. As in the previous case, if a cell in that column is in yellow this indicates

that the loan has been disbursed or fully repaid in the month.

o B: it codifies the year, 2010 (B=1) to 2011 (B=2) and 2012 (B=3). Notice that the last

year is not completed due to the date of the data collection (November 2012), so we

have all the tracks since the second week of Nov 2012.

o C: it represent the number of register’s columns, starting from C=1 first week of the

register year (first week of April 2010) to 157, last week recorded (second week of

November 2012)

LEGENDA:

X = case in which the information does not exist. For example in the weekly data, if the

group did not meet in the 4th week of April, the cells related to that week will have this

symbol ‘X’. it is important to notice that, if the group meets but the client account has no

cash flow, the values are 0.

NA= Not Available. It means that the data exists but we do not possess it. For example if

the loan started before April 2009, we can find the data about the disbursement date in

the register 2009-2010, but we can not reach the information concerning the Savings at

the beginning of the repayment period (at the disbursement date) because the information

is written in the register of 2008-2009, not available. Consequently also the variables that

are calculated from value of this type (not available) are also classified NA because they

can not be computed.



4.2.2 ADDITIONAL VARIABLES IN THE Mother’sBankSPSS SHEET

To avoid overloading the first page of the database, the monthly variables were reported only on

the second sheet, the one that is inserted in the SPSS software for the econometric analysis.

The additional variables to be explained are the following one:

LRBT with T= 0, …, 11: loan repayment barycenter at the T-th month.

1

1

T

t

tT T

t

t

i t

LRB

i

= [weighted sum of the loan installments with the number of month in

which it is provided]/[cumulative sum of the loan installments until

month i]

Charter 4.4: Loan Repayment Barycenter codification along the months

For calculating this variable, we calculated for each month the total loan installment provided

by the client. As logical, for Mother’s Bank it simply consists in the monthly loan installment

while for microcredit we took the sum the weekly installment in one month. Having for each

month 5 columns it is the sum of 5 values.

All the loan installments are considered as made at the beginning of each period, so that for

instance, the repayment of the 6th installment, which has to be made in the 7th month after

the loan disbursement, is considered as made in t=6. In the 12th month after the loan

disbursement the client has to repay the 11th installment, which is then considered as made in

t=11.

If the repayment is regular, with equal loan installments repaid for t=1,...,11, we have that the

barycenter of the repaid installments is in t=1 when the first installments is repaid, in t=1,5

when the second is repaid (t=2), in t=2, when the third is repaid (t=3), and so on.



Table 4.11: Loan Repayment Barycenter values along the months

Finally, when the mother gives the 11th installment, the barycenter is on t=6.

LRRT with T = 0, …, 11: loan repayment regularity at T-th month. It is calculated taking the

LRBT divided by the relative following indicators of a regular repayment:

Table 4.12: Loan Repayment Regularity values along the months

If the value is higher than 1 it means that the repayment barycenter is moved on the right of the

time line, that is the client repaid more in the last installments; in contrast if the value is lower

than 1 it means that the barycenter is before the right point in the time line, that is the client

repaid more in the first installments. Again, these indicators tell how the client is repaying, but not

how much she has repaid.

Thus this indicator has to be associated with the following one that provides the repaid

percentage of the loan. In fact it can happen that the barycenter LRBk is actually on the right

month, but the mother hasn’t already provide enough rupees to be considered a correct payment

from the policy point of view and this is shown by the value of the CRLk.

CRL z with z = 0, …, 11: Cumulative Repaid Loan is an indicator that gives the percentage of the

loan amount already repaid at the month z from the date of disbursement.

CRLz = SOMMA(installment month 0: installment z)/Loan Size

For the Mother’s Bank and for a regular repayment it is (since each month, starting from the

second, the client has to repay 1/11 of the loan):

CRL0 CRL1 CRL2 CRL3 CRL4 CRL5 CRL6 CRL7 CRL8 CRL9 CRL10 CRL11

0,00 1/11= 0,09

2/11= 0,18

3/11= 0,27

4/11= 0,36

5/11= 0,45

6/11= 0,55

7/11= 0,64

8/11= 0,73

9/11= 0,82

10/11= 0,91

11/11= 1,00

Table 4.13: Cumulative Repaid Loan values along the months

t=1 t=2 t=3 t=4 t=5 t=6 t=7 t=8 t=9 t=10 t=11

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00

T: t=1 t=2 t=3 t=4 t=5 t=6 t=7 t=8 t=9 t=10 t=11

LRRT,indir 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00

LRRT LRB1/

1

LRB2/

1,5

LRB3/

2

LRB4/

2,5

LRB5/

3

LRB6/

3,5

LRB7/

4

LRB8/

4,5

LRB9/

5

LRB10/

5,5

LRB11/

6



ISSUE for CRL11:

It should be noticed that, because of the database design, 5 loans are considered defaulters

(Defaulter Indicator equal to 0) but the coefficient of the cumulative loan repaid in 12 months

is 1 (100% of the amount repaid). Their repayment period is lower than 52.72 week, meaning

that the delay consists in maximum 4 days: these exceptions are due to the fact that the cash

flow is organized by weeks, not allowing inserting the installment into a cell that refers to a

day. Consequently it can happen that the first week inserted for the coefficient computation

does not start from the day after the disbursement day, but from maximum 6 days after.

In fact, for example, if the disbursement date is the 1st of November, the loan disbursement is

inserted into the first week of the month (from 1st to 7th day) while the cash flow analysis

considers the time window of 52 weeks from the following one, that goes from the second

week of November (8th-14th day) to the first week of the same month of the following year (1st

-7th day). If the loan is repaid the 3rd of November of the following year, the last installment

falls into the last week considered, resulting a repayment period of 52, 29 weeks and a

complete loan amount repaid at the end of the 52th week (7th of November).

This results in an inconsistency between the Defaulter indicator (DI) and the Cumulative Loan

Repaid in 12 months (CLR11) for 5 cases. But on the other hand this allows signaling a small

delay in the repayment completion.

Only one manual adjustment was done for the case 1373: in fact the mother repays with a

delay of 2 days, resulting in a DI equals to 0 and in a CLR11 equals to 0.45; but, considering that

all the cases with lower RP and 2 cases with directly higher RP have CRL11 equal to 1, we

decided to anticipate the 100% repayment at the 12th month, so that the CRL11 is equal to 1

when RP < 52.75 with no exception

CSz with z = 0, ..., 11 : Cumulative Savings amount is the total amount of savings deposited in

the client account from the date of disbursement to the month z.

CSz = SOMMA (total savings amount at month 0: total savings amount given in month z)

Distance abs (LRR-1) : distance in absolute value between the actual loan repayment regularity

index and the perfect LRR (1)

ABS(LRR-1)



4.2.3 EXPECTED RESULT

After having introduced the research questions and the variables we are going to use as

predictors, we zoom the attention on the variables that should have been adapted for the

programs comparison in order to put in evidence the reasons under them and the consequent

expected results.

In particular there was the need for the conversion of the variables to the same frequency

schedule, consequently the weekly variables in the Pure Microcredit Program have been

converted into monthly data are for Mother’s Bank Program.

For example, looking at the number of loan installments in the repayment period and their

amounts, for the Pure Microcredit Program the clients meet in weekly meetings so it is expected

to provide 44 installments in one year and with installment size equals to Loan size divided by 44.

On the other hand, for Mother’s Bank Program the policy requires 11 installments of higher

amount in 12 months, thus the amount of them should be Loan size divided by 11. If all the data

were left as they are, the possible revealed relationships would not refer to the difference in

performance of the client while on the difference in the policy design, as in this example the loan

installment amount in average is higher in Mother’s Bank Program than in Pure Microcredit

Program, but considering the monthly amount this difference may be reconsidered.

Moreover, considering the relation between the loan size and the number of loan installment, if

this last variable is not transformed, a significant correlation among them can signal that a

program policy pushes higher loan size then the other. Conversely, if the variable refers to the

month for both projects, a positive Pearson correlation coefficient may signal evidence that the

higher the loan size, the higher the number of loan installment needed, independently from the

program.

The main variable considered is the repayment period (RP), and its relationship with the program

(PC) and with the number of loan installments (NLI). The possible results obtainable by such an

analysis are the following ones:



SIGN correlation RP- PC

SIGN correlation RP-NLI

INTERPRETATION

NO NO The performance of the client is independent from the

program and their frequency type, and from the total number

of times the client visit the bank or meets the group.

NO YES The difference in the two programs performance is not

statistically significant so the repayment frequency in the loan

installment is not important but the total number of

installment impacts on the performance: if the correlation is

negative it means that the faster repayments are those with a

low number of installments; if it is positive it means that

repaying in few installments causes a worse performance than

the repayment with higher loan installments. In this case also

the repayment regularity and additional parameters can give

useful information.

YES YES This is the most tricky case, in which one program performs

better than the other, and in addition it seems that the

repayment period depends on the monthly frequency

indicator. In this case it is important both to look at the sign of

the relationships and then the regression results also for the

other variables.

YES NO One program has better performance than the other but this is

due only to the different in the policy and not because of the

adherence to the required frequency rules.

Table 4.14: Analysis of the possible results in the relationship within Repayment Period, Program

Code and Number of Loan installments

Another implicit microfinance principle is the one for which poor clients should be followed in

their repayment not only in terms of frequent meeting but also designing programs where the

effort for the loan repayment is spread all along the repayment period, with the requirement of

constant micro installments.



Thus the number of standard loan installments predictor is inserted: if it has negative beta it

means that the more the clients repay as IIMC requires, the lower the repayment period, thus the

better the performance.

The model aims to assess if a constant behavior in terms of amount of loan installments, savings

deposits and repayment barycenter impacts the performance of the client in terms of repayment

period.

If this principle is confirmed, this may suggest the need of the poor to be supported along the

repayment period, investing time and resources on their training and education. But if the result

of the research reports that the number of weeks a client needs for completing the repayment is

not related to a regular behavior, this may be a peculiar finding to be further explored in order to

manage more efficiently operations.

In this context, indicators of the regularity of the cash flows are analyzed and inserted in the

regression to evaluate their impact on the main dependent variable, the repayment period. They

are also supported by parameters related to the savings so that there is the possibility to see if the

additional effort to save money in parallel of loan installments is a peculiarity of a good

performance.



CHAPTER 5 - PERFORMANCE

COMPARISON MODEL OF 2 MICROCREDIT

PROGRAMS In this chapter the sample on which the model is based will be described and analyzed, from the

outliers’ exclusion process to the general statistical analysis and Pearson Correlation coefficient

explanation. Then the regression is computed and the results are explained, along with additional

test useful in order to demonstrate and better assess the conclusions we arrived at.

5.1 OUTLIERS’ EXCLUSION

An outlying observation, or outlier, is an observation that is significantly different (either very

small or very large) in relation to the observations in the sample. The inclusion or exclusion of such

an observation, especially if the sample size is small, can substantially alter the results of

regression analysis (Gujarati2011). It can seriously bias or influence estimates that may be of

substantive interest, or negatively impact on the assumptions of a statistical test. For these

reasons it is fundamental to deal with them properly in order to improve statistical analysis.

The assessment of outlying observations involves the most relevant variables while heterogeneity

in the population for the control variables is not particularly detrimental for the analysis purposes,

even if its evaluation can provide interesting insights. Therefore the level of strictness chosen for

each variable in evaluating the outliers varies according to the relevance and significance of its

value.

Moreover the selection of the detection method influences the expected outliers’ percentage, as

also the sample size or distribution type of the data do: there are two kinds of outlier detection

methods, the formal tests and the informal tests, respectively called tests of discordance and

outlier labeling methods.

The first type mainly needs test statistics for hypothesis testing: it is powerful under well-behaving

statistical assumptions such as distribution one, but in most of the cases, (such as our research),

the type of distribution is unknown.

On the other hand, most outlier labeling methods (informal tests) generate an interval or criterion

for outlier detection instead of hypothesis testing, and any observation beyond the interval or

criterion is considered as an outlier: if the purpose of the outlier detection, as it actually is in our



case, consists in a preliminary step mainly to find the extreme values away from the majority of

the data regardless of the distribution, the outlier labeling methods may be applicable.

The most commonly used and ease informal methods for detecting outliers are the Standard

Deviation (SD) and the boxplot.

The SD method can be applied in different ways, depending on the needed degree of strictness (k):

it builds a value range from the statistic parameter of the mean (µ) and the standard deviation (σ)

that consists in [µ-kσ; µ+kσ] within the valid observations fall. The “4-sigma region” (µ±4σ)

includes 99.99% of the values for a normal distribution and 97% for symmetric unimodal

distributions and even for arbitrary distributions it includes 94% of the values (Sachs, 1982)

Considering the features of the program, in which the performances of the clients can vary a lot,

we selected the 4SD level that will be also supported by a graphic method of the box plot, about

which a preliminary introduction of it is necessary in order to interpret the graphs.

The boxplot which was developed by Tukey (1977) is helpful since it makes no distributional

assumptions nor does it depend on a mean or standard deviation. The lower quartile (q1) is the

25th percentile, and the upper quartile (q3) is the 75th percentile of the data. The inter-quartile

range (IQR) is defined as the interval between q1 and q3. Tukey defined

inner fences = [q1-(1.5*iqr) ; q3+(1.5*iqr)]

outer fences = [q1-(3*iqr) ; q3+(3*iqr)]

Image 5.1: Graphic representation of the Boxplot method

The observations between an inner fence and its nearby outer fences are considered “outside”

(possible outlier), and anything beyond outer fences is “far out” (probable outlier).



In the following pages the results of these methods’ application are shown.

First 2 sets of variables are the loan related one, then the last table and box plots refer to the

savings predictors. Highlighted in red there can be noticed the cases evaluated as outliers.

The box plot methodology is applied when the SD method suggests the presence of outliers, in

order to verify if it is necessary to delete all the observations detected or only a subgroup.

Image 5.2: Box plots representation of the variables Repayment Period, Number of loan

installments and Loan installments mean per month

Loan size

Repayment period in weeks

Number Loan Installments

Loan Installment mean per month

Standard Loan installment amount

Percentage Number Standard Loan Installment

Loan installments Median

Variance in Loan Installments

N 320 320 320 320 320 320 320 320

Mean 5675,22 47,78 8,83 531,09 283,90 ,78 541,05 103401,95

Std. Error of Mean

204,32 0,44 0,10 19,49 8,95 0,01 21,29 19543,56

Median 4400,00 48,86 9,00 444,19 250,00 0,87 500,00 14400,00

Mode 2200 49,00 11,00 193.96a 300,00 1,00 500,00 0,00

Std. Deviation

3655,08 7,94 1,87 348,65 160,04 0,23 380,89 349605,91

Variance 1.34 E+7 63,00 3,51 1.22 E+5 25612,40 0,05 145080,11 1.22 E+11

Minimum 1100,00 19,57 2,00 85,14 25,00 0,00 100,00 0,00

Maximum 16500,00 86,43 12,00 1960,45 800,00 1,00 3000,00 4.38 E+6

Mean-4SD -8945,09 16,03 1,34 -863,52 -356,26 -0,15 -982,52 -1.29 E+6

Mean+4SD 20295,52 79,53 16,32 1925,70 924,05 1,72 2064,63 1.50 E+6

outliers 988, 1085

1_83_14_9, 1_83_14_8

1_80_10_8, 1_49_7_8

Table 5.1: 4SD values for loan related variables (1st part)



Being the repayment period the most important variable, the 4SD method is strictly applied, thus

the two outliers (case 988 and 1085) are deleted from the sample. For the other loan related

variables, the box plot is verified and in addition the 3SD interval calculated in order to see which

observations can be considered outliers for more than one variable. In addition, for each variable

the main statistic parameters are shown in the table, with the range calculated with the SD

method and then the box-plot graph is inserted for the critical predictors are reported.

Image 5.3: Box plots representation of the variables of variance in monthly loan installments and

variance of (Loan Installment-Standard Loan Installment

Period to repay 70% loan

Loan Repayment Regularity

Median Monthly loan installments

Median (Loan Intallment-SLI)

VAR.P Monthly loan Installments

VAR.P (Loan Intallment-SLI)

N 320 320 320 320 320 320

Mean 38.65 5.90 496.83 104,23 218193.38 160054.47

Std. Error of Mean

0.40 0.07 16.68 9,14 35792.87 25726.20

Median 37.27 5.73 400.00 0,00 35555.56 25590.28

Mode 34.67 5.00 500.00 0.00 0.00 0.00

Std. Deviation

7.21 1.19 298.43 163.51 640282.24 460204.29

Variance 52.00 1.41 89058.89 26735.39 0.41E+12 0.21E+12

Mean-4SD 9.81 1.16 -696.88 -549.81 -2.34 E+6 -1.68 E+6

Mean+4SD 67.50 10.64 1690.54 758.26 2.7 E+6 2.00 E+6

outliers 0_115_3_1

1_80_10_8, 1_49_7_8, 0_22_11_5 0_142_13_5 1_132_21_6

1_80_10_8, 1_49_7_7 0_22_11_5 0_142_13_5

Table 5.2: 4SD values for loan related variables (2nd part)



Analyzing then the savings related variables, the situation is homogeneous: mainly all the variables

have one outliers (case 970), but also the observation 1232 is considered dangerous for the

analysis results, thus it is also deleted.



Table 5.3: 4SD values for savings related variables

Number Savings Deposits

Savings Amount in the RP

Savings Deposits Median

Variance in Savings Deposits

Savings Mean per month in RP

Monthly Savings deposit Median

VAR.P Monthly Savings Deposits

N Valid 283 283 283 283 283 283 283

Missing 37 37 37 37 37 37 37

Mean 7.45 600.82 84.33 108752.69 56.09 86.22 109874.98

Std. Error of Mean

0.20 50.06 17.99 89527.42 4.62 18.51 89491.32

Median 8.50 404.00 50.00 80.44 39.07 50.00 299.00

Mode 3 391 40.00 0.00 33.67 50.00 0.00

Std. Deviation

3.29 842.14 302.70 1506084.33 77.70 311.45 1505476.94

Variance 10.81 709192.60 91627.01 2.27 E+12 6037.33 97000.43 2.27 E+12

Minimum 1.00 7.00 7.00 0.00 0.67 7.00 0.00

Maximum 13.75 10012.00 5006.00 24940036.00 900.98 5006.00 2.49 E+7

Mean-4SD -5.71 -2767.73 -1126.47 -5915584.64 -254.71 -1159.58 -5.91 E+6

Mean+4SD 20.60 3969.36 1295.13 6133090.01 366.89 1332.01 6.13 E+6

outliers 970, 1232 970 970 970 970, 1232, 1216 970 970



Image 5.4: Box plots representation of the savings related variables

In conclusion the observations excluded because considered outliers are 8 out of 320.

In the next section a general statistical analysis of the variables will be exposed.



5.2 GENERAL STATISTIC ANALYSIS

In this section statistic parameters will be evaluated for each variable, in particular special

attention will be given to the relationship of the predictors with the Program Code and the

Repayment Period variables.

This part is divided into 3 subsections: the first one considers the loan related variables, the

second moves to the ones based on the savings cash flows and finally the last analyzes the

monthly parameters.

5.2.1 LOAN RELATED VARIABLES ANALYSIS

The first set of variables considered are those described in the following table.

Table 5.4: Descriptive statistic parameters of the main research variables

The sample has majority of representatives (178

observations) from the Mother’s Bank Program (Program

Code = 0) as the median, mode and the percentage

(57.05%) in the pie show.

Charter 5.1: Program code

percentage pie chart

11,5

Program

Code

Repayment

period in

weeks

Defaulter

Indicator

Loan size Number Loan

Installments

N Valid 312 312 312 312 312

Missing 0 0 0 0 0

Mean .43 47.53 .79 5580.99 8.90

Std. Error of

Mean

.028 .428 .023 199.151 .101

Median .00 48.71 1.00 4400.00 9.00

Mode 0 49.00a 1 3300a 11

Std. Deviation .496 7.558 .404 3517.711 1.785

Variance .246 57.128 .164 12374293.222 3.185

Range 1 52.43 1 15400 10

Minimum 0 19.57 0 1100 2

Maximum 1 72.00 1 16500 12



The repayment period’s parameters demonstrate a general

trend to repay on time: as also it can be noticed from the

histogram, the median falls under the 52 weeks, and even

the mean and mode values are under the year target. This

fact is confirmed by the mean of the default indicator

whose interpretation is that the 79.5% of the loan are

repaid within the year (Default Indicator equals to 1).

Charter 5.2: Frequency Histograms of the Repayment period alone and split in the two Program

Code.

The histogram of the repayment period variable distribution is shown disjointedly for the two

programs.

Evaluating the different programs separately, the repayment period mean in the Mother’s bank

program (48.30) is higher than in the Pure Microcredit program (46.52 weeks) with the range in

the first case of 52 weeks while in the second is 39. However the mother’s bank program has

homogeneous values for mode and median, while the Pure microcredit program has 2 weeks

more for the median (48.39 weeks) and additional 2, 5 weeks more for the mode (50.71). This

means that the Mother Bank program has in average a worse performance in terms of repayment

period but and in addition the variability is higher compared to the Mother’s bank clients’

performance.

Default

indicator

Frequenc

y

Percent

Valid

0 64 20.5

1 248 79.5

Total 312 100.0

Table 5.5: Default Indicator

frequency and percentage



From this fast analysis it is demonstrated that the

mother’s Bank program have in average worse

performances but with lower variance in the

repayment period of the clients thus the

observations are concentrated on the central part of

the distribution. On the other hand the pure

microcredit program performs better by looking at

the mean but the distribution is shifted on the right

with highest values of the repayment period.

The repayment period is also interesting to compare with the variables that consider the time to

repay the 50-60-70 and 80%. The values are inserted in the following tables and represented with

a graph.

Table 5.6: Descriptive statistic parameters of the set of variables Period for repaying XX% loan for

Mother’s Bank program

Microcredit Program

RP 50% loan RP 60% loan RP 70% loan RP 80% loan Repayment Period

N 134 134 134 134 134

Mean 28,23 33,79 37,89 41,96 46,52

Median 26,87 32,50 36,83 41,60 48,29

Mode 25,13 32,93 34,67 42,47 50,71

Std. Deviation 6,108 6,722 7,027 7,206 7,21

Range 40,73 39,00 42,47 45,93 39,00

Minimum 13,00 16,47 16,47 17,33 22,00

Maximum 53,73 55,47 58,93 63,27 61,00 Table 5.7: Descriptive statistic parameters of the set of variables Period for repaying XX% loan for

Microcredit program

Repayment period in weeks

Mother’s Bank

Program

Pure Microcredi

t Pr.

N Valid 178 134

Missing 0 0 Mean 48.30 46.52 Std. Error of Mean

.580 .623

Median 48.93 48.29 Mode 48.14 50.71 Minimum 19.57 22.00 Maximum 72.00 61.00

Table 5.4: Repayment Period statistic parameters in the two programs

Mother's Bank Program

RP 50% loan RP 60% loan RP 70% loan RP 80% loan Repayment Period

N 178 178 178 178 178

Mean 29,63 34,54 38,91 42,71 48,29

Median 28,60 32,93 37,27 41,60 48,92

Mode 27,73 32,07 34.67b 39,87 48,14

Std. Deviation 6,05 6,99 7,22 7,31 7,742

Range 41,60 44,20 44,20 43,33 52,43

Minimum 11,27 15,60 15,60 19,93 19,57

Maximum 52,87 59,80 59,80 63,27 72,00



Consistent with the general behaviour detected in the analysis of the repayment period, also in

these parameters the Mother’s Bank borrowers have higher values comparing to the Pure

Microcredit Program, signaling a constant trend from the percentage of 50% of loan size, to be in

delay with the repayment.

In the graph the mean of the

predictors are plotted separately

for the programs. also here the

mother’s Bank blue line

demonstrate that has a slightly

delay in the repayment of the

different percentage of loan size

comparing to program code 1.

Charter 5.3:Representation of the mean of Period for repaying XX% loan split in the two programs

The next variable to be considered is the Loan size: the distribution’s histogram suggests that the

clients ask for (or the responsibles prefer to provide) not very high amount of loans.

Charter 5.4: Frequency histogram of the Loan Size

Loan size

Frequency Percent Cumulative Percent

1100 11 3.5 3.5 1620 1 .3 3.8 2200 53 17.0 20.8 2750 7 2.2 23.1 3300 54 17.3 40.4 4400 36 11.5 51.9 5500 54 17.3 69.2 6600 17 5.4 74.7 7700 13 4.2 78.8 8800 20 6.4 85.3 9900 1 .3 85.6 11000 28 9.0 94.6 13200 8 2.6 97.1 14300 1 .3 97.4 16500 8 2.6 100.0 Total 312 100.0

Table 5.8:Frequency of the Loan size categories



Even if the mean is higher than 5500 rupees, both the mode and the median falls under this

threshold.

In addition the distribution of this predictors in the two programs is not equal: the clients of the

Mother’s Bank access in average to lower amount compared to the women of Microcredit

Program .

Moreover the loan size categories are evaluated by analyzing the mean of the repayment period

within them. The histogram shows means’ values from 42 to 52 weeks, where a particular

behaviour is detected in the 2,750 rupees loans that perform very well, while the 14,300 category

seems to perform worst than the other (but within the year); however it is represented by only

one observation, thus it is not significant.

Same consideration can be done for the 1,620 and 9,900 rupees loans, not well represented in the

sample.

Charter 5.5: Frequency histogram of the Charter 5.6: Repayment Period Mean histogram Loan Size split in the two programs across the Loan Size categories

It is quite interesting to analyze the number of loan installments: the mean and the median are

lower than the required value (11) but the mode respects it: this implies that the majority of the

clients gives the expected number of installments. The frequency graph was calculated with the

variable as continuous because the values computed from the microcredit program are not

integer, but the observation for the other programs are represented by integer number, so the

graphs is characterized by picks.



Charter 5.7: Frequency diagram of the Charter 5.8: Repayment Period values scatter dots Number of loan installments across the Number of Loan Installments variable

Then the scatter dot chart helps to put in evidence the relationship between the repayment

period and the number of loan installments. A general trend can be identified, then confirmed by

the correlation analysis: the higher the number of loan installments, the higher the repayment

period. The relation is not very defined, as in the left part of the graph the points are rare and less

concentrated than in the higher-right side.

Moving to the relationship between this variable Number of loan installments and the program

code, it is logical to observe that it takes integer number in the Mother’s Bank program, as the

installments are monthly, while for the Microcredit program the total number of weekly

installments are divided by 44 in order to align

the variable with the other program, thus the

values are discrete in type. As it can be seen from

the histogram, in the first case the frequency in

the variables is higher as the number of

installments increases, thus the majority of the

clients tends to repay with number of

installments near 11. Conversely for the

microcredit program the situation is quite

different, having not a positive trends but a not

linear pattern of the bars. Charter 5.9: Frequency Histograms of the Loan

Repayment Barycenter split in the two programs



Finally this predictor is evaluated across the different categories of the loan size: without

considering the 3 lonely observations

that can not well represent the 3

categories, the lower loans present a

broader interquartile interval, while the

last one are perceived to be more

homogeneous in the behavior within

the category. In addition any class of

debt has a peculiar pattern in terms of

outliers. Finally the mean in the boxes

varies from approximately 8 to 10 loan

installments in the repayment period.

Charter 5.10: Number of Loan Installment box plot across different Loan Size categories

% Number Standard

Loan Installme

nt

Loan Installment mean per

month

Standard Loan

installment amount

Median Monthly

loan installment

s

Median (Loan

Installment - SLI)

Var Monthly

loan Installme

nts

Var (Loan Intallment-

SLI)

N Valid 312 312 312 312 312 312

Missing 0 0 0 0 0 0 Mean .79 524.42 282.60 492.19 98.08 173356.38 129105.10 Std. Error of Mean

.013 19.20 9.10 16.57 8.53 24537.96 18182.74

Median .87 438.43 250.00 400.00 .00 35277.78 23439.258 Mode 1 193.96 300.00 500 0 .00 .00a Std. Deviation

.226 339.20 160.68 292.72 150.73 433426.85 321171.157

Variance .051 115057.30 25818.41 85685.91 22719.16 18785883

5000.15 103150912

297.255

Range 1 1875.31 775.00 1425 750 3471650.0

0 3074750.0

0 Minimum 0 85.14 25.00 75 0 .00 .00 Maximum 1 1960.45 800.00 1500 750 3471650 3074750

Table 5.9: Descriptive statistic parameters of the set of variables loan related



The next set of variable starts with the percentage of the number of loan installments with an

amount equal to the standard requirement, thus it is a sub sample of the number of loan

installments. The mean says that in general the client provide 79% of the loan installments with an

amount equal to the standard loan installment required. Quite significant is also the value of the

mode, that suggest a repayment with 100% of the installments that respect the policy regarding

the amount provided.

It is very interesting to see as, across the different loan size categories, the behavior of this

predictor follows the one of the repayment

period: the mean of the two variables in

the different classes of loan suggests that

the performance in terms of time for

repayment is potentially well predicted by

this variable. On the other hand the general

pattern of the histogram does not allow to

make conclusions on a possible relation

between the loan size and the percentage

of standard installments.

Charter 5.11: Histogram of Mean of the Percentage of Number of Standard Loan instalments

across different Loan Size categories with a line of the Mean of the Regression Period

Moreover the predictor is plotted looking at the relation with the number of loan installment,

dividing the sample by programs. A first

consideration from the graph observation is

the very different behavior that the two

programs have: the Microcredit dots (in

green) create a pyramid suggesting that for

high number of installments, the percentage

of the installments that respect the

requirement increases. There are not

observations with the maximum number of

loan installments corresponding to a low

percentage of standard loan installments.

Charter 5.12: Scatter dot of the NLI and of the % of NLI for the two programs



The Mother’s Bank sample takes integer number for the loan installments as they are monthly and

the variable is calculated referring to the monthly installments. Thus the dots are organized into a

grid. It is worthy to notice that in this program it seems quite rare that the clients respects the

policy requirement in terms of loan installments amount: the triangle on the left-high part of the

graph is covered by blue dots.

The next three variables will be considered together: loan installment mean per month (calculated

taking the loan size and dividing it by the repayment period in months) the standard Loan

installment amount (theoretically the required loan installments the policy asks to the client to

repay each month) and finally the median of the monthly loan installments (having considered the

month as frequency period for the installments, the median of the values is taken for each client).

Analyzing the mean of them, the first variable has definitely higher value than the second while it

is quite similar to the third: this means that the repayment period is lower than those established

by the policy (one year) thus in average the client gives more money each month than expected.

This is also confirmed by the median of the monthly repayment that falls nearer the first predictor

than the second. However the difference in the range of values in the sample is very high: the first

variable has a range (1960 rupees) three times higher than that of the standard loan installments

(800 rupees).

Charter 5.13: Scatter dots of the Standard Loan Installment with Loan Installment mean per month

and Loan Installment Median, divided for the two programs



The scatter dots graphs represent the two programs separately: for the Microcredit program

sample the loan size is lower thus the X-axis is different in the two images. Firstly it is possible to

notice that the Mother’s Bank program has a less define trend in the relation between the loan

installments Median and the Standard loan installment amount, while in the Microcredit program

the picture is more clear and regular: all the three variables seem to be related to each other. Thus

the Mother’s Bank clients have a less constant behavior in terms of loan installments across the

loan size categories.

The same analysis is done regarding the repayment period instead of the standard Loan

installment amount.

Charter 5.14: Scatter dots of the Repayment Period with Loan Installment mean per month and

Loan Installment Median, divided for the two programs

In this case the different scale in the Loan installment mean per month variable (in blue) is left in

order to better understand the trend and the results. Being this variable computed with the

repayment period as a dividend, then the hyperbolic curves represent the loan size categories.

With this representation we can see another time if the repayment in each categories is

concentrated around the mean values or if the behavior is different. In Mother’s Bank program

the central categories are plotted with a less continuous line, while in the Microcredit program

also the first have a high variability in terms of repayment period



Considering then the second variable, the Microcredit presents more observations with high

values, and less concentrated parallel lines, while the cases of the other program create a better

defined horizontal line, suggesting more homogeneous behavior also for this variable.

The Median of monthly loan installment can be also compared with the median of the difference

between the loan installment and the standard loan amount required each month.

Taking the mean value of the variable, the difference

between the mean is equal to approximately 210

rupees (492 rs mean of the monthly loan installment

minus 282 rs, mean of the standard loan installment

amount variable) but this value is double of the

average median between the monthly difference (98

rs) and moreover the mode and the median of this

variable take null value, suggesting an overall correct

behavior of the client respect to the loan repayment

cash flow.

Charter 5.15: Frequency histogram of

the Median (Loan Installment-Standard Loan installment)

Looking at its value across the different loan

categories, in general terms the difference

increases as the loan size augments. In addition

the Mother’s Bank programs confirms a more

correct behavior in terms of policy respect as

the mean in most of the class of loan is lower

than that of the Microcredit.

Charter 5.16: Scatter dots of the Median (Loan Installment-Standard Loan installment) the across the Loan size categories



Finally the scatter dot graph that links the

repayment period with this predictor does not

put in evidence any peculiar characteristic in the

behavior.

Charter 5.17: Scatter dots of the Median (Loan

Installment-Standard Loan installment) with the

Repayment Period

The variance in the monthly loan installments will be analyzed with the variance of the difference

between the loan installment and the Standard loan amount, where the first provides information

of the pure repayment of the client while the second compare it with the expected one.

Histogram of the predictors’ distributions are designed distinguishing between the Mother’s Bank

program (Program Code 0) and the Microcredit program (Program Code 1): the pattern is fairly

similar both across the program and for the different variable. The majority of the cases are

concentrated on the proximity of the 0 value of the variances, signifying that in both programs the

clients provided loan installments with amount that does not vary a lot.

Charter 5.18 Frequency histogram of the Variance of (Loan Installment-Standard Loan installment)

and of the Variance of monthly installments divided by the Program Code



Moving to the comparison between loan sizes, the histogram confirms the similar pattern of the

two variables across the categories, but the mean values of them are in general 30% higher for the

variance in monthly loan installments than for the other variable because the second one amortize

the difference by comparing the loan installments amount with the standard requirement, that in

general can be both higher or lower.

In addition a peculiar behavior is

detected for the category of 7 700

rupees that has mean values definitely

distant from those of the previous and

following loan size classes. This is

mainly due to one observation that

pushes the mean high.

In conclusion, with the exception of

the previous case, the variances

variables seem logically correlated with

the loan size.


across Loan size categories with the line of the Mean of the Variance of monthly installments

The next set of scatter dot focus the attention on the relation between the two predictors and the

repayment period: starting from the representation of all the dots, then the range is decreased by

10 times, producing in total the four graphs.

It is interesting to notice as a group of high Values stay on the left part of the graphs, suggesting

that the loans repaid faster have cash flows with a high variance. This probably is due to the fact

that they do not repay with constant loan installments amount higher than the target one, but

they complete the repayment by providing a huge last installments that consequently pushes the

value up.



Charter 5.20: Set of scatter dots of the Repayment Period with the Variance of month Loan

Installment and of the difference between Loan Installment and Standard Loan Installment

In contrast the fast repayments are also detected in the second and third scatter dot, with the

conclusion that the subgroup of loan with low repayment period have a very different behavior in

terms of variances predictors. This is not the case for late repayments, that are represented by a

more dense cloud.



The last set of loan related variable to be analyzed are the described in the following table.

Table 5.10: Descriptive statistic parameters of the set of variables loan related (2nd part)

Theoretically the repayment should start from the second month, in eleven monthly installments

(or 44 weekly installments) and be completed within the year. Thus the 70% of the loan should be

repaid within the 47. 80 weeks ((365-30.4)/7). The mean, median and mode of the variable fall

under this target, signifying a general well performance of the clients.

In the scatter dot graph the most majority of observations with a value higher than 48 weeks

belong to the Mother’s Bank program (blue dots) suggesting that the clients have an higher

variance in the repayment of the first part of the loan, but they converge in terms of repayment

period as previously highlighted by looking at the value of the mode, median and mean that take

similar value in this program. On the other hand the microcredit program dots form a more

homogeneous cloud.

Indeed the histogram for the Mother’s Bank

program (code 0) is more shifted on the upper

values, while the microcredit clients’

observations are concentrate below the 40

weeks.

Charter 5.20: scatter dot of the Number of loan

repayment and the period to repay the 70% of

the loan, spit in 2 subsamples of loan size


Loan Repayment Barycenter


Distance abs (LRR-1)

N Valid 312 312 312 312

Missing 0 0 0 0

Mean 38.49 5.85 .97 .1380

Std. Error of Mean .404 .062 .010 .0071

Median 37.27 5.73 .9545 .110

Mode 35 5.00 .83 .05a

Std. Deviation 7.14 1.107 .185 .125

Variance 50.96 1.226 .034 .016

Range 44 7.80 1.30 .66

Minimum 16 2.05 .34 .00

Maximum 60 9.84 1.64 .66



Considering then the relation with the

repayment period across the different

categories of loan size the mean of

repayment period and of the period to

repay 70% of the loan have the same

trend, as the line and the bar mostly stay

at the same distance.

Charter 5.21 Mean histogram of the Period

to repay 70% of the loan across Loan size

categories with the line of the Mean of the

repayment period itself.

The Loan Repayment Barycenter and the Loan Repayment Regularity express the same concept

and differ by only constant (6): the first one represents the number of the month where the

barycenter of the cash flow lays while the LRR says that the client has a repayment with late

barycenter if its value is higher than 1, otherwise it is an anticipated repayment.

Looking at the statistic parameter the general trend is positive: all of the mode, median and mean

are lower than 1.

The different histograms of the variable’s distributions in the two programs confirm the late

barycenter for the mother’s Bank women while the microcredit program clients prefer to repay

the majority of the loan before the 6th month.

Charter 5.22 Frequency histogram of the Loan

Repayment Barycenter and Period to repay 70% of the loan divided by the Program Code



The cloud representing the repayment

period as a function of the loan

Repayment Barycenter takes an ellitic

form across the diagonal from the first

to the third quarter, suggesting a

positive relation between the two

variables: the higher the barycenter the

higher the repayment period.

Charter 5.23: Scatter dots of the Loan Repayment Barycenter with the Repayment Period

The last variable to be considered in the loan installment related group is the distance in absolute

value between the LRR and the target (value 1). This calculation depurates the variable from the

concept of delay, and extrapolates the simple information of being or not aligned with the

program policy.

The distribution of the variables is divided

depending on the program: it is easy to

see that the mother’s bank program has

an higher concentration of observations in

the proximity of the 0 value, suggesting an

overall behavior in line with the policy.

Charter 5.24: Frequency histogram of the Distance (LRR-1) divided by the Program Code



The scatter dot that links this variable

with the repayment period tells that,

the more the distance the higher the

variability in the number of weeks to

complete the loan repayment: the dots

are concentrated on the left part of the

graph, taking a considerably not high

range of value in the Y-axis. On the

other hand the right part is

characterized by less observations but

with very different repayment periods.

Charter 5.25: Scatter dots of the Repayment Period with the Distance (LRR-1)

Finally the variable is considered along with the

loan repayment barycenter: with the exception

of the 2,750 and 16,500 rupees loans, the

distance, represented by the red line, follows

the LRB trend, with values around 0.15 for the

first predictor and 5.75 for the second

respectively.

Charter 5.26: Mean histogram of the Loan

Repayment Barycenter across Loan size

categories with the line of the Mean of the

Variance of the Distance (LRR-1)

CONCLUSION

The loan related variables seems to be potentially good predictors of the repayment period.

Moreover also the program code seems to be correlated with some of the analyzed variables, that

can be useful to understand the difference in the client performance due to the frequency

repayment policy.



5.2.2 SAVINGS RELATED VARIABLES ANALYSIS

In this section the variables calculated from the savings accounts are analyzed. As already

mentioned, the available number of observations with this type of data is lower than 213 on a 275

cases sample.

Number

Savings

Deposits

Savings

Amount in

the RP

Savings Mean

per month in

RP

Monthly

Savings

deposit

Median

Var Monthly

Savings

Deposits

N Valid 275 275 275 275 275

Missing 37 37 37 37 37

Mean 7.46 527.64 49.86 62.60 4699.05

Std. Error of

Mean

.197 26.99 2.69 3.575 1963.13

Median 8.50 400.00 37.78 50.00 281.08

Mode 3a 391a 33.67 50a .00a

Std. Deviation 3.27 447.59 44.62 59.28 32554.92

Variance 10.67 200333.87 1991.15 3514.65 1059822519.41

Range 13 3187 395.86 793 408608.49

Minimum 1 7 .67 7 .00

Maximum 14 3194 396.53 800 408608.49

Table 5.11: Descriptive statistic parameters of the set of savings variables

The first variable considered is the number of savings deposits provided during the repayment

period.

This predictor is interesting to be compared with the number of loan installments, because the

client has the possibility to provide savings deposits

when she comes to the branch for repaying the

debt.

The scatter does not provide any specific

information about a trend in the relationship: the

concentration of the dots is located on the right high

part, corresponding on high values of both the

variables. However it is worthy to highlight that

some loans repaid with a very low number of Charter 5.26: Scatter dots of the Loan Installments with the Number of Savings Deposits



installments are associated with a very high number of savings deposits, signaling a willingness to

save but a preference to repay with few installments.

In contrast an opposite behavior is noticed on the upper-left part of the graph, where a very low

numbers of savings deposits corresponds to a number from 2 to 12 of loan installments.

Then the mean of this variable is compared

across the loan size, distinguishing between

the programs’ subsamples. The main

characteristic to be signaled is the fact that

the Mother’s Bank clients provide a lower

number of savings deposit for each

categories. In addition no special trend is

detected in terms of a relationship between

the number of savings deposits and the loan

size.

Finally a scatter dot graph is created putting on the y-axis the repayment period and analyzing the

different values this variable takes in the two different programs across the number of savings

deposits.

Most of the categories of it are represented

either by the Mother’s Bank program (blue

dots) or the Pure Microcredit program

(green dots). If both of them have values in

one category, then a vertical bar signals the

difference between the mean of the

repayment period of the two programs. In 5

cases out of 6, the microcredit mothers

perform worse than the woman in Pure

Microcredit service, as the blue dot is higher

than the green one.

Charter 5.27: Mean histogram of the Number

of savings deposits across Loan size categories

divided by the Program Code

Charter 5.28: Mean scatter dot of the

Repayment Period and the Number of Savings

Deposits divided by the Program Code



This result was expected thanks to the previous analysis of the repayment period values in the two

type of microcredit services.

The next variable to be considered is the total

amount of savings the client put in the savings

account during the repayment period.

The 80% of the observations save less than 820

rupees in the repayment period,. This value is

quite low compared to the loan installments the

clients should repay: the mean of the loan size is

5,581 while here it is 528 rupees, consequently

in general terms the client provides 8.64% in

savings and 91.36 % in loan installments of the

total amount of rupees given to IIMC.

Charter 5.29: Frequency histogram of the Savings Amount variable in the Repayment Period

Considering then the histogram of the savings

amount in the repayment period across the

loan size categories, a not very constant and

defined positive trends is detected, as the last

higher categories present quite high different

mean. in the histogram the categories with only

one representative are not shown.

Charter 5.30: Histogram of the Savings Amount

mean across the Loan Size categories

The last scatter dot considered for this variable is the one that represents in green the repayment

period and in blue the number of savings deposits, with the corresponding target value put in

evidence through an horizontal line (at 52 weeks level and 11 loan installment level).

The combination of these 3 information helps to see that the highest value in the savings amount

in the Repayment Period are associated with a number of savings deposits near the target, and in

addition that they repay within the year. However for the range of values near the zero we can see



an initial trend considering the relation with the number of deposits, while the repayment period

has a less define cloud.

Charter 5.31: Scatter

dot of the Savings

Amount in the RP with

the Number of Savings

Deposits (blue dots) and

the Repayment Period

(green dots)

Considering then the savings mean per month, calculated dividing the total amount of savings in

the repayment period by the number of month the client took to completely repay the loan, the

results are shown in the following graphs.

Charter 5.33: Mean histogram of the Savings

Mean per month and the frequency line across

Loan size categories

Charter 5.32:Frequency histogram of

the Savings Mean per month In the

Repayment Period



The mean of this variable is 49.86 rupees, lower than the mean of the monthly savings deposit

median that consider the sum of deposit provided each month. This is confirmed by the frequency

histogram that shows a concentration of observations in the first left part of the histogram.

Moving to the distribution of this variable across the loan size categories, for lower amount (that

have higher cardinality) the savings mean per month is definitely low, while from the 8 800 rupees

loan the average values are higher than the sample mean but, having few representatives for

these categories, they do not impact on the variable mean parameter.

Finally the only observation that can be done evaluating the scatter dot that links the savings

mean per month with the repayment period is the following: for high values of the savings mean

per month, the repayment period stays under the target, but the representative of this subgroup

are few, as the majority of the cases lays on the left part. In addition the Mother’s Bank clients

are spread more in a vertical pattern on the low values of the X-axis, while the Microcredit’s bank

(green dots) clients have an higher variance in the savings mean variable, but lower in the

repayment period.

Charter 5.34: Mean scatter dot

of the Savings Mean per

month and the Repayment

Period divided by Program

Code (Mother’s Bank program

in blue dots, Microcredit

Program in green dots)

The next variable to be considered is the variance of the savings deposits that in the histogram is

represented along the loan size categories: in the 3,300 rupees loans some observations push the

value of the predictor’s mean very high, as also for the following 2 size’s groups.



In addition the mean of the monthly

savings deposits median is

represented through a violet line.

A general positive trend can be

detected as between the loan size

and the monthly savings median

variable, even if the relationship

seems not to be linear.

Charter 5.35: Mean histogram of the Variance in monthly Savings Deposits with line representing

the Mean of the Monthly savings deposit Median across Loan Size categories

Finally the last set of scatter dots helps to understand the link between the variable and the

repayment period. If all the observations are represented, the graph is not clear, thus a zoom is

used in order to clarify the cloud of dots on the left part.

Charter 5.36: Set of scatter dots of the Variance in monthly Savings Deposits and the Repayment

period divided by Program Code



It is important to notice that the dots excluded step by step thanks to the zoom belong to the

Mother’s Bank program (in blue) and this means that its clients have higher variance in the savings

cash flows in comparison to the women in the Microcredit program.

CONCLUSION:

The savings cash flows seem to be more related to the loan size and the program code, than to the

repayment period, for which no particular trend or consideration can be done.



5.2.3 MONTHLY VARIABLES ANALYSIS

CUMULATIVE REPAID LOAN PERCENTAGES

The first set of monthly variables to be analyzed is the cumulative repaid loan percentage, from

the first month (CRL0) to the 12th month (CRL11).

The first graph considers the entire sample and

plot the average value of the variables along

the months, thus CRL11 is less than 1 because

not all the clients repaid the loan within the

year, but the value is quite high. In addition the

intercept between the target of 50% of loan

repaid at CRL6 gives the information that in

average the clients are slightly in delay.

Charter 5.37:Chart representing the mean of the set of Cumulative Repaid Loan variables

From the second graph, where the line is

splitted for evaluating the program code

specific behavior, it is possible to notice that

the trends are similar, with the Microcredit

program line always above the Mother’s

Bank one: this implies that the first program

in average behaves constantly better in

terms of percentage of the loan repaid.

Charter 5.38:Chart representing the mean of

the set of Cumulative Repaid Loan variables,

divided by Program Code (in blue Mother ’s

Bank, in green Microcredit program)

Finally looking at the Loan Size categories pattern, there are mainly 2 lines that move from the

common path (2,750 repaying faster, the 16,500 being in delay until the last month). In addition



the initial part is more homogeneous, while the second half of the CRL are characterized by a less

dense concentration of lines.

Charter 5.39:Chart

representing the mean

of the set of

Cumulative Repaid

Loan variables across

Loan Size categories

LOAN REPAYMENT REGULARITY INDEX

The second group of monthly variables is that of the Loan Repayment Regularity indexes that track

if the repayment has an anticipated or postponed barycenter in the repayment period. It is

important to be noticed that LRRi does not take into consideration the amount repaid at the

month i but only if the repayment cash flows are centered.


the set of Loan Repayment Regularity

variables


the set of Loan Repayment Regularity

variables divided by Program code



As in the previous section, the variable is plotted in one graph: the curve suggests that initially the

clients are ahead on time with a very anticipated barycenter. The value boosts very high from the

second month and has the pick on the 10th month (LRR9).

From the graphs it is important to see that in average none of the months has LRRi mean higher

than 1, thus a barycenter shifted on the second part of the period considered. This means that the

clients disbursed a higher amount of money in the first half of the repayment period than in the

second.

More over here the two programs have no parallel curves: the Mother’s Bank seems to be less

instable than the microcredit but with a more centered barycenter as the mean in each month are

higher (but not greater than 1). This irregularity is in part due to the fact that the first program, the

Mother’s Bank, has a repayment frequency 4 times higher than the second one, thus not providing

one installments impact directly on the LRR of the month, while for the microcredit clients, if they

give only 3 installments out of 4 in the month, the impact on the LRR is lower.

Analyzing the line of the different categories, for this variable the pattern is quite complicated to

be interpreted. The most relevant observations are the following: the 2,750 rupees loans start

with a fairly centered cash flow but the value falls down the last months, meaning that in the first

period the barycenter was

moderately in the middle

but the repayment was

high in terms of amount.

On the other hand the

16,500 rupees loans are the

only category that ends

with a mean higher than 1,

so they tends to have cash

flows shifted on the last

months.

Charter 5.42:Chart representing the mean of the set of Loan Repayment Regularity variables across




CUMULATIVE SAVINGS

The last set of monthly indicators is the cumulative savings amounts.

The curve is linear, with a small decrease in the slope in the last months, signifying that in the last

months the clients concentrate the effort in repaying the loan and provides less amount for the

savings deposit. There is a great difference between the two program slopes, due to the fact that

the Mother’s Bank women provide in general a definitely lower amount of rupees for the savings

amount in comparison with the

Microcredit clients.

Finally the loan size categories are

expected to have slopes proportional

to the loan size. Indeed the higher

line is the one of the 16,500 rupees,

but the 11,000 and the 13,200 are

inverted. For the central categories

the pattern is less defined, while a

very poor performance is registered

for the group of 2,750 rupees loans.

Charter 5.45:Chart representing the

mean of the set of Cumulative

Savings variables across Loan Size

categories


the set of Cumulative savings variables


the set of Cumulative Savings variables divided

by Program code



5.3 CORRELATION ANALYSIS

In order to assess the existing relationship between the variables, a correlation analysis is

computed.

The coefficient selected is the most common, the Pearson Correlation coefficient that is the

division between the covariates of the 2 variables (covxy) with the product of the standard

deviations (sx sy) as the formula shows:

From this standardization of the covariance, the results can take values from -1 to +1, where for

example +1 indicates that the two variables are perfectly positively correlated, so as one variable

increases the other increases by the proportionate amount.

In general terms a commonly used measure of the size of an effect is the following:

R = ± 0.1 small effect

R = ± 0.3 medium effect

R = ± 0.5 large effect

Pearson’s correlation requires only that data are interval for it to be an accurate measure of the

linear relationship.

In addition it is important to mention that this analysis does not give any indication of the

direction of causality, in other words it says nothing about which variable causes the other to

change.

The coefficients are organized into tables, where the green cells put in evidence the correlation

coefficients with significance higher than 0.01 (SIGN**), while the yellow one have significance

between 0.05 and 0.01 (SIGN*).

At first the main dependent and independent variables are analyzed, highlighting the significant

Pearson correlation coefficients through colors (green for 0.01 level, yellow for 0.05 level of

significance).



Table 5.12: Pearson Correlation coefficients of the main variables

Repayment

period

Program

Code

Loan size

Number

Loan Inst.

Abs (NLI-

11/11)

Defaulter

Indicator

Period to

repay 70% loan

Loan Repaym.

Regularity

Distance abs

(LRR-1)

Repayment period 1.0 -.116* 0.0 .444** -

.438** -

.578** .719** .715**

-.271**

Program Code -.116* 1.0 .517** -

.240** .238** 0.1 -0.1 -.122* .064

Loan size 0.0 .517** 1.0 0.0 .044 0.0 .127* 0.1 .000

Number Loan Installments .444** -

.240** 0.0 1.0

-.998**

-0.1 0.0 0.0 -

.454**

Defaulter Indicator -.578** 0.1 0.0 -0.1 .067 1.0 -

.483** -

.489** -.141*

Number Savings Deposits 0.0 .553** .277** 0.1 -.094 0.0 -0.1 0.0 -.083

Savings Amount in the RP 0.0 .461** .472** 0.0 -.039 0.0 0.0 0.0 -.091

Savings Deposits Median -0.1 .204** .337** 0.0 .005 0.0 0.0 -0.1 -.024


-.170** -0.1 -0.1 -.149* .149* 0.1 -0.1 -0.1 .000


-.199** .426** .422** 0.0 .045 0.1 -.141* -

.157** -.032


-0.1 0.1 .213** 0.0 -.014 0.1 0.0 -0.1 -.031

Variance Monthly Savings Deposits

-.129* -0.1 -0.1 -.148* .148* 0.0 0.0 -0.1 -.014


-.265** .515** .947** -

.173** .173** .169** -0.1 -0.1 .079


0.1 -

.585** .253** .196**

-.192**

0.0 .172** .179** -.075

Number Standard Loan Installment

.235** .220** .191** .532** -

.542** 0.0 0.0 0.0

-.352**

Loan installments Median -0.1 .475** .965** -

.157** .156** 0.1 0.1 0.0 .060


-.198** 0.1 .370** -

.479** .480** -0.1 .202** .177** .276**

Period to repay 70% loan .719** -0.1 .127* 0.0 .021 -

.483** 1.0 .899** .003


.715** -.122* 0.1 0.0 -.003 -

.489** .899** 1.0 .010


-.140* .388** .902** -0.1 -.003 .138* -0.1 -0.1 .010

Median (Loan Installment-SLI)

-0.1 .420** .571** -

.469** .081 0.0 .147** .165** .035

Var Monthly loan Installments

-.180** .260** .486** -

.395** .

396** 0.0 .140* .139*

. 257

**

Var (Loan Installment-SLI) -.239** .208** .427** -

.406** .

471** 0.0 0.1 0.1

. 308**

**,* imply significance at 1% and 5% respectively



RP_50% loan

RP_60% loan

RP_70% loan

RP_80% loan

Repayment Period

ProgramCode -.114* -,055 -,071 -,052 -.116*

Repayment Period .542** .636** .721** .829** 1

LRR .887** .898** .902** .856** .715**

DistanceabsLRR1 .141* ,097 ,002 -.133* -.271**

LogLoanSize .125* ,097 .122* ,085 -,003

NumberStandardLoanInstallment -,002 ,008 ,011 ,075 .235**

NumberLoanInstallments -.152** -,091 -,015 .140* .444**

DefaulterIndicator -.411** -.469** -.484** -.521** -.578**


Table 5.13: Pearson Correlation coefficients of the Repayment Period related variables



FREQUENCY IMPACT ON THE PERFORMANCE – repayment period and program

code variables

1. The repayment period and the program code have a significant Pearson correlation

coefficient, but its value is low. This means that in general there is difference in the

performance between the two programs: being a negative coefficient it signals that the

client of Pure Microcredit program (value 1 for the Program Code variable) performs better

than those of the Mothers bank program. This is confirmed by the mean of the repayment

period in the two subsample that is higher for the Mother’s Bank, but in the latter program

there is lower variability if we look at the mode and median values, consistent with the

mean, while in the Microcredit this does not happen. It is worthy to notice that the default

indicator is not correlated with the program code, thus the repayment within the year is

not program correlated.

2. The Repayment Periods variables for different percentages of the loan size are not

correlated with the program code with the exception of the 50% loan percentage.

3. The loan size positively depends on the program code, so in general the microcredit

program disburses higher loan amount than the mothers bank, as already seen from the

previous general statistical analysis. This characteristic does not impact on the relationship

between the RP and PC because of the previous consideration, according to the Pearson

coefficient analysis.

4. It is interesting to see that the repayment period is significantly correlated with the

number of loan installment and this last is positively correlated with the program code. It

means that the higher the number of loan installments, the higher the week for complete

the repayment; but also it says that the clients in the MBP do more monthly visits than the

PURE MICROCREDIT PROGRAM women. This last data maybe is due to the fact that the

number of weekly loan installments was divided by 4 doing the hypothesis that in one

month in average there were 4 meetings and that the client attends all of them. But in

reality the mother could have gone to 2 meetings one month and 2 additional the month

after, but this indicator consider the 4 data as a unique month. For this reason this

parameter aggregates too much the PURE MICROCREDIT PROGRAM information.

5. Considering the adjusted Number of Loan Installments [ABS(NLI-11)/11] that evaluate the

distance from the policy requirement of the loan installment number, the result of the

correlation with the RP is a negative significant parameter: as much as the client repays in



a correct way, as much the performance is good. In other words, if the adjusted parameter

tends to 1 the repayment period decreases. So a regular repayment seems to help to

achieve a better performance.

REPAYMENT VARIANCE AND REGULARITY INDEXES IMPACT ON THE

PERFORMANCE

A. Interesting to be analyzed is the relationship between the repayment period and the Loan

Repayment Regularity index. Indeed it is both significant and also high in value, indicating

that the more the barycenter is shifted on the last month, the higher is the repayment

period.

B. Considering the Repayment period for different percentages of loan size, the number of

standard loan installment is never significant and also changes sign in the 50% variable;

the negative sign is also characteristic of the number of loan installments for the same

variable, while for the RP_80% loan it becomes positive and slightly correlated.

C. Looking at the same variables of point B, the relations with the Loan Repayment Regularity

is coherent with that of the repayment period, in other words all the predictor are

positively correlated and with high significance.

D. Considering the variable Distance [=Absolute(LRB-1)], it is expected that, the higher the

Distance Abs (LRR-1) (barycenter of the repayment far from the 6th month) the lower the

performance. But this does not happen: on the other hand it seems that the more the

barycenter is shifted, the lower is the repayment period, and so the better the

performance is.

E. The repayment period and the savings related variables are correlated only by considering

the variance in the loan installment and the monthly savings deposit provided. Have

better performance those clients who provide constant deposits of low amounts.

F. it is also interesting to see that the PC is significantly related with the standard Loan

installment amount, this suggests that the Microcredit program respectsmore the policy in

terms of loan amount repaid each month.

G. The repayment period and the loan related variances (LI -in the loan installments, m_LI -in

the monthly loan installments, _LI-SLI in the difference between loan installments amount

and standard installment required by the policy) are significantly correlated, with negative



coefficient with values low, near the 0.2. This implies that the higher the variability the

lower the repayment period, that is the opposite to our hypothesis.

H. Same considerations as the point E can be done with the variances in the savings deposits,

that have negative significant coefficient with the repayment period, indicating that the

higher the variance, the lower the repayment period. This is opposite to our hypothesis.

ADDITIONAL CONSIDERATIONS

LOANS SIZE

I. It is also interesting to see that there is a positive correlation between the loan size and the

repayment period necessary in order to pay back 70% of the loan amount. Thus it means

that the higher the loan size, the later the client repays the 70% of the debt. (But the RP for

the complete repayment is not significant correlated with the LS).

J. The loans related variances are positively significantly correlated with both the programs

and the loan size. Thus the microcredit program has a higher variability in the loan

installments than the mother’s bank, as already highlighted in the initial part of this

chapter. In addition for higher loan size the variance is detected to be higher probably

because the loan installments are considered in absolute value and not as a percentage of

the standard loan installments, so a variation in the loan installment of 16,500 IRP debt

results in a higher value in the variable comparing to variation of 1100 IRP installments

amount.

K. The Repayment period for different percentages of loan size is calculated related to the

Logarithm of the Loan Size; the table shows as the coefficients have different sign

comparing with the repayment period, and they are significant for the 50 and 70% loan

size.



SAVINGS RELATED VARIABLES


Table 5.14: Pearson Correlation coefficients of the Savings related variables

I. It seems that the savings have not an impact on the performance in terms of RP, as the

only significant relationships with the RP are with the variance in the savings deposit

Number Savings Deposits


Savings Deposits Median




VAR.P Monthly Savings Deposits

Repayment period 0.0 0.0 -0.1 -.170** -.199** -0.1 -.129*

Program Code .553** .461** .204** -0.1 .426** 0.1 -0.1

Loan size .277** .472** .337** -0.1 .422** .213** -0.1


0.1 0.0 0.0 -.149* 0.0 0.0 -.148*

Defaulter Indicator 0.0 0.0 0.0 0.1 0.1 0.1 0.0

Number Savings Deposits 1.0 .465** 0.1 -0.1 .420** 0.0 -0.1

Savings Amount in the RP .465** 1.0 .698** .374** .970** .588** .404**

Savings Deposits Median 0.1 .698** 1.0 .158** .672** .895** .169**


-0.1 .374** .158** 1.0 .491** .288** .962**


.420** .970** .672** .491** 1.0 .581** .509**


0.0 .588** .895** .288** .581** 1.0 .288**


-0.1 .404** .169** .962** .509** .288** 1.0


.248** .447** .347** 0.0 .455** .229** 0.0


-.402** -.144* 0.1 0.1 -.142* .127* 0.1


.410** .256** 0.0 -0.1 .203** 0.0 -0.1

Loan installments Median

.221** .452** .328** 0.0 .418** .210** 0.0


-0.1 0.1 0.1 0.1 0.1 0.0 0.1

Period to repay 70% loan -0.1 0.0 0.0 -0.1 -.141* 0.0 0.0


0.0 0.0 -0.1 -0.1 -.157** -0.1 -0.1


.170** .439** .339** 0.0 .421** .236** 0.0

Median (Loan Installment-SLI)

.188** .297** .175** 0.1 .271** 0.1 0.1

Var Monthly loan Installments

0.1 .144* .119* 0.0 .167** 0.1 0.0

Var (Loan Installment-SLI) 0.0 0.1 0.1 0.0 .148* 0.1 0.0



amount and the savings median amount provided per month. The higher the variance and

the higher the amount disbursed, the lower the repayment period.

II. Both loans size and program code are significantly correlated with the savings related

variables, with the exception of the variances: the higher the number of the deposits and

their amount, the higher is the repayment period. And in general the Microcredit Program

has higher values for savings data but maybe this is due to the relationship between the

Loan Size and the Program Code, that is positively significant.

III. All the savings related variables have a high dependency among them.

INSTALLMENTS RELATED VARIABLES


Table 5.15: Pearson Correlation coefficients of the Loan related variables (part 1)


Standard Loan Installment


Loan Installments Median

Repayment period -.265** 0.1 .235** -0.1

Program Code .515** -.585** .220** .475**

Loan size .947** .253** .191** .965**

Number Loan Installments -.173** .196** .532** -.157**

Defaulter Indicator .169** 0.0 0.0 0.1

Number Savings Deposits .248** -.402** .410** .221**

Savings Amount in the RP .447** -.144* .256** .452**

Savings Deposits Median .347** 0.1 0.0 .328**

Variance in Savings Deposits 0.0 0.1 -0.1 0.0

Savings Mean per month in RP .455** -.142* .203** .418**

Monthly Savings deposit Median .229** .127* 0.0 .210**

Variance Monthly Savings Deposits 0.0 0.1 -0.1 0.0

Loan Installment mean per month 1.0 .217** .117* .937**

Standard Loan installment amount .217** 1.0 -.125* .273**

Number Standard Loan Installment .117* -.125* 1.0 0.1

Loan installments Median .937** .273** 0.1 1.0

Variance in Loan Installments .449** .199** -0.1 .403**

Period to repay 70% loan -0.1 .172** 0.0 0.1

Loan Repayment Regularity -0.1 .179** 0.0 0.0

Median Monthly loan installments .907** .328** 0.0 .942**

Median (Loan Installment-SLI) .554** 0.0 -.225** .667**

Var Monthly loan Installments .558** 0.1 0.0 .490**

Var (Loan Installment-SLI) .532** 0.1 0.0 .442**





I. They are highly significantly correlated both with the Loans Size and with the Program: the

point is, if one program disbursed higher loans, and the size is correlated with the loan

related variables, it is logical that this last are correlated with the program code.

II. With the Loan Size, the coefficient put in evidence that the variance, the number of

installments and their amount are higher for higher debt.

III. The PURE MICROCREDIT PROGRAM has better performances in the RP and also repays with

higher number of standard loan installments.


Monthly Loan Installments Median

Median of (LI amount-Standard LI)

Variance of monthly loan installments

Variance of (LI amount-Standard LI)

Repayment period -.198** -.140* -0.1 -.180** -.239**

Program Code 0.1 .388** .420** .260** .208**

Loan size .370** .902** .571** .486** .427**

Number Loan Installments -.479** -0.1 -.469** -.395** -.406**

Defaulter Indicator -0.1 .138* 0.0 0.0 0.0

Number Savings Deposits -0.1 .170** .188** 0.1 0.0

Savings Amount in the RP 0.1 .439** .297** .144* 0.1

Savings Deposits Median 0.1 .339** .175** .119* 0.1

Variance in Savings Deposits 0.1 0.0 0.1 0.0 0.0

Savings Mean per month in RP 0.1 .421** .271** .167** .148*

Monthly Savings deposit Median 0.0 .236** 0.1 0.1 0.1


0.1 0.0 0.1 0.0 0.0

Loan Installment mean per month .449** .907** .554** .558** .532**


.199** .328** 0.0 0.1 0.1


-0.1 0.0 -.225** 0.0 0.0

Loan installments Median .403** .942** .667** .490** .442**

Variance in Loan Installments 1.0 .271** .506** .873** .881**

Period to repay 70% loan .202** -0.1 .147** .140* 0.1

Loan Repayment Regularity .177** -0.1 .165** .139* 0.1

Median Monthly loan installments .271** 1.0 .538** .342** .320**

Median (Loan Installment-SLI) .506** .538** 1.0 .576** .489**

Var Monthly loan Installments .873** .342** .576** 1.0 .983**

Var (Loan Installment-SLI) .881** .320** .489** .983** 1.0



LRB

The key observations are that the behavior of the programs in terms of the barycenter of the

payment is detected on the first months, when the Microcredit Program has an earlier barycenter

compared to the Mother’s Bank program.

In addition, trends in the performance of the clients in terms of repayment period cannot be

detected before the 8th month, when progressively the parameters become significantly

correlated and show that the higher the delay in the barycenter, the higher the repayment period.

LRB1 LRB2 LRB3 LRB4 LRB5 LRB6 LRB7 LRB8 LRB9 LRB10 LRB11 LRB12

PC -.298** -.278** -.303** -.206** -.187** -.137* -.154** -.083 -.104 -.073 -.013 -.077

RP .046 .108 .066 .009 -.006 .088 .100 .161** .231** .390** .202** .510**

LRB1 1 .654** .468** .419** .335** .323** .335** .248** .297** .288** .143* .165**

LRB2 .654** 1 .533** .462** .416** .371** .289** .200** .266** .221** .088 .128*

LRB3 .468** .533** 1 .652** .494** .415** .337** .241** .244** .212** .088 .137*

LRB4 .419** .462** .652** 1 .597** .489** .383** .294** .232** .237** .059 .106

LRB5 .335** .416** .494** .597** 1 .630** .457** .347** .299** .252** .136* .143*

LRB6 .323** .371** .415** .489** .630** 1 .601** .495** .428** .446** .240** .293**

LRB7 .335** .289** .337** .383** .457** .601** 1 .783** .626** .582** .327** .336**

LRB8 .248** .200** .241** .294** .347** .495** .783** 1 .729** .633** .344** .377**

LRB9 .297** .266** .244** .232** .299** .428** .626** .729** 1 .797** .481** .494**

LRB10 .288** .221** .212** .237** .252** .446** .582** .633** .797** 1 .637** .675**

LRB11 .143* .088 .088 .059 .136* .240** .327** .344** .481** .637** 1 .670**

LRB12 .165** .128* .137* .106 .143* .293** .336** .377** .494** .675** .670** 1


Table 5.17: Pearson Correlation coefficients of the Loan Regularity Barycenter variables

CRL

CRL0 CRL1 CRL2 CRL3 CRL4 CRL5 CRL6 CRL7 CRL8 CRL9 CRL10 CRL11

PC .297** .229** .151** 0.1 0.1 0.1 0.1 0.1 .121* 0.1 .112* 0.1

RP -.260** -.413** -.478** -.531** -.609** -.656** -.664** -.722** -.713** -.693** -.636** -.515**

CRL1 .689** 1.0 .838** .772** .689** .640** .627** .551** .528** .475** .424** .313**

CRL2 .610** .838** 1.0 .845** .779** .762** .725** .622** .580** .539** .434** .311**

CRL3 .588** .772** .845** 1.0 .879** .821** .780** .688** .632** .582** .469** .337**

CRL4 .491** .689** .779** .879** 1.0 .882** .845** .744** .684** .611** .491** .318**

CRL5 .470** .640** .762** .821** .882** 1.0 .909** .791** .722** .654** .523** .348**

CRL6 .441** .627** .725** .780** .845** .909** 1.0 .839** .770** .697** .589** .398**

CRL7 .369** .551** .622** .688** .744** .791** .839** 1.0 .889** .790** .673** .447**

CRL8 .357** .528** .580** .632** .684** .722** .770** .889** 1.0 .856** .718** .470**

CRL9 .291** .475** .539** .582** .611** .654** .697** .790** .856** 1.0 .832** .546**

CRL10 .222** .424** .434** .469** .491** .523** .589** .673** .718** .832** 1.0 .660**

CRL11 .168** .313** .311** .337** .318** .348** .398** .447** .470** .546** .660** 1.0


Table 5.18: Pearson Correlation coefficients of the Cumulative Repaid Loan variables



The repayment period seems to be significantly correlated with this monthly parameter, thus it can be a good predictor.

CUMULATIVE SAVINGS

CS0 CS1 CS2 CS3 CS4 CS5 CS6 CS7 CS8 CS9 CS10 CS11

PC .270** .385** .475** .497** .516** .534** .459** .443** .468** .480** .475** .478**

RP -.077 -.152* -.158** -.151* -.153* -.129* -.137* -.151* -.125* -.100 -.070 -.046

CS0 1 .861** .798** .758** .728** .700** .678** .638** .631** .626** .602** .603**

CS1 .861** 1 .951** .918** .896** .872** .794** .763** .766** .768** .752** .754**

CS2 .798** .951** 1 .981** .965** .947** .862** .823** .830** .831** .813** .814**

CS3 .758** .918** .981** 1 .985** .974** .889** .848** .860** .863** .844** .845**

CS4 .728** .896** .965** .985** 1 .991** .917** .875** .886** .890** .869** .869**

CS5 .700** .872** .947** .974** .991** 1 .927** .888** .903** .907** .887** .887**

CS6 .678** .794** .862** .889** .917** .927** 1 .964** .962** .956** .929** .925**

CS7 .638** .763** .823** .848** .875** .888** .964** 1 .994** .985** .973** .968**

CS8 .631** .766** .830** .860** .886** .903** .962** .994** 1 .995** .984** .979**

CS9 .626** .768** .831** .863** .890** .907** .956** .985** .995** 1 .991** .988**

CS10 .602** .752** .813** .844** .869** .887** .929** .973** .984** .991** 1 .998**

CS11 .603** .754** .814** .845** .869** .887** .925** .968** .979** .988** .998** 1


Table 5.19: Pearson Correlation coefficients of the Cumulative Savings variables

The program code is significantly and positively correlated with the monthly savings, while the

repayment period does not seem very correlated to them (significant coefficient but very low

values).



5.3. MODEL REGRESSION

5.3.1 THEORETICAL INTRODUCTION

Multiple linear regression attempts to model the relationship between two or more explanatory

variables and a response variable by fitting a linear equation to observed data. More specifically,

this type of regression fits a line through a multi-dimensional cloud of data points.

Formally, the model for multiple linear regression, given n observations, is

yi = 0+ 1 xi1+ 2 xi2+ ...+ pxip+ I for I = 1,2, ...n.

In our case the variable Y is the repayment period, while Xi are a selected group of variables from

the data set explained in the previous chapters.

The statistic software used for the analysis, SPSS, allows to compute different types of regression,

and in the specific case of Multivariate one the approach for the insertion of the predictors should

be carefully selected depending on the objective of the analysis. For this research the hierarchical

method (blockwise entry) suits better the study’s necessity to see the progressive impact of

inserting step by step a sequence of predictors and for evaluating the significant level evolution

along the created models.

The number of predictors included is preferable to be not high in order to not overload the model

and risk of redundancy: consequently not all the variables previously analyzed will be inserted into

the model but only a group is selected as it is explained in the following paragraphs. The final

model is composed by 11 independent variables, whose beta coefficients are calculated by a linear

system of 275 observations.

In hierarchical regression predictors are selected based on past work, literature findings or on logic

reasoning. Then the experimenter decides in which order to enter the predictors into the model.

In our case the microfinance literature lacks of studies based on client cash-flow, but concentrates

the attention on more qualitative variables and experiment that considers other feature of the

microfinance policies.

As a general rule, known predictors (from other research) should be entered into the model first in

order of their importance in predicting the outcome. After known predictors have been entered,

the experimenter can add any new predictors into the model.



5.3.2 MODEL APPLICATION

The multivariate regression is conducted through the block method: starting from an initial model

with only 2 independent variables, step by step additional predictors are inserted in the regression

and the impact of this supplementary information on the model preciseness will be represented

by the R parameter.

The literature concentrates the attention of the evaluation of the performance based on

qualitative variables as demographic data and social interaction information, while here the

predictors are deduced by analyzing the cash flow of the clients in the loan repayment.

Consequently the assumptions made for the selection and insertion of the variables are based on

theoretical considerations and on general principle of microcredit. Certainly, one of the

Microfinance pillars is the idea that the poor, even if without any concrete collateral, are able to

repay a small loan if they are instructed and followed in the repayment path. This concept implies

that the women are facilitated by a regular behavior in the cash flow, both in terms of repayment

frequency and in installments amount.

But before analyzing the performance of the models, the blocks of variables are described along

with the assumptions under which they are selected.

BLOCK 1: Loan Size and Program Code.

First of all the main purpose of this research is to study the client performance due to

repayment frequency differences, thus, having computed the variables by taking a

homogeneous time windows of month, this policy’s peculiarity can be detected only through

the variable Program Code. As already seen in chapter 3, the literature does not provide a

univocal answer on this issue. However we expected that the Mother’s Bank program

performs worse by analyzing the statistics of this variables across the programs and based on

the Pearson correlation analysis in which the coefficient was significant and negative. This

preliminary results suggest thus the idea that the weekly frequency with group meeting policy

performs faster than the monthly installment schedule with individual visit to the branch.

The Loan size is inserted taking its logarithm in order to better highlight the interpretation of

the estimated coefficients as marginal effects (dY/dX). The different studies give

heterogeneous answers on the effect of the loan size on the repayment of the loan. For a



given borrower and duration of the loan, it is argued (Freimer & Gordon, 1965) that the

repayment probability decreases with the size of the loan as the probability of default due to

external factors such as illness or accidental destruction of the borrower’s productive assets,

the difference in endowments and moral hazard or strategic default associated costs.

According to Sharma and Zeller (1997) the greater the loan size, the greater the probability of

unwilling default, but another research conducted by Roslan and Karim (2009) found that the

lower is the loan size and the higher is the probability of default, with this result being justified

by the fact that a too low loan amount can attract people who may not be able to repay and

may need grants, thus the lower limit to the loan size should be calculated carefully. For this

reason it is interesting to see if the loan size has or not an impact on the repayment period and

if it is positive or negative.

BLOCK 2: Number of loan installments

The variable of the number of loan installments is inserted in order to see not only if the different

repayment frequency established by the program policy impacts on the performance, but also if

the attendance to the rules in terms of time rate and the frequent provision of loan installments

can be considered a potential characteristic of degree of performance. In general the results are

not useful alone, so additional variables are inserted in the following steps.

Indeed, for example in general terms a low number of loan installments from one side suggests

that the client was able to repay with high loan amounts and in few times, while the higher the

number of installments the more meetings the clients attended or the more the client went to

Sonarpur. On the other hand the absolute number of correct installments required by the policy is

11 and a lower value of the variable considered does not necessarily imply that the client

completes the loan repayments within the year. But if the beta coefficient will result significant

and with positive value, some consideration of the number of installments defined by the policy

should be done, considering that maybe even if an overall lower number of installments the client

has a good performance. In conclusion the results should be cross compared with the other loan

installment related variables



BLOCK 3: Median of monthly loan installments.

After having assessed if the number of the installments paid by the client is significant and in

which terms, it is interesting to see if the behavior in terms of amount of loan installments impact

on the repayment performance.

For this reason the predictor of the median is inserted, considering the monthly loan installments

in order to homogenize the data across programs. Indeed, as already explained, by summing the

weekly installments for each month the Pure Microcredit Program can be compared to the

Mother’s Bank. It is important to highlights that this variable in theory depends on the loan size as,

the higher the loan, the higher the amount that each month the client provides to the bank. But

considering that the Loan size is not correlated with the dependent variable (Pearson Coefficient

between the Repayment Period and the Log Loan Size has significant level equals to 0.96), it is

worthy to see if the clients with higher amount are those who repay faster than the ones that give

smaller installments. In addition, having considered the median and not the mean this variable is

rich of the additional information of the most preferable loan amount of each client.

BLOCK 4 Number of standard loan installments and Variance in the monthly difference between

the loan installment amount and the standard loan installment required

The decision of inserting this predictor is based on the assumption that, the more the cash

flow of the client is aligned to the policy, the better should result the performance. Thus

this predictor considers the times the client respected the policy in terms of amount

provided to the program each month. The beta coefficient is expected to be negative,

meaning that, the higher the number of standard loan installments provided during the

repayment period (maximum 11) the more easy should be the repayment. On the other

hand the clients that decided to repay faster by providing a smaller number of loan

installments repaid with amounts higher than the standard. Theoretically, and in general

terms, a uneven pattern of cash flows increases the risk of a default or of a late repayment

indicating that the client is experiencing difficulties in the repayment of the loan .

Variance in the monthly difference between the loan installment amount and the

standard loan installment required. This predictor was selected because it is necessary to

see not only the total number of time the client respects the policy in terms of standard

installments paid, but also the variance in the amount comparing to the standard



requirement. We expect a positive beta coefficient that is the higher the variance of the

distance from the standard and the higher the repayment period, assuming that a client

who adheres more strictly to the policy rule has a better performance.

BLOCK 5 Period to repay 70%

This predictor is important to see if it is significant and in which terms the repayment of the first

part of the loan. In other word it helps to highlight if the problems comes in the last installments

provision: does the client prefer to repay an high percentage of the total loan amount in advance?

Is the 70% of the loan repaid a good percentage that provides the program responsible an useful

information on the time of completion of the loan repayment? For this type of predictor, the

percentage was selected in order to be a good representative of the second part of the

repayment: as on the one hand 50% and 60% are values too close to the first part on the other

hand the 80% parameter would have been too close to the dependent variable itself.

The last subsections of this chapter consider also the other parameters in order to check if the

results found can be generalized and consider the marginal effect of the other variables as

representative of the behavior in the second part of the repayment period, as the repayment

period of the 70% of the loan is inserted.

BLOCK 6 Loan Repayment Regularity

This index helps to evaluate if the loans with barycenter located before the 6th month have a

repayment period lower than those with cash flow balance shifted on the last month. It is different

from the previous predictor because it consider 100% of the loan repayment and weights the

installments along the repayment period. It is an index that for each loan is positive and takes the

value 1 if the barycenter is perfectly on the 6th month. As previously pointed out, the theory

suggests that a balanced and regular repayment lowers the risk for defaulting.

BLOCK 7 Distance of the Loan Repayment Regularity index from the target of 1 (in absolute

value)

The difference between this predictor and the pure LRR is the fact that we want to assess if

respecting the policy in terms both of installments amount and in frequency regularity is a



necessary condition for a fast repayment or if a more flexible cash flow is the mirror for a better

performance.

BLOCK 8: Savings amount in the Repayment Period

The first savings related variable inserted in the model is the overall amount that the client saves

during the repayment period: conceptually it is the parallel information of the loan size in the loan

related variables, but in addition it is an index of both the client’s involvement in the program

(savings are not mandatory during the repayment) and also of the savings capacity and money

management, fundamental for a good loan repayment. In addition two different considerations

should be done: on one side a positive beta is expected as for example if the repayment period is 7

months of client A and 12 for client B, the first woman has less time for providing extra rupees for

the savings account, while mother B has higher number of months considered thus higher

possibility to increase the money saved. An opposite observation is the following one: for those

clients more committed into the program save more money than those who use the microfinance

services only for microcredit purpose.

BLOCK 9: Savings Mean per month in the Repayment Period

First we wanted to insert the Median of monthly Savings deposits, as we have done for the loan

installments, but in the model this predictors does not provide any significant contribution in any

blocks. Indeed the beta were always not significant all along the models. On the other side it is

also interesting to see the mean effort of the client to save money taking the period of one month

in order to be consistent with the other monthly variables. This predictors helps to depurate the

previous variables from the repayment period factor.

OBSERVATIONS

In this section the multivariate linear regression model is applied to the sample. As already

pointed out, in total there are 312 observations, but only 275 have complete set of data, while 37

do not have savings related variables available. Thus, having considered also this type of

predictors, the analysis is based on 275 cases.



5.4 REGRESSION RESULTS

The results are interpreted by analyzing the following parameters and the values they take in the

set of models described above.

5.4.1 MODEL PERFORMANCE STATISTIC PARAMETERS

R2:: the most common indicator of the preciseness of the model in predicting the

dependent variable is the R squared. It is the percentage of the variation in the outcome

that can be explained by the model: if the SSM is the amount of variance in the outcome

explained by the model, and SST is the total variation of the data.

R2 = SSM/SST= Sum of Squared explained by the model/Total Sum of Squared

The more this parameter tends to 1, the more the model fits the data in comparison to the

simple average of the dependent variable.

F-test: it is a measure of how much the model has improved the prediction of the outcome

compared to the level of inaccuracy of the model: it is used for comparing statistical

models that have been fitted to a data set, in order to identify the one that best predicts

the population.

In short, a good model should have a large F value greater than 1 at least: it arises by

considering a decomposition of the variability in a collection of data in terms of sums of

squares in other words it is a ratio between the explained variation and the unexplained

variation in the model.

The t-statistic tests tell whether the b-value is different from 0 relative to the variation in

b-values across samples. When the standard error is small even a small deviation from zero

can reflect a meaningful difference because b is representative of the majority of possible

samples. If the standard error is very small, then it means that most samples are likely to

have a b-value similar to the one in our sample (because there is little variation across

samples).

Durbin–Watson test: the assumption of having, for any two observations, the residual

terms independent, is verified by looking at the Durbin-Watson parameter which tests for

serial correlations between errors. Its value can vary between 0 and 4 with a value of 2

meaning that the residuals are uncorrelated. A value greater than 2 indicates a negative

correlation between adjacent residuals, whereas a value below 2 indicates a positive



correlation. The size of the Durbin–Watson statistic depends upon the number of

predictors in the model and the number of observations.

Multicollinearity tests: after having already anaylized the correlation matrix, in which the

Pearson correlation coefficients need not to be high, the following parameter are

considered for checking possible problems of multicollinearity.

o The tolerance (T) measures the influence of one independent variable on all other

independent variables. It is defined as T = 1 – R² for these first step regression

analysis. With T < 0.2 there might be multicollinearity in the data and with T < 0.01

there certainly is.

o Variance Inflation Factor (VIF) of the linear regression is defined as VIF = 1/T.

Similarly with VIF > 10 there is an indication for multicollinearity to be present.

These model performance parameters were taken into consideration during the model

assessment in order to low down the multicollinearity problems detected during the first phases

of the model design.

The set of the selected predictors and the

sequence by which they enter in the model,

as already explained, can be found in the

following table.

Table 5.20: Variables entered in the model at

each step

The model results are evaluated by analyzing the statistic parameters in the next table:

Model R Square

Adjusted R Square

Std. Error of the

Estimate

R Square Change

F Change Degree freedom

1

Degree freedom

2

Sig. F Change

Durbin-Watson

1 .014 .007 7.22 .014 1.912 2 272 .150 2 .152 .143 6.71 .138 44.225 1 271 .000 3 .190 .178 6.58 .037 12.476 1 270 .000 4 .733 .728 3.78 .544 548.475 1 269 .000 5 .750 .745 3.66 .017 18.155 1 268 .000 6 .765 .758 3.57 .015 8.488 2 266 .000 7 .775 .768 3.50 .010 11.810 1 265 .001 8 .776 .768 3.50 .001 1.053 1 264 .306 9 .826 .819 3.09 .050 75.169 1 263 .000 1.835

Table 5.21: Model’s performance parameters along the different steps

Model Variables Entered

1 Program Code, Log Loan Size

2 Number Loan Installments

3 Median of Monthly loan installments

4 Period to repay 70% loan

5 Loan Repayment Regularity

6 Variance Distance(Loan Installment-SLI), Number Standard Loan Installment

7 Distance abs (LRR-1)

8 Savings Amount in the RP

9 Savings Mean per month in RP



The R square value started from a very low number (0.014): this is expected as the predictors

inserted are only 2 (Degree of freedom 1) and thus, with 275 observations, the model can not

accurately predict the dependent variable. In general terms its value grows definitely with the

insertion of the Repayment Period for repaying 70% of the loan: we can conclude that the

behavior in terms of time to repay the first large part of the loan impacts on the predictability of

the dependent variable. In the last sections of the chapter the results of the regression done by

inserting the other percentage related variables gives similar results that support the conclusions

we arrived at.

But it is important to notice that also the Loan Repayment Regularity has a huge influence in the

preciseness of the model. This can not be detected from the present results as this predictor

enters in the block after the repayment period for 70% of the loan. However the regression was

tested by deleting the LRR predictor and the consequence was the loss of predictability power of

the model.

On the other hand the adjusted R2 gives us some idea of how well our model generalizes and

ideally it is desirable to have values very close to the one of R2. It seems that the model can be

generalized because the distance from the previous parameter approximately does not exceed

2%. This shrinkage means that if the model were derived from the population rather than a

sample it would account for approximately 2% less variance in the outcome.

Moving to the standard error of the estimates, they start from a quite high value, but arrives to

less than the half of it, signaling a strong improvement in the preciseness of the model.

If the improvement due to fitting the regression model is much greater than the inaccuracy within

the model, then the value of F will be greater than 1, as it happens in almost all the steps models,

with a relative significance lower than 0.001. The only critical blocks are the first and the eighth. In

the former case, the bad performance is attributed to the low number of variables inserted in the

model, while in the latter case this parameter, along with also the low value or the change in the R

square, signals that the predictor inserted in this block (total savings amount in the repayment

period) do not provide greater preciseness to the model.

Finally, the last column, Durbin-Watson coefficient, informs that the assumption of independent

errors is tenable, being the value close to 2, thus the errors in the regression are independent.



5.4.2 BETA COEFFICIENTS TABLE

In the following table the columns represent the set of blocks and in the lines , for each predictor,

the beta coefficients and their standard error (in parenthesis) are listed. The significant level of

this value are coding with a system of stars: the higher the number of stars, the more the

coefficient is significant.

In addition, the last rows represent the statistic indicators analyzed in the previous section, but

here they are useful in order to extrapolate the contribution of the single predictor.

Variables/Model 1 2 3 4 5 6 7 8 9

(Constant) 41.47***

32.13***

4.58 20.74***

18.69***

12.97**

16.64***

16.68***

28.15***

(-6.27) (5.99) (9.76) (5.65) (5.50) (5.53) (5.53) (5.53) (5.06)

Program Code -1.87 -0.28 -0.27 2.16***

2.32***

2.57***

2.72***

2.89***

1.40***

(-0.96) (0.93) (0.91) (0.53) (0.52) (0.56) (0.55) (0.57) (0.53)

Log Loan Size 1.82 0.35 9.32***

-6.62***

-6.32***

-3.92* -4.27**

-4.32**

-4.58***

(-1.75) (1.64) (3.01) (1.86) (1.80) (1.85) (1.81) (1.81) (1.60)


1.59***

1.51***

1.98***

1.99***

1.95***

1.85***

1.85***

1.34***

(0.24) (0.23) (0.14) (0.13) (0.18) (0.18) (0.18) (0.17)

Median of Monthly loan installments

-0.01***

2.71 E-3 *

2.37 E-3

1.23 E-3

1.27E-3

1.59E-3

1.60E-3

(2.68E-3)

(1.62E-3)

(1.58E-3)

(1.58E-3)

(1.55E-3)

(1.58E-3)

(1.40E-3)


0.81***

0.53***

0.49***

0.48***

0.49***

0.40***

(0.03) (0.07) (0.07) (0.07) (0.07) (0.06)


11.84***

13.24***

13.70***

13.57***

10.14***

(2.78) (2.76) (2.71) (2.71) (2.43)


-2.44 -3.40**

-3.21**

-2.45*

(1.40) (1.40) (1.41) (1.25)

Var Distance(Loan Installment-SLI)

-2.46E-6***

-2.00 E-6**

-2.04E-6**

-1.27E-6*

(8.09E-7)

(8.05E-7)

(8.05E-7)

(7.17E-7)


-6.66**

-6.76**

-4.91**



Table 5.22: Beta coefficient of the multivariate regression

Program Code: this variable is very sensible to the predictors inserted in the model, thus

additional tests were done by inserting an Interaction Effect variable and in addition

checking the result with the Repayment periods of different percentages of the loan, as it is

explained after this section. Indeed, this fundamental predictor is not significant and with

negative values for the first 3 models, but with the insertion of the Period to repay 70% of

the loan it becomes very significant but changes the sign. This means that the clients in the

pure Microcredit program (Program Code = 1) repayments have lower performances in

terms of repayment period compared to the women of Mother’s Bank in the repayment of

the last 30% of loan. From the set of model this delay can be evaluated in a range from

1.40 weeks (model 9) to 2.89 (model 8). The insertion of the savings mean per month

almost cut the half of the beta coefficient. These results are in deep analyzed in the

following sections.

Logarithm of the Loan Size: from the third model, with the insertion of the median of the

monthly loan installments, this predictor becomes highly significant (p < 0.01) with a

positive sign, meaning that the higher is the loan and the slower is the repayment. From

the fourth model, with the insertion of the period to repay 70% loan, the predictor remains

highly significant but with a negative sign. This means that the higher is the loan size and

the faster is the repayment for the last part of the loan. Being the variable the logarithm,

the beta can not be taken as it is in the table: in the last model the 10,000 (Log Loan Size =

* * *

(1.94) (1.94) (1.73)


-5.90E-4

0.02***

(5.75E-4)

(2.42E-3)


-0.21***

(0.02)

R Square 0.014 0.152 0.190 0.733 0.750 0.765 0.775 0.776 0.826

R Square Change 0.014 0.138 0.037 0.544 0.017 0.015 0.010 0.001 0.050

Std. Error of the Estimate

7.23 6.72 6.58 3.78 3.66 3.57 3.50 3.50 3.09

Dependent Variable: Repayment period in weeks.

***,**,* imply significance at 1, 5and 10 percent, respectively; Standard Error in parenthesis.



4) rupees loan is repaid with less than one month (precisely 4.58 weeks) than a loan of

1,000 rupees (Log Loan Size = 3).

The Number of loan installments has a more homogeneous behavior in the set of models:

this predictor is always significant and positive. This means that the more is the number of

loan installments the higher is the repayment period. Having considered monthly loan

installments, it is expected that, the increase of one unit of loan instalment causes the

increase of one month (4 weeks) of the repayment period. But the beta coefficient gives a

different information: indeed its value is always lower than 2. Thus the clients in general do

not provide each month an installments (or 4 installments in the case of the pure

microcredit programs), and an increase in the number suggest a worst performance

comparing to those woman that gives less installments.

The median of monthly loan installments has very low value, due to the fact that the

installments amount goes from a minimum of 100 rupees. Its significance level decreases

as more predictors are inserted into the model, in particular the Repayment period of 70%

of the loan size: this implies that the repayment period is not influenced by the median

amount provided if the focus is on the last part of the repayment. It is important to notice

that, with this insertion, the loan size becomes significant, pushing the beta very high but

positive before the zoom on the last part of the repayment that starts from the 4th model.

The addition of the Period to repay 70% of the loan pushes the R square to a high value,

lowering down the standard error of the estimates. This variable, being also always

significant, results important for the performance evaluation in terms of preciseness. On

the one hand it is important to notice that one more week in this predictor’s value does

not results in the increase of one week in the overall loan repayment, but only of half a

week: indeed the beta coefficients take values near 0.50 in the models’ set. This implies

that most of the clients have a late repayment, thus a delay in the first period of the loan

repayment is then recovered in the last weeks of the repayment. Additional regressions

were computed with different percentages of loan repaid. They are briefly shown in the

following sections.

The Loan Repayment Regularity predictor has similar power of increasing the R square of

the period to repay 70% of the loan predictor if inserted before it. However it was checked

that only if these two variables are inserted together, the results in terms of model



preciseness and variables’ significance level are high. This variable is always significant with

a positive beta, thus the hypothesis of an increase of the repayment period if the

barycenter is shifted after the half of the standard repayment period is confirmed. This

indicator is 1 for a barycenter perfectly in the 6 month, thus an increase of 0.17 (1 / 6)

means a shift of 1 month. Having beta coefficient with values near 13, this implies that a

shift of one month (∆LRR= +0.17) results in an increase in the repayment period equal to

approximately 2 weeks (0.17*13). This confirms the fact that a balanced cash flow is a

signal of a faster repayment.

The hypothesis made on the Number of standard Loan Installments was the following one:

the more the client follows the policy and repay with the amount suggested, the better is

the performance. This is confirmed by the significant beta coefficient that this predictor

takes in the models: the values are always negative, near to 3, consequently the increase of

one installment with standard loan amount decreases the repayment period by 3 weeks for

last part of the loan size to be repaid. We remind however that this predictor was

positively correlated with the repayment period for 100% of the loan size in the Pearson

correlation analysis.

The variable of the variance of the difference between the amount provided by the client

and the standard loan installment was expected to negatively contribute to the

dependent variable, that is to the repayment period: indeed the beta coefficient of this

predictor is always negative, thus the lower is the variance of the distance from the policy

in terms of monthly installments and the better is the client performance.

The distance between the Loan Repayment Regularity index and the target value (1) is

taken in absolute value in order to see if a greater distance either in terms of delay or of

advance repayment causes an increase in the repayment period. Surprisingly the significant

beta coefficient are always negative, thus the higher the distance the less the repayment

period. This information should be crossed compared with the one of the LRR. Indeed in

that case a late repayment contributes in an increase in the weeks for a complete

repayment. It can be deduced that in general terms it is not important that the balance

stay at the 6th month, while that it is not after this month. In conclusion a repayment with a

barycenter shifted on the initial months has definitely higher probability to repay faster

than one with a barycenter after the 6th month.



The insertion of the first savings related variable provides a very low increase in the R

square of the model (0.001). In this block this variable, the Savings Amount in the RP, is

both not significant and also with very low value, but if inserted with the additional

information of the savings mean per month, the coefficient becomes very significant and

signals that an increase of 100 rupees in the savings account of the client during the

repayment period lowers down the performance by increasing the repayment period by 2

weeks. This suggests that the effort the client puts in providing the savings results in a

delay from the loan repayment point of view.

Finally the last predictor inserted, the Savings mean per month, pushes the R square to

0.826 value and lowers down the standard error of the estimate to 3.09. This variable is

significant and has a negative parameter meaning that the higher is the mean amount

saved by the clients and the better is the performance. If this result is analyzed with the

previous one it can be done the hypothesis that, in general terms a great amount saved in

the repayment period decreases the performance but if it is well distributed during the

repayment period it increases the client performance in terms of a shorter repayment

period.

Briefly considering the other performance indicators of the model, F parameter in the Anova test

is always higher than 1 as it can be seen from the table in the annexes.; only in the first model it

has a low value (1.9).

From a multicollinearity point of view, the T index is lower than 0.2 for the Period to repay the

70% of the loan, for the LRR and Log Loan size but in all these cases the VIF values are not higher

than 10, thus we can conclude that the performance of the model is acceptable from the

collinearity side. The main problem is detected in the last model for the savings related variables:

both the T and VIF parameters take values nom complaint with the test of no multicollinearity.

This implies that the two variables are strongly correlated.

In conclusion the analysis of the cash flows can be useful for the prediction of the repayment

period, as it can be deduced from the high R value and the significant level of the variables. In

addition the most relevant variables for the models are those linked to the loan installments.

The family of the savings related predictors resulted not fundamental for the analysis for the

following reasons: firstly other variables were inserted in the model (Number of Savings Deposits,

Median of Monthly Savings Deposits and Variance of Monthly Savings Deposits) but their



contribution to the model was very poor and their beta coefficients have never been significant;

secondly the one finally selected for the last version of the model rose multicollinearity issues. In

conclusion the savings cash flow does not provide useful information for the performance analysis

of the clients in terms of repayment period.



5.4.3 ADDITIONAL EVALUTION ON THE REGRESSION RESULTS

VARIATIONS OF THE INDEPENDENT VARIABLE “PERIOD TO REPAY XX% OF

THE LOAN”.

This subsection considers the variables Repayment period for repaying 50-60-70-80% of the loan

size.

In order to demonstrate that the percentage selected, the 70%, is a good predictor of the first part

of the repayment period, the regression is computed by inserting the other similar predictors in

the same 4th step and the results are analyzed. As the blocks 1, 2 and 3 are not influenced by this

variation, the table starts with the beta coefficients from the 4th block, where previously the

Period to repay 70% of the loan was inserted. Each column represents the model explained in the

previous section but having inserting the different “Period to repay xx% of the loan” variables: in

the first column for example, the results of the beta coefficients are shown for the model where

the variable inserted in the 4th model is the Period to repay 50% of the loan. Accordingly the third

column contains the values already described.

In this way, it is possible to see how the prediction of the total repayment period depends on the

percentage already repaid.

Considering the most important variables, we notice that

1) The Program code has positive and significant beta coefficients in all the blocks and

columns, with the only exception of the block 9, with the period to repay 80% of the loan

variable. This implies first that the 70% percentage does not create a peculiar picture, but

that the results of the models can be generalized to the repayment of the second half of

the loan. This is consistent with the hypothesis that the mother’s bank clients repay faster

the second part of the loan but as the percentage tends to 100% then relationship

between the program code and the repayment period is less evident because in average

the Microcredit program repay faster.

2) The loan size beta has heterogeneous behavior across the different set of models: the

higher the percentage repaid, the higher the significance level. In addition the highest

values are not in the 80% set of model but in the 70% column: the marginal effect of having

a higher loan and being faster in the repayment is more evident if the period to repay the

70% of the loan is considered.



3) The Period to repay 50% of the loan is not significant, thus this variable has not a prediction

power. In addition it has negative beta coefficient from the 5th block, hence, taking the

other variables fixed, the Microcredit Program performs better in comparison to the

Mother’s Bank if we consider the weeks for paying back the first half of the loan.

The Period to repay 60% of the loan has a significant and positive beta, as the 70 and 80%

variables, but the values are definitely lower than the last two. Thus its marginal effect

power is lower than those of the other two predictors.

RP_50% RP_60% RP_70% RP_80%

Model Unstandardized Coefficients B

4 (Constant) 15.57** 15.61** 20.74*** 14.31***

ProgramCode 2.62*** 1.63*** 2.16*** 0.92**

LogLoanSize -3.59 -3.98* -6.62*** -4.13***

NumberLoanInstallments 2.36*** 2.15*** 1.98*** 1.36***

MedianMonthlyloaninstallments -0.0009 0.0009 2.71 E-3 * 0.0016

Period to repay XX loan 0.80*** 0.76*** 0.81*** 0.82***

5 (Constant) 12.37** 14.34** 18.69*** 14.57***

ProgramCode 2.01*** 2.17*** 2.32*** 1.29***

LogLoanSize -2.98 -4.33** -6.32*** -4.71***


MedianMonthlyloaninstallments -0.0001 0.0008 2.37 E-3 0.0019

Period to repay XX loan -0.17* 0.21*** 0.53*** 0.67***

LRR 34.65*** 22.98*** 11.84*** 7.14***

6 (Constant) 6.65 7.69 12.97** 11.57**

ProgramCode 2.70*** 2.66*** 2.57** 1.45***

LogLoanSize -0.85 -1.65 -3.92* -3.42**


MedianMonthlyloaninstallments -0.0014 -0.0005 1.23 E-3 0.0013

Period to repay XX loan -0.08 0.23*** 0.49*** 0.63***

LRR 32.40*** 22.66*** 13.24*** 8.42***

NumberStandardLoanInstallment -4.31*** -3.97*** -2.44 -1.12

VarLoanIntallmentSLI 0.0000** 0.0000*** -2.46E-6*** 0.0000**

7 (Constant) 10.79* 12.38** 16.64*** 13.28***

ProgramCode 2.90*** 2.82*** 2.72*** 1.56***

LogLoanSize -1.39 -2.23 -4.27** -3.54**


MedianMonthlyloaninstallments -0.0012 -0.0003 1.27E-3 0.0012


LRR 31.33*** 21.73*** 13.70*** 9.21***

NumberStandardLoanInstallment -5.32*** -5.03*** -3.40** -1.68

VarLoanIntallmentSLI 0.0000** 0.0000** -2.00 E-06** 0.0000***

DistanceabsLRR1 -7.10*** -7.99*** -6.66*** -3.27*



RP_50% RP_60% RP_70% RP_80%

Model Unstandardized Coefficients B

8 (Constant) 10.79* 12.38** 0.49*** 13.27***

ProgramCode 2.99*** 2.95*** 2.89*** 1.69***

LogLoanSize -1.40 -2.26 -4.32** -3.56**


MedianMonthlyloaninstallments -0.0011 -0.0001 1.59 0.0015


LRR 31.29*** 21.60*** 13.57*** 9.19***

NumberStandardLoanInstallment -5.23*** -4.88*** -3.21** -1.54

VarLoanIntallmentSLI 0.0000** 0.0000** -2.04E-06** 0.0000*

DistanceabsLRR1 -7.16*** -8.09*** -6.76*** -3.35**

SavingsAmountintheRP 0.00 0.00 -5.90E-4 0.00

9 (Constant) 24.83*** 25.81*** 0.40*** 23.72***

ProgramCode 1.27** 1.32** 1.40*** 0.62

LogLoanSize -2.26 -2.99* -4.58*** -3.90***


MedianMonthlyloaninstallments -0.0005 0.0003 1.60E-3 0.0015


LRR 24.30*** 16.03*** 10.14*** 7.14***

NumberStandardLoanInstallment -3.93*** -3.70*** -2.45* -1.19

VarLoanIntallmentSLI 0.0000 0.0000* -1.27E-06* 0.0000

DistanceabsLRR1 -4.94*** -5.82*** -4.91*** -2.35

SavingsAmountintheRP 0.02*** 0.02*** 0.02*** 0.02***

SavingsMeanpermonthinRP -0.23*** -0.22*** -0.21*** -0.18***

Dependent Variable: Repayment period in weeks.


Table 5.23: Beta coefficients of multivariate regression with different RP_XX %loan predictors

With this analysis we demonstrated that, inserting as a predictor the Period to repay a percentage

higher than 50% of the loan, belonging to the Microcredit program means an additional 2 weeks in

average to the repayment period in comparison to the Mother’s Bank program, ceteris paribus.



VARIATIONS OF THE DEPENDENT VARIABLE “REPAYMENT PERIOD OF A

PERCENTAGE XX% OF THE LOAN”

In the previous section we demonstrated that the Mother’s Bank program borrowers repay faster

the second part of the loan, having the beta coefficient of the Program Code dummy variable

positive and significant when the dependent variable is the overall repayment period, that is the

period of the repayment of 100% of the loan, and one predictor is the time to repay the 70% of

the loan (hypothesis tested also for other percentages).

From the Pearson correlation analysis and the general statistical analysis, the Mother’s Bank

resulted with an overall lower repayment period (Pearson correlation negative and significant,

mean and mode value of the repayment period higher for the Mother’s Bank program).

From these two considerations we arrived at the conclusions that the Microcredit Program should

result faster in the performance of the first part of the loan size, while the speed in the repayment

lowers down in the second, in comparison to the Mother’s Bank clients behavior.

A method to demonstrate this hypothesis consists in computing the regression analysis by

inserting not the repayment period for 100% of the loan as dependent variable, but the period to

repay a smaller percentage. In this case the dependent variables selected were only those that do

not refer to the entire repayment period, as the time windows for the analysis is smaller. Hence

the predictors are the Program Code and the Logarithm of the Loan Size, inserted in one step, with

the forced method.

Four regressions were computed, and the results are shown in the following table: each column

refers to one regression where the dependent variables are respectively Period to repay 50% of

the loan, Period to repay 60% of the loan, Period to repay 70% of the loan and finally Period to

repay 80% of the loan.

As it can be noticed, the hypothesis is demonstrated: in all the regressions, the Program code

dummy variable is significant and with beta value negative, thus the Microcredit Program clients

repay faster than the Mother’s Bank clients the first part of the loan. It is important to notice that

considering the 80% and 60% of loan repaid this relation is less evident but always significant.

In addition this analysis confirms the hypothesis that the higher loans are repaid faster only for the

last percentages (beta coefficient negative in the main regression from 4th block) but considering

the first part of the loan the relation is opposite (here the beta coefficients of the LogLoanSize



variable are positive). These results are consistent with the no significant Pearson correlation of

the loan size with the repayment period: having different trend in the first and second part, it is

not possible to generalize a positive or negative relation between the total repayment period and

the amount disbursed.

Dependent Variables

Unstandardized Coefficients with Y = Repayment period of XX% Loan Size

50% 60% 70% 80%

(Constant) 12.22** 21.05*** 21.04*** 29.92***

(4.97) (5.68) (5.86) (6.01)

ProgramCode -2.58*** -1.67* -2.23** -1.62*

(0.76) (0.87) (0.90) (0.92)

LogLoanSize 4.89*** 3.79** 5.02*** 3.59**

(1.39) (1.59) (1.64) (1.68)

Dependent Variable: Repayment period of XX % in weeks.


Table 5.24: Beta coefficients of multivariate regression with

different RP_XX %loan dependent variables



INTERACTION EFFECT:

As both the Program code and the Number of loan installments are significantly correlated with

the repayment period, the question of a possible circle in the correlation coefficients between

these 3 variables rose: we faced the need to demonstrate if the significant relation between the

dependent variable and the Number of loan installments was due to the strong link of this last

predictor with the Program Code. The answer was negative thanks to the application of the

interaction effect method described in the following lines.

Charter 5.46: Scatter dot of the Number of Loan installments values according to the Loan

Repayment variable, divided for each program code (Program Code 0 is Mother’s Bank, Program

Code 1 is Microcredit Program)

From the scatterdots of the Number of loan installments variable in relationship with the

repayment period, are present different patterns mainly only in the typology of the Number of

loan Installment predictor: indeed for mother’s Bank program (represented by the graph on the

left with program code equal to 0) it takes natural value as the program asks to the clients to come

once in a month. On the other side, the Pure Microcredit program (code equals to 1) has

continuous values on the Y axis as the number of weekly meetings are divided by 4 in order to

transform the variable and compare the data across programs.

The interaction effects represent the combined effects of variables on the criterion or dependent

measure: when interaction effect is present, the impact of one variable depends on the level of

the other variable.



For this reason an additional variable was inserted into the regression model in order to point out

if the high significant level of the variables were due to an interaction effect between them: the

Interaction Effect predictor, calculated by multiplying the Program Code and the Number of Loan

Installments.

The product term should be significant in the regression equation in order for the interaction to be

interpretable, but this did not happen. Indeed the insertion of this new parameter causes the loss

of significance in the Program code in the 4th and 5th as it can be seen from the beta coefficient

table.

In addition the Interaction Effect variable’s beta coefficient have very low significant level,

becoming relevant in the last model due to the insertion of the savings variables. But in this last

model we saw there is a multicollinearity issue.

In addition the insertion of this new parameter worse the performance of the model in terms of

multicollinearity: the value of T and VIF for the Program code and the Interaction Effect are always

respectively lower than 0.5 and higher than 30 in all the different models.

All the considerations explained above lead to the conclusion that there is no interaction effect

between the Program Code and the Number of Loan Installments. Thus the set of models

suggested is not the one with the interaction effect variables, but the previous one already

described.



1 2 3 4 5 6 7 8 9

(Constant) 41.47*** 33.42*** 3.59 20.90*** 18.83*** 13.11** 16.86*** 16.87*** 28.50***

(6.27) (6.50) (9.72) (5.68) (5.53) (5.53) (5.52) (5.52) (5.04)

Log Loan Size 1.82 0.31 11.16*** -6.88*** -6.56*** -4.57** -5.00*** -4.97*** -5.41***

(1.75) (1.64) (3.13) (1.97) (1.91) (1.91) (1.88) (1.88) (1.66)

Program Code

-1.87* -2.43 -8.95** 3.21 3.23 5.70** 6.21** 6.05** 5.35**

(.96) (4.26) (4.45) (2.63) (2.55) (2.54) (2.49) (2.50) (2.21)


1.46*** 0.99*** 2.04*** 2.05*** 2.13*** 2.05*** 2.03*** 1.57***

(0.34) (0.35) (0.21) (0.20) (0.23) (0.23) (0.23) (0.21)

Interaction Effect PC NLI

0.25 0.99* -0.12 -0.10 -0.35 -0.40 -0.36 -0.45*

(0.47) (0.50) (0.29) (0.28) (0.28) (0.28) (0.28) (0.25)

Median of Monthly loan installments

-0.01*** 3.00* 2.61E-3 2.03E-3 2.17 E-3 2.35 2.55E-3*

(2.87 E-3)

(1.77 E-3)

(1.72 E-3)

(1.70 E-3) (1.67 E-3) (1.68 E-3) (1.48 E-3)


0.81*** 0.53*** 0.50*** 0.49*** 0.49*** 0.40***

(0.03) (0.07) (0.07) (0.07) (0.07) (0.06)


11.83*** 13.26*** 13.74*** 13.63*** 10.18***

(2.78) (2.75) (2.70) (2.71) (2.42)


-2.53* -3.53** -3.36** -2.63**

(1.40) (1.40) (1.42) (1.25)

Var Distance(Loan Installment-SLI)

-2.64 E-06***

-2.20E-06***

-2.21 E-06***

-1.49 E-06**

(8.22E-07) (8.15E-07)

(8.16E-07) (7.23E-07)


-6.78*** -6.85*** -5.01***

(1.94) (1.94) (1.72)


-4.76E-4 0.02***

(0.58E-3) (2.42E-3)


-0.21***

(0.02)

R Square 0.014 0.153 0.201 0.734 0.750 0.767 0.777 0.778 0.828

R Square Change

0.014 0.139 0.048 0.532 0.017 0.016 0.010 0.001 0.051

Std. Error of the Estimate

7.23 6.72 6.54 3.79 3.67 3.56 3.49 3.49 3.07

a. Dependent Variable: Repayment period in weeks.


Table 5.25: Beta coefficients of multivariate regression with also the Interaction Effect predictor



CHAPTER 6 - CONCLUSIONS The most important conclusions deduced from the multivariate linear regression analysis and the

general statistic analysis and Pearson Correlation coefficients are described below.

6.1 REPAYMENT PERIOD PERFORMANCES IN THE TWO MICROCREDIT PROGRAMS.

The MOTHER’S BANK program repays slower the total loan size but faster the last part of the loan

amount in comparison to the PURE MICROCREDIT program. Hence a higher installments frequency

and group meetings are a better mix for the repayment period but the special formula dedicated

to the mothers of sponsored child, with monthly installments and individual visits to the bank,

allows a faster performance in the last part of the loan repayment, ceteris paribus.

The graph shows that the program code

1, the Microcredit Program, has always

a repayment period lower than the

program code 0, Mother’s Bank, for all

the Cumulative Loan Repaid until the

12th month.

Charter 6.1: mean linear graph representation of the Cumulative Repaid Loan monthly variables,

divided for each program code

This affirmation is also confirmed by the following fact: the collected data start from the loans

disbursed in April 2010, from this date approximately 10% of the loans in Mother’s Bank program

and 5% of the loans in the Microcredit program have not already been fully repaid at July 2013,

that is considering a maximum 3 years delay. Thus in the sample used for the analysis there is an

asymmetry that should be considered in evaluating the regression result: the better performance



of the microcredit program is also demonstrated by the percentage of loans with delayed

payments in the period considered.

In addition, in the first blocks of the regression the sign of the Beta coefficient for the Program

Code is negative, but not significant, then with the insertion of the powerful predictor Period to

repay the 70% of the loan, the coefficient becomes positive and significant. From the general

statistical analysis the examination of the Repayment Period mean suggested that the microcredit

program (46.5 weeks) is faster than the Mother’s Bank one (48.3 weeks) in the completion of the

loan repayment. However the median and the mode values are more different in the first program

(respectively 48.3 and 50.7 weeks) than in the second (48.9 and 48.1 respectively). Accordingly the

additional information provided thanks to the period to repay 70% of the loan predictor unable us

to evaluate more precisely the marginal impact on the repayment performance of belonging to

the two different program focusing the attention on the last amount to be repaid.

The repayment period means in the two subsamples differs of approximately 2 weeks, thus in

theory we expected that the contribution of being from Microcredit Program lowers down the

dependent variable of this amount more or less. This does not happen in the regression once the

Period to repay 70% of the loan variable is considered: on the opposite being from Microcredit

Program, the one with weekly frequency installments, higher the repayment period by 2 weeks.

The total gap is thus equal to 4 weeks. How we can explain it? The beta coefficients explain the

marginal effect. Hence the other variables in total fill this gap: this means that in the Microcredit

program there is lower variability and higher regularity than in the Mother’s Bank program.

In other words: if the mean of the Microcredit Program repayment period is around 46 weeks and

the fact of being in this program contributes by 2 weeks (beta coefficient of the program code

equal to 2 and program code equal to 1), the other 44 weeks are attributed to the other predictors

in the regression.

On the other hand, if the mean of Mother’s Bank repayment period is around 48 weeks and the

fact of being in this program does not contributes (program code equal to 0), the 48 weeks are

attributes to the other predictors of the regression.

In conclusion the Mother’s Bank clients are slower in the overall repayment but faster in the last

part than the Microcredit, according to the positive beta coefficient in the regression from the 4th

block.



6.2 LOAN SIZE CATEGORIES AND THE REPAYMENT PERIOD

There is not a homogeneous relation for the Loan size variable on the repayment period: the

higher loans are repaid slower in the first part but then faster in the second part.

The first affirmation is based on the result of the no significant Pearson correlation between the

repayment period and loan size, along with the graphs analysis. The second one relies on the

significant and positive beta coefficient in the regression with Repayment Period of 50, 60, 70 or

80 % loan size. The third concept is supported by the significant negative is positively correlated

with the repayment period until the 3rd block (here significant), from the 4th it becomes negatively

correlated: this means that in general terms the bigger the loan size, the slower the repayment for

100% of the loan, but in the last part of the loan size, that is once considered the period to repay

the 70% of the loan, the situation is opposite: the greater is the loan size and the better is the

performance in terms of weeks necessary to completely repay the loan. Thus the responsible

should monitor the higher categories of loan size in the first months from the loan disbursement.

6.3 REGULAR REPAYMENT CASH FLOW AND REPAYMENT PERFORMANCE

A delayed repayment (loan repayment barycenter shifted on the last months) negatively impact

on the performance of the loan behavior, however unbalanced cash flow (repayment barycenter

far from the 6th month) are an index of smaller the repayment period.

Indeed the Microcredit program,

faster in the overall repayment, has

lower loan repayment regularity

indicators across all the 12 initial

months of the repayment.

Charter 6.2: mean linear graph

representation of the Loan Repayment

Regularity monthly variables, divided

for each program code



Not necessary a well balanced cash flow is an index of fast repayment: data suggest that the client

with barycenter shifted toward the first months repay faster both the last part of the loan and the

100% of the amount than those with barycenter positioned after the 6th month. This is deduced

by the positive correlation and beta coefficient of the Loan Repayment Regularity index with the

repayment period with also the significant negative correlation and beta coefficient of the

distance (LRR-1) with the dependent variable.

6.4 RESPECT OF THE POLICY IN TERMS OF CASH FLOW AND REPAYMENT PERIOD

There is not a homogeneous relation for the Loan size variable with the repayment period: the

higher loans are repaid slower in the first part but then faster in the second part of the repayment.

The first affirmation is based on the result of the no significant Pearson correlation between the

repayment period and loan size, along with the graphs analysis. The second one relies on the

significant and positive beta coefficient in the regression with Repayment Period of 50, 60, 70 or

80 % of the loan. The third concept (the higher the loan amount, the faster the repayment in the

second part) is supported by the positive beta coefficient with the repayment period until the 3rd

block (here significant), from the 4th it becomes negatively correlated: this means that in general

terms the bigger the loan size, the slower the repayment for 100% of the loan, but in the last part

of the loan size, that is once considered the period to repay the 70% of the loan, the situation is

opposite: the greater is the loan size and the better is the performance in terms of weeks

necessary to completely repay the loan. Thus the responsible should monitor the higher categories

of loan size in the first months from the loan disbursement.

6.5 SAVINGS

Savings predictors are not very relevant for a performance analysis in terms of the repayment

period based on the client cash-flow, but due to multicollinearity issue this conclusion can not be

generalized to research different from ours. In general terms the correlation analysis suggested

that the higher the variance and the mean of savings deposits in the repayment period, the better

the overall performance. However only the latter is significant in the regression along with the

total amount of savings, hence in the last part of the repayment the more the client is able to save

the faster is the repayment.



6.6 ADDITIONAL CONSIDERATIONS

We conclude that the repayment policy of the Microcredit Program with weekly frequency and in

group meeting results in a more homogeneous performance in terms of repayment period range

of values, while the monthly installments system of the Mother’s Bank has a divergent impact on

the client performance.

In this second program the flexibility is higher in the loan installment provision, because the

frequency is monthly and not weekly and there are no fixed date for loan installment provision

(while in the pure microcredit program the women met each week in one specific day at one fixed

hour). Consequently the conclusion is the following: if the client is followed during the repayment

period with a constant requirement of small loan installment provision, than her performance in

terms of repayment period is slower in the last part but in general terms the repayment period is

lower.

Finally the repayment period for the 70% loan size is resulted to be an important predictor of the

total number of weeks. This parameter can be a useful tool for the microcredit program

responsibles in order to control if the client has a high probability to default. As a matter of fact,

the 70% of the loan amount in theory should be repaid within the 8th installments, more precisely

after the first month (no required installments) plus 7.7 month, with a total of 8.7 months. In

weeks, the target for 70% of loan repaid is 37.7 weeks from the date of loan disbursement.

Consequently at the 38th week the responsible can check if the client is or not on time with the

repayment and thus prevent a potential defaulter.

A suggestion for improving the performance of the programs is to compare the target Cumulative

Loan Repaid at each month with the actual values, with a more strict monitoring of the Mother’s

Bank program in the initial months of the repayment period.

In addition a managerial suggestion in terms of policy could be the creation of small groups of

women in the Mother’s Bank program that can help the development of social ties, important

component in the microfinance programs. The proposal is to design groups of 5 clients from the

same village and determine a day in the month for their visit to the IIMC branch.

The hope is that, with this additional factor, the women performance in this monthly frequency

program improve at the level of the Microcredit Program.



On the other hand the CEOs of the seven branches around Surnarpour receive the advice to pay

attention to the last period of the loan repayment, where the clients have a worse performance

than the Mother’s Bank program.



ANNEXES

CHAPTER 3

The table below records the Group Number of the registers collected in photo format.

HATGACHA SPONSORED

HATGACHA NOT SPONSORED

CHAKBERIA SPONSORED

CHAKBERIA NOT SPONSORED

1 20 4 27

5 53 5 55

6 61 9 60

7 64 14 163

17 65 15 164

19 67 21 165

22 70 22 168

36 73 25 175

40 75 30 190

45 76 32 191

47 84 92 200

48 86 102 201

49 91 104

57 123 109

58 188 110

60 208 115

68 227 120

72 121

78 123

80 142

83 143

95 145

97 146

101 150

106 151

108 166

114 167

120 171

132 177

134 180

138 196

155 197

168

175

206

211

237



CHAPTER 5: REGRESSION RESULTS

CORRELATIONS RESULTS



REGRESSION RESULTS



REGRESSION RESULTS WITHOUT PERIOD TO REPAY 70% LOAN

BUT WITH LOAN REGULARITY INDEX



REGRESSION RESULTS WITHOUT PERIOD TO REPAY 70% LOAN

AND WITHOU LOAN REGULARITY INDEX



REGRESSION RESULT WITH DIFFERENT RP XX% LOAN

PREDICTORS

With period to repay 50% Loan



With period to repay 80% Loan



REGRESSION RESULT WITH RP 70% AS DEPENDENT VARIABLES

ONLY THE REGRESSION RESULTS WITH PERIOD

TO REPAY 70% OF THE LOAN AS DEPENDENT

VARIABLES WILL BE SHOWN



REGRESSION RESULTS INTERACTION EFFECT VARIABLE



REGRESSION RESULT OF DIFFERENT METHODS FOR ENTERING

VARIABLES

Computing the multivariate regression through different method, the results suggest that the

variables inserted in the previous analysis were well selected in terms of significance level.



FORWARD METHOD

Starting from 0 predictor in the model, in each step the most relevant variables in terms of beta

coefficient and significant level is enter. In evidence those predictors selected for the regression

previously explained.

Mode

l

R R Square Predictors Added in the step Std. Error of

the Estimate

1 .721a .520 Period to repay 70% loan 5.03240

2 .841b .708 Number Loan Installments 3.93455

3 .856c .733 Variance in Loan Installments 3.76785

4 .866d .750 Loan Repayment Regularity 3.65538

5 .870e .757 Program Code 3.60694

6 .875f .765 Savings Mean per month in RP 3.55656

7 .901g .811 Savings Amount in the RP 3.19369

8 .912h .831 Variance Monthly Savings Deposits 3.02228

9 .914i .835 Log Loan Size 2.99416

10 .916j .838 %Number Loan Installments 2.96996

BACKWARD METHOD

Starting from the insertion of all the variables, in each step the less relevant is deleted from the

model.

Mode

l

R R Square Excluded Variables Std. Error of

the

Estimate

1 .923a .852 Variance in Loan Installments 2.89770

2 .923b .852 Median of Monthly loan installments 2.89203

3 .923c .852 Variance Monthly loan Installments 2.88655

4 .923d .852 Number Savings Deposits 2.88345

5 .922e .851 Median (Loan Intallment-SLI) 2.88660

6 .922f .850 %Number Loan Installments 2.89328

7 .921g .848 Median of Monthly Savings Deposit 2.90054

8 .920h .847 Median of Monthly loan installments 2.90847

However the regression was tested by deleting the LRR predictor and the consequence was the

loss of predictability power of the model. Regression senza lrr e rp_70%



TABLES

Table 1.1 Types of Microfinance institutions

Table 1.2 Characteristics of selected leading Microfinance programs

Table 2.1 Socio-economic indicators in West-Bengali and Parganas District

Table 3.1 Performance of programs with different installments frequency

Table 3.2 Characteristics’ comparison of the two Microfinance programs in IIMC

Table 4.1: Matching savings and loan account in Mother’s Bank program



Table 4.4: Excel columns shotscreen 3 (Loan and repayment general data)

Table 4.5: Excel columns shotscreen 4 (Number Loan Installments and Default Indicator data)

Table 4.6: Excel columns shotscreen 5 (Savings variables)

Table 4.7: Excel columns shotscreen 6 (Loan installments variables)

Table 4.8: Excel columns shotscreen 7 (Regularity variables and outsstanding balance)

Table 4.9: Excel columns shotscreen 8 (Monthly loan installments variables)

Table 4.10: Excel columns shotscreen 9 (Cash flow digitalization)

Table 4.11: Loan Repayment Barycenter values along the months

Table 4.12: Loan Repayment Regularity values along the months

Table 4.13: Cumulative Repaid Loan values along the months

Table 4.14:Analysis of the possible results in the relationship within Repayment Period, Program

Code and Number of Loan installments






Table 5.1: 4SD values for loan related variables (1st part)

Table 5.2: 4SD values for loan related variables (2nd part)

Table 5.3: 4SD values for savings related variables

Table 5.4: Descriptive statistic parameters of the main research variables

Table 5.5: Default Indicator frequency and percentage


Mother’s Bank program


Microcredit program

Table 5.8: Frequency of the Loan size categories

Table 5.9: Descriptive statistic parameters of the set of variables loan related

Table 5.10: Descriptive statistic parameters of the set of variables loan related (2nd part)

Table 5.11: Descriptive statistic parameters of the set of savings variables

Table 5.12: Pearson Correlation coefficients of the main variables

Table 5.13: Pearson Correlation coefficients of the Repayment Period related variables

Table 5.14: Pearson Correlation coefficients of the Savings related variables



Table 5.17: Pearson Correlation coefficients of the Loan Regularity Barycenter variables

Table 5.18: Pearson Correlation coefficients of the Cumulative Repaid Loan variables

Table 5.19: Pearson Correlation coefficients of the Cumulative Savings variables

Table 5.20: Variables entered in the model at each step

Table 5.21: Model’s performance parameters along the different steps

Table 5.22: Beta coefficient of the multivariate regression

Table 5.23: Beta coefficients of multivariate regression with different RP_XX %loan predictors

Table 5.24: Beta coefficients of multivariate regression with different RP_XX %loan dependent variables

Table 5.25: Beta coefficients of multivariate regression with also the Interaction Effect predictor



CHARTERS

Charter 4.2: Collection photo organization for Mother’s Bank program

Charter 4.3: Loan Repayment Regularity codification along the months

Charter 4.4: Loan Repayment Barycenter codification along the months

Charter 5.1: Program code percentage pie chart

Charter 5.2: Frequency Histograms of the Repayment period alone and split in the two Program

Code.

Charter 5.3:Representation of the mean of Period for repaying XX% loan split in the two programs

Charter 5.4: Frequency histogram of the Loan Size

Charter 5.5: Frequency histogram of the Loan Size split in the two programs

Charter 5.6: Repayment Period Mean histogram across the Loan Size categories

Charter 5.7: Frequency diagram of the Number of loan installments

Charter 5.8: Repayment Period values scatter dots across the Number of Loan Installments variable

Charter 5.9: Frequency Histograms of the LoanRepayment Barycenter split in the two programs

Charter 5.10: Number of Loan Installment box plot across different Loan Size categories

Charter 5.11: Histogram of Mean of the Percentage of Number of Standard Loan instalments

across different Loan Size categories with a line of the Mean of the Regression Period

Charter 5.12: Scatter dot of the NLI and of the % of NLI for the two programs

Charter 5.13: Scatter dots of the Standard Loan Installment with Loan Installment mean per month

and Loan Installment Median, divided for the two programs

Charter 5.14: Scatter dots of the Repayment Period with Loan Installment mean per month and

Loan Installment Median, divided for the two programs

Charter 5.15: Frequency histogram of the Median (Loan Installment-Standard Loan installment)

Charter 5.16: Scatter dots of the Median (Loan Installment-Standard Loan installment) the across

the Loan size categories

Charter 5.17: Scatter dots of the Median (Loan Installment-Standard Loan installment) with the

Repayment Period




and of the Variance of monthly installments divided by the Program Code


across Loan size categories with the line of the Mean of the Variance of monthly

installments

Charter 5.20: Scatter dot of the Number of loan repayment and the period to repay the 70% of the

loan, spit in 2 subsamples of loan size

Charter 5.21 Mean histogram of the Period to repay 70% of the loan across Loan size categories

with the line of the Mean of the repayment period itself.

Charter 5.22 Frequency histogram of the Loan Repayment Barycenter and Period to repay 70% of

the loan divided by the Program Code

Charter 5.23: Scatter dots of the Loan Repayment Barycenter with the Repayment Period

Charter 5.24: Frequency histogram of the Distance (LRR-1) divided by the Program Code

Charter 5.25: Scatter dots of the Repayment Period with the Distance (LRR-1)

Charter 5.26: Mean histogram of the Loan Repayment Barycenter across Loan size categories with

the line of the Mean of the Variance of the Distance (LRR-1)

Charter 5.26: Scatter dots of the Loan Installments with the Number of Savings Deposits

Charter 5.27: Mean histogram of the Number of savings deposits across Loan size categories


Charter 5.28: Mean scatter dot of the Repayment Period and the Number of Savings Deposits


Charter 5.29: Frequency histogram of the Savings Amount variable in the Repayment Period

Charter 5.30: Histogram of the Savings Amount mean across the Loan Size categories

Charter 5.31: Scatter dot of the Savings Amount in the RP with the Number of Savings Deposits

(blue dots) and the Repayment Period (green dots)

Charter 5.32:Frequency histogram of the Savings Mean per month In the Repayment Period

Charter 5.33: Mean histogram of the Savings Mean per month and the frequency line across Loan

size categories



Charter 5.34: Mean scatter dot of the Savings Mean per month and the Repayment Period divided

by Program Code (Mother’s Bank program in blue dots, Microcredit Program in green

dots)

Charter 5.35: Mean histogram of the Variance in monthly Savings Deposits with line representing

the Mean of the Monthly savings deposit Median across Loan Size categories

Charter 5.36: Set of scatter dots of the Variance in monthly Savings Deposits and the Repayment

period divided by Program Code

Charter 5.37:Chart representing the mean of the set of Cumulative Repaid Loan variables

Charter 5.38:Chart representing the mean of the set of Cumulative Repaid Loan variables, divided

by Program Code (in blue Mother ’s Bank, in green Microcredit program)

Charter 5.39:Chart representing the mean of the set of Cumulative Repaid Loan variables across


Charter 5.40:Chart representing the mean of the set of Loan Repayment Regularity variables

Charter 5.41:Chart representing the mean of the set of Loan Repayment Regularity variables

divided by Program code

Charter 5.42:Chart representing the mean of the set of Loan Repayment Regularity variables across


Charter 5.43:Chart representing the mean of the set of Cumulative savings variables

Charter 5.44:Chart representing the mean of the set of Cumulative Savings variables divided by

Program code

Charter 5.45:Chart representing the mean of the set of Cumulative Savings variables across Loan

Size categories

Charter 5.46: Scatter dot of the Number of Loan installments values according to the Loan

Repayment variable, divided for each program code (Program Code 0 is Mother’s Bank,

Program Code 1 is Microcredit Program)

Charter 6.1: mean linear graph representation of the Cumulative Repaid Loan monthly variables,

divided for each program code

Charter 6.2: mean linear graph representation of the Loan Repayment Regularity monthly

variables, divided for each program code



IMAGES

Image 4.1: Image 4.2: 2nd page of Microcredit Program Collection Book

Image 4.2: 2nd page of Microcredit Program Collection Book

Image 4.3: 3rd and 4th page of Microcredit Program Collection Book

Image 4.4: Main page of Microcredit Program Collection Book

Image 4.5: Final page of Microcredit Program Collection Book




Image 5.1: Graphic representation of the Boxplot method

Image 5.2: Box plots representation of the variables Repayment Period, Number of loan

installments and Loan installments mean per month



Image 5.4: Box plots representation of the savings related variables



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http://www.adb.org/

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