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
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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
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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
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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
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TABLES .......................................................................................................................................... 194
CHARTERS ..................................................................................................................................... 196
BIBLIOGRAPHY ............................................................................................................................. 200
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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
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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
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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.
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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),
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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.
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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.
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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 $1.25 a day at 2005
international prices. Each country has its own national poverty line.
An issue is not only how poverty is measured but what poverty means. This concept has been
changing along the years: during the early decades of microfinance development (1950s, 1960s)
the bulk of the poor was identified in the rural small farmer’s families, with consequent objective
of raise incomes through agricultural credit subsidies. Then in the early 1980s the dominant
thinking moved to the women as representatives of poor, coping with their situation by running
microenterprises, adding in addition the broader scope of social empowerment (Matin et al.,
2002).
So nowadays the meaning of poverty alleviation takes a broader definition: the needs the poor
face are not only economical and linked to the income, but they involve also social factors and
survival perspective. In fact not only the material needs should be considered in order to evaluate
the poverty level. Thus even if on the one hand the income and consumption variables allow to
compare diverse groups across countries and to analyze the capacity of different people to meet
their immediate material needs, on the other hand the final aims of financial services in this
nontraditional sector is the impact on the secondary factors as livelihood level. In fact, as the well-
known theory of Maslow hierarchy of needs suggests, when people have satisfied their
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physiological needs, they are able to look at the safety and belonging needs towards the self-
actualization.
Microfinance, therefore, acts not only as an economic stimulator for small enterprises but also has
far reaching social impacts.
1.2.2 WOMAN EMPOWERMENT
Among poor, women cover the higher percentage: in its 1995 Human Development Report, the
UNDP reported that 70% of the 1.3 billion people living on less than 1$ 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
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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.
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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).
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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).
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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.
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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
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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).
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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
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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
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(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).
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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
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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
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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:
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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
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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
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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
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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.
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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
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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.
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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
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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
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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
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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:
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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
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(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.
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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:
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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)
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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
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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).
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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).
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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
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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.
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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
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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
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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.
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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
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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
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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.
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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
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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.
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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
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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.
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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.
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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.
Table 4.3: Excel columns shotscreen 2 (client info)
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.
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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:
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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
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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)
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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)
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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.
Period to repay 60% loan xx-yy: the same concept of the previous variable but considering the
50% of the loan amount repaid thus inserting 0,6 in the formula instead of 0,5.
Period to repay 80% loan xx-yy: the same concept of the previous variable but considering the
50% of the loan amount repaid thus inserting 0,8 in the formula instead of 0,6.
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
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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).
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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.POP(value1; … ; valueN)
Var(Loan Installment-SLI): statistic measure of the variability of the differences between the
loan installments and the standard loan installment amount.
VAR.POP(value1; … ; valueN)
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.
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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
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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.
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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.
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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
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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)
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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:
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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.
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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.
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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
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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).
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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)
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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)
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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.
Image 5.3: Box plots representation of the variables of variance in monthly loan installments and
variance of (Loan Installment-Standard Loan Installment
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
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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.
Charter 5.19 Frequency histogram of the Variance of (Loan Installment-Standard Loan installment)
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.
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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.
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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
Period to repay 70% loan
Loan Repayment Barycenter
Loan Repayment Regularity
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
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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
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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
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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.
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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
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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
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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
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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
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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.
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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
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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.
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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
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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.
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
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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
Loan Size categories
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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
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
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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).
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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
Variance in Savings Deposits
-.170** -0.1 -0.1 -.149* .149* 0.1 -0.1 -0.1 .000
Savings Mean per month in RP
-.199** .426** .422** 0.0 .045 0.1 -.141* -
.157** -.032
Monthly Savings deposit Median
-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
Loan Installment mean per month
-.265** .515** .947** -
.173** .173** .169** -0.1 -0.1 .079
Standard Loan installment amount
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
Variance in Loan Installments
-.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
Loan Repayment Regularity
.715** -.122* 0.1 0.0 -.003 -
.489** .899** 1.0 .010
Median Monthly loan installments
-.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
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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**
**,* imply significance at 1% and 5% respectively
Table 5.13: Pearson Correlation coefficients of the Repayment Period related variables
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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
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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
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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.
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SAVINGS RELATED VARIABLES
**,* imply significance at 1% and 5% respectively
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 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
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
Number Loan Installments
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**
Variance in Savings Deposits
-0.1 .374** .158** 1.0 .491** .288** .962**
Savings Mean per month in RP
.420** .970** .672** .491** 1.0 .581** .509**
Monthly Savings deposit Median
0.0 .588** .895** .288** .581** 1.0 .288**
Variance Monthly Savings Deposits
-0.1 .404** .169** .962** .509** .288** 1.0
Loan Installment mean per month
.248** .447** .347** 0.0 .455** .229** 0.0
Standard Loan installment amount
-.402** -.144* 0.1 0.1 -.142* .127* 0.1
Number Standard Loan Installment
.410** .256** 0.0 -0.1 .203** 0.0 -0.1
Loan installments Median
.221** .452** .328** 0.0 .418** .210** 0.0
Variance in Loan Installments
-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
Loan Repayment Regularity
0.0 0.0 -0.1 -0.1 -.157** -0.1 -0.1
Median Monthly loan installments
.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
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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
**,* imply significance at 1% and 5% respectively
Table 5.15: Pearson Correlation coefficients of the Loan related variables (part 1)
Loan Installment mean per month
Standard Loan Installment
Number 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**
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**,* imply significance at 1% and 5% respectively
Table 5.16: Pearson Correlation coefficients of the Loan related variables (part 2)
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.
Variance in 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
Variance Monthly Savings Deposits
0.1 0.0 0.1 0.0 0.0
Loan Installment mean per month .449** .907** .554** .558** .532**
Standard Loan installment amount
.199** .328** 0.0 0.1 0.1
Number Standard Loan Installment
-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
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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
**,* imply significance at 1% and 5% respectively
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
**,* imply significance at 1% and 5% respectively
Table 5.18: Pearson Correlation coefficients of the Cumulative Repaid Loan variables
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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
**,* imply significance at 1% and 5% respectively
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).
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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.
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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
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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
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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
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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
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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.
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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
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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
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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.
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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)
Number Loan Installments
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)
Period to repay 70% loan
0.81***
0.53***
0.49***
0.48***
0.49***
0.40***
(0.03) (0.07) (0.07) (0.07) (0.07) (0.06)
Loan Repayment Regularity
11.84***
13.24***
13.70***
13.57***
10.14***
(2.78) (2.76) (2.71) (2.71) (2.43)
Number Standard Loan Installment
-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)
Distance abs (LRR-1)
-6.66**
-6.76**
-4.91**
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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)
Savings Amount in the RP
-5.90E-4
0.02***
(5.75E-4)
(2.42E-3)
Savings Mean per month in RP
-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.
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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
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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.
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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
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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.
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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.
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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***
NumberLoanInstallments 1.84*** 2.03*** 1.99*** 1.49***
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**
NumberLoanInstallments 2.01*** 2.08*** 1.95*** 1.47***
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**
NumberLoanInstallments 1.92*** 1.96*** 1.85*** 1.44***
MedianMonthlyloaninstallments -0.0012 -0.0003 1.27E-3 0.0012
Period to repay XX loan -0.04 0.26*** 0.48*** 0.61***
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*
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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**
NumberLoanInstallments 1.92*** 1.96*** 1.85*** 1.44***
MedianMonthlyloaninstallments -0.0011 -0.0001 1.59 0.0015
Period to repay XX loan -0.04 0.26*** 0.49*** 0.61***
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***
NumberLoanInstallments 1.33*** 1.39*** 1.34*** 1.07***
MedianMonthlyloaninstallments -0.0005 0.0003 1.60E-3 0.0015
Period to repay XX loan -0.05 0.22*** 0.40*** 0.51***
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.
***,**,* imply significance at 1, 5and 10 percent, respectively; Standard Error in parenthesis.
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.
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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
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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.
***,**,* imply significance at 1, 5and 10 percent, respectively; Standard Error in parenthesis.
Table 5.24: Beta coefficients of multivariate regression with
different RP_XX %loan dependent variables
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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.
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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.
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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)
Number Loan Installments
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)
Period to repay 70% loan
0.81*** 0.53*** 0.50*** 0.49*** 0.49*** 0.40***
(0.03) (0.07) (0.07) (0.07) (0.07) (0.06)
Loan Repayment Regularity
11.83*** 13.26*** 13.74*** 13.63*** 10.18***
(2.78) (2.75) (2.70) (2.71) (2.42)
Number Standard Loan Installment
-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)
Distance abs (LRR-1)
-6.78*** -6.85*** -5.01***
(1.94) (1.94) (1.72)
Savings Amount in the RP
-4.76E-4 0.02***
(0.58E-3) (2.42E-3)
Savings Mean per month in RP
-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.
***,**,* imply significance at 1, 5and 10 percent, respectively; Standard Error in parenthesis.
Table 5.25: Beta coefficients of multivariate regression with also the Interaction Effect predictor
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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
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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.
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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
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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.
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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.
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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.
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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
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CHAPTER 5: REGRESSION RESULTS
CORRELATIONS RESULTS
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REGRESSION RESULTS
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REGRESSION RESULTS WITHOUT PERIOD TO REPAY 70% LOAN
BUT WITH LOAN REGULARITY INDEX
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REGRESSION RESULTS WITHOUT PERIOD TO REPAY 70% LOAN
AND WITHOU LOAN REGULARITY INDEX
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REGRESSION RESULT WITH DIFFERENT RP XX% LOAN
PREDICTORS
With period to repay 50% Loan
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With period to repay 80% Loan
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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
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REGRESSION RESULTS INTERACTION EFFECT VARIABLE
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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.
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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%
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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 4.2: Excel columns shotscreen 1 (client info)
Table 4.3: Excel columns shotscreen 2 (client info)
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
Image 4.6 Request model for Mother’s Bank program
Image 4.7: ERP database for Mother’s Bank program’s loan account
Image 4.8: ERP database for Mother’s Bank program’s savings account
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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
Table 5.6: Descriptive statistic parameters of the set of variables Period for repaying XX% loan for
Mother’s Bank program
Table 5.7: Descriptive statistic parameters of the set of variables Period for repaying XX% loan for
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 5.15: Pearson Correlation coefficients of the Loan related variables (part 1)
Table 5.16: Pearson Correlation coefficients of the Loan related variables (part 2)
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
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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
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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
Charter 5.19 Frequency histogram of the Variance of (Loan Installment-Standard Loan installment)
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
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
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
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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
Loan Size categories
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
Loan Size categories
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
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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 4.6 Request model for Mother’s Bank program
Image 4.7: ERP database for Mother’s Bank program’s loan account
Image 4.8: ERP database for Mother’s Bank program’s savings account
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 5.3: Box plots representation of the variables of variance in monthly loan installments and
variance of (Loan Installment-Standard Loan Installment
Image 5.4: Box plots representation of the savings related variables
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WEBSITES
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