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					                Targeting Individuals for Microfinance Programs:

                            The Impact of Educational Attainment

                             Nicole Adams
                             SIS, American University
                             4400 Massachusetts Avenue NW, Washington DC 20016


Microfinance loan repayment levels are quite low; discouraging banks and other microcredit

institutions from expanding their lending scope to reach the majority of the rural poor. In this

study, I examine candidate client characteristics that affect loan repayment. Mainly, I study the

relationship between loan repayment and educational attainment, but also other borrowing

history variables affecting repayment as well. In particular, data is used from the Matlab

[Bangladesh] Health and Socioeconomic Survey (MHSS) of 1996, to examine the microfinance

borrowing history of 773 individuals from across the country. My findings suggest that greater

levels of educational attainment improve loan repayment rates. I also find that first time

borrowers are less likely to repay their loans. Microenterprises are a vital source of income for

those of the rural poor in emerging economies. It is imperative that microcredit institutions

uphold their mission of issuing microcredit to these clients.

    I am grateful to Andy Schaalman. Andy provided helpful feedback for this empirical analysis.

   1. Introduction

Research Interest

Historically commercial efforts to provide credit and financial services to poor, rural

communities of developing countries have generally failed. To compensate, many governments

developed credit programs to fill this void. These programs yielded mixed results, incentivizing

international organizations, such as USAID and the United Nations, to design and deploy their

own microfinancing programs. These programs too have produced varied results in terms of

program reach and credit access. Of those individuals who did gain access to credit through

these programs, results have indicated there is room for improvement in terms of loan

repayment. Are there measurable characteristics that should be considered for targeting

microfinancing candidates that would ensure greater loan repayment rates, thus further

incentivizing commercial entities and international organizations to extend the reach of their

credit programs?

Research Question

In developing countries, does a microfinance candidate’s education level impact loan repayment?

    Ha: Microfinance client candidates with higher educational attainment are more likely to
    repay their loans.

For this statistical analysis the independent variable is educational attainment. The dependent

variable is household borrowing history of interest-free loans, loan repayment. Other

independent variables that will be measured are: loan amount, months allowed for payback,

number of borrowing transactions, and purpose of loan. These independent variables are

indicators of the interest-free loans granted and may be associated with loan repayment. The

following variables are nominal: loan amount, months allowed for payback, and number of

borrowing transactions. The following variables are ordinal: educational attainment, purpose of

loan, loan repayment. All data is gathered from the Matlab [Bangladesh] Health and

Socioeconomic Survey (MHSS) of 1996.

Microfinance is thought of to be as the most important recent tool for reducing poverty in

developing economies, as credit is important to the incomes of people in these countries.

Microenterprises created from this credit is a critical source of income for the poor.

   2. Literature Review

Over the last couple of decades alleviating poverty in developing economies has become a focus

of many international organizations and a focus for governments, domestically and

internationally. For example, the United Nations has implemented several microfinancing

programs, previously declaring 2005 as the “Year of Microcredit”. Many individuals of these

countries have witnessed a surge in available credit enabling them to support themselves and

their families through creating their own microenterprises. However, the faith in microfinance

does not appear to be based on solid evidence, as interviews with the poor of select countries and

non comprehensive program evaluations have indicated. There is much evidence to suggest that

while microfinance programs may help some, they are not reaching and providing credit to the

vast majority of the rural poor.

Overall, little unbiased research exists to evaluate the impact of these programs, and even further

fewer attempts to examine the relationship between client candidate characteristics and loan

repayment. The literature review for this research includes an overall analysis of empirical

evidence on microcredit, two country (India and Thailand) empirical studies on credit access and

benefits of microcredit, and an empirical assessment of clients of microfinance programs in


Micro-credit is claimed to be the best solution to most of the problems facing the rural poor in

developing countries. However, the implementation of any formal program is plagued by three

challenges: exact targeting of clients of those in need of the program; distinguishing creditworthy

borrowers; and enforcing loan repayment (Chavan and Ramakumar, 2002). Understanding and

working to improve these problems is vital for the survival of microfinance programs. Chavan

and Ramakumar found that while the implemented programs have led to positive income

increases, they have only been marginal adjustments. Perhaps if these errors are address, these

programs could be more successful.

Further, minimizing the costs of default is “crucial for the sustainability of any credit provision

programme or institution” (Chavan and Ramakumar, 2002 p. 959). Some studies have

concluded default rates from these programs are between 50 and 75 percent. Evidence has also

suggested default rates increase with the size of the loan and with return borrowers (Chavan and

Ramakumar, 2002). In essence, curbing the default rate is important for incentivizing

microcredit institutions and borrowers to develop better reaching programs and to participate in

these programs. If high default rates persist, the additional costs will be shifted onto borrowers,

increasing their debt and thus defeated the objective of the program (Chavan and Ramakumar,


Basu and Srivastava similarly find that despite its perceived growing global importance, the

practice of microfinance has been relatively poor at reaching the majority of rural poor and

producing a positive impact over the past decade (2005). These discouraging results stem from
banks’ concerns regarding the safety of their loans. Banks, financial services entities, want to

ensure their loans are safe and “will not take on the time-consuming task of helping groups

manage the bookkeeping of their internal savings and loan accounts” (Basu and Srivastava, 2005

p. 1754). Microfinance programs in emerging economies will not be successful if the right

clients are not targeted, nor the clients are not educated to understand how these programs work.

Basu and Srivastava’s analysis indicates linkage between banks and targeting the poor has

resulted in “mission drift” as pressures to produce good financial results have led to the serving

of richer clients with larger loans (2005).

This drift is a possible explanation for the shallow reach and small impact microfinance

programs have made thus far. Further, Basu and Srivatava analyze what types of communities

are associated with poverty and what candidate client characteristics may indicate why these

programs are not reaching those in need (2005). Their results suggest the percentage of illiterate

households is likely to be correlated with poverty; additionally, higher rates of illiteracy are

associated with fewer “self help groups”/ microcredit institutions in that community (Basu and

Srivatava, 2005).

To reinforce the argument that these programs are not reaching the poor as much as they are the

relatively wealthy, Coleman provides empirical evidence (2006). In his study, weighted t-tests

indicate that program participant households are significantly wealthier than those of

nonparticipants (Coleman, 2006). Further, Coleman interviewed poor rural villagers who stated

they would be interested in joining a microfinance program, but they do not believe they are

qualified to join as these programs are perceived to be for the rich. Coleman’s regressions yield

these villagers understand the concept of loan repayment and show understanding that

creditworthiness is a significant determinant. In interviews with five other villages, Coleman

found that none of the villages identified the rural bank program as one that targeted the poor

(2006). Poor targeting is a significant problem for these programs.

When examining an earlier assessment of microfinance clients in Uganda, the empirical evidence

is somewhat different. The assessment concludes that these programs positively and

significantly had a positive impact on clients (Barnes, Morris, & Gaile, 1999). Further, this

research assessing client characteristics and determines clients tended to have higher educational

attainment than non-clients; this characteristic was found statistically significant (Barnes, et. al,

1999). In fact Barnes, et. al. find that these clients actually invested in further education after

theses programs. Fifty-nine percent of clients cited their microenterprises as the main source of

funds for expenditures on education, averaging $137 a month (Barnes,, 1999).

This literature supports to objective of this research; examining candidate client characteristics is

important for predicting success of microfinance programs. This literature also indicates

educational attainment may be a significant predictor for determining loan repayment.

   3. The Model

Given empirical findings, greater levels of education attainment should be associated with

interest-free loan repayment. From the survey, the significant indicators with the largest amount

of data available are: education attainment, loan amount, months allowed for payback, number of

borrowing transactions, and purpose of loan.

   Loan Repayment = 0 + 1Edu +2 Amount +3Months + 4NBorrow + 5Purpose

   where, Edu = education indicator (primary, secondary, or unattached secondary)
           Amount = loan amount
           Months = months allowed for payback
           NBorrow = number of borrowing transactions
           Purpose = purpose of loan

It is expected that higher levels of educational attainment will result in a negative correlation

with loan repayment; therefore, the more educated an individual is the less likely the loan will

not be repaid. Microcredit institutions tend to have a greater reach into more educated

communities because these individuals are able to understand the purpose of the loan and the

advantages to repayment.

   4. Data

Data collected for this study is at the nominal and ordinal levels of measurement. The data is

analyzed from the Matlab [Bangladesh] Health and Socioeconomic Survey (MHSS) 1996

survey. Only data from Bangladesh is represented in this study.

The dependent variable, loan repayment, was calculated from the survey’s Household Economy,

Borrowing History of Interest-Free Loans; respondents answered “yes” or “no”. The main

independent variable, educational attainment, was calculated from the survey’s Household roster,

Household Members. Education levels were measured in primary, secondary, or unattached

secondary. Loan amount, months allowed for payback, number of borrowing transactions, and

purpose of loan were all measured within the Borrowing History data. Purpose of loan was by

23 different responses. According to the data, the majority of loans were used for business

supplies; the average responses were fish cultivation, rickshaw, and boat.

In total, the Household survey collected data from 4,364 households, capturing 2,687 residential

compounds. The Borrowing Data represented 773 observations. The data is large enough to

make sufficient references to examine a case of microfinance programs and candidate clients.

   5. Estimates and Empirical Findings

The purpose of this study is to determine if higher educational attainment levels increased loan

repayment of microfinance programs emerging economies. This study uses the MHSS 1996

survey to examine microfinance loan repayment in Bangladesh. The MHSS 1996 provides both

the dependent variable and the independent variables (educational attainment, loan amount,

months allowed for payback, number of borrowing transactions, and purpose of loan) from the

Borrowing History data. The null hypothesis states that microfinance clients will all educational

attainment levels will repay their loans. The research hypothesis states that microfinance clients

with higher levels of educational attainment levels will be more likely to repay their loans.

First descriptive statistics were run to determine central tendencies, range, and variability of the

variables. The descriptive statistics give greater detail into the types of microfinance loans that

were issued, as well as to the clients. The majority of those individuals had not repaid their loan

(Figure 1) and attained a primary education level (Figure 2). Loan amounts for this survey

ranged from 50-200,000 takas and the average amount was 5,180.93 takas (Table 1). The

average months allowed for payback was 50 month and the average number of borrowing

transactions was close to 1 (Table 1). Individuals cited the purpose of the loan, which showed

the majority of the loans were used for microenterprise investments. The average responses

were: to purchase a boat, to purchase a rickshaw, or for fish cultivation.

Bivariate correlations were run to determine if there were relationships between any of the

variables (Table 2). Only one significant relationship was found and that was between the two

main variables: loan repayment and educational attainment. This further supports the research

hypothesis, indicating that there is an association between these two variables, which may cause a

change in the other.

Lastly, a probit regression analysis of the dependent variable (loan repayment) was performed to

determine the Best Linear Unbiased Element Model (BLUE) (Table 3). This Model will be used

to determine unbiased estimations of the relationships between the variables; thus, further

determining a decision to accept or reject the null hypothesis. As seen in Table 3, all Models have

a sufficient sample size (773) to rely up on the regression. In all six models, loan repayment and

educational attainment are both significant at the 5% level. The intercept coefficients remain

somewhat stable around the same value throughout all models.

While Model 1 has both significant variables, adjusted R is very low; therefore, this Model is not

the best estimator of the data. Models 2 and 3, both incorporate the significant variables, but also

incorporate highly non-significant models, also making them not the best predictor.

Models 4 and Models 5 both show number of borrowing transactions is significant at the 10%

level, in addition to the intercept and educational attainment. However, Model 4’s adjusted R is

relatively low. This indicates Model 5 is the best Model to estimate the relationships between

these variables; however, because previous empirical findings suggest the number of borrowing

transactions is significant, another model should be created. Model 6 was created to determine the

significant between the three significant variables: loan repayment, educational attainment, and

number of borrowing transactions.
While Model 6 indicates the intercept is still significant, educational attainment becomes less

significant (now at the 10% level) and number of borrowing transactions becomes non-significant.

The intercept’s coefficient also greatly increased. Also, adjusted R is very low. These changes

may indicate some incomplete data; thus, altering the true data. Therefore, Model 5 is the BLUE.

      6. Conclusions and Policy Implications

The results from Model 5 explain there is a negative relationship between loan repayment and

educational attainment; greater levels of educational attainment indicate lower levels of unpaid

loans. This Model also shows a positive relationship between loan repayment and number of

borrowing transactions; the less borrowing transactions indicate lower loan repayment rates.

These results allow us the reject the null hypothesis and accept the research hypothesis.

Further, the results indicate that these relationships can be relied upon 6.6% of the time; this is

relatively low, but it is the second highest adjusted R values of the probit regression. This low

reliability reduces the profoundness of the research hypothesis. While the hypothesis is

significant, the reliability of the data is low.

However, this data presents the opportunity for future research. A more recent, but similar survey

and statistical analysis should be conducted in other emerging economies. It would be interesting

to explore the relationship between loan repayment and number of borrowing transactions, as other

data has indicated the more a client borrows, the less likely they are to repay; while this data, only

seems to have focused on first time borrowers. It would also be interesting to investigate whether

educational attainment is correlated with number of borrowing transactions.

There a two main policy implications from this research. First, microfinance programs

implemented in emerging economies should first target candidate clients with higher levels of

educational attainment in the rural poor areas. Targeting these clients, will ensure greater loan

safety for banks/microcredit institutions; thus, greater levels of loan repayment. These higher

levels of repaid loans will encourage these institutions to expand the scope of their lending

practices, expanding beyond the wealthier individuals and reaching the true potential beneficiaries

of these programs: the rural poor.

Second, the aid in the process of implementing successful microfinance programs, microcredit

institutions should develop educational programs for their clients and candidate clients. An

educational program that provides greater understanding of the borrowing process and reinforces

the importance of loan repayment, will allow for a more educated clientele. A more educated

clientele will yield higher loan repayment rates, incentivizing these institutions to expand their

lending scope; thus, further reaching a larger majority of the rural poor.


 Barnes, C., Morris, G., & Gaile, G. (1999). As Assessment of Clients of Microfinance Programs
        in Uganda. International Journal of Economic Development. 1; 1; pp. 80-121.

 Basu, P. & Srivastava, P. (2005). Microfinance and Rural Credit Access for the Poor in India.
        Economic and Political Weekly. 40; 17; pp. 1747+1749-1756

 Chavan, P. & Ramakumar, R. (2002). Micro-Credit and Rural Poverty: An Analysis of
       Empirical Evidence. Economic and Political Weekly. 37; 10; pp.955-965.

 Coleman, B.E. (2006). Microfinance in Northeast Thailand: Who Benefits and How Much?
       World Development. 34;9; pp. 1612-1638.

 Rahman, O., Menken, J., Foster, A., & Gertler, P. (1996). Matlab [Bangladesh] Health and
      Socioeconomic Survey (MHSS). ICPSR02705-v5. Ann Arbor, MI: Inter-university
      Consortium for Political and Social Research [distributor], 2001. doi:10.3886.
Appendix 1. Tables
Table 1. Descriptive Statistics
           Variable                Mean           Standard Deviation          Coefficient of           Observations
    Loan Repayment                 0.150                 0.357                   2.856                     782

    Educational                    1.430                 0.559                       0.391                 774

    Loan Amount                 5,180.926              12,502.56                     2.413                 783

    Months Allowed for            50.024                 47.919                      0.958                 783

    Number of                      1.309                 0.743                       0.567                 783
    Purpose of Loan               17.156                 12.280                      0.716                 783

Data source: Data are taken from the MHSS, 1996.

Table 2. Correlation Table

Variable          Loan              Educational      Loan Amount      Months             Purpose of       Number of
                  Repayment         Attainment                        Allowed for        Loan             Borrowing
                                                                      Payback                             Transactions
Loan                   1.000
Educational             0.091           1.0000
Attainment            (0.012)
                         773               774
Loan Amount            -0.007            -0.004           1.000
                      (0.841)           (0.916)
                         782               774             783
Months                 -0.030            -0.025           0.029            1.000
Allowed for           (0.410)           (0.482)          (0.416)
Payback                  782               774             783              783
Purpose of              0.008            -0.024           0.015            -0.018            1.000
Loan                  (0.816)           (0.503)          (0.684)          (0.609)
                         782               774             783              783                783
Number of              -0.058             0.012           -0.020           -0.032             0.021           1.00
Borrowing             (0.104)           (0.738)          (0.572)          (0.376)\           (0.559)
Transactions             782               774             783              783                783            783
Data source: Data are taken from from the MHSS, 1996.
Estimates significant at the 5% level are in boldface, p-values in parentheses.

Table 3 . Probit Regression Analysis
          Dependent Variable: Loan Repayment

                          Model 1          Model 2         Model 3           Model 4 *Model 5              Model 6

Intercept                  -1.389           -1.385          -1.336         -1.137          -1.151          -1.194
                           (0.000)          (0.000)         (0.000)        (0.000)         (0.000)         (0.000)

Educational                 0.240            0.240           0.239          0.239           0.240           0.240
Attainment                 (0.012)          (0.012)         (0.013)        (0.013)         (0.013)         (0.012)

Loan Amount                                 -0.786          -0.626         -0.835          -0.824
(1,000,000)                                 (0.844)         (0.876)        (0.835)         (0.837)

Months Allowed                                              -0.000         -0.001          -0.001
For Payback                                                 (0.392)        (0.372)         (0.375)

Number of                                                                  -0.155          -0.155          -0.154
Borrowing                                                                  (0.082)         (0.081)         (0.086)
Purpose of Loan                                                                            (0.853)
N                            773              773             773            773             773             773
R2                          0.013            0.044          0.073           0.035           0.066           0.015
     Estimates significant at the 5% level are in boldface, p-values in parentheses. Estimates significant at the 10%
     level are in boldface and italics. *Denotes best Model.

Appendix 2. Figures
Figure 1. Descriptive Statistics
          Dependent Variable: Loan Repayment

                               Loan Repaid



                                       0            1
 Source: MHSS 1996

*0 = Not Repaid; 1 = Repaid

Figure 2. Descriptive Statistics
          Independent Variable: Educational Attainment





                      PRIMARY                                           SECONDARY
                      UNATTACHED SECONDARY
 Source: MHSS 1996

*Primary = Grades 1-5; Secondar = Grades 6-10; Unattached Secondary = Grades 11 & 12


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