How does competition among microfinance institutions affect lenders and borrowers? A randomized field experiment in Bangladesh

Last registered on June 21, 2019


Trial Information

General Information

How does competition among microfinance institutions affect lenders and borrowers? A randomized field experiment in Bangladesh
Initial registration date
June 13, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 21, 2019, 11:22 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


Primary Investigator

University of Sydney

Other Primary Investigator(s)

Additional Trial Information

Start date
End date
Secondary IDs
Microfinance institutions (MFIs) have achieved phenomenal success in expanding credit to poor households in rural areas all around the world. Indeed, microfinance movement is widely recognized as “an important liberating force” and an “ever more important instrument in the struggle against poverty” (the 2006 Nobel Peace Prize committee while awarding the prize to M. Yunus and the Grameen Bank). In recent years, however, there has been a dramatic increase in competition among the MFIs. Wall Street Journal (27.11.2001) reports that in Bangladesh, “… 23% to 43% of families borrowing from micro lenders in Tangail, borrow from more than one.” Similar phenomenon has also been reported for other developing countries, such as Bolivia, Uganda, and India as well (Vogelsang, 2003, McIntosh et al., 2005, Srinivasan, 2010).

Has increased competition improved the welfare of the rural borrowers? The conventional wisdom on this question appears to be mixed. On one hand, it has been argued that the competition will weed out inefficiencies, improving repayment behavior and lowering of interest rates. Critics of this viewpoint, however, argue that increased competition leads to a breakdown of `the restriction of one borrower from one household’ resulting in higher rates of default. In recent years, this view has gained some prominence and has become an important policy concern of governments in developing countries including that of Bangladesh.

The objective of this study is to evaluate the impact of MFI competition by conducting a randomized control trial (RCT) in Bangladesh. To undertake this study, we collaborate with RDRS, a large local MFI in Bangladesh. Since its inception in 1972, RDRS has largely been operating in the northwest region of the country. It is currently expanding its operation in the northeast region. In these areas, we study the effects of competition on repayment incentives and see whether modification of the existing lending contracts can lead to higher business activities, technology adoption in agriculture, and over all income.
External Link(s)

Registration Citation

Chowdhury, Shyamal. 2019. "How does competition among microfinance institutions affect lenders and borrowers? A randomized field experiment in Bangladesh." AEA RCT Registry. June 21.
Former Citation
Chowdhury, Shyamal. 2019. "How does competition among microfinance institutions affect lenders and borrowers? A randomized field experiment in Bangladesh." AEA RCT Registry. June 21.
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Experimental Details


RDRS, the implementing agency, aims to improve the livelihood of its microfinance clients who are mostly landless rural poor, agricultural labour and marginal farmers with little or no education.

In collaboration with RDRS, we evaluate its microcredit program. Since its inception in 1972, RDRS has largely focused its attention in the northwest region of Bangladesh. This region also has the highest concentration of MFIs within the country. Among other things, RDRS has been providing credit to more than 300,000 of its microfinance clients in over 150 branches. We take advantage of its expansion plan in the northest region of the country and implement treatments in credit markets that vary in terms of MFI competition and as well as other lending sources.

In areas where RDRS planned to expand its operation, we classify villages (clusters) based on their MFI concentration, and randomly assign them to treatment and control groups. Since these areas differ markedly in terms of number of MFIs, our treatment will thus bring exogenous variation in MFI competition in the randomly selected rural credit markets.

In addition, we carry out three cross-treatments: a) use of a standard contract in which repayment starts after two weeks of loan disbursement; b) introducing a new contract in which repayment will start after 12 weeks which is equivalent to agricultural crop cycle in Bangladesh; and c) offering the borrowers a menu of choice between the above mentioned contracts.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Assessment outcomes include:
a) changes in borrowing sources and amounts, default rates and delinquency rates;
b) changes in existing businesses and overall business portfolio including technology adoption in agriculture, business profitability and new business start-up;
c) changes in ownership of assets, savings and debt;
d) changes in housing condition, consumption expenditure, and health and educational expenditure;
e) changes in labor allocation between farm and non-farm and self-employment and wage labor, and changes in land ownership and land contracts; and
f) profile of MFI selected borrowers and treatment group borrowers under the randomized control trial (RCT).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The primary objective of this study is to evaluate the effects of entry of an MFI (RDRS ) in rural credit markets in northeast regions of Bangladesh. We study the project choices and repayment performances of the borrowers in the presence of competition and in addition, we plan to evaluate the performance of a new debt contract that delays the repayment requirement by 12 weeks. In the process, we identify the types/characteristics of borrowers who prefer such contracts to the standard microcredit debt contract. Finally, we estimate the ‘selection bias’ of MFIs by comparing treatment groups generated through our RCT with the treatment groups selected by the MFI.

We randomly select 150 villages (clusters) from two regions– northeast and northwest, where RDRS intends to expand its microcredit operation by opening up four new branches. We first conduct a village census and community survey in 150 villages. Next, we randomly select 20 households in each village from the set of households who are (a) eligible to receive microcredit based on RDRS’s eligibility criteria (household’s landownership should not be more than 1.5 acre, the primary income-earning member works at least 90 days per year as wage laborer, household has at least one earning member between 18 and 50 years old, and educational qualification of any member is not above 10 years of schooling, and (b) that at least one household member is willing to apply for credit from an MFI within the next six months. In addition, we also select 10 households from each village that are eligible (satisfy condition (a)) but have no plans to apply for credit from a MFI (do not satisfy condition (b)).

We conduct a baseline survey of these 00 households from 150 villages. The information from these surveys allow us to rank the villages in terms of MFI competition. Using this ranking, we randomly allocate 50 villages into a control group and the rest 100 villages into a treatment group. The 100 treatment villages is randomized further into three cross-treatment groups: (a) standard contract, (b) delayed repayment contract, and c) a menu of contracts.

Treatment 1 – Standard contract: 33 villages is randomly assigned to this treatment under which 660 eligible and willing households, randomly selected from these villages are offered the standard microcredit contract. In this contract, repayment starts two weeks after loan disbursement. The duration of the loan is one year and has to be repaid in 50 equal installments. While all loans is made in groups, the contracts does not have any ‘group liability’ component with each individual member being liable for his/her loan. Use of such `individual’ loans now have become the standard practice among MFIs in Bangladesh.

Treatment 2 – Delayed repayment contract: Under this contract, repayment starts after 12 weeks of loan disbursement instead of two weeks as is the case in the standard contract. The other features of the standard contract however are kept unchanged. The loan are offered in group under individual liability with the repayment period of one year in 40 equal weekly installments. This treatment is conducted in another set of randomly assigned 33 villages under which 660 eligible and willing households are offered this new debt contract.

Treatment 3 – A menu of contracts: In this treatment, we offer the borrower the choice between two contracts: the standard contract where repayment starts two weeks after the disbursement of loan and delayed repayment contract where repayment starts 12 weeks after the disbursement of loans to choose from. This treatment is assigned to the next 34 villages where 680 eligible and willing households are offered the menu.

Control villages: There are 50 villages out of 150 villages that are randomly assigned as control in the sense that RDRS does not expand its microfinance program in those villages during the duration of this evaluation. We conduct census, household surveys and community surveys similar to the ones that are carried out in the 100 treatment villages.

Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
150 villages
Sample size: planned number of observations
4500 households
Sample size (or number of clusters) by treatment arms
50 villages in control group, 100 villages in three treatment arms (33, 33 and 34 villages)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Minimum Detectable Effect Size is 0.2 SD for a power value of 0.8
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Human Ethics, University of Sydney
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials