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Impacts of Seasonality Adjusted Flexible Micro-credit in Northern Bangladesh
Last registered on August 07, 2017


Trial Information
General Information
Impacts of Seasonality Adjusted Flexible Micro-credit in Northern Bangladesh
Initial registration date
August 05, 2017
Last updated
August 07, 2017 10:28 AM EDT
Primary Investigator
New York University
Other Primary Investigator(s)
PI Affiliation
Hitotsubashi University, Tokyo.
Additional Trial Information
Start date
End date
Secondary IDs
The mismatch between credit repayments and income seasonality implies a challenge for micro-finance institutions (MFIs) working in developing countries. For instance in northern Bangladesh, income and consumption downfalls during the lean season after the transplanting of major paddy crops are a serious threat to the household economy. Poor landless agricultural wage laborers suffer the most due to this seasonality as they face difficulty to smooth their consumption. In designing micro-credit products, MFIs do not usually provide any flexibility or seasonal adjustment during the lean season, however. This is mainly because MFIs are afraid of the possibility that such flexibility might break the repayment discipline of borrowers, resulting in higher default rates. We thus conducted a randomized controlled trial in 2011-12 in northern Bangladesh to test empirically whether seasonality adjusted flexible micro-credit leads to an increase in repayment problems for MFIs and whether it can increase and stabilize consumption of borrower households. Our results suggest no statistically discernible difference among the treatment arms in case of default, overdue amount, or repayment frequency. This is in favor of seasonality adjusted flexible design of micro-credit.
External Link(s)
Registration Citation
Kurosaki, Takashi and Abu Shonchoy. 2017. "Impacts of Seasonality Adjusted Flexible Micro-credit in Northern Bangladesh." AEA RCT Registry. August 07. https://doi.org/10.1257/rct.25-1.0.
Former Citation
Kurosaki, Takashi, Abu Shonchoy and Abu Shonchoy. 2017. "Impacts of Seasonality Adjusted Flexible Micro-credit in Northern Bangladesh." AEA RCT Registry. August 07. http://www.socialscienceregistry.org/trials/25/history/20287.
Experimental Details

Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
- Default 1: binary variable if loan is not repaid at the end of the loan cycle
- Default 2: amount in default at end of the loan cycle, and various intervals after the end of the loan cycle, e.g. 1 month after end of loan cycle, 2 months after, etc.
- Repayment discipline 1: number of weekly repayments missed
- Repayment discipline 2: share of repayments made on time (relative to total number of repayments)
- Savings 1: amount of savings accumulated
- Savings 2: amount of savings withdrawn
- Savings 3: binary indicator if savings have been used for loan repayment
- Various measures of food consumption
- portfolio choice, investment decision and income generating activities
- Coping with shock
- Borrowing
- Seasonal Migration and remittance
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
2.1 RCT Strategy
(1) Inflexible Microcredit as the Control
A typical Grameen-style microcredit scheme proceeds as follows (Armendariz and Morduch 2010): individuals eligible for microcredit first form a group wherein its members are expected to help each other in times of difficulty. Not all members can borrow immediately. It is usually the case that only some of them are offered credit after all members have saved a small amount of money on a regular basis; the rest of them are given credit after the first borrowers successfully repay several installments and all members have continued to save the same small amount on a regular basis. Weekly repayments begin without a long grace period. With typical Grameen-type microcredit, the first lent amount is small, and it is to be repaid in 50 weekly installments within a 12-month period.
Several rationales have been offered for this rigidly designed repayment schedule (Armendariz and Morduch 2010). The success of frequent repayment in minimizing default and delay could be attributed to the early warning mechanism, the lender’s capture of information vis-à-vis the income flow of the borrower, and the borrower’s commitment to save regularly. Repayment in group meetings in front of others also drives regular repayment by those borrowers who would like to maintain their reputation within the village.
Probably on account of these mechanisms, classic Grameen-type microcredit has been successful in maintaining high repayment rates. However, attending weekly meetings regularly puts a high burden on the borrowers in terms of the opportunity costs of their time and financial stress (Field et al. 2012). Relaxing several of the classic Grameen-type features is thus being demanded from borrowers. Academic research has responded to this request, to identify the key element that was the most critically important in guaranteeing high repayment rates. For example, using a field experiment approach, Giné and Karlan (2011) evaluate the impact of removing group liability in the Philippines; they find there was no adverse impact on repayment, as long as public and frequent repayment systems were maintained. On the other hand, recent studies comparing weekly versus monthly installments and based on RCT designs show mixed results. In India, Field and Pande (2008) show no difference between microfinance schemes with weekly and monthly repayment frequencies, as long as repayments were made in public meetings. The same RCT also shows that the change from weekly to monthly repayment greatly reduced borrowers’ financial stress (Field et al. 2012). In contrast, in Indonesia, Feigenberg et al. (2011) find that repayment performance was better when repayments were collected weekly rather than monthly.
Given this background, we adopted the following borrowing and repayment scheme as the control. Borrowers obtain credit of BDT 3,000 and begin repayment after a short, two-week grace period. Repayments are made in 45 installments, each of which is BDT 75 (except for the last one, which is BDT 60), implying a gross interest payment of BDT 360 that is spread throughout the borrowing period of approximately one year. Each of the weekly installments is to be repaid by the borrower at a weekly meeting. The borrower is obliged to attend the weekly meeting, even during the monga period. This design of a traditional or inflexible microcredit scheme is denoted as the “Control.”

(2) Flexible Microcredit as the Treatment
During the monga period, microcredit borrowers may face difficulties in preparing the money needed for regular repayment. To facilitate the demand for repayment flexibility within this context, the treatment relaxes the repayment schedule in two ways during the monga period, which for this purpose is designated as September 20–December 20.
Under the first treatment, “Flexible 1,” a moratorium is temporarily applied to repayments during the designated monga period. During that moratorium, households within the Flexible 1 groups do not pay any installment. After the monga period, the borrowers begin to pay BDT 100 per week, so that their total repayment amount and repayment period would be identical to those of the Control group.

Under the second flexibility treatment, the repayment schedule is changed to feature three monthly installments of BDT 300 each during the designated monga period, instead of 12 weekly repayments of BDT 75 each. After the monga period, borrowers resume paying BDT 75 per week, so that their total repayment amount and repayment period would be the same as those of the control group. We refer to this treatment as “Flexible 2.” This treatment arm provides less flexibility than Flexible 1 (in terms of loan repayment obligation), while it provides better loan collection discipline than Flexible 1.

(3) Randomization of Treatment Arms
To preclude unequal treatment among members within a group, we randomized the four treatment statuses at the borrower-group level. Since our counterpart NGO usually forms one group in one village, our randomization took place at the village level.
Of the list of 90 villages that were under potential treatment by the counterpart NGO, we randomly selected 12 villages for “Control,” 36 for “Flexible 1,” and 24 for “Flexible 2.” In the randomization, we stratified the villages based on their distance from the closest bus station and the location type of the village.

In each village, our counterpart NGO formed a borrower group known as samity, which comprised 20 members who satisfied the NGO’s microcredit criteria and had voiced an interest in receiving microcredit. The member names were then recorded in the samity formation book by the loan officers. In the book, each samity member was assigned a number in ascending order; the members who happened to hold numbers 1–15 were to be offered credit, while those holding numbers 16–20 were kept in the group as observers. This design of randomization was not known to the samity members before the announcement of the treatments. This randomization thus implies the following sample distribution: there are 72 sample villages and 1,440 sample households, one-sixth or one-third of which falls into one of the four treatment arm categories; three-fourths of the sample households (1,080 households) were actual borrowers of microcredit.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Borrowing group level cluster randomization.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
72 Micro-credit borrowing group
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
12 villages for “Control,” 36 for “Flexible 1,” and 24 for “Flexible 2.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With alpha = 0.05, within cluster correlation of 0.10, 1 Standard Deviation and 12 control cluster and 24 treatment cluster, our Minimum Detectable Effect Size for Main Outcomes would be 0.396
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)