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Tailor-Made Microcredit in Rural Morocco. Experimental Evidence on Loan Take-Up and Poverty Impacts
Last registered on November 22, 2019

Pre-Trial

Trial Information
General Information
Title
Tailor-Made Microcredit in Rural Morocco. Experimental Evidence on Loan Take-Up and Poverty Impacts
RCT ID
AEARCTR-0002618
Initial registration date
April 05, 2018
Last updated
November 22, 2019 8:25 AM EST
Location(s)

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Primary Investigator
Affiliation
Université Catholique de Louvain
Other Primary Investigator(s)
PI Affiliation
EBRD
PI Affiliation
J-PAL Europe
PI Affiliation
CREST
Additional Trial Information
Status
On going
Start date
2018-04-09
End date
2021-10-31
Secondary IDs
Abstract
We use a randomized controlled trial (RCT) in rural Morocco to test whether matching loan repayments more closely with expected entrepreneurial cash flows increases the take-up and poverty impact of microcredit. We introduce two new forms of individual-liability ‘tailored’ microcredit: First, a contract with a five-month grace period and second, a contract where the repayment schedule is split into three equal periods (with varying installments). We first randomize individuals (an estimated 3,600 participants) interested in and eligible for a standard loan into either of the two flexible loans or a control loan with the standard contract. We measure the effect on individuals’ repayment behavior, entrepreneurial activities and household consumption. We then randomize the information about the different treatments (both flexible loan types and the standard loan) at the village level (320 villages) through information campaigns and measure the impact on loan take-up and repayment quality at the village level.
External Link(s)
Registration Citation
Citation
Crepon, Bruno et al. 2019. "Tailor-Made Microcredit in Rural Morocco. Experimental Evidence on Loan Take-Up and Poverty Impacts." AEA RCT Registry. November 22. https://doi.org/10.1257/rct.2618-1.1.
Former Citation
Crepon, Bruno et al. 2019. "Tailor-Made Microcredit in Rural Morocco. Experimental Evidence on Loan Take-Up and Poverty Impacts." AEA RCT Registry. November 22. https://www.socialscienceregistry.org/trials/2618/history/57520.
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Experimental Details
Interventions
Intervention(s)
1. Grace period loan: A loan product is offered where borrowers only pay monthly interest during a five-month grace period and both interest and capital thereafter.
2. Tailored loan: A loan product is offered where the repayment schedule is split into three periods of equal length. The borrower and loan officer jointly decide on the monthly amount that the borrower has to repay in each of the three periods in order to more closely match the borrower’s expected cash flows (which may vary significantly over time). The repayment schedule can thus be decomposed into ‘low’ – ‘medium’ – ‘high’ repayment brackets (not necessarily in that order) with potentially substantial variation in the monthly repayment amount across these three brackets. The research team developed an Apache OpenOffice spreadsheet to help loan officers determine the cyclicality of participants’ expected cash flows.
Intervention Start Date
2018-04-09
Intervention End Date
2021-06-30
Primary Outcomes
Primary Outcomes (end points)
The primary outcomes vary across the two parts of the experiment. In Part A, we focus on the profits and revenues derived from the economic activities of the clients as well as on their consumption and income. In Part B, we focus on the number of loans disbursed, repayment rates, and the portfolio at risk. In the second part of the study, we will rely on administrative data to measure loan take-up, amounts borrowed, reimbursement length and frequency, client retention, and portfolio at risk.
Primary Outcomes (explanation)
Revenues (sales) and profits: Total revenues (sales) and profits of the existing businesses over the 12 months before the survey. Income: Total income of the household over the 12 months before the survey (transfers, wage labor, salaried contracts, profits of businesses). Subjective income expectations: Expected profits from entrepreneurial activities over the next 12 months. Expected profits in positive and negative scenario as well as likelihood that profits will be above/below average expectation. Consumption: Per capita consumption of the household over the 12 months before survey (Food consumption (short term) and durables). Credit take-up: Take-up of flexible loans or standard loan. Repayment rate: Repayment status of the loan 30 days after the due date.
Secondary Outcomes
Secondary Outcomes (end points)
Our secondary outcomes are variables that may also be affected by the intervention but are not the core variables that determine whether the interventions have been successful. These include loan use, type and level of investments, time allocation (Part A), and reimbursement length and frequency, amount borrowed, characteristics of the pool of clients taking up loans, and client retention (Part B).
Secondary Outcomes (explanation)
Type of investments: Type of investments since baseline survey (chosen from a pre-specified set). Level of investments: Total value of new investments since the baseline survey. Sum of all investments undertaken. Time allocation: Allocation of household time across self-employment, wage labor, and domestic activities in last seven days before the survey. Time spent on each activity over the last week for each member of the household. Loan amount: Take-up of flexible loan or standard loan. Repayment rate: Repayment status of the loan 30 days after the due date. Client characteristics: Gender, age, type of activity, repayment history (if any).
Experimental Design
Experimental Design
We implement the following interventions:
1. Grace period loan: A loan product is offered where borrowers only pay monthly interest during a five-month grace period and both interest and capital thereafter.
2. Tailored loan: A loan product is offered where the repayment schedule is split into three periods of equal length. The borrower and loan officer jointly decide on the monthly amount that the borrower has to repay in each of the three periods in order to more closely match the borrower’s expected cash flows (which may vary significantly over time). The repayment schedule can thus be decomposed into ‘low’ – ‘medium’ – ‘high’ repayment brackets (not necessarily in that order) with potentially substantial variation in the monthly repayment amount across these three brackets. The research team developed an Apache OpenOffice spreadsheet to help loan officers determine the cyclicality of participants’ expected cash flows.
Experimental Design Details
Not available
Randomization Method
Part A of the study: randomization done at the branch by a computer. Part B of the study: randomization done in office by a computer.
Randomization Unit
We will randomize at the individual level for the first part of the study and at the village level for the second part of the study.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Part B will take place in eight villages in the catchment area of each of the 40 participating branches. This part thus involves 320 villages.
Sample size: planned number of observations
The first two waves of the baseline survey have provided us with preliminary estimates of the daily number of eligible clients that visit the branches and that consent to participate in the study. Since the variation across branches is substantial, we undertook power calculations for three scenarios where we assume total sample sizes of either 3,600; 3,000; or 2,400 clients.
Sample size (or number of clusters) by treatment arms
Part A of the study: 1,200 individuals control, 1,200 individuals tailored loans (type 1), 1,200 individuals tailored loans (type 2). Part B will take place in eight villages in the catchment area of each of the 40 participating branches. This part thus involves 320 villages.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Part A: Individual-level randomization. For a total sample of 3,600 clients (1,200 being offered the standard loans; 1,200 the tailored loans; and 1,200 the grace-period loans), we will be able to detect increases of 4.2 percentage points (5 percent) in the share of households with a self-employment activity and of 5.3 pp (17 percent) in the share of households where at least one household member does day work. With imperfect compliance, effects among clients that will take up the new loan offers will need to be much larger in order to detect them. For example, for a total sample size of 3,000, standardized MDEs increase to 0.21 for a take-up rate of 0.6 and to 0.31 for a take-up rate of a 0.4. This compares to 0.13 in the ITT scenario. Given the observed take-up rates during the beginning of the experiment implementation, it is important to generate a large enough sample. Our goal is therefore to achieve a total sample size of 3,600 clients (1,200 in each arm). For continuous outcomes, we will be able to detect increases of 17 percent for profits, 18 percent for sales, and 14 percent for consumption. These magnitudes of the minimum detectable effects are equivalent to 0.11 of a standard deviation. Minimum detectable effects increase as the sample size decreases: standardized effects increase to 0.13 and 0.14 of a standard deviation for sample sizes of 3,000 clients (1,000 in each group) and 2,400 clients (800 in each group), respectively. Standardized detectable effects do not change significantly for this range of sample sizes. Given the large standard deviation of continuous outcomes, a standardized effect of 0.14 translates, however, into larger minimum detectable effects of 21 percent for profits, 22 percent for sales, and 17 percent for consumption. Part B will take place in eight villages in the catchment area of each of the 40 participating branches. This part thus involves 320 villages. A sample size of 80 villages per treatment group will allow for the detection of impacts on village-level take-up of at least 6.3 percentage points. We calculate these MDE using data from a related study in rural Morocco (Crépon, Devoto, Duflo, and Parienté, 2015) where the standard deviation of this outcome is 0.14.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Innovations for Poverty Action Institutional Review Board
IRB Approval Date
2017-07-28
IRB Approval Number
13891
IRB Name
Commission Nationale de contrôle de la protection des Données à Caractère Personnel (CNDP)
IRB Approval Date
2018-03-27
IRB Approval Number
A-RS-81/2018