Microcredit Made to Measure. Experimental Evidence from Rural Morocco

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.

2018-04-09

2021-06-30

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.

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.

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).

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).

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.

Not available

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.

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.

Yes

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.

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.

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.

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.