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Trial Title
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Tailored Microcredit in Rural Morocco
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Tailor-Made Microcredit in Rural Morocco. Experimental Evidence on Loan Take-Up and Poverty Impacts
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Trial Status
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in_development
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on_going
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Abstract
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Recent evidence on the ability of microcredit to stimulate entrepreneurship and reduce poverty has been sobering. An important reason why overall impacts are small is that relatively few people take up microcredit when it is offered to them. But even for the few that take up small loans, the available empirical evidence suggests a lack of transformative impact.
There might be a number of factors that affect both the demand for microcredit and its impact. For example, typical microcredit contracts (with regular repayments) may not be fully adapted to the needs of the population, especially in rural areas.
We propose to test a couple of changes in the design of microcredit loans (making their repayment more flexible) in rural areas of Morocco, where microcredit take-up is relatively low (Crépon et al., 2015) and entrepreneurial and employment activities are limited. Evaluating the effects of tailored microcredit loans designed to more closely match the financial needs of rural populations will allow us to learn more about how microcredit can be adjusted to make it a more attractive and hence a potentially more effective development tool.
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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.
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Trial End Date
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December 31, 2020
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October 31, 2021
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JEL Code(s)
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D14, G21, O12
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Last Published
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April 06, 2018 05:27 PM
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November 22, 2019 08:25 AM
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Intervention (Public)
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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.
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Intervention End Date
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December 31, 2019
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June 30, 2021
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Primary Outcomes (End Points)
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In the first part of the study, we will collect detailed data on production, income, time allocation, consumption and use of financial services.
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.
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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.
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Primary Outcomes (Explanation)
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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.
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Experimental Design (Public)
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In the first part of the study, potential borrowers will visit one of the participating microcredit branches to ask for a loan during the trial period. If they consent to participate in the study, they will be randomly assigned to one of two treatment groups or to a control group. Those in the treatment groups will have access to one of two tailored microcredit loans while those in the control group will have access to a standard microcredit loan. This part of the study will allow us to estimate the impact of the tailored loans on borrower welfare.
In the second part of the study, villages in the catchment area of the participating microcredit branches will be randomly assigned to one of the two treatment groups or to the control group. Loan officers will then visit the villages to promote one of the two tailored microcredit loans in each of the treatment villages and the standard microcredit loan in the control villages. This part of the study will allow us to estimate the impact of the tailored loans on the demand for credit.
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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.
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Randomization Method
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Part I of the study: randomization done at the branch by a computer
Part II of the study: randomization done in office by a computer
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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.
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Randomization Unit
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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
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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.
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Planned Number of Clusters
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Part II of the study: 240 villages
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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.
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Planned Number of Observations
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Part I of the study: 3,600 individuals
Part II of the study: 240 villages
Part II of the study: 240 villages
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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.
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Sample size (or number of clusters) by treatment arms
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Part I of the study: 1,200 individuals control, 1,200 individuals tailored loans (type 1), 1,200 individuals tailored loans (type 2)
Part II of the study: 80 villages control, 80 villages tailored loans (type 1), 80 villages tailored loans (type 2)
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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.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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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.
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Additional Keyword(s)
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Microcredit, repayment flexibility, loan take-up, poverty alleviation
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Keyword(s)
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Finance
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Finance, Firms And Productivity, Welfare
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Intervention (Hidden)
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We test the following:
1. Standard microcredit contracts lack a sufficiently long grace period. Entrepreneurs may have difficulties repaying loans immediately after disbursement because it takes time for investments to generate revenues.
2. The rigid and linear repayment schedule of the standard microcredit contract is not (sufficiently) relaxed by a generic grace period that is identical for all borrowers and reduces liquidity demands early in the loan cycle. Instead, a repayment schedule is required that matches the expected revenue flows of individual borrowers more closely throughout the loan cycle as a whole.
We have partnered with a leading Moroccan microfinance institution (Al Amana) to test these two hypotheses. We do so by randomly relaxing either of two constraints in individual-liability microcredit contracts. We test the first hypothesis by offering microcredit with a significantly longer grace period (five months instead of one). The availability of such a longer grace period may make projects with a high expected return, but that are initially illiquid, more attractive to borrowers. While prior evidence (Field, Pande, Papp, and Rigol, 2013) shows that the introduction of grace periods (of two months) may change the type of investments that borrowers select, our contribution is to also provide evidence on the broader impacts on borrowers and their households.
We test the second hypothesis by offering microcredit with a repayment schedule that is designed ex ante to more closely match the expected revenues of individual borrowers throughout the repayment period. The availability of such tailor-made loans may make projects with a high expected return, but with highly seasonal revenues (for instance due to natural or religious cycles), more attractive to borrowers. Unlike Barboni and Agarwal (2018) and Battaglia, Gulesci, and Madestam (2018), who both test the impact of ex-post repayment flexibility, in which borrowers are allowed to waive certain repayments during the loan cycle, our loans do not provide repayment flexibility after disbursement. Instead, we match repayments more closely with future (expected) revenue streams before disbursement. Our contribution is therefore to investigate how customizing microcredit more to the needs of individual borrowers (while maintaining ex post repayment discipline) affects loan uptake, investment decisions, and ultimately the welfare of those who take these loans.
To estimate the impact of relaxing these constraints on borrower welfare, we randomize individuals who willingly demonstrate a desire to take out a standard microcredit loan (by visiting a branch and applying for such a loan) and who are eligible for a standard loan. We randomize these individuals into either of the two new flexible loan offers or into a standard loan offer (the control group). By restricting our experiment to individuals willing to take a standard loan, we can evaluate the impact of the flexible loans on borrower welfare while netting out potential selection effects. If the new loan offers would instead have been made available to everyone, then the characteristics of the client pool would likely vary across the different loan offers.
A second important contribution is our focus on changes in borrower selection and repayment performance once tailor-made microcredit becomes available. The individual randomization will allow us to identify the types of borrowers who prefer the new loans versus the standard ones. In addition to this, we randomize loan-type specific information campaigns to stimulate take up at the village level. This allows us to measure differential selection into loan demand across treatment arms.
Overall, this integrated approach helps us to clarify the causal link from offering new loan contracts to borrowing and repayment behavior and to evaluate the effect of these new contracts on borrower welfare in the population that was initially interested in the standard loan offer.
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Secondary Outcomes (End Points)
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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).
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Secondary Outcomes (Explanation)
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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).
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