Targeting Short Term Loans to High Potential Market Vendors

Last registered on December 01, 2023


Trial Information

General Information

Targeting Short Term Loans to High Potential Market Vendors
Initial registration date
November 17, 2023

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
December 01, 2023, 4:50 AM EST

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



Primary Investigator

Harvard Business School

Other Primary Investigator(s)

PI Affiliation
Harvard Business School

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Microentrepreneurs around the world utilize informal credit at exorbitant interest rates. Many of these entrepreneurs have access to, but do not utilize microfinance because the timing of their cash flows does not match the microfinance repayment schedules. We are evaluating the impact of a short-term working capital credit product meant to flexibly match the cash flows of market vendors at interest rates significantly above microcredit but far below the informal sources of credit utilized in these markets. We will evaluate the efficacy of community nominations, formal credit histories, and self reported business characteristics in identifying entrepreneurs with high-growth opportunities and reliable credit risks.

In contrast to traditional microfinance, there is no group element or joint liability associated with this credit product. As such, identifying good borrowers, both those who are likely to repay their loans as well as those who are likely to have high-growth opportunities, presents novel challenges. Alongside evaluating the impact of the credit product, we will evaluate the usefulness of three sources of information for targeting entrepreneurs. 1) Nominations from neighboring vendors (Hussam et al. 2022), 2) Formal credit histories, 3) Self reported business characteristics and investment opportunities.
External Link(s)

Registration Citation

Rigol, Natalia and Ben Roth. 2023. "Targeting Short Term Loans to High Potential Market Vendors ." AEA RCT Registry. December 01.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Business revenue and profit
Household income
Fixed assets and inventory
Use of informal and formal credit
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will enter new markets in Tamil Nadu to interview market vendors in census-style data collection activity. Approximately 2,000 vendors will receive a short survey. Content will focus on current borrowing practices and borrowing needs, as well as financial literacy. This will serve as the baseline.
Our MFI partner will advertise their loan product and screen vendors who expressed an interest in the product.This will consist of 3 activities: a longer survey to collect data with detailed information about their businesses to assist in determining loan eligibility, a query on their equifax score, and discussions with neighboring vendors. They will share this data with us
After eligibility is determined using the above process, we will then randomize at the individual level. 500 eligible vendors will receive the loan (the treatment group) and 500 eligible vendors will receive nothing (the control group).
After both one and three months, we will do a midline and endline survey with all 1,000 participants to collect the same outcomes as in baseline, we will assess whether any of the three sources of screening information predict repayment as well as heterogeneity in the impacts of our loans on livelihoods.
We will also investigate to what extent our loans displace versus supplement existing sources of credit.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
1000 vendors
Sample size: planned number of observations
1000 vendors
Sample size (or number of clusters) by treatment arms
500 treatment, 500 control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Institute for Financial Management and Research (IFMR)
IRB Approval Date
IRB Approval Number