Impact of Microcredit in the Philippines
Last registered on May 24, 2017


Trial Information
General Information
Impact of Microcredit in the Philippines
Initial registration date
March 13, 2014
Last updated
May 24, 2017 11:47 PM EDT
Primary Investigator
Northwestern University
Other Primary Investigator(s)
PI Affiliation
University of Illinois at Urbana Champaign
PI Affiliation
Dartmouth College
Additional Trial Information
Start date
End date
Secondary IDs
This study is a long-term evaluation of the impact of microcredit in the Philippines. In partnership with two microcredit lenders, we introduced a credit scoring system that evaluates the credit worthiness of credit applicants. First-time borrowers who are marginally credit worthy are subject to a random credit decision where most receive an offer of credit but some are randomly rejected. By comparing those who were randomly approved for credit against those who were randomly denied, we will be able to measure the impact of access to microcredit.

This study is a follow-up to Karlan and Zinman (2010).
External Link(s)
Registration Citation
Karlan, Dean, Adam Osman and Jonathan Zinman. 2017. "Impact of Microcredit in the Philippines." AEA RCT Registry. May 24.
Former Citation
Karlan, Dean et al. 2017. "Impact of Microcredit in the Philippines." AEA RCT Registry. May 24.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Consumption, business investment, household income, response to shocks, use of formal insurance
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We used credit scoring software designed in consultation with two microlenders in the Philippines to identify marginally creditworthy loan applicants. Some applicants receive a randomized offer of access to credit while others do not and we will compare these two groups to obtain an unbiased measure of the impact of access to microcredit. More details on the experimental design can be found in Karlan, Osman and Zinman (2013), "Follow the Money: Methods for Identifying Consumption and Investment Responses to a Liquidity Shock."
Experimental Design Details
Randomization Method
Randomization done by computer at time of loan application
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
4,032 loan applicants
Sample size: planned number of observations
4,032 loan applicants
Sample size (or number of clusters) by treatment arms
417 applicants in control group, 3,615 in treatment group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Yale University FAS Human Subjects Committee
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
December 31, 2012, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
December 31, 2012, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
1,601 applicants
Final Sample Size (or Number of Clusters) by Treatment Arms
1,272 accepted applicants served as the treatment and 329 rejected applicants served as the comparison
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers
Measuring the impacts of liquidity shocks on spending is difficult methodologically but important for theory, practice, and policy. We compare three approaches for tackling this question: directly asking borrowers how they spend proceeds from a loan (direct elicitation); asking borrowers using a list randomization technique (indirect elicitation) that allows them to answer discretely in cases where loan uses are at odds with lender policies or social norms; and, a counterfactual analysis in which we compare household and enterprise cash outflows for those in a treatment group, randomly assigned to receive credit, to a control group. The counterfactual analysis yields an estimate that about 100% of loan-financed spending is on business inventory. For the direct and indirect elicitations, we find evidence of both strategic misreporting and “following the cash”: borrowers likely report what they physically did with cash proceeds, rather than counterfactual spending.
Karlan, Dean, Adam Osman, and Jonathan Zinman. 2016. "Follow the money not the cash: Comparing methods for identifying consumption and investment responses to a liquidity shock." Journal of Development Economics 121: 11-23.