Direct and Indirect Impacts of Credit Scoring for Small and Medium Enterprises

Last registered on March 12, 2014
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Pre-Trial

Trial Information

General Information

Title
Direct and Indirect Impacts of Credit Scoring for Small and Medium Enterprises
RCT ID
AEARCTR-0000159
First published
March 12, 2014, 9:31 AM EDT

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

Locations

Country
Philippines
Region

Primary Investigator

Name
Dean Karlan
Affiliation
Northwestern University
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Other Primary Investigator(s)

PI Name
Daniel Keniston
PI Affiliation
Yale University
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PI Name
Pamela Jakiela
PI Affiliation
University of Maryland
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PI Name
Gharad Bryan
PI Affiliation
London School of Economics
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Additional Trial Information

Status
In development
Start date
2014-03-10
End date
2017-03-31
Keywords
Finance & Microfinance
Additional Keywords
Small and medium enterprises
JEL code(s)
Secondary IDs
Abstract
This study measures the impact of providing credit to SMEs, examining both the direct effects on recipient firms and the spillovers on competitors and suppliers. Lack of access to finance remains a core challenge to small firm growth in developing countries. Many SMEs have high returns to capital but if banks cannot identify those borrowers, they may restrict lending or misallocate funds, in turn limiting production and employment growth. Many developing countries do not have credit bureaus or other effective public systems that aggregate information valuable for screening, further exacerbating the challenge of identifying high return borrowers. We evaluate a new credit scoring system implemented by the Development Bank of the Philippines to process its SME loans. The randomized design generates exogenous variation in access to credit among loan applicants, enabling us to measure the impact of credit on SMEs. An important and innovative aspect of this evaluation is its focus on vertical and horizontal spillovers. Firms operate in a web of competitors and suppliers, and providing credit to a single business is likely to have spillover effects on other firms and on consumers. Classic theories of economic development stress the importance of these linkages in the takeoff to development.
External Link(s)

Registration Citation

Citation
Bryan, Gharad et al. 2014. "Direct and Indirect Impacts of Credit Scoring for Small and Medium Enterprises." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.159-1.0
Former Citation
Bryan, Gharad et al. 2014. "Direct and Indirect Impacts of Credit Scoring for Small and Medium Enterprises." AEA RCT Registry. March 12. https://www.socialscienceregistry.org/trials/159/history/1301
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Development Bank of the Philippines will roll out its new Retail Lending Program for Micro and Small Enterprises at 30 bank branches across the country. Under this program, DBP will use a new credit scoring software to make SME lending decisions. This system will determine loan approvals based on verifiable client information and an objective credit score, replacing the current approval process which relies on loan officers’ perceptions about applicants’ creditworthiness. Loan applicants with scores above a certain predetermined threshold will be automatically approved for SME loans; applicants with scores below another threshold will be automatically denied; and applicants with scores between those two thresholds will be randomly approved or denied for loans.
Intervention Start Date
2014-03-10
Intervention End Date
2015-03-09

Primary Outcomes

Primary Outcomes (end points)
business revenue, expenses and profits; firm operations and employees; supplier and customer networks; use of financial services; investment decisions.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
After SMEs submit a loan application, the credit scoring software will assign each applicant a score. Applicants whose scores fall in a pre-defined range just below the minimum score that automatically qualifies someone for a loan will be randomly assigned to either receive a loan or serve as part of the comparison group. During the loan application process, information will be collected on the loan applicant's suppliers and competitors. Those suppliers and competitors comprise the supplier and competitor samples, respectively, and data on those firms will be used to measure the indirect effects of access to credit for SMEs.

Experimental Design Details
Randomization occurs first at the branch-sector level and then at the firm level. At each branch, pre-selected industry sectors will be randomly assigned “targeted” or “non-targeted” status:

• Targeted: In these sectors, we will intensively market the SME loan products to all firms in the catchment area of the bank branch, encouraging them to apply for credit. The third and subsequent applicants in the bubble from these sectors will be approved with 90% probability.
• Non-targeted: These sectors will receive no specific marketing on loan products. The third and subsequent applicants in the bubble from non-targeted sectors will be approved with 10% probability.

Assignment of targeted status will be stratified by sector and branch, so that each sector has an equal number of branches in which it is targeted and non-targeted, and each branch has an equal number of targeted and non-targeted sectors. The mix of targeted and non-targeted sectors will be stratified to balance all permutations across branches. To ensure that each outreach sector in each branch has at least one treatment and one control firm, the first applicant in the bubble from the targeted sector will be randomly allocated a loan with 50% probability. The second applicant from the outreach sector will receive the opposite treatment as the first, with subsequent applicants’ treatment or control assignment probabilities dependent only on whether the sector is targeted or not at that branch.
Randomization Method
Random assignment of loan approval or denial for applicant firms in the credit score "bubble" will be done by the credit scoring software.
Randomization Unit
Firms are randomly assigned to loan approval or denial.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
190 firm clusters
Sample size: planned number of observations
1,710 firms: 190 credit applicant firms, 760 competitor firms, 760 supplier firms.
Sample size (or number of clusters) by treatment arms
95 firm clusters control and 95 firm clusters treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
effect size: 0.15 standard deviations; power: 0.8, size 0.05; baseline to endline correlation: 0.6; intra-cluster correlation: 0.1; 8 firms per cluster.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Innovations for Poverty Action IRB-USA
IRB Approval Date
2013-07-10
IRB Approval Number
13July-003 (SP_444)
IRB Name
Yale University Human Subjects Committee
IRB Approval Date
2013-11-13
IRB Approval Number
1308012524
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials