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Gender Discrimination at the Consumer Credit Market in Chile: Experimental Evidence from a Correspondence Study with Real Borrowers
Last registered on July 25, 2019

Pre-Trial

Trial Information
General Information
Title
Gender Discrimination at the Consumer Credit Market in Chile: Experimental Evidence from a Correspondence Study with Real Borrowers
RCT ID
AEARCTR-0003961
Initial registration date
March 21, 2019
Last updated
July 25, 2019 2:53 PM EDT
Location(s)
Primary Investigator
Affiliation
Universidad de Chile
Other Primary Investigator(s)
PI Affiliation
Uppsala University
PI Affiliation
IADB
PI Affiliation
Universidad de Chile
Additional Trial Information
Status
Completed
Start date
2018-07-12
End date
2018-09-27
Secondary IDs
Abstract
This paper examines the extent to which Chilean banks discriminate against female borrowers in the consumer credit market. By using a self-selected sample of incentivized loan applicants, we match 404 male and female profiles on demographics and credit history information (exactly the same variables used by the banks to evaluate loan applications) to build a balanced sample of applicants across gender. We then ask each applicant to send 4 loan requests through email (with random amounts and loan terms) to 4 different and randomly assigned bank executives who are in charge of selling loans to clients, for a total sample of 1,616 loan applications distributed across more than 600 bank executives. We analyze response, approval, and rejection rates across gender (external margin), as well as amount, interest rate, and term of approved loans (internal margin). We take advantage of a series of gender-preference elicitation pretests conducted to bank executives to infer whether potential differences across applicant's gender are originated on taste-based discrimination (as opposed to statistical) . Finally, we further conduct a salience experiment where emails about the importance and the prevalence of gender discrimination are sent from the governmental authority to the bank executives, so that we can test the effectiveness of information treatments on reducing gender discrimination in the consumer credit market.
External Link(s)
Registration Citation
Citation
Montoya, Ana Maria et al. 2019. "Gender Discrimination at the Consumer Credit Market in Chile: Experimental Evidence from a Correspondence Study with Real Borrowers." AEA RCT Registry. July 25. https://doi.org/10.1257/rct.3961-2.0.
Former Citation
Montoya, Ana Maria et al. 2019. "Gender Discrimination at the Consumer Credit Market in Chile: Experimental Evidence from a Correspondence Study with Real Borrowers." AEA RCT Registry. July 25. https://www.socialscienceregistry.org/trials/3961/history/50669.
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Experimental Details
Interventions
Intervention(s)
This is a randomized control trial where the treatment variable is the gender of the individual that submits the loan request to the assigned loan officer. In particular, this is an RCT implemented in the form of a corresponding study where loan officers randomly receive loan requests from either male or female borrowers according to randomization.
A secondary treatment is an information intervention where one-paragraph email about the importance and the prevalence of gender discrimination is randomly sent from the governmental authority to half of the loan officers considered for this study. Here the treatment variable is whether the loan officer receive the message or not.
Intervention Start Date
2018-07-12
Intervention End Date
2018-09-27
Primary Outcomes
Primary Outcomes (end points)
We analyze response, approval, and rejection rates across gender (external margin), as well as amount, interest rate, and term of approved loans (internal margin). Please see the pre-analysis plan for details
Primary Outcomes (explanation)
Response rate: proportion of loan requests responded by bank executives (from total of loan requests sent by loan applicants).
Approval rate: proportion of loan requests approved by bank executives (from total of loan requests sent by loan applicants).
Rejection rate: proportion of loan requests rejected by bank executives (from total of loan requests sent by loan applicants).
Amount: amount of approved loan
Interest rate: interest rate of approved loan
Term: loan term of approved loan
Please see the pre-analysis plan for details
Secondary Outcomes
Secondary Outcomes (end points)
Please see the pre-analysis plan for details
Secondary Outcomes (explanation)
Please see the pre-analysis plan for details
Experimental Design
Experimental Design
By using a self-selected sample of incentivized loan applicants ("actors"), we match 404 male and female profiles on demographics and credit history information to build a balanced sample of actors across gender. The recruitment of actors was made based on randomly selected emails from DEMRE data, which contain detailed information about emails and test scores of people that have taken the PSU test from 2000 onward (PSU = Prueba de Seleccion Universitaria, a national test on math, verbal, etc. taken every year by any student who applies to the university system). Then, our sample is composed by relatively young professionals with short credit history. Information about incomes and employment status is obtained through wage settlements and social security contributions reports attached by the testers. Finally, data on credit history of each actor is obtained through SBIF administrative records (SBIF = Superintendencia de Bancos e Instituciones Financieras), the national authority for banking regulation. Overall, our prettreatment information allows us to fairly match the set of variables typically used by the banks to evaluate consumer loan applications.

Applicants are asked to send 4 loan requests through email to 4 different and randomly assigned bank executives who are in charge of selling loans to clients. Applicants receive around US$20 for sending the 4 loan applications, for a total sample of 1,616 loan applications distributed across more than 600 bank executives. Loan requests are text-standardized, but randomly vary across amount and loan term so we can test heterogeneous effects across the two dimensions. For each loan application, the actor receives the exact text and email address of the bank executive, so they copy and paste the text with the loan request and send it to the email address from their personal accounts. Responses from the executive are tracked by the actor who resend the information to the research team.

Bank executives are randomly selected from a nationally representative survey of bank executives administered through SBIF. The survey includes information on gender, education, and years of experience, as well as a series of gender-preference elicitation pretests.

Finally, we further conduct a salience experiments where an email about the importance and the prevalence of gender discrimination is randomly sent from the governmental authority to half of the loan officers included in our study, so that we can test the effectiveness of information treatments on reducing gender discrimination in the consumer credit market. The emails were sent at the beginning of the experiment to all the executives at the same time, the half of which randomly contains treatment information.
Experimental Design Details
Randomization Method
Randomization was done in office by a computer
Randomization Unit
There are several randomizations:
1. We randomly assign loan amount to each loan request. There are 9 types. Randomization unit is the loan application, stratified by applicant.
2. We randomly assign loan term to each loan request. There are 9 types. Randomization unit is the loan application, stratified by applicant.
3. We randomly assign 4 loan requests to each tester.
4. We randomly assign loan requests to loan officers. We stratify by region-bank where the loan officer works as well as for loan officer's gender.
5. We randomly assign salience treatment to loan officers. All receive email from SBIF, but half of them additionally receive the information treatment.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
The treatment status of each loan request is given by the tester's gender. The outcome of the loan application (responded, approved, rejected) is endogenous to the decision making process made by the bank executive. Each bank executive will never evaluate more than one application coming from the same actor. Thus, from the eyes of the econometrician, each application is independent from each other. However, these may well be correlated within region-bank, so we cluster the standard errors by region-bank.
Sample size: planned number of observations
Total number of actors: 404 Number of males in sample: 233 Number of females in sample: 171 Number of applications per actor: 4 Total number of loan applications: 404*4 = 1,616
Sample size (or number of clusters) by treatment arms
Number of males in sample: 233
Number of females in sample: 171
Number of applications per actor: 4
Number of female applications in sample (treatment): 233*4 = 932
Number of male applications in sample (control): 171*4 = 684
Total number of loan applications: 1,616

Information Treatment:
Number of loan requests assigned to treated loan officers (treatment): 787
Number of loan requests assigned to control loan officers (control): 795
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For a sample of 684 loan applications made by men and 932 made by women (n1/n2 ratio=0.73), our experimental design is able to identify across-gender differences on the order of 1.1% in any of the external margin outcomes (i.e., response, approval, and/or rejection rate), at the 95% level of statistical significance and with a statistical power of 80%. Likewise, assuming a standard deviation of 1, our experimental design is able to identify across-gender differences on the order of 0.14 standard deviations in any of the external margin outcomes, at the 95% level of statistical significance and with a statistical power of 80%. As detailed in the pre-analysis plan, our hypothesis is that there is discrimination against females (not against males), thus we will report one-sided test of the null hypothesis that discrimination is against female borrowers.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available
Analysis Plan
Analysis Plan Documents
Pre-Analysis Plan

MD5: 94181b3c3f5f362d1f4643feea0ead9d

SHA1: a0534f4a1f1014d18e866e1a592a0df835286ac0

Uploaded At: March 21, 2019

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
September 27, 2018, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
September 27, 2018, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Number of female applications in sample (treatment): 233*4 = 932
Number of male applications in sample (control): 171*4 = 684
Total number of loan applications: 1,616

Information Treatment:
Number of loan requests assigned to treated loan officers (treatment): 787
Number of loan requests assigned to control loan officers (control): 795
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
1,616 loan requests assigned to be sent to a given loan officer. The decision to send or not the loan request is endogenous to the tester.
1,068 loan requests assigned at baseline but actually sent by testers
Hence, 548 attriters (attrition rate=33.91%)
Final Sample Size (or Number of Clusters) by Treatment Arms
At baseline: Number of female applications in sample (treatment): 233*4 = 932 Number of male applications in sample (control): 171*4 = 684 Total number of loan applications: 1,616 At follow up (if sent loan request): Number of female applications in sample (treatment) = 635 Number of male applications in sample (control) = 433 Total number of loan applications: 1,068
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports and Papers
Preliminary Reports
Relevant Papers