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Body Mass and Creditworthiness: Evidence from Loan Officers in Uganda
Last registered on August 27, 2019

Pre-Trial

Trial Information
General Information
Title
Body Mass and Creditworthiness: Evidence from Loan Officers in Uganda
RCT ID
AEARCTR-0004528
Initial registration date
August 21, 2019
Last updated
August 27, 2019 4:56 AM EDT
Location(s)

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Primary Investigator
Affiliation
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2019-09-23
End date
2019-12-23
Secondary IDs
Abstract
In this project I study how individuals' body mass affects access to credit in Uganda. Participants are loan officers from a random subsample of banks and micro financing institutions from Kampala, Mbarara and Gulu. Loan officers are a key step in the assessment of creditworthiness: They screen prospective clients screen prospective clients and refer them to the second stage of the loan approval process; for selected loan profiles, they are also be responsible for the entire loan approval process. In either context, their evaluation is a key step in obtaining access to credit. Hence, the measures of ease of access to credit in the experiment are: a creditworthiness rating; a referral of the applicant to the next step of the loan approval process (binary) or the binary choice of granting the loan or not; conditional on a referral, an assessment of the applicant's likelihood to receive the desired loan at the end of the process; conditional on a not referral, the choice of advising to apply for a smaller loan and an assessment of the applicant's likelihood to receive a smaller loan.

The design builds on Kessler et al. (NBER WP 2019) experiment. After answering a set of questions including demographics, financial literacy and banks characteristics, loan officers are shown 6 hypothetical loan applications which vary by applicants' characteristics and loan profiles. Most notably, each applicant's picture is randomly selected between either a group of underweight/norm weight or a group of overweight/obese pictures. Moreover, each application can either include information on wealth or not. Answers are incentivised by matching loan officers' preferences with real applicants' characteristics and providing referrals to prospective clients.

This set up allows me to study whether (1) body mass affects perceived creditworthiness and referral probability, and (2) whether reducing asymmetric information on income changes the relevance of body mass in determining the decision. As the gender of the applicants is randomly assigned, I will also investigate heterogeneity by gender.
External Link(s)
Registration Citation
Citation
Macchi, Elisa. 2019. "Body Mass and Creditworthiness: Evidence from Loan Officers in Uganda." AEA RCT Registry. August 27. https://doi.org/10.1257/rct.4528-0.1.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-09-23
Intervention End Date
2019-12-23
Primary Outcomes
Primary Outcomes (end points)
The key dependent variables are (1) A 0-10 Likert scale creditworthiness rating (2A) 0-1 choice of referring the applicant to the next step of the process or, when loan officer has full discretionary power, (2B) 0-1 choice of granting the requested loan. Conditional on (2A) == 1, (3) 0-1 ex-ante assessed applicants' likelihood of receiving the requested loan, at the end of the process. Conditional on (2A) == 0, (3A) choice of advising to apply for a smaller loan (with referral). Conditional on (2B) ==0 (3B) choice of advise to apply for a smaller loan (with approval). Conditional on (2A) ==1, ex-ante assessed applicants' likelihood of receiving a half-sized loan, at the end of the process.

The main explanatory variable of interest is body mass of the picture randomly assigned to the application. The second explanatory variable of interest is wealth of the applicant.

The main statistics monitored are the difference in outcome (1) and outcome (2) for applications which are randomly assigned a high body mass picture vs applications which are randomly assigned a low body mass picture. I assess the variation in this difference when the applicant wealth is revealed, and when applicant's wealth is not disclosed. Furthermore, as pre-experimental qualitative research suggested potential differences in return to body mass by gender, I will also investigate heterogeneity in these basic statistics by gender.

In addition to computing basic statistics, I will also identify parameters as in Kessler et al. (2019) by running regressions and interacting parameters in those regressions with the treatment variables (BMI/ Wealth).

The set of regressions that I will perform are the following. On the set of applications without income information, I will estimate for each main outcome:
(*) Y =α x BM +u,
where the error terms are clustered at the enumerator level. To this basic equation, I will then add sequentially 1) loan profiles fixed effect; 2) Bank Tier and Location FE; 3) controls on the applicant: Age, Gender, Occupation, Attractiveness; 4) controls on the loan officer: Age, Gender, Experience, Financial literacy.
As a second step, I will estimate the same model but allowing for heterogeneity by gender of the applicant.

On the set of applications with income information, I will estimate for each main outcome:
(**) Y =α x BM+γ×Wealth+β×Wealth*BM +u,

where the error terms are clustered at the enumerator level. To this basic equation, I will then add sequentially 1) loan profiles fixed effect; 2) Bank Tier and Location FE; 3) controls on the applicant: Age, Gender, Occupation, Attractiveness; 4) controls on the loan officer: Age, Gender, Experience, Financial literacy. As a second step, I will estimate the same model but allowing for heterogeneity by gender of the applicant.

On the full set of application, I will estimate for each main outcome:
(***) Y =α x BM+γ×WealthInformation+β×WealthInformation*BM +u,

where the error terms are clustered at the enumerator level. To this basic equation, I will then add sequentially 1) loan profiles fixed effect; 2) Bank Tier and Location FE; 3) controls on the applicant: Age, Gender, Occupation, Attractiveness; 4) controls on the loan officer: Age, Gender, Experience, Financial literacy. As a second step, I will estimate the same model but allowing for heterogeneity by gender of the applicant.











Primary Outcomes (explanation)
BM: 0/1 dummy, 1 if picture was modified to show higher body mass; 0 if picture was modified to show lower body mass. BM will also be defined continuously based on assessment by similar population via survey.

Wealth: Low/High dummy (Low: Revenue/month: Ush. 1-1.5 million and Profits/month: Ush. 400-600'000 per month; High: Revenue/month: Ush. 3-4 million; Profit/month: Ush. 1-1.5 million)

WealthInformation: 0/1 dummy if wealth information is present.

Financial literacy: FCA of answers to Financial Literacy Section in Questionnaire.

Attractiveness: Assessed by similar population via survey, on a 0-10 Likert scale (Mobius and Rosenblat, 2006).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The design builds on Kessler et al. (NBER WP 2019). After answering a brief questionnaire, loan officers are asked to evaluate 6 hypothetical loan applications, a set. The application in a set are grouped into two loan profiles, which vary by amount requested and repayment rate. The first 3 applications are assigned to the first loan profile (Ush. 1 million, with 6 months repayment); the second 3 applications are assigned to the second loan profile (Ush. 5 million, with 12 months repayment). Notably, for selected Tier 1 banks, who do not lend as low as Ush. 1 million, the first loan profile entails Ush. 10 million, with 12 months repayment. Within each loan profile, the applications vary by wealth category. The first application has no wealth information; the second application is "low wealth"; the third application is "high wealth". I define as cell the intersection of a loan profile and a wealth category. Within each cell, the applicants' occupation, appearance and body mass are cross-randomised, with the caveat that - to avoid suspicion - the same individual appearance cannot repeat within each set with different body mass or occupation.

Using this procedure, I build 23 sets and I randomly select 21 of those and assign each of them an enumerator. Each enumerator is randomly assigned to a set of branches to interview. The set of branches is obtain by randomly selecting 150 between branches and headquarters in Kampala, 28 in Mbarara and 24 in Gulu. For each branch, 1 to 3 loan officers are interviewed. The procedure is as follows: The enumerator asks who are the people that directly in touch with with (potential) borrowing customers. If there are more than 3, the enumerator randomly selects 3 of them to be interviewed. If there are less than 3, everyone is interviewed. From this randomisation technique follows that all the loan officers in a branch will rate the same set of applications. This is again to make sure that no loan officers happens to observe the same individual with different body mass or occupations.

Each loan officers is shown each of the 6 application singularly. First, the applications with no wealth information are shown. Then, the remaining applications are shown in random order. For each application, the loan officer answers the following questions: (1) A 0-10 Likert scale creditworthiness rating (2A) 0-1 choice of referring the applicant to the next step of the process or, when loan officer has full discretionary power, (2B) 0-1 choice of granting the requested loan. Conditional on (2A) == 1, (3) 0-1 ex-ante assessed applicants' likelihood of receiving the requested loan, at the end of the process. Conditional on (2A) == 0, (3A) choice of advising to apply for a smaller loan (with referral). Conditional on (2B) ==0 (3B) choice of advise to apply for a smaller loan (with approval). Conditional on (2A) ==1, ex-ante assessed applicants' likelihood of receiving a half-sized loan, at the end of the process.

When answering the questions, the loan officers are informed that the applicants are hypothetical. Honest answers are incentivised by informing the loan officers that their answers will be used to provide referrals to prospective clients whose characteristics match with their preferences. The matching is performed using a machine learning algorithm, following Kessler et al. (2019).

The design allows to investigate whether (1) body mass affects perceived creditworthiness and referral probability, and (2) whether reducing asymmetric information on income changes the relevance of body mass in determining the decision. It also allows to test whether the estimated relations are heterogenous by gender.
Experimental Design Details
Not available
Randomization Method
computer
Randomization Unit
Enumerator level
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
21 enumerators.
Sample size: planned number of observations
6 applications per loan officer; 1 to 3 loan officers per branch for a total of 202 branches. Planned number of observations ranges from 1'212 to 3636 applications.
Sample size (or number of clusters) by treatment arms
21 clusters in the no- wealth information treatment arm; 21 clusters in the wealth information treatment arm (within loan officer design).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Human Subjects Committee of the Faculty of Economics, Business Administration, and Information Technology
IRB Approval Date
2019-08-20
IRB Approval Number
OEC IRB # 2019-032