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Abstract
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Before
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. 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, meeting with them and their evaluation is a key step in obtaining access to credit.
In the experiment, I randomise body mass of hypothetical applicants, and evaluate how this affects access to credit. My measure of access to credit are 1) the choice of meeting the hypothetical applicant; 2) likelihood of refer the applicant to the next step of the loan application process 3) the expected probability of the applicant to receive the requested loan.
I exploit a design which builds on Bartoz et al. (2016) and Kessler et al. (WP 2019) insights, which allows to test for discrimination and pin down the mechanism at play. Treatment is assigned within subject. In a first treatment arm, I provide no information on the applicant, except picture and age. In a second treatment arm, I allow the loan officer to learn about the applicants' income and occupation. In the third treatment arm, I provide all loan officers the additional information.
After answering a set of questions including demographics, financial literacy and banks characteristics, loan officers are shown 45 hypothetical loan applications which vary by applicants' characteristics and loan profiles. The order of the treatment is fixed, but the applications are randomly assigned to treatment group.
This set up allows me to study whether (1) body mass affects perceived creditworthiness and access to credit, (2) whether reducing asymmetric information on income changes the relevance of body mass in determining the decision, and (3) at which part of the decision making process does the discrimination bites.
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After
In low income countries, overweight and obesity are more widespread among rich individuals. In contexts where income information is limited, high body mass might be exploited as signal of wealth. In this project I study how individuals' body mass affects their chance of accessing credit via a survey experiment in Uganda. Respondents are loan officers from a random subsample of licensed money lending institutions from the metropolitan area of Kampala, the capital. Loan officers are a key step in the process of accessing credit: They screen prospective clients, verify their information and upon verification, determine whether applicants qualify for the selected loan.
In the survey experiment, loan officers evaluate multiple hypothetical loan applications. Following Kessler et al. (WP 2019), truthful answers are incentivised by providing real referrals. In the applications, I randomise body mass of hypothetical applicants, and evaluate how this affects loan officers' evaluations. My measure of access to credit are the loan officer’s 1) choice of meeting the hypothetical applicant; 2) likelihood of qualifying the loan application; 3) charged interest rate. Furthermore, to investigate the mechanism at play, I elicit 4) assessment of the applicant’s creditworthiness and 5) assessment of the applicants' ability to put the loan to productive use.
Loan officers evaluate in total 30 hypothetical applications, which are split into three treatment arms, which vary how much and in which way information on the applicant is presented (within-subject design). The three arms allow to pin down the mechanism at play, by allowing to understand at which point of the decision making process does the discrimination bites.
This set up allows to (1) test whether body mass affects perceived creditworthiness and access to credit, (2) test whether reducing asymmetric information on income changes the relevance of body mass in determining the decision, and (3) understand at which, if any, step of the decision making process does the discrimination bites.
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Trial Start Date
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Before
October 23, 2019
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November 01, 2019
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Trial End Date
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Before
December 23, 2019
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After
September 01, 2020
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Last Published
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Before
October 11, 2019 10:12 AM
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After
October 31, 2019 04:15 PM
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Intervention Start Date
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Before
October 23, 2019
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After
November 01, 2019
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Intervention End Date
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Before
December 23, 2019
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After
September 01, 2020
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Primary Outcomes (End Points)
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Before
Creditworthiness; probability of accessing credit.
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After
Access to credit:
- choice of granting a meeting
- choice of qualifying the application
- interest rate charged
Creditworthiness
Perceived Financial Ability:
- perception as able to put loan money to productive use
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Primary Outcomes (Explanation)
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Before
(1) A Yes/No choice of meeting the applicant to discuss and verify information; (2) a 1-6 Likert scale assessment of the probability of referring the person to the next step of the application process (3) a 1-6 Likert scale assessment of the applicants' probability of receiving the requested loan.
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After
Access to credit:
(1) Meeting: A Yes/No choice of meeting the applicant to discuss and verify information;
(2) Qualifying: a 1-5 Likert scale assessment of the probability of qualifying the requested application.
Creditworthiness: a 1-5 Likert scale assessment of the creditworthiness of the applicant
Perceived Financial Ability: a 1-5 Likert scale assessment of the probability that the applicant puts the money to productive use.
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Experimental Design (Public)
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Before
Loan officers are asked to evaluate 45 hypothetical loan applications, in three sets. Each set is a treatment arm. The order of the treatment arms is fixed, but each hypothetical application is randomly selected into one of each arm for each participant.
The applications are grouped into two loan profiles, which vary by amount requested and repayment rate. The first loan profile is a Ush. 1 million, with 6 months repayment; the second loan profile Ush. 5 million, with 12 months repayment. Notably, I exclude Tier 1 banks from my sample as they generally do not lend as low as Ush. 1 million.
Within each loan profile, the applications vary by characteristics; in each application, the applicants' age and residence are defined statically, while the applicants' occupation, income, portrait, body mass are cross-randomised. In particular, to avoid suspicion, portraits are randomized without replacement across the full set of applications each respondents receives.
Using this procedure, I build 45 loan applications and randomly assign each to a treatment arm. In the first treatment arm, respondents only see the picture, age and loan profile requested. In the second treatment arm, respondents only see the picture, age and loan profile requested but can choose to learn occupation and income of the applicant. In the third treatment arm, participants see all the information (including the picture) at once.
Then, the remaining applications are shown in random order. For each application, the loan officer answers the following questions:
[Main outcomes]
(1) A Yes/No choice of meeting the applicant to discuss and verify information; (2) a 1-6 Likert scale assessment of the probability of referring the person to the next step of the application process (3) a 1-6 Likert scale assessment of the applicants' probability of receiving the requested loan.
[Secondary outcomes]
(4) A 1-6 Likert scale assessment of the probability that the applicant makes productive use of the money.
(5) A 1-6 Likert scale assessment of the probability that the applicant will repay/collateral will be collected, conditional on the investment not being successful.
For loan profiles where the loan officer has full discretionally on the application decision, question (2) and (3) are substituted by question (6) a 1-6 Likert scale assessment of the probability of granting the requested loan.
The set of branches is obtained by randomly selecting 70 branches and headquarters in Kampala. 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.
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, (2) whether reducing asymmetric information on income changes the relevance of body mass in determining the decision, (3) at which point of the decision making process does discrimination bites.
Relevant heterogeneity analysis include: 1) gender; 2) body mass of the loan officer; 3) financial literacy/experience; 4) perception of high body mass. If heterogeneity in the share of applicant invited is detected (either at the loan officers' level or at the institution level), an interesting heterogeneity analysis compares "lemon dropping" types to "cherry picking" ones. The design builds on Kessler et al. (NBER WP 2019) and Bartoz et al. (2016).
At the end of the experiment, loan officers answer a set of questions on financial literacy; perception of high body mass;
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After
Loan officers are asked to evaluate 30 hypothetical loan applications, in three sets. Each set is a treatment arm, the design exploits within subject variation and the order of the treatment arms is fixed. Each treatment arm varies according to how much and which information is presented. In the first treatment arm, respondents only see the picture, age and loan profile requested. In the second treatment arm, respondents only see the picture, age and loan profile requested but can choose to learn occupation and income of the applicant. In the third treatment arm, participants see all the information (including the picture) at once.
Within each treatment arm, the applications vary by characteristics; in each application, the applicants' age, nationality and place of residence are defined statically, while the applicants' loan profile, collateral, reason for loan, occupation, income (profits and revenues), gender, portrait, body mass are cross-randomised. Body mass is randomized by assigning the high body mass or low body mass photomorphed version of the randomly selected portrait. To avoid suspicions, portraits are randomized without replacement across the full set of applications each respondents receives so that no picture is seen twice by the same respondent.
Using the procedure detailed in the appendix, I first build 30 loan applications with all the cross randomized information, except body mass. Then I create the final set of 60 applications by creating two versions (a high body mass or low body mass) of each application. The 10 applications are randomly assigned to each arm stratified by characteristics. The order of the applications within each arm is random.
For each application, the loan officer answers the following questions:
1) Would you like us to refer to you this applicant to meet and discuss this loan application? Yes No
2) Based on your first impression, how likely would you be to approve this loan application? Not at all likely 1 2 3 4 5 Very likely
3) “Creditworthiness describes how likely a person is to repay a financial obligation according to the terms of the agreement.” Based on your first impression, how would you rate the person’s creditworthiness? Not at all creditworthy 1 2 3 4 5 Very creditworthy
4) Based on your first impression, how likely do you think this person would be to put the loan money to productive use? Not at all likely 1 2 3 4 5 Very likely
5) If you had to grant this loan to this person, what is the interest rate would you charge to this applicant?
Lower than standard - Standard - Higher than Standard
Interest rate question is only displayed to loan officers which have discretionality to change interest rates.
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 access to credit and creditworthiness,
(2) whether reducing asymmetric information on income changes the relevance of body mass in determining the decision,
(3) at which point of the decision making process does discrimination bites.
Relevant heterogeneity analysis include: 1) gender of applicant and loan officer; 2) body mass of the loan officer; 3) age of the loan officer; 4) perception of high body mass. If heterogeneity in the share of applicant invited is detected (either at the loan officers' level or at the institution level), I will compare "lemon dropping" types to "cherry picking" ones.
The incentives structure of the experiment is based on the idea that it is worthwhile for loan officers to receive good applicants. This incentives are the strongest when the loan officers' pay is tied to performance. While incentives might still be aligned also in the absence of performance pay (good applicants' referrals reduce screening and verification costs), nonetheless they are likely to be less relevant. Hence, if heterogeneity by performance pay will emerge, I will compare answers of high incentives vs low incentives loan officers.
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Randomization Unit
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Enumerator level
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Loan officer level; Application level.
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Planned Number of Clusters
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Before
200 loan officers
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minimum/expected 250 loan officers.
The sample size is obtained as follows. The set of institutions is obtained by randomly selecting 250 branches in Kampala, Mukono and Wakiso, stratifying by bank tier and location. Notably, commercial banks are excluded from the randomisation, as they are unlikely to lend as little as low as Ush. 1 million. Each selected branch is visited to ask whether they will be willing to participate to the study. If they agree, 1 to 2 loan officers are interviewed, until exhaustion of the list. The target loan officers number is 250 loan officers. If the 250 branches interview fail to reach the target number, we will perform a second randomisation among the remaining branches.
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Planned Number of Observations
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Before
45 applications per loan officer; 1 to 3 loan officers per branch for a total of 200 branches. Planned number of observations is around 6000 applications.
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After
30 applications per loan officer, for a total of expected 250 loan officers.
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Sample size (or number of clusters) by treatment arms
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Before
This is not an RCT. Half of the applications by treatment arm will have high body mass portrait, and half will have a low body mass one.
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This is not an RCT. minimum 250 loan officers per treatment arm, 10 applications per treatment arm.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
In Kessler et al. (2019), the effect of gender on hiring interest for the STEM majors
subsample ranged from 0.399 to 0.516 sd., with a single treatment arm. Because the effect the
effect of body mass is presumably mediated by wealth, I target a smaller MDE. 3000
observations allow to detect a MDE of 0.1 sd in each treatment arm.
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After
In Kessler et al. (2019), the effect of gender on hiring interest for the STEM majors
subsample ranged from 0.399 to 0.516 sd., with a single treatment arm. Because the effect the
effect of body mass is presumably mediated by wealth, I target a smaller MDE.
Using data from power calculations, 2500 observations allow me to pick up for choice of meeting
an effect slightly larger than 0.05 in each arm.
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Secondary Outcomes (End Points)
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Before
financial reliability; financial ability;
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Interest Rate.
Trust in the information provided (only when more information is provided).
Loan's officers attention:
- choice of receiving more information
- time spent on each application
Explicit Bias
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Secondary Outcomes (Explanation)
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Before
Financial reliability: A 1-6 Likert scale assessment of the probability that the applicant makes productive use of the money.
Financial ability: A 1-6 Likert scale assessment of the probability that the applicant will repay/collateral will be collected, conditional on the investment not being successful.
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After
Interest Rate: a 1-3 scale choice of the interest rate charged to the applicant (lower than standard, standard, higher than standard, not applicable). NA is selected for those loan officers who have no discretionality to change interest rate.
Trust in the information provided: A 1-5 Likert scale assessment of the probability that the applicant information is believed to be trustworthy.
Loan's officers attention: Yes/No choice of receiving more information on an applicant.
Explicit Bias: Pointing on a silhouette, whether an individual moving from low to high BM silhouette would more/less/equally likely to receive a loan.
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