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.