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.