We will conduct an RCT with potential clients of a large microfinance institution in Ghana. Each client will be randomly assigned to one of three treatment arms (or to a control group that receives no information) and will be provided with information about the average amounts requested by other similar borrowers (men, women, or both men and women).
We will intervene at the first stage of the loan application process, where potential clients apply for loans via a telephone call with a call center agent of the microcredit company. After collecting basic information (including the initial loan amount requested), in the randomized conditions the call center agent elicits prior beliefs about the typical amounts requested by other men and women with similar characteristics. The client is then provided with information about what other similar borrowers request on average. Exactly what priors are elicited and what information is provided to the client depends on the treatment group into which the client was randomized, as follows:
1. Control group: both priors elicited, no additional information provided
2. Own-gender-info-only treatment: same-gender prior elicited, information about average amount requested by other clients of the same gender
3. Other-gender-info-only treatment: opposite gender prior elicited, information about average amount requested by other clients of opposite gender
4. Double-info treatment: priors elicited for both genders, information about average amount requested by both other male and female clients, order of elicitation and provision of information is randomized.
The average loan amounts provided in the information treatments come from either an XGBoost algorithm or a regression model run on the company's historical data. Both models perform similarly on average, but there are differences in predictions at the individual level. Within each treatment group, we provide either the lower or higher amount with a 50% probability. In the double-info treatment, this implies 4 different combinations, each chosen with a 25% probability.
This design will allow us to test whether and how people respond to information about their own gender, the opposite gender, or both genders, and to pinpoint the mechanisms at play, including whether there are any psychological biases or inconsistencies.
We will conduct the main analysis pooling both genders (and coding treatments as same gender vs. opposite gender) as well as separately for men and women.
We will also conduct the following heterogeneity analyses:
- Heterogeneity by gender, priors and initial request amount.
- Heterogeneity by old vs. new borrowers, as we hypothesize that the largest effect would be for first-time borrowers, since they have the least experience and information about market conditions.