Table 6 can be used to test Prediction 3 of the model, which states that all costumers are offered similar products. Columns 1 and 2 of Table 6 report the total yield after one year that would accrue in the savings account under the assumption of “no usage” (column 1) and “usage” (column 2). “No usage” refers to an activity level without deposits to or withdrawals from the account for one year. In this case, if inactivity fees are not assessed, then the formula to calculate total yield coincides with the APY. In column 2, “usage” assumes an activity level of 2 account inquiries and 2 cash withdrawals per month, penalty for a month long account inactivity, and a penalty if the average balance in the account is below the minimum allowed. As expected, auditors with a preference for investment accounts are offered higher yields as they are more profitable for the institution, but we also find that experienced auditors are given slightly higher yielding savings accounts, especially among those that quoted a low interest rate. Columns 5 and 6 report the total cost of credit products with and without a late payment, respectively. In the case of credit products Prediction 3 is confirmed, because all auditors are offered similar products.<sup>37</sup> Columns 3 and 4 report the difference in total yields between the cheapest savings product that the institution could have offered while meeting the needs of the auditor and the actual product offered. While all auditors were offered more expensive products, experienced auditors appear to receive a cheaper savings product. Columns 7 and 8 report the difference in total costs between the cheapest credit product offered by the institution and the actual loan offered to the auditor. None of the treatment dummies are statistically significant, suggesting that neither financially literacy nor the degree of indebtedness affects the product offered. Auditors are nonetheless offered relatively more expensive products: without late payments, the average difference in the total cost of credit for control auditors is 11.7 percent and 17.4 percent once late payment costs are included. We conclude that Prediction 3 is supported for the matched credit products but not for matched savings products. Appendix tables 4 and 5 include products offered that could not be matched in the market products database by matching them to either the cheapest product or the most expensive product that the institution could have offered. Since every product offered is matched, the number of products in Appendix tables 4 and 5 coincides with the number of auditorinstitution interactions. When we use all products, Prediction 3 is supported for both credit and savings products. Overall, the results seem to conform well with the predictions of the Gabaix and Laibson (2006) model.

Why do staff provide little information voluntarily and offer relatively more expensive products? Table 7 tries to address this question by reporting results from the staff survey conducted in Peru and Ghana. About 70 percent of the staff in each country have university degrees. In Peru there are no

> 37 When the total cost of credit and savings products are regressed against the 9 auditor characteristics and the treatment dummies, the p-value associated with the F-test that all auditor characteristics are jointly zero is 0.992 for credit and 0.172 for savings. This suggests that staff has limited ability to tailor the terms and cost of the product to the characteristics of the client.

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