Can incentives mitigate student overconfidence at grade forecasts?

Research shows that college students exhibit bias in their forecasts of exam performance. Most students are overconfident in their forecasts, academically weaker students are the most overconfident, and top-performing students are underconfident. The literature identifies negative repercussions of these biases, including inadequate preparation for exams. A recurring attribute of this literature is the absence of meaningful incentives for students to forecast accurately. We implement an extra credit scheme to incentivize accurate forecasts. Depending on how forecast bias is measured, the scheme mitigates bias by top-performing students and marginally mitigates bias by other students. Our results have several implications. First, we illustrate an extra credit tool instructors can use to incentivize students to make more thoughtful assessments of their future exam performance. Second, we show how the association between incentives and forecast bias differs across student groups. Finally, we show that results in this literature are sensitive to how bias is measured.     

Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank-Specific Analysis

We present our efforts to develop bank-specific models to test for the presence of mortgage lending discrimination. We discuss the potential for selection and simultaneity biases and delineate the conditions under which a single-equation model is appropriate. The results from three national banks demonstrate that, by incorporating the specific underwriting guidelines of each bank, our alternative approach significantly improves the ability of the model to explain the outcomes of the mortgage lending decision process when compared to a single generic specification applied across all banks. Our results also demonstrate the difficulties encountered in attempting to incorporate the specifics of a bank's underwriting criteria and the remaining potential for omitted-variables problems.