  We show that lenders face more uncertainty when assessing default risk of
historically under-served groups in US credit markets and that this information
disparity is a quantitatively important driver of inefficient and unequal
credit market outcomes. We first document that widely used credit scores are
statistically noisier indicators of default risk for historically under-served
groups. This noise emerges primarily through the explanatory power of the
underlying credit report data (e.g., thin credit files), not through issues
with model fit (e.g., the inability to include protected class in the scoring
model). Estimating a structural model of lending with heterogeneity in
information, we quantify the gains from addressing these information
disparities for the US mortgage market. We find that equalizing the precision
of credit scores can reduce disparities in approval rates and in credit
misallocation for disadvantaged groups by approximately half.
