Abstract We show that banks with high environmental, social, and governance (ESG) ratings issue fewer mortgages in poor localities—in number and dollar amount—than banks with low ESG ratings. This lending disparity happens at both the county and census tract level, worsens in disaster areas of severe hurricane strikes, is robust to alternative ESG ratings (including using only the social (S) component), and cannot be explained by banks’ differential deposit networks. We find no difference in mortgage default rates between high- and low-ESG banks, rejecting an alternative explanation based on differential credit screening quality. We report a complementary, not substitution, relation between high-ESG banks’ mortgage lending and their community development investments (like affordable housing projects) in poor localities. Loan-application-level analyses confirm that high-ESG banks are more likely than low-ESG banks to reject mortgage loans in poor neighborhoods. The evidence hints at social wash: banks deploy prosocial rhetoric and symbolic actions while not lending much in disadvantaged communities, the social function they arguably ought to perform. Community Reinvestment Act (CRA) examinations partially undo the social wash effect.

ABSTRACT We show that firms' investment opportunity sets (IOS) are multidimensional. Analyzing Form 10-K texts, we identify 445 unique keywords that predict firms' future investments during 1995–2009 and combine them into 43 underlying factors. Industry-specific factors include Bio-Pharma, Banking, Information Technology, Oil and Gas, and Retail Stores, while more general factors include Equity Intensity, Debt Intensity, Lease, Going Concern, and Acquisition. These factors form our multidimensional measures of IOS. They outperform Tobin's Q and/or industry fixed effects, in predicting future out-of-sample (2010–2015) investments and related corporate policies, and even inform incrementally over lagged dependent variables. We trace the factors' improved predictive power to their multidimensional nature, which captures IOS-related variation within and between industries, and stability in IOS that allows 10-K texts to be more informative. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G35; M41; M21.