The Journal of Finance

The Gender Gap in Housing Returns

Published: 02/07/2023   |   DOI: 10.1111/jofi.13212

PAUL GOLDSMITH‐PINKHAM, KELLY SHUE

Using detailed transactions data across the United States, we find that single women earn 1.5 percentage points lower annualized returns on housing relative to single men. Forty‐five percent of the gap is explained by transaction timing and location. The remaining gap arises from a 2% gender difference in execution prices at purchase and sale. Consistent with a negotiation channel, women list for less and experience worse negotiated discounts. The gender gap shrinks in tight markets, where negotiation is replaced by quasi‐auctions. Overall, gender differences in housing explain 30% of the gender gap in wealth accumulation for the median household.

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Published: 10/28/2021   |   DOI: 10.1111/jofi.13090

ANDREAS FUSTER, PAUL GOLDSMITH‐PINKHAM, TARUN RAMADORAI, ANSGAR WALTHER

Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.

Bad Credit, No Problem? Credit and Labor Market Consequences of Bad Credit Reports

Published: 06/01/2020   |   DOI: 10.1111/jofi.12954

WILL DOBBIE, PAUL GOLDSMITH‐PINKHAM, NEALE MAHONEY, JAE SONG

We study the financial and labor market impacts of bad credit reports. Using difference‐in‐differences variation from the staggered removal of bankruptcy flags, we show that bankruptcy flag removal leads to economically large increases in credit limits and borrowing. Using administrative tax records linked to personal bankruptcy records, we estimate economically small effects of flag removal on employment and earnings outcomes. We rationalize these contrasting results by showing that, conditional on basic observables, “hidden” bankruptcy flags are strongly correlated with adverse credit market outcomes but have no predictive power for measures of job performance.