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.

The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares. Next, we show how the Bartik instrument weights each of the exposure designs. Finally, we discuss how to assess the plausibility of the research design. We illustrate our results through two applications: estimating the elasticity of labor supply, and estimating the elasticity of substitution between immigrants and natives.

This paper estimates the effect of Chapter 13 bankruptcy protection on financial health using a new data set linking bankruptcy filings to credit bureau records. Our empirical strategy uses the leniency of randomly assigned judges as an instrument for Chapter 13 protection. We find that Chapter 13 protection decreases an index measuring adverse financial events such as civil judgments and repossessions by 0.323 standard deviations and increases the probability of being a homeowner by 13.2 percentage points. Chapter 13 protection has little impact on open unsecured debt but decreases the amount of debt in collections by $1,333.

The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent. However, low barriers to entry and fixed commission rates result in large market share for inexperienced intermediaries. Using micro-level data on 8.5 million listings and a novel research design, we show that house listings by inexperienced agents have a lower probability of selling, and this effect is strongest during the housing bust. We estimate that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition and the remainder from commission variation across experience.

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.

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show these regressions generally fail to estimate convex averages of heterogeneous treatment effects—instead, estimates of each treatment’s effect are contaminated by nonconvex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A reanalysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores.

Municipal bond markets began pricing sea-level rise (SLR) exposure risk in 2013, coinciding with upward revisions to worst-case SLR projections and accompanying uncertainty around these projections. The effect is larger for long-maturity bonds and not solely driven by near-term flood risk. We use a structural model of credit risk to quantify the implied economic impact and distinguish between the effects of underlying asset values and of uncertainty. The SLR exposure premium exhibits a trend different from house prices and is unaffected by house price controls. Together, our results highlight the importance of climate uncertainty in driving municipal bond prices.

We present and discuss preliminary evidence suggesting that credit ratings significantly influenced prices for subprime mortgage-backed securities issued in the period leading up to the recent financial crisis. Ratings are closely correlated with prices even controlling for a rich set of security- and loan-level controls. This incremental variation in ratings has much less predictive power for security defaults, however, based on findings to date from our ongoing research, suggesting prices were excessively sensitive to ratings relative to their informational content.

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.

Can information from a credible messenger shift behavior in an information-saturated environment? In a randomized controlled trial involving twenty-eight million individuals in West Bengal, we find that SMS-delivered video messages containing information about COVID-19 symptoms and health-preserving behaviors recorded by a credible messenger increased adherence to targeted and non-targeted preventive behaviors, measured by two objective measures (symptoms reported to a health worker, and phone usage at home), as well as self-reported behaviors. We find large spillovers onto non-targeted recipients. Credible light-touch messaging can play an important role in crisis response, even when similar information is widely available.