Using U.S. administrative data on worker earnings, we show that increases in risk premia lead to lower labor earnings, particularly for lower-paid workers. These declines are primarily driven by job separations. We build an equilibrium model of labor market search that quantitatively replicates the observed heterogeneity in labor market dynamics across worker earnings levels. Our findings underscore the role of time-varying risk premia as a key driver of labor market fluctuations and highlight the importance of both the job creation and the job destruction margins in understanding the heterogeneity in worker outcomes over the business cycle.

U.S. employers and the federal government devote the equivalent of 1.5% of GDP annually toward promoting defined contribution (DC) retirement savings. Using a new employer-employee linked dataset covering millions of Americans, we show that tax and employer matching incentives disproportionately benefit White and Asian workers compared to their similar-income Hispanic, Black, and American Indian or Alaska Native coworkers. Similarly, these incentives disproportionately benefit those with richer parents compared to those from lower-income families. Breaking the link between contribution choices and saving subsidies through revenue-neutral reforms could close up to one-third of the DC wealth gaps by race and parental income.

For whom has earnings risk changed, and why? We answer these questions by combining the Kalman filter and EM-algorithm to estimate persistent and temporary earnings for every individual at every point in time. We apply our method to administrative earnings linked with survey data. We show that since the 1980s, persistent earnings risk rose by 12.5% for both employed and unemployed workers and the scarring effects of unemployment doubled. At the same time, temporary earnings risk declined. Using education and occupation codes, we show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption.

Administrative earnings data reveal that households are exposed to large, countercyclical idiosyncratic tail risks in labor earnings. I illustrate how these risks affect asset prices within an asset pricing framework with recursive preferences, heterogeneous agents and incomplete markets. Quantitatively, a model in which agents face a time-varying probability of experiencing a rare, idiosyncratic disaster, with parameters disciplined by data, matches the level and dynamics of the equity premium. Stock returns are highly informative about labor market event risk, and, consistent with model predictions, initial claims for unemployment, a proxy for labor market uncertainty, is a highly robust predictor of returns.

Are market experts prone to heuristics, and if so, do they transfer across closely related domains---buying and selling? We investigate this question using a unique dataset of institutional investors with portfolios averaging $573 million. A striking finding emerges: while there is clear evidence of skill in buying, selling decisions underperform substantially---even relative to random selling strategies. This holds despite the similarity between the two decisions in frequency, substance and consequences for performance. Evidence suggests that an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: we document a systematic, costly heuristic process when selling but not when buying.

For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (‘pockets’) are interspersed with long periods with little or no evidence of return predictability. We document this result empirically using a flexible time-varying parameter model which estimates predictive coefficients as a nonparametric function of time and explore possible explanations of this finding, including time-varying risk-premia for which we only find limited support. Conversely, pockets of return predictability are consistent with a sticky expectations model in which investors only slowly update their beliefs about a persistent component in the cash flow process.

Note: A minor coding error impacted some of the results using the original method in the paper. In this note, we show that a simple adjustment to the estimation procedure restores the key results of the published paper.

We use recent advances in natural language processing and large language models to construct novel measures of technology exposure for workers that span almost two centuries. Combining our measures with Census data on occupation employment, we show that technological progress over the 20th century has led to economically meaningful shifts in labor demand across occupations: it has consistently increased demand for occupations with higher education requirements, occupations that pay higher wages, and occupations with a greater fraction of female workers. Using these insights and a calibrated model, we then explore different scenarios for how advances in artificial intelligence (AI) are likely to impact employment trends in the medium run. The model predicts a reversal of past trends, with AI favoring occupations that are lower-educated, lower-paid, and more male-dominated.

We study a general class of consumption-savings problems with recursive preferences. We characterize the sign of the consumption response to arbitrary shocks in terms of the product of two sufficient statistics: the elasticity of intertemporal substitution between contemporaneous consumption and continuation utility (EIS), and the relative elasticity of the marginal value of wealth (REMV). Under homotheticity, the REMV always equals one, so the propensity of the agent to save or dissave is always signed by the relationship of the EIS with unity. We apply our results to derive comparative statics in classical problems of portfolio allocation, consumption-savings with income risk, and entrepreneurial investment. Our results suggest empirical identification strategies for both the value of the EIS and its relationship with unity.

We detail a methodology for estimating the textual similarity between two documents while accounting for the possibility that two different words can have a similar meaning. We illustrate the method's usefulness in facilitating comparisons between documents with very different formats and vocabularies by textually linking occupation task and industry output descriptions with related technologies as described in patent texts; we also examine economic applications of the resultant document similarity measures. In a final application we demonstrate that the method also works well relative to alternatives for comparing documents within the same domain by showing that pairwise textual similarity between occupations' task descriptions strongly predicts the probability that a given worker will transition from one occupation to another. Finally, we offer some suggestions on other potential uses and guidance in implementing the method.

We analyze the supply-side disruptions associated with Covid-19 across firms and workers. To do so, we exploit differences in the ability of workers across industries to work remotely using data from the American Time Use Survey (ATUS). We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced significantly greater declines in employment, significantly more reductions in expected revenue growth, worse stock market performance, and higher expected likelihood of default. In terms of individual employment outcomes, lower-paid workers, especially female workers with young children, were significantly more affected by these disruptions. Last, we combine these ex-ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that most closely replicates the supply-side disruptions resulting from the pandemic.

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We study investor redemptions and portfolio rebalancing decisions of prime money market mutual funds (MMFs) during the Eurozone crisis. We find evidence that investors selectively acquire and act upon information about MMFs' risk exposures. In turn, this provides strong incentives for managers to withdraw funding from issuers whose debt becomes information-sensitive. Consistent with this, we show that MMF managers, particularly those serving the most sophisticated investors, selectively adjust their portfolio risk exposures to avoid information-sensitive European risks, while maintaining or increasing risk exposures to other regions. This mechanism helps to explain the occurrence of selective dry-ups in debt markets where delegation is common and returns to information production are often low.

We study daily money market mutual fund flows at the individual share class level during September 2008. This fine granularity of data facilitates new insights into investor and portfolio holding characteristics conducive to run risk in cash-like asset pools. Empirically, we find that cross-sectional flow data observed during the week of the Lehman failure are consistent with key implications of a simple model of coordination with incomplete information and strategic complementarities. Similar conclusions follow from daily models fitted to capture dynamic interactions between investors with differing levels of sophistication within the same money fund, holding constant the underlying portfolio.

A large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high. More generally, we expect the projection of the stochastic discount factor on the market portfolio--that is, the discounted pricing kernel evaluated at the market portfolio--to be a monotonically decreasing function of the market portfolio. Numerous recent empirical studies appear to contradict this prediction. The nonmonotonicity of empirical pricing kernel estimates has become known as the pricing kernel puzzle. In this paper we propose and apply a formal statistical test of pricing kernel monotonicity. We apply the test using seventeen years of data from the market for European put and call options written on the S&P 500 index. Statistically significant violations of pricing kernel monotonicity occur in a substantial proportion of months, suggesting that observed nonmonotonicities are unlikely to be the product of statistical noise.