Research

Unequal Recovery from Recessions: Skills Learning Among Young Workers (Job Market Paper)

• • Abstract: Graduating during a recession leads to wage losses—about 2% per one-point rise in the unemployment rate—with effects that diminish but persist for over a decade. However, these averages mask substantial heterogeneity: using NLSY data and quantile regression, I find that young workers in higher residual wage quantiles recover significantly faster. This heterogeneity can be attributed to some unobservable characteristics of workers. To quantify the role of endogenous skill investment in recovery and the sources of heterogeneity, I develop a life-cycle model featuring two-dimensional Ben-Porath skill accumulation with workers differ along three unobserved dimensions relevant for wage determination: true abilities, initial self-beliefs about true abilities, and initial stock of skills. This model extends the classical Ben-Porath model by accounting for workers' uncertainties about their skills accumulation and introducing aggregate fluctuations in the demand for skills. Counterfactual analysis shows that the endogenous change in workers' investment in skills accumulation due to the recession explains up to 23% of wage losses every year throughout the recovery period and 92% from the recovery periods even after the recession is over. Heterogeneity in wage losses is primarily driven by differences in true skill accumulation ability, with low-ability workers suffering the most—mainly due to slower recovery in the time they devote to skill accumulation.

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Estimating Demand Shocks from Foot Traffic: A Big-Data Approach

with Marina Azzimonti  and David Wiczer

• • Abstract: This study leverages high-frequency foot traffic data from SafeGraph to estimate demand shocks in customer-facing establishments across New York City’s retail, service, and health sectors. Recognizing that variations in foot traffic can arise from both unpredictable demand shocks and firm-driven strategies to attract customers, we develop a theoretical framework to link foot traffic with underlying demand, isolating establishment-level demand fluctuations from firm-level strategic choices. Implementing this empirically, we employ an unsupervised machine learning approach to classify establishments into distinct categories that are mostly orthogonal to location and sector. We find important heterogeneity in the persistence of shocks, important heterogeneity in their trends, and that estimation on a pooled sample importantly understates the variance experienced by some establishments.

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Declining Defined Benefit Plan Inclusion and the Impact on Savings and Retirement Decisions

with Rosemary Kaiser and Xiaohui Sun

• • Abstract: The last thirty years have seen a dramatic transition from traditional defined benefit retirement plans to defined contribution plans in the United States. To what extent has this transformation influenced the decline in retirement beyond age 65 and individuals’ retirement savings decisions? We empirically document differences in retirement timing by retirement plan type and construct a model where enrollment in different plan types influences endogenous savings and retirement decisions. The model replicates differences in savings and retirement timing observed in the data. The model estimates that the shift towards defined contribution plans alone generated roughly 92% of the decline in retirement among individuals age 65+ observed in the data since the early 1990s. A decomposition reveals that defined benefit plans induce earlier retirement mainly by alleviating the risk associated with an uncertain planning horizon (life expectancy). In contrast, their role in reducing risk associated with uncertain investment returns is less impactful on retirement timing decisions.

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