Research

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

• • Abstract: Numerous studies indicate that graduating and entering the job market during a recession leads to persistent wage losses, initially at 2% per one-point rise in the national unemployment rate and remaining significant over a decade. However, this statistic conceals significant heterogeneity in outcomes. Using data from the National Longitudinal Survey of Youth, I investigate wage recovery differences among workers in various residual wage quantiles through quantile regression. Controlling for observable characteristics like demographics and work experiences, I find workers in higher residual wage quantiles have faster recovery speed. This heterogeneity can be attributed to unobservable worker characteristics. To understand these findings, I present a life-cycle model with multi-dimensional skill accumulation among workers differing in three dimensions. Dimensions related to wage determination but not directly observed from data: true abilities, initial self-beliefs about true abilities, and initial stock of skills. This model extends the standard Ben-Porath model by incorporating idiosyncratic learning processes and allowing for endogenous occupational choices. The model addresses the inefficiency of the recovery process, particularly how recessions alter occupation profiles, especially for those with biased self-beliefs about their true abilities. In an economy with more precise signals, the recovery process is longer. Increased investments in skill accumulation due to more significant information friction serve as a buffer during a recession. The counterfactual experiment reveals that the decline in human capital accumulation due to the recession significantly explains up to 20% of wage losses throughout the recovery period and 90% from the 8th to 15th years after the recession. Moreover, I conduct the half-life analysis and find that unequal recovery among workers primarily links to differences in true skill accumulation abilities rather than initial skill levels or biased self-beliefs.

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

with Marina Azzimonti  and David Wiczer

• • Abstract: Unlike the manufacturing industries, the demand shocks in customer-facing sectors are challenging to measure because the arrival of customers largely influences them. In this paper, we employ high-frequency foot traffic data to estimate demand dynamics in the Service, Retail Trade, and Health sectors from 2018 to 2019 in three New York boroughs: Manhattan, Brooklyn, and Bronx. We begin by eliminating the location-specific and trend biases in sampling, which is the technical contribution of our paper. We find substantial within-year variations in establishments with high foot traffic and large monthly fluctuations in demand, even among establishments with zero growth. Using an unsupervised machine learning approach, we categorize establishments into four groups and then estimate an AR(1) process of year-over-year demand growth patterns, allowing for group-specific effects. We find that the two-step grouped fixed-effects estimators are heterogeneous across clusters, and they are different than pooled regression estimates, indicating that the pooled regression results are not representative. The “Fast-Growth” and “Fast-Declining” groups of establishments also exhibit substantial monthly volatility in demand, suggesting that the growth in demand is not a smooth process. We uncover a negative relationship between persistence and the standard deviation of demand shocks within an establishment, implying similar unconditional variance across groups. Heterogeneity in demand dynamics is observed when restricting attention to a single geographical unit or production sector.

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