The effect of job loss on crime: Evidence using Mass Layoffs across the U.S.

Identification: Mass layoffs (>30% downsizing at the employer level).

Sample: Workers in mass layoff firms regardless of whether displaced (Intend-to-Treat sample) from nine U.S. states.

Method: Difference-in-Differences with a matched comparison group of workers who share similar characteristics.

Main Datasets: 1. LEHD: paired quarterly employee-employer wage income records; 2. CJARS: criminal justice administrative records system.

This paper examines how job loss affects criminal behavior using linked administrative microdata on employment and criminal justice outcomes from nine U.S. states between 1995 and 2020. Many previous studies identify the effect of layoffs using displaced workers from mass layoff events. However, focusing on displaced workers can lead to bias due to reverse causality. A fundamental data limitation is that the precise timing of crime and layoff events is not observed in most datasets, nor is the reason for job separation. Prior studies typically assume a laid off worker at a firm that experiences a mass-layoff event is part of the layoff, but bias can arise if crimes precede and cause layoffs within the quarter when the event occurs. Replicating the conventional research design based on displaced workers, I find a 40 percent spike in felony convictions at the layoff quarter, whereas a bias-corrected estimate is close to zero.

I circumvent reverse causality by using an intent-to-treat design that estimates the layoff effect on all workers at firms experiencing mass layoffs, regardless of whether individual workers are displaced. I use a difference-in-differences strategy to compare workers at mass layoff firms to matched workers who are not in mass-layoff firms within the same state and industry, and with similar demographics, employment histories, and prior criminal records. Using this design, and after adjusting my baseline intent-to-treat parameters to reflect treatment effects on the treated (about 70 percent of individuals at mass-layoff firms are actually laid off), I find layoffs do not significantly affect crime. My estimates are precise enough to rule out effects larger than a 7 percent increase in the probability of a conviction.

Link to the paper: https://drive.google.com/file/d/1drxkk7pMr79xjPqWhtMFmIt6iDl2RksL/view?usp=drive_link

Dissertation Research Grant Award from the Russel Sage Foundation (2025): https://www.russellsage.org/news/fourth-annual-dissertation-research-grants-awarded

The Market Value of Charter Schools: Evidence from Housing Prices in Chicago

Abstract

Charter schools are publicly funded but operate with greater autonomy than traditional neighborhood schools, allowing flexibility in curriculum design and accountability structures. While extensive research has documented the academic impacts of charter schools—particularly the large gains from oversubscribed urban “No Excuses” charters—much less is known about how parents value their presence. Because housing prices reflect parents’ valuation of local educational opportunities, I examine how the housing market responds to charter school openings and traditional neighborhood school closures in Chicago from 1999 to 2022. Using a spatial–temporal difference-in-differences framework and detailed property transaction data, I find little evidence that, on average, charter school openings influence nearby housing prices. However, housing prices increase by approximately 2.9 percent following the opening of “No Excuses” charter schools, suggesting that the market differentiates among charter school types. These results provide updated evidence on how families value the expanding charter sector and indicate that parents place a higher value on certain educational models.

Link to the paper: https://drive.google.com/drive/folders/18S1ogfkZVCu7XdlNvWgXqwdNH_cSS7Cv

Revisiting Job Loss and Mortality: Accounting for Pre-Event Selection from the Absorbing State of Death

(with Pauline Leung)

Identification: Mass layoffs (>30% downsizing at the employer level).

Sample: Workers in mass layoff firms 2 years prior to Mass Layoffs from 30 U.S. states.

Method: Difference-in-Differences with a matched comparison group of workers who share similar characteristics. 

Main Datasets: 1. LEHD: paired quarterly employee-employer wage income records; 2. Numident: social security data, including death records. 

Previous studies estimating the effect of job loss on mortality typically restrict the sample to workers who experienced mass layoffs (Eliason and Storrie, 2009; Sullivan and von Wachter, 2009; Browning and Heinesen, 2012). This sampling strategy implicitly conditions on survival to the layoff event, since only workers who remain in the labor force until the displacement are observed. Thus, pre-trends in mortality cannot be assessed, and estimates may be biased if treated and control workers differ systematically in baseline mortality risk. To address this concern, we construct a sample that includes all individuals employed at Mass Layoff firms two years prior to the layoff events, regardless of whether they were still employed at the time of displacement, in order to estimate the impact of layoffs on mortality. We link individual-level, paired employee–employer microdata to mortality records sourced from Social Security data across 30 U.S. states.

Why Job Loss Hurts More Today: Evidence from the United States (with Pauline Leung)

Each point plots the estimated effect of mass layoffs on employment one (left panel) and three (right panel) years after job loss, by layoff cohort. Estimates are based on administrative employer–employee data from four U.S. states. Vertical bars show 95% confidence intervals. Results are preliminary.

Preliminary analysis using administrative data from four U.S. states (Arizona, Maryland, Pennsylvania, and Wisconsin) suggests that the employment costs of mass layoffs are sizable and may have grown over time. These early findings motivate a broader research agenda to understand how structural shifts in the labor market have changed the consequences of displacement for U.S. workers.