Jeff Sorensen
I am a PhD candidate in Economics at UC Berkeley.
I am on the job market and will be available for interviews at the January 2017 ASSA meetings in Chicago.
My research is in labor economics and focuses on job loss and the importance of firms in workers' labor market outcomes.
You can view my CV here.
Research
Job Market Paper
Firms' layoff rules, the cost of job loss, and asymmetric employer learning
I test for asymmetric information in the labor market by studying how earnings losses of displaced workers vary with the degree of selection in employers’ layoff decisions. Using matched employer-employee data on the universe of mass layoffs in West Germany from 1980 to 2009, I characterize each of the 4,400 mass layoffs by the relative probabilities of displacement for different types of workers. I find substantial heterogeneity in layoff rules across establishments, with the most common types being based on relative tenure, relative wages, or occupation. Laying off workers with low tenure—which I interpret as a less selective layoff rule—has become less common, while laying off workers with low relative wages—which I interpret as a more selective layoff rule—has become more common. I also find an increase in wage-based layoffs and a decrease in tenure-based layoffs in recessions. I develop an asymmetric employer learning model that implies that earnings losses are smaller for workers involved in tenure-based layoffs, since these layoffs do not serve as a negative signal of workers’ productivity. I find strong support for this prediction: earnings losses are 20% smaller for workers displaced in a tenure-based layoff than similar workers displaced in a wage-based layoff. Selective layoffs are particularly costly for less-educated workers and workers in high-skilled service occupations, suggesting that asymmetric learning by incumbent employers is more prevalent for these groups.
Work in progress
Quasi-experimental estimates of the cost of job loss: A regression discontinuity approach using strict seniority layoff rules
I estimate the cost of job loss to workers in a regression discontinuity framework using matched employer-employee data on downsizing establishments that use a strict tenure cutoff to choose which workers to lay off. I identify the tenure cutoff for each establishment utilizing precise timing on job start and end dates, and I then estimate the effect of job displacement on workers' earnings, wages, and days worked using regression discontinuity methods. Pooling together different establishments allows me to estimate the cost of job loss to workers at various levels of tenure. I compare my estimates with those from event studies and other approaches to evaluate the degree of bias in past estimates from selection issues regarding which workers are laid off.
Layoff rules over the business cycle
Using administrative data on the universe of mass layoffs in West Germany from 1980 to 2009, I explore changes in firm layoff behavior over the business cycle and implications of these changes for the labor market. During recessions establishments are less likely to lay off their low-seniority and college-educated workers and more likely to lay off their female workers and workers with low relative wages. These business cycle effects are stronger in the post-1995 period, likely due to liberalizing labor market reforms and a decline in collective bargaining. I investigate the importance of these findings for how the composition of unemployed workers changes over the business cycle, and I find that the cyclicality of establishments' layoff rules can explain part of the increased cost of job loss and increased duration of unemployment for workers displaced in recessions. Finally, I study the relationship between changes in the cyclicality of layoff rules and changes in the cyclicality of labor productivity and consider different models that can explain these results.
Publications
Academic salary compression across disciplines and over time, with James McDonald. Economics of Education Review, 2017, 59, 87-104.
Skewness and kurtosis properties of income distribution models, with James McDonald and Patrick Turley. Review of Income and Wealth, 2013, 59(2), 360-374.