We develop prototype job-to-job flow measures to provide new evidence on labor turnover and earnings dynamics in the Great Recession. We find a sharp drop in job mobility in the Great Recession, much sharper than the previous recession, and higher earnings penalties for job transitions with an intervening nonemployment spell. Focusing on residential construction separators in particular, we find increasing rates of industry change and higher earnings penalties from job change in the Great Recession.

In this paper, we use linked employer-employee data to study the reallocation of heterogeneous workers between heterogeneous firms. We build on recent evidence of a cyclical job ladder that reallocates workers from low-productivity to high-productivity firms through job-to-job moves. In this paper, we turn to the question of who moves up this job ladder and the implications for worker sorting across firms. Not surprisingly, we find that job-to-job moves reallocate younger workers disproportionately from less productive to more productive firms. More surprisingly, especially in the context of the recent literature on assortative matching with on-the-job search, we find that job-to-job moves disproportionately reallocate less educated workers up the job ladder. This finding holds even though we find that more educated workers are more likely to work with more productive firms. We find that while highly educated workers are less likely to match to low-productivity firms, they are also less likely to separate from them, with less educated workers more likely to separate to a better employer in expansions and to be shaken off the ladder (separate to nonemployment) in contractions. Our findings underscore the cyclical role job-to-job moves play in matching workers to better-paying employers.

Analyses of survey data highlight observable person characteristics, such as education and occupation, as critical factors for rising earnings inequality, while industry has an offsetting effect. In contrast, analysis of administrative data highlights that rising between-firm earnings dispersion and, in turn, between-industry earnings dispersion dominates the rise in earnings inequality. We construct a novel integrated dataset based on CPS micro data linked with LEHD administrative records. We find that most of the rise in earnings inequality is accounted for by rising between-industry inequality. This finding reflects a substantial contribution of increased sorting and segregation of observable person characteristics between industries.

Most of the rise in overall earnings inequality from 1996 to 2018 is accounted for by rising between-industry dispersion. The contribution of industries is right-skewed with the top 10 percent of four-digit NAICS industries dominating. The top 10 percent are clustered in high-paying high-tech and low-paying retail sectors. In the top industries, high-wage workers are increasingly sorted to high-wage industries with rising industry premia. In the bottom industries, low-wage workers are increasingly sorted into lowwage industries, with rising employment and falling industry wage premia.

The US workforce has had little change in real wages, income, or earnings since the year 2000. However, even when there is little change in the average rate at which workers are compensated, individual workers experienced a distribution of wage and earnings changes. In this paper, we demonstrate how earnings evolve in the US economy in the years 2001-2014 on a forthcoming dataset on earnings for stayers and transitioners from the U.S. Census Bureau's Job-to-Job Flows data product. We account for the roles of on-the-job earnings growth, job-to-job flows, and nonemployment in the growth of U.S. earnings.