Labor Market Flows, Business Dynamics, and Unemployment (2010)

Measured from establishment-level data on employment gains and losses, job creation and destruction average nearly 8 percent of employment per quarter in the U.S. private sector. Worker flows in the form of establishment-level hires and separations are more than twice as large. (1) These facts summarize the remarkable extent of job and worker flows in U.S. labor markets. They provide powerful motivation for theories of frictional unemployment. In recent research with several coauthors, I explore the relationship of job flows to worker flows, develop methods to improve the measurement of worker flows, investigate job loss and business volatility trends, and provide new evidence on the determinants of long-term movements in the unemployment rate. Job Flows and Worker Flows in the Cross Section Data from the Job Openings and Labor Turnover Survey (JOLTS) display a very tight link between job flows and worker flows in the cross section of employers. In Figure 1 we see that hires rise a bit more than one-for-one with establishment-level job creation. Separations rise a bit more than one-for-one with job destruction. (2) Further investigation reveals that layoffs are the main margin of employment adjustment for establishments with high job destruction rates, while both quits and layoffs are important margins at moderate destruction rates. Many studies find, not surprisingly, that layoffs are much more likely than quits to result in unemployment spells. (3) Thus, higher rates of job destruction bring higher layoff rates and greater worker flows into unemployment. Pitfalls in Measuring Worker Flows from Employer Survey Data A striking feature of Figure 1 is the highly nonlinear relationship of hires and separations to employer growth rates. These relations exhibit pronounced kinks at zero, steep slopes moving away from zero in one direction, and mild slopes with an opposite sign in the other direction. Similar patterns hold for quits and layoffs. These highly nonlinear relations create potential pitfalls in the measurement of worker flows from survey data. To see the issue, observe that aggregate hires, for example, are the weighted sum of hires at establishments with different growth rates, with weights given by the amount of employment at each growth rate. In order to accurately measure aggregate worker flows, it is necessary to combine good estimates for the type of cross-sectional relations in Figure 1 with an accurate measure of the (weighted) cross-sectional distribution of employer growth rates. [FIGURE 1 OMITTED] Using survey data to construct an accurate measure of the growth rate distribution is challenging for two reasons. First, employer surveys typically capture new establishments with a considerable lag. Entrants account for a disproportionate share of hires and, more generally, newer establishments exhibit a much higher incidence of extreme growth rates. (4) Second, survey response rates are correlated with employer growth rates in the cross section. More to the point, and borrowing a line from Robert Hall: the first employee let go from a declining establishment is the person who fills out government surveys. For both reasons, employer surveys tend to produce growth rate distributions with too little mass in the tails. Inspecting Figure 1, it is easy to see why missing tail mass generates a downward bias in worker flow estimates. My coauthors and I study this issue in the JOLTS program, a leading source of information about worker flows and job openings for the U.S. economy. (5) We verify that the growth rate distribution generated by the JOLTS sample has much less tail mass than that implied by the comprehensive Business Employment Dynamics (BED) database. We also develop a method to correct the problem. The key idea is to reweight the cross-sectional distributions of employment growth rates in JOLTS to match the corresponding distributions in the comprehensive BED. …