Estimation of Job-to-Job Flow Rates under Partially Missing Geography
Integration of data from different regions presents challenges for the calculation of entitylevel longitudinal statistics with a strong geographic component: for example, movements between employers, migration, business dynamics, and health statistics. In this paper, we consider the estimation of worker-level employment statistics when the geographies (in our application, US states) over which such measures are defined are partially missing. We focus on the recent pilot set of job-to-job flow statistics produced by the US Census Bureau’s Longitudinal Employer- Household Dynamics (LEHD) program, which measure the frequency of worker movements between jobs and into and out of nonemployment. LEHD’s coverage of the labor force gradually increases during the 1990s and 2000s because some states have a longer time series than others, so employment transitions involving missing states are only partially or not at all observed. We propose and implement a method for estimating national-level job-to-job flow statistics that involves dropping observed states to recover the relationship between missing states and directly tabulated job-to-job flow rates. Using the estimated relationship between the observable characteristics of the missing states and changes in the employment measures, we provide estimates of the rates of job-to-job, and job-to-nonemployment, job-to-nonemploymentto- job flows were all states uniformly available.
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