Abstract
Labor market indicators are critical for policymakers, but measurement error in labor force survey data is known to be substantial. In this paper, I quantify the implications of classification errors in the U.S. Current Population Survey (CPS), in which respondents misreport their true labor force status. Once I correct for measurement error using a latent variable approach, the unemployment rate is on average 0.8 percentage points (ppts) higher than the official unemployment rate, with a maximum of 2.0 ppts higher between 1996 and 2018. This paper further quantifies the contributions to business-cycle fluctuations in the unemployment rate from job separation, job finding, and participation. Correcting for misclassification changes previous studies' results about the contributions of these transition probabilities: job separation accounts for more of the unemployment fluctuations, while participation accounts for fewer. The methodology I propose can be applied to any other labor force survey in which labor force status is observed for three periods.
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