We study the US labor market transitions using a latent variable approach, explicitly modeling the persistent misclassification process and the non-Markovian nature of the underlying true labor force dynamics. A closed-form global identification for misclassification probabilities and labor transition probabilities is established through an eigenvalue-eigenvector decomposition. Contrary to existing studies, our empirical results suggest that the observed data have understated the true mobility in labor force statuses after we account for persistence in both the misclassification errors and the latent true labor force dynamics.

Abstract Colleges can send signals about their quality by adopting new, more alluring names. We study how this affects college choice and labor market performance of college graduates. Administrative data show name-changing colleges enroll higher-aptitude students, with larger effects for alluring-but-misleading name changes and among students with less information. A large resume audit study suggests a small premium for new college names in most jobs, and a significant penalty in lower-status jobs. We characterize student and employer beliefs using web-scraped text, surveys, and other data. Our study shows signals designed to change beliefs can have real, lasting impacts on market outcomes.

Using recent results in the measurement error literature, we show that the official US unemployment rate substantially underestimates the true level of unemployment, due to misclassification errors in the labor force status in the Current Population Survey. During the period from January 1996 to August 2011, the corrected monthly unemployment rates are between 1 and 4.4 percentage points (2.1 percentage points on average) higher than the official rates, and are more sensitive to changes in business cycles. The labor force participation rates, however, are not affected by this correction.