This paper reports evidence on the error properties of survey reports of labor market variables such as earnings and work hours. Our primary data source is the PSID Validation Study, a two-wave panel survey of a sample of workers employed by a large firm which also allowed us access to its very detailed records of its workers earnings. etc. The second data source uses individuals' 1977 and 1978 (March Current Population Survey) reports of earnings, matched to Social Security earnings records. In both data sets, individuals: reports of earnings are fairly accurately reported, and the errors are negatively related to true earnings. The latter property reduces the bias due to measurement error when earnings are used as an independent variable, but (unlike the classical-error case) leads to some bias when earnings are the dependent variable. Measurement-error-induced biases when change in earnings is the variable of interest are larger, but not dramatically so. Various measures of hourly earnings were much less reliable than annual earnings. Retrospective reports of unemployment showed considerable under-reporting, even of long spells.

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.