title: Measurement error in earnings data: Using a mixture model approach to combine survey and register data.
abstract: Survey data on earnings tend to contain measurement error. Administrative data are superior in principle, but they are worthless in case of a mismatch. We develop methods for prediction in mixture factor analysis models that combine both data sources to arrive at a single earnings figure. We apply the methods to a Swedish data set. Our results show that register earnings data perform poorly if there is a (small) probability of a mismatch. Survey earnings data are more reliable, despite their measurement error. Predictors that combine both and take conditional class probabilities into account outperform all other predictors.
authors.affiliation: RAND Corporation, Santa Monica, CA 90407-2138.
authors.forename: Erik
authors.initials: E
authors.lastname: Meijer
authors.forename: Susann
authors.initials: S
authors.lastname: Rohwedder
authors.forename: Tom
authors.initials: T
authors.lastname: Wansbeek
keywords: Factor score; administrative data; finite mixture; structural equation model; validation study