Matching individuals in the Current Population Survey: A distance-based approach

This presentation introduces a set of Stata programs designed to match individuals from year-to-year in the Current Population Survey (CPS) using a distance based measure of similarity . Unlike panel data, the CPS is a repeated cross section of geographic residences, which are continually surveyed regardless of whether the occupants are the same. Previous work has taken the person and household identifiers supplied in the datasets as given and validated or invalidated identifier-derived matches based on demographic variables. This work has focused on selecting the best set of demographic verifiers. Recognizing that there is substantial error in the supplied identifiers, the distance-based approach extends these methods by treating demographic variables as pseudo-identifiers, and selecting matches based on a criterion of distance minimization. This approach possesses several advantages over prior methods. First, by reducing the weight placed on the survey-provided identifiers, the distance approach provides a matching technique that can be uniformly applied across the entire CPS series to create a consistent historical series of CPS matches, even in those years where the survey-provided identifiers are particularly error prone. Second, this approach provides a flexible framework for matching individuals in the CPS, which allows for the selection of pseudo-identifiers to vary based on the measurement of interest. Third, it generates a matched series with low and consistent mismatch rates, which is ideal for measuring secular trends in dynamics, such as income volatility. Several measures of distance and the analytical decisions regarding acceptable year-to-year variation are discussed.