An analysis of classification error for the revised current population survey employment questions

Abstract

The reduced accuracy of the revised classification of unemployed persons in the Current Population Survey (CPS) was documented in Biemer and Bushery (2000). In this paper, we provide additional evidence of this anomaly and attempt to trace the source of the error through extended analysis of the CPS data before and after the redesign. The paper presents an novel approach decomposing the error in a complex classification process, such as the CPS labor force status classification, using Markov Latent Class Analysis (MLCA). To identify the cause of the apparent reduction in unemployed classification accuracy, we identify the key question components that determine the classifications and estimate the contribution of each of these question components to the total error in the classification process. This work provides guidance for further investigation into the root causes of the errors in the collection of labor force data in the CPS possibly through cognitive laboratory and/or field experiments.