Finite mixture models for linked survey and administrative data: estimation and post-estimation

Researchers use finite mixture models (FMMs) to analyze linked survey and administrative data on labor earnings taking account of various types of measurement error in each data source. Different combinations of error-ridden and/or error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a suite of Stata commands to fit FMMs to linked survey-administrative data: there is a general model and seven simpler variants. We also provide post-estimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid variables that combine information from both data sources about the outcome of interest. Our software can also be used to study measurement errors in other variables besides labor earnings.