Augmenting US Census data on industry and occupation of respondents

The U.S. Census Bureau classifies survey respondents into hundreds of detailed industry and occupation categories. The classification systems change periodically, creating breaks in time series. Standard crosswalks and unified category systems bridge the periods but these often leave sparse or empty cells, or induce sharp changes in time series. We propose a methodology to predict standardized industry, occupation, and related variables for each employed respondent in the public use samples from recent Censuses of Population and CPS data. Unlike earlier approaches, predictions draw from micro data on each individual and large training data sets. Tests of the resulting "augmented" data sets can evaluate their consistency with known trends, smoothness criteria, and benchmarks.