Full Citation

Title: Augmenting U.S. Census data on industry and occupation of respondents

Citation Type: Miscellaneous

Publication Year: 2019

ISBN:

ISSN:

DOI:

NSFID:

PMCID:

PMID:

Abstract: 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.

Url: http://econterms.net/innovation/images/2/28/Augmenting-DSAA-paper-handout.pdf

User Submitted?: No

Authors: Meyer, Peter B.; Asher, Kendra

Publisher: U.S. Bureau of Labor Statistics

Data Collections: IPUMS USA

Topics: Other, Population Data Science

Countries: United States