Joseph Staudt

U.S. Census Bureau
Silver Hill Road
Suitland, MD 20746

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: U.S. Census Bureau

NBER Working Papers and Publications

May 2019Automating Response Evaluation for Franchising Questions on the 2017 Economic Census
with Yifang Wei, Lisa Singh, Shawn D. Klimek, J. Bradford Jensen, Andrew L. Baer: w25818
Between the 2007 and 2012 Economic Censuses (EC), the count of franchise-affiliated establishments declined by 9.8%. One reason for this decline was a reduction in resources that the Census Bureau was able to dedicate to the manual evaluation of survey responses in the franchise section of the EC. Extensive manual evaluation in 2007 resulted in many establishments, whose survey forms indicated they were not franchise-affiliated, being recoded as franchise-affiliated. No such evaluation could be undertaken in 2012. In this paper, we examine the potential of using external data harvested from the web in combination with machine learning methods to automate the process of evaluating responses to the franchise section of the 2017 EC. Our method allows us to quickly and accurately identify and ...
April 2019Automating Response Evaluation for Franchising Questions on the 2017 Economic Census
with Andrew L. Baer, J. Bradford Jensen, Shawn D. Klimek, Lisa Singh, Yifang Wei
in Big Data for 21st Century Economic Statistics, Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro
August 2018Occupational Classifications: A Machine Learning Approach
with Akina Ikudo, Julia Lane, Bruce Weinberg: w24951
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional...
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