Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Abstract
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 HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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:
payroll,
industrial,
employee,
employed,
job,
classified,
classification,
classifying,
department,
hiring,
workforce,
employing,
worker,
occupation,
clerical,
associate,
wage data,
employee data
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Bureau of Labor Statistics,
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National Science Foundation,
National Bureau of Economic Research,
Office of Management and Budget,
Current Population Survey,
Longitudinal Business Database,
Employer Identification Numbers,
Federal Register,
Department of Labor,
Standard Occupational Classification,
Longitudinal Employer Household Dynamics,
Information and Communication Technology Survey,
Business Register,
Protected Identification Key,
Ohio State University,
University of Michigan,
Integrated Longitudinal Business Database,
Person Validation System,
Center for Administrative Records Research,
Person Identification Validation System,
National Center for Science and Engineering Statistics
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