Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/185198 
Year of Publication: 
2018
Series/Report no.: 
IZA Discussion Papers No. 11738
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
UMETRICS
occupational classifications
machine learning
administrative data
transaction data
JEL: 
J0
J21
J24
Document Type: 
Working Paper

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