Occupational Classifications: A Machine Learning Approach

Akina Ikudo (), Julia Lane, Joseph Staudt and Bruce Weinberg
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Akina Ikudo: University of California, Los Angeles

No 11738, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: UMETRICS; occupational classifications; machine learning; administrative data; transaction data (search for similar items in EconPapers)
JEL-codes: J0 J21 J24 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2018-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ict and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Occupational Classifications: A Machine Learning Approach (2018)
Working Paper: Occupational Classifications: A Machine Learning Approach (2018)
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