Occupational Classifications: A Machine Learning Approach — by Akina Ikudo, Julia Lane, Joseph Staudt, Bruce Weinberg

Occupational Classifications: A Machine Learning Approach — by Akina Ikudo, Julia Lane, Joseph Staudt, Bruce Weinberg

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Occupational Classifications: A Machine Learning Approach

NBER Working Paper No. 24951
Issued in August 2018
NBER Program(s):Labor Studies

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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Document Object Identifier (DOI): 10.3386/w24951

PublishFinancevia National Bureau of Economic Research Working Papers https://ift.tt/1j89DVYSeptember 3, 2018 at 03:32PM https://ift.tt/1aNsfVT https://ift.tt/1j89DVY https://ift.tt/2LY5qaY

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