Working Paper: NBER ID: w24951
Authors: Akina Ikudo; Julia Lane; Joseph Staudt; Bruce Weinberg
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: Occupational Classification; Machine Learning; Labor Economics
JEL Codes: C8; J01; J24
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| automated machine learning approaches (C45) | reduce labor costs (J39) |
| machine learning (C45) | impact the efficiency of occupational classification systems (J24) |
| challenges in classification process (C38) | hinder the effectiveness of machine learning models (C52) |
| focus on tasks and skills (J24) | shift in conceptualization of occupational data (J21) |
| combined approach of machine learning and manual review (C45) | yield better results in occupational classification (J24) |