Occupational classifications: A machine learning approach

Akina Ikudo, Julia I. Lane, Joseph Staudt, Bruce A. Weinberg

Research output: Contribution to journalArticlepeer-review


Characterizing people's occupations is important for both policy and research. However, as large scale administrative records are increasingly being used to describe labor market activity, it will become important to find new automated approaches to describing occupations. We apply new machine learning techniques to new sources of data and investigate the potential of using algorithms to classify occupations. We find that job titles are both inherently noisy and inconsistent across organizations, but a subset of them can be assigned algorithmically, with little impact on accuracy.

Original languageEnglish (US)
Pages (from-to)57-87
Number of pages31
JournalJournal of Economic and Social Measurement
Issue number2-3
StatePublished - 2020


  • Machine learning
  • administrative data
  • occupations

ASJC Scopus subject areas

  • General Social Sciences


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