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Occupational models from 42 million unstructured job postings.

Nile Dixon1, Marcelle Goggins1, Ethan Ho1

  • 1Research Improving People's Lives, 1 Park Row, Suite 401, Providence, RI 02903, USA.

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Summary
This summary is machine-generated.

This study introduces a novel data resource and Python package (sockit) to classify jobs into occupations using natural language processing. It aids in matching job seekers with roles and analyzing labor market data.

Keywords:
SOC codesemployment servicesjob descriptionsjob titleslabor marketsnatural language processingoccupational hazardsremote workskills

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Area of Science:

  • Labor economics
  • Computational linguistics
  • Public health informatics

Background:

  • Accurate job classification is crucial for economic and public health research, and for workforce development.
  • Existing methods for assigning occupation codes to job postings can be labor-intensive and imprecise.
  • A data-driven approach is needed to model the complex relationships between job titles, skills, and occupational categories.

Purpose of the Study:

  • To develop and present a data resource and computational models for empirically linking unstructured job postings to standardized occupation codes.
  • To create an open-source Python package (sockit) that facilitates the automated assignment of occupation codes and skill extraction from job data.
  • To enhance the analysis of labor market dynamics and improve job matching processes.

Main Methods:

  • Utilized natural language processing (NLP) techniques on over 42 million U.S. job postings from 2019-2021.
  • Developed probabilistic models to associate job titles with occupation codes and job descriptions with relevant skills and occupation codes.
  • Leveraged the Standardized Occupation Coding for Computer-assisted Epidemiological Research (SOCR) method for initial occupation code estimation.

Main Results:

  • Created a comprehensive dataset modeling associations between occupation codes, skills, job titles, and descriptions.
  • The sockit Python package can accurately assign occupation codes to job titles and postings.
  • The package enables skill parsing from job postings and resumes, and estimates occupational similarity.

Conclusions:

  • The developed NLP models and sockit package provide a scalable and accurate method for job classification and skill analysis.
  • This resource can significantly improve the efficiency of labor market research, public health studies, and job matching platforms.
  • Open-source availability of the models and tools promotes wider adoption and further research in occupational data science.