Occupational models from 42 million unstructured job postings.

SOC codes employment services job descriptions job titles labor markets natural language processing occupational hazards remote work skills

Journal

Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765

Informations de publication

Date de publication:
14 Jul 2023
Historique:
received: 16 11 2022
revised: 10 01 2023
accepted: 24 04 2023
medline: 31 7 2023
pubmed: 31 7 2023
entrez: 31 7 2023
Statut: epublish

Résumé

Structuring jobs into occupations is the first step for analysis tasks in many fields of research, including economics and public health, as well as for practical applications like matching job seekers to available jobs. We present a data resource, derived with natural language processing techniques from over 42 million unstructured job postings in the National Labor Exchange, that empirically models the associations between occupation codes (estimated initially by the Standardized Occupation Coding for Computer-assisted Epidemiological Research method), skill keywords, job titles, and full-text job descriptions in the United States during the years 2019 and 2021. We model the probability that a job title is associated with an occupation code and that a job description is associated with skill keywords and occupation codes. Our models are openly available in the

Identifiants

pubmed: 37521040
doi: 10.1016/j.patter.2023.100757
pii: S2666-3899(23)00102-2
pmc: PMC10382938
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100757

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

M.H. is currently senior data scientist at Amazon.com, Inc., but conducted this research prior to starting that role.

Références

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Auteurs

Nile Dixon (N)

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

Marcelle Goggins (M)

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

Ethan Ho (E)

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

Mark Howison (M)

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

Joe Long (J)

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

Emma Northcott (E)

National Association of State Workforce Agencies, 444 N. Capitol Street NW, Suite 300, Washington, DC 20001, USA.
George Washington University, Trachtenberg School of Public Policy and Public Administration, 805 21st Street NW, Washington, DC 20052, USA.

Karen Shen (K)

Research Improving People's Lives, 1 Park Row, Suite 401, Providence, RI 02903, USA.
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA.

Carrie Yeats (C)

National Association of State Workforce Agencies, 444 N. Capitol Street NW, Suite 300, Washington, DC 20001, USA.

Classifications MeSH