Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data

Meisenbacher, Stephen and Nestorov, Svetlozar and Norlander, Peter (2025): Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data.

Abstract

Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 – 2025. We illustrate the potential for research and future uses in education and workforce development.

• Item Type: MPRA Paper
• Language: English
• Keywords: Labor Market Information, Online Job Vacancies, NLP methods, ML, data transparency
• Subjects: J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J23 - Labor Demand; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J63 - Turnover ; Vacancies ; Layoffs

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