Recommending Career Transitions to Job Seekers Using Earnings Estimates, Skills Similarity, and Occupational Demand

Howison, Mark; Long, Joe

doi:10.2139/ssrn.4371445

Cited by 2 publications

(2 citation statements)

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“…We have applied these models in a real-world application to recommend career transitions to job seekers based on skill similarity to their previous occupations. 18 Using these methods, researchers can parse unstructured job description, resume, or job title data in order to conduct analyses that rely on structured SOC codes, which could open up new lines of research that were previously not possible.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, sockit was implemented in a recommendation system that helps job seekers discover new careers, recently deployed by labor departments in Rhode Island, Hawai’i, New Jersey, Colorado, and Maryland of the United States. 18 The entry point for job seekers to these applications is a resume upload or manual entry of previous job titles, which are unstructured data. The algorithm for recommending careers, however, requires structured SOC codes and skill keywords that are estimated from the unstructured input using the methods described in this article.…”

Section: Introductionmentioning

confidence: 99%

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Occupational models from 42 million unstructured job postings

Dixon,

Goggins,

et al. 2023

Patterns

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Occupational models from 42 million unstructured job postings

Dixon,

Goggins,

et al. 2023

Patterns

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Extracting Structured Labor Market Information from Job Postings with Generative AI

Howison,

Ensor,

Maharjan

et al. 2024

Digit. Gov.: Res. Pract.

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Labor market information is an important input to labor, workforce, education, and macroeconomic policy. However, granular and real-time data on labor market trends are lacking; publicly available data from survey samples are released with significant lags and miss critical information such as skills and benefits. We use generative Artificial Intelligence to automatically extract structured labor market information from unstructured online job postings for the entire U.S. labor market. To demonstrate our methodology, we construct a sample of 6,800 job postings stratified by 68 major occupational groups, extract structured information on educational requirements, remote-work flexibility, full-time availability, and benefits, and show how these job characteristics vary across occupations. As a validation, we compare frequencies of educational requirements by occupation from our sample to survey data and find no statistically significant difference. Finally, we discuss the scalability to collections of millions of job postings. Our results establish the feasibility of measuring labor market trends at scale from online job postings thanks to advances in generative AI techniques. Improved access to such insights at scale and in real-time could transform the ability of policy leaders, including federal and state agencies and education providers, to make data-informed decisions that better support the American workforce.

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Recommending Career Transitions to Job Seekers Using Earnings Estimates, Skills Similarity, and Occupational Demand

Cited by 2 publications

References 0 publications

Occupational models from 42 million unstructured job postings

Occupational models from 42 million unstructured job postings

Extracting Structured Labor Market Information from Job Postings with Generative AI

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