Job posting market data

What is LinkUp?

LinkUp is a job market data company that collects postings directly from the career sites of more than 80,000 employers. Because the data comes from the source rather than from job board aggregators, it carries fewer duplicates, ghost postings, and stale listings than competing feeds. S&P uses LinkUp data to construct the S&P 500 LinkUp Jobs Indices.

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What academic researchers should know about LinkUp job postings data

LinkUp publishes U.S. job postings on Dewey with raw posting records dating back to 2007 and aggregated rollups at the company and ticker level. Each record carries the title, full description, location, URL, occupation and sector codes, company identifier, and where applicable the public market ticker. The methodology is what differentiates it: instead of pulling from aggregators, LinkUp's crawlers visit employer career pages directly, so a posting being live in the data closely tracks whether it is actually live on the company website. Aggregate counts of LinkUp postings track BLS nonfarm payrolls closely, which has been validated by independent researchers including students at the University of Minnesota.

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Why academic researchers choose LinkUp on Dewey

Job postings have become the workhorse data source for labor economics, finance, and macroeconomics over the past decade, and the quality of the underlying scrape matters enormously for any conclusion that depends on the level or trend of vacancy counts. LinkUp's source level methodology avoids the duplication and aggregator noise that has dogged competing feeds, and the ticker level identifiers let finance and accounting researchers connect hiring signals to firm fundamentals out of the box. On Dewey, LinkUp pairs naturally with consumer spending data from Consumer Edge, foot traffic and points of interest from Advan Research, commercial property records from RESimplify, and other firm level data for multidisciplinary work. LinkUp was built for quantitative investors and corporate strategy teams, and Dewey is how academics get their hands on it.

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LinkUp academic research ideas and use cases

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Labor market concentration and skill demand. Researchers have used LinkUp vacancy data to test how local labor markets shape wages, skills, and worker outcomes. Hershbein and Macaluso's work on labor market concentration and the demand for skills used LinkUp postings to show that more concentrated local labor markets actually raise skill requirements within the same occupations, with the biggest jumps in cognitive and social skill demand. The same design extends to monopsony, antitrust enforcement, and the geography of opportunity.

COVID, recessions, and business cycle research. LinkUp's posting level depth back to 2007 makes it one of the strongest datasets available for studying labor market shocks. The NBER working paper Corporate Hiring under COVID-19 (Campello, Kankanhalli, and Muthukrishnan, NBER WP 27208) used LinkUp postings to document how the early pandemic reshaped hiring across firms, occupations, and places, with sharp downskilling and concentration effects. The methodology transfers to studying interest rate shocks, tariff announcements, and any disruption to labor demand.

Skills, AI, and the changing nature of work. Because every record includes the full description, researchers can study how job content evolves over time. A collaboration between the University of Maryland, LinkUp, and Outrigger Group used a specialized language model to identify postings that require AI skills and documented a roughly 68 percent rise in U.S. AI postings from late 2022 to late 2024. Researchers can replicate this kind of skill extraction work for any technology, occupation, or regulatory shift.

Macroeconomic nowcasting and policy. LinkUp data is closely correlated with BLS payrolls and well suited for nowcasting employment ahead of official releases. Researchers can build state and metro level indicators, study which regions are entering or leaving downturns first, and evaluate the labor market effects of fiscal and monetary policy in close to real time.

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