280 Characters to Employment: Using Twitter to Quantify Job Vacancies

Abstract

Accurate assessment of workforce needs is critical for designing well-informed economic policy and improving market efficiency. While surveys are the gold standard for estimating when and where workers are needed, they also have important limitations, most notably their substantial costs, dependence on existing and extensive surveying infrastructure, and limited temporal, geographical, and sectorial resolution. Here, we investigate the potential of social media to provide a complementary signal for estimating labor market demand. We introduce a novel statistical approach for extracting information about the location and occupation advertised in job vacancies posted on Twitter. We then construct an aggregate index of labor market demand by occupational class in every major U.S. city from 2015 to 2022, which we evaluate against two sources of official statistics and an index from a large aggregator of online job postings. We find that the newly constructed index is strongly correlated with official statistics and, in some cases, advantageous compared to statistics from job aggregators. Moreover, we demonstrate that our index can robustly improve the prediction of official statistics across occupations and states.