Using AI to Expand the Job Search of Displaced Workers in the Aftermath of the Covid-19 Crisis

To date, more than 38 million workers have filed for unemployment during the COVID-19 crisis. Early evidence suggests that the worst economic impacts will be concentrated among entry-level service workers, who are disproportionately young, female, and from marginalized groups. These populations are often slow to be reintegrated into the economy after economic contractions due at least in part to discrimination and imperfect information, with full recovery from the last decade’s financial crisis, for example, taking more than 7 years.
This inequality in employment outcomes is caused in part by job search frictions that disproportionately affect the ability of low-income minority workers to find good matches. In the proposed study, we investigate whether AI-assisted algorithmic matching of skills and psychometric profiles to overlooked vacancies can help displaced workers from entry-level service occupations in their search for employment. To do so, we propose a large-scale randomized controlled trial (RCT) in the United States building on our prior work that focuses on workers who were displaced from their prior jobs as a result of the COVID-19 pandemic.
The RCT will: 1) Use trained machine learning algorithms from prior experiments to predict job performance across a broad array of recovering occupations from psychometric profiles of job seekers; 2) Share a combination of information on best matched occupations and real vacancies,
highlighting deviations from prior search experience.
We will recruit job seekers from online job portals to complete the psychometric survey and answer questions about demographics, recent work, and job search history (including if they have been recently laid off due to Covid-19). After randomized assignment to one of several experimental groups, we will track employment status, job search success rates, time to reemployment, and compensation.