Algorithmic, skills-based assessments can improve hiring rates for entry level job seekers while reducing search time.
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measurement & evaluation
Many entry-level frontline roles build highly transferable skills, yet jobseekers often search narrowly within familiar occupations because it’s hard to judge how their skills (especially soft skills) translate to roles in new sectors, and job search is time-consuming and costly.
Our experiment tests whether AI-based (predicts performance) and/or preference-based vacancy rankings help jobseekers find jobs faster and move across occupations, without sacrificing wages or satisfaction.
Scale: 1,117 enrolled participants
Target population: Entry-level job seekers
Geography: Seven U.S. metro areas: Atlanta, Chicago, Cleveland, Las Vegas, Phoenix–Tucson, Bay Area (Sacramento–San Francisco–Oakland–San Jose), Seattle.
Occupations/sectors surfaced: Cashier, Childcare, Customer Service, Food Preparation, Orderly/Personal Care, Receptionist/Administrative, Sales Associate, Waiter, Warehousing.
We built a job-search platform populated with real-time vacancies (sourced via Monster.com and Aspen Tech Labs). Participants completed a baseline survey capturing psychometrics, cognitive ability, and job preferences. The platform then displayed vacancies – including in sectors where participants had no prior experience – ranked in one of four ways depending on their treatment arm: (1) by an algorithmic fit score that predicts performance, (2) by stated preferences, (3) by both fit and preferences, or (4) in a standard job-board format. We then studied impacts on (1) likelihood of employment, (2) time to employment, and (3) durability of employment.
Our AI fit score combines psychometric measures with performance on simulated tasks designed to reflect real job requirements, recommending best-fit vacancies without relying on prior titles or sector experience.
- Design: Randomized Controlled Trial with 4 arms (control, preference-only, AI-only, combined).
- Data sources: Baseline survey, follow-up surveys (28 and 91 days), and passive platform usage data (clicks, saves, applications via platform).
- Analysis approach (as described): Longitudinal ANCOVA with strata and location fixed effects; survival analysis planned for time-to-employment.
Employment: AI- and preferences-based rankings led to higher employment; jobseekers were 12% more likely to be employed than control.
Search effort: All treatments reduced search intensity; jobseekers found jobs 30% faster.
In all treatment groups, the number of applications submitted declined, indicating that jobseekers were able to focus on quality of fit rather than volume; interviews and offers stayed stable.
This improved matching can increase wages, employment duration, career growth, and mobility for low-income workers. By introducing an objective skills signal into the job-search process – rather than relying on prior experience and titles – we can direct jobseekers to higher-opportunity, better-fit roles and build a more equitable, efficient labor market. We are seeking (1) partners to expand to other underserved populations (e.g., STARs) and into additional occupations; (2) funders to support further pilots; (3) job platforms and workforce development organizations to strengthen matching; and (4) employers to pilot fit scoring within hiring workflows.
