Rapidly expanding internet access has dramatically changed job search behavior across the developing world. This expansion has coincided with the growth of private-sector online job portals that provide search and matching services. Despite these technological advances, labor markets in many low-income countries continue to face high unemployment and persistently low female labor force participation. As increasingly important labor market intermediaries, online job portals represent a significant opportunity to improve employment outcomes for jobseekers.
In partnership with QuikrJobs—one of India’s largest online job portals for entry-level skilled and semi-skilled occupations—this project implemented a randomized experiment that leveraged the platform’s “big data” to provide targeted search guidance to jobseekers. The study provided more than 5,000 male and female jobseekers with personalized information about their relative skills and local labor market conditions, including callback rates on the platform. The project examined how beliefs about skills and callback rates shaped job search decisions—such as which and how many jobs to apply to, where to search, and how long to continue searching—as well as investments in skills, and how these behaviors ultimately affected employment outcomes.
The intervention was particularly relevant for female jobseekers, who exhibited lower confidence in their abilities relative to men in this setting. Baseline evidence showed that jobseekers had limited information about overall callback rates on the platform and substantially overestimated their chances of receiving callbacks, with women overestimating more than men. While individuals could observe their own callback experiences over time, disentangling individual ability from broader market conditions was difficult. By addressing these information frictions, the project tested whether more accurate beliefs led to more efficient search behavior and improved employment outcomes.
The completed study contributed to the growing experimental literature on labor market search frictions in developing countries. Previous research has focused primarily on improving workers’ ability to signal skills (Abel et al., 2019; Bassi and Nansamba, 2018; Carranza et al., 2019) or reducing search costs through interventions such as transport subsidies and job fairs (Abebe et al., 2017, 2018). This project instead focused on improving jobseekers’ information about local labor market conditions within an online job platform, enabling more informed and potentially more effective search strategies. The analysis documented gender differences in online job search behavior and assessed whether information provision improved labor market outcomes for both men and women.
More broadly, the project contributed to the literature on active labor market policy evaluating programs that assist workers in finding employment (McKenzie, 2017). Unlike many programs in this literature, which are implemented by governments or researchers, this experiment was conducted in partnership with a private-sector online job platform—an increasingly central intermediary in developing country labor markets. This collaboration demonstrated how large-scale digital platforms can be leveraged to improve labor market functioning and provided access to novel, high-frequency microdata on job search behavior in a developing country context.