If workers direct their search to better jobs, the labor market becomes more efficient in theory. We provide novel evidence of directed search for an online job board using data on offered wages, even if employers hide them from applicants. Since explicit-wage ads often target unskilled workers, selection bias affects estimates ignoring hiddenwage ads. We find significant but milder evidence for directed search for hidden (or implicit) wages, suggesting that ad texts and requirements tacitly convey wage information. Moreover, job ad requirements are aligned with their applicants' traits, as predicted in directed search models with heterogeneity.

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We use an unusually rich data from a Chilean job board to document novel facts regarding job search for unemployed and employed seekers. We show how application behavior is influenced by (1) demographics such as gender, age, and marital status, (2) alignment between applicant wage expectations and wage offers, (3) applicant fit into ad requirements such as education, experience, job location and occupation (4) timing variables, including unemployment duration, job tenure (for on-the-job searchers) and business cycle conditions. This empirical evidence can discipline current and future search-theoretical frameworks.

We use online job board data to document novel facts regarding unemployment and on-the-job searches. We define a relevant set of job ads using the bipartite network between ads and applicants and show how applications are affected by demographics such as gender, age, and marital status as well as timing variables like unemployment duration, job tenure, and cyclical conditions. We also study the selective margin, i.e., how the posted-expected wage gap and the applicant fit into ad requirements such as education, experience, location, and occupation affect applications. This evidence can help discipline current and future search theories. JEL Codes: E24, J40, J64