| Abstract: |
Most digital job-search assistance encourages unemployed workers to broaden
their search toward related occupations, targeting one important source of
search inefficiency: insufficient occupational diversification. Our analysis
suggests that the relevant margin of adjustment depends on the underlying
search problem. Building on a detailed analysis of job seekers' beliefs and
search behavior, we identify a large group of pessimistic workers for whom the
main constraints are low search effort and low aspirations, rather than
insufficient occupational diversification. This diagnosis points to an
unexpected intervention: rather than encouraging these workers to search in
new occupations, we encourage them to search more intensively and apply for
better-paying jobs within the occupations they already consider. We evaluate
this diagnosis-based intervention, alongside a standard occupational
recommendation, in a large-scale randomized experiment conducted with the
French Public Employment Service. The motivational intervention increases
search effort, raises reservation wages, and improves reemployment outcomes
along the predicted margins. Occupational recommendations, by contrast,
primarily benefit workers whose search problem lies in the allocation of
attention across occupations and operate by activating existing perceptions
rather than correcting beliefs. More broadly, our findings show how digital
platforms can combine subjective expectations, behavioral data, targeted
interventions, and randomized experimentation to diagnose job seekers' needs
and iteratively improve intervention design. |