Scroll AI takes the way you would scroll a great paper aggregator: quick signal first, deeper critique when something earns your attention, and challenges when a claim feels off.
econ.EMstat.MLGuillaume Bied, Philippe Caillou, Bruno Cr\'epon et al.
·
Mar 23, 2026
Job recommender systems deployed by public employment services are typically optimized for predictive metrics like clicks, applications, or hires rather than job seeker welfare. This paper develops a structural job-search model where vacancy value depends on utility $U$ and hiring probability $p$, deriving a welfare-optimal ranking based on an expected-surplus index $\Gamma(p, U) = p \sigma \log(1 + e^{\Delta(p,U)/\sigma})$. Through two randomized field experiments with the French public employment service, the authors demonstrate that algorithms approximating this theoretical benchmark substantially outperform existing approaches, while formalizing the "inversion problem" where behavior-based rankings diverge from welfare-maximizing ones.
Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives such as clicks, applications, or hires, rather than job seekers' welfare. We develop a job-search model with an application stage in which the value of a vacancy depends on two dimensions: the utility it delivers to the worker and the probability that an application succeeds. The model implies that welfare-optimal RSs rank vacancies by an expected-surplus index combining both, and shows why rankings based solely on utility, hiring probabilities, or observed application behavior are generically suboptimal, an instance of the inversion problem between behavior and welfare. We test these predictions and quantify their practical importance through two randomized field experiments conducted with the French public employment service. The first experiment, comparing existing algorithms and their combinations, provides behavioral evidence that both dimensions shape application decisions. Guided by the model and these results, the second experiment extends the comparison to an RS designed to approximate the welfare-optimal ranking. The experiments generate exogenous variation in the vacancies shown to job seekers, allowing us to estimate the model, validate its behavioral predictions, and construct a welfare metric. Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark. Our results show that embedding predictive tools within a simple job-search framework and combining it with experimental evidence yields recommendation rules with substantial welfare gains in practice.