#jobtech — Public Fediverse posts

Choice Architecture in Occupational Choices
http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0255_lhwpaper.pdf
This study uses a Swiss job board to analyze how rank order and design influence high-stakes occupational choices. Higher rankings increased applications, especially for high-paying and gender-congruent occupations. Users interpreted rank to justify choices aligning with identity, providing field evidence for motivated reasoning. An interactive, visually enriched interface redesign boosted applications and watch list usage. Results show that reducing cognitive load expands the variety of options individuals consider and remember.
#choicearchitecture #motivatedreasoning #laborEconomics #jobtech #ExperimentalEcon
#BoundedRationality

Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
https://docs.iza.org/dp18517.pdf
#AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
#hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.

While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon

Who Gets the Callback? Generative AI and Gender Bias https://d.repec.org/n?u=RePEc:arx:papers:2504.21400&r=&r=lma
"… most #llm models reproduce stereotypical gender associations and systematically recommend equally qualified women for lower-wage roles, indicating occupational segregation.
… These biases stem from entrenched gender patterns in the training data as well as from an agreeableness bias induced during the reinforcement learning from human feedback stage
.…AI-driven hiring may perpetuate biases in the labor market and have implications for #fairness and diversity within firms"
#AI #jobtech #ExperimentalEcon #LaborEconomics #discrimination #bias