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on Neuroeconomics |
| By: | Madison Dell; Enzo Brox; Patricia Palffy; Claudio Schilter; Uschi Backes-Gellner |
| Abstract: | We study how choice architecture in online platforms shapes high-stakes occupational choices through two behavioral mechanisms: motivated reasoning and cognitive load. Using detailed process data from a large online job board and exploiting a quasi-experimental setting, we leverage two sources of exogenous variation in the presentation of occupation recommendations. First, we use random variation in the rank order of equally well-matched occupations to study the effects of motivated reasoning. Our results show that rank order strongly increases the level of users' engagement on the platform and, consequently, the number of occupations to which they apply. Second, we exploit a redesign that transformed the occupation recommendations from a static, text-heavy list into an interactive and visually enriched presentation. The redesign was neither announced nor anticipated, which allows for causal interpretation. We find that this small redesign significantly increases the number of occupations to which users apply, supporting our hypothesis that it reduces cognitive load, leading to increased use of a watch list that keeps more occupations in jobseekers' memory. Our findings provide large-scale field evidence showing that even small changes in platform design significantly and strongly shape consequential career choices. |
| Keywords: | Occupational choice, Choice architecture, Recommender systems, Motivated reasoning, Cognitive load |
| JEL: | D91 J24 D83 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0255 |
| By: | Delaney, Judith (University of Bath); Devereux, Paul (University College Dublin) |
| Abstract: | We use population-level administrative data on secondary school students in England to examine how mathematical and verbal skills shape educational and labour market outcomes. Tracking cohorts from age 16 through higher education and into employment up to age 34, we show that these skills operate through distinct pathways. Verbal skills strongly predict educational attainment - including university enrolment, completion, and postgraduate study - while mathematical skills yield substantially larger earnings returns. At ages 30-34, moving from the 25th to the 75th percentile of the mathematics distribution is associated with 29% higher earnings, compared with 14% for verbal skills. This divergence is partly driven by field-of-study choice: individuals with stronger verbal skills are more likely to enter fields with higher completion rates but lower pay, while those with stronger mathematical skills sort into STEM and other high-paying fields. Gender differences in skills explain the female advantage in higher education and part of the STEM gap, but have limited impact on the gender earnings gap due to offsetting effects across these channels. |
| Keywords: | math skills, verbal skills, college, field of study, STEM |
| JEL: | I26 I24 I21 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18542 |
| By: | Yu, Jiao; Wang, Yi; Gill, Thomas M.; Chen, Xi |
| Abstract: | We estimate the effect of neighborhood disorder on dementia risk among middle-aged and older adults in the United States and identify cardiometabolic dysregulation as a mediating biological pathway. Using data from the Health and Retirement Study (HRS, 2006-2020), we show that exposure to visible neighborhood disorder is associated with higher risk of dementia (Hazard Ratio: 1.37; 95% CI: 1.08-1.74) and higher risk of cognitive impairment no dementia (CIND; HR: 1.50; 95% CI: 1.22-1.85) over a 14-year follow-up. Mediation analysis reveals that a composite cardiometabolic risk score-aggregating seven biomarkers spanning inflammatory, cardiovascular, and metabolic systems-accounts for approximately 16 percent of the total neighborhood disorder-dementia association and 19 percent of the neighborhood disorder-CIND association. These findings are robust to competing-risk regression for mortality, restriction to non-movers, age-at-onset restrictions, and exclusion of pandemic-year data. The results establish neighborhood disorder as a modifiable upstream risk factor for cognitive decline and identify cardiometabolic health as a biologically proximate mediating pathway. The findings have implications for place-based public health policy: community-level interventions that simultaneously reduce visible signs of neighborhood decay and address cardiometabolic risk may yield dementia-prevention dividends beyond what individual-level clinical strategies alone can achieve. |
| Keywords: | dementia, cognitive impairment, neighborhood disorder, cardiometabolic risk, social determinants of health, mediation analysis |
| JEL: | I12 I14 J14 R23 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1744 |
| By: | Casas Pablo (European Commission - JRC); Fernandez Macias Enrique (European Commission - JRC); Martinez Plumed Fernando; Gomez Emilia (European Commission - JRC); Gonzalez Vazquez Ignacio (European Commission - JRC); Salotti Simone (European Commission - JRC) |
| Abstract: | Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evolution of AI exposure in the European labour market from 2008 to 2024 by linking 352 AI benchmarks to 14 cognitive abilities, 108 work tasks and 127 ISCO-3 occupations, weighting benchmarks by their research intensity in the AI literature and thus deriving AI exposure by cognitive ability. Bundling work tasks into occupations based on intensity indicators, we explore occupational exposure to AI. We find that the cognitive abilities most exposed to the recent surge of AI research are ideas-related, such as attention and search, comprehension and expression and logical reasoning. Because the associated information processing and problem-solving tasks are the most transversal across occupations, we find an exponential increase in AI exposure across all occupational categories of workers, even though comparatively high-skilled occupations are more exposed than elementary occupations. This points at a substantial and transversal labour market impact of AI. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:laedte:202602 |
| By: | Marie-Nathalie Jauffret (UniCA - Université Côte d'Azur, SIC.Lab Méditerranée - Laboratoire de Recherche en Sciences de l'Information et de la Communication - UNS - Université Nice Sophia Antipolis (1965 - 2019) - UniCA - Université Côte d'Azur); Éleonora Abreu |
| Abstract: | The integration of biodigitalization into mental health care may lead to the development of technologically assisted hypnosis systems using avatars, AI, and deepfake technologies. These systems have the potential to replace human hypnotherapists, creating an illusion of supervision, misleading patients into believing they are guided by real humans. This illusion raises significant ethical and psychological risks, as patients may not be aware that they are interacting with non-human entities. Technological trends suggest that biodigital practitioners could replace human hypnotherapists within the next decade, raising serious concerns regarding the future of therapeutic hypnosis. This study investigates risks such as cognitive manipulation, increased suggestibility, and reinforcement of harmful behaviors when hypnosis is conducted without human oversight. Semi-structured interviews with 20 early- to mid-career hypnosis professionals revealed that 90% expressed concern over increased suggestibility, 75% warned of risks related to psychological decompensation, and 100% emphasized the inability of automated systems to adapt to patients' emotional states. These findings underscore the critical dangers associated with biodigital hypnotherapy in the absence of human supervision. This research is pioneering in its focused analysis of therapeutic interactions with biodigitals and the significant clinical risks they may introduce. |
| Keywords: | Hypnosis, Biodigitalization, AI therapy, Psychological risks, Mental health |
| Date: | 2025–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05592139 |
| By: | Hecker, Britta (IAB and the University of Bamberg); Shure, Nikki (University College London); Yükselen Saif, Ipek (No longer in academia (formerly IAB and University of Bamberg)) |
| Abstract: | We examine how adolescents' domain-specific confidence shapes subsequent participation in Science, Technology, Engineering, and Mathematics (STEM) study and vocational training, using longitudinal data from a nationally representative cohort of German secondary school students. We show that domain-specific confidence measures provide markedly different predictions from composite confidence indices: in line with established models from educational psychology, higher confidence in mathematics and Information and Communications Technology (ICT) increase the likelihood of entering STEM pathways, whereas higher confidence in reading decreases it. These opposing patterns are obscured when confidence is aggregated into a single measure. Our findings demonstrate the importance of distinguishing between domains when studying non-cognitive determinants of STEM choices and suggest that broad confidence-building interventions may unintentionally reinforce existing gender disparities in STEM participation. |
| Keywords: | confidence, STEM, education, gender |
| JEL: | I24 I23 D91 J24 J16 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18535 |
| By: | Shuhuai Zhang; Shu Wang; Zijun Yao; Chuanhao Li; Xiaozhi Wang; Songfa Zhong; Tracy Xiao Liu |
| Abstract: | Altruism is fundamental to human societies, fostering cooperation and social cohesion. Recent studies suggest that large language models (LLMs) can display human-like prosocial behavior, but the internal computations that produce such behavior remain poorly understood. We investigate the mechanisms underlying LLM altruism using sparse autoencoders (SAEs). In a standard Dictator Game, minimal-pair prompts that differ only in social stance (generous versus selfish) induce large, economically meaningful shifts in allocations. Leveraging this contrast, we identify a set of SAE features (0.024% of all features across the model's layers) whose activations are strongly associated with the behavioral shift. To interpret these features, we use benchmark tasks motivated by dual-process theories to classify a subset as primarily heuristic (System 1) or primarily deliberative (System 2). Causal interventions validate their functional role: activation patching and continuous steering of this feature direction reliably shift allocation distributions, with System 2 features exerting a more proximal influence on the model's final output than System 1 features. The same steering direction generalizes across multiple social-preference games. Together, these results enhance our understanding of artificial cognition by translating altruistic behaviors into identifiable network states and provide a framework for aligning LLM behavior with human values, thereby informing more transparent and value-aligned deployment. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.19260 |