|
on Neuroeconomics |
| By: | Stans, Renske; Ehrmantraut, Laura; Siemers, Malin; Pinger, Pia |
| Abstract: | Do employers seek to attract individuals with more education because it enhances human capital or because it signals higher levels of pre-existing traits? We experimentally vary master’s degree completion rates on applicant résumés and examine how this influences candidates’ desirability and employer perceptions of their productive characteristics. Our findings show that while a completed master’s degree increases desirability, an incomplete master’s degree is perceived by human resource managers as less favourable than a bachelor’s degree. This suggests that employers prefer candidates with higher education mainly because they view the degree as a signal of pre-existing productive traits. Consistent with this, employers perceive both cognitive and non-cognitive traits as stronger in master graduates but non-cognitive traits as weaker in master dropouts compared to bachelor’s degree holders. Overall, perceived cognitive and non-cognitive traits play a larger role in determining a candidate’s attractiveness than expertise. This paper thus provides causal evidence on the origins of the education premium. |
| Keywords: | beliefs |
| JEL: | I23 I26 J23 J24 J31 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20909 |
| By: | Ingvild AlmŒs (IIES, Stockholm University and Norwegian School of Economics); Nina Drange (Statistics Norway); Costas Meghir (Yale University); Henrik Daae Zachrisson (University of Oslo) |
| Abstract: | This paper examines the impact of early childcare on academic achievement for children in grade 5 and grade 9, based on a 2003 policy expansion that created quasi-random variation in slot availability for children aged 1Ð2. Starting childcare one year earlier increases math scores by 9.7% of a standard deviation (SD) in grade 9. Children whose mothers do not hold a high school diploma benefit by a significant 28% of a SD at grade 9, reducing the math achievement gap from children of higher-educated mothers by about one third. We also present evidence of strong improvements for children of immigrants. |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2518 |
| By: | Anna Adamecz (UCL Social Research Institute; HUN-REN Centre for Economic and Regional Studies; IZA); Radina Ilieva (Informa Connect); Nikki Shure (UCL Social Research Institute; IZA) |
| Abstract: | The Dunning-Kruger effect (DKE) states that people with lower levels of the ability tend to self-assess their ability less accurately than people with relatively higher levels of the ability. Thus, the correlation between one’s objective cognitive abilities and self-assessed abilities is higher at higher levels of objective cognitive abilities. There has been much debate as to whether this effect actually exists or is a statistical artefact. This paper replicates and extends Gignac and Zajenkowski (2020) and Dunkel, Nedelec, and van der Linden (2023) to test whether the DKE exists using several measures of ability and nationally representative data from a British birth cohort study. To do this, we construct a measure of objective cognitive abilities using 18 tests conducted at ages 5, 10, and 16, and a measure of subjective self-assessed abilities using estimates of school performance and being clever at ages 10 and 16. We replicate their models and show that the DKE exists in our secondary data. Importantly, we are the first to look at whether this relationship is heterogeneous by gender and find that while the self-assessment bias is gender specific, the DKE is not. The DKE comes from men relatively overestimating and women relatively underestimating their abilities. |
| Keywords: | Dunning-Kruger effect; overconfidence; underconfidence; gender differences |
| JEL: | J16 J24 D90 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:has:discpr:2501 |
| By: | Ennio Bilancini; Leonardo Boncinelli; Pablo Marcos-Prieto; Chiara Nardi |
| Abstract: | Using a one-shot Hawk–Dove game, we experimentally investigate the effect of different cognitive modes—intuitive (induced by Time Pressure), deliberative (by Time Delay), and motivated deliberative (by Time Delay combined with a written motivation)—on the propensity to behave hostilely (i.e., to play Hawk). We also examine whether cognitive modes affect responsiveness to payoff incentives by varying the harshness of conflict. Our results show that intuition significantly increases the likelihood of hostile behavior, while motivated deliberation reduces it. The harshness of conflict does not significantly affect behavior, and we find no evidence that its effect differs across cognitive manipulations. However, when restricting attention to subjects in the pooled delay conditions, the effect of harshness becomes statistically significant, indicating that responsiveness to payoff incentives may require deliberation. Consistently, we find that deliberation increases the likelihood that subjects best respond to their own beliefs |
| JEL: | C72 C90 D91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:dis:wpaper:dis2603 |
| By: | Alan, Sule |
| Abstract: | This chapter examines how schools cultivate socio-emotional skills that influence both individual success and broader social cohesion. Moving beyond the traditional focus on cognitive ability, I argue that education plays a crucial role in fostering traits that promote cooperation, trust, and long-term societal well-being. Drawing on insights from neuroscience, psychology, and economics, I explore how schools shape not only academic and labor market outcomes but also intergenerational beliefs, attitudes, and the formation of social capital. Using evidence from experimental studies, I highlight how school-based interventions can instill perseverance, enhance social learning, and create environments that curb anti-social tendencies, promote prosocial behavior—ultimately influencing the cultural fabric of society. This perspective reframes education as a mechanism for building more equitable and cohesive communities. |
| JEL: | C93 D63 I24 I25 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21289 |
| By: | Márquez, Karina; Scartascini, Carlos |
| Abstract: | Cognitive biases are pervasive, even among policymakers whose decisions have far-reaching welfare consequences. Yet little is known about whether scalable training can mitigate policymakers biases. This paper provides evidence from a randomized controlled trial embedded in an online behavioral economics course for public officials in Latin America and the Caribbean. Participants were randomly assigned to complete a seven-item diagnostic test either before or after the course, measuring cognitive reflection, applied behavioral knowledge, and policy-approach preference. Those tested afterward scored 0.87 standard deviations higher on average, with especially large gains in applied reasoning and problem solving. These results, robust to within-subject comparisons, suggest that short, structured online training can enhance reasoning relevant to public decision-making. The findings both highlight the potential of large-scale cognitive training to improve public sector performance and motivate further research on its policy impact. |
| Keywords: | Training Evaluation;Policymakers;online education;Field experiment |
| JEL: | D91 C93 H83 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14634 |
| By: | Rai, Sudhanshu |
| Abstract: | A growing empirical literature uses pre-built "AI occupational exposure" scores (most prominently the AI Occupational Exposure (AIOE) index of Felten, Raj, and Seamans (2021) and the GPT-4 task-exposure measure of Eloundou et al. (2024)) as occupation-level treatments or predictors for AI's labor-market effects. We show that two of the most-cited scores, AIOE and Eloundou's GPT-4 measure, substantially re-label cognitive task content rather than capturing AI-specific exposure, a construct-validity problem that does not extend to a third, differently-built score (Webb 2020, patent-based). This note horse-races these three measures against transparent cognitive/manual task-content indices and the established Autor–Dorn Routine Task Intensity (RTI) measure. Across 773 occupations: (i) the ten-plus AI "applications" underlying AIOE collapse to a single factor (first principal component ≈ 88%); (ii) AIOE and Eloundou each correlate strongly with a cognitive-ability index (+0.85 / +0.70) and negatively with a manual-ability index (−0.91 / −0.83), correlate 0.86 with each other, but only moderately with RTI (−0.33 / −0.30): the confound is specifically cognitive ability level, not the classic routine-task polarization axis; (iii) each score's positive wage association reverses sign controlling for cognitive content but is barely affected by controlling for RTI; and (iv) the much-cited pre-ChatGPT "AI foresight" wage-divergence pattern collapses to near-zero under the cognitive control (not under the RTI control). Critically, this collapse is not universal: Webb's patent-text-overlap score is essentially uncorrelated with AIOE (r=0.03) and Eloundou (r=−0.03), only weakly related to cognitive content (r=0.13), and its modest wage associations do not reverse under cognitive control. The cognitive-content collapse is a signature of how a score is built: subjective crowd-relatedness ratings (AIOE) or LLM/human task judgments (Eloundou), not an inherent property of occupational AI-exposure measurement. Studies using relatedness- or judgment-based exposure scores should control for cognitive content and re-interpret accordingly; a patent-based measure is not shown here to have the same problem, though its own construct validity is untested. |
| Keywords: | AI occupational exposure, Felten-Raj-Seamans index, GPT-4 task exposure, construct validity, task content measures, routine task intensity, Autor-Dorn RTI, patent-based automation exposure |
| JEL: | C38 C52 C81 J24 O33 |
| Date: | 2026–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129904 |
| By: | Boaz Abramson (Columbia GSB); Job Boerma (University of Wisconsin-Madison); Diego Daruich (University of Southern California); Aleh Tsyvinski (Yale University) |
| Abstract: | We develop a quantitative macroeconomic theory of child mental health. The theory is grounded in child psychiatry, formalized in a life-cycle heterogeneous agent model of child development, and disciplined using micro data on mental health of children and parents. Intergenerational transmission of mental illness arises due to both biological factors and parental behavior. Parents experiencing mental illness have negative expectations and lose time due to rumination. As a result, they invest less in their child's mental health. We use the model to evaluate policies designed to improve child mental health. We show that subsidizing mental health treatment for children generates sizable welfare gains. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2528 |
| By: | Epper, Thomas Frank; Ibsen, Kristoffer; Koch, Alexander K; Nafziger, Julia |
| Abstract: | University dropout is costly, making it a policy priority to identify factors that predict dropout. Using a survey experiment with incoming first-year students linked to long-run administrative outcomes, we assess which information improves dropout prediction beyond standard university records. A small number of targeted, study-specific survey items — especially motivation and expectations about degree completion — substantially improve predictive performance. By contrast, widely used measures of general preferences and traits (such as grit and self-control) add little incremental value — a result that we qualitatively replicate in a large population. Our findings suggest inexpensive, scalable ways to improve dropout predictions. |
| Keywords: | Dropout; Non-cognitive skills; Motivation; Economic preferences; beliefs; Education; Machine learning |
| JEL: | I23 D91 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21263 |
| By: | Anna Adamecz (UCL Social Research Institute, University College London; ELTE Centre for Economic and Regional Studies); John Jerrim (UCL Social Research Institute, University College London); Ágnes Szabó-Morvai (ELTE Centre for Economic and Regional Studies; University of Debrecen, Faculty of Economics and Business) |
| Abstract: | This paper examines the role of dropping out of school in the association between adolescent mental health disorders (MHDs) and subsequent early-life outcomes. Utilising an administrative panel dataset that links education, health, and employment records for half of the Hungarian school population, we track the life outcomes of a school cohort until age 22. Our findings indicate that adolescents diagnosed with an MHD between the ages of 14 and 16 are 5.8 percentage points (or 34%) more likely to drop out of secondary school compared to their peers, even after controlling for social background factors and educational performance. Furthermore, MHDs are associated with poorer early-life outcomes by age 22, including reduced employment rates, an increased likelihood of being neither in education nor employment, lower wages, and higher probabilities of motherhood, abortions, sexually transmitted diseases, and substance abuse. On average, approximately a third of these negative associations are mediated through school dropout but there are substantial differences across these outcomes. We conclude that educational policies aimed at facilitating the school-to-life transition should simultaneously address mental health disorders and dropout prevention. |
| Keywords: | adolescent mental health, school dropout, school-to-work transition, NEET, abortion, substance abuse, sexually transmitted diseases, causal mediation analysis |
| JEL: | I12 I29 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:has:discpr:2609 |