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on Neuroeconomics |
| By: | Cobb-Clark, Deborah A. (University of Sydney); Dahmann, Sarah C. (University of Melbourne); Kamhöfer, Daniel A. (Heinrich Heine University Düsseldorf); Schildberg-Hörisch, Hannah (Heinrich Heine University Düsseldorf) |
| Abstract: | While there is an established positive relationship between self-control and education, the direction of causality remains a matter of debate. We make a contribution to resolving this issue by exploiting a series of Australian and German educational reforms that increased minimum education requirements as a source of exogenous variation in education levels. Instrumental variables estimates suggest that, for people affected by the reforms, an additional year of schooling has no effect on self-control. |
| Keywords: | self-control, quasi-experiments, compulsory schooling reforms, Brief Self-Control Scale |
| JEL: | D90 I26 C26 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16864&r=neu |
| By: | Bastani, Spencer (IFAU - Institute for Evaluation of Labour Market and Education Policy); Karlsson, Kristina (Department of Economics, Uppsala University); Kolsrud, Jonas (Department of Economics and Statistics); Waldenström, Daniel (IFN - Research Institute of Industrial Economics) |
| Abstract: | Using administrative tax and military records, we show that cognitive ability is more strongly associated with capital income than with labor income. This result holds across intensive and extensive margins, across different income types, and after controlling for education, occupation, inheritance, and parental background. Higher ability individuals save more, are better at selecting high-return stocks, hold more risky assets, and are less likely to live hand-to-mouth. Capital market returns are higher for cognitive ability than for non-cognitive skills, and the difference is stable over time. Rising capital shares, fueled by technological progress, could therefore exacerbate cognitive ability-based economic inequality. |
| Keywords: | Ability; Skills; Labor Earnings; Capital income; Wealth; Taxation |
| JEL: | D31 G11 H20 J24 |
| Date: | 2024–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:vxesta:2024_001&r=neu |
| By: | Miriam Gensowski (Rockwool Foundation, CEBI (U. of Copenhagen), and IZA); Mette Goertz (University of Copenhagen, Dep. of Economics and CEBI and IZA) |
| Abstract: | Is the education-health gradient inflated because both education and health are associated with unobserved socio-emotional skills? Revisiting the literature, we find that the gradient is reduced by 30-45% by fine-grained personality facets and Locus of Control. Traditional aggregated Big-Five scales, in contrast, have a much smaller and mostly insignificant contribution to the gradient. We decompose the gradient into its components with an order-invariant method, and use sibling-fixed effects to address that much of the observed education-health gradient reflects associations rather than causal relationships. There are education-health gradients even within sibling pairs; personality facets reduce these gradients by 30% or more. Our analyses use an extraordinarily large survey (N=28, 261) linked to high-quality administrative registers with information on SES background and objective health outcomes. |
| Keywords: | Inequality; Health-Education Gradient; Personality; Big Five-2 Inventory; Sibling Fixed Effects. |
| JEL: | I14 I12 I24 I31 |
| Date: | 2023–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kucebi:2304&r=neu |
| By: | NISHIMURA Kazuo; YAGI Tadashi |
| Abstract: | The ability to visualize things differs from person to person. For example, some people can remember the face of the first person they meet, while others cannot. Others think linguistically, as typified by self-talk. In other words, there are differences in the cognitive abilities of individuals. In this paper, we analyze how differences in cognitive ability affect the degree of achievement in work and the degree to which one is good at or bad at learning subjects. Specifically, we ask respondents about their ways of thinking in a questionnaire survey, and index the spatial, imagery, and verbal abilities of each respondent. We then analyzed the relationship between the abilities used in the occupation in which they are currently engaged, their strengths and weaknesses in the subjects they study, and their strengths and weaknesses in other tasks. The results will serve to provide one method of determining the right person for the right job and also provide a method of developing competence. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:eti:rdpsjp:24008&r=neu |
| By: | Ana Costa-Ramón (University of Zurich); N. Meltem Daysal (University of Copenhagen); Ana Rodriguez-González (Lund University) |
| Abstract: | What is the impact of the oral contraceptive pill on the mental health of adolescent girls? Using administrative data from Denmark and exploiting the variation in the timing of pill initiation in an event study design, we find that the likelihood of a depression diagnosis and antidepressant use increases shortly after pill initiation. We then uncover substantial variation in primary care providers' tendency to prescribe the pill to adolescents, unrelated to patient characteristics. Being assigned to a high prescribing physician strongly predicts pill use by age 16 and leads to worse mental health outcomes between ages 16-18. |
| Keywords: | Contraceptive pill, mental health, adolescents, prescribing practices. |
| JEL: | I12 J13 |
| Date: | 2023–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kucebi:2305&r=neu |
| By: | Mario Passalacqua (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal); Robert Pellerin (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal); Esma Yahia (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université); Florian Magnani (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, ECM - École Centrale de Marseille); Frédéric Rosin (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université); Laurent Joblot (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université); Pierre-Majorique Léger (HEC Montréal - HEC Montréal) |
| Abstract: | The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human-AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators' technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology. |
| Keywords: | Human-centered AI, training curriculum, motivation, self-determination theory, industry 5.0 |
| Date: | 2024–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04487695&r=neu |