|
on Neuroeconomics |
| By: | Lavy, Victor; Rachkovski, Genia; Yoresh, Omry |
| Abstract: | Literature has shown that air pollution can have short- and long-term adverse effects on physiological and cognitive performance. In this study, we estimate the effect of increased pollution levels on the likelihood of accidents at construction sites, a significant factor related to productivity losses in the labor market. Using data from all construction sites and pollution monitoring stations in Israel, we find a strong and significant causal effect of nitrogen dioxide (NO2), one of the primary air pollutants, on construction site accidents. We find that a 10-ppb increase in NO2 levels increases the likelihood of an accident by as much as 25 %. Importantly, our findings suggest that these effects are non-linear. While moderate pollution levels, according to EPA standards, compared to clean air levels, increase the likelihood of accidents by 138 %, unhealthy levels increase it by 377 %. We present a mechanism where the effect of pollution is exacerbated under conditions of high cognitive strain or reduced awareness. Finally, we perform a cost-benefit analysis, supported by a nonparametric estimation calculating the implied number of accidents due to NO2 exposure, and examine a potential welfare-improving policy to subsidize the closure of construction sites on highly polluted days. |
| Keywords: | workplace accidents; labor productivity; air pollution; government policy |
| JEL: | R14 J01 |
| Date: | 2025–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129773 |
| By: | So Kuroki; Yingtao Tian; Kou Misaki; Takashi Ikegami; Takuya Akiba; Yujin Tang |
| Abstract: | The study of emergent behaviors in large language model (LLM)-driven multi-agent systems is a critical research challenge, yet progress is limited by a lack of principled methodologies for controlled experimentation. To address this, we introduce Shachi, a formal methodology and modular framework that decomposes an agent's policy into core cognitive components: Configuration for intrinsic traits, Memory for contextual persistence, and Tools for expanded capabilities, all orchestrated by an LLM reasoning engine. This principled architecture moves beyond brittle, ad-hoc agent designs and enables the systematic analysis of how specific architectural choices influence collective behavior. We validate our methodology on a comprehensive 10-task benchmark and demonstrate its power through novel scientific inquiries. Critically, we establish the external validity of our approach by modeling a real-world U.S. tariff shock, showing that agent behaviors align with observed market reactions only when their cognitive architecture is appropriately configured with memory and tools. Our work provides a rigorous, open-source foundation for building and evaluating LLM agents, aimed at fostering more cumulative and scientifically grounded research. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.21862 |
| By: | Jane Greve; Mette T. Jensen; Esben Agerbo; John Cawley |
| Abstract: | This paper contributes to the literature on the impact of early-life health on education by estimating the effect of genetic predisposition to a higher body mass index (BMI) on educational attainment and related outcomes. The identification strategy exploits the randomness in which genes one inherits from one's parents by estimating sibling fixed effects models of the polygenic score for a higher BMI. These models are estimated using rich administrative data from Denmark for over 14, 000 full siblings. We find that a one-standard-deviation increase in the genetic predisposition to a higher BMI is associated with a 1.4 percentage point (4.4%) lower probability of earning a high school diploma, a 1.7 percentage point (12.3%) lower probability of a college degree, and a 1.7 percentage point (3.7%) higher probability of vocational training. An investigation into mechanisms suggests that youth with a greater genetic predisposition to a higher BMI are more likely to report being bullied, have greater school absences, and lower test scores. |
| JEL: | I1 I14 I2 I23 I24 J13 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34322 |