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on Big Data |
| By: | MI Jie; LI Chao; KEELEY Alexander Ryota; ZHANG Jiaxu; SHI Bo; MANAGI Shunsuke |
| Abstract: | This study explores pervasive gender disparities in subjective well-being (SWB) by analyzing over 2.5 million responses collected from 168 countries between 2004 and 2022. This study uses an exogenous switching treatment effect model (ESTEM) and machine learning techniques to examine both inherent and societal factors that contribute to the gender disparity in SWB. The findings reveal that while men are naturally inclined to report higher well-being, external societal pressures significantly lower their SWB, leading to a paradox: women, despite facing more societal obstacles, often report higher SWB. In addition, the gender gap in societal treatment has widened over time, exacerbating disparities in well-being. This widening gap is primarily fueled by rigid societal norms and unequal treatment of genders across various contexts. This study underscores the urgent need for policy interventions aimed at dismantling these societal norms and promoting inclusive environments where all genders can thrive equally. By addressing both inherent and external factors, such policies can reduce the gap in well-being and foster a more equitable and supportive social framework. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25021 |
| By: | LI Chao; MI Jie; ZHANG Jiaxu; KEELEY Alexander Ryota; SHI Bo; MANAGI Shunsuke |
| Abstract: | This study delves into the complex causes of low well-being among middle-aged individuals by analyzing over 1.9 million global responses from 168 countries between 2009 and 2022. Employing an exogenous switching treatment effect model and advanced machine learning techniques, this study identifies a U-shaped relationship between age and well-being, where middle-aged individuals experience the lowest levels of well-being. The present study reveals that middle-aged individuals face significantly poorer external treatment compared with the younger and older populations, contributing to a noticeable decrease in their well-being. Conversely, older adults benefit from inherent factors that boost their well-being, illustrating a positive relationship between age and well-being at older ages. Furthermore, the widening disparity in external treatment between age groups over time is particularly pronounced for middle-aged individuals. These findings provide crucial insights for policymakers, emphasizing the need for targeted interventions that address the external challenges disproportionately faced by middle-aged individuals. By understanding and addressing these external disparities, policies can be developed to enhance overall well-being across all age groups. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25019 |
| By: | Rim Bahroun; Nadine Levratto; Mounir Amdaoud |
| Abstract: | Integrated into public policies aimed at fostering entrepreneurship, thePÉPITE plan (Student Hubs for Innovation, Transfer, and Entrepreneurship) plays a keyrole in promoting an entrepreneurial culture in higher education. Through the NationalStudent-Entrepreneur Status (SNEE), it provides tailored support for business creation.However, existing data reveal significant disparities among different PÉPITE hubs interms of trajectories and impact.This study seeks to document this heterogeneity througha semantic analysis of the documents produced by PÉPITE, from the competition phaseto reporting, using the SpaCy natural language processing model. Our findings highlighta strong homogeneity in the content of reports, reflecting a high degree of alignmentwith institutional expectations. However, they also suggest that greater consideration oflocal specificities and increased flexibility could enhance the program’s effectiveness andstrengthen its impact on student entrepreneurial engagement. |
| Keywords: | Student entrepreneurship, PÉPITE, Semantic analysis, Natural language processing |
| JEL: | I23 M13 O32 J24 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:drm:wpaper:2025-20 |
| By: | Eléonore Dodivers (Université Côte d'Azur, CNRS, GREDEG, France); Ismaël Rafaï (Toulouse School of Economics, Toulouse School of Management) |
| Abstract: | This paper investigates Artificial intelligence Large Language Models (AI-LLM) social preferences’ in Dictator Games. Brookins and Debacker (2024, Economics Bulletin) previously observed a tendency of ChatGPT-3.5 to give away half its endowment in a standard Dictator Game and interpreted this as an expression of fairness. We replicate their experiment and introduce a multiplicative factor on donations which varies the efficiency of the transfer. Varying transfer efficiency disentangles three donation explanations (inequality aversion, altruism, or focal point). Our results show that ChatGPT-3.5 donations should be interpreted as a focal point rather than the expression of fairness. In contrast, a more advanced version (ChatGPT-4o) made decisions that are better explained by altruistic motives than inequality aversion. Our study highlights the necessity to explore the parameter space, when designing experiments to study AI-LLM preferences. |
| Keywords: | Artificial Intelligence, Large Language Models, Dictator Games, Experimental Economics, Social Preferences |
| JEL: | D90 O33 C02 C91 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-09 |
| By: | João A. Bastos |
| Abstract: | A deep learning binary classifier is proposed to test if asset returns follow martingale difference sequences. The Neyman-Pearson classification paradigm is applied to control the type I error of the test. In Monte Carlo simulations, I find that this approach has better power properties than variance ratio and portmanteau tests against several alternative processes. I apply this procedure to a large set of exchange rate returns and find that it detects several potential deviations from the martingale difference hypothesis that the conventional statistical tests fail to capture. |
| Keywords: | Martingale difference hypothesis; Convolutional network; Variance ratio test; Portmanteau test; Exchange rates. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03742025 |