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on Computational Economics |
| By: | Marco Amendola; Marcelo C. Pereira |
| Abstract: | The paper examines the macroeconomic effects of fiscal policy under varying economic conditions. The analysis is conducted using a closed-economy agent-based model, where macroeconomic outcomes of fiscal intervention emerge from the bottom up as the result of interactions between heterogeneous agents in different markets, with feedback loops between demand, supply, and the financial sector. The model simulation results indicate that expansionary fiscal policies generate significant positive effects on aggregate output, with a public consumption multiplier of 1.6 on average, and an income tax multiplier of approximately 1.0. Notably, the effectiveness of a public direct consumption stimulus exhibits significant non-linearities, with multipliers reaching up to 3.5 during periods of economic slack and 2.5 during times of high financial fragility. In contrast, income tax rate multiplier appears largely acyclical. Overall, this analysis contributes to the growing and unsettled debate on the state-dependent effects of fiscal policy, providing model-based insights into this crucial topic. |
| Keywords: | Fiscal policy; State-dependent fiscal multipliers; Agent-based models; Non-linear dynamics |
| Date: | 2025–04–07 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2025/10 |
| 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: | Costantiello, Alberto; Drago, Carlo; Arnone, Massimo; Leogrande, Angelo |
| Abstract: | This study investigates the relationship between Research Intensity (RI) and a range of Environmental, Social, and Governance (ESG) variables for Italian regions using machine learning algorithms and panel data models. The study seeks to identify the most predictive variables of research intensity from a range of cultural, environmental, socio-economic, and governance indicators. Support Vector Machine, Random Forest, k-Nearest Neighbors, and Neural Network algorithms are used to ascertain comparative predictive power. Feature importance analysis identifies education levels, in particular tertiary education qualifications, and technological infrastructure as most predictive of research intensity. Regional differences in research intensity are also investigated on the basis of political representation, healthcare accessibility, material consumption, and cultural investment variables. Results indicate that economically developed regions with sufficient research capacity are more research-intensive but can also face environmental sustainability and social inclusiveness issues. The study concludes that policy measures to enable education, technological innovation, environmental management, and governance improvement are required to spur research capacity in Italian regions. The study also provides insight into the use of research intensity in informing broader ESG objectives, including policy intervention for mitigating regional imbalances. Future studies should provide insight into the dynamic interaction effects of research intensity and ESG variables over time using more sophisticated machine learning techniques to further enhance predictive power. |
| Keywords: | Research Intensity, ESG Factors, Machine Learning, Panel Data Models, Italian Regions. |
| JEL: | C23 I23 O32 Q56 R58 |
| Date: | 2024–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124185 |
| By: | Barbrook-Johnson, Peter; Fu, Yuan |
| Abstract: | The use of causal systems mapping in interdisciplinary and policy research has increased in recent years. Causal system maps typically rely on stakeholder opinion for their creation. This works well but does not make use of all available literature and can be time-consuming. For most topics, there is an abundance of text data in easily identifiable journal papers, grey literature, and policy documents. Using this data to support causal systems mapping exercises has the potential to make them more comprehensive and connected to evidence. There is also potential for the creation of maps using this data, to be done quickly, if the processes used become routine. In this paper, we develop an approach using Natural Language Processing (NLP) techniques and text data from journal papers to create preliminary causal system maps. Using the example topic of power sector decarbonisation policies and comparisons to a related participatory exercise, we consider the best techniques to use, the workflows which might speed up mapping exercises, and potential risks. The approach produces familiar factors and logical individual relationships, but causal maps with structure that mirrors attention in the literature rather than real causal patterns, and which overemphasise connections directly between policies and outcomes, rather than longer more realistic causal chains. We highlight the importance of choice of documents and sections of documents to use, and that the NLP workflow is full of subjective judgements and decisions. We argue that a clear purpose must be identified before beginning, to inform these choices; purely exploratory, which are relatively common with systems mapping exercises, are likely to be flawed. |
| Keywords: | Natural language processing, Pretrained language model, Deep learning, Systems mapping, Causal maps, Policy analysis, Decarbonisation policy |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2025-09 |
| By: | Giacomo Ravaioli; Francesco Lamperti; Andrea Roventini; Tiago Domingos |
| Abstract: | Climate change and economic inequality are two critical and interlinked global challenges. The feasibility of jointly reducing greenhouse gas emissions and inequality has often been questioned. Here, we aim to test whether a properly designed mix of progressive and environmental fiscal policies can effectively reduce both while improving economic dynamics. We extend the DSK integrated-assessment agent-based model to combine an income class-based analysis of inequality with an improved accounting of emissions. We calibrate the model to the European Union and employ it to explore how fiscal policies can tackle the coevolution of income inequality and carbon emission. The results show that no single policy in our portfolio can simultaneously reduce inequality and emissions. Redistributing income increases aggregate consumption and hence emissions, whereas environmental taxes risk hampering economic growth and stability. In contrast, a combination of progressive fiscal policies, green subsidies to reduce carbon intensity of production and a mild carbon tax achieves both goals, while increasing employment, growth, stability and the consumption of low-income households. A potential trade-off emerges between increasing economic growth and reducing emissions, mediated by the extent to which green innovations lead to higher productivity. In conclusion, our results show that moving towards a sustainable and inclusive economy needs the co-design of distributive, innovation and mitigation policies. |
| Keywords: | climate policies, inequality, mitigation, just transition, ecological macroeconomics, agent-based modelling |
| Date: | 2025–04–14 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2025/14 |
| By: | Leiashvili, Paata |
| Abstract: | The book Symmetric Model of Economic Equilibrium: Dialogue with Artificial Intelligence is a unique experiment that blends economic theory with cutting-edge technology. It consists of a record of dialogues between the author and the artificial intelligence system Grok 3, with the central theme being the exploration of the Symmetric Model of Economic Equilibrium. This model introduces a novel perspective on the economy as a self-regulating system, where micro- and macro-levels are interconnected through cyclical flows and feedback loops, ensuring its integrity and adaptability. The book includes chat sessions in which the AI evaluates the model‘s mathematical rigor, economic logic, and practical significance. It examines the model‘s advantages over traditional approaches and its potential applications in economic policy and the development of analytical tools. The dialogue underscores the value of an interdisciplinary approach, integrating economic theory, dialectics, second-order cybernetics, and the capabilities of artificial intelligence. It illustrates how engaging with AI can enhance the understanding of complex economic processes and provide fresh momentum for further research in this field. The book is aimed at economists, AI researchers, and anyone interested in innovative directions for the advancement of economic science. |
| Keywords: | economic theory, methodology, second-order cybernetics, general economic equilibrium model, pricing, economic cycles, self-regulation, nonlinearity in the economy, economic policy |
| JEL: | A10 D50 E31 E32 E60 E66 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124088 |
| By: | Koundouri, Phoebe; Alamanos, Angelos; Arampatzidis, Ioannis; Devves1, Stathis; Sachs, Jeffrey D |
| Abstract: | As global commitments to decarbonization intensify, energy-emission models are becoming increasingly vital for policymaking, offering data-driven insights to evaluate the feasibility and impact of climate strategies. These models help governments design evidence-based policies, assess mitigation pathways, and ensure alignment with national and international targets, such as the Paris Agreement and the EU Green Deal. Researchers often spend a lot of time considering their modelling choices to develop the best possible tools in terms of data-requirements, accuracy, computational demand, while there is always a ‘debate’ of complexity versus explicability and ready-to-use models for policymaking. Especially for energy-emissions models, given their increasing policy-relevance, and the need to provide insights fast for short-term policies (e.g. 2030, or 2050 net-zero goals), such considerations become increasingly pressing. In this paper, we present two different versions of the same energy-emissions model, and we run them for the same study area, planning horizon, and scenario analysis. The two versions differ only in how they approach complexity: Version1 is a more ‘detailed’, complex model, while Version2 is a ‘simpler’ and less data-hungry one. A set of evaluation criteria was then used to qualitatively compare these two versions, based on modelling- and policymaking-related considerations, debating modelers’ and policymakers’ expectations and preferences. We reflect on best modelling practices, discuss different goal-dependent approaches, providing useful guidance for modelers and policymakers |
| Keywords: | Energy-emissions modelling; Decarbonization pathways; Model development; LEAP; Models to policy |
| JEL: | C63 O33 Q41 Q50 Q58 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124147 |
| By: | Drago, Carlo; Arnone, Massimo; Leogrande, Angelo |
| Abstract: | The paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N₂O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, the paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of the study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N₂O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emissions mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets. |
| Date: | 2025–03–20 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4r8ux_v1 |
| By: | Luca Fontanelli; Mattia Guerini; Raffaele Miniaci; Angelo Secchi |
| Abstract: | While artificial intelligence (AI) adoption holds the potential to enhance business operations through improved forecasting and automation, its relation with average productivity growth remain highly heterogeneous across firms. This paper shifts the focus and investigates the impact of predictive artificial intelligence (AI) on the volatility of firms' productivity growth rates. Using firm-level data from the 2019 French ICT survey, we provide robust evidence that AI use is associated with increased volatility. This relationship persists across multiple robustness checks, including analyses addressing causality concerns. To propose a possible mechanisms underlying this effect, we compare firms that purchase AI from external providers ("AI buyers") and those that develop AI in-house ("AI developers"). Our results show that heightened volatility is concentrated among AI buyers, whereas firms that develop AI internally experience no such effect. Finally, we find that AI-induced volatility among "AI buyers" is mitigated in firms with a higher share of ICT engineers and technicians, suggesting that AI's successful integration requires complementary human capital. |
| Keywords: | Artificial intelligence, productivity growth volatility, coarsened exact matching |
| Date: | 2025–04–07 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2025/12 |
| 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 |
| By: | Tello, Mario D.; Tello-Trillo, Daniel Sebastian; Rojas Lara, Pablo Enrique |
| Abstract: | This paper uses seven standard market power indicators (price-cost margin, and six drawn upon the production approach) to estimate the effect market power on the rate of change of total factor productivity for a sample of formal manufacturing firms of Peru for the period 2002-2019. After applying exogeneity tests and implementing panel data with fixed effects instrumental variable method, the results are not clear about the causal relationship between market power and firms' TFP. However, when the Double-Debiased machine learning (DML) causal method is applied for fixed effects panel data with and without instruments, firms market power robustly seems not to affect their respective total factor productivity regardless of the market power indicators and instruments used. The paper also presents four examples which are consistent with this causal result suggesting that the relationship between market power and productivity needs to be analyzed on a case-by-case basis considering the product development of sectors, the influence and activities of firms and economic groups in the domestic economy and foreign markets, and the level of development of the country's productive structure. |
| Keywords: | Market power;total factor productivity;Causal machine learning |
| JEL: | D24 L11 L60 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14044 |
| 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 |