This paper argues that military buildups lead to a significant rise in greenhouse gas emissions and can disrupt the green transition. Identifying military spending shocks, I use local projections to show that a percentage point rise in the military spending share leads to a 1-1.5% rise in total emissions, as well as a 1% rise in emission intensity. Using a dynamic production network model calibrated for the US, I find that a permanent shock of the same size would increase total emissions by between 0.36% and 1.81%, and emission intensity by between 0.22% and 1.5%. The model indicates that fossil fuel and energy-intensive firms experience a considerable expansion in response to such a shock, which could create political obstacles for the green transition. Similarly, investment in renewables and green R&D could be crowded out by defence spending, further hindering the energy transition. Policymakers can use carbon prices or green subsidies to counteract these effects, the latter likely being more efficient due to political and social constraints.
{"title":"The Green Peace Dividend: the Effects of Militarization on Emissions and the Green Transition","authors":"Balázs Markó","doi":"arxiv-2408.16419","DOIUrl":"https://doi.org/arxiv-2408.16419","url":null,"abstract":"This paper argues that military buildups lead to a significant rise in\u0000greenhouse gas emissions and can disrupt the green transition. Identifying\u0000military spending shocks, I use local projections to show that a percentage\u0000point rise in the military spending share leads to a 1-1.5% rise in total\u0000emissions, as well as a 1% rise in emission intensity. Using a dynamic\u0000production network model calibrated for the US, I find that a permanent shock\u0000of the same size would increase total emissions by between 0.36% and 1.81%, and\u0000emission intensity by between 0.22% and 1.5%. The model indicates that fossil\u0000fuel and energy-intensive firms experience a considerable expansion in response\u0000to such a shock, which could create political obstacles for the green\u0000transition. Similarly, investment in renewables and green R&D could be crowded\u0000out by defence spending, further hindering the energy transition. Policymakers\u0000can use carbon prices or green subsidies to counteract these effects, the\u0000latter likely being more efficient due to political and social constraints.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital.
{"title":"The Turing Valley: How AI Capabilities Shape Labor Income","authors":"Enrique Ide, Eduard Talamàs","doi":"arxiv-2408.16443","DOIUrl":"https://doi.org/arxiv-2408.16443","url":null,"abstract":"Do improvements in Artificial Intelligence (AI) benefit workers? We study how\u0000AI capabilities influence labor income in a competitive economy where\u0000production requires multidimensional knowledge, and firms organize production\u0000by matching humans and AI-powered machines in hierarchies designed to use\u0000knowledge efficiently. We show that advancements in AI in dimensions where\u0000machines underperform humans decrease total labor income, while advancements in\u0000dimensions where machines outperform humans increase it. Hence, if AI initially\u0000underperforms humans in all dimensions and improves gradually, total labor\u0000income initially declines before rising. We also characterize the AI that\u0000maximizes labor income. When humans are sufficiently weak in all knowledge\u0000dimensions, labor income is maximized when AI is as good as possible in all\u0000dimensions. Otherwise, labor income is maximized when AI simultaneously\u0000performs as poorly as possible in the dimensions where humans are relatively\u0000strong and as well as possible in the dimensions where humans are relatively\u0000weak. Our results suggest that choosing the direction of AI development can\u0000create significant divisions between the interests of labor and capital.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"396 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One key in real-life Nash equilibrium applications is to calibrate players' cost functions. To leverage the approximation ability of neural networks, we proposed a general framework for optimizing and learning Nash equilibrium using neural networks to estimate players' cost functions. Depending on the availability of data, we propose two approaches (a) the two-stage approach: we need the data pair of players' strategy and relevant function value to first learn the players' cost functions by monotonic neural networks or graph neural networks, and then solve the Nash equilibrium with the learned neural networks; (b) the joint approach: we use the data of partial true observation of the equilibrium and contextual information (e.g., weather) to optimize and learn Nash equilibrium simultaneously. The problem is formulated as an optimization problem with equilibrium constraints and solved using a modified Backpropagation Algorithm. The proposed methods are validated in numerical experiments.
{"title":"A General Framework for Optimizing and Learning Nash Equilibrium","authors":"Di Zhang, Wei Gu, Qing Jin","doi":"arxiv-2408.16260","DOIUrl":"https://doi.org/arxiv-2408.16260","url":null,"abstract":"One key in real-life Nash equilibrium applications is to calibrate players'\u0000cost functions. To leverage the approximation ability of neural networks, we\u0000proposed a general framework for optimizing and learning Nash equilibrium using\u0000neural networks to estimate players' cost functions. Depending on the\u0000availability of data, we propose two approaches (a) the two-stage approach: we\u0000need the data pair of players' strategy and relevant function value to first\u0000learn the players' cost functions by monotonic neural networks or graph neural\u0000networks, and then solve the Nash equilibrium with the learned neural networks;\u0000(b) the joint approach: we use the data of partial true observation of the\u0000equilibrium and contextual information (e.g., weather) to optimize and learn\u0000Nash equilibrium simultaneously. The problem is formulated as an optimization\u0000problem with equilibrium constraints and solved using a modified\u0000Backpropagation Algorithm. The proposed methods are validated in numerical\u0000experiments.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
American income inequality, generally estimated with tax data, in the 20th century is widely recognized to have followed a U-curve, though debates persist over the extent of this curve, specifically regarding how high the peaks are and how deep the trough is. These debates focus on assumptions about defining income and handling deductions. However, the choice of interpolation methods for using tax authorities' tabular data to estimate the income of the richest centiles -- especially when no micro-files are available -- has not been discussed. This is crucial because tabular data were consistently used from 1917 to 1965. In this paper, we show that there is an alternative to the standard method of Pareto Interpolation (PI). We demonstrate that this alternative -- Maximum Entropy (ME) -- provides more accurate results and leads to significant revisions in the shape of the U-curve of income inequality.
人们普遍认为，20 世纪美国的收入不平等（一般通过税收数据估算）呈现出一条 U 型曲线，但关于这条曲线的范围，特别是关于峰值有多高和谷值有多深的争论一直存在。这些争论主要集中在对收入的定义和扣除额的处理上。然而，在使用税务机关的表格数据估算最富裕阶层的收入时，尤其是在没有微观档案的情况下，如何选择内插法还没有得到讨论。这一点至关重要，因为从 1917 年到 1965 年，我们一直使用表格数据。在本文中，我们展示了帕累托内插法（PI）的标准方法之外的另一种方法。我们证明，这种替代方法--最大熵（ME）--能提供更准确的结果，并能显著修正收入不平等的 U 曲线形状。
{"title":"Pareto's Limits: Improving Inequality Estimates in America, 1917 to 1965","authors":"Vincent Geloso, Alexis Akira Toda","doi":"arxiv-2408.16861","DOIUrl":"https://doi.org/arxiv-2408.16861","url":null,"abstract":"American income inequality, generally estimated with tax data, in the 20th\u0000century is widely recognized to have followed a U-curve, though debates persist\u0000over the extent of this curve, specifically regarding how high the peaks are\u0000and how deep the trough is. These debates focus on assumptions about defining\u0000income and handling deductions. However, the choice of interpolation methods\u0000for using tax authorities' tabular data to estimate the income of the richest\u0000centiles -- especially when no micro-files are available -- has not been\u0000discussed. This is crucial because tabular data were consistently used from\u00001917 to 1965. In this paper, we show that there is an alternative to the\u0000standard method of Pareto Interpolation (PI). We demonstrate that this\u0000alternative -- Maximum Entropy (ME) -- provides more accurate results and leads\u0000to significant revisions in the shape of the U-curve of income inequality.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) like GPT-4 have shown promise in replicating human-like responses in various psychological experiments. However, the extent to which LLMs can effectively replace human subjects across diverse experimental contexts remains unclear. Here, we conduct a large-scale study replicating 154 psychological experiments from top social science journals with 618 main effects and 138 interaction effects using GPT-4 as a simulated participant. We find that GPT-4 successfully replicates 76.0 percent of main effects and 47.0 percent of interaction effects observed in the original studies, closely mirroring human responses in both direction and significance. However, only 19.44 percent of GPT-4's replicated confidence intervals contain the original effect sizes, with the majority of replicated effect sizes exceeding the 95 percent confidence interval of the original studies. Additionally, there is a 71.6 percent rate of unexpected significant results where the original studies reported null findings, suggesting potential overestimation or false positives. Our results demonstrate the potential of LLMs as powerful tools in psychological research but also emphasize the need for caution in interpreting AI-driven findings. While LLMs can complement human studies, they cannot yet fully replace the nuanced insights provided by human subjects.
{"title":"Can AI Replace Human Subjects? A Large-Scale Replication of Psychological Experiments with LLMs","authors":"Ziyan Cui, Ning Li, Huaikang Zhou","doi":"arxiv-2409.00128","DOIUrl":"https://doi.org/arxiv-2409.00128","url":null,"abstract":"Artificial Intelligence (AI) is increasingly being integrated into scientific\u0000research, particularly in the social sciences, where understanding human\u0000behavior is critical. Large Language Models (LLMs) like GPT-4 have shown\u0000promise in replicating human-like responses in various psychological\u0000experiments. However, the extent to which LLMs can effectively replace human\u0000subjects across diverse experimental contexts remains unclear. Here, we conduct\u0000a large-scale study replicating 154 psychological experiments from top social\u0000science journals with 618 main effects and 138 interaction effects using GPT-4\u0000as a simulated participant. We find that GPT-4 successfully replicates 76.0\u0000percent of main effects and 47.0 percent of interaction effects observed in the\u0000original studies, closely mirroring human responses in both direction and\u0000significance. However, only 19.44 percent of GPT-4's replicated confidence\u0000intervals contain the original effect sizes, with the majority of replicated\u0000effect sizes exceeding the 95 percent confidence interval of the original\u0000studies. Additionally, there is a 71.6 percent rate of unexpected significant\u0000results where the original studies reported null findings, suggesting potential\u0000overestimation or false positives. Our results demonstrate the potential of\u0000LLMs as powerful tools in psychological research but also emphasize the need\u0000for caution in interpreting AI-driven findings. While LLMs can complement human\u0000studies, they cannot yet fully replace the nuanced insights provided by human\u0000subjects.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of distributed energy resources (DERs) into wholesale energy markets can greatly enhance grid flexibility, improve market efficiency, and contribute to a more sustainable energy future. As DERs -- such as solar PV panels and energy storage -- proliferate, effective mechanisms are needed to ensure that small prosumers can participate meaningfully in these markets. We study a wholesale market model featuring multiple DER aggregators, each controlling a portfolio of DER resources and bidding into the market on behalf of the DER asset owners. The key of our approach lies in recognizing the repeated nature of market interactions the ability of participants to learn and adapt over time. Specifically, Aggregators repeatedly interact with each other and with other suppliers in the wholesale market, collectively shaping wholesale electricity prices (aka the locational marginal prices (LMPs)). We model this multi-agent interaction using a mean-field game (MFG), which uses market information -- reflecting the average behavior of market participants -- to enable each aggregator to predict long-term LMP trends and make informed decisions. For each aggregator, because they control the DERs within their portfolio under certain contract structures, we employ a mean-field control (MFC) approach (as opposed to a MFG) to learn an optimal policy that maximizes the total rewards of the DERs under their management. We also propose a reinforcement learning (RL)-based method to help each agent learn optimal strategies within the MFG framework, enhancing their ability to adapt to market conditions and uncertainties. Numerical simulations show that LMPs quickly reach a steady state in the hybrid mean-field approach. Furthermore, our results demonstrate that the combination of energy storage and mean-field learning significantly reduces price volatility compared to scenarios without storage.
将分布式能源资源（DER）纳入能源批发市场，可以大大提高电网的灵活性，提高市场效率，并有助于实现更可持续的能源未来。随着太阳能光伏板和储能等 DER 的激增，需要建立有效的机制来确保小型消费者能够有意义地参与这些市场。Westudy 的批发市场模式以多个 DER 聚合器为特色，每个聚合器控制一个 DER 资源组合，并代表 DER 资产所有者参与市场竞标。我们的方法的关键在于认识到市场互动的反复性以及参与者随时间学习和适应的能力。具体来说，聚合器在批发市场中与其他供应商反复互动，共同影响批发电价（又称本地边际价格 (LMP)）。我们使用均场博弈（MFG）来模拟这种多代理互动，该博弈使用市场信息（反映市场参与者的平均行为），使每个聚合者都能预测 LMP 的长期趋势，并做出明智的决策。对于每个聚合器而言，由于它们根据特定的合同结构控制其投资组合中的 DER，因此我们采用均场控制（MFC）方法（而非 MFG）来学习最优策略，使其管理下的 DER 的总回报最大化。我们还提出了基于强化学习（RL）的方法，以帮助每个代理在 MFG 框架内学习最优策略，从而增强其适应市场条件和不确定性的能力。数值模拟表明，在混合均值场方法中，LMPs 很快就能达到稳定状态。此外，我们的研究结果表明，与没有储能的情况相比，储能和均值场学习的结合大大降低了价格波动。
{"title":"Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach","authors":"Jun He, Andrew L. Liu","doi":"arxiv-2409.00107","DOIUrl":"https://doi.org/arxiv-2409.00107","url":null,"abstract":"The integration of distributed energy resources (DERs) into wholesale energy\u0000markets can greatly enhance grid flexibility, improve market efficiency, and\u0000contribute to a more sustainable energy future. As DERs -- such as solar PV\u0000panels and energy storage -- proliferate, effective mechanisms are needed to\u0000ensure that small prosumers can participate meaningfully in these markets. We\u0000study a wholesale market model featuring multiple DER aggregators, each\u0000controlling a portfolio of DER resources and bidding into the market on behalf\u0000of the DER asset owners. The key of our approach lies in recognizing the\u0000repeated nature of market interactions the ability of participants to learn and\u0000adapt over time. Specifically, Aggregators repeatedly interact with each other\u0000and with other suppliers in the wholesale market, collectively shaping\u0000wholesale electricity prices (aka the locational marginal prices (LMPs)). We\u0000model this multi-agent interaction using a mean-field game (MFG), which uses\u0000market information -- reflecting the average behavior of market participants --\u0000to enable each aggregator to predict long-term LMP trends and make informed\u0000decisions. For each aggregator, because they control the DERs within their\u0000portfolio under certain contract structures, we employ a mean-field control\u0000(MFC) approach (as opposed to a MFG) to learn an optimal policy that maximizes\u0000the total rewards of the DERs under their management. We also propose a\u0000reinforcement learning (RL)-based method to help each agent learn optimal\u0000strategies within the MFG framework, enhancing their ability to adapt to market\u0000conditions and uncertainties. Numerical simulations show that LMPs quickly\u0000reach a steady state in the hybrid mean-field approach. Furthermore, our\u0000results demonstrate that the combination of energy storage and mean-field\u0000learning significantly reduces price volatility compared to scenarios without\u0000storage.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Response times contain information about economically relevant but unobserved variables like willingness to pay, preference intensity, quality, or happiness. Here, we provide a general characterization of the properties of latent variables that can be detected using response time data. Our characterization generalizes various results in the literature, helps to solve identification problems of binary response models, and paves the way for many new applications. We apply the result to test the hypothesis that marginal happiness is decreasing in income, a principle that is commonly accepted but so far not established empirically.
{"title":"Time is Knowledge: What Response Times Reveal","authors":"Jean-Michel Benkert, Shuo Liu, Nick Netzer","doi":"arxiv-2408.14872","DOIUrl":"https://doi.org/arxiv-2408.14872","url":null,"abstract":"Response times contain information about economically relevant but unobserved\u0000variables like willingness to pay, preference intensity, quality, or happiness.\u0000Here, we provide a general characterization of the properties of latent\u0000variables that can be detected using response time data. Our characterization\u0000generalizes various results in the literature, helps to solve identification\u0000problems of binary response models, and paves the way for many new\u0000applications. We apply the result to test the hypothesis that marginal\u0000happiness is decreasing in income, a principle that is commonly accepted but so\u0000far not established empirically.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the relationship between economic growth and CO$_2$ emissions across European regions from 1990 to 2022, specifically concerning the dynamics of emissions growth rates through different phases of the European Union Emissions Trading System (EU ETS). We find that emissions dynamics exhibit significant volatility influenced by changing policy frameworks. Furthermore, the distribution of emissions growth rates is asymmetric and displays fat tails, suggesting the potential for extreme emissions events. We identify marked disparities across regions: less developed regions experience higher emissions growth rates and greater volatility compared to many developed areas, which show a trend of declining emissions and reduced volatility. Our findings highlight the sensitivity of emissions to policy changes and emphasise the need for clear and effective governance in emissions trading schemes.
{"title":"Regional emission dynamics across phases of the EU ETS","authors":"Marco Dueñas, Antoine Mandel","doi":"arxiv-2408.15438","DOIUrl":"https://doi.org/arxiv-2408.15438","url":null,"abstract":"This paper explores the relationship between economic growth and CO$_2$\u0000emissions across European regions from 1990 to 2022, specifically concerning\u0000the dynamics of emissions growth rates through different phases of the European\u0000Union Emissions Trading System (EU ETS). We find that emissions dynamics\u0000exhibit significant volatility influenced by changing policy frameworks.\u0000Furthermore, the distribution of emissions growth rates is asymmetric and\u0000displays fat tails, suggesting the potential for extreme emissions events. We\u0000identify marked disparities across regions: less developed regions experience\u0000higher emissions growth rates and greater volatility compared to many developed\u0000areas, which show a trend of declining emissions and reduced volatility. Our\u0000findings highlight the sensitivity of emissions to policy changes and emphasise\u0000the need for clear and effective governance in emissions trading schemes.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A one-size-fits-all paradigm that only adapts the scale and immediate outcome of climate investment to economic circumstances will provide a short-lived, economically inadequate response to climate issues; given the limited resources allocated to green finance, it stands to reason that the shortcomings of this will be exacerbated by the fact that it comes at the cost of long-term, self-perpetuating, systemic solutions. Financial commitments that do not consider the capital structure of green finance in an economy will cumulatively dis-aggregate the economic cost of climate investment, to erode the competitive advantage of the most innovative economies, while simultaneously imposing the greatest financial burden on economies that are most vulnerable to the impact of climate change; such disaggregation will also leave 'middle' economies in a state of flux - honouring similar financial commitments to vulnerable or highly developed peers, but unable to generate comparable return, yet sufficiently insulated from the impact of extreme climate phenomena to not organically develop solutions. In the face of these changing realities, green innovation needs to expand beyond technology and address systemic inefficiencies - lack of clear responsibility, ambiguously defined commitments, and inadequate checks & balances to name a few. Clever application of financial engineering demonstrates promise, and simple measures like carbon-credit exchanges have been effective in mitigating imperfections at the grassroots level. We believe that information- and incentive-centric systemic advancements can usher a fresh wave of green innovation that stands on the shoulders of giants to ensure effective implementation of technological breakthroughs; economic development that will create an international community equipped with a robust framework to deal with long-term crises in a strategic manner.
{"title":"The Climate Cost of Climate Investment: A Two-Period Perspective","authors":"Shaunak Kulkarni, Rohan Ajay Dubey","doi":"arxiv-2408.14359","DOIUrl":"https://doi.org/arxiv-2408.14359","url":null,"abstract":"A one-size-fits-all paradigm that only adapts the scale and immediate outcome\u0000of climate investment to economic circumstances will provide a short-lived,\u0000economically inadequate response to climate issues; given the limited resources\u0000allocated to green finance, it stands to reason that the shortcomings of this\u0000will be exacerbated by the fact that it comes at the cost of long-term,\u0000self-perpetuating, systemic solutions. Financial commitments that do not\u0000consider the capital structure of green finance in an economy will cumulatively\u0000dis-aggregate the economic cost of climate investment, to erode the competitive\u0000advantage of the most innovative economies, while simultaneously imposing the\u0000greatest financial burden on economies that are most vulnerable to the impact\u0000of climate change; such disaggregation will also leave 'middle' economies in a\u0000state of flux - honouring similar financial commitments to vulnerable or highly\u0000developed peers, but unable to generate comparable return, yet sufficiently\u0000insulated from the impact of extreme climate phenomena to not organically\u0000develop solutions. In the face of these changing realities, green innovation needs to expand\u0000beyond technology and address systemic inefficiencies - lack of clear\u0000responsibility, ambiguously defined commitments, and inadequate checks &\u0000balances to name a few. Clever application of financial engineering demonstrates promise, and simple\u0000measures like carbon-credit exchanges have been effective in mitigating\u0000imperfections at the grassroots level. We believe that information- and\u0000incentive-centric systemic advancements can usher a fresh wave of green\u0000innovation that stands on the shoulders of giants to ensure effective\u0000implementation of technological breakthroughs; economic development that will\u0000create an international community equipped with a robust framework to deal with\u0000long-term crises in a strategic manner.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Does public insurance reduce uninsured long-term care (LTC) risks in developing countries, where informal insurance predominates? This paper exploits the rollout of LTC insurance in China around 2016 to examine the impact of public LTC insurance on healthy workers' labor supply, a critical self-insurance channel. We find that workers eligible for public LTC insurance were less likely to engage in labor work and worked fewer weeks annually following the policy change, suggesting a mitigation of uninsured risks. However, these impacts were insignificant among those with strong informal insurance coverage. Parallel changes in anticipated formal care use corroborate these findings. While our results reveal that public LTC insurance provides limited additional risk-sharing when informal insurance predominates, they also underscore its growing importance.
{"title":"Insuring Long-Term Care in Developing Countries: The Interaction between Formal and Informal Insurance","authors":"Jiayi Wen, Xiaoqing Yu","doi":"arxiv-2408.14243","DOIUrl":"https://doi.org/arxiv-2408.14243","url":null,"abstract":"Does public insurance reduce uninsured long-term care (LTC) risks in\u0000developing countries, where informal insurance predominates? This paper\u0000exploits the rollout of LTC insurance in China around 2016 to examine the\u0000impact of public LTC insurance on healthy workers' labor supply, a critical\u0000self-insurance channel. We find that workers eligible for public LTC insurance\u0000were less likely to engage in labor work and worked fewer weeks annually\u0000following the policy change, suggesting a mitigation of uninsured risks.\u0000However, these impacts were insignificant among those with strong informal\u0000insurance coverage. Parallel changes in anticipated formal care use corroborate\u0000these findings. While our results reveal that public LTC insurance provides\u0000limited additional risk-sharing when informal insurance predominates, they also\u0000underscore its growing importance.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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