The rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta-contribution by highlighting a significant risk of unproductive knowledge exchanges in entrepreneurship research amid the AI revolution. It offers strategies to mitigate this risk and provides guidance for future AI-based studies to enhance their collective impact and relevance. Drawing on Akerlof's renowned market-for-lemons concept, we identify the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape (e.g., complexities around construct validity, theory building, and research relevance). Such asymmetries are particularly deeply ingrained due to what we term the double-black-box puzzle, where the widely recognized black box nature of AI methods intersects with the black box nature of the entrepreneurship phenomenon driven by inherent uncertainty. As a result, these asymmetries could lead to an increase in suboptimal research products that go undetected, collectively creating a market for lemons that undermines the field's well-being, reputation, and impact. However, importantly, if these risks can be mitigated, the AI revolution could herald a new golden era for entrepreneurship research. We discuss the necessary actions to elevate the field to a higher level of AI resilience while steadfastly maintaining its foundational principles and core values.
{"title":"A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research","authors":"Martin Obschonka, Moren Levesque","doi":"arxiv-2409.08890","DOIUrl":"https://doi.org/arxiv-2409.08890","url":null,"abstract":"The rapid expansion of AI adoption (e.g., using machine learning, deep\u0000learning, or large language models as research methods) and the increasing\u0000availability of big data have the potential to bring about the most significant\u0000transformation in entrepreneurship scholarship the field has ever witnessed.\u0000This article makes a pressing meta-contribution by highlighting a significant\u0000risk of unproductive knowledge exchanges in entrepreneurship research amid the\u0000AI revolution. It offers strategies to mitigate this risk and provides guidance\u0000for future AI-based studies to enhance their collective impact and relevance.\u0000Drawing on Akerlof's renowned market-for-lemons concept, we identify the\u0000potential for significant knowledge asymmetries emerging from the field's\u0000evolution into its current landscape (e.g., complexities around construct\u0000validity, theory building, and research relevance). Such asymmetries are\u0000particularly deeply ingrained due to what we term the double-black-box puzzle,\u0000where the widely recognized black box nature of AI methods intersects with the\u0000black box nature of the entrepreneurship phenomenon driven by inherent\u0000uncertainty. As a result, these asymmetries could lead to an increase in\u0000suboptimal research products that go undetected, collectively creating a market\u0000for lemons that undermines the field's well-being, reputation, and impact.\u0000However, importantly, if these risks can be mitigated, the AI revolution could\u0000herald a new golden era for entrepreneurship research. We discuss the necessary\u0000actions to elevate the field to a higher level of AI resilience while\u0000steadfastly maintaining its foundational principles and core values.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259577","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}
Shu Takahashi, Kento Yamamoto, Shumpei Kobayashi, Ryoma Kondo, Ryohei Hisano
The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network.
{"title":"Dynamic Link and Flow Prediction in Bank Transfer Networks","authors":"Shu Takahashi, Kento Yamamoto, Shumpei Kobayashi, Ryoma Kondo, Ryohei Hisano","doi":"arxiv-2409.08718","DOIUrl":"https://doi.org/arxiv-2409.08718","url":null,"abstract":"The prediction of both the existence and weight of network links at future\u0000time points is essential as complex networks evolve over time. Traditional\u0000methods, such as vector autoregression and factor models, have been applied to\u0000small, dense networks, but become computationally impractical for large-scale,\u0000sparse, and complex networks. Some machine learning models address dynamic link\u0000prediction, but few address the simultaneous prediction of both link presence\u0000and weight. Therefore, we introduce a novel model that dynamically predicts\u0000link presence and weight by dividing the task into two sub-tasks: predicting\u0000remittance ratios and forecasting the total remittance volume. We use a\u0000self-attention mechanism that combines temporal-topological neighborhood\u0000features to predict remittance ratios and use a separate model to forecast the\u0000total remittance volume. We achieve the final prediction by multiplying the\u0000outputs of these models. We validated our approach using two real-world\u0000datasets: a cryptocurrency network and bank transfer network.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259578","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 introduces an economic framework to assess optimal longevity risk transfers between institutions, focusing on the interactions between a buyer exposed to long-term longevity risk and a seller offering longevity protection. While most longevity risk transfers have occurred in the reinsurance sector, where global reinsurers provide long-term protections, the capital market for longevity risk transfer has struggled to gain traction, resulting in only a few short-term instruments. We investigate how differences in risk aversion between the two parties affect the equilibrium structure of longevity risk transfer contracts, contrasting `static' contracts that offer long-term protection with `dynamic' contracts that provide short-term, variable coverage. Our analysis shows that static contracts are preferred by more risk-averse buyers, while dynamic contracts are favored by more risk-averse sellers who are reluctant to commit to long-term agreements. When incorporating information asymmetry through ambiguity, we find that ambiguity can cause more risk-averse sellers to stop offering long-term contracts. With the assumption that global reinsurers, acting as sellers in the reinsurance sector and buyers in the capital market, are generally less risk-averse than other participants, our findings provide theoretical explanations for current market dynamics and suggest that short-term instruments offer valuable initial steps toward developing an efficient and active capital market for longevity risk transfer.
{"title":"Contract Structure and Risk Aversion in Longevity Risk Transfers","authors":"David Landriault, Bin Li, Hong Li, Yuanyuan Zhang","doi":"arxiv-2409.08914","DOIUrl":"https://doi.org/arxiv-2409.08914","url":null,"abstract":"This paper introduces an economic framework to assess optimal longevity risk\u0000transfers between institutions, focusing on the interactions between a buyer\u0000exposed to long-term longevity risk and a seller offering longevity protection.\u0000While most longevity risk transfers have occurred in the reinsurance sector,\u0000where global reinsurers provide long-term protections, the capital market for\u0000longevity risk transfer has struggled to gain traction, resulting in only a few\u0000short-term instruments. We investigate how differences in risk aversion between\u0000the two parties affect the equilibrium structure of longevity risk transfer\u0000contracts, contrasting `static' contracts that offer long-term protection with\u0000`dynamic' contracts that provide short-term, variable coverage. Our analysis\u0000shows that static contracts are preferred by more risk-averse buyers, while\u0000dynamic contracts are favored by more risk-averse sellers who are reluctant to\u0000commit to long-term agreements. When incorporating information asymmetry\u0000through ambiguity, we find that ambiguity can cause more risk-averse sellers to\u0000stop offering long-term contracts. With the assumption that global reinsurers,\u0000acting as sellers in the reinsurance sector and buyers in the capital market,\u0000are generally less risk-averse than other participants, our findings provide\u0000theoretical explanations for current market dynamics and suggest that\u0000short-term instruments offer valuable initial steps toward developing an\u0000efficient and active capital market for longevity risk transfer.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259576","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}
Anton Pichler, Jan Hurt, Tobias Reisch, Johannes Stangl, Stefan Thurner
The Russian invasion of Ukraine on February 24, 2022 entailed the threat of a drastic and sudden reduction of natural gas supply to the European Union. This paper presents a techno-economic analysis of the consequences of a sudden gas supply shock to Austria, one of the most dependent countries on imports of Russian gas. Our analysis comprises (a) a detailed assessment of supply and demand side countermeasures to mitigate the immediate shortfall in Russian gas imports, (b) a mapping of the net reduction in gas supply to industrial sectors to quantify direct economic shocks and expected relative reductions in gross output and (c) the quantification of higher-order economic impacts through using a dynamic out-of-equilibrium input-output model. Our results show that potential economic consequences can range from relatively mild to highly severe, depending on the implementation and success of counteracting mitigation measures. We find that securing alternative gas imports, storage management, and incentivizing fuel switching represent the most important short-term policy levers to mitigate the adverse impacts of a sudden import stop.
{"title":"Economic impacts of a drastic gas supply shock and short-term mitigation strategies","authors":"Anton Pichler, Jan Hurt, Tobias Reisch, Johannes Stangl, Stefan Thurner","doi":"arxiv-2409.07981","DOIUrl":"https://doi.org/arxiv-2409.07981","url":null,"abstract":"The Russian invasion of Ukraine on February 24, 2022 entailed the threat of a\u0000drastic and sudden reduction of natural gas supply to the European Union. This\u0000paper presents a techno-economic analysis of the consequences of a sudden gas\u0000supply shock to Austria, one of the most dependent countries on imports of\u0000Russian gas. Our analysis comprises (a) a detailed assessment of supply and\u0000demand side countermeasures to mitigate the immediate shortfall in Russian gas\u0000imports, (b) a mapping of the net reduction in gas supply to industrial sectors\u0000to quantify direct economic shocks and expected relative reductions in gross\u0000output and (c) the quantification of higher-order economic impacts through\u0000using a dynamic out-of-equilibrium input-output model. Our results show that\u0000potential economic consequences can range from relatively mild to highly\u0000severe, depending on the implementation and success of counteracting mitigation\u0000measures. We find that securing alternative gas imports, storage management,\u0000and incentivizing fuel switching represent the most important short-term policy\u0000levers to mitigate the adverse impacts of a sudden import stop.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192959","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}
In 1880, Keio, a private school in Japan, was in jeopardy of being closed. To cope with the situation, the school first created a fundraising campaign during the 18801-90 period. The school was established in 1857, and since 1861, the list covering all students academic record has been distributed not only to teachers but also to all students. Individual-level historical academic record was integrated with the list of contributors. Using the data, we compared persons who had learned in Keio before and after the system was introduced. The main findings are presented as follows. first, graduates who share the academic record are more likely to contribute, and their amount of donation is larger; second, the class size is negatively correlated with the likelihood of contribution and with its amount; and third, academic performance, as shown in the list, is positively correlated with the likelihood of contribution but not with the amount of donation, using a sub-sample of those who shared the list. The introduction of the system strengthened the community network and role of social image shared by the members. This resulted in a successful fundraising for the school, an unprecedented feat in the history of Japan.
{"title":"The first alumni donation in 1880 in Japan: social image and the open-academic record system","authors":"Eiji Yamamura","doi":"arxiv-2409.08415","DOIUrl":"https://doi.org/arxiv-2409.08415","url":null,"abstract":"In 1880, Keio, a private school in Japan, was in jeopardy of being closed. To\u0000cope with the situation, the school first created a fundraising campaign during\u0000the 18801-90 period. The school was established in 1857, and since 1861, the\u0000list covering all students academic record has been distributed not only to\u0000teachers but also to all students. Individual-level historical academic record\u0000was integrated with the list of contributors. Using the data, we compared\u0000persons who had learned in Keio before and after the system was introduced. The\u0000main findings are presented as follows. first, graduates who share the academic\u0000record are more likely to contribute, and their amount of donation is larger;\u0000second, the class size is negatively correlated with the likelihood of\u0000contribution and with its amount; and third, academic performance, as shown in\u0000the list, is positively correlated with the likelihood of contribution but not\u0000with the amount of donation, using a sub-sample of those who shared the list.\u0000The introduction of the system strengthened the community network and role of\u0000social image shared by the members. This resulted in a successful fundraising\u0000for the school, an unprecedented feat in the history of Japan.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259579","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}
Doron Yeverechyahu, Raveesh Mayya, Gal Oestreicher-Singer
Generative AI (GenAI) has been shown to enhance individual productivity in a guided setting. While it is also likely to transform processes in a collaborative work setting, it is unclear what trajectory this transformation will follow. Collaborative environment is characterized by a blend of origination tasks that involve building something from scratch and iteration tasks that involve refining on others' work. Whether GenAI affects these two aspects of collaborative work and to what extent is an open empirical question. We study this question within the open-source development landscape, a prime example of collaborative innovation, where contributions are voluntary and unguided. Specifically, we focus on the launch of GitHub Copilot in October 2021 and leverage a natural experiment in which GitHub Copilot (a programming-focused LLM) selectively rolled out support for Python, but not for R. We observe a significant jump in overall contributions, suggesting that GenAI effectively augments collaborative innovation in an unguided setting. Interestingly, Copilot's launch increased maintenance-related contributions, which are mostly iterative tasks involving building on others' work, significantly more than code-development contributions, which are mostly origination tasks involving standalone contributions. This disparity was exacerbated in active projects with extensive coding activity, raising concerns that, as GenAI models improve to accommodate richer context, the gap between origination and iterative solutions may widen. We discuss practical and policy implications to incentivize high-value innovative solutions.
{"title":"The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot","authors":"Doron Yeverechyahu, Raveesh Mayya, Gal Oestreicher-Singer","doi":"arxiv-2409.08379","DOIUrl":"https://doi.org/arxiv-2409.08379","url":null,"abstract":"Generative AI (GenAI) has been shown to enhance individual productivity in a\u0000guided setting. While it is also likely to transform processes in a\u0000collaborative work setting, it is unclear what trajectory this transformation\u0000will follow. Collaborative environment is characterized by a blend of\u0000origination tasks that involve building something from scratch and iteration\u0000tasks that involve refining on others' work. Whether GenAI affects these two\u0000aspects of collaborative work and to what extent is an open empirical question.\u0000We study this question within the open-source development landscape, a prime\u0000example of collaborative innovation, where contributions are voluntary and\u0000unguided. Specifically, we focus on the launch of GitHub Copilot in October\u00002021 and leverage a natural experiment in which GitHub Copilot (a\u0000programming-focused LLM) selectively rolled out support for Python, but not for\u0000R. We observe a significant jump in overall contributions, suggesting that\u0000GenAI effectively augments collaborative innovation in an unguided setting.\u0000Interestingly, Copilot's launch increased maintenance-related contributions,\u0000which are mostly iterative tasks involving building on others' work,\u0000significantly more than code-development contributions, which are mostly\u0000origination tasks involving standalone contributions. This disparity was\u0000exacerbated in active projects with extensive coding activity, raising concerns\u0000that, as GenAI models improve to accommodate richer context, the gap between\u0000origination and iterative solutions may widen. We discuss practical and policy\u0000implications to incentivize high-value innovative solutions.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259580","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 study explores the potential of large language models (LLMs) to conduct market experiments, aiming to understand their capability to comprehend competitive market dynamics. We model the behavior of market agents in a controlled experimental setting, assessing their ability to converge toward competitive equilibria. The results reveal the challenges current LLMs face in replicating the dynamic decision-making processes characteristic of human trading behavior. Unlike humans, LLMs lacked the capacity to achieve market equilibrium. The research demonstrates that while LLMs provide a valuable tool for scalable and reproducible market simulations, their current limitations necessitate further advancements to fully capture the complexities of market behavior. Future work that enhances dynamic learning capabilities and incorporates elements of behavioral economics could improve the effectiveness of LLMs in the economic domain, providing new insights into market dynamics and aiding in the refinement of economic policies.
{"title":"An Experimental Study of Competitive Market Behavior Through LLMs","authors":"Jingru Jia, Zehua Yuan","doi":"arxiv-2409.08357","DOIUrl":"https://doi.org/arxiv-2409.08357","url":null,"abstract":"This study explores the potential of large language models (LLMs) to conduct\u0000market experiments, aiming to understand their capability to comprehend\u0000competitive market dynamics. We model the behavior of market agents in a\u0000controlled experimental setting, assessing their ability to converge toward\u0000competitive equilibria. The results reveal the challenges current LLMs face in\u0000replicating the dynamic decision-making processes characteristic of human\u0000trading behavior. Unlike humans, LLMs lacked the capacity to achieve market\u0000equilibrium. The research demonstrates that while LLMs provide a valuable tool\u0000for scalable and reproducible market simulations, their current limitations\u0000necessitate further advancements to fully capture the complexities of market\u0000behavior. Future work that enhances dynamic learning capabilities and\u0000incorporates elements of behavioral economics could improve the effectiveness\u0000of LLMs in the economic domain, providing new insights into market dynamics and\u0000aiding in the refinement of economic policies.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259581","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}
Francisco José Zamudio Sánchez, Javier Jiménez Machorro, Roxana Arana Ovalle, Hildegardo Martínez Silverio
This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying levels of inequality depending on their position within the distribution, particularly with respect to those at the top. The key assumption is that for individuals in lower positions, inequalities referenced to the top positions have greater impact on their well-being and the inequality relative to maximum is the most critical. The IDRM fulfills desirable theoretical properties which were used for its evaluation and comparison against widely accepted measures in inequality literature. From this perspective, the IDRM is shown to be as robust as traditional measures and outperforms the Gini and Dalton indices by satisfying eight out of nine key properties, including decomposability across population subgroups. In a comparative analysis using income data from 58 countries and microdata from Mexico, with the Gini, Theil, and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency, sensitivity to inequality, reduced bias in grouped data, and enhanced precision. This index reflects the varying forms of income distribution, showing heightened sensitivity to the magnitude of inequality.
{"title":"Un índice discreto sensible a la desigualdad","authors":"Francisco José Zamudio Sánchez, Javier Jiménez Machorro, Roxana Arana Ovalle, Hildegardo Martínez Silverio","doi":"arxiv-2409.07538","DOIUrl":"https://doi.org/arxiv-2409.07538","url":null,"abstract":"This paper introduces the Relative Inequality Index at the Maximum (IDRM), a\u0000novel and intuitive measure designed to capture inequality within a population,\u0000such as income inequality. The index is based on the idea that individuals\u0000experience varying levels of inequality depending on their position within the\u0000distribution, particularly with respect to those at the top. The key assumption\u0000is that for individuals in lower positions, inequalities referenced to the top\u0000positions have greater impact on their well-being and the inequality relative\u0000to maximum is the most critical. The IDRM fulfills desirable theoretical\u0000properties which were used for its evaluation and comparison against widely\u0000accepted measures in inequality literature. From this perspective, the IDRM is\u0000shown to be as robust as traditional measures and outperforms the Gini and\u0000Dalton indices by satisfying eight out of nine key properties, including\u0000decomposability across population subgroups. In a comparative analysis using\u0000income data from 58 countries and microdata from Mexico, with the Gini, Theil,\u0000and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency,\u0000sensitivity to inequality, reduced bias in grouped data, and enhanced\u0000precision. This index reflects the varying forms of income distribution,\u0000showing heightened sensitivity to the magnitude of inequality.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192944","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}
Anna Josephson, Jeffrey D. Michler, Talip Kilic, Siobhan Murray
The availability of weather data from remotely sensed Earth observation (EO) data has reduced the cost of including weather variables in econometric models. Weather variables are common instrumental variables used to predict economic outcomes and serve as an input into modelling crop yields for rainfed agriculture. The use of EO data in econometric applications has only recently been met with a critical assessment of the suitability and quality of this data in economics. We quantify the significance and magnitude of the effect of measurement error in EO data in the context of smallholder agricultural productivity. We find that different measurement methods from different EO sources: findings are not robust to the choice of EO dataset and outcomes are not simply affine transformations of one another. This begs caution on the part of researchers using these data and suggests that robustness checks should include testing alternative sources of EO data.
从遥感地球观测（EO）数据中获取天气数据降低了将天气变量纳入计量经济学模型的成本。天气变量是用于预测经济结果的常见工具变量，也是雨水灌溉农业作物产量建模的输入变量。在计量经济学应用中使用环 境观测数据时，最近才对这些数据在经济学中的适用性和质量进行了严格评估。我们以小农农业生产率为背景，量化了 EO 数据测量误差影响的意义和程度。我们发现，来自不同环 境观测数据源的不同测量方法：研究结果并不因选择的环境观测数据集而稳健，结果也不是简单的仿射变换。这就要求研究人员在使用这些数据时要谨慎，并建议稳健性检查应包括测试替代的环 境观测数据源。
{"title":"The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Context","authors":"Anna Josephson, Jeffrey D. Michler, Talip Kilic, Siobhan Murray","doi":"arxiv-2409.07506","DOIUrl":"https://doi.org/arxiv-2409.07506","url":null,"abstract":"The availability of weather data from remotely sensed Earth observation (EO)\u0000data has reduced the cost of including weather variables in econometric models.\u0000Weather variables are common instrumental variables used to predict economic\u0000outcomes and serve as an input into modelling crop yields for rainfed\u0000agriculture. The use of EO data in econometric applications has only recently\u0000been met with a critical assessment of the suitability and quality of this data\u0000in economics. We quantify the significance and magnitude of the effect of\u0000measurement error in EO data in the context of smallholder agricultural\u0000productivity. We find that different measurement methods from different EO\u0000sources: findings are not robust to the choice of EO dataset and outcomes are\u0000not simply affine transformations of one another. This begs caution on the part\u0000of researchers using these data and suggests that robustness checks should\u0000include testing alternative sources of EO data.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192958","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 review article examines the challenge of eliciting truthful information from multiple individuals when such information cannot be verified against an objective truth, a problem known as information elicitation without verification (IEWV). This article reviews over 25 mechanisms designed to incentivize truth-telling in such scenarios, and their effectiveness in empirical studies. The analysis finds that although many mechanisms theoretically ensure truthfulness as a Bayesian Nash Equilibrium, empirical evidence of such mechanisms working in practice is very limited and generally weak. Consequently, more empirical research is needed to validate mechanisms. Given that many mechanisms are very complex and cannot be easily conveyed to research subjects, this review suggests that simpler, more intuitive mechanisms may be easier to test and apply.
{"title":"Mechanisms for belief elicitation without ground truth","authors":"Niklas Valentin Lehmann","doi":"arxiv-2409.07277","DOIUrl":"https://doi.org/arxiv-2409.07277","url":null,"abstract":"This review article examines the challenge of eliciting truthful information\u0000from multiple individuals when such information cannot be verified against an\u0000objective truth, a problem known as information elicitation without\u0000verification (IEWV). This article reviews over 25 mechanisms designed to\u0000incentivize truth-telling in such scenarios, and their effectiveness in\u0000empirical studies. The analysis finds that although many mechanisms\u0000theoretically ensure truthfulness as a Bayesian Nash Equilibrium, empirical\u0000evidence of such mechanisms working in practice is very limited and generally\u0000weak. Consequently, more empirical research is needed to validate mechanisms.\u0000Given that many mechanisms are very complex and cannot be easily conveyed to\u0000research subjects, this review suggests that simpler, more intuitive mechanisms\u0000may be easier to test and apply.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192960","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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