Escaping the middle-income trap requires a country to develop indigenous technological capabilities for high value-added innovation. This study examines the role of second-tier patent systems, known as utility models (UMs), in promoting such capability acquisition in less developed countries. UMs are designed to incentivize incremental and adaptive innovation through lower novelty standards than patents, but their long-term impact on the capability acquisition process remains underexplored. Using South Korea as a case study and drawing on the characteristics of technological regimes in catching-up economies, we present three key findings: First, the country's post-catch-up frontier technologies (U.S. patents) are more impactful (highly cited) when they build on Korean domestic UMs. This suggests that UM-based imitative and adaptive learning laid the foundation for the country's globally competitive capabilities. Second, the impact of UM-based learning diminishes as the country's economy develops. Third, frontier technologies rooted in UMs contribute more to the country's own specialization than to follow-on innovations by foreign actors, compared to technologies without UM linkages. We discuss how technological regimes and industrial policies in catching-up economies interact with the UM system to bridge the catching-up (imitation- and adaptation-based) and post-catching-up (specialization- and creativity-based) phases.
要摆脱中等收入陷阱，一个国家就必须发展本土技术能力，实现高附加值创新。本研究探讨了被称为实用新型（UMs）的二级专利制度在促进欠发达国家获得这种能力方面的作用。实用新型旨在通过低于专利的新颖性标准来激励渐进式和适应性创新，但其对能力获取过程的长期影响仍未得到充分探索。以韩国为案例，并借鉴追赶型经济体技术体制的特点，我们提出了三项重要发现：首先，当韩国的追赶型前沿技术（美国专利）建立在韩国国内的统一管理基础上时，其影响力更大（引用率更高）。这表明，基于 UM 的模仿和适应性学习为韩国的全球竞争能力奠定了基础。其次，随着国家经济的发展，基于 UM 的学习的影响会逐渐减弱。第三，与没有UM联系的技术相比，植根于UM的前沿技术对国家自身专业化的贡献要大于外国参与者的后续创新。我们讨论了追赶型经济体的技术制度和产业政策如何与统一市场体系相互作用，从而在追赶（基于模仿和适应）和后追赶（基于专业化和创造性）阶段之间架起桥梁。
{"title":"From catch-up to frontier: The utility model as a learning device to escape the middle-income trap","authors":"Su Jung Jee, Kerstin Hötte","doi":"arxiv-2408.14205","DOIUrl":"https://doi.org/arxiv-2408.14205","url":null,"abstract":"Escaping the middle-income trap requires a country to develop indigenous\u0000technological capabilities for high value-added innovation. This study examines\u0000the role of second-tier patent systems, known as utility models (UMs), in\u0000promoting such capability acquisition in less developed countries. UMs are\u0000designed to incentivize incremental and adaptive innovation through lower\u0000novelty standards than patents, but their long-term impact on the capability\u0000acquisition process remains underexplored. Using South Korea as a case study\u0000and drawing on the characteristics of technological regimes in catching-up\u0000economies, we present three key findings: First, the country's post-catch-up\u0000frontier technologies (U.S. patents) are more impactful (highly cited) when\u0000they build on Korean domestic UMs. This suggests that UM-based imitative and\u0000adaptive learning laid the foundation for the country's globally competitive\u0000capabilities. Second, the impact of UM-based learning diminishes as the\u0000country's economy develops. Third, frontier technologies rooted in UMs\u0000contribute more to the country's own specialization than to follow-on\u0000innovations by foreign actors, compared to technologies without UM linkages. We\u0000discuss how technological regimes and industrial policies in catching-up\u0000economies interact with the UM system to bridge the catching-up (imitation- and\u0000adaptation-based) and post-catching-up (specialization- and creativity-based)\u0000phases.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192813","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}
What impact does import competition have on firms' production organizational choices? Existing literature has predominantly focused on the relationship between import competition and firms' global production networks, with less attention given to domestic. We first develop a Nash-bargaining model to guide our empirical analysis, then utilize tariff changes as an exogenous shock to test our theoretical hypotheses using a database of Chinese listed firms from 2000 to 2023. Our findings indicate that a decrease in downstream tariffs lead to an increase in vertical integration. In our mechanism tests, we discover that a reduction in upstream tariffs also enhances this effect. Moreover, the impact of tariff reductions on vertical integration is primarily observed in industries with high asset specificity, indicating that asset-specificity is a crucial mechanism. We further explore whether import competition encourages vertical integration for technological acquisition purpose, the effect is found only among high-tech firms, while it's absent in non-high-tech firms. Our research provides new perspectives and evidence on how firms optimize their production organization in the process of globalization.
{"title":"Import competition and domestic vertical integration: Theory and Evidence from Chinese firms","authors":"Xin Du, Xiaoxia Shi","doi":"arxiv-2408.13706","DOIUrl":"https://doi.org/arxiv-2408.13706","url":null,"abstract":"What impact does import competition have on firms' production organizational\u0000choices? Existing literature has predominantly focused on the relationship\u0000between import competition and firms' global production networks, with less\u0000attention given to domestic. We first develop a Nash-bargaining model to guide\u0000our empirical analysis, then utilize tariff changes as an exogenous shock to\u0000test our theoretical hypotheses using a database of Chinese listed firms from\u00002000 to 2023. Our findings indicate that a decrease in downstream tariffs lead\u0000to an increase in vertical integration. In our mechanism tests, we discover\u0000that a reduction in upstream tariffs also enhances this effect. Moreover, the\u0000impact of tariff reductions on vertical integration is primarily observed in\u0000industries with high asset specificity, indicating that asset-specificity is a\u0000crucial mechanism. We further explore whether import competition encourages\u0000vertical integration for technological acquisition purpose, the effect is found\u0000only among high-tech firms, while it's absent in non-high-tech firms. Our\u0000research provides new perspectives and evidence on how firms optimize their\u0000production organization in the process of globalization.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193035","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 ESG rating disagreement and total factor productivity (TFP) based on data from Chinese domestic ESG rating agencies and financial data of A-share listed companies in China from 2015 to 2022. On one hand, the empirical results show that ESG rating disagreement reduces corporate TFP, a conclusion that is validated through multiple robustness tests. The mechanism analysis reveals an interaction effect between green innovation and ESG rating disagreement. Specifically, in firms without ESG rating disagreement, green innovation promotes the improvement of TFP; however, in firms with disagreement, although ESG rating disagreement may drive green innovation, this does not lead to an increase in TFP. Furthermore, ESG rating disagreement lower corporate TFP by increasing financing constraints. The heterogeneity analysis indicates that this effect is more pronounced in non-state-owned, asset-intensive, and low-pollution enterprises. On the other hand, XGBoost regression demonstrates that ESG rating disagreement play a significant role in predicting TFP, with SHAP values showing that the main effects are more evident in firms with larger ESG rating disagreement.
{"title":"ESG Rating Disagreement and Corporate Total Factor Productivity:Inference and Prediction","authors":"Zhanli Li","doi":"arxiv-2408.13895","DOIUrl":"https://doi.org/arxiv-2408.13895","url":null,"abstract":"This paper explores the relationship between ESG rating disagreement and\u0000total factor productivity (TFP) based on data from Chinese domestic ESG rating\u0000agencies and financial data of A-share listed companies in China from 2015 to\u00002022. On one hand, the empirical results show that ESG rating disagreement\u0000reduces corporate TFP, a conclusion that is validated through multiple\u0000robustness tests. The mechanism analysis reveals an interaction effect between\u0000green innovation and ESG rating disagreement. Specifically, in firms without\u0000ESG rating disagreement, green innovation promotes the improvement of TFP;\u0000however, in firms with disagreement, although ESG rating disagreement may drive\u0000green innovation, this does not lead to an increase in TFP. Furthermore, ESG\u0000rating disagreement lower corporate TFP by increasing financing constraints.\u0000The heterogeneity analysis indicates that this effect is more pronounced in\u0000non-state-owned, asset-intensive, and low-pollution enterprises. On the other\u0000hand, XGBoost regression demonstrates that ESG rating disagreement play a\u0000significant role in predicting TFP, with SHAP values showing that the main\u0000effects are more evident in firms with larger ESG rating disagreement.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193034","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}
Leonardo Matone, Ben Abramowitz, Nicholas Mattei, Avinash Balakrishnan
Aggregating the preferences of multiple agents into a collective decision is a common step in many important problems across areas of computer science including information retrieval, reinforcement learning, and recommender systems. As Social Choice Theory has shown, the problem of designing algorithms for aggregation rules with specific properties (axioms) can be difficult, or provably impossible in some cases. Instead of designing algorithms by hand, one can learn aggregation rules, particularly voting rules, from data. However, the prior work in this area has required extremely large models, or been limited by the choice of preference representation, i.e., embedding. We recast the problem of designing a good voting rule into one of learning probabilistic versions of voting rules that output distributions over a set of candidates. Specifically, we use neural networks to learn probabilistic social choice functions from the literature. We show that embeddings of preference profiles derived from the social choice literature allows us to learn existing voting rules more efficiently and scale to larger populations of voters more easily than other work if the embedding is tailored to the learning objective. Moreover, we show that rules learned using embeddings can be tweaked to create novel voting rules with improved axiomatic properties. Namely, we show that existing voting rules require only minor modification to combat a probabilistic version of the No Show Paradox.
{"title":"DeepVoting: Learning Voting Rules with Tailored Embeddings","authors":"Leonardo Matone, Ben Abramowitz, Nicholas Mattei, Avinash Balakrishnan","doi":"arxiv-2408.13630","DOIUrl":"https://doi.org/arxiv-2408.13630","url":null,"abstract":"Aggregating the preferences of multiple agents into a collective decision is\u0000a common step in many important problems across areas of computer science\u0000including information retrieval, reinforcement learning, and recommender\u0000systems. As Social Choice Theory has shown, the problem of designing algorithms\u0000for aggregation rules with specific properties (axioms) can be difficult, or\u0000provably impossible in some cases. Instead of designing algorithms by hand, one\u0000can learn aggregation rules, particularly voting rules, from data. However, the\u0000prior work in this area has required extremely large models, or been limited by\u0000the choice of preference representation, i.e., embedding. We recast the problem\u0000of designing a good voting rule into one of learning probabilistic versions of\u0000voting rules that output distributions over a set of candidates. Specifically,\u0000we use neural networks to learn probabilistic social choice functions from the\u0000literature. We show that embeddings of preference profiles derived from the\u0000social choice literature allows us to learn existing voting rules more\u0000efficiently and scale to larger populations of voters more easily than other\u0000work if the embedding is tailored to the learning objective. Moreover, we show\u0000that rules learned using embeddings can be tweaked to create novel voting rules\u0000with improved axiomatic properties. Namely, we show that existing voting rules\u0000require only minor modification to combat a probabilistic version of the No\u0000Show Paradox.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192815","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}
Generative Artificial Intelligence constitutes a new wave of automation. There is broad agreement among economists that humanity is potentially entering into a period of profound change. However, significant uncertainties and disagreements exist concerning a variety of overlapping topics: the share of jobs in which human labour is displaced and/or reinstated through automation; the effects on income inequality; the effects on job satisfaction; and, finally, what policy changes ought to be pursued to reduce potential negative impacts. This literature review seeks to clarify this landscape by mapping out key disagreements between positions, and to identify the critical elements upon which such disagreements rest. By surveying the current literature, the effects of AI on the future of work will be clarified.
{"title":"The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review","authors":"Caleb Peppiatt","doi":"arxiv-2408.13300","DOIUrl":"https://doi.org/arxiv-2408.13300","url":null,"abstract":"Generative Artificial Intelligence constitutes a new wave of automation.\u0000There is broad agreement among economists that humanity is potentially entering\u0000into a period of profound change. However, significant uncertainties and\u0000disagreements exist concerning a variety of overlapping topics: the share of\u0000jobs in which human labour is displaced and/or reinstated through automation;\u0000the effects on income inequality; the effects on job satisfaction; and,\u0000finally, what policy changes ought to be pursued to reduce potential negative\u0000impacts. This literature review seeks to clarify this landscape by mapping out\u0000key disagreements between positions, and to identify the critical elements upon\u0000which such disagreements rest. By surveying the current literature, the effects\u0000of AI on the future of work will be clarified.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192814","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 examines how market risks impact the sustainability and performance of the New Pension System (NPS). NPS relies on defined contributions from both employees and employers to build a corpus during the employee's service period. Upon retirement, employees use the corpus fund to sustain their livelihood. A critical concern for individuals is whether the corpus will grow sufficiently to be sustainable or if it will deplete, leaving them financially vulnerable at an advanced age. We explore the impact of market risks on the performance of the corpus resulting from the NPS. To address this, we quantify market risks using Monte Carlo simulations with historical data to model their impact on NPS. We quantify the risk of pension corpus being insufficient and the cost to the Government to hedge the risk arising from guaranteeing the pension.
{"title":"Understanding the Effect of Market Risks on New Pension System and Government Responsibility","authors":"Sourish Das, Bikramaditya Datta, Shiv Ratan Tiwari","doi":"arxiv-2408.13200","DOIUrl":"https://doi.org/arxiv-2408.13200","url":null,"abstract":"This study examines how market risks impact the sustainability and\u0000performance of the New Pension System (NPS). NPS relies on defined\u0000contributions from both employees and employers to build a corpus during the\u0000employee's service period. Upon retirement, employees use the corpus fund to\u0000sustain their livelihood. A critical concern for individuals is whether the\u0000corpus will grow sufficiently to be sustainable or if it will deplete, leaving\u0000them financially vulnerable at an advanced age. We explore the impact of market\u0000risks on the performance of the corpus resulting from the NPS. To address this,\u0000we quantify market risks using Monte Carlo simulations with historical data to\u0000model their impact on NPS. We quantify the risk of pension corpus being\u0000insufficient and the cost to the Government to hedge the risk arising from\u0000guaranteeing the pension.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192818","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 causal impact of education opportunities on rural areas by exploiting the higher education expansion (HEE) in China in 1999. By utilizing the detailed census data, the cohort-based difference-in-differences design indicates that the HEE increased college attendance and encouraged more people to attend senior high schools and that the effect is more significant in rural areas. Then we apply a similar approach to a novel panel data set of rural villages and households to examine the effect of education opportunities on rural areas. We find contrasting impacts on income and life quality between villages and households. Villages in provinces with higher HEE magnitudes underwent a drop in the average income and worse living facilities. On the contrary, households sending out migrants after the HEE experienced an increase in their per capita income. The phenomenon where villages experienced a ``brain drain'' and households with migrants gained after the HEE is explained by the fact that education could serve as a way to overcome the barrier of rural-urban migration. Our findings highlight the opposed impacts of education opportunities on rural development and household welfare in rural areas.
{"title":"Education Opportunities for Rural Areas: Evidence from China's Higher Education Expansion","authors":"Ande Shen, Jiwei Zhou","doi":"arxiv-2408.12915","DOIUrl":"https://doi.org/arxiv-2408.12915","url":null,"abstract":"This paper explores the causal impact of education opportunities on rural\u0000areas by exploiting the higher education expansion (HEE) in China in 1999. By\u0000utilizing the detailed census data, the cohort-based difference-in-differences\u0000design indicates that the HEE increased college attendance and encouraged more\u0000people to attend senior high schools and that the effect is more significant in\u0000rural areas. Then we apply a similar approach to a novel panel data set of\u0000rural villages and households to examine the effect of education opportunities\u0000on rural areas. We find contrasting impacts on income and life quality between\u0000villages and households. Villages in provinces with higher HEE magnitudes\u0000underwent a drop in the average income and worse living facilities. On the\u0000contrary, households sending out migrants after the HEE experienced an increase\u0000in their per capita income. The phenomenon where villages experienced a ``brain\u0000drain'' and households with migrants gained after the HEE is explained by the\u0000fact that education could serve as a way to overcome the barrier of rural-urban\u0000migration. Our findings highlight the opposed impacts of education\u0000opportunities on rural development and household welfare in rural areas.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192817","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 the context of global trade, cross-border commodity pricing largely determines the competitiveness and market share of businesses. However, existing methodologies often prove inadequate, as they lack the agility and precision required to effectively respond to the dynamic international markets. Time series data is of great significance in commodity pricing and can reveal market dynamics and trends. Therefore, we propose a new method based on the hybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate prediction and optimization of cross-border commodity pricing strategies through in-depth analysis and optimization of time series data. Our model undergoes experimental validation across multiple datasets. The results show that our method achieves significant performance advantages on datasets such as UNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our model reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly better than other models. On the IMF and WITS datasets, our method also achieves similar excellent performance. These experimental results verify the effectiveness and reliability of our model in the field of cross-border commodity pricing. Overall, this study provides an important reference for enterprises to formulate more reasonable and effective cross-border commodity pricing strategies, thereby enhancing market competitiveness and profitability. At the same time, our method also lays a foundation for the application of deep learning in the fields of international trade and economic strategy optimization, which has important theoretical and practical significance.
在全球贸易背景下，跨境商品定价在很大程度上决定了企业的竞争力和市场份额。时间序列数据对商品定价具有重要意义，可以揭示市场动态和趋势。因此，我们提出了一种基于混合神经网络模型 CNN-BiGRU-SSA 的新方法。目标是通过对时间序列数据的深入分析和优化，实现跨境商品定价策略的准确预测和优化。我们的模型在多个数据集上进行了实验验证。结果表明，我们的方法在贸发会议、国际货币基金组织、WITS 和中国海关等数据集上取得了显著的性能优势。例如，在 UNCTAD 数据集上，我们的模型将 MAE 降至 4.357，RMSE 降至 5.406，R2 降至 0.961，明显优于其他模型。在 IMF 和 WITS 数据集上，我们的方法也取得了类似的优异成绩。这些实验结果验证了我们的模型在跨境商品定价领域的有效性和可靠性。同时，我们的方法也为深度学习在国际贸易和经济战略优化领域的应用奠定了基础，具有重要的理论和实践意义。
{"title":"Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis","authors":"Lijuan Wang, Yijia Hu, Yan Zhou","doi":"arxiv-2408.12115","DOIUrl":"https://doi.org/arxiv-2408.12115","url":null,"abstract":"In the context of global trade, cross-border commodity pricing largely\u0000determines the competitiveness and market share of businesses. However,\u0000existing methodologies often prove inadequate, as they lack the agility and\u0000precision required to effectively respond to the dynamic international markets.\u0000Time series data is of great significance in commodity pricing and can reveal\u0000market dynamics and trends. Therefore, we propose a new method based on the\u0000hybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate\u0000prediction and optimization of cross-border commodity pricing strategies\u0000through in-depth analysis and optimization of time series data. Our model\u0000undergoes experimental validation across multiple datasets. The results show\u0000that our method achieves significant performance advantages on datasets such as\u0000UNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our\u0000model reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly\u0000better than other models. On the IMF and WITS datasets, our method also\u0000achieves similar excellent performance. These experimental results verify the\u0000effectiveness and reliability of our model in the field of cross-border\u0000commodity pricing. Overall, this study provides an important reference for\u0000enterprises to formulate more reasonable and effective cross-border commodity\u0000pricing strategies, thereby enhancing market competitiveness and profitability.\u0000At the same time, our method also lays a foundation for the application of deep\u0000learning in the fields of international trade and economic strategy\u0000optimization, which has important theoretical and practical significance.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192825","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}
Su Jung Jee, Kerstin Hötte, Caoimhe Ring, Robert Burrell
This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation.
{"title":"Making intellectual property rights work for climate technology transfer and innovation in developing countries","authors":"Su Jung Jee, Kerstin Hötte, Caoimhe Ring, Robert Burrell","doi":"arxiv-2408.12338","DOIUrl":"https://doi.org/arxiv-2408.12338","url":null,"abstract":"This study investigates the controversial role of Intellectual Property\u0000Rights (IPRs) in climate technology transfer and innovation in developing\u0000countries. Using a systematic literature review and expert interviews, we\u0000assess the role of IPRs on three sources of climate technology: (1)\u0000international technology transfer, (2) adaptive innovation, and (3) indigenous\u0000innovation. Our contributions are threefold. First, patents have limited impact\u0000in any of these channels, suggesting that current debates over IPRs may be\u0000directed towards the wrong targets. Second, trademarks and utility models\u0000provide incentives for climate innovation in the countries studied. Third,\u0000drawing from the results, we develop a framework to guide policy on how IPRs\u0000can work better in the broader context of climate and trade policies, outlining\u0000distinct mechanisms to support mitigation and adaptation. Our results indicate\u0000that market mechanisms, especially trade and demand-pull policies, should be\u0000prioritised for mitigation solutions. Adaptation differs, relying more on\u0000indigenous innovation due to local needs and low demand. Institutional\u0000mechanisms, such as finance and co-development, should be prioritised to build\u0000innovation capacities for adaptation.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192819","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}
We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, consumption, and monetary flow. Each agent type has distinct objectives, such as households seeking utility from consumption and the central bank targeting inflation and production. We define this multi-agent economic system using an OpenAI Gym-style environment, enabling agents to optimize their objectives through reinforcement learning. Standard multi-agent reinforcement learning (MARL) schemes, like independent learning, enable agents to learn concurrently but do not address whether the resulting strategies are at equilibrium. This study integrates the Policy Space Response Oracle (PSRO) algorithm, which has shown superior performance over independent MARL in games with homogeneous agents, with economic agent-based modeling. We use PSRO to develop agent policies approximating Nash equilibria of the empirical economic game, thereby linking to economic equilibria. Our results demonstrate that PSRO strategies achieve lower regret values than independent MARL strategies in our economic system with four agent types. This work aims to bridge artificial intelligence, economics, and empirical game theory towards future research.
{"title":"Empirical Equilibria in Agent-based Economic systems with Learning agents","authors":"Kshama Dwarakanath, Svitlana Vyetrenko, Tucker Balch","doi":"arxiv-2408.12038","DOIUrl":"https://doi.org/arxiv-2408.12038","url":null,"abstract":"We present an agent-based simulator for economic systems with heterogeneous\u0000households, firms, central bank, and government agents. These agents interact\u0000to define production, consumption, and monetary flow. Each agent type has\u0000distinct objectives, such as households seeking utility from consumption and\u0000the central bank targeting inflation and production. We define this multi-agent\u0000economic system using an OpenAI Gym-style environment, enabling agents to\u0000optimize their objectives through reinforcement learning. Standard multi-agent\u0000reinforcement learning (MARL) schemes, like independent learning, enable agents\u0000to learn concurrently but do not address whether the resulting strategies are\u0000at equilibrium. This study integrates the Policy Space Response Oracle (PSRO)\u0000algorithm, which has shown superior performance over independent MARL in games\u0000with homogeneous agents, with economic agent-based modeling. We use PSRO to\u0000develop agent policies approximating Nash equilibria of the empirical economic\u0000game, thereby linking to economic equilibria. Our results demonstrate that PSRO\u0000strategies achieve lower regret values than independent MARL strategies in our\u0000economic system with four agent types. This work aims to bridge artificial\u0000intelligence, economics, and empirical game theory towards future research.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192822","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：