In economies without monetary transfers, token systems serve as an alternative to sustain cooperation, alleviate free riding, and increase efficiency. This paper studies whether a token-based economy can be effective in marketplaces with thin exogenous supply. We consider a marketplace in which at each time period one agent requests a service, one agent provides the service, and one token (artificial currency) is used to pay for service provision. The number of tokens each agent has represents the difference between the amount of service provisions and service requests by the agent. We are interested in the behavior of this economy when very few agents are available to provide the requested service. Since balancing the number of tokens across agents is key to sustain cooperation, the agent with the minimum amount of tokens is selected to provide service among the available agents. When exactly one random agent is available to provide service, we show that the token distribution is unstable. However, already when just two random agents are available to provide service, the token distribution is stable, in the sense that agents' token balance is unlikely to deviate much from their initial endowment, and agents return to their initial endowment in finite expected time. Our results mirror the power of two choices paradigm in load balancing problems. Supported by numerical simulations using kidney exchange data, our findings suggest that token systems may generate efficient outcomes in kidney exchange marketplaces by sustaining cooperation between hospitals.
{"title":"The Power of Two in Token Systems","authors":"I. Ashlagi, Süleyman Kerimov, Omer Tamuz","doi":"10.2139/ssrn.4835080","DOIUrl":"https://doi.org/10.2139/ssrn.4835080","url":null,"abstract":"In economies without monetary transfers, token systems serve as an alternative to sustain cooperation, alleviate free riding, and increase efficiency. This paper studies whether a token-based economy can be effective in marketplaces with thin exogenous supply. We consider a marketplace in which at each time period one agent requests a service, one agent provides the service, and one token (artificial currency) is used to pay for service provision. The number of tokens each agent has represents the difference between the amount of service provisions and service requests by the agent. We are interested in the behavior of this economy when very few agents are available to provide the requested service. Since balancing the number of tokens across agents is key to sustain cooperation, the agent with the minimum amount of tokens is selected to provide service among the available agents. When exactly one random agent is available to provide service, we show that the token distribution is unstable. However, already when just two random agents are available to provide service, the token distribution is stable, in the sense that agents' token balance is unlikely to deviate much from their initial endowment, and agents return to their initial endowment in finite expected time. Our results mirror the power of two choices paradigm in load balancing problems. Supported by numerical simulations using kidney exchange data, our findings suggest that token systems may generate efficient outcomes in kidney exchange marketplaces by sustaining cooperation between hospitals.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122615","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}
Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share, while overlooking the influence on consumers’ perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand’s future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper. Through a consumer-facing automobile search platform, we collect data on new cars’ inventories of more than 20,000 dealerships in the United States from August 2020 to March 2021. We measure brand perceptions using 273,991 responses by in-market consumers collected by YouGov. To address endogeneity concerns, we model the effects of inventory and variety on perceived brand strength using three different empirical approaches: 1) high-dimensional fixed effects, 2) instrumental variables, and 3) causal forest. Across all analyses, we find that inventory has a positive effect on perceived brand strength but the main effect of product variety is not significant. Our second contribution is related to the fact that past research on inventory and variety does not, for the most part, investigate systematic heterogeneity due to brand- or consumer-specific factors that impact the effectiveness of inventory or variety; to help fill this gap in the literature, we investigate the role of two important and theoretically motivated moderators: consumer income and luxury status of the brand. We find that consumers’ income levels and brands’ luxury status negatively (positively) moderate the effects of product inventory (variety) on perceived brand strength. Our results have managerial implications for effective assortment planning under scarcity, determining the right range of product offerings for luxury versus non-luxury brands, optimizing the customer’s online browsing experience, and targeting advertisements based on brand and consumer characteristics.
{"title":"Impact of Inventory Levels and Product Variety on Consumers’ Perceptions of Brands","authors":"Koushyar Rajavi, Sina Golara, Sajad Modaresi","doi":"10.2139/ssrn.4793724","DOIUrl":"https://doi.org/10.2139/ssrn.4793724","url":null,"abstract":"Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share, while overlooking the influence on consumers’ perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand’s future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper. Through a consumer-facing automobile search platform, we collect data on new cars’ inventories of more than 20,000 dealerships in the United States from August 2020 to March 2021. We measure brand perceptions using 273,991 responses by in-market consumers collected by YouGov. To address endogeneity concerns, we model the effects of inventory and variety on perceived brand strength using three different empirical approaches: 1) high-dimensional fixed effects, 2) instrumental variables, and 3) causal forest. Across all analyses, we find that inventory has a positive effect on perceived brand strength but the main effect of product variety is not significant. Our second contribution is related to the fact that past research on inventory and variety does not, for the most part, investigate systematic heterogeneity due to brand- or consumer-specific factors that impact the effectiveness of inventory or variety; to help fill this gap in the literature, we investigate the role of two important and theoretically motivated moderators: consumer income and luxury status of the brand. We find that consumers’ income levels and brands’ luxury status negatively (positively) moderate the effects of product inventory (variety) on perceived brand strength. Our results have managerial implications for effective assortment planning under scarcity, determining the right range of product offerings for luxury versus non-luxury brands, optimizing the customer’s online browsing experience, and targeting advertisements based on brand and consumer characteristics.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124274","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 propose a deep learning algorithm for high dimensional optimal stopping problems. Our method is inspired by the penalty method for solving free boundary PDEs. Within our approach, the penalized PDE is approximated using the Deep BSDE framework proposed by cite{weinan2017deep}, which leads us to coin the term"Deep Penalty Method (DPM)"to refer to our algorithm. We show that the error of the DPM can be bounded by the loss function and $O(frac{1}{lambda})+O(lambda h) +O(sqrt{h})$, where $h$ is the step size in time and $lambda$ is the penalty parameter. This finding emphasizes the need for careful consideration when selecting the penalization parameter and suggests that the discretization error converges at a rate of order $frac{1}{2}$. We validate the efficacy of the DPM through numerical tests conducted on a high-dimensional optimal stopping model in the area of American option pricing. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm.
{"title":"Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems","authors":"Yunfei Peng, Pengyu Wei, Wei Wei","doi":"10.2139/ssrn.4839092","DOIUrl":"https://doi.org/10.2139/ssrn.4839092","url":null,"abstract":"We propose a deep learning algorithm for high dimensional optimal stopping problems. Our method is inspired by the penalty method for solving free boundary PDEs. Within our approach, the penalized PDE is approximated using the Deep BSDE framework proposed by cite{weinan2017deep}, which leads us to coin the term\"Deep Penalty Method (DPM)\"to refer to our algorithm. We show that the error of the DPM can be bounded by the loss function and $O(frac{1}{lambda})+O(lambda h) +O(sqrt{h})$, where $h$ is the step size in time and $lambda$ is the penalty parameter. This finding emphasizes the need for careful consideration when selecting the penalization parameter and suggests that the discretization error converges at a rate of order $frac{1}{2}$. We validate the efficacy of the DPM through numerical tests conducted on a high-dimensional optimal stopping model in the area of American option pricing. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124913","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 final version of the 2023 Merger Guidelines, which were issued in December 2023, is a vast improvement over an earlier draft—which indicates that the Agencies took the many comments that they received on a draft very seriously. These Guidelines break some new ground that older Guidelines did not address, and make many positive contributions, which this paper spells out. They are also excessively nostalgic for a past era, however, and this may explain their propen sity to treat empirical questions as issues of law: This is one way to insulate these Guidelines from further revision. The excessive reliance on one decision, Brown Shoe, is unfortunate—particularly since that decision has been so often repudiated, even by the Supreme Court itself. This paper pays particular attention to: the Guidelines’ treatment of structural triggers and direct measures of competitive effects; their aggressive position on potential competition mergers; their willingness to weigh a “trend” toward concentration as a factor; and their treatment of serial acquisitions. The Guidelines include a welcome new section on mergers involving multi-sided networks, although their view of networks is too one-sided; and the Guidelines also contain an expanded section on mergers with harmful effects on suppliers—including labor. The Guidelines’ treatment of market definition is likely to lead to underenforcement because they define markets too broadly. Finally, the Guidelines could have made better use of recent retrospective studies—many of which would have provided further support for the substantive positions that the Guidelines take.
{"title":"The 2023 Merger Guidelines: Law, Fact, and Method","authors":"Herbert Hovenkamp","doi":"10.2139/ssrn.4684465","DOIUrl":"https://doi.org/10.2139/ssrn.4684465","url":null,"abstract":"The final version of the 2023 Merger Guidelines, which were issued in December 2023, is a vast improvement over an earlier draft—which indicates that the Agencies took the many comments that they received on a draft very seriously. These Guidelines break some new ground that older Guidelines did not address, and make many positive contributions, which this paper spells out. They are also excessively nostalgic for a past era, however, and this may explain their propen sity to treat empirical questions as issues of law: This is one way to insulate these Guidelines from further revision. The excessive reliance on one decision, Brown Shoe, is unfortunate—particularly since that decision has been so often repudiated, even by the Supreme Court itself. This paper pays particular attention to: the Guidelines’ treatment of structural triggers and direct measures of competitive effects; their aggressive position on potential competition mergers; their willingness to weigh a “trend” toward concentration as a factor; and their treatment of serial acquisitions. The Guidelines include a welcome new section on mergers involving multi-sided networks, although their view of networks is too one-sided; and the Guidelines also contain an expanded section on mergers with harmful effects on suppliers—including labor. The Guidelines’ treatment of market definition is likely to lead to underenforcement because they define markets too broadly. Finally, the Guidelines could have made better use of recent retrospective studies—many of which would have provided further support for the substantive positions that the Guidelines take.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125775","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 evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design. We tested three incentive schemes: Flat (constant pay regardless of treatment quantity), Progressive (pay increases with the number of treatments), and Regressive (penalties for overtreatment) to assess their influence on the adoption and effectiveness of AI assistance. Our findings demonstrate that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned. Diagnostic accuracy improved by 17% to 37%, depending on the incentive scheme. Adoption of AI advice was high, with approximately half of the participants modifying their decisions based on AI input across all settings. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI's potential to mitigate non-monetary incentives and enhance social welfare. Our results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.
{"title":"Overcoming Medical Overuse with AI Assistance: An Experimental Investigation","authors":"Ziyi Wang, Lijia Wei, Lian Xue","doi":"10.2139/ssrn.4828970","DOIUrl":"https://doi.org/10.2139/ssrn.4828970","url":null,"abstract":"This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design. We tested three incentive schemes: Flat (constant pay regardless of treatment quantity), Progressive (pay increases with the number of treatments), and Regressive (penalties for overtreatment) to assess their influence on the adoption and effectiveness of AI assistance. Our findings demonstrate that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned. Diagnostic accuracy improved by 17% to 37%, depending on the incentive scheme. Adoption of AI advice was high, with approximately half of the participants modifying their decisions based on AI input across all settings. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI's potential to mitigate non-monetary incentives and enhance social welfare. Our results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126758","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}
Nicolás Ajzenman, Martin Ardanaz, Guillermo Cruces, German Feierherd, Ignacio Lunghi
Recent literature highlights a paradox in corruption prevention messaging: instead of reducing tolerance for corruption, such campaigns can inadvertently intensify it by priming the existence of corruption while failing to diminish citizens beliefs about government misbehavior. Building on Cheeseman and Peiffer (2022), which demonstrates that messages focused on combating corruption often backfire among individuals with preexisting negative perceptions of corruption, we posit that an effective strategy to mitigate backfiring involves shifting those pessimistic perceptions before delivering the corruption eradication messages. To test our hypothesis, we conducted a randomized survey experiment within the context of a major institutional reform to reduce tax agency corruption in Honduras. Results confirm the backfiring findings of previous literature, but also show that our approach effectively mitigates perceived corruption and diminishes the propensity for tax evasion, especially among skeptics.
{"title":"Unraveling the Paradox of Anticorruption Messaging: Experimental Evidence from a Tax Administration Reform","authors":"Nicolás Ajzenman, Martin Ardanaz, Guillermo Cruces, German Feierherd, Ignacio Lunghi","doi":"10.2139/ssrn.4795363","DOIUrl":"https://doi.org/10.2139/ssrn.4795363","url":null,"abstract":"Recent literature highlights a paradox in corruption prevention messaging: instead of reducing tolerance for corruption, such campaigns can inadvertently intensify it by priming the existence of corruption while failing to diminish citizens beliefs about government misbehavior. Building on Cheeseman and Peiffer (2022), which demonstrates that messages focused on combating corruption often backfire among individuals with preexisting negative perceptions of corruption, we posit that an effective strategy to mitigate backfiring involves shifting those pessimistic perceptions before delivering the corruption eradication messages. To test our hypothesis, we conducted a randomized survey experiment within the context of a major institutional reform to reduce tax agency corruption in Honduras. Results confirm the backfiring findings of previous literature, but also show that our approach effectively mitigates perceived corruption and diminishes the propensity for tax evasion, especially among skeptics.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126524","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}
E. Charalambakis, Federica Teppa, Athanasios Tsiortas
This article analyses the consumer’s decision to apply for credit and the probability of the credit being accepted in the euro area during a period characterized by the unprecedented concomitance of events and changing borrowing conditions linked to the global coronavirus disease 2019 (COVID-19) pandemic and the Russian invasion of Ukraine. We use data between 2020Q1 and 2023Q2 from the ECB’s Consumer Expectations Survey. We find that credit demand is the highest when the first lockdown ends and it drops when supportive monetary compensation schemes are implemented. There is evidence that constrained households are significantly less likely to apply for credit. Credit is more likely to be accepted under favourable borrowing conditions and after the approval of national recovery plans. We also find that demographic, economic factors, perceptions, and expectations are associated with the demand for credit and the credit grant.
{"title":"Consumer participation in the credit market during the COVID-19 pandemic and beyond","authors":"E. Charalambakis, Federica Teppa, Athanasios Tsiortas","doi":"10.2139/ssrn.4757772","DOIUrl":"https://doi.org/10.2139/ssrn.4757772","url":null,"abstract":"\u0000 This article analyses the consumer’s decision to apply for credit and the probability of the credit being accepted in the euro area during a period characterized by the unprecedented concomitance of events and changing borrowing conditions linked to the global coronavirus disease 2019 (COVID-19) pandemic and the Russian invasion of Ukraine. We use data between 2020Q1 and 2023Q2 from the ECB’s Consumer Expectations Survey. We find that credit demand is the highest when the first lockdown ends and it drops when supportive monetary compensation schemes are implemented. There is evidence that constrained households are significantly less likely to apply for credit. Credit is more likely to be accepted under favourable borrowing conditions and after the approval of national recovery plans. We also find that demographic, economic factors, perceptions, and expectations are associated with the demand for credit and the credit grant.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126383","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}
Salma Elomari-Kessab, Guillaume Maitrier, J. Bonart, J. Bouchaud
Understanding the micro-dynamics of asset prices in modern electronic order books is crucial for investors and regulators. In this paper, we use an order by order Eurostoxx database spanning over 3 years to analyze the joint dynamics of prices and order flow. In order to alleviate various problems caused by high-frequency noise, we propose a double coarse-graining procedure that allows us to extract meaningful information at the minute time scale. We use Principal Component Analysis to construct"microstructure modes"that describe the most common flow/return patterns and allow one to separate them into bid-ask symmetric and bid-ask anti-symmetric. We define and calibrate a Vector Auto-Regressive (VAR) model that encodes the dynamical evolution of these modes. The parameters of the VAR model are found to be extremely stable in time, and lead to relatively high $R^2$ prediction scores, especially for symmetric liquidity modes. The VAR model becomes marginally unstable as more lags are included, reflecting the long-memory nature of flows and giving some further credence to the possibility of"endogenous liquidity crises". Although very satisfactory on several counts, we show that our VAR framework does not account for the well known square-root law of price impact.
{"title":"Microstructure Modes -- Disentangling the Joint Dynamics of Prices & Order Flow","authors":"Salma Elomari-Kessab, Guillaume Maitrier, J. Bonart, J. Bouchaud","doi":"10.2139/ssrn.4831906","DOIUrl":"https://doi.org/10.2139/ssrn.4831906","url":null,"abstract":"Understanding the micro-dynamics of asset prices in modern electronic order books is crucial for investors and regulators. In this paper, we use an order by order Eurostoxx database spanning over 3 years to analyze the joint dynamics of prices and order flow. In order to alleviate various problems caused by high-frequency noise, we propose a double coarse-graining procedure that allows us to extract meaningful information at the minute time scale. We use Principal Component Analysis to construct\"microstructure modes\"that describe the most common flow/return patterns and allow one to separate them into bid-ask symmetric and bid-ask anti-symmetric. We define and calibrate a Vector Auto-Regressive (VAR) model that encodes the dynamical evolution of these modes. The parameters of the VAR model are found to be extremely stable in time, and lead to relatively high $R^2$ prediction scores, especially for symmetric liquidity modes. The VAR model becomes marginally unstable as more lags are included, reflecting the long-memory nature of flows and giving some further credence to the possibility of\"endogenous liquidity crises\". Although very satisfactory on several counts, we show that our VAR framework does not account for the well known square-root law of price impact.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126153","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}
Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategies for estimating idea production functions. We explore various methods, including naive approaches, linear regression, maximum likelihood estimation, and Bayesian inference, each suited to different data availability settings. Through case studies ranging from total factor productivity to software R&D, we show how to apply these methodologies in practice. Our synthesis provides researchers with guidance on strategies for characterizing idea production functions and highlights obstacles that must be addressed through further empirical validation.
{"title":"Estimating Idea Production: A Methodological Survey","authors":"Ege Erdil, T. Besiroglu, Anson Ho","doi":"10.2139/ssrn.4814445","DOIUrl":"https://doi.org/10.2139/ssrn.4814445","url":null,"abstract":"Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategies for estimating idea production functions. We explore various methods, including naive approaches, linear regression, maximum likelihood estimation, and Bayesian inference, each suited to different data availability settings. Through case studies ranging from total factor productivity to software R&D, we show how to apply these methodologies in practice. Our synthesis provides researchers with guidance on strategies for characterizing idea production functions and highlights obstacles that must be addressed through further empirical validation.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126763","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 article reviews the literature on the economic impacts of disasters caused by extreme weather and climate events to draw lessons on how societies can better manage these risks. While evidence that richer, better-governed societies suffer less and recover faster from climate extremes suggests adaptation, knowledge gaps remain, and little is known about the efficiency of specific adaptation actions. I review various “no or low” regrets adaptation options that are recommended when uncertainties over climate change impacts are high. I discuss how governments can play an important role in adaptation by directly providing public goods to manage disaster risks or by facilitating private agents’ adaptation responses but also highlight the political economy of policy and coordination failures.
{"title":"Extreme Weather Events and Climate Change: Economic Impacts and Adaptation Policies","authors":"Susana Ferreira","doi":"10.2139/ssrn.4691170","DOIUrl":"https://doi.org/10.2139/ssrn.4691170","url":null,"abstract":"This article reviews the literature on the economic impacts of disasters caused by extreme weather and climate events to draw lessons on how societies can better manage these risks. While evidence that richer, better-governed societies suffer less and recover faster from climate extremes suggests adaptation, knowledge gaps remain, and little is known about the efficiency of specific adaptation actions. I review various “no or low” regrets adaptation options that are recommended when uncertainties over climate change impacts are high. I discuss how governments can play an important role in adaptation by directly providing public goods to manage disaster risks or by facilitating private agents’ adaptation responses but also highlight the political economy of policy and coordination failures.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129063","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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