Apple iOS is a closed platform;Google Android is open. In this paper,we combine data on iOS and Android tablet sales with data on the top 1000 mobile applications from both platforms for five European countries and estimate a structural demand model.We find that the quality of applications affects tablet demand. We then run two counterfactuals. In line with our theory, the exclusion of low-quality applications is beneficial to tablet producers in both platforms but is more pronounced for Apple.Tablet producers in the platform with lower quality applications gain most from cross-platform app interoperability.
{"title":"Platform Competition in the Tablet PC Market: The Effect of Application Quality","authors":"T. Doan, Fabio M. Manenti, Franco Mariuzzo","doi":"10.2139/ssrn.3744115","DOIUrl":"https://doi.org/10.2139/ssrn.3744115","url":null,"abstract":"Apple iOS is a closed platform;Google Android is open. In this paper,we combine data on iOS and Android tablet sales with data on the top 1000 mobile applications from both platforms for five European countries and estimate a structural demand model.We find that the quality of applications affects tablet demand. We then run two counterfactuals. In line with our theory, the exclusion of low-quality applications is beneficial to tablet producers in both platforms but is more pronounced for Apple.Tablet producers in the platform with lower quality applications gain most from cross-platform app interoperability.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"422 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120977866","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}
Sharing markets have been associated with several unintended consequences, and policymakers have formulated a range of interventions. As a response, platform-owners resort to a wide range of strategies. We examine the impact of algorithmic regulation on both matchings, as well as market exit in the largest home-sharing market using a quasi-natural experiment. We find that algorithmic regulation led to both a decrease in matches, as well as an increase in the likelihood of market exit for the affected listings. We provide evidence that not all listings experience same effects; listings owned by hosts who own reputation badges experience a greater drop in matches. In contrast, we find that listings owned by hosts who own reputation badges are not highly likely to exit the market than other listings. We discuss the ability of sharing platforms to exercise control over market design, as well as implications for policymakers and market designers.
{"title":"To Share or Not to Share? Assessing the Impact of Algorithmic Regulation in a Peer-to-Peer Market","authors":"Shagun Tripathi, Harris Kyriakou","doi":"10.2139/ssrn.3741933","DOIUrl":"https://doi.org/10.2139/ssrn.3741933","url":null,"abstract":"Sharing markets have been associated with several unintended consequences, and policymakers have formulated a range of interventions. As a response, platform-owners resort to a wide range of strategies. We examine the impact of algorithmic regulation on both matchings, as well as market exit in the largest home-sharing market using a quasi-natural experiment. We find that algorithmic regulation led to both a decrease in matches, as well as an increase in the likelihood of market exit for the affected listings. We provide evidence that not all listings experience same effects; listings owned by hosts who own reputation badges experience a greater drop in matches. In contrast, we find that listings owned by hosts who own reputation badges are not highly likely to exit the market than other listings. We discuss the ability of sharing platforms to exercise control over market design, as well as implications for policymakers and market designers.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131403721","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}
Problem definition: This paper addresses the question whether or not self-learning algorithms can learn to collude instead of compete against each other, without violating existing competition law. Academic/practical relevance: This question is practically relevant (and hotly debated) for competition regulators, and academically relevant in the area of analysis of multi-agent data-driven algorithms. Methodology: We construct a price algorithm based on simultaneous-perturbation Kiefer–Wolfowitz recursions. We derive theoretical bounds on its limiting behavior of prices and revenues, in the case that both sellers in a duopoly independently use the algorithm, and in the case that one seller uses the algorithm and the other seller sets prices competitively. Results: We mathematically prove that, if implemented independently by two price-setting firms in a duopoly, prices will converge to those that maximize the firms’ joint revenue in case this is profitable for both firms, and to a competitive equilibrium otherwise. We prove this latter convergence result under the assumption that the firms use a misspecified monopolist demand model, thereby providing evidence for the so-called market-response hypothesis that both firms’ pricing as a monopolist may result in convergence to a competitive equilibrium. If the competitor is not willing to collaborate but prices according to a strategy from a certain class of strategies, we prove that the prices generated by our algorithm converge to a best-response to the competitor’s limit price. Managerial implications: Our algorithm can learn to collude under self-play while simultaneously learn to price competitively against a ‘regular’ competitor, in a setting where the price-demand relation is unknown and within the boundaries of competition law. This demonstrates that algorithmic collusion is a genuine threat in realistic market scenarios. Moreover, our work exemplifies how algorithms can be explicitly designed to learn to collude, and demonstrates that algorithmic collusion is facilitated (a) by the empirically observed practice of (explicitly or implicitly) sharing demand information, and (b) by allowing different firms in a market to use the same price algorithm. These are important and concrete insights for lawmakers and competition policy professionals struggling with how to respond to algorithmic collusion.
{"title":"Learning to Collude in a Pricing Duopoly","authors":"J. Meylahn, A. V. Boer","doi":"10.2139/ssrn.3741385","DOIUrl":"https://doi.org/10.2139/ssrn.3741385","url":null,"abstract":"Problem definition: This paper addresses the question whether or not self-learning algorithms can learn to collude instead of compete against each other, without violating existing competition law. Academic/practical relevance: This question is practically relevant (and hotly debated) for competition regulators, and academically relevant in the area of analysis of multi-agent data-driven algorithms. Methodology: We construct a price algorithm based on simultaneous-perturbation Kiefer–Wolfowitz recursions. We derive theoretical bounds on its limiting behavior of prices and revenues, in the case that both sellers in a duopoly independently use the algorithm, and in the case that one seller uses the algorithm and the other seller sets prices competitively. Results: We mathematically prove that, if implemented independently by two price-setting firms in a duopoly, prices will converge to those that maximize the firms’ joint revenue in case this is profitable for both firms, and to a competitive equilibrium otherwise. We prove this latter convergence result under the assumption that the firms use a misspecified monopolist demand model, thereby providing evidence for the so-called market-response hypothesis that both firms’ pricing as a monopolist may result in convergence to a competitive equilibrium. If the competitor is not willing to collaborate but prices according to a strategy from a certain class of strategies, we prove that the prices generated by our algorithm converge to a best-response to the competitor’s limit price. Managerial implications: Our algorithm can learn to collude under self-play while simultaneously learn to price competitively against a ‘regular’ competitor, in a setting where the price-demand relation is unknown and within the boundaries of competition law. This demonstrates that algorithmic collusion is a genuine threat in realistic market scenarios. Moreover, our work exemplifies how algorithms can be explicitly designed to learn to collude, and demonstrates that algorithmic collusion is facilitated (a) by the empirically observed practice of (explicitly or implicitly) sharing demand information, and (b) by allowing different firms in a market to use the same price algorithm. These are important and concrete insights for lawmakers and competition policy professionals struggling with how to respond to algorithmic collusion.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131056522","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 consider a firm who sells a single product with finite inventory over a finite horizon via dynamic pricing. The market size is a polynomial function of cumulative historic sales. The firm does not know the coefficients in the market size function before the start of the season and must learn it over time. The firm aims at finding a pricing policy that yields as much revenue as possible. We show that the firm's revenue is upper bounded by her optimal revenue in a setting that she perfectly knew all coefficients in the market size function ex ante and the system is deterministic (fluid model). For this fluid model, we show that by replacing prices with sales quantities as the decision variables, the problem becomes a convex program that can be efficiently solved. We propose a maximum likelihood estimate - reoptimized (MR) policy. Under this policy, in each period, the firm performs learning and optimization jobs. In the learning job, the firm uses the maximum likelihood estimate approach to form a point estimate of unknown coefficients. In the optimization job, the firm resolves the fluid model with updated information on remaining inventory, remaining horizon and the estimate of the unknown coefficients. We establish an upper bound of the regret of our policy for the regime that the initial inventory and the length of the horizon are proportionally scaled up.
{"title":"Dynamic Pricing in an Unknown and Sales-dependent Evolving Marketplace","authors":"Yiwei Chen, Fangzhao Zhang","doi":"10.2139/ssrn.3740107","DOIUrl":"https://doi.org/10.2139/ssrn.3740107","url":null,"abstract":"We consider a firm who sells a single product with finite inventory over a finite horizon via dynamic pricing. The market size is a polynomial function of cumulative historic sales. The firm does not know the coefficients in the market size function before the start of the season and must learn it over time. The firm aims at finding a pricing policy that yields as much revenue as possible. \u0000 \u0000We show that the firm's revenue is upper bounded by her optimal revenue in a setting that she perfectly knew all coefficients in the market size function ex ante and the system is deterministic (fluid model). For this fluid model, we show that by replacing prices with sales quantities as the decision variables, the problem becomes a convex program that can be efficiently solved. \u0000 \u0000We propose a maximum likelihood estimate - reoptimized (MR) policy. Under this policy, in each period, the firm performs learning and optimization jobs. In the learning job, the firm uses the maximum likelihood estimate approach to form a point estimate of unknown coefficients. In the optimization job, the firm resolves the fluid model with updated information on remaining inventory, remaining horizon and the estimate of the unknown coefficients. We establish an upper bound of the regret of our policy for the regime that the initial inventory and the length of the horizon are proportionally scaled up.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121745252","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}
Abstract This paper proposes a model of the ridesourcing market in presence of traffic congestion and with the provision of both solo and pooling services. Our analysis of the first-best solution shows that, under a highly congested scenario, the ridesourcing platform may enjoy non-negative profits. However, when congestion is low, the ridesourcing market must be subsidized due to low marginal costs of operation. This mirrors previous findings in the traditional taxi literature. We also demonstrate that a commission cap on the solo service combined with a congestion toll (however small) on ridesourcing vehicles can induce any desired, sustainable equilibrium under the assumption of homogeneous value of travel time and sufficient supply of homogeneous drivers. Furthermore, numerical experiments suggest that the most important problem that a regulator should address when faced with a monopoly may not be that of congestion but rather that of market power. Indeed, when congestion is high, similar to previous findings in the literature, decisions by the monopolist tend to mirror that of the regulator. This is because customers on the platform must also bear the congestion cost, which hurts the platform’s revenues. Additionally, numerical examples reveal that, even when accounting for heterogeneity in the value of travel time, the commission cap is able to achieve the second-best–whether combined with a toll or not. This confirms the effectiveness of the commission cap strategy illustrated in previous literature and provides decision makers with a simple, non-intrusive mechanism for regulating the market.
{"title":"Regulating Ridesourcing Services with Product Differentiation and Congestion Externality","authors":"D. Vignon, Yafeng Yin, Jintao Ke","doi":"10.2139/ssrn.3531372","DOIUrl":"https://doi.org/10.2139/ssrn.3531372","url":null,"abstract":"Abstract This paper proposes a model of the ridesourcing market in presence of traffic congestion and with the provision of both solo and pooling services. Our analysis of the first-best solution shows that, under a highly congested scenario, the ridesourcing platform may enjoy non-negative profits. However, when congestion is low, the ridesourcing market must be subsidized due to low marginal costs of operation. This mirrors previous findings in the traditional taxi literature. We also demonstrate that a commission cap on the solo service combined with a congestion toll (however small) on ridesourcing vehicles can induce any desired, sustainable equilibrium under the assumption of homogeneous value of travel time and sufficient supply of homogeneous drivers. Furthermore, numerical experiments suggest that the most important problem that a regulator should address when faced with a monopoly may not be that of congestion but rather that of market power. Indeed, when congestion is high, similar to previous findings in the literature, decisions by the monopolist tend to mirror that of the regulator. This is because customers on the platform must also bear the congestion cost, which hurts the platform’s revenues. Additionally, numerical examples reveal that, even when accounting for heterogeneity in the value of travel time, the commission cap is able to achieve the second-best–whether combined with a toll or not. This confirms the effectiveness of the commission cap strategy illustrated in previous literature and provides decision makers with a simple, non-intrusive mechanism for regulating the market.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"32 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993672","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}
Sharing platforms such as Airbnb provide rich information on the good or service to be exchanged, as well as elaborate mechanisms to protect against fraud. Thus, one could argue that contracts concluded via such platforms achieve a high level of completeness. According to economic theory, the identity of transaction partners should therefore be more or less irrelevant.
Yet, these platforms often also provide an elaborate rating system and reveal considerable information about the transaction partners. These features are often explicitly described as tools to increase trust between the transaction partners, which would not be important in a complete contract setting.
In this paper, we empirically analyze whether personal information about hosts provided in Airbnb actually influences prices on that platform using two different data sets. Results indicate that partner-specific information in fact has only a comparatively weak influence on prices, and that the importance of premise-specific vs. partner-specific information varies with the type of premise being rented.
{"title":"In Whose Bed Shall I Sleep Tonight? The Impact of Transaction-Specific vs. Partner-Specific Information on Pricing in a Sharing Platform","authors":"Ayşegül Engin, R. Vetschera","doi":"10.2139/ssrn.3732749","DOIUrl":"https://doi.org/10.2139/ssrn.3732749","url":null,"abstract":"Sharing platforms such as Airbnb provide rich information on the good or service to be exchanged, as well as elaborate mechanisms to protect against fraud. Thus, one could argue that contracts concluded via such platforms achieve a high level of completeness. According to economic theory, the identity of transaction partners should therefore be more or less irrelevant.<br><br>Yet, these platforms often also provide an elaborate rating system and reveal considerable information about the transaction partners. These features are often explicitly described as tools to increase trust between the transaction partners, which would not be important in a complete contract setting.<br> <br>In this paper, we empirically analyze whether personal information about hosts provided in Airbnb actually influences prices on that platform using two different data sets. Results indicate that partner-specific information in fact has only a comparatively weak influence on prices, and that the importance of premise-specific vs. partner-specific information varies with the type of premise being rented.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"24 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956833","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}
Over the last two decades, grassroots altruism, enabled through online platforms such as DonorsChoose.org, has resulted in the successful funding of numerous essential public school projects across the country. While such channels become critical fundraising mechanisms, there is an unintended possibility of the crowding out of these sources by governmental initiatives that aim to address public school welfare and quality of education. In this study, with a focus on major public policy announcements, we examine whether there is an unintended effect of external measures, such as the signing of the Every Student Succeeds Act (ESSA), on grassroots altruism, as observed on online philanthropy platforms. We surmise that, in such platforms, donors could become complacent and take comfort in the cognizance of an external agency addressing the problems they care about — we term this the “savior effect”. Importantly, from our analysis of panel data on an education crowdfunding platform, we find (a) a decline in donations toward public school projects on the platform, and (b) that donations become more local, disproportionately impacting schools with high concentrations of low-income and minority students, which receive fewer instructional resources to begin with. Our work has important policy implications for public schools, donor communities, and online fundraising platforms.
{"title":"Unintended Consequences: The Effect of Education Policy Announcements on Online Philanthropy","authors":"Anqi Wu, Aravinda Garimella, Ramanath Subramanyam","doi":"10.2139/ssrn.3779413","DOIUrl":"https://doi.org/10.2139/ssrn.3779413","url":null,"abstract":"Over the last two decades, grassroots altruism, enabled through online platforms such as DonorsChoose.org, has resulted in the successful funding of numerous essential public school projects across the country. While such channels become critical fundraising mechanisms, there is an unintended possibility of the crowding out of these sources by governmental initiatives that aim to address public school welfare and quality of education. In this study, with a focus on major public policy announcements, we examine whether there is an unintended effect of external measures, such as the signing of the Every Student Succeeds Act (ESSA), on grassroots altruism, as observed on online philanthropy platforms. We surmise that, in such platforms, donors could become complacent and take comfort in the cognizance of an external agency addressing the problems they care about — we term this the “savior effect”. Importantly, from our analysis of panel data on an education crowdfunding platform, we find (a) a decline in donations toward public school projects on the platform, and (b) that donations become more local, disproportionately impacting schools with high concentrations of low-income and minority students, which receive fewer instructional resources to begin with. Our work has important policy implications for public schools, donor communities, and online fundraising platforms.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131484373","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}
Problem Definition: Many digital platforms provide a search environment for consumers to evaluate sellers' products. We investigate a strategic platform's preference in search pattern (parallel pattern or sequential pattern) to keep in check consumers' search behavior and sellers' price and assortment reactions. Academic/Practical Relevance: Although both parallel and sequential patterns are prevalent in the online shopping environment, few studies have considered the platform's preferences in these search patterns, and implications in relevant operations management problems---sellers' assortment decisions in our paper. Methodology: We use the multinomial logit choice model to analytically explore the platform's preference in search pattern in anticipation that a specific pattern will affect the interactions between consumers and sellers. Consumers optimally choose the number of sellers to visit and the amount of product attributes to evaluate, and sellers optimally choose their prices and assortment levels, with all decisions being affected by which pattern the platform selects. Results: In our benchmark model with exogenous assortment level, our results show that the platform prefers parallel (sequential) pattern when the search cost is small (large) or when the assortment level is high (low). However, when the assortment level is a decision, the platform's preference will be altered qualitatively; that is, the platform prefers parallel (sequential) pattern when the search cost is large (small). We have identified several novel effects that are built off the fundamental difference between parallel and sequential patterns and use them to explain the platform's search-pattern preference. Interestingly, our paper shows that the platform can strategically use operational means (assortment prevention effect) and marketing means (pricing prevention effect) to manipulate consumers' search to maximize its profit. Managerial Implications: Our analytical predictions are consistent with several interesting observations in practice and shed some light on how a strategic platform designs its search environment and monetizes assortment management service.
{"title":"Parallel or Sequential? Platforms' Search-Pattern Preference: The Role of Assortment","authors":"Qingwei Jin, Lin Liu, Yi Yang","doi":"10.2139/ssrn.3721973","DOIUrl":"https://doi.org/10.2139/ssrn.3721973","url":null,"abstract":"Problem Definition: Many digital platforms provide a search environment for consumers to evaluate sellers' products. We investigate a strategic platform's preference in search pattern (parallel pattern or sequential pattern) to keep in check consumers' search behavior and sellers' price and assortment reactions. Academic/Practical Relevance: Although both parallel and sequential patterns are prevalent in the online shopping environment, few studies have considered the platform's preferences in these search patterns, and implications in relevant operations management problems---sellers' assortment decisions in our paper. Methodology: We use the multinomial logit choice model to analytically explore the platform's preference in search pattern in anticipation that a specific pattern will affect the interactions between consumers and sellers. Consumers optimally choose the number of sellers to visit and the amount of product attributes to evaluate, and sellers optimally choose their prices and assortment levels, with all decisions being affected by which pattern the platform selects. Results: In our benchmark model with exogenous assortment level, our results show that the platform prefers parallel (sequential) pattern when the search cost is small (large) or when the assortment level is high (low). However, when the assortment level is a decision, the platform's preference will be altered qualitatively; that is, the platform prefers parallel (sequential) pattern when the search cost is large (small). We have identified several novel effects that are built off the fundamental difference between parallel and sequential patterns and use them to explain the platform's search-pattern preference. Interestingly, our paper shows that the platform can strategically use operational means (assortment prevention effect) and marketing means (pricing prevention effect) to manipulate consumers' search to maximize its profit. Managerial Implications: Our analytical predictions are consistent with several interesting observations in practice and shed some light on how a strategic platform designs its search environment and monetizes assortment management service.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875422","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 last decade, new technologies have led to a boom in real-time pricing. I analyze the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a counterfactual with uniform pricing, surge pricing increases total welfare by 1.59% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 5.25% of gross revenue, whereas driver surplus and platform profits decrease by 1.81% and 1.77% of gross revenue, respectively. Riders at all income levels benefit, while disparities in driver surplus are magnified.
{"title":"Who Benefits from Surge Pricing?","authors":"Juan-Camilo Castillo","doi":"10.2139/ssrn.3245533","DOIUrl":"https://doi.org/10.2139/ssrn.3245533","url":null,"abstract":"In the last decade, new technologies have led to a boom in real-time pricing. I analyze the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a counterfactual with uniform pricing, surge pricing increases total welfare by 1.59% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 5.25% of gross revenue, whereas driver surplus and platform profits decrease by 1.81% and 1.77% of gross revenue, respectively. Riders at all income levels benefit, while disparities in driver surplus are magnified.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369148","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 develop an economic model of autonomous vehicle (AV) ride-hailing markets in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and an unlimited potential supply of human drivers (HVs). We analyze market outcomes under two dispatch platform designs (common platform vs. independent platforms) and two levels of AV competition (monopoly AV vs. competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform design and the level of competition. In the extreme case, we show if AVs and HVs operate on independent dispatch platforms and there is a monopoly AVs supplier, then prices are even higher than they are in a pure HV market. When AVs are introduced on a common dispatch platform, we show that whether the equilibrium price is reduced depends on the level of AV competition. If AVs are owned by a monopoly firm, then the equilibrium price is the same as in a pure HV market. In fact, the only market design that leads to unambiguously lower prices in all demand scenarios is when AVs and HVs operate on a common dispatch platform and the AV supply is competitive. Our results illustrate the critical role market design and competition plays in realizing potential welfare gains from AVs.
{"title":"Autonomous Vehicle Market Design","authors":"Zhen Lian, G. V. van Ryzin","doi":"10.2139/ssrn.3716491","DOIUrl":"https://doi.org/10.2139/ssrn.3716491","url":null,"abstract":"We develop an economic model of autonomous vehicle (AV) ride-hailing markets in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and an unlimited potential supply of human drivers (HVs). We analyze market outcomes under two dispatch platform designs (common platform vs. independent platforms) and two levels of AV competition (monopoly AV vs. competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform design and the level of competition. In the extreme case, we show if AVs and HVs operate on independent dispatch platforms and there is a monopoly AVs supplier, then prices are even higher than they are in a pure HV market. When AVs are introduced on a common dispatch platform, we show that whether the equilibrium price is reduced depends on the level of AV competition. If AVs are owned by a monopoly firm, then the equilibrium price is the same as in a pure HV market. In fact, the only market design that leads to unambiguously lower prices in all demand scenarios is when AVs and HVs operate on a common dispatch platform and the AV supply is competitive. Our results illustrate the critical role market design and competition plays in realizing potential welfare gains from AVs.","PeriodicalId":150569,"journal":{"name":"IO: Theory eJournal","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128150804","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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