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on Regulation |
| By: | Yukihiro Nishimura (Osaka University and CESifo) |
| Abstract: | Online markets like app stores are typically characterized by a monopoly who set prices on both sides — the prices of the network good (such as iPhone) and the commission fee to participating firms. There is an ongoing concerns on the welfare consequences of imperfect competition, where the antitrust authorities in the EU are keen about the monopolistic commission fee. With online apps as a representative example, this study investigates the welfare effects of price ceiling policies. The following results are shown. If the network-size externality on apps’ price is stronger than the app variety’s network externality, then, first, the price ceiling on the network good increases both the producer surplus of the app developers and the consumer surplus of the end-users. Second, in contrast, the price ceiling on the commission fee for the developers reduces the consumer surplus. The reverse proposition holds when the order of the strength of two network externalities is reversed. By the level of the unconstrained equilibrium commission fee, a regulator can identify which policy would make both consumers and developers better off. |
| Keywords: | Digital economy; Platform; Antitrust pricing; Network externality |
| JEL: | F23 L13 D85 K21 L86 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2505 |
| By: | Rémi Avignon; Claire Chambolle; Etienne Guigue; Hugo Molina |
| Abstract: | This article bridges monopoly, monopsony, and countervailing power theories to analyze their welfare implications in a vertical supply chain. We develop a bilateral monopoly model with bargaining that accommodates upstream monopsony and downstream monopoly power. In equilibrium, the ‘‘short-side rule'' applies: the quantity exchanged is determined by the firm willing to trade less. Welfare is maximized when each firm's bargaining power exactly countervails the other's market power. Otherwise, double marginalization arises in the form of double markdownization under excessive downstream bargaining power, or double markupization under excessive upstream bargaining power. We offer novel insights for price regulation and competition policy. |
| Keywords: | markups, markdowns, bargaining, countervailing buyer power, monopsony power, bilateral monopoly |
| JEL: | C78 D42 J42 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12026 |
| By: | Khan, Abhimanyu; Peeters, Ronald |
| Abstract: | We characterize stable market structures under price-competition in differentiated markets when multiple cartels may form. Market structures without cartelisation are never stable and always involve multiple small cartels, and, but for one knife-edge case, only involves multiple small cartels. Combined with the result that the unique stable market structure under quantity-competition is also characterised by multiple small cartels, this underscores the importance of considering the possibility of multiple cartels in competition policy. Comparing stable market structures under price and quantity competition, we find that prices and profits are higher under price-competition whenever the market is sufficiently differentiated or sufficiently concentrated. |
| Keywords: | multiple cartels; stable cartels; price competition; differentiated markets |
| JEL: | C70 D43 L13 |
| Date: | 2025–07–03 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125199 |
| By: | Zach Y. Brown; Alexander MacKay |
| Abstract: | We examine a model in which one firm uses a pricing algorithm that enables faster pricing and multi-period commitment. We characterize a coercive equilibrium in which the algorithmic firm maximizes its profits subject to the incentive compatibility constraint of its rival. By adopting an algorithm that enables faster pricing and (imperfect) commitment, a firm can unilaterally induce substantially higher equilibrium prices even when its rival maximizes short-run profits and cannot collude. The algorithmic firm can earn profits that exceed its share of collusive profits, and coercive equilibrium outcomes can be worse for consumers than collusive outcomes. In extensions, we incorporate simple learning by the rival, and we explore the implications for platform design. |
| JEL: | D43 L13 L40 L81 L86 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34070 |
| By: | Christopher R. Knittel; Juan Ramon L. Senga; Shen Wang |
| Abstract: | Data centers are among the fastest-growing electricity consumers, raising concerns about their impact on grid operations and decarbonization goals. Their temporal flexibility—the ability to shift workloads over time—offers a source of demand-side flexibility. We model power systems in three U.S. regions: Mid-Atlantic, Texas, and WECC, under varying flexibility levels. We evaluate flexibility's effects on grid operations, investment, system costs, and emissions. Across all scenarios, flexible data centers reduce costs by shifting load from peak to off-peak hours, flattening net demand, and supporting renewable and baseload resources. This load shifting facilitates renewable integration while improving the utilization of existing baseload capacity. As a result, the emissions impact depends on which effect dominates. Higher renewable penetration increases the emissions-reduction potential of data center flexibility, while lower shares favor baseload generation and may raise emissions. Our findings highlight the importance of aligning data center flexibility with renewable deployment and regional conditions. |
| JEL: | D61 L94 Q41 Q48 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34065 |
| By: | Christoph Carnehl; Anton Sobolev; Konrad Stahl; André Stenzel; Konrad O. Stahl |
| Abstract: | We study information design in a vertically differentiated market. Two firms offer products of ex-ante unknown qualities. A third party designs a system to publicly disclose information. More precise information guides consumers toward their preferred product but increases expected product differentiation, allowing firms to raise prices. Full disclosure of the product ranking alone suffices to maximize industry profits. Consumer surplus is maximized, however, whenever no information about the product ranking is disclosed, as the benefit of competitive pricing always dominates the loss from suboptimal choices. The provision of public information on product quality becomes questionable. |
| Keywords: | information design, vertical product differentiation, quality rankings, competition |
| JEL: | D43 D82 L13 L15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12038 |
| By: | F. Nguyen |
| Abstract: | The "free trial" followed by automatic renewal is a dominant business model in the digital economy. Standard models explain trials as a mechanism for consumers to learn their valuation for a product. We propose a complementary theory based on the rational inattention framework. Consumers know their valuation but face a cognitive cost to remember to cancel an unwanted subscription. We model this using a Shannon entropy-based cost of information processing, where a consumer's baseline attention level decays with the length of the trial period. This creates a novel trade-off for a monopolist firm: a longer trial increases "inattentive revenue" from consumers who fail to cancel, but it also lowers ex-ante consumer utility, making the initial offer less attractive. We show that this trade-off leads to an interior optimal trial length, even for products where value-learning is instantaneous. Our model, under standard assumptions about demand elasticity and the distribution of consumer valuations, generates sharp, testable predictions about the relationship between contract terms. We find that the optimal renewal price and trial length are complements: firms offering longer trials will also set higher post-trial prices. We analyze the impact of policies aimed at curbing consumer exploitation, such as "click-to-cancel" regulations. We show that such policies, by making attention effectively cheaper, lead firms to reduce trial lengths. The effect on price depends directly on the elasticity of demand from loyal subscribers. We also extend the model to include paid trials, showing that introductory prices and trial lengths act as strategic substitutes. Our framework provides a micro-founded explanation for common features of subscription contracts and offers a new lens through which to evaluate consumer protection policies in digital markets. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06422 |
| By: | Koski, Heli |
| Abstract: | Abstract This paper examines the effects of data privacy regulation on R&D investment in the pharmaceutical and biotechnology sectors. In these industries, access to personal health data is essential for innovation, particularly in clinical research. Leveraging a firm-level panel of the world’s top R&D investors from 2013 to 2023, we exploit the staggered implementation of major data protection regimes to estimate their causal impact. Using a dynamic event-study design, we find that stricter privacy regulation leads to a significant decline in R&D spending. By year four after implementation, treated firms reduced R&D investment by approximately 39 percent. The effects are heterogeneous: firms without foreign affiliates and small and medium-sized enterprises experience larger declines. Our findings suggest that privacy regulation may constrain the foundations of data-driven innovation and shape the geographic distribution of R&D activity. |
| Keywords: | Privacy regulation, R&D investment, Innovation, Pharmaceuticals, Biotechnology, Firm-level panel, GDPR, Compliance costs |
| JEL: | D22 K23 L65 O32 O38 |
| Date: | 2025–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:130 |
| By: | Leonardo Bursztyn; Matthew Gentzkow; Rafael Jimenez-Duran; Aaron Leonard; Filip Milojevic; Christopher Roth; Matthew Gentzkow |
| Abstract: | Market definition is essential for antitrust analysis, but challenging in settings with network effects, where substitution patterns depend on changes in network size. To address this challenge, we conduct an incentivized experiment to measure substitution patterns for TikTok, a popular social media platform. Our experiment, conducted during a time of high uncertainty about a potential U.S. TikTok ban, compares changes in the valuation of other social apps under individual and collective TikTok deactivations. Consistent with a simple framework, the valuations of alternative social apps increase more in response to a collective TikTok ban than to an individual TikTok deactivation. Our framework and estimates highlight that individual and collective treatments can even lead to qualitatively different conclusions about which alternative goods are substitutes. |
| Keywords: | markets, network goods, coordination, collective interventions |
| JEL: | D83 D91 P16 J15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12049 |
| By: | Tomaso Duso; Joseph E., Jr. Harrington; Carl Kreuzberg; Geza Sapi |
| Abstract: | Competition authorities increasingly rely on economic screening tools to identify markets where firms deviate from competitive norms. Traditional screening methods assume that collusion occurs through secret agreements. However, recent research highlights that firms can use public announcements to coordinate decisions, reducing competition while avoiding detection. We propose a novel approach to screening for collusion in public corporate statements. Using natural language processing, we analyze more than 300, 000 earnings call transcripts issued worldwide between 2004 and 2022. By identifying expressions commonly associated with collusion, our method provides competition authorities with a tool to detect potentially anticompetitive behavior in public communications. Our approach can extend beyond earnings calls to other sources, such as news articles, trade press, and industry reports. Our method informed the European Commission’s 2024 unannounced inspections in the car tire sector, prompted by concerns over price coordination through public communication. |
| Keywords: | communication, collusion, NLP, screening, text analysis |
| JEL: | C23 D22 L1 L4 L64 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12029 |
| By: | Winston Wei Dou; Itay Goldstein; Yan Ji |
| Abstract: | The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets. Alongside the benefits, it raises concerns for collusion. This study first develops a model to explore the possibility of collusion among informed speculators in a theoretical environment. We then conduct simulation experiments, replacing the speculators in the model with informed AI speculators who trade based on reinforcement-learning algorithms. We show that they autonomously sustain collusive supra-competitive profits without agreement, communication, or intent. Such collusion undermines competition and market efficiency. We demonstrate that two separate mechanisms are underlying this collusion and characterize when each one arises. |
| JEL: | D43 G10 G14 L13 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34054 |
| By: | Justin Katz; Hunt Allcott |
| Abstract: | We present a new model of competition between digital media platforms with targeted advertising. The model adds new insights around how user heterogeneity and overlap, along with user and advertiser substitution patterns, determine equilibrium ad load. We apply the model to evaluate the proposed separation of Facebook and Instagram. We estimate structural parameters using evidence on diminishing returns to advertising from a new randomized experiment and information on user overlap, diversion ratios, and price elasticity from earlier experiments. In counterfactual simulations, a Facebook-Instagram separation increases ad loads, transferring surplus from platforms and users to advertisers, with limited total surplus effects. |
| JEL: | D12 L1 L4 L86 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34028 |
| By: | Jamie Hansen-Lewis; Michelle M. Marcus |
| Abstract: | Targeting distributional impacts is gaining importance in the design of environmental policy. To achieve this, policy makers are adopting advances in air transport models to predict the benefits of air emissions regulation. These models offer policy makers accuracy in the spatial distribution of ambient air quality improvements for a given emissions reduction, but do not take into account behavioral responses to environmental policies. We consider how the failure to account for behavioral responses when making policy predictions may have important implications for the ultimate distributional impact of such policies. We compare the distributional impacts of maritime emission regulation predicted from the policy maker's air transport model to the realized distributional impacts. We then decompose the prediction error from two components: model error, whereby the predictions of air transport models fail to account for behavioral responses of polluting firms, and sorting error, whereby the targeted population migrates. |
| JEL: | Q5 Q51 Q52 Q53 Q58 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34055 |
| By: | Amine Allouah; Omar Besbes; Josu\'e D Figueroa; Yash Kanoria; Akshit Kumar |
| Abstract: | Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, vision-language-model (VLM) agents can parse webpages, evaluate products, and transact. This raises a fundamental question: what do AI agents buy, and why? We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent with a fully programmable mock marketplace to study this question. We first conduct basic rationality checks in the context of simple tasks, and then, by randomizing product positions, prices, ratings, reviews, sponsored tags, and platform endorsements, we obtain causal estimates of how frontier VLMs actually shop. Models show strong but heterogeneous position effects: all favor the top row, yet different models prefer different columns, undermining the assumption of a universal "top" rank. They penalize sponsored tags and reward endorsements. Sensitivities to price, ratings, and reviews are directionally human-like but vary sharply in magnitude across models. Motivated by scenarios where sellers use AI agents to optimize product listings, we show that a seller-side agent that makes minor tweaks to product descriptions, targeting AI buyer preferences, can deliver substantial market-share gains if AI-mediated shopping dominates. We also find that modal product choices can differ across models and, in some cases, demand may concentrate on a few select products, raising competition questions. Together, our results illuminate how AI agents may behave in e-commerce settings and surface concrete seller strategy, platform design, and regulatory questions in an AI-mediated ecosystem. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02630 |
| By: | Md Mahadi Hasan |
| Abstract: | I develop a theoretical model to examine how the rise of autonomous AI (artificial intelligence) agents disrupts two-sided digital advertising markets. Through this framework, I demonstrate that users' rational, private decisions to delegate browsing to agents create a negative externality, precipitating declines in ad prices, publisher revenues, and overall market efficiency. The model identifies the conditions under which publisher interventions such as blocking AI agents or imposing tolls may mitigate these effects, although they risk fragmenting access and value. I formalize the resulting inefficiency as an ``attention lemons" problem, where synthetic agent traffic dilutes the quality of attention sold to advertisers, generating adverse selection. To address this, I propose a Pigouvian correction mechanism: a per-delegation fee designed to internalize the externality and restore welfare. The model demonstrates that, for an individual publisher, charging AI agents toll fees for access strictly dominates both the `Blocking' and `Null (inaction)' strategies. Finally, I characterize a critical tipping point beyond which unchecked delegation triggers a collapse of the ad-funded digital market. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.22435 |
| By: | Sinan Aral; Seth G Benzell; Avinash Collis; Christos Nicolaides |
| Abstract: | We use representative, incentive-compatible online choice experiments involving 19, 923 Facebook, Instagram, LinkedIn, and X users in the US to provide the first large-scale, empirical measurement of local network effects in the digital economy. Our analysis reveals social media platform value ranges from $78 to $101 per consumer, per month, on average, and that 20-34% of that value is explained by local network effects. We also find 1) stronger ties are more valuable on Facebook and Instagram, while weaker ties are more valuable on LinkedIn and X; 2) connections known through work are most valuable on LinkedIn and least valuable on Facebook, and people looking for work value LinkedIn significantly more and Facebook significantly less than people not looking for work; 3) men value connections to women on social media significantly more than they value connections to other men, particularly on Instagram, Facebook and X, while women value connections to men and women equally; 4) white consumers value relationships with other white consumers significantly more than they value relationships with non-white consumers on Facebook while, on Instagram, connections to alters eighteen years old or younger are valued significantly more than any other age group-two patterns not seen on any other platforms. Social media platforms individually generate between $53B and $215B in consumer surplus per year in the US alone. These results suggest social media generates significant value, local network effects drive a substantial fraction of that value and that these effects vary across platforms, consumers, and connections. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04545 |
| By: | Gardner, Benjamin (University of Surrey); Walker, Ian (Swansea University); Daly, James Edward Michael; Brown, Julia (University of Portsmouth); Voss, Sofie; Pereira-Doel, Pablo (University of Surrey) |
| Abstract: | The UK water sector wants to embrace behavioural science to alleviate water scarcity. We co-created, with over 100 individuals from 60 organisations within the UK water sector, an agenda to identify behaviour change priorities. Three activities were undertaken: a workshop, online survey, and webinar. Written input (workshop, webinar) was synthesised to identify and thematise key questions. Survey data quantified the prioritisation of questions. Of seven themes identified, five focused on consumers (identifying behaviour change targets, understanding water use perceptions and behaviour, navigating public acceptability, developing behavioural solutions, contextualising behaviour change), and two on the water sector (building capacity and knowledge, moving beyond behaviour change). Fixing leaks, showering, and toilet-flushing were deemed the most important behaviours to change. Prioritised knowledge gaps focused on identifying behaviours to target, and delivering effective and acceptable water efficiency initiatives. The agenda can be used to guide future domestic water efficiency behaviour change research and action. |
| Date: | 2025–07–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:9j6zk_v3 |
| By: | Souryabrata Mohapatra (Indian Institute of Technology, Jodhpur); Amit Mitra (National Council of Applied Economic Research); Sanjib Pohit (National Council of Applied Economic Research) |
| Abstract: | India is facing a looming water crisis driven by rapid urbanisation, population growth, groundwater depletion, and climate variability. Despite receiving over 3, 800 billion cubic metres of annual precipitation, the country utilises less than one-third effectively due to uneven rainfall distribution, inadequate storage infrastructure, and poor water governance. Per capita water availability has declined sharply, while demand is projected to double by 2030. Agriculture remains the dominant water consumer, though industrial and domestic demands are rising rapidly. Groundwater over-extraction—particularly in states like Punjab, Rajasthan, and Delhi—has led to critical depletion, with 22% of groundwater blocks categorised as overexploited. Climate change further exacerbates water stress through erratic monsoons, glacial retreats, and increasing droughts and floods. This paper assesses systemic challenges across the water sector in India, from source sustainability to end-use efficiency. It also highlights policy evolution, institutional bottlenecks, and emerging governance initiatives. The study emphasises the urgent need for integrated water resource management, investment in wastewater recycling, demand-side interventions, and climate-resilient infrastructure to ensure water security. A multi-pronged strategy is essential for safeguarding livelihoods, supporting economic development, and achieving long-term sustainability. |
| Keywords: | Water scarcity, Groundwater depletion, Climate change, Urbanisation, Water governance and management, India |
| JEL: | O21 Q25 Q53 R11 |
| Date: | 2025–04–03 |
| URL: | https://d.repec.org/n?u=RePEc:nca:ncaerw:182 |
| By: | Coppola, Antonio; Clayton, Christopher |
| Abstract: | We examine whether and how granular, real-time predictive models should be integrated into central banks' macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between implementing models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a setting in which private portfolios react endogenously to the regulator's model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is optimally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction. |
| Date: | 2025–07–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:xwsje_v1 |