|
on Industrial Organization |
| By: | Herbert Dawid; Ali Kashefi; Gerd Muehlheusser |
| Abstract: | Should platforms be held legally responsible for harm caused by products or services offered by third-party sellers? We address this question in a novel setting in which product safety (the likelihood of accidents) is determined by third-party sellers, while the consumer price is set by a monopolistic platform that pays sellers a commission. Relevant examples include platforms providing ride-hailing services (e.g. Uber and Lyft) or health care services (e.g. Teladoc). Sellers decide whether or not to become active on the platform, and consumers' willingness to pay for the platform good increases with the number of active sellers (platform externality). When an accident occurs, the harm is divided between the platform and the respective seller according to a liability rule set ex ante by a welfare-maximizing policymaker. Sellers' incentive to provide high product safety is driven by market demand, which in turn is determined by the platform's pricing scheme, balancing the trade-off between market revenue and expected liability costs. The platform may optimally set a price below the monopoly level to boost demand, thereby triggering active sellers to provide high product safety (market channel). The socially optimal liability rule is dichotomous: it either assigns no liability to the platform, or the maximum liability share for which the platform exploits the market channel to induce high product safety from the sellers. Holding the platform liable is socially desirable only when it induces lower consumer prices. |
| Keywords: | platform liability, consumer pricing, product safety, product liability |
| JEL: | K13 L13 L15 L86 D42 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13033 |
| By: | Juan-Jose Ganuza (Comision Nacional de los Mercados y la Competencia (CNMC), Spain); Ivan Rendo (Toulouse School of Economics, Toulouse, France) |
| Abstract: | Piracy is central to digital markets, where identical content can be accessed legally or through unauthorized channels. While easier piracy seems likely to intensify competition and lower prices, it also reduces profitability and discourages entry. We develop a circular-city model with free entry in which consumers jointly choose content and access mode. Stronger enforcement raises prices for a fixed number of firms but also attracts competitors. Prices rise with enforcement in less competitive markets but can follow an inverted U in more competitive markets, where entry eventually pushes prices down. Extensions show that piracy can also weaken competition and raise prices by attracting consumers most willing to switch between legal providers. |
| Keywords: | competition; piracy; entry; digital markets |
| JEL: | L13 L82 K42 D43 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2602 |
| By: | Byung-Cheol Kim (Culverhouse College of Business, University of Alabama, Tuscaloosa, AL, USA); Keeyoung Rhee (Department of Economics, Sungkyunkwan University, Seoul, Korea) |
| Abstract: | Firms increasingly outsource innovation to AI platforms serving their competitors, allowing one client's problem solving to improve what the platform delivers to another. We study a duopoly in which firms choose between in-house innovation and a shared AI platform. Cross-client learning occurs when both firms use the platform. Stronger learning can initially support joint use, yet eventually destabilize it as each firm's problem solving increasingly benefits its rival. Too many or too few firms may adopt the platform relative to a coordinated benchmark that maximizes their combined payoff subject to symmetric effort. Blocking cross-client learning can reduce competitive harm yet leave the firms collectively worse off. |
| Keywords: | shared AI platforms; outsourcing; coopetition; cross-client learning; technology adoption |
| JEL: | D43 L22 L24 O32 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2601 |
| By: | Juan Camilo Castillo (Department of Economics, University of Pennsylvania, Philadelphia, Pennsylvania, and NBER); Alejandro Sabal (Department of Economics, University of Chicago, Chicago, Illinois); Jaume Vives-i-Bastida (Department of Economics, New York University, New York, New York) |
| Abstract: | Delivery platforms provide an easy way for small restaurants to reach a large customer base with little upfront investment. However, small restaurants often face high commissions, while large, anchor restaurants receive preferential rates. We study competition in the delivery app industry and the economic forces behind this market structure. We develop a model in which platforms set commissions for small restaurants and negotiate over listing and commissions with anchor restaurants. Restaurants then set prices, and consumers choose which platforms to install and which restaurants to order from. We estimate our model using order-level data from a major European delivery app. We then run counterfactual simulations to understand what drives high commissions, measure how surplus is split among market participants, and evaluate the effects of additional platform entry. |
| Keywords: | delivery platforms; two-sided markets; bargaining; exclusivity; anchor restaurants |
| JEL: | L14 L83 L86 C78 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2606 |
| By: | Joshua S. Gans |
| Abstract: | How much liability should AI providers bear when their services enable both attack and defence? Liability can improve welfare while increasing harm. Providers sell a common input to productive users, attackers and defenders. Within a defended contest, a higher common price reduces effort without changing attack success or attacker profits, saving resources and improving the target's security payoff. Compensation weakens defence and raises attacker profits. Optimal liability balances these effects against productive exclusion. Greater competition can lower optimal liability; every such decline must end at an outcome retaining defence. With cybersecurity access fixed, monopoly can warrant partial liability but never full liability when provision is worthwhile. When guardrails preserving productive uses are available, strong competition favours universal guarding socially but encourages unilateral removal at insufficient liability. At a fixed provider count, sufficiently many productive users ensure a pure equilibrium with universal guarding under high liability. A universal-guarding requirement makes liability redundant. Under monopoly, adoption follows a unique liability threshold, while zero liability remains uniquely optimal for a range of parameters with sufficiently many productive users. |
| JEL: | D62 K13 L13 L51 L86 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35828 |
| By: | Jonathan Becker; Corina Boar; Virgiliu Midrigan |
| Abstract: | Importers are few and large, have higher labor productivity and pass through cost changes to prices incompletely. We study optimal tariffs in a model consistent with these facts. Firms pay a fixed cost to import and charge markups that increase with size. Market power implies that importers are too few and too small relative to the efficient allocations. Tariffs amplify this distortion. We derive a formula that relates the optimal tariff not only to the foreign export supply elasticity but also to how much distortions amplify the effect of trade costs on welfare. In our calibrated economy the optimal tariff is negative and decreases with country size. |
| JEL: | E10 F12 F13 L11 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35829 |
| By: | Federico Boffa (Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy); Chiara Farronato (Harvard Business School, Boston, MA, USA); Alessandro Fedele (Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy); Eugenio Levi (Link Campus University, Rome, Italy); Andrea Mantovani (TBS Business School, Toulouse, France) |
| Abstract: | Large language models (LLMs) are increasingly used by consumers to search for information, compare alternatives, and obtain purchasing advice. As conversational AI systems progressively complement traditional search engines and recommender systems, they may influence consumer choice by determining which products receive visibility and attention. Despite their growing importance, little is known about how LLMs construct product recommendations, whether they concentrate attention on a narrow set of products, or whether vertically integrated providers favor affiliated products and services. This paper conducts an audit study of leading LLMs, including ChatGPT, Gemini, Claude, DeepSeek, Grok, and Mistral. Using standardized prompts and repeated queries, we collect recommendations across a broad range of products and service categories that differ in market characteristics and the presence of vertically integrated products. We characterize recommendations along several dimensions, including breadth, first-position concentration, prominence, best-overall selections, and personalization. We find substantial heterogeneity across models in the breadth and structure of their recommendations. At the same time, first-position recommendations are highly concentrated in a substantial subset of markets. In 20 of the 100 product and service categories, a single product is listed first in more than three quarters of responses. We also find that some LLMs are likely to recommend products affiliated with their providers. |
| Keywords: | large language models; empirical audit; product recommendations; AI; algorithmic bias |
| JEL: | L15 L41 L86 D83 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2609 |