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on Economic Design |
| By: | Yeon-Koo Che; Olivier Tercieux |
| Abstract: | Top Trading Cycles (TTC) is Pareto efficient and strategy-proof and explicitly uses agents' priorities. Although TTC favors higher-priority agents in each round, we show that this priority advantage vanishes as the market grows large under a canonical random model of preferences and priorities. In the limit, TTC produces assignments with virtually the same incidence of justified envy as Random Serial Dictatorship (RSD) -- a mechanism entirely blind to priorities. This stark asymptotic equivalence implies that TTC effectively fails to satisfy standard fairness criteria in large markets, casting significant doubt on its practical appeal for balancing efficiency and fairness. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.10819 |
| By: | Florian Brandl; Warut Suksompong; Nicholas Teh |
| Abstract: | We consider the fair allocation of indivisible goods with binary valuations. In this setting, the maximum Nash welfare rule, the leximin rule, and all additive welfarist rules with a strictly concave function coincide. We show that for any number of agents, this rule is the only rule that satisfies envy-freeness up to one good, strategyproofness, neutrality, minimal completeness, and invariance under disapproving unassigned goods (IDU). Moreover, we present an alternative characterization for two agents, where we replace IDU with non-redundancy and resource-monotonicity. In both characterizations, all axioms are necessary. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.10064 |
| By: | Bergemann, Dirk; Bojko, Marek; Duetting, Paul; Paes Leme, Renato; Xu, Haifeng; Zuo, Song |
| Abstract: | We study mechanism design when agents have private preferences and private information about a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions. To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that hey achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is affine in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate. We demonstrate applications to digital advertising auctions and large language model (LLM)-based mechanisms, where user engagement naturally reveals relevant information. |
| Keywords: | Large Language Models |
| JEL: | D47 D82 D83 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20227 |
| By: | Bergemann, Dirk; Bonatti, Alessandro; Wu, Nick |
| Abstract: | In digital advertising, auctions determine the allocation of sponsored search, sponsored product, or display advertisements. The bids in these auctions for attention are largely generated by auto-bidding algorithms that are driven by platform-provided data. We analyze the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms. First, we consider the equilibrium bidding behavior of an individual advertiser who controls the auto-bidding algorithm through the choice of their budget. Second, we examine the interaction when all bidders use budget-controlled bidding algorithms. Finally, we derive the bidding algorithm that maximizes the platform revenue while ensuring that all advertisers continue to participate. |
| Keywords: | Data; Advertising; Competition; Auctions |
| JEL: | D44 D82 D83 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20263 |
| By: | Eliaz, Kfir; Eilat, Ran |
| Abstract: | We incorporate negative reciprocity into strategic information transmission, examining how a receiver's response to perceived manipulation influences optimal information design. In one setting, greater signal inaccuracy increases the likelihood that the receiver disregards it; in another, inaccuracy shifts the receiver's preferences unfavorably for the sender. In both cases, the revelation principle fails, yet we characterize the set of posterior beliefs on which an optimal signal is supported. While full revelation may be optimal in one setting, it is never so in the other. Our findings connect information design with behavioral economics, highlighting the implications of design-dependent preferences. |
| Keywords: | Negative-reciprocity; Information design |
| JEL: | D82 D91 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20295 |
| By: | Yuan Deng; Yilin Li; Wei Tang; Hanrui Zhang |
| Abstract: | We study the limits of third-degree price discrimination when the production cost is Bayesian and private to the seller, generalizing the seminal work of Bergemann, Brooks and Morris (2015). The rough setup is the following: A monopoly seller sets different prices for buyers in different "segments" of the market so as to maximize seller surplus. Different ways in which the aggregate market is decomposed into segments lead to different welfare outcomes, i.e., (seller surplus, buyer surplus) pairs. When the production cost is Bayesian, the region of achievable welfare outcomes can exhibit complex shapes beyond the clean characterization by Bergemann, Brooks and Morris for the case with a fixed cost. We show that with a Bayesian cost, this region coincides with a proper projection of a polytope defined by a polynomial number of linear constraints, the essential ones of which correspond to flow conservation in a "discounted" flow network. As a result, we give a polynomial-time algorithm that computes optimal market segmentations in terms of any linear combination of the seller surplus and the buyer surplus. En route, we establish the following structural property: Any market can be written as a convex combination of "extremal markets" in a way preserving the seller surplus and the buyer surplus. These extremal markets are piecewise equal-surplus with respect to different possible costs, generalizing a similar notion introduced by Bergemann, Brooks and Morris when the cost is fixed. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.12615 |
| By: | Antonio Nicol\`o; Pietro Salmaso; Riccardo D. Saulle |
| Abstract: | In many matching markets, agents care not only about their own partners but also about the matches formed by others. With externalities, stability depends on what agents believe would happen after a deviation. We introduce rationalizable conjectures: beliefs that survive iterated elimination, in the spirit of rationalizability in non-cooperative games. These beliefs define conjecture-rationalizable stability, a solution concept that always exists, extends Gale--Shapley stability, and coincides with it when externalities are absent. We also introduce rationalizable matchings, a non-equilibrium counterpart, and show that every conjecture-rationalizable stable matching is rationalizable. In matching with couples, our concept yields non-empty predictions even when standard stability is vacuous. Finally, we provide an epistemic foundation: rationalizability is behaviorally implied by pairwise rationality and common belief in pairwise rationality, while conjecture-rationalizable stability additionally requires belief correctness. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.28028 |
| By: | Bergemann, Dirk; Bonatti, Alessandro; Smolin, Alex |
| Abstract: | We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints. Users with similar aggregate value-scale characteristics choose similar levels of fine-tuning and token consumption. The optimal mechanism can be implemented through menus of two-part tariffs, with higher markups for more intensive users. Our results rationalize observed industry practices such as tiered pricing based on model customization and usage levels. |
| Keywords: | Large Language Models |
| JEL: | D47 D82 D83 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20226 |
| By: | Kiho Yoon |
| Abstract: | Consignment auctions, in which bidders first receive free initial endowments of a good and must then consign them to a subsequent uniform price auction, are often used in emissions allowance trading for the environmental regulation of greenhouse gas emissions. We study consignment auctions where many asymmetric bidders have flat demands up to their respective quantity constraints. We first characterize the equilibrium outcome and then examine the effects of initial endowments and total supply. If bidders' initial endowments increase or the total supply decreases, the equilibrium price increases whereas the social welfare and the auctioneer's revenue may increase or decrease. In particular, the revenue may increase even though fewer units remain in the hands of the auctioneer since an increase in initial endowments can prevent the low price equilibrium resulting from demand reduction. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.03648 |
| By: | Dizon-Ross, Rebecca; Zucker, Ariel |
| Abstract: | Personalizing policies can theoretically increase their effectiveness. However, personalization is difficult when individual types are unobservable and the preferences of policymakers and individuals are not aligned, which could cause individuals to misreport their type. Mechanism design offers a strategy to overcome this issue: offer an “incentive-compatible†menu of policy choices designed to induce participants to select the variant intended for their type. Using a field experiment that personalized incentives for exercise among 6, 800 adults with diabetes and hypertension in urban India, we show that personalizing with an incentive-compatible choice menu substantially improves program performance, increasing the treatment effect of incentives on exercise by 80% without increasing program costs relative to a one-size-fits-all benchmark. Mechanism design achieves similar performance to personalizing with an extensive set of observable variables, but without the high data requirements or the risk that participants might manipulate their observables. |
| Keywords: | Incentives |
| JEL: | I12 D82 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20113 |
| By: | Omar Inverso; Emilio Tuosto; Dragisa Zunic |
| Abstract: | This paper argues that AI-agent alignment in markets should not be understood only as a property of agents, but also as a property of the interaction infrastructure in which agents act. In financial markets, this infrastructure is the market core: the rule system that determines how orders enter, interact, match, persist, and stabilize. If this fundamental interaction layer allows or rewards undesired behaviour, then higher-level alignment of agents may be insufficient. We propose to view fundamental market design as a layer of AI-agent alignment. Alongside the important work of computational economics in modelling agents, strategies, and learning, we focus on a complementary but more fundamental layer: the formal modelling of the market core itself. Market design, especially at the level of the core mechanism, can benefit from a rigour characteristic of theoretical computer science. This gives a transparent-box model of the market, whose core properties can be formally specified and reasoned about. It also lets us treat the trading venue not as a static order book, but as a computational process combining resident orders with incoming order flow, and ask which computational model, perhaps yet unknown, naturally lies at its core. This perspective is especially relevant for markets populated by adaptive or AI agents. Such agents may learn what the mechanism rewards, including speed, delay, liquidity provision, or manipulation. These behaviours are not only properties of individual agents, but may emerge from the agent-mechanism system. We therefore argue that transparent formal models of market cores can support incentive-oriented analysis and the design of mechanisms in which desirable behaviours are structurally favoured and undesirable behaviours are harder to sustain. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.09702 |
| By: | Antonio Cabrales; Wenhao Cheng |
| Abstract: | This paper studies how organizations should jointly design evaluation rules and assign workers when performance depends on both effort and non-discretionary advantage. Agents choose effort in positions linked by a competition network, while their effective advantage depends on own type and spillovers through a second network. The planner chooses both the assignment and the effort weight in evaluation. Equilibrium effort rises with a position's Katz-Bonacich centrality and falls with effective advantage. The optimal evaluation rule generally differs from true output. When effort is more important in production, the planner lowers the effort weight and uses negative assortative assignment to strengthen incentives. When advantage is more important, the planner raises the effort weight and uses positive assortative assignment to exploit spillovers. We also study a constraint requiring assignments to be pairwise stable, which creates an output loss depending on the intensity of competition. |
| Keywords: | relative performance evaluation, worker assignment, organizational design, incentives; contests, network games, peer effects, spillovers, assortative matching |
| JEL: | D23 D85 C72 J33 M52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12816 |
| By: | Ge, Houtian; Gomez, Miguel; Jablonski, Rebecca; Nie, Xiaodong |
| Abstract: | Public food procurement contracts are commonly awarded through competitive sealed-bid procedures in which vendors strategically determine bid prices while competing for institutional food supply contracts. However, procurement outcomes depend not only on underlying costs but also on strategic vendor responses within competitive bidding environments. This paper develops an integrated empirical and structural framework to analyze strategic bidding behavior in public lettuce procurement. First, a lognormal bid-distribution model is estimated in which conditional bid distributions vary according to procurement costs, local sourcing status, vendor size, and lettuce product category. Second, the estimated probability density and cumulative distribution functions are incorporated into a Bayes Nash equilibrium model of first-price procurement auctions to derive optimal equilibrium bid prices under alternative competition scenarios. Results indicate substantial cost pass-through into submitted bids and significant heterogeneity across vendor types and lettuce categories. Small vendors submit substantially higher bids than comparable large vendors. Local sourcing is associated with modestly higher bid prices, although the estimated effect is not statistically significant after controlling for procurement costs, vendor size, and product categories. Increased bidder participation substantially reduces equilibrium markups through intensified competitive pressure. More broadly, the study demonstrates how empirically estimated bid distributions can be integrated with structural auction theory to evaluate strategic vendor behavior in public food procurement markets. The paper contributes an empirically calibrated Bayes Nash equilibrium framework for analyzing strategic bidding behavior and equilibrium pricing in first-price public food procurement auctions. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404368 |
| By: | Zi Yang Kang; Mitchell Watt |
| Abstract: | This paper studies how topping up -- allowing recipients of in-kind transfers to supplement subsidized consumption in a private market -- affects optimal redistribution. Consumers can access a competitive private market, while a social planner offers an alternative nonlinear price schedule. We show that the effect of topping up depends on the correlation between redistributive priority and demand. When the correlation is positive, topping up does not affect the optimal mechanism. When the correlation is negative, topping up weakens screening and reduces redistribution. At the extensive margin, topping up reduces the set of environments in which intervention is optimal. At the intensive margin, topping up weakly reduces both the scope of a free public option and the mass of consumers served, and shifts redistribution away from the consumers with the highest redistributive priority. We characterize the optimal mechanisms and show how topping up changes the comparative statics of optimal redistribution with respect to redistributive priorities. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.28919 |
| By: | Furkan Sezer |
| Abstract: | We study information design when a designer with commitment shapes the information of strategically interacting, far-sighted agents whose actions drive a persistent, controlled Markov state. We introduce the Markov Bayes correlated equilibrium (Markov BCE), the controlled-Markov generalisation of the BCE of Bergemann and Morris (2016), characterised by a dynamic obedience condition that adds a continuation-value term to the static one and reduces to it when actions cannot move the state. Recommending actions is without loss; the designer's problem is recursive in the agents' promised continuation utilities and is solved by a set-valued backward-induction algorithm whose optimum exists and lies between the no-disclosure and first-best values. For linear-quadratic-Gaussian payoffs the obedience condition becomes a covariance condition with a modified interaction matrix, and the stationary case reduces to an algebraic Riccati equation. When agents instead learn the transition, we identify the rent an agent earns from a model of the dynamics sharper than the designer anticipates: it is non-negative, zero at the known-dynamics benchmark, and deterred only by building slack into obedience. Under persistent excitation the cumulative rent grows logarithmically as heterogeneous agents' estimates converge. Two worked examples, in congestion and resource coordination, together with a numerical study illustrate the theory. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.04308 |
| By: | Chengcheng Wang; Zexin Ye |
| Abstract: | As firms increasingly adopt AI-powered pricing algorithms, a key and urgent policy concern is how to regulate the potential algorithmic collusion. This paper approaches the regulatory question through the lens of information design and examines how different disclosure rules, committed to by a third-party intermediary, shape learning outcomes when firms delegate pricing to Q-learning algorithms under stochastic demand. We analyze three disclosure rules: no disclosure, full disclosure, and upper censorship. Upper censorship, which truthfully reveals low-demand states while pooling high-demand ones, delivers higher profits than full disclosure, consistent with theoretical predictions. However, we uncover a profit reversal: when the discount factor is high, no disclosure yields higher profits than full disclosure, whereas when the discount factor is low, full disclosure performs better. This pattern is exactly the opposite of what classical collusion theory predicts. Overall, these findings show that Q-learning agents respond systematically to the information structure and further suggest that restricting information sharing may backfire when algorithms are sufficiently patient, highlighting the need to reassess regulatory approaches in AI-mediated markets. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.04345 |
| By: | Geleta, Solomon |
| Abstract: | Organic transition support programs are an incentive-design problem under asymmetric information. Policymakers cannot observe farm-specific transition costs, yield outcomes, or compliance capabilities. As a result, uniform and static payment structures can generate information rents for low-cost adopters, fail to attract high-cost but potentially high-value participants, and create moral hazard during the multi-year transition period when compliance is costly to monitor. We develop an integrated bio-economic and mechanism-design framework to evaluate how federal organic transition programs affect adoption incentives, transition costs, compliance behavior, and program efficiency. Farm-level linear-programming models are calibrated to USDA Census of Agriculture microdata for conventional operations and certified organic farms to generate farm-specific conversion-cost distributions, which serve as the type space for mechanism evaluation. Programs are assessed on six criteria: individual-rationality satisfaction, incentive-compatibility compliance, participation rates, targeting efficiency, information rents, and deadweight loss relative to the full-information first-best benchmark. Static support scenarios, including certification cost share through the Organic Certification Cost Share Program (OCCSP) and practice-based conservation payments through the Environmental Quality Incentives Program (EQIP), do not achieve break-even within the transition period for the majority of farms in our simulations. Cumulative four-year returns remain negative under all static scenarios evaluated. A theoretical dynamic mechanism, budget-matched to the Environmental Quality Incentives Program–Organic Transition Initiative (EQIP-OTI) over the three-year transition window and structured as a front-loaded, declining payment conditioned on verifiable transition milestones, achieves positive Year 1 returns, the highest targeting correlation, and the lowest rents-to-budget ratio among all evaluated configurations, outperforming all static programs on fiscal efficiency. The highest steady-state adoption rate (86.3 percent individual-rationality satisfaction) is achieved by combining market premiums with practice-based cost share, but this scenario also produces the largest deadweight loss (35.1 percent of first-best welfare) due to additive information rents. These results establish a quantitative efficiency-adoption tradeoff at the core of organic transition program design. Effective policy should use dynamic, declining payment structures as the core mechanism, offer sizeand production-type-differentiated contract menus to exploit observable heterogeneity, condition continuation on verifiable milestones rather than self-reported costs, and integrate financial transfers with technical assistance and market-development support. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404389 |
| By: | Decarolis, Francesco; Pellegrinetti, Tommaso; Rovigatti, Gabriele; Rovigatti, Michele; Shakhgildyan, Ksenia |
| Abstract: | This paper examines how proprietary algorithms used by dominant digital platforms create informational advantages in search auctions, reshaping market competition. Using experimental evidence and counterfactual simulations, we quantify the impact of algorithmic bidding on auction outcomes and competitive dynamics. Our findings reveal how platforms can leverage superior information to significantly improve their revenues, distorting competition and creating welfare losses for independent advertisers. We also show why platforms prefer selling a bidding algorithm service over directly selling data. These results highlight the need for greater scrutiny of algorithmic decision-making in platform markets, offering new insights for competition policy in digital economies. |
| Keywords: | Collusion |
| JEL: | C73 D82 D83 D18 D44 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19983 |
| By: | Mohammad Rashid; Hema Yoganarasimhan |
| Abstract: | Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes. We study this trade-off using a large-scale randomized field experiment on an Android app store, where over five million users are exposed to one through six sponsored slots. Increasing ad load raises revenue by up to 43%, but reduces total search conversions by up to 5% and daily engagement by up to 2.2%. These average effects mask substantial heterogeneity: additional slots generate large revenue gains for high-ad-conversion queries, but little or negative marginal revenue for low-conversion queries. The trade-off also shifts within query as advertiser composition changes, such as brand-advertiser presence. Motivated by these findings, we design and deploy a novel adaptive algorithm -- exploration-augmented Locally Adaptive Ad Load (e-LAAL). e-LAAL combines LAAL, a model-free query-level decision rule that updates ad-load recommendations using recent outcomes, with static exploration arms that maintain support and provide fixed-policy counterfactual benchmarks. We provide a finite-time dynamic-regret guarantee for the e-LAAL architecture. In a platform-level production deployment serving 22.3 million users and 77.6 million searches, e-LAAL improves the empirical revenue--conversion trade-off relative to deployed static benchmarks and outperforms uniform and historical query-dependent static benchmarks. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.14418 |
| By: | Jongjin Park; Hyungbin Park |
| Abstract: | This paper studies the recovery of uncertainty from dynamic sublinear valuation rules. A robust valuation assigns each payoff its worst-case expected value across plausible models under uncertainty and induces a dynamic sublinear valuation rule. While valuation rules are observable in practice, the underlying uncertainty structure is latent. First, we show that the latent uncertainty structure can be identified from an observed valuation rule and provide an explicit procedure for recovering it. Second, we develop the notion of time consistency for uncertainty structures as the uncertainty-side counterpart of time consistency in valuation. Third, we characterize all time-consistent uncertainty structures that represent a given valuation rule. Finally, we develop nonparametric estimators for recovering uncertainty from limited valuation data. These results overturn the traditional Knightian view that uncertainty is inherently non-measurable. Indeed, valuation contains sufficient information to identify, characterize, and statistically recover the uncertainty structures that generate it. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.29572 |
| By: | Enache, Andreea; Rhodes, Andrew |
| Abstract: | We consider a setting in which a platform matches buyers and sellers, who then wish to transact with each other multiple times. The platform charges fees for hosting transactions, but also offers convenience benefits. We consider two scenarios. In one scenario, all transactions must occur on the platform; in the other scenario, buyers and sellers can disintermediate the platform after the first transaction, and do subsequent transactions offline. We find that the platform reacts to disintermediation by using a ``front-loaded'' pricing scheme, whereby it charges more for earlier transactions. We also show that sometimes the platform is better off when disintermediation is possible---because it can use disintermediation to screen users' private information about their convenience benefits. Buyers are not necessarily better off when they can disintermediate, due to the way in which the platform adjusts its fees. |
| Keywords: | Platforms |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20298 |
| By: | De Fraja, Gianni; Sakovics, Jozsef |
| Abstract: | This paper is a theoretical analysis of the consequences of workplace discrimination. We prove that discrimination against a group at lower levels of the hierarchy affects the pay of members of the same group at higher levels, leading to a "pay gap" relative to non-discriminated workers. These spillovers in turn induce firms to alter the match between workers and jobs for the discriminated group, potentially leading to a "glass ceiling". The phenomenon can occur even in firms where "equal pay for equal jobs" appears to be adhered to. The explanation is based on the standard participation and incentive constraints: the need to compensate workers for the direct discrimination they suffer, to induce them to work, and the need to maintain pay differentials between job levels, to provide effort incentives. We end the paper showing that neither competition among workers, nor competition among firms for workers eliminates these spillovers. |
| Keywords: | Discrimination; Minorities; Glass ceiling; Gender pay gap; Earnings inequality; Principal-agent model; Mechanism design |
| JEL: | J71 J70 M52 M14 D82 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20056 |
| By: | David Imhof; Thierry Madi\`es; Martin Huber |
| Abstract: | This paper analyzes the internal organization and economic effects of a bid-rigging cartel in the road construction sector of the Swiss canton of Ticino, active from 1999 to 2005. Using exceptionally rich documentary evidence, we reconstruct how cartel members coordinated bids and allocated contracts under a formal agreement known as the 'convention'. We show that, despite the absence of side payments, the cartel implemented a cost-based allocation mechanism that closely approximated the first-best collusive outcome. Regression and machine-learning analyses indicate that observable cost proxies systematically predict both winning bids and bid rankings. The evidence further suggests that cartel members strategically mimicked competitive bidding behavior, allowing them to evade standard econometric detection methods. Using double machine learning, we estimate average overcharges of at least 45\%, and potentially substantially higher, highlighting the significant financial harm caused by this sophisticated form of collusion. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30470 |
| By: | Eliaz, Kfir; Spiegler, Ran |
| Abstract: | A monopolist curates a database for users seeking to learn a parameter's value: "nowcasters" focus on its current value, while "forecasters" target its long-run value. The monopolist designs a menu of contracts described by fees and data-access levels, balancing revenue and data-storage costs. The optimal menu offers full access to historical data, while current data is fully provided to nowcasters but may be withheld from forecasters. Compared to the social optimum, the monopolist oversupplies historical data, undersupplies current data, and may provide excessive data overall. |
| Keywords: | Data markets |
| JEL: | D42 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20299 |