Information Economics and Policy最新文献

We analyse the incentives of a data broker to sell consumer-level data that enable personalised pricing to compete with firms when only a fraction of consumers — centred around one firm that we label “central” — are profiled. The central firm can potentially benefit from the data more than the rival ones (“peripheral”). We show that the data broker may decide not to sell the dataset to the central firm and instead trade with its peripheral competitors. In particular, we identify a strategic reaction of competitors that want to prevent that data increase competition.

Music streaming platforms’ models for sharing revenues with content providers have been the subject of intense debate for nearly a decade. The dominating model involves pooling platform revenues and allocating these funds to songs based on a song's share of the total number of platform streams. Since this model has several controversial consequences, alternative models have been proposed. This paper uses a novel approach to assess the two most discussed models – the “user-centric” and the “artist-centric”. Our approach relies on a unique data set of 154,505 streaming platform users (890 million streams) and simulates how a large-scale implementation of these models may reallocate revenues across different songs and rightsholders. We disentangle the static effects of a transition to a “user-centric” or an “artist-centric” model across each of six different song characteristics. We then compare the results of the two models. We show that contrary to its objective, an artist-centric payment system does not significantly improve remuneration to professional artists while the user-centric payment system would generate more significant changes in revenue reallocation, mainly at the expense of Rap & Hip-hop songs, superstars and new releases. Finally, we analyze the positions of the various stakeholders with regard to each of them.

We consider a duopoly model with history-based price discrimination where firms inherit asymmetric shares of consumers that they might partly recognize according to the degree of information completeness. We analyze the impact of the amount of information on market configuration, profits and welfare. With high degrees of information completeness and sufficiently small asymmetries in the market shares, firms are more likely to use aggressive pricing strategies, both poaching rival's consumers. Otherwise, firms adopt different pricing strategies and price discrimination is enforced only by the smaller firm. Greater information completeness has a non-monotonic effect on profits and a decreasing effect on welfare. Finally, we show that the case with perfect information is a special case whose results do not hold when even a small degree of uncertainty is introduced in the game.

Data on websites that hosted job boards and CV banks in the U.S. from 2000 to 2011 reveal that websites imposed fewer restrictions (in terms of the duration of use) and lower fees for job searchers relative to employers. This asymmetry in the treatment (or the terms of use) changed as the relative scarcity of job searchers and job vacancies in the labor market in which the websites offered their services changed. Compared with job searchers, employers faced less stringent restrictions and lower fees when job searchers were scarce relative to job openings. These adjustments imply that the value of using an employment website changes with the number of potential users and the probability of finding a quality match. We find that these adjustments were most pronounced for websites that relied exclusively on employers and job searchers for their content (job ads and CVs). Whereas existing literature on the role that network size plays in intermediaries’ decision-making has focused on prices, our findings reveal that this focus can overlook other adjustments that affect the terms of use. Given that these adjustments in our context may result in longer periods during which CVs and job ads remain online, our findings suggest that the optimal design of intermediaries must include tools that help users sort through stale information.

Two federal programs in the United States subsidize internet access for rural healthcare providers, namely, Healthcare Connect Fund (HCF) and the Telecom Program. HCF uses a subsidy mechanism that strongly incentivizes healthcare providers to shop for faster or cheaper internet. Telecom does not incentivize shopping. Theoretically, this predicts that HCF must achieve faster or cheaper internet than Telecom. I empirically test this question and find that Telecom subsidy recipients pay 132-179% more than HCF subsidy recipients on similar internet plans. Evidence point to Telecom's poor incentive design as the root cause. Eliminating this price gap would save American taxpayers $143 million annually. The findings highlight the power of program design, showcase the unintended consequences of misaligned incentives, and encourage policymakers to thoroughly examine program features, the impact on consumer behavior, and the effect on competition and market outcomes.

This article studies the impact of algorithmic pricing on market competition when firms collect data to charge personalized prices to their past customers. Pricing algorithms offer to each firm a rich set of pricing strategies combining first and third-degree price discrimination: they can choose for each of their past customers whether to charge them personalized or homogeneous prices. The optimal targeting strategy of each firm consists in charging personalized prices to past customers with the highest willingness to pay and a homogeneous price to the remaining consumers, including past customers with a low valuation on whom a firm has information. This targeting strategy maximizes rent extraction while softening competition between firms compared to classical models where firms target all past customers. In turn, price-undercutting and poaching practices are not sustainable with behavior-based algorithmic pricing, resulting in greater industry profits.

We investigate patent litigation, settlements and R&D incentives on a market where two firms develop technologies in order to obtain patents and produce goods. Firms may sell IP rights to a Patent Assertion Entity (PAE) that acts as intermediary for patent monetization. We find that compared to simultaneous market entry, the effect of this so-called patent privateering is mitigated if firms enter sequentially. Furthermore, we show that privateering may decrease industry profits by distortion of R&D incentives even when there is no rent extraction by the PAE.

We study collusion sustainability in an infinitely repeated game in which firms might price discriminate, by offering personalized prices for the share of consumers they have information about. We do not impose any restrictions to the distribution of consumers and the product characteristic space. In such a general framework we show that when firms share their personal information about consumers, collusion is more difficult to sustain. We also show that, for intermediate levels of the discount factor, an antitrust policy aiming to discourage joint profit maximization and to maximize the consumer surplus should allow information sharing between firms. Instead, a ban on information sharing is optimal only if firms have imperfect information about their own consumers.

Technological development and better information systems potentially increase firms' abilities to use personalized pricing. Should firms take advantage of such an opportunity, or should they rather charge all consumers the same price (uniform pricing)? It might seem obvious that it is optimal for an individual firm to use personalized pricing; if it does, it can charge each consumer a price equal to her maximal willingness to pay. No other price plan can possibly yield higher profits. However, we show that if a firm is expected to use personalized pricing, then it effectively eliminates a rival's possibility to select values on non-price variables, such as horizontal differentiation, that can soften competition and increase profits for all firms. Once we take this into account, we might no longer expect that personalized pricing is a dominant strategy. Indeed, we show that it may be a dominant strategy for a firm to commit to uniform pricing prior to the rival's choice of non-price variables if it has the ability to do so.

This paper assesses the impact of mobile network sharing in Europe during the 2000-2019 period, looking at 140 mobile operators in 29 countries. We find that - consistent with economic theory - network sharing generated significant benefits for operators and consumers, including lower prices and improved network coverage and quality. This was driven by cost reductions, higher returns on investment and increased competition. These effects materialized heterogeneously, with the impact of network sharing depending on the type of sharing, the technology cycle in which it is entered into as well as the market position and size of the operators entering the agreement. This has important implications going forward as it shows that network sharing can play a vital role in the deployment of new 5G networks and that the technological and market specificity of each type of sharing agreement can significantly affect its outcomes.