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CfP: Workshop on Economic Governance and Legitimacy

TILEC, the Tilburg Law and Economics Center, will be organizing a workshop on “Economic Governance and Legitimacy” at Tilburg University, the Netherlands, on May 19-20, 2022.

A foundational question for any economic governance system concerns the legitimacy of its rules, where legitimacy is defined as the degree to which individual citizens believe they have a moral obligation to obey the ruler (Bisin, Rubin, Seror, and Verdier, 2021). Obviously, if (most) people believe the ruler (president, queen, chieftain, dictator, association director, influencer, etc.) has the right to rule, ruling becomes cheaper, quicker, and more efficient. But what are the origins of legitimacy in political, legal, and social systems across the world? Why do some players have a lot of influence and are listened to by many followers, whereas others do not (even if their arguments or proposals may be better)?

During a multidisciplinary and discussion-intensive two-day on-site workshop, we aim to learn from theoretical, empirical, experimental, and conceptual papers addressing the topic from various angles.

Keynote addresses will cross disciplinary boundaries between economics and law (Gillian Hadfield, Toronto), sociology (Sonja Opper, Bocconi), political science (Gérard Roland, Berkeley), and religion studies (Jared Rubin, Chapman).

The deadline for paper submissions is January 16, 2022. Papers should be submitted in PDF format to TILECgovernance@tilburguniversity.edu. More details are in the call for papers and at the Workshop website.

“Consumers’ Privacy Choices in the Era of Big Data” forthcoming in Games and Economic Behavior

In 2013, Edward Snowden shocked the world by revealing large surveillance programs of US intelligence services. In 2012, Sebastian Dengler and I had started to think about privacy from an economic perspective. Of course, we were not the only ones, as this interim review article shows. It turned out to be a hard task to trade off the costs and benefits of privacy against other goods. Therefore, we are very happy that this work has now borne fruit.

Our paper, “Consumers’ Privacy Choices in the Era of Big Data” (working paper version), has just been accepted for publication in Games and Economic Behavior. There, combining Industrial Organization, Behavioral Economics, and insights about digital markets, we start from the observation that recent progress in information technologies provides sellers with detailed knowledge about consumers’ preferences, approaching perfect price discrimination in the limit. We construct a model where consumers with less strategic sophistication than the seller’s pricing algorithm face a trade-off when buying. They choose between a direct, transaction cost-free sales channel and a privacy-protecting, but costly, anonymous channel. We show that the anonymous channel is used even in the absence of an explicit taste for privacy if consumers are not too strategically sophisticated. This provides a micro-foundation for consumers’ privacy choices. Some consumers benefit but others suffer from their anonymization.

Video “Mandatory Data Sharing on Data-driven Markets: Why, When, and How?”

The University of Passau (Germany) dedicated a series of talks to the platform economy this summer. A diverse set of scholars had the opportunity (and time) to browse through various research projects and to point out connections and uncharted territories. In my contribution, now on video, I could tell the full story of the idea to implement mandatory sharing of user-generated data on data-driven markets: from economic theory via the development of a “test for data-drivenness,” its exemplification (by experimental testing with a search engine and in a representative consumer panel) up to the current draft of the Digital Markets Act and our proposal how to implement mandatory data sharing in practice.

The Nonprofit’s Dilemma

Nonprofit firms producing services that are of broad public concern — mission-driven organizations — are a key part of the economy in many countries, especially in “care” sectors (healthcare, childcare, care for the elderly…). They mostly pay lower wages than for-profit firms and often use low-powered incentive schemes, which has been explained by binding financial constraints and the threat to attract wrong worker types if wages are increased. Yet, they face higher labor turnover than for-profit firms, which is very costly.

Together with Yilong Xu, in a new, short discussion paper, I construct a simple model that reproduces these stylized facts, explains the high labor turnover of mission-driven organizations, and suggests a way out of this nonprofit’s dilemma, based on insights from the economic psychology literature. We construct testable empirical hypotheses and offer managerial and policy implications.

Post-Academic Training on Competition Law & Digital Regulation

The Tilburg Institute for Law, Technology, and Society (TILT) & the Tilburg Law and Economics Center (TILEC) jointly offer a post-academic training on Competition Law & Digital Regulation in January 2022. The program provides practitioners at law firms, in-house legal counsels, policy officials, or officials at regulatory authorities and sectoral bodies with a comprehensive overview of the legal issues relating to the application of competition law in the digital sector and the ongoing policy as well as legislative initiatives regarding the regulation of platforms and data.

The program is taught by a mix of distinguished practitioners who have direct experience of the rules and by an academic team whose members participate actively in the policy debates.

More information and registration is available here

“Governance of Data Sharing” forthcoming in Research Policy

The latest paper of our long-term project studying the economics of data-driven markets is concerned with implementing the policy solution that was developed by previous work: “Governance of Data Sharing: a Law & Economics Proposal” (co-authored with Inge Graef) has had quick uptake and will be published in a special issue on the “Governance of AI” at Research Policy.

To the best of our knowledge, this is the first actual design proposal how and with which governance structure (i.e. which allocation of rights and duties) mandatory data sharing can best be implemented. It is currently considered by EU authorities revising the proposal of the Digital Markets Act, a key piece of EU-legislation aiming to regulate online platforms.

More details (working paper version): To prevent market tipping, which inhibits innovation, there is an urgent need to mandate sharing of user information in data-driven markets. Existing legal mechanisms to impose data sharing under EU competition law and data portability under the GDPR are not sufficient to tackle this problem. Mandated data sharing requires the design of a governance structure that combines elements of economically efficient centralization with legally necessary decentralization. We identify three feasible options. One is to centralize investigations and enforcement in a European Data Sharing Agency (EDSA), while decision-making power lies with National Competition Authorities in a Board of Supervisors. The second option is to set up a Data Sharing Cooperation Network coordinated through a European Data Sharing Board, with the National Competition Authority best placed to run the investigation adjudicating and enforcing the mandatory data-sharing decision across the EU. A third option is to mix both governance structures and to task national authorities to investigate and adjudicate and the EU-level EDSA with enforcement of data sharing.

Member of EU Expert Group on the Online Platform Economy

The European Commission’s Digital Services and Platforms unit is responsible for the new key legislation regarding online platforms, namely the Digital Services Act and the Digital Markets Act (both proposed in December 2020). In 2018, they put up an expert group to the EU Observatory on the Online Platform Economy, which should advise the Commission on the main trends of the online platform economy and analyze potentially harmful practices there.

Now a set of new members has been appointed to the expert group, me included. I am looking forward to learn a lot and to discuss relevant issues with the group and Commission representatives.

New Working paper on Governance of Standard-Setting Organizations

Standard-setting organizations (SSOs) are a very interesting species of man-made contractual arrangements: They are usually nonprofits, i.e. they do not sell products or services and distribute profits to their owners. However, in contrast to many other clubs, their members are not individuals but profit-maximizing firms, i.e. organizations themselves. Moreover, often these firms have very heterogeneous interests. For instance, often some members are (upstream) innovators with patented technologies, whereas other members are (downstream) implementers, who take out a license of diverse technologies and sell products to final consumers. According to common wisdom among organizational economists, such heterogeneity in objectives should deteriorate the efficiency of SSOs (see, e.g. here). Nevertheless, many SSOs fulfill crucial roles as intermediaries in innovative industries. This opens the question, how they manage to square the circle.

In a new working paper, “Membership, Governance, and Lobbying  in Standard-Setting Organizations” (joint with Clemens Fiedler and Maria Larrain), we try to better understand the internal workings (aka governance) of SSOs. Specifically, inspired by recent empirical results, we construct a game-theoretic model, with which we study the incentives of heterogeneous innovators and implementers to join an SSO, which is endogenously formed. We also study the effect of SSO governance on membership incentives and on members’ lobbying efforts to get their technologies included in the standard. We show that, depending on parameter realizations, one of four equilibrium types arises uniquely. The results can reconcile existing evidence, especially that many SSO member firms are small. We show that raising the influence of implementers within the SSO increases the standard’s market coverage and lowers royalty rates but it erodes innovators’ incentives to contribute to the standard. We also show that both large innovators and large implementers have incentives to make the standard more inclusive, which decreases quality and damages smaller firms.

Mandatory Data Sharing: Development of a Test & Governance Structure

A year ago, a group of TILEC researchers (combining expertise in economics, law, and econometrics and teaming up with CentERdata’s competence in data science and consumer research) was charged by the German Federal Ministry of Finance (BMF) to develop a suitable indicator for the identification and delineation of data-driven markets and, based on this, approaches to data governance. In particular, the task was to develop a methodology for measuring the data-driven nature of a market (i.e., a test for data drivenness) and the market dominance of individual providers, to apply this procedure in a selected industry, and to explore a suitable data governance structure and possible regulatory implementation.

With recent and ongoing progress in the legislation and regulation of data-based industries, both at national and EU-levels (e.g. the Digital Markets Act and Digital Services Act), this project has become even more topical.

The German Tagesspiegel, a daily newspaper, has already reported about the results. The full research report (in German) is here. The first openly accessible research document is a working paper, titled “Governance of Data Sharing: a Law & Economics Proposal” (joint with Inge Graef). There is more to come.

The main results of the BMF-project are as follows:

  1. The developed econometric test for data-driven markets follows the basic question: how long does it take a provider who starts without user-generated data on user preferences and characteristics and hypothetically “does everything right”[1] to catch up with the competitor with the largest market share? If the answer is “less than 3-5 years,” a market is not (sufficiently) data-driven. If the answer is “longer than 5 years,” then the market is data driven. In the latter case, the feedback loop by which having more access to data leads to higher quality, which necessarily increases the market leader’s market share, is very strong. Without regulatory intervention, there is then no hope of a change in the market structure. This has a negative impact on the incentives for innovation of both potential market entrants and the market leader. Due to the great market power of the dominant provider, it leaves room for multiple abuses to the detriment of users/consumers.
  2. The test for data-driven markets consists of two parts: the assessment of the role of different features in shaping the demand of users and the assessment of the quality feedback loop. To illustrate its use in practice, the test for data drivenness was applied in the market for internet search engines. There, a discrete-choice experiment with 821 participants showed that both a reduction in the quality of the search results and an increase in the number of ads and the degree of personalization of the search engine have a significantly negative effect on user satisfaction. The negative evaluation of personalization implies a preference for the protection of their privacy. However, we found that respondents rated quality approximately twice as highly as the other two characteristics, personalization level and advertising (each on a 5-level scale). This shows the dominant importance of search engine quality compared to other product characteristics for user satisfaction (and therefore demand) in this market.
  3. Furthermore, the results show significant interactions of the degree of personalization with both the type of search query and the degree of transparency. The negative effect of the degree of personalization on user satisfaction was significantly stronger for a health-related search query than for a harmless search query — and significantly stronger if the privacy information was transparent (and not hidden).[2]
  4. In an experiment with the search engine Cliqz from Munich, the amount of user-generated data to which the search algorithm had access to in order to answer a user’s search query was artificially varied. It showed that giving a small search engine access to more user-generated data would greatly improve its search quality. This is especially true for rare search queries, regardless of the exact measure of search quality. For these more than 70% of all search queries, no quality saturation could be determined through access to more and more user-generated data. Human evaluators of the search results qualitatively confirmed these results based on machine-calculated quality measures of search engines: More user-generated data lead to higher quality for rare search queries.
  5. In summary, the test for data drivenness shows a clear result: the search engine market is data driven. With significantly less user-generated data than the leading search engine, it is impossible to achieve a market share on this market that comes close to the market leader, even in the medium term. Therefore, this market is not competitive.
  6. With regard to an appropriate governance structure for mandatory data sharing, we found that the existing legal mechanisms for enforcing a data-sharing obligation under EU competition law and for facilitating data portability under the GDPR are not sufficient.
  7. In any data-governance structure, regulators must perform three essential tasks: investigating potentially data-driven markets (i.e., performing the test for data-drivenness), deciding whether a market is data driven and exactly which data must be shared by whom, with whom, in what way (that is, evaluating the test result), and technically implementing and legally enforcing the data sharing obligation.
  8. Due to institutional limitations resulting from the EU Treaties, the design of the data-sharing obligation requires a governance structure that combines elements of an economically efficient centralization with a legally necessary decentralization of data sharing. Our analyses show three feasible governance structures:
    1. Relatively centralized: The investigation of a potentially data-driven market and the enforcement of the data-sharing obligation will be centralized in a new European Data Sharing Agency (EDSA), while the joint decision-making power of the national competition authorities will lie with a supervisory body.
    2. Decentralized: A Data Sharing Cooperation Network (DSCN) will be established, coordinated by a European Data Sharing Board, which will include the presidents of all 27 national competition authorities. The DSCN decides on the data-driven nature of a market. The national competition authority best placed to investigate a potentially data-driven market acts as the lead national competition authority (so-called Lead NCA), which investigates and enforces the data-sharing obligation throughout the EU.
    3. Mixed: The national competition authorities are charged with investigation (Lead NCA) and decision making (DSCN). The centralized EDSA is responsible for the enforcement of the data-sharing obligation.

Existing enforcement approaches in data protection and consumer law have already demonstrated the feasibility of such arrangements. By incorporating data protection and intellectual property considerations into the governance design itself, the governance structures proposed here offer a concrete approach to future data regulation that combines legal and economic insights and can be easily taken up by policy makers.

The report leads to the following policy implications:

  1. In data-driven markets, competitors of a dominant firm have no chance without political intervention to achieve a market share close to that of the market leader in the medium term. Therefore, we recommend the creation of new legal tools for regulating data-driven markets. Specifically, we recommend the introduction of mandatory data sharing of user-generated data.
  2. Because the market for search engines is data-driven (see result 5 above), we recommend the introduction of a data sharing obligation for user-generated data in this market.
  3. Regardless of a specific market, we recommend the following design principles for mandatory data sharing:
  4. Only raw data should have to be shared, which can be stored almost free of charge by the provider via the automated storage of the interaction between user and provider. The analysis of this data is the responsibility of each recipient. In the search engine market this corresponds to search log data.
  5. In a data-driven market, all providers with a market share of at least 30% should be obliged to share their user-generated data. This results in a maximum of three providers per market that have to share data. This number decreases the more the market is monopolized.
  6. On the receiving side, any organization that is active in the respective market or that can explain how it would serve the users of this market with the data should be given access to the shared data. This should apply regardless of the organizational form of the receiving party, that is, both to for-profit, non-profit and public organizations.
  7. On the one hand, our analysis of the available mechanisms of competition and data protection law shows that these are not sufficient to avoid monopolistic tendencies in data-driven markets. On the other hand, all three proposed options for data governance (see result 8 above) already take into account the limitations imposed by data protection and intellectual property protection. We therefore recommend implementing one of the three governance options, including newly created institutions and communication channels.
  8. When trading off the pros and cons of centralized and decentralized governance, we see an advantage in the “mixed” governance structure: the technical infrastructure required to enforce the data-sharing obligation does not need to be duplicated between national competition authorities, as this takes place at EU level within the EDSA. At the same time, there is no need to create new investigative and enforcement powers at EU level, as the national competition authorities select a lead national competition authority that is best placed to take over a particular case. The NCAs thus share the burden of using the resources within the DSCN. Due to this combination of features, we regard the “mixed” governance structure optimal and recommend this option.
  9. For efficiency, data security and privacy considerations, we recommend that user-generated data is not forwarded to organizations with recipient rights, but rather that it is consolidated and shielded in a data pool, operated by the Lead NCA/EDSA’s technology department. Organizations that have a right to access the shared data should be given the opportunity to have their ML algorithms trained in the pool. Only the algorithms of the receiving companies — and no human being — get access to the raw data, but cannot take it out of the data pool. Instead, they can only transfer the findings from their analyses to the outside world, where a multitude of providers can now compete with each other in a meaningful way.

[1] This means that the provider makes the most user-friendly decision regarding all product features that influence user satisfaction (even if it costs her/him revenues in the short term).

[2] “Health-related” search query: Search for burnout symptoms. “Harmless” query: search for restaurants.

“Competing with Big Data” forthcoming in Journal of Industrial Economics

For years, my paper “Competing with Big Data” (joint with Christoph Schottmüller), which analyzes competition on data-driven markets and makes a policy proposal how to mitigate their main problem, has been around and widely discussed in (competition) policy circles. See here, here, here and here.

Now, it has also found its place in the academic literature: it will be published in the Journal of Industrial Economics. This step will hopefully contribute to convince critics of the policy proposal (mandatory data sharing) that its economic foundations are rigorous and that a duty for dominant firms to grant their competitors on data-driven markets access to data about their users’ preferences and characteristics in anonymized way would reestablish incentives to innovate for all involved firms.