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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 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.

Big Data and Democracy

A lot has been written in the public press about fake news and the apparently magic abilities of novel, data-driven technologies to manipulate voters’ beliefs and, hence, the outcomes of democratic elections. The problem is rooted in recent developments regarding the information available about voters, means to provide information to voters, and the nature of information acquisition by voters.

These developments concern (a) data-driven voter research and the possibility of political microtargeting, and (b) news consumption of growing numbers of people using social media and news aggregators that obfuscate the origin of news, leading to voter unawareness about the news sender’s identity. Academic empirical research on the topic is scarce but growing. Theoretical research, however, that carefully analyses the ability of political interest groups to spread messages via social media to voters in a credible way, is lacking.

In a new working paper, “Big Data and Democracy” (joint with Freek van Gils and Wieland Müller), we provide a theoretical framework in which we can analyze the effects that microtargeting by political interest groups and unawareness of voters about the political position of a news sender have on election outcomes in comparison to “conventional” news reporting. The model framework does not only offer new theoretical insights, it also allows the theory-based discussion of policy proposals, such as to ban microtargeting or to require news platforms to signal the political orientation of a news item’s originator.

P.S. This is the sister paper to “Competing with Big Data” (joint with Christoph Schottmüller), which has had and is having quite some policy impact. Where “Competing with Big Data” studies effects of datafication on competition on data-driven markets, “Big Data and Democracy” studies effects of datafication on democratic elections and the process of political opinion formation. In both papers, we identify the core problem and analyze solution proposals, trying to inform policy makers.

Data Sharing on Data-Driven Markets: a new Study and a Research Project

Recently, I described the latest developments regarding efforts to establish a law mandating data sharing of information on users’ preferences and characteristics on data-driven markets (the economic rationale is here). The post ended with the statement: “We are actually in contact with that political party [in Germany, which made data sharing part of their platforms for the EU elections]. It will be interesting to see whether and how research can have real impact, which may impact the lives of virtually all Internet users (and beyond).”

Now we are a step further. Friedrich-Ebert-Stiftung, a foundation close to Germany’s Socialdemocratic Party, asked me “to develop some recommendations on how the idea can be implemented in legislative practice.” The outcome is a slim 15-page essay, titled “Competition Policy and Data Sharing on Data-driven Markets: Steps Towards Legal Implementation.” It tries to give (preliminary) answers to key questions:

  1. How to identify a data-driven market empirically?
  2. What information should be shared on which market?
  3. How can user information be anonymized and how can re-identification of individuals (technically or legally) be avoided?
  4. Who should share data?
  5. Who should have the right to get access to the shared data? At what price?
  6. How should data sharing be organized? What is the optimal governance structure?

Importantly, the answers given here are (informed) ideas, not fundamental research. But that is coming, too. The German Finance Ministry has commissioned a group of TILEC-researchers (combining expertise in economics, law, econometrics, data science, and consumer research) to study the key questions in the list above more seriously. Specifically, our tasks are:

  1. To develop an empirical test that could be applied by, say, a competition authority, which then could show that a market is “data driven”, or not. This would inform policy makers/regulators whether intervention by mandatory data sharing is innovation improving and, hence, positive for users on that market. We are also asked to apply that test to one industry, as a proof of concept.
  2. We are to develop a governance structure of data sharing, which implies to answer questions 2-6 listed above (and to show that it coincides with EU and German law).

The results of these studies are due in fall 2020. Hence, the saga will be continued.

 

Competition Policy and Data Sharing on Data-driven Markets: Moving towards Policy Making

The datafication of our lives, driven both by the availability of so much data and technological progress in artificial intelligence, is ongoing. One key element of this unstoppable process, which brings us many fabulous technologies but also exposes us to new threats, is the development of so-called data-driven markets, which has growing implications for all parts of the economy. In earlier work (especially here), we analyzed competition on data-driven markets and showed a very strong tendency towards monopolization/market tipping. Market tipping leads to lower innovation incentives of both the challengers and the dominant firm on these markets. Based on the analysis, we made a policy proposal, a specific kind of mandatory data sharing between competitors, and showed that this can avoid market tipping—at least in theory. More details are here.

Since 2016, policy makers have expressed grown interest in this research and in the policy proposal. Now, several European governments and the EU Commission have moved forward. For instance, the Dutch Economics Ministry recently wrote an open letter (in Dutch, English, French, and German!) to the Dutch parliament (guess why the many languages!), suggesting to significantly reform EU competition law such that competition authorities can already intervene in certain (data-driven, tipping) markets before it is too late, even if the dominant form has not abused its market power or cartelized the market, which current EU competition law requires. While this is already in line with our proposals, which have been expressed several times at that ministry, the letter picks up our data sharing proposal explicitly (on p.5 of the English version):

"Gatekeeper platforms may for example have access to certain data, such as specific consumer preferences, which other businesses need in order to compete, thereby making use of the platform unavoidable. In such cases the authority could force the platform to share the data in question with the businesses that require it under reasonable conditions."

Moreover, Sébastien Soriano, the head of France’s Electronic Communications and Postal Regulatory Authority (ARCEP), has put up five interesting proposals how (and why) to regulate big tech firms. His main reason (just as here) is to defend incentives of innovators, who cannot make business at all or face a very uneven playing-field, against big tech firms. Notably, one of his examples picks up our mandatory data-sharing proposal (the link to this website is in Soriano’s original):

"Data sharing mechanisms could also be used for competitive purpose. Economists have shown that, in data-driven markets, data sharing would allow us to regain our capacity to innovate: “Data sharing (voluntary, or not) eliminates the mechanism causing data-driven markets to tip.” This is no small issue: it would amount to protecting the innovation economy and ensuring that innovation is not the sole dominion of a small handful of people. This is why the idea is catching on more and more. One of Germany’s main political party’s actually made it part of their platform for the EU elections."

We are actually in contact with that political party. It will be interesting to see whether and how research can have real impact, which may impact the lives of virtually all Internet users (and beyond).

Update: In August 2019, the Dutch Competition Authority (ACM) published a short position paper supporting the above-mentioned open letter of the Dutch Economics Ministry. Specifically, they support “the introduction of an ex-ante intervention mechanism to prevent anti-competitive behaviour by dominant companies acting as gatekeeper to the relevant online ecosystem. We think introducing the mechanism by way of adding an extra tool to Regulation 1/2003, to be applied by the European Commission and the member states’ competition authorities, could be explored.” This is a great success as the ACM was rather skeptical about such ex ante measures, holding that existing EU competition law would be enough to confine dominant firms in data-driven markets, about a year ago.

What is Economic Governance? (in 2 videos)

A new initiative, Learn IOE, has been started at the University Paris Dauphine (where IOE refers to Institutional and Organizational Economics). The idea is to provide a stack of short video lectures about fundamental or current topics in IOE. In one of my contributions, I explain the concept of economic governance. In another one, I introduce a classification of economic governance institutions.

In short, economic governance studies how fundamental economic problems, such as contract enforcement, collective action, and the protection of property rights, can be tackled by careful institutional design. The categorization introduced in the video helps in a comparative fashion to identify the type of governance institution that is best suited to solve a specific application. In the next step, the (institutional and organizational) details of the optimal type have to be designed.

For an application of this concept to the cloud computing industry, see this! A previous paper that only did step 1 and characterized the optimal type of the governance institution for cloud computing is here.

 

New EU Regulation on Online Platforms cites “Competing with Big Data”

In February 2019, the European Parliament, the Council of the European Union and the European Commission agreed on new European rules to improve fairness of online platforms’ trading practices. This nice fact sheet explains the rules in a nutshell.

The background document to the regulation cites my “Competing with Big Data” paper (joint with Christoph Schottmüller), which has already had policy impact earlier.

 

“Data Science for Entrepreneurship Research” forthcoming in Small Business Economics

Most of my recent research studies the consequences of datafication (the recent rise of big data and artificial intelligence) on markets, politics, organizations, and societies. This time it’s different. In “Data Science for Entrepreneurship Research: Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands,” co-authored with Patricia Prüfer and just accepted for publication in Small Business Economics, we describe the most prominent data science methods suitable for research in the social sciences (here: applied to the domain of entrepreneurship) and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature.

As a showcase of how to use data science techniques, based on a data set of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills. We find that both entrepreneurial and digital skills are in increased demand for managerial positions in the Netherlands over the entire period 2012-2017. We also find (less surprisingly) that due to the hugely growing importance of datafication, amongst digital skills, those on ‘Digital transformation’ and ‘Big data and analytics’ are most valued by managers’ employers (less so for other professions). What is surprising, however, is that one could expect that demand for digital skills would increase most. Our empirical results, however, show the opposite: entrepreneurial skills were significantly more relevant over the six-year period studied. Moreover, the absolute importance of this skill type in managerial job vacancies has increased even more than digital skills.