A team of researchers at TILEC, the Tilburg Law and Economics Center, has submitted a contribution responding to a call issued by the European Commission’s Directorate-General for Competition (DG COMP) on on “Shaping competition policy in the era of digitization.” The call elicits contributions from interested stakeholders that can inform the discussion at a conference DG COMP organizes in January of 2019.
According to DG COMP, the conference is “designed to provide input to the Commission’s ongoing reflection process about how competition policy can best serve European consumers in a fast-changing world. The conference may also help to identify problems and solutions as markets go through rapid changes. The objective is to identify the key upcoming digital challenges and their implications for competition policy.”
TILEC’s contribution revolves around the three panels that will be featured at the conference: (1) competition, data, privacy and AI, (2) digital platforms’ market power, (3) preserving digital innovation through competition policy. It summarizes recent research undertaken by TILEC researchers in these fields and derives implications for competition policy.
Lisa Bernstein (Chicago Law School), together with a formidable set of other scholars, organizes a fascinating meeting next week, where the ins and outs of relational contracting and transacting in the shadow of the law will be discussed. Promises, rules, social norms, property rights, productivity, and incentive contracts combine a range of topics relevant for relationships where the future matters. The program is populated by exquisite legal scholars, economists, and political scientists. For a glimpse, click here!
My recent work with Cedric Argenton and Christoph Schottmueller on competition in data-driven markets had some policy impact (details here). The key proposal we put forward and analyzed in those theory papers was to require competitors in data-driven markets to share their user information – data about the preferences and characteristics of users gained as a virtually free byproduct of offering certain services, e.g. search engines or online platforms.
Now ESB, a Dutch economic policy outlet, published an entire special issue on such mandated data sharing. Naturally, authors from different backgrounds disagree about the right measures to prevent monopolization of markets. Together with legal scholar Inge Graef, a colleague at the Tilburg Law and Economics Center (TILEC), I explain the original reasoning in a nutshell and discuss policy implementation options, both from a legal and an economic perspective. Our 4-page essay, a bit sensationally called “Mandated data sharing is a necessity in specific sectors,” is here. Financieele Dagblad, a Dutch (language) daily newspaper, wrote about it here.
Is it possible to elicit the ideological positions of voters by having information about the structure of their social media networks? Can we make predictions about upcoming policy changes by analyzing the speeches of statesmen, although they could be considered cheap talk? Is there a way to analyze all court decisions of a jurisdiction in order to identify individual biases of judges, thereby suggesting a way how to make the legal system more impartial? Or can we develop a reliable index of organized crime and subversion in industrial areas, typical hotbeds of such crimes, taking into account a wide range of Internet, social media and administrative data sources? These questions are within the domain of Institutional and Organizational Economics (IOE). And all of them could not be seriously studied, let alone answered, by traditional empirical methods. Data science, a new toolkit combining statistics with computer science, is changing this.
Together with Patricia Prüfer, I have written a brief introductory essay that will be published in a handbook, “A Research Agenda for New Institutional Economics,” edited by Claude Menard and Mary Shirley. We describe the most prominent data science techniques that lend themselves to analyses of the governance structures of institutions and organizations. Several examples using data science to analyze legal, political, and social institutions are introduced. Then we sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory. All this is amended by links to literature and Internet resources and to the most relevant text mining tools and download sources, showing how to get started with data science methods independently.
Do religious people hold different moral views than non-religious people? Given that a good part of religious teachings are ethical, we could think so. But where exactly are the differences between Catholics, Protestants, and other believers—and non-religious people? And, more importantly for policy insights, are there significant differences not only in views but also in economically relevant actions across denominations or even between clearly identifiable groups within one congregation?
“Religion, Moral Attitudes & Economic Behavior” (joint with Isadora Kirchmaier and Stefan Trautmann) studies and answers such questions empirically, based on a representative survey of the Dutch population and an experimental game with monetary payoffs played with the survey respondents. The paper is now forthcoming in the Journal of Economic Behavior & Organization. Its background and some details are here. The final working paper version is here.
One year ago, Christoph Schottmüller and I put out “Competing with Big Data,” a theory-paper offering a definition and analysis of “data-driven markets”—and a policy proposal to overcome the main problem, market tipping. Based on the idea of an earlier paper (Argenton and Prüfer, 2012), we studied the consequences of a (currently fictive) regulatory requirement for dominant firms in data-driven markets (think of search engines, many digital platform markets, self-driving cars, etc.) to share their data on user preferences and characteristics with each other. We showed that such a policy intervention can mitigate the strong tendency of data-driven markets towards monopolization and that in most relevant cases the net welfare effects would be highly positive.
Since then, this paper—and especially the policy proposal—has been discussed widely both in academic and in policy circles. In January 2018, the Secretary General of the Dutch Ministry of Economic Affairs adopted our view on data sharing (Camps, 2018, p.3):
“one can think of data used for search engine optimisation, such as users’ clickstream following certain search queries. By increasing access to such anonymised clickstream data, other parties in different markets can use them for further innovation. At the same time, a strong concentration of large internet companies on these markets can be avoided (Prüfer and Schottmüller, 2017). One can think of the markets for digital maps, retail and, in the future, autonomous cars.”
Similarly, in his latest book, Viktor Mayer-Schönberger, who co-authored a highly successful book on the economic and social consequences of big data, attributes :
“Rather than algorithmic transparency, regulators wanting to ensure competitive markets should mandate the sharing of data. To this end, economists Jens Prüfer and Christoph Schottmüller offer an intriguing idea. They suggest that large players using feedback data must share such data (stripped of obvious personal identifiers, and stringently ensuring that privacy is not being unduly compromised) with their competitors. Calculating the effect of such mandated data sharing over a wide spectrum of scenarios, they see an overall net benefit in most cases, especially when one incumbent is close to dominating a market. Building on this idea, we suggest what we term a progressive data-sharing mandate.” (Mayer-Schönberger and Ramge, 2018, p.167)
So, for now we keep the watch from the ivory tower and view to which extent these influential multiplicators may help policy implementation. And we are thinking about an empirical validation of several data-driven sectors …
The Society for Institutional and Organizational Economics (SIOE) just publicized the call for papers for the SIOE 2018 conference, which will be held at HEC Montreal, Canada, on June 21-23, 2018. Keynote lectures will be given by Naomi Lamoreaux (Yale) and Nobel Laureate Jean Tirole (Toulouse), representing nicely SIOE’s two intellectual pillars, institutions and organizations.
The exquisite Program Committee, chaired by President-Elect Francine Lafontaine (Michigan), invites you to submit your proposal to present a paper at the conference. Paper proposals are due by February 5th, 2018.