Working Papers (see also at SSRN):
“Data Science for Institutional and Organizational Economics” (with Patricia Prüfer); TILEC Discussion Paper No. 2018-011.
- To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and 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.
“Competing with Big Data” (with Christoph Schottmüller); TILEC Discussion Paper No. 2017-006, CentER Discussion Paper No. 2017-007.
- This paper studies competition in data-driven markets, that is, markets where the cost of quality production is decreasing in the amount of machine-generated data about user preferences or characteristics, which is an inseparable byproduct of using services offered in such markets. This gives rise to data-driven indirect network effects. We construct a dynamic model of R&D competition, where duopolists repeatedly determine their innovation investments, and show that such markets tip under very mild conditions, moving towards monopoly. In a tipped market, innovation incentives both for the dominant firm and for competitors are small. We also show under which conditions a dominant firm in one market can leverage its position to a connected market, thereby initiating a domino effect. We show that market tipping can be avoided if competitors share their user information.
“Consumers’ Privacy Choices in the Era of Big Data” (with Sebastian Dengler); TILEC Discussion Paper No. 2018-014, CentER Discussion Paper No. 2018-012.
- 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.
“Trusting Privacy in the Cloud;” (updated: 10 April 2018); TILEC Discussion Paper No. 2014-047, CentER Discussion Paper No. 2014-073.
- Cloud computing technologies can increase innovation and economic growth considerably. Because of privacy concerns, however, many users underutilize cloud technologies. This paper designs an institution attenuating the problem: a two-layered certification scheme built around a private, nonprofit organization called cloud association. This association is governed by representatives of both users and cloud service providers and sources auditing and certification of providers out to independent for-profit certifiers. It is shown how this institution incentivizes providers to produce high data security, and users with strong privacy preferences to trust them and pay a premium for their services..
An earlier version was formerly distributed under the title “Semi-Public Competitions”; CentER Discussion Paper, No. 2009-33; TILEC Discussion Paper, No. 2008-023.
- The process of innovation is driven by two main factors: new inventions and institutions supporting the transformation of inventions into marketable innovations. This paper studies such an institution, called an innovation contest, and shows that it can mitigate a dilemma on the market for ideas. The sponsor of an innovation contest publicizes the ranking of winners, which motivates entrepreneurs to participate in the contest. But information about losers remains private with the sponsor. This allows him to place better informed bids on valuable losers’ projects. Efficiency increases because both entrepreneurs and investors have better incentives to enter the market.
Work in Progress:
- “Believing in Making a Difference” (with Xu YiLong)
- Nonprofit firms active in the production of public goods – mission-driven organizations – face higher labor turnover than firms producing private goods for a profit. Simultaneously, they pay lower wages 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. We construct a 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. Workers who seek employment in the nonprofit sector learn the true philanthropic impact of their work on the job only, which can lead to disappointment. Some of the disappointed workers leave the firm but others costly manipulate their own recollection of the facts and keep believing in making a difference. We construct testable empirical hypotheses and offer managerial and policy implications.
- “Clash of Classification Institutions” (with Gillian Hadfield and Vatsalya Srivastava)
- “Thick Norms and Thin Laws” (with Gillian Hadfield and Vatsalya Srivastava)
“Membership in Standard Setting Organizations” (with Maria Larraín Aylwin)
- “Democracy and Big Data” (with Freek van Gils and Wieland Müller)
- “General and Specialized Courts: Objectivity vs. Expertise in Adjudication” (with Scott Masten)
- “Public Hospitals are More Effective but Private Hospitals are More Efficient” (with Lapo Filistrucchi)