Working Papers (see also at SSRN):
“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.
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:
- “Data Science for the Entrepreneurial (Research) Process” (with Patricia Prüfer)
- A machine-learning algorithm trained on survey data among 200 students had an accuracy of 80% when predicting entrepreneurial inclination in individuals not encountered before. With another data science technique, a Self-Organizing Map approach, differences in firm size and structures could be used to predict antecedents of firm survival and success. By studying personality differences between (superstar) entrepreneurs and (superstar) managers with a language-based personality assessment tool, it could be shown that managers appear considerably more entrepreneurial than entrepreneurs in various personality traits. Therefore, we ask: what should entrepreneurial firms and individuals learn about data science? How can, and how should, researchers react to the challenges and opportunities offered by big data and artificial intelligence? We depict data-driven organizations, explain the concept of data maturity, and sketch the practical steps an organization should undertake to increase its data maturity, thereby making better use of available technological opportunities. Complementarily, we address researchers by sketching the most prominent data science methods suitable for entrepreneurship research. We exemplify these methods by surveying recent relevant studies and show how data science techniques have been used to study important entrepreneurship research questions that could not, or not to the same extent, be studied without these techniques. We conclude by comparing the main strengths and limitations of data science techniques with traditional empirical research methods and its relation to theory.
- “Clash of Classification Institutions” (with Gillian Hadfield and Vatsalya Srivastava)
- Classification institutions assign a normative label, acceptable or wrongful, to human behavior: laws, social norms, religious rules, cultural traditions, etc. Thereby they shape the expectations about other people’s behavior, reduce uncertainty, and create trust in other’s actions. We construct a dynamic model where two classification institutions with different enforcement mechanisms, social norms and legal order, clash. We show how laws crowd out norms, and when and how norms decay gradually, where more and more players first stop enforcing and then stop complying with the norm as time proceeds. We also show that the existence of legal order can undermine norms, even if legal order cannot enforce its own laws very effectively. In such a case, players may rationally ignore the classification of both norms and laws and engage in novel behavior, implying the breakdown of both governance mechanisms. Finally, we apply the model to issues of immigration, developing countries and colonization, former Soviet republics, failed states, and how to organize a multicultural society.
- “Membership, Governance, and Lobbying in Standard-Setting Organizations” (with Maria Larraín Aylwin)
- Standard-setting organizations (SSOs) are collectively self-governed industry associations, formed by innovators and implementers. They are the main organizational form to agree on and manage technical standards, which have significant positive welfare effects. SSO self-governance avoids monopolization and increases a standard’s acceptance but also introduces specific issues. Constructing a model, we study the incentives of heterogeneous innovators and implementers to join a 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 create intuition regarding firms’ trade-off, which depends on the beliefs of active members to get their technologies included in the standard and the intensity of knowledge spillovers.
- “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.
- “Classification Through Thick and Thin: Permissive Norms and Strict Laws” (with Gillian Hadfield and Vatsalya Srivastava)
- “News Platforms, Voter Manipulation, and Political Outcomes” (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)