Affiliations:

Universität Mannheim
ZEW Mannheim
CRC TR 224 - EPoS
MaCCI

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Research

• Search on a Grid: Directed Consumer Search with Correlated Products - SSRN

  Abstract: The consumer search literature mostly considers independently distributed products. In contrast, I study a model of directed search with infinitely many products whose valuations are correlated through shared attributes. I propose a tractable, systematic, history-dependent scoring system based on nests of correlated products that leverages the predictability of the optimal search process along different paths. This scoring system generates an optimal search policy conceptually equivalent to the familiar optimal search policy with independently distributed search products. The policy instructs the consumer to inspect unrelated products until an attribute the realization of which surpasses the added informational value of inspecting two new attributes is found. The search paths emerging from this policy match recent evidence of consumer learning through search, and can rationalize backtracking to a previously abandoned attribute.

• Consumer Search and Firm Strategy with Multi-Attribute Products - SSRN

Abstract: I analyze a model of directed search in which a consumer inspects products that share attributes with each others. The consumer discovers her valuation for the attributes of the inspected products and adapts her search strategy based on what she has learned. The consumer anticipates the optimal paths that arise after different realizations; this generates a search rule that accounts for learning systematically. In this search environment, a multiproduct seller commits to a menu of horizontally differentiated products. The seller can exploit the fact that the emerging search paths reveal the consumer's preferences: by setting different prices for \textit{ex ante} identical products, the seller can encourage specific paths to arise and exploit the information that the consumer learned through search. In some cases, the seller optimally limits the set of available products.

• Platform-Enabled Information Disclosure - with Martin Peitz - SSRN - Video presentation

Abstract: We analyze consumers’ voluntary information disclosure in a platform setting. For given consumer participation, the platform and sellers tend to prefer limited disclosure of consumer valuations, in contrast to consumers. With endogenous consumer participation, seller and platform incentives may be misaligned, and sellers may be better off when consumers can disclose their valuations. A regulator acting in the best interest of consumers and/or sellers may want to intervene and force the platform to employ a disclosure technology that enables consumers to voluntarily disclose information from a richer message space.

• Not as Good as it Used to be: Do Streaming Platforms Penalize Quality? - with Luca Sandrini - SSRN

Abstract: We study the incentives of a streaming platform to bias consumption when products are vertically differentiated. The platform offers mixed bundles of content to monetize consumers' interest in variety and pays royalties to sellers based on the effective consumption of the content they produce. When products are not vertically differentiated, the platform has no incentive to bias consumption in equilibrium: the platform being active represents a Pareto-improvement compared to the case in which she is not. With vertical differentiation, royalties can differ; the platform always biases recommendations in favor of the cheapest content, which hurts consumers and the high-quality seller. Biased recommendation always diminishes the incentives of a seller to increase the quality of her content for a given demand. If a significant share of the users is ex-ante unaware of the existence of the sellers the platform can bias recommendations more freely, but joining the platform encourages investment in quality. The bias, however, can lead to inefficient allocation of R&D efforts. From a policy perspective, we propose this as a novel rationale for regulating algorithmic recommendations in streaming platforms.

• Regulatory Compliance with Limited Enforceability: Evidence from Privacy Policies - with Bernhard Ganglmair, Julia Krämer - SSRN

Abstract: The EU General Data Protection Regulation (GDPR) of 2018 introduced stringent transparency rules compelling firms to disclose, in accessible language, details of their data collection, processing, and use. The specifics of the disclosure requirement are objective, and its compliance is easily verifiable; readability, however, is subjective and difficult to enforce. We use a simple inspection model to show how this asymmetric enforceability of regulatory rules and the corresponding firm compliance are linked. We then examine this link empirically using a large sample of privacy policies from German firms. We use text-as-data techniques to construct measures of disclosure and readability and show that firms increased the disclosure volume, but the readability of their privacy policies did not improve. Larger firms in concentrated industries demonstrated a stronger response in readability compliance, potentially due to heightened regulatory scrutiny. Moreover, data protection authorities with larger budgets induce better readability compliance without effects on disclosure.

Other

• Mikroökonomik A, 2021/2022 - Teaching Assistant for Prof. Thomas Tröger, Universität Mannheim
• Mikroökonomik A, 2019/2020 - Teaching Assistant for Prof. Thomas Tröger, Universität Mannheim

• MaCCI Annual Conference 2024 - Scientific Contact
• MaCCI Annual Conference 2023 - Scientific Contact
• MaCCI Annual Conference 2022 - Scientific Contact
• MaCCI Annual Conference 2021 - Scientific Contact

• CCE - Andrea Lofaro Prize for the Best Papers on Industrial Organization Written by Young Researchers (2024) for Consumer Search and Firm Strategy with Multi-Attribute Products
• Jornadas de Economia Industrial - Young Economist Award (2023) for Consumer Search and Firm Strategy with Multi-Attribute Products
• CRESSE - CPI Best Paper Award for Young Researchers (2023) for Consumer Search and Firm Strategy with Multi-Attribute Products (was: Multi-Attribute Search)