Research

Working papers

• Do I Really Want to Buy This? Preference Discovery and Consumer Search. Joint with Tobias Klein and Christoph Walsh. [PDF]
  • One of the most invoked assumptions in economics is that consumers know their preferences when making choices. Although theories and experiments in psychology and behavioral economics suggest that this may be unrealistic, there is relatively little evidence from the field on this question. In this paper, we use detailed clickstream data from a large Central Asian online platform to study the extent to which consumers learn about their preferences while searching for a smartphone. To quantify the speed at which this takes place and account for other factors, most notably that consumers obtain additional product information when they inspect product pages, we estimate a rich search model in which consumers learn about their willingness to pay each time they visit the checkout page. Consumers initially underestimate their price sensitivity and update it along the way. Taking this into account shows that consumers are more price sensitive than a standard search model would predict, and an intervention that prompts consumers to end their search early can lead to potential welfare loss.
  • Selected presentations: SEG Tilburg, Tilburg-Rotterdam Workshop on Digital Markets 2024
• Estimating Sequential Search Models Based on a Partial Ranking Representation. [arXiv] [Slides]
  • Consumers are increasingly shopping online, and more and more datasets documenting consumer search are becoming available. One way to make use of such data is to estimate a sequential search model. However, this remains challenging due to the need to solve or approximate high-dimensional integrals. One of the reasons is that many inequality conditions implied by the model and used to construct the likelihood function depend on multiple unobservables revealed in the search process. This paper introduces a novel representation of inequalities implied by a broad class of sequential search models, showing that the empirical content of these models is effectively captured through a specific partial ranking of available actions. This representation suffers less from interdependencies due to the unobservables and is more convenient for empirical application. Leveraging this insight, I simplify the GHK-style simulation-based likelihood estimator. The proposed estimator is easy to implement, provides greater flexibility for handling incomplete or missing search data, is able to incorporate additional ranking information, and can accommodate more complex search processes, such as those involving product discovery. I demonstrate that the estimator achieves robust performance while maintaining a relatively low computational cost, making it a practical and versatile tool for researchers and practitioners.
  • Selected presentations: SEG Tilburg

Work in Progress

• Higher Price, Better Quality? Resolve the Endogeneity in Search Decisions.
  • Consumers typically search before making a purchase to resolve product uncertainty under imperfect information. A key factor driving their search decisions is their private evaluation of the product. However, this evaluation often exhibits an endogenous relationship with price, as consumers tend to associate higher prices with better quality. This creates endogeneity between search decisions and product prices beyond the consumer’s price sensitivity in purchase. I developed a novel econometric method demonstrating how using instrumental variables can address this endogeneity, enabling accurate estimation of consumers’ preferences in purchase.