Publications/ Accepted Papers:
with Marco J.W. Kotschedoff (equal authorship), 2020
Marketing Science 39(1): 253-280
Abstract: This paper estimates an individual level demand model for eggs differentiated by animal welfare. Typically, after minimum quality standards for eggs are raised, the price of higher quality eggs falls. As a result, consumer welfare is redistributed from households who do not value animal welfare to households who are willing to pay a premium for animal welfare. In our analysis of German household data, we find that on average, households with higher income are willing to pay more for eggs that provide higher animal welfare. This provides evidence that higher minimum quality standards have a regressive impact. In counter-factual scenarios, we estimate the cost reduction that would be needed to offset the regressive effect, and find that as retailers' pricing power increases, the cost reduction must be higher. Finally, we consider hypothetical future scenarios that continue to increase the minimum quality standard until only the highest quality eggs remain on the market.
with Peter Kurz and Thomas Otter, 2020
Quantitative Marketing and Economics 18(4): 343–380
Abstract: Models of consumer heterogeneity play a pivotal role in marketing and economics, specifically in random coefficient or mixed logit models for aggregate or individual data and in hierarchical Bayesian models of heterogeneity. In applications, the inferential target often pertains to a population beyond the sample of consumers providing the data. For example, optimal prices inferred from the model are expected to be optimal in the population and not just optimal in the observed, finite sample. The population model, random coefficients distribution, or heterogeneity distribution is the natural and correct basis for generalizations from the observed sample to the market. However, in many if not most applications standard heterogeneity models such as the multivariate normal, or its finite mixture generalization lack economic rationality because they support regions of the parameter space that contradict basic economic arguments. For example, such population distributions support positive price coefficients or preferences against fuel-efficiency in cars. Likely as a consequence, it is common practice in applied research to rely on the collection of individual level mean estimates of consumers as a representation of population preferences that often substantially reduce the support for parameters in violation of economic expectations. To overcome the choice between relying on a mis-specified heterogeneity distribution and the collection of individual level means that fail to measure heterogeneity consistently, we develop an approach that facilitates the formulation of more economically faithful heterogeneity distributions based on prior constraints. In the common situation where the heterogeneity distribution comprises both constrained and unconstrained coefficients (e.g., brand and price coefficients), the choice of subjective prior parameters is an unresolved challenge. As a solution to this problem, we propose a marginal-conditional decomposition that avoids the conflict between wanting to be more informative about constrained parameters and only weakly informative about unconstrained parameters. We show how to efficiently sample from the implied posterior and illustrate the merits of our prior as well as the drawbacks of relying on means of individual level preferences for decision-making in two illustrative case studies.
with Marco J.W. Kotschedoff, Arjen van Lin, Bart J. Bronnenberg and Erica van Herpen, 2023
Journal of Marketing Research 60(1): 92-109
Winner of the 2023 Paul E. Green Award
Abstract: Obesity is increasing worldwide and the problem is particularly serious among lower income groups. Front-of-pack nutritional warning labels are a prominent regulatory tool that have been implemented or are currently debated in many countries. Existing studies document that warning labels incentivize consumers to substitute away from unhealthy products. However, not much is known about manufacturers’ price reoptimizations in response to consumers’ (dis-)utility for warning labels. Using household purchase data in the cereal category, this paper studies the adjustments of prices after the mandatory introduction of warning labels in Chile. We show that warning labels lead to higher prices of labeled cereals because of a segmentation effect and because of increased product differentiation. In contrast, prices of unlabeled products tend to drop or at least increase less, incentivizing price sensitive consumers to remain in the category. We decompose post-labeling market share adjustments into a direct effect that fixes prices at initial levels after regulation and a total effect that accounts for price reoptimizations. Our findings point to self-enforcing effects of a warning label regulation as the price adjustments amplify the policy maker’s goal of reducing unhealthy nutritional intake, especially because market forces incentivize low-income segments to consider healthier alternatives as well.
with Peter Kurz and Thomas Otter
Journal of Marketing Research, 60(5): 968-986
Abstract: Standard Choice-Based Conjoint (CBC) models often ignore or insufficiently approximate consumers’ budget constraints, despite the prominent role of budget constraints in economic theory. The authors offer a theoretically motivated improvement to the CBC model that is especially appropriate for high-ticket durable goods and develop a Bayesian method for the inference of unobserved budget constraints. The proposed method leverages respondents’ stated budget constraints that suffer from measurement error and respondents’ financial demographic variables as additional information to reduce the dependency on functional form assumptions in the estimation. The authors show that accounting for budget constraints substantially increases model fit and the accuracy of competitive pricing in an industry-grade discrete-choice experiment on consumer preferences for high-end laptops. The proposed model performs better than the canonical linear price benchmark model, which is not flexible enough to approximate budget constraints. In theory, more flexible utility specifications, such as the non-linear dummy price model, can approximate consumers’ budget constraints. However, they perform poorly when only finite data are available. The authors conclude that applied researchers in industry and academia will benefit from having a better tool for estimating budgets in high-ticket categories.
with Hannes Datta
Marketing Science, 44(1): 54-64
Abstract: We provide estimates of the drivers of playlist followers on Spotify. We base our analysis on a unique panel data set for 30,000+ popular playlists and combine it with data on how prominently these playlists are featured in the Spotify app. Using two-way fixed effects and staggered synthetic difference in difference models, we compare the short-term effect of two important demand factors in our data - featuring playlists on Spotify's Search Page and adding songs by exceptionally popular major label artists to playlists. We find that users prefer to follow playlists featured in the app. According to our estimates, being featured on the Search Page raises daily playlist followers by 0.95% - which is about two times larger than the effect on followers of including a song by an exceptionally popular major label artist (0.45%). Our examination of playlist demand has two important implications. First, Spotify can effectively guide user attention to certain playlists, fueling concerns among industry executives and artists about its potential to favor some producers by promoting selected content. Second, popular artists signed with major labels play an important role in attracting followers to playlists on Spotify.
Working Papers:
with B.J. Bronnenberg, T. Bui, B. Deleersnyder, L. Haerkens, G. Knox, A. Paley, R. Smith and S. Staebler
Conditionally Accepted at the Journal of Marketing's Special Issue on Research-Driven Apps.
with J. Barrett and B.J. Bronnenberg
with Marco J.W. Kotschedoff and Anita Rao
Presented at: Stanford Marketing for Environmental Sustainability Conference 2023, Christmas Camp at Tilburg University 2023
with Daniel Ershov and Adam N. Smith
Work in Progress:
Sizing the Market for Plant-based Meat Replacements (with B. Bronnenberg and T. Otter)
Preference Measurement with Vertically Ordered Prices (with T. Kosyakova, A. Smith and T. Otter)