Working Papers

[1] Platform Design, Consumer Behavior, and Market Power, joint with Adrian Düll, Florian Heiss, Florian Kerzenmacher, und Joachim Winter [pdf]

We analyze how the design of an online retail platform shapes the market power of sellers competing on that platform. To this end, we study consumer behavior on a generic price comparison webpage and derive pricing equilibria from a structural demand model estimated on consumers‘ choices. Consumers exhibit heterogeneous degrees of price sensitivity, even in settings without physical search costs and straightforward price comparisons. As a consequence, the pure-strategy pricing equilibrium features price dispersion among identical firms. Design features of the platform – the presentation of a default through a buy box, the number of transaction dimensions, and the ranking algorithm – matter for price dispersion and sellers‘ markups, sometimes in interdependent and non-monotonic ways. Simplifying price comparisons can increase equilibrium markups as it may cause a subset of sellers to focus on price-insensitive consumers.

[2] Competitive Markets, Add-On Prices, and Bounded Rationality [pdf]

We analyze the trade of differentiated products with potentially hidden add-on prices. Boundedly rational consumers mistakenly believe that product choice has no effect on the probability of incurring hidden add-on charges. However, all consumers correctly anticipate their equilibrium expenses, so there are no “surprise charges.” Shrouding equilibria with inefficient trade exist, but only if the market is sufficiently competitive. There is an optimal maximal add-on price that defines the scope for profitable exploitative innovation. Further, product and exploitative innovation are complements, and the presence of boundedly rational consumers can generate innovation incentives that improve welfare relative to the rational benchmark.

[3] Persuasion of Loss-Averse Receivers Through Early Offers, joint with Heiko Karle and Rune Vølund [pdf]

We study a simple bargaining model in which the sender can make an early offer to the receiver. Initially, the sender has private information about the value of the receiver’s outside option. The receiver learns this value before she chooses between the sender’s early offer and her outside option. Nevertheless, if the receiver is expectation-based loss averse, the sender can persuade her to accept an offer that is inferior to her outside option. This result is due to the interaction of two effects: the attachment effect that makes it costly for the receiver to reject an offer that she planned to accept, and the uncertainty effect which renders the acceptance of the sender’s offer as the preferred plan since it creates peace of mind at an early stage. If the receiver faces uncertainty in multiple dimensions, the main result holds for all degrees of loss aversion. Thus, expectation-based loss-averse preferences imply that there is scope for persuasion through signaling even if the receiver has all payoff-relevant information at the decision stage.

[4] Evaluating Search Cost Models: Estimation and Prediction, joint with Adrian Düll, Heiko Karle, and Simon Martin [pdf]

The classic search models assume that consumers adhere to a particular method of search (sequential or non-sequential) and that they know the true price distribution. In this paper, we evaluate how well the search cost estimates from classic models predict search outcomes – the amount of search and purchase prices – when these assumptions are violated. To this end, we conduct an online experiment in which we vary searchers‘ information about the price distribution of a homogeneous good. For each treatment, we (i) estimate search costs, (ii) fit each model to the estimated search cost distribution to obtain in- and out-of-sample predictions about outcomes, and (iii) compare predicted and realized outcomes. We find that the prediction performance of each model is largely robust to violations of the informational assumption. Further, the prediction performance of the sequential and non-sequential search model are similar, despite the fact that the search environment strongly favors sequential search.