Johannes Kandelhardt

We study the impact of consumer inattention on market outcomes for the US ready-to-eat cereal market by estimating a discrete-type mixed logit model with heterogeneous consideration sets within and between consumer types. The full information benchmark model is statistically rejected against all limited consumer attention specifications. Under the full information assumption own-price elasticities are inflated and cross-price elasticities are an order of magnitude smaller than those of our most preferred limited consumer attention specification. Product-level markups are higher under limited attention and are estimated by all models to increase over the period from 2006 to 2020. The consideration proxy that best fits the observable data has on average six products, while there are on average 153 products in the market. While consumer behavior is best explained by limited attention, our model selection tests indicate that firms on average expect consumers to be fully informed when setting prices.

Demand estimation is a crucial step in structural modelling. In order to catch fundamentals when simulating counterfactuals and provide welfare-enhancing policy advice, the present paper combines the Berry, Levinsohn, and Pakes (1995) model and the logit mixed logit model of Train (2016) to estimate random taste heterogeneity using highly flexible parametric distributions. The model operates solely on aggregate data but offers the option of incorporating micro data, closely aligning higher moments of the mixture distribution with the data. The common assumption of normally distributed preferences is shown to be a source of bias, resulting in non-trivially biased estimates of elasticities, market power, and welfare changes resulting from mergers.

We analyse how US product labelling policies affect consumer and producer behaviour using a reduced-form empirical approach, and quantify the welfare implications for consumers in a structural model.

We investigate how data aggregation influences estimated parameters and demand elasticities through information loss and methodological differences in estimation.