Günter J. Hitsch

Do Switching Costs Make Markets Less Competitive

The conventional wisdom in economic theory holds that switching costs make markets less competitive. This article challenges this claim. The authors formulate an empirically realistic model of dynamic price competition that allows for differentiated products and imperfect lock-in. They calibrate this model with data from frequently purchased packaged goods markets. These data are ideal in the sense that they have the necessary variation to identify switching costs separately from consumer heterogeneity. Equally important, consumers exhibit inertia in their brand choices, a form of psychological switching cost. This makes the results applicable to the broad range of products that are distinctly identified (i.e., branded) rather than just to products for which there is a product adoption cost or explicit switching fee. In the simulations, prices are as much as 18% lower with than without switching costs. More important, equilibrium prices do not increase even in the presence of switching costs that are of the same order of magnitude as product price.

Category Pricing with State-Dependent Utility

There is substantial literature documenting the presence of state-dependent utility with packaged goods data. Typically, a form of brand loyalty is detected whereby there is a higher probability of purchasing the same brand as has been purchased in the recent past. The economic significance of the measured loyalty remains an open question. We consider the category pricing problem and demonstrate that the presence of loyalty materially affects optimal pricing. The prices of higher quality products decline relative to those of lower quality when loyalty is introduced into the model. Given the well-known problems with the confounding of state dependence and consumer heterogeneity, loyalty must be measured in a model which allows for an unknown and possibly highly nonnormal distribution of heterogeneity. We implement a highly flexible model of heterogeneity using multivariate mixtures of normals in a hierarchical choice model. We use an Euler equations approach to the solution of the dynamic pricing problem which allows us to consider a very large number of consumer types.

Commentaries and Rejoinder to "Do Switching Costs Make Markets Less Competitive?"

Vol. XLVI (August 2009), 446–452 446

Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation

We discuss how to construct optimal targeting policies and document the difference in profits from alternative targeting policies by using estimation approaches that are based on recent advances in causal inference and machine learning. We introduce an approach to evaluate the profit of any targeting policy using only one single randomized sample. This approach is qualitatively equivalent to conducting a field test, but reduces the cost of multiple field tests because all comparisons can be conducted in only one sample. The approach allows us to compare many alternative optimal targeting policies that are constructed based on different estimates of the conditional average treatment effect, i.e. the incremental effect of targeting. We draw a conceptual distinction between methods that predict the conditional average treatment effect indirectly via the conditional expectation function trained on the outcome level, and methods that directly predict the conditional average treatment effect. We propose a new direct estimation method, called treatment effect projection. The empirical application is to a catalog mailing with a high-dimensional set of customer features. We find that the optimal targeting policies based on the direct estimation methods typically outperform the indirect estimation methods, both in the validation sets from the same population from which the training set is drawn and in the data obtained one year after the training set was collected. In particular, the treatment effect projection performs similar to the recently introduced causal forest of Wager and Athey (2017). We also compare targeting policies based on conditional average treatment effects with a sophisticated application of the traditional CRM approach that is based on a prediction of the outcome level. Even though based on a conceptually incorrect metric — outcome levels — the sophisticated application of the traditional CRM approach often yields larger profits than the targeting policies based on the indirect estimation methods.

Income and Wealth Effects on Private-Label Demand: Evidence from the Great Recession

We measure the causal effects of income and wealth on the demand for private-label products. Prior research suggests that these effects are large and, in particular, that private-label demand rises during recessions. Our empirical analysis is based on a comprehensive household-level transactions database matched with price information from store-level scanner data and wealth data based on local house value indices. The Great Recession provides a key source of the variation in our data, showing a large and geographically diverse impact on household incomes over time. We estimate income and wealth effects using “within” variation of income, at the household level, and wealth, at the zip code level. Our estimates can be interpreted as income and wealth effects consistent with a consumer demand model based on utility maximization. We establish a precisely measured negative effect of income on private-label shares. The effect of wealth is negative but not precisely measured. However, the estimated effect sizes...

Prices and Promotions in U.S. Retail Markets: Evidence from Big Data

We document the degree of price dispersion and the similarities as well as differences in pricing and promotion strategies across stores in the U.S. retail (grocery) industry. Our analysis is based on “big data�? that allow us to draw general conclusions based on the prices for close to 50,000 products (UPC’s) in 17,184 stores that belong to 81 different retail chains. Both at the national and local market level we find a substantial degree of price dispersion for UPC’s and brands at a given moment in time. We document that both persistent base price differences across stores and price promotions contribute to the overall price variance, and we provide a decomposition of the price variance into base price and promotion components. There is substantial heterogeneity in the degree of price dispersion across products. Some of this heterogeneity can be explained by the degree of product penetration (adoption by households) and the number of retail chains that carry a product at the market level. Prices and promotions are more homogenous at the retail chain than at the market level. In particular, within local markets, prices and promotions are substantially more similar within stores that belong to the same chain than across stores that belong to different chains. Furthermore, the incidence of price promotions is strongly coordinated within retail chains, both at the local market level and nationally. We present evidence, based on store-level demand estimates for 2,000 brands, that price elasticities and promotion effects at the local market level are substantially more similar within stores that belong to the same chain than across stores belonging to different retailers. Moreover, we find that retailers can not easily distinguish, in a statistical sense, among the price elasticities and promotion effects across stores using retailer-level data. Hence, the limited level of price discrimination across stores by retail chains likely reflects demand similarity and the inability to distinguish demand across the stores in a local market. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.