Matthew Osborne

Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock

By April 2013, the FCCs recent bill-shock agreement with cellular carriers requires consumers be notified when exceeding usage allowances. Will the agreement help or hurt consumers? To answer this question, we estimate a model of consumer plan choice, usage, and learning using a panel of cellular bills. Our model predicts that the agreement will lower average consumer welfare by

Consumer Learning, Switching Costs, and Heterogeneity: A Structural Examination

2 per year because firms will respond by raising monthly fees. Our approach is based on novel evidence that consumers are inattentive to past usage (meaning that bill-shock alerts are informative) and advances structural modeling of demand in situations where multipart tariffs induce marginal-price uncertainty. Additionally, our model estimates show that an average consumer underestimates both the mean and variance of future calling. These biases cost consumers

Competition Among Spatially Differentiated Firms: An Empirical Model with an Application to Cement

42 per year at existing prices. Moreover, absent bias, the bill-shock agreement would have little to no effect.

Competition Among Spatially Differentiated Firms: An Estimator with an Application to Cement

This paper develops and estimates a model of forward-looking consumer learning with switching costs using household level scanner data from a frequently purchased product category. This is novel because current models of consumer purchase behavior assume that only one of these types of dynamics is present, not both at the same time. My model estimates support the presence of both learning and switching costs in this product category. The estimates show that before consuming new products, consumers are unsure of their tastes for them, and subsequently learn their tastes by purchase and consumption of new products. Switching costs are large, comprising roughly 30 percent of the cost of a medium sized package of the product. Additionally, the model incorporates very rich individual level unobserved heterogeneity in price sensitivities, tastes, and switching costs, and the amount by which consumers learn. To show that my model produces different implications than a model with learning or switching costs only, I estimate two more specifications, one without each type of dynamics, and simulate counterfactuals that are of interest to managers and policymakers. I find that intertemporal elasticities are underestimated when either type of dynamics is left out, by as much as 90%. Informative advertising is also affected by the presence of switching costs, although the direction of the bias is not signed. Leaving out dynamics also has a large impact on long-term elasticities, which are used by antitrust policymakers to evaluate the impact of mergers. When learning is ignored, cross elasticities are underestimated by as much as 45%. When switching costs are ignored, both own and cross elasticities are underestimated.

Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling

The theoretical literature of industrial organization shows that the distances between consumers and firms have first-order implications for competitive outcomes whenever transportation costs are large. To assess these effects empirically, we develop a structural model of competition among spatially differentiated firms and introduce a GMM estimator that recovers the structural parameters with only regional-level data. We apply the model and estimator to the portland cement industry. The estimation fits, both in-sample and out-of-sample, demonstrate that the framework explains well the salient features of competition. We estimate transportation costs to be

Frequency versus Depth: How Changing the Temporal Process of Promotions Impacts Demand for a Storable Good

0.30 per tonne-mile, given diesel prices at the 2000 level, and show that these costs constrain shipping distances and provide firms with localized market power. To demonstrate policy-relevance, we conduct counter-factual simulations that quantify competitive harm from a hypothetical merger. We are able to map the distribution of harm over geographic space and identify the divestiture that best mitigates harm.

Accounting for Household Production in the National Accounts, 1965-2010

We develop an estimator for models of competition among spatially differentiated firms. In contrast to existing methods (e.g., Houde (2009)), the estimator has flexible data requirements and is implementable with data that are observed at any level of aggregation. Further, the estimator is the first to be applicable to models in which firms price discriminate among consumers based on location. We apply the estimator to the portland cement industry in the U.S. Southwest over 1983-2003. We estimate transportation costs to be

Spatial differentiation and price discrimination in the cement industry: evidence from a structural model

0.30 per tonne-mile and show that, given the topology of the U.S. Southwest, these transportation costs permit more geographically isolated plants to discriminate among consumers. We conduct a counterfactual experiment and determine that disallowing this spatial price discrimination would increase consumer surplus by

Pass-through in a concentrated industry: empirical evidence and regulatory implications

12 million annually, relative to a volume of commerce of

Approximating the Cost-of-Living Index for a Storable Good

1.3 billion. Heretofore it has not been possible examine the surplus implications of spatial price discrimination in specific, real-world settings; these implications have been known to be ambiguous theoretically since at least Gronberg and Meyer (1982) and Katz (1984). Additionally, our methodology can be used to construct transportation margins, which are an important component of input-output tables.