State Dependence and Alternative Explanations for Consumer Inertia

For many consumer packaged goods products, researchers have documented a form of state dependence whereby consumers become "loyal" to products they have consumed in the past. That is, consumers behave as though there is a utility premium from continuing to purchase the same product as they have purchased in the past or, equivalently, there is a psychological cost to switching products. However, it has not been established that this form of state dependence can be identified in the presence of consumer heterogeneity of an unknown form. Most importantly, before this inertia can be given a structural interpretation and used in policy experiments such as counterfactual pricing exercises,alternative explanations which might give rise to similar consumer behavior must be ruled out. We develop a flexible model of heterogeneity which can be given a semi-parametric interpretation and rule out alternative explanations for positive state dependence such as autocorrelated choice errors, consumer search, or consumer learning.

Advertising Bans and the Substitutability of Online and Offline Advertising

The authors examine whether the growth of the Internet has reduced the effectiveness of government regulation of advertising. They combine nonexperimental variation in local regulation of offline alcohol advertising with data from field tests that randomized exposure to online advertising for 275 different online advertising campaigns to 61,580 people. The results show that people are 8% less likely to say that they will purchase an alcoholic beverage in states that have alcohol advertising bans compared with states that do not. For consumers exposed to online advertising, this gap narrows to 3%. There are similar effects for four changes in local offline alcohol advertising restrictions when advertising effectiveness is observed both before and after the change. The effect of online advertising is disproportionately high for new products and for products with low awareness in places that have bans. This suggests that online advertising could reduce the effectiveness of attempts to regulate offline advertising channels because online advertising substitutes for (rather than complements) offline advertising.Google (Firm)WPP (Firm

An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty

This paper considers the decision problem of a firm that is uncertain about the demand, and hence profitability, of a new product. We develop a model of a decision maker who sequentially learns about the true product profitability from observed product sales. Based on the current information, the decision maker decides whether to scrap the product. Central to this decision problem are sequential information gathering, and the option value of scrapping the product at any point in time. The model predicts the optimal demand for information (e.g., in the form of test marketing), and it predicts how the launch or exit policy depends on the firm's demand uncertainty. Furthermore, it predicts what fraction of newly developed products should be launched on average, and what fraction of these products will “fail,” i.e., exit. The model is solved using numerical dynamic programming techniques. We present an application of the model to the case of the U.S. ready-to-eat breakfast cereal industry. Simulations show that the value of reducing uncertainty can be large, and that under higher uncertainty firms should strongly increase the fraction of all new product opportunities launched, even if their point estimate of profits is negative. Alternative, simpler decision rules are shown to lead to large profit losses compared to our method. Finally, we find that the high observed exit rate in the U.S. ready-to-eat cereal industry is optimal and to be expected based on our model.new product strategy, product launch, product exit, managerial decision making under uncertainty, Bayesian learning, numerical dynamic programming, dynamic structural models

Preliminary and Incomplete. Comments Welcome

We compare alternative numerical methods for approximating solutions to continuous-state dynamic programming (DP) problems. We distinguish two approaches: discrete approximation and parametric approximation. In the former, the continuous state space is discretized into a finite number of points N, and the resulting finite-state DP problem is solved numerically. In the latter, a function associated with the DP problem such as the value function, the policy function, or some other related function is approximated by a smooth function of K unknown parameters. Values of the parameters are chosen so that the parametric function approximates the true function as closely as possible. We focus on approximations that are linear in parameters, i.e. where the parametric approximation is a linear combination of K basis functions. We also focus on methods that approximate the value function V as the solution to the Bellman equation associated with the DP problem. In finite state DP problems the method of policy iteration is an effective iterative method for solving the Bellman equation that converges to V in a finite number of steps. Each iteration involves a policy valuation step that computes the value function Vα corresponding to a trial policy α. We show how policy iteration can be extended to continuous-state DP problems. For discrete approximation, we refer to the resulting algorithm as discrete policy iteration (DPI). Eac

Tipping and Concentration in Markets with Indirect Network Effects

This paper develops a framework for measuring “tipping”—the increase in a firm's market share dominance caused by indirect network effects. Our measure compares the expected concentration in a market to the hypothetical expected concentration that would arise in the absence of indirect network effects. In practice, this measure requires a model that can predict the counterfactual market concentration under different parameter values capturing the strength of indirect network effects. We build such a model for the case of dynamic standards competition in a market characterized by the classic hardware/software paradigm. To demonstrate its applicability, we calibrate it using demand estimates and other data from the 32/64-bit generation of video game consoles, a canonical example of standards competition with indirect network effects. In our example, we find that indirect network effects can lead to a strong, economically significant increase in market concentration. We also find important roles for beliefs on both the demand side, as consumers tend to pick the product they expect to win the standards war, and on the supply side, as firms engage in penetration pricing to invest in growing their networks.dynamic oligopoly, indirect network effects, tipping, standards war, high technology

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.dynamic pricing, loyalty, state dependence, consumer heterogeneity