4 research outputs found
Welfare Effects of Pharmaceutical Informative Advertising
No full textPharmaceutical markets are characterized by a high degree of innovation, complexity and uncertainty, especially markets of idiosyncratic symptomatolgy and response to treatment such as the antidepressant market. It may, therefore, be unreasonable to assume that consumers are aware of all antidepressants for sale at the time of purchase, as is the case in traditional models of consumer choice. Such an assumption will bias demand curves towards being more elastic and the evaluation of consumer welfare downwards. This paper, therefore, aims at analyzing and evaluating the effects of promotions by pharmaceutical firms on patient welfare taking into account the interaction of multiple agents (patients, physicians, insurance companies and pharmaceutical companies) in the decision process. I present an empirical discrete-choice model of limited information, where advertising influences the set of drugs from which a purchase choice is made. The estimation technique incorporates both macro- and micro-level data. Estimation results indicate that pharmaceutical firms use advertising media to target high-income households and households with more comprehensive prescription drug insurance schemes through their physicians or directly. Model comparison shows that limited information leads to less elastic demand curves and larger estimates of patient welfare due to pharmaceutical innovation that exacerbate the moral hazard issue that coexists with insurance coverage.Advertising, Health, Information, Moral Hazard, Pharmaceuticals, Welfare
The Pure Characteristics Demand Model 1
No full text1 We are very pleased to have presented this paper at the Festschrift in honor of Dan McFadden. Dan opened up the world of discrete choice modeling in empirical researh, with a lasting impact on our field. We also appreciate helpful comments from Don Brown, Paris Cleanthous, Gowtham Gowrisankarn, Minjae Song and from many seminar participants over the many years that we worked on this project. In this paper we consider a class of discrete choice models in which consumers care about a finite set of product characteristics. These models have been used extensively in the theoretical literature on product differentiation and the goal of this paper is to translate them into a form that is useful for empirical work. Most recent econometric applications of discrete choice models implicitly let the dimension of the characteristic space increase with the number of products. The two models have different theoretical properties, and these, in turn, have quite pronounced implications for the likely impacts o
Kernel and wavelet density estimators on manifolds and more general metric spaces
No full textWe consider the problem of estimating the density of observations taking values in classical or nonclassical spaces such as manifolds and more general metric spaces. Our setting is quite general but also sufficiently rich in allowing the development of smooth functional calculus with well localized spectral kernels, Besov regularity spaces, and wavelet type systems. Kernel and both linear and nonlinear wavelet density estimators are introduced and studied. Convergence rates for these estimators are established, which are analogous to the existing results in the classical setting of real-valued variables
