Harikesh S. Nair

Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders

The authors quantify the impact of social interactions and peer effects in the context of physicians’ prescription choices. Using detailed individual-level prescription data, along with self-reported social network information, the authors document that physician prescription behavior is significantly influenced by the behavior of research-active specialists, or “opinion leaders,” in the physicians reference group. The authors leverage a natural experiment in the category: New guidelines released about the therapeutic nature of the focal drug generated conditions in which physicians were more likely to be influenced by the behavior of specialist physicians in their network. The authors (1) find important, statistically significant peer effects that are robust across model specifications; (2) document asymmetries in response to marketing activity across nominators and opinion leaders; (3) measure the incremental value to firms of directing targeted sales force activity to these opinion leaders; and (4) present estimates of the social multiplier of detailing in this category.

Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games

Firms in durable good product markets face incentives to intertemporally price discriminate, by setting high initial prices to sell to consumers with the highest willingness to pay, and cutting prices thereafter to appeal to those with lower willingness to pay. A critical determinant of the profitability of such pricing policies is the extent to which consumers anticipate future price declines, and delay purchases. I develop a framework to investigate empirically the optimal pricing over time of a firm selling a durable-good product to such strategic consumers. Prices in the model are equilibrium outcomes of a game played between forward-looking consumers who strategically delay purchases to avail of lower prices in the future, and a forward-looking firm that takes this consumer behavior into account in formulating its optimal pricing policy. The model outlines first, a dynamic model of demand incorporating forward-looking consumer behavior, and second, an algorithm to compute the optimal dynamic sequence of prices given these demand estimates. The model is solved using numerical dynamic programming techniques. I present an empirical application to the market for video-games in the US. The results indicate that consumer forward-looking behavior has a significant effect on optimal pricing of games in the industry. Simulations reveal that the profit losses of ignoring forward-looking behavior by consumers are large and economically significant, and suggest that market research that provides information regarding the extent of discounting by consumers is valuable to video-game firms.

Diffusion of New Pharmaceutical Drugs in Developing and Developed Nations

In the context of introducing new products around the world, it is important to understand the relative attractiveness of various countries in terms of maximum penetration potential and diffusion speed. In this paper, we examine these market characteristics for a new category of prescription drugs in both developing and developed countries. Using data from fifteen countries, and a logistic specification in the Hierarchical Bayesian framework, we report the differences in diffusion speed and maximum penetration potential between developing and developed nations. Our methodology accounts for the limited number of data observations as well as serial correlation and endogeneity problems that arise in the analysis. The principal findings include: (i) Compared to developed countries, developing nations tend to have lower diffusion speeds and maximum penetration levels; (ii) Laggard developed countries have higher speeds. However, laggard developing countries do not have higher diffusion speeds; (iii) Per capita expenditures on healthcare have a positive effect on diffusion speed (particularly for developed countries), while higher prices tend to decrease diffusion speed. The paper concludes by identifying useful avenues for additional research.

Intertemporal Price Discrimination with Forward-Looking Consumers: Application to the US Market for Console Video-Games

Firms in durable good product markets face incentives to intertemporally price discriminate, by setting high initial prices to sell to consumers with the highest willingness to pay, and cutting prices thereafter to appeal to those with lower willingness to pay. A critical determinant of the profitability of such pricing policies is the extent to which consumers anticipate future price declines, and delay purchases. We develop a framework to investigate empirically the optimal pricing over time of a firm selling a durable-good product to such strategic consumers. Prices in our model are equilibrium outcomes of a game played between forward-looking consumers who strategically delay purchases to avail of lower prices in the future, and a forward-looking firm that takes this consumer behavior into account in formulating its optimal pricing policy. The model incorporates first, a method to infer estimates of demand under dynamic consumer behavior, and second, an algorithm to compute the optimal sequence of prices given these demand estimates. The model is solved using numerical dynamic programming techniques. We present an empirical application to the market for video-games in the US. The results indicate that consumer forward-looking behavior has a significant effect on optimal pricing and profits of games in the industry. Simulations reveal that the profit losses of ignoring forward-looking behavior by consumers are large and economically significant, and suggest that market research that provides information regarding the extent of discounting by consumers is valuable to video-game firms.

Retail Competition and the Dynamics of Demand for Tied Goods

We present a demand system for tied goods incorporating dynamics arising from the tied nature of the products and the stockpiling induced by storability and durability. We accommodate competition across tied good systems and competing downstream retail formats by endogenizing the retail format at which consumers choose to stockpile inventory. This facilitates measurement of long-run retail substitution effects and yields estimates of complementarities within, and substitution across, competing systems of tied goods. We present an empirical application to an archetypal tied goods category: razors and blades. We discuss the implications of measured effects for manufacturer pricing when selling the tied products through an oligopolistic downstream retail channel and assess the extent to which retail substitution reduces channel conflict.

Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants

Adjustment of an operating parameter of an analog electronic circuit is effectuated through a set of adjustment resistances ( 22 ) that can be configured from outside the circuit to modulate the value of resistances (R 1 , R 2 ) in the circuit and thus to adjust the value of the parameter. Fusible elements ( 20 ) each associated with one of the said adjustment resistances are selected and activated to configure the resistances of the adjustment device. A combinational logic circuit ( 18 ) receives a control signal as input applied from outside the circuit onto a terminal (C) operates to select one of the fusible elements ( 20 ) as a function of a signal applied thereto.

Estimating Causal Installed-Base Effects: A Bias-Correction Approach

New empirical models of consumer demand that incorporate social preferences, observational learning, word-of-mouth or network effects have the feature that the adoption of others in the reference group - the “installed-base�? - has a causal effect on current adoption behavior. Estimation of such causal installed-base effects is challenging due to the potential for spurious correlation between the adoption of agents, arising from endogenous assortive matching into social groups (or homophily) and from the existence of unobservables across agents that are correlated. In the absence of experimental variation, the preferred solution is to control for these using a rich specification of fixed-effects, which is feasible with panel data. We show that fixed effects estimators of this sort are inconsistent in the presence of installed-base effects; in our simulations, random-effects specifications perform even worse. Our analysis reveals the tension faced by the applied empiricist in this area: a rich control for unobservables increases the credibility of the reported causal effects, but the incorporation of these controls introduces biases of a new kind in this class of models. We present two solutions: an instrumental variable approach, and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The bias-correction approach is tractable in this context because we are able to exploit the structure of the problem to solve analytically for the asymptotic bias of the installed base estimator, and to incorporate it into the estimation routine. Our approach has implications for the measurement of social effects using non-experimental data, and for measuring marketing-mix effects in the presence of state-dependence in demand, more generally. Our empirical application to the adoption of the Toyota Prius Hybrid in California reveals evidence for social influence in diffusion, and demonstrates the importance of incorporating proper controls for the biases we identify.

VMT MIX MODELING FOR MOBILE SOURCE EMISSIONS FORECASTING: FORMULATION AND EMPIRICAL APPLICATION

A fractional split model is proposed and implemented that predicts the vehicle miles traveled (VMT) mix on links as a function of the functional roadway classification of the link, the physical attributes of the link, the operating conditions on the link, and the attributes of the traffic analysis zone in which the link lies. The fractional split model is a useful formulation for VMT-mix analysis because it accommodates boundary values of fractional VMT in a vehicle class, is easy to estimate using commonly available econometric software, and is easy to apply in forecasting mode to predict the VMT mix on each link of a network. The empirical analysis applies the fraction split model structure to estimate a VMT-mix model for the Dallas–Fort Worth metropolitan region in Texas. The results of model evaluation also are presented.

Retail Competition and the Dynamics of Consumer Demand for Tied Goods

We empirically investigate the demand for tied goods sold through competing retail channels. Tied good pricing strategies commonly involve a low price on the initial purchase (i.e. the primary good) to drive adoption, and a substantial markup on aftermarket goods to capture value. However, if the goods are sold through downstream channels, retail market power and a misalignment of incentives could distort the relative prices of primary and aftermarket goods. To evaluate whether retail competition is strong enough to prevent such distortions, we explore the commonly noted example of razors and blades, which are sold through drug, grocery, mass merchandising, and club stores. We specify a forward-looking demand model that incorporates dynamics arising from the tied good nature of the products and the stockpiling and durability aspects of razors and blades. Furthermore, we allow intertemporal substitution in the purchase of both razors and blades to occur across channels as well as time. This modeling feature enables a novel approach to measuring retail competition in single category demand analyses. Our estimates indicate that there is substantial cross-channel substitution in razors, but some retail market power in blades. However, the channel with the most market power in blades, club stores, specializes in high volume customers that would adopt a razor even if blade prices are higher. This suggests that the manufacturer can achieve its desired level of razor adoption without vertical restraints, though blade sales may be slightly reduced by double marginalization.

Modeling soak-time distribution of trips for mobile source emissions forecasting: Techniques and applications

The soak time of vehicle trip starts is defined as the duration of time in which the vehicle’s engine is not operating and that precedes a successful vehicle start. The temporal distribution of the soak time in an area is an important determinant of areawide mobile source emissions. In this study, a methodology for modeling soak-time durations is formulated and implemented. The methodology involves estimation of models using vehicle trip data from household travel surveys and supplementary zonal demographic and land use data. The effectiveness of the methodology lies in its easy application at the traffic zonal level within a metropolitan region to obtain zone-specific soak-time distributions by time of day and origin activity purpose. The methodology is applied to estimate soak-time duration models for the Dallas–Fort Worth area of Texas.