Model Of Consumer Demand Research Articles

Vertical Integration and Exclusivity in Platform and Two-Sided Markets

This paper measures the impact of vertically integrated and exclusive software on industry structure and welfare in the sixth-generation of the US video game industry (2000–2005). I specify and estimate a dynamic model of both consumer demand for hardware and software products, and software demand for hardware platforms. I use estimates to simulate market outcomes had platforms been unable to own or contract exclusively with software. Driven by increased software compatibility, hardware and software sales would have increased by 7 percent and 58 percent and consumer welfare by $1.5 billion. Gains would be realized only by the incumbent, suggesting exclusivity favored the entrant platforms. (JEL D12, L13, L22, L63, L86)

The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference

In recent years, many researchers have devoted their attention to using search traffic information gathered from Google Insights to carry out consumer attitude research. The purpose of this study is to assess the effectiveness of using search traffic information to analyze actual consumer attitudes regarding a product. By comparing the results of conventional survey-based attitude research with the results of search traffic information, this study reveals that search traffic information indicates consumers' level of interest regarding a product, the product attributes that they are considering, and the importance of each attribute to them. Also, it demonstrates the potential benefits of search traffic analysis, which can be useful for forecasting consumer preferences regarding products. Focusing on the Prius, a hybrid car, this study shows that search traffic information serves as an accurate indicator of consumer attitudes, and even succeeds in identifying consumers' hidden attitudes toward the Prius, which can be explained by cognitive dissonance theory. Finally, this study utilizes search traffic information to forecast changes in consumer attitudes and to develop an econometric model of consumer demand for the Prius by incorporating environmental variables such as the WTI (West Texas Intermediate) price. This study concludes that search traffic information offers new potential advantages, in that it not only overcomes the limitations imposed by the high cost of conducting surveys, in terms of money and time, but also helps to reduce the distortions caused by conscious or unconscious errors committed by survey respondents.

Firm's objective function and product and process R&D

Firms undertake different kinds of R&D activities. They do product R&D (R&D aimed at improving the quality of existing products, and creating new products). They also do process R&D (R&D aimed at lowering the cost of making existing and new products). Moreover, firms often do both product and process R&D simultaneously. As far as the objective of firms is concerned, this need not be limited to profit-maximization only. Rather, firms may have a broader objective, where they care about profits as well as consumer surplus. This paper studies effects of a firm having a general objective function (that takes into consideration both profits and consumer surplus) on its product and process R&D choices, and corresponding implications.I consider product and process R&D choices of firms in an infinite horizon set-up with discrete time. Firms in my framework can simultaneously do both product and process R&D in every period, face a discrete-choice model of consumer demand with vertical product differentiation, and maximize a discounted, weighted sum of their profits and consumer surplus over the infinite time horizon.I show how process and product R&D differ from each other in my framework, and the role of a firm's objective function in this regard. I compare process and product R&D choices across firms that differ in their objective function, and illustrate effects of providing general R&D subsidies (subsidies given for any R&D, regardless of whether it is product or process R&D) to firms. I also characterize how in my framework, the choice of process R&D in total R&D — R&D composition — by an individual firm varies over time, and how process and product R&D choices, process and product R&D productivity, and the choice of R&D composition vary across firms that differ in size but are otherwise similar.

A Parsimonious Structural Model of Individual Demand for Multiple Related Goods

Nearly all theoretically motivated models of consumer demand for multiple goods assume additive separability in preferences, i.e. the consumption utility of each good x is independent of the quantity demanded of another good y. This is a strong restriction that makes the solution of the consumer’s utility maximization problem computationally tractable. This paper shows that assuming preferences are weakly separable yields a similar simplification. It offers a theoretically founded model of consumer demand for continuous quantities of related goods. It also proposes a Bayesian estimation approach with a parsimonious parameterization that allows for corner solutions. It is the first structural model of individual demand for multiple goods which relaxes additive separability and does not suffer from a curse of dimensionality in the number of chosen goods. The model is estimated using data on Chilean advertisers’ television audience purchases. We find that advertisers prefer spreading expenditures across time blocks more so than spreading expenditures across networks within a time block. The model nests additively separable preferences as a special case, but the data reject this case. We illustrate how a television network could use the model to assess the consequences of different advertisement pricing policies conditional on competitive response assumptions.

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

New empirical models of consumer demand that incorporate social effects seek to measure the causal effect of past adopter's behavior—the “installed-base”—on current adoption behavior. Identifying such causal effects is challenging due to several alternative confounds that generate correlation in agents' actions. In the absence of experimental variation, a preferred solution has been to control for these spurious correlations using a rich specification of fixed effects. The authors show that fixed-effects estimators of this sort are inconsistent in the presence of installed-base effects; in simulations, random-effects specifications perform even worse. The analysis reveals the tension the applied empiricist faces 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. The authors present two solutions: a modified version of an instrumental variable approach and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The empirical application to the adoption of the Toyota Prius Hybrid in California shows evidence for social influence in diffusion and reveals that implementing the bias correction reverses the sign of the measured installed-base effect. The authors also discuss implications of the results for identification of models in marketing involving state dependence in demand, and incorporating discrete games of strategic interaction.

DEMAND FOR DIFFERENTIATED DURABLE PRODUCTS: THE CASE OF THE U.S. COMPUTER PRINTER MARKET

This paper develops an estimation technique for analyzing the impact of technological change on the dynamics of consumer demand in a differentiated durable products industry. The paper presents a dynamic model of consumer demand for differentiated durable products that explicitly accounts for consumers' expectations of future product quality and consumers' outflow from the market that arises endogenously from their purchase decisions. The timing of consumers' purchases is formalized as an optimal stopping problem. A solution to that problem defines the hazard rate of product adoptions, while the nested discrete choice model determines the alternative‐specific purchase probabilities. Integrating individual decisions over the population distribution generates rich dynamics of aggregate and product‐level sales. The empirical part of the paper applies the model to data on the U.S. computer printer market for 1998–1999. The estimates support the hypothesis of consumers' forward‐looking behavior, allowing for better demand forecasts and improved measures of welfare gains from introducing new products. (JEL L11, C35, D91)

A Computationally Efficient MCMC Approach to Estimating Structural Models of Aggregate Demand

This paper develops a computationally efficient approach to structural estimation of random coefficients logit models of consumer demand using product-level data. The conventional GMM estimation approach relies on two computationally intensive fixed point algorithms developed by Rust (1987) and Berry, Levinsohn, and Pakes (1995). We transform the GMM estimator into a quasi-Bayesian (Laplace type) framework and develop a new Markov Chain Monte Carlo (MCMC) approach that solves the fixed point problems incrementally with Markov chain simulation. The proposed approach has two main advantages. First, it reduces the computational burden involved with the nested fixed point approach. In doing so, the proposed algorithm does not sacrifice the rich consumer heterogeneity. Second, our proposed estimation approach requires only moment condition as the GMM estimator, therefore avoiding the risk of misspecification bias in equilibrium frameworks. This contrasts with alternative approaches such as simulated maximum likelihood, control function, and Bayesian methods that require explicitly specified likelihood model.

MEASURING THE WELFARE GAIN FROM PERSONAL COMPUTERS

The welfare gain to consumers from the introduction of personal computers (PCs) is estimated. A simple model of consumer demand is formulated that uses a slightly modified version of standard preferences. The modification permits marginal utility, and hence total utility, to be finite when the consumption of computers is zero. This implies that the good will not be consumed at a high enough price. It also bounds the consumer surplus derived from the product. The model is calibrated/estimated using standard national income and product account data. The welfare gain from the introduction of PCs is 2%–3% of consumption expenditure. (JELE01, E21, O33, O47)

Adaptive learning in service operations

We propose a decision analytics approach that leverages adaptive learning in the refinement of service operations. We aim to integrate service design and service pricing with downstream operational decision-making related to service provision. This approach involves: collecting consumer data and establishing consumer behavior models; integrating consumer behavior models with models for service operation decision-making; and iteratively evaluating service designs based on service delivery performance that evolves over time due to learning. We discuss how this approach enables service providers to set time-differentiated prices and evaluate the impact on transportation network performance. We use agent-based simulation to illustrate the application of our approach to the operations of a public rail transportation firm in a European urban setting. Our findings suggest that knowing the impacts of consumer responses in service operations is essential for devising cost-effective and value-bearing service designs. Our approach can support service providers who wish to adjust their pricing, consumer demand and capacity management models, and to develop more effective market forecasts of performance through adaptive learning, in the presence of “big data” from consumers and operations.

Structural Workshop Paper—Discrete-Choice Models of Consumer Demand in Marketing

Marketing researchers have used models of consumer demand to forecast future sales, to describe and test theories of behavior, and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain an econometric system that describes consumers' choices over available options, and to thus characterize product demand. The basic framework has been augmented significantly to account for quantity choices, to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multicategory purchases), and to allow for asymmetric switching between brands across different price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The first objective is to articulate the main goals of demand analysis—forecasting, measurement, and testing—and to highlight several considerations associated with these goals. Our second objective is to describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and we review extensions that have appeared in the marketing literature. The third objective is to explore a few emerging directions in demand analysis, including considering demand-side dynamics, combining purchase data with primary information, and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see the potential for new directions in future research.

Focus on Authors

Focus on Authors

Automobile replacement: a dynamic structural approach

This article specifies and estimates a structural dynamic model of consumer demand for new and used durable goods. Its primary contribution is to provide an explicit estimation procedure for transaction costs. Identification of transaction costs is achieved from the variation in the share of consumers choosing to hold a given car type each period, and from the share of consumers choosing to purchase the same car type that period. Specifically, I estimate a random‐coefficient discrete‐choice model that incorporates a dynamic optimal stopping problem. I apply this model to evaluate the impact of scrappage subsidies on the Italian automobile market.

Exploring the effects of aggregation error in the estimation of consumer demand elasticities

Errors introduced by using aggregate data in estimating a consumer demand model have long been a concern. We study the effects of such errors on elasticity estimates derived from AIDS and QUAIDS models. Based on a survey of published articles, a generic parameterization of the income distribution, and the range of Gini coefficients reported for 28 OECD countries, we generate and analyze a large number of “observations” on the differences between elasticities calculated at the aggregate level and those calculated at the micro level. We suggest a procedure for evaluating the likely range of aggregation error when a model is estimated with aggregate data.

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.

Scenarios for regional passenger car fleets and their CO2 emissions

Passenger car traffic is among the main contributors to anthropogenic greenhouse gas (GHG) emissions, which are responsible for climate change. It is also an important indicator used to forecast these emissions in integrated climate-economic models. This paper develops scenarios for global passenger car stock until 2050. The study adopts a global regionalized approach, encompassing 11 world regions. Car stock projections are obtained using a multi-model approach, which includes a consumer demand model based on utility maximization, a non-linear Gompertz model and a panel estimate of the income elasticity of demand for cars. The main hypothesis underlying these projections is that preferences for purchasing cars are similar across cultures and nations and that the demand for cars is largely determined by disposable income. We apply scenarios for the average traffic volume and fuel efficiency developed in previous work together with the average carbon content of fuels to obtain the CO2 emissions.

Pricing strategies for tied digital contents and devices

Media companies are increasingly offering digital content to consumers. Many of these companies are tying digital content with their proprietary digital devices. In this study, we develop a consumer demand model for digital device and digital content based on a constant elasticity demand function. In modeling consumer valuation of the digital device, we take consumer surplus on digital content into account. We further derive equilibrium prices for digital devices based on an oligopoly competition model with horizontal product differentiation. We analyze the equilibrium prices and how prices affect firm profits. We find product differentiation and the level of product substitutability affect prices. We also find that content price plays a significant role in affecting the price of the digital device. Content price can either increase or decrease the tied digital device price depending on the profit margin and demand elasticity of the digital content. We further analyze how content and device prices affect their respective profits and the overall profit of the firm. We extend our model to vertical product differentiation and find vertical product differentiation and the level of product quality affect prices.

Measuring the Welfare Gain from Personal Computers: A Macroeconomic Approach

The welfare gain to consumers from the introduction of personal computers is estimated here. A simple model of consumer demand is formulated that uses a slightly modified version of standard preferences. The modification permits marginal utility, and hence total utility, to be finite when the consumption of computers is zero. This implies that the good won't be consumed at a high enough price. It also bounds the consumer surplus derived from the product. The model is calibrated/estimated using standard national income and product account data. The welfare gain from the introduction of personal computers is in the range of 2 to 3 percent of consumption expenditure.

Demand Forecasting in Transport: Overview and Modeling Advances

The main purpose of this paper is to comprehensively explore and productively overview the growing research field of demand forecasting in transport. In this analytic context, it seeks to describe, critically discuss and fruitfully elaborate on relevant mechanisms and models of demand forecasting, as well as on the particular development and implementation of systematic (or system-wide) approaches. The overview of various theoretical and methodological developments in current prediction models eventually advocates the use of consumer demand models (of dynamic character) to predict demand shares among alternative modes of transport.

Marketing Models of Consumer Demand

Marketing researchers have used models of consumer demand to forecast future sales; to describe and test theories of consumer behavior; and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain a statistical system that describes consumers’ choices over available options, and to thus characterize product demand. The basic model has been augmented significantly to account for quantity choice decisions; to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multi-category purchases); and to allow for asymmetric switching between brands across different price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The first objective is to articulate the main goals of demand analysis - forecasting, measurement and testing - and to highlight the desiderata associated with these goals. Our second objective is describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and review extensions that have appeared in the marketing literature. The third objective is to explore interesting emerging directions in demand analysis including considering demand-side dynamics; combining purchase data with primary information; and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see potential for new directions in future research.

Location and competition in retail banking

We investigate the impact of banking deregulation during the 1990s on consumer welfare. We estimate a spatial model of consumer demand for retail bank deposits that explicitly accounts for consumer disutility from distance traveled. This is important given the substantial changes in banks' branch networks observed in the data. Our model indicates that cross-price elasticities between banks whose branches are close to consumers (‘close’ banks) are larger than those between ‘far’ banks and more than double the cross-price elasticity of ‘close’ banks with respect to ‘far’ banks. We distinguish between thrifts and other banks and find that within-thrift competitive effects are stronger than within-bank effects or those between thrifts and banks. We use our estimates to predict the effect of changes in market structure on consumer welfare following the branching deregulation of the Riegle–Neal Act of 1994. Our results indicate that the median household gained around $60 per year from the changes. Approximately two thirds of the gains come from within-market changes in market structure. The gains were greater in markets with high initial numbers of banks than elsewhere.