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The U.S. 2009 Tobacco Control Act opened the door for new antismoking policies by giving the Food and Drug Administration broad regulatory authority over the tobacco industry. We develop a behavioral welfare economics approach to conduct cost-benefit analysis of FDA tobacco regulations. We use a simple two-period model to develop expressions for the impact of tobacco control policies on social welfare. Our model includes: nudge and paternalistic regulations; an excise tax on cigarettes; internalities created by period 1 versus period 2 consumption; and externalities from cigarette consumption. Our analytical expressions show that in the presence of uncorrected internalities and externalities, a nudge or a tax to reduce cigarette consumption improves social welfare. In sharp contrast, a paternalistic regulation might either improve or worsen social welfare. Another important result is that the social welfare gains from new policies do not only depend on the size of the internalities and externalities, but also depend on the extent to which current policies already correct the problems. We link our analytical expressions to the graphical approach used in most previous studies and discuss the information needed to complete cost-benefit analysis of tobacco regulations. We use our model as a framework to reexamine the evidence base for strong conclusions about the size of the internalities, which is the key information needed.

This chapter is an up-to-date survey of the state-of-the art in consumer demand analysis. We review (and evaluate) advances in a number of related areas, in the spirit of the recent survey paper by Barnett and Serletis (2008). In doing so, we only deal with consumer choice in a static framework, ignoring a number of important issues, such as, the effects of demographic or other variables that affect demand, welfare comparisons across households (equivalence scales), and the many issues concerning aggregation across consumers.

Lewbel and Pendakur (2009) developed the idea of implicit Marshallian demands. Implicit Marshallian demand systems allow the incorporation of both unobserved preference heterogeneity and complex Engel curves into consumer demand analysis, circumventing the standard problems associated with combining rationality with either unobserved heterogeneity or high rank in demand (or both). They also developed the exact affine Stone index (EASI) implicit Marshallian demand system wherein much of the demand system is linearised and thus relatively easy to implement and estimate. This chapter offers a less technical introduction to implicit Marshallian demands in general and to the EASI demand system in particular. I show how to implement the EASI demand system, paying special attention to tricks that allow the investigator to further simplify the problem without sacrificing too much in terms of model flexibility. STATA code to implement the simplified models is included throughout the text and in an appendix.

This chapter presents the differential approach to applied demand analysis. The demand systems of this approach are general, having coefficients that are not necessarily constant. We consider the Rotterdam parameterization of differential demand systems and derive the absolute and relative price versions of the Rotterdam model, due to Theil (1965) and Barten (1966). We address estimation issues and point out that, unlike most parametric and semi-nonparametric demand systems, the Rotterdam model is econometrically regular.

This chapter presents a summary of the literature on the influence of the Internet and other digital innovations on markets, consumers, and firms. The review leads to a list of topics in need of research in the general areas of big data, online and mobile advertising, consumer search, online privacy, online reviews, social networks, platforms for online transactions, and the impact of the Internet on retail markets, including multi-channel and omni-channel retailing. We discuss the big data approaches that have been applied to problems of targeting and positioning and suggest areas for further development of these approaches. We also discuss the emerging area of mobile advertising, which can further enhance targeting. On the consumer side, the evidence indicates that the Internet has greatly lowered the costs of search and access to retailers. Much of the consumer data are transmitted to sellers, and much of the online advertising is transmitted to consumers, through platforms, such as Google. We conclude that better models of competition among these platform firms are needed and that they need to be examined for anti-trust violations. While online retailing has grown rapidly, it still has a relatively small share of retail sales. Since sellers can combine the advantages of online and offline channels, it has been common for sellers to branch into multi-channel retailing. Given the increased availability of detailed consumer data, omni-channel selling, which emphasizes strategies for the various touchpoints that lead to a transaction, is an area for further development.

The chapter estimates the cost of maintaining a child, at different ages, the cost of being single, and the cost of additional adults present in a family, with the aim of making comparable the income levels of different households. The study investigates the issue of econometric identification of equivalence scales within a demand system modified to include demographic characteristics consistently with economic theory. It shows that a robust estimation of equivalence scales must take into formal consideration the problem of econometric identification. The estimate also puts forward all-encompassing demographic specifications to identify costs due to differences in needs, household lifestyles, and economies of scale.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

This chapter examines the seeming paradox that although children may be a net cost to parents, they may nonetheless play a key role in underwriting the cost of large families. Maya time allocation and reproductive history data are used to approach children’s economic value from two methodological perspectives: wealth flows and the timing of children’s economic contributions. While Maya children are expensive to raise, when viewed in light of the timing of their labor supply across the demographic life cycle of the family, children’s economic contributions enable Maya parents to continue childbearing and raise more children than they might otherwise be able.