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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.

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and parsimonious multiple discrete-continuous extreme value (MDCEV) econometric approach to handle such multiple discreteness was formulated by Bhat (2005) within the broader Kuhn–Tucker (KT) multiple discrete-continuous economic consumer demand model of Wales and Woodland (1983). In this chapter, the focus is on presenting the basic MDCEV model structure, discussing its estimation and use in prediction, formulating extensions of the basic MDCEV structure, and presenting applications of the model. The paper examines several issues associated with the MDCEV model and other extant KT multiple discrete-continuous models. Specifically, the paper discusses the utility function form that enables clarity in the role of each parameter in the utility specification, presents identification considerations associated with both the utility functional form as well as the stochastic nature of the utility specification, extends the MDCEV model to the case of price variation across goods and to general error covariance structures, discusses the relationship between earlier KT-based multiple discrete-continuous models, and illustrates the many technical nuances and identification considerations of the multiple discrete-continuous model structure. Finally, we discuss the many applications of MDCEV model and its extensions in various fields.

We review the interventions that promote motivation in academic contexts, with a focus on two primary questions: How can we motivate students to take more STEM courses? Once in those STEM courses, how can we keep students motivated and promote their academic achievement?

We have approached these two motivational questions from several perspectives, examining the theoretical issues with basic laboratory research, conducting longitudinal questionnaire studies in classrooms, and developing interventions implemented in different STEM contexts. Our research is grounded in three theories that we believe are complementary: expectancy-value theory (Eccles & Wigfield, 2002), interest theory (Hidi & Renninger, 2006), and self-affirmation theory (Steele, 1988). As social psychologists, we have focused on motivational theory and used experimental methods, with an emphasis on values – students’ perceptions of the value of academic tasks and students’ personal values that shape their experiences in academic contexts.

We review the experimental field studies in high-school science and college psychology classes, in which utility-value interventions promoted interest and performance for high-school students in science classes and for undergraduate students in psychology courses. We also review a randomized intervention in which parents received information about the utility value of math and science for their teens in high school; this intervention led students to take nearly one semester more of science and mathematics, compared with the control group. Finally, we review an experimental study of values affirmation in a college biology course and found that the intervention improved performance and retention for first-generation college students, closing the social-class achievement gap by 50%. We conclude by discussing the mechanisms through which these interventions work.

These interventions are exciting for their broad applicability in improving students’ academic choices and performance, they are also exciting regarding their potential for contributions to basic science. The combination of laboratory experiments and field experiments is advancing our understanding of the motivational principles and almost certainly will continue to do so. At the same time, interventions may benefit from becoming increasingly targeted at specific motivational processes that are effective with particular groups or in particular contexts.

In previous research (Abou-Zeid et al., 2008), we postulated that people report different levels of travel happiness under routine and nonroutine conditions and supported this hypothesis through an experiment requiring habitual car drivers to switch temporarily to public transportation. This chapter develops a general modeling framework that extends random utility models by using happiness measures as indicators of utility in addition to the standard choice indicators, and applies the framework to modeling happiness and travel mode switching using the data collected in the experiment. The model consists of structural equations for pretreatment (remembered) and posttreatment (decision) utilities and explicitly represents their correlations, and measurement equations expressing the choice and the pretreatment and posttreatment happiness measures as a function of the corresponding utilities. The results of the empirical model are preliminary but support the premise that the extended modeling framework, which includes happiness, will potentially enhance behavioral models based on random utility theory by making them more efficient.

The chapter outlines the principles underlying relative utility models, discusses the results of empirical applications and critically assesses the usefulness of this specification against commonly used random utility models and other context dependence models. It also discusses how relative utility can be viewed as a generalisation of context dependency.

In contrast to the conventional concept of random utility, relative utility assumes that decision-makers derive utility from their choices relative to some threshold(s) or reference points. Relative utility models thus systematically specify the utility against such thresholds or reference points.

Examples in the chapter show that relative utility model perform well in comparison to conventional utility-maximising models in some circumstances.

Examples of relative utility models are rare in transportation research. The chapter shows that several recent models can be viewed as special cases of relative utility models.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

The paper published below was prepared by Taylor Ostrander for Frank Knight’s course, Economic Theory, Economics 301, during the Fall 1933 quarter.

With the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.

We model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.

First, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.

Our findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.