Joshua Schwartzstein is an assistant professor of business administration in the Negotiation, Organizations & Markets Unit. Before joining HBS, he was an assistant professor of economics at Dartmouth College.
Professor Schwartzstein’s primary research area is behavioral economics. He focuses on incorporating more psychologically realistic assumptions into economic models, and on applying these models to shed light on market outcomes and optimal public policy. In recent work, he has studied how the recognition that people make mistakes in medical-care decisions affects the analysis of health insurance policies, how relative thinking influences consumer choice, and how selective attention impacts the way people learn to use technologies. His research has appeared in the Quarterly Journal of Economics, the Journal of the European Economic Association, the Annual Review of Economics, and the Journal of Law and Economics. It has also been referenced in The New York Times, Science, and Health Affairs.
Professor Schwartzstein holds a PhD in economics from Harvard University and a BA in behavioral economics, economics, and mathematics from Cornell University.
Consumers suffer significant losses from not acting on available information. These losses stem from frictions such as search costs, switching costs, and rational inattention, as well as what we call mental gaps resulting from wrong priors/worldviews, or relevant features of a problem not being top of mind. Most research studying such losses does not empirically distinguish between these mechanisms. Instead, we show that most highly cited papers in this area presume one mechanism underlies consumer choices and assume away other potential explanations or collapse many mechanisms altogether. We discuss the empirical difficulties that arise in distinguishing between different mechanisms as well as some promising approaches for making progress in doing so. We also assess when it is more or less important for researchers to distinguish between these mechanisms. Approaches that seek to identify true value from demand, without specifying mechanisms behind this wedge, are most useful when researchers are interested in evaluating allocation policies that strongly steer consumers towards better options with regulation, traditional policy instruments, and defaults. On the other hand, understanding the precise mechanisms underlying consumer losses is essential to predicting the impact of mechanism policies aimed primarily at reducing specific frictions or mental gaps without otherwise steering consumers. We make the case that papers engaging with these questions empirically should be clear about whether their analyses distinguish between mechanisms behind poorly informed choices and what that implies for the questions they can answer. We present examples from several empirical contexts to highlight these distinctions.
People make personal plans regarding whether, when, where, and how to undertake certain actions. We discuss three questions related to personal plans. First, what are the effects of plans on behavior? Second, when are plans formed? Third, how do plans deviate from optimality? For each of these questions, we (a) offer a brief overview of research that sheds light on the issue and (b) identify gaps in current knowledge. We emphasize connections to the growing theoretical literature that gives personal plans a substantive role, but we conclude that more research is needed, especially on the latter two questions we cover.
A fundamental implication of standard moral hazard models is overuse of low-value medical care because copays are lower than costs. In these models, the demand curve alone can be used to make welfare statements, a fact relied on by much empirical work. There is ample evidence, though, that people misuse care for a different reason: mistakes or "behavioral hazard." Much high-value care is underused even when patient costs are low, and some useless care is bought even when patients face the full cost. In the presence of behavioral hazard, welfare calculations using only the demand curve can be off by orders of magnitude or even be the wrong sign. We derive optimal copay formulas that incorporate both moral and behavioral hazard, providing a theoretical foundation for value-based insurance design and a way to interpret behavioral "nudges." Once behavioral hazard is taken into account, health insurance can do more than just provide financial protection—it can also improve health care efficiency.
We consider a model of technological learning under which people "learn through noticing": they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may persistently be off the production frontier because they fail to notice important features of the data they possess. We also develop predictions on when these learning failures are likely to occur, as well as on the types of interventions that can help people learn. We test the model's predictions in a field experiment with seaweed farmers. The survey data reveal that these farmers do not attend to pod size, a particular input dimension. Experimental trials suggest that farmers are particularly far from optimizing
this dimension. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, behavioral changes occur only after the farmers are presented with summaries that highlight previously unattended-to relationships in the data.
We propose an activity-generating theory of regulation. When courts make errors, tort litigation becomes unpredictable and as such imposes risk on firms, thereby discouraging entry, innovation, and other socially desirable activity. When social returns to activity are higher than private returns, it may pay the society to generate some information ex ante about how risky firms are and to impose safety standards based on that information. In some situations, compliance with such standards should entirely preempt tort liability; in others, it should merely reduce penalties. By reducing litigation risk, this type of regulation can raise welfare.
Research in behavioral public finance has blossomed in recent years, producing diverse empirical and theoretical insights. This article develops a single framework with which to understand these advances. Rather than drawing out the consequences of specific psychological assumptions, the framework takes a reduced-form approach to behavioral modeling. It emphasizes the difference between decision and experienced utility that underlies most behavioral models. We use this framework to examine the behavioral
implications for canonical public finance problems involving the provision of social insurance, commodity taxation, and correcting externalities. We show how deeper principles undergird much work in this area and that many insights are not specific to a single psychological assumption.
We present a model of uninformative persuasion in which individuals "think coarsely": they group situations into categories and apply the same model of inference to all situations within a category. Coarse thinking exhibits two features that persuaders take advantage of: (i) transference, whereby individuals transfer the informational content of a given message from situations in a category where it is useful to those where it is not, and (ii) framing, whereby objectively useless information influences individuals' choice of category. The model sheds light on uninformative advertising and product branding, as well as on some otherwise anomalous evidence on mutual fund advertising.
This chapter summarizes research in behavioral health economics, focusing on insurance markets and product markets in health care. We argue that the prevalence of choice difficulties and biases leading to mistakes in these markets establish a special place for them in economic analysis. In addition, we argue that while the behavioral health-economics literature has done a better job documenting consumer-choice mistakes in insurance and treatment choices than explaining why those mistakes occur, it is clear that we should not ignore these mistakes in our analyses. We document evidence showing that consumers leave lots of money on the table in their insurance-plan choices, sometimes thousands of dollars. This is true both when consumers make active choices (e.g., they do not have a default plan) and when they make passive choices (e.g., they have a default plan). We discuss the implications of this body of work for the design and regulation of insurance markets, including the interaction between consumer choice difficulties or biases and adverse selection. We then document evidence on consumer mistakes in health-care utilization and treatment choices, especially in response to changes in prices such as copayments and deductibles. We show how choice difficulties or biases may lead patients to respond to such increases in patient cost-sharing by reducing demand for high-value care, muddying the traditional argument that the price elasticity of demand for medical care meaningfully captures the degree of moral hazard. We conclude with directions for future research.
We present a framework for analyzing “model persuasion,” where persuaders influence people’s beliefs by proposing models (likelihood functions) that suggest how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find models more compelling when they better explain the past. A key tradeoff persuaders face is that models that better fit the data induce less movement in receivers’ beliefs. Model persuasion sometimes makes the receiver worse off than if he interprets data through the lens of a default model. The receiver is most misled by persuasion when there is a lot of publicly available data that is open to interpretation and exhibits randomness, as this gives the persuader “wiggle room” to highlight false patterns. Even when the receiver is exposed to the true model, the wrong model often wins because it better fits the past. Competition more broadly pushes persuaders towards overfitting available data and as a result tends to neutralize the data by leading receivers to view it as unsurprising. With multiple receivers, a persuader is more effective when receivers share similar priors and default interpretations. We
illustrate with examples from finance, politics, and law.
A common critique of models of mistaken beliefs is that people should recognize their error after observations they thought were unlikely. This paper develops a framework for assessing when a given error is likely to be discovered, in the sense that the error-maker will deem her mistaken theory implausible. The central premise of our approach is that people channel their attention through the lens of their mistaken theory, meaning a person may ignore or discard information her mistaken theory leads her to consider unimportant. We propose solution concepts embedding such channeled attention that predict when a mistaken theory will persist in the long run even with negligible costs of attention, and we use this framework to study the “attentional stability” of common errors and psychological biases. While many costly errors are prone to persist, in some situations a person will recognize her mistakes via “incidental learning”: when the data she values, given her mistaken theory, happen to also tell her how unlikely her theory is. We investigate which combinations of errors, situations, and preferences tend to induce such incidental learning vs. factors that render erroneous beliefs stable. We show, for example, that a person may never realize her self-control problem even when it leads to damaging behavior and may never notice the correlation in others’ advice even when that failure leads her to follow repetitive advice too much. More generally, we show that for every error there exists an environment where the error persists and is costly. Uncertainty about the optimal action paves the way for incidental learning, while being dogmatic creates a barrier.
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings, the model predicts context effects such as the attraction effect, but predicts meaningful bounds on such effects driven by the intrinsic utility for the choices. In risky environments, a person is less likely to exert effort in a money-earning activity if he had expected to earn higher returns or if there is greater income uncertainty. In intertemporal consumption, relative thinking induces a tendency to overspend and for a person to act more impatient if infrequently allotted large amounts to consume than if frequently allotted a small amount to consume, or especially the greater the uncertainty in future consumption utility.
Luca, Michael, Joshua Schwartzstein, and Gauri Subramani. "Managing Diversity and Inclusion at Yelp." Harvard Business School Teaching Note 918-039, March 2018.
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This case explores the industry-wide lack of employee diversity in the technology sector and Yelp’s decision to take a leadership position in identifying strategies to increase diversity. The goal of the case is to provide an opportunity for students to develop a framework for understanding the factors that might lead to a less diverse workforce and for evaluating approaches to increase diversity. The case opens in 2014, when Yelp hired Rachel Williams into a newly created position—the Head of Diversity and Inclusion. Rachel was tasked with developing strategies to ensure that Yelp was attracting and retaining a diverse, productive workforce and creating a welcoming environment for all employees. The case puts students in Rachel’s footsteps upon being hired and tasks them to propose a set of changes for Yelp to make in order to increase the diversity of the workforce.
