This grant funds efforts by Allessandro Acquisti at Carnegie Mellon University to examine, through laboratory, online, and field experiments, how Privacy Enhancing Technology (PET) can affect consumer behavior and welfare. Examples of PET tools include ad blockers like Ghostery, surveillance blockers like Tor, and cookie blockers like Beef Taco. Acquisti and his team will have PET software installed on the computers of some experimental subjects and then observe how their online behavior changes relative to a control group. They will then measure and analyze the subsequent differences in consumer behavior, like purchases or sites visited, as well as changes in the prices, products, or search results offered by websites and search engines to the two groups. The work promises to provide valuable new data on how concerns about privacy shape the way we conduct our lives online.

This grant funds research by University of Michigan economist and professor of education Brian Jacob, who has designed a randomized controlled trial to study the effects of behavioral interventions on enrollment in the Teacher Loan Forgiveness (TLF) program. The TLF is a federal initiative that forgives up to $17,500 in student loans to teachers who teach for five years in a school serving students from low-income families.  The complicated, multistage qualification process for the program offers a unique opportunity to test how various interventions might work, by randomly assigning applicants to different groups during the process and subjecting them to slightly different form designs, requirements, defaults, and choice architectures. The TLF thus serves as an excellent opportunity to study how to design federal benefits programs in ways that maximize their uptake. Funds from this grant will support Jacob and his research team as they conduct this two-year study.

Behavioral economists tout examples of how small changes in the way options are presented can have large effects on the decisions people make. The term “nudging” refers to such “choice architecture” modifications that help, but do not force, people to behave more in line with how they wish they could. To count as a nudge, the behavioral intervention should be easy and inexpensive to disregard. So, for example, putting fruit at eye level is a nudge; banning junk food is not. Large-scale experiments, both by academics and by governments, have shown that nudging can help people eat better, reduce their energy consumption, or save more for retirement. These are relatively straightforward applications, though. Others raise harder questions about who ultimately benefits, who loses, and by how much. For example, do people like being nudged? Should people like being nudged? All things considered, when does nudging actually make society better off? Does it matter much if people know they are being nudged? This grant funds a series of experiments by University of Chicago economist John List to examine these and related issues. List’s team has designed two large randomized controlled trials with almost 50,000 subjects in total, one focused on energy conservation and another on food choices. Along with measuring the direct effects of nudges, List will rigorously examine participants’ decisions to opt in or out of being nudged, allowing him to estimate any associated welfare losses experienced by consumers.

Behavioral economics catalogs examples of how people fail to act as naпve economic models say they should. In theory, such examples should lead to revised models of economic behavior that are more sophisticated, nuanced, and accurate. These have been slow in coming. To date, behavioral economists have been more concerned with classifications and applications than with foundations, representations, or explanations. Courses and textbooks tend to take up one anomaly or bias after another, without much of a conceptual or analytic framework to offer. Funds from this grant support a project by Harvard economist Andrei Shleifer to develop a theoretical framework that can systematically accommodate many of the anomalous behaviors detected by behavioral economists. Shleifer will attempt to do this through further development of “salience theory,” which hypothesizes that certain facts or pieces of information can appear more salient or command more attention at the moment of decision. These salient facts are then overweighted by decision-makers relative to their nonsalient cousins, causing decision-makers to deviate from the rational behavior predicted by, say, expected utility theory. Grant funds will support Shleifer as he continues to develop salience theory and use it to incorporate the diverse insights of behavioral economics into satisfying, predictive models of human economic behavior. Topics to be explored include the role stereotypes and generalization play in decision-making, how being surprised affects salience, and how attitudes about what is or is not normal shape what people pay attention to.

Empirical articles and the data they use have not always been carefully connected. That makes it hard to replicate findings, to reuse data, or to build on previous work rather than just duplicating it. This grants supports the development and expansion of a new platform, DUOS (Dataset-Utilization Open Search), that links existing papers with the standard datasets and methodologies they use. Conceived by Svetlozar Nestorov of Loyola University, the system allows researchers, graduate students, and policymakers to find the published results of performing particular kinds of calculations on particular sets of survey data. Nestorov’s initial work has focused on the Current Population Survey, the primary source of labor force statistics in the United States. Student research assistants have manually compiled hundreds of linkages between the survey and the published academic literature. This information constitutes a training set for machine-learning algorithms that, when sufficiently developed, will be able to scan the online literature and extract links automatically. Grant funds support the continuation of Nestorov’s work and its expansion to other datasets, including the Survey of Income and Program Participation (SIPP) run by the U.S. Census, and the Panel Study of Income Dynamics (PSID) funded by NSF. Once developed, tested, and refined, Nestorov’s machine-learning software for automating DUOS operations will be made freely available for use in fields besides economics.

What do behavioral economists really know? Lessons learned so far seem more about isolated, but intriguing, examples rather than coherent or unifying principles. What counts as accepted doctrine is based almost exclusively on empirical results about particular phenomena such as loss aversion, probability weighting, altruism, hyperbolic discounting, and social comparisons. One would expect, therefore, that experts would be rather good at predicting the outcomes of standard experiments about standard topics in behavioral economics. This grant funds a research project by Devin Pope of Chicago and Stefano DellaVigna of Berkeley that test that hypothesis. First, Pope and DellaVigna will ask experts to forecast the effects of 17 different behavioral interventions or “nudges” in standard, simple, familiar, and carefully specified experiments. Second, they will run these experiments as described in a common setting. A large number of subjects will be asked to perform an effortful 10-minute task online. Each will be assigned to one of the 17 different framings, incentive structures, or other treatments. Just by keeping everything else equal except these behavioral interventions, the experimenters will be able to draw conclusions about the relative magnitudes and probabilities of various effects. Third, they will compare the expert forecasts with the experimental results. It is possible, of course, that all the predictions will turn out to be quite accurate—or not. In any case, such an exercise should help identify what behavioral economists do agree upon and, therefore, what we have learned from behavioral economics.

Among big questions about the economics of science, two of the most important and challenging concern investments in research and development (R&D): How do the inputs to R&D map into scientific breakthroughs? And how do the inputs to R&D map into broader social returns? This grant funds efforts by Benjamin Jones of Northwestern University to make fresh progress on each of these questions. First Jones will focus on the productivity of scientific teams, investigating how the characteristics of individual team members contribute to overall performance in different contexts. We know little about what makes effective scientific collaboration. For theoretical work, perhaps the strength of the strongest researcher drives results; in the lab, perhaps the strength of the weakest researcher matters most; and, in other situations, it may be some kind of average over everyone. Jones will use output and productivity data on scientific team composition to try to understand how these different skills and training fit together to influence scientific productivity. In a second effort, Jones will investigate the time delays between investments in and payoffs from R&D. Starting with NSF and NIH grant numbers, he will link newly available microeconomic data that trace how long it takes in various fields for grants to turn into papers, for papers to turn into patents, and for patents to turn into adopted technologies. Jones will then use these data to calculate societal returns to government investment in science.