Grants Database

The Foundation awards approximately 200 grants per year (excluding the Sloan Research Fellowships), totaling roughly $80 million dollars in annual commitments in support of research and education in science, technology, engineering, mathematics, and economics. This database contains grants for currently operating programs going back to 2008. For grants from prior years and for now-completed programs, see the annual reports section of this website.

Grants Database

Grantee
Amount
City
Year
  • grantee: Carnegie Mellon University
    amount: $333,090
    city: Pittsburgh, PA
    year: 2015

    To investigate how the availability and deployment of privacy enhancing technologies affect consumer behavior and welfare

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Alessandro Acquisti

    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.

    To investigate how the availability and deployment of privacy enhancing technologies affect consumer behavior and welfare

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  • grantee: University of Michigan
    amount: $486,501
    city: Ann Arbor, MI
    year: 2015

    To explore the relationship between behavioral nudges and intrinsic motivation by conducting field experiments

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Brian Jacob

    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.

    To explore the relationship between behavioral nudges and intrinsic motivation by conducting field experiments

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  • grantee: The University of Chicago
    amount: $580,003
    city: Chicago, IL
    year: 2015

    To study experimentally the welfare economics of nudging and other behavioral interventions

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator John List

    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.

    To study experimentally the welfare economics of nudging and other behavioral interventions

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  • grantee: Harvard University
    amount: $286,695
    city: Cambridge, MA
    year: 2015

    To fashion fundamental concepts and models for behavioral economics based on theories of context-dependent choice

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Andrei Shleifer

    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.

    To fashion fundamental concepts and models for behavioral economics based on theories of context-dependent choice

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  • grantee: Loyola University Chicago
    amount: $207,000
    city: Chicago, IL
    year: 2015

    To catalogue the use of datasets and methodologies in empirical economic research publications

    • Program Economics
    • Initiative Empirical Economic Research Enablers (EERE)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Svetlozar Nestorov

    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.

    To catalogue the use of datasets and methodologies in empirical economic research publications

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  • grantee: RAND Corporation
    amount: $20,000
    city: Santa Monica, CA
    year: 2015

    To promote research on behavioral economics and household finance by co-sponsoring the 7th annual RAND Forum on Behavioral Finance

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Krishna Kumar

    To promote research on behavioral economics and household finance by co-sponsoring the 7th annual RAND Forum on Behavioral Finance

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  • grantee: The University of Chicago
    amount: $214,690
    city: Chicago, IL
    year: 2015

    To elicit and study experts’ prior predictions about the outcomes of experiments in behavioral economics

    • Program Economics
    • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Devin Pope

    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.

    To elicit and study experts’ prior predictions about the outcomes of experiments in behavioral economics

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  • grantee: Northwestern University
    amount: $258,536
    city: Evanston, IL
    year: 2015

    To improve estimates of how research investments translate into breakthroughs by scientific teams, and how scientific breakthroughs translate into eventual economic growth

    • Program Economics
    • Initiative Economic Analysis of Science and Technology (EAST)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Benjamin Jones

    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.

    To improve estimates of how research investments translate into breakthroughs by scientific teams, and how scientific breakthroughs translate into eventual economic growth

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  • grantee: University of Pennsylvania
    amount: $494,015
    city: Philadelphia, PA
    year: 2015

    To develop, analyze, and evaluate data science algorithms that provably protect privacy while avoiding overfitting and false discovery

    • Program Economics
    • Initiative Empirical Economic Research Enablers (EERE)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Aaron Roth

    This grant supports University of Pennsylvania computer scientist Aaron Roth in his work to develop, analyze, and evaluate “differentially private” algorithms for use in scientific discovery. First developed by mathematicians concerned about privacy, differentially private algorithms are ways of querying sensitive datasets. An algorithm or database query is “differentially private” if the results it returns would be provably the same even if an individual record were randomly replaced by another record in the queried dataset. Since the results such algorithms return do not depend on whether a given record is or is not included in the dataset, one cannot reverse engineer who is in the dataset from the results it generates. The privacy of the data is thereby protected. As it happens, this privacy protecting feature has uses outside the concern to protect privacy. Differentially private algorithms also prevent data mining and overfitting. Since differentially private algorithms produce the same results regardless of whether a given observation is randomly replaced by another, it is difficult to use them to craft results tailored to the particularities of the data you happen to have collected. At present, however, differentially private algorithms are more exciting in theory than in practice. They tend to be laborious and slow. What’s needed is further development and testing of such algorithms with scientific applications in mind. Dr. Roth is working on just such an approach, trying to develop practical applications of differentially private algorithms that are streamlined and reliable enough to be used in everyday scientific practice and analysis.

    To develop, analyze, and evaluate data science algorithms that provably protect privacy while avoiding overfitting and false discovery

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  • grantee: California Institute of Technology
    amount: $283,935
    city: Pasadena, CA
    year: 2015

    To conduct replication studies on economics papers after running prediction markets that subjectively assess the probability of confirmations

    • Program Economics
    • Initiative Empirical Economic Research Enablers (EERE)
    • Sub-program Economic Institutions, Behavior, & Performance
    • Investigator Colin Camerer

    This grant funds a project lead by California Institute of Technology economist Colin Camerer to attempt to replicate the findings of 18 seminal papers in economics. Working with the original authors, Camerer has selected highly influential, highly cited papers that all deal with between-subject treatment effects that appeared between 2011 and 2014 in either the American Economic Review or the Quarterly Journal of Economics. Camerer and his team have worked with the original authors to design the replication experiments and have agreed in advance about what kinds of findings will constitute a confirmation and which will not. His team will also run a prediction market where knowledgeable economic experts can trade bets on the likelihood that various results are confirmed by the new data. The project will thereby not only measure whether these 18 experimental results can be replicated, but whether and to what extent the community of economists is able to reliably predict such replication when it is likely to happen and whether expert confidence serves as a good indicator of future replicability in economics.

    To conduct replication studies on economics papers after running prediction markets that subjectively assess the probability of confirmations

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