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.

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.

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.

This grant funds three projects by the University of Chicago’s Macro-Financial Modeling (MFM) initiative. Led by University of Chicago economist and Nobel laureate Lars Peter Hansen and Andrew Lo of MIT, the MFM initiative is a group of distinguished economists, business professors, and other finance experts who have come together to meet the challenges of modeling the complex interactions between the real economy and modern financial institutions. The first supported project is a summer school for graduate students, which will bring young scholars from a variety of intellectual backgrounds to the University of Chicago to introduce them to macro-finanical modeling and to work on specific projects related to it. The second is an open call competition for new or crowd-sourced solutions to problems posed by the MFM initiative. The call will elicit the best thinking from outside the group, encourage innovative and creative approaches to established problems, and expand the reach of the initiative to those not yet involved in the program.  The third project is the development and construction of an online platform for comparing and archiving various macro-financial models. This platform will allow MFM scholars to compare, contrast, and evaluate different models and will spur integrative work that may lead to the combination or improvement of existing models.

Funds from this grant continue operational support to the NBER Working Group on Household Finance, a group of researchers from economics departments, business schools, government, and industry who come together to work on questions about household balance sheets and financial decision-making. Under the leadership of Brigitte Madrian of Harvard and Steve Zeldes of Columbia, the group holds regular meetings, shares new developments in the field, identifies gaps in the research literature and promising ways to fill them, develops research projects, and convenes a well-attended biennial meeting on the economics of household finance. Additional initiatives planned for the next three years include a postdoctoral fellowship program to help engage the next generation of rising economists in the field of household finance, and a project focused on developing new methods, standards, and courses related to the use of administrative and government data.

People eligible for government benefits do not always make use of them. This goes for everything from health insurance subsidies to federal weatherization incentives to tax breaks for retirement savings to student loan forgiveness plans. For social scientists, particularly behavioral economists, the underutilization of such benefits is a vexing puzzle. Working with the Benefits Data Trust, Matt Notowidigdo of Northwestern has secured records and permissions to run a randomized controlled trial on the uptake of benefits under the Supplemental Nutrition Assistance Program (SNAP). Although there have been studies of SNAP before, none have been randomized controlled trials. The study calls for over 30,000 eligible seniors to be slated by chance for one of three treatments: the control group gets nothing special; a “low touch” group will receive information about enrolling; and the “high touch” group will also receive assistance with preparing the necessary paperwork. Researchers will then analyze the collected data about who actually enrolls. Funds from this grant will support Notowidigdo and his team in executing the experiment and analyzing the results. None of Notowidigdo’s efforts aim to address potentially ideological questions about the existence or generosity of SNAP or social welfare programs in general. Rather, the aim is to generate empirical economic evidence that will help economists test different theories about what factors drive uptake of social safety net programs and how such programs can be administered effectively and efficiently.

Funds from this grant continue support to the Macro Finance Society (MFS) a group of prominent scholars in economics and finance who first came together in 2013 to advance the development of macroeconomic models that incorporate our best thinking about the interaction between the real economy and the financial sector. Grant funds will support a host of related activities over the next two years, including a series of biannual workshops and associated outreach activities, the development of a lasting repository for relevant data and code, the creation of a long-term financial sustainability plan for the society, and operational funds to offset the professional research and travel of members.

This grant supports a research project by economists Jay Battacharya (Stanford Medical School), Jon Levin (Stanford), Liran Einav (Stanford), and Amy Finkelstein (MIT) to use newly available datasets to compare the cost and performance of Medicare and Medicare Advantage to private insurance plans that cover similarly situated consumers. The team will examine a broad range of questions relevant to health care policy by comparing data on public insurance plan performance provided by the Center for Medicare & Medicaid Services with newly available data on private plan performance compiled by the Health Care Cost Institute. Issues to be examined include the differences in health care costs, services, and prices in public and private plans; what features of public or private plan structure account for these differences; and whether private insurance plans can or do deliver comparable outcomes to Medicare at lower costs.

Any given research protocol entails a trade-off between privacy and accuracy. At one extreme, locking up data so no one can use it gives privacy but no accuracy or utility. At the other, fully open data provides plenty of accuracy and utility, but no privacy. In between are other protocols—like ones using fully homomorphic encryption, multiparty secure computation, or differential privacy—that provide differing combinations of accuracy and privacy. Together, one can imagine all these protocols forming a production possibility set. This grant supports a project by Cornell economist John Abowd to characterize the “efficient frontier” of such protocols. These are ones with the property that no other conceivable protocol could deliver more accuracy without sacrificing some privacy, or more privacy without sacrificing some accuracy. After assembling a library of such protocols, Abowd and his team will explore and measure public attitudes among these protocols and the tradeoffs, helping us understand public preferences toward the tradeoffs between accuracy and privacy.