This grant funds a project by Hunt Allcott, Dmitry Taubinsky, and Jonathan Zinman to four common kinds of consumer decisions and then use those models to analyze the welfare implications of potential policy interventions aimed at altering these decisions. They plan to examine supposed “mistakes” people make making decisions about sugar-sweetened beverages, credit card borrowing, checking account overdrafts, and college enrollment. In each context, the research team will start by formulating a theoretical model that can accommodate a range of consumer behaviors. Next, they will perform empirical analyses using experimental, quasi-experimental, and survey designs to identify biases and test predictions. Then they will analyze the empirical welfare implications various regulatory or other interventions aimed at altering consumer choices in these areas. In addition to covering data collection costs, grant funds will support a research assistants and a single project manager for all four studies.

While advances in information technology may be changing our lives, they are not necessarily translating into greater productivity or prosperity for the economy as a whole. Having peaked in the 1950s, productivity growth fell dramatically in the U.S. from 2004 to 2008. The last five years have seen some of the lowest levels since the U.S. began collecting such statistics. This grant funds a project by John Van Reenen of MIT and Nicholas Bloom of Stanford to study how R&D investment in the U.S. affects the productivity growth rate. In previous work, Van Reenen and Bloom have identified how organizational innovations affect worker productivity. This grant funds an extension of that work as the team tries to isolate the role research and development plays in productivity by studying what they call “Ideas-TFP.” From a macroeconomic view, the team will examine trends in Ideas-TFP across countries and regions. At the meso level, they will concentrate on a few key sectors, such as medical and agricultural innovation. Focusing further on individual firms, they will compile private and public data about R&D spending as well as patenting and new product introductions. Of particular concern to the team will be constructing measures of market “dynamism” that reflect the rates at which jobs and firms are created or destroyed. The datasets compiled and shared by this project will also help other recent grantees, since estimating the returns on R&D is one of the animating and abiding goals for the Sloan Foundation’s subprogram on the Economic Analysis of Science and Technology.

This grant funds a suite of three research projects by Heidi Williams of MIT to estimate the returns on R&D investments. All three projects deal with how private and public interests diverge and to what extent that divergence is mitigated or created by the patent system. The first study asks how much “stealing” from previous innovations may increase private returns without necessarily increasing social returns. Working with Daron Acemoglu (MIT), Williams will measure “citations stolen” from patents that served as “prior art” for a given innovation. Because advances do not always come about due to new knowledge per se, but rather due to marginal or technical improvements on existing technologies, “follow-on innovations” can earn more private returns than warranted by the new social value they create. Williams will compare citation data between successful and unsuccessful patent applications to help quantify the extent to which progress depends on substituting new ideas for old ones, rather than the generation of completely new or disruptive capabilities. For the second study, Williams and co-authors Pat Kline (University of California, Berkeley), Nevianna Petkova (U.S. Office of Tax Analysis), and Owen Zidar (University of Chicago Booth School of Business) will merge data on U.S. patent applications with IRS tax records to investigate which firms and which workers profit from a patent. By carrying out event studies, the team will specifically trace how spillovers accrue to private parties other than the original inventors. The third study, with Eric Budish (Chicago Booth) and Ben Roin (MIT Sloan School of Management), seeks evidence to support the common but unproven assumption that patents increase innovation. What happens, for example, when the patent for a basic ingredient expires, but a “new use” is found during the unprotected period? Preliminary findings indicate a drop-off in for-profit (but not publicly funded) research on a drug once a generic competitor can enter the market. Williams and her collaborators will estimate the social value of “missing” research investments that private interests are not undertaking now, but would if incentive systems were different.

The Conversation U.S. (TCUS) is an independent and nonprofit news outlet that producing popular articles by academics about their research. Articles on the site are written and titled by researchers themselves, edited in cooperation with skilled journalists, and then published under a Creative Commons CC-BY license, allowing other publications like The Atlantic, Washington Post, and New York Times to republish them to their own readers. Since its founding in 2014, some 3,400 scholars from 525 universities have written for TCUS. Including republished articles, the number of “reads” has grown to more than six million per month. Funds from this grant provide two years of support to the Business and Economics desk at TCUS, allowing the continued publication of articles on timely topics in economics and finance. In addition to defraying operational costs, grant funds will support the hiring of a researcher responsible for identifying top professors whose academic work is timely and compelling enough for TCUS to turn into popular, authoritative, and important news.

Suppose you observe people making economic decisions that do not appear to be in their own best interest. Say they are not saving enough for retirement. Policymakers may decide to “nudge” those people into saving more, following the precepts put forth by Sunstein and Thaler in their popular book about behavioral economics. But is this, on balance, a good idea? Perhaps some people have good reasons to “undersave” (e.g., perhaps the person has a wealthy spouse). Evaluating a policy “nudge” like programs to increase saving would involve asking if the net benefit to those targeted outweighs the cost of the intervention. Determining when this is the case is a problem in “Behavioral Welfare Economics” and involves important questions about when choices represent mistakes on the part of the chooser, when they do not, and how much choosers value the opportunity to correct mistakes they make.  This grant supports work by Sandro Ambuehl from the University of Toronto and Doug Bernheim from Stanford to study how to measure welfare losses incurred due to irrational mistakes. The team will field several experiments that give subjects two financial choices that look different but are actually the same and then measure both the subjects’ willingness to pay for one option over the other and their willingness to pay to have the choice between options simplified. The results promise to shed new light on how the choosers value the ability to make good decisions and how that value is related to the likely costs of a poor choice.

Over the past 30 years, behavioral research has succeeded in generating many examples of how people routinely violate traditional economic assumptions that human actors are rational optimizers of their preferences. So far, however, behavioral economists have not succeeded in generating a coherent set of principles that could replace these assumptions. The grant funds a project led by Mike Woodford of Columbia, Andrew Caplin from NYU, and Ernst Fehr from the University of Zurich, to take up this challenge. The team hypothesizes that much of what we have observed in behavioral economics can be systematized and explained through the lens of attentional constraints. There are also limits on how much attention people can pay to any given situation or decision. Nonoptimal decisions like those observed by behavioral economists can be best explained, they theorize, by thinking of them as the result of the allocation of limited attentional or decision-making resources. Grant funds will support the team as they work on developing and testing this theory. Additional funds support a series of summer schools and workshops to further engage the larger community of scholars on these issues.

The Institute for Research on Innovation and Science (IRIS) provides data and data management services in support of fundamental research on the results of public and private investments in discovery, innovation, and education. Partnering with dozens of research universities, IRIS collects and processes administrative data, links those files with restricted federal microdata, and make the fully documented results available to researchers. Data compiled by IRIS bear on a host of interesting issues about the practice of modern science in a university setting, including return on investment, the productivity of scientific teams, and whether university science labs have spillover effects on local economies. Funds from this grant provide three years of support to IRIS to expand its operations and facilitate use of IRIS data by researchers.

Digitization changes everything. The rapid decline in marginal costs for information storage, processing, and networking, for example, challenges many basic assumptions of textbook economics. Traditional concepts and analytical tools provide limited help understanding recent phenomena such as on-demand labor markets, zero-cost reproduction of copyrighted material, or exclusively ad-supported consumption goods. This grant provides three years of continued support to the Economics of Digitization Working Group at the National Bureau of Economic Research. Under the leadership of Professors Shane Greenstein and Josh Lerner from Harvard and Scott Stern from MIT, the working group brings together top scholars to address issues such as digital markets for books, music, and the news; online privacy and piracy; government regulation of the internet; the economic implications of artificial intelligence; and the economics of two-sided markets. Grant funds will support two meetings of the working group per year, an annual student tutorial, a small grant program to support new work on the economics of digitization, and outreach and support to the growing community of researchers interested in working on these and related issues.  

Improving the reliability of empirical research will require many strategies over many years. One “theory of change” is to start at the beginning by targeting undergraduates during their first experiences with collecting, processing, and interpreting data. If inculcated in college, good habits and rigorous expectations can last a lifetime. The benefits will be seen not only among those who go on to become academics, but also among those who become doctors, lawyers, leaders, and informed citizens generally. With this motivation, economist Richard Ball has developed the Teaching Integrity in Empirical Research (TIER) Protocol, which guides novice researchers on how to work with data. Starting with liberal arts colleges, over 120 faculty have participated in extended workshops on how to teach this protocol, and it is being used in 25 courses, and been featured in webinars run by the American Statistical Association. Funds from this grant provide support to Ball to continue expanding valuable partnerships, training programs, and curricular development projects for the TIER protocol, with particular emphasis on improving its footprint at research universities.

Funds from this grant support a project by economist Bruce Meyer of the University of Chicago to create a rich new dataset for the measurement of U.S. household income. Partnering with the Census Bureau, Meyer plans to link and reconcile data from a host of important, but currently separate government surveys and data sources, including the Current Population Survey, the Consumer Expenditure Survey, and American Community Survey, the Survey of Income and Program Participation, tax return data from the IRS, and information from important government programs like SNAP and TANF. The resulting dataset, to be called the Comprehensive Income Dataset, would significantly expand the analytic power of these datasets taken separately and would also ease several well-known obstacles to the measurement of U.S. household income. Grant funds will support the initial construction of the dataset, which will then be made available for use by scholars through Federal Statistical Data Research Centers.