How do people decide between consuming more today and saving more for the future? Mainstream macroeconomists have one answer: the Euler Equation. Simply put, it says that an optimizing agent will consume today up to the point where adding one more unit now would provide the same utility that could be expected if consuming that extra unit were deferred until tomorrow instead. In principle, the attitude expressed by the Euler Equation seems reasonable enough. Surely if you knew that having a second dessert right now, for example, would not be as enjoyable as having that dessert tomorrow, you would do well to wait. Yet as a practical matter, such calculations are difficult or impossible for individuals to make. And we all know from experience that hardly anyone ever tries. Real people rely on heuristics at best, and are sometimes not only inconsistent but also self-defeating. The Euler Equation also has theoretical implications that limit its applicability to the real world. For instance, to a population governed by the Euler Equation, the timing of consumption does not depend on when income arrives. So Euler populations will not alter their behavior in response income events like tax cuts. But clearly people in the real world do so alter their behavior. This grant funds the research of Emi Nakamura and Jon Steinsson of Columbia University to test alternatives to the Euler Equation against a uniquely comprehensive dataset of the consumer behavior of the residents of Iceland, which has for years usefully kept records of nearly every financial transaction in the country. The goal is to devise a replacement for the Euler Equation and to create new macroeconomic models that are both less naпve and more useful in predicting consumer behavior in the real world.

Effective government regulations can improve citizens’ health, safety, and financial well-being and reduce market imperfections. On the other hand, regulations that are poorly designed or implemented can impair markets, impose burdens, and impede innovation. There are potential benefits from regulatory interventions that mitigate imperfections but also potential costs from necessarily imperfect regulation. The challenge is to find an appropriate balance. This grant provides support for an initiative at the Brookings Institution to found a new, evidence-based, non-ideological Center on Regulation and Markets. In recent years, the trend has been for academics interested in regulation to specialize in environmental, health, labor, or other specific regulatory contexts. While this approach has many merits, such specialization deprives the field of the insights and wisdom that come from the wider study of regulation as such. The new Brookings Center will aim to recapture those insights and revitalize regulatory economics by incorporating recent behavioral, technological, societal, and legal perspectives. The new Center will initially concentrate on three work streams: Regulatory Processes and Perspectives, Market and Government Failures, and the Regulation of Financial Markets. Specific topics range from autonomous vehicles and the sharing economy to bankruptcy law and cost/benefit estimation methods. Outputs will include peer-reviewed papers, policy briefs, roundtables, and conferences.

Representing roughly $4 trillion globally, private capital plays an outsize role in productivity trends since its investments traditionally promote innovation and reorganization. Since private equity is private, however, there is very little available data on how venture capital or private equity firms invest in companies. What few studies we do have comes from proprietary data that cannot be shared and thus cannot be subjected to normal scientific attempts to replicate or check findings and results. Josh Lerner, a distinguished scholar at the Harvard Business School, is so keen on making data about this sector more available to academic researchers that he established a nonprofit, the Private Capital Research Institute (PCRI), explicitly for that purpose. This grant funds a project by Lerner and his team at PCRI to compile a large dataset of Certificates of Incorporation (COIs). COIs filings record significant details about the provision of private capital, including information on the capital structure and key terms of venture capital deals along with important information about valuation. Though supposedly public, COIs are in practice quite difficult to obtain or study other than one at a time. Lerner and the PCRI staff will use grant funds acquire approximately 6,000 COIs and begin compile a database that tracks 20-30 variables contained in COIs. The database will then be made available to academic researchers for research.

Evaluating the impact of proposed changes to the law requires predicting how people’s behavior will change in response to this or that policy change. These predications are made using microsimulations. Researchers compile data from a representative sample of the population, run models that estimate what those individuals will do in response to changes in, say, the tax code, and then aggregate the results. This is a traditional tool not just for economists but also for the study of traffic, finance, epidemiology, and crowds. The problem with microsimulations, however, is that they are computationally unwieldy. Running a sophisticated model requires lots of time and computing power. Funds from this grant support efforts by Robert McClelland at the Urban Institute’s Tax Policy Center (TPC) to take the next big step in microsimulation by harnessing the power of cloud-based computing. McClelland will move the TPC’s existing tax policy evaluation microsimulation models to the cloud, allowing the models to both be run faster and to allow multiple simulations to be run at once. This will make it routinely practical, for example, to see how robust results are to changes in parameter choices, to evaluate many different policy options and see which works best, and to handle nonlinearities due to thresholds in the tax code where different rules kick in or out. Basic statistical tasks—like obtaining variances, building confidence intervals, or testing hypotheses—should run in a matter of hours rather than months. These new capabilities will greatly enhance how useful TPC’s models are for rapidly understanding proposed changes in the tax code. The TPC team will then test these new capabilities by investigating three specific research questions: How does uncertainty in growth rates and recession timing affect projected tax revenues? How does sampling variation affect model behavior? And how can tax policies improve distributional outcomes without reducing revenue? Lastly, TPC will also launch an interactive website where the public can explore and visualize tax plans of their own design in real time.

This work funds methodological work by economist Susan Athey, who is aiming to develop rigorous new statistical algorithms that will allow machine learning programs to isolate causal relationships in large, complex datasets. Athey is building special new tools to handle methodological tasks that economists care about but often find challenging. These include novel techniques for taking heterogeneity into account while estimating treatment effects, calculating optimal policies, and testing hypotheses in very large and varied populations. Athey’s focus will be on computing algorithms that are particularly useful for evaluating policy interventions and that enable one to isolate how policy changes differentially affect the behavior of heterogeneous populations. As a result of her work, she expects to publish several pieces in peer reviewed statistical and econometric journals and all the algorithms, code, documentation, and nonproprietary data Athey and her team generates will be made freely available to other researchers.

Though it has been ten years since the Great Recession, the comprehensive macroeconomic models in use at central banks, government agencies, and other large financial institutions are not noticeably improved from a decade ago. Conversations with leaders of those institutions point to two fundamental flaws in traditional models, namely, the assumptions about representative agents and about rational expectations. These imply not only that the economy evolves as if there is only one consumer and only one firm but also that the consumer and the firm make optimal decisions based on predictions that are realized. Why are macroeconomists so reluctant to give up these stultifying assumptions? Because as hard as it is to run models with those assumptions, it is nearly impossible to compute much without them. Chris Carroll of Johns Hopkins University wants to fix this situation. While serving as chief economist at the Consumer Financial Protection Bureau (CFPB), he started constructing an open source computational tool kit for macroeconomists that can specifically handle non-rational heterogeneous agents. The platform, the Heterogeneous Agents Resources Kit (HARK), is capable of modeling how microeconomic interactions among heterogeneous agents can lead to macroeconomic outcomes different from those predicted by traditional techniques. It is also possible to assign less-than-rational behaviors—such as hyperbolic discounting, anchoring, or herding—to parts of the population. Running simulations under those circumstances can reveal phenomena that traditional models can neither explain nor even generate. This grant provides three years of support to Carroll as he further expands and develops HARK and creates tools to facilitate its use.

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.