This grant funds work by economists Daron Acemoglu of MIT and Pascual Restrepo of Boston University, who are investigating the economics of robotics and automation. These two researchers have begun developing a conceptual framework to understand how robotics is affecting the economy. The effects of new automation technologies, they maintain, can best be understood by explicitly examining how fast and how thoroughly they replace human labor in the performance of specific tasks. One virtue of such a framework is that it helps distinguish between the “displacement effect” of automation—the way it can reduce demand for certain kinds of labor—and the “productivity effect” of automation—the way it can increase the value of certain sorts of labor by making laborers more productive. Using this framework, Acemoglu and Restrepo estimate that an increase of one new robot per thousand workers in the U.S. economy reduces the ratio of employment to population by 0.5 percentage points and reduces average wages by 1 percent in a local labor market with the average U.S. exposure to robots relative to a local labor market with no exposure to robots. Grant funds will support the extension and refinement of Acemoglu and Restrepo’s work, including plans to disaggregate effects across various labor markets by studying long-term and fine-grain data at the firm level. The project promises to generate at least six academic papers based on this work.

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.