The importance of macroeconomic statistics compiled by the government, such as Gross Domestic Product (GDP) and the Consumer Price Index (CPI), is difficult to overstate. The data used to calculate these statistics are collected through surveys fielded by a host of differing government agencies, including the Bureau of Labor Statistics, the Bureau of Economic Analysis, and the Census Bureau. These surveys have significant and well-known methodological limitations leading to inaccuracies, substantial lag times, sampling distortions, and the need for (often significant) revisions. The worrying methodological basis of many economic statistics stands at odds with the increasingly high-quality data available about the economy. Vast improvements in the ability of retailers, for instance, to electronically track transactions provide a wealth of data for price and quantity measurement that is several orders of magnitude richer than currently captured in government surveys. This grant funds a pilot project by a team led by John Haltiwanger at the University of Maryland, College Park, to develop new, more accurate, and more timely methods to calculate portions of GDP and CPI using administrative data collected by retail firms. Partnering with several large retailers, the team will compile a large set of administrative data bearing on retail prices and quantities produced and sold, document how these data can be acquired and harvested, use the data to calculate portions of CPI and GDP, and then issue a report comparing and contrasting these calculations along several dimensions with current methodologies. Though it is too much to hope that such a project will, by itself, change the way government economic statistics are calculated, this project is an important proof of concept demonstrating one potential path to what all agree is badly needed reform.

This grant funds a dozen conferences to be held over two years that are designed as community-building exercises for a new Administrative Data Research Network (ADRN). The ADRN is a linked network of Administrative Data Research Facilities, research centers devoted to facilitating the use of administrative data by researchers by lowering transaction costs and increasing the reproducibility of research conducted on administrative data. Conference topics will focus on common difficulties encountered when working with administrative datasets, including securing data access, privacy and anonymity, data ethics, documenting and versioning data, data use agreements, and archiving. Conference outreach will target not only researchers, but key officials in government and industry who have access to administrative data. If successful, these conferences will serve as important gatherings to build consensus around standards, priorities, and best practices in the growing community of researchers working with administrative data.

Though considered by most economists to be the gold standard method for testing hypotheses, randomized controlled trials (RCTs) are nonetheless difficult to design, implement, or interpret in the field. That is why the Abdul Latif Jameel Poverty Action Lab (J-PAL) was founded at MIT in 2003. J-PAL has been a staunch champion of RCT methods, providing funds and expert guidance in the design and implementation of RCTs. Initially focused on developmental economics, the J-PAL network expanded its focus to the U.S. with the 2013 launch of J-PAL North America. The goal of this new network is to promote rigorous study of economic issues in the United States and its neighbors, both by catalyzing high-quality research directly and by strengthening the capacity of institutions and individuals to conduct and understand such work. This grant provides three years of core operating support for J-PAL North America’s activities. Supported activities include salary support for core J-PAL staff, publication costs, training expenses, and development of workshops and toolkits for researchers wishing to field RCTs in North America.

Suppose you, as a researcher, have succeeded in wrangling important and sensitive data from a government or corporate source, data that is valuable to the research community. Because your data are sensitive, however, you want to share only with appropriately trained and responsible scholars who can be trusted to treat the data ethically. Suppose now a request comes in from someone who wants to study the data. What do you do? Not every researcher is savvy about the technical, privacy, or legal compliance issues related to sensitive administrative data. You could investigate the individual and draft an agreement for them to sign. But starting from scratch to answer each new request, with all the associated inefficiencies, is time consuming and costly. Wouldn’t it be much better if you could begin instead by asking your data seeker for some standard researcher certification, a kind of Good Housekeeping Seal of Approval that would indicate what kind of training and track record they have? This grant funds a project by Maggie Levenstein, executive director of the Interuniversity Consortium for Political and Social Research (ICPSR), to design, test, implement, and promote just such a researcher credentialing system for use by her own and other institutions holding sensitive administrative data. The widespread adoption of such a system could significantly decrease the transaction costs associated with access to administrative data, increase the analysis of important though sensitive datasets, and promote responsible training and research protocols concerning preregistration, anonymization, reproducibility, and other research practices.

One goal of the Foundation’s grantmaking in economics is to lower barriers that impede obtaining and using naturally occurring datasets for sound and reproducible research. One strategy is to identify, strengthen, and, in some cases, create intermediaries who can manage the relationships between data generators and academic researchers. We refer to such an intermediary as an Administrative Data Research Facility (ADRF). These facilities will, in turn, work more effectively if they can be linked together into a network that facilitates the sharing of standards, best practices, and data among ADRFs. Such networks, however, require a dedicated coordinator to ensure their proper functioning. This grant to the University of Pennsylvania provides two years of (partial) salary support for a network coordinator devoted to servicing the growing needs of the ADRF community. Although this network organizer will work with various ADRFs, he or she will be initially hosted by the University of Pennsylvania’s Actionable Intelligence for Social Policy (AISP) project, headed by Dennis Culhane. AISP has emerged as a leader in developing practical procedures and protocols for conducting research that uses administrative data and is thus well positioned within the community to successfully host this important position.

Better management of the economy requires better macroeconomic models that can be used to predict the consequences of this or that economic policy choice, proposal, or regime. Better economic models, in turn, require the ability to compare, contrast, and evaluate the predictions of various models across a variety of scenarios. How do professional economists, financial regulators, and central bankers compare one macroeconomic model to another? As it turns out, they don’t. Not only are there no widely accepted ways to compare models, there is little agreement about what criteria make one model better than another. Enter economist Volker Wieland, who is approaching this problem head-on. Collaborating with a wide network of economists in Europe and the U.S., Wieland has developed what he calls the Macro Model Database (MMD), a software platform that can upload, store, and run different macroeconomic models, allowing researchers to confront models with the same historical or synthetic scenarios and compare the predictions subsequently produced. Funds from this grant support a project to make the Macro Model Database fully open source, documenting and disseminating the underlying code to any expert interested in using it. By making it easier for researchers to upload and explore models, Wieland and his team (including Stanford economist John Taylor) plan to double the number of models available on its platform. Additional grant funds support the establishment of a research network based at the Center for Economic Policy Research (CEPR) in London to further promote the MMD’s use within the research community.

The ability to accurately predict the U.S. labor force participation rate among older workers is important, not least because it bears significantly on the finances of the Social Security system. Traditional methods for predicting this crucial statistic involve extrapolating from past trends. Past trends, however, may not continue. Over the past 25 years, for instance, the labor force participation rate of the population aged 60 to 69 has been increasing, in part because Americans in their 60s were getting progressively healthier. But recent studies suggest this is no longer the case. What effects, if any, will the halting of this trend toward better health in older Americans have on labor force participation rates? This grant funds the work of researchers Susann Rohwedder and Michael Hurd, who are examining this issue. Using twelve waves of data from the Health and Retirement Study (HRS), Rohwedder and Hurd will study how labor force participation at older ages has increased even as some determinants of participation have worsened and examine whether the trend toward working at older ages is likely to continue, especially in view of adverse trends in health. One particular focus of their work will be the relationship between labor force participation rates and individuals’ forecasts about how long they will continue to work as they age, examining how predictive these forecasts have been in the past and how their predictive power varies along multiple dimensions. Once this relationship is better understood, the hope is to use this knowledge to inform forecasts of labor force participation rates going forward.

This grant supports the planning of a project led by Richard Johnson and Eugene Steuerle of the Urban Institute to identify, simulate, and evaluate policy reforms that, taken alone, as well as simultaneously, would reduce work disincentives at older ages and more equitably and efficiently provide retirement benefits to older adults. In so doing, this larger project would provide important new information about the likely costs and benefits of reforming Social Security, Medicare, employer-sponsored retirement plans, and tax incentives for retirement saving. The larger project will use DYNASIM, the Urban Institute’s dynamic microsimulation model, to simulate the likely impact of potential retirement program reforms across a vast array of dimensions, including effects on employment at older ages; on older adults’ household wealth; on annual income; on lifetime Social Security benefits; on income tax payments; and on out-of-pocket spending on medical care. The team will also model the effects of hypothetical reforms on government revenues and outlays. The planning activities funded by this grant will lay the groundwork for the larger project by implementing necessary enhancements to DYNASIM, specifying criteria for evaluating policy reforms, and making the case for the need to reform retirement programs to eliminate work disincentives.

Funds from this grant to the Tribeca Film Institute (TFI) support two years of the Sloan Student Grand Jury Prize, an annual prize awarded to the single best science-themed student screenplay produced by a student at one of the Foundation’s six film school partners. The award package is $50,000 per year, of which $30,000 goes directly to the student filmmaker. The balance of the award funds support a noted industry mentor to guide the project, a committed science advisor, and other marketing and distribution efforts to maximize the screenplay’s chances of production. The aim of this effort is to stimulate greater interest and excitement among the six participating film schools and film students by awarding a “best of the best” prize and fast-tracking the winning project for development so it becomes a major career opportunity. The Student Grand Jury Prize offers enhanced visibility, prestige, and a career boost to the student winner working on a science-themed script and fosters healthy competition within the film school program.

This grant supports an initiative at New York University’s Tisch School of the Arts (NYU Tisch) to incentivize the creation of feature films about science and technology through an annual award of $100,000 given to help an outstanding science-themed student film project move to production. Each year a distinguished committee of filmmakers and scientists from NYU’s Kanbar Institute of Film and Television will publicize the award among students and accept and evaluate film treatments. Filmmakers selected as semifinalists receive a $5,000 award to produce their script and are connected with an expert scholar to serve as a mentor and to ensure the accuracy of the scientific work and characters presented. The winning filmmaker from among the semifinalists will receive $100,000 to be used to move the script into production. Scripts are eligible only if they explore scientific or technological themes or employ scientists, mathematicians, or engineers as major characters. In addition, NYU holds an annual reception for the winner and engages in media outreach to publicize the awards. Grant funds provide core operating support for this awards program for three years.