Across many experimental settings, from precision medicine to science funding, different interventions may be much more effective for some individuals than others. In such cases, both researchers and decision makers would like to know more than the “average treatment effect.” Doing the most good with limited resources can also require targeting interventions toward those who will benefit from them most—if only we could identify those subgroups in advance, or, in other words, if only we could estimate “personalized treatment effects” (PTEs). Consider a school district deeply concerned about devasting learning losses during the pandemic. Preliminary studies indicate that high-dosage tutoring (HDT) is a costly but highly effective form of small group instruction that can help teachers double or triple mathematics learning each year. Another effective yet lower-cost option is to replace some in-person instruction with time on a high-quality computer-assisted-learning platform. This treatment is called “sustainable high-dosage tutoring” (SHDT). On average, students seem to benefit from SHDT as much as HDT. The problem is that certain students hardly benefit from the technology-assisted component at all, and thus should be treated with the more expensive high-dosage tutoring. Personalized treatment effect estimation allows the school district to identify these students. Jens Ludwig at the University of Chicago seeks to advance the estimation of PTEs when data is obtained from large-scale field experiments. His approach builds on fundamental techniques developed by Sloan grantee Susan Athey, whose “random forest” algorithm carries out machine learning by exploring many different “decision trees.” Ludwig will extend these methods, leveraging recent experimental data involving eight U.S. school districts where a total of 20,000 students were randomly assigned to a control group, to high-dosage tutoring (HDT), or to sustainable high-dosage tutoring (SHDT). Ludwig’s team will develop PTE estimation methods for identifying which of the variables observable in advance best predict how students vary in their treatment response to HDT and SHDT. They will devise and test practical rules for helping schools assign different types of students to HDT or SHDT based on these individual characteristics. This personalized approach has the potential to maximize learning gains and optimize resource allocation, offering a cost-effective solution to reversing pandemic learning loss in STEM education and allowing the same tutoring program budget to benefit a greater number of students The project will also produce a practical guide for researchers on PTE estimation methods, helping to ensure robust application of this methodology across other contexts outside of STEM education.

Private equity (PE) firms buy companies they can reconfigure and then sell at a profit. Across various sectors, some of these firms have a reputation for saddling the businesses they purchase with debt, replacing management, prioritizing short-term value over long-run sustainability, and then flipping what is left as soon as they can. In the U.S. childcare market, PE firms now own nine of the 11 largest for-profit chains. These have done quite well financially even as smaller community-based childcare providers have struggled in the wake of COVID-19. New research documenting higher mortality rates and lower care quality in PE-owned nursing homes raises concerns about what might be in store for the even more lightly regulated market for childcare. On the one hand, PE investment could increase quality and maintain reasonable prices that attract consumers and enhance profitability. Or PE may result in rising prices, quality reductions, and steep charges for the privilege of being managed by the firm. Without good data on childcare markets, most public discourse is based on anecdotes and speculation. Chris Herbst (Arizona State University) and Jessica Brown (University of South Carolina) will compile new data and conduct four academic studies analyzing PE’s impact on the market for childcare. Their first paper will identify PE-owned childcare facilities using business registry data and document how they compare to non-PE-owned centers. The second will examine what happens to individual enterprises after they are acquired by PE in terms of prices, wages, employment, and performance on state inspections. Using structural estimation methods, the third and fourth papers will explore how PE entry affects both local childcare centers not owned by PE as well as public early education providers like Head Start. By demonstrating the utility of new data and methods, this work will jumpstart the conversation about childcare management in academic circles and inspire other researchers to begin work on this topic as well.

Recent supply chain disruptions have highlighted the need for resilience in critical industries. Firms often prioritize efficiency over resiliency, leading to underinvestment in robust production methods. The CHIPS and Science Act passed by Congress in 2022 tasked the White House Office of Science and Technology Policy with creating a National Technology Strategy. It also tasked the NSF’s new Directorate for Technology, Innovation, and Partnerships (TIP) with identifying and evaluating investments in key technologies. As is often the case, however, directives like this do not imply pre-existence of the expertise, methodologies, or even the data necessary to carry them out. Building on previous Sloan-funded research related to critical technologies and resiliency, Erica Fuchs will lead a research initiative on the techno-economics of critical industries and their supply chains, concentrating on developing exemplary methodologies, datasets, and meaningful research results related to the energy storage, semiconductors, and medical supplies industries. The multidisciplinary team includes experts on public policy generally and resiliency interventions in particular, economists to analyze firm reactions and societal implications, as well as engineers to assess interoperability in production methods and industry vulnerabilities. Based at Carnegie Mellon, the research team will bring together experts from institutions like MIT and Arizona State to regularly present results, comparing cross-case insights, challenges, and solutions, and to receive feedback from industry and government stakeholders and from an academic advisory board. The methods, data, and policy-relevant research developed from this initiative will help make the U.S. economy more resilient and potentially lead to whole new multidisciplinary fields or institutions devoted to critical technology assessment.

In 2021, more than 100 economists and public policy scholars signed an open letter supporting investments in home-and-community-based services (HCBS) that address the caregiving needs of elderly Americans. “Investing in home care,” they wrote, “makes economic sense: it will create jobs, seniors and their families prefer it, it will reduce spending by shifting people out of nursing homes and improve care outcomes.”   The challenge, however, is not whether to provide the elderly with HCBS, but precisely how. To do the most good with limited resources, which services should be offered to whom? Faced with such complicated decisions, economists like to imagine quasi-experiments where you can vary the parameters and see what happens. In the case of HCBS, such interventions are actually being performed all the time. Specifically, federal Medicaid programs both cover and document nearly half of all spending on formal long-term care; differences across states in how they allocate that spending create an opportunity for learning which policies work best for whom.   To enable such research, PI Gal Wettstein and his team at Boston College’s Center for Retirement Research will compile and publish fine-grained data about how states have been, since 2000, changing their reimbursement plans for over 60 different HCBS services. Wettstein’s team has assembled resources like this in the past that many academics have cited, analyzed, and linked with other datasets. In this case, three papers are already planned and will be supported under the grant. The first of these concerns informal caregiving, specifically the impact of HCBS funding schemes on the probability that informal care is provided, on the hours of informal care provided, and on what types of help with activities of daily living (ADLs) and instrumental activities of daily living (IADLs) are provided. The study will also assess how HCBS coverage affects the health and well-being of spousal caregivers, as well as the ability of adult child caregivers to move further away from the care recipient—perhaps in search of better employment or housing, for example. The second study concerns the workforce of formal caregivers, particularly how coverage of home care services affects labor market factors such as: the employment of registered nurses, licensed practical nurses, and nurse aides; the wages in these occupations; and their demographic composition. This is of special concern because the nurse aide workforce is typically low-wage, and disproportionally composed of immigrants and people of color. And the third research paper concerns health outcomes of long-term care recipients, including not only their probability of hospitalization, their rate of acquiring more ADL and IADL limitations, and their mortality rates, but also their self-assessed health and life satisfaction. While some literature suggests that more generous spending on HCBS overall does improve health outcomes, say by reducing unnecessary hospitalizations, researchers have previously been unable to isolate which types of services are particularly effective without the detailed data this project will provide.

Why do some regions of the United States flourish while others flounder? Economists have long studied such questions, of course. What has changed recently is the federal government’s active commitment to address regional disparities and inequities. This introduces new demand for practical answers as well as new occasions for experimentation and data collection—not to mention new opportunities to influence billions in new spending, much of it specifically intended to help turn technological innovations into sustainable businesses.   In response, the National Bureau of Economic Research (NBER) proposes to unite three of its traditional initiatives: the Entrepreneurship Working Group, the Entrepreneurship Research Boot Camp, and the Productivity, Innovation, and Entrepreneurship Program. Led by co-PIs David Robinson (Duke), Sabrina Howell (NYU Stern), and Josh Lerner (Harvard Business School), this joint project will specifically focus on how economic processes and policies can encourage or impede the launch of new businesses that commercialize innovations and help regions prosper.   The project will, for example, run a workshop each fall on place-based questions in entrepreneurship and innovation. Rather than solely featuring new results from academic research, however, these gatherings will also include presentations from private investors and government officials—including representatives from the NSF and the Commerce Department—who can discuss the structure of new federal programs for promoting innovation in areas of the country that have historically been left, or pushed, behind. Near the end of the project, NBER will host a capstone conference for policymakers, practitioners, and journalists in Washington. Papers from that meeting will distill practical lessons learned for publication in an edited volume.   To support graduate students and other early-career researchers, NBER’s Entrepreneurship Boot Camp meets for a week each summer and has an excellent reputation. Sloan funding will specifically provide lectures and training about spatial disparities in economic development. Campers will further discuss, dissect, and disseminate research presented at other meetings funded by this grant as well.   Guiding all these activities will be an advisory panel of public officials and private entrepreneurs. They will not only help prioritize research directions, but also help plan and participate in the NBER meetings. Above all, they will help make sure researchers are asking the right questions, accessing the right data, accumulating the right connections, and accelerating our understanding of regional innovation. 

For decades, questions have been raised about whether the postdoctoral system in the United States is achieving its goals. The United States currently supports more than 60,000 postdocs spread about the sciences, engineering, and medicine. Overall, less than a third move on to tenure-track positions. Biomedicine stands out both for the scale of its reliance on postdocs and for the limited number of faculty openings for them to fill. The National Institutes of Health (NIH) funds almost half of all postdoctoral fellowships, and it spends over $1.6 billion per year doing so. NIH has begun to discuss alternative funding schemes for young researchers. In other countries, for example, recent PhDs routinely find long-term, respectable, and satisfying work as “staff scientists.” How could something like that work here? What would it cost? Whether to fund more postdocs or staff scientists, that is the question. And it is a hard one requiring insights from labor economics, public finance, the science of science funding, experimental economics, etc. With Sloan support, economists Donna Ginther (Kansas) and Bruce Weinberg (Ohio State) will lead research on whether postdocs and staff scientists currently function as complements or substitutes in the production of research output. They will make use of administrative data about grant spending compiled by the Institute for Research on Innovation and Science (IRIS), a resource that Weinberg helped launch with Sloan funding. They will also use text analysis to study author contribution statements and postings for biomedical job openings in industry. This will allow comparisons of how the roles of staff scientists and postdocs progress generally and, in particular, whether postdoctoral training does provide benefits for those who end up in industry and for their employers. Results will be incorporated into the “Science of Science Funding” project at NBER, which Sloan also supports.

When running experiments, a social scientist can expect to observe the outcome of treating a given individual at a given time under given circumstances. But what the experimenter never gets to see directly is the counterfactual outcome that would have been obtained had the very same individual not been treated at that very same time under those very same circumstances.   That is why so many empirical social scientists love randomized controlled trials (RCTs). By assigning people randomly to either the treatment or control group, RCTs are designed to make sure there are no statistical differences between one group and the other at the outset. If the experiment reveals statistically significant differences between those two groups when it is over, then we have good reason to conclude that the intervention must be the cause. One big problem, though, is that RCTs are often impractical or even unethical. And unless researchers have access to very large samples, they usually produce reliable estimates of average treatment effects only,   Andrew Gelman of Columbia University has dedicated his career to developing and applying techniques that provide empirical evidence in complicated settings where desirable data may be incomplete or unavailable. Like many social scientists, he favors “Bayesian” methods over “Frequentist” ones that natural scientists prefer since, in many cases, they actually can repeat the same experiment over and over. Bayesians start with prior beliefs about a probability distribution in question, then systematically adjust those beliefs based on all the experimental evidence. Gelman specifically constructs reasonable priors using a process of “multilevel modeling.” His approach recognizes that treatment effects are heterogeneous from the outset and provides additional structure for breaking down those estimates by subgroup even when data are sparse.   Together with Jori Korpershoek, Gelman will develop a unified Bayesian approach to estimating causal effects in cross sectional data from both panels and time series (i.e., the kinds of data most empirical economists study). They aim to express standard ways of estimating average treatment effects as cases of a more comprehensive algorithm in which each method amounts to comparing differently weighted means. Rather than having researchers concoct specific weights, as is current practice, Gelman and Korpershoek will prescribe methods for estimating the weights that will in turn produce better and more efficient estimators.  Their open-source and user-friendly software will extend those methods to allow for the reliable estimation of individual treatment effects based on Bayesian multilevel modeling. 

The Sloan Foundation previously funded Misty Heggeness from the University of Kansas to collect and present data about the economics of caregiving through a dashboard—called the Care Board—that she is launching online. The Care Board will cover both paid and unpaid forms of childcare, elder care, and housework to demonstrate the value and feasibility of measuring these activities systematically and comprehensively.   That pilot is off to a great start and has attracted significant attention from academic researchers and government officials alike. Several federal agencies have expressed a compelling interest in developing official caregiving statistics, but none currently has the authority, budget, or expertise to start anything like the Care Board. When federal statistical agencies need to coordinate like this, either on technical matters or funding requests, they often turn to CNSTAT, the Committee on National Statistics at the National Academy of Sciences.   Half of this grant therefore funds a CNSTAT study panel on caregiving statistics that will meet ten times over the next two years. That panel will issue a peer-reviewed report making the case for precisely how and why the federal government should take on this task. The design decisions, use cases, and test results for the Care Board will be discussed throughout by this panel and form the basis of its recommendations. CNSTAT will then disseminate the report to Congress with all the authority and credibility that only the National Academies can muster.   The other half supports putting a data-rich Care Board online. One of the main challenges at this stage is carrying through plans to access and process confidential administrative data about everything from tax deductions to public assistance program participation. Also, new surveys, new categorization schemes, and new ways of inferring statistics are all under intense development and testing.   Working through the independent research unit at University of Massachusetts Amherst called the Political Economy Research Institute (PERI), PI Nancy Folbre will manage the synergies between the two workstreams. Having devoted her career to the study of caregiving, Folbre is among the most respected and accomplished economists in this field. She and Heggeness will work to achieve a shared vision for sustainably providing important data about the economics of caregiving.

In 2022, the Sloan Economics Program funded three postdoctoral fellowships at the National Bureau for Economic Research (NBER) for scholars studying racial and ethnic disparities in economic outcomes. This grant renews Sloan support for three more appointments during 2025-2028. Each fellow will spend a year at the NBER’s Cambridge headquarters pursuing their research agenda, interacting with NBER scholars, and participating in professional activities throughout the Boston area. Each will also benefit from mentoring by a senior professor who shares their research interests and who will arrange training and networking opportunities. To facilitate interaction and foster a sense of community, fellows will be invited and funded to attend the NBER Summer Institute in subsequent years. The distinguished Selection Committee—led by PI Juan Carlos Suárez Serrato (Stanford)—includes Trevor Logan (Ohio State), Jim Poterba (MIT), and Ebonya Washington (Columbia). They are looking for potential to contribute to the scientific analysis of racial and ethnic disparities in economic outcomes, with priority given to recruiting candidates from diverse backgrounds. Promising research areas include racial and ethnic differences in family structure, socioeconomic status and health care access, and experiences with the criminal justice system, and the extent to which discrimination and neighborhood effects contribute to these disparities.