@article{e5b8f583974d4601a45ed6e1ad8a4b0c,
title = "Federal funding of doctoral recipients: What can be learned from linked data",
abstract = "This technical note describes the results of a pilot approach to link administrative and survey data to better describe the richness and complexity of the research enterprise. In particular, we demonstrate how multiple funding channels can be studied by bringing together two disparate datasets: UMETRICS, which is based on university payroll and financial records, and the Survey of Earned Doctorates (SED), which is one of the most important US survey datasets about the doctoral workforce. We show how it is possible to link data on research funding and the doctorally qualified workforce to describe how many individuals are supported in different disciplines and by different agencies. We outline the potential for more work as the UMETRICS data expands to incorporate more linkages and more access is provided.",
keywords = "Doctoral workforce, Linked survey transaction data, Research impact, Survey of earned doctorates, UMETRICS",
author = "Chang, {Wan Ying} and Wei Cheng and Julia Lane and Bruce Weinberg",
note = "Funding Information: Our pilot showed that it will be possible to use the data to describe the complexity of federal funding. Federal funding is an extremely important source of support for doctoral recipients in these research-intensive universities – almost half (46%) of doctoral recipients were federally funded in the two years prior to receiving their doctoral degree. Almost 56% of those who reported being supported on a research assistantship received at least some federal support in the two years before completing. About 44% of those supported on a fellowship or a scholarship, 46% of those on a teaching assistantship, and 49% of those on a grant, also received some federal support in two years before completing. Funding Information: There are now new administrative data that can be combined with the Survey of Earned Doctorates to fill the gap. The STAR METRICS project, which was initiated by federal agencies in 2009 in response to the Roadmap findings, was intended to ( National Science Board, 2015 ) provide policymakers with a better understanding of the process of research and ( Romer, 1990 ) provide the research community with a common data infrastructure that connected research funding with research outcomes ( Lane et al., 2015 ). Since it was impossible to collect and link data on all individuals supported by research funding from across federal agencies, the STAR METRICS approach drew the information directly from the research organizations themselves. The key information came from administrative grant records, which contain record level information on wage payments made from federal grants to all university personnel, including doctoral recipients. Funding Information: The second gap is the inability to link between grant funding and the support for doctoral recipients, which makes it is impossible for a funding agency to say how many doctoral students or postdoctoral fellows are funded to do research in a particular field. As a result, there is no information about the pipeline of scientists. Even the agencies that provide data publicly do not capture information about doctoral recipients in a manner that can be linked and used to trace labor market outcomes. For example, at the National Science Foundation (NSF) “Relevant information is currently distributed across different formats and systems. Proposal data are currently stored in both structured (database) and unstructured (pdf) formats” ( National Science Foundation, 2010 ). At NIH a recent task force was “frustrated and sometimes stymied throughout its study by the lack of comprehensive data regarding biomedical researchers” ( National Institutes of Health, 2012 ). Funding Information: The data permit an examination of the importance of federal funding by discipline. As is clear from an examination of , the NIH provided funding for roughly 40% of the federally funded cohort; NSF supported slightly more. The DOD supported just over one in ten and DOE roughly one in ten, all depending on the horizon over which support is calculated. Fig. 2 Funding Information: This research was supported by NSF SciSIP Awards 1064220 and 1262447; NSF Education and Human Resources DGE Awards 1348691, 1547507, 1348701, 1535399, 1535370; NSF NCSES award 1423706; and the Ewing Marion Kaufman and Alfred P. Sloan Foundations. Weinberg is also grateful for support from R24 AG048059, R24 HD058484, UL1 TR000090, and the National Institute on Aging and the Office of Behavioral and Social Science Research via P01AG039347 (on which Weinberg and his work were supported directly by the National Bureau of Economic Research and indirectly by Ohio State). Data were generously provided by ProQuest and by the Committee on Institutional Cooperation and its member institutions. We thank Cameron Conrad for research support, Greg Carr, Marietta Harrison, David Mayo, Mark Sweet, Jeff Van Horn and Stephanie Willis for help with data issues, and Jay Walsh, Roy Weiss, and Carol Whitacre for their continuing support. The research agenda draws on work with many coauthors. Opinions expressed are those of the authors and do not necessarily represent the views of the National Science Foundation, the National Institutes of Health, or the Kauffman or Sloan Foundations. Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",
year = "2019",
month = jul,
doi = "10.1016/j.respol.2019.03.001",
language = "English (US)",
volume = "48",
pages = "1487--1492",
journal = "Research Policy",
issn = "0048-7333",
publisher = "Elsevier",
number = "6",
}