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Federal funding of doctoral recipients: What can be learned from linked data

Author

Listed:
  • Chang, Wan-Ying
  • Cheng, Wei
  • Lane, Julia
  • Weinberg, Bruce

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.

Suggested Citation

  • Chang, Wan-Ying & Cheng, Wei & Lane, Julia & Weinberg, Bruce, 2019. "Federal funding of doctoral recipients: What can be learned from linked data," Research Policy, Elsevier, vol. 48(6), pages 1487-1492.
    • Handle: RePEc:eee:respol:v:48:y:2019:i:6:p:1487-1492
    DOI: 10.1016/j.respol.2019.03.001
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    References listed on IDEAS

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    1. Nathan Goldschlag & Javier Miranda, 2020. "Business dynamics statistics of High Tech industries," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 29(1), pages 3-30, January.
    2. Nathan Goldschlag & Julia Lane & Bruce A. Weinberg & Nikolas Zolas, 2019. "Proximity and economic activity: An analysis of vendor‐university transactions," Journal of Regional Science, Wiley Blackwell, vol. 59(1), pages 163-182, January.
    3. Lane, Julia I. & Owen-Smith, Jason & Rosen, Rebecca F. & Weinberg, Bruce A., 2015. "New linked data on research investments: Scientific workforce, productivity, and public value," Research Policy, Elsevier, vol. 44(9), pages 1659-1671.
    4. John J. Abowd & John Haltiwanger & Julia Lane, 2004. "Integrated Longitudinal Employer-Employee Data for the United States," American Economic Review, American Economic Association, vol. 94(2), pages 224-229, May.
    5. Valerie K. Bostwick & Bruce A. Weinberg, 2022. "Nevertheless She Persisted? Gender Peer Effects in Doctoral STEM Programs," Journal of Labor Economics, University of Chicago Press, vol. 40(2), pages 397-436.
    6. Jason Gush & Adam Jaffe & Victoria Larsen & Athene Laws, 2018. "The effect of public funding on research output: the New Zealand Marsden Fund," New Zealand Economic Papers, Taylor & Francis Journals, vol. 52(2), pages 227-248, May.
    7. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    8. Catherine Buffington & Benjamin Cerf & Christina Jones & Bruce A. Weinberg, 2016. "STEM Training and Early Career Outcomes of Female and Male Graduate Students: Evidence from UMETRICS Data Linked to the 2010 Census," American Economic Review, American Economic Association, vol. 106(5), pages 333-338, May.
    9. Glennon, Britta & Lane, Julia & Sodhi, Ridhima, 2018. "Money for Something: The Links between Research Funding and Innovation," IZA Discussion Papers 11711, Institute of Labor Economics (IZA).
    10. Reinhilde Veugelers, 2016. "Getting the most from public R&D spending in times of budgetary austerity," Bruegel Working Papers 13004, Bruegel.
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    Cited by:

    1. Jason Coupet & Yuhao Ba, 2022. "Benchmarking university technology transfer performance with external research funding: a stochastic frontier analysis," The Journal of Technology Transfer, Springer, vol. 47(2), pages 605-620, April.
    2. Graddy-Reed, Alexandra & Lanahan, Lauren & D'Agostino, Jesse, 2021. "Training across the academy: The impact of R&D funding on graduate students," Research Policy, Elsevier, vol. 50(5).
    3. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.

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    More about this item

    Keywords

    UMETRICS; Linked survey transaction data; Doctoral workforce; Survey of earned doctorates; Research impact;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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