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Partisan Disparities in the Use of Science in Policy

Author

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  • Furnas, Alexander C
  • LaPira, Timothy Michael

    (James Madison University)

  • Wang, Dashun

Abstract

Science, long considered a cornerstone in shaping policy decisions, is increasingly vital in addressing contemporary societal challenges. However, it remains unclear whether science is used differently by policymakers with different partisan commitments. Here we combine large-scale datasets capturing science, policy, and their interactions to systematically examine the partisan differences in the use of science in policy across both the federal government and ideological think tanks in the United States. We find that the use of science in policy documents has featured a steady increase over the last 25 years, highlighting science’s growing relevance in policymaking. However, this marked increase masks stark and systematic partisan differences in the amount, content, and character of science used in policy. Democratic-controlled congressional committees and left-leaning think tanks cite substantially more science, and more impactful science, compared to their Republican and right-leaning counterparts. Moreover, the two factions cite substantively different science, with only about 5% of scientific papers being cited by both parties, underscoring a strikingly low degree of bipartisan engagement with scientific literature. We find that the uncovered large partisan disparities are rather universal across time, scientific fields, policy institutions, and issue areas, and they are not simply driven by differing policy agendas. Probing potential mechanisms, we field an original survey of over 3,000 political elites and policymakers, finding substantial partisan differences in trust in scientists and scientific institutions, which potentially contribute to the observed disparities in science use. Overall, amidst rising political polarization and science’s increasingly critical role in informing policy, this paper uncovers systematic partisan disparities in the use and trust of science, which may have wide-ranging implications for science and society at large.

Suggested Citation

  • Furnas, Alexander C & LaPira, Timothy Michael & Wang, Dashun, 2024. "Partisan Disparities in the Use of Science in Policy," SocArXiv aep9v_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:aep9v_v1
    DOI: 10.31219/osf.io/aep9v_v1
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    References listed on IDEAS

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    1. Lutz Bornmann, 2013. "What is societal impact of research and how can it be assessed? a literature survey," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 217-233, February.
    2. Druckman, James N. & Peterson, Erik & Slothuus, Rune, 2013. "How Elite Partisan Polarization Affects Public Opinion Formation," American Political Science Review, Cambridge University Press, vol. 107(1), pages 57-79, February.
    3. Crosson, Jesse M. & Furnas, Alexander C. & Lorenz, Geoffrey M., 2020. "Polarized Pluralism: Organizational Preferences and Biases in the American Pressure System," American Political Science Review, Cambridge University Press, vol. 114(4), pages 1117-1137, November.
    4. Shouhuai Xu & Moti Yung & Jingguo Wang, 2021. "Seeking Foundations for the Science of Cyber Security," Information Systems Frontiers, Springer, vol. 23(2), pages 263-267, April.
    5. James N. Druckman, 2022. "Threats to Science: Politicization, Misinformation, and Inequalities," The ANNALS of the American Academy of Political and Social Science, , vol. 700(1), pages 8-24, March.
    6. Kari Kelton & Kenneth R. Fleischmann & William A. Wallace, 2008. "Trust in digital information," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(3), pages 363-374, February.
    7. Hall, Richard L. & Deardorff, Alan V., 2006. "Lobbying as Legislative Subsidy," American Political Science Review, Cambridge University Press, vol. 100(1), pages 69-84, February.
    8. Abhay S. D. Rajput & Sangeeta Sharma, 2021. "India: draft science policy calls for public engagement," Nature, Nature, vol. 592(7852), pages 26-26, April.
    9. Jean J. Wang & Sarah X. Shao & Fred Y. Ye, 2021. "Identifying 'seed' papers in sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6001-6011, July.
    10. Daniel Sarewitz, 2013. "Science must be seen to bridge the political divide," Nature, Nature, vol. 493(7430), pages 7-7, January.
    11. John Wilkerson & David Smith & Nicholas Stramp, 2015. "Tracing the Flow of Policy Ideas in Legislatures: A Text Reuse Approach," American Journal of Political Science, John Wiley & Sons, vol. 59(4), pages 943-956, October.
    12. Ban, Pamela & Park, Ju Yeon & You, Hye Young, 2023. "How Are Politicians Informed? Witnesses and Information Provision in Congress," American Political Science Review, Cambridge University Press, vol. 117(1), pages 122-139, February.
    13. Johan S. G. Chu & James A. Evans, 2021. "Slowed canonical progress in large fields of science," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(41), pages 2021636118-, October.
    14. Yian Yin & Yuxiao Dong & Kuansan Wang & Dashun Wang & Benjamin F. Jones, 2022. "Public use and public funding of science," Nature Human Behaviour, Nature, vol. 6(10), pages 1344-1350, October.
    15. Lutz Bornmann & Robin Haunschild & Werner Marx, 2016. "Policy documents as sources for measuring societal impact: how often is climate change research mentioned in policy-related documents?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1477-1495, December.
    16. Esterling, Kevin M., 2007. "Buying Expertise: Campaign Contributions and Attention to Policy Analysis in Congressional Committees," American Political Science Review, Cambridge University Press, vol. 101(1), pages 93-109, February.
    17. Mozer, Reagan & Miratrix, Luke & Kaufman, Aaron Russell & Jason Anastasopoulos, L., 2020. "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality," Political Analysis, Cambridge University Press, vol. 28(4), pages 445-468, October.
    18. Robin Haunschild & Lutz Bornmann, 2017. "How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1209-1216, March.
    19. William J. Ball, 1995. "A Pragmatic Framework for the Evaluation of Policy Arguments," Review of Policy Research, Policy Studies Organization, vol. 14(1‐2), pages 3-24, March.
    20. Shalabh, 2021. "Statistical inference via data science," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1155-1155, July.
    21. Alexander A. Kaurov & Viktoria Cologna & Charlie Tyson & Naomi Oreskes, 2022. "Trends in American scientists’ political donations and implications for trust in science," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    22. Lisa Mandle & Analisa Shields-Estrada & Rebecca Chaplin-Kramer & Matthew G. E. Mitchell & Leah L. Bremer & Jesse D. Gourevitch & Peter Hawthorne & Justin A. Johnson & Brian E. Robinson & Jeffrey R. Sm, 2021. "Increasing decision relevance of ecosystem service science," Nature Sustainability, Nature, vol. 4(2), pages 161-169, February.
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