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NIH biomedical funding: evidence of executive dominance in swing-voter states during presidential elections

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Abstract

This paper explores the role of presidential politics in influencing the distribution of the National Institutes of Health (NIH) funding. In particular, it investigates how the distribution of NIH funding is stirred towards institutions located in swing-voter US states during presidential elections. In doing so, it fills a gap left in the literature on the political economy of the NIH, which previously focused on the role of membership in the Committees on Appropriations in both chambers of the US Congress. First, it is found that NIH funded performers in states where the Presidential Electoral Importance (PEI) increases by 1 %, receive, on average 0.7–0.8 % more funding. Second, this effect is robust to three additional checks. Third, I run heterogeneity tests, where the direction and change of the elasticity coefficient fit plausible assumptions on the mechanism of presidential influence on NIH funding in swing-voter states. I finally estimate, that the average lower bound of the overall impact of PEI on the NIH budget is between 2 and 3 %. It reaches a maximum of 10 % for specific states, fiscal years, and presidential mandates.

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Notes

  1. The details reported in this section can be found at the following website: http://www.nih.gov/about-nih/what-we-do/budget (Accessed on February 2016).

  2. In particular, some institutes may be assigned a very broad competence on a specific disease, but can be constrained on other dimensions, for example, the research has to be dedicated only to basic or applied research, but not to both.

  3. Moreover, the NIH, together with providing systematic public research for the discovery of new treatments for health improvement, supports about 432,000 jobs across the United States. Some sources report also that $1 in NIH funding is estimated to generate $2.21 in local economic growth. See: http://www.nih.gov/about/impact/index.htm (Accessed online February 2016).

  4. The bill is then submitted to the Congress for adjustments, and indicates the total amount of funds for the next fiscal year, presenting details on the total budget assigned to every NIH institute or center.

  5. See http://www.npr.org/templates/story/story.php?storyId=11207876 (Accessed online February 2016).

  6. See http://www.the-scientist.com/?articles.view/articleNo/25677/title/Bush-vetoes-NIH-budget-increase/ (Access February 2016). Part of the Bush administration’s opposition was based on seeing unnecessary political pork and earmarking in the bill. The political struggle took went so far that, according to some analysts, the president’s veto marked the end of the stable bipartisan agreement about NIH funding that had doubled the resources dedicated to the public medical research funding for the agency during the 1998–2003 period, see: http://www.nationalhealthcouncil.org/NHC_Files/news/12-18-07-news_04-07-09.pdf (Accessed online February 2016).

  7. This strand of literature follows, among others, previous empirical tests run for other agencies, as in Faith et al. (1982) and Weingast and Moran (1983), who test Posner’s (1969) conjecture that Federal Trade Commission rulings tend to be more favorable in districts with congressional oversight committee membership. Hunter and Nelson (1995) study the relationship between Congress and the Internal Revenue Service. They find that the IRS allocates more resources for tax enforcement towards states not represented by oversight committee members. Kosnik (2006) reports that congressional representation affects the Federal Energy Regulatory Commission’s relicensing of hydroelectric dams. More recently, Ryan (2014) explores how congressional dominance affects the allocation of H1N1 (swine flu) doses. The paper finds that states with Democratic House members seated on key oversight committees were very responsive in sending about 60,000 more doses for each legislator in the first phase of the flu epidemics.

  8. I will make clearer how this relevance is measured in the data section.

  9. Reifler and Lazarus (2010) focus on party affiliation at the level of congressional districts.

  10. On the other hand, an answer to this question is not trivial. For example, Larcinese et al. (2006) find a role for presidential politics in the distribution of the federal budget across US states, but the drivers are based on support for the incumbent president and same party affiliation, while no evidence is found supporting the swing voter effect. This paper finds instead that being a swing voter state is an important driver of more NIH funding. Another advantage of my approach is that my unit of observation is the single performer, so any concern of simultaneity bias should be cast away, in that the likelihood of a single institution manipulating presidential elections so to increase the contestability in their state of residence is likely to be zero.

  11. Data were obtained by signing a Freedom of Information Act (FOIA). I am very grateful to the NIH Office of Extramural Research for sharing the NIH CRISP data under FOIA#40443.

  12. In the case of the District of Columbia, data on presidential importance are available, but not on congressmen or possible appropriators. This implied the exclusion of about 2165 NIH performers out of more than 83,000 from the dataset. When including DC the size of presidential electoral importance is still relevant in size and significant at the 1 % level. However, a full comparison is not possible.

  13. Presidential election data are available from the website http://www.presidency.ucsb.edu, which provides information on all US presidential contests from 1789 to 2012.

  14. Here I follow the rule given in Cameron and Miller (2015) and apply clustering on the base of the largest geographical unit, and US states are the largest geographical unit in this dataset.

  15. I also tried regressions using the total appropriators for House and Senate separately. The coefficients are still insignificant for both variables. Thus for simplicity, I report the total number of appropriators. Results are available on request.

  16. Results are available on request as well.

  17. I recall here that PEI coefficients are to be interpreted as elasticities. Given that PEI is a constructed scale, this is the appropriate measure to gauge the entirety of the effect of a change in PEI on institution-level funding.

  18. State fixed effects are justified in that a small number of institutions (212 out of 16,517) in fact changed their state of residence during the 43-year period under scrutiny. However, adding this control does not change the main results obtained without state fixed effects. Moreover, having such a small number of performers moving across states should remove doubt that the effect obtained is the result of the self-selection process of institutions looking to operate in highly contestable states to obtain additional funding.

  19. Note that, in doing this, I do not lose observations (still 87,395 in the sample) because, while I do not have NIH data for fiscal years before 1972, I do have data on PEI from the 1968 presidential election and this allows building that variable for the first period in which NIH data are available.

  20. On the other hand, averaging out districts’ results at the state level would add too much noise to this variable, making it a non-trustworthy indicator of the institutional exposure to House’s contestability.

  21. H_CONT and S_CONT were constructed by collecting voting data on winners and the first of the non-elected competitors, and by subtracting the percentage difference in the two shares from 100. This created again a contestability scale varying from 100 to 0, similar to the one used for PEI. While several ways of gauging contestability are possible, I think the one adopted here has the advantage of making the three measures for House, Senate, and presidential elections easily comparable. More details on the construction of these measures are in the notes section of Table 10.

  22. Results are also robust to removing outliers following several criteria: Removing residual outliers below the first percentile and above the 99th; also by removing residuals below the 5th and above the 95th percentile. In another test I remove all residuals larger than 4/N (Cook’s test). In all cases, the PEI variable returns the predicted positive sign, which is fairly stable and significant at the 1 % level. Results are available by request.

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Acknowledgments

I am really thankful to the Editors of this Journal and to an anonymous referee for very useful feedback and suggestions on this paper. I am also grateful to Mark Bonica and Christis Tombazos. I would like to thank Muyang Zhang, and other participants at the Fourth Annual International Workshop on Economic Analysis of Institutions, Xiamen University, April 16th 2016. I gratefully acknowledge the cooperation of the NIH Office of Extramural Research for sharing the NIH CRISP data under FOIA#4044, and to Shiyi Qiu for excellent research assistance. Any error, omission or mistake in this paper is my only responsibility.

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  1. School of Public Economics and Administration, Shanghai University of Finance and Economics (SUFE), 777 Guoding Road, 200433, Shanghai, China

    Alberto Batinti

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Batinti, A. NIH biomedical funding: evidence of executive dominance in swing-voter states during presidential elections. Public Choice 168, 239–263 (2016). https://doi.org/10.1007/s11127-016-0358-z

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