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Profiling giants: the networks and influence of Buchanan and Tullock

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Abstract

This paper uses network analysis to measure the position and influence of two prominent academics, James M. Buchanan and Gordon Tullock, founders of Public Choice theory. First, an account of their parallel lives is given. Second, a review of the literature and of the standard centrality measures is provided, insisting on their relevance to assess an author’s importance in a given network. Third, detailing the publication records and, overall, co-authorship networks of the two scholars, their respective influence is analyzed through the lens of network analysis. Their academic genealogy is also explored. It is shown in particular that: (1) Buchanan and Tullock’s careers followed parallel paths and co-founded Public Choice theory and the journal of the same name, although the two had few common works; (2) though being apparently very similar as to their centrality in the co-authoring network under scrutiny, their ego-networks are structured very differently, revealing diverse positions in the field and, thus, different influence on the discipline.

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Notes

  1. The count goes up to seven when taking into account a reply to a comment; see Buchanan and Tullock (1976a).

  2. RePEc stands for Research Papers in Economics. The depository can be accessed at the following URL: repec.org. For a description of the rankings derived from RePEc see, e.g., Zimmermann (2013). The count here excludes reviews of books.

  3. It appears that the archives reveal that Buchanan and Tullock put together a NSF proposal for a project, under the heading “The Calculus of Control”, on which they collaborated and of which they laid out various parts and pieces. In retrospect, while they never published that volume, they published all of its various constituent parts in various separate works. It is thus probable that they had many discussions, which did not end in joint publications. We owe this point to a referee.

  4. Available at the following URL: http://files.libertyfund.org/files/1063/Buchanan_0102-03_EBk_v6.0.pdf (last consulted, 12 February 2017).

  5. Compared to the medical sciences or physics, for example, where contributions are more often decomposed and attributed explicitly.

  6. That conclusion is also apparent if one reads the separately signed appendices to the Calculus: Buchanan was more of a political philosopher and Tullock more of an economist/game theorist—in 1962, at least.

  7. Some information is now available on the Internet, and historians of economic thought can take a much deeper dive into the connections between the two than we can intend to do here.

  8. Compilation realized in March 2017.

  9. Formally, HHI = s 21  + s 22  + ··· + s 2 n , where n is the total number of journals ranked by decreasing order and s i denotes the share of the ith journal. HHI lies in the [0,1] interval. Larger values of the index indicate that the author’s publications are more concentrated within the n journals.

  10. Fourcade et al. (2015) document the parochialism of the economics discipline relative to other social sciences. Buchanan certainly was different from other American economists, as his track record (as well as his publication record) reveals connections with other disciplines, sometimes far from economics. That is less true for Tullock.

  11. Not that we would hint that any of the two authors considered here has consciously aimed at such an influence throughout his life, especially so as we look at their networks in hindsight. See, again, Buchanan’s “Notes on Nobelity” (Buchanan 2001), where he admits that he has financially benefited from the celebrity status associated with the Nobel Prize—although probably much less than he could have, had he accepted all of the invitations based only on his sudden notoriety—but has refused to become a columnist on all and every issue.

  12. In some ways, such merging is reminiscent of the classification of Mancur Olson’s 1962 review of The Calculus of Consent in the “other disciplines” (Medema 2000, p. 316).

  13. An opposite perspective is given, for example, by Hill (1999), who considers how Christianity is part of public choice (that is, how its main hypotheses with regard to people’s behavior, in particular, fit with Christian beliefs).

  14. An ego-network is the network surrounding a particular individual. In other words, it includes the individual surveyed and his or her immediate contacts (Newman 2010, p. 45ff.).

  15. Note that betweenness centrality in citation networks can be interpreted as an indicator of journals’ interdisciplinariness (see, e.g., Mutschke 2003; Leydesdorff 2007).

  16. An alternative definition of the global clustering coefficient of an undirected graph is the number of triangles in it (Luce and Perry 1949; Wasserman and Faust 1994). Formally, for a network G:

    $$C(G) = \frac{3 \times number\;of\;triangles}{{number\;of\;connected\;triplets\;of\;co{ - }authors}}$$

  17. And, as demonstrated by Schoch et al. (2017), that is even truer if the network differs from stereotypical ones (i.e., stars or circles).

  18. We chose to limit our analysis to the second-degree co-authors both for tractability reasons and to concentrate on short-distance links.

  19. All graphs contained herein were generated with Pajek.

  20. An alternative way to apprehend the set of authors linked through direct or indirect co-authorships with Buchanan and Tullock is to analyze the two scholars’ ego-networks. Buchanan and Tullock gather 969 and 943 direct and indirect co-authors involving 1122 and 1059 relationships, respectively. However, focusing on ego-networks blurs the analysis, as they, by definition, darken the overlapping part(s) of the networks. Nevertheless, for the sake of completeness, we report these two ego-networks in a graphical form, with their corresponding measures for the 40 top co-authors (that is, the equivalent of Table 2) in the online supplementary material appendix.

  21. That is, the sub-networks obtaining when considering only the authors with, respectively, at least two or three links in the 1621-author network.

  22. So-called “triadic” reduction mentioned above.

  23. Limiting the analysis to the top 40 authors actually is the result of space limitations. The full set of results is available from the authors upon request.

  24. According to a standard feature-scaling transformation of the measures in a [0,100] range.

  25. “A clique is a maximal subset of the vertices in an undirected network such that every member of the set is connected by an edge to every other.” (Newman 2010; italics in the original).

  26. Although this was not guaranteed from the start, and the “community” aspect even brought negative sides, at least in the beginning, as is witnessed by the refusal of the Ford Foundation to deliver financial support in the early days (see Levy and Peart 2014).

  27. Because of space constraints, the full set of codes for the co-authors analyzed in the paper is available as supplementary material (online Appendix).

  28. So-called “triadic” reduction, mentioned above.

  29. We owe this interpretation to a referee.

  30. The theses are available from the following website: https://vtechworks.lib.vt.edu/handle/10919/5534. However, not all the theses are already digitized, and the period of time covered does not yet go as far enough back in time to fully address what we require.

  31. The listing is available as a supplementary online appendix.

References

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

    Article  Google Scholar 

  • Atkinson, A. (1987). James M. Buchanan’s contributions to economics. The Scandinavian Journal of Economics, 89(1), 5–15.

    Article  Google Scholar 

  • Azoulay, P., Zivin, J. G., & Wang, J. (2010). Superstar extinction. Quarterly Journal of Economics, 125(2), 549–589.

    Article  Google Scholar 

  • Besancenot, D., Huynh, K., & Serranito, F. (2017). Co-authorship and individual research productivity in economics: Assessing the assortative matching hypothesis. Economic Modelling, 66(C), 61–80.

    Article  Google Scholar 

  • Bidault, F., & Hildebrand, Th. (2014). The distribution of partnership returns: Evidence from co-authorships in economics journals. Research Policy, 43, 1002–1013.

    Article  Google Scholar 

  • Boettke, P. J. (2008). Maximizing behavior & market forces: The microfoundations of spontaneous order theorizing in Gordon Tullock’s contributions to Smithian political economy. Public Choice, 135(1–2), 3–10.

    Article  Google Scholar 

  • Boettke P. J. (2014). The smelly little orthodoxies which are now contending for our souls. Coordination Problem, blog post, http://www.coordinationproblem.org/2014/06/the-smelly-little-orthodoxies-which-are-now-contending-for-our-souls.html.

  • Boettke, P. J., Fink, A., & Smith, D. J. (2012). The impact of Nobel Prize winners in economics: Mainline vs. mainstream. American Journal of Economics and Sociology, 71, 1219–1249.

    Article  Google Scholar 

  • Boettke, P. J., & Marciano, A. (2015). The past, present and future of Virginia political economy. Public Choice, 163(1–2), 53–65.

    Article  Google Scholar 

  • Buchanan, J. M. (1964). What should economists do? Southern Economic Journal, 30(3), 213–222.

    Article  Google Scholar 

  • Buchanan, J.M. (2001). Notes on nobelity. http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1986/buchanan-article.html. Accessed September 29, 2016.

  • Buchanan, J. M., Tollison, R. D., & Tullock, G. (Eds.). (1980). Toward a theory of the rent-seeking society (Vol. 4). College Station: Texas A&M University Press.

    Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1962). The calculus of consent: Logical foundations of constitutional democracy. Ann Arbor: University of Michigan Press.

    Book  Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1964). Economic analogues to the generalization argument. Ethics, 74, 300–301.

    Article  Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1965). Public and private interaction under reciprocal externalities. In J. Margolis (Ed.), The public economy of urban communities (pp. 52–73). Washington D.C.: Resources for the future.

    Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1975). Polluters’ profits and political response: Direct controls versus taxes. American Economic Review, 65, 139–147.

    Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1976a). Reply. American Economic Review, 66, 983–984.

    Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1976b). The politics of bureaucracy and planning. In A. L. Chickering (Ed.), The politics of planning: A review and critique of centralized economic planning (pp. 225–273). San Francisco: Institute for contemporary studies.

    Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1977). The expanding public sector: Wagner squared. Public Choice, 31, 147–150.

    Article  Google Scholar 

  • Buchanan, J. M., & Tullock, G. (1981). An American perspective. In A. Seldon (Ed.), The emerging consensus, IEA 1957–1981 (pp. 79–97). London: The institute of economic affairs.

    Google Scholar 

  • De Stefano, D., Giordano, G., & Vitale, M. P. (2011). Issues in the analysis of co-authorship networks. Quality & Quantity, 45(5), 1091–1107.

    Article  Google Scholar 

  • Dolton, P. (2017). Identifying social network effects. Economic Record, 93, 1–15.

    Article  Google Scholar 

  • Ductor, L., Fafchamps, M., Goyal, S., & van der Leij, M. J. (2014). Social networks and research output. The Review of Economic and Statistics, 96(5), 936–948.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2015). How to become an important player in scientific collaboration networks. Journal of Informetrics, 9, 809–825.

    Article  Google Scholar 

  • Ellison, G. (2013). How does the market use citation data? The Hirsch index in economics. American Economic Journal: Applied Economics, 5(3), 63–90.

    Google Scholar 

  • Fourcade, M., Ollion, E., & Algan, Y. (2015). The superiority of economists. Journal of Economic Perspectives, 29(1), 89–114.

    Article  Google Scholar 

  • Freeman, L. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.

    Article  Google Scholar 

  • Gaumont, N., Magnien, C., & Latapy, M. (2016). Finding remarkably dense sequences of contacts in link streams. Social Networks Analysis and Mining, 6, 87–101.

    Article  Google Scholar 

  • Goyal, S., van der Leij, M. J., & Moraga-González, J. L. (2006). Economics: An emerging small world. Journal of Political Economy, 114(2), 403–412.

    Article  Google Scholar 

  • Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In J. Scott & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 340–369). Thousand Oaks: SAGE.

    Google Scholar 

  • Hill, P. J. (1999). Public choice: A review. Faith & Economics, 34(Fall), 1–10.

    Google Scholar 

  • Hollis, A. (2001). Co-authorship and the output of academic economists. Labour Economics, 8(4), 503–530.

    Article  Google Scholar 

  • Laband, D. N., & Tollison, R. D. (2000). Intellectual collaboration. Journal of Political Economy, 108(3), 632–662.

    Article  Google Scholar 

  • Latapy M., Viard T., Magnien C. (2017). Stream graphs and link streams for the modeling of interactions over time. arXiv:1710.04073.

  • Levy D. M., Peart S. J. (2014). Almost wholly negative: The Ford foundation’s appraisal of the Virginia school, Working Paper.

  • Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.

    Article  Google Scholar 

  • Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 14(1), 95–116.

    Article  Google Scholar 

  • McKenzie, R. B., & Galar, R. (2004). The production and diffusion of Public Choice political economy: Reflections on the VPI Center. American Journal of Economics and Sociology, 63(1), 19–49.

    Article  Google Scholar 

  • McKenzie, R. B., & Tullock, G. (1975). The new world of economics: Explorations into the human experience. Homewood, Ill: Richard D. Irwin.

    Google Scholar 

  • Medema, S. G. (2000). Related disciplines: The professionalization of Public Choice analysis. The History of Applied Economics: History of Political Economy Annual Supplement, 32, 289–323.

    Article  Google Scholar 

  • Medema, S. G. (2011). Public Choice and the notion of creative communities. History of Political Economy, 43(Spring), 225–246.

    Article  Google Scholar 

  • Mutschke, P. (2003). Mining networks and central entities in digital libraries. A graph theoretic approach applied to co-author networks. Advances in Intelligent Data Analysis, 2810, 155–166.

    Google Scholar 

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences (PNAS), 98(2), 404–409.

    Article  Google Scholar 

  • Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Ostrom, V. (1993). Epistemic choice and Public Choice. Public Choice, 77(1), 163–176.

    Article  Google Scholar 

  • Parisi, F., Luppi, B., & Guerra, A. (2017). Gordon Tullock and the Virginia School of Law and Economics. Constitutional Political Economy, 28(1), 48–61.

    Article  Google Scholar 

  • Qi, X., Duval, R. D., Christensen, K., Fuller, E., Spahiu, A., Wu, Q., et al. (2013). Terrorist networks, network energy and node removal: A new measure of centrality based on Laplacian energy. Social Networking, 2, 19–31.

    Article  Google Scholar 

  • Qi, X., Fuller, E., Wu, Q., Wu, Y., & Zhang, C. (2012). Laplacian centrality: A new centrality measure for weighted networks. Information Sciences, 194(1), 240–253.

    Article  Google Scholar 

  • Schoch, D., Valente, T. W., & Brandes, U. (2017). Correlations among centrality indices and a class of uniquely ranked graphs. Social Networks, 50, 46–54.

    Article  Google Scholar 

  • Schram, A. (2016). Gordon Tullock and experimental public choice. Constitutional Political Economy, 27(2), 214–226.

    Article  Google Scholar 

  • Tollison, R. D. (1991). Graduate students in Virginia Political Economy: 1957–1991, Center for Study of Public Choice, Occasional Paper on Virginia Political Economy. Fairfax, VA: George Mason University.

    Google Scholar 

  • Valente, T. W., Coronges, K., Lakon, C., & Costenbader, E. (2008). How correlated are network centrality measures? Connections, 28(1), 16–26.

    Google Scholar 

  • Vaughn, K. I. (2015). How James Buchanan came to George Mason University. Journal of Private Enterprise, 30(2), 103–109.

    Google Scholar 

  • Wagner, R. (2004). Public Choice as an academic enterprise: Charlottesville, Blacksburg, and Fairfax retrospectively viewed. American Journal of Economics and Sociology, 63(1), 55–74.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Yin, L., Kretschmer, H., Hanneman, R. A., & Liu, Z. (2006). Connection and stratification in research collaboration: An analysis of the COLLNET network. Information Processing and Management, 42, 1599–1613.

    Article  Google Scholar 

  • Zimmermann, Ch. (2013). Academic rankings with RePEc. Econometrics, 1(3), 249–280.

    Article  Google Scholar 

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Acknowledgements

Without implication, we thank the editors and the reviewers of the journal for stimulating suggestions, as well as Stefan Balev, Bruno Beaufils, Hamza Bennani, Peter Boettke, Hakim Hammadou, Daniel Houser, Fabio Padovano and Yann Secq for useful discussions and remarks, as well as participants in the workshop on “Interdisciplinary approaches on co-authorship and scientific networks” (Le Havre, May 2016). Steven Medema deserves special thanks for his precious help in effectively getting Robert Tollison’s 1991 paper (containing the list of Virginia’s Political Economy Graduate students).

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  1. LEM - CNRS (UMR 9221), Université Lille 1 Sciences et Technologies, Cité Scientifique, Bâtiment SH2, 59655, Villeneuve d’Ascq Cedex, France

    Etienne Farvaque

  2. CIRANO, Montréal, QC, Canada

    Etienne Farvaque

  3. EDEHN (EA 7263), Université du Havre, 76600, Le Havre, France

    Frédéric Gannon

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Farvaque, E., Gannon, F. Profiling giants: the networks and influence of Buchanan and Tullock. Public Choice 175, 277–302 (2018). https://doi.org/10.1007/s11127-018-0535-3

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