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When TED talks, does anyone listen? A new dataset on political leadership

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Abstract

The Technocratic and Education Dataset (TED) provides comprehensive new data on the educational and professional backgrounds of the heads of government of all sovereign states between 1946 and 2015. TED details the educational and employment credentials of 1733 unique heads of government, and provides additional information on their demographic backgrounds and military experience. TED comes in leader-level and country-year versions. These data make three major contributions to the study of leadership. First, TED offers a longer time series than most extant data sets on leadership. Second, TED offers data on a broader cross section of countries, facilitating scholarship on a wider variety of countries, including non-OECD ones, which are excluded from many existing datasets on leaders. Third, by offering detailed data on the educational and employment experiences of leaders, TED helps scholars interested in the mechanisms underlying the effects of these experiences generate more rigorous tests of their theories. TED, therefore, represents a major step forward for those interested in leadership. In this article, we introduce TED and use it to show how the pool of international leaders has changed over time. We end with an empirical application of the data in which we use leadership characteristics to predict countries’ sovereign credit ratings. The article concludes with a discussion of other potential applications of these new data.

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Data availability

The datasets created for and analyzed for this study, as well as all replication code, are available in the corresponding author’s Dataverse repository, https://www.dataverse.harvard.edu/dataverse/ted/.

Notes

  1. A large body of relevant research contributes to this debate. In addition to many of those already cited in this paragraph, notable contributors include Wade and Veneroso (1998) and Chwieroth (2007).

  2. Missing data fall into one of two categories: the effective head of government cannot clearly be identified or pertinent information cannot be found on that leader.

  3. To convert the leader data set into a country-year format, we follow the rule of coding for the year the values of the leader who was in power on January 1st of the year in question. Scholars who prefer to follow a different coding scheme can do so using the original leader-level data.

  4. 2022 US News Rankings are available at https://www.usnews.com/education/best-global-universities/rankings (accessed 28 January 2022).

  5. Research assistants were instructed on how to identify credible sources before beginning work on TED, and received guidance from the authors on useful sources of information.

  6. Not all scholars agree, with many arguing that sovereign credit ratings generally lag, rather than predict, shifts in market perceptions and major financial crises (Cox & McCubbins, 2015; González-Rozada & Yeyati, 2008; Reinhart, 2002).

  7. The public sector corruption is reverse-scaled so that higher scores indicate less corruption. Substantive patterns are similar for S&P’s.

  8. These countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, West Germany, unified Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States. Of course, these countries are not immune to default risk, as made clear by Greece’s experience in 2010–12, or to downgrades, as experienced by Spain and other countries in the European periphery. We estimate separate models with the developed countries; these are reported in the appendix.

  9. The conventional approach in existing research on sovereign debt ratings is to transform the ordinal ratings variables by taking a natural log of a linear transformation (Archer et al., 2007; Beaulieu et al., 2012). However, the twenty-plus categories in each dependent variable should make them suitable for estimation using linear regression without transformation; doing so produces findings that are, in our opinion, easier to interpret. In the Appendix, we provide the results estimated using the log linear-transformed versions of these three dependent variables. We find that the transformations have no effect on our findings.

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Authors and Affiliations

  1. George Mason University, Arlington, VA, USA

    Thomas Edward Flores

  2. Office of the Naval Inspector General, Department of Defense, DC, Washington, USA

    Gabriella Lloyd

  3. Georgetown University, DC, Washington, USA

    Irfan Nooruddin

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  1. Thomas Edward Flores
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  2. Gabriella Lloyd
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  3. Irfan Nooruddin
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Contributions

a. research design and conceptualization: T.F. (35%), G.L. (35%), I.N. (30%).

b. statistical analysis: T.F. (35%), G.L. (35%), I.N. (30%).

c. writing: T.F. (35%), G.L. (35%), I.N. (30%).

1) The order of the authors is chosen alphabetically.

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Correspondence to Irfan Nooruddin.

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Flores, T.E., Lloyd, G. & Nooruddin, I. When TED talks, does anyone listen? A new dataset on political leadership. Rev Int Organ 18, 169–199 (2023). https://doi.org/10.1007/s11558-022-09461-5

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