Explaining case selection in African politics research

ABSTRACT

It is common to test general claims about politics in Africa with evidence from a small number of countries. This note examines patterns in the countries of study in two Anglophone African politics journals over two decades. Countries with larger populations are studied more, while former French colonies are under-represented. There is no bias towards former-British colonies once one controls for population and former-French colonial status. These biases suggest that Anglophone research on African politics produces results not about African politics in general, but rather about politics in a narrow and novel subset of countries.

KEYWORDS:

• Case selection
• methodology
• bibliometrics

Acknowledgements

I would like to thank Tyler Oishi for research assistance; Scott Weathers for his work on the dataset; and Maya Berinzon, Laura Seay, and Dylan Craig for comments on a related paper. Talk of ‘Africa’ in the context of this paper refers to Sub-Saharan Africa.

Disclosure statement

No potential conflict of interest was reported by the authors.

Note on contributor

Ryan C. Briggs is an Assistant Professor in the Department of Political Science at Virginia Tech. He studies the political economy of poverty alleviation and the politics of foreign aid. His work has been published in journals such as African Affairs, International Organisation, and World Development. He can be reached at: [email protected].

ORCID

Ryan C. Briggs http://orcid.org/0000-0002-3933-8119

Notes

1. For a discussion on case studies and case selection, see King, Keohane, and Verba (1994), Brady and Collier (2010), and George and Bennett (2005).

2. Veenendaal and Corbett (2015) argue that there can be large theoretical payoffs to studying small states.

3. For more information on the coding of the dataset, see the original paper by Briggs and Weathers (2016).

4. Some articles were purely theoretical and others focused on, for example, American or French policies towards Africa.

5. For countries that were originally colonised by Germany, I chose the European country that held control after World War I.

6. The islands dropped from the map are Cape Verde, Comoros, Sao Tome and Principe, Seychelles, and Mauritius. The map also excludes the South African Prince Edward Islands.

7. The scaling was done using ScapeToad, which deforms the map using the Gaster/Newmann diffusion algorithm (Gastner and Newman 2004).

8. In a t-test with unequal variances, the difference is statistically significant (p = 0.035).

9. In a t-test with unequal variances, the difference between former French colonies and countries that were neither British nor French colonies is significant at p < 0.1.

10. Population, GDP, and area all come from the World Bank's World Development Indicators. Polity scores and the variable measuring major episodes of political violence come from the Center For Systemic Peace (2014). Cape Verde, Guinea-Bissau, the Seychelles, and Sao Tome and Principe lack data on either the violence or Polity scores (or both) and so are dropped from the main analysis. The results of the analyses are similar if the aforementioned independent variables are dropped and these countries are included.

11. If a variable had zeros, then it was logged after 0.1 was added.

12. To see the percentage change in a log-level regression: (e^0.849 − 1) × 100 ≈ 130. Ex-British colonies are written about 130% more than would otherwise be expected.

13. The figure is derived as before: (e^−0.923 − 1) × 100 ≈ −60. Ex-French colonies are written about 60% less than would otherwise be expected.