The present study is devoted to the phenomenon of gerrymandering on the example of US Congressional elections. The boundaries of US Congressional electoral districts were used for the analysis. The division of states into districts was based on census data from 2000 to 2020. Data on small administrative units (counties) similarly relied on census data. This article theorizes the prospects of applying inferential network analysis methods to study spatial data of state county area distributions among electoral districts. In particular, methods of statistical models based on Exponential Random Graph Model (ERGM) can be adapted to assess patterns of aggregation or redistribution of individual counties between single electoral districts. Such models allow identifying statistically significant effects of electoral engineering factors in the form of strategic manipulation of constituency boundaries (“hand-drawing”), whereby the unification of counties is not based on the geographical proximity of populated areas, but is dictated by the specific socio-demographic and political characteristics of individual counties. The use of network analysis is conditioned by the “dyad” structure of spatial data, in which individual observations (counties) are united by pairwise ties of common affiliation to constituencies, which in turn leads to the formation of an affiliation network graph. This method potentially allows describing the mechanism of redistribution of small electoral units within constituencies. The researchers tested hypotheses about the socio-political factors that influence this mechanism - the role of partisan interests, as well as the role of suppression and protection of racial minorities as important factors shaping American political discourse. The results of the network analysis demonstrate the significance of socio-demographic predictors, confirming the relationship between the racial composition of county populations and the geospatial slicing of electoral districts. The effects for any factor are not homogeneous within the country, showing significantly different results between states. The resulting estimates do not allow for a clear conclusion about the hypotheses, as the groups of states where particular attributes are evident are not always subject to meaningful eneralization. The results generally illustrate the euristic potential of adapting the methodology of network analysis to solve theoretical problems of political geography.
network analysis, gerrymandering, electoral geography, USA, elections
1. Cain B.E. (1985), Assessing the partisan effects of redistricting, American Political Science Review, vol. 79, no. 2, pp. 320-333.
2. Campagna J., Grofman B. (1990), Party control and partisan bias in 1980s congressional redistricting, The Journal of Politics, vol. 52, no. 4, pp. 1242-1257. EDN: https://elibrary.ru/EPWKMT
3. Caughey D., Tausanovitch C., Warshaw C. (2017), Partisan gerrymandering and the political process: effects on roll-call voting and state policies, Election Law Journal: Rules, Politics, and Policy, vol. 16, no. 4, pp. 453-469.
4. Chatterjee T. et al. (2020), On theoretical and empirical algorithmic analysis of the efficiency gap measure in partisan gerrymandering, Journal of Combinatorial Optimization, vol. 40, no. 2, pp. 512-546. DOI: https://doi.org/10.1007/s10878-020-00589-x; EDN: https://elibrary.ru/RRKGTR
5. Cohen-Zemach A., Lewenberg Y., Rosenschein J.S. (2018), Gerrymandering over graphs, Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, Stockholm, pp. 274-282.
6. Fan C. et al. (2015), A spatiotemporal compactness pattern analysis of congressional districts to assess partisan gerrymandering: a case study with California and North Carolina, Annals of the Association of American Geographers, vol. 105, no. 4, pp. 736-753.
7. Friedman J.N., Holden R.T. (2009), The rising incumbent reelection rate: what's gerrymandering got to do with it?, The Journal of Politics, vol. 71, no. 2, pp. 593-611.
8. Glazer A., Grofman B., Robbins M. (1987), Partisan and incumbency effects of 1970s congressional redistricting, American Journal of Political Science, vol. 31, no. 3, pp. 680-707.
9. Goedert N. (2014), Gerrymandering or geography? How Democrats won the popular vote but lost the Congress in 2012, Research & Politics, vol. 1, no. 1, pp. 205-212.
10. Lublin D. (1997), The paradox of representation: Racial gerrymandering and minority interests in Congress, Princeton, NJ: Princeton University Press, 176 p. DOI: https://doi.org/10.1515/9780691221397
11. Lusher D., Koskinen J., Robins G. (eds.) (2013), Exponential random graph models for social networks: Theory, methods, and applications, Cambridge: Cambridge University Press, 331 p. DOI: https://doi.org/10.1017/CBO9780511894701
12. McCarty N., Poole K.T., Rosenthal H. (2009), Does gerrymandering cause polarization?, American Journal of Political Science, vol. 53, no. 3, pp. 666-680.
13. McGhee E. (2020), Partisan gerrymandering and political science, Annual Review of Political Science, vol. 23, no. 1, pp. 171-185. DOI: https://doi.org/10.1146/annurev-polisci-060118-045351; EDN: https://elibrary.ru/BXXTIK
14. Morrill R. (2018), Electoral geography and gerrymandering: Space and politics, Reordering the World: Geopolitical perspectives on the 21st century, eds. Demko G.J., Wood W.R. NY: Routledge, pp. 117-138.
15. Noragon J.L. (1973), Redistricting, political outcomes, and gerrymandering in the 1960s, Annals of the New York Academy of Sciences, vol. 219, no. 1, pp. 314-333.
16. Polsby D.D., Popper R.D. (1993), Ugly: An inquiry into the problem of racial gerrymandering under the Voting Rights Act, Michigan Law Review, vol. 92, no. 3, pp. 652-682.
17. Stephanopoulos N.O. (2017), The causes and consequences of gerrymandering, William & Mary Law Review, vol. 59, pp. 2115-2158.
18. Stephanopoulos N.O., McGhee E.M. (2015), Partisan gerrymandering and the efficiency gap, University of Chicago Law Review, no. 82, pp. 831-900.
19. Waymer D., Heath R.L. (2016), Black voter dilution, American exceptionalism, and racial gerrymandering: The paradox of the positive in political public relations, Journal of Black Studies, vol. 47, no. 7, pp. 635-658.
20. Xu C. et al. (2023), Hybrid tree visualizations for analysis of gerrymandering, International Symposium on Visual Computing, Cham: Springer Nature Switzerland, pp. 85-96.
