Introduction
Several previous studies have found robust relationships between spatial properties of a country’s capital city and that country’s propensity for conflict and misgovernance.
Perceptions of this linkage also have an effect on “coup-proofing” decisions made by national governments. A recent BBC interview with Equatorial Guinea’s President Teodoro Obiang, for example, highlighted this as a factor behind his decision to relocate the capital city:
It’s the remoteness of Oyala that makes it so appealing to President Obiang. In a rare interview he described how rebels had recently plotted a seaborne assault on his palace in the current capital, Malabo. ‘We need a secure place for my government and for future governments. That’s why we have created Oyala, to guarantee the government of Equatorial Guinea.’ (Sackur 2012)
This case is far from exceptional, as an even more recent Washington Post article points out with respect to Myanmar’s decision to move its capital from Yangon to Naypyidaw:
Analysts have described the decision as motivated by a desire to secure the military’s seat of power from any threat of protests or invasions. (Berger 2021)
Most of these studies, however, are based on observations of conflict events. In this study, we study the more fundamental variable of a capital’s distance from the population centroid of the country.
Literature Review
Campante, Do, and Guimaraes (2019) analyzes the relationship between the location of a capital city and the degree of conflict and misgovernance in a given country. Their two key findings are that:
Conflict is more likely to emerge (and dislodge incumbents) closer to the capital
and
Isolated capitals are associated with misgovernance.
This first finding is illustrated in Figure 1
Methodology
The population centroids we use herein might require some explanation, since the term “centroid” can be ambiguous.
Here, the population centroids are drawn from Hall et al. (2019)
Exploratory Data Analysis (EDA)
Here we plot the base GIS objects we’re analyzing: the location of each capital city (in purple) and each population centroid (in yellow).
We then construct an area-normalized measure of capital-centroid distance \(\text{dist}^{\textsf{AN}}\), using the formula
\[ \text{dist}^{\textsf{AN}}_i = \text{dist}_i / \sqrt{\text{area}_i}. \]
A plot of this measure by country looks as follows:
Hypothesis Testing (Regression)
geounit | iso_a3 | OBJECTID | ID_0 | NAME_ENGLI | OUT_FLAG | NAME | dist | area | scaled_dist | total_score | geometry | centroid | capital |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tanzania | TZA | 227 | 227 | Tanzania | 0 | Dar es Salaam | 324758.1 [m] | 885800 | 345.0580 [m] | 0.007 | MULTIPOLYGON (((33.90371 -0… | POINT (36.5813 -5.612844) | POINT (39.2664 -6.798067) |
Canada | CAN | 42 | 42 | Canada | 0 | Ottawa | 1410811.7 [m] | 8788700 | 475.8902 [m] | 0.001 | MULTIPOLYGON (((-122.84 49,… | POINT (-92.673 51.33108) | POINT (-75.70196 45.41864) |
United States of America | USA | 244 | 244 | United States | 0 | Washington, D.C. | 1227411.4 [m] | 9147420 | 405.8269 [m] | 0.022 | MULTIPOLYGON (((-122.84 49,… | POINT (-91.24719 39.43566) | POINT (-77.01136 38.9015) |
Kazakhstan | KAZ | 117 | 117 | Kazakhstan | 0 | Nur-Sultan | 227074.6 [m] | 2699700 | 138.2009 [m] | 0.010 | MULTIPOLYGON (((87.35997 49… | POINT (69.7252 49.45229) | POINT (71.42777 51.18113) |
Uzbekistan | UZB | 246 | 246 | Uzbekistan | 0 | Tashkent | 168011.1 [m] | 440653 | 253.0985 [m] | 0.005 | MULTIPOLYGON (((55.96819 41… | POINT (67.77264 40.30358) | POINT (69.26882 41.30383) |
Papua New Guinea | PNG | 175 | 175 | Papua New Guinea | 0 | Port Moresby | 289887.1 [m] | 452860 | 430.7714 [m] | 0.025 | MULTIPOLYGON (((141.0002 -2… | POINT (146.2921 -7.014699) | POINT (147.1925 -9.464708) |
Discussion
Conclusion
Our evidence indicates that the spatial dynamics of conflict differ from the spatial dynamics of misgovernance. Whereas