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Up to now, we have not given Montesquieu his due; we have not specifically included geography as a variable. Model 3 does this by employing a «Mediterranean» variable with values of 1 for the 22 countries bordering the sea and 0 for the 19 EU countries not on the sea12. Model 3 added the binary Mediterranean variable to the previous model containing country wealth and absence of parliamentary parties. Doing so improved the explained variance of RL only slightly, to 85 percent. However, the Mediterranean variable was statistically significant in the expanded equation, indicating that the model was more properly specified.
The regression statistics generated by the three models are summarized in Table 2. It reports standardized (beta) coefficients instead of unstandardized b-coefficients to better reflect the relative impacts of the independent variables across the equations.
Table 2
Effects of wealth, parties, and location on RL Scores for 41 Nations
a All coefficients are significant far beyond the conventional. 05 level.
b n.s. = not significant in initial run; variable was dropped and analysis re-run.
The strong effects of country wealth on Rule of Law scores are clear in all three models. Because wealthy countries tend to have political parties and also tend not be on the Mediterranean, the wealth effect diminished slightly as the party system variable was added in Model 2 and the Mediterranean variable was added in Model 3. Both models adjusted for the effects of the new variables, which had been masked by country wealth.
Figure 4 graphs the results of the regression equation for Model 3, plotting each country’s predicted Rule of Law scores – based on country wealth, absence of parties, and location on the Mediterranean Sea – against its actual scores for 2011. A few of the 41 countries are identified by name to illustrate the analysis. Compare Figure 4 with Figure 3, which plotted Rule of Law scores by location and EU status. Sweden, Finland, Montenegro, and Lebanon all rated higher on Rule of Law than predicted by the regression line, while Luxembourg, Italy, and Libya all rated below. Inevitable measurement error accounts for some of the deviations from the prediction line, while country specific factors presumably account for the remainder.
Figure 4. Predicted effects of wealth, parties, and location on Rule of Law
Discussions of Mediterranean politics often resort to stereotypes recalling Montesquieu’s belief that «passions» arise from «warm climates». Montesquieu did not distinguish the Mediterranean climate from that in northern Europe, but others have when discussing the recent Eurozone crisis. Involving problems with sovereign debt, banking, and economic growth, the Eurozone crisis (which began in 2009) was most severe in five countries: Cyprus, Greece, Ireland, Italy, Portugal, and Spain (5). That four of the five were Mediterranean countries fueled old regional stereotypes, which crept into political discourse about resolving the crisis. One analyst described the discourse this way: «Maybe the problem is those southerners lolling in the northerners, rigid beyond reason, so gloomy in their own lives that they're determined to see the southerners suffer» (11).
In truth, politics in European countries bordering the Mediterranean have been demonstrably more volatile than politics in their northern neighbors. According to the 2007 WGI measure, «Political Stability and Absence of Violence» (described above and scored two years before the Eurozone crisis), the 19 EU members not bordering the Mediterranean Sea scored significantly higher on political stability than the 9 members on the sea (0.87 vs 0.62)13.
One need not succumb to stereotypes, however, to conclude that countries bordering on the Mediterranean Sea tended to rate significantly lower on Rule of Law in 2011, even after controlling for country wealth and the absence of party politics. The difference is a genuine source of concern for the EU’s «European Neighborhood Policy». Why this difference exists requires more sophisticated analysis than attributing it to climate.
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12
Qualitative binary variables with values of 1 or 0 are called «dummy» variables in regression analysis. Other models using alternative dummy variables for region fared no better than this one with only one dummy variable.
13
The difference was significant at the. 05 level using a one-tailed test.