Актуальные проблемы Европы №2 / 2014
Шрифт:
The XVIII century French philosopher Charles-Louis Montesquieu might explain differences between European and African countries in Rule of Law by differences in climate. He wrote that «passions disclose themselves earlier» in «warm climates» 9 . Northern European countries score higher in Rule of Law than African countries, he might say, because of climatic differences. The temperate European climate, presumably, favors rational development of rule of law in public affairs, while the hot African climate produces hot-blooded politics and authoritarian government. Long before Montesquieu wrote, however, inhabitants in the mild climates of today’s European territories were mired in ignorance during the «middle ages» of the V through the XV centuries, while Arabs in hot climates of today’s northern Africa embraced a «golden age» of science, philosophy, medicine, and government during the VIII to the XIII centuries.
9
«In warm climates, where despotic power generally prevails, the passions disclose themselves earlier, and are sooner extinguished; the understanding is sooner ripened; they are less in danger of squandering their fortunes; there is less facility of distinguishing themselves in the world; less communication between young people, who are confined at home; they marry much earlier, and consequently may be sooner of age than in our European climates. In Turkey they are of age at fifteen» (13).
Clearly, complex historical factors account for contemporary differences in the political development of the Mediterranean countries. This study seeks only to explain differences in their 2011 Rule of Law scores in terms of country size, wealth, and party systems. It will follow the worldwide analysis of 212 polities employed in
Concerning country party systems, three traits were studied:
1) the degree of interparty competition – measured both by the presence of parliamentary parties and by the strength of the second largest party in parliament;
2) the fragmentation of the party system – measured by the number of parties seated in parliament (9, p. 135–148);
3) the stability of its party system – measured by the change in seat distribution between the two most recent elections (9, p. 163–172).
The book attempted to explain variation in all six Worldwide Governance Indicators (WGI) for 2007 (treated in turn as separate dependent variables) using the two social variables (size and wealth) and the party system variables (competitiveness, fragmentation, and instability). In summary, both country size and wealth together explained from 41 to 67 percentage of the variance in the various WGI measures of governance.
Adding party system competitiveness to the explanation raised the explained variance to the range of 58 to 69 percent 10 . In general, party competition made statistically significant contributions to the explanation. However, party system fragmentation did not make statistically significant contributions. Moreover, party system stability proved to be significant only for the subset of 130 «electoral democracies». For example, the nine one-party systems in the 212 countries, proved to be high in «stability» but not in governance.
10
Party competition was not used to predict to the WGI measure, Voice and Accountability, for it already included some judgments of party competition.
The question in the present study is how well the same theoretical model can explain 2011 «WGI Rule of Law scores» for the chosen subset of 41 nations: 9 of which border on the Mediterranean Sea but are not in the European Union; 4 are EU candidate states on the sea’s border; 9 are EU members bordering on the sea (except for Portugal); and 19 are EU members not on the sea. The 2011 data were the most recent data posted by the WGI researchers at the time this study was undertaken. That data on country area and GDP per capita income were taken from earlier in the 2000-s should be of no concern, for countries rarely change much in area over short periods and countries’ relative income is highly correlated over time. That data on party systems come from earlier in the 2000-s is of more concern and may introduce errors in the analysis. Unfortunately, those are the only data available for this short study.
Data on the 41 countries were subjected to ordinary regression analysis in Model 1, which used RL scores as the dependent variable and country size and wealth as the dependent variables. Country size proved to be statistically insignificant, and country wealth alone explained 76 percent of the variance in RL scores spread along the vertical axis in Figure 3 11 .
That was substantially more than the 66 percent of explained variance using both variables in the larger worldwide analysis. Country size was probably insignificant in this analysis because Monaco was the only microstate among the 41 countries. In the worldwide study of 212 nations, 32 (15 percent of the total) were smaller than 1,000 square kilometers. Excepting Monaco, most of the other 40 nations were
11
The adjusted R-square was 0.758. Adjusted R-squares will be reported in all subsequence instances, rounded to two decimal places.
One interpretation leaps out from Model 1: the observed variation in Rule of Law scores among Mediterranean nations and non-Mediterranean EU nations is mainly attributable to differences in country wealth. That suggests that geographical location and therefore climate has no significant effect on Rule of Law ratings – notwithstanding Montesquieu’s contention. Poorer nations – e.g., EU members like Romania and Bulgaria – tend to score low on Rule of Law, regardless of their geographical location.
In social research, there often is not much opportunity for additional explanation beyond explaining 76 percent of the variance, but that was not true when expanding the analysis this time. Model 2 dropped country size (insignificant in the previous analysis) and kept country wealth while adding two party system variables: whether or not parliamentary parties existed (No Parties) and the strength of the second largest party among the existing parties.
The explained variance jumped to 83 percent, but only the absence of parliamentary parties was significant, not the strength of parliamentary party competition. Compared to countries worldwide, most of the 41 countries in this study had high levels of party competition, producing relatively little variance for explaining differences in RL scores. Including the «no parties» variable, however, brought the two cases of Lebanon and Libya closer to the regression line, improving the fit and the explained variance. Both countries had no public parliamentary parties and also had very low scores on Rule of Law.
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 sea 12 . 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.
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.
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.