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GeoJournal

, Volume 60, Issue 1, pp 3–17 | Cite as

Democratic Values in a Globalizing World: A Multilevel Analysis of Geographic Contexts

  • John O'Loughlin
Article

Abstract

Geographers contend that regional and national contexts are important mediating and controlling influences on globalization processes. However, to reach this conclusion, geographers have been forced to engage in rather convoluted statistical manipulations to try to isolate the so-called ‘geographic factor’. Recent developments in multilevel statistical modeling offer a more precise and suitable methodology for examination of contextual factors in political behavior if the data have been collected in a hierarchical manner with respondents grouped into lower-level and higher-level districts. The World Values Survey data (collected in three waves from 1980 to 1997) for 65 countries are ideally suited to examination of the hypothesis that democratic beliefs and practices are globalizing. Using three key predictors (trust in fellow citizens, political interest, and volunteerism) for the sample of 91,160 respondents, it is evident that regional (for the 550 regions) and country settings (between 55 and 65 countries) are important predictors of political behavior, on the order of about 10% and 20%, respectively. Respondent characteristics account for about 70% of the variance explained. Ideology is far more significant than many of the usual demographic characteristics in explaining political behavior cross-nationally. Dramatic differences between established and new democracies clarify the political globalization process and global regions (Latin America, Eastern Europe, Western Europe, etc) also emerge as significant factors. Multilevel modeling of survey data offers a compromise between the aggregate data analysis preferred by geographers and the emphasis on surveys in a non-geographic context preferred by political scientists.

Keywords

Multilevel Modeling Social Trust Political Interest National Context Political Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • John O'Loughlin
    • 1
  1. 1.Institute of Behavioral Science and Department of GeographyUniversity of ColoradoBoulderU.S.A.

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