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19. See the Appendix; the two dependent variables are four-point scales that sum the answers of three yes/no items. Zero means the respondent did not participate in any political activity, and three means participation in all three. Missing value is treated as a negative answer by assumption.Google Scholar

20. The CSES is available at www.cses.org, and we use the April 10, 2006, release. The East Asia Barometer is available at http://eacsurvey.law.ntu.edu.tw/.Google Scholar

21. We adopt the definition of internal efficacy proposed by Niemi, Richard, Craig, Stephen, and Mattei, Franco, “Measuring Internal Political Efficacy in the 1988 National Election Study,” American Political Science Review 85 (1991): 1407–1413. “Internal efficacy refers to beliefs about one's own competence to understand, and to participate effectively in, politics” (p. 1407). The items we use are also two of the original seven items in their article, with minor modification of phrases.Google Scholar

22. The basic specification of the SEM model is the same as an ordinal logistic regression model. The only difference is that we treat Internal Efficacy as an intervening variable to be explained by other independent variables.Google Scholar

23. Our analysis also shows relatively weak partisanship in East Asian countries as opposed to Western democracies. The analysis by Emile Sheng in this issue attributes this to the fact that East Asian democracies have had a relatively short time span of electoral and partisan politics. There has not yet been enough time to institutionalize partisanship through mechanisms such as cumulative electoral experiences and parental socialization. See also Dalton, Russell J. and Weldon, Steve, “Partisanship and Party System Institutionalization,” Party Politics 13 (2007): 179–196.Google Scholar

24. Electoral participation encompasses having voted in the last election, trying to persuade others how to vote, and participating in campaign activities. See Appendix for additional detail.Google Scholar

25. Also see Karp, Jeffrey and Banducci, Susan, “Party Mobilization and Political Participation in New and Old Democracies,” Party Politics 13 (2007): 275–277.CrossRefGoogle Scholar

26. Nonelectoral participation includes three items: (1) contacting politicians or officials, (2) protest or demonstration, and (3) work with others to share concern.Google Scholar

27. Tables 1 and 2 are the results of ordinal regressions for electoral and nonelectoral participation. Each ordinal regression views the model as three separate binary logistic regressions by dichotomizing the (0,1,2,3) ordinal scale into a (0,1) binary scale. For example, threshold (0) combines categories into (0/1,2,3), threshold category (1) into (0,1/2,3), and threshold category (2) into (0,1,2/3). Therefore, the threshold (0) represents “the constant of the binary logistic regression” if we recode the four-point scale into 0 and 1 (1,2, and 3) and run the binary logistic regression. Threshold (1) means “the constant” if we recode the ordinal dependent variable by category 1 into a binary variable and run the regression. Similar interpretation can apply to threshold (2).Google Scholar

28. The expected probability is calculated with a baseline profile for those who are youngest and have the lowest level of religiosity, perception of corruption, and education.Google Scholar

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31. A reasonable speculation for the poor fit of Taiwan and Japan is that there must be some country-specific factors beyond the scope of our model. We believe these factors are related to citizen-initiative contact about personal issues since the two countries for a long time had the same electoral system (single, nontransferable vote) and a very similar party system (one party dominated). Under this system, nonelectoral participation is usually initiated for local or personal purposes and most politicians try to exploit the chances of marketing themselves and demonstrate their political impact to win popular support. This is a key factor of intraparty competition under the one party-dominated system with SNTV rule with multiple party candidates in a district. See Horiuchi, Yusaku, Institutions, Incentives and Electoral Participation in Japan: Cross-Level and Cross-National Perspectives (London: RoutledgeCurzon, 2005).CrossRefGoogle Scholar

32. Model specification at the individual level of HGLM is exactly the same as ordinal logistic regression. But at country level, two dummy variables are added to the HGLM model, one signifying established democracies and the other Asian emerging democracies (Taiwan, South Korea, Hong Kong, and Philippines), to verify if the random component shows significant unexplained variance. For the sake of space, we do not report the detailed results of our HGLM models. Only a summary of the findings is reported here. Interested readers are welcome to send us a request for a copy of detailed results. Only twenty-five countries are included in the HGLM model. We excluded Japan, Bulgaria, Denmark, Germany, the Netherlands, and New Zealand because of very high missing values in some variables.Google Scholar

33. This finding is based on lack of statistical significance for the contextual variables, specifically two dummy variables, Established Democracy and Asian Democracy, adding to the country-level part of multilevel specification.Google Scholar

34. We also ran ordinal logistic regressions for EAB data. Since the results are consistent with the SEM analysis, we only report the latter.Google Scholar

35. We add more explanatory variables to the country-specific model one at a time depending on which variable has the largest modification index. We stop adding new variables when the fit statistics pass the acceptable level. We apply the same specification for each country sample, and overfitting may be a potential problem. However, since the aim of our SEM modeling is to explore peculiarity of each country instead of finding a universal structure of causal relationships, we decide to let the data speak for themselves. The acceptable level of the fit statistics is as follows: CFI is greater than or equal to 0.95, TLI is greater than or equal to 0.9, RMSEA is less than 0.06, WRMR is less than 0.9. See Muthén, Linda K. and Muthén, Bengt O., Mplus User's Guide: Statistical Analysis with Latent Variables (Los Angeles: Muthén and Muthén, 2001), p. 362.Google Scholar

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