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Evaluations of the quality of the representative channel and unequal participation

Comparative European Politics Aims and scope

Abstract

This article analyzes how individual-level assessments of the quality and functioning of the representative channel affect citizens’ likelihood to turn out to vote and to engage in alternative forms of non-institutionalized participation, and whether these relationships are moderated by individual resources as measured by education. Relying on novel data from the sixth round of the European Social Survey on how European citizens evaluate different aspects of democracy we show that negative evaluations of the quality of the representative channel discourage voting, but only promote participation in demonstrations among the highly educated. These findings highlight potential inequalities in citizens’ ability to voice their political demands: while highly educated individuals are likely to translate their negative evaluations of the institutional channel of representation into non-institutionalized forms of participation, in the presence of negative evaluations low educated individuals are simply more likely to withdraw from politics.

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Notes

  1. Although still focusing on the integrity of the electoral process Carreras and İrepoğlu (2013) and Norris (2014) rely on more than one indicator to operationalize their electoral integrity/malpractice measures.

  2. The studies by Hiskey and Bowler (2005), and Levin and Alvarez (2009) focused on Mexico where allegations of electoral fraud have been common in the last decades. Carreras and İrepoğlu (2013) focused on Latin American countries, which clearly differ in their levels of democracy. Although Norris’ (2014) analysis adopted a global outlook, a great number of the 18 countries included in her sample cannot be considered full democracies as attested by their scores in Freedom House indexes, and the few established democracies included in her analysis (Australia, Chile, Netherlands, Poland, Slovenia and Uruguay) functioned as a control (Norris, 2014, p. 64). The exception to this pattern is Birch’s (2010) study, which included a great number of established democracies.

  3. Carreras and İrepoğlu (2013 p. 612) show that while distrust in the fairness of elections is quite high in regions like Latin America this is not the case in Europe.

  4. Some recent studies challenge the view that differences in turnout across education groups are small in all countries. Gallego (2015) uncovered substantial country differences in turnout inequalities related to education. Likewise, Armingeon and Schädel (2015) recently argued that there are remarkable cross-country and temporal differences with respect to voting inequalities related to education. In any case, Gallego (2015, p. 25) points that in most countries the overall differences in turnout rates for citizens with different levels of education are moderate in size, and Armingeon and Schädel (2015) identify an average difference in turnout rates between those with the highest and the lowest education of just 4.9 percent (for the 1999–2009 decade).

  5. Our final sample includes 27 countries. We exclude Russia and Ukraine because they cannot be considered fully democratic. None of these countries had a score above 6 in the Polity IV dataset.We exclude countries that are not fully democratic because voting and demonstrating, as well as answers to questions related to the functioning of democracy, might be distorted by the non-democratic character of these regimes.

  6. The question wording and descriptive statistics of all items used in this article can be found in appendix A.

  7. The same factor solution is obtained when factor analyzing these indicators in each of the countries separately. In all countries only one factor with an eigenvalue higher than one is extracted, and in all cases all indicators have a factor loading above the 0.3 threshold.

  8. If instead of relying on the factor scores we rely on an index obtained through the sum of the four indicators we obtain very similar results that lead us to the same conclusions for all the analyses presented below (results available upon request).

  9. Respondents not eligible to vote have been excluded from all the analyses.

  10. This choice is motivated by the fact that it is not possible to directly measure winner/loser status according to the party voted by the respondent, because this variable predicts success perfectly in non-linear models in which voting is specified as the dependent variable.

  11. To ensure that our results are not driven by our model estimation decisions we replicate all the analyses using random-intercepts logistic and multinomial logistic models. Empty random-intercepts models reveal that the amount of variance at the country level is 8.6 per cent for voting, and 13.7 per cent for demonstrating. Following recent analysis of political participation (Dalton et al, 2010; Marien and Christensen, 2013; Braun and Hutter, 2014) we introduce in these models a country-level control for the openness of the political system. To operationalize this variable we follow Dalton et al (2010) who rely on the World Bank rule of law indicator to measure system openness. This choice is motivated by this being the only system openness proxy (among the ones used in previous studies) that is available for all the countries in our sample. These multilevel models also include a country-level variable measuring the enforcement of compulsory voting in national elections. These models, which can be found in appendix C, do not alter the substantive results and lead us to the same conclusions.

  12. Independent variables are rescaled so that numeric inputs represent the effect of the mean ±1 standard deviation. Binary predictors are not rescaled.

  13. We have re-estimated these predictions relying on adjusted predictions at representative values (APRs) instead of average adjusted predictions (AAPs). We have estimated APRs of the likelihood of turning out to vote for a young individual with a low level of political interest. In this case the adjusted predictions of voting change from 0.46 for an individual with the worst evaluations to 0.62 for and individual with the best evaluations. That is, in this case the change in the adjusted prediction of turning out to vote is of 0.16.

  14. In the case of voting a contrast of the statistical significance of the average marginal effects of the evaluations reveals that there is no statistically significant difference in the effect of the evaluations between the three education groups.

  15. In the case of demonstrating a contrast of the statistical significance of the average marginal effects of the evaluations reveals that there are statistically significant differences in the effect of the evaluations between all these three education groups. Moreover, while the negative average marginal effects of the evaluations are statistically significant at the 0.001 level for those with secondary and university education, the positive average marginal effect of the evaluations fails to reach conventional levels of statistical significance for those with primary education.

  16. The high proportion of voters in the sample is likely to be caused by turnout overestimation because of social desirability bias.

  17. Even if only 503 respondents fall in the only demonstrates category, it is meaningful to separate those respondents from those who both vote and demonstrate since their attitudinal profile is likely to be quite different (for example, they should have more negative evaluations of the functioning of representative channel than those who both vote and demonstrate).

  18. For those with university and secondary education, the average marginal effects associated to a one-unit change in the evaluations factor indicate that, for them, more negative evaluations statistically significantly increase the likelihood of both voting and demonstrating, while this is not the case for those with primary education.

  19. As in the previous case, for those with university and secondary education, the average marginal effects associated to a one-unit change in the evaluations factor indicate that, for them, more negative evaluations statistically significantly increase the likelihood of demonstrating only, while this is not the case for those with primary education.

  20. Birch (2010) and Norris (2014) acknowledge this potential pitfall when analyzing the relationship between electoral integrity and political participation.

  21. This variable measuring non-conventional activity takes the value 1 if the respondent performed any of the following actions in the last 12 months: joined a demonstration, boycotted a product or signed a petition. The categorical variable of participation is also re-estimated with the category of only demonstrates becoming only non-conventional, and the category both votes and demonstrates becoming both votes and non-conventional.

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Acknowledgements

The authors would like to thank Hanspeter Kriesi, the two anonymous reviewers and editors of Comparative European Politics, the participants in the 2014 ECPR General Conference panel on political participation, and the members of the Political Behaviour Colloquium at the European University Institute for their useful comments and suggestions on earlier versions of this article. They also acknowledge generous support from the Salvador de Madariaga Program and the Spanish Ministry of Education.

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Appendices

Appendix A

Table A1

Table A1 Descriptive statistics and question wording

Appendix B

Table B1

Table B1 Logistic fixed-effects regression results

Table B2

Table B2 Multinomial logistic fixed-effects regression results

Appendix C

Table C1

Table C1 Multilevel random-intercepts logistic regression results

Table C2

Table C2 Multilevel random-intercepts multinomial logistic regression results

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Hernández, E., Ares, M. Evaluations of the quality of the representative channel and unequal participation. Comp Eur Polit 16, 351–384 (2018). https://doi.org/10.1057/cep.2015.45

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