CIVIC ENGAGEMENT AND CORRUPTION IN 20 EUROPEAN DEMOCRACIES

Abstract

This paper analyzes the relation between different forms of civic engagement and corruption. This first of all extends earlier analyses linking generalized trust to corruption by incorporating another element from the social capital complex (namely formal forms of civic engagement). Second, based on the idea that social networks’ beneficial or harmful impact may depend on their characteristics, it investigates how the structure of social networks (i.e., inclusive vs. exclusive and isolated vs. connected) matters. Evaluating the engagement –corruption nexus for a cross-section of 20 European democracies in 2002/2003, we confirm that social networks are linked to corruption even when controlling for the effect of generalized trust, and that their relation to corruption is type-specific. These findings survive under various model specifications and robustness checks.

Keywords:

• corruption
• civil society
• networks
• voluntary associations
• European Social Survey

Notes

¹While O'Connell (2003): 243) employs the ‘average number of organizational memberships per person’ as a measure of social capital and connects this to ‘the perception of corruption in each society’, his work does not simultaneously control for the effect of trust – as we do – nor does he assess the potentially different roles played by various types of associations (see later).

²Unlike particularized trust, which is based on personal knowledge and, hence, restricted to a specific social unit (Freitag et al. 2009), generalized trust can be defined as an ‘estimate of the probability of trustworthiness, p*, for the average person’ (Coleman 1990: 104). It is therefore independent of specific persons or groups, and based on the assumption that most people are part of the own ‘moral community’ (Uslaner 2003).

³Some recent studies have similarly addressed potential differences between the effects of various types of social networks (i.e., friends, family, voluntary organizations) on economic development and growth (e.g., Beugelsdijk and Smulders 2003; Sabatini 2009). However, such studies often implicitly presuppose that all voluntary associations have the same effect. Yet, in reality, there is likely to be significant variability across such associations (e.g., Knack and Keefer 1997; Paxton 1999; Zmerli 2003; Coffé and Geys 2007; van Deth 2010). Using recently developed methodological tools, this variability will be explicitly taken into account and exploited in our analysis (see later).

⁴According to these same authors, human rights and environmental organizations are typical examples of inclusive networks, while professional interest groups, social clubs, and student fraternities are key examples of exclusive groups (more details later).

⁵The ESS is a biennial survey with minimum effective sample size of 1,500 respondents for each country (800 for countries with a population below 2 million). The first round (2002/2003) was carried out in 22 European countries and contained a special focus on civic engagement. Note that the civic engagement questions were not included in Switzerland and the Czech Republic, such that these countries are excluded.

⁶Evidently, we exclude the ‘other’ category in making this distinction (and similarly later).

⁷With respect to religious groups, we also ran all regressions below excluding religious organizations – as suggested by Putnam (1993). This leaves our results qualitatively unaffected.

⁸This correction involves a simple OLS regression where the average number of participations of individuals participating in a given association type in country i is the dependent variable and the total number of participants of these same association types in a given country the explanatory variable. Higher (lower) residuals from this regression indicate associations having more (less) interconnections than its participant-base would suggest, implying higher (lower) connectedness net of the participant-size effect (see also Coffé and Geys 2008; Geys and Murdoch 2008).

⁹While this follows Paxton (2002, 2007), selection of just three associations as isolated is obviously ad hoc. Still, re-estimating the model taking four associations as isolated, makes no difference to the results (available upon request).

¹⁰Generalized trust is measured using respondents’ answers to: ‘Generally speaking, would you say that most people can be trusted, or that you can't be too careful in dealing with people?’ We use the average score for each country, which lies on a scale from 0 (‘you can't be too careful’) to 10 (‘most people can be trusted’). Although the credibility of this measure depends on what respondents understand under ‘most people’ (i.e., the so-called ‘radius of trust’ problem), recent research has shown that the ‘question seems to work well in Western and affluent nations’ (Delhey et al., 2011: 25), which are the focus of the current analysis.

¹¹Note that all models were tested for influential cases using Cook's D as test statistic. Exclusion of such cases leaves our results qualitatively unaffected (details available upon request). If anything, removing any outliers tends to slightly improve our results.

¹²Unfortunately, a more direct approach to address the causality issue using instrumental variable techniques is unfeasible by the lack of viable instruments. This is why we refrain from drawing final causal inferences throughout the paper.