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Can Corruption Erode Confidence in Political Institutions Among European Countries? Comparing the Effects of Different Measures of Perceived Corruption

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Abstract

The purpose of this study is to analyze the effects of corruption on institutional confidence through testing alternative perceptions-based indexes of corruption. Scholars who have investigated this topic have often employed only indicators of corruption based on experts’ surveys. In this article we also consider a new index of corruption developed aggregating citizens’ perceptions. The first part of the paper explores the levels of corruption perceived by the citizens of EU member states, stressing the differences with the experts’ opinions. The second part tests, through a multivariate analysis, the impact of citizens’ and experts’ perceptions-based indexes of corruption on institutional confidence. The main results show that experts and citizens tend to express similar opinions on the extent of corruption in EU member states though, especially in some countries, these actors present some noticeable differences. Nevertheless, irrespective of the indexes used, more corrupt countries are characterized by lower levels of confidence in parliament and government. This relationship holds even controlling for the presence of reverse causality between corruption and confidence.

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Notes

  1. For information and data on CPI see www.transparency.org. CC and the other Worldwide Governance Indicators are available at http://info.worldbank.org/governance/wgi/index.asp.

  2. More precisely, we have used questions from QB1 to QB7 included in Eurobarometer 72.2. The text of the questions can be found in the EB72.2 basic bilingual questionnaire (pp. 15–20), available from: https://dbk.gesis.org/dbksearch/SDesc2.asp?ll=10&notabs=&af=&nf=&search=&search2=&db=E&no=4976.

  3. The active variables, which contribute to the formation of the axes factors, included in our MCA are 90. In this analysis we have not considered the variables which modalities presented a frequency percentage less than 2.

  4. Since eigenvalues obtained by MCA give a pessimistic evaluation of the variability explained by factorial axes, we have computed the variance considering only eigenvalues higher than 1/k, where k is the number of variables used in the analysis.

  5. Figure 1 does not report the position of those modalities and countries that collapse in the center of the two axes.

  6. We have used the QB1 (see note 3 for the related documentation). QB1 is the following: “For each of the following statements, could you please tell me whether you totally agree, tend to agree, tend to disagree or totally disagree with it. There is corruption in local/regional/national institutions in [country].” We have dichotomized original answers in the following way: “totally agree” and “tend to agree” in the category “agree” (0) and “tend to disagree” and “totally disagree” in the category “disagree” (1).

  7. We have run a null model without country-level or individual-level predictors which allows us to decompose the total variance in our dependent variables between individual and country levels. From the value of intra-class correlation we can infer that country-level variance ranges between the 18 % (CONFGOV) and the 20 % (CONFPARL) of the total variance. In other words, around the 20 % of the difference in the level of institutional confidence can be explained by the fact that a respondent lives in one country instead of another.

  8. Because both the dependent variables are indexes and could be treated as approximation of continuous variables, the models are estimated by the xtreg command in STATA. We have run the Hausman test to compare the performance of the fixed and the random effect models. The result suggests the use of random effects models.

  9. The question used is the following: “Please tell me how much you personally trust [NATIONALITY] parliament(government) using a scale from 1 to 10 where [1] means ‘you do not trust the institution at all' and [10] means ‘you trust it completely'”.

  10. The procedure used to obtain MAJ is exactly the same described in Lijphart (1999: 243–250). The original Lijphart’s dataset has been updated using different sources. Data on electoral results, parliaments and governments composition are gathered from ParlGov Dataset (www.parlgov.org), while data on the index of corporativism are taken from Siaroff (2003) and Roberts (2006).

  11. Data for these two variables are taken from World Development Indicators. See http://data.worldbank.org/data-catalog/world-development-indicators.

  12. As a robustness check we have also run additional models which contemporaneously test CEB and CPI as well as CEB and CC. We have not included CPI and CC in the same models because they are almost perfectly correlated. Results are available upon request. In all these models only CEB displays a positive and significant effect on both CONFPARL and CONFGOV, whereas CPI and CC turn to be insignificant. Given that institutional confidence is directly gathered from ordinary citizens’ opinions, it is not unsurprising that only CEB, which is measured based on different country residents’ perceptions, is significantly related to confidence. This result represents a further confirmation of the validity of our new corruption measure and points out that experts’ perceptions based measures can be flawed in explaining confidence, recommending the necessity to use different corruption measures in future analyses. However, scholars should be careful of this result because it can be ascribed to a problem of collinearity among different corruption measures. Given the high level of correlation between CEB and CPI/CC, it is most likely that CEB “absorbs” all the significance of the relation between corruption and institutional confidence.

  13. For simplicity we present the robustness checks only for CEB, but the same procedure has been also applied on the other corruption indexes. Graphs are available upon request.

  14. For a more complete explanation of the algebra adopted in the following procedure we remand to Leamer (1981) and Clausen et al. (2011).

  15. We are very grateful to Aart Kraay that sent us the excel do-file to plot the graphs reported in Fig. 4.

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Authors and Affiliations

  1. Department of Social and Political Sciences, Università degli Studi di Milano, Via Conservatorio, 7, 20122, Milan, Italy

    Alessandro Pellegata

  2. Department of Political and Social Sciences, Università degli Studi di Catania, Via Vittorio Emanuele II, 8, 95131, Catania, Italy

    Vincenzo Memoli

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  1. Alessandro Pellegata
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  2. Vincenzo Memoli
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Correspondence to Alessandro Pellegata.

Appendix

Appendix

See Table 5.

Table 5 Labels of the variables’ modalities which appear in Fig. 1
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Pellegata, A., Memoli, V. Can Corruption Erode Confidence in Political Institutions Among European Countries? Comparing the Effects of Different Measures of Perceived Corruption. Soc Indic Res 128, 391–412 (2016). https://doi.org/10.1007/s11205-015-1036-0

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