This paper has contributed to the research on the relationship between corruption and support for an authoritarian leader. We were able to show that levels of corruption do indeed matter.
Higher levels of corruption influence citizens and increase their support for a strong leader, who does not have to bother with parliament and elections. Citizens are more willing to disregard political institutions or important aspects of democracy. This is remarkable and highlights the importance of democracies to actively fight corruption and keep corrupt practices at a minimum. Moreover, we can observe that inequality produces the same effect. Over long periods of time, rising inequality within a country leads to higher support for authoritarianism.
We could further confirm academic findings (Chong & Gradstein, 2018; Wike & Fetterolf, 2018) that levels of education, age, and income are negatively correlated and refute Foa &
Mounk’s (2016) claim that undemocratic tendencies (at least support for an authoritarian leader) are rising among higher earing citizens. The economy, however, seems to be less influential.
Neither changes over time, nor differences between countries seem to influence citizens’ desire for a strong leader.
However, we were not able to show, whether citizens compare their own country over time which leads them to those drastic measures or if the comparison to other countries is what drives them. Once split up, we could not find a significant result for either. This most likely means that it is a mixture of both. We cannot see whether it is because of within-country variance, cross-national variance, or an absolute effect. In addition, we could find no interaction effect between corruption and education. It seems that corruption does not impact higher or lower educated citizens more when it comes to the support for an authoritarian leader.
Based on existing literature we have learned that corruption has several negative effects, all of which in turn influence citizens and their willingness to disregard “democratic guardrails”
which are existential for a well-functioning democracy. Ironically, once authoritarian leaders are in power, without checks and balances, levels of corruption are very likely to increase, further amplifying the problem.
This thesis has empirically shown how detrimental corruption is and to what extent it threatens democracies. This is especially remarkable, as the subject of our research were members of the European Union and therefore consolidated democracies, that met the Copenhagen Criteria
28
when they entered. If we can see these effects in consolidated democracies, we can assume that they are even stronger for newly established, unconsolidated democracies.
However, the survey is limited by its rather short time span from 1999 to 2017 with only three waves and the use of members of the European Union only. Further research could find out if this effect holds up in democracies outside of the EU and if we can see the effect also in other regime types. Moreover, future research could focus on the question if high levels of support for a strong leader, who does not have to bother with parliament and elections means that citizen reject democracy. As Lavric & Bieber (2021) have shown in the Western Balkans, citizens did not think that a strong leader, unrestrained by parliament and elections is conflicting with their idea of democracy. This might be true in other consolidated democracies as well. Further research that focuses not only on citizens’ support for authoritarian alternatives but also on their understanding of democracy, is needed.
We found empirical proof that corruption has detrimental effects on democracy and increases the support for democratic alternatives. This is of great societal as well as academic relevance and might help in the fight against democratic backsliding.
29
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Declaration of Authorship
I, Martin Winroither, hereby declare that the master’s thesis submitted is my own unaided work. All direct or indirect sources used are acknowledged as references. This paper was not previously presented to another examination board and has not been published.
Amsterdam, 10th June 2022 _________________________
City and Date Signature of author
36
Appendix
Appendix I: GDP per capita (PPP)
1999 2008 2017 Mean
Austria 27606,48303 41316,22518 54172,98679 41031,89833
Bulgaria 5807,811701 14335,7146 21469,94544 13871,15725
Croatia 9960,338531 20921,54056 27154,08469 19345,32126
Czech Republic 15396,31178 27974,48713 38824,88792 27398,56228
Denmark 26653,8239 41278,32683 55356,68078 41096,27717
Estonia 8412,066776 22802,34218 33821,93291 21678,78062
Finland 24767,28067 40083,69727 47570,13358 37473,70384
France 24307,50281 35095,26568 44577,06457 34659,94435
Germany 26291,21108 37802,38743 53071,45557 39055,01803
Hungary 10873,24786 20727,88997 29501,12042 20367,41942
Italy 25602,38947 35523,27131 41581,12079 34235,59386
Lithuania 7820,717972 20721,0132 33761,87124 20767,86747
Netherlands 29268,5361 46420,20168 55088,6338 43592,45719
Poland 10040,89838 18303,35292 30064,50348 19469,58493
Portugal 17718,15157 26665,8373 33044,71674 25809,56854
Romania 5596,472489 16782,22907 27141,90093 16506,8675
Slovak Republic 10714,17713 23725,85776 30061,55147 21500,52879
Slovenia 17083,25172 29604,29147 36507,55304 27731,69874
Spain 19954,29022 33263,2724 39528,92539 30915,496
Sweden 27471,55827 42158,2957 51947,95425 40525,93607
Source: WorldBank
37 Appendix II: Gini coefficient
2001 2008 2017 Mean
Austria 24 27,7 27,9 26,53333
Bulgaria 26 35,9 40,2 34,03333
Croatia - - 29,9 -
Czech Republic 25 24,7 24,5 24,73333
Denmark 22 25,1 27,6 24,9
Estonia 35 30,9 31,6 32,5
Finland 27 26,3 25,3 26,2
France 27 29,8 28,8 28,53333
Germany 25 30,2 29,1 28,1
Hungary 25 25,2 28,1 26,1
Italy 29 31,2 32,7 30,96667
Lithuania 31 34,5 37,6 34,36667
Netherlands 27 27,6 27,1 27,23333
Poland 30 32 29,2 30,4
Portugal 37 35,8 33,5 35,43333
Romania 30 35,9 33,1 33
Slovak Republic - 23,7 23,2 -
Slovenia 22 23,4 23,7 23,03333
Spain 33 32,4 34,1 33,16667
Sweden 24 25,1 28 25,7
Source: EuroStat
38 Apendix III: Results
III a.) Full regression model
39 Appendix III: Results
III b.) Full regression model: Interaction between corruption and education
40 Appendix III: Results
III c.) Full regression model: Split up of country-level indicators
41 Appendix III: Results
III d.) Full regression model: Split up of country-level indicators (interaction between corruption (compared between countries) and education)
42 Appendix III: Results
III e.) Full regression model: Split up of country-level indicators (interaction between corruption (within countries over time) and education)