Global economic crisis and corruption

See also Yamamura (2014) for the evidence that natural disasters lead to more public sector corruption at the country level.

There is also a large literature on the country-level determinants of corruption, as well as on the country- and firm-level effects of corruption. See, for example, Iwasaki and Suzuki (2012), Dreher and Siemers (2009), Serra (2006) and Yamamura (2014) for the role of country-level structural reforms, capital controls, GDP per capita, political stability, colonial past and natural disasters in determining corruption. For the effects of corruption, much effort has been devoted to finding out whether corruption has a harmful or beneficial effect on outcomes such as GDP growth and firm entry. For example, Mauro (1995), Meon and Sekkat (2005) and Johnson et al. (2011) find a harmful effect of corruption on economic growth. At the same time, Dreher and Gassebner (2013) find that corruption facilitates firm entry, and Mendez (2014) shows that corrupt officials may increase firms’ compliance with regulations.

Whether corruption persists in the long-run is open to some debate in the literature. For example, whilst Rose-Ackerman (1978) and Shleifer and Vishny (1993) argue that competition among officials reduces corruption to zero, this outcome depends on how powerful the central authority is (Priks 2012).

On the downside, the respondents may be reluctant to talk about the actual corruption exchanges in which they were involved. However, the stigma associated with honestly answering questions about bribery may be lower in high-corruption (such as transition economies) than low-corruption countries (such as industrialised Western democracies). This is because in high-corruption countries bribery is viewed as inevitable and part of the system (Hunt 2007).

The main dataset includes Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, the Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, former Yugoslavian Republic of Macedonia, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan.

Table A1 of the online appendix contains information on the share of respondents who contacted a particular type of public official and the number of respondents who paid a bribe.

This distribution of answers excludes respondents who did not provide an answer to the question (6.6 % of the sample). The crisis questions non-respondents, however, are not excluded from the econometric analysis—all regressions, looking at general effects of crisis, contain a crisis-question-missing-value dummy (results for this variable are not reported and are available on request).

These coefficients are Pearson correlations. We have also calculated tetrachoric correlations (a technique suitable for calculating correlations between dichotomous variables); they did not exceed 0.35. In addition, we have tested for multicollinearity by obtaining the variance inflation factors (VIF) after estimating the model. The VIFs for the different effects of crisis ranged from 1.03 to 1.24, being well below the critical value of 10.

The post-estimation Variance Inflation Factor analysis, again, suggests that the multicollineraity is unlikely to be an issue: VIFs for the expenditure effect variables ranged from 1.08 to 1.35, which is lower than the critical value of 10.

The respondents were asked, “Please imagine a ten-step ladder where on the bottom, the first step, stand the poorest 10 % people in our country, and on the highest step, the tenth, stand the richest 10 % of people in our country. On which step of the ten is your household today?”.

The wealth index was constructed using principal component analysis. Unfortunately, the survey does not contain information on the actual household (or individual) income. We considered using information on household expenditures on different goods (food, utilities, transport, education, health, clothing and durable goods) as a proxy for household income, but decided against it, as the “do not know”/non-response rate for several of these expenditure categories is as high as 18 % (and much higher for particular countries – e.g., 66 % missing answers for the expenditure on durable goods in Uzbekistan), and the proportion of missing values of the composite expenditure variable is 46 %.

We have also estimated the model with Poisson regression; the results are consistent with the OLS model and are available on request.

We have also checked for possible non-linearities in the wealth-bribery relationship, by estimating the models with the wealth index and its square. The results suggest an inverted U-shaped relationship, with the implied inflection point equal to 2.89 and 1.34 on the wealth index scale for the models explaining general bribery and the number of officials bribed, respectively. It is should be noted that only two percent of respondents had a wealth indexes above 2.89 and those of 15 percent exceeded 1.34. This effectively means that, for the overwhelming majority of our sample, the likelihood of bribery and the range of public officials bribed was increasing with household wealth.

This finding, however, should be interpreted with caution. Some people habitually complain about everything, including being the crisis victims and the extortion targets for public officials. Unfortunately, with the present data, we are not able to isolate such potential “kvetching” effects (Kaufmann and Wei 2000 p.16); the use of longitudinal data, which would allow isolating unobserved personality trait effects, represents a possible solution and a way forward.

In this paper we concentrate on the effects of crisis on the likelihood of bribery, controlling for individual-level characteristics. The discussion of these individual-level characteristics on bribery in Western Europe can be found in our related work (Ivlevs and Hinks 2014).

The income groups were created according to the World Bank country income classification for 2009.