Assessment of corruption impact on economic growth

Abstract

This paper analyzes relationship between corruption and economic growth and results of empirical research about corruption level impact on economic growth in politically free and not free countries during the period of 1996–2013. The theme of corruption impact on different economic phenomena is not new. Rigorous study of corruption by economists was commenced in the 1980s. However, it was noticed that the number of research in corruption level impact on economic growth field is low. Most of existing research is not complex. Basic statistical methods, such as correlation analysis or binary linear regression analysis are used in most of research only. It was also noticed that only one measure of corruption is used in most of research. Research paper consists of 3 parts. In the first part theoretical analysis of corruption level impact on economic growth is represented. By comparing scientific literature it was noticed that approach of corruption impact on economic growth among researchers is not the same. On the one hand, there is a dominant approach, that corruption negatively affects economic growth. On the other hand, there is approach that economic growth is maximized with small but positive corruption level. There is approach that relationship between corruption level and economic growth is not the same in different countries also. For example I. Ehrlich and F. T. Lui (1999) assume that this relationship depends on political regime of a country. In the second part methods of empirical research are introduced. Research was made for 30 countries. It’s 14,56 % of general population. Secondary data from various databases were used. Corruption level was measured by 3 different corruption perception indices – Transparency International index, ICRG index and IMD index. Economic growth was measured by real GDP growth. Correlation analysis, analysis of variance (ANOVA), and panel-data regression analysis were used in this research. F-statistics, t-statistics, adjusted R squared, adjusted Akaike’s information criterion, Bayesian information criterion were used to assess fitness of regression equations. The problem of endogeneity was eliminated by using fixed-effects method. Post-hoc analysis was also made, by using 5 different tests: Bonferroni correction test, Scheffe’s test, Tamhan’s T2 multiple comparison test, Fisher’s last significant difference test (LSD) and Tukey’s honest significant difference (HSD) test. In the third part the results of empirical research are represented. The strong correlation between 3 corruption perception indices was found. Pearson’s correlation coefficient values vary between 0,8642 and 0,9499. It was found that there is no statistically significant difference between value means of different corruption perception indices by using analysis of variance and post-hoc analysis. No statistically significant linear relationship between corruption level and economic growth was found in group of politically not-free countries. However, statistically significant second power relationship between corruption level and economic growth was found in group of these countries. Economic growth maximizing levels were also calculated. They vary from 7,1 to 9 and depend from regressors and measures of corruption that are used. These results imply that GDP growth reaches its maximum level for small levels of corruption. In contrast, no statistically significant linear or second power relationship between corruption level and economic growth was found in group of politically free countries.