The above results find evidence that an increase in per capita income increases corruption in low income countries and it reduces corruption in high income countries. The result suggests that a non-linear relationship may exist between per capita income and corruption. The scatter plots in Figure 4.3 below, illustrate the close relationship between real GDP per capita and corruption with the best fitted Kernel curves. It is evident that there exists a non-linear relationship between real GDP per capita and corruption.
To evaluate the possibility of a non-linear relationship between per capita income and the level of corruption more systematically, the study re-estimates the model based on a non-linear framework of quadratic form depicted in equation (4.4) for the LICs, MICs, HICs, and for all countries in the sample. The non-linear relationship between income per capita and corruption are represented by the linear and squared term of log
real GDP per capita of a second degree polynomial function in equation (4.4). The PLS estimation results of the quadratic function are displayed in Table 4.4.
Column (12) presents the non-linear estimation results for all countries whereas columns (13), (14) and (15) show the results for LICs, MICs and HICs, respectively, for the period 1995-2004. The results confirm the existence of a non-linear Kuznets relationship between real GDP per capita and corruption. The inverted-U-shaped Kuznets relationship states that the corruption level increases initially but then reduces in the course of a country’s economic development.41 The estimated correlation value between CPI and log (RGDP)-of 6.243 on the linear term is positive and is a negative value of 0.443 on the squared term for all countries. The coefficients are statistically significant at the conventional level except for the low income countries. In addition, the inclusion of the second-order polynomial term improves the model’s goodness of fit for the MICs, HICs and for all country cases. The estimated negative sign for the second-degree polynomial of log (RGDP) reveals that a concave function better fits the data than the simple linear and cubic functions.42 The calculated adjusted R2 improves most for the high income countries (the adjusted R2 changes from 0.576 to 0.597 (see
Table 4.4 column (15)) for HICs).
Table 4.4 Non-linear relationship between per capita income and corruption
(12) All countries (13) LICs (14) MICs (15) HICs Log (RGDP) 6.243*** (0.585) 1.129 (1.402) 4.690** (2.438) 28.676*** (8.363) [Log (RGDP)]2 -0.443*** (0.036) -0.073 (0.107) -0.324** (0.149) -1.590*** (0.434) Adjusted R2 0.797 0.260 0.551 0.597 Observations 982 170 480 350 Wald test (p- value) 0.001 0.004 0.006 0.006 Note: CPI is corruption perception index, LICs is low-income countries, MICs is middle-income countries and HICs is high-income countries.
All equations control for adult literacy rate, level of education, income inequality, unemployment rate, democracy and economic freedom. White heteroscedasticity corrected standard errors are in parenthesise. ***, **, * indicate significance level at the 1 percent, 5 percent and 10 percent respectively.
41 See Kuznets (1955) for details.
42 The second degree non-linear function illustrates the non-monotonicity of per capita income and
corruption well. As the cubic function of log real GDP per capita decreases the R2 value of the regression.
Figure 4.3 Kernel fit plots of log (RGDP) and corruption for LICs, MICs and HICs LICs 0 2 4 6 8 10 5. 2 5.6 6.0 6.4 6. 8 7. 2 7.6 8.0 8.4 Log (RGDP) C P I
Kernel Fit (Epanec hnikov, h= 0.4461)
MICs 0 2 4 6 8 10 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Log (RGDP) C P I
Kernel Fit (Epanec hnikov, h= 0.3677)
HICs 0 2 4 6 8 10 8. 4 8. 8 9. 2 9. 6 10. 0 10. 4 10. 8 Log (RGDP) C P I
Kernel Fit (Epanec hnikov, h= 0.2661)
Note: RGDP is real gross domestic product per capita, LICs is low-income countries, MICs is middle- income countries and HICs is high-income countries.
The evidence of low-income, middle-income and high-income countries provides support for a non-linear relationship between per capita income and corruption level. The turning points are estimated, based on equation (4.3) for the LICs, MICs, HICs and for all countries at which the relationship switches from positive to negative, shown in Table 4.5. The turning point for all countries in the sample, LICs and MICs, is approximately at its value of 7, whereas the turning point for HICs is 9. The result suggests that poor countries with an extremely low level of income exhibit a high level of corruption in the early stages of economic development, and the corruption level increases until they reach the turning point at which the average level of corruption is at its maximum; once past the turning point, corruption level tends to become substantially lower at more mature stages of development with a high level of income.
Table 4.5 Turning points of the real GDP per capita
All Country LICs MICs HICs
Turning point 7.046275 7.732877 7.237654 9.01761 RGDP at the turning
point
1147.734 2280.329 1389.784 8239.336 Note: CPI is corruption perception index, LICs is low-income countries, MICs is middle-income countries and HICs is high-income countries.
Table 4.5 also displays the value of real GDP per capita at each of the turning points where corruption level starts decreasing for all countries, LICs, MICs and HICs. It is seen that the value of real GDP per capita at the turning point for high income countries becomes $8239.336 (1990 U.S. dollars), which is quite high in comparison with the income levels of LICs and MICs. The RGDP values of MICs suggests that few middle income countries already achieve the required levels of income of HICs to break the turning point but the average real GDP per capita of $1434.60 (1990 U.S. dollars) of LICs illustrates that low income countries are far behind the required levels of income for corruption to decrease. Furthermore, the average incomes levels of high- income countries are above the turning point level of income, which demonstrates that high-income countries have already reached the required level of development and, therefore, experience low levels of corruption.
Overall, the non-linear relationship between real GDP per capita and corruption is quite evident in HICs. The higher stages of economic development make it possible for the HICs to pay higher compensation to bureaucrats, which in turn helps to deter
corruption.43 Transparency International’s corruption perception index 2004 supports the theoretical expectations about the correlations among corruption and income. It is observed that the countries possessing well developed and highly integrated economies are among the least corrupt. Nations that fall into this situation include Denmark, Finland, Iceland, Canada, New Zealand, and Sweden. On the other hand, the most corrupt countries are traditionally viewed as having less developed and less integrated economies. Examples of some of the most corrupt nations include Bangladesh, Indonesia, Kenya, and Nigeria.
4.5 Sensitivity Analysis
The preceding section has shown the results of testing the various hypotheses to explain the levels of variation in corruption - in particular, the relationship between real GDP per capita and the degree of corruption across regions and countries. To check the robustness of the results, this section re-estimates the basic regression for the cross-section analysis and the results are discussed in the subsection 4.5.1. In addition, the two-stage least square methods are used to test the existence of endogeneity bias (subsection 4.5.2). An alternative measure of corruption is also utilised in both the cases.