**3.4 The dynamic model**

The dynamic panel model we propose is defined as follows:

$$Growth\_{\rm{la}} = \alpha\_{i} + \rho Growth\_{\rm{la}-1} + \beta Inf\_{\rm{la}} + \gamma Trad\_{\rm{la}} + \mu Fdi\_{\rm{la}} + \delta Icrg\_{\rm{la}} + \lambda Icrg\_{\rm{la}}^{2} + \varepsilon\_{\rm{ra}} \tag{4}$$

Where Growthit-1 represents per capita lagged GDP growth rate.

The results of the estimation are reported in **Table 3**, which shows that H0 hypothesis of the validity of the instruments is not rejected (the probability of Sargan statistics exceeds 5%, which means that instruments are in all exogenous). Similarly, there is no order 2 serial autocorrelation (probability of Arellano & Bond AR test (2) is greater than 5%). This allows us to assert that the GMM system model is appropriate and specifies well the instruments, with no heteroscedasticity or autocorrelation problems.

This method is more robust than the previous one. **Table 3** confirms our hypothesis that corruption negatively affects growth (−1.86). However, square corruption positively affects growth (0.33). The results obtained by the two methods (static and dynamic) confirm the positive impact of investment on growth.

The estimated model is written as follows:

$$\begin{array}{l} \text{Growth} = 2.885 + 0.0505 \text{lpf} + 0.0419 \text{Trad} - 1.865 \text{lerg} + 0.332 \text{lerg}^2 \\ + 0.0213 \text{Fdi} + 0.098 \text{Growth}\_{-1} \end{array} \tag{5}$$

*The Impact of Corruption on Economic Growth: A Nonlinear Evidence DOI: http://dx.doi.org/10.5772/intechopen.108876*


*AR (2): Arellano and Bond test of null of zero second-order serial correlation, distributed N (0, 1) under null. Sargan test: is a statistical test used to check for over-identifying restrictions in a statistical model. t-statistics are displayed in parentheses under the coefficient estimates.\*test statistic is significant at the 1% level. \*\*test statistic is significant at the 5% level and.*

*\*\*\*the numbers in parentheses are p-values.*

#### **Table 3.**

*Estimation of the model by GMM.*

By analogy to Section 2.3, determining the threshold effect shows that an optimum is achieved by Icrg = 1.865/0.664 = 2.81. This value confirms our hypothesis on the relevance of moderate corruption to achieve an optimal growth value.

#### **4. Results and discussion**

The concave function (**Figures 1** and **2**) may be interpreted in the following way. Corruption, which facilitates tax evasion, has two types of effects in economics. It offers households an opportunity of tax savings that can be consumed or invested, as tax evasion leads to a transfer of public resources to private agents [32, 33]. This could improve growth up to a certain threshold. The optimal threshold represents the reversal point of the curve; otherwise, the country may suffer underdevelopment like several countries immersed in corruption.

This corruption, if significant, will reduce state resources because of productive public spending, which will lead to a loss in economic growth, which sooner or later will lead to an uprising calling for establishing democratic principles and good governance.

This result may surprise those who advocate the negative effects of corruption, but it can be explained by the fact that administrative delays resulting from absence of "bribes" paid in a corrupt economy may dampen economic growth and reduce economic development.

#### **5. Conclusion**

The aim of this paper is to examine the impact of corruption on economic growth. The empirical literature that reported a linear relationship between corruption

and economic development failed to differentiate between growth-enhancing and growth-reducing levels of corruption.

In our study, we have presented evidence that suggests the existence of humpshaped relationship between corruption and growth, which shows the existence of a nonlinear relationship between these two variables. This nonlinear result shows that growth increases at middle-corruption and decreases as nations achieve higher level of governance (low corruption). In other words, the results indicate that higher or lower levels of corruption negatively affect growth. Minimum corruption can be beneficial to economic growth. This confirms some theories that assume that corruption "lubricates the economic cycle" and produces the most efficient economies. However, this lubricating effect has a threshold beyond which it becomes a threat to economic growth. Conversely, lack of corruption may be a mechanism that slows down growth.
