The Effect of Corruption on the Productivity of a Country - Essay Example

GDP and Corruption: Introduction: This paper analysis the effect of corruption on the productivity of a country, corruption is defined as the use of an individual's position for personal gains. In terms of corruption countries are ranked as most corrupt countries and those that are clean countries, Finland, Switzerland, Singapore, Denmark, New Zealand and Sweden are some of the countries that have least corruption incidences in the world while countries such as Sudan, Chad and Guinea are considered to be the most corrupt countries in the world. From this list it is evident that the most clean countries have higher GDP levels than the most corrupt countries and also that the most corrupt countries majority of them are African countries. The main objective of this paper is to use econometrics methods to show the relationship between GDP and corruption, results show that the higher the level of corruption in a country the lower the level of GDP, correlation coefficients show that as the level of corruption in a country increases GDP declines, results show that African countries have higher mean corruption levels than the other non African countries. Economic motivation: A study by Mauro (1995) showed that corruption had an effect on the level of investment in a country, in his study he found out that corruption discouraged foreign direct investment. Ackerman (1998) states that if India was to reduce its corruption levels then it would attract foreign direct investment at the same level as the attraction attained through tax incentives, Another study by Tanzi and Davoodi (1997) showed that corruption tends to increase the level of government spending, these funds are not used for their intended purpose and therefore spending on education and health is reduced resulting to poor economic growth and human capital development. Bardhan (1997) study shows that corruption also affects income distribution and this increases poverty levels in a country, Krueger (1994) states that corruption affects the election of leaders and the decisions made by these leaders may adversely affect the economy. The level of GDP is a key indicator of the welfare and economic development in an economy, the level of GDP in a country is determined by a number of factors which include the interest rates, inflation, investment, infrastructure, human capital, capital stock, production, exports and consumption, however less emphasis has been put on the importance of eliminating corruption practices as a way to influence economic growth. In this paper we focus on the effect of corruption on the GDP level of a country, we use data on corruption index from transparency international and GDP level from the world bank, we use linear regression models to estimate the relationship between these variables and show that corruption affects the level of GDP, limitations include unavailability of data for countries and the violation of linear regression assumptions on auto regression given that we will be considering cross sectional data. The following is a description of the data and econometric modelling. Data: Data used was retrieved from transparency international website that indicate corruption index for the year 2007 for countries all over the world, data on GDP in US dollars for the year 2007 was retrieved from the world bank website. A sample of 152 countries was considered in the study and this was due to the availability of data. The following chart represents the countries considered in this study where countries are categorized into African and non African countries: Only 3 % of the countries are African while approximately 97% were non African countries, analysis on the GDP levels showed that the mean GDP levels for non African countries was relatively higher compared to the mean GDP for African countries, the following chart summarizes the results: Results also show that the mean corruption index value for African countries was relatively lower than the mean corruption index for non African countries, the chart below summarizes the results: From the transparency international data the index scale ranges from 1 to 10, the value 1 represents most corrupt nation and value 10 represents the cleanest country, from the above chart therefore it is evident that African countries considered in the study are more corrupt than non African countries, also that the GDP level is relatively lower showing that there is a possibility that the level of corruption may affect production in a country given that the African countries are more corrupt than non African countries. An analysis on the correlation coefficient between the level of GDP and corruption index show that there is a positive but weak correlation between the two variables, the correlation coefficient value is 0.274082 showing that as the value of corruption index increases the GDP level also increases, this means that the lower the level of corruption in a country the higher the level of GDP. correlation GDP African CPI GDP 1 0.274082 African -0.15058 1 -0.33138 CPI 0.274082 -0.33138 1 From the above table it is also evident that the correlation coefficient between GDP and African is negative, this means that as the dummy variable African increases then the GDP level declines, the CPI level also shows that as the Dummy variable African increases the CPI value declines, this shows that as the dummy variable increases GDP and CPI index declines, showing that African countries arte more likely to have lower GDP and higher corruption levels. The mean for the corruption index for all the countries considered in the study amounts to 4.14 while the mean GDP level amounts to 343,505, the following table summarizes the results: N = 152 CPI mean GDP mean mean 4.139474 343505.9013 Econometric method: Sample data contains three variables and this include the level of GDP in 2007 per country, corruption index and the dummy variable that has a value of 1 is a country is an African country and the value 0 if the country is non African, we first estimate a model that will show the effect of corruption on GDP, we state our model as follows: GDP = a + b CPI Where a is a constant, b is the slope of the model and CPI is the corruption index, the following table summarizes the results of the linear regression model using Eviews: Dependent Variable: GDP Method: Least Squares Date: 04/24/09 Time: 16:13 Sample: 1 152 Included observations: 152 Variable Coefficient Std. Error t-Statistic Prob. C -326937.5 216460.8 -1.510377 0.1331 CPI 161963.4 46401.73 3.490462 0.0006 R-squared 0.075121 Mean dependent var 343505.9 Adjusted R-squared 0.068955 S.D. dependent var 1275232. S.E. of regression 1230480. Akaike info criterion 30.89678 Sum squared resid 2.27E+14 Schwarz criterion 30.93657 Log likelihood -2346.155 F-statistic 12.18332 Durbin-Watson stat 2.118941 Prob(F-statistic) 0.000633 From the output we state our model as follows: GDP = -326937.5 + 161963.4CPI From the above model it is evident that the autonomous value or the Y intercept of our model is -326937.5, this means that if we hold all factors constant and the value of corruption index is zero then the level of GDP will be -326937.5. The value of the slope is 161963.4, if we hold all factors constant and increase the corruption index by one unit then the GDP level will increase by 161963.4 units. This model therefore demonstrates that if we reduce the level of corruption in country the GDP level is likely to increase, the correlation of determination value is 0.075121 and this means that the explanatory variables only explain 7.5% of the deviations in GDP. Hypothesis testing on the statistical significance of the estimated values at 95% is summarized by the table below; we state our hypothesis as follows: Slope: Null hypothesis H0: b = 0 Alternative hypothesis Ha: b 0 Constant: Null hypothesis H0: b = 0 Alternative hypothesis Ha: b 0 Coefficient Standard error T statistics T critical 95% Constant a -326937.5 216460.8 -1.5103774 1.95996 Slope b 161963.4 46401.73 3.490460377 1.95996 The T critical value at 95% level of test is + or - 1.95996, From the above table we consider the constant statistics, the T statistics value for the constant is less that + or - 1.95996 and for this reason we accept the null hypothesis that the constant is equal to zero, we therefore conclude that the constant is not statistically significant at the 95% level of test. Considering the slope the T statistics value is 3.490460377 and because this value is greater than 1.95996 we reject the null hypothesis, by rejecting the null hypothesis we accept the alternative hypothesis that the slope value is not equal to zero, this means that the slope value estimated is statistically significant at the 95% level of test. We now consider joint statistics of the significance of the two estimated coefficients; in this case we state the null and alternative hypothesis as follows: Null hypothesis H0: a=b = 0 Alternative hypothesis Ha: ab 0 From the Eview output the F statistics value is 12.18332, the F critical value given that alpha = 0.10 is 2.70554, and because the F statistics value is greater than the F critical value we reject the null hypothesis, this means that the joint statistical test show that the two coefficients are statistically significant. From the above model therefore it is evident that a decline in corruption levels will induce an increase in the level of GDP in a country, this is indicated by the increase in the corruption index will result into an increase in GDP by 161963.4 units if we hold all other factors constant, if we hold all other factors constant and the value of the corruption index is zero meaning that the country is the most corrupt then the GDP level will be -326937.5 if we hold all other factors constant. We now specify our model by adding a dummy variable, this dummy variable will help indicate whether a country is an African country or a non African country, the dummy variable = if country is African and dummy = 0 if the country is non African, we therefore e3xtend our model as follows: GDP = a + b1 CPI + b2 African Where a is a constant, b1 is the coefficient of corruption index and b2 is the coefficient of the dummy variable African, the following Eview output summarizes the results: Dependent Variable: GDP Method: Least Squares Date: 04/24/09 Time: 16:10 Sample: 1 152 Included observations: 152 Variable Coefficient Std. Error t-Statistic Prob. C -218940.2 254828.6 -0.859167 0.3916 AFRICAN -189420.7 235135.1 -0.805582 0.4218 CPI 148819.0 49238.26 3.022425 0.0030 R-squared 0.079131 Mean dependent var 343505.9 Adjusted R-squared 0.066771 S.D. dependent var 1275232. S.E. of regression 1231922. Akaike info criterion 30.90559 Sum squared resid 2.26E+14 Schwarz criterion 30.96527 Log likelihood -2345.825 F-statistic 6.401886 Durbin-Watson stat 2.130369 Prob(F-statistic) 0.002151 From the output above we state the estimated model as follows: GDP = -218940.2 +148819.0 CPI - 189420.7African From the model it is evident that if we hold all factors constant and the value of CPI and African is zero then the GDP level is equal to -218940.2, if we hold all factors constant and increase the value of CPI meaning that we reduce corruption the value of GDP will increase by 148819.0, also if we hold all factors constant and increase the value of African by one unit then the GDP level will reduce by -189420.7 units. From this model it is evident that GDP level depends on the level of corruption and also whether the country is an African country or not, it is evident that reducing corruption levels will result into an increase in GDP level, also a country being an African country means that the level of GDP will decline. Statistical significance of the estimated coefficients is summarized in the table below: coefficient Standard error T statistics T critical 95% constant -218940.2 254828.6 -0.859167 1.95996 AFRICAN -189420.7 235135.1 -0.805582 1.95996 CPI 148819 49238.26 3.022425 1.95996 From the above table it is evident that we reject the null hypothesis for the CPI index and accept the null hypothesis for the constant and the African coefficient, this means that only the CPI index is statistically significance at the 95% level of test and the other coefficients are not statistically significant. Joint statistical significance of the estimated coefficients show that the F statistics value is 6.401886 and the F critical value given that alpha = 0.10 is 2.70554, this means that we reject the null hypothesis that: H0: a=b1 =b2 = 0 and accept the alternative hypothesis that Ha: ab1 b2 0 which means that their joint F test at a = 0.1 means that they are statistically significant. Conclusion: Corruption affects a number of sectors in the economy, this include the fact that corruption may reduce the level of domestic and foreign investment and because these are key GDP determinants a reduction in investment means less employment opportunities and higher poverty levels. Corruption also affects government spending, inappropriate use of public funds will reduce human capital development, poor infrastructure and poor population health and this will means that there will be lower levels of GDP. The above analysis of data show that the corruption level in a country will affect the level of GDP, the estimated econometric model that depict the relationship between corruption index and GDP shows that if we increase the corruption index which means that corruption level is declining then the GDP level increases, declines in GDP levels are a s result of reduced economic activities due to corruption such as reduced investment levels in an economy. Another finding is that African countries are more corrupt than the non African countries; the mean CPI index for African countries is 3.004651163 while the mean for non African countries is 4.587155963 Meaning that African countries are more corrupt than non African countries, an analysis on the difference in GDP level show that African countries GDP mean value is 38,788 thousand dollars while for the non African countries the mean GDP value is 463,716 and this shows that the non African countries have a relatively higher GDP than the African countries and this difference can be explained by the difference in corruption levels. Limitations of this study is that cross sectional data used in estimating regression model may exhibit autocorrelation which means that the assumption of the linear regression may not be met, autocorrelation refers to the correlation of error terms of the estimated model. Another limitation is that the CPI index may not be effective enough to estimate the level of corruption in country and therefore estimations may be biased, further studies should include the effect of corruption on the various sectors of an economy, studies aimed at the analysis of those sectors of the economy that are highly affected by corruption. References: Anne Krueger (1994). The Political Economy of Rent-seeking Society, American Economic Review, 64(3): page 291 to 303. Paolo Mauro (1995). Corruption and Growth: Quarterly Journal of Economics, issue 110: page 681 to 712 Pranab Bardhan (1997). Corruption and Development: A Review of Issues, Journal of Economic Literature, Vol. 35, page 1320 to 1346. Susan Ackerman (1998). Bribes and Gifts: Economics Values, and Organization, Cambridge: Cambridge University Press. Tanzi Vito and Hamid Davoodi (1997). Corruption, Public Investment, and Growth: IMF Working Paper, retrieved 23rd April, from http://www.imf.org/external/pubs/ft/wp/wp97139.pdf Transparency international (2009). corruption index data for 2007, retrieved 23rd April, from World Bank website (2009). GDP levels by country data 2007, retrieved 23rd April, from Read More