Determinants of Corruption and its Relation to GDP : ( A Panel study )

This paper investigates the determinants of corruption, and highlights its relation to the growth. The corruption index is taken from Transparency International. We used the more recent data set from 1995 to 2005. First part of the study is based on determinants of corruption. The empirical results indicate that real GDP per capita, secondary school enrolment, public spending on education, FDI, and unemployment rate are important determinants of corruption. Through combination of these variables we can change the level of corruption. Second part of the study is based on the relationship between corruption and level of GDP per capita and growth of GDP in Barro regression. The results indicate that corruption is more relevant to level of GDP per capita. Section 1: Introduction Corruption has been around for a very long time and will remain in the future unless governments can figure out effective ways to combat it (Mauro 1997). This is not going to be easy. Although the study of the causes and consequences of corruption has a long history in economics, and most of the developed countries have controlled it, but still its effects are large in developing countries. In recent years, the detrimental effects of bureaucratic corruption gained attention from development economists as well as international financial institutions and policymakers. Corruption, which was previously ignored and mentioned only with caution, has taken a center stage. Nonetheless, corruption is not a new phenomenon. It is as old as government itself. The current literature on corruption highlights its harmful effects on growth (see Klitgaard 1988, Shleifer and Vishny 1993, Mauro 1995, Cheung 1996, and Bardhan 1997). Some studies found the determinants of corruption. The relevant analytical problem is not to assess the harmfulness of corruption but to explore why different political systems foster different levels of corruption. Recent years have seen a renewed interest in studying corruption. There is a long history of models attempting to identify its causes, yet its empirical analysis is complicated. These complications are due to difficulties of obtaining the data and also some estimation complexities are involved. ∗ Authors are Associate Professor, Chairman, Department of Economics, Punjab University, Lahore and Assistant Professor at Economics Department, F.C. College University, Lahore. Determinants of Corruption and its Relation to GDP 28 So in this paper an effort is made to identify the major determinants of corruption, and also examine the relationship between growth and corruption. For its relation with growth this paper also tries to examine the relation between GDP per capita and corruption. In order to achieve the objective of the study we used the data for corruption, which is available for 11 years (1995-2005). Then we used averaged values for all variables in our study. This paper found some important determinants through which, we can control the level of corruption. The main determinants found by this study are, real GDP per capita, secondary school enrolment, public spending on education, unemployment rate, and FDI. Our results are different from other studies due to the effect of three important variables, which are secondary school enrolment (SED), public spending on education (PSED) and unemployment rate (UNEMP). No other study found these relationships significantly with corruption. In the second place we analyzed the relationship between corruption and growth. For this purpose we used Barro type equation, by using fixed effect and also with the set of control variables. According to results of this paper, it is found that in growth model the corruption does not affect the growth of GDP per capita, while corruption does affect the log of GDP per capita. So the relationship between corruption and level of real GDP per capita is important and same results are found by Paldam (2002). The paper is further divided as following. 1.2: Organization of the Study Further paper is continuing from section 2, which describes some empirical and theoretical literature and also some discussion on objectives and variables used in the study. Section 3 introduced the model used in the study, while results are reported in section 4. Concluding remarks are given in section 5. Section 2: Review OF Literature