2. Contagion Corruption and Financial Development: Evidence from a Panel of Regions DSA Conference Aberdeen, UK 14 th October 2011. Muhammad Tariq Majeed & Ronald MacDonald University of Glasgow, UK

3. Introduction Corruption is a serious issue and a major obstacle to development. According to World Bank more than US$ 1 trillion is paid in bribes each year Countries that tackle corruption could increase per capita incomes by a staggering 400 percent. "Fighting corruption is a global challenge. Corruption in European countries, on average, has increased 22% over last two decades (Majeed, 2011) IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

4. Outline Theory Research Questions Model Data Description and Sources Results Academic Contribution Conclusion IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

5. Theory Lack of competition, in product or/and financial markets, increases corruption because rent seeking activities increase in the absence of competition. Theoretical studies predict an ambiguous effect of competition on corruption. On the one hand, lack of competition generates rents (supra normal profits) for entrepreneurs, thereby motivating bureaucrats to ask for bribery (Foellmi and Oechslin (2007). On the other hand, the presence of these rents increases the values of monitoring the bureaucracy in a society (Ades and Di Tella (1999). Since neighbour countries share similar political cultures and institutions, cross-border spill over effects of corruption are likely outcome. IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

6. Research Questions (1) Does financial liberalization reduce corruption? (2)Is the relationship between high financial liberalization and corruption perhaps non-monotonic? (3) Do corruption in neighbouring countries, regional panels and past levels of corruption matter in shaping the link? IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

7. Model Equation 3 includes another key determinant of corruption, the military in politics (MP), that has recently been introduced by Majeed and MacDonald (2010). Where (i= 1… N; t=1… T), Cit is a perceived corruption index, PCYit is per capita income to measure the level of economic development, FLit represents the degree of financial liberalization, Xit represents a set of control variables based on the existing corruption literature. The expected sign for the key parameter of interest, β2, is negative. Equation 2 introduces non-monotonic form to capture the possible present of a threshold level of financial liberalization in shaping the relationship between financial development and corruption. Equation 4 models contagion nature of corruption where wij is an adjacency- related weight. α is an intercept while β is a K ×1 parameter vector for the covariates collected in xi. Two parameter, λ and ρ, measure the intensity (strength) of interdependence, where λ denotes the spatial lag and ρ represents the spatial correlation in the residuals. IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

8. Variables Sources Corruption International Country Risk Guide Military in Politics Inflation IFS database. Credit as % of GDP M2 as % of GDP Economic Development World Bank database. Trade Liberalization Government Spending Remittances Data IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

9. Data: Scatter plots for Spatial Corruption IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

10. Table 1: Corruption and FL: Regional Panel Estimation VariablesDependent Variable: Corruption FL-0.004 (-9.92)* -0.001 (-3.71)* -0.002 (-7.15)* -0.002 (-7.16)* -0.002 (-7.00)* -0.002 (-6.77)* -0.002 (-6.70)* -0.002 (-7.31)* PCY-0.000 (-4.22)* -0.000 (-3.40)* -0.000 (-2.72)* -0.000 (-3.51)* -0.000 (-6.79)* -0.000 (-2.35)* -0.000 (-3.77)* Govt. Spending -.04 (-3.13)* -.05 (-4.97)* -.04 (-3.57)* -.03 (-3.5)* -.04 (-3.97)* -.05 (-5.59)* -.05 (-5.28)* Rule of Law0.4 (7.15)* -0.44 (-10.12)* -0.27 (-4.97)* -0.63 (-15.25)* -0.49 (-12.53)* -0.35 (-6.90)* -0.3 (-3.30)* Trade Openness 0.01 (12.29)* 0.01 (11.43)* 0.01 (10.01)* 0.01 (10.49)* 0.01 (8.89)* 0.02 (11.29)* Military in Politics 0.26 (4.67)* Govt. Stability 0.17 (9.64)* Investment Profile 0.115 (7.72)* Democracy0.17 (3.56)* Internal conflict -0.08 (1.7)*** R0.320.750.860.870.900.890.86 F98.40 (0.000)159.96 (0.000) 249.58 (0.000) 232.28 (0.000) 314.99 (0.000) 276.19 (0.000) 221.70 (0.000) 210.43 (0.000) Observations216215 IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

11. Table 2:Corruption and Financial Liberalization: Non-linearity VariablesDependent Variable: Corruption Index by TI Dependent Variable: Corruption Index by WB Dependent Variable: Corruption Index by ICRG FL-0.018 (-4.91)* -0.014 (-4.04)* -0.008 (-4.93)* -0.006 (-4.00)* -0.006 (-2.30)** -0.004 (-1.41) PCY-0.000 (-10.77)* -0.000 (-9.04)* -0.000 (-9.64)* -0.000 (-7.84)* -0.000 (-5.20)* -0.000 (-3.63)* Economic Freedom -0.26 (-4.42)* -0.26 (-4.86)* -0.16 (-6.40)* -0.17 (-7.25)* -0.25 (-6.29)* -0.26 (-6.85)* Govt. Spending -.03 (-1.60)*** -.009 (-0.52) -.015 (-1.86)*** -.003 (-0.45) -.001 (-0.07) -.015 (-1.22) Rule of Law-0.34 (-3.66)* -0.19 (-4.53)* -0.24 (-3.56)* FL Square0.000 (4.22)* 0.000 (3.67)* 0.000 (4.16)* 0.000 (3.58)* 0.000 (2.20)** 0.000 (1.60) R0.820.840.820.850.610.66 F96.47 (0.000) 91.96 0.000) 100.37 (0.000) 101.87 (0.000) 35.30 (0.000) 34.66 (0.000) Observation s 113 116 IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

12. Table 3 : Cross-border Effects of Corruption VariablesSWC(99-03)SWC(94-98)SWC(89-93)SWC(84-88) SWC0.21 (2.31)* 0.19 (2.41)* 0.19 (2.43)* 0.19 (2.42)* PCY-0.000 (-2.33)* -0.000 (-1.26) -0.000 (-1.26) -0.000 (-0.25) Democracy-0.21 (-3.89) -0.25 (-4.77)* -0.16 (-2.43)* -0.27 (-5.26)* Bureaucracy Quality-0.30 (-3.18)* -0.24 (-2.72)* -0.26 (-5.0)* -0.21 (-2.35)* Rule of Law-0.24 (-3.69)* -0.35 (-5.36)* -0.36 (-5.41)* -0.41 (-6.15)* R0.760.80 0.81 Observations134125123117 IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

13. The importance of financial market reforms in combating corruption has been highlighted in the theoretical literature but has not been systemically tested empirically. To best of our knowledge, this study provides a first pass at testing this relationship using both linear and non-monotonic forms of the relationship between corruption and financial liberalization. This study introduces the concept of regional panels. Academic Contribution IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

14. The results imply that a one standard deviation increase in financial liberalization is associated with a decrease in corruption of 0.20 points, or 16 percent of a standard deviation in the corruption index. The analysis also indicates the presence of a threshold implying that financial liberalization is beneficial only up to a threshold level and after the threshold is reached corruption increases. Finally, results of the study show that a policy in a neighboring country that reduces corruption by one standard deviation in the past five to ten years will reduce corruption in the home country by 0.12 points. IntroductionOutlinesTheoryResearch QuestionsModelDataResultsContributionConclusion

15. Thank You!