Contagion Corruption and Financial Development: Evidence from a Panel of Regions
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Transcript of Contagion Corruption and Financial Development: Evidence from a Panel of Regions
Contagion Corruption and Financial Development: Evidence from a Panel of Regions
DSA ConferenceAberdeen, UK
14th October 2011.
Muhammad Tariq Majeed& Ronald MacDonald
University of Glasgow, UK
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)
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Outline • Theory
• Research Questions
• Model
• Data Description and Sources
• Results
• Academic Contribution
• Conclusion
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
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.
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
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?
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Model
1.....321 ittitititititit XFLPCYC
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.
2.....42
321 ittititititititit XFIFIPCYC
3.....542
321 ittitititititititit XMPFIFIPCYC
4.....;11 j
N
j ijiij
N
j iji wpXcwC
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
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
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Data: Scatter plots for Spatial Corruption0
24
6C
orr
uptio
n In
dex
0 1 2 3Spacious Weighted Index 5 Year Average Lag (1999-2003)
Spacious analysis of Corruption
Brunei
China
Hong Kong
Indonesia
Japan
Malaysia
Myanmar
Papua New Guinea
Philippines
Singapore
ThailandVietnam
12
34
5C
orr
uptio
n In
de
x
0 100 200 300 400High Financial Intermediation
1984-2007
East Asia and PacificHigh Financial Intermediation
02
46
8T
ransp
are
ncy
Inte
rnatio
nal C
orr
uptio
n I
ndex
0 100 200 300 400Financial Liberalization
Fitted values tii
Financial Liberalization and Corruption (1996-2007)
01
23
4W
orld B
ank C
orr
uption I
ndex
0 100 200 300 400Financial Liberalization
Fitted values WBC
Financial Liberalization and Corruption (1996-2007)
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Table 1: Corruption and FL: Regional Panel EstimationVariables Dependent 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 Law 0.4(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)*
TradeOpenness
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)*
Democracy 0.17(3.56)*
Internal conflict
-0.08(1.7)***
R 0.32 0.75 0.86 0.87 0.90 0.89 0.86 0.86
F 98.40 (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)
Observations 216 215 215 215 215 215 215 215
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Table 2:Corruption and Financial Liberalization: Non-linearity Variables Dependent Variable:
Corruption Index by TIDependent 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 Square 0.000(4.22)*
0.000(3.67)*
0.000(4.16)*
0.000(3.58)*
0.000(2.20)**
0.000(1.60)
R 0.82 0.84 0.82 0.85 0.61 0.66
F 96.47 (0.000)
91.96 0.000)
100.37(0.000)
101.87 (0.000)
35.30 (0.000)
34.66 (0.000)
Observations
113 113 116 116 116 116
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Table 3 : Cross-border Effects of Corruption
Variables SWC(99-03) SWC(94-98) SWC(89-93) SWC(84-88)
SWC 0.21(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)*
R 0.76 0.80 0.80 0.81
Observations 134 125 123 117
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
• 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
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
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.
Introduction Outlines Theory Research Questions Model Data Results Contribution Conclusion
Conclusion
Thank You!