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Contagion Corruption and Financial Development: Evidence from a Panel of Regions. Muhammad Tariq Majeed & Ronald MacDonald University of Glasgow, UK. DSA Conference Aberdeen, UK 14 th October 2011. Introduction. Corruption is a serious issue and a major obstacle to development.

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Contagion corruption and financial development evidence from a panel of regions

Contagion Corruption and Financial Development: Evidence from a Panel of Regions

Muhammad Tariq Majeed

& Ronald MacDonald

University of Glasgow, UK

DSA Conference

Aberdeen, UK

14th October 2011.


Introduction
Introduction from a Panel of Regions

  • 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)


Outline
Outline from a Panel of Regions

  • Theory

  • Research Questions

  • Model

  • Data Description and Sources

  • Results

  • Academic Contribution

  • Conclusion


Theory
Theory from a Panel of Regions

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.


Research questions
Research Questions from a Panel of Regions

(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?


Model

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.

Model

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 3 includes another key determinant of corruption, the military in politics (MP), that has recently been introduced by Majeed and MacDonald (2010).

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.


Data 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.


Data: Scatter plots for Spatial Corruption 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.


Table 1 corruption and fl regional panel estimation
Table 1: Corruption and FL: Regional Panel Estimation 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.


Table 2 corruption and financial liberalization non linearity
Table 2: 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.Corruption and Financial Liberalization: Non-linearity


Table 3 cross border effects of corruption
Table 3 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.: Cross-border Effects of Corruption


Academic contribution
Academic Contribution 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.

  • 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.


Conclusion
Conclusion 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.

  • 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.


Thank you

Thank You! 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.


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