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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.
Muhammad Tariq Majeed
& Ronald MacDonald
University of Glasgow, UK
14th October 2011.
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.
(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?
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 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.