Identification and Ranking of Effective Factors on Financial Corruption based on the Bayesian approach (A Case Study of countries rich with natural resources)

Bohloolvand, Elahe; Arabi, Hadi

doi:10.22096/esp.2022.137574.1420

Identification and Ranking of Effective Factors on Financial Corruption based on the Bayesian approach (A Case Study of countries rich with natural resources)

Document Type : Original Article

Authors

elahe Bohloolvand ¹

hadi Arabi ²

¹ PhD student, Faculty of Economics and Management, Qom University, Qom, Iran.

² Associate Professor, Faculty of Economics and Management, Qom University, Qom, Iran.

10.22096/esp.2022.137574.1420

Abstract

Abstract:
Financial Corruption is one of the aspects of inefficiency in societies that undermines the economic growth and development of countries and is considered by planners and policymakers. Therefore, this study identifies 22 effective variables on financial corruption index and ranks them using the Bayesian Model Averaging (BMA) in 26 countries rich with natural resources during 1996-2017.
Inclusion probability indicates that political stability, democracy, economic globalization, property rights, socialist legal origin, ethnic fractionalization and language fractionalization are the most effective factors on financial corruption index.
Also the variables of investment freedom, total natural resources rents and GDP per capita take second to fourth rank regarding their effects on financial corruption model.
the variables of trade freedom, military expenditure and size of government with 51%, 45% and 21% probability of occurrence have affected corruption in a in a negative direction. On the other hand, there is no significant relationship between the variables of business freedom and monetary freedom and the corruption index in these countries, due to the low probability of these variables in the model. It seems these variables can affect corruption only through other channels of economic freedom in the model.

Keywords

Financial Corruption

natural resources

Bayesian Model Averaging (BMA)

Weighted Average Least Squares (WALS)

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