ABSTRACT
The variation in the level of economic development across countries has been proposed as an explanation for the disparity in the level of corruption that is observed. As a country evolves from one stage of economic development to another and its social institutions as a result become more refined and sophisticated, their capacity to tackle corruption and poor governance practices becomes increasingly better. Improvements in the overall quality of institutions, including better policing and justice systems, increase their capacity to detect and deter corruption. This evolution of institutional quality improves social and economic well-being of society, which in turn pressures regulators, legislators and politicians to continue in the fight against corruption. The objective of this paper is to examine how economic development mediated by improvements in the quality of social institutions impacts on the level of corruption. Lessons from worldwide trends, including the Asia-Pacific region, provide opportunities for countries to enact strategic measures that can accelerate the fight against corruption.
Acknowledgments
We would like to thank Lynge Nielsen for the algorithm used in the construction of economic development and to the two anonymous referees for their helpful thoughts and suggestions.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Christopher Bajada
Christopher Bajada has a PhD in Economics (UNSW) and has taught economics in a variety of undergraduate and postgraduate courses, including the Executive MBA. His main research interests include tax compliance and money laundering, economics of education, management practices and innovation. He has been a recipient of several teaching awards including a national citation award and has held the role of Associate Dean (Education). Christopher has extensive experience in consultation with industry and government.
Mikhail Shashnov
Mikhail Shashnov has a PhD in Economics with a major in Statistics (Moscow State University of Economics, Statistics and Informatics) and has held a range of positions including financial analyst, economist (with Moscow Central Bank) and as an academic researcher. Mikhail has also taught undergraduate courses in accounting and statistics at several institutions. Mikhail is a fully qualified CPA and has research interests in data science and analytics including applying machine learning algorithms to solve business problems.