Corruption and economic growth in some selected transitional economies

Purpose - The general objective of the paper is to investigate the impact of corruption and other institutional factors on economic growth in some selected transitional economies for the period of 1990‐2004 and make policy recommendations for combating it. Specifically, the study attempts to: assess whether corruption has any impact on the growth of the sample countries; examine whether simultaneous policy reform focussing on accountability and rule of law impact positively on growth of these economies; and investigate whether corruption in these countries exhibit the efficient grease syndrome. Design/methodology/approach - The indices for corruption and other institutional variables were drawn from International Country Risk Guide (ICRG – PRS) for the period of 1990‐2004, the polity data were obtained from the Polity IV, while the real gross domestic product (GDP) per capita growth were obtained from the Penn World 6.2. The study covered the period between 1990 and 2004 that coincides with the real transition of these economies from centrally planned to market economies. It adopts the panel data framework, the fixed effect, the random effect and the maximum likelihood estimation techniques for the analysis. Findings - The study's findings support Mauro's hypothesis that corruption has a negative impact on the economies. However, the study cannot find a robust statistical evidence to support the efficient grease hypothesis of Leff and Huntington. Research limitations/implications - The paper recommends policy efforts that would strengthen accountability and bureaucratic quality, reduce discretionary power, ethnic fractionalisation and military involvement in politics with a view to enhancing social responsibility practices at both micro and macro levels. Originality/value - Unlike previous studies that focussed on single cross‐country regression with an assumption of identical aggregate production function for all countries, this study adopts the panel data framework that makes it possible to allow for differences in the form of unobservable individual country effects. The paper employs the fixed effect, the random effect and the maximum likelihood estimation techniques.