ASSESSMENT OF THE IMPACT OF CORRUPTION ON ECONOMIC GROWTH IN THE CONTEXT OF THE EU'S ANTI-CORRUPTION POLICY

Abstract

The purpose of this paper is to analyse the impact of corruption on economic growth in EU Member States, taking into account the key elements of the EU's anti-corruption policy. Methodology. The study used GDP as the dependent variable to measure economic growth and Transparency International's Corruption Perceptions Index as the independent variable. Control variables such as GDP per capita, inflation, trade openness, political stability and industrial value added were also included in the statistical model to account for confounding factors affecting the relationship between corruption and economic growth. Historical data series for these variables were collected for the period 2013-2021 for 27 European Union member countries, and the data were normalised to make all indices comparable. The study used multiple linear regression to predict the outcome of the response variable. The results show that GDP per capita has the strongest positive effect on GDP growth, while the CPI has a positive coefficient but no statistically significant effect on GDP growth. Other independent variables, including trade openness, political stability, inflation and industrial value added, also have no statistically significant impact on GDP growth. The predicted growth rates for most countries were relatively accurate, with some exceeding expectations and others falling short. Practical implications. The research suggests that policymakers should focus their anti-corruption efforts on objectives other than promoting economic growth, such as increasing government transparency and accountability, improving public trust in institutions and reducing social inequality. The finding that GDP per capita has the strongest positive effect on GDP growth also highlights the importance of policies that promote economic development, such as investment in education, infrastructure and innovation. Policymakers could also use information on which countries exceeded or fell short of their predicted GDP growth rates to assess the effectiveness of their policies and make adjustments where necessary. Value/оriginality. Applying multiple linear regression using the proposed variables for different analysed periods of data series could be used in evaluating the impact of corruption on economic parameters of different countries set.