Assessment of the Impact of Corruption on Economic Growth in the Context of the EU's Anti-corruption Policy

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/originality. 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.


Introduction
Corruption has been identified as a critical obstacle to economic growth and development in many countries around the world. In recent decades, numerous studies have focused on the detrimental effects of corruption on various aspects of economic performance, such as investment, productivity and income growth. Despite the increasing attention paid to the issue, corruption remains a significant challenge in many regions, including the European Union (EU).
The EU is a region of diverse countries, each with its own political, legal and economic systems. The issue of corruption is recognised as a major challenge in several EU Member States. Corruption undermines the integrity of institutions and public trust in governments, which has a negative impact on economic growth and the overall wellbeing of society.
Studying the impact of corruption on economic growth in EU member states is crucial for policymakers, businesses and investors. This research can help identify the root causes of corruption and develop effective policies to mitigate its negative effects. In addition, understanding the relationship between corruption and economic growth can inform decisions on resource allocation, investment and regulatory frameworks.
The basic idea of this study is to analyse the impact of corruption on economic growth in EU member countries, taking into account the key elements of EU anti-corruption policy. In accordance with the purpose of the article, the objectives of the article are to review the literature on the relationship between corruption and economic growth, analyse the EU anti-corruption policy, propose a methodology for modelling a regression between economic growth as a dependent variable, corruption as an independent variable and a set of control variables, and carry out this modelling.

Literature Review
Since the first studies pointed to the impact of corruption on various economic parameters, numerous studies have emerged to address this relationship. Some authors (Farrag, Ezzat, 2016) have contributed to the literature by examining the impact of corruption on growth in little studied countries in Europe and MENA. The results of this paper are also interesting because they show that the region, as a proxy for the level of development, significantly influences the impact of corruption on economic growth.
Another study investigates the impact of corruption on economic growth by testing the hypothesis that the relationship between these two variables is non-linear, assessing whether the belief that corruption has a detrimental effect on the economy is always true (Trabelsi, 2023). The author uses a panel data analysis and finds that corruption can have a positive effect on growth. The results suggest that beyond an optimal threshold, both high and low levels of corruption can reduce economic growth. Below this optimal threshold, a moderate level of corruption, defined by the point of inflection of the marginal effect of corruption on growth, could be beneficial for economic growth.
Another author examines corruption in developing countries, focusing on two aspects: how corruption is measured and its impact on economic performance (Spyromitros, Panagiotidis, 2022). The study analyses data from 83 developing countries between 2012 and 2018, using different data processing techniques. The study critically assesses different approaches to corruption indexes, highlighting the most appropriate measures for statistical analysis of perceptions and experiences. The study finds that corruption hampers economic growth in developing countries, although different levels of corruption have different effects on growth in different regions.
Investment, human development, government growth and institutional quality are identified as key drivers of economic growth.
The working paper (Afonso, Fortes Leitão Rodrigues, 2022) concludes that corruption has a negative effect on the level and growth of GDP, and that reducing corruption has a smaller benefit for developing economies, regardless of the size of government. Government size alone does not explain the impact of corruption on economic activity, but the level of efficiency of public services is crucial. The paper suggests that private investment is a potential transmission channel for corruption.
There is a paper that examines the impact of corruption on inflation in the European Union during the transition from socialist economies to modern entrepreneurial societies (Piplica, 2011). The corruption perception index is used as a measure of corruption and the consumer price index as a measure of inflation. The study assumes a certain degree of liberalisation of the domestic economy, measured by the cumulative liberalisation index, in order to examine the impact of corruption on inflation. The study finds a weak positive impact of corruption on inflation and that the impact is largely indirect and time-lagged. The study also suggests that indicators of the observed transition period related to Croatia fit the observed data of other transition countries.
Some research (Pluskota, 2020) suggests that the relationship between corruption and innovation, and corruption and economic growth, is not linear but parabolic. This means that the effect of corruption on economic growth and innovation is not uniform across all levels of corruption. The study suggests that low levels of corruption are beneficial for economic growth because corruption can help solve other economic problems, such as bureaucracy. However, this is only the case when the state is not functioning effectively. In other words, corruption can support economic growth when there are shortcomings in government performance.
The literature review suggests that there is a gap in research on the impact of corruption on economic growth in the context of EU anti-corruption policy. While there are numerous studies on the relationship between corruption and economic growth, few have examined this relationship in the context of EU anti-corruption policy. Moreover, the existing studies focus on developing and transition countries, with few studies on European countries. This gap in the literature is significant because the EU has a strong anti-corruption policy framework, and understanding the impact of corruption on economic growth in the context of this policy framework is important for policymakers and researchers alike.

EU Anti-Corruption Policy Framework
The EU's anti-corruption infrastructure includes several components, in particular: 1. International and EU conventions, standards, and guidelines. The laws and policies of EU Member States have been significantly influenced by the adoption of international conventions, standards and guidelines, notably by the Council of Europe, the Organisation for Economic Cooperation and Development (OECD) and the United Nations (UN). These instruments are the result of global cooperation and reflect a broad consensus on the negative consequences of corruption.
The OECD Convention on Combating Bribery of Foreign Public Officials in International Business Transactions (1997) has been adopted and ratified by 23 EU Member States. The OECD Working Group on Bribery in International Business Transactions monitors the performance of the Parties and a new Recommendation was adopted in 2021 to complement the Anti-Bribery Convention.
The UN Convention against Corruption (2003) (UNCAC) covers five main areas and requires States Parties to take measures such as promoting transparency, integrity and accountability of public officials. The EU and all its Member States are parties to the UNCAC. The Implementation Review Mechanism (IRM) is a peer review process for the effective implementation of the Convention.
The Council of Europe has adopted a comprehensive approach to the problem of corruption, including the Twenty Guiding Principles for the Fight against Corruption and the Criminal Law Convention on Corruption. The Group of States against Corruption (GRECO) monitors the implementation of the Criminal Law Convention and the Civil Law Convention. Full EU membership of GRECO is envisaged and an agreement between the Council of Europe and the EU on the modalities of membership would have to take into account the specific nature of the EU and its institutions.
The EU Financial Regulation (2018) and the Anti-Fraud Strategy (2019) are key legal instruments to protect the EU budget. Effective governance and sound financial management require detailed policies and rules to manage conflicts of interest. Specialised EU agencies such as the European Public Prosecutor's Office, the European Anti-Fraud Office, the European Union Agency for Criminal Justice Cooperation and the European Union Agency for Law Enforcement Cooperation are responsible for protecting the EU budget. The Early Detection and Exclusion System (EDES), set up by the European Commission in 2016, identifies and prevents dubious economic operators from receiving funds and imposes financial sanctions on them. The EDES rules apply to all contracts, grants, agreements, prizes, financial instruments and paid experts, and the system maintains a database of economic operators who are ineligible for EU funding or subject to financial sanctions (Huliaieva, 2022).
2. Institutional framework. The European Anti-Fraud Office (OLAF) was set up in 1999 to detect fraud and corruption affecting the financial interests of the EU. OLAF conducts administrative investigations and informs national authorities when it suspects that a criminal investigation is necessary. Between 2010 and 2021, OLAF recommended the recovery of more than €8 billion for the EU budget and made 3,200 recommendations for judicial, financial, disciplinary and administrative action by national and EU authorities. The European Public Prosecutor's Office (EPPO) is the EU's first supranational prosecution service with the power to investigate and prosecute offences against the EU's financial interests. This includes VAT fraud and related offences. The EPPO will become operational in June 2021 and will launch its first major corruption investigation in July 2021. Europol, Eurojust and the European Anti-Corruption Network (EACN) facilitate the exchange of information and operational cooperation between national authorities. The EACN is modelled on the European Partners against Corruption (EPAC), which brings together anticorruption authorities and police oversight bodies from Council of Europe member states.
3. Monitoring mechanisms. The EU lacked an effective anti-corruption evaluation mechanism, which led to the launch of a bi-annual EU Anti-Corruption Report (EU Anti-Corruption Report, 2014). The report was based on existing international evaluation mechanisms, Eurobarometer surveys and information from experts and civil society. Although it included country-specific recommendations, there was no formal process for evaluating their implementation. The Commission abandoned the report in 2017, shifting its focus to tackling corruption in the context of the European Semester on economic governance. The current EU approach to corruption emphasises the rule of law, resulting in limited and disjointed evaluation mechanisms that assess selected aspects of relevant policies or specific Member States. These mechanisms include the Cooperation and Verification Mechanism (CVM) and the EU Justice Scoreboard. These mechanisms feed into the Annual Report on the Rule of Law, which has been criticised for being an 'after-theevent' reporting mechanism that does not provide a comprehensive picture of systemic corruption issues across the EU. The EU's recent focus on strengthening the rule of law may not be well suited to tackling the complex and multi-faceted corruption issues across the EU. The European Semester on economic governance has only made corruption-related recommendations to selected Member States.
4. International companies' corruption control tools. Self-regulation, defining unacceptable behaviour through codes of conduct (e.g. Transparency International, Organisation for Economic Co-operation and Development Guidelines for Multinational Enterprises). Corporate governance, or the systems, structures and procedures used to manage and control the company and ensure compliance. The prevention of corruption is to some extent regulated by the EU through public procurement legislation and a system of obligations for European companies by establishing compliance with international accounting and auditing standards (Oliinyk, Sierova, 2022).
Based on the research presented, it can be concluded that the EU has put in place several anti-corruption measures, including international and EU conventions, institutional frameworks and monitoring mechanisms. However, the effectiveness of these measures in fighting corruption and promoting economic growth may be limited by inconsistent evaluation mechanisms and a lack of comprehensive reporting on systemic corruption issues across the EU. In addition, international corporate anti-corruption tools, such as selfregulation and corporate governance, can help prevent corruption, but the EU must continue to regulate and enforce compliance with international standards to effectively combat corruption.

Methodology
In order to determine the impact of corruption on economic growth in EU member states, time series of historical data were analysed. The analysis process includes the following steps: 1. Data collection. This study uses Transparency International's Corruption Perceptions Index as a measure of corruption, given its widespread use in recent studies. The CPI is an annual publication that assesses the perceived level of corruption in a country's public sector by assigning a score on a scale of 0-100. A score of 0 indicates high levels of perceived corruption, while a score of 100 represents a very clean public sector. The CPI is a reliable and established measure of corruption, making it an appropriate choice for this research.
In this study, the dependent variable is GDP, which is intended to measure economic growth. For this reason, GDP in USD from the World Bank was used.
To ensure the validity of the research, control variables based on empirical and theoretical studies are included in the statistical model to address confounding factors that affect the relationship between corruption and economic growth: 1) GDP per capita. In line with previous studies, this study considers GDP per capita as a critical control variable, as the theory suggests that developing countries have a higher growth potential than developed countries. GDP per capita is valued in current USD and is taken from World Bank data.
2) Consumer Price Index (inflation). This study includes inflation as a control variable in the main econometric model, as it is a classic variable in economic growth models and has been shown to create distortions and hinder productivity. The indicator shows the annual rate of change in consumer prices. Data are taken from the UNCTADstat database of the United Nations Conference on Trade and Development.
3) Trade openness. International trade enhances economic growth and therefore this study controls for trade openness in the empirical model. This indicator is constructed as the sum of trade in goods and services as a share of GDP. Data are collected from the UNCTADstat database of the United Nations Conference on Trade and Development. 4) Political stability and absence of violence/ terrorism (political stability). Change of government, used as a measure of political instability, leads to uncertainty and reduces the supply of capital and labour, increasing the risk of capital loss. The establishment of property rights becomes difficult in such an environment, hindering productivity gains. Therefore, economic growth is expected to be lower in countries and periods with high government turnover. Data are from the World Bank. 5) Value added by industry (VA). Industry value added, also known as gross domestic product (GDP) by industry, is the contribution of a private or government sector to total GDP. It provides a measure of the contribution of industry to the economy as a whole. By controlling for the value added of each industry, it is possible to distinguish the impact of each industry on economic growth and ensure that any observed effects are not due to industry size alone. Data are taken from the World Bank.
2. Definition of a set of countries. In order to achieve the objective of the paper, historical data series of the above variables were collected for the period 2013-2021 for the 27 member states of the European Union.
3. Data preparation. Once the data collection was completed, the historical series for CPI, GDP, GDP per capita, FDI, Trade Openness, Political Stability and Industrial VA were normalised to make all indices comparable. For normalisation, each data was converted into yearly percentage change (YPC) to show the annual growth rate according to the equation: After normalisation, average variables for the period 2013-2021 were calculated for each country.
For the purposes of this study, the multiple linear regression method was used. The multiple linear regression method simply aims to predict the outcome of a response variable. The following equation is used: (2) where, for i = observations: y i = dependent variable, x i = explanatory variables, b 0 = y-intercept (constant term), b p = slope coefficients for each explanatory variable, h = the model's error term (also known as the residuals).
Referring to the multiple linear regression equation above, in this example: yi = dependent variable -GDP, x i1 = CPI, x i2 = GDP per capita, x i3 = Trade Openness, x i4 = Political Stability; x i5 = Industry VA.

Findings
Summary of the descriptive statistics of all variables in this study demonstrates that the GDP has grown at an average of 3.06% over the period of 2013-2021 which indicates a positive growth of selected countries ( Table 1). The minimum value is detected for Greece (-1,18%), the maximum refers to Ireland (9,03%). The CPI indicates an average growth of 2,11% for all countries which shows a strong decrease of corruption.
The lowest CPI value is -0.14 for Greece, and the highest is 8.54 (Ireland). Table 2 presents the Pearson's correlation analysis of the variables GDP, CPI, GDP per capita, trade openness, political stability, inflation and industrial VA. The table shows the correlation coefficients between the variables, with values ranging from -1 to 1.
The results show that GDP has a positive and strong correlation with GDP per capita (r = 0.909) and a moderate positive correlation with inflation (r = 0.411). On the other hand, GDP has a negative and moderate correlation with trade openness (r = -0.634) and political stability (r = -0.013), suggesting that countries with higher political stability and trade openness tend to have higher GDP.
The CPI has a moderate positive correlation with political stability (r = 0.239) and a weak negative correlation with GDP per capita (r = -0.138) and inflation (r = -0.148). Trade openness has a moderate negative correlation with GDP per capita (r = -0.500) and a weak positive correlation with inflation (r = 0.124).
The results also show that industrial VA has a negative and moderate correlation with trade openness (r = -0.475) and a weak positive correlation with GDP per capita (r = 0.273). There is a weak positive correlation between industrial VA and inflation (r = 0.020) and a very weak positive correlation between industrial VA and GDP (r = 0.017).
Overall, these results suggest that political stability and trade openness are important factors contributing to higher GDP, while industrial VA seems to be weakly related to the other variables. These results show that the parameter of lack of multicellularity between the variables is satisfied, as there is no correlation coefficient equal to or greater than 0.8. Table 3 shows the results of a regression analysis aimed at identifying the factors influencing GDP growth in a set of 27 countries.

Source: authors' calculations
The results show that the regression model has a high R-squared value of 0.9219, indicating that 92.19% of the variability in GDP can be explained by the independent variables. The F-test also shows that the overall model is statistically significant at a very low level of significance (p < 0.0000000005).
Among the independent variables, GDP per capita has the strongest positive effect on GDP growth, with a coefficient of 1.0809 and a very low p-value of 0.000000001, indicating a high level of statistical significance. This means that as GDP per capita increases, GDP growth is also expected to increase. Corruption (CPI) has a positive coefficient of -0.063, but with a p-value of 0.6324, the effect is not statistically significant. Therefore, the data do not provide sufficient evidence to conclude that a decrease in the perception of corruption (i.e., an increase in the CPI) has a significant impact on GDP growth.
Other independent variables, including trade openness, political stability, inflation and industrial VA, also have no statistically significant impact on GDP growth. The p-values for these variables are all above the conventional significance level of 0.05.
In conclusion, the results suggest that GDP per capita is the most important factor influencing GDP growth in these 27 countries. Although some of the other independent variables have coefficients that suggest an effect on GDP growth, they are not statistically significant, indicating that any observed relationship could be due to chance. Figure 1 shows the comparison between the average predicted GDP growth and the average real GDP growth for the EU Member States for the period 2012-2021. The average predicted GDP growth is calculated on the basis of the analysis carried out, while the average real GDP growth represents the actual growth rate of the economy.
It shows that of the 27 EU Member States, Ireland had the highest average predicted GDP growth of 8.98%, which was slightly lower than the average real GDP growth of 9.03%. Malta had the second highest predicted growth rate of 6.95%, which was also higher than its actual growth rate of 6.52%. Romania and Lithuania had predicted growth rates above 6% and both performed well in reality, with actual growth rates of 6.01% and 5.11% respectively.
On the other hand, Greece had the lowest predicted GDP growth rate of -0.42% and also the lowest real GDP growth rate of -1.18%. Italy's predicted growth rate was only 0.41% and its actual growth rate was slightly lower at 0.31%. Other countries where the predicted growth rate was lower than the actual growth rate were Spain and Slovakia. In general, the figure shows that the predicted GDP growth rates for most countries were relatively accurate, with some countries exceeding expectations and others falling short.

Conclusions
The aim of this paper was 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.
The literature review revealed a gap in the research on the impact of corruption on economic growth. While there are numerous studies on the relationship between corruption and economic growth, few have examined this relationship in the context of EU anti-corruption policy. Moreover, the existing studies focus on developing and transition countries, with few studies on European countries.
The EU has adopted several measures to combat corruption, including conventions, institutional frameworks and monitoring mechanisms. However, the effectiveness of these measures in fighting corruption and promoting economic growth may be limited by inconsistent evaluation mechanisms and insufficient reporting on systemic corruption issues across the EU. In addition, while self-regulation and corporate governance tools used by international companies can help prevent corruption, the EU must continue to regulate and enforce compliance with international standards to effectively combat corruption.
The aim of the study was to assess the impact of corruption on economic growth in EU Member States. Data were collected from Transparency International's Corruption Perceptions Index and World Bank data on GDP, GDP per capita, inflation, trade openness, political stability and industrial value added. The data were normalised and analysed using multiple linear regression. The study aimed to control for confounding variables such as GDP per capita, inflation, trade openness, political stability and industrial value added.
The study examined the relationship between GDP growth and several independent variables, including GDP per capita, corruption, trade openness, political stability, inflation and industrial VA in 27 countries. The results show that GDP per capita has the strongest positive effect on GDP growth, while 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 effect on GDP growth. The predicted growth rates for most countries were relatively accurate, with some exceeding expectations and others falling short.
The research findings could be used by policymakers to inform their decision-making processes related to the development and implementation of anti-corruption policies. In particular, the finding that corruption does not have a statistically significant impact on GDP growth in the 27 EU Member States studied suggests that policymakers may want to focus their anticorruption efforts on other objectives beyond 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.
Future research could include further analysis of the impact of specific independent variables on GDP growth, such as different aspects of political stability or different measures of corruption. In addition, it may be useful to examine the impact of these variables on specific sectors or industries within the economy. Furthermore, future research could also focus on analysing the effectiveness of anti-corruption policies in the EU and how they could be improved to promote economic growth. The publication was prepared as part of the implementation of the Jean Monnet project Module "EU Anti-Corruption Policy: Key Elements, Focus on Sustainable Development, Lessons for Ukraine" (EU AntiCor, 101047968 -EU AntiCor -ERASMUS-JMO-2021-HEI-TCH-RSCH, Erasmus+ programme)