Determinants of corruption: a panel data analysis of Visegrad countries

Equilibrium. Quarterly Journal of Economics and Economic Policy Volume 17 Issue 1 March 2022 p-ISSN 1689-765X, e-ISSN 2353-3293 www.economic-policy.pl Copyright © Instytut Badań Gospodarczych / Institute of Economic Research (Poland) This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ORIGINAL ARTICLE Citation: Linhartová, V., & Halásková, M. (2022). Determinants of corruption: a panel data analysis of Visegrad countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79. doi: 10.24136/eq.2022.003 Contact to corresponding author: Veronika Linhartová, veronika.linhar[email protected] Article history: Received: 6.08.2021; Accepted: 28.01.2022; Published online: 25.03.2022 Veronika Linhartová Ambis University, Czechia orcid.org/0000-0003-1270-0744 Martina Halásková VŠB – Technical University of Ostrava, Czechia orcid.org/0000-0002-6812-6158 Determinants of corruption: a panel data analysis of Visegrad countries JEL Classification: C33; D73; P2 Keywords: corruption; control of corruption, determinants, panel data analysis; Visegrad countries Abstract Research background: Corruption is a phenomenon that has no borders, thus hindering the proper functioning of the social, economic, and legal systems of a given state. As the rankings assessing the level of corruption in various countries show, transition economies are more vulnerable to corruption than countries that have not undergone changes in the political and economic order. The Visegrad group is an example of such countries. Despite their efforts, these countries’ governments have yet to match the evaluation of corruption indices for developed European countries. Purpose of the article: This study analyses the determinants of corruption in Visegrad countries to identify which determinants are the most impactful and thus should be the focus of Visegrad countries’governments when creating anti-corruption policies. Methods: Data for the period 1996–2019 from the databases of the World Bank, Transparency International, and the European Central Bank were used for panel data analysis. The study uses a comprehensive set of economic, socio-cultural, and political determinants that can influence corruption. The purpose of this large set of variables is to prevent possible distortion owing to omitted variables. Findings & value added: The results of the analysis of panel data show the main determinants of corruption in Visegrad countries are economic, political, and socio-cultural (phase of economic Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 52 development, openness of the economy, size of the public sector, degree of urbanization, and women's share in the labour force). A significant effect was also demonstrated in the case of regulatory quality and public sector wages. The findings can serve as a valuable resource for policymakers to develop government policies in individual countries and to implement effective anti-corruption tools. Introduction Today, corruption is one of society's most serious problems, occurring in all countries of the world, regardless of their economic and social maturity. However, some countries find it more challenging to cope with the side effects arising from the long history of corruption (Popova & Post, 2018, pp. 231–244; Meyer-Sahling & Mikkelsen, 2020). The preconditions for the emergence of corruption in individual countries and institutions are associated with economic transformation. Problems can also be seen in the new dimension of economic governance, which is related to the misorientation of resources (Svensson, 2005, pp. 19–42). When corruption began to develop, many countries, including those in Eastern Europe, were ill prepared for those undesirable changes. The Visegrad countries are an example of such countries (Sicakova-Beblava & Beblavy, 2016, pp. 295–313; Meyer, 2019, pp. 220–233; Snegovaya, 2020, pp. 1162–1182). Their economies are embedded in the legacy of communism, which affects corruption as an accompanying phenomenon of transformation processes (Liptakova, 2020, pp. 81–102; Naxera, 2020, pp. 671–673; Pirro & Della Porta, 2021, pp. 433–450). In particular, as a result of privatisation, transforming economies can be described as more vulnerable to corruption. One of the prerequisites for the development of corruption in such countries is government officials who demand bribes and kickbacks from private agents for state-owned businesses (Holmes, 1999). Some researchers have already applied panel data to assess the determinants of corruption in different countries (Elbahnasawy & Revier, 2012, pp. 311–333; Picón & Boehm, 2019, pp. 88–100, Bitterhout & Simo-Kengne, 2020, pp. 1–23). Based on the literature, several variables appear to affect the corruption environment in a country. Particular attention is paid to solving current issues of corruption in the European Union (EU) countries, Organisation for Economic Cooperation and Development (OECD) nations, developed and developing countries, as well as the unique problems in individual countries. Like the aforementioned studies, this study applies the analysis of panel data to a set of transition economies, specifically the Visegrad countries. These countries share a similar character in terms of economics and the political environment, rely on the same traditions, and Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 53 promote cooperation and stability in the wider region of Central Europe. To date, little research emphasising the influence of selected determinants of corruption in the Visegrad countries has been conducted. Thus, this study attempts to fill the gap in this research area. A wide range of variables that were frequently utilised in previous studies (Billger & Goel, 2009, pp. 299– 305; Elbahnasawy & Revier, 2012, pp. 311–333; Picón & Boehm, 2019, pp. 88–100; Bitterhout & Simo-Kengne, 2020, pp. 1–23) were selected for the analysis examining the determinants of corruption and verified to determine which of these are decisive factors for corruption in the Visegrad group. This paper aims to identify the determinants of corruption in the Visegrad group that are most likely to affect corruption and ascertain which determinants the governments should concentrate on in their anticorruption policies. To achieve this goal, the following two hypotheses are verified: H1: Countries with a higher level of perception of corruption according to the CPI are characterised by lower corruption control. H2: Corruption in the Visegrad countries is influenced by selected economic, political, and socio-cultural determinants. The paper is structured into six parts. The introduction specifies the problems associated with the origin and causes of corruption in the Visegrad countries. The following section provides an overview of the literature on this issue. In the methodology, the data and method used are described. The results section evaluates corruption in V4 countries and the influence of selected determinants on the level of corruption in the V4 countries using panel data analysis. The next section compares our results with those of other studies. The conclusion summarises the findings and provides suggestions for further research. Literature review There are several perspectives from scholars and institutions concerning corruption that provide a unified definition for the phenomenon. At its core, corruption is an undesirable phenomenon that affects a country's development. It is also an abuse of public power for private gain (Aidt, 2009, pp. 271–291). According to Picón and Boehm (2019, pp. 88–100), corruption can be described as non-compliance with rules and established principles, Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 54 where its frequent manifestations are bribes, embezzlement of funds, or manipulation of information. Corrupt behaviour can also be a means of influencing the rules of the game. These perspectives cover political, economic, bureaucratic, legal, social, and even moral dimensions (Tanzi, 1998, pp. 559–594; Treisman, 2000, pp. 399–457; Serra, 2006, pp. 225–256; Kasik, 2013, pp. 287–291; Montes & Paschoal, 2016, pp. 146–150; Popova & Post, 2018, pp. 231–244; Moldogaziev & Liu, 2020, pp. 475–504; Sviderskyi & Lubentsov, 2020, pp. 125–129). According to Ochulor (2011, pp. 223–228), the source and direction in defining corruption are normally anchored to the author or scholar's disciplinary background. There is no universally unique nor accepted definition in the academic discourse or among members of the general public for corruption. Kwong (2015) believes that under different moral values, standards, and economic organisations, the prohibited actions and forms that corruption takes in the social and institutional systems of developed and developing societies differ. In defining corruption, Nye (1967, p. 418) referred to it as ‘an attitude that violates rules or deviates from the ethical public duties due to privateregard influence.’ Elbahnasawy and Revier (2012, pp. 311–333) comprehensively assessed the economic, political, and sociocultural determinants of corruption using panel data analysis. Another study by Picón and Boehm (2019, pp. 88–100) examined whether different determinants of corruption exist in countries with different levels of corruption. Despite many studies on this topic, to date, there has been no clear consensus on the root causes of corruption (Knack & Omar, 2000; Krajewska & Makowski, 2017, pp. 325–339; Liptakova, 2020, pp. 81–102; Laurent, 2021, pp. 65–91). Several economic variables could impact the benefits or costs of paying or accepting bribes. For example, civil servants' wage rate compared to the private sector wage rate was found to influence the level of corruption (Tanzi, 1998, pp. 559– 594). Kotlánová and Kotlán (2013, pp. 660–667) examined the institutional environment’s effect on the perception of corruption and the influence of selected determinants using a dynamic panel regression model and concluded that the institutional environment has a significant impact on the causes and spread of corruption. According to the literature, the economic determinants of corruption include gross domestic product (GDP) per capita, openness to foreign trade, and the size of the public sector (Elbahnasawy & Revier, 2012, pp. 311– 333). Some (Lederman et al., 2005, pp. 1–35; Serra, 2006, pp. 225–256; Mustapha, 2014, pp. 1–5) argue that the level of economic development of a country (according to GDP/capita) affects the level of corruption and that higher education may increase the likelihood of detecting corrupt practices. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 55 Several studies have shown that a high degree of economic development, as indicated by GDP/capita, has a positive effect on corruption (Deyshappriya, 2015, pp. 135–147; Knack & Azfar, 2003, pp. 1–18; Lederman et al., 2005, pp. 1–35; Serra, 2006, pp. 225–256). At the same time, corruption is an influencing variable, i.e. corruption determines the degree of economic development (Bentzen, 2012, pp. 167–184; Feruni et al., 2020). The causal relationship can be tested by regression analysis. According to Treisman (2000, pp. 399–457) and Knack and Omar (2000), greater openness to foreign trade is associated with lower levels of corruption. Elbahnasawy and Revier (2012, pp. 311–333) emphasise that the economic determinants of corruption include trade restrictions such as tariffs and quotas and that increasing natural resources is also an important factor. Similarly, Zhan (2017) and Williams and Le Billon (2017) argued that large amounts of natural resources are an important determinant of a country's level of corruption because they create opportunities for rent-seeking. As Fisman and Gatti (2002, pp. 325–345) and Corrado and Rossetti (2018, pp. 1126–1139) demonstrate, the larger the size of the public sector (defined by a government consumption), the greater the number of government contracts for which bribes can be offered. Picón and Boehm (2019, pp. 88–100) argue that a more equal distribution of income in the economy reduces the incidence of corruption. Another view is offered by studies evaluating the socio-cultural determinants of corruption (e.g. Treisman, 2000, pp. 399–457; Serra, 2006, pp. 225–256; Fisman & Gatti, 2002, pp. 325–345; Elbahnasawy & Revier, 2012, pp. 311–333). Research (Treisman, 2000, pp. 399–457; Fisman & Gatti, 2002, pp. 325–345) has shown that the preconditions in large countries increase the likelihood of civil servant bribes. In addition, Swamy et al. (2001, pp. 25–55) have found that the level of corruption is lower in countries where women occupy a larger share of parliamentary seats and a larger share of the labour force. According to Feruni et al., 2020, a lower degree of urbanization, i.e. a higher concentration of the rural population, also negatively affects the incidence of corruption in the country. According to Treisman (2000, pp. 399–457), Serra (2006, pp. 225–256), and Elbahnasawy and Revier (2012, pp. 311–333), the country’s legal system is a factor influencing the incidence of corruption. The authors emphasise the importance of the existence of a common-law tradition and draw attention to the risk of corruption associated with corrupt government officials. Popova and Post (2018, pp. 231–244), searched for answers to the question of whether Eastern European courts are effectively restricting policies and upholding the rule of law. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 56 Political determinants are analysed in the literature from other variables influencing corruption (e.g. Treisman, 2000, pp. 399–457; Lederman et al., 2005, pp. 1–35; Pirvu, 2015, pp. 65–82; Sicakova-Beblava & Beblavy, 2016, pp. 295–313; Snegovaya, 2020, pp. 1162–1182; Laurent, 2021, pp. 65–91). Some of the aggregated global governance indicators (WGI) include the Political Stability and the Voice and Accountability Indices (Kaufmann et al., 2006). The importance of political stability in influencing corruption has also been emphasised in other studies (e.g. Lederman et. al., 2005, pp. 1–35; Serra, 2006, pp. 225–256) as political instability may make officials more prone to accepting or even demanding bribes. Some of the extent, literature addresses current issues of corruption in the Visegrad countries (e.g. Kasik, 2013, pp. 287–291; Sicakova-Beblava & Beblava, 2016, pp. 295–313; Merickova et al., 2017, pp. 99–120; Meyer, 2019, pp. 220–233; Snegovaya, 2020, pp. 1162–1182; Pirro & Della Porta, 2021, pp. 433–450). Specifically, these studies emphasise the differences in the forms of corruption and its measurement, potential solutions, and anticorruption measures in connection with their common historical development and reform tendencies, including the modernization of public administration and the public sector. Meyer-Sahling and Mikkelsen (2020) and Meyer (2019, pp. 220–233) assessed the effectiveness of disciplinary and ethical codes in reducing corruption in the civil service and government interventions in the conditions of Poland. Krajewska and Makowski (2017, pp. 325–339) highlight that anti-corruption policy poses a threat to the standards of a democratic state. Pirro and Della Porta (2021, pp. 433–450) and Snegovaya (2020, pp. 1162–1182) address the effects of corruption in Hungary and reveal its political context. Lendvorský et al. (2021, pp. 1–15) examine the enforceability of legal responsibility for corrupt acts against public officials in Slovakia for the period 1994–2020. Their results point to low levels of accountability, especially under left-wing governments. Němec et. al. (2021, pp. 1–16) assess the economic impacts of corruption on the size of the shadow economy, sources of economic growth, and the tax burden in the Czech Republic and generalise the effects of corruption and its consequences for other post-communist EU member states. In connection with approaches analysing the consequences of corruption, several studies should be mentioned (e.g. Billger & Goel, 2009, pp. 299–305; Picón & Boehm, 2019, pp. 88–100; Bitterhout & Simo-Kengne, 2020, pp. 1–23). Picón and Boehm (2019, pp. 88–100) examine whether the determinants of corruption in highly corrupt countries differ from those that can be found in less corrupt nations. They conclude that some variables may have different effects on countries with different levels of corruption. Conversely, other determinants may have the same impact on countries Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 57 with different levels of corruption. Picón and Boehm (2019, pp. 88–100) argue that variables such as the size of the government and the proportion of the Protestant population are good predictors of the level of corruption, but only for the most and least corrupt countries and not those with medium levels of corruption. However, other variables, such as the degree of democracy, economic freedom, and income levels, remain critical determinants for all levels of corruption. This suggests that if there are differences according to the level of corruption, the policies adopted to control this phenomenon should likewise be distinguished. According to several studies, a more comprehensive and interdisciplinary approach to understanding the full picture of corruption is essential (Park, 2003, pp. 28–49; Elbahnasawy & Revier, 2012, pp. 311–333; Picón & Boehm, 2019, pp. 88–100). According to Bitterhout and Simo-Kengne (2020, pp. 1–23), using different indices of corruption perception may be useful to provide an accurate view of corruption. A distinction must always be made between the tool used to measure the perceptions of corruption perception and how its results differ from the actual experience with corruption. These facts allow for the correct conclusions to be drawn in individual research. Corruption is difficult to quantify as a statistical variable. The main reason is that most of the data about corruption are not available in the form of ‘hard’ data. Comparable objective data in this regard are practically nonexistent; thus, most surveys and measurements are based on the subjective evaluation of respondents (Kauffman et al., 2006; Olken, 2009, pp. 950– 966; Bitterhout & Simo-Kengne, 2020, pp. 1–23). Corruption level indicators are, therefore, based on so-called ‘soft’ data, which show considerable variability depending on the situation and time they are obtained. On the other hand, if there is an interest in fighting corruption, it is necessary to quantify this phenomenon and determine its extent in individual countries. Existing indicators of the level of corruption are, therefore, widely used, despite their shortcomings. Such indicators include the currently bestknown corruption indicator, the Corruption Perceptions Index (CPI), which is published annually by Transparency International (Transparency International, 2019, 2020). The CPI belongs to the category of composite indices, which are compiled by a combination of several indicators of corruption, thus including more information and eliminating possible one-sided deviations of the obtained results. Another group of indices consists of the socalled ‘expert evaluations’, most notably the Control of Corruption (CC) Index. The CC indicator belongs to a wide group of worldwide governance indicators compiled by the World Bank (Linhartová & Volejníková, 2015, pp. 25–39). Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 58 Research method In line with the goal of this paper, two hypotheses are verified: H1: Countries with a higher level of perception of corruption according to the CPI are characterised by lower corruption control. H2: Corruption in the V4 countries is influenced by selected economic, political, and socio-cultural determinants. Data This study uses the indicators from Transparency International, the World Bank database, and the European Central Bank. The dependent variables are the Corruption Perception Index (CPI) and the Control of Corruption Index (CC). The Corruption Perception Index (CPI) ranks countries based on how corrupt a country’s public sector is perceived to be, with a score of 0 representing a very high level of corruption and a score of 100 representing a very low level (Transparency International, 2019). The Control of Corruption (CC) Index captures ‘perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests’ (Kauffmann et al., 2010, p. 4). The index ranges from -2.5 or 0 (a completely corrupted government) to 2.5 or 100 (no corruption). Indicators for assessing the impact of corruption are chosen to reflect the widest possible range of current variables that may affect corruption (economic, political, and socio-cultural). Based on the literature research (Park, 2003, pp. 29–48; Kaufman et al., 2006, 2010; Elbahnasawy & Revier, 2012, pp. 311–333; Ghaniy & Hastiadi, 2016, pp. 1–10; Picón & Boehm, 2019, pp. 88–100; Bitterhout & Simo-Kengne, 2020, pp. 1–23) the most frequently mentioned determinants of corruption were selected as the independent influencing variables. Independent indicators used in our analysis are from the World Bank's world development indicators (worldwide governance indicators) and focus on a specific area of governance quality (Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, and Rule of Law). These indicators range from -2.5 to 2.5, where the highest possible value of the indicator is desirable (Kaufmann et al., 2010, pp.1–29). ‘Voice and Accountability (VA) captures perceptions of the extent to which a country's citizens can participate in selecting their government. Political Stability (PS) captures perceptions of the likelihood that the government will be destabilized or overthrown. Gov- Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 59 ernment Effectiveness (GE) captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures. Rule of Law (RL) captures perceptions of the extent to which agents have confidence in and abide by the rules of society. Regulatory Quality (RQ) captures perceptions of the ability of the government to formulate and implement policies and regulations“(Kaufmann et al., 2010, p. 4). All indicators range from -2.5 or 0 to 2.5 or 100. Other independent variables in the analysis were taken from World Bank database (indicators 1-9) and European Central Bank (indicator 10). Those indicators are as follows: 1) Gross Domestic Product (PPP GDP/capita), where the purchase price is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products, 2) Openness to Trade represents the external balance on goods and services, which is calculated by the total number of exports of goods and services minus the total number of imports of goods and services (previously non-factor services), and 3) Natural Source Endowment (% of merchandise exports), which is defined by ores and metals exports (% of merchandise exports), 4) Government consumption (% of GDP), which is defined by the general government final consumption expenditure (% of GDP), 5) Country Population, 6) Rural Population, 7) Percentage of Females in the Labour Force, and 8) Percentage of Seats Held by Women in National Parliaments (%), which is defined by the proportion of seats held by women in national parliaments (%), 9) the Gini index, which measures the extent to which the distribution of income among individuals or households within an economy deviates from a perfectly equal distribution (World Bank, 2022), and 10) Public Sector Wage, which is expressed by the unit labour costs in the public sector (an index) (European Central Bank, 2022). The sample of countries consists of Visegrad group countries (Czechia, Slovakia, Hungary, and Poland), which are analysed for the period 1996– 2019. This time series is sufficient to demonstrate the possible influence of independent variables on the level of corruption. Table 1 provides an overview of the basic statistics of the data used. The correlation matrix of the dataset is presented in Figure 1. Methods The most commonly used method for estimating the effect of several independent variables on one dependent variable is multiple regression analysis. The simplest multidimensional regression model contains two explanatory variables. Multiple regression analysis is suitable for time se- Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 66 Svensson, 2005, pp. 19–42; Matei & Matei, 2009, pp. 145–171; Ochulor, 2011, pp. 223–228; Sviderskyi & Lubentsov, 2020, pp. 125–129). Elsewhere, political and economic liberalisation has simply exposed corrupt practices that were previously hidden. According to Blagojevič and Damijan (2013, pp. 133–158), the period of transformation is mainly caused by the spillover of corrupt practices in trade and services and public administration. For these reasons, corruption is perceived as a serious problem, particularly by post-communist transforming economies, including the analysed Visegrad countries. Although several economists have already addressed the determinants of corruption, scholars have yet to reach a consensus on the causes of corruption (Tanzi 1998, pp. 559–594; Lederman et al., 2005, pp. 1–35; Serra, 2006, pp. 225–256; Elbahnasawy & Revier, 2012, pp. 311–333; Kasik, 2013, pp. 287–291; Pirvu, 2015, pp. 65–82; Montes & Paschoal, 2016, pp. 146–150; Popova & Post, 2018, pp. 231–244; Snegovaya, 2020, pp. 1162– 1182; Laurent, 2021, pp. 65–91). In connection with the aim of this paper, Hypothesis 1 was verified: Countries with a higher level of perception of corruption according to the CPI are characterised by lower corruption control. For the V4 countries in 2000, 2010, and 2019, it can therefore be confirmed that a higher level of corruption in most countries for the period under review is accompanied by a lower level of corruption control and vice versa (see Table 2). If we evaluate the level of corruption and its relationship with corruption control in the same years for other groups of European countries with traditional public administrations (i.e. within the continental model of public administration, the Scandinavian model, and the southern European model), we find similar results to the Visegrad group. Moreover, in the continental traditional model countries or the Mediterranean/Southern European countries employing the public administration model, a higher level of perception of corruption is accompanied by a lower level of corruption control and vice versa. Although each group of countries is characterised by a similar tradition of public administration and human resources management system, we also find differences between countries in one group. The Scandinavian countries have long had the lowest level of corruption in Europe, which is due to their sophisticated government control and legislative system, and this fact also reflects the results of corruption control. The only exception is Estonia, which has a higher level of perception of corruption and a lower level of corruption control. However, Estonia has long followed the Finnish system, which is also reflected in the improving results of the corruption assessment in 2019 (a reduction of the corruption rate and an increase in the control of corruption). Based on the results of the CPI and CC Indices in other European Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 67 countries (see Table 2), we can state that countries with higher levels of corruption have less sophisticated systems of corruption control and vice versa. According to Billger and Goel (2009, pp. 299–305), the existing level of corruption depends on the capacity to control it. Previous studies have found that the causes of corruption are different in highly corrupt countries compared to the least corrupt countries. They also found that some principles of corruption control could be reconsidered, especially among the most and least corrupt nations. Hypothesis 2 was also verified in this study: Corruption in the V4 countries is influenced by selected economic, political, and sociocultural determinants. Results with the use of panel data analysis show that corruption in countries with lower GDP per capita is larger in scale than in richer countries (see Table 3). This supports the findings of several studies (e.g., Knack & Omar, 2000; Elbahnasawy & Revier, 2012, pp. 311–333) and confirms the same technique can be applied to evaluate the Visegrad countries. Many studies (e.g. Knack & Omar, 2000) consider a level of economic openness to be closely linked to lower levels of corruption. These conclusions were also confirmed for the selected transition economies. The size of the public sector measured by Government Consumption negatively affects corruption in the Visegrad countries. The public sector scale and the associated rent-seeking activities generate opportunities for corruption in a selected set of countries. This confirmed the conclusion of Fisman and Gatti (2002, pp. 325–345). The extent of urbanization and the size of the urban population also positively affect the level of corruption. Some studies (e.g. Swamy et al., 2001, pp. 25–55) consider women to be less prone to corrupt practices. This conclusion was also confirmed for the selected transition economies. Finally, public sector wages also affect the level of corruption. Countries with higher salaries in the public sector perform better in corruption indices. Such a conclusion confirms Tanzi’s (1998, pp. 559– 594) conclusions for the Visegrad states. Of the political determinants, only the influence of Regulatory Quality was confirmed for the Visegrad states. Other determinants of corruption listed in the literature search section have not been proven for this group. It is essential to mention that the analysis results cannot be generalised, and apply only to the selected set of countries and the particular time period. The results of our research have showed that for the period 1996–2019 in the V4 countries, economic and socio-cultural determinants were the predominant influence on corruption. However, the impact of political determinants in our case of regulatory quality concerning corruption control was also confirmed. Similar to our research, other studies confirm the influence of several determinants (economic, political, social, or historical) on corruption (e.g. Nowak, 2001; Lederman et al., Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 68 2005, pp. 1–35; Ochulor, 2011, pp. 223–228; Montes & Paschoal, 2016, pp. 146–150; Merickova et al., 2017, pp. 99–120; Liptakova, 2020, pp. 81– 102; Meyer-Sahling & Mikkelsen, 2020; Laurent, 2021, pp. 65–91). The impact of indicators on corruption perceptions and control varies from country to country due to different approaches to tackling corruption, the degree of corruption (Billger & Goel, 2009, pp. 299–305; Bitterhout & Simo-Kengne, 2020, pp. 1–23), and the type and form of corruption (Olken, 2009, pp. 950–964; Mustapha, 2014, pp. 1–5; Picón & Boehm, 2019, pp. 88–100). Based on the literature on corruption determinants, a trend of the influence of economic, political and socio-cultural indicators at the level of perception of corruption and corruption control can be assumed (Elbahnasawy & Revier, 2012, pp. 311–333; Picón & Boehm, 2019, pp. 88–100) Compared to our study, Park (2003, pp. 29–48) provides incremental information on the determinants of corruption, showing that the main variables that determine the level of corruption include economic freedom, socio-political stability, the rule of law, and national culture. Ghaniy and Hastiadi (2016, pp. 1–10) add that there are differences in the determinants of corruption between the groups of developing and developed countries, measured by development indicators, the CPI, and other indices. The significant impact on the perceived level of corruption in these countries is confirmed by the level of development, democracy, economic freedom, education, political stability or religion. Conclusions This study examined the determinants of corruption by analysing panel data using a large data set for the Visegrad countries and an extensive time series. Although there are many studies examining the root causes of corruption, this is still a relatively unexplored area and no real consensus on the basic determinants of corruption has been reached. Hypothesis 1, which states that the level of perception of corruption is influenced by the level of control of corruption, was verified. The results show that countries with a higher level of perception of corruption are characterised by lower corruption control. The evaluation of the level of perceived corruption and corruption control in the Visegrad countries in the years 2000, 2010, and 2019 has showed that all countries with a higher level of perceived corruption (measured by CPI) are characterised by a lower level of corruption control (measured by CC) compared to other European countries. A panel analysis of data from the Visegrad countries for the period 1996–2019 was performed to verify Hypothesis 2, which Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 69 concerns corruption's main determinants. As a result, Hypothesis 2 was verified. The results of the analysis showed that the main determinants of corruption in the Visegrad countries were economic and political (the phase of economic development, openness of the economy, the size of the public sector, the degree of urbanization, and the share of women in the labour force). These indicators were significant in both models: the index of perceived corruption and in the control of corruption. Significance was also demonstrated in the case of the Regulatory Quality and Public Sector Wage. All variables are statistically significant with the country's level of corruption and the Visegrad countries should pay due attention to them in their anti-corruption policies. The conclusions on the determinants of corruption open a new space for further analysis of the specific causes of corrupt practices in other European countries. Across Europe, the results of corruption indices greatly differ. Therefore, the analysis of the specific causes of these different results is entirely appropriate and deserves more space. However, for anti-corruption measures to be as effective as possible, it is necessary to focus on the most problematic areas. Several surveys in the V4 countries show that there are no specific indicators for measuring and evaluating corruption in the public sector. If an agreement is reached on the specific causes of corruption and appropriate indicators are set for measuring corruption, it will also be possible to focus on effective anti-corruption measures in these countries. The limitation of the results of the analysis of the determinants of corruption can be seen in the fundamental problem of quantifying corruption; corruption is difficult to identify and quantify. Most of the currently existing indicators are based on soft data. On the other hand, the corruption indicators compiled by the World Bank and Transparency International which were used in this study are the most reliable ones available at this time for economic analyses. The authors of this article see as a topic for further research not only the solution of the aforementioned issues of corruption in the Visegrad countries, but also their broader context within the European and international contexts. We also see the evaluation of corruption and the influence of legislative determinants, institutional quality or indicators of political accountability using certain international indices (e.g. rule of law, freedom of the press or global competitiveness) as topics for future research. This area has not yet been explored in-depth from an empirical research point of view and thus offers opportunities for further scientific research. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79 70 References Aidt, T. S. (2009). 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Public Organization Review, 1(4), 465–486. Doi: 10.1023/A:1013740000213. Acknowledgments This paper was supported within projects VEGA 1/0683/21 “Generation Gap and Provision of Public Services and Administration “; SGS Project SP2022/74 “Computational Intelligence in the Prediction of Economic Quantities, Data Mining and Economic Process Modeling”; IGA project KEM-2022-01 „Support for scientific publications of the Department of Economics and Management for 2022“. Annex Table 1. Descriptive statistics of used variables Variable Mean Median Std. Dev. Min Max CPI 19.80 5.00 23.00 3.40 63.00 CC 0.41 0.39 0.20 -0.01 0.82 Voice and Accountability 0.93 0.97 0.17 0.32 1.18 Political Stability 0.86 0.91 0.23 0.15 1.26 Government Effectiveness 0.75 0.75 0.18 0.37 1.10 Regulatory Quality 0.97 1.00 0.19 0.51 1.31 Rule of Law 0.71 0.71 0.23 0.16 1.15 GDP/capita (PPP) 2.24e +004 2.28e +004 5.22e +003 1.20e +004 3.34e +004 Openness to trade 0.25 -0.29 4.47 -13.20 8.75 Natural resources endowment 2.76 2.29 1.19 1.17 5.86 Government consumption 19.80 19.70 1.55 17.10 25.30 Country population 1.60e +007 1.02e +007 1.30e +007 5.37e +006 3.87e +007 Rural population 35.60 36.80 7.10 25.50 46.30 % of females in labor force 60.30 61.40 4.22 48.60 69.30 % of seats held by women 16.70 17.00 5.23 8.30 29.10 GINI 29.00 27.70 3.24 24.70 38.00 Public sector wage 76.30 81.20 23.40 26.80 124.00 Source: own calculations based on Transparency International (2022a), World Bank (2021,2022) and European Central Bank (2022).