On the relationship between income and control of corruption in the Eurozone.

• Author: Lopez-Gomez, Laura

1. Introduction

   The connection between institutions and economic performance is well-established. North (1990) first demonstrated that the quality of institutions plays a significant role in economic growth, leading to a plethora of subsequent studies on this relationship. However, the concepts of institutions and institutional quality are broad, covering various aspects. As a result, some authors have chosen to focus specifically on certain elements, with corruption being one of the most widely studied.

   Following this line of work, the aim of this study is to examine the relationship between income per capita and control of corruption in the Euro Area (EA) and determine if the pattern observed in previous studies holds true. Assuming a positive causal relationship between the two variables - where greater control of corruption leads to higher income per capita and vice versa - we use panel Granger causality tests to analyze if there is a predictive capacity between them.

   There is a significant amount of research exploring this connection between corrupt behaviors and economic development. Mauro (1995) was one of the first to empirically demonstrate that corruption has a detrimental effect on growth. Since then, many authors have continued to investigate this relationship, utilizing advanced and sophisticated techniques to examine whether the effect is consistent across all countries. However, these studies have yielded a range of results, making it challenging to assert that the relationship between economic performance and corruption is always straightforward and bidirectional.

   Despite the variety of findings, the causality relationship between corruption and income per capita is clearly established and the positive causal relationship between per capita income and control of countries' corruption levels is evident. In regards to this two-way causality, some authors such as Chong and Calderon (2000), Littvay and Donica (2006), and Aidt, Dutta, and Sena (2008) suggest that it only occurs in certain groups of countries. They employ different estimation procedures to uncover cross-country evidence that the relationship between corruption and economic performance varies among different countries or groups of countries.

   Law, Lim, and Ismail (2013) sought to validate the causality patterns between institutional quality and economic performance using panel Granger causality techniques. They analyzed 60 countries using two different datasets: the International Country Risk Guide (ICRG) database and the World Governance Indicators (WGI) from the World Bank. The authors applied the panel causality procedures developed by Hurlin and Venet (2001) and Hurlin (2004) to address heterogeneity and cross-section dependence. Their findings suggest that there is a bidirectional causality between income per capita and institutional quality for the full sample, however, this pattern is not consistent across all countries. In high-income countries, institutional quality is found to foster economic development, while in developing countries, higher levels of income are associated with better institutional quality. With regard to corruption, they found that while there is a bidirectional causality for the full sample, this pattern is not supported in richer economies, where the results do not indicate that corrupt behavior has a significant impact on real GDP per capita.

   The study of institutional quality and corruption has also been developed for smaller areas, such as Europe. Several studies have analyzed, not the causal relationship, which they assume from the outset, but whether European countries have managed to converge in terms of institutions. Fernandez-Villaverde, Garicano and Santos (2013), Papaioannou (2016), Schonfelder and Wagner (2019), Glawe and Wagner (2021), Beyaert, Garcia-Solanes and Lopez-Gomez (2019, 2021) and Perez-Moreno, Barcena-Martin and Ritzen (2020, 2021) have highlighted a lack of institutional convergence among European countries, with some specifically noting a gap in terms of corruption within the Eurozone. Blackburn, Bose and Haque (2006) theorize that this lack of institutional convergence hinders income convergence. This demonstrates that the relationship between corruption and per capita income is crucial to understand the economic development of countries.

   Our focus is not to delve into the causal relationship itself, but rather the joint behavior of these variables in recent years and the possibility of a bidirectional relationship. Understanding this behavior helps us to determine if efforts to fight corruption are being maintained and what implications they have on income, as well as to comprehend why corruption divergences have been detected in the Economic and Monetary Union (EMU).

   Detecting these behavioral patterns in an economically integrated region such as the Eurozone is critical for the implementation of public policies. Institutional asymmetries create significant structural macroeconomic imbalances that hinder the desired economic integration and income convergence, which were the primary objectives of the Delors Report (1988) and the Maastricht Treaty (1992). In the specific case of corruption, the absence of effective measures to monitor and deter corrupt practices can lead to misallocation of resources in corrupt countries. This, in turn, can negatively impact not only the well-being of the country in question, but also other EMU countries that may have discontinued providing aid to such nations.

   This paper employs an advanced panel Granger causality test developed by Dumitrescu and Hurlin (2012), which is particularly suited for analyzing the Eurozone, as it accounts for both cross-section dependence and heterogeneity. We apply the test to the entire EMU, as well as different subgroups of euro countries that share similar economic, political, and historical features. With this, our objective is to identify the sources of any possible joint relationship between control of corruption and per capita income. To further explore the interaction between these variables, we also conduct the Pedroni (2004) panel cointegration test, which can detect long-term relationships between different variables.

   Our findings show that increases in control of corruption do not predict increases in per capita income for the EMU as a whole or for its different subgroups. We observe a desynchronization between control of corruption and per capita income after the Great Recession, for both the core and peripheral countries. Furthermore, we find evidence of a long-term relationship between control of corruption and GDP per capita in Estonia, Latvia, Lithuania, Slovakia, and Slovenia.

   These results have policy implications, particularly with regard to income per capita convergence among EA members, and also provide crucial insights for the development of anti-corruption policies by Euro Area authorities.

   This paper is structured as follows: Section 2 provides a review of the literature on control of corruption and income. Section 3 describes the data and methodology used in the study. In Section 4, we present the results of the panel Granger causality and panel cointegration analysis. Section 5 presents a discussion of the results and economic policy derivations. Finally, Section 6 offers some concluding remarks.

2. Literature review

   The literature on the relationship between income level and corruption has primarily focused on understanding the impact of corrupt behaviors on long-term economic performance. The findings from this literature are varied. Many studies suggest that there is a negative relationship between economic growth and corruption, while others propose that certain types of corruption may facilitate growth in countries or regions with poor institutional quality. These conflicting hypotheses suggest that the connection between economic development and corruption may be more complex and varied among different countries or groups of countries than previously thought.

   In this context, Myrdal (1968) and Kurer (1993) argue that corruption always negatively impacts growth as it leads to the misallocation of resources. Mauro (1995) presents empirical evidence of the negative impact of corruption on growth through the use of an instrumental variable approach. For their part, Meon and Sekkat (2005) investigate the interplay between institutional quality and levels of corruption, and through the use of multiplicative variables within a linear model, they find that corruption has a negative effect on growth regardless of the legal context. Similarly, Aidt (2011) identifies a strong negative correlation between GDP and levels of corruption.

   Grundler and Potrafke (2019) argue that corruption negatively impacts growth by reducing foreign direct investment, particularly in autocratic countries. Sharma and Mitra also conclude that control of corruption is associated with higher levels of growth and income. Conversely, Li, Xu, and Zou (2000) confirm the findings of Mauro (1995) but with a less severe impact. However, the hypothesis that corruption is bad is not supported by all the literature. Authors such as Leff (1964) or Huntington (1968) suggest that in situations where bureaucracy is prevalent, corruption may facilitate growth by streamlining bureaucratic processes.

   Most of these studies, which use large samples, assume the existence of a bidirectional causality between corrupt behaviors and income. However, other papers that analyze specific countries or smaller groups of countries find limited evidence of this relationship. For example, Treisman (2000) and Paldam (2002) argue that corruption leads to poverty, but as countries develop, this relationship weakens, suggesting that as a country's GDP increases, the bidirectional relationship becomes less pronounced and the level of GDP primarily influences the magnitude of corruption. According to Beyaert, Garcia-Solanes, and Lopez-Gomez...