Imagine this: you're trying to solve a complex puzzle where every piece represents a different facet of economic performance. Now, imagine that the shape of some of these pieces are influenced by the quality of the institutions in place—things like governance, legal frameworks, and anti-corruption measures. As it turns out, the fit of these pieces can make or break the overall picture of economic growth. Institutions, in this sense, are not just background players; they are fundamental to how the economic puzzle comes together.

Welcome to the world of my research, where I explore this intricate relationship between institutional quality and economic growth. My work dives into how institutions shape economic dynamics, focusing on issues like corruption, the impact of the euro, and the role of institutional frameworks in economic convergence.

Discovering Corruption’s Effect on Economic Growth

When we think about the factors that drive or hinder economic growth, corruption often surfaces as a prime suspect. But how exactly does corruption influence the economic trajectory of a nation? Our recent study (Beyaert, García-Solanes and Lopez-Gomez, 2023) takes a fresh approach to answering this question, using innovative regression tree analysis and advanced econometric techniques to explore the nuanced effects of corruption on growth across 103 countries over the period from 1996 to 2017.

Traditional economic growth models, like those developed by Barro and Sala-i-Martin (1992) and Mankiw et al. (1992), have typically treated corruption as a direct drag on growth. However, this study challenges that assumption by suggesting that corruption's impact is more indirect—and complex—than previously thought. Rather than applying a one-size-fits-all model, the researchers utilized regression tree methodology, which allowed them to segment countries into different subgroups based on specific characteristics (or "split variables") and then estimate distinct growth models for each group.

The regression tree algorithm was allowed to determine endogenously whether the group divisions should be based on corruption, “Rule of Law”, or a combination of both. In particular, the analysis showed that separating countries into just two primary groups was best: those with high levels of corruption and those with low levels of corruption.

This segmentation is crucial as it reveals how the impact of traditional growth factors—like capital accumulation and population growth—varies significantly across these groups. In nations with higher levels of corruption, these traditional growth determinants play a more significant role compared to countries with lower corruption levels. Essentially, corruption seems to exacerbate the influence of these factors, making them more critical in determining economic outcomes.

Data for the study were sourced from authoritative references such as the International Country Risk Guide (ICRG) and the World Bank's Worldwide Governance Indicators. These sources provided key variables, including levels of corruption and the quality of the Rule of Law, which are essential for understanding the institutional landscape of each country.

To enhance accuracy, we employed the GUIDE (Generalized Unbiased Interaction Detection and Estimation) methodology, which addresses the selection bias issues common in other analytical methods like the CART algorithm. The GUIDE method facilitated a more precise identification of split variables, leading to more reliable and nuanced results. It resulted in two groups, as detailed in Table 1.

Table 1. Countries Grouped by Corruption Levels Detected by Regression Tree

High Corruption Group

Low Corruption Group

Algeria, Angola, Argentina, Albania, Armenia, Belarus, Bangladesh, Bolivia, Burkina Faso, Cameroon, China, Rep. Congo, Cote d’Ivoire, Dominican Republic, Egypt, Gabon, Ghana, Guatemala, Guinea-Bissau, Honduras, Indonesia, India, Jamaica, Kenya, Latvia, Mali, Malawi, Mexico, Mozambique, Niger, Nigeria, Pakistan, Papua New Guinea, Panama, Paraguay, Philippines, Russia, Sierra Leone, Togo, Thailand, Tunisia, Turkey, Uganda, Ukrania, Zimbabwe

Australia, Austria, Bahrain, Belgium, Botswana, Brazil, Bulgaria, Canada, Chile, Colombia, Costa Rica, Croatia, Czech Republic, Denmark, Estonia, Ecuador, El Salvador, Finland, France, Gambia, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Jordan, Korea, Rep., Kuwait, Lithuania, Luxembourg, Malaysia, Madagascar, Morocco, Namibia, Netherlands, Nicaragua, New Zealand, Norway, Peru, Poland, Portugal, Romania, Singapore, Senegal, Slovak Republic, Slovenia, Spain, Sri Lanka, Sweden, Switzerland, United Kingdom, United States, Uruguay, Zambia

^{Source: https://doi.org/10.1108/AEA-11-2021-0297. Copyright © 2022, Arielle Beyaert, José García-Solanes and Laura Lopez-Gomez. Published in Applied Economic Analysis. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license.}

Subsequently, after dividing the sample into these two subgroups, a traditional cross-sectional Solow model was estimated, incorporating standard drivers such as investment, initial GDP per capita, and population growth. To ensure the robustness of these findings, the researchers utilized instrumental variable estimations, drawing on the methodology of Gründler and Potrafke (2019) to refine and enhance the instruments employed. This advanced econometric technique addresses the issue of endogeneity—a common challenge in growth studies—by ensuring that the identified relationships are truly causal rather than merely correlational. By improving the instruments and applying them within each identified subgroup, the study provides a more reliable estimation of the growth models.

One of the study's key revelations is that corruption does not have a statistically significant direct effect on economic growth in either the most corrupt or least corrupt countries. Instead, its influence is more subtle, indirectly altering how traditional growth determinants operate; the algorithm revealed growth parameter differences between countries, showing that those with higher levels of corruption were farther away from reaching an economic steady state. These findings resonate with the work of Durlauf et al. (2001), which identifies parameter heterogeneity in the Solow model, suggesting that corruption contributes significantly to this variability.

The evidence points to a clear pattern: the more corrupt a country, the more pronounced the effect of growth determinants. This means that corrupt countries are generally further from their economic steady state, and thus, experience a stronger impact from factors like investment.

Interestingly, the study also highlights a developmental divide: more developed nations typically exhibit lower corruption levels, while less developed countries struggle with higher corruption. This finding emphasizes the dual challenge facing developing nations—not only must they fight corruption, but they must also focus on enhancing investment and managing population growth effectively.

Geographically, the analysis identifies three major regions: Europe and North America, with low corruption; Africa and Latin America, characterized by higher corruption levels often linked to their colonial histories; and Asia, marked by significant corruption variability. Notably, China and Russia, despite both being part of the high corruption group, exhibit divergent growth trajectories. Russia’s corruption has been a drag on its growth, while China has managed to sustain rapid growth rates despite similar corruption levels. The difference is attributed to the more organized nature of corruption in China compared to Russia.

In conclusion, this study urges policymakers and economists to reconsider how they model and address the effects of corruption on economic growth. It’s important for policymakers to recognize that improving institutional quality can help lower corruption. By recognizing that corruption's impact is largely indirect and varies significantly across different contexts, and by employing more sophisticated econometric techniques like improved instrumental variables, more tailored and effective strategies can be developed to foster economic development, especially in countries where corruption remains a significant hurdle.

Regional Case Study: How the Eurozone Navigates Corruption and Institutional Quality

While global discussions often focus on the broad impacts of economic integration, it’s essential to zoom in on specific regions to truly understand these effects. Shifting the lens to the Eurozone, my research delves into the influence of the euro on corruption control within this unique economic area. Using a counterfactual analysis based on the methodology of Abadie, Diamond, and Hainmüller (2015) and conducted by Beyaert, García-Solanes, and Lopez-Gomez (2023), we explored the hypothetical scenario of a Eurozone without the euro. Surprisingly, the findings suggest that the introduction of the euro has not worsened corruption control in southern Eurozone countries. Instead, economic integration and standardized practices seem to have a stabilizing effect on corruption levels.

Still, the Eurozone has not achieved institutional convergence, as highlighted by Beyaert, García-Solanes, and Lopez-Gomez (2019) and García-Solanes, Beyaert, and Lopez-Gomez (2021). There are notable asymmetries in institutional quality among member states, creating macroeconomic imbalances, economic frictions, and inefficiencies, which potentially reduce the Eurozone's overall GDP (García-Solanes et al., 2023). This underscores the need for more uniform institutional standards to address these disparities.

My research delves further into this economic convergence within the Eurozone. Using Phillips and Sul (2007, 2009) tests and a logit model, my findings suggest that successful economic convergence heavily depends on strong normative and judicial frameworks (Lopez-Gomez, forthcoming 2024). Countries with robust governance and legal systems tend to converge more effectively, while those with weaker institutions face persistent economic gaps.

Finally, an analysis of income per capita and corruption control using panel Granger causality and cointegration tests (Lopez-Gomez, 2023) reveals no bidirectional causal relationship between economic growth and improved corruption control. The Great Recession further weakened this relationship, especially post-2008, with notable exceptions in Eastern Eurozone countries.

Taken as a whole, the results of my research indicate that anti-corruption measures need to be tailored to the specific institutional contexts of each country, as a one-size-fits-all approach is unlikely to be effective.

The Broader Implications

Looking at the broader picture, my research consistently highlights one key theme: institutions are more than just background structures, they are pivotal in shaping economic behavior and outcomes. It’s a complex but crucial relationship, and unraveling how institutions interact with broader economic dynamics is essential for crafting effective solutions. Understanding these interconnections is key to developing policies that not only address immediate issues but also foster long-term stability and growth.

So, where does this leave us? The future of economic growth and development hinges on our ability to enhance institutional quality. This means not just fighting corruption or reforming legal systems, but fostering environments where effective governance can flourish. The journey ahead is filled with challenges, but also immense opportunities.

As we continue to piece together the puzzle of economic growth, one thing is clear: the role of institutions cannot be overstated. They are the hidden gears that drive economic performance.

Thanks for joining me on this exploration of how institutions and economic growth intersect. It’s a journey through models, data, and analysis, driven by a passion for understanding and improving our economic systems. Here’s to the ongoing quest to piecing together the puzzle of economic growth!

References

^{Abadie, A., Diamond, A., and Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495-510.}

^{Beyaert A., García-Solanes J., Lopez-Gomez L. (2019). Do institutions of the euro area converge?, Economic Systems, 43(3-4), 100720.}

^{Beyaert A., García-Solanes J., Lopez-Gomez L. (2023). Corruption, quality of institutions and growth. Applied Economic Analysis. https://doi.org/10.1108/AEA-11-2021-0297}

^{Beyaert A., García-Solanes J., Lopez-Gomez L. (2023). Did the euro really increase corruption in the eurozone?, Hacienda Pública Española-Review of Publics Economics, 3-36.}

^{García-Solanes. J, Beyaert, A and Lopez-Gomez, L. (2021). Clubes de convergencia en la zona del Euro, Papeles de Economía Española.}

^{García-Solanes. J, Beyaert, A and Lopez-Gomez, L. (2023).Convergence in Formal and Informal Institutions and Long-Run Economic Performance in the Euro Area. The Routledge Handbook of Comparative Economics Systems, 256.}

^{Lopez-Gomez, L. (2023). On the relationship between income and control of corruption in the Eurozone. The European Journal of Comparative Economics, 20 (1), 3-37. http://dx.doi.org/10.25428/1824-2979/020}

^{Lopez-Gomez, L. (2024). The role of political institutions on the economic convergence process. Journal of institutional economics. Forthcoming.}

^{Phillips, P. C., and Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75(6), 1771-1855.}

^{Phillips, P. C., and Sul, D. (2009). Economic transition and growth. Journal of applied econometrics, 24(7), 1153-1185.}

 

^{Header image credit: Pixabay.}