Dynamic Interconnections between Corruption and Economic Growth

TL;DR

This paper models the complex, dynamic relationship between corruption and economic growth globally, revealing how they influence each other and offering insights for policy development using advanced time-series and network analysis.

Contribution

It introduces a coupled stochastic equations model combined with graph theory and Granger causality to analyze international corruption-growth interdependencies.

Findings

Corruption can temporarily boost economic activity under certain conditions.

Interdependence patterns between corruption and growth are identified globally.

A network of corruption and growth influences across countries is constructed.

Abstract

This study explores the dynamic relationship between corruption and economic growth through an approach based on a system of stochastic equations. In the context of globalization and economic interdependencies, corruption not only affects investment and distorts markets, but it can also, under certain conditions, temporarily boost economic activity. Using data from the Gross Domestic Product (GDP) and the Corruption Perception Index (CPI), we implement a time-series-based model to capture the interactions between these two variables. Through a coupled vector autoregressive equations system, our model identifies patterns of interdependence between economic fluctuations and perceptions of corruption at a global level. Employing graph theory and Granger causality, we build a network of interconnections that illustrates how corruption dynamics in one country can influence economic growth and corruption perception in others. The results provide a robust tool for analyzing international political-economic relationships and can serve as a basis for designing policies that promote transparency and sustainable development.