Dynamic Interconnections between Corruption and Economic Growth

TL;DR

This paper tackles the dynamic, cross-country relationship between $GDP$ growth and corruption perceptions ($CPI$) within a global network of 13 economies. It develops a system of two coupled VARs and uses four Granger-causality–based adjacency matrices to map domestic and international spillovers, forming multilayer signed networks to characterize grease versus sand dynamics. The analysis reveals that shocks propagate across borders through trade, FDI, and information channels, with large economies shaping others while corruption perceptions can both hinder and momentarily boost activity depending on context. The framework provides a quantitative tool for evaluating international policy interventions aimed at transparency and coordinated governance to mitigate contagion and support sustainable growth.

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.

Figures (4)

• Figure 1: (a) The graph associated with $\hat{\mathbb{G}}_{\Phi}$ illustrates the relationship between the GDP of one country and that of others (blue arrows indicate direct positive effects, red arrows inverse effects). (b) The graph associated with $\hat{\mathbb{G}}_{\Pi}$ depicts the influence of a country’s CPI on the GDP of other countries (blue arrows indicate direct effects, red arrows inverse effects). (c) The graph associated with $\hat{\mathbb{G}}_{\Psi}$ shows the influence of a country’s CPI on the CPI of other countries (blue arrows indicate direct effects, red arrows inverse effects). (d) The graph associated with $\hat{\mathbb{G}}_{\Gamma}$ illustrates the influence of a country’s GDP on the CPI of other countries (blue arrows indicate direct effects, red arrows inverse effects). For a more detailed assessment of the degree of connectivity and structural properties among these graphs, refer to the network metrics summarized in Table \ref{['tab:network_metrics']}, which include measures such as centrality, density, and clustering coefficients
• Figure 2: Series charts for the South America group. The upper chart shows the sampled GDP series, while the lower chart shows the sampled CPI series.
• Figure 3: Series charts for the Europe group. The upper chart shows the sampled GDP series, while the lower chart shows the sampled CPI series.
• Figure 4: Series plots for the USA & Others group. The top plot displays the sampled GDP series, while the bottom plot shows the sampled CPI series.