Empirical Modeling of the Impact of Money Laundering on Governance, Economic and Financial Sectors in CEMAC Countries

With renewed uncertainties, market vagaries and poor governance in CEMAC countries, we attempt to understand the dynamics of money laundering based on empirical modeling of corruption. In panel data, this modeling is based on Completely Modified Least Squares (FMOLS, DOLS), impulse response functions (IRF) from the Vector Error Correction Model (PVECM) and a quadratic approach in Generalized Moments (SGMM). Over a period from 2000 to 2024, the analysis of stationarity specifies the persistent nature of corruption with an asymptotic convergence. The results suggest that the operational implications, on the fight against money laundering, concern adjustments in governance, unemployment, GDP, public debt, the primary balance, the financial system and the attractiveness of FDI in relation to the fight against corruption. The GMM system defines a threshold for which countries with a corruption perception index lower than 25.33 are exposed to the negative effects of money laundering; beyond this threshold, these effects are attenuated on economic growth. Robustness checks reveal that CEMAC States can structurally carry out institutional and political reforms, in order to adequately combat money laundering deviance, while those integrating uncertainties (notably stochastic shocks) reveal an unstable trajectory of corruption with differentiated results if corrective measures are not anticipated.