INVESTIGATING THE VENEZUELAN HUMANITARIAN AND MIGRATORY CRISIS: AN FMEA AND ETA ANALYSIS VIA MULTIPLE REGRESSION

Abstract

This article addresses the migratory and humanitarian crisis in Venezuela, focusing on the analysis of the economic and social roots of this complex phenomenon. To comprehensively understand and approach the crisis, the Failure Mode and Effects Analysis (FMEA) and Event Tree Analysis (ETA) methodologies were applied, with results compared to those generated by two Artificial Intelligences (AI): Google Bard and ChatGPT. Additionally, the study employed a robust multiple regression econometric model to investigate the impact of socioeconomic variables on Venezuelan net migration from 1998 to 2022. The findings revealed that factors such as inflation, gross debt, oil production, and mortality rate played significant roles in the observed mass migration. These conclusions are essential for understanding the underlying economic dynamics of the crisis and may contribute to the development of more effective and sustainable policies to address it.