Regional inequality in Mexico’s post-pandemic economic recovery

Abstract

This study analysed how the Covid-19 pandemic has affected pre-existing regional economic disparities in Mexico by comparing economic recovery between tourism and nontourism regions. Using a blend of econometrics and machine learning methods, this study examines major economic variables, such as gross domestic product, unemployment rate (UR), and foreign direct investment (FDI), between 2018 and 2023. This approach combines synthetic control method, random forest, and hierarchical clustering to address the differential regional recovery given economic structural variations. Findings highlight a glaring economic dichotomy: the southeast and southwest regions of the USA that mainly are reliant on tourism experienced massive economic shocks and much longer recovery timelines owing to their dependence on tourism and informal labor markets. Conversely, more diverse economies, such as the northeast, maintained a more stable recovery through consistent FDI, which fostered some level of economic protection. SHAP and random forest analyses revealed that UR and FDI are key drivers of economic recovery. The unemployment rate negatively impacts tourism-dependent regions, whereas foreign direct investment fosters recovery in nontourismdependent regions. These results indicate the need for economic diversification to act as a buffer from external shocks. Effective regional policies should work toward decreasing the reliance on tourism in vulnerable regions while making those areas more competitive for investment, thereby contributing to a more balanced recovery. Such an approach may cultivate structural resilience, thereby reducing the negative impact of future shocks and more broadly controlling for economic growth across Mexico.