The impact of COVID-19, vaccination, and non-pharmaceutical policies on Ethiopian trade: a structural gravity using semi-parametric machine learning

Abstract

The COVID-19 outbreak in early 2020 had a significant impact on human health and the economy. To effectively manage this pandemic, we must balance economic losses and health benefits. We must use real-time data to accomplish this. The literature currently available for Ethiopia is based on simulations of pre-COVID macroeconomic data and economic indicators. Using a Poisson pseudo maximum likelihood estimator for a machine learning semi-mixed effect model (SMEM) of structural gravity, this study estimated the impact of COVID-19 cases and deaths, vaccinations, and non-pharmaceutical policies on Ethiopian trade. Cases, deaths, and the stringency index all have statistically significant negative effects on bilateral exports and imports. Vaccinations, on the other hand, have a statistically significant positive impact on both bilateral exports and imports. When compared to bilateral imports, Ethiopia's bilateral exports suffer greatly. Import substitution and increased vaccination coverage and rates are the policy implications.

Keywords

• Pandemic
• bilateral trade
• Poisson pseudo maximum likelihood
• semi-mixed effects model