IMPACT OF COVID-19 PANDEMIC, NON-PHARMACEUTICAL, AND PHARMACEUTICAL(VACCINATION) RESPONSE POLICIES ON SUSTAINABLE DEVELOPMENT IN ETHIOPIA: MICROMACRO PERSPECTIVE

Abstract

Real-time data, state-of-the-art models, multi-dimensional impact assessments, and comprehensiveness are needed to reduce pandemic development impacts. This research supplements early efforts done in mitigating the pandemic effects and can be used as an input in mitigation efforts of future pandemics if any. This study investigates COVID-19's non-pharmaceutical and pharmaceutical response policies impact on sustainable development objectives (micro-macro, short-run-long-run, dynamic and systemic, parametric-non-parametric, reduced-form-structural, direct-indirect, exogenousendogenous). Near-real-time databases (high-frequency COVID-19 household phone survey data, standard gravity model variables, quarterly reports of national accounts, COVID-19 pandemic daily data, Google trends) and state-of-the-art models (e.g. semiparametric panel data models, Bayesian econometrics, Rasch model, and other machine learning models) are used to satisfy pandemic mitigation's quick and reliable information demand. The pandemic has Micro-level and short-run impacts. Short-run and long-run evidence are inconsistent. Micro- and macro and other dimensions of impact results are mixed. One-dimensional impact measurements obscure important information. Pandemic uncertainty in national account system or a multiplier effect analysis only explains exports (SDG 17) (10.45%), state fragility (SDG 16) (6.08%) (correlation, not causation based on economic theory and context) and catastrophe uncertainty (SDG 13) (by 15.31 percent). Pandemic factors only explain total exports. Other national account system variables explained each other better than the pandemic (transport CPI (SDG 7) explains 18.38% of food CPI (SDG 2) variance, while food CPI explains 26.98% of general CPI variation (SDG 1)). Non-pharmaceutical and pharmaceutical COVID-19 responses have different effects. The former declines demand for services(SDG 9) and bilateral exports (e.g., the non-pharmaceutical policy elasticity of demand for tourism is -0.49 and the COVID-19 interaction term elasticity of bilateral exports is -0.10 in the non-pharmaceutical regression). The latter enhances service demand and bilateral exports (the pharmaceutical (vaccination) policy elasticity of retail trade (SDG 17) is 0.04 and the COVID-19 elasticity of bilateral exports is 0.05). Variable ICT demand (SDG 9) is co-integrated with both non-pharmaceutical and pharmaceutical COVID-19 response policies. This is due to COVID-19 and zoom software-a proxy for ICT demand long absence and exponential cooccurrence. ICT demand is the variable highly influenced by non-pharmaceutical (Nonpharmaceutical policy elasticity of ICT demand is 0.33) and slightly influenced by pharmaceutical (vaccine) policies (pharmaceutical policy elasticity of ICT demand is - 0.11). ICT demand adjust speedily to its long-run equilibrium (-0.90 for nonpharmaceutical policy shocks and -0.80 for pharmaceutical (vaccine) policy shocks). Bilateral import in Ethiopia are a variable to observe since it is unaffected by the pandemic burden (cases and deaths) or response policies. Ethiopia's permissive nonpharmaceutical policies, low vaccine coverage, and the weak pandemic may be to blame. Again, for bilateral import (SDG 17) (e.g. it is not affected by GDP (SDG 8)), the important factors in its regression are different from those for bilateral export, deviating from trade literature. Again, Only the first lag of total export explains (99%) total import variation in national accounts. The interaction term, which enters the trade and demand for service model due to overlapping COVID-19 cases, fatalities, non-pharmaceutical xix policies, and pharmaceutical policies, has a negative significant coefficient on bilateral export regressed on the four COVID-19-related variables. (COVID-19 interaction term elasticity of bilateral export is -0.13, -0.11, -0.10, -0.15 for COVID-19 cases, COVID-19 deaths, non-pharmaceutical policy, and pharmaceutical (vaccination) policy respectively). When included in the model, it takes the sign of COVID-19 cases, fatalities, and nonpharmaceutical policies but not vaccination policies since it is more connected with the pandemic burden and non-pharmaceutical policies. For the bilateral export sample, the interaction term is interpreted. Bilateral export is explained by state fragility (SDG 16). (the state fragility elasticity of bilateral export is 0.80). Ethiopia exports more to fragile states. Short-term pandemic effects differ by consumption quintile (SDG 1), gender (SDG 5), sector (rural/urban) (SDG 11), region/ethnicity (SDG 10) and time. Poor, rural, and female-headed households are affected more. June 2020–April 2021 show improvements than May 2020. The pandemic's effects were short-run. Macro models show little long-run pandemic effect or co-integration with other variables except ICT demand. As expected, highest-order (exogenous) variables aren't explained by lower-order variables (the most endogenous ones). All conventional gravity model variables are at their expected signs. This research at least has three original results. Physical size (area) and technological size (patent) variables that are usually absent from the gravity model (and are sources of endogeneity) found to be statistically significant determinants of trade (SDG 17) (area (SDG 8) has linear negative effect and patent (SDG 9) has linear positive effect); two, mean GDP (SDG 8) is found to have a statistically significant non-linear relationship with bilateral export (it has non-linear positive effect); three, mean uncertainty (SDG 13) caused by man-made and natural disasters found to be statistically significant determinant of bilateral import (negative effect). Micro- and macro-analyses indicate the pandemic's shot-run impact. Low- and middle-income employee income growth, improving producers' knowledge, focusing on the COVID-19 vaccine, promoting online learning, eliminating all forms of violence, reducing water pollution, digitalizing the energy industry, work-fromhome options, automation, eliminating outcomes inequities, decarbonization strategies, and fostering collaborations are few of the policy implications of the entire recommendation.