The economic damage of COVID-19 on regional economies: an application of a spatial computable general equilibrium model to South Korea

The spatial distribution of the local Moran’s \(I\) index is typically divided into four types: high-high (HH), high-low (HL), low–high (LH), and low-low (LL). An HH type has high values around the high value, an LL type has low values around the low value, an HL type has low values around the high value, and an LH type has high values around the low value. The two latter types are often outliers.

The patterns and the confirmed cases and fatalities of COVID-19 have been discussed within the context of educational environments (Abedi et al. 2021; Goutte et al. 2020), demographic structures (Stojkoski et al. 2020; Hassan et al. 2020), health or medical care conditions (Stojkoski et al. 2020; Chaudhry et al. 2020; Ehlert 2021) and spatial income levels. For example, the number of COVID-19 cases were found to be negatively associated with national income levels (Hassan et al. 2020; Chaudhry et al. 2020; Sannigrahi et al. 2020), and additionally, the lower the income level, the greater the risk of COVID-19 (Abedi et al. 2021; Goutte et al. 2020). Meanwhile, spatial regression models have also been developed in order to take into account the spatial dependency amongst the regions and the subsequent regional spillover effects of disease spread between neighboring areas (Sannigrahi et al. 2020; Ehlert 2021). Sannigrahi et al. (2020) found socio-demographic factors such as national indicators of income, poverty and population were positively associated with COVID-19 cases and deaths in 31 European countries. Ehlert (2021) shed light on the positive relations between average age, population density and the number of people employed in elderly care with the levels of COVID-19 cases and death rates in Germany, as well as their negative relationship with infant population density and medical doctor numbers. Additionally, several studies quantified the impact of SD policies on COVID-19 transmission and mortality (Chaudhry et al. 2020; Qiu et al. 2020; Ehlert 2021).

As discussed previously, any given SD intervention is expected to prevent the spread of COVID-19, but will also subsequently cause an economic slowdown due to the decreases in private consumption and industrial production. To assess the overall economic impacts of this SD measure, it is essential to explore the concept of what VSL represents in terms of the benefits of reduced mortality risk. If individuals are willing to pay $10,000 for a 1/1000 reduction in the underlying risk of death, then the VSL, measured in terms of an individual’s willingness to pay, is $10 million. Thunström et al. (2020) compared the present value of lives saved (benefits) to the present value of the difference in GDP lost without and with the SD intervention (costs). If the value of reduced mortality risk was assumed to be $10 million, then following U.S. federal agency guidelines, the net benefits of the SD can be calculated as $5.16 trillion, over one-fifth of the U.S. GDP. Greenstone and Nigam (2020) estimated the benefits of reduced mortality risk as $7.9 trillion, over one-third of the U.S. GDP. Robinson et al. (2020) adjusted VSL for life expectancy at the age of death, and produced VSL estimates of $10.63 million for an invariant population-average VSL approach, $4.47 million for a constant value per statistical life-year approach, and $8.31 million for a VSL approach which follows an inverse-U pattern that peaks in middle age. Since the cost of COVID-19 should include the reduced mortality risk, as well non-fatal health impacts such as damages to health, Kniesner and Sullivan (2020) estimated COVID-19's non-fatal economic losses from the cumulative cases and hospitalizations within the U.S. They calibrated an overall non-fatal valuation of $2.2 trillion for the U.S., a figure almost 40% higher figure than the estimated $1.6 trillion cost of COVID-19 fatalities. Viscusi (2020) revealed that when morbidity effects were adjusted with income elasticities in over 100 countries, the expected health losses increased from 10 to 40%, implying that non-fatal infections are as economically serious as fatal infections.

Theoretical details of the SCGE model are found in the literature listed in References.

The health-loss effect is not directly derived from the SCGE model, since it is regarded as an economic value of non-market or unproductive impact as lost lives as discussed in Matus et al. (2012). It means that the medical expenditures do not produce demand effects on the regional economy.

Seoul REA includes Seoul, the capital city, while Busan REA covers Busan, the second-largest city and the fifth-busiest container-port city in the world.

The SD intervention results in reducing Daegu’s GRP by 3.188% which can be decomposed into the agglomeration-loss effect (1.415%), the health-loss effect (1.733%) and the labor productivity (0.040%).

In fact, the Korean government implemented the uniform SD policy nationwide in the early days of COVID-19, but when the second wave occurred in the SMA, different SD policies were applied in SMA and ROK. Since the third wave, locally-tailored SD policies were allowed in each regions considering different contexts.