This article describes the relationship between countries' lockdown responses to the COVID-19 pandemic and those countries' political rights and civil liberties, macroeconomic variables, and vulnerability to the virus. Political rights and civil liberties cannot explain the differences in lockdown timing across countries. Countries with high contagion exposure due to weak water sanitation and weak health systems locked down their economies as fast as possible to reduce contagion. However, countries more vulnerable to COVID-19 due to large fractions of elderly and smokers in the population did not respond differently from less-vulnerable countries. Interestingly, macroeconomic variables that did affect the timing of lockdowns were the sizes of a country's financial and trading sectors, even when differences in income and population density are taken into account.
Why did some countries lock down their economies faster than others? Was lockdown speed related to a country's economic condition, democratic rights and civil liberties, or vulnerability to contagion and death? This article aims at understanding differences across countries in response to the COVID-19 pandemic, focusing on lockdown speed.
Figure 1A shows that there is dispersion in countries' responses to COVID-19. After having 100 confirmed cases, some countries remained fully open, while others had closed their economy even before reaching this point. In addition, while many countries locked down their economy after having a "sufficient" number of confirmed cases, as shown in Figure 1B, what each country considered sufficient seems to vary. For many countries 100 cases was enough, while for others 2,000 cases was not enough to restrict activity.
Recently, a wide literature combining economic models with the SIR model of contagion, used by public health specialists, has highlighted the trade-off between reducing economic activity by reducing social interaction at businesses and reducing the rate of infections. At the heart of the standard SIR model, there is a matching function that mixes susceptible individuals (S) with infected individuals (I). Because private and public returns to social distancing differ due to externalities not taken into account by private agents, intervention could be desirable. Farboodi, Jarosch, and Shimer (2020) show that optimal policy reduces social interaction immediately relative to laissez-fare where individuals only reduce activity once the risk of infection becomes nonnegligible. Optimal social distancing starts as soon as the disease emerges, immediately imposing social distancing, for example, through stay-at-home orders. This sudden drop in activity delays the spread of infection and hence "buys time." That is, prior to development of a cure, social distancing enormously reduces expected fatalities and yields a substantial welfare gain, albeit at the cost of a reduction in social and economic activity.
The first and main hypothesis of this study is that lockdown timing and intensity in response to the pandemic can be explained by country differences in political rights and civil liberties. The lack of political rights and civil liberties can improve the externality problem. In other words, less individual liberty might imply more coordination and compliance, making the solution to the problem closer to the social optimum.
On one hand, on January 23, 2020, with a population of more than 11 million and less than 500 confirmed cases, Wuhan, China, was the first city to lock down; several other cities in the province of Hubei, China, followed immediately. On the other hand, on March 19, with a population of 36 million and already more than 1,000 confirmed cases, California was the first U.S. state to issue a statewide stay-at-home order. China and the Unites States have very different degrees of civil liberties, and stay-at-home orders have been of different intensities and at different points in time. For example, while many cities in China faced a full quarantine where people were not allowed to leave their homes, this did not happen at any point in the United States.
Disparity in the responses of the United States and China to COVID-19 could be driven by the differences in the countries' individual liberties. How did countries between these two extremes respond to COVID-19? I would expect to see that countries with high levels of freedom, political rights, and civil liberties would take significantly longer to force their population to quarantine and issue lockdowns. However, I do not find evidence to support the idea that countries with lower levels of freedom responded faster to the pandemic through lockdowns.
This finding is consistent with Frey, Chen, and Presidente (2020). Using the Oxford COVID-19 Government Response Tracker (OxCGRT), they test the belief that autocratic governments have been more effective in reducing the movement of people to curb the spread of COVID-19. They find that autocratic regimes imposed more-stringent lockdowns and relied more on contact tracing. However, they find no evidence that autocratic governments were more effective in reducing travel and find evidence to the contrary: Countries with democratically accountable governments introduced less-stringent lockdowns but were approximately 20 percent more effective in reducing mobility than their less-democratic counterparts who enforced the same level of policy stringency. Cronert (2020) investigates the institutional determinants of the timing of COVID-19-related school closures around the world, focusing on the role of democracy and administrative state capacity. That study finds that other things being equal, democratic countries tended to implement school closures quicker than those with a more authoritarian regime, while countries with high government effectiveness tended to take longer to implement school closures than those with less-effective state apparatuses. Similarly, Bosancianu et al. (2020) find that, to date, political and social variables such as populist governments, right-leaning governments, or women-led governments have little explanatory power over and above simple demographic and health indicators.
Second, I study whether differences in health systems and sanitation services as well as fatal vulnerability to the virus can explain the variation in country responses. The health status of the population measures the set of individuals potentially susceptible to the virus. In the SIR model, infected people transmit the virus to susceptible people at a rate that depends on the nature of the virus and on the frequency of social interactions. Populations with potentially better health might not get infected as easily from contact with individuals with the disease or if infected might have a lower likelihood of death. Additionally, populations with better sanitation services might also have a reduced likelihood of contagion. Similarly, hospital capacity could affect the payoff function of the policymaker. For example, some models assume that there is a capacity constraint in the health care system and that part of the reason to lock down is to avoid exceeding hospital capacity (Jones, Philippon, and Venkateswaran, 2020). One would expect that countries that are more vulnerable to contagion, have a weak health system to fight the virus, or have a large fraction of the population with pre-existing characteristics that increase the likelihood of death would respond faster to the pandemic. I find that countries that were highly exposed to fast COVID-19 contagion had a much faster response than countries that had better access to safe drinking water and sanitation. However, countries more vulnerable to COVID-19 due to a large fraction of elderly or smokers in the population do not seem to have responded faster than less-vulnerable countries. Nevertheless, after controlling for differences in income, none of these health- and sanitation-related variables can explain the variation in country responses to COVID-19. It is worth noting that this article is silent regarding whether differences in responses across countries are due to differences in sanitation and health systems or differences in incomes across countries, since these variables are highly correlated.
Early in 2020, the general expectation was that the coronavirus pandemic's effects would be more severe in developing countries than in advanced economies, on both the public health and economic fronts. According to Goldberg and Reed (2020), preliminary evidence as of June 2020 supports a more optimistic assessment. According to their investigation, to date, most low- and middle-income countries have had a significantly lower death toll per capita than richer countries, a pattern they attribute primarily to younger populations and limited obesity. On the economic front, emerging market and developing economies have seen massive capital outflows and large price declines for certain commodities, especially oil and non-precious metals; however, Goldberg and Reed (2020) suggest that these changes are in line with earlier commodity price shocks. They conclude that in the long run, the highest costs may be due to the indirect effects of virus containment policies on poverty, health, and education as well as to the effects of the accelerating deglobalization of emerging market and developing economies.
Finally, I look at whether economic variables that could affect the resources that countries have to navigate the impact of a substantial fall in economic activity could be responsible for the variation in responses. Macroeconomic variables could be changing the payoff function of the policymaker. Rich economies might have a different threshold for minimum consumption than poor countries; hence, they might be able to afford larger investment in health at the expense of the economy. I find that once differences in income and population density are taken into account, differences in the sizes of the financial and trading sectors can additionally explain variation in responses across countries. Demirguc-Kunt, Lokshin, and Torre (2020) provide an estimate of the economic impacts of the non-pharmaceutical interventions implemented by countries in Europe and Central Asia over the initial stages of the COVID-19 pandemic. Their results suggest that non-pharmaceutical interventions led to about a 10 percent decline in economic activity across the regions. On average, countries that implemented non-pharmaceutical interventions in the early stages of the pandemic appear to have had better short-term economic outcomes and lower cumulative mortality, compared with countries that imposed non-pharmaceutical interventions during the later stages of the pandemic. In part, this is because the interventions have been less stringent. Moreover, there is evidence that COVID-19 mortality at the peak of a local outbreak has been lower in countries that acted earlier.
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