Discussions of COVID-19 policy have often centered around two metrics: robust economic performance, as measured by GDP, and public health, as measured by deaths attributable to COVID-19. Places that do better on either metric, or both, are often judged to have had a better policy response than those that do worse. While this mode of analysis can be useful, there are reasons for caution.

First, those goals have sometimes been erroneously framed as opposing ones. While they are different in many ways, they are not in direct opposition; some communities have learned to operate safely within the presence of the virus and demonstrated that there is not necessarily a policy tradeoff between health and the economy.¹ There are tradeoffs, but there are win-win choices as well.

^{Source: Our World in Data}

But there is also a second and less-discussed problem with this mode of analysis: the measures are overly simple, and obscure many below-the-surface trends worthy of more discussion. Coronavirus deaths are not the only measure of protection of population health. Gross domestic product is not the only measure of how an economy weathered the pandemic. These broad measures oversimplify to the detriment of our understanding of how countries have individually fought the virus.

Furthermore, it is plausible that these broad measures are driven in part by non-policy factors as well. Therefore, while they are a reasonable first pass at measuring the overall success of controlling and mitigating harm from the virus, the measures do not reveal how much of that success came from policy, and how much came from other factors.

Below we will consider some below-the-surface trends that can help us understand the quality of policy response a bit better, in health and in economics, respectively.

Looking Beyond the Headline Numbers in Health

One of the largest non-policy factors to adjust for in considering COVID-19 response is in demographics. Although capacity, resources and expertise on fighting the virus can prevent deaths, it is easier to do so in a younger population than an older one. For example, coronavirus deaths in nursing homes, where tenants are elderly and frequently have underlying health conditions, comprise nearly 4 in 10 coronavirus deaths nationally in the U.S. even though only 6 percent of known coronavirus cases occurred in nursing home facilities thus far.² It therefore makes some sense to consider demographics when observing state death rates. For example, Vermont has the lowest death rate from COVID-19 and has done so despite having one of the oldest average populations in the United States. This makes its success all the more impressive.

Another consideration is timing. Consider, for example, the New York metropolitan area. While cases and subsequent deaths were initially concentrated in this area early on in the second quarter, known cases were widespread across states by June. Fortunately, as noted in a previous post and shown in the chart below, deaths were not nearly as high among the rest of the country compared to the initial toll seen in New York and New Jersey.³

^{Source: The COVID Tracking Project at The Atlantic}

Judging on deaths alone, the data would imply that the New York City metro area (defined as New York and New Jersey in this case), and states similarly along the Northeast Corridor, were ineffective in their policy response as compared to the rest of the country. However, this may be too harsh a judgment; we learned much in the critical months following the initial outbreak in terms of how to treat critical care coronavirus patients. We also learned what policies, like use of non-medical masks and admission protocols to long-term care facilities, did a better job at preventing spread among high-risk populations. Even now, New York and New Jersey have lower daily infection rates and deaths per million relative to the rest of the country, with the severe losses experienced this spring still raw in recent memory.

A third factor to adjust for is different data collection practices. International comparisons are difficult because the U.S. has some of the most reliable (albeit still incomplete) coronavirus data, and this data provides more transparency regarding the impact of the virus than is available in many other heavily populated countries.⁴ Nonetheless, the evidence based on the data we do have – excess deaths since the start of the pandemic through early November – suggest that the U.S. is in line with the European experience, and lower than Latin American regions for which there is reliable data.⁵

Finally, a reason to look more broadly and beyond deaths alone is that other health concerns must also be factored into general population health. Provisional data suggests, for example, that the opioid crisis continues to rise unchecked over the course of the pandemic.⁶ The mental health effects of job loss and furlough also have important implications for general population well-being.⁷ Additionally, whether from outright lockdowns or risk avoidance, the pandemic has had unavoidable negative ramifications on health care administration, leading to a decrease in routine vaccinations and health screenings to detect diseases such as diabetes and cancer.⁸

Q2 GDP Contraction vs. Deaths per Million

^{Source: Q2 2020 Population and Real GDP estimates by state from Bureau of Economic Analysis. Deaths from The COVID Tracking Project at The Atlantic}

It is worth considering some hypotheses on why this might be. The most fruitful of these is that different industry mixes have different levels of vulnerability. The underlying sectors the coronavirus affects—especially travel, tourism, food and accommodations—and their relative importance to economic activity overall can vary significantly from one economy to the next. For instance, the District’s GDP is likely less affected because a significant portion of District jobs are relatively secure -- and relatively easy to do from home -- government or government-related jobs. On the other hand, Hawaii has seen very low deaths per million, but suffered the largest GDP loss by comparison, as much of its economy is dependent upon accommodation and food service. Nevada too saw one of the largest declines in GDP and heavily depends on accommodation and food service. Considering industry mix helps us understand the GDP numbers somewhat better.

Even with a strong understanding of the data, and some appropriate adjustments for factors like demographics or industry mix, it may be difficult to truly understand how policy response affected these numbers. There is no one style of policy response that can be measured on a single dimension. Instead, approach and quality of execution of different combinations of policies to control spread of the virus have varied significantly from one state to the next as well as one country to the next. Like individual U.S. states, many countries opted for broad lockdowns in the spring in effort to curb virus spread. Other countries took a more localized approach, or alternatively, had an existing contact tracing and testing approach that could be implemented quickly. Furthermore, policy is only effective when adhered to, and even then, lowering risk is not the same as eliminating it. These types of policy variations, their timing, and public adherence make measuring relative success in virus containment of any particular combination of policies at a variety of government levels even more difficult.

While several countries could be considered models of success in handling the pandemic, their experiences seem diverse and do not always yield obvious takeaways. South Korea and Japan, in particular, managed to control virus outbreak with less restrictive measures than countries like Vietnam and ultimately Singapore, which made use of lockdowns and government-controlled quarantining. One theory is that experience with previous pathogens may have sufficiently shored up norms of face mask use and social distancing.⁹ However, despite emphasis on mask use and social distancing, European countries and U.S. states have so far been unable to replicate the success.

Overall, the data on country- and state-level COVID-19 experience are diverse and yield fewer obvious policy lessons than one would hope. Nonetheless, there is still work to do. Although third quarter GDP in the U.S. posted significant gains, bouncing back at a record annualized rate, we have yet to make up for the economic ground ceded to the pandemic’s effects. As the coronavirus resurges across much of the globe in the final quarter of the year, many of the countries that had experienced initial success in tamping down cases are now seeing new record flare ups and rising deaths once more.

This time, the phenomenon of pandemic fatigue is trying our collective vigilance.¹⁰ Governments at every level are left in an even more fiscally precarious position, with depleted resources in the face of an unrelenting adversary. A number of countries are hesitant to embrace another round of rigid lockdowns as cooler temperatures drive people indoors, opting for partial lockdowns on sources at highest risk of potential outbreaks like restaurants and bars.¹¹ And yet this time, we are also more equipped with the hard lessons learned in the earlier stages of the pandemic. Trusting in citizens to make responsible decisions, many countries remain hopeful that these lessons are committed to memory throughout the coming winter.¹²

Christina King
Senior Economist