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Why Do COVID-19 Death Rates Differ Wildly From Place to Place?

As the coronavirus pandemic rages on, the wide chasm between countries’ death rates has become soberingly clear.

虽然平均Covid-19百万人死亡人数— as of January 20 — stands at 266 globally, many countries are well below or above that number. In South Korea, the number is 25 deaths per million, and in Australia, it’s 36. Yet in Italy, they’re experiencing 1,384 deaths per million people, in the United Kingdom, it’s 1,377, and in the United States, it’s 1,277. Similar differences are apparent in每10万人在美国各国死亡。


Sergio Rebelo是凯洛格的财务教授,Martin Eichenbaum那a professor of economics at Northwestern, andMathias Trabandt.FreieUniversität柏林决定看他们是否可以隔离任何有助于解释为什么一些地区失去的变量比其他地区更加遗忘。他们探讨了人均GDP的各种措施,每1000人的医生份额和65岁以上人口的份额 - 寻找与Covid-19死亡率有着强烈相关的任何措施。


The team had a hunch going in. They expected that the percentage of the population over 65 in a given region would best predict its number of COVID-19 deaths per million. They also suspected that GDP per capita would be a factor, and that high-income regions would have lower mortality rates. Other variables they thought might be in play included the share of doctors per 1,000 people, the size of the average household, the proportion of urban to rural areas and perhaps even the average temperature, since colder climates may be more conducive to the virus’s spread.

As it turned out, none of these variables emerged as good predictors of COVID-19 deaths. But another variable did: A country’s level of pre-COVID income inequality was by far the best predictor of the COVID-19 death rate. The same finding emerged when the researchers studied variations in COVID-19 death rates across U.S. states: Higher death rates tend to occur in U.S. states with higher pre-COVID income inequality.

“That is a striking, unexpected finding,” Rebelo says. “All those other variables didn’t really matter when pre-COVID income inequality was included.”

Once they identified this key predictor, the researchers decided to explore the role of income inequality in COVID-19 death outcomes.


Modeling a Pandemic Economy


“This task proved more difficult than we expected,” Rebelo says. Conventional business-cycle models use certain assumptions about how the economy behaves — and the COVID-19 economy turned those assumptions on their head.

Typically, consumption among high-income people is smooth over time. But research looking at 2020 consumption and employment patterns inPortugal英国。那Spainand the U.S.has documented a different response: Consumption among high-income people dropped dramatically, even as this segment’s income stayed steadier than that of lower-income groups. On the other hand, poorer people experienced a bigger blow to their income but cut their spending less dramatically.

“Usually, you’d expect to see relatively smooth consumption for high-income people,” Rebelo explains. “Here, it is this group that has the most volatile consumption.”



Their model makes the realistic assumption that higher-income people tend to work in sectors that produce tradable goods, meaning goods that can be consumed without geographic constraints — such as anything sold online. Lower-income people tend to work in sectors that produce non-tradable goods, meaning goods that have to be locally consumed — such as eating in a restaurant, staying in a hotel or going to a live entertainment show.

Motivated by empirical evidence, the researchers assume that working or consuming non-tradable goods, like restaurant meals, exposes people to a higher risk of COVID-19 infection. The demand for these types of goods fell disproportionately as people tried to reduce their exposure to the virus. This drop in demand created massive job losses for low-income people.

A Double Blow

The researchers’ model reveals two central ways in which lower-income people suffered more during the pandemic.



Here, the model offers some strong implications: It shows that the death rate among low-income Americans would have been a staggering 30% lower if this group had the same COVID-19 case-fatality rate as high-income people. The researchers surmise that lower-income people presumably came into the pandemic with more preexisting health conditions that made them much more predisposed to succumb to the virus than those with more money.

“The model implies,” the authors write, “that preexisting inequality is a powerful force generating inequality in death.”


The researchers use the model to compute what the death rate would have looked like in late 2020 if the U.S. hadn’t used any containment policies, such as closing some businesses. If such policies had never gone into effect, the model finds, mortality rates would have been roughly 31% higher for low-income people and 27% higher for those earning more. At the same time, these policies disproportionately reduced the employment and earnings of low-income workers, heightening income inequality.

“We had to use blunt containment policies to reduce the death toll,” Rebelo says. “But that containment exacerbated how much the poor suffered in terms of their income and employment.”

The government tried to alleviate this suffering by sending stimulus checks to low-income people. These checks increased the demand for non-tradable goods and helped improve the employment prospects for people in these sectors, without significantly worsening the mortality rate for anyone.

Closing Yawning Gaps in Health Care and Income


“这不会是我们将面临的最后一个流行病,”Rebelo说,指的是预测到来的研究机构rise in zoonotic infections。“We should be getting ready for the next epidemic, and part of getting ready is to have a more equitable health care system.”


Martin Eichenbaum

Charles Moskos Professor of Economics at Northwestern University

Martin Eichenbaum教授是西北大学Weinberg艺术与科学学院经济学院的Charles Moskos教授,是国际宏观经济学(CIM)中心的联合主任。

    Sergio Rebelo

    MUFG Bank Distinguished Professor of International Finance at Kellogg School of Management

    Sergio Rebelo is the MUFG Bank Distinguished Professor of International Finance at the Kellogg School of Management, where he has served as Chair of the Finance Department. Professor Rebelo does research on macroeconomics and international finance. He has studied the causes of business cycles, the impact of economic policy on economic growth, and the sources of exchange rate fluctuations.

      Mathias Trabandt.

      Research Fellow at Freie Universität Berlin

      Mathias Trabandt.joined the Department of Macroeconomics at Freie Universität Berlin as a Research Fellow in April 2017. His research focuses on macroeconomics, monetary economics, public economics, labour economics, international macroeconomics, financial frictions, and applied econometrics.

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