Lit in Review: The impact of epidemics on historical economics, part 1

The most recent Journal of Economic Literature includes four essays on how historical epidemics and pandemics affected major macroeconomic variables. Together, they account for 170-someodd pages, which I will summarize below. Each of them is a detailed literature review on decades of historical research. While they are dense, they are for the most part readable. Part 2 will summarize three articles from The Journal of Economic Perspectives on Macro Policy in the Pandemic.


“Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses” – D. Bloom, M. Kuhn, and K. Prettner The greatest strength of this paper is in critically discussing the various methodologies and theories we have available to even answer the question of how epidemics affect the economy. This is aside from the problem that “narrow economic considerations take inadequate account of the ethical, normative, and political dimensions of decisions that relate to saving lives.”

Generally, micro-based methods that focus on the impacts on individuals and add them up ignore indirect, complex interactions that macro-based methods do capture. For instance, increasing the probability that a 15 year old survives to age 60 by 10 percentage points (roughly equivalent to moving from India to China) increases labor productivity by 9.1 percent. On the other hand, most macro models miss behavioral responses are an insufficiently complex. One problem is that my individual incentive to take preventative actions depends on everyone else. This is something I noticed in my own life – here in Texas where almost no one wore a mask, I had a strong incentive to stay masked myself; when we traveled to any state west of us, almost everyone was masked and surfaces were regularly cleaned, so I felt much less urgency to wear a mask myself. Their conclusion is that diseases will be difficult to eradicate via “private actions alone.” They therefore conclude that some form of government lockdown is likely to be warranted.

Epidemics will have different impacts on the economy depending on a) disease-specific characteristics (how much do they impact working-age population, how much long-term damage do they do, etc) b) population characteristics, particularly how much poverty there is and c) country characteristics, particularly government capacity. Because of this, the same epidemic might have minor impacts in one country, create a poverty trap in a second, impose economic hardship in a third while leaving long-run health mostly untouched, or leaving the economy mostly unaffected but harming health and increasing the incidence of other diseases in a fourth.

“Epidemics, Inequality, and Poverty in Preindustrial and Early Industrial Times” – G. Alfani Most important point: epidemics reduce poverty by either a) changing society/laws/markets in ways that are pro-poor and b) killing more poor people than other socioeconomic groups. If a particular disease leads more to the latter, then there will be very small impacts of disease on poverty. Standard intermediate macroeconomics says that wages come from productivity and the more land or physical capital each worker has, the higher their wages will be. Because of this, the usual story I tell my students about the Black Death that killed off 20-35% of western Europe but left the capital alone is that it raised wages for the poorest and created a large middle class, setting the stage for the Renaissance. Alfani shows Gini coefficients [measures of inequality] falling by 30 percent or more.

But this didn’t happen everywhere. “Government intervention may have suppressed wage bargaining for an extended period of time” in post-Colombus Mexico (Scheidel 2017), or Black-Death-era Spain (Álvarez-Nodal and Prados de la Escosura, 2013), and Poland.

And it didn’t happen always. Repeated epidemics in the 17th century that were as deadly as the Black Death in some communities didn’t seem to reduce inequality at all, either in total or compared to what happened in communities that were unaffected. Why not? One difference is that when epidemics happened more often, governments changed inheritance rules to ensure large amounts of wealth stayed controlled by only a few. He also argues that demand for labor will decrease, and if it decreases as much as the labor supply, wages may not increase at all. On top of these effects, I infer from his paper that later epidemics killed a higher percent of skilled workers than the Black Death did, and that stunted any change in the skill premium. Then there are diseases like cholera that not only hit poor areas hardest, but tended to increase and concentrate the negative aspects of poverty.

Alfani and Murphy (2017): “From the fifteenth century, most plagues were particularly harsh on the poor. This has to do both with the poor’s relatively unhealthy living areas, but also with how they were treated during the epidemics. Once doctors and health authorities noticed that plague mortality tended to be higher in the poorest parts of the city, they began to see the poor themselves as the potential culprits of the spread of the infection.” That attitude is contrasted with efforts to improve sanitation and nutrition to both reduce disease and improve the lives of the poor.

“The 1918 Influenza Pandemic and Its Lessons for COVID-19” – B. Beach, K. Clay, and M. Saavedra “The first lesson from 1918 is that the health effects were large and diffuse” and we may never know just how large because of inaccurate record keeping, “issues that also undermine our ability to quantify the impact of COVID-19.” The second lesson: The Spanish flu epidemic was more likely to kill working-age adults, so it had a major long-run labor supply shock which COVID is unlikely to cause, even though both have caused recessions.

Among the differences between the two are that epidemics were not unusual in 1918 and it happened right at the end of World War I, which had upset many economies already and led to falling productivity for reasons unrelated to the pandemic. We have also documented a wide range of negative health impacts from the 1918 epidemic and are only beginning to document the longer-term impacts of COVID, which will have to be studied in the future.

Interestingly, while there was some attempt at social distancing and closing society down in 1918, it was much shorter-lived and not as severe as what we tried during COVID. While they were “somewhat effective at reducing mortality in 1918, … the extent to which more restrictive [regulations] would have further reduced pandemic mortality remains debated.”

“The Economic Impact of the Black Death” – R. Jedwab, N. Johnson, and M. Koyama There are three primary lenses through which economists have viewed the Black Death. Malthusians argue that smaller populations increase wages (by raising the capital/labor or land/labor ratios) and lower inequality. The “Smithian” view is that larger populations are necessary for a greater division of labor, specialization, and larger markets that support important technologies. The third strand focuses on the role of institutions, both as causes and effects.

“In the very short run [the Black Death] caused a breakdown in markets and economic activity more generally.” In a longer run sense, though, England, Spain, and Italy had very different divergences between wages and productivity. Put another way, England had larger Smithian effects than Spain or Italy and Italy had the largest Malthusian effects. Thus, rather than one model being “right” and the other “wrong,” there is more of a continuum, moderated in part by institutions.

In the years after the plague, people moved out of rural areas to the cities that had been hardest hit because wages had increased more there, which also increased reforestation. In Western Europe, workers’ bargaining power increased, eroding the institution of serfdom. Craft guilds increased dramatically, though their net effect is questionable – decreasing competition through monopoly power but increasing human capital accumulation through apprenticeships. States grew in size and influence, perhaps because there were fewer people to oppose them, with growing taxation accompanying investment in public health and the ability to impose quarantines.

Nightcap

  1. The surprising lexical history of infectious disease Charles McNamara, Commonweal
  2. Immigration and virologic hysteria Michael Agovino, Not Even Past
  3. Against scarcity Marilynne Robinson, NYRB
  4. Can we escape from information overload? Tom Lamont, 1843

A History of Plagues

As COVID-19 continues to spread, fears and extraordinary predictions have also gone viral. While facing a new infectious threat, the unknowns of how new traits of our societies worldwide or of this novel coronavirus impact its spread. Though no two pandemics are equivalent, I thought it best to face this new threat armed with knowledge from past infectious episodes. The best inoculation against a plague of panic is to use evidence gained through billions of deaths, thousands of years, and a few vital breakthroughs to prepare our knowledge of today’s biological crises, social prognosis, and choices.

Below, I address three key questions: First, what precedents do we have for infections with catastrophic potential across societies? Second, what are the greatest killers and how do pandemics compare? Lastly, what are our greatest accomplishments in fighting infectious diseases?

As foundation for understanding how threats like COVID-19 come about and how their hosts fight back, I recommend reading The Red Queen concerning the evolutionary impact and mechanisms of host-disease competition and listening to Sam Harris’ “The Plague Years” podcast with Matt McCarthy from August 2019, which predated COVID-19 but had a strangely prophetic discussion of in-hospital strategies to mitigate drug resistance and their direct relation to evolutionary competition.

  • The Biggest Killers:

Infectious diseases plagued humanity throughout prehistory and history, with a dramatic decrease in the number of infectious disease deaths coming in the past 200 years. In 1900, the leading killers of people were (1) Influenza, (2) Tuberculosis, and (3) Intestinal diseases, whereas now we die from (1) Heart disease, (2) Cancer, and (3) Stroke, all chronic conditions. This graph shows not that humans have vanquished infectious disease as a threat, but that in the never-ending war of evolutionary one-upmanship, we have won battles consistently since 1920 forward. When paired with Jonathan Haidt’s Most Important Graph in the World, this vindicates humanity’s methods of scientific and economic progress toward human flourishing.Death rates

However, if the CDC had earlier data, it would show a huge range of diseases that dwarf wars and famines and dictators as causes of death in the premodern world. If we look to the history of plagues, we are really looking at the history of humanity’s greatest killers.

The sources on the history of pandemics are astonishingly sparse/non-comprehensive. I created the following graphs only by combining evidence and estimates from the WHO, CDC, Wikipedia, Our World in Data, VisualCapitalist, and others (lowest estimates shown where ranges were presented) for both major historic pandemics and for ongoing communicable disease threats. This is not a complete dataset, and I will continue to add to it, but it shows representative death counts from across major infectious disease episodes, as well as the death rate per year based on world population estimates. See the end of this post for the full underlying data. First, the top 12 “plagues” in history:

Capture disease top 12

 

Note: blue=min, orange=max across the sources I examined. For ongoing diseases with year-by-year WHO evidence, like tuberculosis, measles, and cholera, I grouped mortality in 5-year spans (except AIDS, which does not have good estimates from the 1980s-90s, so I reported based on total estimated deaths).

Now, let’s look at the plagues that were lowest on my list (number 55-66). Again, my list was not comprehensive, but this should provide context for COVID-19:

Capture covid

As we can see, the 11,400 people who have died from COVID-19 recently passed Ebola to take the 61st (out of 66) place on our list of plagues. Note again that several ongoing diseases were recorded in 5-year increments, and COVID-19 still comes in under the death rates for cholera. Even more notably, it has 0.015% as many victims as the plague in the 14th Century,

  • In Context of Current Infectious Diseases:

For recent/ongoing diseases, it is easier to compare year-by-year data. Adding UNAIDS to our sources, we found the following rates of death across some of the leading infectious causes of death. Again, this is not comprehensive, but helps put COVID-19 (the small red dot, so far in the first 3 months of 2020) in context:

Capture diseases by year

Note: darker segments of lines are my own estimates; full data at bottom of the post. I did not include influenza due to the lack of good sources on a year-by-year basis, but a Lancet article found that 291,000-645,000 deaths from influenza in a year is predictable based on data from 1999-2015.

None of this is to say that COVID-19 is not a major threat to human health globally–it is, and precautions could save lives. However, it should show us that there are major threats to human health globally all the time, that we must continue to fight. These trendlines tend to be going the right direction, but our war for survival has many foes, and will have more emerge in the future, and we should expend our resources in fighting them rationally based on the benefits to human health, not panic or headlines.

  • The Eradication List:

As we think about the way to address COVID-19, we should keep in mind that this fight against infectious disease builds upon work so amazing that most internet junkies approach new infectious diseases with fear of the unknown, rather than tired acceptance that most humans succumb to them. That is a recent innovation in the human experience, and the strategies used to fight other diseases can inform our work now to reduce human suffering.

While influenzas may be impossible to eradicate (in part due to an evolved strategy of constantly changing antigens), I wanted to direct everyone to an ever-growing monument to human achievement, the Eradication List. While humans have eradicated only a few infectious diseases, the amazing thing is that we can discuss which diseases may in fact disappear as threats through the work of scientists.

On that happy note, I leave you here. More History of Plagues to come, in Volume 2: Vectors, Vaccines, and Virulence!

Disease Start Year End Year Death Toll (low) Death Toll (high) Deaths per 100,000 people per year (global)
Antonine Plague 165 180 5,000,000 5,000,000 164.5
Plague of Justinian 541 542 25,000,000 100,000,000 6,250.0
Japanese Smallpox Epidemic 735 737 1,000,000 1,000,000 158.7
Bubonic Plague 1347 1351 75,000,000 200,000,000 4,166.7
Smallpox (Central and South America) 1520 1591 56,000,000 56,000,000 172.8
Cocoliztli (Mexico) 1545 1545 12,000,000 15,000,000 2,666.7
Cocoliztli resurgence (Mexico) 1576 1576 2,000,000 2,000,000 444.4
17th Century Plagues 1600 1699 3,000,000 3,000,000 6.0
18th Century Plagues 1700 1799 600,000 600,000 1.0
New World Measles 1700 1799 2,000,000 2,000,000 3.3
Smallpox (North America) 1763 1782 400,000 500,000 2.6
Cholera Pandemic (India, 1817-60) 1817 1860 15,000,000 15,000,000 34.1
Cholera Pandemic (International, 1824-37) 1824 1837 305,000 305,000 2.2
Great Plains Smallpox 1837 1837 17,200 17,200 1.7
Cholera Pandemic (International, 1846-60) 1846 1860 1,488,000 1,488,000 8.3
Hawaiian Plagues 1848 1849 40,000 40,000 1.7
Yellow Fever 1850 1899 100,000 150,000 0.2
The Third Plague (Bubonic) 1855 1855 12,000,000 12,000,000 1,000.0
Cholera Pandemic (International, 1863-75) 1863 1875 170,000 170,000 1.1
Indian Smallpox 1868 1907 4,700,000 4,700,000 9.8
Franco-Prussian Smallpox 1870 1875 500,000 500,000 6.9
Cholera Pandemic (International, 1881-96) 1881 1896 846,000 846,000 4.4
Russian Flu 1889 1890 1,000,000 1,000,000 41.7
Cholera Pandemic (India and Russia) 1899 1923 1,300,000 1,300,000 3.3
Cholera Pandemic (Philippenes) 1902 1904 200,000 200,000 4.2
Spanish Flu 1918 1919 40,000,000 100,000,000 1,250.0
Cholera (International, 1950-54) 1950 1954 316,201 316,201 2.4
Cholera (International, 1955-59) 1955 1959 186,055 186,055 1.3
Asian Flu 1957 1958 1,100,000 1,100,000 19.1
Cholera (International, 1960-64) 1960 1964 110,449 110,449 0.7
Cholera (International, 1965-69) 1965 1969 22,244 22,244 0.1
Hong Kong Flu 1968 1970 1,000,000 1,000,000 9.4
Cholera (International, 1970-75) 1970 1974 62,053 62,053 0.3
Cholera (International, 1975-79) 1975 1979 20,038 20,038 0.1
Cholera (International, 1980-84) 1980 1984 12,714 12,714 0.1
AIDS 1981 2020 25,000,000 35,000,000 13.8
Measles (International, 1985) 1985 1989 4,800,000 4,800,000 19.7
Cholera (International, 1985-89) 1985 1989 15,655 15,655 0.1
Measles (International, 1990-94) 1990 1994 2,900,000 2,900,000 10.9
Cholera (International, 1990-94) 1990 1994 47,829 47,829 0.2
Malaria (International, 1990-94) 1990 1994 3,549,921 3,549,921 13.3
Measles (International, 1995-99) 1995 1999 2,400,000 2,400,000 8.4
Cholera (International, 1995-99) 1995 1999 37,887 37,887 0.1
Malaria (International, 1995-99) 1995 1999 3,987,145 3,987,145 13.9
Measles (International, 2000-04) 2000 2004 2,300,000 2,300,000 7.5
Malaria (International, 2000-04) 2000 2004 4,516,664 4,516,664 14.7
Tuberculosis (International, 2000-04) 2000 2004 7,890,000 8,890,000 25.7
Cholera (International, 2000-04) 2000 2004 16,969 16,969 0.1
SARS 2002 2003 770 770 0.0
Measles (International, 2005-09) 2005 2009 1,300,000 1,300,000 4.0
Malaria (International, 2005-09) 2005 2009 4,438,106 4,438,106 13.6
Tuberculosis (International, 2005-09) 2005 2009 7,210,000 8,010,000 22.0
Cholera (International, 2005-09) 2005 2009 22,694 22,694 0.1
Swine Flu 2009 2010 200,000 500,000 1.5
Measles (International, 2010-14) 2010 2014 700,000 700,000 2.0
Malaria (International, 2010-14) 2010 2014 3,674,781 3,674,781 10.6
Tuberculosis (International, 2010-14) 2010 2014 6,480,000 7,250,000 18.6
Cholera (International, 2010-14) 2010 2014 22,691 22,691 0.1
MERS 2012 2020 850 850 0.0
Ebola 2014 2016 11,300 11,300 0.1
Malaria (International, 2015-17) 2015 2017 1,907,872 1,907,872 8.6
Tuberculosis (International, 2015-18) 2015 2018 4,800,000 5,440,000 16.3
Cholera (International, 2015-16) 2015 2016 3,724 3,724 0.0
Measles (International, 2019) 2019 2019 140,000 140,000 1.8
COVID-19 2019 2020 11,400 11,400 0.1

 

Year Malaria Cholera Measles Tuberculosis Meningitis HIV/AIDS COVID-19
1990 672,518 2,487 670,000 1,903 310,000
1991 692,990 19,302 550,000 1,777 360,000
1992 711,535 8,214 700,000 2,482 440,000
1993 729,735 6,761 540,000 1,986 540,000
1994 743,143 10,750 540,000 3,335 620,000
1995 761,617 5,045 400,000 4,787 720,000
1996 777,012 6,418 510,000 3,325 870,000
1997 797,091 6,371 420,000 5,254 1,060,000
1998 816,733 10,832 560,000 4,929 1,210,000
1999 834,692 9,221 550,000 2,705 1,390,000
2000 851,785 5,269 555,000 1,700,000 4,298 1,540,000
2001 885,057 2,897 550,000 1,680,000 6,398 1,680,000
2002 911,230 4,564 415,000 1,710,000 6,122 1,820,000
2003 934,048 1,894 490,000 1,670,000 7,441 1,965,000
2004 934,544 2,345 370,000 1,610,000 6,428 2,003,000
2005 927,109 2,272 375,000 1,590,000 6,671 2,000,000
2006 909,899 6,300 240,000 1,550,000 4,720 1,880,000
2007 895,528 4,033 170,000 1,520,000 7,028 1,740,000
2008 874,087 5,143 180,000 1,480,000 4,363 1,630,000
2009 831,483 4,946 190,000 1,450,000 3,187 1,530,000
2010 788,442 7,543 170,000 1,420,000 2,198 1,460,000
2011 755,544 7,781 200,000 1,400,000 3,726 1,400,000
2012 725,676 3,034 150,000 1,370,000 3,926 1,340,000
2013 710,114 2,102 160,000 1,350,000 3,453 1,290,000
2014 695,005 2,231 120,000 1,340,000 2,992 1,240,000
2015 662,164 1,304 150,000 1,310,000 1,190,000
2016 625,883 2,420 90,000 1,290,000 1,170,000
2017 619,825 100,000 1,270,000 1,150,000
2018 1,240,000
2019
2020 16,514