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This paper analyses the costs and benefits of lockdown policies in the face of COVID-19. What matters for people is the quality and length of lives and one should measure costs and benefits in terms of those things. That raises difficulties in measurement, particularly in valuing potential lives saved. We draw upon guidelines used in the UK for public health decisions, as well as other measures, which allow a comparison between health effects and other economic effects. We look at evidence on the effectiveness of past severe restrictions applied in European countries, focusing on the evidence from the UK. The paper considers policy options for the degree to which restrictions are eased. There is a need to normalise how we view COVID because its costs and risks are comparable to other health problems (such as cancer, heart problems, diabetes) where governments have made resource decisions for decades. The lockdown is a public health policy and we have valued its impact using the tools that guide health care decisions in the UK public health system. The evidence suggests that the costs of continuing severe restrictions in the UK are large relative to likely benefits so that a substantial easing in general restrictions in favour of more targeted measures is warranted.
R60 NatioNal iNstitute ecoNomic Review No. 253 august 2020
*Professor of Economics, Imperial College, London. E-mail: **RES Consortium. E-mail: ***The
School of Medicine, University of Manchester. E-mail: The authors thank two anonymous referees and Jagjit Chadha for
many helpful comments on an earlier draft.
David Miles,* Mike Stedman** and Adrian Heald***
This paper analyses the costs and benefits of lockdown policies in the face of COVID-19. What matters for people is
the quality and length of lives and one should measure costs and benefits in terms of those things. That raises difficulties
in measurement, particularly in valuing potential lives saved. We draw upon guidelines used in the UK for public health
decisions, as well as other measures, which allow a comparison between health effects and other economic effects. We
look at evidence on the effectiveness of past severe restrictions applied in European countries, focusing on the evidence
from the UK. The paper considers policy options for the degree to which restrictions are eased. There is a need to
normalise how we view COVID because its costs and risks are comparable to other health problems (such as cancer, heart
problems, diabetes) where governments have made resource decisions for decades. The lockdown is a public health policy
and we have valued its impact using the tools that guide health care decisions in the UK public health system. The evidence
suggests that the costs of continuing severe restrictions in the UK are large relative to likely benefits so that a substantial
easing in general restrictions in favour of more targeted measures is warranted.
Keywords: value of lives saved, cost benefit analysis, COVID-19.
JEL codes: I18; D61; E65.
In early March 2020 it seemed that the COVID-19 virus
in the United Kingdom was spreading exponentially with
no clear sign of imminent slowing; the fatality rate was
unknown and the ability of the National Health Service
(NHS) to deal with rapidly rising numbers of seriously
ill people was unclear. Estimates made at that time put
the likely level of UK deaths if there was no change in
behaviour at 500,000 (Ferguson et al., 2020). Based on
that and other assessments, the UK government followed
the example of several other European countries in
introducing severe restrictions on individual movement
that were backed by legislation and actively policed. The
key message was to stay at home; this was a lockdown.
This served both to slow the spread of the virus and
to signal in a very clear way that people needed to
change behaviours quickly; but it also generated great
costs. The extent to which the lockdown contributed
to a subsequent slowing in the rate of new infections
and deaths is not easy to estimate precisely, but that it
did bring it down signicantly seems clear. But whether
keeping such tight restrictions in place for three months
(until restrictions began to be eased substantially at the
end of June) was warranted, given the large costs, is
very far from clear. This paper summarises the evidence
on this in an assessment of costs and benets of severe
restrictions – ‘lockdowns’. We do so to inform the
decisions on how restrictions in the UK should be lifted.
What we mean by a lockdown is wide ranging government
restrictions on activity that dramatically reduce mobility
and the ability of people to work. A lockdown obviously
goes beyond guidance and it also goes further than
closing just schools, bars, sports venues and restaurants.
It includes general restrictions on movement and involves
rules that people should stay at home except in exceptional
circumstances. Such a lockdown was introduced in the
UK in the second half of March 2020 and only began to
be eased some three months later.
While it is clear that the cost of the lockdown in the UK
has been large, just how great it is will not be known
for many years. This cost – as well as the benets of the
three-month lockdown – should be measured in terms of
human welfare in the form of impacts upon the length
and quality of lives. Such measurement is profoundly
difcult. Yet measurement of the costs of restrictions
© National Institute of Economic and Social Research, 2020.
DOI: 10.1017/nie.2020.30
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The cost of severe restrictions plausibly rises more than
in proportion to the length of a lockdown. Two months
of missed treatments for cancer, of company closures, of
postponed screening for serious health conditions, of lessons
missed at schools and universities, of many people living in
very stressful situations is likely more than twice as bad as
one month. In contrast the benets of maintaining a severe
needs to be weighed against benets of different levels
of restrictions to assess what is the best policy now. A
signicant part of costs and benets is in potential lives
lost and saved under different policies. We draw upon
guidelines used in the UK for public health decisions,
and other measures, to allow a comparison between
health effects and other economic effects.
Figure 1. Number of reported cases (7-day rolling average) per million national population
Source: Our World in Data COVID-19 dataset. Our World in Data
is a collaborative effort between researchers at the University of
Oxford, who are the scientific editors of the website content, and
the non-profit organisation Global Change Data Lab, which publishes
and maintains the website and the data tools https://ourworldindata.
org/coronavirus. Downloaded 1/7/2020.
23/02 01/03 08 /03 15 /03 22/03 29/03 05 /04 12 /04 19/04 26 /04 03 /05 10/05 17/05 24 /05 31 /05 07/06 14/06 21 /06 28 /06
Reported cases ro lling previous 7 day average /mil lion populati on
Belg i um De n mark F ran c e Germa ny
Italy Netherlands Norway P o rtu g al
Spa in Sweden United Ki ngdom
Spa in
Be lgium
United Kingdom
F ra nce
Ge rm any
Cumulative up to 30/6/2020
R62 NatioNal iNstitute ecoNomic Review No. 253 august 2020
set of restrictions – the lockdown – may be diminishing
(see for example Bongaerts et al., 2020). Decisions on how
to ease restrictions are therefore of immediate signicance.
In this article we aim to calibrate what the costs and benets
of sustained severe restrictions might be and what that
implies about the policy that should now be followed. We
look at evidence from many countries, focusing particularly
on European countries with similar levels of income and of
healthcare resources. We then draw out what this implies
for policy in the UK. We nd that the costs of a three-
month lockdown are likely to have become high relative
to benets so that a continuation of severe restrictions is
unlikely to be warranted.
Figure 2. Number of deaths reported associated with COVID-19 cases (7-day rolling average) per million of the national
Source: Our World in Data COVID-19 dataset. Downloaded
19/02 26/02 04/03 11/03 18/03 25/03 01/04 08/04 15/04 22/04 29/04 06/05 13/05 20/05 27/05 03/06 10/06 17/06 24/06
Reported deaths rolling previous 7 day average /million
Be lgium Denmark F ra n ce Ge rm an y
Italy Netherlands Nor way Portugal
Spa in Sweden United Kingdom
Be lgium
United Kingdom
Spa in
F ra nce
Ge rm any
Cumulative up to 30/6/2020
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Figure 3. Excess deaths in weeks 8–21 2020(a)
Source: The Human Mortality Database, Department of Demography
at the University of California, Max Planck Institute for Demographic
Research, Center on the Economics and Development of Aging (CEA), Downloaded 9/6/2020.
Note: (a) Difference from average in same week in previous 3 years
(2017/2018/2019) and shown as % of average.
Weekly excess deaths as % of expected
Week Ending
Be lgium Denmark F ra n ce Ge rm an y
Italy Netherlands Norway Portugal
Spa in Sweden Great Britain
23/02 01/03 08/03 15/03 22/03 29/03 05/04 12/04 19/04 26/04 03/05 10/05 17/05 24/05 31/05
Section 1 summarises evidence on recorded cases of the virus,
deaths, and excess deaths – all in the context of restrictions
adopted. Section 2 turns to issues of interpretation,
focusing on how much of the slowdown in the spread of
the virus, and of deaths attributed to it, may have been due
to lockdowns and how much to the curves turning down
independently of severe restrictions. Section 3 summarises
evidence from countries with different policies and draws
some conclusions on the scale of benets of the lockdown.
Section 4 focuses on the costs of restrictive policies to slow
the spread of the infections; section 5 brings costs and
benets together and section 6 considers policy options for
coming out of lockdown. Conclusions are drawn in a
nal section.
Spa in
Great Britain
Be lgium
F ra nce
Ge rm any
Cumulative % up to 3/06/20
42,539, 21%
58,393, 20%
52,479, 18%
7,057, 13%
7,831, 11%
23,649, 8%
2,515, 6%
1,642, 3%
–3,374, 1%
–779, –3%
–766, –4%
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1. Recorded cases, deaths and excess
Figure 1 shows the number of new positive cases tested
for in several European countries between early 2020 and
the end of June. Figure 2 shows cumulative deaths where
there is evidence that the deceased had the COVID-19
infection. Figure 3 shows a measure of excess deaths –
that is total deaths in excess of the average of such deaths
over the comparable months in previous years. Figure
4 shows a measure of the stringency of government
restrictions introduced in European countries to counter
the spread of the virus.
Several measurement issues make it difcult to draw
conclusions from these data with high condence. The
number of tests undertaken (relative to population) varies
within countries over time and also between countries and
this will clearly affect the numbers who tested positive for
the virus and the prevalence of untested cases. While data
on the numbers of deaths is reliable, ascribing death to
the virus is not – even if one could accurately measure the
numbers who have died with the virus the prevalence of
co-morbidities means that drawing conclusions about the
scale of deaths caused by the virus is more problematic.
Focusing on ‘excess deaths’ is a different way of assessing
the impact of the virus but since some deaths will be a
result of restrictions, rather than infections, it is also an
imperfect measure.
Despite measurement issues, certain conclusions from
the data in gures 1–4 seem robust.
(i) The spread of the infection after the rst few recorded
cases within European countries was extremely rapid
Government response stringency index ((0 to 100, 100 = strictest))
United Kingdom Portugal F ra nce Denmark
Netherlands Germ any B e lgiu m Sweden
Italy Spain No rway
Figure 4. Date of implementation and relaxation of national responses in selected countries
Source: Blavatnik School of Government, University of Oxford,
Note: The series is the COVID-19 Government Response Stringency Index which is a composite measure based on nine response indicators including
school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response).
22/01 05/02 19/02 04/03 18/03 01/04 15/04 29/04 13/05 27/05 10/06 24/06
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and consistent with initial reproduction numbers (the
average number of people infected by each person with
the virus) far in excess of 1 and quite likely close to 3.
(ii) In late February or early March 2020 many European
countries brought in severe restrictions on movement,
meaning that the majority of populations stayed
home and numbers able to work fell dramatically.
Such restrictions came in earlier in countries where the
numbers recorded with the infection had risen sharply
earliest – most notably Italy. There were also places
where restrictions were much less severe than in most
countries – most notably in Sweden.
(iii) New measured cases of the infection and of deaths
ascribed to the virus were signicantly lower within
a few weeks of restrictions being introduced. There
is some evidence of a attening in new cases ahead of
severe restrictions being introduced.
(iv) The slowing in new infections and in deaths has been
marked in all countries during late March and into
April 2020, though the severity of restrictions and the
timing of those restrictions differs.
2. How much was due to lockdowns?
What is not clear from data on measured deaths of
people who are recorded as having the virus, on tested
new cases of the infection and on excess deaths is the
precise extent to which they have fallen because of (in
many cases severe) restrictions on the population. There
are at least three reasons why new infections and deaths
could have fallen, perhaps sharply, even with much more
limited government restrictions short of a lockdown: (i)
individuals would have altered their behaviour (washing
hands more frequently, avoiding crowded spaces, staying
home if you have symptoms) with no legal restrictions
on ability to leave the home and with much more limited
disruption to life; (ii) a degree of immunity may have built
up by the time severe restrictions were introduced because
the infection may have spread quite widely and largely
unnoticed with the asymptomatic a large fraction of the
infected. (iii) a signicant proportion of the population
may have been effectively immune from the virus when
lockdowns started not just because of recovery from past
infections that conferred a degree of immunity but also
because some proportion of the population was never
susceptible. All three factors may have played some role,
and all would mean that deaths and new infections would
have slowed, at least to some extent, in the absence of
the sort of severe government restrictions introduced in
the UK where people were told to stay at home except in
exceptional circumstances.
These three factors are not mutually exclusive and
there is some (less than conclusive and often disputed)
evidence that each of them may have played some role.
An Oxford University research team used death data to
estimate the proportion of the population who might
have built up some form of immunity before the UK
lockdown was introduced in mid-March. They put that
fraction at around 60 per cent (Lourenço et al., (2020).
Stedman et al. (2020) used data on differences in the
spread of the infection across English regions to assess
how many might have been infected and put that fraction
at similarly high levels. Dimdore-Miles and Miles (2020)
tted a SIR (Susceptible-Infected-Recovered) model to
data on new cases of infections across several countries
and estimated that the numbers who might have been
infected with no (or few) symptoms was likely to be
ten times or more as large as those who had symptoms
and were more likely to have been tested up to late
April 2020. This is at the high end of estimates of the
asymptomatic as a proportion of the infected.
Wieland (2020) modelled the spread of the infection
across Germany and concluded that infections were
past their peak and starting to decline ahead of the
introduction of government restrictions there. The
results were summarised thus: “In a large majority
of German counties, the epidemic curve has attened
before the social ban was established (March 23). In
a minority of counties, the peak was already exceeded
before school closures.
Friston et al. (2020) concluded that the numbers of
people not susceptible to the COVID-19 virus were
substantial before lockdowns were introduced and that
the virus may have been burning itself out.
Despite these pieces of evidence, direct measures
of how many people in the wider population have
been infected by COVID-19, and the extent to which
immunity from the virus has been built up by that
route, are not high. Most estimates based on limited
testing of a random sample of the population for
antibodies put the level of those who have had the
infection in European countries where the virus has
spread most rapidly at 5–10 per cent, though in some
areas within countries it is still high enough to have
had a signicant impact on R.1
While there are reasons to believe that the spread of the
infection may have slowed short of a lockdown which
kept most people at home, it remains highly likely that
this level of restriction did bring the spread down faster
than it otherwise would.
R66 NatioNal iNstitute ecoNomic Review No. 253 august 2020
3. The evidence from countries with
different policies: Sweden vs UK
In contrast to many other European countries the
Swedish strategy has been one of adopting much less
restrictive measures that are far short of a lockdown (see
gure 4). In terms of the health impacts, there is mixed
evidence over how different they are compared to those
in countries that adopted lockdown policies. Cases of
the infection – relative to population - have been higher
than in most other European countries (gure 1); deaths
have not been higher (gure 2). Excess deaths in Sweden
up to end-June have been one third the levels seen in
Italy, Spain and Great Britain.
Interpretation of the raw numbers is therefore not
straightforward. The study by Born et al. (2020)
estimates how the infection might have spread if Sweden
had imposed a lockdown like many other European
countries. They nd essentially no difference in the likely
path of infections.
But the study by Conyon et al. (2020), which compares
deaths in Sweden with those in Norway and Denmark,
nds strong evidence that the looser restrictions in
Sweden compared with its close neighbours led to
signicantly more people dying. The comparison with
immediate neighbours is telling because they are more
similar in terms of climate, health care systems and
density of population than most other countries.
UK data show a signicantly higher cumulative death
rate than Sweden (gure 2 above); Financial Times
estimates, as well as those shown in gure 3 above, put
excess deaths relative to population in the UK at more
than three times the Swedish level by end-June 2020. UK
density of population, age structure and distribution of
income is different from Sweden (and from the European
average) which muddies direct comparison. Figure 3
shows that Sweden sits near the middle of the pack for
European countries. While cumulative death rates for
Sweden remain markedly higher than in its immediate
neighbours, they are not very different from European
averages. Cases of new infections in Sweden during June
2020 did, however, move higher, unlike in most other
European countries where they continued to decline
even as lockdowns have begun to be eased.
There is some, limited, evidence that looser restrictions
in Sweden have meant a lower hit to economic activity.
A recent study by Mackie et al. (2020) notes the stark
contrast between the relative health experience of Sweden
with its immediate neighbours in Denmark and Norway
(much worse) and with the UK (signicantly better). That
report also estimates that the economic performance has
up to June 2020 been somewhat better than Denmark and
Norway and markedly better than the UK. They estimate
that the Swedish economy has benetted by around 6.7
per cent of GDP relative to following the UK prole and
by 2 per cent and 1.4 per cent relative to the Danish and
Norwegian proles respectively.
Krueger, Uhlig and Xie (2020) assess how economies
might have evolved during the pandemic with few
government restrictions. They conclude:
“One may view our results as the “Swedish” outcome:
Sweden has largely avoided government restrictions on
economic activity, allowing people to make their own
choices. These private incentives and well-functioning
labour-and social-insurance markets, we submit, may
solve the COVID19-spread on their own, mitigating
the decline in economic activity.
How effective was the lockdown in the UK?
There is contradictory evidence on the effectiveness of
the three-month lockdown strategy in the UK. It is hard
to be sure of the precise scale of the health benets: they
range from very few lives saved to a high of perhaps
450,000 lives saved (that is the difference between the
500,000 or so deaths projected by Ferguson et al, 2020,
on the basis of no change in behaviour and the 50,000
or so deaths that might have resulted in the UK by early
June 2020). Figures for lives saved in the UK at the
extreme ends of that spectrum (near zero or as high as
450,000) seem implausible.
The fall in deaths soon after lockdowns is so clear
across many countries that it is very unlikely that those
severe restrictions had no signicant impact at all on
lives lost. Deb et al. (2020) nd signicant impacts of
severe restrictions on new cases of the infection. The
chances that such a sharp slowing in new infections
should consistently have happened across countries
some weeks after the most severe restrictions were
introduced, yet not be partly a result of them, seems
But there are also reasons to be sceptical of gures at
the high end of that scale which puts the saving of lives
from the lockdown in the UK at several hundreds of
the low cost of effective forms of behavioural change
(washing hands, avoiding crowds, staying home if
you have symptoms) adopted by individuals makes
miles, stedmaN aNd Heald liviNg witH covid-19: balaNciNg costs agaiNst beNefits iN tHe face of tHe viRus R67
it rather unlikely that in the UK there would have
been 500,000 deaths even with no government
restrictions; the 500,000 gure from Ferguson et al.
(2020) was based on an assumption of no change at
all in individual behaviour;
the evidence of a turn in the curve before lockdowns
are likely to have had much effect is much disputed
but not easily dismissed;
even if lockdowns stopped such huge numbers of deaths
over the period March-June 2020, they may not have
permanently stopped them happening if wider immunity
has not signicantly risen so that any substantial easing
of restrictions will just bring them back;
in the UK deaths were concentrated in care homes
for the elderly (where around 30 per cent of deaths
have occurred) and have been disproportionately
among older people so a blanket lockdown (‘don’t
leave home’) may have been inefcient – it generated
substantial costs (see below) and may have yielded
limited health benets over and above what might
have been achieved with measures which focused on
groups most at risk.
4. The costs of lockdowns
Measurement issues for the costs of lockdowns are
different from those that make the assessment of the
benets difcult, but they are also signicant. While
some of the costs are fairly clear and immediate (GDP
is lower, the scal decit is higher, unemployment has
risen a great deal), even here it is not straightforward to
judge their true scale of cost because of two issues: (a)
how permanent will the losses be? (b) how great would
such problems have been even with no lockdown?
Costs which will come further down the road because
of disruption to healthcare and to education are harder
again to measure relative to the more immediate effects
on economic production and employment.
A great deal of evidence is already emerging on the
(narrow) economic impacts of restrictions. Estimates
made by Deb et al. (2020) to identify the particular
effect of restrictive policies (lockdown) suggest that they
reduced economic activity by 15 per cent in the 30 days
after they were adopted. They nd that stay-at-home
requirements and workplace closures are the costliest
in economic terms. Preliminary estimates from the UK
Ofce for National Statistics showed a slightly more
than 20 per cent fall in GDP in April 2020, the rst full
month after the lockdown. Bonadio et al. (2020) put the
impact on output and incomes (i.e. GDP) of policies to
counter the spread of the infection on GDP averaged
across 64 countries even higher, at around 30 per cent.
Aum et al. (2020) estimate that around one half of all job
losses in the UK and US can be attributed to lockdowns.
Coibion et al. (2020 a) estimate that there were 20 million
lost jobs in the US by 8 April triggered overwhelmingly
by government restrictions, far more than jobs lost
over the entire Great Recession. Furthermore, many
of those losing jobs were not actively looking to nd
new ones. Participation in the labour force declined by
7 percentage points, an unparalleled fall that dwarfs the
three percentage point cumulative decline that occurred
from 2008 to 2016 after the nancial crisis. In a related
paper the same authors undertake surveys of behaviour
and economic outcomes across US regions with different
degrees of restrictions. They conclude:
“We observe a dramatic decline in employment and
consumer spending as well as a bleak outlook for
the next few years. Our estimates suggest that this
economic catastrophe can be largely accounted by
lockdowns. (Coibion, 2020b)
Around 9 million people (one quarter of the workforce)
have been furloughed in the UK and paid largely by the
government. The OBR reported in May that UK net
government debt rose by over 17 per cent of GDP on a
year earlier to around 100 per cent in April. Extra debt
issuance is likely to be at least 10 per cent of GDP in 2020;
the stock of debt will be well above 100 per cent of GDP
by the end of 2020 and likely to go higher in 2021.
For the UK, the Ofce for Budget Responsibility (OBR)
and the Bank of England estimate that GDP is likely to
have fallen by between 25 per cent and 35 per cent in
2020Q2 and by 10–15 per cent in 2020 relative to 2019;
unemployment may rise to around 10 per cent. The OBR
central estimate, and the illustrative scenario for the
Bank of England made in May 2020, is that in 2020 UK
GDP will be around 13–14 per cent lower than in 2019.
The June Organisation for Economic Cooperation and
Development forecast is for an 11.5 per cent decline in UK
output in 2020 and for output to remain lower in 2021
than it was in 2019. In May, the National Institute of
Economic and Social Research estimated that over a 10-
year period UK output would be lower by a cumulative
amount of around 35 per cent of annual GDP, with much
of that coming in 2020 and 2021.
The estimates from the Bank of England and the OBR
assume that restrictions are eased after June and that
effectively the lockdown is then soon over; it seems
R68 NatioNal iNstitute ecoNomic Review No. 253 august 2020
plausible that their estimates of economic cost are
therefore estimates of the impact of the lockdown that
has been in place in the UK from March to June and
not of a continuation of the lockdown into the second
half of 2020 and beyond. The OBR is explicit about this;
in describing their forecasts they note: “The table below
summarises the results of our three-month lockdown
scenario where economic activity would gradually return
to normal over the subsequent three months. The
Bank of England in its May economic assessment takes
a similar line:Underlying the illustrative scenario for
both the UK and the rest of the world is an assumption
that enforced social distancing measures remain in place
until early June and that they are then lifted gradually
over the following four months, until the end of Q3”.
In that illustrative scenario GDP in 2020 is 14 per cent
below the 2019 level (Table 1A, Bank of England May
Monetary Policy Report). But it is hard to be sure how
these assessments would have been different with much
less restrictive policies; economic activity would almost
certainly have been lower, at least to some extent.
Many elements of the cost of the lockdown in the UK are
not reected at all in current incomes, employment and
GDP. Health costs – including mental health – are not yet
showing up in a measurable way. They are likely to be
large and long lasting. Referrals for cancer investigations
were 70 per cent down in April 2020;3 there were hardly
any follow-up routine appointments for long-term
conditions in UK Primary Care between mid-March
2020 and the beginning of June 2020; outpatient seen
were 64 per cent down and elective admissions were
75 per cent down;4 attended appointments in General
Practice were down 35 per cent.5 The impact of the
stress of the lockdown on anyone with a pre-existing
mental health condition, let alone the population as a
whole, is yet to be determined.
The cost from disrupted education of children and
students will be felt over a horizon of many years, even
5. Bringing costs and benefits together:
Bringing together costs and benets is necessary if good
policy decisions are to be made. There is no simple way
to do this that is clearly ethically justiable, empirically
reliable and widely accepted. But to make no assessment
is just to make policy in a vacuum. One approach is to
focus on quality adjusted life years (QALYs) that may
have been saved as a result of restrictions that have been
in place in the UK up to the end of June and to convert
that to a metric that can be compared with estimates of
the cost of the restrictions. That is the strategy we follow.
We then go on to make estimates of costs and benets
of alternative ways forward with restrictions eased to
different extents.
We make use of the guidelines established in the UK by
the National Institute for Health and Care Excellence
(NICE) for the use of resources in the UK health
system (see NICE, 2013). These are guidelines applied
to resource decisions that have a direct impact on lives
saved. It is hard to see how you could run a public health
care system without such rules. The guidelines in the UK
set out by NICE are that treatments that are expected
to increase life expectancy for a patient by one year (in
quality of life adjusted years, QALYs) should cost no
more than £30,000. We apply that gure to possible
total numbers of QALYs saved by restrictions to estimate
their benet. We also assess the sensitivity of results to
using much higher gures for the value of potential extra
years of life.
To implement this we need to assess how many likely
extra years of good life might be enjoyed by the people
who would have died but for a lockdown. We assume
that the age and health of those who would have died
is similar to that of those who have died with the virus.
The Ofce for National Statistics (ONS) has been
publishing each week the number of deaths where
COVID-19 has been recorded as a possible cause by
quinary age and gender. In total up to week ending 22
May this was 43,694 in England and Wales. (This total
is 21 per cent below the excess all causes deaths gure
of 55,504 up to the week ending 24 May (Week 21)
calculated by comparing the actual recorded number in
2020 to the average deaths over previous three years in
the same period).
By applying the average life expectancy6 to the actual
recorded COVID-19 deaths by age and gender, a total
life expectancy years lost can calculated. The table below
shows the calculation. Average life expectancy loss comes
out at 10.1 years per COVID-19 death. (The average life
expectancy years lost for a non-COVID death is only
slightly higher at 11.4, conrming that the age prole
for COVID mortality matches natural mortality.) The
median COVID-19 age at death is around 80 and the
average life years lost for the older 50 per cent is ve
years and the for younger 50 per cent is fteen years.
The average gure of 10.1 years of life lost does not
account for the fact that those who have died with
COVID-19 have often been in poor health, conditional
on their age. In their detailed study of 23,804 hospital
miles, stedmaN aNd Heald liviNg witH covid-19: balaNciNg costs agaiNst beNefits iN tHe face of tHe viRus R69
deaths in England from COVID-19 from 1 March 2020
to 11 May 2020, Valabhji et al. (2020) found that various
different life-shortening risk factors were signicantly
more prevalent in those patients who died of COVID-19
than in the general population. This included Diabetes
(33 per cent vs 5 per cent), and previous hospital
admission for signicant cardiovascular comorbidities
including coronary heart disease (31 per cent vs 3.5 per
cent), cerebrovascular disease (19.8 per cent vs 1.5 per
cent) and heart failure (17.7 per cent vs 1 per cent).
Other comorbidities such as dementia in its various
forms, chronic obstructive pulmonary disease (COPD),
vitamin D deciency, and hyperlipidaemia were not
collected and compared, but it is plausible that these
would also show similar levels of differences. Each of
these comorbidities has been shown to increase the
risk of early death signicantly. The National Diabetes
Audit in its mortality study7 found that the presence of
diabetes increases a person’s standard mortality risk by
a factor of 1.6.
It is, therefore, plausible that those patients who died of
COVID-19 were, on average, already in relatively poor
health for their age and this poor health would give them
a life expectancy, on average, signicantly below that of
the age-equivalent general population.
These comorbidities and conditions also reduce the
person’s quality of life, as well as its quantity (see Clarke
et al., 2002). The impact of poor heath through long-term
conditions and comorbidities are usually incorporated
into modelling through a quality of life utility factor
which ranges from 1 (healthy) to 0 (death); this is used
to adjust the total life years. Beaudet et al. (2014), found
that the basic type 2 diabetes without complication had
a factor of 0.79 and then other comorbidities would
reduce this further, including Myocardial infarction
−0.06, Ischemic heart disease −0.09, Heart failure −0.11,
and Stroke –0.16. An average poor health utility factor
of 0.8 could be applied to the population of those who
have died with COVID-19.
A substantial downwards adjustment to the ten years
estimate of the residual life expectancy based on the
general population would seem appropriate for the
group who have died with COVID-19. To get to an
estimate of average quality adjusted years of life lost,
Table 1. The age structure of deaths in 2020
Up to Total Deaths COVID-19 Total Deaths Life Expectancy (Years) Total Expected Life
24 May 2020
Age Group Male Female Male Female Male Female Non COVID COVID-19
<1 576 446 2 0 79.3 82.9 82,476 159
1–4 89 64 0 1 77.1 80.7 11,949 81
5–9 56 31 0 0 72.6 76.3 6,432 0
10–14 61 54 0 1 67.7 71.3 7,907 71
15–19 203 100 5 4 62.7 66.3 18,787 579
20–24 325 142 13 9 57.8 61.4 26,214 1,305
25–29 465 208 29 16 53 56.5 33,950 2,440
30–34 654 358 46 29 48.2 51.6 46,259 3,712
35–39 1,003 635 65 49 43.4 46.7 68,081 5,110
40–44 1,406 856 139 81 38.7 41.9 81,497 8,772
45–49 2,326 1,568 256 153 34.1 37.1 123,136 14,412
50–54 3,884 2,469 476 277 29.6 32.5 172,061 23,085
55–59 5,633 3,625 876 417 25.2 27.9 209,590 33,741
60–64 7,640 5,019 1,234 617 21 23.6 238,458 40,495
65–69 10,575 6,848 1,643 845 17.1 19.3 268,990 44,464
70–74 16,314 11,331 2,665 1,406 13.4 15.4 335,960 57,429
75–79 20,131 15,452 3,640 2,235 10.2 11.7 322,234 63,145
80–84 24,854 22,256 4,782 3,478 7.3 8.5 306,689 64,625
85–89 25,537 28,624 4,650 4,234 5.1 5.9 249,665 48,572
90+ 22,873 42,080 3,812 5,509 2.9 3.2 172,665 28,711
Total 144,605 142,166 24,333 19,361 2,783,000 440,907
Mean age 76.9 81.5 78.7 82.5
Non Covid-19 243,077 Covid-19 43,694 Lost Years/Death 11.4 10.1
Note: Total expected life years lost is the sum of lives lost multiplied by remaining life expectancy for each age group. Average lost years per death is
total expected life years lost divided by total deaths.
R70 NatioNal iNstitute ecoNomic Review No. 253 august 2020
some adjustment for both lower life expectancy, and
perhaps also for lower quality of life, is justiable.
How great an adjustment is hard to be precise
about, but it might plausibly be by one half. In the
calculations below we apply either no adjustments for
co-morbidities or an adjustment of one-half, using lost
average quality adjusted years per COVID19 death of
ten or ve years.
A cost benefit analysis of the lockdown:
Suppose that a group of people who each had an
expected quality-adjusted remaining years of life of
ten years, and who might have died with the virus,
has been spared that because of severe government
restrictions (‘the lockdown’). We will assume that the
benet of the restrictions that prevented such deaths
are the value of ten quality-adjusted years of life
multiplied by the number of lives saved. The NICE
£30,000 threshold is an assessment of the (maximum)
resource cost that would be justied for the UK health
service to make an expected saving of one quality
adjusted year of life. To save ten QALY would be
worth up to £300,000.
We initially apply this gure of £300,000 (or a gure of
£150,000 if we make an adjustment for co-morbidities
and take ve average quality-adjusted life years lost per
death) to estimates of the possible number of lives saved
as a result of lockdowns to give an overall benet number.
(The impact of using much higher values for extra years
of life is discussed shortly.) We compare that aggregate
number with an estimate of the cost of the lockdown. As
noted above there is no single, reliable estimate of lives
that have been saved by the UK lockdown and nor is there
a widely accepted single gure for the comprehensive
overall cost of the lockdown (which should include lost
and damaged lives into the future as a result of severe
restrictions and not just lost incomes in 2020). There is
much uncertainty here and presenting a single estimate
of costs and benets is not sensible. So we present a
range of estimated costs and benets based on a wide
range of assumptions that we think encompass plausible
upper and lower limits on both costs and benets.
At the high end of estimated lives saved is the
difference between the projected deaths from the
study of Professor Ferguson’s group at Imperial
assuming no change in behaviour (500,000) less an
estimate of excess UK deaths (approximately 60,000
by June 2020). This 440,000 net lives saved number
is likely to be a signicant overestimate of likely lives
saved. As noted above it does not account for changes
in behaviour that would have occurred without the
government lockdown; it does not count future higher
deaths from side effects of the lockdown (extra cancer
deaths for example); and it does not allow for the fact
that some of those ‘saved’ deaths may just have been
postponed because when restrictions are eased, and in
the absence of a vaccine or of widespread immunity,
deaths may pick up again. (If the epidemic is dying out
anyway those deaths will not come as the lockdown is
eased but in this case the 440,000 saved lives is also
excessive because the lockdown may have come as a
decline in infections was happening for other reasons).
At the other end of the spectrum would be estimates of
net saved lives that are effectively zero. That too seems
very unlikely. We set the lowest estimated net saved lives
well above that and use (rather arbitrarily) a ‘lowest’
estimate of 20,000.
For each life saved we apply a factor of either ve or ten
quality-adjusted extra years of life, each initially valued
with the NICE guideline gure of £30,000.
On the cost side, the lowest estimate is just to count the
GDP that would have been produced in 2020 but for
the lockdown established in March and assuming the
lockdown to be eased from the end of June. This assumes
a rapid bounce-back by the end of the year so there is
no effect on incomes and output from the start of 2021
onwards. That was the scenario envisaged by the Bank of
England in their May 2020 assessment of the economic
outlook, when they put the GDP loss in 2020 at around
14 per cent. The OBR estimate for lost output in 2020,
also based on an assumed rapid recovery in the second
half of the year, is close to 13 per cent. It seems plausible
that a large fraction of these estimates of lost output is due
to the lockdown. Even absent a government mandated
lockdown there would have been some reduction in
incomes, but with no restrictions on leaving home and
most workplaces still open it seems likely the hit to
GDP would have been far less. Simulations undertaken
by Bradley et al. (2020), using a model of behavioural
choice about work calibrated to the UK, show that absent
a lockdown falls in output and employment would be
very much lower than with a lengthy lockdown. If the
lockdown effect was two thirds of what the OBR and
Bank of England suggest is the loss in GDP for 2020, that
might imply around a 9 per cent fall in GDP as a direct
result of it. That is around £200 billion.
That 9 per cent of GDP cost is likely to be a low-end
estimate of overall costs of the UK lockdown from mid-
March to late June; it assumes lost output from the rst
half of 2020 comes back quickly, it ignores wider health
miles, stedmaN aNd Heald liviNg witH covid-19: balaNciNg costs agaiNst beNefits iN tHe face of tHe viRus R71
costs of future lives damaged,9 and it assumes zero costs
from disruption to the education of the young.10
At the high end of the spectrum would be an estimate of
15 per cent of GDP lost in 2020 and lower output for the
next few years on top of that, as economic activity does
not return to normal for several years with some rms
permanently damaged by the lockdown and the large rise
in unemployment slow to be reversed, even if restrictions
are quickly removed from mid-2020. A shortfall of GDP
of 15 per cent in 2020, 7.5 per cent in 2021 and 2.5 per
cent in 2022, would be at the more pessimistic end of
the spectrum for the impact of the March-June lockdown,
though for many economists such a gure seems realistic
rather than pessimistic.11 The cumulative lost output
would then be 25 per cent of GDP.
It should be stressed that these are scenarios – not
forecasts. But we believe they cover high and low ends of
a plausible range for both costs and benets of lockdown.
Tables 2 and 3 below show the cost-benet calculations
of the lockdown based on such ranges: in each cell we
report three numbers: benets (+), costs (–) and (in red)
the balance of the two – all measured as £ billions.
The fous here is on the lockdown that was in place
up to the end of June. In the next section we consider
policy going forwards from then and factor in explicitly
scenarios where infections and deaths rise again as a
result of relaxing restrictions.
For every permutation of lives saved and GDP lost the
costs of lockdown exceed the benets. Even if lives saved
Table 2. Benefits (+), costs (–) and net benefits(a) of March-June UK lockdown; converted to an index of £bn, 5 QALY
are assumed lost for each COVID-19 death
9% GDP loss 15% GDP loss 20% GDP loss 25% GDP loss
Lives not lost
440,000 £66b, –£200b, £66b, –£330b, £66b, –£440b, £66b, –£550b,
–£134b –£264b –£374b –£484b
200,000 £30b, –£200b, £30b, –£330b, £30b, –£440b, £30b, –£550b,
–£170b –£300b –£410b –£520b
100,000 £15b, –£200b, £15b, –£330b, £15b, –£440b, £15b, –£550b,
–£185b –£315b –£425b –£535b
50,000 £8b, –£200b, £8b, –£330b, £8b, –£440b, £8b, –£550b,
–£192b –£322b –£432b –£542b
20,000 £3b, –£200b, £3b, –£330b, £3b, –£440b, £3b, –£550b,
–£197b –£327b –437b –£547b
Notes: Each life saved is estimated to result in 5 more quality adjusted years of life. The NICE resource threshold of £30,000 is applied to each of these
quality adjusted years. The money value of GDP losses is taken as a proportion of 2019 GDP of £2.2 trillion. All resulting figures are in £ billions.
(a) Net benefits are shown in red.
Table 3. Benefits (+), costs (–) and net benefits(a) of March-June UK lockdown; converted to an index of £bn, 10 QALY
are assumed lost for each COVID-19 death
9% GDP loss 15% GDP loss 20% GDP loss 25% GDP loss
Lives not lost
440,000 £132b, –£200b, £132b, –£330b, £132b, –£440b, £132b, –£550b,
–£68b –£198b –£308b –£418b
200,000 £60b, –£200b, £60b, –£330b, £60b, –£440b, £60b, –£550b,
–£140b –£270b –£380b –£490b
100,000 £30b, –£200b, £30b, –£330b, £30b, –£440b, £30b, –£550b,
–£170b –£300b –£410b –£520b
50,000 £15b, –£200b, £15b, –£330b, £15b, –£440b, £15b, –£550b,
–£185b –£315b –£425b –£535b
20,000 £6b, –£200b, £6b, –£330b, £6b, –£440b, £6b, –£550b,
–£194b –£324b –434b –£544b
Notes: Each life saved is estimated to result in 10 more quality adjusted years of life. The NICE resource threshold of £30,000 is applied to each of these
quality adjusted years. The money value of GDP losses is taken as a proportion of 2019 GDP of £2.2 trillion. All resulting figures are in £ billions.
(a) Net benefits are shown in red.
R72 NatioNal iNstitute ecoNomic Review No. 253 august 2020
are as high as 440,000, each of which means an extra ten
years of quality adjusted life – and when the lost output
(assumed to be a sufcient and comprehensive measure of
all costs of the lockdown) is simply the likely shortfall in
incomes in 2020 – costs are still over 50 per cent higher
than the benets of a three month lockdown (benets =
£132 billion; costs = £200 billion). In all other cases costs
are a multiple of benets. In most cases costs are 10 times
or more the scale of benets. This result reects the fact
that the economic costs of a three month lockdown – even
on the most conservative estimate of £200 billion – is
probably far larger than annual total expenditure on the
UK national health service (which runs at around £130
billion); the benets of that level of resources applied to
health and using the NICE guidelines would be expected to
generate far more lives saved than is plausibly attributable
to the lockdown in the UK.
Another way of making the same point is that the cost
per QALY saved of the lockdown looks to be far in
excess (generally by a factor of at least 3 and often by a
factor of 10 and more) of that considered acceptable for
health treatments in the UK.
Sensitivity to the value of extra years of life:
Taken at face value, the numbers in tables 2 and 3
would suggest a lockdown that lasted three months
was not an effective policy and that severe restrictions
should have been eased sooner because the economic
costs of such protracted restrictions on mobility have
been high relative to likely benets. But there is much
uncertainty, indeed controversy, about how to value
potential lives saved and that conclusion is sensitive to
whether using the NICE gure of £30,000 for valuing
a potential year of life saved is sensible. As noted by
Layard et al. (2020), a much higher gure per QALY of
£60,000 is often advocated. If we use that gure all the
benet numbers in tables 2 and 3 would be doubled.
But of the 40 permutations of scenarios in tables 2 and
3 the result that costs of a three-month lockdown are
greater than benets would then only be overturned in
one case.
Might it be that such calculations massively understate
the benets of lockdown? That would be the case if
the value of potential years of life saved is dramatically
too low; some would say that it is. Layard et al. (2020)
advocate enormously higher levels.12 Estimates used in
the US for the statistical value of a life place it slightly
above $10 million (see Viscusi, 2020 and 2018, and
Murphy and Topel, 2006). For a new-born who might
expect 80 good years of life, the NICE £30,000 number
would generate a life value of £2.4 million – just under
a third of the value if a statistical life is worth $10
million. Adler (2020) presents powerful arguments why
using such high VSL gures may be inappropriate for
assessing policy with respect to COVID-19, especially if
the impact of lockdowns falls disproportionately on the
less well off.
Goldstein and Lee (2020) note that US health economists
use values of around $125,000 per year of life. That is
a bit over three times the NICE gure. However, the
£30,000 gure per QALY is the gure used in resource
decisions within the UK health system. It is not an
arbitrary number. It is not based on likely future earnings
lost or the value of future consumption – calculations that
are open to the moral objection that they reduce the value
of human life to how much people would have spent on
commodities. Instead the gure we use for the value of
a QALY is a measure of what is considered the highest
level of resources (i.e. what part of GDP) in the UK health
system that should be used to generate extra quality
adjusted years of life – and it is saving of lives which is
what the lockdown was for. In using this yardstick, we are
treating decisions on how to face COVID-19 in the same
way as decisions in the UK are made about resources to
apply to the treatment of cancer, heart disease, dementia
and diabetes. On that basis it would seem as though the
benets of the three-month lockdown were likely to have
been lower – perhaps far lower – than its costs. Yet even
if one used a valuation of a QALY three times as great, the
gures in tables 2 and 3 (with benets raised by a factor
of 3) would still generate costs of the lockdown in excess
of benets in nearly all the cases considered.
That judgement is, however, made with the benet of
hindsight; we now know more about the scale of the
economic costs of the lockdown than was known in
March 2020, and also know about how deaths and
new infections have evolved across Europe. The more
interesting policy issue is what it is best to do now; how
quickly should the lockdown be eased given what we
know now? That issue we consider in the next section.
6. What policy to adopt now?
We apply a similar cost-benet methodology to consider
policy options for the level of restrictions applied in the
UK over the next 3 months (July–September 2020). The
options we consider fall under two broad headings:
1) Carry on with only very limited easing of restrictions.
2) Move quickly to minimal lockdown (easing restrictions
for the general population rapidly, focusing resources
on high risk groups and relying on existing tracking
miles, stedmaN aNd Heald liviNg witH covid-19: balaNciNg costs agaiNst beNefits iN tHe face of tHe viRus R73
of the cases/deaths to help prevent re-emergence of
the virus).
We consider the following scenarios for the consequences
of each policy for the evolution of COVID-19 deaths:
1. Very limited easing of restrictions results in a
continuing steady fall in the death rate over 13 weeks
down to single gures per week at the end of three
months. Each week deaths are assumed to be 0.7 x
deaths of the previous week.
2. For the policy of more rapid easing of restrictions we
consider three possible scenarios:
(i) Deaths continue to fall but at a signicantly slower rate
than with a slow and limited easing of lockdown; each
week deaths are 0.9 x deaths of the previous week.
(ii) Deaths move back to the start-June level of 1,230 per
week and stay there.
(iii)Deaths steadily increase back up to levels seen at the
height of the UK pandemic; each week over a three-
month period they are 15 per cent higher than the
week before.
These are macabre thought experiments and many
will feel uneasy at such calculations. But there are
implications in terms of deaths and misery on both sides
of the ledger from any policy. To think such comparisons
are distasteful is to not face that reality.
The assumed paths of deaths under the four scenarios
is shown in table 4. In each case we set the initial level
of deaths in the week prior to each scenario at the last
ONS recorded gure for UK deaths in the week to 12
June (1230 deaths).
The implied cost of the extra deaths from the easing
policies (under scenarios i, ii. and iii) are shown towards
the bottom of the table. These are the projected excess
deaths under each easing scenario relative to the policy
of continuing with the lockdown multiplied by the lost
QALY per death and valued at £30,000 per QALY. These
numbers are in £ billions and should be set against the
estimated benets from easing the lockdown.
Our low-end estimate of the (narrowly dened) cost of the
March to June lockdown was 9 per cent of GDP – a gure
of £200 billion. One might assume that a continuation
of the lockdown over the next three months with only
a very limited easing of restrictions generates a further
cost of the same size. But rapid easing of restrictions
is unlikely to mean narrowly dened economic activity
(GDP) just bounces back. A conservative estimate of the
narrowly dened economic benets of quickly easing the
lockdown is that the £200 billion cost under lockdown
Table 4. Deaths and costs of deaths under different unlocking scenarios
from 12/6/2020 Continue Lockdown (0.7) Ease Scenario i (0.9) Ease Scenario ii (1) Ease Scenario iii (1.15)
week 1 861 1,107 1,230 1,415
week 2 603 996 1,230 1,627
week 3 422 896 1,230 1,871
week 4 295 806 1,230 2,152
week 5 207 725 1,230 2,475
week 6 145 653 1,230 2,846
week 7 102 588 1,230 3,273
week 8 71 529 1,230 3,764
week 9 50 476 1,230 4,329
week 10 35 428 1,230 4,978
week 11 25 385 1,230 5,725
week 12 18 347 1,230 6,584
week 13 13 312 1,230 7,572
Total expected deaths 2,847 8,248 15,990 48,611
Additional expected deaths compared to Lockdown 5,401 13,143 45,764
Cost of easing (£ billions) – each add. death = 5 QALY
valued at £30,000 £0.81 £1.97 £6.86
Cost of easing (£ billions) – each add. death = 10 QALY
valued at £30,000 £1.62 £3.94 £13.73
Note: Deaths are assumed to evolve week by week from the level in the week ending 12 June (1230) by a factor 0.7; 0.9; 1.0; 1.15 for the lockdown and
scenarios i, ii and iii respectively. So, for example, under the most pessimistic scenario iii, deaths each week are 1.15 times deaths in the previous week.
R74 NatioNal iNstitute ecoNomic Review No. 253 august 2020
might become half that size. This would generate a
benet from easing of £100 billion over three months to
be set against any extra lives lost.
Based on that assumption, under all scenarios the cost
of easing restrictions is much lower than its benets –
the maximum cost of £14 billion should be set against
an estimate of benets of £100 billion. One would
need to value QALYs at over seven times the NICE
guideline value of £30,000 to make a continuation of
the lockdown warranted.
Figure 5 presents the same information in a slightly
different way, calculating the net costs of an extension
of the lockdown relative to a policy of immediate
easing. To be clear what we are comparing here are
the costs and benets over a three-month period. The
two alternative strategies would leave the country in
different positions at the end of three months – under
no change to the lockdown the scenario is one with low
deaths and low numbers of people currently infected
at the end of three months. Under the alternative
scenarios the current numbers of those infected at the
end of three months would be greater (and under the
worse scenario would be growing). If it was obvious
that starting from the rst scenario three months
ahead (continued lockdown) was clearly better than
the scenarios where more people were infected (the
three easing scenarios) then it would not be a complete
analysis just to focus on costs and benets over the rst
three months. But in fact it is not obvious that starting
from lockdown three months ahead with low numbers
infected is better. The numbers of people who had ever
been infected would be lower than under the other
scenarios and so the susceptible population would be
greater, so that the impact of then easing restrictions, in
the absence of a vaccine, would be worse.
Once again we stress that the favourable assessment
of easing restrictions versus continuing with a blanket
lockdown is conditional on an assumption about the
value of potential extra years of life. But it would take a
valuation of extra possible years of life massively higher
than that used to guide public health decisions in the
UK to mean that a continuation of lockdown plausibly
looked the better strategy.
Notes: The net extra economic costs of the lockdown relative to easing of restrictions is assumed to be £100 billion. To
that is added the cost of lives lost under lockdown. The benefit of lives not lost, relative to the easing of restrictions, is
then deducted from the lockdown costs to generate a net cost figure under the three scenarios. The easing scenarios are:
(i) deaths still decline but slower than in lockdown, (ii) deaths remain at start June 2020 levels, (iii) deaths increase again
back up to April 2020 peak levels. The equivalent cost/QALY is calculated by dividing the lockdown costs (£100 billion)
by the net number of lives not lost in that scenario times the number of QALYs for each death.
£60 £70 £80 £90 £100 £110 £120
Economic Impact of further 3months Lockdown
QALY Value of Additional 2847 lives lost
Scenario 1: QALY Value of 8248 lives not lost
Scenario 2: QALY Value of 15990 lives not lost
Scenario 3: QALY Value of 48611 lives not lost
Lowest Lockdown Net Costs
Economic Impact of further 3months Lockdown
QALY Value of Additional 2847 lives lost
Scenario 1: QALY Value of 8248 lives not lost
Scenario 2: QALY Value of 15990 lives not lost
Scenario 3: QALY Value of 48611 lives not lost
Lowest Lockdown Net Costs
Cumulative Economic Costs & QALY Value £ billion
Figure 5. Scenarios for costs and benefits of different policies
£1.2b (equiv.£3.7m/QALY)
£2.4b (equiv.£1.5m/QALY)
£7.3b (equiv.£0.44m/QALY)
£2.5b (equiv.£1.9m/QALY)
£4.8b (equiv.£0.8m/QALY)
£14.6b (equiv.£0.22m/QALY)
miles, stedmaN aNd Heald liviNg witH covid-19: balaNciNg costs agaiNst beNefits iN tHe face of tHe viRus R75
7. Conclusions
We nd that the costs of the three-month lockdown in
the UK are likely to have been high relative to benets,
so that a continuation of severe restrictions is unlikely to
be warranted. There is a need to normalise how we view
COVID-19 because its costs and risks are comparable to
other health problems (such as cancer, heart problems,
diabetes) where governments have made resource
decisions for decades. Treating possible future COVID-19
deaths as if little else matters is going to lead to bad
outcomes. Good decision-making does not mean paying
little attention to the collateral damage that comes from
responding to a worst case COVID-19 scenario.
The lockdown is a public health policy and we have
valued its impact using the tools that guide health care
decisions in the UK public health system. On that basis,
and taking a wide range of scenarios of costs and benets
of severe restrictions, we nd that having extended the
lockdown for as long as three months is likely to have
generated costs that are greater than likely benets.
Weighing up costs and benets of maintaining general
and severe restrictions is necessary. That is how decisions
over a wide range of public policy issues are made –
many directly concerning public health issues. While
there are inevitably risks in easing restrictions there are
very clear costs in not doing so – a policy of ‘let’s wait
until things are clearer’ is not reliably prudent.
We nd that a movement away from blanket restrictions
that bring large, lasting and widespread costs, and
towards measures targeted specically at groups most at
risk is now prudent. Such a policy should probably have
been started before the end of June 2020.
1 It is possible that serology testing for past COVID-19 infection
based on the presence of antibodies is not picking up cases
where the infected had very few symptoms and not identifying
others who are nonetheless not susceptible to the virus.
6 Using ONS Life Tables based on the normal mortality
rates for each cardinal age and gender: (https://www.
nationallifetablesunitedkingdomreferencetables.7 Th e i r
mortality study is available at
report-2--complications-and-mortality-2017-18 .
8 In fact the lasting effects of job losses in the UK seem very
substantial. Typical estimates are of a lasting wage penalty from
unemployment of 8–10% and an employment penalty of 6-9%
(Arulampalam et al., 2001, Tumino 2015). The impacts are
particularly severe for young people.
9 Recent work by, among others, Carol Propper, of Imperial
College, and researchers at the Institute for Fiscal Studies (IFS)
suggests that the relatively modest increases in unemployment
associated with the 2008–9 financial crisis may have resulted
in 900,000 more people of working age suffering from chronic
health problems. See Propper et al. (2020).
10 Work by Anna Vignoles and Simon Burgess shows that such
costs are likely to be significant.
11 Layard et al. have a central estimate of the total lost GDP from
a lockdown that was lifted at the end of June of 22.8%; if the
lockdown lasted one month longer, their central estimate is that
the total cumulative loss of GDP would be 29.4%. Gottlieb et
al. (2020) conclude that “Overall, a realistic lockdown policy
implies GDP losses of 20–25% on an annualized basis”
12 In an annex they say that they would use a value for a QALY
that is 25 times greater than the NHS guideline level. To use
figures this high would seem to imply that the health system in
the UK should have massively more resources – not just higher
by 10% or 20% above its current level but so as to take spending
to a vastly greater share of GDP. If that really reflected how
people value potential years of life saved one would need to ask
why the political system has never generated a level of health
spending anywhere near those levels, either in the UK or any
other country. One would also need to ask why private health
spending in the UK is not greatly higher than 1.5% of GDP.
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... Essas posições encontram suporte na teoria econômica neoliberal. Um artigo publicado na National Institute Economic Review aplicou o valor monetário à vida humana e usou isso para concluir que o confinamento teve um custo muito alto (Miles;Stedman;Heald, 2020). ...
... Essas posições encontram suporte na teoria econômica neoliberal. Um artigo publicado na National Institute Economic Review aplicou o valor monetário à vida humana e usou isso para concluir que o confinamento teve um custo muito alto (Miles;Stedman;Heald, 2020). ...
... Essas posições encontram suporte na teoria econômica neoliberal. Um artigo publicado na National Institute Economic Review aplicou o valor monetário à vida humana e usou isso para concluir que o confinamento teve um custo muito alto (Miles;Stedman;Heald, 2020). ...
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A saúde planetária vê o capitalismo neoliberal como um mediador-chave nas crises socioecológicas, uma posição que é reverberada em muitos dos comentários sobre a Covid-19. Nessa Visão Pessoal, estabeleço uma teoria econômica que enfatiza alguns dos modos como a conceituação do capitalismo neoliberal sobre valor tem informado respostas à Covid-19. Por meio da intersecção das teorias econômicas ecológica, feminista e marxista, desenvolvo uma análise do capitalismo neoliberal como uma forma histórica específica de economia. Identifico a acumulação de valor de troca como uma tendência central do capitalismo neoliberal e argumento que esta tendência cria barreiras para a produção de outras formas de valor. Então analiso as implicações dessa tendência no contexto das respostas à Covid-19. Argumento que recursos e trabalho fluem para a produção de valor de troca, em detrimento de outras formas de valor. Consequentemente, a economia global capitalista tem uma capacidade produtiva sem precedentes mas utiliza pouco desta capacidade para criar condições que melhorem e mantenham a saúde das pessoas. Para sermos mais resilientes a outras crises vindouras, acadêmicos e acadêmicas, agentes de políticas públicas e ativistas deveriam se engajar em trabalhos teóricos que possibilitassem à economia global reconhecer as múltiplas formas de valor e trabalho político que as incorpore nas instituições sociais.
... I test the whole spectrum of COVID policies in terms of varying degrees of severity to determine whether moderation is indeed the best approach. If so, it is crucial to be able to identify optimally moderate measures within the spectrum of policy responses because restrictions and lockdowns have an immense effect on economic and psychological well being that eventually translate into negative health outcomes [5][6][7]. Because these effects are complex and difficult to quantify, there is a general propensity to focus solely on the positive, immediate effects of NPIs. ...
... Consequently, there are no further gains to be achieved beyond the SI range of 51-60. The socially optimal SI range, however, must account not only for the positive effects of NPIs, but also for the significant impact on physical and mental health [42][43][44][45][46][47][48] and economic costs that result from restrictions (for example, see [5][6][7]). While this would require a full cost-benefit analysis [49][50][51][52] that is beyond the scope of this paper, it is possible to derive the approximate upper bound of the socially optimal SI level with a single assumption about the cost profiles of different SI levels: that the costs are monotonically increasing in the SI level. ...
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Background: Early evaluations of the effectiveness of non-pharmaceutical intervention (NPI) mandates were constrained by the lack of empirical data, thereby also limiting model sophistication (e.g., models did not take into account the endogeneity of key variables). Methods: Observational analysis using a behavioral four-equation structural model that accounts for the endogeneity of many variables and correlated unobservable country characteristics. The dataset includes information from 132 countries from February 15, 2020, to April 14, 2021, with data on confirmed cases and deaths, mobility, vaccination and testing rates, and NPI stringency. The main outcomes of interest are the growth rates of confirmed cases and deaths. Results: There were strongly decreasing returns to more stringent NPI mandates. No additional impact was found for NPI mandates beyond a Stringency Index range of 51-60 for cases and 41-50 for deaths. A nonrestrictive policy of extensive and open testing constituted 51% [27% to 76%] of the impact on pandemic dynamics of the optimal NPIs. Reductions in mobility were found to increase, not decrease, both case [Formula: see text] and death growth rates [Formula: see text]. More stringent restrictions on gatherings and international movement were found to be effective. Governments conditioned policy choices on recent pandemic dynamics, and were found to be more hesitant in de-escalating NPIs than they were in imposing them. Conclusion: At least 90% of the maximum effectiveness of NPI mandates is attainable with interventions associated with a Stringency Index in the range of 31-40, which impose minimal negative social externalities. This was significantly less than the average stringency level of implemented policies around the world during the same time period.
... However, where formal processes for health sector priority setting exist, in the majority of cases they are designed to inform health policy decisions made at the margin; typically appraising the adoption of specific health interventions or technology (referred to as health technology assessment (HTA)) 3,4 . Periodically, wider efforts are made to assess whether the range of services included in Universal Health Care (UHC) health benefit packages being delivered across the health sector is optimal [5][6][7][8][9] . ...
... Same section where you discuss health vs economic welfare indeed -I wonder also whether you want to discuss other refs here giving a negative net benefit or offer a more nuanced approach (i.e. suggest cut offs beyond which things cease to be CE etc.) -for HICs and LMICs eg 7 , this, 8,9 and similar work undertaken in Malawi. I have serious issues with VSL in the UK and LMICs (worse even extrapolating from overestimated UK and US estimates!) to the extent that it is explicitly uninterested in opportunity costs. ...
Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy; highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries; where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.
... However, the authors were unable to establish how the pandemic provoked financial and budgetary losses and which regulatory interventions should be implemented to reduce these consequences. Another publication [45] analyzed the advantages and disadvantages of state policy under the conditions of COVID-19 and assessed the social losses from the point of view of the most critical medical and social parameters-quality and length of life. However, the systematic assessment of the effectiveness of state policy and the determining parameters of this effectiveness in countering the spread of the negative consequences of the pandemic on these parameters has not been carried out. ...
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The authors investigated the coronavirus pandemic as a health and safety risk factor for sustainable state development. The main purpose is to determine the cause-and-effect relationships between the key spheres of society life: economic, financial–budgetary, political-–institutional. The authors hypothesize that these spheres influence each other and that this influence becomes more obvious and important to consider during significant shifts such as health threats and transformations in the public health system. As part of the calculations, the methodology of canonical regression analysis was used, which made it possible to evaluate the influence of a set of indicators using the construction of a correlation matrix. Aggregation of the complex of development indicators for each direction was carried out, and their mutual influence and degree of importance within each group of indicators was determined. The identified interdependencies are valuable for predicting the state of various industries in the future. It was concluded that there were no significant changes between the indicators of the analyzed components of a country’s development in the pre-and post-pandemic period. This makes it possible to state with a high probability that forecasting in the long-term perspective of a country’s development is possible based on the degree of interrelationships between the indicators of individual areas of development. Forecasting can also be based on the trends occurring in a specific related field to correct the upward or downward movement of a particular indicator, and to change the functioning of the complex system under the influence of threats to public health.
... (2) The urgent need is to examine the issue by a far more comprehensive approach, taking into account all relevant factors together rather than depending on one particular element (health preventive measures) for a holistic perspective of the problem. (3) There is proper gap in exploring the context-based strategies because cultural and other factors, such as social and economic situation, political environment, and cultural system must be considered when comparing strategies across nations [40][41][42]. (4) There is lack of reliable framework in the pandemic literature that present and compile major containment strategies based on their significance level. To fill these gaps, Interpretive Structural Modeling (ISM) is employed to address these critical issues [43]. ...
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The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that “strong leadership and control” and “awareness on social media” play significant roles in reducing pandemic challenges, while “promoting online purchase behavior” and “online education” are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.
... By trading off life-years for improvements in each of these health states, the value that people attach to a particular state can be located on a scale between zero for death and one for full health [5]. Given the widespread use of QALYs in the UK, it is surprising that they have not featured prominently in appraisals of pandemic response policies either; see Miles et al. (2020) [6] as an exception. ...
Governments in liberal democracies pursue social welfare, but in many different ways. The wellbeing approach instead asks: Why not focus directly on increasing measured human happiness? Why not try to improve people’s overall quality of life, as it is subjectively seen by citizens themselves? The radical implications of this stance include shifting attention to previously neglected areas (such as mental health and ‘social infrastructure’ services) and developing defensible measures of overall wellbeing or quality of life indicators. Can one ‘master’ concept of wellbeing work to create more holism in policy-making? Or should we stick with multiple metrics? These debates have been live in relation to an alternative ‘capacities’ approaches, and they are well-developed in health policymaking. Most recently, the connections between wellbeing and political participation have come into sharper focus. Wellbeing remains a contested concept, one that can be interpreted and used differently, with consequences for how it is incorporated into policy decisions. By bringing together scholars from economics, psychology and behavioural science, philosophy and political science, the authors explore how different disciplinary approaches can contribute to the study of wellbeing and how this can shape policy priorities.
... inhabitants (16). The same is true for Europe: Miles and colleagues listed excess deaths of 21% for Spain, 20% for the UK, 18% for Italy down to 6% for Sweden, 3% for Portugal, −1% for Germany, −3% for Denmark and −4% for Norway during the first wave of the pandemic (17). ...
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Aim: To clarify the high variability in COVID-19-related deaths during the first wave of the pandemic, we conducted a modeling study using publicly available data. Materials and methods: We used 13 population- and country-specific variables to predict the number of population-standardized COVID-19-related deaths in 43 European countries using generalized linear models: the test-standardized number of SARS-CoV-2-cases, population density, life expectancy, severity of governmental responses, influenza-vaccination coverage in the elderly, vitamin D status, smoking and diabetes prevalence, cardiovascular disease death rate, number of hospital beds, gross domestic product, human development index and percentage of people older than 65 years. Results: We found that test-standardized number of SARS-CoV-2-cases and flu vaccination coverage in the elderly were the most important predictors, together with vitamin D status, gross domestic product, population density and government response severity explaining roughly two-thirds of the variation in COVID-19 related deaths. The latter variable was positively, but only weakly associated with the outcome, i.e., deaths were higher in countries with more severe government response. Higher flu vaccination coverage and low vitamin D status were associated with more COVID-19 related deaths. Most other predictors appeared to be negligible. Conclusion: Adequate vitamin D levels are important, while flu-vaccination in the elderly and stronger government response were putative aggravating factors of COVID-19 related deaths. These results may inform protection strategies against future infectious disease outbreaks.
... It is therefore not surprising to see, in the midst of the Covid-19 crisis, liberal economists proposing theories to justify a lack of action to protect health. For example, Miles et al. conclude that, by monetizing human life, containment strategies come at far too high an economic cost (123). ...
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In hindsight, the early response of liberal governments to the SARS-CoV-2 pandemic was chaotic and generally inefficient. Though one might be tempted to attribute these failures to the incompetence of certain political decision-makers, we propose another explanation. Global threats require a coordinated international response, which is only possible if the threat is perceived in the same way by all, and if government priorities are similar. The effectiveness of the response also relies on massive adhesion of citizens to the measures imposed, which in turn requires trust in government. Our hypothesis is that certain fundamental features of liberalism complicate such global and collective responses: neutrality of the state and primacy of the individual over collective society. Liberalism considers that institutions and public policy must not be designed to favor any specific conception of the common good. That which is best for all is usually determined by a “competition of opinions,” which frequently leads to scientific expertise being considered as only one opinion among many. Liberalism also imposes strict respect for individual freedoms and private interests and tends to reject any form of collectivism or dictate imposed by the common good. In order to solve these structural problems and improve society's management of global threats, we make several proposals, such as the introduction of a minimal and consensual definition of the common good and the promotion of a health policy guided by One Health-like concepts. Overall, our analysis suggests that because political ideologies provide their own definitions of the common good and the place of scientific knowledge in the governance process and can thus affect the response to global threats, they should be urgently taken into consideration by public health experts.
... We conclude, therefore, that restrictions have become less effective at curbing non-essential travel, which may alter the cost-benefit calculus of lockdowns. This is an important finding given that the economic, social, and secondary-health costs of prolonged periods of restrictions are so high [32]. If restrictions are becoming less effective then this will necessarily change their value and it will be incumbent on governments to reconsider whether restrictions, which have worked in the past, will continue to be effective in the future, and to base future policy on a robust understanding of any change in their potential. ...
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As countries struggled with SARS-COV2 outbreaks at the beginning of 2021, many citizens found themselves in yet another period of increasing travel restrictions, if not a strict lockdown. At the same time there was concern that further restrictions would prove to be less effective due to a range of reasons including increasing pandemic fatigue or the lack of appropriate supports. In this study we investigate whether restrictions remained effective as a way to limit non-essential travel in order to curb virus transmission. We do this by analysing adherence during periods of increasing and decreasing restrictions in 125 countries during three different 4-month phases, early (March—June 2020), middle (July—October 2020), and late (November 2020—February 2021) over the course of the first year of the pandemic, and prior to significant population-wide vaccination. We use the strength of the relationship between restriction levels and the level of personal mobility associated with non-essential travel in order to determine the degree of adherence to the restrictions imposed. We show that there is evidence of a significant decrease in adherence to restrictions during the middle and late phases of the pandemic, compared with the early phase. Our analysis further suggests that this decrease in adherence is due to changes in mobility rather than changes in restrictions. We conclude, therefore, that restrictions have become less effective at curbing non-essential travel, which may alter the cost-benefit analysis of restrictions and lockdowns, thus highlighting the need for governments to reconsider large-scale restrictions as a containment strategy in the future, in favour of more focused or flexible mitigation approaches.
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Background During 2020-21, the United States used a multifaceted approach to control SARS-CoV-2 (Covid-19) and reduce mortality and morbidity. This included non-medical interventions (NMIs), aggressive vaccine development and deployment, and research into more effective approaches to medically treat Covid-19. Each approach had both costs and benefits. The objective of this study was to calculate the Incremental Cost Effectiveness Ratio (ICER) for three major Covid-19 policies: NMIs, vaccine development and deployment (Vaccines), and therapeutics and care improvements within the hospital setting (HTCI). Methods To simulate the number of QALYs lost per scenario, we developed a multi-risk Susceptible-Infected-Recovered (SIR) model where infection and fatality rates vary between regions. We use a two equation SIR model. The first equation represents changes in the number of infections and is a function of the susceptible population, the infection rate and the recovery rate. The second equation shows the changes in the susceptible population as people recover. Key costs included loss of economic productivity, reduced future earnings due to educational closures, inpatient spending and the cost of vaccine development. Benefits included reductions in Covid-19 related deaths, which were offset in some models by additional cancer deaths due to care delays. Results The largest cost is the reduction in economic output associated with NMI ($1.7 trillion); the second most significant cost is the educational shutdowns, with estimated reduced lifetime earnings of $523B. The total estimated cost of vaccine development is $55B. HTCI had the lowest cost per QALY gained vs “do nothing” with a cost of $2,089 per QALY gained. Vaccines cost $34,777 per QALY gained in isolation, while NMIs alone were dominated by other options. HTCI alone dominated most alternatives, except the combination of HTCI and Vaccines ($58,528 per QALY gained) and HTCI, Vaccines and NMIs ($3.4m per QALY gained). Conclusions HTCI was the most cost effective and was well justified under any standard cost effectiveness threshold. The cost per QALY gained for vaccine development, either alone or in concert with other approaches, is well within the standard for cost effectiveness. NMIs reduced deaths and saved QALYs, but the cost per QALY gained is well outside the usual accepted limits.
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This paper develops a choice-theoretic equilibrium model of the labor market in the presence of a pandemic. It includes heterogeneity in productivity, age and the ability to work from home. Worker and firm behavior changes in the presence of the virus, which itself has equilibrium consequences for the infection rate. The model is calibrated to the UK and counterfactual lockdown measures are evaluated. We find a different response in both the evolution of the virus and the labor market with different lockdown policies. A laissez-faire approach results in lives lost and acts as negative shock to the economy. A lockdown policy, absent any other intervention, will reduce the lives lost but increase the economic burden. Consistent with recent evidence, we find that the economic costs from lockdown are most felt by those earning the least. Finally, we introduce a job retention scheme as implemented by the UK Government and find that it spreads the economic hardship more equitably.
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While most countries imposed a lockdown in response to the first wave of COVID-19 infections, Sweden did not. To quantify the lockdown effect, we approximate a counterfactual lockdown scenario for Sweden through the outcome in a synthetic control unit. We find, first, that a 9-week lockdown in the first half of 2020 would have reduced infections and deaths by about 75% and 38%, respectively. Second, the lockdown effect starts to materialize with a delay of 3–4 weeks only. Third, the actual adjustment of mobility patterns in Sweden suggests there has been substantial voluntary social restraint, although the adjustment was less strong than under the lockdown scenario. Lastly, we find that a lockdown would not have caused much additional output loss.
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Significance What would a hypothetical 1 million US deaths in the COVID-19 epidemic mean for mortality of individuals at the population level? Life expectancy for 2020 would drop by 2.9 y. Those dying would lose an average of 11.7 y of expected remaining life, while for the general population the loss of remaining life would be 0.2 y for elders (at age 80) and much less at younger ages. Mortality per person would be less than that of the Spanish flu, but closer to that of the opioid and HIV/AIDS epidemics, while far more concentrated in time. The standard valuation of averting 1.75 million deaths would be many trillions of dollars.
We study the role of global supply chains in the impact of the Covid-19 pandemic on GDP growth using a multi-sector quantitative framework implemented on 64 countries. We discipline the labor supply shock across sectors and countries using the fraction of work in the sector that can be done from home, interacted with the stringency with which countries imposed lockdown measures. One quarter of the total model-implied real GDP decline is due to transmission through global supply chains. However, “renationalization” of global supply chains does not in general make countries more resilient to pandemic-induced contractions in labor supply. This is because eliminating reliance on foreign inputs increases reliance on the domestic inputs, which are also disrupted due to nationwide lockdowns. In fact, trade can insulate a country imposing a stringent lockdown from the pandemic-shock, as its foreign inputs are less disrupted than its domestic ones. Finally, unilateral lifting of the lockdowns in the largest economies can contribute as much as 2.5% to GDP growth in some of their smaller trade partners.
Containment measures are crucial to halt the spread of the 2019 COVID-19 pandemic but entail large short-term economic costs. This paper tries to quantify these effects using daily global data on real-time containment measures and indicators of economic activity such as Nitrogen Dioxide (NO2) emissions, flights, energy consumption, maritime trade, and mobility indices. Results suggest that containment measures have had, on average, a very large impact on economic activity—equivalent to a loss of about 15 percent in industrial production over a 30-day period following their implementation. Using novel data on fiscal and monetary policy measures used in response to the crisis, we find that these policy measures were effective in mitigating some of these economic costs. We also find that while workplace closures and stay-at-home orders are more effective in curbing infections, they are associated with the largest economic costs. Finally, while easing of containment measures has led to a pickup in economic activity, the effect has been lower (in absolute value) than that from the tightening of measures.
The value of statistical life (VSL) is a risk-to-money conversion factor that can be used to accurately approximate an individual’s willingness-to-pay for a small change in fatality risk. If an individual’s VSL is (say) $7 million, then she will be willing to pay approximately $7 for a 1-in-1-million risk reduction, $70 for a 1-in-100,000 risk reduction, and so forth. VSL has played a central role in the rapidly emerging economics literature about COVID-19. Many papers use VSL to assign a monetary value to the lifesaving benefits of social-distancing policies, so as to balance those benefits against lost income and other policy costs. This is not surprising, since VSL (known in the U.K. as “VPF”: value of a prevented fatality) has been a key tool in governmental cost-benefit analysis for decades and is well established among economists. Despite its familiarity, VSL is a flawed tool for analyzing social-distancing policy—and risk regulation more generally. The standard justification for cost-benefit analysis appeals to Kaldor- Hicks efficiency (potential Pareto superiority). But VSL is only an approximation to individual willingness to pay, which may become quite inaccurate for policies that mitigate large risks (such as the risks posed by COVID-19)—and thus can recommend policies that fail the Kaldor- Hicks test. This paper uses a simulation model of social-distancing policy to illustrate the deficiencies of VSL. I criticize VSL-based cost-benefit analysis from a number of angles. Its recommendations with respect to social distancing deviate dramatically from the recommendations of a utilitarian or prioritarian social welfare function. In the model here, it does indeed diverge from Kaldor- Hicks efficiency. And its relative valuation of risks and financial costs among groups differentiated by age and income lacks intuitive support. Economists writing about COVID-19 need to reconsider using VSL