Tag: value of a statistical life

Posted on

Health vs the economy: is there a trade off?

Throughout the pandemic, the fight against COVID-19 has often been framed in terms of striking a balance between the health of the public and the health of the economy. This leads to the assumption that a trade-off must exist between these two objectives. Countries, therefore, have to decide between lives and livelihoods. However, one year on since lockdowns swept the globe the evidence suggests that the trade-off between sacrificing lives and sacrificing the economy is not necessarily clear cut.

Controlling the virus

Restrictions such as social distancing and lockdowns were introduced in order to minimise the spread of the virus, prevent hospitals from being overwhelmed, and ultimately save lives. However, as these measures are put in place, schools were closed, businesses and factories stopped operating, and economic activity shrank. This would suggest therefore, that society inevitably faces a trade-off between lost lives versus lost livelihoods.

It could be argued, therefore, that in the short run these interventions create a ‘health–wealth trade-off’. The lockdown restrictions save lives by preventing transmission, but they came at the cost of lost output, income and therefore GDP. This would also imply that the trade-off works in reverse when the lockdown restrictions are eased. As measures are relaxed, the economy can begin to recover but at the cost of an increased threat of the virus spreading again.

What are the costs?

In order to work out if a trade-off exists and what costs are involved, there must be a monetary value placed on human life. While this may seem unethical, governments, civil courts, regulatory bodies and companies do it all the time. The very existence of the life insurance industry is testament to the fact that human lives can be measured in monetary terms. One approach to measuring valuing life, commonly used by economists who conduct cost-benefit analyses, is the ‘value of statistical life’. It measures the loss or gain that arises from changes in the incidence of death, by eliciting people’s willingness to pay for small reductions in the probability of death, or their willingness to accept compensation in exchange for tolerating a small increase in the chance of death. (see the blog Lockdown – again. Is it worth it?)

Take the example of a complete lockdown. The potential number of lives saved can be estimated based on infection and fatality rates estimated from epidemiological models. This can then be multiplied by value of statistical life to compute the monetary value of saved lives. If this number exceeds the economic costs of a complete lockdown, then we know that it is desirable.

The trade-off between lost lives versus the economy is often erroneously viewed as an all-or-nothing choice between complete lockdown versus zero restrictions. However, in reality, there is a continuum in stringency of restrictions and it is not an all-or-nothing comparison.

Death rates vs downturns

In order to explore the existence of this trade-off, we can compare the health and economic impacts of the pandemic in different countries. If such a trade-off exists, then countries with lower death rates should have experienced larger economic downturns. However, when comparing the COVID-19 death rates with GDP data, the result is the opposite: countries that have managed to protect their population’s health in the pandemic have generally also protected their economy too. This suggests that there was never a simple binary trade-off between the two factors. Those countries that experienced the biggest first wave of excess deaths, also had the biggest hits to the economy.

The UK was the hardest hit of similar countries on both measures within the G7 group of industrialised countries. The shape of the recession in the UK from the pandemic and lockdowns was extraordinary and historic. However, it was also unique as there was a very sharp fall followed by a rapid rebound. Over 2020, GDP saw the largest hit in three centuries; larger than any single year of the Great Wars or the 1920s Depression.

Studies of the declines in GDP contradict the idea of a trade-off, showing that countries that suffered the most severe economic downturns, such as Peru, Spain and the UK, were generally among the countries with the highest COVID-19 death rates. There are countries that have experienced the reverse too; Taiwan, South Korea, and Lithuania all experienced modest declines in economic output but have also managed to keep the death rate low.

It should also be noted that some countries that had similar falls in GDP experienced very different death rates from each other. When comparing the USA and Sweden with Denmark and Poland, they all saw similar declines in the economy with contractions of around 8–9%. However, the USA and Sweden recorded 5–10 times more deaths per million. This therefore suggests that there is no clear trade-off between the health of the population and the health of the economy.

There will be many different factors that impact on the death rate for each individual country and by how much the economy has been affected. Such factors will even go beyond the policy decisions that have been made throughout the pandemic about how best to suppress the transmission of the virus. However, from the data available, there is no clear evidence to suggest that a trade-off between the health and the economy exists. If anything, it suggests that the relationship works in the opposite direction.

Save the economy by saving lives

Given the arguments against the existence of the trade-off, it could be argued that in order to limit the economic damage caused by the pandemic, the focus needs to start and end with controlling the spread of the virus. Experiments that have been conducted across the world definitively show that no country can prevent the economic damage without first addressing the pandemic that causes it. Those countries that acted swiftly in implementing harsh measures to control the virus, are now reopening in stages and their economies are growing. Countries such as China, Australia, New Zealand, Iceland, and Singapore, which all invested primarily in swift coronavirus suppression, have effectively eliminated the virus and are seeing their economies begin to grow again.

China, in particular, stands out amongst this group of countries. The Chinese authorities acted very quickly, and firmly, but also the levels of compliance of the population have been very high. However, it could be argued that few countries possess the infrastructure that exists in China to facilitate such high compliance. The fact that the lockdown in China was so effective reduced both losses to the economy and the need for stimulus measures. China is also one of the few countries that have achieved a “V-shaped” recovery. Countries such as Korea, Norway and Finland also appear to have responded relatively well.

Most of the countries that prioritised supporting their economies and resisted, limited, or prematurely curtailed interventions to control the pandemic faced runaway rates of infection and further national lockdowns. The examples of the UK, the USA and Brazil are often quoted, with many arguing that these countries responded too late and too haphazardly. Both have experienced high numbers of deaths.

Conclusion

Discussions around the responses to the pandemic and what appropriate action should be taken have predominately been about how countries can strike the balance between protecting people’s health and protecting the economy. However, from observing the GDP data available there is no clear evidence of a definitive trade-off; rather the relationship between the health and economic impacts of the pandemic goes in the opposite direction. As well as saving lives, countries controlling the outbreak effectively may have adopted the best economic strategy too. It is important to recognise that many factors have affected the death rate and the impact on the economy, and the full impacts of the pandemic are yet to be seen. However, it is by no means clear that the trade-off between greater emphasis on sacrificing lives or sacrificing the economy is as real as has been suggested. If such a trade-off does exist, it is, at best, a weak one.

Articles

Questions

  1. Define and explain the difference between a substitute and complementary good.
  2. Using your answer to question 1, describe the existence of a trade-off.
  3. Discuss the reasons why the trade-off between health and the economy would work in the opposite direction.
Posted on

Lockdown – again.  Is it worth it?

Mid-December saw a rapid rise in coronavirus cases in London and the South East and parts of eastern and central southern England. This was due to a new strain of Covid, which is more infectious. In response, the UK government introduced a new tier 4 level of restrictions for these areas from 20 December. These amount to a complete lockdown. The devolved administrations also announced lockdowns. In addition, the Christmas relaxation of rules was tightened across the UK. Households (up to three) were only allowed to get together on Christmas day and not the days either side (or one day between 23 and 27 December in the case of Northern Ireland). Tier 4 residents were not allowed to visit other households even on Christmas day.

The lockdowns aimed to slow the spread of the virus and reduce deaths. But this comes at a considerable short-term economic cost, especially to the retail and leisure sectors, which are required to close while the lockdowns remain in force. In taking the decision to introduce these tougher measures, the four administrations had to weigh up the benefits of reduced deaths and illness and pressure on the NHS against the short-term economic damage. As far a long-term economic damage is concerned, this might be even greater if lockdowns were not imposed and the virus spread more rapidly.

In a blog back in September, we examined the use of cost–benefit analysis (CBA) to aid decision-making about such decisions. The following is an updated version of that blog.

The use of cost–benefit analysis

It is commonplace to use cost–benefit analysis (CBA) in assessing public policies, such as whether to build a new hospital, road or rail line. Various attempts in the past few months have been made to use CBA in assessing policies to reduce the spread of the coronavirus. These have involved weighing up the costs and benefits of national or local lockdowns or other containment measures. But, as with other areas where CBA is used, there are serious problems of measuring costs and benefits and assessing risks. This is particularly problematic where human life is involved and where a value has to be attached to a life saved or lost.

The first step in a CBA is to identify the benefits and costs of the policy.

Identifying the benefits and costs of the lockdown

The benefits of the lockdown include lives saved and a reduction in suffering, not only for those who otherwise would have caught the virus but also for their family and friends. It also includes lives saved from other diseases whose treatment would have been put (even more) on hold if the pandemic had been allowed to rage and more people were hospitalised with the virus. In material terms, there is the benefit of saving in healthcare and medicines and the saving of labour resources. Then there are the environmental gains from less traffic and polluting activities.

On the cost side, there is the decline in output from businesses being shut and people being furloughed or not being able to find work. There is also a cost if schools have to close and children’s education is thereby compromised. Then there is the personal cost to people of being confined to home, a cost that could be great for those in cramped living conditions or in abusive relationships. Over the longer term, there is a cost from people becoming deskilled and firms not investing – so-called scarring effects. Here there are the direct effects and the multiplier effects on the rest of the economy.

Estimating uncertain outcomes

It is difficult enough identifying all the costs and benefits, but many occur in the future and here there is the problem of estimating the probability of their occurrence and their likely magnitude. Just how many lives will be saved from the policy and just how much will the economy be affected? Epidemiological and economic models can help, but there is a huge degree of uncertainty over predictions made about the spread of the disease, especially with a new strain of the virus, and the economic effects, especially over the longer term.

One estimate of the number of lives saved was made by Miles et al. in the NIESR paper linked below. A figure of 440 000 was calculated by subtracting the 60 000 actual excess deaths over the period of the first lockdown (March to June 2020) from a figure of 500 000 lives lost which, according to predictions, would have been the consequence of no lockdown. However, the authors acknowledge that this is likely to be a considerable overestimate because:

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.

Some help in estimating likely outcomes from locking down or not locking down the economy can be gained by comparing countries which have taken different approaches. The final article in the first list below compares the approaches in the UK and Sweden. Sweden had much lighter control measures than the UK and did not impose a lockdown. Using comparisons of the two approaches, the authors estimate that some 20 000 lives were saved by the lockdown – considerably less than the 440 000 estimate.

Estimating the value of a human life

To assess whether the saving of 20 000 lives was ‘worth it’, a value would have to be put on a life saved. Although putting a monetary value on a human life may be repugnant to many people, such calculations are made whenever a project is assessed which either saves or costs lives. As we say in the 10th edition of Economics (page 381):

Some people argue ‘You can’t put a price on a human life: life is priceless.’ But just what are they saying here? Are they saying that life has an infinite value? If so, the project must be carried out whatever the costs, and even if other benefits are zero! Clearly, when evaluating lives saved from the project, a value less than infinity must be given.
 
Other people might argue that human life cannot be treated like other costs and benefits and put into mathematical calculations. But what are these people saying? That the question of lives saved should be excluded from the cost–benefit study? If so, the implication is that life has a zero value! Again this is clearly not the case.

In practice, there are two approaches used to measure the value of a human life.

The first uses the value of a statistical life (VSL). This is based on the amount extra the average person would need to be paid to work in a job where there is a known probability of losing their life. So if people on average needed to be paid an extra £10 000 to work in a job with a 1% chance of losing their life, they would be valuing a life at £1 000 000 (£10 000/0.01). To avoid the obvious problem of young people’s lives being valued the same as old people’s ones, even though a 20 year-old on average will live much longer than a 70 year-old, a more common measure is the value of a statistical life year (VSLY).

A problem with VSL or VSLY measures is that they only take into account the quantity of years of life lost or saved, not the quality.

A second measure rectifies this problem. This is the ‘quality of life adjusted year (QALY)’. This involves giving a value to a year of full health and then reducing it according to how much people’s quality of life is reduced by illness, injury or poverty. The problem with this measure is the moral one that a sick or disabled person’s life is being valued less than the life of a healthy person. But it is usual to make such adjustments when considering medical intervention with limited resources.

One adjustment often made to QALYs or VSLYs is to discount years, so that one year gained would be given the full value and each subsequent year would be discounted by a certain percentage from the previous year – say, 3%. This would give a lower weighting to years in the distant future than years in the near future and hence would reduce the gap in predicted gains from a policy between young and old people.

Cost–effectiveness analysis (CEA)

Even using QALYs, there is still the problem of measuring life and health/sickness. A simpler approach is to use cost–effectiveness analysis (CEA). This takes a social goal, such as reducing the virus production rate (R) below 1 (e.g. to 0.9), and then finding the least-cost way of achieving this. As Mark Carney says in his third Reith Lecture:

As advocated by the economists Nick Stern and Tim Besley, the ideal is to define our core purpose first and then determine the most cost-effective interventions to achieve this goal. Such cost–effectiveness analysis explicitly seeks to achieve society’s values.

Cost–effectiveness analysis can take account of various externalities – as many of the costs will be – by giving them a value. For example, the costs of a lockdown to people in the hospitality sector or to the education of the young could be estimated and included in the costs. The analysis can also take into account issues of fairness by identifying the effects on inequality when certain groups suffer particularly badly from Covid or lockdown policies – groups such as the poor, the elderly and children. Achieving the goal of a specific R for the least cost, including external costs and attaching higher weights on the effects on certain groups then becomes the goal. As Carney says:

R brings public health and economics together. Relaxations of restrictions increase R, with economic, health and social consequences. A strategic approach to Covid is the best combination of policies to achieve the desired level of infection control at minimum economic cost with due respect for inequality, mental health and other social consequences, and calculating those costs then provides guidance when considering different containment strategies. That means paying attention to the impact on measures of fairness, the social returns to education, intergenerational equity and economic dynamism.

Conclusion

Given the uncertainties surrounding the measurement of the number of lives saved and the difficulties of assigning a value to them, and given the difficulties of estimating the economic and social effects of lockdowns, it is not surprising that the conclusions of a cost–benefit analysis, or even a cost–effectiveness analysis of a lockdown will be contentious. But, at least such analysis can help to inform discussion and drive future policy decisions. And a cost–effectiveness analysis can be a practical way of helping politicians reach difficult decisions about life and death and the economy.

Articles (original blog)

Articles (additional)

Questions

  1. What are the arguments for and against putting a monetary value on a life saved?
  2. Are QALYs the best way of measuring lives saved from a policy such as a lockdown?
  3. Compare the relative merits of cost–benefit analysis and cost–effectiveness analysis.
  4. If the outcomes of a lockdown are highly uncertain, does this strengthen or weaken the case for a lockdown? Explain.
  5. What specific problems are there in estimating the number of lives saved by a lockdown?
  6. How might the age distribution of people dying from Covid-19 affect the calculation of the cost of these deaths (or the benefits or avoiding them)?
  7. How might you estimate the costs to people who suffer long-term health effects from having had Covid-19?
  8. What are the arguments for and against using discounting in estimating future QALYs?
  9. The Department of Transport currently uses a figure of £1 958 303 (in 2018 prices) for the value of a life saved from a road safety project. Find out how this is figure derived and comment on it. See Box 12.5 in Economics 10th edition and Accident and casualty costs, Tables RAS60001 and RA60003, (Department of Transport, 2019).
Posted on

Assessing policies to tackle coronavirus: the use of cost–benefit analysis

It is commonplace to use cost–benefit analysis (CBA) in assessing public policies, such as whether to build a new hospital, road or rail line. Various attempts in the past few months have been made to use CBA in assessing policies to reduce the spread of the coronavirus. These have involved weighing up the costs and benefits of national or local lockdowns or other containment measures. But, as with other areas where CBA is used, there are serious problems of measuring costs and benefits and assessing risks. This is particularly problematic where human life is involved and where a value has to be attached to a life saved or lost.

Take the case of whether the government should have imposed a lockdown: an important question if there were to be a second wave and the government was considering introducing a second lockdown. The first step in a CBA is to identify the benefits and costs of the policy.

Identifying the benefits and costs of the lockdown

The benefits of the lockdown include lives saved and a reduction in suffering, not only for those who otherwise would have caught the virus but also for their family and friends. It also includes lives saved from other diseases whose treatment would have been put (even more) on hold if the pandemic had been allowed to rage and more people were hospitalised with the virus. In material terms, there is the benefit of saving in healthcare and medicines and the saving of labour resources. Then there are the environmental gains from less traffic and polluting activities.

On the cost side, there is the decline in output from businesses being shut and people being furloughed or not being able to find work. There is also a cost from schools being closed and children’s education being compromised. Then there is the personal cost to people of being confined to home, a cost that could be great for those in cramped living conditions or in abusive relationships. Over the longer term, there is a cost from people becoming deskilled and firms not investing – so-called scarring effects. Here there are the direct effects and the multiplier effects on the rest of the economy.

Estimating uncertain outcomes

It is difficult enough identifying all the costs and benefits, but many occur in the future and here there is the problem of estimating the probability of their occurrence and their likely magnitude. Just how many lives will be saved from the policy and just how much will the economy be affected? Epidemiological and economic models can help, but there is a huge degree of uncertainty over predictions made about the spread of the disease and the economic effects, especially over the longer term.

One estimate of the number of lives saved was made by Miles et al. in the NIESR paper linked below. A figure of 440 000 was calculated by subtracting the 60 000 actual excess deaths over the period of the lockdown from a figure of 500 000 lives lost which, according to predictions, would have been the consequence of no lockdown. However, the authors acknowledge that this is likely to be a considerable overestimate because:

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.

Some help in estimating likely outcomes from locking down or not locking down the economy can be gained by comparing countries which have taken different approaches. The final article below compares the approaches in the UK and Sweden. Sweden had much lighter control measures than the UK and did not impose a lockdown. Using comparisons of the two approaches, the authors estimate that some 20 000 lives were saved by the lockdown – considerably less than the 440 000 estimate.

Estimating the value of a human life

To assess whether the saving of 20 000 lives was ‘worth it’, a value would have to be put on a life saved. Although putting a monetary value on a human life may be repugnant to many people, such calculations are made whenever a project is assessed which either saves or costs lives. As we say in the 10th edition of Economics (page 381):

Some people argue ‘You can’t put a price on a human life: life is priceless.’ But just what are they saying here? Are they saying that life has an infinite value? If so, the project must be carried out whatever the costs, and even if other benefits are zero! Clearly, when evaluating lives saved from the project, a value less than infinity must be given.
 
Other people might argue that human life cannot be treated like other costs and benefits and put into mathematical calculations. But what are these people saying? That the question of lives saved should be excluded from the cost–benefit study? If so, the implication is that life has a zero value! Again this is clearly not the case.

In practice there are two approaches used to measuring the value of a human life.

The first uses the value of a statistical life (VSL). This is based on the amount extra the average person would need to be paid to work in a job where there is a known probability of losing their life. So if people on average needed to be paid an extra £10 000 to work in a job with a 1% chance of losing their life, they would be valuing a life at £1 000 000 (£10 000/0.01). To avoid the obvious problem of young people’s lives being valued the same as old people’s ones, even though a 20 year-old on average will live much longer than a 70 year-old, a more common measure is the value of a statistical life year (VSLY).

A problem with VSL or VSLY measures is that they only take into account the quantity of years of life lost or saved, not the quality.

A second measure rectifies this problem. This is the ‘quality of life adjusted year (QALY)’. This involves giving a value to a year of full health and then reducing it according to how much people’s quality of life is reduced by illness, injury or poverty. The problem with this measure is the moral one that a sick or disabled person’s life is being valued less than the life of a healthy person. But it is usual to make such adjustments when considering medical intervention with limited resources.

One adjustment often made to QALYs or VSLYs is to discount years, so that one year gained would be given the full value and each subsequent year would be discounted by a certain percentage from the previous year – say, 3%. This would give a lower weighting to years in the distant future than years in the near future and hence would reduce the gap in predicted gains from a policy between young and old people.

Conclusion

Given the uncertainties surrounding the measurement of the number of lives saved and the difficulties of assigning a value to them, it is not surprising that the conclusions of a cost–benefit analysis of a lockdown will be contentious. And we have yet to see what the long-term effects on the economy will be. But, at least a cost–benefit analysis of the lockdown can help to inform discussion and help to drive future policy decisions about tackling a second wave, whether internationally, nationally or locally.

Articles

Questions

  1. What are the arguments for and against putting a monetary value on a life saved?
  2. Are QALYs the best way of measuring lives saved from a policy such as a lockdown?
  3. If the outcomes of a lockdown are highly uncertain, does this strengthen or weaken the case for a lockdown? Explain.
  4. What specific problems are there in estimating the number of lives saved by a lockdown?
  5. How might the age distribution of people dying from Covid-19 affect the calculation of the cost of these deaths (or the benefits or avoiding them)?
  6. How might you estimate the costs to people who suffer long-term health effects from having had Covid-19?
  7. What are the arguments for and against using discounting in estimating future QALYs?
  8. The Department of Transport currently uses a figure of £1 958 303 (in 2018 prices) for the value of a life saved from a road safety project. Find out how this is figure derived and comment on it. See Box 12.5 in Economics 10th edition and Accident and casualty costs, Tables RAS60001 and RA60003, (Department of Transport, 2019).