Tag: Uncertainty

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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).
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The macroeconomic outlook over the next few years: a Covid hangover

Back in June, we examined the macroeconomic forecasts of the three agencies, the IMF, the OECD and the European Commission, all of which publish forecasts every six months. The IMF has recently published its latest World Economic Outlook (WEO) and its accompanying database. Unlike the April WEO, which, given the huge uncertainty surrounding the pandemic and its economic effects, only forecast as far as 2021, the latest version forecasts as far ahead as 2025.

In essence the picture is similar to that painted in April. The IMF predicts a large-scale fall in GDP and rise in unemployment, government borrowing and government debt for 2020 (compared with 2019) across virtually all countries.

World real GDP is predicted to fall by 4.4%. For many countries the fall will be much steeper. In the UK, GDP is predicted to fall by 9.8%; in the eurozone, by 8.3%; in India, by 10.3%; in Italy, by 10.8%; in Spain, by 12.8%. There will then be somewhat of a ‘bounce back’ in GDP in 2021, but not to the levels of 2019. World real GDP is predicted to rise by 5.2% in 2021. (Click here for a PowerPoint of the growth chart.)

Unemployment will peak in some countries in 2020 and in others in 2021 depending on the speed of recovery from recession and the mobility of labour. (Click here for a PowerPoint of the unemployment chart.)

Inflation is set to fall from already low levels. Several countries are expected to see falling prices.

Government deficits (negative net lending) will be sharply higher in 2020 as a result of government measures to support workers and firms affected by lockdowns and falling demand. Governments will also receive reduced tax revenues. (Click here for a PowerPoint of the general government net lending chart.)

Government debt will consequently rise more rapidly. Deficits are predicted to fall in 2021 as economies recover and hence the rise in debt will slow down or in some cases, such as Germany, even fall. (Click here for a PowerPoint of the general government gross debt chart.)

After the rebound in 2021, global growth is then expected to slow to around 3.5% by 2025. This compares with an average of 3.8% from 2000 to 2019. Growth of advanced economies is expected to slow to 1.7%. It averaged 1.9% from 2000 to 2019. For emerging market and developing countries it is expected to slow to 4.7% from an average of 5.7% from 2000 to 2019. These figures suggest some longer-term scarring effects from the pandemic.

Uncertainties

In the short term, the greatest uncertainty concerns the extent of the second wave, the measures put in place to contain the spread of the virus and the compensation provided by governments to businesses and workers. The WEO report was prepared when the second wave was only just beginning. It could well be that countries will experience a deeper recession in 2000 and into 2021 than predicted by the IMF.

This is recognised in the forecast.

The persistence of the shock remains uncertain and relates to factors inherently difficult to predict, including the path of the pandemic, the adjustment costs it imposes on the economy, the effectiveness of the economic policy response, and the evolution of financial sentiment.

With some businesses forced to close, others operating at reduced capacity because of social distancing in the workplace and with dampened demand, many countries may find output falling again. The extent will to a large extent depend on the levels of government support.

In the medium term, it is assumed that there will be a vaccine and that economies can begin functioning normally again. However, the report does recognise the long-term scarring effects caused by low levels of investment, deskilling and demotivation of the parts of the workforce, loss of capacity and disruptions to various supply chains.

The deep downturn this year will damage supply potential to varying degrees across economies. The impact will depend on various factors … including the extent of firm closures, exit of discouraged workers from the labour force, and resource mismatches (sectoral, occupational and geographic).

One of the greatest uncertainties in the medium term concerns the stance of fiscal and monetary policies. Will governments continue to run large deficits to support demand or will they attempt to reduce deficits by raising taxes and/or reducing benefits and/or cutting government current or capital expenditure?

Will central banks continue with large-scale quantitative easing and ultra-low or even negative interest rates? Will they use novel forms of monetary policy, such as directly funding government deficits with new money or providing money directly to citizens through a ‘helicopter’ scheme (see the 2016 blog, New UK monetary policy measures – somewhat short of the kitchen sink)?

Forecasting at the current time is fraught with uncertainty. However, reports such as the WEO are useful in identifying the various factors influencing the economy and how seriously they may impact on variables such as growth, unemployment and government deficits.

Report, speeches and data

Articles

Questions

  1. Explain what is meant by ‘scarring effects’. Identify various ways in which the pandemic is likely to affect aggregate supply over the longer term.
  2. Consider the arguments for and against governments continuing to run large budget deficits over the next few years.
  3. What are the arguments for and against using ‘helicopter money’ in the current circumstances?
  4. On purely economic grounds, what are the arguments for imposing much stricter lockdowns when Covid-19 rates are rising rapidly?
  5. Chose two countries other than the UK, one industrialised and one developing. Consider what policies they are pursuing to achieve an optimal balance between limiting the spread of the virus and protecting the economy.
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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).
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A global supply-side shock: the impact of the coronavirus (COVID-19) outbreak

The global economic impact of the coronavirus outbreak is uncertain but potentially very large. There has already been a massive effect on China, with large parts of the Chinese economy shut down. As the disease spreads to other countries, they too will experience supply shocks as schools and workplaces close down and travel restrictions are imposed. This has already happened in South Korea, Japan and Italy. The size of these effects is still unknown and will depend on the effectiveness of the containment measures that countries are putting in place and on the behaviour of people in self isolating if they have any symptoms or even possible exposure.

The OECD in its March 2020 interim Economic Assessment: Coronavirus: The world economy at risk estimates that global economic growth will be around half a percentage point lower than previously forecast – down from 2.9% to 2.4%. But this is based on the assumption that ‘the epidemic peaks in China in the first quarter of 2020 and outbreaks in other countries prove mild and contained.’ If the disease develops into a pandemic, as many health officials are predicting, the global economic effect could be much larger. In such cases, the OECD predicts a halving of global economic growth to 1.5%. But even this may be overoptimistic, with growing talk of a global recession.

Governments and central banks around the world are already planning measures to boost aggregate demand. The Federal Reserve, as an emergency measure on 3 March, reduced the Federal Funds rate by half a percentage point from the range of 1.5–1.75% to 1.0–1.25%. This was the first emergency rate cut since 2008.

Economic uncertainty

With considerable uncertainty about the spread of the disease and how effective containment measures will be, stock markets have fallen dramatically. The FTSE 100 fell by nearly 14% in the second half of February, before recovering slightly at the beginning of March. It then fell by a further 7.7% on 9 March – the biggest one-day fall since the 2008 financial crisis. This was specifically in response to a plunge in oil prices as Russia and Saudi Arabia engaged in a price war. But it also reflected growing pessimism about the economic impact of the coronavirus as the global spread of the epidemic accelerated and countries were contemplating more draconian lock-down measures.

Firms have been drawing up contingency plans to respond to panic buying of essential items and falling demand for other goods. Supply-chain managers are working out how to respond to these changes and to disruptions to supplies from China and other affected countries.

Firms are also having to plan for disruptions to labour supply. Large numbers of employees may fall sick or be advised/required to stay at home. Or they may have to stay at home to look after children whose schools are closed. For some firms, having their staff working from home will be easy; for others it will be impossible.

Some industries will be particularly badly hit, such as airlines, cruise lines and travel companies. Budget airlines have cancelled several flights and travel companies are beginning to offer substantial discounts. Manufacturing firms which are dependent on supplies from affected countries have also been badly hit. This is reflected in their share prices, which have seen large falls.

Longer-term effects

Uncertainty could have longer-term impacts on aggregate supply if firms decide to put investment on hold. This would also impact on the capital goods industries which supply machinery and equipment to investing firms. For the UK, already having suffered from Brexit uncertainty, this further uncertainty could prove very damaging for economic growth.

While aggregate supply is likely to fall, or at least to grow less quickly, what will happen to the balance of aggregate demand and supply is less clear. A temporary rise in demand, as people stock up, could see a surge in prices, unless supermarkets and other firms are keen to demonstrate that they are not profiting from the disease. In the longer term, if aggregate demand continues to grow at past rates, it will probably outstrip the growth in aggregate supply and result in rising inflation. If, however, demand is subdued, as uncertainty about their own economic situation leads people to cut back on spending, inflation and even the price level may fall.

How quickly the global economy will ‘bounce back’ depends on how long the outbreak lasts and whether it becomes a serious pandemic and on how much investment has been affected. At the current time, it is impossible to predict with any accuracy the timing and scale of any such bounce back.

Articles

eBook

Questions

  1. Using a supply and demand diagram, illustrate the fall in stock market prices caused by concerns over the effects of the coronavirus.
  2. Using either (i) an aggregate demand and supply diagram or (ii) a DAD/DAS diagram, illustrate how a fall in aggregate supply as a result of the economic effects of the coronavirus would lead to (a) a fall in real income and (i) a fall in the price level or (ii) a fall in inflation; (b) a fall in real income and (i) a rise in the price level or (ii) a rise in inflation.
  3. What would be the likely effects of central banks (a) cutting interest rates; (b) engaging in further quantitative easing?
  4. What would be the likely effects of governments running a larger budget deficit as a means of boosting the economy?
  5. Distinguish between stabilising and destabilising speculation. How would you characterise the speculation that has taken place on stock markets in response to the coronavirus?
  6. What are the implications of people being paid on zero-hour contracts of the government requiring workplaces to close?
  7. What long-term changes to working practices and government policy could result from short-term adjustments to the epidemic?
  8. Is the long-term macroeconomic impact of the coronavirus likely to be zero, as economies bounce back? Explain.
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Evolving Economics

The linked article below, by Evan Davis, assesses the state of economics. He argues that economics has had some major successes over the years in providing a framework for understanding how economies function and how to increase incomes and well-being more generally.

Over the last few decades, economists have …had an influence over every aspect of our lives. …And during this era in which economists have reigned, the world has notched up some marked successes. The reduction in the proportion of human beings living in abject poverty over the last thirty years has been extraordinary.

With the development of concepts such as opportunity cost, the prisoners’ dilemma, comparative advantage and the paradox of thrift, economics has helped to shape the way policymakers perceive economic issues and policies.

These concepts are ‘threshold concepts’. Understanding and being able to relate and apply these core economic concepts helps you to ‘think like an economist’ and to relate the different parts of the subject to each other. Both Economics (10th edition) and Essentials of Economics (8th edition) examine 15 of these threshold concepts. Each time a threshold concept is used in the text, a ‘TC’ icon appears in the margin with the appropriate number. By locating them in this way, you can see their use in a variety of contexts.

But despite the insights provided by traditional economics into the various problems that society faces, the discipline of economics has faced criticism, especially since the financial crisis, which most economists did not foresee.

Even Davis identifies two major shortcomings of the discipline – both beginning with ‘C’. ‘One is complexity, the other is community.’

In terms of complexity, the criticism is that economic models are often based on simplistic assumptions, such as ‘rational maximising behaviour’. This might make it easier to express the models mathematically, but mathematical elegance does not necessarily translate into predictive accuracy. Such models do not capture the ‘messiness’ of the real world.

These models have a certain theoretical elegance but there is now an increasing sense that economies do not evolve along a well-defined mathematical path, but in a far more messy way. The individual players within the economy face radical uncertainty; they adapt and learn as they go; they watch what everybody else does. The economy stumbles along in a process of slow discovery, full of feedback loops.

As far as ‘community’ is concerned, people do not just act as self-interested individuals. Their actions are often governed by how other people behave and also by how their own actions will affect other people, such as family, friends, colleagues or society more generally.

And the same applies to firms. They will be influenced by various other firms, such as competitors, trend setters and suppliers and also by a range of stakeholders – not just shareholders, but also workers, customers, local communities, etc. A firm’s aim is thus unlikely to be simple short-term profit maximisation.

And this broader set of interests translates into policy. The neoliberal free-market, laissez-faire approach to policy is challenged by the desire to take account of broader questions of equity, community and social justice. However privately efficient a free market is, it does not take account of the full social and environmental costs and benefits of firms’ and consumers’ actions or a fair distribution of income and wealth.

It would be wrong, however, to say that economics has not responded to these complexities and concerns. The analysis of externalities, income distribution, incentives, herd behaviour, uncertainty, speculation, cumulative causation and institutional values and biases are increasingly embedded in the economics curriculum and in economic research. What is more, behavioural economics is becoming increasingly mainstream in examining the behaviour of consumers, workers, firms and government. We have tried to reflect these developments in successive editions of our four textbooks.

Article

Questions

  1. Write a brief defence of traditional economic analysis (i.e. that based on the assumption of ‘rational economic behaviour’).
  2. What are the shortcomings of traditional economic analysis?
  3. What is meant by ‘behavioural economics’ and how does it address the concerns raised in Evan Davis’ article?
  4. How is herd behaviour relevant to explaining macroeconomic fluctuations?
  5. Identify various stakeholder groups of an energy company. What influence are they likely to have on the company’s behaviour?
  6. In an era of social media, web-based information and e-commerce, why might it be necessary to rethink the concept of GDP and its measurement?
  7. What is meant by an efficient stock market? Why may the stock market not be efficient?