Artificial intelligence is having a profound effect on economies and society. From production, to services, to healthcare, to pharmaceuticals; to education, to research, to data analysis; to software, to search engines; to planning, to communication, to legal services, to social media – to our everyday lives, AI is transforming the way humans interact. And that transformation is likely to accelerate. But what will be the effects on GDP, on consumption, on jobs, on the distribution of income, and human welfare in general? These are profound questions and ones that economists and other social scientists are pondering. Here we look at some of the issues and possible scenarios.
According to the Merrill/Bank of America article linked below, when asked about the potential for AI, ChatGPT replied:
AI holds immense potential to drive innovation, improve decision-making processes and tackle complex problems across various fields, positively impacting society.
But the magnitude and distribution of the effects on society and economic activity are hard to predict. Perhaps the easiest is the effect on GDP. AI can analyse and interpret data to meet economic goals. It can do this much more extensively and much quicker than using pre-AI software. This will enable higher productivity across a range of manufacturing and service industries. According to the Merrill/Bank of America article, ‘global revenue associated with AI software, hardware, service and sales will likely grow at 19% per year’. With productivity languishing in many countries as they struggle to recover from the pandemic, high inflation and high debt, this massive boost to productivity will be welcome.
But whilst AI may lead to productivity growth, its magnitude is very hard to predict. Both the ‘low-productivity future’ and the ‘high-productivity future’ described in the IMF article linked below are plausible. Productivity growth from AI may be confined to a few sectors, with many workers displaced into jobs where they are less productive. Or, the growth in productivity may affect many sectors, with ‘AI applied to a substantial share of the tasks done by most workers’.
Even if AI does massively boost the growth in world GDP, the distribution is likely to be highly uneven, both between countries and within countries. This could widen the gap between rich and poor and create a range of social tensions.
In terms of countries, the main beneficiaries will be developed countries in North America, Europe and Asia and rapidly developing countries, largely in Asia, such as China and India. Poorer developing countries’ access to the fruits of AI will be more limited and they could lose competitive advantage in a number of labour-intensive industries.
Then there is growing inequality between the companies controlling AI systems and other economic actors. Just as companies such as Microsoft, Apple, Google and Meta grew rich as computing, the Internet and social media grew and developed, so these and other companies at the forefront of AI development and supply will grow rich, along with their senior executives. The question then is how much will other companies and individuals benefit. Partly, it will depend on how much production can be adapted and developed in light of the possibilities that AI presents. Partly, it will depend on competition within the AI software market. There is, and will continue to be, a rush to develop and patent software so as to deliver and maintain monopoly profits. It is likely that only a few companies will emerge dominant – a natural oligopoly.
Then there is the likely growth of inequality between individuals. The reason is that AI will have different effects in different parts of the labour market.
The labour market
In some industries, AI will enhance labour productivity. It will be a tool that will be used by workers to improve the service they offer or the items they produce. In other cases, it will replace labour. It will not simply be a tool used by labour, but will do the job itself. Workers will be displaced and structural unemployment is likely to rise. The quicker the displacement process, the more will such unemployment rise. People may be forced to take more menial jobs in the service sector. This, in turn, will drive down the wages in such jobs and employers may find it more convenient to use gig workers than employ workers on full- or part-time contracts with holidays and other rights and benefits.
But the development of AI may also lead to the creation of other high-productivity jobs. As the Goldman Sachs article linked below states:
Jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth… For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals. There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services.
Nevertheless, people could still lose their jobs before being re-employed elsewhere.
The possible rise in structural unemployment raises the question of retraining provision and its funding and whether workers would be required to undertake such retraining. It also raises the question of whether there should be a universal basic income so that the additional income from AI can be spread more widely. This income would be paid in addition to any wages that people earn. But a universal basic income would require finance. How could AI be taxed? What would be the effects on incentives and investment in the AI industry? The Guardian article, linked below, explores some of these issues.
The increased GDP from AI will lead to higher levels of consumption. The resulting increase in demand for labour will go some way to offsetting the effects of workers being displaced by AI. There may be new employment opportunities in the service sector in areas such as sport and recreation, where there is an emphasis on human interaction and where, therefore, humans have an advantage over AI.
Another issue raised is whether people need to work so many hours. Is there an argument for a four-day or even three-day week? We explored these issues in a recent blog in the context of low productivity growth. The arguments become more compelling when productivity growth is high.
AI users are not all benign. As we are beginning to see, AI opens the possibility for sophisticated crime, including cyberattacks, fraud and extortion as the technology makes the acquisition and misuse of data, and the development of malware and phishing much easier.
Another set of issues arises in education. What knowledge should students be expected to acquire? Should the focus of education continue to shift towards analytical skills and understanding away from the simple acquisition of knowledge and techniques. This has been a development in recent years and could accelerate. Then there is the question of assessment. Generative AI creates a range of possibilities for plagiarism and other forms of cheating. How should modes of assessment change to reflect this problem? Should there be a greater shift towards exams or towards project work that encourages the use of AI?
Finally, there is the issue of the sort of society we want to achieve. Work is not just about producing goods and services for us as consumers – work is an important part of life. To the extent that AI can enhance working life and take away a lot of routine and boring tasks, then society gains. To the extent, however, that it replaces work that involved judgement and human interaction, then society might lose. More might be produced, but we might be less fulfilled.
- The Macroeconomics of Artificial Intelligence
IMF publications, Erik Brynjolfsson and Gabriel Unger (December 2023)
- Economic impacts of artificial intelligence (AI)
European Parliamentary Research Service, Marcin Szczepański (July 2019)
- Artificial intelligence: A real game changer
Chief Investment Office, Merrill/Bank of America (July 2023)
- Generative AI could raise global GDP by 7%
Goldman Sachs, Joseph Briggs (5/4/23)
- The macroeconomic impact of artificial intelligence
PwC, Jonathan Gillham, Lucy Rimmington, Hugh Dance, Gerard Verweij, Anand Rao, Kate Barnard Roberts and Mark Paich (February 2018)
- How genAI is revolutionizing the field of economics
CNN, Bryan Mena and Samantha Delouya (12/10/23)
- AI-powered digital colleagues are here. Some ‘safe’ jobs could be vulnerable.
BBC Worklife, Sam Becker (30/11/23)
- Generative AI and Its Economic Impact: What You Need to Know
Investopedia, Jim Probasco (1/12/23)
- AI is coming for our jobs! Could universal basic income be the solution?
The Guardian Philippa Kelly (16/11/23)
- CFPB chief’s warning: AI is a ‘natural oligopoly’ in the making
Politico, Sam Sutton (21/11/23)
- Which industries are most likely to benefit from the development of AI?
- Distinguish between labour-replacing and labour-augmenting technological progress in the context of AI.
- How could AI reduce the amount of labour per unit of output and yet result in an increase in employment?
- What people are most likely to (a) gain, (b) lose from the increasing use of AI?
- Is the distribution of income likely to become more equal or less equal with the development and adoption of AI? Explain.
- What policies could governments adopt to spread the gains from AI more equally?
To finance budget deficits, governments have to borrow. They can borrow short-term by issuing Treasury bills, typically for 1, 3 or 6 months. These do not earn interest and hence are sold at a discount below the face value. The rate of discount depends on supply and demand and will reflect short-term market rates of interest. Alternatively, governments can borrow long-term by issuing bonds. In the UK, these government securities are known as ‘gilts’ or ‘gilt-edged securities’. In the USA they are known as ‘treasury bonds’, ‘T-bonds’ or simply ‘treasuries’. In the EU, countries separately issue bonds but the European Commission also issues bonds.
In the UK, gilts are issued by the Debt Management Office on behalf of the Treasury. Although there are index-linked gilts, the largest proportion of gilts are conventional gilts. These pay a fixed sum of money per annum per £100 of face value. This is known as the ‘coupon payment’ and the rate is set at the time of issue. The ‘coupon rate’ is the payment per annum as a percentage of the bond’s face value:
Payments are made six-monthly. Each issue also has a maturity date, at which point the bonds will be redeemed at face value. For example, a 4½% Treasury Gilt 2028 bond has a coupon rate of 4½% and thus pays £4.50 per annum (£2.25 every six months) for each £100 of face value. The issue will be redeemed in June 2028 at face value. The issue was made in June 2023 and thus represented a 5-year bond. Gilts are issued for varying lengths of time from 2 to 55 years. At present, there are 61 different conventional issues of bonds, with maturity dates varying from January 2024 to October 2073.
Bonds can be sold on the secondary market (i.e. the stock market) before maturity. The market price, however, is unlikely to be the coupon price (i.e. the face value). The lower the coupon rate relative to current interest rates, the less valuable the bond will be. For example, if interest rates rise, and hence new bonds pay a higher coupon rate, the market price of existing bonds paying a lower coupon rate must fall. Thus bond prices vary inversely with interest rates.
The market price also depends on how close the bonds are to maturity. The closer the maturity date, the closer the market price of the bond will be to the face value.
Bond yields: current yield
A bond’s yield is the percentage return that a person buying the bond receives. If a newly issued bond is bought at the coupon price, its yield is the coupon rate.
However, if an existing bond is bought on the secondary market (the stock market), the yield must reflect the coupon payments relative to the purchase price, not the coupon price. We can distinguish between the ‘current yield’ and the ‘yield to maturity’.
The current yield is the coupon payment as a percentage of the current market price of the bond:
Assume a bond were originally issued at 2% (its coupon rate) and thus pays £2 per annum. In the meantime, however, assume that interest rates have risen and new bonds now have a coupon rate of 4%, paying £4 per annum for each £100 invested. To persuade people to buy old bonds with a coupon rate of 2%, their market prices must fall below their face value (their coupon price). If their price halved, then they would pay £2 for every £50 of their market price and hence their current yield would be 4% (£2/£50 × 100).
Bond yields: yield to maturity (YTM)
But the current yield does not give the true yield – it is only an approximation. The true yield must take into account not just the market price but also the maturity value and the length of time to maturity (and the frequency of payments too, which we will ignore here). The closer a bond is to its maturity date, the higher/lower will be the true yield if the price is below/above the coupon price: in other words, the closer will the market price be to the coupon price for any given market rate of interest.
A more accurate measure of a bond’s yield is thus the ‘yield to maturity’ (YTM). This is the interest rate which makes the present value of all a bond’s future cash flows equal to its current price. These cash flows include all coupon payments and the payment of the face value on maturity. But future cash flows must be discounted to take into account the fact that money received in the future is worth less than money received now, since money received now could then earn interest.
The yield to maturity is the internal rate of return (IRR) of the bond. This is the discount rate which makes the present value (PV) of all the bond’s future cash flows (including the maturity payment of the coupon price) equal to its current market price. For simplicity, we assume that coupon payments are made annually. The formula is the one where the bond’s current market price is given by:
Where: t is the year; n is the number of years to maturity; YTM is the yield to maturity.
Thus if a bond paid £5 each year and had a maturity value of £100 and if current interest rates were higher than 5%, giving a yield to maturity of 8%, then the bond price would be:
In other words, with a coupon rate of 5% and a higher YTM of 8%, the bond with a face value of £100 and five years to maturity would be worth only £88.02 today.
If you know the market price of a given bond, you can work out its YTM by substituting in the above formula. The following table gives examples.
The higher the YTM, the lower the market price of a bond. Since the YTM reflects in part current rates of interest, so the higher the rate of interest, the lower the market price of any given bond. Thus bond yields vary directly with interest rates and bond prices vary inversely. You can see this clearly from the table. You can also see that market bond prices converge on the face value as the maturity date approaches.
Recent activity in bond markets
Investing in government bonds is regarded as very safe. Coupon payments are guaranteed, as is repayment of the face value on the maturity date. For this reason, many pension funds hold a lot of government bonds issued by financially trustworthy governments. But in recent months, bond prices in the secondary market have fallen substantially as interest rates have risen. For those holding existing bonds, this means that their value has fallen. For governments wishing to borrow by issuing new bonds, the cost has risen as they have to offer a higher coupon rate to attract buyers. This make it more expensive to finance government debt.
The chart shows the yield on 10-year government bonds. It is calculated using the ‘par value’ approach. This gives the coupon rate that would have to be paid for the market price of a bond to equal its face value. Clearly, as interest rates rise, a bond would have to pay a higher coupon rate for this to happen. (This, of course, is only hypothetical to give an estimate of market rates, as coupon rates are fixed at the time of a bond’s issue.)
Par values reflect both yield to maturity and also expectations of future interest rates. The higher people expect future interest rates to be, the higher must par values be to reflect this.
In the years following the financial crisis of 2007–8 and the subsequent recession, and again during the COVID pandemic, central banks cut interest rates and supported this by quantitative easing. This involved central banks buying existing bonds on the secondary market and paying for them with newly created (electronic) money. This drove up bond prices and drove down yields (as the chart shows). This helped support the policy of low interest rates. This was a boon to governments, which were able to borrow cheaply.
This has all changed. With quantitative tightening replacing quantitative easing, central banks have been engaging in asset sales, thereby driving down bond prices and driving up yields. Again, this can be seen in the chart. This has helped to support a policy of higher interest rates.
Problems of higher bond yields/lower bond prices
Although lower bond prices and higher yields have supported a tighter monetary policy, which has been used to fight inflation, this has created problems.
First, it has increased the cost of financing government debt. In 2007/8, UK public-sector net debt was £567bn (35.6% of GDP). The Office for Budget Responsibility forecasts that it will be £2702bn (103.1% of GDP in the current financial year – 2023/24). Not only, therefore, are coupon rates higher for new government borrowing, but the level of borrowing is now a much higher proportion of GDP. In 2020/21, central government debt interest payments were 1.2% of GDP; by 2022/23, they were 4.4% (excluding interest on gilts held in the Bank of England, under the Asset Purchase Facility (quantitative easing)).
In the USA, there have been similar increases in government debt and debt interest payments. Debt has increased from $9tn in 2007 to $33.6tn today. Again, with higher interest rates, debt interest as a percentage of GDP has risen: from 1.5% of GDP in 2021 to a forecast 2.5% in 2023 and 3% in 2024. What is more, 31 per cent of US government bonds will mature next year and will need refinancing – at higher coupon rates.
There is a similar picture in other developed countries. Clearly, higher interest payments leave less government revenue for other purposes, such as health and education.
Second, many pension funds, banks and other investment companies hold large quantities of bonds. As their price falls, so this reduces the value of these companies’ assets and makes it harder to finance new purchases, or payments or loans to customers. However, the fact that new bonds pay higher interest rates means that when existing bond holdings mature, the money can be reinvested at higher rates.
Third, bonds are often used by companies as collateral against which to borrow and invest in new capital. As bond prices fall, this can hamper companies’ ability to invest, which will lead to lower economic growth.
Fourth, higher bond yields divert demand away from equities (shares). With equity markets falling back or at best ceasing to rise, this erodes the value of savings in equities and may make it harder for firms to finance investment through new issues.
At the core of all these problems is inflation and budget deficits. Central banks have responded by raising interest rates. This drives up bond yields and drives down bond prices. But bond prices and yields depend not just on current interest rates, but also on expectations about future interest rates. Expectations currently are that budget deficits will be slow to fall as governments seek to support their economies post-COVID. Also expectations are that inflation, even though it is falling, is not falling as fast as originally expected – a problem that could be exacerbated if global tensions increase as a result of the ongoing war in Ukraine, the Israel/Gaza war and possible increased tensions with China concerning disputes in the China Sea and over Taiwan. Greater risks drive up bond yields as investors demand a higher interest premium.
Information and data
- Why do bond prices and bond yields vary inversely?
- How are bond yields and prices affected by expectations?
- Why are ‘current yield’ and ‘yield to maturity’ different?
- What is likely to happen to bond prices and yields in the coming months? Explain your reasoning.
- What constraints do bond markets place on fiscal policy?
- Would it be desirable for central banks to pause their policy of quantitative tightening?
Speculation in markets can lead to wild swings in prices as exuberance drives up prices and
pessimism leads to price crashes. When the rise in price exceeds underlying fundamentals, such as profit, the result is a bubble. And bubbles burst.
There have been many examples of bubbles throughout history. One of the most famous is that of tulips in the 17th century. As Box 2.4 in Essential Economics for Business (6th edition) explains:
Between November 1636 and February 1637, there was a 20-fold increase in the price of tulip bulbs, such that a skilled worker’s annual salary would not even cover the price of one bulb. Some were even worth more than a luxury home! But, only three months later, their price had fallen by 99 per cent. Some traders refused to pay the high price and others began to sell their tulips. Prices began falling. This dampened demand (as tulips were seen to be a poor investment) and encouraged more people to sell their tulips. Soon the price was in freefall, with everyone selling. The bubble had burst .
Another example was the South Sea Bubble of 1720. Here, shares in the South Sea Company, given a monopoly by the British government to trade with South America, increased by 900% before collapsing through a lack of trade.
Another, more recent, example is that of Poseidon. This was an Australian nickel mining company which announced in September 1969 that it had discovered a large seam of nickel at Mount Windarra, WA. What followed was a bubble. The share price rose from $0.80 in mid-1969 to a peak of $280 in February 1970 and then crashed to just a few dollars.
Other examples are the Dotcom bubble of the 1990s, the US housing bubble of the mid-2000s and BitCoin, which has seen more than one bubble.
Bubbles always burst eventually. If you buy at a low price and sell at the peak, you can make a lot of money. But many will get their fingers burnt. Those who come late into the market may pay a high price and, if they are slow to sell, can then make a large loss.
GameStop shares – an unlikely candidate for a bubble
The most recent example of a bubble is GameStop. This is a chain of shops in the USA selling games, consoles and other electronic items. During the pandemic it has struggled, as games consumers have turned to online sellers of consoles and online games. It has been forced to close a number of stores. In July 2020, its share price was around $4. With the general recovery in stock markets, this drifted upwards to just under $20 by 12 January 2021.
Then the bubble began.
Hedge fund shorting
Believing that the GameStop shares were now overvalued and likely to fall, many hedge funds started shorting the shares. Shorting (or ‘short selling’) is where investors borrow shares for a fee and immediately sell them on at the current price, agreeing to return them to the lender on a specified day in the near future (the ‘expiration date’). But as the investors have sold the shares they borrowed, they must now buy them at the current price on or before the expiration date so they can return them to the lenders. If the price falls between the two dates, the investors will gain. For example, if you borrow shares and immediately sell them at a current price of £5 and then by the expiration date the price has fallen to $2 and you buy them back at that price to return them to the lender, you make a £3 profit.
But this is a risky strategy. If the price rises between the two dates, investors will lose – as events were to prove.
The swarm of small investors
Enter the ‘armchair investor’. During lockdown, small-scale amateur investing in shares has become a popular activity, with people seeking to make easy gains from the comfort of their own homes. This has been facilitated by online trading platforms such as Robinhood and Trading212. These are easy and cheap, or even free, to use.
What is more, many users of these sites were also collaborating on social media platforms, such as Reddit. They were encouraging each other to buy shares in GameStop and some other companies. In fact, many of these small investors were seeing it as a battle with large-scale institutional investors, such as hedge funds – a David vs. Goliath battle.
With swarms of small investors buying GameStop, its share price surged. From $20 on 12 January, it doubled in price within two days and had reached $77 by 25 January. The frenzy on Reddit then really gathered pace. The share price peaked at $468 early on 28 January. It then fell to $126 less than two hours later, only to rise again to $354 at the beginning of the next day.
Many large investors who had shorted GameStop shares made big losses. Analytics firm Ortex estimated that hedge funds lost a total of $12.5 billion in January. Many small investors, however, who bought early and sold at the peak made huge gains. Other small investors who got the timing wrong made large losses.
And it was not just GameStop. Social media were buzzing with suggestions about buying shares in other poorly performing companies that large-scale institutional investors were shorting. Another target was silver and silver mines. At one point, silver prices rose by more than 10% on 1 February. However, money invested in silver is huge relative to GameStop and hence small investors were unlikely to shift prices by anything like as much as GameStop shares.
Amidst this turmoil, the US Securities and Exchange Commission (SEC) issued a statement on 29 January. It warned that it was working closely with other regulators and the US stock exchange ‘to ensure that regulated entities uphold their obligations to protect investors and to identify and pursue potential wrongdoing’. It remains to be seen, however, what it can do to curb the concerted activities of small investors. Perhaps, only the experience of bubbles bursting and the severe losses that can result will make small investors think twice about backing failing companies. Some Davids may beat Goliath; others will be defeated.
- GameStop: The competing forces trading blows over lowly gaming retaile
Sky News (30/1/21)
- Tempted to join the GameStop ‘angry mob’? Lessons on bubbles, market abuse and stock picking from the investment experts… including perma-bear Albert Edwards
This is Money, Tanya Jefferies (29/1/21)
- A year ago on Reddit I suggested investing in GameStop. But I never expected this
The Guardian, Desmund Delaney (29/1/21)
- The real lesson of the GameStop story is the power of the swarm
The Guardian, Brett Scott (30/1/21)
- GameStop: What is it and why is it trending?
BBC News, Kirsty Grant (29/1/21)
- GameStop: Global watchdogs sound alarm as shares frenzy grows
BBC News (30/1/21)
- The GameStop affair is like tulip mania on steroids
The Guardian, Dan Davies (29/1/21)
- GameStop news: Short sellers lose $19bn as Omar says billionaires who pressured apps should go to jail
Independent, Andy Gregory, Graig Graziosi and Justin Vallejo (30/1/21)
- Robinhood tightens GameStop trading curbs again as SEC weighs in
Financial Times, Michael Mackenzie, Colby Smith, Kiran Stacey and Miles Kruppa (29/1/21)
- SEC Issues Vague Threats Against Everyone Involved in the GameStop Stock Saga
Gizmodo, Andrew Couts (29/1/21)
- SEC warns it is monitoring trade after GameStop surge
RTE News (29/1/21)
- GameStop short-squeeze losses at $12.5 billion YTD – Ortex data
- GameStop: I’m one of the WallStreetBets ‘degenerates’ – here’s why retail trading craze is just getting started
The Conversation, Mohammad Rajjaque (3/2/21)
- What the GameStop games really mean
Shares Magazine, Russ Mould (4/2/21)
- Distinguish between stabilising and destabilising speculation.
- Use a demand and supply diagram to illustrate destabilising speculation.
- Explain how short selling contributed to the financial crisis of 2007/8 (see Box 2.7 in Economics (10th edition) or Box 3.4 in Essentials of Economics (8th edition)).
- Why won’t shares such as GameStop go on rising rapidly in price for ever? What limits the rise?
- Find out some other shares that have been trending among small investors. Why were these specific shares targeted?
- How has quantitative easing impacted on stock markets? What might be the effect of a winding down of QE or even the use of quantitative tightening?
In a series of five podcasts, broadcast on BBC Radio 4 in the first week of January 2021, Amol Rajan and guests examine different aspects of inequality and consider the concept of fairness.
As the notes to the programme state:
The pandemic brought renewed focus on how we value those who have kept shelves stacked, transport running and the old and sick cared for. So is now the time to bring about a fundamental shift in how our society and economy work?
The first podcast, linked below, examines the distribution of wealth in the UK and how it has changed over time. It looks at how rising property and share prices and a lightly taxed inheritance system have widened inequality of wealth.
It also examines rising inequality of incomes, a problem made worse by rising wealth inequality, the move to zero-hour contracts, gig working and short-term contracts, the lack of social mobility, austerity following the financial crisis of 2007–9 and the lockdowns and restrictions to contain the coronavirus pandemic, with layoffs, people put on furlough and more and more having to turn to food banks.
Is this rising inequality fair? Should fairness be considered entirely in monetary terms, or should it be considered more broadly in social terms? These are issues discussed by the guests. They also look at what policies can be pursued. If the pay of health and care workers, for example, don’t reflect their value to our society, what can be done to increase their pay? If wealth is very unequally distributed, should it be redistributed and how?
The questions below are based directly on the issues covered in the podcast in the order they are discussed.
- In what ways has Covid-19 been the great ‘unequaliser’?
- What scarring/hysteresis effects are there likely to be from the pandemic?
- To what extent is it true that ‘the more your job benefits other people, the less you get paid’?
- How has the pandemic affected inter-generational inequality?
- How have changes in house prices skewed wealth in the UK over the past decade?
- How have changes in the pension system contributed to inter-generational inequality?
- How has quantitative easing affected the distribution of wealth?
- Why is care work so poorly paid and how can the problem be addressed?
- How desirable is the pursuit of wealth?
- How would you set about defining ‘fairness’?
- Is a mix of taxation and benefits the best means of tackling economic unfairness?
- How would you set about deciding an optimum rate of inheritance tax?
- How do you account for the growth of in-work poverty?
- In what ways could wealth be taxed? What are the advantages and disadvantages of such taxes?
Since the financial crisis of 2008–9, the UK has experienced the lowest growth in productivity for the past 250 years. This is the conclusion of a recent paper published in the National Institute Economics Review. Titled, Is the UK Productivity Slowdown Unprecedented, the authors, Nicholas Crafts of the University of Sussex and Terence C Mills of Loughborough University, argue that ‘the current productivity slowdown has resulted in productivity being 19.7 per cent below the pre-2008 trend path in 2018. This is nearly double the previous worst productivity shortfall ten years after the start of a downturn.’
According to ONS figures, productivity (output per hour worked) peaked in 2007 Q4. It did not regain this level until 2011 Q1 and by 2019 Q3 was still only 2.4% above the 2007 Q4 level. This represents an average annual growth rate over the period of just 0.28%. By contrast, the average annual growth rate of productivity for the 35 years prior to 2007 was 2.30%.
The chart illustrates this and shows the productivity gap, which is the amount by which output per hour is below trend output per hour from 1971 to 2007. By 2019 Q3 this gap was 27.5%. (Click here for a PowerPoint of the chart.) Clearly, this lack of growth in productivity over the past 12 years has severe implications for living standards. Labour productivity is a key determinant of potential GDP, which, in turn, is the major limiter of actual GDP.
Crafts and Mills explore the reasons for this dramatic slowdown in productivity. They identify three primary reasons.
The first is a slowdown in the impact of developments in ICT on productivity. The office and production revolutions that developments in computing and its uses had brought about have now become universal. New developments in ICT are now largely in terms of greater speed of computing and greater sophistication of software. Perhaps with an acceleration in the development of artificial intelligence and robotics, productivity growth may well increase in the relatively near future (see third article below).
The second cause is the prolonged impact of the banking crisis, with banks more cautious about lending and firms more cautious about borrowing for investment. What is more, the decline in investment directly impacts on potential output, and layoffs or restructuring can leave people with redundant skills. There is a hysteresis effect.
The third cause identified by Crafts and Mills is Brexit. Brexit and the uncertainty surrounding it has resulted in a decline in investment and ‘a diversion of top-management time towards Brexit planning and a relative shrinking of highly-productive exporters compared with less productive domestically orientated firms’.
- How suitable is output (GDP) per hour as a measure of labour productivity?
- Compare this measure of productivity with other measures.
- According to Crafts and Mills, what is the size of the impact of each of their three explanations of the productivity slowdown?
- Would you expect the growth in productivity to return to pre-2007 levels over the coming years? Explain.
- Explain the underlying model for obtaining trend productivity growth rates used by Crafts and Mills.
- Explain and comment on each of the six figures in the Crafts and Mills paper.
- What policies should the government adopt to increase productivity growth?