In the first of a series of updated blogs focusing on the importance of the distinction between nominal and real values we look at the issue of earnings. Here we update the blog Getting Real with Pay written back in February 2019. Then, we noted how the macroeconomic environment since the financial crisis of the late 2000s had continued to affect people’s pay. Specifically, we observed that there had been no growth in real or inflation-adjusted pay. In other words, people were no better off in 2019 than in 2008.
In this updated blog, we consider to what extent the picture has changed five years down the line. While we do not consider the distributional impact on pay, the aggregate picture nonetheless continues to paint a very stark picture, with consequences for living standards and financial wellbeing.
While the distinction between nominal and real values is perhaps best known in relation to GDP and economic growth, the distinction is also applied frequently to analyse the movement of one price relative to prices in general. One example is that of movements in pay (earnings) relative to consumer prices.
Pay reflects the price of labour. The value of our actual pay is our nominal pay. If our pay rises more quickly than consumer prices, then our real pay increases. This means that our purchasing power rises and so the volume of goods and services we can afford increases. On the other hand, if our actual pay rises less quickly than consumer prices then our real pay falls. When real pay falls, purchasing power falls and the volume of goods and services we can afford falls.
Figures from the Office for National Statistics show that in January 2000 regular weekly pay (excluding bonuses and before taxes and other deductions from pay) was £293. By April 2024 this had risen to £640. This is an increase of 118 per cent. Over the same period, the consumer prices index known as the CPIH, which, unlike the better-known CPI, includes owner-occupied housing costs and council tax, rose by 82 per cent. Therefore, the figures are consistent with a rise both in nominal and real pay between January 2000 to April 2024. However, this masks a rather different picture that has emerged since the global financial crisis of the late 2000s.
Chart 1 shows the annual percentage changes in actual (nominal) regular weekly pay and the CPIH since January 2001. Each value is simply the percentage change from 12 months earlier. The period up to June 2008 saw the annual growth of weekly pay outstrip the growth of consumer prices – the blue line in the chart is above the red dashed line. Therefore, the real value of pay rose. However, from June 2008 to August 2014 pay growth consistently fell short of the rate of consumer price inflation – the blue line is below the red dashed line. The result was that average real weekly pay fell. (Click here to download a PowerPoint copy of the chart.)
Chart 2 show the average levels of nominal and real weekly pay. The real series is adjusted for inflation. It is calculated by deflating the nominal pay values by the CPIH. Since the CPIH is a price index whose value averages 100 across 2015, the real pay values are at constant 2015 consumer prices. From the chart, we can see that the real value of weekly pay peaked in April 2008 at £473 at 2015 prices. The subsequent period saw rates of pay increases that were lower than rates of consumer price inflation. This meant that by March 2014 the real value of weekly pay had fallen by 6.3 per cent to £443 at 2015 prices. (Click here to download a PowerPoint copy of the chart.)
Although real (inflation-adjusted) pay recovered a little after 2014, 2017 again saw consumer price inflation rates greater than those of pay inflation (see Chart 1). This meant that at the start of 2018 real earnings were 3.2 per cent lower than their 2008-peak (see Chart 2). Real earnings then began to recover, buoyed by the economic rebound following the relaxation of COVID lockdown measures and increasing staffing pressures. Real earnings finally passed their 2008-peak in August 2020. By April 2021 regular weekly pay reached £491 at 2015 prices which was 3.8 per cent above the pre-global financial crisis peak.
However, the boost to real wages was to be short-lived as inflationary pressures rose markedly. While some of this was attributable to the same pressures that were driving up wages, inflationary pressures were fuelled further by the commodity price shock arising from Russia’s invasion of Ukraine and, in particular, its impact on energy prices. This saw the CPIH inflation rate rise to 9.6 per cent in October 2022 (while the CPI inflation rate peaked in the same month at 11.1 per cent). The result was that real weekly earnings fell by 2.7 per cent between January and October 2022 to stand at £471 at 2015 consumer prices. Consequently, average pay was once again below its pre-global financial crisis level.
Although inflationary pressures have recently weakened and real earnings have begun to recover, real regular weekly earnings in April 20024 (£486 at 2015 prices) were a mere 2.7 per cent higher than back in the first half of 2008. This compares to a nominal increase of around 58 per cent over the same period thereby demonstrating the importance of the distinction between nominal and real values in understanding what developments in pay mean for the purchasing power of households.
Chart 3 reinforces the importance of the nominal-real distinction. It shows nicely the sustained period of real pay deflation (negative rates of pay inflation) that followed the financial crisis, and the significant rates of real pay deflation associated with the recent inflation shock.
The result is that since June 2008 the average annual rate of growth of real regular weekly pay has been 0.1 per cent, despite nominal pay increasing at an annual rate of 2.9 per cent. In contrast, the period from January 2001 to May 2008 saw real regular weekly pay grow at an annual rate of 2.1 per cent with nominal pay growing at an annual rate of 4.0 per cent. (Click here to download a PowerPoint copy of the chart.)
If we think about the growth of nominal earnings, we can identify two important determinants.
The first is the expected rate of inflation. Workers will understandably want wage growth at least to match the growth in prices so as to maintain their purchasing power.
The second factor is the growth in labour productivity. Firms will be more willing to grant pay increases if workers are more productive, since productivity helps to offset pay increases and maintain firms’ profit margins. Consequently, since over time the actual rate of inflation will tend to mirror the expected rate, the growth of real pay is closely related to the growth of labour productivity. This is significant because, as John discusses in his blog The Productivity Puzzle (14 April 2024), labour productivity growth in the UK, as measured by national output per worker hour, has stalled since the global financial crisis.
Understanding the stagnation of real earnings therefore nicely highlights the interconnectedness of economic variables. In this case, it highlights the connections between productivity, levels of investment and people’s purchasing power. It is not surprising, therefore, that the stagnation of both real earnings and productivity growth since the global financial crisis have become two of the most keenly debated macroeconomic issues of recent times. Indeed, it is likely that their behaviour will continue to shape macroeconomic debates and broader conversations around government policy for some time.
Articles
Questions
- Using the examples of both GDP and earnings, explain how the distinction between nominal and real relates to the distinction between values and volumes.
- In what circumstances would an increase in actual pay translate into a reduction in real pay?
- In what circumstances would a decrease in actual pay translate into an increase in real pay?
- What factors might explain the reduction in real rates of pay seen in the UK following the financial crisis of 2007–8?
- Of what importance might the growth in real rates of pay be for consumption and aggregate demand?
- Why is the growth of real pay an indicator of financial well-being? What other indicators might be included in measuring financial well-being?
- Assume that you have been asked to undertake a distributional analysis of real earnings since the financial crisis. What might be the focus of your analysis? What information would you therefore need to collect?
Global long-term economic growth has slowed dramatically since the financial crisis of 2007–8. This can be illustrated by comparing the two 20-year periods 1988 to 2007 and 2009 to 2028 (where IMF forecasts are used for 2024 to 2028: see WEO Database under the Data link below). Over the two periods, average annual world growth fell from 3.8% to 3.1%. In advanced countries it fell from 2.9% to 1.6% and in developing countries from 4.8% to 4.3%. In the UK it fell from 2.4% to 1.2%, in the USA from 3.1% to 1.8% and in Japan from 1.9% to 0.5%.
In the UK, labour productivity growth in the production industries was 6.85% per annum from 1998 to 2006. If this growth rate had been maintained, productivity would have been 204% higher by the end of 2023 than it actually was. This is shown in the chart (click here for a PowerPoint).
The key driver of long-term economic growth is labour productivity, which can best be measured by real GDP per hour worked. This depends on three things: the amount of capital per worker, the productivity of this capital and the efficiency of workers themselves – the latter two giving total factor productivity (TFP). Productivity growth has slowed, and with it the long-term rate of economic growth.
If we are measuring growth in output per head of the population, as opposed to simple growth in output, then another important factor is the proportion of the population that works. With ageing populations, many countries are facing an increase in the proportion of people not working. In most countries, these demographic pressures are likely to increase.
A major determinant of long-term economic growth and productivity is investment. Investment has been badly affected by crises, such as the financial crisis and COVID, and by geopolitical tensions, such as the war in Ukraine and tensions between the USA and China and potential trade wars. It has also been adversely affected by government attempts to deal with rising debt caused by interventions following the financial crisis and COVID. The fiscal squeeze and, more recently higher interest rates, have dampened short-term growth and discouraged investment, thereby dampening long-term growth.
Another factor adversely affecting productivity has been a lower growth of allocative efficiency. Competition in many industries has declined as the rate of new firms entering and exiting markets has slowed. The result has been an increase in concentration and a growth in supernormal profits.
In the UK’s case, growth prospects have also been damaged by Brexit. According to Bank of England and OBR estimates, Brexit has reduced productivity by around 4% (see the blog: The costs of Brexit: a clearer picture). For many companies in the UK, Brexit has hugely increased the administrative burdens of trading with the EU. It has also reduced investment and led to a slower growth in the capital stock.
The UK’s poor productivity growth over many yeas is examined in the blog The UK’s poor productivity record.
Boosting productivity
So, how could productivity be increased and what policies could help the process?
Artificial intelligence. One important driver of productivity growth is technological advance. The rapid advance in AI and its adoption across much of industry is likely to have a dramatic effect on working practices and output. Estimates by the IMF suggest that some 40% of jobs globally and 60% in advanced countries could be affected – some replaced and others complemented and enhanced by AI. The opportunities for raising incomes are huge, but so too are the dangers of displacing workers and deepening inequality, as some higher-paid jobs are enhanced by AI, while many lower paid jobs are little affected and other jobs disappear.
AI is also likely to increase returns to capital. This may help to drive investment and further boost economic growth. However, the increased returns to capital are also likely to exacerbate inequality.
To guard against the growth of market power and its abuse, competition policies may need strengthening to ensure that the benefits of AI are widely spread and that new entrants are encouraged. Also training and retraining opportunities to allow workers to embrace AI and increase their mobility will need to be provided.
Training. And it is not just training in the use of AI that is important. Training generally is a key ingredient in encouraging productivity growth. In the UK, there has been a decline in investment in adult education and training, with a 70% reduction since the early 2000s in the number of adults undertaking publicly-funded training, and with average spending on training by employers decreasing by 27% per trainee since 2011. The Institute for Fiscal Studies identifies five main policy levers to address this: “public funding of qualifications and skills programmes, loans to learners, training subsidies, taxation of training and the regulation of training” (see link in articles below).
Competition. Another factor likely to enhance productivity is competition, both internationally and within countries. Removing trade restrictions could boost productivity growth; erecting barriers to protect inefficient domestic industry would reduce it.
Investment. Policies to encourage investment are also key to productivity growth. Private-sector investment can be encouraged by tax incentives. For example, in the UK the Annual Investment Allowance allows businesses to claim 100% of the cost of plant and machinery up to £1m in the year it is incurred. However, for tax relief to produce significant effects on investment, companies need to believe that the policy will stay and not be changed as economic circumstances or governments change.
Public-sector investment is also key. Good road and rail infrastructure and public transport are vital in encouraging private investment and labour mobility. And investment in health, education and training are a key part in encouraging the development of human capital. Many countries, the UK included, cut back on public-sector capital investment after the financial crisis and this has had a dampening effect on economic growth.
Regional policy. External economies of scale could be encouraged by setting up development areas in various regions. Particular industries could be attracted to specific areas, where local skilled workers, managerial expertise and shared infrastructure can benefit all the firms in the industry. These ‘agglomeration economies’ have been very limited in the UK compared with many other countries with much stronger regional economies.
Changing the aims and governance of firms. A change in corporate structure and governance could also help to drive investment and productivity. According to research by the think tank, Demos (see the B Lab UK article and the second report below), if legislation required companies to consider the social, economic and environmental impact of their business alongside profitability, this could have a dramatic effect on productivity. If businesses were required to be ‘purpose-led’, considering the interests of all their stakeholders, this supply-side reform could dramatically increase growth and well-being.
Such stakeholder-governed businesses currently outperform their peers with higher levels of investment, innovation, product development and output. They also have higher levels of staff engagement and satisfaction.
Articles
- World Must Prioritize Productivity Reforms to Revive Medium-Term Growth
IMF Blog, Nan Li and Diaa Noureldin (10/4/24)
- Why has productivity slowed down?
Oxford Martin School News, Ian Goldin, Pantelis Koutroumpis, François Lafond and Julian Winkler (18/3/24)
- How can the UK revive its ailing productivity?
Economics Observatory, Michelle Kilfoyle (14/3/24)
- With the UK creeping out of recession, here’s an economist’s brief guide to improving productivity
The Conversation, Nigel Driffield (13/3/24)
- UK economy nearly a third smaller thanks to ‘catastrophically bad’ productivity slowdown
City A.M., Chris Dorrell (12/3/24)
- Can AI help solve the UK’s public sector productivity puzzle?
City A.M., Chris Dorrell (11/3/24)
- AI Will Transform the Global Economy. Let’s Make Sure it Benefits Humanity
IMF Blog, Kristalina Georgieva (14/1/24)
- Productivity and Investment: Time to Manage the Project of Renewal
NIESR, Paul Fisher (12/3/24)
- Productivity trends using key national accounts indicators
Eurostat (15/3/24)
- New report says change to company law could add £149bn to the UK economy
B Lab UK (28/11/23)
- Investment in training and skills: Green Budget Chapter 9
Institute for Fiscal Studies, Imran Tahir (12/10/23)
Reports
Data
Questions
- Why has global productivity growth been lower since 2008 than before 2008?
- Why has the UK’s productivity growth been lower than many other advanced economies?
- How does the short-run macroeconomic environment affect long-term growth?
- Find out why Japan’s productivity growth has been so poor compared with other countries.
- What are likely to be the most effective means of increasing productivity growth?
- How may demand management policies affect the supply side of the economy?
- How may the adoption of an ESG framework by companies for setting objectives affect productivity growth?
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’.
Growing inequality?
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.
Other issues
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.
Articles
- 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)
Questions
- 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.
Bond prices
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.
Articles
Information and data
Questions
- 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?
Have you ever wondered how your job affects your happiness? We all know that not all jobs are created equal. Some are awesome, while others … not so much. Well, it turns out that employment status and the type of work you do can have a big impact on how you feel – especially in developing countries where labour markets are usually tighter and switching between jobs can be more difficult.
A recent study by Carmichael, Darko and Vasilakos (2021) uses survey data from Ethiopia, Peru, India and Vietnam to answer this very question. The study found that the quality of work is a big deal when it comes to how young people feel. Not all jobs are ‘good jobs’ that automatically make you feel great. Although your wellbeing is likely to be higher when you’re in employment than when you’re not, there are certain job attributes that can push that ‘employment premium’ up or down. This is especially important to understand in countries like many in sub-Saharan Africa, where there aren’t many formal jobs, and people often end up overqualified for what they do.
What job attributes lead to higher wellbeing?
What then are the job attributes that are correlated with higher levels of wellbeing? The first is money: Okay, we know money can’t buy happiness, but it can certainly make life easier. We were therefore hardly surprised to find a positive and statistically significant association between hourly earnings and wellbeing.
We were also not surprised to find that a ‘poor working environment’ has a strong and highly significant negative effect on wellbeing.
Finally, feeling proud of your work is also found to be a strongly significant determinant of your wellbeing. After all, people tend to excel in things they like doing, which is probably part of the ‘transmission mechanism’ between ‘work pride’ and ‘subjective wellbeing’.
Which one of these attributes did you think had the greatest effect on wellbeing? Let me guess, many of you will say ‘earnings’. But then you would be wrong. Earnings were indeed positively associated with wellbeing and statistically significant at just about the 10% level, whereas work pride was very strongly statistically significant at the 1% level and had an effect on wellbeing that was four times greater than hourly earnings.
Putting yourself in a poor working environment on the other hand would reduce your wellbeing by almost twice as much as the earnings coefficient.
Policy implications
What does all this mean for policy-makers? If we want to make life better for young people in low-income countries, we need to tackle the problems from multiple angles.
First, young people need to be helped to get the skills they need for the job market. This can be done through things like training programmes and apprenticeships. However, not all of these programmes are created equal. Some have great results, and others not so much.
But that’s not the whole story. In many countries, there’s a massive informal job market. It’s a place where people work but often don’t have the rights or protections that formal employees do. So, even if young people get trained, they might not find the ‘good’ jobs they’re hoping for.
Changes also need to be made on a much bigger scale. This often includes decentralising public investment to include rural areas, improving infrastructure, and encouraging private investment. Strengthening labour market rules and social protection can help too, by making sure that work is safe and fair.
In a nutshell, where you work and what kind of work you do can make a big difference to how you feel.
Conclusions
If policy-makers want to help young people in low-income countries, they need both to give them the skills they require and to create better job opportunities. But policy-makers also need to make bigger changes to the way things work, like boosting production and making sure jobs are safe and fair.
In the end, it’s about making life better for young people around the world. Let’s keep working on it!
Articles
- Well-being and employment of young people in Ethiopia, India, Peru and Vietnam: Is work enough?
Development Policy Review, Fiona Carmichael, Christian K. Darko and Nicholas Vasilakos (18/5/21)
- The search for ‘meaning’ at work
BBC Worklife, Kate Morgan (7/9/22)
- Job Satisfaction Is Rising: What’s Behind The Surprising Tend
Forbes, Tracy Brower (4/6/23)
- Young workers are embracing AI, job satisfaction rising: 2023 Young Generation in Tech report
Silicon Canals (4/10/23)
- ‘These jobs can be respectable too’: Why youths in China are abandoning white-collar jobs for ‘light labor’
CNBC, Goh Chiew Tong (6/6/23)
- Does Work Make You Happy? Evidence from the World Happiness Report
Harvard Business Review, Jan-Emmanuel De Neve and George Ward (20/3/17)
- Worker well-being is in demand as organizational culture shifts
American Psychological Association, Monitor on Psychology, Heather Stringer (1/1/23)
- Understanding children’s work and youth employment outcomes in Indonesia
Understanding Children’s Work (UCW) Programme, Villa Aldobrandini and V. Panisperna (June 2012)
- Where are We with Young People’s Wellbeing? Evidence from Nigerian Demographic and Health Surveys 2003–2013
Social Indicators Research, pp.803–33, Boniface Ayanbekongshie Ushie and Ekerette Emmanuel Udoh (November 2016)
- Employment Status and Well-Being Among Young Individuals. Why Do We Observe Cross-Country Differences?
Social Indicators Research, Dominik Buttler (29/6/22)
- Employment Mismatches Drive Expectational Earnings Errors among Mozambican Graduates
The World Bank Economic Review, Sam Jones, Ricardo Santos and Gimelgo Xirinda (27/7/23)
- Youth Employment and Skills Development in The Gambia
World Bank Working Paper 217, Nathalie Lahire, Richard Johanson and Ryoko Tomita Wilcox (2011)
Questions
- How does the quality of work impact the happiness and wellbeing of young people in low- and middle-income countries (LMICs), and why is this significant in the context of job opportunities in sub-Saharan Africa?
- What are some potential solutions and strategies discussed in the article for improving the wellbeing of young people in LMICs, particularly in the context of employment and job opportunities?
- Have you ever experienced a job that significantly (positively or negatively) impacted your wellbeing or happiness? Reflect on your experience and how it influenced your overall life satisfaction?
- How is AI likely to affect the wellbeing of young professional workers?
- How is the pandemic likely to have affected job satisfaction?