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?
We have examined inflation in several blogs in recent months. With inflation at levels not seen for 40 years, this is hardly surprising. One question we’ve examined is whether the policy response has been correct. For example, in July, we asked whether the Bank of England had raised interest rates too much, too late. In judging policy, one useful distinction is between demand-pull inflation and cost-push inflation. Do they require the same policy response? Is raising interest rates to get inflation down to the target rate equally applicable to inflation caused by excessive demand and inflation caused by rising costs, where those rising costs are not caused by rising demand?
In terms of aggregate demand and supply, demand-pull inflation is shown by continuing rightward shifts in aggregate demand (AD); cost-push inflation is shown by continuing leftward/upward shifts in short-run aggregate supply (SRAS). This is illustrated in the following diagram, which shows a single shift in aggregate demand or short-run aggregate supply. For inflation to continue, rather than being a single rise in prices, the curves must continue to shift.
As you can see, the effects on real GDP (Y) are quite different. A rise in aggregate demand will tend to increase GDP (as long as capacity constraints allow). A rise in costs, and hence an upward shift in short-run aggregate supply, will lead to a fall in GDP as firms cut output in the face of rising costs and as consumers consume less as the cost of living rises.
The inflation experienced by the UK and other countries in recent months has been largely of the cost-push variety. Causes include: supply-chain bottlenecks as economies opened up after COVID-19; the war in Ukraine and its effects on oil and gas supplies and various grains; and avian flu and poor harvests from droughts and floods associated with global warming resulting in a fall in food supplies. These all led to a rise in prices. In the UK’s case, this was compounded by Brexit, which added to firms’ administrative costs and, according to the Bank of England, was estimated to cause a long-term fall in productivity of around 3 to 4 per cent.
The rise in costs had the effect of shifting short-run aggregate supply upwards to the left. As well as leading to a rise in prices and a cost-of-living squeeze, the rising costs dampened expenditure.
This was compounded by a tightening of fiscal policy as governments attempted to tackle public-sector deficits and debt, which had soared with the support measures during the pandemic. It was also compounded by rising interest rates as central banks attempted to bring inflation back to target.
Monetary policy response
Central banks are generally charged with keeping inflation in the medium term at a target rate set by the government or the central bank itself. For most developed countries, this is 2% (see table in the blog, Should central bank targets be changed?). So is raising interest rates the correct policy response to cost-push inflation?
One argument is that monetary policy is inappropriate in the face of supply shocks. The supply shocks themselves have the effect of dampening demand. Raising interest rates will compound this effect, resulting in lower growth or even a recession. If the supply shocks are temporary, such as supply-chain disruptions caused by lockdowns during the pandemic, then it might be better to ride out the problem and not raise interest rates or raise them by only a small amount. Already cost pressures are easing in some areas as supplies have risen.
If, however, the fall in aggregate supply is more persistent, such as from climate-related declines in harvests or the Ukraine war dragging on, or new disruptions to supply associated with the Israel–Gaza war, or, in the UK’s case, with Brexit, then real aggregate demand may need to be reduced in order to match the lower aggregate supply. Or, at the very least, the growth in aggregate demand may need to be slowed to match the slower growth in aggregate supply.
Huw Pill, the Chief Economist at the Bank of England, in a podcast from the Columbia Law School (see links below), argued that people should recognise that the rise in costs has made them poorer. If they respond to the rising costs by seeking higher wages, or in the case of businesses, by putting up prices, this will simply stoke inflation. In these circumstances, raising interest rates to cool aggregate demand may reduce people’s ability to gain higher wages or put up prices.
Another argument for raising interest rates in the face of cost-push inflation is when those cost increases are felt more than in other countries. The USA has suffered less from cost pressures than the UK. On the other hand, its growth rate is higher, suggesting that its inflation, albeit lower than in the UK, is more of the demand-pull variety. Despite its inflation rate being lower than in the UK, the problem of excess demand has led the Fed to adopt an aggressive interest rate policy. Its target rate is 5.25% to 5.50%, while the Bank of England’s is 5.25%. In order to prevent short-term capital outflows and a resulting depreciation in the pound, further stoking inflation, the Bank of England has been under pressure to mirror interest rate rises in the USA, the eurozone and elsewhere.
Blogs on this site
Information and data
- How may monetary policy affect inflationary expectations?
- If cost-push inflation makes people generally poorer, what role does the government have in making the distribution of a cut in real income a fair one?
- In the context of cost-push inflation, how might the authorities prevent a wage–price spiral?
- With reference to the second article above, explain the ‘monetary policy conundrum’ faced by the Bank of Japan.
- If central banks have a single policy instrument, namely changes in interest rates, how may conflicts arise when there is more than one macroeconomic objective?
- Is Russia’s rise in inflation the result of cost or demand pressures, or a mixture of the two (see articles above)?
The distinction between nominal and real values in one of the ‘threshold concepts’ in economics. These are concepts that are fundamental to a discipline and which occur again and again. The distinction between nominal and real values is particularly important when interpreting and analysing data. We show its importance here when analysing the latest retail sales data from the Office for National Statistics.
Retail sales relate to spending on items such as food, clothing, footwear, and household goods (see). They involve sales by retailers directly to end consumers whether in store or online. The retail sales index for Great Britain is based on a monthly survey of around 5000 retailers across England, Scotland and Wales and is thought to capture around 93 per cent of turnover in the sector.
Estimates of retail sales are published in index form. There are two indices published by the ONS: a value and volume measure. The value index reflects the total turnover of business, while the volume index adjusts the value index for price changes. Hence, the value estimates are nominal, while the volume estimates are real. The key point here is that the nominal estimates reflect both price and volume changes, whereas the real estimates adjust for price movements to capture only volume changes.
The headline ONS figures for September 2023 showed a 0.9 per cent volume fall in the volume of retail sales, following a 0.4 per cent rise in August. In value terms, September saw a 0.2 per cent fall in retail sales following a 0.9 per cent rise in August. Monthly changes can be quite volatile, even after seasonal adjustment, and sensitive to peculiar factors. For example, the unusually warm weather this September helped to depress expenditure on clothes. It is, therefore, sensible to take a longer-term view when looking for clearer patterns in spending behaviour.
Chart 1 plots the value and volume of retail sales in Great Britain since 1996. (Click here for a PowerPoint of this and the other two charts). In value terms, retail sales spending increased by 165 per cent, whereas in volume terms, spending increased by 73 per cent. This difference is expected in the presence of rising prices, since nominal growth, as we have just noted, reflects both price and volume changes. The chart is notable for capturing two periods where the volume of retail spending ceased to grow. The first of these is following the global financial crisis of the late 2000s. The period from 2008 to 2013 saw the volume of retail sales stagnate and flatline with a recovery in volumes only really starting to take hold in 2014. Yet in nominal terms retail sales grew by around 16 per cent.
The second of the two periods is the decline in the volume of retail sales from 2021. To help illustrate this more clearly, Chart 2 zooms in on retail sales over the past five years or so. We can see a significant divergence between the volume and value of retail sales. Between April 2021 and September 2023, the volume of retail sales fell by 11%. In contrast, the value of retail sales increased by 8.4%. The impact of the inflationary shock and the consequent cost-of-living crisis that emerged from 2021 is therefore demonstrated starkly by the chart, not least the severe drag that it has had on the volume of retail spending. This has meant that the aggregate volume of retail sales in September 2023 was only back to the levels of mid-2018.
Finally, Chart 3 shows the patterns in the volumes of retailing by four categories since 2018: specifically, food stores, predominantly non-food stores, non-store retail, and automotive fuel. The largest fall in the volume of retail sales has been experienced by non-store retailing – largely online retailing. From its peak in December 2021, non-store retail sales decreased by 18% up to September 2023. While this needs to be set in the context of the volume of non-store retail purchases being 15% higher than in February 2020 before the pandemic lockdowns were introduced, it is nonetheless indicative of the pressures facing online retailers.
Importantly, the final chart shows that the pressures in retailing are widespread. Spending volumes on automotive fuels, and in food and non-food stores are all below 2019 levels. The likelihood is that these pressures will persist for some time to come. This inevitably has potential implications for retailers and, of course, for those that work in the sector.
- Why does an increase in the value of retail sales not necessarily mean that their volume has increased?
- In the presence of deflation, which will be higher: nominal or real growth rates?
- Discuss the factors that could explain the patterns in the volume of spending observed in the different categories of retail sales in Chart 3.
- Discuss what types of retail products might be more or less sensitive to the macroeconomic environment.
- Conduct a survey of recent media reports to prepare a briefing discussing examples of retailers who have struggled or thrived in the recent economic environment.
- What do you understand by the concepts of ‘consumer confidence’ and ‘economic uncertainty’? How might these affect the volume of retail spending?
- Discuss the proposition that the retail sales data cast doubt on whether people are ‘forward-looking consumption smoothers’.
China has been an economic powerhouse in recent decades – a powerhouse that has helped to drive the world economy through trade and both inward and outward investment. At the same time, its low-priced exports have helped to dampen world inflation. But is all this changing? Is China, to use President Biden’s words, a ‘ticking time bomb’?
China’s economic growth rate is slowing, with the quarterly growth in GDP falling from 2.2% in Q1 this year to 0.8% in Q2. Even though public-sector investment rose by 8.1% in the first six months of this year, private-sector investment fell by 0.2%, reflecting waning business confidence. And manufacturing output declined in August. But, despite slowing growth, the Chinese government is unlikely to use expansionary fiscal policy because of worries about growing public-sector debt.
The property market
One of the biggest worries for the Chinese economy is the property market. The annual rate of property investment fell by 20.6% in June this year and new home prices fell by 0.2% in July (compared with June). The annual rate of price increase for new homes was negative throughout 2022, being as low as minus 1.6% in November 2022; it was minus 0.1% in the year to July 2023, putting new-home prices at 2.4% below their August 2021 level. However, these are official statistics. According to the Japan Times article linked below, which reports Bloomberg evidence, property agents and private data providers report much bigger falls, with existing home prices falling by at least 15% in many cities.
Falling home prices have made home-owners poorer and this wealth effect acts as a brake on spending. The result is that, unlike in many Western countries, there has been no post-pandemic bounce back in spending. There has also been a dampening effect on local authority spending. During the property boom they financed a proportion of their spending by selling land to property developers. That source of revenue has now largely dried up. And as public-sector revenues have been constrained, so this has constrained infrastructure spending – a major source of growth in China.
The government, however, has been unwilling to compensate for this by encouraging private investment and has tightened regulation of the financial sector. The result has been a decline in new jobs and a rise in unemployment, especially among graduates, where new white collar jobs in urban areas are declining. According to the BBC News article linked below, “In July, figures showed a record 21.3% of jobseekers between the ages of 16 and 25 were out of work”.
The fall in demand has caused consumer prices to fall. In the year to July 2023, they fell by 0.3%. Even though core inflation is still positive (0.8%), the likelihood of price reductions in the near future discourages spending as people hold back, waiting for prices to fall further. This further dampens the economy. This is a problem that was experienced in Japan over many years.
Despite slowing economic growth, Chinese annual growth in GDP for 2023 is still expected to be around 4.5% – much lower than the average rate for 9.5% from 1991 to 2019, but considerably higher than the average of 1.1% forecast for 2023 for the G7 countries. Nevertheless, China’s exports fell by 14.5% in the year to July 2023 and imports fell by 12.5%. The fall in imports represents a fall in exports to China from the rest of the world and hence a fall in injections to the rest-of-the-world economy. Currently China’s role as a powerhouse of the world has gone into reverse.
- Using PowerPoint or Excel, plot the growth rate of Chinese real GDP, real exports and real imports from 1990 to 2024 (using forecasts for 2023 and 2024). Use data from the IMF’s World Economic Outlook database. Comment on the figures.
- Explain the wealth effect from falling home prices.
- Why may official figures understate the magnitude of home price deflation?
- Explain the foreign trade multiplier and its relevance to other countries when the volume of Chinese imports changes. What determines the size of this multiplier for a specific country?
- How does the nature of the political system in China affect the likely policy response to the problems identified in this blog?
- Is there any good news for the rest of the world from the slowdown in the Chinese economy?