Category: Essentials of Economics: Ch 12

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Getting real with growth

In the second of a series of blogs looking at applications of the distinction between nominal and real indicators, we revisit the blog Getting Real with Growth last updated in October 2021.

In this blog, we discuss how, in making a meaningful comparison over time of a country’s national income and, therefore, the aggregate purchasing power of its people, we need to take inflation into account. Likewise, if we want to analyse changes in the volume of production, we need to eliminate the effects of price changes on GDP. This is important when analysing the business cycle and identifying periods of boom or bust. Hence, in this updated blog we take another look at what real GDP data reveal about both longer-term economic growth and the extent of economic volatility – or what we refer to as the twin characteristics of economic growth.

Real and nominal GDP

The nominal (or current-price) estimate for UK gross domestic product in 2023 was £2.687 trillion. The estimate of national output or national income is based primarily on the production of final goods and services and, hence, purchased by the final user. It therefore largely excludes intermediate goods and services: i.e. goods and services that are transformed or used up in the process of making something else, although data on imports and exports do include intermediate goods and services. The 2023 figure represents a nominal increase in national income of 7.2 per cent on the £2.51 trillion recorded in 2022. These values make no adjustment for inflation and therefore reflect the prices of output that were prevailing at the time.

Chart 1 shows current-price estimates of GDP from 1955, when the value of GDP was estimated at £19.2 billion. The £2.687 trillion figure recorded for 2023 is an increase of over 140 times that in 1955, a figure that rises to 160 times if we compare the 1950 value with the latest IMF estimate for 2027. However, if we want to make a more meaningful comparison of the country’s national income we need to adjust for inflation. (Click here to download a PowerPoint of the chart.)

Long-term growth in real GDP

If we measure GDP at constant prices, we eliminate the effect of inflation. To construct a constant-price series for GDP, a process known as chain-linking is used. This involves taking consecutive pairs of years, e.g. 2022 and 2023, and estimating what GDP would be in the most recent year (in this case, 2023) if the previous year’s prices (i.e. 2022) had continued to prevail. By calculating the percentage change from the previous year’s GDP value we have an estimate of the volume change. If this is repeated for other pairs of years, we have a series of percentage changes that capture the volume changes from year-to-year. Finally, a reference year is chosen and the percentage volume changes are applied backwards and forwards from the nominal GDP value for the reference year.

In effect, a real GDP series creates a quantity measure in monetary terms. Chart 1 shows GDP at constant 2019 prices (real GDP) alongside GDP at current prices (nominal GDP). Consider first the real GDP numbers for 1955 and 2023. GDP in 1950 at 2019 prices was £491.2 billion. This is higher than the current-price value because prices in 2019 (the reference year) were higher than those in 1955. Meanwhile, GDP in 2023 when measured at 2019 prices was £2.273 trillion. This constant-price value is smaller than the corresponding current-price value because prices in 2019 where lower than those in 2023.

Between 1955 and 2023 real GDP increased 4.6 times. If we extend the period to 2027, again using the latest IMF estimates, the increase is 4.9 times. Because we have removed the effect of inflation, the real growth figure is much lower than the nominal growth figure.

Crucially, what we are left with is an indicator of the long-term growth in the volume of the economy’s output and hence an increase in national income that is backed up by an increase in production. Whereas nominal growth rates are affected by changes in both volumes and prices, real growth rates reflect only changes in volumes.

The upward trajectory observed in constant-price GDP is therefore evidence of positive longer-term growth. This is one of the twin characteristics of growth.

Short-term fluctuations in the growth of real GDP

The second characteristic is fluctuations in the rate of growth from period to period. We can see this second characteristic more clearly by plotting the percentage change in real GDP from year to year.

Chart 2 shows the annual rate of growth in real GDP each year from 1955 to 2025. From it, we see the inherent instability that is a key characteristic of the macroeconomic environment. This instability is, of course, mirrored in the output path of real GDP in Chart 1, but the annual rates of growth show the instability more clearly. We can readily see the impact on national output of the global financial crisis of 2007–8 and the global COVID pandemic.

In 2009, constant-price GDP in the UK fell by 4.6 per cent, whereas current-price GDP fell by 2.8 per cent. Then, in 2020, constant-price GDP and, hence, the volume of national output fell by 10.4 per cent, as compared to a 5.8 per cent fall in current-price GDP. These global, ‘once-in-a-generation’ shocks are stark examples of the instability that characterises economies and which generate the ‘ups and downs’ in an economy’s output path, known more simply as ‘the business cycle’. (Click here to download a PowerPoint copy of the chart.)

Determinants of long-and short-term growth

The twin characteristics of growth can be seen simultaneously by combining the output path (shown by the levels of real GDP) with the annual rates of growth. This is shown in Chart 3. The longer-term growth seen in the economy’s output path is generally argued to be driven by the quantity and quality of the economy’s resources, and their effectiveness when combined in production (i.e. their productivity). In other words, it is the supply side of the economy that determines the trajectory of the output path over the longer term. (Click here to download a PowerPoint copy of the chart.)

However, the fluctuations we observe in short-term growth rates tend to reflect shocks, also known as impulses, that originate either from the ability and or willingness of purchasers to consume (demand-side shocks) or producers to supply (supply-side shocks). These impulses are then amplified (or ‘propagated’) via the multiplier, expectations and other factors, and their effects, therefore, transmitted through the economy. Unusually in the case of the pandemic, the lockdown measures employed by governments around the world resulted in simultaneous negative aggregate demand and aggregate supply shocks.

Persistence effects

Explanations of the business cycle and of long-term growth are not mutually exclusive. The shocks and the propagation mechanisms that help to create and shape the business cycle can themselves have enduring or persistent effects on output. The global financial crisis, fuelled by unsustainable lending and the overstretch of private-sector balance sheets, which then spilt over to the public sector as governments attempted to stabilise the financial system and support aggregate demand, is argued by some to have created the conditions for low-growth persistence seen in many countries in the 2010s. This type of persistence is known as hysteresis as it originates from a negative demand shock.

Economists and policymakers were similarly concerned that the pandemic would also generate persistence in the form of scarring effects that might again affect the economy’s output path. Such concerns help to explain why many governments introduced furlough schemes to protect jobs and employment income, as well as provide grants or loans to business.

Per capita output

To finish, it is important to recognise that, when thinking about living standards, it is the growth in real GDP per capita that we need to consider. A rise in real GDP will only lead to a rise in overall living standards if it is faster than the rise in population.

Our final chart therefore replicates Chart 3 but for real GDP per capita. Between 1955 and 2023 real GDP per capita grew by a factor of 3.45, which increases to 3.6 when we consider the period up to 2027. The average rate of growth of real GDP per capita up to 2023 was 1.87 per cent (lower than the 2.34 per cent increase in real GDP).

But the rate of increase in real GDP per capita was much higher before 2007 than it has been since. If we look at the period up to 2007 and, hence, before the global financial crisis, the figure is 2.32 per cent (2.7 per cent for real GDP), whereas from 2008 to 2023 the average rate of growth of real GDP per capita was a mere 0.42 per cent (1.1 per cent for real GDP). (Click here to download a PowerPoint copy of the chart.)

The final chart therefore reiterates the messages from recent blogs, such as Getting Real with Pay and The Productivity Puzzle, that long-term economic growth and the growth of real wages have slowed dramatically since the financial crisis. This has had important implications for the wellbeing of all sectors of the economy. The stagnation of living standards is therefore one of the most important economic issues of our time. It is one that the incoming Labour government will be keen to address.

Data and Reports

Articles

Questions

  1. What do you understand by the term ‘macroeconomic environment’? What data could be used to describe the macroeconomic environment?
  2. When a country experiences positive rates of inflation, which is higher: nominal economic growth or real economic growth?
  3. Does an increase in nominal GDP mean a country’s production has increased? Explain your answer.
  4. Does a decrease in nominal GDP mean a country’s production has decreased? Explain your answer.
  5. Why does a change in the growth of real GDP allow us to focus on what has happened to the volume of production?
  6. What does the concept of the ‘business cycle’ have to do with real rates of economic growth?
  7. When would falls in real GDP be classified as a recession?
  8. Distinguish between the concepts of ‘short-term growth’ and ‘longer-term growth’.
  9. What do you understand by the term ‘persistence’ in macroeconomics? Given examples of persistence effects and the means by which they can be generated?
  10. Discuss the proposition that the pandemic could have a positive effect on longer-term growth rates because of the ways that people and business have had to adapt.
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Getting real with pay

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

  1. Using the examples of both GDP and earnings, explain how the distinction between nominal and real relates to the distinction between values and volumes.
  2. In what circumstances would an increase in actual pay translate into a reduction in real pay?
  3. In what circumstances would a decrease in actual pay translate into an increase in real pay?
  4. What factors might explain the reduction in real rates of pay seen in the UK following the financial crisis of 2007–8?
  5. Of what importance might the growth in real rates of pay be for consumption and aggregate demand?
  6. Why is the growth of real pay an indicator of financial well-being? What other indicators might be included in measuring financial well-being?
  7. 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?
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The approach to monetary and fiscal policy

The following blog is inspired by my teaching of macroeconomic issues to my final year students at Aston University. In the classes we’ve been discussing important aspects of monetary and fiscal policy design. What has become clear to me and my students is that the trade-offs which characterise the discipline of economics are certainly alive and well in the current environment in which monetary and fiscal policy choices are being made.

To demonstrate this we consider here some of the discussions we’ve had in class around central bank independence and monetary policy mandates. We’ve also looked at fiscal policy. Here we’ve examined the state of the public finances and the importance that seems to be attached to debt stabilisation and the imposition of debt rules.

Delegation and central bank mandates

My teaching this term began by introducing my students to one of the most important and influential monetary policy models. This is the model of Kydland and Prescott. Their model, published in the Journal of Political Economy in 1977 has become the theoretical bedrock for the modern-day operational independence of central banks.1

The model explores how systemically high inflation can become established in economies when policymakers have the political incentive to lower unemployment or increase output above its long-run equilibrium value. This may be the case if governments operate monetary policy rather than the central bank (of if the central bank operates monetary policy but follows government objectives). By adopting expansionary monetary policy, governments can increase their popularity.

But this is likely to be short-lived, as any increased economic activity will only be temporary (assuming that the natural-rate hypothesis holds). Soon, inflation will rise.

But, if an election is on the horizon, there may be enough time to boost output and employment before inflation rises. In other words, an expectations-augmented Phillips curve may present governments with an incentive to loosen monetary policy and worry about the inflation consequences after the election.

However, the resulting ‘inflation surprise’ through the loosening of monetary policy means a fall in real pay and therefore in purchasing power. If people suspect that governments will be tempted to loosen policy, they will keep their expectations of inflation higher than the socially optimal inflation rate. Consequently, low-inflation targets lack credibility when governments have the temptation to loosen monetary policy. Such targets are time-inconsistent because governments have an incentive to renege and deliver higher inflation through a looser monetary policy. The result is an inflation bias.

Central bank independence
To prevent this inflationary bias arising, many central banks around the world have been given some form of operational independence with a mandate centred around an inflation-rate target. By delegating monetary policy to a more conservative central bank, the problem of inflationary bias can be addressed.

Yet central bank independence is not without its own issues and this has been an important part of the discussions with my students. Today, many economies are continuing to experience the effects of the inflationary shocks that began in 2021 (see Chart 1 for the UK CPI inflation rate: click here for a PowerPoint). The question is whether the appointment of a conservative or hard-nosed hawkish central banker trades off the stabilisation of inflation for greater volatility in output or unemployment.

The inflation–output stabilisation trade-off is closely associated with the works of Kenneth Rogoff2 and John Taylor3. The latter is known for his monetary policy rule, which has become known as the ‘Taylor rule’. This advocates that a rules-based central bank ought to place weight on both inflation and output stabilisation.

This is not without its own issues, however, since, by also placing weight on output stabilisation, we are again introducing the possibility of greater inflationary bias in policy making. Hence, while the act of delegation and a rules- or target-based approach may mitigate the extent of the bias relative to that in the Kydland and Prescott model, there nonetheless still remain issues around the design of the optimal framework for the conduct of monetary policy.

Indeed, the announcement that the UK had moved into recession in the last two quarters of 2023 can be seen as evidence that an otherwise abstract theoretical trade-off between inflation and output stabilisation is actually very real.

My classroom discussions have also shown how economic theory struggles to identify an optimal inflation-rate target. Beyond accepting that a low and stable inflation rate is desirable, it is difficult to address fully the student who asks what is so special about a 2% inflation target. Would not a 3% target, for example, be preferable, they might ask?

Whilst this may sound somewhat trivial, it has real-world consequences. In a world that now seems to be characterised by greater supply-side volatility and by more frequent inflation shocks than we were used to in recent history, might a higher inflation rate target be preferable? Certainly, one could argue that, with an inflation–output stabilisation trade-off, there is the possibility that monetary policy could be unduly restrictive in our potential new macroeconomic reality. Hence, we might come to see governments and central banks in the near future revisiting the mandates that frame the operation of their monetary policy. Time will tell.

Fiscal policy and debt stabilisation

The second topic area that I have been discussing in my final-year macroeconomics classes has centred around fiscal policy and the state of the public finances. The context for this is that we have seen a significant increase in public debt-to-GDP ratios over the past couple of decades as the public sector has attempted to absorb significant economic shocks. These include the global financial crisis of 2007–8, the COVID-19 pandemic and the cost-of-living crisis. These interventions in the case of the UK have seen its public debt-to-GDP ratio more than triple since the early 2000s to close to 100% (see Chart 2: click here for a PowerPoint).

Understandably, given the stresses placed on the public finances, economists have increasingly debated issues around debt sustainability. These debates have been mirrored by politicians and policymakers. A key question is whether to have a public debt rule. The UK has in recent years adopted such a rule. The arguments for a rule centre on ensuring sound public finances and maintaining the confidence of investors to purchases public debt. A debt rule therefore places a discipline on fiscal policy, with implications for taxation and spending.

How easy it is to stick to a debt rule depends on three key factors. It will be harder to stick to the rule:

  • The higher the current debt-to-GDP ratio and hence the more it needs to be reduced to meet the rule.
  • The higher the rate of interest and hence the greater the cost of servicing the public debt.
  • The lower the rate of economic growth and hence the less quickly will tax revenues rise.

With a given debt-to-GDP ratio, a given average interest rate payable on its debt, and a given rate of economic growth, we can determine the primary fiscal balance relative to GDP a government would need to meet for the debt-to-GDP ratio to remain stable. This is known as the ‘debt-stabilising primary balance’. The primary balance is the difference between a government’s receipts and its expenditures less the interest payments on its debt.

This fiscal arithmetic is important in determining a government’s fiscal choices. It shows the implications for spending and taxation. These implications become ever more important and impactful on people, businesses, and society when the fiscal arithmetic becomes less favourable. This is a situation that appears to be increasingly the case for many countries, including the UK, as the rate of interest on public debt rises relative to a country’s rate of economic growth. As this happens, governments are increasingly required to run healthier primary balances. This of course implies a tightening of their fiscal stance.

Hence, the fiscal conversations with my students have focused on both the benefits and the costs of debt-stabilisation. In respect of the costs, a few issues have arisen.

First, as with the inflation-rate target, it is hard to identify an optimal public debt-to-GDP ratio number. While the fiscal arithmetic may offer some clue, it is not straightforward to address the question as to whether a debt-to-GDP ratio of say 100% or 120% would be excessive for the UK.

Second, it is possible that the debt stabilisation itself can make the fiscal arithmetic of debt stabilisation more difficult. This occurs if fiscal consolidation itself hinders long-term economic growth, which then makes the fiscal arithmetic more difficult. This again points to the difficulties in designing policy frameworks, whether they be for monetary or fiscal policy.

Third, a focus on debt stabilisation alone ignores the fact that there are two sides to any sector’s balance sheet. It would be very unusual when assessing the well-being of businesses or households if we were to ignore the asset side of their balance sheet. Yet, this is precisely the danger of focusing on public debt at the exclusion of what fiscal choices can mean for public-sector assets, from which we all can potentially benefit. Hence, some would suggest a more balanced approach to assessing the soundness of the public finances might involve a net worth (assets less liabilities) measure. This has parallels with the debates around whether mandates of central banks should be broader.

Applications in macroeconomics

What my teaching of a topics-based macroeconomics module this term has vividly demonstrated is that concepts, theories, and models come alive, and are capable of being understood better, when they are used to shine a light on real-world issues. The light being shone on monetary and fiscal policy in today’s turbulent macroeconomic environment is perhaps understandably very bright.

Indeed, the light being shone on fiscal policy in the UK and some other countries facing an upcoming election, is intensified further with the state of the public finances shaping much of the public discourse on fiscal choices. Hopefully, my students will continue to debate these important issues beyond their graduation, stressing their importance for people’s lives and, in doing so, going beyond the abstract.

References

  1. Rules rather than discretion: The inconsistency of optimal plans
    The Journal of Political Economy, Finn E Kydland and Edward C. Prescott (1977, 85(3), pp 473–92)

  2. The optimal degree of commitment to an intermediate monetary target
    Quarterly Journal of Economics, Kenneth Rogoff (November 1985, 100(4), pp 1169–89)

  3. Discretion versus policy rules in practice
    Carnegie-Rochester Conference Series on Public Policy, John B Taylor (December 1993, 39, pp 195–214)

Articles

Questions

  1. What is meant by time-inconsistent monetary policy announcements? How has this concept been important for the way in which many central banks now conduct monetary policy?
  2. What is meant by a ‘conservative’ central banker? Why is the appointment of this type of central banker thought to be important in affecting inflation?
  3. What is the contemporary macroeconomic relevance of the inflation–output (or inflation–unemployment) stabilisation trade-off?
  4. How is the primary balance different from the actual budget balance?
  5. What do you understand by the concept of ‘the fiscal arithmetic’. Explain how each element of the fiscal arithmetic affects the debt-stabilising primary balance?
  6. Analyse the costs of benefits of a debt-based fiscal rule.
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Worries about the Chinese economy

According to the IMF, Chinese GDP grew by 5.2% in 2023 and is predicted to grow by 4.6% this year. Such growth rates would be extremely welcome to most developed countries. UK growth in 2023 was a mere 0.5% and is forecast to be only 0.6% in 2024. Advanced economies as a whole only grew by 1.6% in 2023 and are forecast to grow by only 1.5% this year. Also, with the exception of India, the Philippines and Indonesia, which grew by 6.7%, 5.3% and 5.0% respectively in 2023 and are forecast to grow by 6.5%, 6.0% and 5.0% this year, Chinese growth also compares very favourably with other developing countries, which as a weighted average grew by 4.1% last year and are forecast to grow at the same rate this year.

But in the past, Chinese growth was much higher and was a major driver of global growth. Over the period 1980 to 2018, Chinese economic growth averaged 9.5% – more than twice the average rate of developing countries (4.5%) and nearly four times the average rate of advanced countries (2.4%) (see chart – click here for a PowerPoint of the chart).

Not only is Chinese growth now much lower, but it is set to decline further. The IMF forecasts that in 2025, Chinese growth will have fallen to 4.1% – below the forecast developing-country average of 4.2% and well below that of India (6.5%).

Causes of slowing Chinese growth

There are a number of factors that have come together to contribute to falling economic growth rates – growth rates that otherwise would have been expected to be considerably higher as the Chinese economy reopened after severe Covid lockdowns.

Property market
China has experienced a property boom over the past 20 years years as the government has encouraged construction in residential blocks and in factories and offices. The sector has accounted for some 20% of economic activity. But for many years, demand outstripped supply as consumers chose to invest in property, partly because of a lack of attractive alternatives for their considerable savings and partly because property prices were expected to go on rising. This lead to speculation on the part of both buyers and property developers. Consumers rushed to buy property before prices rose further and property developers borrowed considerably to buy land, which local authorities encouraged, as it provided a valuable source of revenue.

But now there is considerable overcapacity in the sector and new building has declined over the past three years. According to the IMF:

Housing starts have fallen by more than 60 per cent relative to pre-pandemic levels, a historically rapid pace only seen in the largest housing busts in cross-country experience in the last three decades. Sales have fallen amid homebuyer concerns that developers lack sufficient financing to complete projects and that prices will decline in the future.

As a result, many property developers have become unviable. At the end of January, the Chinese property giant, Evergrande, was ordered to liquidate by a Hong Kong court, after the judge ruled that the company did not have a workable plan to restructure around $300bn of debt. Over 50 Chinese property developers have defaulted or missed payments since 2020. The liquidation of Evergrande and worries about the viability of other Chinese property developers is likely to send shockwaves around the Chinese property market and more widely around Chinese investment markets.

Overcapacity
Rapid investment over many years has led to a large rise in industrial capacity. This has outstripped demand. The problem could get worse as investment, including state investment, is diverted from the property sector to manufacturing, especially electric vehicles. But with domestic demand dampened, this could lead to increased dumping on international markets – something that could spark trade wars with the USA and other trading partners (see below). Worries about this in China are increasing as the possibility of a second Trump presidency looks more possible. The Chinese authorities are keen to expand aggregate demand to tackle this overcapacity.

Uncertainty
Consumer and investor confidence are low. This is leading to severe deflationary pressures. If consumers face a decline in the value of their property, this wealth effect could further constrain their spending. This will, in turn, dampen industrial investment.

Uncertainty is beginning to affect foreign companies based in China. Many foreign companies are now making a loss in China or are at best breaking even. This could lead to disinvestment and add to deflationary pressures.

The Chinese stock market and policy responses
Lack of confidence in the Chinese economy is reflected in falling share prices. The Shanghai SSE Composite Index (an index of all stocks traded on the Shanghai Stock Exchange) has fallen dramatically in recent months. From a high of 3703 in September 2021, it had fallen to 2702 on 5 Feb 2024 – a fall of 27%. It is now below the level at the beginning of 2010 (see chart: click here for a PowerPoint). On 5 February alone, some 1800 stocks fell by over 10% in Shanghai and Shenzhen. People were sensing a rout and investors expressed their frustration and anger on social media, including the social media account of the US Embassy. The next day, the authorities intervened and bought large quantities of key stocks. China’s sovereign wealth fund announced that it would increase its purchase of shares to support the country’s stock markets. The SSE Composite rose 4.1% on 6 February and the Shenzhen Component Index rose 6.2%.

However, the rally eased as investors waited to see what more fundamental measures the authorities would take to support the stock markets and the economy more generally. Policies are needed to boost the wider economy and encourage a growth in consumer and business confidence.

Interest rates have been cut four times since the beginning of 2022, when the prime loan rate was cut from 3.85% to 3.7%. The last cut was from 3.55% to 3.45% in August 2023. But this has been insufficient to provide the necessary boost to aggregate demand. Further cuts in interest rates are possible and the government has said that it will use proactive fiscal and effective monetary policy in response to the languishing economy. However, government debt is already high, which limits the room for expansionary fiscal policy, and consumers are highly risk averse and have a high propensity to save.

Graduate unemployment
China has seen investment in education as an important means of increasing human capital and growth. But with a slowing economy, there are are more young people graduating each year than there are graduate jobs available. Official data show that for the group aged 16–24, the unemployment rate was 14.9% in December. This compares with an overall urban unemployment rate of 5.1%. Many graduates are forced to take non-graduate jobs and graduate jobs are being offered at reduced salaries. This will have a further dampening effect on aggregate demand.

Demographics
China’s one-child policy, which it pursued from 1980 to 2016, plus improved health and social care leading to greater longevity, has led to an ageing population and a shrinking workforce. This is despite recent increases in unemployment in the 16–24 age group. The greater the ratio of dependants to workers, the greater the brake on growth as taxes and savings are increasingly used to provide various forms of support.

Effects on the rest of the world

China has been a major driver of world economic growth. With a slowing Chinese economy, this will provide less stimulus to growth in other countries. Many multinational companies, including chip makers, cosmetics companies and chemical companies, earn considerable revenue from China. For example, the USA exports over $190 billion of goods and services to China and these support over 1 million jobs in the USA. A slowdown in China will have repercussions for many companies around the world.

There is also the concern that Chinese manufacturers may dump products on world markets at less than average (total) cost to shift stock and keep production up. This could undermine industry in many countries and could initiate a protectionist response. Already Donald Trump is talking about imposing a 10% tariff on most imported goods if he is elected again in November. Such tariffs could be considerably higher on imports from China. If Joe Biden is re-elected, he too may impose tariffs on Chinese goods if they are thought to be unfairly subsidised. US (and possibly EU) tariffs on Chinese goods could lead to a similar response from China, resulting in a trade war – a negative sum game.

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Questions

  1. Why is China experiencing slowing growth and is growth likely to pick up over the next five years?
  2. How does the situation in China today compare with that in Japan 30 years ago?
  3. What policies could the Chinese government pursue to stimulate economic growth?
  4. What policies were enacted towards China during the Trump presidency from 2017 to 2020?
  5. Would you advise the Chinese central bank to cut interest rates further? Explain.
  6. Should China introduce generous child support for families, no matter the number of children?
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The AI revolution

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.

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Questions

  1. Which industries are most likely to benefit from the development of AI?
  2. Distinguish between labour-replacing and labour-augmenting technological progress in the context of AI.
  3. How could AI reduce the amount of labour per unit of output and yet result in an increase in employment?
  4. What people are most likely to (a) gain, (b) lose from the increasing use of AI?
  5. Is the distribution of income likely to become more equal or less equal with the development and adoption of AI? Explain.
  6. What policies could governments adopt to spread the gains from AI more equally?