Category: Economics: Ch 21

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Fed independence under attack

Donald Trump is keen to lower US interest rates substantially and rapidly in order to provide a boost to the US economy. He is also keen to reduce the cost of living for US citizens and sees lower interest rates as a means of reducing the burden of debt servicing for both consumers and firms alike.

But interest rates are set by the US central bank, the Federal Reserve (the ‘Fed’), which is formally independent from government. This independence is seen as important for providing stability to the US economy and removing monetary policy from short-term political pressures to cut interest rates. Succumbing to political pressures would be likely to create uncertainty and damage long-term stability and growth.

Yet President Trump is pushing the Fed to lower interest rates rapidly and despite three cuts in a row of 0.25 percentage points in the last part of 2025 (see chart below), he thinks this as too little and is annoyed by suggestions that the Fed is unlikely to lower rates again for a while. He has put great pressure on Jerome Powell, the Fed Chair, to go further and faster and has threatened to replace him before his term expires in May this year. He has also made clear that he is likely to appoint someone more willing to cutting rates.

The Federal Reserve headquarters in Washington is currently being renovated. The nine-year project is costing $2.5 billion and is due to be completed next year. President Trump has declared that the project’s costs are excessive and unnecessary.

On 11 January, Federal prosecutors confirmed that they were opening a criminal investigation into Powell, accusing him of lying to Congress in his June 2025 testimony regarding the scope and costs of the renovations.

Powell responded by posting a video in which he claimed that the real reason that he was being threatened with criminal charges was not because of the renovations but because the Fed had ignored President Trump’s pressure and had set interest rates:

based on our best assessment of what will serve the public, rather than following the preferences of the President. This is about whether the Fed will be able to continue to set interest rates based on evidence and economic conditions – or whether, instead, monetary policy will be directed by political pressure or intimidation.

The Fed’s mandate

The Federal Reserve Board decides on monetary policy and then the Federal Open Market Committee (FOMC) decides how to carry it out. It decides on interest rates and asset sales or purchases. The FOMC meets eight times a year.

The Fed is independent of both the President and Congress, and its Chair is generally regarded as having great power in determining the country’s economic policy.

Since 1977, the Fed’s statutory mandate has been to promote the goals of stable prices and maximum employment. Because of the reference to both prices and employment, the mandate is commonly referred to as a ‘dual mandate’. Its inflation target is 2 per cent over the long run with ‘well anchored’ inflationary expectations.

The dual mandate is unlike that of the Bank of England, the European Central Bank, the Bank of Japan and most other central banks, which all have a single key mandate of achieving a target of a 2 per cent annual rate of consumer price inflation over a particular time period.

With a dual mandate, the two objectives may well conflict from time to time. Moreover, changes in monetary policy affect these objectives with a lag and potentially over different time horizons. Hence, an assessment may have to be made of which is the most pressing problem. This does give some leeway in setting interest rates somewhat lower than if there were a single inflation-rate target. Nevertheless, the assessment is in terms of how best to achieve the mandate and not to meet current political goals.

Statement by former Fed Chairs and Governors

On 12 January, three former Chairs of the Federal Reserve (Janet Yellen, Ben Bernanke and Alan Greenspan), four former Treasury Secretaries (Timothy Geithner, Jacob Lew, Henry Paulson and Robert Rubin) and seven other top former economic officials issued the following statement (see Substack link in the Articles section below):

The Federal Reserve’s independence and the public’s perception of that independence are critical for economic performance, including achieving the goals Congress has set for the Federal Reserve of stable prices, maximum employment, and moderate long-term interest rates. The reported criminal inquiry into Federal Reserve Chair Jay Powell is an unprecedented attempt to use prosecutorial attacks to undermine that independence. This is how monetary policy is made in emerging markets with weak institutions, with highly negative consequences for inflation and the functioning of their economies more broadly. It has no place in the United States whose greatest strength is the rule of law, which is at the foundation of our economic success.

Response of investors

What will happen to the dollar, US bond prices, share prices and US inflation, and what will happen to investment, depends on how people respond to the threat to the Fed’s independence. Initially, there was little response from markets, with investors probably concluding that President Trump is unlikely to be able to sway FOMC members. What is more, several Republican lawmakers have begun criticising the Trump administration’s criminal investigation, making it harder for the President to influence Fed decisions.

Even if Powell is replaced, either in the short term or in May, by a chair keen to pursue the Trump agenda, that chair will still be just one of twelve voting members of the FOMC.

Seven are appointed by the President, but serve for staggered 14-year terms. Four have been appointed by President Trump, but the other three were appointed by President Biden, although one – Lisa Cook – is being indicted by the Supreme Court for mortgage fraud, with the hearing scheduled for January 21. She claims that this is a trumped-up charge to provide grounds for removing her from the Fed. If she is removed, President Trump could appoint a replacement minded to cut rates.

The other five members include the President of the New York Fed and four of the eleven other regional Fed Presidents serving in rotation. These four are generally hawkish and would oppose early rate cuts.

Thus it is unlikely that President Trump will succeed in pushing the Fed to lower interest rates earlier than they would have done. For that reason, markets have remained relatively sanguine.

Nevertheless, Donald Trump’s actions could well cause investors to become more worried. Will he try to find other ways to undermine the Fed? Will his actions over Venezuela, Cuba, Greenland and Iran, let alone his policies towards Ukraine and Russia and towards Israel and Gaza, heighten global uncertainty? Will his actions towards Venezuela and his desire to take over Greenland embolden China to attempt to annex Taiwan, and Russia to continue to resist plans to end the war in Ukraine or to make stronger demands?

Such developments could cause investor confidence to wane and for stock markets to fall. Time will tell. I think we need a crystal ball!

Videos

Articles

Questions

  1. What are the arguments for central bank independence?
  2. What are the arguments for control of monetary policy by the central government?
  3. Assess the above arguments.
  4. Find out what has happened to interest rates, the US stock market and the dollar since this blog was written.
  5. How do the fiscal decisions by government affect monetary policy?
  6. Compare the benefits of the dual mandate system of the Fed with those of the single mandate of the Bank of England and ECB.
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Will AI make the world less equal?

With businesses increasing their use of AI, this is likely to have significant effects on employment. But how will this affect the distribution of income, both within countries and between countries?

In some ways, AI is likely to increase inequality within countries as it displaces low-skilled workers and enhances the productivity of higher-skilled workers. In other ways, it could reduce inequality by allowing lower-skilled workers to increase their productivity, while displacing some higher-skilled workers and managers through the increased adoption of automated processes.

The effect of AI on the distribution of income between countries will depend crucially on its accessibility. If it is widely available to low-income countries, it could significantly enhance the productivity of small businesses and workers in such countries and help to reduce the income gap with the richer world. If the gains in such countries, however, are largely experienced by multinational companies, whether in mines and plantations, or in labour-intensive industries, such as garment production, few of the gains may accrue to workers and global inequality may increase.

Redistribution within a country

The deployment of AI may result in labour displacement. AI is likely to replace both manual and white-collar jobs that involve straightforward and repetitive tasks. These include: routine clerical work, such as data entry, filing and scheduling; paralegal work, contract drafting and legal research; consulting, business research and market analysis; accounting and bookkeeping; financial trading; proofreading, copy mark-up and translation; graphic design; machine operation; warehouse work, where AI-enabled warehouse robots do many receiving, sorting, stacking, retrieval, carrying and loading tasks (e.g. Amazon’s Sequoia robotic system); basic coding or document sifting; market research and advertising design; call-centre work, such as enquiry handling, sales, telemarketing and customer service; hospitality reception; sales cashiers in supermarkets and stores; analysis of health data and diagnosis. Such jobs can all be performed by AI assistants, AI assisted robots or chat bots.

Women are likely to be disproportionately affected because they perform a higher share of the administrative and service roles most exposed to AI.

Workers displaced by AI may find that they can find employment only in lower-paid jobs. Examples include direct customer-facing roles, such as bar staff, shop assistants, hairdressers and nail and beauty consultants.

Such job displacement by AI is likely to redistribute income from relatively low-skilled labour to capital: a redistribution from wages to profits. This will tend to lead to greater inequality.

AI is also likely to lead to a redistribution of income towards certain types of high-skilled labour that are difficult to replace with AI but which could be enhanced by it. Take the case of skilled traders, such as plumbers, electricians and carpenters. They might be able to use AI in their work to enhance their productivity, through diagnosis, planning, problem-solving, measurement, etc. but the AI would not displace them. Instead, it could increase their incomes by allowing them to do their work more efficiently or effectively and thus increase their output per hour and enhance their hourly reward. Another example is architecture, where AI can automate repetitive tasks and open up new design possibilities, allowing architects to focus on creativity, flexibility, aesthetics, empathy with clients and ethical decision-making.

An important distinction is between disembodied and embodied AI investment. Disembodied AI investment could include AI ‘assistants’, such as ChatGPT and other software that can be used in existing jobs to enhance productivity. Such investment can usually be rolled out relatively quickly. Although the extra productivity may allow some reduction in the number of workers, disembodied AI investment is likely to be less disruptive than embodied AI investment. The latter includes robotics and automation, where workers are replaced by machines. This would require more investment and may be slower to be adopted.

Then there are jobs that will be created by AI. These include prompt engineers, who develop questions and prompt techniques to optimise AI output; health tech experts, who help organisations implement new medical AI products; AI educators, who train people in the uses of AI in the workplace; ethics advisors, who help companies ensure that their uses of AI are aligned with their values, responsibilities and goals; and cybersecurity experts who put systems in place to prevent AI stealing sensitive information. Such jobs may be relatively highly paid.

In other cases, the gains from AI in employment are likely to accrue mainly to the consumer, with probably little change in the incomes of the workers themselves. This is particularly the case in parts of the public sector where wages/salaries are only very loosely related to productivity and where a large part of the work involves providing a personal service. For example, health professionals’ productivity could be enhanced by AI, which could allow faster and more accurate diagnosis, more efficient monitoring and greater accuracy in surgery. The main gainers would be the patients, with probably little change in the incomes of the health professionals themselves. Teachers’ productivity could be improved by allowing more rapid and efficient marking, preparation of materials and record keeping, allowing more time to be spent with students. Again, the main gainers would be the students, with little change in teachers’ incomes. Other jobs in this category include social workers, therapists, solicitors and barristers, HR specialists, senior managers and musicians.

Thus there is likely to be a distribution away from lower-skilled workers to both capital and higher-skilled workers who can use AI, to people who work in new jobs created by AI and to the consumers of certain services.

AI will accelerate productivity growth and, with it, GDP growth, but will probably displace workers faster than new roles emerge. This is likely to increase inequality and be a major challenge for society. Can the labour market adapt? Could the effects be modified if people moved to a four- or three-day week? Will governments introduce statutory limits to weekly working hours? Will training and education adapt to the new demands of employers?

Redistribution between countries

AI threatens to widen the global rich–poor divide. It will give wealthier nations a productivity and innovation edge, which could displace low-skilled jobs in low-income nations. Labour-intensive production could be replaced by automated production, with the capital owned by the multinational companies of just a few countries, such as the USA and China, which between them account for 40% of global corporate AI R&D spending. For some companies, it would make sense to relocate production to rich countries, or certain wealthier developing countries, with better digital infrastructure, advanced data systems and more reliable power supply.

For other companies, however, production might still be based in low-income countries to take advantage of low-cost local materials. But there would still be a redistribution from wages in such countries to the profits of multinationals.

But it is not just in manufacturing where low-income countries are vulnerable to the integration of AI. Several countries, such as India, the Philippines, Mexico and Egypt have seen considerable investment in call centres and IT services for business process outsourcing and customer services. AI now poses a threat to employment in this industry as it has the potential to replace large numbers of workers.

AI-related job losses could exacerbate unemployment and deepen poverty in poorer countries, which, with limited resources, limited training and underdeveloped social protection systems, are less equipped to absorb economic and social shocks. This will further widen the global divide. In the case of embodied AI investment, it may only be possible in low-income countries through multinational investment and could displace many traditional jobs, with much of the benefit going in additional multinational profit.

But it is not all bad news for low-income countries. AI-driven innovations in healthcare, education, and agriculture, if adopted in poor countries, can make a significant contribution to raising living standards and can slow, or even reverse, the widening gap between rich and poor nations. Some of the greatest potential is in small-scale agriculture. Smallholders can boost crop yields though precision farming powered by AI; AI tools can help farmers buy seeds, fertilisers and animals and sell their produce at optimum times and prices; AI-enabled education tools can help farmers learn new techniques.

Articles

Questions

  1. What types of job are most vulnerable to AI?
  2. How will AI change the comparative advantage of low-income countries and what effect will it be likely to have on the pattern of global trade?
  3. Assess alternative policies that governments in high-income countries can adopt to offset the growth in inequality caused by the increasing use of AI.
  4. What policies can governments in low-income countries or aid agencies adopt to offset the growth in inequality within low-income countries and between high- and low-income countries?
  5. How might the growth of AI affect your own approach to career development?
  6. Is AI likely to increase or decrease economic power? Explain.
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World Uncertainty Index

We continue to live through incredibly turbulent times. In the past decade or so we have experienced a global financial crisis, a global health emergency, seen the UK’s departure from the European Union, and witnessed increasing levels of geopolitical tension and conflict. Add to this the effects from the climate emergency and it easy to see why the issue of economic uncertainty is so important when thinking about a country’s economic prospects.

In this blog we consider how we can capture this uncertainty through a World Uncertainty Index and the ways by which economic uncertainty impacts on the macroeconomic environment.

World Uncertainty Index

Hites Ahir, Nicholas Bloom and Davide Furceri have constructed a measure of uncertainty known as the World Uncertainty Index (WUI). This tracks uncertainty around the world using the process of ‘text mining’ the country reports produced by the Economist Intelligence Unit. The words searched for are ‘uncertain’, ‘uncertainty’ and ‘uncertainties’ and a tally is recorded based on the number of times they occur per 1000 words of text. To produce the index this figure is then multiplied up by 100 000. A higher number therefore indicates a greater level of uncertainty. For more information on the construction of the index see the 2022 article by Ahir, Bloom and Furceri linked below.

Figure 1 (click here for a PowerPoint) shows the WUI both globally and in the UK quarterly since 1991. The global index covers 143 countries and is presented as both a simple average and a GDP weighted average. The UK WUI is also shown. This is a three-quarter weighted average, the authors’ preferred measure for individual countries, where increasing weights of 0.1, 0.3 and 0.6 are used for the three most recent quarters.

From Figure 1 we can see how the level of uncertainty has been particularly volatile over the past decade or more. Events such as the sovereign debt crisis in parts of Europe in the early 2010s, the Brexit referendum in 2016, the COVID-pandemic in 2020–21 and the invasion of Ukraine in 2022 all played their part in affecting uncertainty domestically and internationally.

Uncertainty, risk-aversion and aggregate demand

Now the question turns to how uncertainty affects economies. One way of addressing this is to think about ways in which uncertainty affects the choices that people and businesses make. In doing so, we could think about the impact of uncertainty on components of aggregate demand, such as household consumption and investment, or capital expenditures by firms.

As Figure 2 shows (click here for a PowerPoint), investment is particularly volatile, and much more so than household spending. Some of this can be attributed to the ‘lumpiness’ of investment decisions since these expenditures tend to be characterised by indivisibility and irreversibility. This means that they are often relatively costly to finance and are ‘all or nothing’ decisions. In the context of uncertainty, it can make sense therefore for firms to wait for news that makes the future clearer. In this sense, we can think of uncertainty rather like a fog that firms are peering through. The thicker the fog, the more uncertain the future and the more cautious firms are likely to be.

The greater caution that many firms are likely to adopt in more uncertain times is consistent with the property of risk-aversion that we often attribute to a range of economic agents. When applied to household spending decisions, risk-aversion is often used to explain why households are willing to hold a buffer stock of savings to self-insure against unforeseen events and their future financial outcomes being worse than expected. Hence, in more uncertain times households are likely to want to increase this buffer further.

The theory of buffer-stock saving was popularised by Christopher Carroll in 1992 (see link below). It implies that in the presence of uncertainty, people are prepared to consume less today in order to increase levels of saving, pay off existing debts, or borrow less relative to that in the absence of uncertainty. The extent of the buffer of financial wealth that people want to hold will depend on their own appetite for risk, the level of uncertainty, and the moderating effect from their own impatience and, hence, present bias for consuming today.

Risk aversion is consistent with the property of diminishing marginal utility of income or consumption. In other words, as people’s total spending volumes increase, their levels of utility or satisfaction increase but at an increasingly slower rate. It is this which explains why individuals are willing to engage with the financial system to reallocate their expected life-time earnings and have a smoother consumption profile than would otherwise be the case from their fluctuating incomes.

Yet diminishing marginal utility not only explains consumption smoothing, but also why people are willing to engage with the financial system to have financial buffers as self-insurance. It explains why people save more or borrow less today than suggested by our base-line consumption smoothing model. It is the result of people’s greater dislike (and loss of utility) from their financial affairs being worse than expected than their like (and additional utility) from them being better than expected. This tendency is only likely to increase the more uncertain times are. The result is that uncertainty tends to lower household consumption with perhaps ‘big-ticket items’, such as cars, furniture, and expensive electronic goods, being particularly sensitive to uncertainty.

Uncertainty and confidence

Uncertainty does not just affect risk; it also affects confidence. Risk and confidence are often considered together, not least because their effects in generating and transmitting shocks can be difficult to disentangle.

We can think of confidence as capturing our mood or sentiment, particularly with respect to future economic developments. Figure 3 plots the Uncertainty Index for the UK alongside the OECD’s composite consumer and business confidence indicators. Values above 100 for the confidence indicators indicate greater confidence about the future economic situation and near-term business environment, while values below 100 indicate pessimism towards the future economic and business environments.

Figure 3 suggests that the relationship between confidence and uncertainty is rather more complex than perhaps is generally understood (click here for a PowerPoint). Haddow, Hare, Hooley and Shakir (see link below) argue that the evidence tends to point to changes in uncertainty affecting confidence, but with less evidence that changes in confidence affect uncertainty.

To illustrate this, consider the global financial crisis of the late 2000s. The argument can be made that the heightened uncertainty about future prospects for households and businesses helped to erode their confidence in the future. The result was that people and businesses revised down their expectations of the future (pessimism). However, although people were more pessimistic about the future, this was more likely to have been the result of uncertainty rather than the cause of further uncertainty.

Conclusion

For economists and policymakers alike, indicators of uncertainty, such as the Ahir, Bloom and Furceri World Uncertainty Index, are invaluable tools in understanding and forecasting behaviour and the likely economic outcomes that follow. Some uncertainty is inevitable, but the persistence of greater uncertainty since the global financial crisis of the late 2000s compares quite starkly with the relatively lower and more stable levels of uncertainty seen from the mid-1990s up to the crisis. Hence the recent frequency and size of changes in uncertainty show how important it to understand how uncertainty effects transmit through economies.

Academic papers

Articles

Data

Questions

  1. (a) Explain what is meant by the concept of diminishing marginal utility of consumption.
    (b) Explain how this concept helps us to understand both consumption smoothing and the motivation to engage in buffer-stock saving.
  2. Explain the distinction between confidence and uncertainty when analysing macroeconomic shocks.
  3. Discuss which types of expenditures you think are likely to be most susceptible to uncertainty shocks.
  4. Discuss how economic uncertainty might affect productivity and the growth of potential output.
  5. How might the interconnectedness of economies affect the transmission of uncertainty effects through economies?
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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.

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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?