Category: Economics: Ch 11

Posted on

AI: benefits but catastrophic risk



Artificial intelligence (AI) has become a key technology in the 21st Century. Businesses use AI systems to analyse data, automate routine tasks, improve customer service, write software, create content and even assist with decision-making. As AI improves and becomes even more capable, governments, economists, businesses and society are debating its effects on productivity, employment and economic growth – and also its potentially extreme dangers.

Some commentators compare AI to earlier technological revolutions such as the steam engine, electricity and the Internet. They argue that AI will continue to increase productivity, create new industries and improve living standards. Others worry that AI may eliminate a large numbers of jobs, increase inequality, concentrate economic power in a few firms and, in the most extreme scenarios, pose a threat to humanity itself.

These debates raise important questions including how society should evaluate the potential risks of a technology whose benefits may be enormous but whose long-term consequences remain uncertain.

AI and the labour market

Historically, technological change has had both positive and negative effects on employment. Automation reduced the demand for many agricultural workers, while creating jobs in manufacturing. Computers automated clerical tasks but generated entirely new industries in software, telecommunications and digital services.

AI appears likely to follow a similar pattern. According to the World Economic Forum, advances in AI, robotics and information-processing technologies are expected to transform labour markets significantly during the second half of the 2020s, creating demand for new skills while reducing demand for others. The fastest-growing skills are expected to include AI and big data, technological literacy and cybersecurity.

The potential benefits

AI may benefit labour markets in several ways:

  • Higher productivity: Workers can complete tasks more quickly with AI assistance.
  • New occupations: Demand has emerged for AI engineers, prompt specialists, data scientists and AI governance professionals.
  • Better decision-making: Firms can use AI to improve forecasting, inventory management and customer service.
  • Complementing human skills: AI may perform repetitive tasks, allowing employees to focus on creativity, problem-solving and interpersonal activities.
  • Economic growth: Higher productivity can increase profits, wages and living standards over time.

Many economists argue that AI will not simply replace workers but will change the tasks they perform. Research from the OECD suggests that even highly AI-exposed occupations continue to require management, communication, collaboration and social skills that technologies struggle to replicate.

The potential costs

At the same time, AI may create significant labour-market challenges. Many white-collar occupations previously considered relatively safe from automation are becoming vulnerable. Generative AI systems can draft reports, analyse legal documents, write computer code and create marketing content. This means that some professional and administrative roles may face considerable disruption.

The World Economic Forum reports that business leaders have differing expectations about the effects of AI. In a 2026 survey (see link below), around 54 per cent expected AI to displace existing jobs, while only 24 per cent expected it to create new jobs within their organisations. Economists have identified several potential problems:

  • Structural unemployment: workers in industries that are in decline may struggle to find employment requiring their existing skills.
  • Increased income inequality: there may be a growing income disparity between highly skilled and less-skilled workers.
  • Growing market power: the largest technology firms that own the most advanced AI systems may see their market power grow further creating dominance in certain areas.
  • Regional inequalities: AI-related investment may become concentrated in particular cities and countries, exacerbating regional inequalities within and between countries.
  • Pressure on governments: to address issues of structural unemployment and increasing inequality, governments may be forced to expand retraining and social-support programmes.

AI and catastrophic risk

Most economic discussion around AI focuses on employment and productivity. However, some researchers argue that the most significant risks from AI may be much broader.

Economists distinguish between ordinary risks and catastrophic risks. Catastrophic risks involve events with a very low probability of occurring but potentially enormous consequences. Examples include nuclear accidents, pandemics and certain climate-related disasters.

AI raises similar concerns. Advanced AI systems could potentially be used to conduct cyberattacks, spread misinformation, disrupt critical infrastructure or support the development of dangerous technologies. Some researchers have even suggested that highly advanced AI systems could pose an existential risk to humanity if they become sufficiently powerful and are not properly controlled.

This became a widely discussed topic in the media in September 2026 following the resignation of an employee, Jacob Coxon, from AI firm, Anthropic. He said that people working on AI were ‘genuinely frightened’ about how quickly AI was advancing and what it might mean for the future of humanity. He said:

I believe that if we don’t slow down at the current rate of progress, there is a strong chance that we could all die in the immediate future.

Other researchers have raised similar concerns and there have since been calls from some of the biggest AI companies for regulation of AI to prevent this.

The policy debate and CBA

All of this creates a challenge for cost-benefit analysis. Suppose AI generates trillions of pounds of economic benefits. But, if there is also a very small probability of catastrophic harm, how should policymakers weigh up the two?

Traditional cost-benefit analysis values risk by multiplying the size of a potential outcome by its probability. However, this approach becomes problematic when both the probability and the consequences are highly uncertain. The risks may be extremely difficult to estimate, while the potential costs could be vast and affect future generations. For this reason, governments and firms increasingly use scenario analysis, stress testing and AI safety assessments to evaluate potential risks. These approaches attempt to prepare for extreme outcomes rather than relying solely on probability calculations.

Supporters of AI argue that technological progress has historically improved living standards and that restricting AI too heavily could reduce innovation and economic growth. Critics argue that uncertainty about potentially catastrophic outcomes justifies a more cautious approach.

The debate therefore extends beyond labour economics to the wider issue of managing catastrophic risk. As with nuclear power or climate change, policymakers must decide how much risk society is willing to accept in exchange for potentially large economic benefits.

Articles

Reports

Questions

  1. How might AI increase productivity while also causing unemployment in some sectors? Which sectors are likely to be affected the most?
  2. Why is it difficult to estimate the costs and benefits of advanced AI?
  3. Assume that a disaster is estimated to cost society £1000 billion (£1 000 000 000 000). The chances of the disaster occurring are said to be minute, however. Estimates vary from a probability of one in a million to one in a billion. What estimate of this cost would you include in a cost–benefit analysis?
  4. Why are many low-income countries apparently prepared to accept riskier projects than are high-income ones?
  5. Discuss whether the greatest economic challenge posed by AI is (a) job displacement; (b) increased inequality; (c) market concentration and the power of large technology firms; or (d) catastrophic long-term risks.
  6. Read the article by Bill Gates, The turbulent AI era is here. The choices we make now are critical. According to him, what steps should the world take to ensure that ‘AI will be a force for good and leave everyone better off’?
Posted on

Manchesterism: will greater devolution boost economic growth and help tackle inequality?


Andy Burnham is set to become UK Prime Minister on 20 July if no-one else stands to replace Keir Starmer. In a speech on 29 June, he outlined his economic vision. Central to this is devolution, where a greater number of economic decisions would be taken locally rather than by central government. This approach has been dubbed ‘Manchesterism’, in reference to his time as Mayor of Greater Manchester from 2017 to earlier this year. Under his mayoralty, Greater Manchester (GM) achieved faster economic growth than other regions or cities in the UK. From 2017 to 2023, GM’s gross value added grew by an average of 6.6% per annum and the city of Manchester’s by 8.4% – the highest of any city in the UK. The UK average was 4.9% and London’s was 4.6%.

The UK, especially England, is one of the least devolved of the OECD countries. One aspect of this is taxation. The chart shows local taxes and, in the case of federal countries, state/regional/provincial taxes too. (Click here for a PowerPoint.)

Only 4.9% of UK tax revenue is in the form of local taxes (council tax and 50% of business rates) and the amount that can be raised in council tax is capped by the central government. The remainder of UK tax revenue goes to central government in the form of income taxes, social security taxes (national insurance), VAT, excise duties, etc. This compares with an average of 7.1% local taxes and 24.5% local plus regional taxes across the 17 OECD countries shown in the chart.

Andy Burnham plans to shift some of the spending and tax-raising powers from Whitehall to metro mayors and local councils. The aim is to stimulate productivity and economic growth at a regional and local level by tailoring support and incentives to local needs and strengths. Local leaders will be best positioned to understand these needs and strengths and will be able to customise spending and support appropriately.

Examples of the types of greater autonomy over decision making would include:

  • Control over adult education budgets to allow them to be tailored to provide training and apprenticeships to meet the skills requirements of existing and emerging industries in the area;
  • Forming partnerships with local universities to support research and development that complements regional economic growth;
  • Providing greater funding for and control over local transport infrastructure, including roads, buses, trams, trains, etc., with local consultation to make them fit for the local population and businesses;
  • Tailoring business incentives to local needs and to the needs of the businesses themselves so as to attract an increase in investment;
  • Greatly expanding council house building, which has virtually dried up in recent years, with mayors and/or local authorities empowered to develop local housing strategies, including affordable housing programmes, and to direct housing investment funding to particular housing developments in areas of greatest need.

Andy Burnham has pledged to stick to the government’s two existing fiscal rules:
a) The Stability Rule (Fiscal Mandate): the current (day-to-day) budget must be in balance or surplus. In other words the provision of salaries, public services, state pensions, welfare, etc. must be covered by government revenues (largely taxation). The government should borrow only to fund long-term investment.
b) The Debt Rule (Stock Mandate): each year, public-sector net financial liabilities (PSNFL) must be forecast by the OBR to be falling as a share of GDP compared to the previous year in three years’ time. This acts as a break on the amount of borrowing for long-term investment.

It is likely, therefore, that there will be little extra government money for investment. Rather, the policy involves a redistribution of public-sector investment from central government to mayoral/local authorities.

In theory, such a policy of devolution need not see a redistribution from richer to poorer regions, but that might be part of the policy when the details are published. The UK has a bigger gap in productivity (GDP per worker) between its capital city and other large cities than in do other countries. Birmingham, Sheffield, Leeds, Newcastle, etc., as well as Cardiff, Glasgow, Edinburgh and Belfast, lag further behind London in output per head and economic growth than do other European countries lag behind their capital city. Thus the gap between Paris and Lyon, Toulouse and Marseille is narrower; as is that between Belin and Munich, Hamburg and Frankfurt. It is a similar picture in Spain and Italy. In the USA, some cities, such as San Francisco, outperform Washington DC and New York. A redistribution of government funding from London to the regions could see their incomes rise faster without having too much impact on London, which would still continue to attract large amounts of private investment.

Overall, there would be little increase in government funding. ‘Manchesterism’ is not, therefore, a demand-side policy. It is a supply-side policy – directing funds to areas where, combined with local incentives and local knowledge, the funding could yield greater returns and thereby increase potential GDP.

But this is not to say that there is no effect on aggregate demand. The hope is that devolution along the lines outlined by Andy Burnham will attract increased private investment, thereby increasing actual GDP as well as further increasing potential GDP.

We wait to see the details over the coming weeks.

Articles

Videos

Data

Questions

  1. Use an aggregate demand and supply diagram (simple or dynamic) to illustrate the effects on real GDP of a successful devolution strategy.
  2. Find out the details of the last Conservative government’s ‘levelling up’ policy. Was it similar in aims to that of ‘Manchesterism’?
  3. How could a policy of devolution as outlined by Andy Burnham affect income distribution within regions?
  4. Find out about the approach to regional policy in the EU. Is it similar to that being advocated by Andy Burnham?
  5. What is meant by ‘regional multipliers’? Why might they differ from the national multiplier?
Posted on

Inequality: the ‘defining issue of our day’?

Wes Streeting and Andy Burnham are seeking to become UK Prime Minister in a challenge to Keir Starmer. They have both responded to an essay by Tony Blair, former Labour Prime Minister, where he argued that current Labour policies were holding back business. But the essay never mentioned inequality. According to Burnham and Streeting, inequality and the related issue of poverty are fundamental to the crises facing society in western democracies. Countries’ economic success is typically measured in terms of growth in GDP. But when the benefits of growth go largely to those at the top of the income scale, while people on lower incomes struggle to make ends meet, this feeds resentment. Populist politicians stoke such resentment and offer simplistic solutions, such as protectionism, blaming outsiders and promising a return to better times.

But just what has happened to inequality over recent years and has poverty deepened? How are inequality and poverty affecting people’s lives and what is the impact on the economy? And what policies should governments follow to tackle the problem?

Income inequality

The chart shows UK inequality as given by the Gini coefficient, where 1 represents complete inequality, with one person earning the whole of national income and 0 represents perfect equality, with everyone earning the same. The higher the figure, therefore, the greater the inequality. As you can see, inequality is greatest when looking at original income – that is, income before taxes and benefits. Gross income includes benefits, and disposable income is income after both benefits and taxes. You can see that both benefits and taxes reduce inequality. When we take housing costs into account with the disposable income measure, however, inequality increases.

The chart shows that income inequality rose until the early 2000s, since when there have been only slight changes, although there has been a small decline recently.

The UK has higher income inequality than most high-income countries, although it is not as high as in the USA. It is sixth most unequal of the 38 OECD countries and the most unequal OECD member in Europe.

Globally, in 2025, the top 10% of the world’s population earned 53% of global income, while the bottom half earned just 8%. The reports listed below provide data and analysis on UK and global inequality.

Wealth inequality

When we turn to wealth, inequality in the UK is even greater. The richest 10% of households hold around 41% of wealth, while the poorest 50% hold just under 10%. The Gini coefficient is around 0.6. This has been drive by a rise in property and share prices and the system of inheritance whereby family wealth can accumulate over the generations.

Globally, the top 10% of the world’s population held 75% of global wealth in 2025, whereas the bottom 50% held just 2%. And a tiny group of people – the top 0.001% of the adult population (about 56,000 individuals) – held about 6% of global wealth, up from 4% in 1995. Such extreme wealth inequality has thus increased.

Inequality and poverty

There is no single measure of poverty. It could be measured in terms of basic needs. Here poverty would be where a person is unable to afford basic food, shelter, heating and lighting, clothing, footwear and basic toiletries. Normally, however, it is measured in relative terms. A typical measure, and one used by the Joseph Rowntree Foundation, is based on a proportion of median income. Poverty is defined as income below 60% of the median income, with deep poverty below 50% and very deep poverty below 40%.

In 2023/24, 14.2 million people were in poverty (20% of the population), of whom around 4.5 million were children. Of the 14.2 million, 6.8 million people (nearly half) were in very deep poverty,

Causes of poverty include one or more of the following: low skills or education, low pay, unemployment, inadequate benefits or a benefit system that is confusing or difficult to access, chronic sickness, disability, unavailability or cost of suitable housing, discrimination, a breakdown of personal relationships, substance abuse, abuse from others, a criminal record. Once in poverty, it becomes difficult to escape as people become deskilled, demotivated and judged by society.

But even if people are not earning less than 60% of median income, they can still struggle to escape inequality. Many people have low skills; many routine jobs are being replaced by automation or AI; many graduates face high debts; people struggle to get on the housing ladder; the rising cost of basic items dampens real incomes, especially of the low paid; people may face discrimination of various sorts; people do not have an option of joining a union in their workplace; people may have a large number of dependants.

The policy agenda

If inequality rises up the political agenda in the UK, especially with a potential leadership race in the Labour party, what might politicians focus on? The government has already done the following:

  • It has raised the minimum wage (the ‘National Living Wage’) substantially from £10.42 in 2023/24 to £11.44 in 2024/25, to £12.21 in 2025/26 and lowered the age limit from 23 to 21. There have been larger percentage rises for 18–20 year-olds and those under 18.
  • The two-child limit to the child benefit element in Universal Credit has been scrapped and so now parents are eligible for benefits for all children.
  • The Employment Rights Act has ended exploitative zero-hour contracts by providing rights to guaranteed hours.
  • It has expanded free school meal entitlements.
  • It has capped Universal Credit debt deductions at 15% of increased incomes (down from 25%) to help the poorest households retain more of their monthly income.
  • It has expanded free school meals and made more money available for free nursery place.
  • Landlords can no longer evict tenants for no reason; they must have a valid reason such as wanting to sell the property or severe rent arrears.
  • Landlords cannot increase rents more than once per year and tenants can appeal excessive or above-market rent increases to an independent tribunal.

But despite these policy measures, many claim that they will do too little to tackle inequality and poverty. Some on the left argue that taxes on property and other forms of wealth will be required to tackle wealth inequality. Others argue that more emphasis on education and training is necessary to provide workers with the skills to earn more in the labour market. Others argue for greater expenditure on public services.

Generally, however, measures to tackle inequality and poverty require government expenditure, which must be funded. This is why many on the centre left argue that economic growth is a necessary condition for any significant redistribution. It is, they argue, the best way of providing the tax revenue to fund redistribution.

Incentives and disincentives

Many on the right argue that redistributing incomes through higher taxes and benefits will act as a disincentive to work and to invest. As we argue in Essentials of Economics, higher income taxes could discourage people from working and investing; higher wealth taxes could discourage people from saving and investing.

The key to analysing these arguments is to distinguish between the income effect and the substitution effect of raising taxes. Raising income tax does two things.

  • It reduces disposable incomes. People therefore are encouraged to work more in an attempt to maintain their consumption of goods and services. This is the income effect. ‘I have to work more to make up for the higher taxes’, a person might say.
  • It reduces the opportunity cost of leisure. Since higher income taxes reduce take-home pay, an extra hour taken in leisure now involves a smaller sacrifice in consumption. Thus people may substitute leisure for consumption, and work less. This is called the substitution effect. ‘What is the point of doing overtime’, another person might say, ‘if so much of the overtime pay is going in taxes?’

The relative size of the income and substitution effects is likely to differ for different types of people. For example, the income effect is likely to dominate for those people with a substantial proportion of long-term commitments, such as those with families, with mortgages and other debts. They may feel forced to work more to maintain their disposable income. Clearly for such people, higher taxes are not a disincentive to work. The income effect is also likely to be relatively large for people on higher incomes, for whom an increase in tax rates represents a substantial cut in income.

The substitution effect is likely to dominate for those with few commitments: those whose families have left home, the single, and second income earners in families where that second income is not relied on for ‘essential’ consumption. A rise in tax rates for these people is likely to encourage them to work less.

Although high income earners may work more when there is a tax rise, they may still be discouraged by a steeply progressive tax structure. If they have to pay very high marginal rates of tax, it may simply not be worth their while seeking promotion or working harder.

What those on the centre and left argue is that tackling inequality and poverty requires more than just changing the tax and benefits system. What is required is policies that encourage greater upward social mobility, greater social cohesion and greater expenditure on infrastructure that will support the poor, such as greater expenditure on education and training, on support for very young children, on preventative healthcare, on social housing and on local public transport.

Articles

Reports

Data

Questions

  1. Is the UK becoming more or less equal? Does the answer depend on how inequality is measured?
  2. Is the world becoming more or less equal?
  3. Summarise the arguments against redistributing incomes from the rich to the poor.
  4. Summarise the arguments in favour of redistributing incomes from the rich to the poor.
  5. Explain the income and substitution effects of making income tax more progressive.
  6. How is the greater adoption of AI likely to affect income distribution?
  7. How does social mobility affect income distribution? What measures can be adopted to increase social mobility?
  8. Compare the relative merits and problems of raising income taxes, wealth taxes and expenditure taxes as means of redistributing incomes more equally.
Posted on

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.
Posted on

How to increase productivity – the key to economic growth

The productivity gap between the UK and its main competitors is significant. In 2024, compared to the UK, output per hour worked was 10.0% higher in France, 19.8% higher in Germany and 41.1% higher in the USA. These percentages are in purchasing-power parity terms: in other words, they reflect the purchasing power of the respective currencies – the pound, the euro and the US dollar.

GDP per hour worked (in PPP terms) is normally regarded as the best measure of labour productivity. An alternative measure is GDP per worker, but this does not take into account the length of the working year. Using this measure, the gap with the USA is even higher as workers in the USA work longer hours and have fewer days holiday per year than in the UK.

The productivity gap is not a new phenomenon. It has been substantial and growing over the past 20 years. (The exception was in 2020 during lockdowns when many of the least productive sectors, such as hospitality, were forced to close temporarily.)

The productivity gap is shown in the two figures. Both figures show labour productivity for the UK, France, Germany and the USA from 1995 to 2024.

Figure 1 shows output (GDP) per hour, measured in US dollars in PPP terms.

Figure 2 shows output (GDP) per hour relative to the UK, with the UK set at 100. The gap narrowed somewhat up to the early 2000s, but since then has widened.

Low UK productivity has been a source of concern for UK governments and business for many years. Not only does it constrain the growth in living standards, it also make the UK less attractive as a source of inward investment and less competitive internationally.

Part of the reason for low UK productivity compared to that in other countries is a low level of investment. As a proportion of GDP, the UK has persistently had the lowest, or almost the lowest, level of investment of its major competitors. This is illustrated in Table 1.

It is generally recognised by government, business and economists that if the economy is to be successful, the productivity gap must be closed. But there is no ‘quick fix’. The policies necessary to achieve increased productivity are long term. There is also a recognition that the productivity problem is a multi-faceted one and that to deal with it requires policy initiatives on a broad front: initiatives that encompass institutional changes as well as adjustments in policy.

So what can be done to improve productivity and how can this be achieved at the micro as well as the macro level?

Improving productivity: things that government can do

Encouraging investment. Over the years, UK governments have increased investment allowances, enabling firms to offset the cost of investment against pre-tax profit, thereby reducing their tax liability. For example, in the UK, companies can offset a multiple of research and development costs against corporation tax. The rate of relief for small and medium-sized enterprises (SMEs) allows companies that work in science and technology to deduct an extra 86% of their qualifying expenditure from their trading profit in addition to the normal 100% deduction: i.e. a total of 186% deduction. Meanwhile, since April 2016, larger companies have been able to claim a R&D expenditure credit, initially worth 11 per cent of R&D expenditures, then 12 per cent from 2018 and 13 per cent from 2020. This was then raised to 20 per cent from 2023.

Strengthening competition. A number of studies have revealed that, with increasing market share, business productivity growth slows. As a result, government policy sought to strengthen competition policy. The Competition Act 1998, which came into force in March 2000, and the Enterprise Act of 2002, enhanced the powers of the Office of Fair Trading (OFT) (a predecessor to the Competition and Markets Authority) in respect to dealing with anti-competitive practices. It was given the ability to impose large fines on firms which had been found guilty of exploiting a dominant market position. Today, one of the strategic goals of the Competition and Markets Authority (CMA) is the aim of ‘extending competition frontiers’ in order to improve the way competition works.

Encouraging an enterprise culture. The creation of an enterprise culture is seen as a crucial factor not only to encourage innovation but also to stimulate technological progress. Innovation and technological progress are crucial to sustaining growth and raising living standards. The UK government launched the Small Business Service in April 2000, later renamed Business and Industry. Its role is to co-ordinate small-business policy within government and liaise with business, providing advice and information. However, according to the OECD, there remains considerable scope for increasing the level of government support for entrepreneurship in the UK.

Improving productivity: things that organisations can do

In the podcast from the BBC’s The Bottom Line series, titled ‘Productivity: How Can British Business Work Smarter’ (see link below), Evan Davis and guests discuss what productivity really looks like in practice – from offices and factories, to call centres and operating theatres.’ The episode identifies a number of ways in which labour productivity can be improved. These include:

  • People could work harder;
  • Workers could be better trained and more skilled and thus able to produce more per hour;
  • Capital could be increased so that workers have more equipment or tools to enable them to produce more, or there could be greater automation, releasing labour to work on other tasks;
  • Workplaces could be arranged more efficiently so that less time is spent moving from task to task;
  • Systems could put in place to ensure that tasks are done correctly the first time and that time is not wasted having to repeat them or put them right;
  • Workers could be better incentivised to work efficiently, whether through direct pay or promotion prospects, or by increasing job satisfaction or by management being better attuned to what motivates workers and makes them feel valued;
  • Firms could move to higher-value products, so that workers produce a greater value of output per hour.

The three contributors to the programme discuss various initiatives in their organisations (an electronics manufacturer, NHS foundation trusts and a provider of office services to other organisations).

They also discuss the role that AI plays, or could play, in doing otherwise time-consuming tasks, such as recording and paying invoices and record keeping in offices; writing grants or producing policy documents; analysing X-ray results in hospitals and performing preliminary diagnoses when patients present with various symptoms; recording conversations/consultations and then sorting, summarising and transcribing them; building AI capabilities into machines or robots to enable them to respond to different specifications or circumstances; software development where AI writes the code. Often, there is a shortage of time for workers to do more creative things. AI can help release more time by doing a lot of the mundane tasks or allowing people to do them much quicker.

There are huge possibilities for increasing labour productivity at an organisational level. The successful organisations will be those that can grasp these possibilities – and in many cases they will be incentivised to so so as it will improve their profitability or other outcomes.

Podcast

Articles

Data

Questions

  1. In what different ways can productivity be measured? What is the most appropriate measure for assessing the effect of productivity on (a) GDP and (b) human welfare generally?
  2. Why has the UK had a lower level of labour productivity than France, Germany and the USA for many years? What can UK governments do to help close this gap?
  3. Find out how Japanese labour productivity has compared with that in the UK over the past 30 years and explain your findings.
  4. Research an organisation of your choice to find out ways in which labour productivity could be increased.
  5. Identify various ways in which AI can improve productivity. Will organisations be incentivised to adopt them?
  6. Has Brexit affected UK labour productivity and, if so, how and why?