Artificial intelligence is having a profound effect on economies and society. From production, to services, to healthcare, to pharmaceuticals; to education, to research, to data analysis; to software, to search engines; to planning, to communication, to legal services, to social media – to our everyday lives, AI is transforming the way humans interact. And that transformation is likely to accelerate. But what will be the effects on GDP, on consumption, on jobs, on the distribution of income, and human welfare in general? These are profound questions and ones that economists and other social scientists are pondering. Here we look at some of the issues and possible scenarios.
According to the Merrill/Bank of America article linked below, when asked about the potential for AI, ChatGPT replied:
AI holds immense potential to drive innovation, improve decision-making processes and tackle complex problems across various fields, positively impacting society.
But the magnitude and distribution of the effects on society and economic activity are hard to predict. Perhaps the easiest is the effect on GDP. AI can analyse and interpret data to meet economic goals. It can do this much more extensively and much quicker than using pre-AI software. This will enable higher productivity across a range of manufacturing and service industries. According to the Merrill/Bank of America article, ‘global revenue associated with AI software, hardware, service and sales will likely grow at 19% per year’. With productivity languishing in many countries as they struggle to recover from the pandemic, high inflation and high debt, this massive boost to productivity will be welcome.
But whilst AI may lead to productivity growth, its magnitude is very hard to predict. Both the ‘low-productivity future’ and the ‘high-productivity future’ described in the IMF article linked below are plausible. Productivity growth from AI may be confined to a few sectors, with many workers displaced into jobs where they are less productive. Or, the growth in productivity may affect many sectors, with ‘AI applied to a substantial share of the tasks done by most workers’.
Even if AI does massively boost the growth in world GDP, the distribution is likely to be highly uneven, both between countries and within countries. This could widen the gap between rich and poor and create a range of social tensions.
In terms of countries, the main beneficiaries will be developed countries in North America, Europe and Asia and rapidly developing countries, largely in Asia, such as China and India. Poorer developing countries’ access to the fruits of AI will be more limited and they could lose competitive advantage in a number of labour-intensive industries.
Then there is growing inequality between the companies controlling AI systems and other economic actors. Just as companies such as Microsoft, Apple, Google and Meta grew rich as computing, the Internet and social media grew and developed, so these and other companies at the forefront of AI development and supply will grow rich, along with their senior executives. The question then is how much will other companies and individuals benefit. Partly, it will depend on how much production can be adapted and developed in light of the possibilities that AI presents. Partly, it will depend on competition within the AI software market. There is, and will continue to be, a rush to develop and patent software so as to deliver and maintain monopoly profits. It is likely that only a few companies will emerge dominant – a natural oligopoly.
Then there is the likely growth of inequality between individuals. The reason is that AI will have different effects in different parts of the labour market.
The labour market
In some industries, AI will enhance labour productivity. It will be a tool that will be used by workers to improve the service they offer or the items they produce. In other cases, it will replace labour. It will not simply be a tool used by labour, but will do the job itself. Workers will be displaced and structural unemployment is likely to rise. The quicker the displacement process, the more will such unemployment rise. People may be forced to take more menial jobs in the service sector. This, in turn, will drive down the wages in such jobs and employers may find it more convenient to use gig workers than employ workers on full- or part-time contracts with holidays and other rights and benefits.
But the development of AI may also lead to the creation of other high-productivity jobs. As the Goldman Sachs article linked below states:
Jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth… For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals. There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services.
Nevertheless, people could still lose their jobs before being re-employed elsewhere.
The possible rise in structural unemployment raises the question of retraining provision and its funding and whether workers would be required to undertake such retraining. It also raises the question of whether there should be a universal basic income so that the additional income from AI can be spread more widely. This income would be paid in addition to any wages that people earn. But a universal basic income would require finance. How could AI be taxed? What would be the effects on incentives and investment in the AI industry? The Guardian article, linked below, explores some of these issues.
The increased GDP from AI will lead to higher levels of consumption. The resulting increase in demand for labour will go some way to offsetting the effects of workers being displaced by AI. There may be new employment opportunities in the service sector in areas such as sport and recreation, where there is an emphasis on human interaction and where, therefore, humans have an advantage over AI.
Another issue raised is whether people need to work so many hours. Is there an argument for a four-day or even three-day week? We explored these issues in a recent blog in the context of low productivity growth. The arguments become more compelling when productivity growth is high.
AI users are not all benign. As we are beginning to see, AI opens the possibility for sophisticated crime, including cyberattacks, fraud and extortion as the technology makes the acquisition and misuse of data, and the development of malware and phishing much easier.
Another set of issues arises in education. What knowledge should students be expected to acquire? Should the focus of education continue to shift towards analytical skills and understanding away from the simple acquisition of knowledge and techniques. This has been a development in recent years and could accelerate. Then there is the question of assessment. Generative AI creates a range of possibilities for plagiarism and other forms of cheating. How should modes of assessment change to reflect this problem? Should there be a greater shift towards exams or towards project work that encourages the use of AI?
Finally, there is the issue of the sort of society we want to achieve. Work is not just about producing goods and services for us as consumers – work is an important part of life. To the extent that AI can enhance working life and take away a lot of routine and boring tasks, then society gains. To the extent, however, that it replaces work that involved judgement and human interaction, then society might lose. More might be produced, but we might be less fulfilled.
- The Macroeconomics of Artificial Intelligence
IMF publications, Erik Brynjolfsson and Gabriel Unger (December 2023)
- Economic impacts of artificial intelligence (AI)
European Parliamentary Research Service, Marcin Szczepański (July 2019)
- Artificial intelligence: A real game changer
Chief Investment Office, Merrill/Bank of America (July 2023)
- Generative AI could raise global GDP by 7%
Goldman Sachs, Joseph Briggs (5/4/23)
- The macroeconomic impact of artificial intelligence
PwC, Jonathan Gillham, Lucy Rimmington, Hugh Dance, Gerard Verweij, Anand Rao, Kate Barnard Roberts and Mark Paich (February 2018)
- How genAI is revolutionizing the field of economics
CNN, Bryan Mena and Samantha Delouya (12/10/23)
- AI-powered digital colleagues are here. Some ‘safe’ jobs could be vulnerable.
BBC Worklife, Sam Becker (30/11/23)
- Generative AI and Its Economic Impact: What You Need to Know
Investopedia, Jim Probasco (1/12/23)
- AI is coming for our jobs! Could universal basic income be the solution?
The Guardian Philippa Kelly (16/11/23)
- CFPB chief’s warning: AI is a ‘natural oligopoly’ in the making
Politico, Sam Sutton (21/11/23)
- Which industries are most likely to benefit from the development of AI?
- Distinguish between labour-replacing and labour-augmenting technological progress in the context of AI.
- How could AI reduce the amount of labour per unit of output and yet result in an increase in employment?
- What people are most likely to (a) gain, (b) lose from the increasing use of AI?
- Is the distribution of income likely to become more equal or less equal with the development and adoption of AI? Explain.
- What policies could governments adopt to spread the gains from AI more equally?
The government has announced outlines of the new system of immigration controls from January 2021 when the Brexit transition period is scheduled to finish. It plans to introduce an Australian-style points-based system. This will apply to all EU and Non-EU citizens. The aim is to attract skilled workers, while preventing non-skilled or low-skilled workers from entering the UK for employment.
But even skilled workers will need to meet three criteria in order to obtain a work visa: (i) having the offer of a job paying a minimum of £25,600 per annum, except in designated jobs where there is a shortage of labour; (ii) being able to speak English; (iii) having qualifications equivalent to A levels.
To apply for a work visa, applicants must have at least 70 points according to the following table:
In certain jobs where there is a shortage of labour, designated by the Migration Advisory Committee (MAC), immigrants will be able to earn a lower income, provided it is above £20,480 per annum. They will earn 20 points for such jobs, which can offset not meeting the £25,600 threshold. Such jobs could include those in healthcare and farming. There will also be temporary visas for seasonal workers, such as fruit pickers.
The government argues that the new system will encourage employers to substitute technology for labour, with greater investment in equipment and computers. This would increase labour productivity and wages without reducing employment.
This is illustrated in the diagram, which illustrates a low-paid job which will be impacted by the restrictions. If there is a rise in productivity through technological change, the marginal revenue product of labour curve shifts upwards from MRPL1 to MRPL2 and offsets the leftward shift in labour supply (caused by the decline in immigration) from ACL1 to ACL2 and the marginal cost of labour from MCL1 to MCL2. Employment is where the marginal cost of labour equals the marginal revenue product of labour. This remains at Q1. Wages are given by the supply curve of labour and rise from W1 to W1. (Click here for a PowerPoint of the diagram.)
Even if the upward shift in the MRPL curve is not sufficient to offset the leftward shift in the labour supply curve, wages will still rise, but there will be a fall in employment.
In higher-paid skilled jobs where people meet the points requirement, there will be little effect on wages and employment, except where people are generally discouraged by a points system, even if they have the points themselves.
The government also argues that there is a large pool of UK residents who can take up jobs that would otherwise have been filled by immigrants. The Home Secretary referred to the 8.48 million people who are economically inactive who could fill jobs no longer filled by immigrants. However, as the data show, most of these people are not available for work. Some 2.3 million are students, 1.9 million are carers at home looking after relatives, 2.1 million are long-term sick and 1.1 million are retired. Only 1.9 million (22.1% of the economically inactive) would like a job and not all these would be able to take up one (e.g. the long-term sick).
One the biggest problems concerns low-paid sectors where it is very difficult to substitute capital for labour through use of technology. Examples include social care, health care, the leisure and hospitality industry and certain jobs in farming. There could be severe shortages of labour in such industries. It remains to be seen whether such industries will be given exemptions or more relaxed conditions by the government in line with advice from the Migration Advisory Committee.
More details will emerge of the points system in the coming months. It will be interesting to see how responsive the government will be to the concerns of employers and workers.
- Find out how the proposed points-based system for immigration differs from the current system that applies to non-EU citizens.
- What will be the likely impact of reducing immigration of unskilled and low-skilled people?
- What barriers are there to substituting capital for labour in the caring and leisure sectors?
- What would be the macroeconomic effects of a substantial reduction in immigration?
The UK’s Low Pay Commission has just published its annual report. This shows that the lowest-paid 20% of workers aged 25 and over benefited from last April’s 4.4% rise in the ‘National Living Wage (NLW)’, the name the government gives to the statutory minimum wage for people in this age group. Although only around 6.5% of such workers are paid at the NLW, when it rises this tends to push up wage rates which are just above the NLW as employers seek to maintain the differential.
If the new NLW is above the equilibrium rate for those receiving it, it would be expected that firms would respond by employing fewer workers. However, the Low Pay Commission found no evidence that rises in the NLW caused unemployment. Instead, employers responded by combinations of increasing prices, accepting lower profit margins, restructuring their workforce and reducing the gaps between pay bands.
Over the longer term, employers often seek to increase labour productivity to offset the higher cost per worker of paying increased minimum wage rates. This, however, could lead to a reduction in employment if it involves substituting capital for labour or if greater labour efficiency does not result in a sufficient increase in total output to compensate for an increase in output per worker.
Report and data
- Demonstrate on a supply and demand diagram for a perfectly competitive labour market the impact of a rise in the minimum wage on employment and unemployment in that market. Assume that the market is initially in equilibrium at the previous minimum wage rate.
- For what reasons in such markets may a rise in the minimum wage not lead to a rise in unemployment?
- Now demonstrate the effect of a rise in the minimum wage in a monopsonistic market. Assume that the previous minimum wage was previously being paid by the employer.
- For what reasons may the employer in the previous question choose to retain employment at the current level?
- For what reasons may the effect of a rise in the minimum wage be different in the long run from the short run?
- How can employers avoid paying the minimum wage (a) when workers work in the ‘gig’ economy; (b) when workers have to travel as part of their job: e.g. care workers moving from house to house; (c) workers working from home producing items for an employer, such as clothing or jewelry, or providing a service such as telesales?
The articles below examine the rise of the sharing economy and how technology might allow it to develop. A sharing economy is where owners of property, equipment, vehicles, tools, etc. rent them out for periods of time, perhaps very short periods. The point about such a system is that the renter deals directly with the property owner – although sometimes initially through an agency. Airbnb and Uber are two examples.
So far the sharing economy has not developed very far. But the development of smart technology will soon make a whole range of short-term renting contracts possible. It will allow the contracts to be enforced without the need for administrators, lawyers, accountants, bankers or the police. Payments will be made electronically and automatically, and penalties, too, could be applied automatically for not abiding by the contract.
One development that will aid this process is a secure electronic way of keeping records and processing payments without the need for a central authority, such as a government, a bank or a company. It involves the use of ‘blockchains‘ (see also). The technology, used in Bitcoin, involves storing data widely across networks, which allows the data to be shared. The data are secure and access is via individuals having a ‘private key’ to parts of the database relevant to them. The database builds in blocks, where each block records a set of transactions. The blocks build over time and are linked to each other in a logical order (i.e. in ‘chains’) to allow tracking back to previous blocks.
Blockchain technology could help the sharing economy to grow substantially. It could significantly cut down the cost of sharing information about possible rental opportunities and demands, and allow minimal-cost secure transactions between owner and renter. As the IBM developerWorks article states:
Rather than use Uber, Airbnb or eBay to connect with other people, blockchain services allow individuals to connect, share, and transact directly, ushering in the real sharing economy. Blockchain is the platform that enables real peer-to-peer transactions and a true ‘sharing economy’.
New technology may soon resurrect the sharing economy in a very radical form The Guardian, Ben Tarnoff (17/10/16)
Blockchain and the sharing economy 2.0 IBM developerWorks, Lawrence Lundy (12/5/16)
2016 is set to become the most interesting year yet in the life story of the sharing economy Nesta, Helen Goulden (Dec 2015)
Blockchain Explained Business Insider, Tina Wadhwa and Dan Bobkoff (16/10/16)
A parliament without a parliamentarian Interfluidity, Steve Randy Waldman (19/6/16)
Blockchain and open innovation: What does the future hold Tech City News, Jamie QIU (17/10/16)
Banks will not adopt blockchain fast Financial Times, Oliver Bussmann (14/10/16)
Blockchain-based IoT project does drone deliveries using Ethereum International Business Times, Ian Allison (14/10/16)
- What do you understand by the ‘sharing economy’?
- Give some current examples of the sharing economy? What other goods or services might be suitable for sharing if the technology allowed?
- How could blockchain technology be used to cut out the co-ordinating role carried out by companies such as Uber, eBay and Airbnb and make their respective services a pure sharing economy?
- Where could blockchain technology be used other than in the sharing economy?
- How can blockchain technology not only record property rights but also enforce them?
- What are the implications of blockchain technology for employment and unemployment? Explain.
- How might attitudes towards using the sharing economy develop over time and why?
- Referring to the first article above, what do you think of Toyota’s use of blockchain to punish people who fall behind on their car payments? Explain your thinking.
- Would the use of blockchain technology in the sharing economy make markets more competitive? Could it make them perfectly competitive? Explain.
Women in the UK on average earn less per hour than men. According to the Annual Survey of Hours and Earnings, the mean hourly pay for women in 2015 was 17.5% less than that for men. This figure is for all employees, full and part time. As far as full-time employees is concerned, the gap was slightly smaller at 13.9%. Nevertheless, as you can see from Table 6 in the linked Excel file, these gaps have decreased in recent years – but only slightly.
A recent paper from the Institute for Fiscal Studies has disaggregated the figures to give a better picture of this wage gap. It finds that having children is a major contributing factor to the gap. It also finds that this has a bigger impact on the earnings of graduates and those without a degree but with A levels.
On entry to the labour market, men and women earn roughly the same. People’s wages tend to rise during their 20s, but men’s rise slightly faster than women’s, causing a pay gap to open and widen – but slowly at first. Average (mean) men’s wages continue to grow during their 30s and a bit during their 40s. However, average women’s wages flatline. Thus the wage gap grows substantially, especially for the higher educated.
The paper argues that the arrival of children is a major contributing factor to this picture. It looks at the gap before and after the arrival of children. “The crucial observation is that the gap opens up gradually after the first child arrives and continues to widen for many years after that point.” By 12 years after the first child is born, the wage gap has widened to 33%.
The paper does not offer reasons for the small gap that exists before the arrival of children. But it does give possible reasons for the widening gap after having children. A major one, it suggests, has to do with labour market experience.
“As women are likely to do less paid work after the arrival of children, the level of labour market experience they have falls further and further behind that of their male counterparts, and the wage gap therefore widens.” They may also miss out on promotions.
Each year a woman spends away from the labour market is associated with an average 2% drop in pay compared with those who remain in work. For those with at least A levels, the penalty is 4%; but there is no drop in pay for those without A levels.
Other possible explanations include mothers taking work that requires a lower skill level, and at lower hourly pay, in order to gain flexibility in working hours. However, the evidence suggests that women who move to part-time work on having a child suffer no immediate drop in pay. But their hourly pay does grow more slowly, thus contributing to a widening of the gap.
Another explanation is employers exercising market power to discriminate against women with children. The paper does not consider this explanation.
The articles discussing the paper look at policy implications and identify various things that can be done to narrow the gap. Read the paper and articles and try answering the questions below.
Videos and podcasts
IFS: gender pay gap widens after first child Compendium of News Reports from BBC News at Six, Channel 4 News, ITV News at Ten and BBC Newsnight from Incorrigible Forever on YouTube (23/8/16)
Gender Pay Gap Hits Women With Children Hardest Sky News (23/8/16)
In Business: Supportive partner = success at work World of Business, BBC Radio 4, Peter Day (25/8/15)
Gender Pay Gap More or Less, BBC Radio 4, Tim Harford (26/8/16)
Gender pay gap: Why do mums increasingly earn less? BBC Victoria Derbyshire programme (23/8/16)
UK women still far adrift on salary and promotion as gender pay gap remains a gulf The Guardian, Katie Allen (23/8/16)
Gender pay gap: mothers returning to work earn a third less than men The Telegraph, Tim Wallace (23/8/16)
Mothers’ pay lags far behind men BBC News (23/8/16)
Four ways the gender pay gap isn’t all it seems BBC News Magazine, Simon Maybin (29/8/16)
Six ways to tackle the gender pay gap BBC News, Emma Atkinson (23/8/16)
Wage gap for UK women unchanged in 20 years Financial Times, Gemma Tetlow (23/8/16)
The UK’s slow march to gender pay equality Financial Times (23/8/16)
Gender Pay Gap For Mothers Widens For 12 Years After Having Children, New Research Finds Huffington Post, Jack Sommers (23/8/16)
Motherhood costs women a third of their salary compared to men, report reveals Independent, Joe Watts (23/8/16)
The gender pay gap means that more women will be in poverty later in life – but there is something the government can do Independent, Claire Turner (26/8/16)
Gender pay gap won’t close until 2069, says Deloitte The Guardian, Katie Allen (24/9/16)
Papers and Reports
Gender wage gap grows year on year after childbirth as mothers in low-hours jobs see no wage progression IFS Press Release (23/8/16)
The Gender Wage Gap IFS Briefing Note BN18, William Elming , Robert Joyce and Monica Costa Dias (23/8/16)
Women in STEM: Technology, career pathways and the gender pay gap Deloitte (September 2016)
Gender pay differences: Annual Survey of Hours and Earnings: 2015 Provisional Results ONS Statistical Bulletin (18/11/15)
All data related to Annual Survey of Hours and Earnings: 2015 Provisional Results ONS datasets (18/11/15)
ASHE 1997 to 2015 selected estimates (See Tables 1 to 4, 6 and 9) ONS dataset (18/11/15)
All Employees – ASHE: Table 1 ONS dataset (18/11/15)
- Identify possible reasons for the wage gap between men and women.
- Why is the median wage gap different from the mean wage gap?
- Why is the wage penalty for periods without work greater for more highly educated women?
- To what extent is the gender wage gap a reflection of marginal productivity differences?
- Is the gender pay gap primarily about men and women being paid differently for doing the same job?
- What evidence is provided by the Chartered Management Institute (CMI) on women’s lack of pay progression?
- What could the government do to reduce the wage gap?
- Discuss the relative effectiveness of different policy alternatives.