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?
Since the financial crisis of 2008–9, the UK has experienced the lowest growth in productivity for the past 250 years. This is the conclusion of a recent paper published in the National Institute Economics Review. Titled, Is the UK Productivity Slowdown Unprecedented, the authors, Nicholas Crafts of the University of Sussex and Terence C Mills of Loughborough University, argue that ‘the current productivity slowdown has resulted in productivity being 19.7 per cent below the pre-2008 trend path in 2018. This is nearly double the previous worst productivity shortfall ten years after the start of a downturn.’
According to ONS figures, productivity (output per hour worked) peaked in 2007 Q4. It did not regain this level until 2011 Q1 and by 2019 Q3 was still only 2.4% above the 2007 Q4 level. This represents an average annual growth rate over the period of just 0.28%. By contrast, the average annual growth rate of productivity for the 35 years prior to 2007 was 2.30%.
The chart illustrates this and shows the productivity gap, which is the amount by which output per hour is below trend output per hour from 1971 to 2007. By 2019 Q3 this gap was 27.5%. (Click here for a PowerPoint of the chart.) Clearly, this lack of growth in productivity over the past 12 years has severe implications for living standards. Labour productivity is a key determinant of potential GDP, which, in turn, is the major limiter of actual GDP.
Crafts and Mills explore the reasons for this dramatic slowdown in productivity. They identify three primary reasons.
The first is a slowdown in the impact of developments in ICT on productivity. The office and production revolutions that developments in computing and its uses had brought about have now become universal. New developments in ICT are now largely in terms of greater speed of computing and greater sophistication of software. Perhaps with an acceleration in the development of artificial intelligence and robotics, productivity growth may well increase in the relatively near future (see third article below).
The second cause is the prolonged impact of the banking crisis, with banks more cautious about lending and firms more cautious about borrowing for investment. What is more, the decline in investment directly impacts on potential output, and layoffs or restructuring can leave people with redundant skills. There is a hysteresis effect.
The third cause identified by Crafts and Mills is Brexit. Brexit and the uncertainty surrounding it has resulted in a decline in investment and ‘a diversion of top-management time towards Brexit planning and a relative shrinking of highly-productive exporters compared with less productive domestically orientated firms’.
- How suitable is output (GDP) per hour as a measure of labour productivity?
- Compare this measure of productivity with other measures.
- According to Crafts and Mills, what is the size of the impact of each of their three explanations of the productivity slowdown?
- Would you expect the growth in productivity to return to pre-2007 levels over the coming years? Explain.
- Explain the underlying model for obtaining trend productivity growth rates used by Crafts and Mills.
- Explain and comment on each of the six figures in the Crafts and Mills paper.
- What policies should the government adopt to increase productivity growth?
How would your life be without the internet? For many of you, this is a question that may be difficult to answer – as the internet has probably been an integral part of your life, probably since a very young age. We use internet infrastructure (broadband, 4G, 5G) to communicate, to shop, to educate ourselves, to keep in touch with each other, to buy and sell goods and services. We use it to seek and find new information, to learn how to cook, to download music, to watch movies. We also use the internet to make fast payments, transfer money between accounts, manage our ISA or our pension fund, set up direct debits and pay our credit-card bills.
I could spend hours writing about all the things that we do over the internet these days, and I would probably never manage to come up with a complete list. Just think about how many hours you spend online every day. Most likely, much of your waking time is spent using internet-based services one way or another (including apps on your phone, streaming on your phone, tablet or your smart TV and similar). If your access to the internet was disrupted, you would certainly feel the difference. What if you just couldn’t afford to have computer or internet access? What effect would that have on your education, your ability to find a job, and your income?
Martin Jenkins, a former homeless man, now entrepreneur, thinks that the magnitude of this effect is rather significant. In fact, he is so convinced about the importance of bringing the internet to poorer households, that he recently founded a company, Neptune, offering low-income households in the Bronx district of New York free access to online education, healthcare and finance portals. His venture was mentioned in a recent (and very interesting) BBC article – a link to which can be found at the end of this blog. But is internet connectivity really that important when it comes to economic and labour market outcomes? And is there a systematic link between economic growth and internet penetration rates?
These are all questions that have been the subject of intensive debate over the last few years, in the context of both developed and developing economies. Indeed, the ‘digital divide’ as it is known (the economic gap between the internet haves and have nots) is not something that concerns only developing countries. According to a recent policy brief published by the New York City Comptroller:
More than one-third (34 percent) of households in the Bronx lack broadband at home, compared to 30 percent in Brooklyn, 26 percent in Queens, 22 percent in Staten Island, and 21 percent in Manhattan.
The report goes on to present data on the percentage of households with internet connection at home by NYC district, and it does not take advanced econometric skills for one to notice that there is a clear link between median district income and broadband access. Wealthier districts (e.g. Manhattan Community District 1 & 2 – Battery Park City, Greenwich Village & Soho PUMA), tend to have a significantly higher share of households with broadband access, than less affluent ones (e.g. NYC-Brooklyn Community District 13 – Brighton Beach & Coney Island PUMA) – 88% of total households compared with 58%.
But, do these large variations in internet connectivity matter? The evidence is mixed. On the one hand, there are several studies that find a clear, strong link between internet penetration and economic growth. Czernich et al (2011), for instance, using data on OECD countries over the period 1996–2007, find that “a 10 percentage point increase in broadband penetration raised annual per capita growth by 0.9–1.5 percentage points”.
Another study by Koutroumpis (2018) examined the effect of rolling out broadband in the UK.
For the UK, the speed increase contributed 1.71% to GDP in total and 0.12% annually. Combining the effect of the adoption and speed changes increased UK GDP by 6.99% cumulatively and 0.49% annually on average”. (pp.10–11)
The evidence is less clear, however, when one tries to estimate the benefits between different types of workers – low and high skilled. In a recent paper, Atasoy (2013) finds that:
gaining access to broadband services in a county is associated with approximately a 1.8 percentage point increase in the employment rate, with larger effects in rural and isolated areas.
But then he adds:
most of the employment gains result from existing firms increasing the scale of their labor demand and from growth in the labor force. These results are consistent with a theoretical model in which broadband technology is complementary to skilled workers, with larger effects among college-educated workers and in industries and occupations that employ more college-educated workers.
Similarly, Forman et al (2009) analyse the effect of business use of advanced internet technology and local variation in US wage growth, over the period 1995–2000. Their findings show that:
Advanced internet technology is associated with larger wage growth in places that were already well off. These are places with highly educated and large urban populations, and concentration of IT-intensive industry. Overall, advanced internet explains over half of the difference in wage growth between these counties and all others.
How important then is internet access as a determinant of growth and economic activity and what role does it have in bridging economic disparities between communities? The answer to this question is most likely ‘very important’ – but less straightforward than one might have assumed.
- Comptroller, New York City, Internet Inequality
- Czernich, N., Falck, O., Kretschmer, T. and Woessmann, L., 2011, Broadband infrastructure and economic growth, The Economic Journal, 121(552), pp.505–32
- Koutroumpis, P., 2018, The economic impact of broadband: evidence from OECD countries, Ofcom
- Atasoy, H., 2013, The effects of broadband internet expansion on labor market outcomes, ILR Review, 66(2), pp.315–45
- Forman, C., Goldfarb, A. and Greenstein, S., 2009, The Internet and Local Wages: Convergence or Divergence? (No. w14750), National Bureau of Economic Research
- Is there a link between economic growth and internet access? Discuss, using examples.
- Explain the arguments for and against government intervention to subsidise internet access of poorer households.
- How important is the internet to you and your day to day life? Take a day offline (yes, really – a whole day). Then come back and write about it.
Cloud computing is growing rapidly and has started to dominate many parts of the IT market. Cloud revenues are rising at around 25% per year and, according to Jeremy Duke of Synergy Research Group:
“Major barriers to cloud adoption are now almost a thing of the past, especially on the public-cloud side. Cloud technologies are now generating massive revenues for technology vendors and cloud service providers, and yet there are still many years of strong growth ahead.”
The market leader in cloud services (as opposed to cloud hardware) is Amazon Web Services (AWS), a subsidiary of Amazon. At the end of 2016, it had a market share of around 40%, larger than the next three competitors (Microsoft, Google and IBM), combined. AWS originated cloud computing some 10 years ago. It is set to have generated revenue of $13 billion in 2016.
The cloud computing services market is an oligopoly, with a significant market leader, AWS. But is the competition from other players in the market, including IT giants, such as Google, Microsoft, IBM and Oracle, enough to guarantee that the market stays competitive and that prices will fall as technology improves and costs fall?
Certainly all the major players are investing heavily in new services, better infrastructure and marketing. And they are already established suppliers in other sectors of the IT market. Microsoft and Google, in particular, are strong contenders to AWS. Nevertheless, as the first article states:
Neither Google nor Microsoft have an easy task since AWS will continue to be an innovation machine with a widely recognized brand among the all-important developer community. Both Amazon’s major competitors have an opportunity to solidify themselves as strong alternatives in what is turning into a public cloud oligopoly.
While Amazon dominates cloud infrastructure, an oligopoly is emerging. Which will buyers bet on? diginomica, Kurt Marko (16/2/17)
Study: AWS has 45% share of public cloud infrastructure market — more than Microsoft, Google, IBM combined GeekWire, Dan Richman (31/10/16)
Cloud computing revenues jumped 25% in 2016, with strong growth ahead, researcher says GeekWire, Dan Richman (4/1/17)
Press releases Synergy Research Group
- Distinguish the different segments of the cloud computing market.
- What competitive advantages does AWS have over its major rivals?
- What specific advantages does Microsoft have in the cloud computing market?
- Is the amount of competition in the cloud computing market enough to prevent the firms from charging excessive prices to their customers? How might you assess what is ‘excessive’?
- What barriers to entry are there in the cloud computing market? Should they be a worry for competition authorities?
- Are the any network economies in cloud computing? What might they be?
- Cloud computing is a rapidly developing industry (for example, the relatively recent development of cloud containers). How does the speed of development impact on competition?
- How would market saturation affect competition and the behaviour of the major players?
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.