Global long-term economic growth has slowed dramatically since the financial crisis of 2007–8. This can be illustrated by comparing the two 20-year periods 1988 to 2007 and 2009 to 2028 (where IMF forecasts are used for 2024 to 2028: see WEO Database under the Data link below). Over the two periods, average annual world growth fell from 3.8% to 3.1%. In advanced countries it fell from 2.9% to 1.6% and in developing countries from 4.8% to 4.3%. In the UK it fell from 2.4% to 1.2%, in the USA from 3.1% to 1.8% and in Japan from 1.9% to 0.5%.
In the UK, labour productivity growth in the production industries was 6.85% per annum from 1998 to 2006. If this growth rate had been maintained, productivity would have been 204% higher by the end of 2023 than it actually was. This is shown in the chart (click here for a PowerPoint).
The key driver of long-term economic growth is labour productivity, which can best be measured by real GDP per hour worked. This depends on three things: the amount of capital per worker, the productivity of this capital and the efficiency of workers themselves – the latter two giving total factor productivity (TFP). Productivity growth has slowed, and with it the long-term rate of economic growth.
If we are measuring growth in output per head of the population, as opposed to simple growth in output, then another important factor is the proportion of the population that works. With ageing populations, many countries are facing an increase in the proportion of people not working. In most countries, these demographic pressures are likely to increase.
A major determinant of long-term economic growth and productivity is investment. Investment has been badly affected by crises, such as the financial crisis and COVID, and by geopolitical tensions, such as the war in Ukraine and tensions between the USA and China and potential trade wars. It has also been adversely affected by government attempts to deal with rising debt caused by interventions following the financial crisis and COVID. The fiscal squeeze and, more recently higher interest rates, have dampened short-term growth and discouraged investment, thereby dampening long-term growth.
Another factor adversely affecting productivity has been a lower growth of allocative efficiency. Competition in many industries has declined as the rate of new firms entering and exiting markets has slowed. The result has been an increase in concentration and a growth in supernormal profits.
In the UK’s case, growth prospects have also been damaged by Brexit. According to Bank of England and OBR estimates, Brexit has reduced productivity by around 4% (see the blog: The costs of Brexit: a clearer picture). For many companies in the UK, Brexit has hugely increased the administrative burdens of trading with the EU. It has also reduced investment and led to a slower growth in the capital stock.
The UK’s poor productivity growth over many yeas is examined in the blog The UK’s poor productivity record.
Boosting productivity
So, how could productivity be increased and what policies could help the process?
Artificial intelligence. One important driver of productivity growth is technological advance. The rapid advance in AI and its adoption across much of industry is likely to have a dramatic effect on working practices and output. Estimates by the IMF suggest that some 40% of jobs globally and 60% in advanced countries could be affected – some replaced and others complemented and enhanced by AI. The opportunities for raising incomes are huge, but so too are the dangers of displacing workers and deepening inequality, as some higher-paid jobs are enhanced by AI, while many lower paid jobs are little affected and other jobs disappear.
AI is also likely to increase returns to capital. This may help to drive investment and further boost economic growth. However, the increased returns to capital are also likely to exacerbate inequality.
To guard against the growth of market power and its abuse, competition policies may need strengthening to ensure that the benefits of AI are widely spread and that new entrants are encouraged. Also training and retraining opportunities to allow workers to embrace AI and increase their mobility will need to be provided.
Training. And it is not just training in the use of AI that is important. Training generally is a key ingredient in encouraging productivity growth. In the UK, there has been a decline in investment in adult education and training, with a 70% reduction since the early 2000s in the number of adults undertaking publicly-funded training, and with average spending on training by employers decreasing by 27% per trainee since 2011. The Institute for Fiscal Studies identifies five main policy levers to address this: “public funding of qualifications and skills programmes, loans to learners, training subsidies, taxation of training and the regulation of training” (see link in articles below).
Competition. Another factor likely to enhance productivity is competition, both internationally and within countries. Removing trade restrictions could boost productivity growth; erecting barriers to protect inefficient domestic industry would reduce it.
Investment. Policies to encourage investment are also key to productivity growth. Private-sector investment can be encouraged by tax incentives. For example, in the UK the Annual Investment Allowance allows businesses to claim 100% of the cost of plant and machinery up to £1m in the year it is incurred. However, for tax relief to produce significant effects on investment, companies need to believe that the policy will stay and not be changed as economic circumstances or governments change.
Public-sector investment is also key. Good road and rail infrastructure and public transport are vital in encouraging private investment and labour mobility. And investment in health, education and training are a key part in encouraging the development of human capital. Many countries, the UK included, cut back on public-sector capital investment after the financial crisis and this has had a dampening effect on economic growth.
Regional policy. External economies of scale could be encouraged by setting up development areas in various regions. Particular industries could be attracted to specific areas, where local skilled workers, managerial expertise and shared infrastructure can benefit all the firms in the industry. These ‘agglomeration economies’ have been very limited in the UK compared with many other countries with much stronger regional economies.
Changing the aims and governance of firms. A change in corporate structure and governance could also help to drive investment and productivity. According to research by the think tank, Demos (see the B Lab UK article and the second report below), if legislation required companies to consider the social, economic and environmental impact of their business alongside profitability, this could have a dramatic effect on productivity. If businesses were required to be ‘purpose-led’, considering the interests of all their stakeholders, this supply-side reform could dramatically increase growth and well-being.
Such stakeholder-governed businesses currently outperform their peers with higher levels of investment, innovation, product development and output. They also have higher levels of staff engagement and satisfaction.
Articles
- World Must Prioritize Productivity Reforms to Revive Medium-Term Growth
IMF Blog, Nan Li and Diaa Noureldin (10/4/24)
- Why has productivity slowed down?
Oxford Martin School News, Ian Goldin, Pantelis Koutroumpis, François Lafond and Julian Winkler (18/3/24)
- How can the UK revive its ailing productivity?
Economics Observatory, Michelle Kilfoyle (14/3/24)
- With the UK creeping out of recession, here’s an economist’s brief guide to improving productivity
The Conversation, Nigel Driffield (13/3/24)
- UK economy nearly a third smaller thanks to ‘catastrophically bad’ productivity slowdown
City A.M., Chris Dorrell (12/3/24)
- Can AI help solve the UK’s public sector productivity puzzle?
City A.M., Chris Dorrell (11/3/24)
- AI Will Transform the Global Economy. Let’s Make Sure it Benefits Humanity
IMF Blog, Kristalina Georgieva (14/1/24)
- Productivity and Investment: Time to Manage the Project of Renewal
NIESR, Paul Fisher (12/3/24)
- Productivity trends using key national accounts indicators
Eurostat (15/3/24)
- New report says change to company law could add £149bn to the UK economy
B Lab UK (28/11/23)
- Investment in training and skills: Green Budget Chapter 9
Institute for Fiscal Studies, Imran Tahir (12/10/23)
Reports
Data
Questions
- Why has global productivity growth been lower since 2008 than before 2008?
- Why has the UK’s productivity growth been lower than many other advanced economies?
- How does the short-run macroeconomic environment affect long-term growth?
- Find out why Japan’s productivity growth has been so poor compared with other countries.
- What are likely to be the most effective means of increasing productivity growth?
- How may demand management policies affect the supply side of the economy?
- How may the adoption of an ESG framework by companies for setting objectives affect productivity growth?
Artificial intelligence is having a profound effect on economies and society. From production, to services, to healthcare, to pharmaceuticals; to education, to research, to data analysis; to software, to search engines; to planning, to communication, to legal services, to social media – to our everyday lives, AI is transforming the way humans interact. And that transformation is likely to accelerate. But what will be the effects on GDP, on consumption, on jobs, on the distribution of income, and human welfare in general? These are profound questions and ones that economists and other social scientists are pondering. Here we look at some of the issues and possible scenarios.
According to the Merrill/Bank of America article linked below, when asked about the potential for AI, ChatGPT replied:
AI holds immense potential to drive innovation, improve decision-making processes and tackle complex problems across various fields, positively impacting society.
But the magnitude and distribution of the effects on society and economic activity are hard to predict. Perhaps the easiest is the effect on GDP. AI can analyse and interpret data to meet economic goals. It can do this much more extensively and much quicker than using pre-AI software. This will enable higher productivity across a range of manufacturing and service industries. According to the Merrill/Bank of America article, ‘global revenue associated with AI software, hardware, service and sales will likely grow at 19% per year’. With productivity languishing in many countries as they struggle to recover from the pandemic, high inflation and high debt, this massive boost to productivity will be welcome.
But whilst AI may lead to productivity growth, its magnitude is very hard to predict. Both the ‘low-productivity future’ and the ‘high-productivity future’ described in the IMF article linked below are plausible. Productivity growth from AI may be confined to a few sectors, with many workers displaced into jobs where they are less productive. Or, the growth in productivity may affect many sectors, with ‘AI applied to a substantial share of the tasks done by most workers’.
Growing inequality?
Even if AI does massively boost the growth in world GDP, the distribution is likely to be highly uneven, both between countries and within countries. This could widen the gap between rich and poor and create a range of social tensions.
In terms of countries, the main beneficiaries will be developed countries in North America, Europe and Asia and rapidly developing countries, largely in Asia, such as China and India. Poorer developing countries’ access to the fruits of AI will be more limited and they could lose competitive advantage in a number of labour-intensive industries.
Then there is growing inequality between the companies controlling AI systems and other economic actors. Just as companies such as Microsoft, Apple, Google and Meta grew rich as computing, the Internet and social media grew and developed, so these and other companies at the forefront of AI development and supply will grow rich, along with their senior executives. The question then is how much will other companies and individuals benefit. Partly, it will depend on how much production can be adapted and developed in light of the possibilities that AI presents. Partly, it will depend on competition within the AI software market. There is, and will continue to be, a rush to develop and patent software so as to deliver and maintain monopoly profits. It is likely that only a few companies will emerge dominant – a natural oligopoly.
Then there is the likely growth of inequality between individuals. The reason is that AI will have different effects in different parts of the labour market.
The labour market
In some industries, AI will enhance labour productivity. It will be a tool that will be used by workers to improve the service they offer or the items they produce. In other cases, it will replace labour. It will not simply be a tool used by labour, but will do the job itself. Workers will be displaced and structural unemployment is likely to rise. The quicker the displacement process, the more will such unemployment rise. People may be forced to take more menial jobs in the service sector. This, in turn, will drive down the wages in such jobs and employers may find it more convenient to use gig workers than employ workers on full- or part-time contracts with holidays and other rights and benefits.
But the development of AI may also lead to the creation of other high-productivity jobs. As the Goldman Sachs article linked below states:
Jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth… For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals. There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services.
Nevertheless, people could still lose their jobs before being re-employed elsewhere.
The possible rise in structural unemployment raises the question of retraining provision and its funding and whether workers would be required to undertake such retraining. It also raises the question of whether there should be a universal basic income so that the additional income from AI can be spread more widely. This income would be paid in addition to any wages that people earn. But a universal basic income would require finance. How could AI be taxed? What would be the effects on incentives and investment in the AI industry? The Guardian article, linked below, explores some of these issues.
The increased GDP from AI will lead to higher levels of consumption. The resulting increase in demand for labour will go some way to offsetting the effects of workers being displaced by AI. There may be new employment opportunities in the service sector in areas such as sport and recreation, where there is an emphasis on human interaction and where, therefore, humans have an advantage over AI.
Another issue raised is whether people need to work so many hours. Is there an argument for a four-day or even three-day week? We explored these issues in a recent blog in the context of low productivity growth. The arguments become more compelling when productivity growth is high.
Other issues
AI users are not all benign. As we are beginning to see, AI opens the possibility for sophisticated crime, including cyberattacks, fraud and extortion as the technology makes the acquisition and misuse of data, and the development of malware and phishing much easier.
Another set of issues arises in education. What knowledge should students be expected to acquire? Should the focus of education continue to shift towards analytical skills and understanding away from the simple acquisition of knowledge and techniques. This has been a development in recent years and could accelerate. Then there is the question of assessment. Generative AI creates a range of possibilities for plagiarism and other forms of cheating. How should modes of assessment change to reflect this problem? Should there be a greater shift towards exams or towards project work that encourages the use of AI?
Finally, there is the issue of the sort of society we want to achieve. Work is not just about producing goods and services for us as consumers – work is an important part of life. To the extent that AI can enhance working life and take away a lot of routine and boring tasks, then society gains. To the extent, however, that it replaces work that involved judgement and human interaction, then society might lose. More might be produced, but we might be less fulfilled.
Articles
- 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)
Questions
- 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?
Have you ever wondered how your job affects your happiness? We all know that not all jobs are created equal. Some are awesome, while others … not so much. Well, it turns out that employment status and the type of work you do can have a big impact on how you feel – especially in developing countries where labour markets are usually tighter and switching between jobs can be more difficult.
A recent study by Carmichael, Darko and Vasilakos (2021) uses survey data from Ethiopia, Peru, India and Vietnam to answer this very question. The study found that the quality of work is a big deal when it comes to how young people feel. Not all jobs are ‘good jobs’ that automatically make you feel great. Although your wellbeing is likely to be higher when you’re in employment than when you’re not, there are certain job attributes that can push that ‘employment premium’ up or down. This is especially important to understand in countries like many in sub-Saharan Africa, where there aren’t many formal jobs, and people often end up overqualified for what they do.
What job attributes lead to higher wellbeing?
What then are the job attributes that are correlated with higher levels of wellbeing? The first is money: Okay, we know money can’t buy happiness, but it can certainly make life easier. We were therefore hardly surprised to find a positive and statistically significant association between hourly earnings and wellbeing.
We were also not surprised to find that a ‘poor working environment’ has a strong and highly significant negative effect on wellbeing.
Finally, feeling proud of your work is also found to be a strongly significant determinant of your wellbeing. After all, people tend to excel in things they like doing, which is probably part of the ‘transmission mechanism’ between ‘work pride’ and ‘subjective wellbeing’.
Which one of these attributes did you think had the greatest effect on wellbeing? Let me guess, many of you will say ‘earnings’. But then you would be wrong. Earnings were indeed positively associated with wellbeing and statistically significant at just about the 10% level, whereas work pride was very strongly statistically significant at the 1% level and had an effect on wellbeing that was four times greater than hourly earnings.
Putting yourself in a poor working environment on the other hand would reduce your wellbeing by almost twice as much as the earnings coefficient.
Policy implications
What does all this mean for policy-makers? If we want to make life better for young people in low-income countries, we need to tackle the problems from multiple angles.
First, young people need to be helped to get the skills they need for the job market. This can be done through things like training programmes and apprenticeships. However, not all of these programmes are created equal. Some have great results, and others not so much.
But that’s not the whole story. In many countries, there’s a massive informal job market. It’s a place where people work but often don’t have the rights or protections that formal employees do. So, even if young people get trained, they might not find the ‘good’ jobs they’re hoping for.
Changes also need to be made on a much bigger scale. This often includes decentralising public investment to include rural areas, improving infrastructure, and encouraging private investment. Strengthening labour market rules and social protection can help too, by making sure that work is safe and fair.
In a nutshell, where you work and what kind of work you do can make a big difference to how you feel.
Conclusions
If policy-makers want to help young people in low-income countries, they need both to give them the skills they require and to create better job opportunities. But policy-makers also need to make bigger changes to the way things work, like boosting production and making sure jobs are safe and fair.
In the end, it’s about making life better for young people around the world. Let’s keep working on it!
Articles
- Well-being and employment of young people in Ethiopia, India, Peru and Vietnam: Is work enough?
Development Policy Review, Fiona Carmichael, Christian K. Darko and Nicholas Vasilakos (18/5/21)
- The search for ‘meaning’ at work
BBC Worklife, Kate Morgan (7/9/22)
- Job Satisfaction Is Rising: What’s Behind The Surprising Tend
Forbes, Tracy Brower (4/6/23)
- Young workers are embracing AI, job satisfaction rising: 2023 Young Generation in Tech report
Silicon Canals (4/10/23)
- ‘These jobs can be respectable too’: Why youths in China are abandoning white-collar jobs for ‘light labor’
CNBC, Goh Chiew Tong (6/6/23)
- Does Work Make You Happy? Evidence from the World Happiness Report
Harvard Business Review, Jan-Emmanuel De Neve and George Ward (20/3/17)
- Worker well-being is in demand as organizational culture shifts
American Psychological Association, Monitor on Psychology, Heather Stringer (1/1/23)
- Understanding children’s work and youth employment outcomes in Indonesia
Understanding Children’s Work (UCW) Programme, Villa Aldobrandini and V. Panisperna (June 2012)
- Where are We with Young People’s Wellbeing? Evidence from Nigerian Demographic and Health Surveys 2003–2013
Social Indicators Research, pp.803–33, Boniface Ayanbekongshie Ushie and Ekerette Emmanuel Udoh (November 2016)
- Employment Status and Well-Being Among Young Individuals. Why Do We Observe Cross-Country Differences?
Social Indicators Research, Dominik Buttler (29/6/22)
- Employment Mismatches Drive Expectational Earnings Errors among Mozambican Graduates
The World Bank Economic Review, Sam Jones, Ricardo Santos and Gimelgo Xirinda (27/7/23)
- Youth Employment and Skills Development in The Gambia
World Bank Working Paper 217, Nathalie Lahire, Richard Johanson and Ryoko Tomita Wilcox (2011)
Questions
- How does the quality of work impact the happiness and wellbeing of young people in low- and middle-income countries (LMICs), and why is this significant in the context of job opportunities in sub-Saharan Africa?
- What are some potential solutions and strategies discussed in the article for improving the wellbeing of young people in LMICs, particularly in the context of employment and job opportunities?
- Have you ever experienced a job that significantly (positively or negatively) impacted your wellbeing or happiness? Reflect on your experience and how it influenced your overall life satisfaction?
- How is AI likely to affect the wellbeing of young professional workers?
- How is the pandemic likely to have affected job satisfaction?
In two previous posts, one at the end of 2019 and one in July 2021, we looked at moves around the world to introduce a four-day working week, with no increase in hours on the days worked and no reduction in weekly pay. Firms would gain if increased worker energy and motivation resulted in a gain in output. They would also gain if fewer hours resulted in lower costs.
Workers would be likely to gain from less stress and burnout and a better work–life balance. What is more, firms’ and workers’ carbon footprint could be reduced as less time was spent at work and in commuting.
If the same output could be produced with fewer hours worked, this would represent an increase in labour productivity measured in output per hour.
The UK’s poor productivity record since 2008
Since the financial crisis of 2007–8, the growth in UK productivity has been sluggish. This is illustrated in the chart, which looks at the production industries: i.e. it excludes services, where average productivity growth tends to be slower. (Click here for a PowerPoint of the chart.)
Prior to the crisis, from 1998 to 2007, UK productivity in the production industries grew at an annual rate of 6.1%. From 2007 to the start of the pandemic in 2020, the average annual productivity growth rate in these industries was a mere 0.5%.
It grew rapidly for a short time at the start of the pandemic, but this was because many businesses temporarily shut down or went to part-time working, and many of these temporary job cuts were low-wage/low productivity jobs. If you take services, the effect was even stronger as sectors such as hospitality, leisure and retail were particularly affected and labour productivity in these sectors tends to be low. As industries opened up and took on more workers, so average productivity fell back. In the four quarters to 2022 Q3 (the latest data available), productivity in the production industries fell by 6.8%.
If you project the average productivity growth rate from 1998 to 2007 of 6.1% forwards (see grey dashed line), then by 2022 Q3, output per hour in the production industries would have been 21/4 times (125%) higher than it actually was. This is a huge productivity gap.
Productivity in the UK is lower than in many other competitor countries. According to the ONS, output per hour in the UK in 2021 was $59.14 in the UK. This compares with an average of $64.93 for the G7 countries, $66.75 in France, £68.30 in Germany, $74.84 in the USA, $84.46 in Norway and $128.21 in Ireland. It is lower, however, in Italy ($54.59), Canada ($53.97) and Japan ($47.28).
As we saw in the blog, The UK’s poor productivity record, low UK productivity is caused by a number of factors, not least the lack of investment in physical capital, both by private companies and in public infrastructure, and the lack of investment in training. Other factors include short-termist attitudes of both politicians and management and generally poor management practices. But one cause is the poor motivation of many workers and the feeling of being overworked. One solution to this is the four-day week.
Latest evidence on the four-day week
Results have just been released of a pilot programme involving 61 companies and non-profit organisations in the UK and nearly 3000 workers. They took part in a six-month trial of a four-day week, with no increase in hours on the days worked and no loss in pay for employees – in other words, 100% of the pay for 80% of the time. The trial was a success, with 91% of organisations planning to continue with the four-day week and a further 4% leaning towards doing so.
The model adopted varied across companies, depending on what was seen as most suitable for them. Some gave everyone Friday off; others let staff choose which day to have off; others let staff work 80% of the hours on a flexible basis.
There was little difference in outcomes across different types of businesses. Compared with the same period last year, revenues rose by an average of 35%; sick days fell by two-thirds and 57% fewer staff left the firms. There were significant increases in well-being, with 39% saying they were less stressed, 40% that they were sleeping better; 75% that they had reduced levels of burnout and 54% that it was easier to achieve a good work–life balance. There were also positive environmental outcomes, with average commuting time falling by half an hour per week.
There is growing pressure around the world for employers to move to a four-day week and this pilot provides evidence that it significantly increases productivity and well-being.
Articles
- Results from world’s largest 4 day week trial bring good news for the future of work
4 Day Week Global, Charlotte Lockhart (21/2/23)
- Four-day week: ‘major breakthrough’ as most UK firms in trial extend changes
The Guardian, Heather Stewart (21/2/23)
- Senedd committee backs four-day working week trial in Wales
The Guardian, Steven Morris (24/1/23)
- ‘Major breakthrough’: Most firms say they’ll stick with a four-day working week after successful trial
Sky News, Alice Porter (21/2/23)
- Major four-day week trial shows most companies see massive staff mental health benefits and profit increase
Independent, Anna Wise (21/2/23)
- Four-day week: Which countries have embraced it and how’s it going so far?
euronews, Josephine Joly and Luke Hurst (23/2/23)
- Firms stick to four-day week after trial ends
BBC News, Simon Read, Lucy Hooker & Emma Simpson (21/2/23)
- The climate benefits of a four-day workweek
BBC Future Planet, Giada Ferraglioni and Sergio Colombo (21/2/23)
- Four-day working week: why UK businesses and workers will continue with new work pattern, plus pros and cons
National World, Rochelle Barrand (22/2/23)
- Most companies in UK four-day week trial to continue with flexible working
Financial Times, Daniel Thomas and Emma Jacobs (21/2/23)
- The pros and cons of a four-day working week
Financial Times, Editorial (13/2/23)
- Explaining the UK’s productivity slowdown: Views of leading economists
VoxEU, Ethan Ilzetzki (11/3/20)
- Why the promised fourth industrial revolution hasn’t happened yet
The Conversation, Richard Markoff and Ralf Seifert (27/2/23)
Questions
- What are the possible advantages of moving to a four-day week?
- What are the possible disadvantages of moving to a four-day week?
- What types of companies or organisations are (a) most likely, (b) least likely to gain from a four-day week?
- Why has the UK’s productivity growth been lower than that of many of its major competitors?
- Why, if you use a log scale on the vertical axis, is a constant rate of growth shown as a straight line? What would a constant rate of growth line look like if you used a normal arithmetical scale for the vertical axis?
- Find out what is meant by the ‘fourth industrial revolution’. Does this hold out the hope of significant productivity improvements in the near future? (See, for example, last link above.)
It’s been a while since I last blogged about labour markets and, in particular, about the effect of automation on wages and employment. My most recent post on this topic was on the 14th of April 2018 and it was mostly a reflection on some interesting findings that had been reported by Acemoglu et al (2017). More specifically, Acemoglu and Restrepo (2017) developed a theoretical framework to evaluate the effect of AI on employment and wages. They concluded that the effect was negative and potentially sizeable (for a more detailed discussion see my blog).
Using a model in which robots compete against human labor in the production of different tasks, we show that robots may reduce employment and wages … According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18–0.34 percentage points and wages by 0.25–0.5 percent.
Since then, I have seen a constant stream of news on my news feed about the development of ever more advanced industrial robots and artificial intelligence. And this was not because of some spooky coincidence (or worse). It has been merely a reflection of the speed at which technology has been progressing in this field.
There are now robots that can run, jump, hold conversations with humans, do gymnastics (and even sweat for it!) and more. It is really impressive how fast change has been happening recently in this field – and, unsurprisingly, it has stimulated the interest of labour economists!
A paper that has recently come to my attention on this subject is by Graetz and Michaels (2018). The authors put together a panel dataset on robot adoption within seventeen countries from 1993 to 2007 and use advanced econometric techniques to evaluate the effect of these technologies on employment and productivity growth. Their analysis focuses exclusively on developed economies (due to data limitations, as they explain) – but their results are nevertheless intriguing:
We study here for the first time the relationship between industrial robots and economic outcomes across much of the developed world. Using a panel of industries in seventeen countries from 1993 to 2007, we find that increased use of industrial robots is associated with increases in labor productivity. We find that the contribution of increased use of robots to productivity growth is substantial and calculate using conservative estimates that it comes to 0.36 percentage points, accounting for 15% of the aggregate economy-wide productivity growth.
The pattern that we document is robust to including various controls for country trends and changes in the composition of labor and other capital inputs. We also find that robot densification is associated with increases in both total factor productivity and wages, and reductions in output prices. We find no significant relationship between the increased use of industrial robots and overall employment, although we find that robots may be reducing the employment of low-skilled workers.
This is very positive news for most – except, of course, for low-skilled workers. Indeed, like Acemoglu and Restrepo (2017) and many others, this study shows that the effect of automation on employment and labour market outcomes is unlikely to be uniform across all types of workers. Low-skilled workers are found again to be likely to lose out and be significantly displaced by these technologies.
And if you are wondering which sectors are likely to be disrupted most/first by automation, the rankings developed by McKinsey and Company (see chart below) would give you an idea of where the disruption is likely to start. Unsurprisingly, the sectors that seem to be the most vulnerable, are the ones that use the highest share of low-skilled labour.
Articles
Questions
- “The effect of automation on wages and employment is likely to be positive overall”. Discuss.
- Using examples and anecdotal evidence, do you agree with these findings?
- Using Google Scholar, put together a list of 5 recent (i.e. 2015 or later) articles and working papers on labour markets and automation. Compare and discuss their findings.