Tag: training

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.

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Questions

  1. Which industries are most likely to benefit from the development of AI?
  2. Distinguish between labour-replacing and labour-augmenting technological progress in the context of AI.
  3. How could AI reduce the amount of labour per unit of output and yet result in an increase in employment?
  4. What people are most likely to (a) gain, (b) lose from the increasing use of AI?
  5. Is the distribution of income likely to become more equal or less equal with the development and adoption of AI? Explain.
  6. 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

Questions

  1. 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?
  2. 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?
  3. 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?
  4. How is AI likely to affect the wellbeing of young professional workers?
  5. 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

Questions

  1. What are the possible advantages of moving to a four-day week?
  2. What are the possible disadvantages of moving to a four-day week?
  3. What types of companies or organisations are (a) most likely, (b) least likely to gain from a four-day week?
  4. Why has the UK’s productivity growth been lower than that of many of its major competitors?
  5. 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?
  6. 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.)

In its latest World Economic Outlook update, the IMF forecasts that the UK in 2023 will be the worst performing economy in the G7. Unlike all the other countries and regions in the report, only the UK economy is set to shrink. UK real GDP is forecast to fall by 0.6% in 2023 (see Figure 1: click here for a PowerPoint). In the USA it is forecast to rise by 1.4%, in Germany by 0.1%, in France by 0.7% and in Japan by 1.8%. GDP in advanced countries as a whole is forecast to grow by 1.2%, while world output is forecast to grow by 2.9%. Developing countries are forecast to grow by 4.0%, with China and India forecast to grow by 5.2% and 6.1%, respectively. And things are not forecast to be a lot better for the UK in 2024, with growth of 0.9% – bottom equal with Japan and Italy.

Low projected growth in the UK in part reflects the tighter fiscal and monetary policies being implemented to curb inflation, which is slow to fall thanks to tight labour markets and persistently higher energy prices. The UK is particularly exposed to high wholesale gas prices, with a larger share of its energy coming from natural gas than most countries.

But the UK’s lower forecast growth relative to other countries reflects a longer-term problem in the UK and that is the slow rate of productivity growth. This is illustrated in Figure 2, which shows output (GDP) per hour worked in major economies, indexed at 100 in 2008 (click here for a PowerPoint). As you can see, the growth in productivity in the UK has lagged behind that of the other economies. The average annual percentage growth in productivity is shown next to each country. The UK’s growth in productivity since 2008 has been a mere 0.3% per annum.

Causes of low productivity/low productivity growth

A major cause of low productivity growth is low levels of investment in physical capital. Figure 3 shows investment (gross capital formation) as a percentage of GDP for the G7 countries from the 2007–8 financial crisis to the year before the pandemic (click here for a PowerPoint). As you can see, the UK performs the worst of the seven countries.

Part of the reason for the low level of private investment is uncertainty. Firms have been discouraged from investing because of a lack of economic growth and fears that this was likely to remain subdued. The problem was compounded by Brexit, with many firms uncertain about their future markets, especially in the EU. COVID affected investment, as it did in all countries, but supply chain problems in the aftermath of COVID have been worse for the UK than many countries. Also, the UK has been particularly exposed to the effects of higher gas prices following the Russian invasion of Ukraine, as a large proportion of electricity is generated from natural gas and natural gas is the major fuel for home heating.

Part of the reason is an environment that is unconducive for investment. Access to finance for investment is more difficult in the UK and more costly than in many countries. The financial system tends to have a short-term focus, with an emphasises on dividends and short-term returns rather than on the long-term gains from investment. This is compounded by physical infrastructure problems with a lack of investment in energy, road and rail and a slow roll out of advances in telecoms.


To help fund investment and drive economic growth, in 2021 the UK government established a government-owned UK Infrastructure Bank. This has access to £22 billion of funds. However, as The Conversation article below points out:

According to a January 2023 report from Westminster’s Public Accounts Committee, 18 months after its launch the bank had only deployed ‘£1 billion of its £22 billion capital to 10 deals’, and had employed just 16 permanent staff ‘against a target of 320’. The committee also said it was ‘not convinced the bank has a strategic view of where it best needs to target its investments’.

Short-termism is dominant in politics, with ministers keen on short-term results in time for the next election, rather than focusing on the long term when they may no longer be in office. When the government is keen to cut taxes and find ways of cutting government expenditure, it is often easier politically to cut capital expenditure rather than current expenditure. The Treasury oversees fiscal policy and its focus tends to be short term. What is needed is a government department where the focus is on the long term.

One problem that has impacted on productivity is the relatively large number of people working for minimum wages or a little above. Low wages discourage firms from making labour-saving investment and thereby increasing labour productivity. It will be interesting to see whether the labour shortages in the UK, resulting from people retiring early post-COVID and EU workers leaving, will encourage firms to make labour-saving investment.

Another issue is company taxation. Until recently, countries have tended to compete corporate taxes down in order to attract inward investment. This was stemmed somewhat by the international agreement at the OECD that Multinational Enterprises (MNEs) will be subject to a minimum 15% corporate tax rate from 2023. The UK is increasing corporation tax from 19% to 25% from April 2023. It remains to be seen what disincentive effect this will have on inward investment. Although the new rate is similar to, or slightly lower, than other major economies, there are some exceptions. Ireland will have a rate of just 15% and is seen as a major alternative to the UK for inward investment, especially with its focus on cheaper green energy. AstraZeneca has just announced that instead of building its new ‘state-of-the-art’ manufacturing plant in England close to its two existing plats in NW England, it will build it in Ireland instead, quoting the UK’s ‘discouraging’ tax rates and price capping for drugs by the NHS.

And it is not just physical investment that affects productivity, it is the quality of labour. Although a higher proportion of young people go to university (close to 50%) than in many other countries, the nature of the skills sets acquired may not be particularly relevant to employers.

What is more, relatively few participate in vocational education and training. Only 32% of 18-year olds have had any vocational training. This compares with other countries, such as Austria, Denmark and Switzerland where the figure is over 65%. Also a greater percentage of firms in other countries, such as Germany, employ people on vocational training schemes.

Another aspect of labour quality is the quality of management. Poor management practices in the UK and inadequate management training and incentives have resulted in a productivity gap with other countries. According to research by Bloom, Sadun & Van Reenen (see linked article below, in particular Figure A5) the UK has an especially large productivity gap with the USA compared with other countries and the highest percentage of this gap of any country accounted for by poor management.

Solutions

Increasing productivity requires a long-term approach by both business and government. Policy should be consistent, with no ‘chopping and changing’. The more that policy is changed, the less certain will business be and the more cautious about investing.

As far a government investment is concerned, capital investment needs to be maintained at a high level if significant improvements are to be made in the infrastructure necessary to support increased growth rates. As far as private investment is concerned, there needs to be a focus on incentives and finance. If education and training are to drive productivity improvements, then there needs to be a focus on the acquisition of transferable skills.

Such policies are not difficult to identify. Carrying them out in a political environment focused on the short term is much more difficult.

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Data

Questions

  1. What features of the UK economic and political environment help to explain its poor productivity growth record?
  2. What are the arguments for and against making higher education more vocational?
  3. Find out what policies have been adopted in a country of your choice to improve productivity. Are there any lessons that the UK could learn from this experience?
  4. How could the UK attract more inward foreign direct investment? Would the outcome be wholly desirable?
  5. What is the relationship between inequality and labour productivity?
  6. What are the arguments for and against encouraging more immigration in the current economic environment?
  7. Could smarter taxes ease the UK’s productivity crisis?

Where you live in Great Britain can have a profound effect on your earning potential. According to a report published by the Social Mobility Commission, there is a growing geographical divide, with more affluent areas getting relatively richer, while ‘many other parts of the country are being left behind economically and hollowed out socially’.

The Commission uses a Social Mobility Index to rank the 324 local authorities in England. The index is a measure of the social mobility prospects for people from disadvantaged backgrounds. It is ‘made up of 16 key performance indicators spanning each major life stage’.

The index shows that children from disadvantaged backgrounds have lower educational attainment, poorer initial jobs and poorer prospects for advancement in the labour market. Often they are stuck in low paid jobs with little chance of getting on the housing ladder and fewer chances of moving away from the area.

The problem is not simply one of a North-South divide or one of inner cities versus the suburbs. Many inner-city areas have been regenerated, with high incomes and high social and geographical mobility. Other inner-city areas remain deprived.

The worst performing areas are remote rural or coastal areas and former industrial areas, where industries have closed. As the author of the report states in the Guardian article linked below:

These areas have fewer specialist teachers, fewer good schools, fewer good jobs and worse transport links. … Many of these areas have suffered from a lack of regeneration: few high-paying industries are located there, and they often exhibit relatively limited job opportunities and clusters of low pay.

The problem often exists within areas, with some streets exhibiting growing affluence, where the residents have high levels of social mobility, while other streets have poor housing and considerable levels of poverty and deprivation. Average incomes for such areas thus mask this type of growing divide within areas. Indeed, some of the richest areas have worse outcomes for disadvantaged children than generally poorer areas.

There are various regional and local multiplier effects that worsen the situation. Where people from disadvantaged backgrounds are successful, they tend to move away from the deprived areas to more affluent ones, thereby boosting the local economy in such areas and providing no stimulus to the deprived areas. And so the divide grows.

Policies, according to the report, need to focus public investment, and incentives for private investment, in deprived areas. They should not focus simply on whole regions. You can read the specific policy recommendations in the articles below.

Articles

Social mobility is a stark postcode lottery. Too many in Britain are being left behind The Guardian, Alan Milburn (28/11/17)
State of the Nation – Sector Response FE News (28/11/17)
Social mobility: the worst places to grow up poor BBC News, Judith Burns and Adina Campbell (28/11/17)
How Britain’s richest regions offer worst prospects for poor young people Independent, May Bulman (28/11/17)
Small Towns Worst Places In Britain For Social Mobility, New ‘State Of The Nation’ Report Reveals Huffington Post, Paul Waugh (28/11/17)

Report

Social mobility in Great Britain: fifth state of the nation report Social Mobility Commission, News (28/11/17)
Fifth State of the Nation Report Social Mobility Commission, News (28/11/17)

Questions

  1. Explain how local multipliers operate.
  2. What is the relationship between social immobility as identified in the report and the elasticity of supply of labour in specific jobs?
  3. What is the link between geographical, occupational and social mobility?
  4. Explain why, apart from London, English cities are ‘punching below their weight on social mobility outcomes’.
  5. Go through each of the key policy recommendations of the report and consider the feasibility of introducing them.
  6. What policies could be adopted to retain good teachers in schools in deprived areas?
  7. To what extent might an increased provision of training ease the problem of social mobility?
  8. Investigate policies adopted in other European countries to tackle local deprivation. Are there lessons that can be learned by the UK government, devolved governments, local authorities or other agencies?