The IFS has launched a major five-year review into all aspects of inequality. The review is led by Sir Angus Deaton, the Scottish-born Professor of Economics and International Affairs at Princeton University. In 2015, he was awarded the Nobel Prize in Economic Sciences for his analysis of consumption, poverty, and welfare. The review will cover all aspects of inequality, including inequality of income, wealth, health, life-span, education, social mobility, housing, opportunity and political access, and by gender, age, ethnicity, family and geography. It will look at trends in and causes of inequality, the impacts of globalisation and political change, barriers to tackling inequality and poverty, and at various policy measures.
Although the published Gini coefficient in England and Wales has not changed much over the past 15 years, largely because of support given to the poor by tax credits, it did rise from 31.7 to 33.2 from 2015/16 to 2017/18 (the latest year for which figures are available). Other measures of inequality, however, have changed more dramatically. There is huge geographical inequality in income in the UK, reflected in inequality in health. Average weekly earnings in London are 66% higher than in the north east of England. And, according to the IFS, ‘Men in the most affluent areas can expect to live nearly 10 years longer than those in the most deprived areas, and this gap is widening’.
The UK has the greatest inequality of income of developed countries, with the exception of the USA. The IFS warns that the UK could follow the USA:
…where wages for non-college-educated men have not risen for five decades, and where rising mortality for less-educated white men and women in middle age has caused average life expectancy in America to fall for the last three years – something that has not happened for a century. We have not experienced anything similar in the UK but we have now had a decade of stagnant wages and there is recent evidence that ‘deaths of despair’ – deaths from suicide and drug and alcohol abuse – are now rising among middle-aged Britons. Sir Angus will go on to say:
‘I think that people getting rich is a good thing, especially when it brings prosperity to others. But the other kind of getting rich, “taking” rather than “making”, rent-seeking rather than creating, enriching the few at the expense of the many, taking the free out of free markets, is making a mockery of democracy. In that world, inequality and misery are intimate companions.’
The initial report, which introduces the IFS Deaton Review, points to some possible causes of growing inequality, including the dramatic decline in union membership, which now stands at just 13% of private-sector employees, with more flexible labour markets with growing numbers of workers on temporary or zero-hour contracts. Other causes include growing globalisation, rapid technological change making some skills redundant, the power of large companies and their shareholders, large pay rises given to senior executives, growing inequality of access to education and changing family environments with more single parents.
About one in six children in the UK are born to single parents – a phenomenon that is heavily concentrated in low-income and low-educated families, and is significantly less prevalent in continental Europe.
Then there is the huge growth in housing inequality as house prices and rents have risen faster than incomes. Home ownership has increasingly become beyond the reach of many young people, while many older people live in relative housing wealth. Generational inequality is another major factor that the Deaton Review will consider.
Inequalities in different dimensions – income, work, mental and physical health, families and relationships – are likely to reinforce one another. They may result in, and stem from, other inequalities in wealth, cultural capital, social networks and political voice. Inequality cannot be reduced to any one dimension: it is the culmination of myriad forms of privilege and disadvantage.
The review will consider policy alternatives to tackle the various aspects of inequality, from changes to the tax and benefit system, to legislation on corporate behaviour, to investment in various structural resources, such as health and education. As the summary to the initial report states:
The Deaton Review will identify policy responses to the inequalities we face today. It will assess the relative merits of available policy options – taxes and benefits, labour market policies, education, competition policy, ownership structures and regulations – and consider how policies in different spheres can be designed to complement each other and minimise adverse effects. We aim not just to further our understanding of inequalities in the twenty-first century, but to equip policymakers with the knowledge and tools to tackle those inequalities.
IFS Deaton Review
- Identify different aspects of inequality. Choose two or three aspects and examine how they are related.
- Why has inequality widened in most developed countries over the past 20 years?
- What is meant by ‘rent seeking’? Why may it be seen as undesirable? Can it be justified and, if so, on what grounds?
- What policies could be adopted to tackle poverty?
- What trade-offs might there be between greater equality and faster economic growth?
- What policies could be adopted that would both reduce inequality and boost long-term economic growth?
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.
- “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.
Donald Trump has suggested that the Fed should cut interest rates by 1 percentage point and engage in a further round of quantitative easing. He wants to see monetary policy used to give a substantial boost to US economic growth at a time when inflation is below target. In a pair of tweets just before the meeting of the Fed to decide on interest rates, he said:
China is adding great stimulus to its economy while at the same time keeping interest rates low. Our Federal Reserve has incessantly lifted interest rates, even though inflation is very low, and instituted a very big dose of quantitative tightening. We have the potential to go up like a rocket if we did some lowering of rates, like one point, and some quantitative easing. Yes, we are doing very well at 3.2% GDP, but with our wonderfully low inflation, we could be setting major records &, at the same time, make our National Debt start to look small!
But would this be an appropriate policy? The first issue concerns the independence of the Fed.
It is supposed to take decisions removed from the political arena. This means sticking to its inflation target of 2 per cent over the medium term – the target it has officially had since January 2012. To do this, it adjusts the federal funds interest rate and the magnitude of any bond buying programme (quantitative easing) or bond selling programme (quantitative tightening).
The Fed is supposed to assess the evidence concerning the pressures on inflation (e.g. changes in aggregate demand) and what inflation is likely to be over the medium term in the absence of any changes in monetary policy. If the Federal Open Market Committee (FOMC) expects inflation to exceed 2 per cent over the medium term, it will probably raise the federal funds rate; if it expects inflation to be below the target it will probably lower the federal funds rate.
In the case of the economy being in recession, and thus probably considerably undershooting the target, it may also engage in quantitative easing (QE). If the economy is growing strongly, it may sell some of its portfolio of bonds and thus engage in quantitative tightening (QT).
Since December 2015 the Fed has been raising interest rates by 0.25 percentage points at a time in a series of steps, so that the federal funds rate stands at between 2.25% and 2.5% (see chart). And since October 2017, it has also been engaged in quantitative tightening. In recent months it has been selling up to $50 billion of assets per month from its holdings of around $4000 billion and so far has reduced them by around £500 billion. It has, however, announced that the programme of QT will end in the second half of 2019.
This does raise the question of whether the FOMC is succumbing to political pressure to cease QT and put interest rate rises on hold. If so, it is going against its remit to base its policy purely on evidence. The Fed, however, maintains that its caution reflects uncertainty about the global economy.
The second issue is whether Trump’s proposed policy is a wise one.
Caution about further rises in interest rates and further QT is very different from the strongly expansionary monetary policy that President Trump proposes. The economy is already growing at 3.2%, which is above the rate of growth in potential output, of around 1.8% to 2.0%. The output gap (the percentage amount that actual GDP exceeds potential GDP) is positive. The IMF forecasts that the gap will be 1.4% in 2019 and 1.3% in 2020 and 2021. This means that the economy is operating at above normal capacity working and this will eventually start to drive up inflation. Any further stimulus will exacerbate the problem of excess demand. And a large stimulus, as proposed by Donald Trump, will cause serious overheating in the medium term, even if it does stimulate growth in the short term.
For these reasons, the Fed resisted calls for a large cut in interest rates and a return to quantitative easing. Instead it chose to keep interest rates on hold at its meeting on 1 May 2019.
But if the Fed had done as Donald Trump would have liked, the economy would probably be growing very strongly at the time of the next US election in November next year. It would be a good example of the start of a political business cycle – something that is rarer nowadays with the independence of central banks.
- What are the arguments for central bank independence?
- Are there any arguments against central bank independence?
- Explain what is meant by an ‘output gap’? Why is it important to be clear on what is meant by ‘potential output’?
- Would there be any supply-side effects of a strong monetary stimulus to the US economy at the current time? If so, what are they?
- Explain what is meant by the ‘political business’ cycle? Are governments in the UK, USA or the eurozone using macroeconomic policy to take advantage of the electoral cycle?
- The Fed seems to be ending its programme of quantitative tightening (QT). Why might that be so and is it a good idea?
- If inflation is caused by cost-push pressures, should central banks stick rigidly to inflation targets? Explain.
- How are expectations likely to affect the success of a monetary stimulus?
Latest resesarch from the independent American think tank The Conference Board paints a worrying picture about the growth of UK labour productivity. While global growth in labour productivity has weakened following the financial crisis, its weakness in the UK is singled out in the Board’s 2019 Productivity Brief. It finds that amongst large mature economies the decline in labour productivity growth rates has been greatest in the UK. This has important implications for the country’s longer-term well-being and, specifically, it peoples’ living standards.
The UK saw the growth in real GDP (national output) fall from 1.8 per cent in 2017 to 1.4 per cent in 2018. The Conference Board predicts that this will fall further to 0.8 per cent in 2019. In the context of living standards, the growth in real GDP per capita is particularly important. An increase in the population will, other things being equal, lower living standards because more people will be sharing a given amount of real national income. The growth in real GDP per capita fell from 1.1 per cent in 2017 to 0.7 per cent in 2018 and is predicted to fall to just 0.1 per cent in 2019.
Chart 1 shows the annual rates of growth in real GDP and real GDP per capita from the 1950s. The average growth rates are 2.4 and 1.9 per cent respectively. The other series shown is the annual growth in real GDP per person employed. This is a measure of the growth in labour productivity. Its average annual growth rate is also 1.9 per cent. This illustrates the intrinsic long-run relationship between labour productivity growth and the growth rate of GDP per capita and hence in general living stanadards. (Click here to download a PowerPoint copy of the chart.)
In the short term, rates of growth in output per worker (labour productivity) and GDP per capita (general living standards) can be less similar. For example, when unemployment rates rise labour productivity rates may be little affected despite GDP per capita falling. Nonetheless, the important point here is the close long-run relationship between the growth in labour productivity and GDP per capita. This then raises an important question: what factors contribute to the growth in output and labour productivity?
An approach known as growth accounting helps to identify four key contributors to the growth of total output. The first is the quantity of labour, commonly measured in labour hours. The second is the quality of labour, also known as labour composition. Third is capital services which are physical inputs into production and include machinery, structures and IT capital. Capital services are affected by quantity and quality, but, unlike labour, it is practically more difficult to separate out these dimensions. Fourth, is Total Factor Productivity (TFP).
TFP it is essentially the residual contribution to output growth that cannot be explained by changes in the quantity and quality of the individual inputs. Hence, in principle, it is capturing changes in how effectively the labour and capital inputs are being employed and combined in production. The Conference Board’s Productivity Brief describes the growth in TFP as providing ‘a more accurate picture of the overall efficiency by which capital, labour and skills are combined in the production process’.
Chart 2 shows Conference Board estimates of the percentage point contribution of these four sources of growth since 1990. Over this period, output growth averaged 2 per cent per year. The contribution of capital services and, hence, what is known as capital accumulation is particularly significant at 1.5 percentage points per year. This has been significantly larger than the contribution of labour hours which averaged only 0.3 percentage points per year since 1990. This evidences the importance played by capital deepening for output growth in the UK. (Click here to download a PowerPoint copy of the chart.)
Capital deepening captures the growth in capital services relative to the growth in the labour input. It takes on even greater significance when we think about the growth in labour productivity since, after all, this is the growth in output relative to the quantity of labour. It is significant though that since 2015 the growth of capital services has contributed only 1 percentage point to output growth while the growth of labour hours has contributed an average of 0.7 percentage points. This points to a slowdown in capital deepening and hence in the growth of labour productivity.
Chart 2 also illustrates the importance of TFP growth to overall output growth. It is also important (along with capital deepening and the growth in labour quality) for the growth in labour productivity. Interestingly, we observe significant fluctuations in the growth of TFP. This is thought to reflect fluctuations in the utilisation of inputs. For example, if the utilisation of inputs falls (rises) when output falls (increases) this will be mirrored by a disproportionately large fall (increase) in TFP. In the longer-term, however, changes in TFP capture aspects of technological progress and advancement that enable more effective production methods and techniques to be deployed. In other words, the growth of TFP captures the ability of production to benefit from the advancement in ideas, products, processes and know-how.
A decline in the growth in TFP growth following the financial crisis is found quite widely in mature economies. The annual rate of growth of TFP across mature economies fell from 0.5 per cent year in 2000-2007 to 0.2 per cent in 2010-2017. In the UK this fall was from 0.5 per cent to -0.1 per cent. Hence, the decline in TFP growth of 0.6 percentage points between 2010 and 2017 was double the 0.3 percentage point fall across all mature economies. In 2018 the Conference Board estimate that TFP in the UK fell by 0.1 percent further exacerbating the downward pressure on labour productivity.
As our final chart shows, it is the magnitude to which labour productivity has eased following the financial crisis that sets the UK apart. While across all mature economies the growth of output per labour hour (another measure of labour productivity growth) fell from an average of 2.3 per cent per year in 2000-2007 to 1.2 per cent in 2010-2017, in the UK the fall was from 2.2 per cent to 0.5 per cent per year. (Click here to download a PowerPoint copy of the chart.)
While the productivity problem facing the UK is not new, the latest figures comes as a very timely reminder of the extent of the problem. To some extent the uncertainty around Brexit and the negative impact on capital accumulation has only helped to exacerbate the problem. But, this may mask a more systemic problem facing the UK. Getting to the root of this problem matters. It matters most significantly for our long-term wellbeing and prosperity. The productivity gap with our major industrial competitors is a gap that policymakers need not only to be mindful of but one that needs closing.
- What do you understand by the term labour productivity. How could we measure it?
- Why is it important to look at the growth of output per capita when assessing the benefits of long-term growth?
- Why is labour productivity important for the long-term well-being of a country?
- What do you understand by the method of growth accounting?
- What is the distinction between capital accumulation and capital deepening?
- What might explain why the growth of labour productivity has been lower in the years following the post-financial crisis?
- What do you understand by Total Factor Productivity (TFP)?
- What does the long-term growth of TFP attempt to capture?
- If you were an economic advisor to the government, what types of policy initiatives might you recommend for a government concerned about low rates of growth of labour productivity?
It is perhaps timely given the ongoing uncertainty around Brexit to revisit and update our blog Desperately seeking confidence written back in January. Consumer and business confidence reflects the sentiment, emotion, or anxiety of consumers and businesses. Confidence surveys therefore try to capture these feelings of optimism or pessimism. They may then provide us with timely information for the short-term prospects for private-sector spending. For example, declining levels of confidence might be expected to play a part in weakening the growth of consumption and investment spending.
Attempts are made to measure confidence through the use of surveys. One long-standing survey is that conducted for the European Commission. Each month consumers and firms across the European Union are asked a series of questions, the answers to which are used to compile indicators of consumer and business confidence. For instance, consumers are asked about how they expect their financial position to change. They are offered various options such as ‘get a lot better, ‘get a lot worse’ and balances are then calculated on the basis of positive and negative replies.
The chart plots confidence in the UK for consumers and different sectors of business since the mid 1990s. The chart captures the volatility of confidence. This volatility is generally greater amongst businesses than consumers, and especially so in the construction sector. (Click here to download a PowerPoint copy of the chart.)
Confidence measures rebounded across all sectors during the 2010s, with positive balances being recorded consistently from 2013 to 2016 in services, retail and industry. Subsequently, confidence indicators became more erratic though often remaining at above-average levels. However, confidence indicators have eased across the board in recent months. In some cases the easing has been stark. For example, the confidence balance in the service sector, which contributes about 80 per cent of the economy’s national income, fell from +10.9 in February 2018 to -16.2 in February 2019, though recovering slightly to -9.2 in March 2019.
Chart 2 shows how the recent easing of consumer confidence has seen the confidence balance fall below its long-term (median) average of -7. In March 2019 the balance stood at -11.7 the lowest figure since November 2013. To put the easing into further perspective, the consumer confidence balance had been as high as +8.2 in September 2015. (Click here to download a PowerPoint copy of the chart.)
Changes in confidence are used frequently as an example of a demand shock. In reality changes in consumer confidence are often likely to be an amplifier of shocks rather than the source. For example, the collapse in aggregate demand in 2007/8 that followed the ‘credit crunch’, the severe tightening of credit conditions and financial distress of many sectors of the economy is likely to have been amplified by the collapse in consumer confidence. The weakening of confidence since 2016 is perhaps a purer example of a ‘confidence shock’. Nonetheless, falls in confidence, whether they amplify existing shocks or are the source of shocks, are often a signal of greater economic uncertainty.
Greater uncertainty is likely to go and hand in hand with lower confidence and is likely to reflect greater uncertainty about future income streams. The result is that people and businesses become more prudent. In the context of households this implies a greater willingness to engage in self-insurance through increased saving. This is known as buffer stock or precautionary saving. Alternatively, people may reducing levels of borrowing. In uncertain times prudence can dominate our impatience that encourages us to spend.
Chart 3 plots the paths of the UK household-sector saving ratio and consumer confidence. The saving ratio approximates the proportion of disposable income saved by the household sector. What we might expect to see, if greater uncertainty induces buffer-stock saving, is for falls in confidence to lead to a rise in the saving ratio. Conversely, less uncertainty as proxied by a rise in confidence would lead to a fall in the saving ratio. (Click here to download a PowerPoint of the chart.)
The chart provides some evidence of this. The early 1990s and late 2000s coincided with both waning confidence and a rising saving ratio, whilst the rising confidence seen in the late 1990s coincided with a fall in the saving ratio. However, the easing of confidence since 2016 has coincided with a period where the saving ratio has been historically low. In the first quarter of 2017 the saving ratio was just 3.3 per cent. Although the saving ratio has ticked up a little, in the final quarter of 2018 it remained historically low at just 4.9 per cent. Hence, the available data on the saving ratio does not provide clear evidence of the more cautious behaviour we might expect with waning confidence.
Consider now patterns in the consumer confidence balance alongside the annual rate of growth of consumer credit (net of repayments) to individuals by banks and building societies. Consumer credit is borrowing by individuals to finance current expenditure on goods and services.
Data on consumer credit is more timely than that for the saving ratio. Therefore, Chart 4 shows the relationship between consumer confidence and consumer credit into 2019. We observe a reasonably close association consumer credit growth and consumer confidence. Certainty, the recent easing in confidence is mirrored by an easing in the annual growth of net consumer credit. (Click here to download a PowerPoint of the chart.)
The year-to-year growth in net consumer credit has eased considerably since the peak of 10.9 per cent in November 2016. In February 2019 the annual growth rate of net consumer credit had fallen back to 6.3 per cent, its lowest rate since September 2014. As we noted in our recent blog Riding the consumer credit cycle (again) it is hard to look much past the effect of Brexit in acting as a lid on the growth in consumer credit. Therefore, while the recent falls in consumer confidence have yet to markedly affect the saving ratio they may instead be driving the slowdown in consumer credit. The effect will be to weaken the growth of consumer spending.
- Draw up a series of factors that you think might affect both consumer and business confidence. How similar are both these lists?
- Which of the following statements is likely to be more accurate: (a) Confidence drives economic activity or (b) Economic activity drives confidence?
- What macroeconomic indicators would those compiling the consumer and business confidence indicators expect each indicator to predict?
- What is meant by the concept of ‘prudence’ in the context of spending? What factors might determine the level of prudence
- How might prudence be expected to affect spending behaviour?
- How might we distinguish between confidence ‘shocks’ and confidence as a ‘propagator’ of shocks?
- What is meant by buffer stock or precautionary saving? Draw up a list of factors that are likely to affect levels of buffer stock saving.
- If economic uncertainty is perceived to have increased how could this affect the consumption, saving and borrowing decisions of people?