Back to the 80s: Projections for living standards and inequality in the UK

Adam Corlett, Economic Analyst and Stephen Clarke, Research and Policy Analyst, Resolution Foundation

The UK economy has, in many respects, performed well recently. Last week it was revealed that GDP grew by 2 per cent in 2016, above the OECD average, and higher than forecasters expected when the country voted to leave the European Union. Employment is at a record high and average wages, although still 4 per cent below their pre-crisis peak, have been growing at a rate of around 2 per cent a year in real terms. Yet dark clouds are forming on the horizon, particularly for those on low and middle incomes.

Our annual audit of Living Standards across the UK, which uses the macroeconomic forecasts of the independent Office for Budget Responsibility as well as expected tax and benefit rates, shows that growth in typical household incomes will slow sharply in the next few years.

Indeed, the strong income growth of the past few years has likely already ended. Very low inflation – driven by oil price falls – and rising employment could not have been expected to last forever, and inflation has picked up quickly since the EU referendum vote cut the value of Sterling. Looking at the next four years as a whole we project cumulative growth in average working-age incomes of only 1.7 per cent (or 0.4 per cent a year). This is the result of forecasts of above-target inflation, poor wage growth, no employment growth, and tightening fiscal policy.

But even more worrying is how this meagre growth is likely to be shared. While incomes are projected to stagnate for those in the middle, they are expected to rise (albeit weakly) for those at the top and fall significantly for those at the bottom – as shown in the figure below. The poorest quarter of working-age households are projected to be around 5-15 per cent worse off in 2020-21 than this year. In contrast, the highest income quarter would rise by 4-5 per cent.

The result is the worst period of household income growth for the poorest half of households since records began in the mid-1960s. The skewed growth would also represent the largest increase in inequality since the premiership of Margaret Thatcher, and would take inequality (measured here after housing costs) to new heights. This is illustrated below for three common inequality measures, with the most dramatic rise being in terms of the ratio of household income of the 90th percentile compared to the 10th, reflecting the extremely large fall in income at the bottom.

The large inequality increases of the 1980s – which until now have never really been repeated in the UK – can also clearly be seen. However, that was a period when incomes generally rose across the distribution – and significantly faster at the top. The current period is therefore unprecedented in combining weak overall growth with rising inequality and falling incomes at the bottom. To put it another way the next few years could be like the 1980s but without the feel-good factor.

Notes: The 80/20 ratio is the income of a household richer than eight out of ten households divided by that of one richer than only two in ten households; the 90/10 ratio is similarly constructed; and the Palma ratio is the income share of the top 10 per cent divided by the income share of the bottom 40 per cent.

So why does this projection look so bad and what can be done to change it?

The UK’s anaemic wage growth is linked to poor productivity growth, which has dogged the UK for a decade now. The OECD has drawn attention to the low levels of infrastructure spending in the UK and a lack of investment in human capital. Greater public and corporate investment could help spur greater productivity growth. Reducing the cost of housing could make a big difference too. Current mortgagors are benefiting from continued low borrowing costs but we can’t rely on record low interest rates forever.

There is also scope to continue the remarkable employment growth of recent years. Despite the record high, many parts of the country and many groups still have much lower labour market engagement. Our research suggests that addressing such disparities could put around 2 million more people in work by 2020-21.

But, beyond the rate of growth, how that growth is shared is in many ways a simple policy choice. The dismal projection for poorer working-age households (and those with children especially) is in large part due to welfare cuts of over £12 billion inherited by the new PM. These include a freeze in almost all working-age benefits until 2020, despite rising and higher-than-expected inflation; cuts to the generosity of in-work support; and large reductions in support for new families with more than two children. At the other end of the spectrum, the government is introducing tax cuts that will predominantly benefit middle to higher income households. While the government is seeking to bring down its fiscal deficit, how this burden falls – and what level of inequality it wants to see in this country – is entirely its own choice.

Useful links

OECD work on inequality

OECD work on the United kingdom

Structural Policies and Distributional Consequences

NAECChristian Kastrop, Director of the Policy Studies Branch, Economics Department, OECD

In a majority of OECD countries, growth over the past three decades has been associated with growing disparities in household income. This suggests that some of the forces driving GDP have also fuelled inequalities. As a result, gains in household disposable incomes generally have not matched those in GDP per capita and the gap has been particularly large among poorer households and the lower-middle class. An important policy question is whether some of the policy changes driving GDP may in addition play a “hidden” role on inequality. New empirical evidence produced by the OECD on the effects of structural policies on households’ incomes across the distribution scale has identified potential policy trade-offs and complementarities between efficiency and equity.

Labour market policy reforms

Labour market policy reforms are often designed to boost aggregate employment through behavioural effects such as labour supply incentives, and via this channel, GDP per capita. At the same time, these policies also affect income inequality through their impact on the earnings distribution. For some reforms, these two impacts on measures of inequality may be offsetting each other. For example, reducing unemployment benefits and lowering the statutory minimum wage relative to median wages are associated with both higher wage dispersion and higher employment rates among low-skilled workers. This may result in a very small net change in inequality among the working-age population, while the impact on overall inequality is uncertain. For other reforms, however, wage and employment effects may reinforce each other, resulting in both stronger growth and less inequality. This could be the case of policy reforms aimed at easing the strictness of job protection on regular contracts as a way to tackle labour market duality, i.e. the existence of separate segments where comparable workers enjoy different wage conditions and job protection.

Tax policy

Many tax policies raise well-known trade-offs with respect to growth and equity objectives. Economic theory and empirical evidence suggest that the tax structure influences macroeconomic efficiency. In particular, that direct taxes have relatively more distortionary effects by reducing incentives to work and invest. One of the highest ranked growth-friendly tax reforms, shifting the tax burden away from income taxes to consumption and property taxes, may in principle have adverse effects on inequality through various channels. For instance, reform-driven positive employment effects can be counterbalanced by increased income dispersion resulting from lower tax progressivity. Also, empirical evidence suggests that consumption taxes can be regressive, at least in the short run. There is ambiguity with respect to the distributional effects of property taxes. On the one hand, depending on how they are designed, recurrent taxes on immovable property can be regressive with respect to disposable incomes; on the other hand, inheritance and capital gains tax clearly reduce wealth inequality.

Product market regulation

Relaxing anti-competitive product market regulation can bring productivity and employment gains in the long run, therefore spurring economic growth. However, the impact on income inequality is uncertain and empirical evidence generally inconclusive. This is because employment gains may be at least partly offset by changes in the wage dispersion, as more intense product market competition tends to reduce the bargaining power of workers. Recent evidence has shown however that reducing barriers to competition is found to lift incomes of the lower-middle class by more than GDP per capita. Research also shows that linking well-tailored employment and product market reforms could bring additional gains on growth and equality.

Globalisation and technological progress

There is some consensus, in both developed and, to a lesser extent, developing countries, that globalisation is a growth-enhancing force. But there is no consensus, and mixed empirical evidence, about the distributional implications. Economic globalisation involves increased exposure to international trade and financial and capital movements, increased mobility of production factors (workers and capital) and increased fragmentation of the production process in Global Value Chains (GVC). The effects of globalisation on overall income inequality have mainly focused on the earnings dispersion channel as opposed to the employment channel. Available evidence would seem to suggest that globalisation-induced inequality effects are mainly driven by greater wage dispersion, in particular arising from changes in the skill and industry composition of labour demand.

Stronger export intensity based on sound and dynamic competitiveness is found to boost long-run GDP per capita and average household disposable income. Such effects hold across the distribution of household income, with stronger estimated gains for the poor – implying reduced inequality. Overall, these findings signal synergies across policy objectives, i.e. that reforms enhancing competitiveness aimed at encouraging exports among domestic firms could boost efficiency and equity.

Globalisation may also affect income distribution insofar as increased trade and international capital flows facilitate the diffusion of technology, increasing thereby wage dispersion via mechanisms such as skill-biased technological change. To the extent that skill-biased technological change shifts demand of labour towards higher skills and especially when this increase in demand is not matched by a sufficient increase in the supply of skilled workers, technical progress may increase wage inequality. The implications of this hypothesis for inequality have found empirical support for many OECD countries. Going further, recent evidence strongly suggests that skill-biased trade specialisation is associated with higher wage inequality, even accounting for technological change.

Technological progress, as measured by the share of investment in communication technology (ICT) in overall investment, is found to boost long-run GDP per capita and average household disposable incomes. Average household income gains hold across the distribution and as a result, there is no evidence of inequality effects.

Taking these findings into account, the OECD is following up designing general but also country tailored policy frameworks which avoid and minimise trade-offs in the short and long run. This encompasses the right mix and sequence of employment and product market reforms, together with science, innovation, education and redistribution systems with taxes and benefits in cash or kind.

Useful links
Economic Policy Reforms: Going for Growth

Connecting the dots on income inequality: what do official sources suggest when adjusted for top incomes?  Nicolas Ruiz, on OECD Economics Department, on OECD Ecoscope blog

OECD work on labour markets, human capital and inequality

Learn to Earn: Skills, Inequality and Well-being

NAECAndreas Schleicher, Director of the OECD Directorate for Education and Skills

Jobs, wealth and individual well-being depend on nothing more than on what people know and what they can do with what they know. There is no shortcut to equipping people with the right skills and to providing them with opportunities to use these skills effectively. If there’s one lesson the global economy has taught us over the last few years, it’s that we cannot simply bail ourselves out of a crisis, that we cannot solely stimulate ourselves out of a crisis and that we cannot just print money our way out of a crisis.

But we can do much better with equipping more people with better skills to collaborate, compete and connect in ways that lead to better jobs and better lives and drive our economies forward. The OECD’s Skills Survey shows that poor skills severely limit people’s access to better-paying and more-rewarding jobs. It works the same way for nations: The distribution of skills has significant implications for how the benefits of economic growth are shared within societies. In the end, productivity is about working smarter, not just working harder.

Put simply, where large shares of adults have poor skills, it becomes difficult to introduce productivity-enhancing technologies and new ways of working, which then stalls improvements in living standards. Importantly, skills affect more than earnings and employment. In all countries with comparable data, adults with lower skills are far more likely than those with better literacy skills to report poor health, to perceive themselves as objects rather than actors in political processes, and to have less trust in others. It is for these reasons that the new Sustainable Development goals (SDGs) formulate their goals no longer just in terms of years of education, but in terms of the skills that people attain.

In short, without the right skills, people languish on the margins of society, technological progress will not translate into economic growth, and countries can’t compete in the global economy. We simply can’t develop fair and inclusive policies and engage with all citizens if a lack of proficiency in basic skills prevents people from fully participating in society. That is especially important for today’s youth, who cannot compete on experience or social networks in ways that older people can.

All that said, skills are only valuable when they are used effectively, and some countries are far better than others in making good use of their talent. While the US has a limited skills base, it is extracting good value from it. The reverse is true for Japan, where rigid labour-market arrangements prevent many high-skilled individuals, most notably women, from reaping the rewards that should accrue to them. But underuse of skills is visible in many countries, and not just for women. It is also common among young and foreign-born workers and among people employed in small enterprises. Employers may need to offer greater flexibility in the workplace. Labour unions may need to reconsider their stance on rebalancing employment protection for permanent and temporary workers. The bottom line is that unused human capital represents a waste of skills and of initial investment in those skills. And as the demand for skills changes, unused skills can become obsolete; and skills that are unused during inactivity are bound to atrophy over time. Conversely, the more individuals use their skills and engage in complex and demanding tasks, both at work and elsewhere, the more likely it is that skills-decline due to ageing can be prevented.

In some countries, skills mismatch is a serious challenge that is mirrored in people’s earnings prospects and in their productivity. Knowing which skills are needed in the labour market and which educational pathways will get people to where they want to be is essential. The under-utilisation of skills, in specific jobs in the short to medium term can lead to skills loss. Workers whose skills are under-used in their current jobs earn less than workers who are well-matched to their jobs and tend to be less satisfied at work. This situation tends to generate more employee turnover, which is likely to affect a firm’s productivity. Under-skilling is also likely to affect productivity and, as with skills shortages, slow the rate at which more efficient technologies and approaches to work are adopted.

Developing the right skills and using these effectively needs to become everyone’s business: governments, which can design financial incentives and favourable tax policies; education systems, which can foster entrepreneurship as well as offer vocational training; employers, who can invest in learning; labour unions, which help that investments in training are reflected in better-quality jobs and higher salaries; and individuals, who can take better advantage of learning opportunities.

Countries also need to take a hard look at who should pay for what, when and how. Governments need to design financial incentives and tax policies that encourage individuals and employers to invest in post-compulsory education and training. Some individuals can shoulder more of the financial burden for tertiary education, and funding can be linked more closely to graduation rates, provided individuals have access to income-contingent loans and means-tested grants.

It’s worth getting this right. If the industrialised world raised its learning outcomes by 25 PISA points, the level of improvement that we have seen in a country like Brazil or Poland over the last decade, its economies could be richer by over 40 trillion euros over the lifetime of today’s students. Many countries still have a recession to fight, but the cost of low educational performance is the equivalent of a permanent economic recession.

Useful links

OECD Skills Outlook 2015 – Youth, Skills and Employability

 6th International Summit on the Teaching Profession (ISTP), Berlin, 3-4 March

Are you in the 1%?

It's good news: I'm in the 1%!
It’s good news: I’m in the 1%!

Oliver Denk, OECD Economics Department

The 1% are back in the news following last week’s Oxfam report claiming that the world’s 62 richest billionaires own as much wealth as the poorest 3.6 billion people on the planet combined. But what about labour income rather than wealth: Who are the 1% when earnings are counted, and not shares, property, and so on? We have a good idea of how much they earn thanks to the administrative records studied by researchers like Thomas Piketty. But these studies don’t actually tell us much about the personal characteristics of the top earners, such as their education, occupation, or the industry they work in.

That’s where my new research comes in, which for the first time puts hard numbers on who the top earners are across 18 European countries. The data source I use is the Eurostat Structure of Earnings Survey for 2010. It is the largest harmonised dataset on employees’ earnings across Europe, with a total of 10 million observations.

Thanks to these vast data, I was able to compare the top 1% earners with the bottom 99%, focusing on the employee’s age, gender, and highest attained level of education, in addition to the number of years the employee has been with the firm, industry, and occupation. You can find the details of the sample, analysis and results in OECD Economics Department Working Paper N°1274.

So, what do the data show? I’m sure you’ll be as unsurprised as me to learn that, whatever the country, if you’re a middle-aged man working as a financier, doctor, or engineer you’ve a better chance than most of being among the top 1% of earners. The typical person in the top 1% is male, in his 40s or 50s, has a tertiary education degree, works in finance or manufacturing, and is a chief executive, manager or professional.

Digging deeper shows that the top 1% have an average age of 47, hence are five years older than the average worker in the bottom 99%. Around 80-85% in the top 1% are men versus 50-55% in the bottom 99%, and the share of men among the top 1% is actually above 90% in Germany and Luxembourg. Likewise, 80-85% among the top 1% completed tertiary education, compared with 30-35% among the bottom 99%.

There are however important differences between countries and regions. And these appear to be connected to political and economic institutions. Thus, some of the policies your governments are choosing may matter for whether you are in the 1%. I’ll highlight a few of these differences.

Top earners are disproportionately younger, often in their 30s, in Eastern Europe (the Czech Republic, Estonia, Hungary, Poland and the Slovak Republic). At first glance, you might think this is because the workforce is younger in these countries than elsewhere, but the analysis doesn’t support this explanation. The much younger age of top earners in Eastern Europe is probably related to the economic transformation of these countries after the fall of the Iron Curtain. Workers already in the labour market during the 1980s, the last years of communism in Eastern Europe, have less chance than in Western Europe of having moved up to the top 25 years later.

In countries where overall female employment is higher, more of the top 1% are women. The paper does not attempt to establish that it is higher female employment, rather than a related factor, that “causes” more women to be at the top. Nevertheless, one way to interpret this finding is that public policies to broaden female participation in the labour market might also have the benefit of facilitating high-paying careers for women.

Length of career with a particular employer shows contrasting results. One in five top 1% earners has worked for the same employer for more than 20 years. On the other hand, almost one-third of the 1% are new recruits. This pattern is quite different though in Southern Europe (Greece, Italy, Portugal and Spain) where top earners tend to have stayed much longer with their current firm than other workers. The difference could be a sign of stronger family ties or lower labour market flexibility at the top in these economies.

Health professionals are a large group of top earners in several countries, and how much they are paid appears to be linked with life expectancy for the population as a whole. The data suggest: wealthy doctors=healthy people, or that life expectancy is higher the larger the share of the top 1% who are health professionals. Spain and Italy, for example, have both the best-paid doctors, relative to other occupations, and the highest life expectancy, though the analysis does not preclude that better weather, nutrition or other factors might be at work.

Finally, industry structure can affect how concentrated labour income is. Comparing countries with one another shows that the more of the 1% who work in finance, the higher is the share in total earnings that goes to the top 1%, and the smaller is the share that goes to the bottom 99%. That’s one indication that more finance may increase inequality. In earlier work with Boris Cournède and Peter Hoeller, I showed that financial expansion more generally, of bank credit or stock markets, is connected with a widening of the income distribution. Now we’ve come full circle, as the Oxfam report actually draws on our results.

What next? The analysis suggests several questions to be explored in future work, for example trying to establish causality for some of the correlations. This kind of data could also serve as the basis of a study of what’s known as the “rent extraction view”, according to which sectors that are more strongly regulated relative to other sectors and other countries attract more top 1% incomes. And of course it would be interesting to extend the geographical scope if suitable data became available for other countries.

Useful links

Income Inequality: The Gap between Rich and Poor, Brian Keeley, OECD Insights, 2015

OECD Economics Department

OECD work on inequality

OECD data on income inequality

OECD Economics Department Working Papers

Is long-term earnings inequality growing? Evidence from German baby-boomers and their parents

Neither cohort looks too happy
Neither cohort looks too happy

Today’s post is by Giacomo Corneo of the Free University of Berlin, following a talk given in the OECD Directorate for Employment, Labour and Social Affairs Seminar Series in October 2014.

Modern welfare economics suggests that lifetime income is a main determinant of how well individuals fare in economic terms. However, most analyses of income inequality are based on yearly data that might be poorly correlated with lifetime incomes. Typically, these analyses include individuals of different age and educational attainment, whose incomes in a given year may be little representative of their long-term incomes. As sample composition changes over time, it is unclear whether the increase of inequality that is often found in cross-sectional analyses corresponds to a similar evolution of long-term inequality or is simply due to sample changes.

In a recent study, we pinned down the evolution of intra-cohort lifetime earnings inequality in Germany. Starting with the cohort born in 1935, we computed the distribution of lifelong earnings among employees who were born in a same year. Our analysis exploits a rich dataset of the German social security system that includes monthly information about earnings, employment status, sickness and other variables of interest for some 240,000 individuals. Based on this, we built a sample that covers about 80 % of the West German labor force in a typical year.

For the cohorts born between 1935 and 1952, we can compute for each individual his or her lifetime earnings, defined as discounted earnings received between age 17 and age 60. For these cohorts we can thus compare lifetime inequality to annual inequality using the Gini coefficient. We find that the distribution of lifetime earnings for these cohorts is rather compressed, with a Gini coefficient that is less than two thirds of the average value of the Gini coefficients of the distributions of yearly earnings. In other words, yearly earnings show far greater inequality than lifetime earnings. This big difference is caused by the mobility of the individuals in the distribution of yearly earnings during their life cycle – the fact that the same individual may rank low in the distribution of annual earnings in some years and rank high in others. Over a lifetime, the ups and downs offset each other to some extent and make the income distribution less unequal.

But how is lifetime inequality evolving over time? Is it increasing, similarly to what is happening in terms of annual inequality?

It is instructive to compare the lifetime inequality experienced by the baby-boomers – who in Germany were born in the 1960s – with their parents. The parents correspond to the oldest cohorts in our sample, while the baby-boomers are now entering their fifties, so that their lifetime earnings cannot be measured yet. In order to gauge the evolution of long-term inequality up to the baby-boomers, we generalized the concept of lifetime earnings to one of up-to-age-X earnings (UAX). These are defined as the present value of earnings received until some age X and discounted to the year when the individual turned age 17. Lifetime earnings are thus a special case of UAX for X equal to 60.

The figure below shows the Gini-coefficient of representative UAX distributions for all cohorts in our sample, the youngest being born in 1972. It shows an upward trend of lifetime inequality, with a secular rise from the cohorts born in the mid-1930s to those born in the early 1970s. (The figure refers to men; our findings for women are qualitatively similar, albeit less strong.)

Gini coefficients of UAX for cohorts 1935-1972, men only

gini

Source: Research Data Centre of the German Pension Insurance System (FDZ-RV), own calculations using weighted data.

In quantitative terms, the intergenerational change that we have detected is considerable. Take for instance the cohort born in 1935 (fathers) and the cohort born in 1963 (sons). The Gini-coefficient of the UA-45 distribution for the fathers equals almost 0.13. The Gini-coefficient of the UA-45 distribution for the sons equals 0.23. This implies a rise of inequality by 85 % and substituting the 1963 cohort with the 1972 cohort yields a rise of inequality by about 100 %. This marks a deep difference between the baby-boom generation and its parents.

Our rich dataset allows us to detect further striking intergenerational change in terms of labor-market outcomes. An important dimension of it relates to pay uncertainty – i.e. the extent to which employees of different cohorts could count on the labor market in order to achieve stable living standards. Statistically, pay uncertainty can be measured by the transitory variance of wages measured when the cohort was 40-years-old. The earnings histories of the baby-boomers and their fathers reveal that pay uncertainty has doubled from one generation to the next.

Another striking intergenerational mutation pertains to exclusion from the labor market. This can be captured by the fraction of a cohort that experienced more than twelve months of unemployment before reaching age 40. For those born in 1935, only 2.3 % of them experienced more than one year of unemployment before they turned 40. For the cohort born in 1963, 28.2 % had that experience. This means that in contrast to their fathers, a substantial fraction of the baby-boom cohort lacked a full integration in the labor market.

In sum, from one generation to the next, Germany has moved from having a rather homogeneous workforce to having a quite heterogeneous one. Compared to their parents, German baby-boomers are substantially more unequal in terms of long-term earnings, are subject to a much stronger pay uncertainty, and are considerably more likely to experience long spells of unemployment. Arguably, this intergenerational change has lowered the cohesion of the workforce and its members’ feeling of sharing a common fate – with potentially far-reaching social and political implications.

Useful links

Income inequality from a lifetime perspective, Giacomo Corneo, Freie Universität Berlin School of Business & Economics Discussion Paper, Economics, 2014/30

OECD work on income inequality and poverty

OECD Income distribution database

OECD Earnings database

Equal pay for equal work

1914 2014To mark the centenary of The First World War, we will be publishing a series of articles looking at what has changed over the last century in a number of domains. Today’s post is by Monika Queisser, head of the Social Policy Division in the OECD’s Employment, Labour and Social Affairs Directorate.

Imagine a world where women are the ones repairing cars, driving buses, building roads and houses, mining coal, fighting fires and ploughing fields, with men nowhere to be seen. It sounds like Utopia, dystopia or highly unlikely, depending on your point of view. Even if girls often do better in school than boys there is still a clear-cut gender divide in the fields of study young people chose and in the areas of work they pursue. Boys are more likely to go for science, engineering and maths while girls probably pick health and the humanities.

A 100 years ago, however, this scenario of a female-dominated world of work, including in traditionally male professions, was the reality in much of Europe. With men having been called to war, women were filling their jobs to keep the countries running. In the UK, women’s employment rates just about doubled from 23% to 47% during the war, and munitions factories became the biggest employers of women (called “canaries” because the poisonous chemicals used to make TNT turned their skin yellow).

But even though women were doing what was considered men’s work, they were still only being paid women’s rates. Not that women weren’t bothered by the pay gap or political issues. As early as 1909, a mostly female led famous strike, called the Uprising of the 20,000, shook the garment industry in New York and spread further as more and more workers joined the picketers. In Russia, women played a major role in the strikes and revolts that led up to the 1917 revolution and already during the 1905 revolution, there were more women in the Assembly of Russian Factory and Plant Workers than there were men in all the socialist organisations in St. Petersburg combined.

In London, women working on buses and streetcars went on strike in 1918 demanding better pay. As a result, the question of the gender pay gap was studied by a special Committee of the British War Cabinet which published a Report on Women in Industry in 1919. The report was widely disseminated and even received an excellent review in the American Economic Review in its June 1920 edition. While the report endorsed equal pay for equal work it also found that for many occupations women’s output was lower than that of men. This justified paying female workers lower wages, which would also be better overall for women since paying women higher wages might actually reduce their employment. In addition, it found that women didn’t need to be paid as much as men since they had no family responsibilities, automatically assuming that any woman who was working must be single, although by 1918 nearly 40% of all women workers were married.

But the report was actually split in two: a majority report and a minority report by Beatrice Webb, a social rights campaigner who was also one of the founders of the London School of Economics. She forcefully argued against this and several other assumptions about women’s output and productivity.

The gender wage gap got governments worried about what would happen when the men returned from the battlefields and found their jobs taken by the women. These concerns turned out to be unfounded. When the men came back, many women were either fired or retreated back to home and hearth, leaving the work responsibilities to the men. And if they continued to work in the same factories they did so at lower wages while the men got better pay.

So what’s changed over the past century? About half of the economic growth in OECD countries in the past 50 years is due to increased educational attainment, particularly among women, but women still earn on average 15.3% in OECD countries. At the top of the pay scale, the gender gap is even higher, 21%, suggesting the continued presence of a glass ceiling. The average pay gap between men and women widens to 22% in families with one or more children. For couples without children, the gap is 7%. Overall the wage penalty for having children is on average 14%, with Japan and Korea showing the greatest gap.

The impact of pay inequality is dramatic over a woman’s lifetime. Having worked less in formal employment, but having carried out much more unpaid work at home, many women will retire on lower pensions and see out their final years in poverty. Living an average of nearly 6 years longer than men, women over 65 are today more than one and a half times more likely to live in poverty than men in the same age bracket. Mrs Webb would be appalled.

Useful links

OECD work on gender

Is inequality good or bad for growth?

Inequality and growthIf you’ve been following the income inequality debate, you’ll know there’s been much discussion of the question in the headline above. Until just a few years ago, it’s probably fair to say that mainstream opinion leaned towards the “good for growth” side of the debate. Yes, inequality might leave a bad taste in the mouth, but it was worth it if it meant a strong economy.

This case rests on three main arguments: First, inequality creates incentives for entrepreneurs. Second, wealthy types are a source of investment for the economy. Third, when the state tries to reduce inequality by taxing wealth and transferring it to the less well-off, some of these resources will be lost in the “leaky bucket” of bureaucracy and administration. From an economic perspective, that’s inefficient.

All these arguments have some merit, and indeed few would disagree with the idea that some level of inequality is necessary in a modern economy. But over the past couple of years, the bigger proposition – that inequality is good, or at least not bad, for growth – has come under increasing fire, including from the IMF, the OECD and even Standard & Poor’s. And now comes new research from the OECD indicating that “income inequality has curbed economic growth significantly”.

Much of the coverage of rising inequality has focused on the incomes of “the 1%”. But the OECD research, which was led by Michael Förster and Federico Cingano, indicates that it’s the situation of people at the other end of the earnings scale that has the biggest impact on growth. These lower-income households are not a small group. They represent some 40% of the population, comprising families that, from a social perspective, might be called lower-middle and working class.

Where overall inequality is higher in a society, a clear pattern emerges: People from such backgrounds invest much less in developing their human capital – essentially their education and skills. By contrast, it has almost no impact on the educational investment of middle-income and wealthy families. The implications for social mobility are clear – an ever-widening education and earnings gap between society’s haves and have-nots.

This gap is seen not just in the length of time people spend in education but also in their skill levels. At the risk of swamping you in data, this is strikingly evident in the chart below. It divides the overall population into three groups based on parental education background, or PEB (which is used to represent socioeconomic status) – high, medium and low levels of education. The chart then looks at the average numeracy scores of people from each of these three groups in the OECD’s Adult Skills Survey and charts them against levels of inequality as measured by the Gini coefficient (where 0 equals absolute equality and 1 equals absolute inequality).

b

Where inequality is relatively low (around 0.20), the gap in numeracy levels between the three groups is relatively modest. But, on the other end of the scale, where inequality is higher (around 0.36 – a little more than in the UK today and a little less than in the US), the score of people from poorer backgrounds is markedly lower; by contrast, the scores of people from middle and high-income families don’t change much.

How does this affect growth? As an economist might say, it’s “inefficient” – workers with higher skill levels can contribute more to the economy. If a large swathe of the population is unable to invest in its skills, that’s bad news for the economy.

Just how bad is clear from the OECD research. It estimates that rising inequality knocked more than 10 percentage points off growth in Mexico and New Zealand in the two decades up to the Great Recession. The impact of rising inequality was also felt – albeit not as strongly – in a number of other OECD countries, including Italy, the UK and the US and even in countries with relatively low levels of inequality like Sweden, Finland and Norway

To be sure, the debate over inequality and growth will certainly continue. Just last week (before publication of the new OECD paper), Nobel laureate Paul Krugman admitted he was a “skeptic” who remained to be convinced of the link. But the fact that the debate is happening at all is surely a good thing. Rising inequality is one of the most significant socioeconomic trends of our time. Understanding its possible impact on our societies and economies has surely never been more important.

Useful links

OECD work on income inequality and poverty

Focus on Inequality and Growth (OECD, 9 Dec. 2014)

Trends in Income Inequality and its Impact on Economic Growth (OECD Working Paper, 2014)