Perspectives on Global Development 2021

From Protest to Progress?

Report

Perspectives on Global Development

30 June 2021

1. Discontent in an era of growth

Abstract

This chapter charts the evolution of the global economy during the three decades prior to the COVID-19 pandemic and examines possible links to the rise in discontent around the world. It analyses four tendencies to explain why discontent has surged despite a prolonged period of economic growth in advanced and developing economies: rising inequalities, uneven progress in broader well-being indicators, changes in production that are putting pressure on large portions of the global labour force, and the worsening environmental crisis. These phenomena underline the importance of looking beyond gross domestic product when analysing sustainable development.

Introduction

Ceux qui ont dit que tout est bien ont dit une sottise : il fallait dire que tout est au mieux (Voltaire, 1759[1])

This edition of Perspectives on Global Development is about discontent. The report examines the origins, manifestations and consequences of discontent globally and explores what can be done about it. As the term is applied in this report, discontent is neither unhappiness nor anger; rather, it is a mental state created by frustration, unmet aspirations and powerlessness. It is also a collective rather than individual phenomenon. Discontent is not a term commonly associated with economics; its psychological and communal implications are better suited to sociological or political analysis. Economic factors are nonetheless central to the causes and consequences of discontent.

This report demonstrates that discontent is on the rise across the world, in advanced and developing economies alike. This chapter examines the global economy prior to the coronavirus (COVID-19) pandemic to confront what looks at first glance to be a paradox. Why was discontent rising around the world when gross domestic product (GDP) and wealth were rising almost uninterruptedly in countries at all income levels, poverty rates had declined, and the gap between rich and poor countries was narrowing? This chapter identifies four keys that both unlock this paradox and underline the importance of looking beyond GDP: the inequalities and vulnerabilities that characterised the growth in income and wealth over the past 30 years; the uneven trends in broader well-being measures over the same period; the negative impact of new patterns of production and consumption on social cohesion and individual well-being; and the environmental damage that has put humanity’s long-term survival at risk.

This report is written during the COVID-19 pandemic, which spread rapidly across the world during the first half of 2020 and continues to inflict a terrible human cost on a global scale in 2021. Much of the data in this chapter (and the rest of the report) pre-date the multiple crises triggered by the pandemic. While it is impossible to predict the long-term consequences of the pandemic, most developing economies are expected to take much longer to recover than advanced economies. Global poverty, unemployment and inequality have risen as a result of the pandemic, which has undone many of the gains in living standards achieved over the past three decades.

The world pre-COVID-19: The best of times?

Over the three decades before the COVID-19 pandemic, countries world wide witnessed sustained GDP growth. Between 1990 and 2018, average GDP per capita almost doubled from USD 8 975 to USD 15 941 (United States dollar) (Figure 1.1). GDP growth rates were positive for all country income groups every year, except for low-income countries in 1992 and high-income countries in 2009. A remarkable feature of this period is that global GDP growth owed more to low- and lower middle-income countries than to high-income countries, indicating convergence between developing and advanced economies. Moreover, as discussed below, the period was marked by a steady decline in global poverty rates and the emergence of middle classes globally.

It should be noted, however, the global financial crisis in 2008-09 changed the complexion of the global economy. Although growth resumed thereafter in advanced economies, it was reliant on monetary stimulus, which inflated asset prices and benefited the holders of capital; meanwhile, austerity policies implemented to control debt levels choked growth and weakened public services (OECD, 2016[2]). The fates of Wall Street and Main Street diverged, exposing inequalities that did not go unnoticed. While developing countries weathered the storm, their growth prospects gradually weakened, policy space narrowed, and debt levels rose. As Kose and Ohnsorge (2019[3]) note, emerging economies became more vulnerable to economic shocks than they were before the global financial crisis. The global economy was poorly prepared for the COVID-19 pandemic (Box 1.1).

Box 1.1. The COVID-19 pandemic will have long-term effects on developing countries

This report was written during the COVID-19 pandemic, which spread rapidly around the world during the first half of 2020 and continues in 2021. The worst global health crisis in a century has precipitated the worst global economic crisis since the Great Depression, perhaps longer. The pandemic is undoing some of the headline achievements of the global economy over the past 30 years while simultaneously exposing and exacerbating the associated structural weaknesses discussed in this chapter, in particular the inequalities within and between countries.

The global economy contracted by some 3.3% in 2020 (IMF, 2021[4]). Advanced economies shrunk by 4.7% and emerging and developing economies by 2.2%; the People’s Republic of China (hereafter, “China”) was one of the few countries in the world whose economy grew in 2020. Latin America and the Caribbean was the most affected region; its economy contracted by an estimated 7.0%. Advanced economies were able to muster an unprecedented response to the crisis, accounting for the vast majority of the USD 16 trillion on fiscal policy actions announced globally up to the time of writing (IMF, 2021[5]).

Output in advanced economies is expected to rebound to pre-crisis levels faster than some developing economies: advanced economies have received the vast majority of vaccines produced to date and fiscal policies implemented during the pandemic protected against long-term scarring. In general, developing countries were restricted to a much more limited fiscal response; most were constrained by high debt levels coming into the crisis, capital flight at the start of the crisis, and a sustained, significant fall in public revenues during the crisis. Concerns are growing of a divergent and asynchronous recovery between advanced and developing economies that would significantly increase global inequalities (IMF, 2021[4]), (OECD, 2021[6]).

The COVID-19 pandemic has reversed recent progress in reducing extreme poverty. According to World Bank estimates in January 2021, between 119 and 124 million people fell into extreme poverty in 2020, with South Asia accounting for approximately half of those newly in poverty (Lakner et al., 2021[7]). This represents the first global increase in extreme poverty since 1998. Using the USD 5.50 per day poverty line, the same estimates calculated an increase in poverty attributable to COVID-19 of between 202 and 210 million people. The combination of economic slowdown and public health measures has had a profound impact on access to work: according to the International Labour Organization (ILO) (2021[8]), there was an 8.8% reduction in global hours worked, equivalent to 255 million jobs. The ILO expects that the effects of the pandemic will continue to weigh on job numbers into 2022 (ILO, 2021[9]).

The pandemic is also expected to set back progress on a wide number of development indicators. For example, COVID-19 increased the number of malnourished people by between 83 and 132 million (FAO et al., 2020[10]). The impact on children’s education was unprecedented: temporary closures kept approximately 1.6 billion students out of school in 2020, causing the proportion of primary school children in learning poverty to increase to above 50% world wide (Azevedo, 2020[11]). This will have long-term consequences for children’s learning, with knock-on effects on their labour market prospects.

Inequality across a wide number of dimensions is expected to worsen as a result of the pandemic. In broad terms, the better-off have been relatively unaffected by the pandemic: they are in jobs that allow them to work remotely, their children are in schools that provide remote learning, and they enjoy the Internet access essential for both. They also have access to health systems and social protection mechanisms if they do fall sick or lose their jobs. Meanwhile, there are large portions of the global population that are unable to work or learn remotely and whose day-to-day subsistence is threatened by lockdown measures. There is a considerable risk that these inequalities will persist long after the pandemic is under control.

The period from 1990 to 2018 coincided with an intensification of globalisation, broadly defined as the expansion of economic and social relations between countries. Increased trade and a diffusion of global production reinforced income gains by lowering the cost of many goods, especially those produced in China. The effect has benefited consumers everywhere, but low-income individuals benefited particularly from the decline in prices of tradable goods (Fajgelbaum and Khandelwal, 2014[12]).

Figure 1.1. Global economic growth rates, 1990-2018

Recent decades have also been a time of unprecedented wealth creation. Between 2000 and 2019, aggregate global wealth per adult rose by 125% from about USD 31 400 to USD 70 840, with an average annual growth rate of 4.5%, far exceeding population growth (Figure 1.2). Wealth increased continuously during this period, except in 2009. After this point, a slightly higher proportion of people’s wealth consisted of financial assets, which recovered faster from the global financial crisis than non-financial wealth.

Figure 1.2. Global trends in wealth creation, 2000-19

A new geography of wealth creation has emerged, marked by the growing prominence of Asian countries. There has been a relative shift of wealth from America and Europe to Asia (Figure 1.3), which has been a central theme of previous editions of this report, starting with 2010 (OECD, 2010[15]). Wealth creation has been particularly strong in China and India. Between 2000 and 2019, North America’s share of global wealth declined from 38% to 32% and Europe’s share from 29% to 25%. By contrast, China’s and India’s share of global wealth increased from 3% to 18% and from 1% to 3%, respectively.

Figure 1.3. Wealth share by region in 2000 and 2019

The geographical distribution of income has also shifted in favour of Asian countries, at the relative expense of Europe, Latin America and the Middle East. In 1990, China and India were hardly present in the global top 10% of global income earners; by 2016, they represented a substantial share not only of the top 10% but also of the top 1%. North America remains the region with the largest proportion of ultra-high-net-worth individuals.

Extreme poverty has declined, but not everyone is satisfied

The three decades prior to the COVID-19 crisis witnessed a large decline in the proportion of the global population in extreme poverty. The global extreme poverty rate declined from 36% in 1990 to 10% in 2015 (Figure 1.4). The decline in poverty was particularly large in middle-income countries, largely China and India, although some 62% of the world’s extreme poor live in middle-income countries (World Bank, 2020[16]). In low-income countries, poverty has declined but remains widespread (Piketty, Saez and Alvaredo, 2018[17]).

Figure 1.4. Global trends in poverty reduction, 1990-2015

The decline in poverty, often hailed as a central achievement of globalisation over the past 30 years, is not universally acclaimed. Philip Alston, a United Nations rapporteur on extreme poverty and human rights, questioned the adequacy of the global poverty measure of USD 1.90 per day, saying it was “scandalously unambitious” and reflects “a staggeringly low standard of living, well below any reasonable conception of a life with dignity” (Alston, 2020[18]). He said the benchmark hid the true extent of global poverty: using the poverty line for upper middle-income countries of USD 5.50 per day, poverty has hardly declined since 1990.

Combining absolute poverty data with a relative poverty indicator for a more welfarist approach to poverty reinforces this less positive analysis. This approach, used by Ravallion (2019[19]), generates a much higher number of people living in poverty globally: while the World Bank calculated that 10% of the global population was in poverty in 2014, this study calculates the number to be above 30%, or more than 2 billion people. In developing countries, the number of individuals in absolute poverty declined between 1990 and 2014, but the number living in relative poverty increased.

The majority of individuals who have left extreme poverty over the past 30 years are at great risk of falling back. According to Edward and Sumner, this “vulnerable” cohort has grown by 1.6 billion people in the past 30 years. A key factor behind their vulnerability is low social protection coverage, particularly in developing countries (Edward and Sumner, 2017[20]). Less than half the world’s population was covered by one or more social protection programmes before the crisis; in low-income countries, this stood at below 20% (Figure 1.5). In high-income countries, coverage is almost universal for the poorest quintile. Regional analysis shows the largest coverage gaps in South Asia and sub-Saharan Africa.

As Chapter 4 explains, the COVID-19 pandemic exposed the size of the vulnerable population globally and prompted an unprecedented scale-up in social protection in almost every country (Gentilini et al., 2021[21]). However, the majority of these emergency interventions were timebound, lasting four months on average, as developing countries struggled with the impact of the crisis on their public finances. While the pandemic has demonstrated the importance of universal social protection coverage, it is far from clear whether such an outcome will be a legacy of COVID-19.

Figure 1.5. Share of total population and the poorest quintile with access to social protection

Middle classes have emerged globally but their prospects are uncertain

Another success story of globalisation is the emergence of middle classes around the world. This is not the same as a “global middle class”, a term that would imply that the middle class in every country – regardless of country income level – enjoys the same level of income and security. However, the aspirations implied by middle-class status (and the frustration that emerges when these aspirations are not realised) are an important driver of discontent the world over, as Chapter discusses.

In 2018, more than 50% of the global population was classified as middle class, a phenomenon driven by the success of developing countries in reducing poverty (Kharas and Hamel, 2018[24]). Asia has accounted for the majority of growth in middle classes globally, with particularly strong gains in China and India, as well as a growing contribution from Southeast Asia (World Bank, 2018[25]). The middle class in Latin America grew from 21.1% to 35.4% of the population between 2000 and 2015, although it had started to shrink even before the COVID-19 pandemic (OECD/CAF/ECLAC/EU, 2019[26]). Africa’s middle class increased from 108 million people in 1990 to 247 million by 2013 (AUC/OECD, 2019[27]). Trends varied in the Middle East during the first decade of the 2000s: the middle class grew strongly in Syria and Tunisia but shrunk in Egypt and Yemen (Dang and Ianchovichina, 2016[28]).

Ravallion captures the vulnerability of the emerging middle classes (2010[29]). He calculates that 1.2 billion people joined the middle class (measured in income terms) in developing countries between 1990 and 2005, 80% of whom came from Asia and half from China alone. However, he recognises that “middle class” in the context of developing countries bears no relation to how the term is applied in advanced economies and that it has different implications even across developing countries: only 100 million of the 1.2 billion would be classified as non-poor in every developing country. The COVID‑19 pandemic has exposed these vulnerabilities: 41% of Latin America’s population was classified as middle income in 2019; in 2020, this proportion fell to 37% (ECLAC, 2021[30]).

Meanwhile, the middle class in advanced economies has been shrinking. The middle class has shrunk in most OECD countries, amid meagre income growth, rising living costs and increasingly vulnerable employment (OECD, 2019[31]). Social mobility has also declined, with middle-class children less likely to achieve the same standard of living as their parents, much less exceed it. While almost 70% of baby boomers were part of middle-income households in their twenties, only 60% of millennials are today.

These diverse trends are captured by the “elephant curve” in Milanovic (2016[32]), which displays strong relative consumption growth between 1988 and 2008 for the first seven deciles of the global income distribution, a sharp decline in relative gains between the seventh and ninth deciles, then strong growth in the tenth. Edward and Sumner calculate that there are 2.2 billion people earning above USD 10 per day; among this number, the consumption of 400 million people in China and the 700 million people in the world’s richest decile has grown strongly, but gains in consumption have been below the global average for the remaining 1.1 billion (2017[20]).

Gains in income and wealth have not been equally shared

The first key to understanding discontent in a time of plenty is that the gains in income and wealth identified have not been shared equally, either within or between countries. Within-country income inequality has risen in many countries, particularly among advanced economies, and remains high in a number of developing countries. It is an important component of the broader inequalities identified later in the report as a key factor behind rising discontent, weakening social cohesion and political upheaval. While there has been convergence in terms of the global income distribution and a decline in between-country inequality since 1990, China’s emergence has been by far the largest factor in both phenomena; many developing economies remain far behind advanced economies.

Large income inequalities are emerging within countries

Income inequality across the population as a whole has increased in a number of countries that experienced rapid economic growth. Between 2000 and 2015, income inequality as measured by the Gini index1 increased considerably in a number of advanced economies, albeit from relatively low levels. It also increased in some developing countries, notably China and India (Figure 1.6). Income inequality declined in many middle-income countries in Latin America, although there is evidence that it started to increase after 2015 (OECD/CAF/ECLAC/EU, 2019[26]).

Figure 1.6. Income inequality in 2000 and 2015

Figure 1.7. Relative income inequality (Gini index) by region

Difference to global median (36.4)

There are clear discrepancies in inequality among regions. The most unequal region is the Americas, where all countries in the sample below have higher Gini than the global median. Income inequality is also above the global median in most countries in sub-Saharan Africa but is more heterogeneous in Asia and Oceania (Figure 1.7). While inequality in almost all European countries is below the global median, the upwards trajectory is of concern.

Income growth and wealth creation have been concentrated among a relatively small group globally. Overall, it is estimated that today 82% of the global wealth belongs to the top 10% richest individuals, while less than 1% is in the hands of the lower half of the world population (Credit Suisse Research Institute, 2019[34]). Between 1990 and 2015, the income share of the global top 1% increased from 18% to 20%, with a peak at 22% in 2006 and 2007, while that of the bottom 50% has stagnated at around 9% (Piketty, Saez and Alvaredo, 2018[17]).

Income inequality tends to have a negative effect on life satisfaction. People in less equal countries (in terms of income) tend to be less satisfied with their lives (Figure 1.8). However, the channels through which inequality leads to discontent at a collective level are complex, as Chapter 3 explains.

Figure 1.8. Life satisfaction and inequality

Variations in economic productivity across regions are an important contributor to within-country inequality. The OECD demonstrates that “two thirds of OECD countries have regions where productivity, a proxy for wages and economic prosperity, have stagnated or declined for a decade” and that “economic gaps across small regions have … almost constantly increased since 2000, reflecting both an increasing concentration of economic activities in cities and the difficulties of small remote regions to keep pace with the national frontier” (2020[36]). The OECD calculates that “one in four persons in the OECD lives in a region that is falling further behind the high‑productivity regions in their country” (2016[37]). According to the OECD, “[on] average, productivity in the least productive region of a country is 46% lower than productivity in its most productive region” (2019[38]). Box 1.2 examines fluctuating regional fortunes in China in recent decades and outlines how the government has responded.

The OECD also finds that “in one‑third of OECD countries, productivity growth has been concentrated in a single, already highly productive, region that is usually home to the country’s largest city” (2019[38]). Although the story is much more complicated than an urban-rural divide, rural areas across the OECD are lagging. According to the OECD, “[in] 2017, GDP per capita in rural regions was 13 percentage points (p.p.) below the average, 16 p.p. lower in labour productivity levels and 8 p.p. lower in employment rates. Rural regions, especially those far from cities, have felt the effects of the 2008 global financial crisis more strongly, leaving many of them in a vulnerable position” (2020[39]). The report notes the demographic pressures facing rural regions, whose populations are ageing and getting smaller.

There are also significant inequalities between different groups within countries. An obvious example is the gender pay gap, which stands at 13% in the OECD and 20% across the world (OECD, 2019[40]). Pay gaps are driven by structural factors including horizontal and vertical segregation in the labour force; Chapter 3 examines the gender-based structural inequalities – and the discontent these generate – in greater detail.

China accounts for the majority of the decline in between-country inequality

Between-country inequality declined between 1988 and 2013 (World Bank, 2016[41]). However, this global convergence was driven principally by income growth in India and (especially) China: due to the size of their populations, rising household incomes in these countries have a major impact on the global income distribution. As Edward and Sumner argue, “between-country changes are so dominated by China’s rise that the fall in global inequality largely evaporates once China is excluded from analysis” (2017[20]). It is nonetheless important to note that China’s Gini coefficient increased from around 0.30 in the early 1980s to 0.49 in 2008 before declining (NBSC, 2017[42]; OECD, 2019[43]; Sicular, 2013[44]).

Income inequalities between countries remain very large. In 2018, among the sample of 187 countries for which data are available, GDP per capita in the richest country (Qatar) was 170 times higher than in the poorest country (Burundi). Using the global median GDP (equivalent to USD 12 316 in 2011 prices) as a benchmark, Africa stands out as the poorest region, with most countries far below the world median. By contrast, Europe appears the richest region, while in the Americas and Asia and Pacific regions, there are both relatively very rich and poor countries.

In 2019, the top ten wealthiest countries were found in Western Europe, North America and the Asia Pacific region, while the poorest nation were all in the Africa region and Haiti in the Americas. In Switzerland, the richest country in 2019, wealth per adult was 1 058 times higher than in Sudan, the poorest country. Looking at the evolution of wealth per adult since 2000 shows a diverging trend, with per capita wealth growing fastest in North America (Figure 1.9).

Figure 1.9. Global trends in wealth per adult by region, 2000-19

Box 1.2. Regional inequality and government responses in China

China’s economy has expanded dramatically since opening up in the 1980s, with average annual GDP growth of 9% to 10% in the three decades before 2020. This was accompanied by rising inequality, both for the population as a whole and across regions. Under Deng Xiaoping’s slogan “Let some people get rich first”, a principle akin to trickle-down economics, some regions benefited disproportionally from government initiatives such as Special Economic Zones and from trade liberalisation, attracting large amounts of domestic and foreign investments and human resources while leaving other regions behind. Nineteen out of 31 regions in mainland China showed widening gaps with the regional average in terms of gross regional product (GRP) per capita from 1993-97 to 2003-07 (Figure 1.10).

While the gaps have narrowed since then, the number of regions below the average (20) exceed those above (11), a ratio that has barely changed since the early 1990s. GRP per capita for the richest region is almost five times as large as that for the poorest region. Inequality of regional income has also contributed to disparities in the quality of public services, such as health care and education, as well as coverage of social security, between wealthier coastal provinces and centrally administered municipalities and less developed inland provinces and autonomous regions (IBRD/IFC/MIGA, 2019[45]).

Figure 1.10. Evolution of GRP per capita in China (five-year average), 1993-97, 2003-07 and 2015-19

Percentage deviation from regional average

Studies have identified a number of drivers of regional inequality in China. Using data from the 1950s to 2000, Kanbur and Zhang concluded that regional inequality, modelled as inland-coastal inequality, was mainly driven by three factors: decentralisation, trade and heavy industry (2005[47]). Fiscal decentralisation (notably after 1984) allowed local governments greater freedom to raise revenues and finance economic development, which benefits regions with a more diverse economic structure and a larger revenue base (mainly coastal regions) while hindering development of regions relying on agriculture or a single industry. Trade liberalisation, especially since China opened up to global markets after joining the World Trade Organization (WTO) in 2001, has also benefited coastal provinces disproportionally due to their geographical advantages and preferential policies from the central government.

Heavy industries, which were mostly based in inland provinces such as the rust belt of Heilongjiang, Jilin and Liaoning, helped reduce regional inequality before 1979. However, this resulted in these inland provinces relying almost solely on military and heavy industries for their economies; their prospects deteriorated as liberalisation and the end of the Cold War prompted a shift of national focus to light manufacturing and trade. All three rust-belt provinces dropped significantly in the ranking of regional income since 1993 (Figure 1.11). Huang and Chen found that certain types of central government transfer payments could be anti-equalising, for example tax rebates, where regions with higher budgetary revenues obtain more, and specific-purpose transfers, where allocation is not rule-based and can be subject to political influence (2012[48]).

Figure 1.11. Ranking of GRP per capita for China’s rust-belt provinces, 1993-2019

Ranking across 31 regions in mainland China

To address inequality, the government of China has adopted a variety of measures, including progressive income tax reform, raising minimum wages, supporting rural development and farmers, expanding social security coverage, supporting less developed regions, enacting poverty-reduction policies and promoting financial inclusion (Jain-Chandra et al., 2018[49]). Some of these measures aim to reduce inequality nationally, while others are more closely linked to reducing spatial inequality between rich coastal regions and lagging inland regions. For example, the 2000 Western Development Strategy was specifically designed to support the development of China’s western regions, including six inland provinces, five autonomous regions and the centrally administered city of Chongqing, through subsidies to infrastructure development, preferential policies for domestic and foreign investment, and increased central government transfers. In addition, general-purpose transfer payments from central to local governments, which aim to equalise basic government incomes across regions, have increased substantially in recent years, from less than 50% of total government transfer payments before 2010 to 90% in 2019 (MOF, 2010[50]; 2019[51]). The largest beneficiaries of general-purpose transfer payments are inland provinces and ethnic autonomous regions (Figure 1.12).

Figure 1.12. General-purpose transfer payments per capita from central to local governments in China, 2015 and 2018

Not all well-being indicators have kept pace with income growth

Rising discontent during a period of sustained economic growth underscores the need to look beyond GDP. People’s living standards are determined by a broad range of factors that are captured by the OECD well-being framework and other multi-dimensional welfare indicators. These elements include health, housing, life satisfaction, the quality of the environment, social connections, the quality of work, work-life balance, safety, and knowledge and skills (OECD, 2020[53]). Policies and strategies that specifically aim to achieve improvements across all these metrics are essential for sustainable and inclusive development (OECD, 2019[54]). To reflect the critical importance of environmental issues, the United Nations Development Programme’s Human Development Report 2020 introduced the Planetary pressures-adjusted Human Development Index, which takes into account countries’ carbon dioxide (CO₂) emissions and material footprint (UNDP, 2020[55]).

A well-being indicator of particular relevance for this report is life satisfaction, which declined globally between 2006 and 2018, albeit with regional variations (Figure 1.13A). The global decline during this period was in large part driven by lower middle-income countries; life satisfaction declined sharply in this group after 2010. Over the same period, there was a sharp rise in negative feelings of anger, sadness and worry world wide: almost 40% of people reported feeling worried in 2018, up from 30% in 2006 (Figure 1.13B). However, saying that people are discontent because they are not satisfied with their lives does not advance our argument; other well-being indicators must be examined to find the cause of this dissatisfaction. As with measures of income and wealth, it is necessary to look beneath aggregate data.

Figure 1.13. Global trends in life satisfaction and negative feelings, 2006-19

Many well-being indicators have improved globally over the past three decades. Between 1990 and 2017, there have been significant increases in life expectancy, average years of schooling and access to improved sanitation facilities in countries at all income levels (Figure 1.14). A key factor behind these improvements was an expansion of fiscal space in developing countries, which financed a substantial increase in public spending per capita even in contexts of rapid population growth. Increases in public revenues have outpaced economic growth, leading to increases in tax-to-GDP ratios across developing regions: the average tax-to-GDP ratio for Latin America rose from 15.9% in 1990 to 23.1% in 2018, while for Africa, it rose from 13.1% to 16.5% over the same period (OECD/AUC/ATAF, 2020[56]).

Figure 1.14. Global trends in key well-being indicators, 1990-2017

Figure 1.15. Relative life expectancy at birth by region, 2018

Life expectancy relative to global median

Large inequalities in the quality of life exist across countries. For example, while the last three decades witnessed a great improvement in global health outcomes, particularly among low-income countries, inequality of life expectancy is still large across regions and countries (Figure 1.15). In 2018, average life expectancy ranged from 53 years in the Central African Republic to 83 years in Japan and Hong Kong, China.

There tend to be large inequalities in well-being within countries. For example, children in poor households have a much higher rate of stunting than those in rich households across developing countries in different income groups (Figure 1.16). The gap tends to be larger in both low-income and lower middle-income countries than in upper middle-income countries, where it is more extreme in countries with high levels of income inequality. Stunting reinforces inequalities by reducing the physical and cognitive development of children and thus reducing their capacity in adult life.

There are also regional dimensions to within-country inequalities. Using a composite well-being indicator for 26 OECD countries that combines income, unemployment and health, Veneri and Murtin show that regional disparities in multi-dimensional living standards are higher than for income alone (2016[57]). According to Brezzi and Diaz Ramirez, as much as 40% of the explained variation of OECD residents’ self-reported life satisfaction can be accounted for by regional characteristics and 60% by individual characteristics (2016[58]).

Figure 1.16. Stunting rate comparison between the bottom and top income quintiles

Certain well-being indicators worsened in the decade preceding the COVID-19 pandemic, most notably those related to nutrition. The number and rate of people who were malnourished increased between 2014 and 2019 in all developing regions except the Middle East and North Africa (FAO, 2020[59]). Close to 2 billion people were moderately food insecure in 2019. As Badie points out, the failure of governments and the international system as a whole to ensure that the global population has access to sufficient food at a time of unprecedented wealth weakens the legitimacy of local, national and global institutions alike (2020[60]). As much as 17% of total global food production went to waste in 2019 (UNEP, 2021[61]).

Slow progress towards achieving the United Nations Sustainable Development Goals (SDGs) demonstrates the distance from achieving an adequate standard of living for the global population. To analyse the trajectory of well-being indicators, it is necessary to consider not only the historical level but also the desired objective. The SDGs, agreed by the United Nations in 2015, represent a universal commitment by all countries to ensuring an acceptable standard of living. By the end of 2019, progress towards achieving the 17 goals was already behind schedule; as a result of the COVID-19 pandemic, many countries will fall even further behind (United Nations, 2020[62]).

The global labour force is increasingly atomised and polarised

The third key to understanding the rise of discontent is understanding the structure of economic growth over the past three decades, in particular how it has affected the global labour force. Employment is inextricably linked to living standards: as well generating an income with which an individual may improve their material quality of life (and that of their families), employment can also teach useful skills, improve self-esteem, enhance social interactions and provide a sense of security about the future. At the same time, work can also be a source of illness or stress, shrink time available for social interactions and be a source of vulnerability.

Employment is therefore a necessary condition for a good life, but it does not guarantee it. This section outlines how trends in global employment over the past 30 years, often linked to profound changes in the organisation of production and consumer behaviour, have adversely affected both the material and non-material benefits of work for large parts of the global labour force. It also explains how they have driven inequality within countries and given rise to a new global class: the precariat.

Labour is under pressure from all sides

For all but the most highly skilled workers, labour is under pressure around the world. The labour share of income declined sharply between the 1980s and the global financial crisis in 2008-09; a similar decline occurred across developing countries, starting in the 1990s (Dao et al., 2017[63]). Over the past three decades, returns to capital have grown significantly more than returns to labour, and wages have not kept pace with productivity gains (Box 1.3). While workers are increasingly vulnerable to shifts in global production and the vagaries of international trade, liberalisation of international capital flows has made it easier for multinational enterprises and holders of capital to seek out returns from anywhere in the world and insulate themselves from risks.

Occupations have enjoyed various fortunes since the 1980s. Examining the employment share of various occupations in the United States between 1980 and 2010, Autor finds that the share of employment in managerial roles increased sharply over this period, while service employment and high-skilled (“white collar”) technical work also grew (2015[64]). Over the same period, there were steep declines in the employment share of skilled and unskilled blue-collar occupations. Similar trends have been evident across the OECD, where demand for high-skilled jobs has increased but has declined for mid-level skills, while demand for low-skilled jobs in the service sector has increased, driven by retail and personal care (OECD, 2020[65]). Shifts in production between advanced and developing economies associated with globalisation are part of this story, but so too is technology, changes in domestic demand and, to a lesser extent, demographic change (Tella and Rodrik, 2019[66]).

The accession of China to the WTO in 2001 was a defining moment in the recent phase of globalisation and in the evolution of labour markets in advanced economies. According to Acemoglu et al., job losses in the United States directly or indirectly linked to China’s increased prominence in the 2000s accounted for almost 20% of jobs lost in the country’s manufacturing sector between 1999 and 2011 (2016[67]). Declines in European manufacturing employment were also significant; while the impact might have been lower in countries with strong labour protection, the China shock made it harder for the unemployed to find work (Aghelmaleki, Bachmann and Stiebale, 2019[68]).

There is growing concern that many developing countries, especially in Africa and Latin America, are deindustrialising before developing large-scale manufacturing sectors. Although internationally competitive firms specialising in particular products often exist in these regions and are integrated into global value chains (GVCs), this does not guarantee the growth of a broad-based manufacturing sector. This so-called premature deindustrialisation poses major challenges for employment creation, formalisation, diversification and productivity growth. This is a particular concern for Africa’s rapidly growing (and increasingly urban) working-age population, for whom decent employment opportunities are scarce (AUC/OECD, 2019[27]).

The dislocation and vulnerability of workers associated with global trade have been compounded by rapid technological change since the 1990s. The Third Industrial Revolution, which spanned the period between the late 1960s and the first decade of the 21st century, was characterised by advances in computing technology that powered the rise of automation, the birth of the Internet and advances in telecommunications more broadly. These changes were a critical enabler of globalisation. In the Fourth Industrial Revolution (also known as IR4), which began around 2010, technology is blurring the distinction between the physical, digital and biological spheres through advances in artificial intelligence, the Internet of Things, the capacity to harness big data, and 3D printing (World Economic Forum, 2016[69]).

Technological change, particularly in automation, is weakening job security. Some 47% of jobs in the United States are at high risk of being automated (Frey and Osborne, 2017[70]). The average proportion of jobs at risk is 14% in the OECD but varies greatly across countries (Nedelkoska and Quintin, 2018[71]). A major change in automation is that it is not just routine tasks that are at risk; as the OECD notes, “with the advent of Big Data, artificial intelligence, the Internet of Things and ever-increasing computing power, non-routine tasks are also increasingly likely to become automated” (2017[72]).

As technology has evolved over the 20th and 21st centuries, so too has the organisation of production. Fordist production systems that revolutionised production in the first half of the 20th century evolved to focus less on scale and cost efficiency and more on differentiation, providing customers with products that were tailored to their specific needs, often through the addition of services. These changes entailed a shift in the structure of employment: demand increased for white-collar workers adept at using new technologies and administering differentiation and just-in time supply chains designed to eliminate waste (a major focus of the Toyota Production System, for example), while automation reduced demand for routine manual work. As globalisation has intensified, with a concomitant lengthening and complexity of supply chains, production is ever-more international.

The advent of IR4 is accelerating the post-Fordist evolution in production techniques and demand for different types of worker. It has also fundamentally altered the structure of employment and consumption patterns, primarily through the impact of the platform economy, which functions as “a digital service that facilitates interactions between two or more distinct but interdependent sets of users (whether firms or individuals) who interact through the service via the Internet” (OECD, 2019[73]). It includes (although is not limited to) the gig economy, whereby piece work is outsourced to individuals with appropriate skills, who are essentially self-employed independent contractors.

Box 1.3. Wages have not kept pace with productivity gains

Labour productivity growth, measured by annual growth rate of real GDP per worker, has accelerated over the past three decades (Figure 1.17). However, there is a large variation in productivity growth by country income group and by region. The largest increases have been in upper middle-income and lower middle-income countries and in the Asia and Pacific region, driven by the contributions of China and India. Aggregate productivity figures in advanced economies mask a significant divergence between firms at the technological frontier and the rest; productivity has risen sharply among the former but much less among the latter (Andrews, Criscuolo and Gal, 2016[74]; Unger, 2019[75]). These firm-level inequalities have knock-on effects for inequalities between individuals and between regions (OECD, 2018[76]).

Figure 1.17. Labour productivity growth by income group and region, 2000-17

Labour productivity has increased more than real wages over the last two decades (Figure 1.18). This trend has been particularly evident in high-income countries, although firms at the technological frontier are once again the exception, paying salaries that keep pace with productivity gains (OECD, 2018[78]). Recent trends such as the prioritisation of shareholders’ interests, linking managerial compensation to financial markets, the disconnect between managerial compensation and long-term value creation, and the decoupling of wages and workers’ productivity have been especially detrimental to workers (Beal and Astakhova, 2017[79]; ILO, 2018[80]; Keeley, 2015[81]).

The gap between labour productivity and wage growth is particularly pronounced in firms engaged in international trade. Although exporting and importing firms appear more productive than firms that do not engage in trade and tend to pay higher wages than their non-trading counterparts, the productivity premiums for exporting and importing firms outweigh the wage premium by 13 and 5 percentage points, respectively (ILO, 2017[82]). Hence, although some workers have become increasingly productive across the world, the benefits of their work tend to have increasingly accrued to capital income and to those at the top of the income distribution.

Figure 1.18. Trends in labour productivity and wages, 1999-2017

The distribution of value added from GVCs raises equity issues. GVCs offer a number of potential benefits to firms and countries, including output and export growth and, most importantly, productivity spillovers (OECD, 2015[83]). However, evidence shows that profits are largely captured by firms responsible for the design and marketing of the product, often in the most advanced countries. According to the OECD-WTO database on trade in value added, 67% of total global value created under GVCs accrues to global lead firms from OECD countries, while only 25% and 8% of total value added is shared among entities from emerging and low-income countries, respectively, where most workers reside (Banga, 2013[84]).

Individuals in the gig economy are less protected and less connected to each other. Employers gain “wider and more flexible access to talented workers, including those with specialised skills, as well as a faster hiring process, lower costs and potentially round-the-clock productivity”, while workers enjoy greater flexibility to find work that interests them, perhaps even abroad (OECD, 2019[73]). However, workers in the gig economy are often not covered by social security arrangements or other forms of worker protection. Moreover, they might have minimal interaction with other workers in the same sector. As such, today’s workforce is ever more atomised and their capacity to act collectively severely constrained.

Social protection systems in OECD countries are struggling to adapt to the changing structure of work, with large gaps in coverage emerging. In the case of the gig economy, for example, who should be responsible for employer contributions to social insurance schemes, how can involuntary loss of work be determined in the absence of an employer, and how should benefit calculations and means tests take into account fluctuations in income (OECD, 2018[85])? In the absence of an employer, new employment arrangements have been accused of free-riding on welfare states that provide income support of last resort.

Consumers are becoming more connected in diverse ways. E-commerce, whereby consumers purchase goods and services on line rather than in person, was increasingly prevalent globally even before COVID‑19 struck; restrictions imposed to control the pandemic have accelerated this phenomenon and with it the retail market share of platforms. Another innovation driven by the platform economy is its collaborative potential: in the so-called sharing economy, consumers are able to share products, with a group of people thus sharing the cost of an item they will use only intermittently rather than each having to purchase an item themselves.

The costs incurred by individuals using the platforms that facilitate these exchanges are typically extremely low. However, the providers are able to generate revenues by selling the data these interactions generate to third parties. There are clear efficiencies built into the platform economy, which is capable of linking buyers and sellers on an individual basis, as well as advertisers and target consumers. However, these efficiencies should be considered against the evidence of a high degree of market concentration among the leading platforms and their ability to gain a competitive advantage over traditional retailers in tax terms.

The speed, scope and complexity of IR4 is disrupting societies. All four industrial revolutions have changed the way people live, work and interact, and thus have had a profound impact on society as a whole. The high speed, broad scope and complex mechanisms with and through which these changes occur under IR4 is compounding this upheaval. Individuals and industries that are able to lead or at least keep pace with advances associated with IR4 are able to derive significant gains in income and wealth that will be much harder to access for those that are not. As IR4 becomes more pervasive in day-to-day life, reshaping not only production and consumption but also the relationships between citizens and institutions, it has ever-greater potential to divide people between those with the skills and resources to navigate digital societies and those without. Exclusion means not only inferior employment prospects but also diminished capacity to participate in society as a whole.

Informality, insecurity and working poverty are widespread

The majority of the world’s working population are in informal employment and, as a consequence, confront high risks and vulnerabilities, as amply demonstrated by the COVID-19 pandemic. The informal workforce comprises some 2 billion workers, representing 61% of the total including agriculture and 50% excluding agriculture (ICLS, 2003[86]; ICLS, 1993[87]; ILO, 2018[88]). Some 1.7 billion informal workers, or 85%, work in the informal economy. In low-income countries, nearly 90% of the labour force work in the informal economy and face high job insecurity and poor working conditions (ILO, 2019[89]). These workers are likely to account for a significant proportion of the new precariat (Box 1.4).

In developed countries, some 18% of workers are engaged in informal employment (OECD/ILO, 2019[90]). Non-standard forms of employment, which are not informal but which are characterised by self-employment, temporary work and independent contracting, have grown rapidly in the 2000s across several OECD countries, especially among the young. Some of these jobs, particularly in the gig or platform economy, are linked to technological advances, but many others are in traditional personal services, where employers are looking to maximise their flexibility through new employment relationships and part-time contracts (OECD, 2019[91]).

Stable employment is increasingly rare. Across European OECD countries, 69% of employees who have been unemployed have histories of non-standard dependent employment. Workers with these contracts have proven especially vulnerable during the COVID-19 pandemic; before the crisis struck, the OECD calculated that non-standard workers were 40% to 50% less likely to receive any form of income support when out of work than standard employees (OECD, 2020[65]).

In many countries, developed and developing, having a job does not guarantee a decent living standard or an escape from poverty. In 2018, workers in moderate or extreme poverty accounted for more than half of employment in low-income countries and more than one-quarter in lower middle-income countries. Working poverty is particularly common among informal economy workers, for whom the incidence is twice that found among formal workers (OECD/ILO, 2019[90]).

According to ILO and UNICEF, progress in reducing child labour stagnated between 2016 and 2020: shortly before the COVID-19 pandemic, 160 million children – almost one in ten children worldwide – were working. Half of them were engaged in hazardous work (ILO/UNICEF, 2021[92]). The authors expect child labour to rise further as a result of the pandemic unless there is a major scale-up of social protection in developing countries.

Box 1.4. The rise of the precariat

O masters, lords and rulers in all lands,
How will the future reckon with this Man?
How answer his brute question in that hour
When whirlwinds of rebellion shake the world?
(Markham, 1899[93])

Economic trends of the past three decades have given rise to a new social taxonomy based on different types of worker. New groupings have evolved partly from the opposing blocs that defined industrial relations in the 19th and 20th centuries, but they are no longer defined via a binary distinction between capital and labour. Nor is it straightforward to divide the global labour force according to a class system except with reference to income level (which only captures part of what class identity entails).

A new taxonomy is emerging that distinguishes (in crude terms) between the winners and losers of globalisation and technological progress. The Occupy Wall Street protests of 2011 railed against “the 1%” as symbolic of the glaring inequality of the era. However, this term underestimates the size of the winners category without necessarily capturing the dynamics at the very top of the income distribution: in the United States, for example, the income and wealth of the top 0.1% of the income distribution has grown particularly strongly (Saez and Zucman, 2020[94]). In its place, the term “elite” has become more prominent in the discourse around inequality.

The connotations of elite in its current usage are linked to economic factors, notably its constituents’ capacity to take advantage of the opportunities offered by the changes in production discussed in this chapter, in particular the growth of the knowledge economy. However, the term also entails a set of attitudes and way of life that separate this group from the rest of the population; as such, its implications are also cultural. This confluence of cultural and economic factors resonates with sociologists’ depictions of changes in the structure of modern societies.

For example, Castells contends that globalisation, changes in production and advances in information and communications technology have created a “network society” that is not bound by national borders (2004[95]). In his framework, certain workers are included in the network society and others not; he articulates a divide between “those who are the source of innovation and value to the network society [and] those who merely carry out instructions”. He also adds a third group: “those who are irrelevant whether as workers (not enough education, living in marginal areas with inadequate infrastructure for participation in global production) or as consumers (too poor to be part of the global market).” The winners and losers are easy to locate in this framework, which he warns will inevitably lead to inequality and polarisation.

The “symbolic analysts” of Reich fall into the first of Castell’s categories (1992[96]). According to this vision, the knowledge economy has created a new category of worker – symbolic analysts – who are valued for their capacity for creative thought in bringing product and service together rather than for their contribution to standardised production alone. Reich contends that symbolic analysts tend to interact through networks (as described by Castells) and work collaboratively, which means they often cluster in a particular area. He argues that countries in the 21st century must invest heavily in education (to provide children with the skills to become symbolic analysts) and physical infrastructure (to attract symbolic analysts to cluster in a particular place). The importance of clustering as a social phenomenon is echoed by Florida, who proposes that the economic prospects of cities depends on their capacity to attract the “creative class” through a combination of “technology, talent and tolerance” (2002[97]). The tendency discussed earlier in this chapter for national productivity gains to be concentrated in one or two big cities suggests this is indeed happening.

At the same time, a number of categories have emerged for individuals who do not form part of this class. Not all of these can necessarily be considered the losers from globalisation: recalling the elephant curve discussed earlier in this chapter, it is important to distinguish between those groups (principally in developing countries) whose incomes have risen sufficiently that they are no longer in poverty and the squeezed middle class of advanced economies, whose incomes have risen very slightly over the past 30 years. Nonetheless, these groups are bound by vulnerability and uncertainty about the future.

Standing posits the emergence of a new global mass class that he terms the “precariat”, which is defined according to three dimensions (2011[98]). First, members of this class are pressured to accept a life of unstable, insecure labour, in which casualisation is extended by indirect labour, crowd labour and on-call contracts. The second dimension is their reliance on money wages, which have been falling in real terms while becoming more volatile and unpredictable; this group has lost access to non-wage benefits (paid leave, medical leave, occupational pensions) that provide labour security. The third dimension of the precariat is a loss of rights across the civil, cultural, social, economic and political spheres.

Giraud recalls the third category of Castells’ scheme, warning that globalisation has rendered a portion of the global workforce “useless” in economic terms (2015[99]). They are either unemployed, on low incomes with only intermittent access to employment or in subsistence activities (often agriculture). This group relies on nomadic jobs generated by shifting patterns of global production, while more productive workers enjoy sedentary jobs with much greater security. The capacity of those reliant on nomadic jobs is limited by their lack of skills and poverty; their situation is rendered more precarious by the threat of automation. The low likelihood of career progress is a source of immense frustration.

These new groups of workers – defined by their starkly different situations and prospects – are a transnational phenomenon. As Sassen argues, globalisation is bringing together both broad categories of worker in what she terms “global cities”, whose economic purpose is better understood in terms of the global economy than the national (1991[100]). The inhabitants of these cities are likely to come from all parts of the world, whether they be high- or low-skilled, with the latter servicing the former. This phenomenon creates a social structure with an hourglass rather than a diamond configuration, wherein the people at the bottom struggle with the increased cost of living driven by those at the top; they share a living space but inhabit very different realities. The emergence of global cities also exacerbates the cultural differences between major cities and secondary cities or rural areas.

Both Standing and Giraud foresee dire political consequences of the phenomena they identify, ranging from growth in support for extremist political parties to deep societal fractures, if measures are not taken to improve the situation of this new class of worker. However, in a recent essay, Standing also explains the “transformative potential” of the precariat, which “in Europe at least, is becoming conscious of itself as a coherent group opposed to the dominant power structure” (2018[101]). He recognises that the political power of the precariat is nonetheless weakened by its tendency to fracture into three groups – the atavists, the nostalgics and the progressives – that have different outlooks and agendas.

The vulnerability of the precariat is reflected in a large inequality in social insurance coverage between the bottom and top income quintiles. As social insurance is mainly provided to workers in the formal sector, and formal employment accounts for a small proportion of the labour market in most low- and middle-income countries, social insurance coverage is extremely low globally. Indeed, as Figure 1.19 shows, there is almost no coverage for people in the bottom of the income distribution and very low coverage for the better-off households in low-income countries. The gap varies significantly among middle-income countries; it is as high as 40% and above in countries like Brazil, Mexico and Thailand, while there is no gap in countries such as Bosnia and Herzegovina.

Figure 1.19. Social insurance coverage comparison between the bottom and top income quintiles

The employment prospects for young and migrant workers are not promising. Changes to the nature of employment have often affected young workers more than older generations, which are more likely to have been engaged in standard forms of employment. Since the 1990s, successive cohorts of young workers in OECD countries have been increasingly less likely to enter the labour market in middle-skill jobs and are now likely to be in low-level jobs that are below the level for which they are educated. Meanwhile, in developing countries and especially across Africa, young people are particularly likely to be in informal employment (Lorenceau, Rim and Savitki, 2021[102]).

COVID-19 has demonstrated the key role of migrant workers. Across Europe and other regions, migrant workers are particularly likely to be in low-skilled, non-standard employment. The COVID‑19 pandemic has demonstrated the critical role they play in the economy. According to Fasani and Mazza, on average across Europe, 13% of key workers are immigrants, a figure that rises in sectors such as personal healthcare workers, drivers and food processing (2020[103]). At the same time, mortality among ethnic minorities has been high relative to the population as a whole in a number of European countries (Crouch, 2020[104]).

Humanity has brought environmental catastrophe upon itself

The environmental impact of global economic activity provides the fourth key to understanding today’s discontent. As this section outlines, the world faces multiple environmental crisis, including rising temperatures, biodiversity loss and pollution, which entail the destruction of natural systems on which humanity depends for its survival. It has been posited that humanity is living through – and directly responsible for – the sixth mass extinction on earth in 540 million years (Barnosky et al., 2011[105]). Global environmental movements, in particular Fridays for Future, have grown rapidly in recent years and are increasingly influential on the world stage.

There are clear links between economic growth and environmental damage (Acheampong, 2018[106]; Aye and Ebruvwiyo, 2017[107]). It is estimated that human activities each year consume more than 1.7 times the resources generated by the biosphere, and it requires 20 months for the earth to regenerate what humans use in a year (Global Footprint Network, 2017[108]). Incorporating a country’s environmental footprint within the Human Development Index (HDI) has a large impact on national rankings: more than 50 countries drop out of the very high human development group (UNDP, 2020[55]). Meanwhile, Costa Rica, Moldova and Panama move up at least 30 places on the HDI once their environmental impact is taken into account, underlining that environmental costs are not an inevitable result of economic progress but rather the outcome of policy choices (Dang and Serajuddin, 2020[109]; Wu, Zhu and Zhu, 2018[110]).

Environmental degradation is taking a toll on people’s physical and mental well-being. Lawrance et al. demonstrate the severe impact of climate change on people’s mental health (2021[111]). The negative effects of air pollution on happiness and life satisfaction have been observed across Europe (Ferreira et al., 2013[112]) and globally (Welsch, 2007[113]). Climate change and deforestation also take a toll on subjective well-being (Krekel and MacKerron, 2020[114]; Maddison and Rehdanz, 2011[115]).

Carbon emissions and environmental inequality

Global CO₂ emissions closely reflect humanity’s environmental impact since the first Industrial Revolution and are the principal driver of climate change. The earth’s average temperature between 2016 and 2020 is expected to be the hottest on record at a level 1.1°C higher than in the period 1850-1900 (generally known as pre-industrial levels) (WMO, 2020[116]). Temperature increases attributable to human activity are already responsible for significant proportion of heat-related deaths across the world, especially in Asia and Latin America (Vicedo-Cabrera et al., 2021[117]). However, the consequences of climate change extend beyond rising temperatures: they are behind an increasing frequency and intensity of high-impact weather events, such as droughts, storms and flooding, which cause massive human and economic damage.

Between 1970 and 2020, extreme weather caused more than 11 000 disasters, took 2 million lives and caused economic losses of USD 3.6 trillion: over these 50 years, the number of disasters and their economic cost increased by five and seven times, respectively (WMO, 2020[118]). By 2050, the number of people at risk of flooding will increase from 1.2 billion to 1.6 billion, while the number living in areas with severe water shortages will increase from 1.9 billion to 2.7-3.2 billion (WMO, 2020[119]).

Per capita CO₂ emissions increased from 4.2 to 5.0 metric tonnes between 1990 and 2014, closely following upwards trends in GDP per capita (Figure 1.20). Over this period, high-income countries had a carbon footprint that was more than ten times larger than that of low-income countries. However, carbon emissions have evolved unevenly across country income groups. While a downward trend in emissions has been observed in high-income countries since 2010, the reverse is true in upper middle-income countries and, to a lesser extent, in lower middle-income countries.

A “polluter elite” has emerged that mirrors the growing inequalities discussed in this chapter (Kenner, 2019[120]). According to the United Nations Environment Programme, “the combined emissions of the richest 1% of the global population account for more than twice the combined emissions of the poorest 50%” (2020[121]). The same report finds that to limit global temperature increase by 2050 to 1.5°C, as set out by the Paris Agreement, this elite would need to reduce their current emissions by a factor of 30 while the emissions of the poorest 50% could still increase by around three times their current level. As this report identifies, the question of how countries can reduce emissions in ways that reflect these vast discrepancies are an emerging source of political tensions in many places.

Figure 1.20. Global trends in CO2 emissions per capita and GDP per capita, 1990-2014

Shifts in global industrial production are changing the distribution of emissions. Wealthier countries have exported manufacturing (and thus the associated carbon emissions) to developing countries, thereby allowing them to reduce their carbon footprint without changing consumption patterns. Initially, this trend led to a sharp increase in emissions by China, but changes in the structure of China’s production and an associated fragmentation of regional value chains have resulted in emissions shifting to countries such as Indonesia, Thailand and Viet Nam (Meng et al., 2018[122]).

Developing countries are bearing the brunt of climate change, despite their minimal contribution to global emissions. Countries that rely more on agriculture for subsistence and economic activity are more susceptible to changes in temperature and extreme weather. At the same time, many developing countries lack the financial resources to invest in climate change mitigation, such as flood defences, as well as early warning systems (EWS). According to the World Meteorological Organization, one in three people are not covered by EWS, most of whom live in least-developed countries (LDCs); almost 90% of LDCs and small-island developing states identified EWS as their top priority for mitigating climate change but most lack the financial resources and capacity required to establish such systems (2020[118]).

As countries start to plan long-term reductions in carbon emissions to achieve “net zero” by 2050 (meaning the same volume of carbon emissions is removed from the atmosphere as is produced in a given year), developing countries are dealing with the immediate reality of the climate crisis. As African Development Bank President Dr. Akinwumi Adesina said in April 2021, Africa “loses USD 7 billion to USD 15 billion a year to climate change, and this will rise to USD 50 billion per year by 2040…Africa is not at net zero. Africa is at ground zero” (Harvey, 2021[123]). The cost of dealing with the impacts of extreme weather events and other consequences of climate change is worsening developing countries’ debt situation, compounding the financial impact of the COVID‑19 pandemic. At the same time, as Chapter 5 explains, a number of developing countries are reliant on exports of hydrocarbons to finance their development.

Biodiversity loss poses an existential threat to humanity. Global economic activity is having an ever-larger impact on the natural world: more than 33% of the world’s land surface and nearly 75% of freshwater resources are now devoted to crop or livestock production to feed the growing global population and adapt to changing consumption patterns (IPBES, 2019[124]). This is associated with a sharp decline in the abundance of species and massive degradation of natural systems; approximately 1 million species are threatened with extinction. This will have dire consequences for earth’s human inhabitants, for example by destroying food systems and posing threats to global health, as demonstrated by the COVID‑19 pandemic.

Figure 1.21. Forest land area coverage by income group, 1990-2016

Deforestation continues at a rapid pace in the Global South. Deforestation is threatening ecosystems and human lives due to the loss of biodiversity, scarcity of natural resources, and pollution (FAO and UNEP, 2020[125]). Forest area measured as a percentage of land area is decreasing everywhere and at a particularly rapid rate in lower middle-income and low-income countries (Figure 1.21). Biodiversity in developing countries is also increasingly threatened by invasive alien species, which globalisation has helped to spread outside their natural habitats and climate change has allowed to settle in new environments (Early et al., 2016[126]).

Meanwhile, human beings are increasingly exposed to air and water pollution, which cost at least 9 million lives annually (UN Environment, 2019[127]). As measured by global concentrations of fine particulate matter less than 2.5 µm in diameter (PM2.5), air pollution increased rapidly from 1990 and reached a peak in 2011 far above the World Health Organization’s annual safety threshold of 10 µg/m³(Figure 1.22). Air pollution has decreased in high-income and upper middle-income countries and increased in lower middle-income and low-income countries, especially in Africa. Air quality in advanced economies is four times better than in lower middle-income countries, and almost three times better than in low-income and upper middle-income countries. Today, the most polluted regions are South Asia and the Middle East and North Africa.

Figure 1.22. Global trends in PM2.5 concentration and GDP per capita, 1990-2017

Conclusion

This chapter provides the global context for the rise in discontent discussed in this report over the past 30 years. It explains that there is no paradox behind the fact that discontent has worsened during a period of sustained economic growth: gains in income and wealth achieved in the three decades before 2020 were unsustainable socially, environmentally and politically. They gave rise to widening inequalities and were accompanied by social upheaval caused by changes in the way people live and work. In telling this story, the chapter reinforces the inadequacy of GDP growth as an indicator of development or living standards. It also demonstrates that developing countries continue to face immense economic, social and environmental challenges even though they were an important driver of global growth.

These trends do not explain global discontent in themselves. As the following two chapters discuss, discontent is a complex phenomenon to diagnose and understand. Although economic factors are important, so too are sociological and political factors: discontent has a broad set of short- and long-term, contingent and structural causes that vary across countries. Nonetheless, there will be evidence of the four keys to discontent identified in this chapter throughout the report.

The chapter also demonstrates that global trends contribute to discontent and that the response to discontent must, to a certain extent, therefore be international. International co-operation on the environment is critical and urgent, but it cannot end there: improving living standards, providing secure employment and fostering social cohesion are the bedrocks of national development but they are also projects with a strong global dimension.

References

[67] Acemoglu, D. et al. (2016), “Import competition and the great US employment sag of the 2000s”, Journal of Labor Economics, Vol. 34/S1, pp. 141-198, http://dx.doi.org/10.1086/682384.

[106] Acheampong, A. (2018), “Economic growth, CO2 emissions and energy consumption: What causes what and where?”, Energy Economics, Vol. 74, pp. 677-692, https://doi.org/10.1016/j.eneco.2018.07.022.

[68] Aghelmaleki, H., R. Bachmann and J. Stiebale (2019), “The China shock, employment protection, and European jobs”, DICE Discussion Paper, No. 328, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf, https://EconPapers.repec.org/RePEc:zbw:dicedp:328 (accessed on 22 January 2021).

[18] Alston, P. (2020), Report on the parlous state of poverty eradication, UN-OHCHR, Geneva, http://www.ohchr.org/EN/Issues/Poverty/Pages/parlous.aspx (accessed on 21 January 2021).

[74] Andrews, D., C. Criscuolo and P. Gal (2016), “The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy”, OECD Productivity Working Papers, No. 5, OECD, Paris, http://www.oecd-ilibrary.org/economics/the-best-versus-the-rest_63629cc9-en (accessed on 23 January 2021).

[27] AUC/OECD (2019), Africa’s Development Dynamics 2019: Achieving Productive Transformation, OECD Publishing, Paris/African Union Commission, Addis Ababa, https://dx.doi.org/10.1787/c1cd7de0-en.

[64] Autor, D. (2015), “Why are there still so many jobs? The history and future of workplace automation”, Journal of Economic Perspectives, Vol. 29/3, pp. 3-30, http://dx.doi.org/10.1257/jep.29.3.3.

[107] Aye, G. and P. Ebruvwiyo (2017), “Effect of economic growth on CO2 emission in developing countries: Evidence from a dynamic panel threshold model”, Cogent Economics & Finance, Vol. 5, pp. 1-22, https://doi.org/10.1080/23322039.2017.1379239.

[11] Azevedo, J. (2020), Learning Poverty in the Time of COVID-19 : A Crisis within a Crisis, World Bank, Washington, DC, https://openknowledge.worldbank.org/handle/10986/34850 (accessed on 26 January 2021).

[60] Badie, B. (2020), Inter-socialités - Le monde n’est plus géopolitique, CNRS Éditions, Paris.

[84] Banga, R. (2013), Measuring Value in Global Value Chains, UNCTAD, Geneva, https://unctad.org/system/files/official-document/ecidc2013misc1_bp8.pdf.

[105] Barnosky, A. et al. (2011), “Has the Earth’s sixth mass extinction already arrived?”, Nature, Vol. 471/7336, pp. 51-57, http://dx.doi.org/10.1038/nature09678.

[79] Beal, B. and M. Astakhova (2017), “Management and Income Inequality: A Review and Conceptual Framework”, Journal of Business Ethics, Vol. 142, pp. 1-23, https://ideas.repec.org/a/kap/jbuset/v142y2017i1d10.1007_s10551-015-2762-6.html.

[58] Brezzi, M. and M. Diaz Ramirez (2016), “Building subjective well-being indicators at the subnational level: A preliminary assessment in OECD regions”, OECD Regional Development Working Papers, No. 2016/03, OECD Publishing, Paris, https://doi.org/10.1787/5jm2hhcjftvh-en (accessed on 26 April 2021).

[95] Castells, M. (2004), The Network Society: A Cross-Cultural Perspective, Edward Elgar Publishing, Cheltenham, United Kingdom, http://www.e-elgar.com/shop/gbp/the-network-society-9781843765059.html (accessed on 24 January 2021).

[14] Credit Suisse Research Institute (2019), Global wealth databook 2019, Credit Suisse Research Insitute, Zürich, http://www.credit-suisse.com/media/assets/corporate/docs/about-us/research/publications/global-wealth-databook-2019.pdf (accessed on 21 January 2021).

[34] Credit Suisse Research Institute (2019), Global Wealth Report 2019, Credit Suisse Research Institute, Zürich, http://www.credit-suisse.com/about-us/en/reports-research/global-wealth-report.html.

[104] Crouch, C. (2020), Social Europe - A Manifesto, Social Europe, Berlin, http://www.socialeurope.eu/book/social-europe-a-manifesto (accessed on 25 January 2021).

[28] Dang, H. and E. Ianchovichina (2016), Middle-class dynamics and the Arab Spring, The Brookings Institution, Washington, DC, http://www.brookings.edu/blog/future-development/2016/03/18/middle-class-dynamics-and-the-arab-spring/ (accessed on 21 January 2021).

[109] Dang, H. and U. Serajuddin (2020), “Tracking the sustainable development goals: Emerging measurement challenges and further reflections”, World Development, Vol. 127, https://ideas.repec.org/a/eee/wdevel/v127y2020ics0305750x19301445.html.

[63] Dao, M. et al. (2017), “Why Is Labor Receiving a Smaller Share of Global Income? Theory and Empirical Evidence”, IMF Working Paper, No. 17/169, International Monetary Fund, Washington, DC, http://www.imf.org/en/Publications/WP/Issues/2017/07/24/Why-Is-Labor-Receiving-a-Smaller-Share-of-Global-Income-Theory-and-Empirical-Evidence-45102 (accessed on 22 January 2021).

[126] Early, R. et al. (2016), “Global threats from invasive alien species in the twenty-first century and national response capacities”, Nature Communications, Vol. 7/1, pp. 1-9, http://dx.doi.org/10.1038/ncomms12485.

[30] ECLAC (2021), Social Panorama of Latin America 2020, United Nations, Santiago, http://www.cepal.org/en/publicaciones/ps (accessed on 9 March 2021).

[20] Edward, P. and A. Sumner (2017), “Global Inequality and Global Poverty Since the Cold War: How robust is the optimistic narrative?”, in Van Bergeijk, P. and R. Van Der Hoeven (eds.), Sustainable Development Goals and Income Inequality, Edward Elgar Publishing, Cheltenham, United Kingdom, https://eprint.ncl.ac.uk/file_store/production/232747/A86FE330-0644-4B43-97CE-1F1982E4388C.pdf (accessed on 21 January 2021).

[12] Fajgelbaum, P. and A. Khandelwal (2014), “Measuring the Unequal Gains from Trade”, NBER Working Paper, No. 20331, http://www.nber.org/papers/w20331 (accessed on 21 January 2021).

[59] FAO (2020), FAOSTAT, Food and Agriculture Organisation of the United Nations, Rome, http://www.fao.org/faostat/en/#data/FS/visualize (accessed on 26 January 2021).

[10] FAO et al. (2020), The State of Food Security and Nutrition in the World 2020: Transforming food systems for affordable healthy diets, Food and Agriculture Organization of the United Nations, Rome, http://dx.doi.org/10.4060/ca9692en.

[125] FAO and UNEP (2020), The State of the World’s Forests 2020. Forests, biodiversity and people, Food and Agriculture Organisation of the United Nations and United Nations Environment Programme, Rome, http://dx.doi.org/10.4060/ca8642en.

[103] Fasani, F. and J. Mazza (2020), Immigrant Key Workers: Their Contribution to Europe’s COVID-19 Response, European Commission, Brussels, https://knowledge4policy.ec.europa.eu/publication/immigrant-key-workers-their-contribution-europes-covid-19-response_en (accessed on 25 January 2021).

[112] Ferreira, S. et al. (2013), “Life satisfaction and air quality in Europe”, Ecological Economics, Vol. 88, pp. 1-10, http://dx.doi.org/10.1016/j.ecolecon.2009.08.011.

[97] Florida, R. (2002), The rise of the creative class: and how it’s transforming work, leisure, community and everyday life, Basic Books, New York, http://www.basicbooks.com/titles/richard-florida/the-rise-of-the-creative-class/9781541617735/ (accessed on 25 January 2021).

[70] Frey, C. and M. Osborne (2017), “The future of employment: How susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114, pp. 254-280, http://dx.doi.org/10.1016/j.techfore.2016.08.019.

[35] Gallup (2020), Gallup Analytics, Gallup, Inc., Washington, DC, http://www.gallup.com/analytics/213617/gallup-analytics.aspx (accessed on 22 January 2021).

[21] Gentilini, U. et al. (2021), Social Protection and jobs responses to COVID-19 : a real-time review of country measures (14 May 2021 update), World Bank, Washington, DC., https://openknowledge.worldbank.org/handle/10986/33635.

[99] Giraud, P. (2015), L’homme inutile. Du bon usage de l’économie, Odile Jacob Publishing, Paris, https://en.odilejacob.fr/catalogue/human-sciences/economics-and-finance/redundant-people_9782738133113.php (accessed on 25 January 2021).

[108] Global Footprint Network (2017), National Footprint Accounts: 2017 Public Data Package, Global Footprint Network.

[123] Harvey, F. (2021), “Wealthy nations ‘failing to help developing world tackle climate crisis’”, The Gaurdian, https://www.theguardian.com/environment/2021/apr/24/wealthy-nations-failing-to-help-developing-world-tackle-climate-crisis (accessed on 26 April 2021).

[114] Helliwell, J. (ed.) (2020), The Environment and Happiness: How Our Environment Shapes How We Feel and Evaluate Our Lives, Sustainable Development Solutions Network, New York, https://worldhappiness.report/ed/2020/how-environmental-quality-affects-our-happiness/.

[48] Huang, B. and K. Chen (2012), “Are intergovernmental transfers in China equalizing?”, China Economic Review, Vol. 23/3, pp. 534-551, https://doi.org/10.1016/j.chieco.2012.01.001.

[45] IBRD/IFC/MIGA (2019), Country Partnership Framework for the People’s Republic of China FY2020-2025, International Bank for Reconstruction and Development/International Finance Corporation/Multilateral Investment Guarantee Agency, Washington, DC.

[86] ICLS (2003), Guidelines concerning a statistical definition of informal employment, ILO Seventeenth International Conference of Labour Statisticians, Geneva, http://ilo.org/global/statistics-and-databases/standards-and-guidelines/guidelines-adopted-by-international-conferences-of-labour-statisticians/WCMS_087622/lang--en/index.htm.

[87] ICLS (1993), Resolution concerning the measurement of employment in the informal sector, ILO Fifteenth International Conference of Labour Statisticians, Geneva, http://ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087484/lang--en/index.htm.

[8] ILO (2021), ILO Monitor: COVID-19 and the world of work. 7th edition, ILO, Geneva, http://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_767028/lang--en/index.htm (accessed on 26 January 2021).

[9] ILO (2021), World employment and social outlook: trends 2021, International Labour Office, Geneva, https://www.ilo.org/global/research/global-reports/weso/trends2021/lang--en/index.htm.

[77] ILO (2020), ILOSTAT, International Labour Organization, Geneva, https://ilostat.ilo.org/ (accessed on 27 January 2021).

[89] ILO (2019), World Employment Social Outlook: Trends 2019, International Labour Organization, Geneva, http://www.ilo.org/global/research/global-reports/weso/2019/WCMS_670542/lang--en/index.htm.

[80] ILO (2018), Global Wage Report 2018/19: What lies behind gender pay gaps, International Labour Organization, Geneva, http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_650553.pdf.

[88] ILO (2018), Women and Men in the Informal Economy: A Statistical Picture. Third Edition, International Labour Organization, Geneva, http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_626831.pdf.

[82] ILO (2017), World Employment and Social Outlook 2017. Sustainable enterprises and jobs: Formal enterprises and decent work, International Labour Organization, Geneva, http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_579893.pdf.

[92] ILO/UNICEF (2021), Child labour: global estimates 2020, trends and the road forward, International Labour Office and United Nations Children’s Fund, New York, https://www.ilo.org/ipec/Informationresources/WCMS_797515/lang--en/index.htm.

[5] IMF (2021), Fiscal Monitor: A fair shot, International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/FM/Issues/2021/03/29/fiscal-monitor-april-2021.

[4] IMF (2021), World Economic Outlook: Managing Divergent Recoveries, International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021.

[124] IPBES (2019), Global Assessment Report on Biodiversity and Ecosystem Services, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services secretariat, Bonn, Germany, https://ipbes.net/global-assessment (accessed on 25 January 2021).

[49] Jain-Chandra, S. et al. (2018), “Inequality in China – Trends, Drivers and Policy Remedies”, IMF Working Paper, WP/18/127, http://www.imf.org/~/media/Files/Publications/WP/2018/wp18127.ashx.

[47] Kanbur, R. and X. Zhang (2005), “Fifty Years of Regional Inequality in China: a Journey Through Central Planning, Reform, and Openness”, Review of Development Economics, Vol. 9/1, pp. 87-106, https://doi.org/10.1111/j.1467-9361.2005.00265.x.

[81] Keeley, B. (2015), Income Inequality: The Gap between Rich and Poor, OECD Insights, OECD, https://doi.org/10.1787/9789264246010-en.

[120] Kenner, D. (2019), Carbon inequality: the role of the richest in climate change, Routledge, https://www.routledge.com/Carbon-Inequality-The-Role-of-the-Richest-in-Climate-Change/Kenner/p/book/9780367727666.

[24] Kharas, H. and K. Hamel (2018), A global tipping point: Half the world is now middle class or wealthier, The Brookings Institution, Washington, DC, http://www.brookings.edu/blog/future-development/2018/09/27/a-global-tipping-point-half-the-world-is-now-middle-class-or-wealthier/ (accessed on 21 January 2021).

[3] Kose, M. and F. Ohnsorge (2019), A Decade After the Global Recession, World Bank Group, Washington, DC, http://dx.doi.org/10.1596/978-1-4648-1527-0.

[7] Lakner, C. et al. (2021), Updated estimates of the impact of COVID-19 on global poverty: Looking back at 2020 and the outlook for 2021, World Bank, Washington, DC, https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty-looking-back-2020-and-outlook-2021 (accessed on 26 January 2021).

[111] Lawrance, E. et al. (2021), “The impact of climate change on mental health and emotional wellbeing: current evidence and implications for policy and practice”, Grantham Institute briefing paper no 36.

[102] Lorenceau, A., J. Rim and T. Savitki (2021), “Youth aspirations and the reality of jobs in Africa”, OECD Development Policy Papers, Vol. 38, https://doi.org/10.1787/2d089001-en.

[115] Maddison, D. and K. Rehdanz (2011), “The impact of climate on life satisfaction”, Ecological Economics, Vol. 70/12, pp. 2437-2445, https://doi.org/10.1016/j.ecolecon.2011.07.027.

[93] Markham, E. (1899), The man with the hoe and other poems, Doubleday & McClure, New York, http://www.loc.gov/item/74234262/.

[122] Meng, J. et al. (2018), “The rise of South-South trade and its effect on global CO2 emissions”, Nature Communications, Vol. 9/1, pp. 1-7, http://dx.doi.org/10.1038/s41467-018-04337-y.

[32] Milanovic, B. (2016), Global Inequality: A New Approach for the Age of Globalization, Harvard University Press,Cambridge, MA, http://www.jstor.org/stable/j.ctvjghwk4 (accessed on 21 January 2021).

[52] MOF (2020), Central government budget, Ministry of Finance of China, Beijing, http://www.mof.gov.cn/zyyjsgkpt/zyzfyjs/zyys/.

[51] MOF (2019), Central to local transfer payment budget in 2019, Ministry of Finance of China, Beijing, http://yss.mof.gov.cn/2019zyczys/201904/t20190402_3210773.htm.

[50] MOF (2010), Tax rebate and transfer system, Ministry of Finance of China, Beijing, http://www.mof.gov.cn/zhuantihuigu/czjbqk/cztz/201011/t20101101_345458.html.

[46] NBSC (2020), Statistical Data (database), National Bureau of Statistics of China, Beijing, http://www.stats.gov.cn/.

[42] NBSC (2017), 2003-2016 national residents’ disposable personal income Gini coefficients, National Bureau of Statistics of China, Beijing, http://www.stats.gov.cn/ztjc/zdtjgz/yblh/zysj/201710/t20171010_1540710.html.

[71] Nedelkoska, L. and G. Quintin (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris, http://dx.doi.org/10.1787/1815199X.

[6] OECD (2021), OECD Economic Outlook, Interim Report March 2021, OECD Publishing, Paris, https://doi.org/10.1787/34bfd999-en.

[53] OECD (2020), How’s Life? 2020: Measuring Well-being, OECD Publishing, Paris, https://dx.doi.org/10.1787/9870c393-en.

[65] OECD (2020), OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://dx.doi.org/10.1787/1686c758-en.

[36] OECD (2020), OECD Regions and Cities at a Glance 2020, OECD Publishing, Paris, https://dx.doi.org/10.1787/959d5ba0-en.

[39] OECD (2020), Rural Well-being: Geography of Opportunities, OECD Rural Studies, OECD Publishing, Paris, https://dx.doi.org/10.1787/d25cef80-en.

[73] OECD (2019), An Introduction to Online Platforms and Their Role in the Digital Transformation, OECD Publishing, Paris, https://dx.doi.org/10.1787/53e5f593-en.

[43] OECD (2019), OECD Economic Surveys: China 2019, OECD Publishing, Paris, https://dx.doi.org/10.1787/eco_surveys-chn-2019-en.

[91] OECD (2019), OECD Employment Outlook 2019: The Future of Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9ee00155-en.

[38] OECD (2019), OECD Regional Outlook 2019: Leveraging Megatrends for Cities and Rural Areas, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264312838-en.

[54] OECD (2019), Perspectives on Global Development 2019: Rethinking Development Strategies, OECD Publishing, Paris, https://dx.doi.org/10.1787/persp_glob_dev-2019-en.

[40] OECD (2019), SIGI 2019 Global report : transforming challenges into opportunities, Social Institutions and Gender Index, OECD Publishing, Paris, https://doi.org/10.1787/bc56d212-en.

[31] OECD (2019), Under Pressure: The Squeezed Middle Class, OECD Publishing, Paris, https://dx.doi.org/10.1787/689afed1-en.

[78] OECD (2018), OECD Economic Outlook, Volume 2018 Issue 2, OECD Publishing, Paris, https://dx.doi.org/10.1787/eco_outlook-v2018-2-en.

[85] OECD (2018), The Future of Social Protection: What Works for Non-standard Workers?, OECD Publishing, Paris, https://doi.org/10.1787/9789264306943-e (accessed on 24 January 2021).

[76] OECD (2018), The Productivity-Inclusiveness Nexus, OECD Publishing, Paris, https://doi.org/10.1787/9789264292932-en (accessed on 23 January 2021).

[72] OECD (2017), Future of Work and Skills, OECD Publishing, Paris, http://www.oecd.org/els/emp/wcms_556984.pdf (accessed on 23 January 2021).

[2] OECD (2016), OECD Economic Outlook, Volume 2016 Issue 2, OECD Publishing, Paris, https://dx.doi.org/10.1787/eco_outlook-v2016-2-en.

[37] OECD (2016), OECD Regional Outlook 2016: Productive Regions for Inclusive Societies, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264260245-en.

[83] OECD (2015), The Future of Productivity, OECD Publishing, Paris, https://doi.org/10.1787/9789264248533-en.

[15] OECD (2010), Perspectives on Global Development 2010: Shifting Wealth, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264084728-en.

[56] OECD/AUC/ATAF (2020), Revenue Statistics in Africa 2020: 1990-2018, OECD Publishing, Paris, https://doi.org/10.1787/14e1edb1-en-fr (accessed on 21 January 2021).

[26] OECD/CAF/ECLAC/EU (2019), Latin American Economic Outlook 2019: Development in Transition, OECD Publishing, Paris, https://doi.org/10.1787/g2g9ff18-en.

[90] OECD/ILO (2019), Tackling Vulnerability in the Informal Economy, OECD Publishing, Paris, https://doi.org/10.1787/939b7bcd-en.

[33] Our World in Data (2020), Our World in Data, https://ourworldindata.org/ (accessed on 22 January 2021).

[17] Piketty, T., E. Saez and F. Alvaredo (2018), World Inequality Report 2018, World Inequality Lab. Paris, https://wir2018.wid.world/.

[19] Ravallion, M. (2019), “On Measuring Global Poverty”, NBER Working Papers, No. 26211, https://examplewordpresscom61323.files.wordpress.com/2019/09/measuring-global-poverty-w26211.pdf (accessed on 21 January 2021).

[29] Ravallion, M. (2010), “The Developing World’s Bulging (but Vulnerable) Middle Class”, World Development, Vol. 38/4, pp. 445-454, http://dx.doi.org/10.1016/j.worlddev.2009.11.007.

[96] Reich, R. (1992), The Work of Nations: Preparing Ourselves for 21st-Century Capitalism, Knopf Publishing, New York, http://www.penguinrandomhouse.com/books/140056/the-work-of-nations-by-robert-b-reich/ (accessed on 24 January 2021).

[94] Saez, E. and G. Zucman (2020), “The Rise of Income and Wealth Inequality in America: Evidence from Distributional Macroeconomic Accounts”, NBER Working Paper, No. 27922, National Bureau of Economic Research, Cambridge, MA, http://dx.doi.org/10.3386/w27922.

[100] Sassen, S. (1991), The Global City, Princeton University Press, Princeton, NJ, https://press.princeton.edu/books/paperback/9780691070636/the-global-city.

[44] Sicular, T. (2013), “The Challenge of High Inequality in China”, Inequality in Focus, Vol. 2/2, http://www.worldbank.org/content/dam/Worldbank/document/Poverty%20documents/Inequality-In-Focus-0813.pdf.

[101] Standing, G. (2018), The Precariat: Today’s Transformative Class?, Great Transition Initiative, https://greattransition.org/publication/precariat-transformative-class (accessed on 26 April 2021).

[98] Standing, G. (2011), The Precariat: The New Dangerous Class, Bloomsbury Academic, London, http://www.bloomsbury.com/uk/the-precariat-9781849664561/ (accessed on 25 January 2021).

[66] Tella, R. and D. Rodrik (2019), “Labor Market Shocks and the Demand for Trade Protection: Evidence from Online Surveys”, NBER Working Papers, No. 25705, National Bureau of Economic Research, Cambridge, MA, http://dx.doi.org/10.3386/w25705.

[127] UN Environment (2019), Global Environment Outlook – GEO-6: Healthy Planet, Healthy People, Cambridge University Press, Cambridge, United Kingdom, https://doi.org/10.1017/9781108627146.001.

[55] UNDP (2020), Human Development Report 2020 – The next frontier: Human development and the Anthropocene, United Nations Development Programme, New York, http://hdr.undp.org/sites/default/files/hdr2020.pdf (accessed on 9 March 2021).

[61] UNEP (2021), Food Waste Index Report 2021, United Nations Environment Programme, Nairobi, http://www.unep.org/resources/report/unep-food-waste-index-report-2021 (accessed on 9 March 2021).

[121] UNEP (2020), Emissions Gap Report 2020, United Nations Environment Programme, Nairobi, https://www.unep.org/emissions-gap-report-2020.

[75] Unger, R. (2019), “Rethinking the Knowledge Economy”, American Affairs Journal, Vol. III/2, https://americanaffairsjournal.org/2019/05/rethinking-the-knowledge-economy/ (accessed on 23 January 2021).

[62] United Nations (2020), The Sustainable Development Goals Report 2020, United Nations, New York, https://unstats.un.org/sdgs/report/2020/ (accessed on 27 January 2021).

[57] Veneri, P. and F. Murtin (2016), “Where is inclusive growth happening? Mapping multi-dimensional living standards in OECD regions”, OECD Statistics Working Papers, No. 2016/1, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jm3nptzwsxq-en.

[117] Vicedo-Cabrera, A. et al. (2021), “The burden of heat-related mortality attributable to recent human-induced climate change”, Nature Climate Change 2021, pp. 1-9, http://dx.doi.org/10.1038/s41558-021-01058-x.

[1] Voltaire (1759), Candide, ou l’optimisme.

[113] Welsch, H. (2007), “Environmental welfare analysis: A life satisfaction approach”, Ecological Economics, Vol. 62/3-4, pp. 544-551, https://doi.org/10.1016/j.ecolecon.2006.07.017.

[118] WMO (2020), 2020 State of Climate Services, World Meteorological Organization, Geneva, https://library.wmo.int/index.php?lvl=notice_display&id=21777#.YA72CehKjIW (accessed on 25 January 2021).

[116] WMO (2020), Provisional Report on the State of the Global Climate 2020, World Meteorological Organization, Geneva, https://library.wmo.int/index.php?lvl=notice_display&id=21804#.YA70VehKjIU (accessed on 25 January 2021).

[119] WMO (2020), United in Science 2020, World Meteorological Organization, Geneva, https://public.wmo.int/en/resources/united_in_science (accessed on 25 January 2021).

[22] World Bank (2020), ASPIRE: The Atlas of Social Protection Indicators of Resilience and Equity, World Bank Group, Washington, DC, http://www.worldbank.org/en/data/datatopics/aspire (accessed on 21 January 2021).

[16] World Bank (2020), Middle Income Countries Overview, World Bank Group, Washington, DC, http://www.worldbank.org/en/country/mic/overview (accessed on 21 January 2021).

[13] World Bank (2020), World Development Indicators (database), World Bank Group, Washington, DC, https://databank.worldbank.org/source/world-development-indicators (accessed on 21 January 2021).

[25] World Bank (2018), Riding the Wave: An East Asian Miracle for the 21st Century, World Bank, Washington, DC, http://dx.doi.org/10.1596/978-1-4648-1145-6.

[23] World Bank (2018), The State of Social Safety Nets 2018, World Bank Group, Washington, DC, http://dx.doi.org/10.1596/978-1-4648-1254-5.

[41] World Bank (2016), Poverty and Shared Prosperity 2016: Taking on Inequality, World Bank Group, Washington, DC, http://dx.doi.org/10.1596/978-1-4648-0958-3.

[69] World Economic Forum (2016), The Fourth Industrial Revolution: what it means and how to respond, World Economic Forum, Cologny, Switzerland, http://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ (accessed on 22 January 2021).

[110] Wu, Y., Q. Zhu and B. Zhu (2018), “Decoupling analysis of world economic growth and CO2 emissions: A study comparing developed and developing countries”, Journal of Cleaner Production, pp. 94-103, https://doi.org/10.1016/j.jclepro.2018.04.139.

Note

← 1. The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus, a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality (World Bank, 2020[13]).

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