Andy Sumner
King’s College London
Arief Anshory Yusuf
Padjadjaran University, Indonesia
Andy Sumner
King’s College London
Arief Anshory Yusuf
Padjadjaran University, Indonesia
Ending global poverty and reducing inequalities are interlinked and urgently needed in the wake of the pandemic. Climate change and the rising costs of debt servicing in developing countries threaten to exacerbate poverty and hinder efforts to reduce inequalities. Drawing on a wide range of recent data and evidence, this chapter discusses the relevance of current poverty definitions. It reviews the complex relationship between changing levels of inequality, poverty and economic growth as well as the implications for development co-operation and national policy making. It underscores what it will take to end global poverty and reduce inequalities and the urgent necessity to do so. It concludes that the annual cost of ending poverty is not prohibitive but the price of failing to end poverty could be disastrous to billions of people.
The authors are grateful to Ida Mc Donnell, Eleanor Carey, Danielle Mallon and Harsh Desai for comments and inputs.
It is urgent to end poverty and reduce inequalities now before doing so becomes harder and more costly in the face of climate-induced impacts of extreme weather, shifting agriculture patterns, rising sea levels, and potentially mass migration between and within countries.
Ending poverty and reducing inequalities will not be easy. Global poverty is split between fragile and conflict-affected situations (FCAS) where progress is difficult and non-FCAS countries and between countries where official development assistance (ODA) really matters and those where it does not.
Economic growth and rising incomes are important, but the power of growth to reduce poverty is stronger when income inequality is low or falling; and growth may bypass the poorest sometimes, especially when income inequality is high.
Livelihood strategies, or how income is generated, are shaped by key human, social, natural, physical and financial capital assets, the income derived from these assets, and the provision and quality of public goods.
Effective national policies to end poverty and reduce inequalities include social assistance such as cash transfers, social insurance such as health insurance, policies that expand education opportunities, and active labour market policies.
OECD countries should differentiate to a greater extent their partnerships between countries where ODA really matters and those where ODA is less important. In countries where ODA really matters for public service delivery, more ODA and debt relief are key. In countries where ODA is less important as share of public finance, policy coherence is essential in, for example, OECD countries’ policies on trade, technical assistance, and co-financing global and regional public goods.
Ending poverty and reducing inequalities matter for any number of well-known reasons. Some are rooted in values related to meritocracy, fairness and life chances. Others stress that ending poverty and addressing inequalities are essential for sustainable development. In the absence of countervailing forces or policies, for instance, high- or rising income inequality can potentially negatively impact long-run economic growth, health and education outcomes, and even the quality of governance – in terms of institutional quality, corruption, political participation, democracy and support for democracy. Of course, none of those effects are inevitable. They are contingent on various factors. Most important, public policy can intervene to ensure they are neutralised.
Ending poverty is closely related to inequalities. It is likely to be faster with stable or falling income inequality. Evidence shows that reducing poverty depends not only on average income growth but on changes in income inequality and the initial level of inequality (Ferreira and Ravallion, 2011[1]; Ravallion, 1997[2]). In short, the higher the initial level of income inequality, the higher the rate of growth that will be needed to achieve poverty reduction. Indeed, a decline of just 1% a year in the Gini index for each country could reduce the global extreme poverty headcount by almost 90 million people by 2030 (Lakner et al., 2022[3]).
Indeed, a decline of just 1% a year in the Gini index for each country could reduce the global extreme poverty headcount by almost 90 million people by 2030 (Lakner et al., 2022[3]).
Ending poverty and reducing inequalities are urgent intertwined tasks. The next decade and beyond are likely to be shaped by long-run stressors as well as sudden, acute shocks related to climate change and other major global factors. Cost-wise, it would be cheaper and easier to end poverty and reduce inequalities now rather than later amid climate stressors and shocks.1 Doing so is also sensible preparation for a hotter climate. In short, it is crucial to end absolute poverty and reduce inequalities before climate change makes reducing poverty and addressing inequalities harder due to more frequent extreme weather, shifting agriculture patterns, changing sea levels, and potentially mass migration between and within countries. In fact, ending poverty and reducing inequalities may increase climate resilience (see Chapter 4) and relieve, to some extent at least, some of the pressure on people to migrate in the future.
Cost-wise, it would be cheaper and easier to end poverty and reduce inequalities now rather than later amid climate stressors and shocks.
To better understand the urgency, it is important to consider how all people – those in poverty as well as those that are not – make a livelihood for themselves and their family or household. One’s livelihood strategy, or how income is generated, is shaped by one’s assets, the income derived from these assets, and the provision and quality of public goods.2 There are five types of assets that households can hold, commonly identified as human, social, natural, physical and financial capital. A set of contextual as well as governance factors mediate the value of the assets, their accumulation (or deterioration) and income flows from them. These factors include the state and management of the macroeconomy (and thus employment opportunities), exposure to stressors and shocks, exposure to discrimination, the distribution of rights to public resources, and the set of public policies in place to support improved living standards and insure collectively against risk.3 Governments can support the accumulation of people’s assets in numerous ways, redistribute assets, and put in place insurance mechanisms against stressors and shocks.
The underlying cause of a person living in extreme or absolute poverty is simple: having insufficient assets necessary to generate a reasonable income, the unequal distribution of those assets across society, and governance failures leading to uninsured exposure to stressors and shocks (including conflict) and/or macroeconomic instability. People seek to generate or build assets to build a livelihood strategy that will lead to better outcomes. For those living near or in poverty in a rural setting, for example, where most of the extreme poor live, this may mean agricultural intensification and/or extensification, livelihood diversification, or rural-to-urban migration, for example. Public policy can help support these endeavours.
The underlying cause of a person living in extreme or absolute poverty is simple: having insufficient assets necessary to generate a reasonable income, the unequal distribution of those assets across society, and governance failures leading to uninsured exposure to stressors and shocks (including conflict) and/or macroeconomic instability.
Understanding poverty through this “assets” lens highlights the urgency to reduce both poverty and inequalities in the face of climate change. A hotter climate is likely to entail more frequent and more substantial stressors and shocks. Shocks, in turn, will erode existing assets and make asset accumulation harder since they increase exposure to harm while the decrease in assets diminishes capacities to cope over time. Stressors and shocks also contribute to people falling (back) into poverty.
The answer is asset building at the household and at a societal level through public policy. An enabling environment for asset building determines if someone escapes poverty. In their empirical survey based on longitudinal data sets from low-income countries (LICs) and middle-income countries (MICs), Dercon and Shapiro (2007[4]) identified a lack of asset accumulation as the leading cause of remaining in poverty alongside contextual and governance factors such as social exclusion, discrimination, or being in remote or otherwise disadvantaged areas. On the other hand, according to the survey, falling into poverty is linked to stressors and shocks such as illness and health-related expenses, social and customary expenses (e.g. the costs of marriages and funerals), high-interest private loans, crop disease, and drought and irrigation failure. To deal even with temporary shocks, people may turn to selling assets, a strategy that may result in greater insecurity in the longer term. The COVID-19 pandemic is a perfect example of asset erosion – through distressed sales, missed education and reduced nutrition. Risk-averse strategies become more important under conditions of compound, repeat and complex stressors and shocks that will be more likely with climate change.
There is no doubt that climate change will increase the frequency and severity of stressors and shocks, notably extreme weather incidents, rising sea levels, and changes in agricultural patterns and especially agricultural productivity. LICs and MICs will have greater exposure, first because these stressors and shocks will affect a higher share of their population and second because government revenues are more limited, governance less responsive and state capacity may be weaker (than in OECD countries which will face the same kind of stressors and shocks but are better placed to cope). Further, exposure to these stressors and shocks will have a substantial impact among those already living in or near poverty.
Dang, Hallegatte and Trinh’s (2024[5]) detailed review of empirical studies on the impact of climate change on poverty and inequality is highly instructive. First, rising temperatures are empirically associated with increased poverty and inequality, with greater vulnerability in poorer countries and sub-Saharan Africa. High temperatures are also associated with more premature births, low birth weights, lower school attendance and poorer student performance. Second, variations in rainfall are associated with a higher likelihood of conflict and migration exacerbating poverty and inequality, and further shifts in agricultural patterns can lead to food prices and food insecurity, making matters worse. Cross-country empirical studies of the impact of climate change on inequality tend to find that rising temperatures lead to rising income inequality in the short term and often in the long run. The findings of single-country studies related to inequality and climate change impact are mixed. Some single-country country studies find rising inequality due to climate change. Other single-country studies do not.
What magnitude of exposure is possible? Drawing on estimates in Adom (2024[6]); Wiebe, Robinson and Cattaneo (2019[7]); CIESIN and CIDR (2023[8]); and the Centre for Research on the Epidemiology of Disasters (2024[9]), climate impacts may include the following:
An estimated 60 million people in Africa and 500 million in Asia are at risk of sea level rises due to living in low elevation coastal areas.
About 1.2 billion people in Africa and 4.5 billion in Asia are exposed to extreme weather due to climate change.
In Africa, agriculture productivity losses are forecast to be in the range of 10-20% by 2050 (relative to 2008) and the number of people at risk of hunger in sub-Saharan Africa could be close to 200 million. In Asia, productivity losses in agriculture are estimated at 2-12% by 2050, and in Asia and the Pacific the numbers of people at risk of hunger could also be over 200 million.
In sum, the likely impacts of climate change mean there is a real urgency to end poverty and address inequalities. However, ambition will need to go beyond ending extreme poverty because USD 2.15 a day is a very low level of income. This income measure does not necessarily mean someone can afford good nutrition, schooling or healthcare. Ending absolute poverty is more ambitious but achieving this means putting in place policies that ensure people don’t fall back into absolute poverty. Climate change is likely to heighten the risk of falling back into poverty. Thus, ambitions need to be higher before the consequences of climate change become more evident.
The end of absolute poverty means that no one is living without something that is essential for a minimum standard of living. Basic needs include sufficient monetary income (SDG 1), adequate nutrition (SDG 2), access to healthcare (SDG 3), quality education (SDG 4), and access to safe drinking water and sanitation (SDG 6). This list is in line with other global attempts to define poverty in the major United Nations (UN) summits of the 1990s, such as the Social Development Summit in 1995, which ultimately influenced the Millennium Development Goals for 2015 and their successors, the SDGs for 2030, which all countries negotiated together and signed up to achieve.
...averages and numbers alone do not tell the full story of poverty.
It is important to remember that averages and numbers alone do not tell the full story of poverty as people live it and that there are limits to what the data capture. While a multidimensional conceptualisation of poverty is built into the SDGs, a participatory process across 60 countries revealed additional or hidden dimensions. Narayan et al. (2000, pp. 4-5[10]) found that people living in poverty cited not only the objective dimensions of poverty – the experience of hunger and ill health – but also the subjective dimensions, or how it feels to live in poverty, that manifest in a sense of shame, powerlessness and humiliation.
When estimating poverty levels and trends, attention often turns to monetary poverty first. Monetary poverty lines at the national level are usually determined based on the amount of monetary expenditure necessary to afford approximately 2 100 calories daily, as advised by the World Health Organization, plus essential non-food items. Globally, and to compare across countries, the World Bank uses three thresholds to measure monetary poverty: USD 2.15, USD 3.65 and USD 6.85 a day per person in 2017 US dollars purchasing power parity (PPP). These three thresholds are the median of harmonised national poverty lines in LICs, lower middle-income countries (LMICs) and upper middle-income countries, respectively. Another method to measure poverty is the global Multidimensional Poverty Index (MPI), developed by the United Nations Development Programme (UNDP), and the Oxford Poverty and Human Development Initiative (OPHI), which is a composite of ten indicators of education, health, nutrition and assets.
For the purposes of this discussion, each poverty measure is labelled as follows:
Extreme poverty refers to those living with less than USD 2.15 per day per person.
Absolute poverty refers to those living with less than USD 3.65 per day per person (referred to as moderate poverty by the World Bank) and includes those living in extreme monetary poverty.
Multidimensional poverty encompasses those living without basic education, or in health poverty or without basic assets or undernourished (and a household is poor if someone is deprived in more than one-third of the weighted indicators).
The USD 2.15 a day line – extreme poverty – is used in the SDGs, and ending global poverty is unfortunately often equated with ending extreme poverty at this line.4 Here is the rub, though: most of the world’s extreme poor do not live in LICs but in LMICs, where the average national poverty line is higher (the USD 3.65 line). So, the more relevant poverty threshold to where most of the world’s extreme poor live (and the world’s absolute poor), is the USD 3.65 poverty line. For comparison purposes, the average poverty line in high-income countries (HICs) is about USD 25 per day, though relative poverty lines rather than absolute poverty lines are generally used in HICs.
...people living in poverty cited not only the objective dimensions of poverty – the experience of hunger and ill health – but also the subjective dimensions, or how it feels to live in poverty, that manifest in a sense of shame, powerlessness and humiliation.
A significant question is can someone live on USD 2.15 per day? Does it buy essential goods while covering health and education costs? Indeed, extreme monetary poverty might be inadequate to portray the multidimensional nature of poverty. When the number of people in poverty across LICs and MICs is considered, the correlation between monetary and multidimensional poverty headcounts are much stronger at the USD 3.65 line than at the USD 2.15 line (Sumner and Yusuf, forthcoming[11]). This, too, suggests that ending absolute poverty ought to be the ambition of public policy.
The persistence of child stunting (low height or weight for their age) further illustrates why ending extreme monetary poverty may be an insufficient target. Many countries that have successfully reduced extreme monetary poverty, and may even end extreme monetary poverty by 2030, have surprisingly high levels of child stunting. For example, in some countries with extreme poverty at or below 10% in Southeast Asia, one in four or even one in three children under 5 are stunted (Sumner and Yusuf, forthcoming[11]). Stunting in childhood has long-run impacts on physical and cognitive development and as such hinders capacities to build up assets. Stunting is caused a wide set of deprivations that culminate in the condition, notably poor maternal nutrition during pregnancy and poor infant nutrition in early years, repeated infections or repeated diarrhoea, and poor sanitation and hygiene. So, is monetary poverty the right way to measure poverty?
A significant question is can someone live on USD 2.15 per day? Does it buy essential goods while covering health and education costs?
Estimating poverty trends using monetary poverty as a measure, moreover, may not fully account for likely fluctuations in levels of poverty. Being temporarily out of monetary poverty, for instance, does not imply being free of the risk of falling back in the future. People may move in and out of poverty over time, and when leaving poverty, people do not move out of poverty in one big jump to a prosperous life. Rather, the move is slow and gradual.
Indeed, insecurity is a big problem. Millions of people globally live barely above the USD 2.15 line and barely above the USD 3.65 line and are at risk of falling (back) into poverty due to stressors and shocks, as the pandemic showed (Sumner, Ortiz-Juarez and Hoy, 2021[12]). This clustering of people around the poverty lines also underlines how hypersensitive estimates of global poverty are to the precise value of the poverty line used. In fact, each increase in the USD 2.15 poverty line of USD 0.20 increases the global poverty headcount by about 150 million additional people on average (Figure 2.1). Another way to look at this is that approximately 700 million people live below the extreme poverty line of USD 2.15 – but another billion or so people live above that line of extreme poverty but still in absolute poverty (under the USD 3.65 line).
In fact, the poverty headcount rises to as high as over 5 billion people using the highest poverty line of USD 14 per person per day (Figure 2.1). This threshold is used by the World Bank in Latin America. It is associated, in longitudinal studies, with a low probability of falling into absolute poverty in Latin America, which is measured at the upper middle-income country poverty line of USD 6.85 per day (see López-Calva and Ortiz‑Juarez (2014[13]) and Fernandez, Olivieri and Sanchez (2023[14])). In short, the risk of falling back into poverty diminishes to a low level only for people living on at least USD 14 per day.
An alternative to monetary poverty measures is to aggregate the basic non-monetary dimensions of poverty, as the UNDP and OPHI do annually to produce a measure of multidimensional poverty (Alkire et al., 2015[16]). Their MPI consists of ten indicators across 3 dimensions: 1) health (nutrition and child mortality); 2) education (years of schooling and attendance rate); and 3) standards of living (access to electricity, sanitation, potable water, housing, and cooking fuel type and household assets). Though the MPI does not consider all the dimensions of poverty identified by poor people themselves in the survey by Narayan et al. (2000[10]), it portrays dimensions of poverty beyond what is possible to portray with monetary poverty measures. Compared with the approximately 700 million people living in extreme monetary poverty across the world, for instance, over a billion people live in multidimensional poverty. This is another flag that the income level at the extreme poverty line (USD 2.15) is potentially too low to be able to buy the food necessary to be nourished, and pay the costs related to schooling and healthcare. For these reasons, USD 3.65 may be a more reasonable poverty line.
Compared with the approximately 700 million people living in extreme monetary poverty across the world, for instance, over a billion people live in multidimensional poverty.
Those in monetary and non-monetary poverty are overwhelmingly young and rural, though agriculture employment may no longer be their dominant form of work. Focusing on monetary poverty, Castañeda et al. (2018[17]) found those living in extreme poverty to be two-thirds working in agriculture, with 80% residing in rural areas and 45% children under the age of 15 years old. A somewhat different profile emerged from an analysis based on the UNDP-OPHI multidimensional poverty measure (MPI), which concurred on the youth and rural nature of poverty but found that one-third to one-half of poor households have no member employed in agriculture (Robles Aguilar and Sumner, 2020[18]).
In terms of the scale of the task, the good news is that since the 1990s, monetary poverty has been clearly declining as a share of the total global population. This trend is observed at both monetary poverty lines noted, though with an uptick during the pandemic as people fell back into poverty (Figure 2.2). In most countries, since 2010, aggregated multidimensional poverty has declined too (Figure 2.3). Further, the various poverty-related SDGs – undernutrition, stunting, under-5 mortality, maternal mortality and access to safe water – all improved over the 2000s quite rapidly (Figure 2.4).
However, the post-pandemic context of slow growth, price shocks and higher debt servicing costs have pushed the poverty-related SDGs off track. Projections by Yusuf et al. (2023[20]) show that the strong declines of the 2000s and onward have stalled (Figure 2.4). In 2030, it is projected that over 600 million people will remain in extreme monetary poverty and 665 million people will be undernourished.
Alarmingly, the number of undernourished people will be higher in 2030 than before the COVID crisis and, in fact, higher than in 2015 when the SDGs were agreed upon. While rates of stunting and infant and maternal mortality have improved dramatically, especially since 2000, it is also projected that in 2030 more than one in five children will be stunted. Additionally, although under-5 mortality and maternal mortality should continue to improve, the progress will be slow. Access to safe water could also remain a major challenge and in 2030, 800 million people could still lack access to safe water and potentially 1.7 billion people may still not have access to basic sanitation (Figure 2.4).
The setbacks could be particularly striking in sub-Saharan Africa, where extreme monetary poverty (USD 2.15) is projected to increase. The better news is that extreme poverty is projected to decline in South Asia. This pattern is also reflected in forecasts for undernutrition, which is on a rising trend in sub-Saharan Africa but a declining trend in South Asia. However, South Asia has and will have high stunting rates, with projections of more than a quarter of children experiencing stunting in 2030. Nevertheless, under-5 mortality and maternal mortality rates are projected to slowly improve. In sub-Saharan Africa, though gradually decreasing, these rates are expected to remain exceptionally high. While South Asia has lower mortality rates, slow progress is expected in the years leading to 2030.
Inequality refers to differences in income (and other things) between individuals globally or within a country. Poverty, in contrast, is understood as insufficient assets to generate reasonable income which may be caused, in part, by the unequal distribution across society of the assets needed to generate reasonable income. In other words, poverty and inequality are interlinked and mutually dependent, and changes in one impact the other.
Reducing income inequality can best be thought of as increasing the share of income accruing to the poorest parts of the population. Achieving this may only be politically possible in a context of a growing economy and rising incomes for everyone. It is important to note that income differences between individuals often intersect and overlap with differences between groups, creating complex patterns of exclusion and disadvantage by gender, ethnicity and subnational region. Further, extreme poverty in many countries is related to entrenched inequalities and thus a strategic reason why more emphasis is needed on policies to mitigate inequality.
...that income differences between individuals often intersect and overlap with differences between groups, creating complex patterns of exclusion and disadvantage by gender, ethnicity and subnational region
Global income inequality – inequality between all individuals in the world – has been falling for some time, largely driven by the fast growth of per capita income in the People’s Republic of China and other MICs (Gradín, 2024[22]; Kanbur, Ortiz-Juarez and Sumner, 2024[23]). That said, this era is likely over, and there may be a global inequality “boomerang” on the horizon. In recent years, MICs, especially China, have been moving closer to the income levels of HICs while pulling further ahead of LICs. If these patterns of economic growth continue, even at a slower pace, the inequality-increasing consequences at the global level may well blunt any inequality-reducing effects of favourable national forces (Kanbur, Ortiz-Juarez and Sumner, 2024[23]).
At the same time, the International Monetary Fund’s latest growth forecasts suggest weak growth ahead for many of the world’s poorest countries (IMF, 2024[24]), which exacerbates the pessimistic outlook for global income inequality.
Income inequality has fallen in some countries, notably in Latin America, but risen in other LICs and MICs. This trend is observed whether measuring changes in income inequality through the Gini index or in terms of the share of income accruing to the richest 10% of the population over time or the share of income held by the poorest 40%.
What do the data say? In sum (based on (UNU-WIDER, 2023[25])) thus:
1. Within-country income inequality is the highest in countries in sub-Saharan Africa.
2. Within-country income inequality has fallen from high levels in many Latin American countries, with the income shares of the poorest 40% rising and the shares of the richest 10% falling since 1985. This is due in large part to anti-poverty social assistance programmes and policies, the expansion of basic education, and labour market interventions, including rising real minimum wages (Cornia, 2012[26]; Lustig, López-Calva and Ortiz-Juarez, 2013[27]).
3. Across sub-Saharan Africa and South Asia, trends are very mixed. The data show the poorest in sub-Saharan Africa countries have the lowest shares of national income though those shares in some sub-Saharan Africa countries have improved since the mid-1980s. The richest in sub-Saharan Africa have the highest shares of national income.
The share of income accruing to the poorest 40% of the population typically ranges from 10% to 20% (population weighted) across LICs and MICs. It is particularly low in LICs in sub-Saharan Africa. The share of income of the richest 10% in these countries ranges from 25% to 45% (population weighted), with the richest holding a particularly hefty share in LICs in sub-Saharan Africa and LMICs in South Asia.
It is important to note that these data are based on the available household survey data, which may not fully capture the incomes of the richest. This is because very rich households are less likely to appear in survey data. To address this, some analyses have sought to look at tax data to estimate the share of the richest (Alvaredo et al., 2016[28]; Bourguignon, 2015[29]). Innovative methodologies to create distributional national accounts have also recently emerged. Estimates of the share of income of the top 1% suggest not only that within-country inequality is higher than surveys show but that there is heterogeneity within regions.
The World Bank has a new goal to address income inequality within countries with a Gini coefficient above 0.40. However, a Gini coefficient higher than about 0.30 – close to the average for developed countries and the OECD median of 0.33 – is already considered to hinder future economic growth.
What does this mean? In their analysis of the relationship between income inequality and economic development, Grigoli and Robles (2017[30]) show that when the Gini rises above 0.27 there is a slowdown in future economic growth. At low levels of inequality, the impact of inequality on economic growth is positive. Then at inequality levels above 0.27, the impact of inequality on economic growth switches from positive to negative. Almost all developing countries have inequality above this level. In short, it is likely inequality is already slowing economic growth. This 0.27 is an inequality “tipping point” where inequality above this level can start to hinder economic growth.
We also know that changes in income inequality tend to happen at the top and bottom of society. Palma (2011[31]) has noted that there is a surprising stability in this regard, with the “middle” that is sandwiched between the richest 10% and poorest 40% of the population always capturing about 50% of national income. This distribution varies little across countries and over time (Cobham, Schlögl and Sumner, 2016[32]). Rather, it is the shares of the richest and poorest groups that vary dramatically across countries, and thus changes in income inequality can be seen as the result of political competition between the richest and the poorest or the result of the “middle” siding with one group or the other. Moreover, the critical threshold around a Gini of about 0.30, above which income inequality becomes a drag on future economic growth, hinders future prosperity for everyone, including the rich.
Ending poverty and reducing inequalities are complex endeavours. One difficulty relates to people falling back into poverty; another relates to shifts in the location of poverty; yet another to the changing significance of ODA and the limits of relying on economic growth. Moreover, high levels of inequality can have negative impacts on social cohesion in societies (Box 2.1). In addition, the post-pandemic debt is draining national governments of resources that could be used for ending poverty and reducing inequalities while also widening the development finance gap. Indeed, there is a growing sense of zero-sum game between ODA inflows and debt servicing outflows to creditors. One way of looking at this it that debt servicing costs are diminishing – offsetting – the potential positive effects of ODA resources and this is especially the case in the poorest countries.
The minimum aspiration should be ending global monetary poverty at the USD 3.65 line and having policies in place to ensure people do not fall back below that line.
Moreover, reducing monetary poverty is not enough given the many other dimensions of poverty. Addressing undernutrition, limited education and poor health as well as a lack of access to clean water and basic sanitation matters, too – not only intrinsically but instrumentally to ensure that people do not fall back into poverty due to poor nutrition or ill health, for example.
Where the world’s poor live is changing over time. All forms of poverty are increasingly focused in sub-Saharan Africa (Figure 2.5). South Asia will still have a sizeable proportion of the world’s extreme poor. Likewise, sub-Saharan Africa and South Asia will have the largest concentration of undernutrition and stunting, though sub-Saharan Africa’s share will expand. The prospects for the reduction of both extreme monetary poverty and other forms of poverty are weakest in sub-Saharan Africa, though notable challenges will remain in South Asia and elsewhere.
Social cohesion – defined as the sense of solidarity among a community or country - and its relationship with inequality has garnered interest in policy discussion (Cox, Fiedler and Mross, 2023[35]; Fiedler, 2023[36]). Higher levels of inequality are associated with lower levels of trust in others and in government (Rothstein and Uslaner, 2005[37]), which can weaken social cohesion, particularly in large societies with different perceptions of deprivation between social groups at opposite ends of the income spectrum (Bjørnskov, 2008[38]). Cohesive societies are more resilient to external shocks and disasters (Jewett et al., 2021[39]). However, despite its importance for achieving more prosperous, inclusive and peaceful societies, the relationship between inequality and social cohesion receives limited empirical investigation, especially in low- and middle-income countries (Delhey et al., 2018[40]).
Recent research finds that efforts to build social cohesion are likely to mitigate vulnerabilities of societies, foster societal peace and enable sustainable development (Lalot et al., 2022[41]; Orazani, Reynolds and Osborne, 2023[42]; Sonnenfeld et al., 2021[43]). For example, (Lalot et al., 2022[41]; Orazani, Reynolds and Osborne, 2023[42]; Sonnenfeld et al., 2021[43]). For example having fair and transparent eligibility criteria for targeted social protection programmes and communicating them clearly could also improve the institutional dimension of social cohesion in addition to reducing poverty and inequality (Burchi et al., 2022[44]).
Research has found that, in a subsample of approximately 30 African countries, vertical inequality had a mild negative correlation with three attributes of social cohesion – trust, inclusive identity and co-operation for the common good. This was the case for trust in societies: higher levels of inequality were usually associated with lower levels of social trust and social co-operation. In general, these findings indicate there is a role for development co-operation actors to support the dual objectives of reduced inequality and improved social cohesion through well developed and context-specific inequality-reducing policies.
Source: This contribution was provided by the German Institute of Development and Sustainability
In some countries ODA is essential for the government to function reasonably and to deliver basic services. In others it is much less significant thanks to expanding domestic resources that the government has at its disposal. Furthermore, some countries are FCAS, and some are not. Thus, development co-operation needs to be tailored for four types of country contexts which should shape its priorities and approach to reducing poverty and inequalities (Table 2.1). This creates complexity for donors in the sense that the partnerships with governments will be quite different in each context.
ODA caters for only two of the four contexts – those where ODA is significant – but these countries are home to only half of the global extreme poor, almost half of the world’s multidimensional poor and just a third of the world’s absolute poor. The remainder of each type of global poverty is in countries where ODA is less significant (Sumner and Yusuf, forthcoming[45]).
Approximately 40% of global extreme monetary poverty and multidimensional poverty was concentrated in fragile/conflict-affected situations in 2022 and a third of absolute poverty – but some of these amounts are in FCAS that are low ODA dependency countries. Looking ahead, projections suggest that more of the world’s poverty – of all types – will be more concentrated in FCAS countries by 2030. The share is forecast to continue to increase and approach 60% in 2030 (Yusuf et al., 2023[20]).
So where do the world’s poor live today in terms of the four contexts? Sumner and Yusuf (forthcoming[45]) note that:
FCAS, high ODA/GNI (gross national income) countries account for a third of extreme poverty, a third of multidimensional poverty and a fifth of absolute monetary poverty
non-FCAS, low ODA/GNI countries account for a third of extreme poverty, approaching half of multidimensional poverty and over a half of absolute monetary poverty
the remainder is split between non-FCAS, high ODA/GNI countries and FCAS, low ODA/GNI countries, with the former having larger proportions of each type of poverty.
Significance of ODA relative to recipient’s GNI |
||
---|---|---|
High |
Low |
|
FCAS |
Type I High aid dependency and fragile/conflict-affected situations |
Type II Low aid dependency and fragile/conflict-affected situations |
Non-FCAS |
Type III High aid dependency and non-fragile/conflict-affected situations |
Type IV Low aid dependency and non-fragile/conflict-affected situations |
Notes: ODA: official development assistance; GNI: gross national income; FCAS: fragile and conflict-affected situations. Significance of ODA relative to recipient’s GNI = net ODA received as a percentage of GNI.
There is much that development co-operation can do and already does in places where ODA matters less. Potential avenues are policy coherence (e.g. trade policies and supporting new global tax rules); supporting more open policy processes (though this can look like political meddling); widening the evidence base in policy making by bringing evidence from other contexts and supporting national, regional and global think tanks and research institutes; technical assistance; and co-financing global and regional public goods.
Economic growth and shifts in income inequality can induce changes in monetary poverty. On average, the poverty headcount falls and the incomes of the poor rise in line with income growth (Dollar and Kraay, 2002[46]; Dollar, Kleineberg and Kraay, 2016[47]; Kraay, 2006[48]) but not always. Some recent analyses have reopened the debate around the relationship between economic growth and poverty with a focus on “immiserizing growth” episodes – episodes in which average incomes rise but the incomes of the poor or the poorest do not. According to Shaffer (2023[49]), who provides the most up-to-date estimates using 1990‑2023 PIP data, poverty does not fall and/or the incomes of the poorest do not rise in one in six of all growth episodes, defined as episodes of rising average incomes for at least five years (Table 2.2).
...reopened the debate around the relationship between economic growth and poverty with a focus on “immiserizing growth” episodes – episodes in which average incomes rise but the incomes of the poor or the poorest do not.
These anti-poor episodes of growth are particularly evident in sub-Saharan Africa, where the income of the poorest 10% of the population does not rise in nearly a quarter of the 50 growth episodes over the 1990‑2023 period. Why? The reason is that countries in sub-Saharan Africa have high levels of income inequality and the poorest in the region have low shares of income. Thus, the poorest benefit less – and sometimes not at all – from rises in average income. This could reflect, as noted by Shaffer (2023[49]), that there is a minimum level of assets required to accumulate more assets and raise incomes. Examples include minimum land or herd sizes or cash advances to buy farming inputs. Or it could be that conflict may lead to loss of assets, reduced production and insecurity for the poorest even amid overall economic growth. Another possible explanation is that stressors and shocks among the poorest such as droughts, floods and harvest failures are so substantial that they negate attempts at progress. In short, exposure to harms such as conflict, natural disasters and other stressors severely hinders asset accumulation.
Poorest 10% |
Poorest 20% |
|||||
---|---|---|---|---|---|---|
# of episodes without income rise |
Total # of episodes |
% of episodes without income rise |
# of episodes without income rise |
Total # of episodes |
% of episodes without income rise |
|
East Asia and Pacific |
4 |
23 |
17.4 |
3 |
30 |
10.0 |
Europe and Central Asia |
0 |
9 |
0.0 |
0 |
13 |
0.0 |
Latin America |
11 |
53 |
20.8 |
9 |
56 |
16.1 |
Middle East and North Africa |
1 |
7 |
14.3 |
3 |
16 |
18.8 |
South Asia |
0 |
13 |
0.0 |
0 |
14 |
0.0 |
Sub-Saharan Africa |
12 |
50 |
24.0 |
12 |
53 |
22.6 |
Total |
28 |
155 |
18.1 |
27 |
182 |
14.8 |
Note: Growth episodes are defined as episodes of rising average incomes for at least five years.
Source: Data from Shaffer (2023[49]), “Growth, poverty and immiserizing growth: Empirical evidence”, https://d.docs.live.net/3c6a1bfca63c80ba/DCR 2024/CHAPTER 1 FROM IDA/10.13140/RG.2.2.10061.20961.
One of the biggest constraints on the ability of both LICs and MICs to end poverty and address inequalities is the cost of post-pandemic debt service. Debt servicing levels have risen dramatically. The United Nations estimates that the average LIC and MIC has net interest payments of over 8% of government revenue in 2024 (UNDP, 2024[50]). In fact, in many of the poorest countries with the highest levels of extreme and absolute monetary poverty and of multidimensional poverty, net interest is at or above 5% of government revenue and surpasses 10% in a number of them (Figure 2.6 and Figure 2.7). More than half of low-income countries (LICs) are either already in debt distress or are at high risk of debt distress (IMF, 2024[24]).
These costs of debt service also represent a substantial “offsetting” of ODA resources provided to the poorest countries because of fungibility. And the burden is growing. For example, in 2022, the poorest 75 countries paid approximately USD 90 billion in external public debt service, of which interest payments accounted for approximately USD 25 billion (Albert et al., 2023[51]). Furthermore, external public debt service paid by these 75 poorest countries (which are International Development Association-eligible countries) is estimated to rise to approximately USD 125 billion per year in 2023 and 2024 (Albert et al., 2023[51]).
Diversification in creditor structure has hardened terms at which borrowing countries access debt, as new lenders tend to offer less concessional debt (Albinet and Kessler, 2022[52]). At the same time, this shift has been limited, due to the concomitant increase in multilateral development finance (Kessler, 2022[53]). At the macroeconomic level, multilateral banks have sought to alleviate funding constraints by providing additional loans (Albinet and Kessler, 2022[52]). Yet, net external flows were projected to turn negative in 2024 (Diwan and Harnoys-Vannier, 2024[54]) for low and lower middle-income countries as more money flowed out, raising questions on the efficiency of the aid system where dollars meant to reduce poverty are diverted to reimburse creditors. Development providers thus face a difficult dilemma: either play their role as counter-cyclical actors, but at the risk of mainly reimbursing other creditors, and with very little new money entering illiquid countries; or allow them to fail even though those countries do not seem insolvent (Diwan and Harnoys-Vannier, 2024[54]).
Estimates of the cost of ending poverty are indicative rather than precise given there are administrative and logistical costs to delivering any transfer system. However, the infrastructure necessary for social transfer schemes do exist in many LICs and MICs. One measure to approximate this cost is the annual monetised value of the poverty gap, which is shown in Table 2.3. This is the cost of raising those who are living below the poverty line to an income level equivalent to the poverty line.
Table 2.3 sets out new cost estimates using this measure for ending extreme monetary poverty (USD 2.15 per day) and for ending monetary poverty (USD 3.65 per day). The former is over USD 65 billion in current US dollars per year, which is 0.07% of global GNI and 0.12% of the GNI of high-income OECD countries. To end absolute monetary poverty, the estimate is inevitably a larger amount: around USD 325 billion in current US dollars per year or 0.32% of global GNI and 0.56% of the GNI of high-income OECD countries.
What would be the cost per person living in poverty? Measured at the two monetary poverty lines, the annual per-person cost is small, as it is the total cost divided by the number of people living in poverty. Thus, the cost is approximately USD 100 per year per person living in extreme poverty and approaching USD 200 per year per person living in absolute poverty. ODA-dependent FCAS countries account for the largest share (around 35%) of the cost of ending extreme monetary poverty. However, a large share of the cost of ending absolute poverty (41%) is concentrated in countries that are not FCAS and have low ODA as a share of GNI. This again points to the importance of ODA in the ODA-dependent FCAS countries and to OECD countries’ policy coherence in the non-ODA-dependent and non-FCAS countries.
In short, the financial cost of ending poverty is not prohibitive vis-à-vis current ODA levels or the share of global GNI or OECD high-income countries’ GNI. Of course, these values are only the monetised poverty gap, and administrative costs challenges are not considered. Nor is the technical challenge of reaching people, which should not be underestimated. Nonetheless, the estimates do suggest that ending global poverty would not be financially impossible in relation to global GNI or the GNI of high-income OECD countries.
Estimates of the cost of addressing specific components of multidimensional poverty are available, but some are dated and typically pre-pandemic cost estimates. Generally, comparability across estimates is limited due to the different survey years to estimate each type of poverty, differences in the average cost of ending each type of poverty within and between countries, and different underlying assumptions, making it challenging to estimate the costs (Kenny and Snyder, 2017[55]; Manuel et al., 2019[56]; Watkins and Nwajiaku-Dahou, 2024[57]).
In contrast to poverty, the direct cost of addressing inequalities, in principle, is zero. However, this does not take into account administrative costs associated with income transfers or with other policies such as expanding tax collection and enforcing minimum wage legislation. All of these policies incur costs of delivering the policy outcome. The barriers are rather political such as vested interests, what is perceived as possible, and who the winners and losers may be (see discussion in Coolin, Sapienza and Sumner (2024[58])). It is not just about the cost.
USD 2.15 poverty line |
USD 3.65 poverty line |
|
---|---|---|
Annual cost, current USD, billion |
67.28 |
324.90 |
Share of annual cost by country type: |
||
Low ODA and FCAS (%) |
14.55 |
13.91 |
Low ODA and non-FCAS (%) |
25.44 |
41.00 |
High ODA and FCAS (%) |
35.40 |
25.88 |
High ODA and non-FCAS (%) |
24.32 |
18.95 |
Annual cost per person in poverty, current USD |
97.2 |
185.2 |
% global GNI |
0.07 |
0.32 |
% OECD HIC GNI |
0.12 |
0.56 |
Notes: ODA: official development assistance; FCAS: fragile and conflict-affected situations; GNI: gross national income; OECD HIC GNI = high-income OECD countries. Estimates are based on countries with populations of more than 1 million with available data (103 countries).
Sources: Sumner and Yusuf (forthcoming[45]) based on World Bank (2024[15]), Poverty and Inequality Platform, https://pip.worldbank.org/home Bank (2024[21]), World Development Indicators (database), https://databank.worldbank.org/source/world-development-indicators.
The next decade and beyond are likely to be shaped by shocks and stressors related to climate change and other major global factors. Ending poverty and reducing inequalities now, as soon as possible, would be cheaper and easier in a relative sense, that is in relation to the cost of doing so amid climate shocks and stressors. It would also be sensible, in preparation for a hotter climate, to end poverty and reduce inequalities before climate change-induced impacts make these tasks harder in the face of extreme weather, shifting agriculture patterns, rising sea levels, and potentially mass migration between and within countries.
Ending poverty and reducing inequalities now, as soon as possible, would be cheaper and easier in a relative sense, that is in relation to the cost of doing so amid climate shocks and stressors.
The scale of the task to end poverty and reduce inequalities is undeniably substantial, economic growth and rising incomes are important. That said, growth itself is no guarantee of poverty reduction and may even bypass the poorest sometimes. Public policies to ensure that the direct and indirect benefits of growth are broadly distributed are thus essential. Changes in non-monetary poverty, however, relate more to the quality and provision of public goods – for example, education and health. There is substantial scope for national policies, particularly those to support welfare such as social policies, to change the trends and levels of poverty and inequalities. However, the sharp increase in debt service costs since the pandemic has significantly limited national social policy spending, especially in the poorest countries. This does need urgent attention, potentially in a similar vein to the Heavily Indebted Poor Countries Initiative and the related Multilateral Debt Relief Initiative programmes of the mid-1990s onwards that led to debt relief of USD 100 billion for the poorest countries (World Bank, 2024[59]). A renewed case may be made as part of an “SDG-compact” or reparations for climate-related loss and damage.
LIC and MIC governments do have considerable control over domestic policies, including national macroeconomic policies, labour market policies, wealth inequality policies, fiscal policy (taxation and transfers) and government spending on public goods. But this requires active policy regimes – social spending, transfers and policies – orientated towards that end, thus shaping the accumulation and distribution of assets.
Finally, the challenge of ending poverty and reducing inequalities is evolving with important implications for development co-operation and development finance. The shifting characteristics and location of global poverty are reasonably clear. In the years ahead, economic growth may not be sufficient to raise the incomes of the poor. The expansion of income transfers, well-funded public goods, insurance mechanisms and active labour market policies are required to end poverty and address inequalities. Further, roughly half of the world’s poorest live in countries where ODA really matters to the functioning of their governments, including in conflict and post--affected situations where ending poverty is particularly difficult. The other half of the world’s poorest live in countries where ODA is less important due to sufficient domestic resources. What really matters in such countries is policy coherence on the part of OECD countries. The legacy of the pandemic has been higher debt servicing and a resulting fiscal squeeze impacting the public systems that need to deliver poverty and inequalities reduction. Without a doubt, new financing will be needed for the poorest countries.
[6] Adom, P. (2024), “The socioeconomic impact of climate change in developing countries in the next decades”, CGD Working Paper, No. 681, Center for Global Development, Washington, DC, https://www.cgdev.org/sites/default/files/socioeconomic-impact-climate-change-developing-countries-next-decades.pdf.
[51] Albert, M. et al. (2023), Global Economic Prospects, World Bank, Global Economic Prospects Washington, DC, http://documents.worldbank.org/curated/en/099060723202024954/BOSIB03b0337cf0dd096470ad6bbabb2581.
[52] Albinet, C. and M. Kessler (2022), “The coming debt crisis: Monitoring liquidity and solvency risks”, Working Paper 1, Finance for Development Lab, https://findevlab.org/wp-content/uploads/2022/12/FDL_CAMK_DebtService.pdf.
[16] Alkire, S. et al. (2015), Multidimensional Poverty Measurement and Analysis, Oxford Academic Books, Oxford, United Kingdom, https://doi.org/10.1093/acprof:oso/9780199689491.001.0001.
[28] Alvaredo, F. et al. (2016), “Distributional national accounts guidelines: Methods and concepts used in the WID.world”, WID.world Working Paper, No. 2016/2, https://wid.world/document/dinaguidelines-v1.
[38] Bjørnskov, C. (2008), “Social trust and fractionalization: A possible reinterpretation”, European Sociological Review, Vol. 24/3, pp. 271-283, https://doi.org/10.1093/ESR/JCN004.
[29] Bourguignon, F. (2015), “Conférence présidentielle: Revisiting the debate on inequality and economic development”, Revue d’économie Politique, Vol. 125/5, pp. 633-663, http://www.jstor.org/stable/43860236.
[44] Burchi, F. et al. (2022), “Disentangling the relationship between social protection and social cohesion: Introduction to the special issue”, European Journal of Development Research, Vol. 34/3, pp. 1195-1215, https://doi.org/10.1057/S41287-022-00532-2/TABLES/1.
[17] Castañeda, A. et al. (2018), “A new profile of the global poor”, World Development, Vol. 101, pp. 250-267, https://doi.org/10.1016/j.worlddev.2017.08.002.
[9] Centre for Research on the Epidemiology of Disasters (2024), The International Disaster Database, http://www.emdat.be.
[8] CIESIN and CIDR (2023), Low Elevation Coastal Zone: Urban-Rural Population and Land Area Estimates v3 (1990, 2000, 2015) (database), https://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v3.
[32] Cobham, A., L. Schlögl and A. Sumner (2016), “Inequality and the tails: The Palma proposition and ratio”, Global Policy, Vol. 7/1, pp. 25-36, https://doi.org/10.1111/1758-5899.12320.
[58] Coolin, G., E. Sapienza and A. Sumner (2024), “The politics of inequality: Why are governance systems not more responsive to the unequal distribution of income and wealth?”, UNDP Working Paper, United Nations Development Programme, New York, NY, https://www.undp.org/publications/politics-inequality-why-are-governance-systems-not-more-responsive-unequal-distribution-income-and-wealth.
[26] Cornia, G. (2012), “Inequality trends and their determinants: Latin America over 1990-2010”, WIDER Working Paper, No. 9/2012, United Nations University World Institute for Development Economics Research, Helsinki, https://www.wider.unu.edu/publication/inequality-trends-and-their-determinants.
[35] Cox, F., C. Fiedler and K. Mross (2023), “Strengthening social cohesion in conflict-affected societies: Potential, patterns and pitfalls”, IDOS Policy Brief, No. 3-2023, German Institute of Development and Sustainability, Bonn, Germany, https://doi.org/10.23661/ipb3.2023 (accessed on 5 July 2024).
[5] Dang, H., S. Hallegatte and T. Trinh (2024), “Does global warming worsen poverty and inequality? An updated review”, World Bank Policy Research Working Paper, No. 10697, World Bank, Washington, DC.
[40] Delhey, J. et al. (2018), “Social cohesion and its correlates: A comparison of western and Asian societies”, Comparative Sociology, Vol. 17/3-4, pp. 426-455, https://doi.org/10.1163/15691330-12341468.
[4] Dercon, S. and J. Shapiro (2007), “Moving on, staying behind, getting lost: Lessons on poverty mobility from longitudinal data”, GPRG-WPS, No. 075, Global Poverty Research Group, https://ora.ox.ac.uk/objects/uuid:5703a9c0-ed5c-4878-b48f-222b875373f6/download_file?safe_filename=gprg-wps-075.pdf&file_format=application%2Fpdf&type_of_work=Working+paper.
[54] Diwan, I. and B. Harnoys-Vannier (2024), “The collapse of external finance to developing countries”, https://findevlab.org/the-collapse-of-external-finance-to-developing-countries.
[47] Dollar, D., T. Kleineberg and A. Kraay (2016), “Growth still is good for the poor”, European Economic Review, Vol. 81, pp. 68-85, https://doi.org/10.1016/j.euroecorev.2015.05.008.
[46] Dollar, D. and A. Kraay (2002), “Growth is good for the poor”, Journal of Economic Growth, Vol. 7/3, pp. 195-225, https://www.jstor.org/stable/40216063.
[14] Fernandez, J., S. Olivieri and D. Sanchez (2023), “A methodology for updating international middle-class lines for the Latin American and Caribbean region”, Policy Research Working Paper, No. 10447, World Bank Group, Washington, DC, https://documents1.worldbank.org/curated/en/099727305162311612/pdf/IDU04368d6810dbd50416a099d90d1cba9d5db21.pdf.
[1] Ferreira, F. and M. Ravallion (2011), Poverty and Inequality: The Global Context, Oxford University Press, Oxford, United Kingdom, https://doi.org/10.1093/oxfordhb/9780199606061.013.0024.
[36] Fiedler, C. (2023), “What do we know about how armed conflict affects social cohesion? A review of the empirical literature”, International Studies Review, Vol. 25/3, https://doi.org/10.1093/ISR/VIAD030.
[22] Gradín, C. (2024), “Revisiting the trends in global inequality”, World Development, Vol. 179, p. 106607, https://doi.org/10.1016/j.worlddev.2024.106607.
[30] Grigoli, F. and A. Robles (2017), “Inequality overhang”, IMF Working Papers, No. 2017/076, International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/WP/Issues/2017/03/28/Inequality-Overhang-44774.
[24] IMF (2024), World Economic Outlook: Steady but Slow – Resilience Amid Divergence, International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/WEO/Issues/2024/04/16/world-economic-outlook-april-2024.
[34] IMF (2023), World Economic Outlook: Navigating Global Divergences, International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/WEO/Issues/2023/10/10/world-economic-outlook-october-2023 (accessed on April 2024).
[39] Jewett, R. et al. (2021), “Social Cohesion and Community Resilience During COVID-19 and Pandemics: A Rapid Scoping Review to Inform the United Nations Research Roadmap for COVID-19 Recovery”, https://doi.org/10.1177/0020731421997092, Vol. 51/3, pp. 325-336, https://doi.org/10.1177/0020731421997092.
[23] Kanbur, R., E. Ortiz-Juarez and A. Sumner (2024), “Is the era of declining global income inequality over?”, Structural Change and Economic Dynamics, Vol. 70, pp. 45-55, https://doi.org/10.1016/j.strueco.2024.01.002.
[55] Kenny, C. and M. Snyder (2017), “Meeting the Sustainable Development Goal zero targets: What could we do?”, CGD Working Paper, No. 472, Center for Global Development, Washington, DC, https://www.cgdev.org/publication/meeting-sustainable-development-goal-zero-targets-what-could-we-do.
[53] Kessler, M. (2022), “Supercharging multilateral development banks”, https://findevlab.org/supercharging-multilateral-development-banks.
[48] Kraay, A. (2006), “When is growth pro-poor? Evidence from a panel of countries”, Journal of Development Economics, Vol. 80/1, pp. 198-227, https://doi.org/10.1016/j.jdeveco.2005.02.004.
[3] Lakner, C. et al. (2022), “How much does reducing inequality matter for global poverty?”, Journal of Economic Inequality, Vol. 20/3, pp. 559-585, https://doi.org/10.1007/s10888-021-09510-w.
[41] Lalot, F. et al. (2022), “The social cohesion investment: Communities that invested in integration programmes are showing greater social cohesion in the midst of the COVID-19 pandemic”, Journal of Community & Applied Social Psychology, Vol. 32/3, pp. 536–554, https://doi.org/10.1002/casp.2522.
[13] López-Calva, L. and E. Ortiz-Juarez (2014), “A vulnerability approach to the definition of the middle class”, Journal of Economic Inequality, Vol. 12/1, pp. 23-47, https://doi.org/10.1007/s10888-012-9240-5.
[27] Lustig, N., L. López-Calva and E. Ortiz-Juarez (2013), “Declining inequality in Latin America in the 2000s: The cases of Argentina, Brazil, and Mexico”, World Development, Vol. 44, pp. 129-141, https://doi.org/10.1016/j.worlddev.2012.09.013.
[56] Manuel, M. et al. (2019), Financing the End of Extreme Poverty: 2019 Update, Overseas Development Institute, London, https://media.odi.org/documents/12907.pdf.
[10] Narayan, D. et al. (2000), Voices of the Poor: Crying Out for Change, World Bank Group, Washington, DC, http://documents.worldbank.org/curated/en/501121468325204794/Voices-of-the-poor-crying-out-for-change.
[19] OPHI; UNDP (2023), Global Multidimensional Poverty Index (MPI): Unstacking Global Poverty – Data for High Impact Action, Oxford Poverty and Human Development Initiative, Oxford, United Kingdom; United Nations Development Programme, New York, NY, https://ophi.org.uk/global-mpi/2023 (accessed on April 2024).
[42] Orazani, S., K. Reynolds and H. Osborne (2023), “What works and why in interventions to strengthen social cohesion: A systematic review”, Journal of Applied Social Psychology, Vol. 53/10, pp. 938-995, https://doi.org/10.1111/jasp.12990.
[31] Palma, J. (2011), “Homogeneous middles vs. heterogeneous tails, and the end of the ‘Inverted‐U’: It’s all about the share of the rich”, Development and Change, Vol. 42/1, pp. 87-153, https://doi.org/10.1111/j.1467-7660.2011.01694.x.
[2] Ravallion, M. (1997), “Can high-inequality developing countries escape absolute poverty?”, Economics Letters, Vol. 56/1, pp. 51–57, https://doi.org/10.1016/S0165-1765(97)00117-1.
[18] Robles Aguilar, G. and A. Sumner (2020), “Who are the world’s poor? A new profile of global multidimensional poverty”, World Development, Vol. 126, p. 104716, https://doi.org/10.1016/j.worlddev.2019.104716.
[37] Rothstein, B. and E. Uslaner (2005), “All for all: Equality, corruption, and social trust”, World Politics, Vol. 58/1, pp. 41-72, https://doi.org/10.1353/WP.2006.0022.
[49] Shaffer, P. (2023), “Growth, poverty and immiserizing growth: Empirical evidence”, Q-Squared Working Paper, No. 72, Trent University, Peterborough, Ontario, Canada, https://doi.org/10.13140/RG.2.2.10061.20961 (accessed on May 2024).
[43] Sonnenfeld, A. et al. (2021), “Strengthening intergroup social cohesion in fragile situations”, 3ie Systematic Review, Vol. 46/2021, https://doi.org/10.23846/SR00046.
[12] Sumner, A., E. Ortiz-Juarez and C. Hoy (2021), “Measuring global poverty before and during the pandemic: A political economy of overoptimism”, Third World Quarterly, Vol. 43/1, pp. 1-17, https://doi.org/10.1080/01436597.2021.1995712.
[45] Sumner, A. and A. Yusuf (forthcoming), “Costing the end of global poverty”, UNU-WIDER Working Paper, World Institute for Development Economics Research, Helsinki.
[11] Sumner, A. and A. Yusuf (forthcoming), “New estimates of the cost of ending poverty and Its global distribution”, UNU-WIDER Working Paper, United Nations University World Institute for Development Economics Research, Helsinki, forthcoming.
[50] UNDP (2024), No Soft Landing for Developing Economies, United Nations Development Programme, New York, NY, https://www.undp.org/publications/no-soft-landing-developing-economies.
[33] United Nations (2023), Statistics – SDG Indicators Database, https://unstats.un.org/sdgs/dataportal (accessed on March 2024).
[25] UNU-WIDER (2023), World Income Inequality Database (WIID). Version 28 November 2023 (database), United Nations University World Institute for Development Economics Research (UNU-WIDER), Helsinki, https://www.wider.unu.edu/database/world-income-inequality-database-wiid#WIIDcomp (accessed on 2 May 2024).
[57] Watkins, K. and K. Nwajiaku-Dahou (2024), Financing the Fight Against Poverty and Hunger: Mobilising Resources for a Sustainable Development Goal Reset, Overseas Development Institute, London, https://odi.org/en/publications/financing-the-fight-against-poverty-and-hunger-mobilising-resources-for-a-sustainable-development-goal-reset.
[7] Wiebe, K., S. Robinson and A. Cattaneo (2019), “Climate change, agriculture and food security: Impacts and the potential for adaptation and mitigation”, in Campanhola, C. and S. Pandey (eds.), Sustainable Food and Agriculture: An Integrated Approach, Food and Agriculture Organization, Rome, https://doi.org/10.1016/B978-0-12-812134-4.00004-2.
[59] World Bank (2024), “Heavily Indebted Poor Countries (HIPC) Initiative”, https://www.worldbank.org/en/topic/debt/brief/hipc.
[15] World Bank (2024), Poverty and Inequality Platform (PIP) version 20240324, https://pip.worldbank.org/home (accessed on June 2024).
[21] World Bank (2024), World Development Indicators (database), https://databank.worldbank.org/source/world-development-indicators (accessed on June 2024).
[20] Yusuf, A. et al. (2023), “Will economic growth be sufficient to end global poverty? New projections of the UN Sustainable Development Goals”, WIDER Working Paper, No. 2023/12, United Nations University World Institute for Development Economics Research, Helsinki, https://doi.org/10.35188/UNU-WIDER/2023/431-1 (accessed on June 2024).
← 1. The Stern Review on the Economics of Climate Change made a similar argument that the benefits of early action outweigh the economic costs of not acting. See: https://webarchive.nationalarchives.gov.uk/ukgwa/20100407172811/https:/www.hm-treasury.gov.uk/stern_review_report.htm.
← 2. This approach of conceptualising people’s livelihood strategy is sometimes referred to as an asset-based approach or sustainable livelihoods approach and is associated with the seminal paper by Chambers and Conway, https://www.ids.ac.uk/publications/sustainable-rural-livelihoods-practical-concepts-for-the-21st-century.
← 3. Shocks refer to sudden-onset risk events such as floods, droughts and price spikes. Stressors refer to more gradual shifts such as regular non-catastrophic flooding, land degradation and socio-economic marginalisation. A shock may be short in duration, but its impacts can persist long after the initial event and erode assets that support resilience.
← 4. There is also some contention over whether ending extreme poverty means zero extreme poverty, with SDG target 1.1 calling for eradicating extreme poverty everywhere by 2030 and the World Bank’s goal is to reduce extreme poverty to under 3% of the global population by 2030.