Development Co-operation Report 2024

Tackling Poverty and Inequalities through the Green Transition

Report

Development Co-operation Report

17 July 2024

Available in:

English
français

Download PDF

14. Poverty and inequalities focus of official development assistance and its measurement

Harsh Desai
Development Co-operation Directorate, OECD

Abstract

To what extent does development finance, specifically official development assistance (ODA), target the reduction of poverty and inequalities? After analysing the results from existing measures of aid targeting, this chapter shares findings from innovative approaches to provide a more complete view of the current state of targeting for poverty and inequalities across bilateral and multilateral providers, relevant sectors and geographies. It provides new evidence on ODA allocations relative to where the poorest and most vulnerable people are living. The chapter concludes with suggestions for development co-operation providers to reinvigorate the fight against poverty and inequalities in the context of the green transition agenda by improving resource allocations, targeting and measurement.

The author is grateful for research assistance, guidance and peer review from Yasmin Ahmad, Mark Baldock, Elena Bernaldo de Quiros, Olivier Bouret, Emily Bosch, Eleanor Carey, Gabrielle Germé, Renwick Irvine, Anthony Kiernan, Danielle Mallon, Ida Mc Donnell, Haje Schütte, José Suárez Sarrazin, Lou Turroques and Sandie Xu from the Development Co-operation Directorate, OECD.

Key Messages

New and better measures are needed to understand how official development assistance (ODA) is targeting poverty and inequalities worldwide – and how it can do better in observing relationships between aid, poverty and inequalities.
9.5% of DAC members’ bilateral ODA in 2022 (USD 19.6 billion) went to grants for poverty-reducing sectors as measured by indicator SDG 1.a.1. This is down from a historical high of 11.5% in 2021 and consistent with the average from 2012 to 2022.
Comparison of ODA allocations across regions shows that providers collectively allocated the biggest share of their ODA (2020-22 average) to sub-Saharan Africa, where poverty and inequalities are most heavily concentrated.
Amidst a changing context for ODA, the findings in this chapter suggest a waning focus on poverty and inequalities despite policy commitments. When adjusting for the distribution of poverty worldwide, upper middle-income countries (UMICs) received more aid than least development countries (LDCs) in 2022. This was not the case in 2012.
Investing aid to facilitate the green transition and address the climate crisis will not automatically address the poverty and inequalities crisis. Providers need to make their climate finance more poverty- and inequality-sensitive, and vice versa.

ODA has an important role to play in reducing poverty and inequalities, but better measures of this relationship are needed to inform better targeting

International agendas to foster a just and equitable world that leaves no one behind highlight the goals of eradicating poverty and reducing inequalities. These objectives feature prominently in the OECD Development Assistance Committee’s (DAC) current mandate (1960[1]) and in the latest DAC High Level Meeting communiqué, in which the DAC committed to helping partner countries manage potential trade-offs involved in reducing poverty and inequalities while achieving sustainable development and net zero transitions (OECD, 2023[2]). ODA is called on to support partner countries to achieve and balance these myriad goals. While it is not the sole resource available for low-income countries (LICs) or middle-income countries (MICs), and providers are not always meeting their international targets to provide ODA (OECD, 2023[3]), ODA is a stable and predictable source of external funding for sustainable development (Figure ‎14.1).

Figure ‎14.1. ODA is a dependable source of external financing that has increased gradually in recent years

Official development assistance, other official flows, foreign direct investment, and remittances for ODA-eligible low- and middle-income countries, 2010-22

The findings presented in this chapter confirm the need for a more comprehensive, granular and nuanced menu of indicators to help ODA reach the poorest and most vulnerable countries, as well as the poorest and most vulnerable people within those countries. Standard measures provide a useful but still piecemeal view of whether ODA is going where needs are greatest. With rising demands on development finance (see Chapter 12), better targeting tools can guide providers in allocating aid according to where and in what form it will have mutually beneficial outcomes for poverty and inequalities reduction and net zero transitions. The analysis and suite of indicators tested in this chapter, based on measures of ODA overall and to key sectors and cross-cutting priorities across countries, can inform discussions on how to measure whether ODA is doing its job.

The ten countries with the highest extreme poverty headcount in 2022 received on average just 8.6% of concessional development finance for climate and environment from official sources in 2021-22.

Better targeting to reduce poverty and inequalities is an especially important ambition given that providers are spending more of their development finance to promote the green transition (OECD, 2023[8]). Climate finance from official bilateral and multilateral sources almost doubled over the period from 2013 to 2021 (OECD, 2023[8]). While the increased attention to climate is welcome, it is important for climate finance – and development finance more broadly – to tackle climate change while also benefitting the world’s poorest and most vulnerable. However, the top ten countries with the highest extreme poverty headcount in 2022 received on average just 8.6% of concessional development finance for climate and environment1 from official sources in 2021-22 (OECD, 2023[9]). Ensuring that efforts to tackle climate change also address poverty and inequalities is especially important given that LDCs, where poverty and inequalities are concentrated, are receiving a growing share of concessional development finance for climate and environment, which increased from 17.5% on average in 2011-12 to 31.1% in 2021-22 (Figure ‎14.2). In the same period, the share of such finance to MICs declined from 55.7% on average in 2011-12 to 43.8% in 2021-22. The decline to MICs was especially pronounced in UMICs: their share of such finance dropped from 20.9% on average in 2011-12 to 12.8% in 2021-22, while the share to lower middle-income countries (LMICs) dropped from 34.8% on average in 2011-12 to 31.0% in 2021-22.

Figure ‎14.2. LDCs are receiving greater shares of concessional development finance for climate and environment from official sources, with corresponding declines in the share of such financing to MICs

Development finance for climate and the environment, all official providers, 2011/12-2021/22 (2-year averages)

Based on existing indicators, ODA is not targeting poverty and inequalities well

Fresh crises and expanded global goals have placed additional demands and pressures on ODA. The findings presented in this section, based on commonly used measures of the relative share and volume of ODA by region or across and within country income groupings, suggest that ODA is not targeting needs arising from poverty and inequalities worldwide.2 In response to a changing global context for development co-operation, ODA is also focusing on certain priorities instead of or at the expense of others (Calleja and Cichocka, 2022[10]), and there are signs that ODA has not adapted to the nature of poverty in the wake of the COVID-19 pandemic, particularly extreme poverty. Current measures of ODA allocations for poverty and inequalities reduction, however, give a fragmented picture, are over-reliant on proxies (such as the share of ODA to LDCs or to sub-Saharan Africa), and are narrow relative to the scale of the problem. They also fall short in providing a comparative perspective that can guide better allocations, especially to where the poorest and most vulnerable are actually living, and towards the policies that are more conducive to the reduction of poverty and inequalities (see Chapters 3 and 4). Respondents to a DAC survey on poverty and inequalities (see Chapter 12) cited a lack of data, measurements, and diagnostics as a primary challenge to the better targeting of poverty and inequalities.

Correlating ODA with national-level poverty and inequalities measures shows no clear relationship

A typical approach to measuring the relationship between ODA, poverty and inequalities is to examine the allocation of ODA across partner countries against common measures of poverty and inequalities such as extreme poverty, multidimensional poverty or the Gini index. Strikingly, for the most recent data on ODA allocations, these correlations do not show a clear relationship between aid, poverty and inequalities (Figure ‎14.3). If a clear pattern were to exist, a diagonal line would be evident in the figure suggesting higher or lower levels of aid with increasing levels of poverty or inequalities. Figure ‎14.3 does suggest that some of the LICs with high levels of extreme poverty, both in 2022 and projected in 2030, are receiving low amounts of aid. This finding highlights the value of observing relationships between aid, poverty and inequalities disaggregated by income groups, which is further explored in this chapter.

One explanation for the lack of correlation is that ODA is allocated to address a variety of development challenges, which have evolved over time. Historically, reducing poverty and inequalities has just been one of many reasons for giving ODA (Kenny and Yang, 2021[11]; OECD, 2019[12]). Providers also are motivated by national interest and, as in the case of aid for the environment or disease preparedness, a desire to preserve the global commons (Gulrajani and Calleja, 2019[13]; Melonio, Naudet and Rioux, 2022[14]). These reasons may change over time and may or may not be co-ordinated across providers. It is important for future work to enable more fine-grained analysis across provider groups or between providers that considers their motivations and relative ability to target poverty and inequalities.

Figure ‎14.3. No clear correlation between ODA, poverty and inequalities

Official development assistance from all official providers (2020-22 average), by partner country, relative to extreme poverty in 2022, projected extreme poverty in 2030, multidimensional poverty, and Gini index

Measuring ODA to LDCs is not sufficient to assess targeting to poverty and inequalities

LDCs face structural barriers to their sustainable development, including persistent high rates of poverty and inequalities (see Chapters 2 and 3). In 2022, these countries accounted for 14% of the world’s population but half the number of people in extreme poverty worldwide and just over a third (36%) of people in absolute poverty (World Bank, 2024[15]; UN, 2022[19]; Castaneda Aguilar et al., 2024[20]). In recognition of these challenges, Sustainable Development Goal 17.2 sets a target for providers to allocate between 0.15% and 0.20% of their gross national income in ODA to LDCs. In 2022, though, only 3 of 31 DAC member countries met this target and overall, DAC members’ ODA to LDCs amounted to just 21.5% of their total ODA – the lowest share in the period analysed from 1996 (23% of the total) forward (Figure ‎14.4). This share reflects, in part, an increase in members’ aid to Ukraine in response to Russia’s aggression, as well as increased support for the costs of processing and hosting Ukrainian refugees in provider countries. Based on the volume and share of ODA to LDCs, a frequently cited measure of targeting to poverty and inequalities, ODA is therefore not aligned to needs. However, on its own, this indicator is not enough to guide providers on the extent to which their aid is targeting to poverty and inequalities. For one, ODA to LDCs is merely a proxy in relation to the concentration of poverty and inequalities in this country grouping. Second, it does not indicate whether LDCs received aid that was proportional to their levels of poverty or inequalities or to that of other countries.

Figure ‎14.4. The share of DAC countries’ total ODA to LDCs in 2022 was the lowest in the period analysed from 1996 forward

Share of DAC countries’ total (bilateral and imputed multilateral) official development assistance to Least Developed Countries, 1996-2022

Measuring ODA relative to levels of extreme poverty across income groups gives a more comprehensive view

An alternative to the LDC proxy is measuring aid across DAC income groups relative to their levels of poverty, specifically by comparing shares of ODA received relative to shares of people in poverty in each group. This approach moves beyond the broad measure of the share of ODA to poorer countries (e.g. LDCs or sub-Saharan Africa) towards considering the distribution of ODA and poverty across groups. As illustrated in Figure ‎14.5, findings from this approach suggest that ODA, as measured by country programmable aid (CPA) received3, was not targeting extreme poverty more in poorer countries (e.g. LDCs) than in richer ones (e.g. UMICs) in 2022. The data suggest that ODA did a better job targeting country groups that had the highest share of people in extreme poverty ten years earlier – in 2012. At that time, development co-operation was operating in a different global context that, in turn, led to different purposes for ODA (Calleja and Cichocka, 2022[10]).

Analysing these findings against the hypothesis that global shares of CPA would correlate with global distribution of extreme poverty by country grouping provides another perspective on aid targeting.4 The LDCs would have received 33% of CPA in 2012 and 51% of CPA in 2022, while the data show little alignment: they received 40% of CPA in 2012 and 34% in 2022. LMICs on the other hand received less CPA than their share of extreme poverty in 2012 and a higher share relative to their share of extreme poverty in 2022. At the same time, while UMICs’ relative share of extreme poverty dropped from 16.2% in 2012 to 4.7% in 2022, they received more aid than their share of people in extreme poverty would imply in both years. While the influx of aid to Ukraine (USD 23.7 billion from all official providers) may have partly contributed to the finding in 2022, the data in 2019 also suggest a misalignment: LDCs accounted for 49% of people in extreme poverty in 2019 and received 44% of CPA, while LMICs accounted for 45% but received 41% of CPA. Meanwhile, 5.5% of the world’s extreme poor in 2019 were in UMICs, which received 14% of CPA.

Testing these relationships using the multidimensional poverty indicator produces the same findings. Based on the latest available data for each individual country, 48% of people in multidimensional poverty live in LDCs, 44% live in LMICs and 8% in UMICs. Both LMICs and UMICs received a larger share of CPA than their share of multidimensional poverty would imply in 2022, and LDCs received a lower share, across DAC income groups.

Wealthier countries’ ability to pay for social services buttresses the case for targeting ODA to LDCs, where it can have a greater impact in filling financing gaps and helping the poorest.

Figure ‎14.5 further suggests that current aid targeting is overestimating extreme poverty in MICs and underestimating it in LDCs and LICs. Additionally, many MICs have the ability to raise sufficient revenue to finance social sectors that address poverty and inequalities, though political and technical constraints explain why some MICs may not do so (Manuel et al., 2020[21]). Countries’ response to the COVID-19 pandemic offers an illustration: to mitigate the pandemic’s impacts in 2020 and 2021, UMICs spent 2.5% of their gross domestic product (GDP) on social protection and labour programmes while LMICs spent 1.7% and LICs spent 1.3% of GDP (Gentilini et al., 2022[22]). Indeed, in some of the largest UMICs, governments’ fiscal support to households mitigated the impact of the pandemic on poverty and even led to reductions in poverty in some cases (World Bank, 2022[23]). Wealthier countries’ ability to pay for social services buttresses the case for targeting ODA to LDCs, where it can have a greater impact in filling financing gaps and helping the poorest.

Aid in MICs

At the same time, the findings should not discount the value of aid to MICs for poverty and inequalities reduction. They accounted for 55% of the world’s extreme poor in 20245 (World Bank, 2024[24]). Inequalities are also persistent challenges in MICs, highlighting the need for a balance between investments in poverty and inequality objectives (UNU-WIDER, 2023[16]). Fragile contexts are also increasingly middle income, underscoring the multidimensional challenges to their development beyond national wealth (OECD, 2022[25]). Aid in MICs can also address important development priorities or pockets of poverty, particularly among vulnerable populations such as refugees or other populations that are the furthest behind (Kenny, Dissanayake and Plant, 2020[26]; OECD, 2023[27]; OECD, 2019[12]). These findings highlight the value of additional work within the DAC to clarify the role of aid in MICs, a recommendation first presented in the High-Level Panel report for the DAC in 2017 (OECD DAC, 2017[28]).

Figure ‎14.5. Aid allocations relative to the global distribution of extreme poverty suggest that in 2022, providers underestimated levels of extreme poverty in LDCs and overestimated them in LMICs and UMICs

Share of country programmable aid from all official providers and share of people in extreme poverty by DAC income group, 2012, 2019, 2022, and 2030 (projected)

Regional allocations suggest aid prioritises poverty and inequalities hotspots, such as sub-Saharan Africa, with some gaps

Another way to assess the targeting of ODA is to compare ODA allocations across regions. This approach shows that providers collectively allocated the biggest share of their ODA (2020-22 average) to sub-Saharan Africa, where poverty and inequalities are most heavily concentrated (Figure ‎14.6). However, it does not show whether sub-Saharan Africa received levels of ODA that were proportional to its levels of poverty and inequalities, or how ODA to sub-Saharan Africa compared in this regard with ODA to other regions. The regional comparison also highlights an additional challenge: the poorest regions are not necessarily places where inequality is high, and apart from sub-Saharan Africa, bilateral ODA allocations did not align with a region’s level of needs as defined by poverty and inequalities. South Asia, for instance, has a higher rate of extreme poverty than other regions but received less ODA in terms of volume. This finding is driven by India, which has the largest number of people in extreme poverty worldwide but is not among the top recipients of net ODA. Similarly, the Latin America and Caribbean region received a lower share of ODA than other regions yet has the third-highest rate of inequality after sub-Saharan Africa and Middle East and North Africa. These comparisons broadly illustrate the challenges in optimising ODA allocations and the potential trade-offs between a poverty and an inequality focus. The share of ODA unallocated by region is also rising and increased from 29.5% in 2012 to 38.4% in 2022, which further assesses aid targeting across regions difficult as these allocations are multi-regional or global in nature.

Figure ‎14.6. Providers are allocating their ODA to sub-Saharan Africa, where poverty and inequalities are most concentrated, but ODA to other regions does not align as well with vulnerabilities

Official development assistance (2020-22 average), extreme poverty headcount, and Gini index, by World Bank region

Sectoral allocations: potential for improving indicators for better targeting

The UN Sustainable Development Goal (SDG) indicator framework also provides a basis for measuring ODA to poverty and inequalities, specifically through SDG 1.a.1.6 The OECD is the custodian agency of this indicator, which allows for a disaggregated analysis of each provider’s allocation to key sectors that matter for reducing poverty and inequalities. It thereby provides insight into how ODA can be spent better for these objectives. Though the indicator’s definition is too narrow to sufficiently capture the extent of ODA’s contribution to reducing poverty and inequalities, it is an important part of a broader framework of measurement. For example, the indicator could be broadened to include sectors such as social protection and justice, as well as to include concessional loans rather than only grants in its measurement.

Using this indicator, 9.5% of DAC members’ bilateral ODA in 2022 (USD 19.6 billion) went to grants for poverty-reducing sectors, down from a historical high of 11.5% in 2021 and consistent with the average from 2012 to 2022. Analysis across specific sectors shows a growing concentration of ODA grants to health services since the start of the COVID-19 pandemic, with ODA to basic health services within the health sector increasing by 169% from 2019 to 2022. ODA to basic education and to water supply and sanitation declined by 27.9% and 4.7%, respectively, over the same period while other poverty-reducing sectors experienced modest increases (Figure ‎14.7). These trends highlight the balancing act inherent in targeting ODA to both poverty and inequalities. While investments in health were undoubtedly important to help countries respond to the pandemic, such a concentration in this sector risks fostering aid dependency and undermining the resilience of health systems (OECD, 2023[30]); additionally, corresponding declines in other sectors could be a concern for specific strategies that aim to address both poverty and inequalities. Increasing ODA to health while decreasing it for education is a prime example of a potentially self-defeating trade-off given the links between investments in human capital formation and reductions in intergenerational poverty and inequalities (Collin and Weil, 2018[31]; OECD, 2023[3]).

Figure ‎14.7. DAC members have concentrated their bilateral aid to poverty reduction on health services since the pandemic

Official development assistance disbursements from DAC members to poverty reduction, 2010-22

Testing a new approach for measuring ODA targeting of poverty and inequalities

To address and overcome the limitations of current proxies and measures, this section proposes and tests a new approach built around a coherent set of indicators, with the objective of allowing a better understanding of the extent to which ODA targets poverty and inequalities – and how it can do this better in the future. The list of indicators is not exhaustive; rather, it is intended as a contribution to an ongoing conversation about measuring aid targeting in forums such as the DAC, the OECD-DAC Community of Practice on Poverty and Inequalities and other international initiatives. The discussion here is therefore a starting point to reach a common standard that can guide better allocation, monitoring and accountability.

The novelty of this method is its combination of statistical presentations of ODA with poverty and inequalities indicators in a way that can inform policy discussions on the targeting of ODA. It aims to balance the measurement of ODA’s contribution to poverty and to inequalities, as these are complementary but distinct objectives. It therefore uses distinct measures for these objectives rather than conflating them. At the same time, as ending poverty and inequalities is a linked-up mission, ODA has a vital role in tackling them together (Seery and Seghers, 2019[32]).

It includes the following key features (Infographic 14.1):

Comparisons across key sectors and country groups: Analysing ODA and CPA overall, as well as to key sectors and thematic areas that affect poverty and inequalities across groups of countries. Despite its potential drawbacks, a sectoral and thematic approach to measuring aid targeting has some important advantages: it is scalable to different policy objectives and vulnerable demographic groups, comparable across providers, and capitalises on the level of detail provided in the OECD’s database on aid activities.
Demographic characteristics of partner countries: Assessing ODA allocation per person and per person in extreme or absolute poverty.7 These measures adjust for the size of the population in partner countries, as well as the number of people in extreme or absolute poverty and therefore in greatest need of aid, thereby enabling for a disaggregated understanding of aid targeting that facilitates reliable comparisons across countries or groups of countries.
Sub-national targeting: Opening the door to measure aid targeting within countries at the sub-national level. This perspective can help address an important limitation in current approaches that measure aid targeting across countries but not whether the aid reaches the poorest within countries.

Infographic ‎14.1. More comprehensive framework for measuring aid targeting in relation to poverty and inequalities

The sectors included in the framework as shown in Infographic 14.1 reflect key priorities identified by DAC members, international policy commitments and other relevant sectors that merit further consideration in an analysis of poverty and inequalities8 (OECD, 2022[33]). The justice sector,9 for example, merits further analysis given recent research on the links between justice, inclusive growth, and the reduction of poverty and inequalities worldwide (OECD/Pathfinders for Peaceful, Just and Inclusive Societies, 2023[34]; OECD, 2022[25]). Another example is food security,10 reflected most prominently in the Brazilian G20 Presidency’s call for a global alliance against hunger and poverty (G20 Brasil 2024, 2024[35]).

The following subsections show findings from testing the framework and investigating whether its measures can better guide the allocation of ODA to poverty and inequalities and therefore be developed by DAC members.

Test 1: Geographic allocations according to per capita levels of extreme poverty

The analysis in this section focuses on extreme poverty in consideration of the ambition of SDG 1.1 to eradicate extreme poverty for all people everywhere. Extreme poverty is still prevalent, and as a result of recent crises, the number of people living in extreme poverty has increased (Castaneda Aguilar et al., 2024[20]). At the same time, as shown in Chapter 2, even if extreme poverty is eradicated, at least a billion people would still be living in absolute poverty. Additionally, even countries with low levels of extreme poverty could face high inequalities. Therefore, where relevant, the framework also allows for an analysis of aid targeting to higher poverty lines and in support of addressing inequalities. The following discussion draws insights from measuring aid per person in extreme poverty. First, disaggregated groups of LDCs are compared with UMICs and LMICs and aid to LICs is compared with aid to MICs (Figure ‎14.8). Second, a comparison is made across provider groups, including multilateral versus bilateral providers, rather than by aid overall, which indicates that certain providers may target their aid better than others (Figure ‎14.9).

Historically, the LDCs have received a larger volume of ODA (including concessional outflows from multilateral organisations) than LMICs or UMICs (Figure ‎14.8). 2022 was an exception due to the influx of ODA to Ukraine, which is an LMIC. Additionally, when allocations are tracked and measured using ODA per person, the LDCs have also generally received more ODA than LMICs or UMICs. However, the finding reverses when measuring ODA relative to the global distribution of extreme poverty and using ODA per person in extreme poverty. In 2022, LDCs received about six times more aid per person than UMICs. UMICs, however, received three times more ODA per person in extreme poverty than LDCs. Similarly, LDCs received twice more ODA per person than LMICs, but LMICs received 1.3 times more ODA per person in extreme poverty than LDCs.

In 2012, however, LDCs received more ODA per person in extreme poverty than UMICs or LMICs. This finding is in line with the analysis of the distribution across income groups of CPA relative to extreme poverty, which indicated that it targeted extreme poverty more closely in 2012 than in 2022. From 2012 to 2022, ODA per person in extreme poverty jumped by 471% in UMICs and 218% in LMICs but increased only modestly in LDCs (by 12%) (Figure ‎14.8). Yet over the same period, the volume of ODA and concessional flows to UMICs and LDCs rose at similar rates (26% and 31%, respectively), while increasing by 120% to LMICs, based on countries that were in a given DAC income group at that time.11

...should DAC members decide to target aid more explicitly to extreme poverty, that is to allocate according to per person in extreme poverty, then current ODA would need to be re-allocated.

Therefore, the different growth rates of ODA per person in extreme poverty reflect the number of people in extreme poverty increasing in LDCs (by 16%) but declining in LMICs (by 31%) and UMICs (by 78%) over time, with allocations not having adapted to this change. For LDCs and UMICs to receive equal levels of ODA per person in extreme poverty in 2022, there would need to be a trade-off: ODA to UMICs would have to be reduced by two-thirds and that entire amount reallocated to LDCs. Therefore, should DAC members decide to target aid more explicitly to extreme poverty, that is to allocate according to per person in extreme poverty, then current ODA would need to be re-allocated. The general finding on ODA per person in extreme poverty also holds when assessing ODA targeting for absolute poverty. In 2012, LDCs received twice as much ODA per poor person than did UMICs; ten years later, in 2022, UMICs received twice as much per poor person than LDCs. Therefore, across both poverty lines, UMICs received more aid than LDCs when adjusting for the number of people in poverty.

Figure ‎14.8. While LDCs have received a greater volume of ODA than LMICs or UMICs historically (except in 2022), ODA per person in extreme poverty has grown significantly in UMICs but only modestly in LDCs over time

ODA from all official providers, volumes and per person in extreme poverty, Least Developed Countries, lower middle-income countries, and upper middle-income countries (DAC income groups), 2010-22

Allocations to and through multilateral organisations

Measured as per person in extreme poverty, outflows from multilateral organisations also did not target LDCs over UMICs in recent years, with the exception of 2019 (Figure ‎14.9). Nonetheless, their targeting has systematically been more pro-poor than that of bilateral providers. Bilateral ODA channelled through the multilateral system was also more pro-poor than bilateral ODA channelled in other ways. This finding highlights the importance of the multilateral system in the mission to end poverty and inequalities, a theme that is explored further in the forthcoming OECD Multilateral Development Finance Report 2024.

Figure ‎14.9. Aid per person in extreme poverty from and through multilateral organisations did not target LDCs over UMICs, except in 2019

ODA per person in extreme poverty, least developed countries and upper-middle income countries (DAC income groups), 2019-22, based on DAC countries’ ODA earmarked through multilateral organisations, DAC countries’ ODA not earmarked through multilateral organisations, and outflows from multilateral organisations

Across World Bank income groups

Tracking ODA per person in extreme poverty across World Bank income groupings shows divergence over the last ten years. In 2012, LICs received 1.8 times more ODA per person in extreme poverty than MICs. Ten years later, in 2022, MICs received 1.6 times more ODA per person in extreme poverty than LICs (Figure ‎14.10). This shift can partly be explained by crisis responsiveness of ODA:

Ukraine received a significant volume of ODA in 2022 (USD 28.7 billion) but had a very low number of people in extreme poverty (less than one million people). However, the data suggest that the increase of ODA to Ukraine in response to Russian aggression does not entirely explain the finding.
Extreme poverty rose worldwide after the COVID-19 outbreak; by 2022, it had declined to pre-pandemic levels in MICs but not in LICs (Yonzan, Gerszon Mahler and Lakner, 2023[36]). Meanwhile, ODA to MICs increased by 66% from 2019 to 2022, while ODA to LICs declined by only 10% over the same period.
At the absolute poverty line, LICs have historically received higher ODA per poor person than MICs. The gap, however, has been shrinking: LICs received almost three times more ODA per poor person than MICs in 2012 but only 1.2 times as much per poor person in 2022.

These findings suggest that ODA has not adapted to the geography of poverty after the outbreak of the COVID-19 pandemic, particularly extreme poverty.

Figure ‎14.10. ODA has yet to adapt to the geography of poverty post pandemic

Total ODA per person in extreme poverty, low-income countries and middle-income countries (World Bank income groups), 2012-22

Test 2: Allocations to poverty-focused sectors relative to per capita poverty levels

A sectoral approach to measurement can redirect attention to opportunities for win-win outcomes rather than trade-offs. Certain sectors – chiefly food security, justice, poverty reduction (basic social services) and social protection – matter more than others for poverty and inequalities reduction. Overall, these four sectors received only 19.5% of DAC members’ bilateral ODA in 2022. Tracking allocations by geography found that LICs received more DAC bilateral ODA than MICs per person in extreme poverty in only two of these sectors: food security and poverty reduction (Figure ‎14.11). In 2022, LICs received almost three times more food security ODA per person in extreme poverty than MICs. LICs also received slightly more aid per person in extreme poverty for poverty reduction than MICs. Nevertheless, the gap between LICs and MICs in this regard has narrowed since 2012. Tipping the balance back to favour LICs more significantly will be important to ensure that aid in the green transition continues to benefit poverty- and inequality-reducing sectors in the poorest countries.

Figure ‎14.11. DAC members’ bilateral ODA to food security and poverty reduction sectors, measured as aid per person in extreme poverty, targeted low-income over middle-income countries in 2022

DAC members’ bilateral ODA per person in extreme poverty, low-income countries and middle-income countries (World Bank income groups), 2022, across food security, poverty reduction, social protection, and justice sectors

Test 3: Inequality-focused ODA

As discussed, the pandemic ushered in shifts in ODA allocations by sector, with more aid concentrated in the health sector and aid declining, or only modestly increasing, to other sectors associated with poverty reduction (Figure ‎14.7). The picture shifted as well for aid to specific inequality-reducing sectors, namely social protection and justice, in terms of volume and aid per person in extreme poverty in different country groupings (Figure ‎14.12). Historically, these sectors have received a low share of DAC bilateral ODA – 1% for social protection and 1.0-3.5% for justice – compared with sectors such as health (9-14%) and education (5-8%). Another indicator of a inequality-focused ODA – aid to gender equality and women’s empowerment – also suggests a declining focus.

Social protection and justice

In response to the COVID-19 pandemic, providers nearly tripled the volume of their ODA to social protection from 2019 to 2020, with social protection as a share of DAC members’ bilateral ODA rising from 0.6% to 1.6% of the total. From 2020 to 2022, such ODA declined by almost a third but was still higher than pre-pandemic levels. This suggests providers recognise the importance of sustained investments in social protection, though their reduced investments since 2020 are a cause for concern. Additionally, the bulk of the increase from 2019 to 2020 was concentrated in three providers: France, Germany, and the European Union Institutions, in descending order.

On the other hand, ODA to justice declined from 3.3% of total aid in 2012 to 1.4% in 2022, with declines in volume each year from 2018 to 2022. This trend indicates a missed opportunity for providers to address important drivers of inequality, particularly horizontal inequalities, with their aid (OECD, 2022[25]).

Figure ‎14.12. DAC members’ bilateral ODA to social protection and justice, already a small share of total ODA, has declined since 2020

Bilateral official development assistance from DAC members to justice and social protection, 2012-22

Gender equality and women’s empowerment

Overall, the focus of DAC members’ aid to gender equality and women’s empowerment is declining. After consistently increasing in volume and as a share of DAC members’ bilateral allocable ODA over the years, gender focused aid dropped from 45% of the total from 2019-20 and 43% of the total in 2021-22 (OECD, 2023[37]). This decrease is concerning given the feminisation of poverty, particularly since the pandemic and in the face of concurrent crises (UNDP and UN Women, 2023[38]), and is inconsistent with findings from the OECD survey of donor approaches that DAC members measure their targeting of inequality principally through the lens of gender (see Chapter 12). Other OECD research explores trends in finance for gender equality, with a breakdown across sectors and thematic areas relevant to women’s empowerment. Box ‎14.1 reviews other initiatives developed by DAC members, civil society organisations and multilateral institutions to better measure how development co-operation is targeting dimensions of poverty and particularly inequalities.

Box ‎14.1. Initiatives to better track development finance targeted to reducing poverty and inequalities

There are a number of initiatives underway by providers, civil society and others to measure poverty and inequalities in a more granular way and provide a deeper understanding of trends and gaps in the targeting of poverty and inequalities.

In June 2023, the European Union Directorate-General for International Partnerships introduced the Inequality Marker (I-Marker) to “reinforce the inequality reduction effect of development interventions by assessing whether, and to what extent, inequality reduction is an objective of a donor’s intervention and, therefore, how likely it will have an impact on reducing within country inequalities” (European Commission, 2023[39]).
Sightsavers, an international non-government organisation, has developed a data visualisation dashboard on the disability and gender markers used by DAC members that draws from OECD Creditor Reporting System data from 2018 to 2022 (Sightsavers, 2024[40]). The interactive dashboard breaks down the allocation of OECD funding by disability and gender to track the use and coverage of each DAC markers. Data can be disaggregated by country, year, and score or objective. The dashboard also provides a comparative analysis of the intersection of different markers – for instance, the share of ODA marked as gender equitable that is also disability inclusive.
UN Women has explored options for introducing disability inclusion markers to its results monitoring and reporting systems with the aim of enhancing data on development co-operation interventions that “aim to promote rights and equality for persons with disabilities” (UN Women, 2022[41]).
With its Shandia platform, the Global Alliance of Territorial Communities aims to monitor funding allocated for and received by Indigenous peoples and local communities. The Alliance collects these data with a view to developing a simple methodology or framework that would improve reporting on and tracking of funding and could also be used by its institutions, donors, allies and partner organisations (Global Alliance of Territorial Communities, 2023[42]).

Source: Sightsavers (2024[40]), View our disability and gender markers https://www.sightsavers.org/policy-and-advocacy/ (please note: the link to the dashboard appears next to the section, “Our key policy and advocacy achievements”); UN Women (2022[41]), Disability Inclusion Markers, https://www.unwomen.org/sites/default/files/2022-01/Brief-Disability-inclusion-markers-en.pdf; Global Alliance of Territorial Communities (2023[42]), Shandia, https://globalalliance.me/shandia/#top; European Commission (2023[39]), The European Commission Inequality Marker (I-Marker), https://data.europa.eu/doi/10.2841/637400.

Test 4: Aid to global environmental objectives

Ahead of COP26, the DAC issued a joint declaration to align its aid to the goals of the Paris Agreement, including an acknowledgement of the links between tackling climate change and reducing poverty and inequalities (OECD, 2021[43]). Among other commitments in the Declaration, the DAC committed to adaptation finance for LDCs and small island developing states. From 2010 to 2018, DAC members committed more of their bilateral ODA towards mitigation than adaptation objectives, with both objectives receiving roughly equal amounts in 2019-20 and 2021-22. At the same time, LDCs received almost twice as much bilateral ODA from DAC members for adaptation than for mitigation on average in 2021-22, a higher ratio than in the years prior to the COVID-19 pandemic. Additionally, more bilateral climate adaptation ODA from DAC members went to LDCs than to UMICs or to LMICs.

However, data on the distribution of ODA per person in extreme poverty show a different picture on the poverty and inequalities focus of this aid. Adaptation ODA has not kept up with the concentration of extreme poverty in LDCs over time. In 2011-12, LDCs and UMICs received roughly equal levels of adaptation ODA per person in extreme poverty, while LDCs received slightly more than LMICs. LDCs continued to receive more of such aid per person in extreme poverty than LMICs until 2019-2020. Meanwhile, UMICs received on average five times more adaptation aid per person in extreme poverty than LDCs in 2021-22, and LMICs received on average slightly more than LDCs (Figure ‎14.13). The evidence therefore suggests that in 2021-22, UMICs and LMICs received more support than LDCs to adapt to the impacts of climate change, relative to their share of the world’s poorest. And yet, these LDCs are more exposed to climate change and less able to adapt to it, with the consequences especially concentrated on the poorest and most vulnerable (Georgieva, Gaspar and Pazarbasioglu, 2022[44]). These findings comparing LDCs and UMICs are consistent when measured per person in absolute poverty, while LDCs have consistently received more climate adaptation aid per person in absolute poverty than LMICs over time (Figure ‎14.13).

At the same time, LDCs received almost twice as much bilateral ODA from DAC members for adaptation than for mitigation on average in 2021-22, a higher ratio than in the years prior to the COVID-19 pandemic.

Climate adaptation can help address climate change, poverty and inequalities simultaneously. However, the mutual benefits are not automatic. Providers cannot assume that they are targeting poverty when they target climate. One way to show this point is to measure ODA per person exposed to extreme weather events, as set out in a pilot methodology for measuring vulnerability to climate shocks12 described by Doan et al. (2023[45]). In 2022, more ODA went to LDCs than UMICs relative to where people exposed to extreme weather events were living (based on 2019 estimates). Using this measure, LDCs received almost seven times more ODA than UMICs but less ODA than UMICs per poor person exposed to extreme weather events. Therefore, more ODA went to poorer countries (e.g. LDCs) based on their exposure to climate events but not based on their levels of poverty. In other words, aid has targeted climate but not poverty or the intersection of climate and poverty.

Figure ‎14.13. DAC members' bilateral ODA for climate adaptation has not kept up with the concentration of poverty in LDCs

Bilateral official development assistance for climate adaptation from DAC members, by DAC income group, 2011/12-2021/22 (2-year averages)

Though further work is needed to better define the links between ODA and climate vulnerability, these initial findings suggest that ODA is focused on fostering the green transition, but it could be more poverty and inequalities focused. This is consistent with the findings in this chapter that more ODA is flowing to wealthier countries (e.g. UMICs and LMICs) than poorer ones (e.g. LDCs) based on the distribution of poverty and inequalities worldwide. At the same time, understanding the relationships between aid, climate, poverty and inequalities requires moving “beyond the tyranny of averages” (Custer et al., 2017, p. 2[46]) to assess aid targeting at a subnational level (Box ‎14.2). Such spatial targeting allows for a more precise exploration of dynamics within countries and across vulnerable groups, reconciling a poverty and inequalities focus in line with the global agenda to leave no one behind (Cohen, Desai and Kharas, 2019[47]).

Box ‎14.2. A subnational perspective can help reconcile poverty and inequality objectives in the context of the green transition

An analysis of aid targeting at the subnational level has significant potential but faces barriers in terms of the availability of data. Such an assessment requires data on both inputs (aid) and outputs or outcomes (poverty and inequalities) at spatially disaggregated levels worldwide. Various initiatives exist to produce data on outputs and outcomes ranging from applications of remote sensing to the use of household surveys (Doan et al., 2023[45]; Parks, Bergeron and Tierney, 2016[48]). Data on aid at the subnational level, especially data from official sources, are more limited, though partner countries’ aid information management systems are a source (Manuel et al., 2019[49]), as are recent unofficial initiatives to geo-reference these data for public use (see, for example, AidData (https://www.aiddata.org/geoquery) and the Geocoded Official Development Assistance Dataset initiative (https://godad.uni-goettingen.de/home/)).

These efforts have facilitated research on the determinants and effects of aid within countries. Though results are varied across studies, there is little evidence that aid systematically targets poorer areas within countries (BenYishay, DiLorenzo and Dolan, 2022[50]; Manuel et al., 2019[49]). It may even target richer areas that are not as remote as poorer ones (Briggs, 2021[51]). A comprehensive review of the literature on subnational targeting is beyond the scope of this report, though Briggs (2024[52]) offers a recent review. However, findings in the literature are largely limited to certain providers or a subset of partner countries, which makes it difficult to generalise the policy conclusions.

The majority of DAC respondents to the survey about their approaches to poverty and inequalities in their development co-operation (see Chapter 12) indicated that they have not developed systems to track aid at the subnational level but are interested in doing so. A systematic initiative to collect and publish these data comprehensively could build further momentum for this work.

Sources: Doan et al. (2023[45]), Counting people exposed to, vulnerable to, or at high risk from climate shocks, https://documents1.worldbank.org/curated/en/099602511292336760/pdf/IDU07639ca570f3cb048db09bf60fc2cc82df22d.pdf; Parks, Bergeron, and Tierney (2016[48]), Foreign aid and conflict: What we know and what we need to know, https://www.taylorfrancis.com/chapters/edit/10.4324/9781315625614-12/foreign-aid-conflict-bradley-parks-caroline-bergeron-michael-tierney; Manuel et al. (2019[49]), Subnational Investment in Human Capital, https://odi.org/en/publications/subnational-investment-in-human-capital/; BenYishay, DiLorenzo and Dolan (2022[50]), The economic efficiency of aid targeting, https://doi.org/10.1016/j.worlddev.2022.106062; Briggs (2021[51]), Why does aid not target the poorest?, https://doi.org/10.1093/isq/sqab035; Briggs (2024[52]), Aid targeting, https://doi.org/10.4337/9781800886810.00016.

Ways forward

This chapter’s analysis shows providers are targeting their aid to certain crises worldwide, such as the climate crisis, the crisis arising from the COVID-19 pandemic, or the current geopolitical crisis arising from Russia’s aggression in Ukraine. However, as this fast-paced crisis context has placed additional demands on development co-operation, ODA has not kept up in its business-as-usual form with the growing need to address poverty and inequalities more decisively. Additionally, current measures or proxies fall short in guiding allocations. ODA is unique in its ability to support a global redistribution of wealth by providing a concessional resource to countries that need it the most. Even absent large increases in ODA, better targeting of existing ODA could make a significant difference in reducing poverty and inequalities worldwide. The following concrete steps can shift attention to poverty and inequalities in line with ODA commitments.

Even absent large increases in ODA, better targeting of existing ODA could make a significant difference in reducing poverty and inequalities worldwide.

Deploy better measures of aid targeting for better monitoring and accountability. There are different approaches to measuring the targeting of ODA to poverty and inequalities.
- The measures analysed in this chapter can serve as a starting point for bodies such as the DAC and its Community of Practice on Poverty and Inequalities to develop a shared methodology to assess how their ODA is targeting poverty and inequalities. This initiative can be part of a broader effort to develop DAC guidance on poverty and inequalities reduction as mandated in the 2023 DAC High Level Meeting communiqué.
- Proposed measures such as ODA per person in extreme poverty raise questions about aid effectiveness and the global distribution of aid relative to the cost of ending poverty and inequalities. Future work should explore these issues, with specific measures developed to enable an evidence-based discussion among providers.
- There is an opportunity to explore the potential for analysis of aid within countries and make data more readily available for it. As the custodian agency for official aid statistics, the OECD can facilitate efforts to collect, curate and publish geo-referenced aid data. The OECD could also collaborate with partner institutions to pair these aid data with geo-referenced data on outputs and outcomes.
- There is momentum to use CPA, rather than the broader ODA concept, to measure flows going to partner countries as part of long-term development co-operation arrangements. This measure is especially salient for the analysis of whether aid is targeting poverty and inequalities in partner countries.
Develop a toolkit to integrate poverty and inequalities considerations within aid overall and in relation to climate. Sectors such as social protection, climate adaptation and gender equality, among others, open the door for tackling poverty and inequalities in the green transition. Providers cannot assume that investing in addressing the climate crisis or in facilitating the green transition will automatically mean that support is flowing to poorer countries or people. They should develop explicit criteria, guidelines and measures to integrate a poverty and inequalities perspective in their climate programming and vice versa. Examples are available on the OECD Tools, Insights and Practices platform and can be an avenue for peer learning, much like the Development Co-operation Peer Reviews. Further work by the DAC to define a concrete approach for targeting poverty and inequalities in members’ development co-operation can revitalise the role of ODA in supporting the green transition while keeping its focus on the world’s poorest and most vulnerable.

References

[48] Backer, D., R. Bhavnani and P. Huth (eds.) (2016), Foreign aid and conflict: What we know and what we need to know, Routledge, London, https://www.taylorfrancis.com/chapters/edit/10.4324/9781315625614-12/foreign-aid-conflict-bradley-parks-caroline-bergeron-michael-tierney.

[50] BenYishay, A., M. DiLorenzo and C. Dolan (2022), “The economic efficiency of aid targeting”, World Development, Vol. 160, https://doi.org/10.1016/j.worlddev.2022.106062.

[52] Briggs, R. (2024), “Aid targeting”, in Desai, R., S. Devarajan and J. Tobin (eds.), Handbook of Aid and Development, Elgar, Cheltenham, UK, https://doi.org/10.4337/9781800886810.00016.

[51] Briggs, R. (2021), “Why does aid not target the poorest?”, International Studies Quarterly, Vol. 65/3, pp. 739-752, https://doi.org/10.1093/isq/sqab035.

[10] Calleja, R. and B. Cichocka (2022), “Development effectiveness in the ’new normal’: What do the changing roles and purposes of ODA mean for the effectiveness agenda?”, CGD Policy Paper, No. 255, Center for Global Development, Washington, DC, https://www.cgdev.org/sites/default/files/development-effectiveness-new-normal-what-do-changing-roles-and-purposes-oda-mean.pdf.

[20] Castaneda Aguilar, R. et al. (2024), “March 2024 global poverty update from the World Bank: First estimates of global poverty until 2022 from survey data”, World Bank Data Blog, https://blogs.worldbank.org/en/opendata/march-2024-global-poverty-update-from-the-world-bank--first-esti.

[47] Cohen, J., R. Desai and H. Kharas (2019), “Spatial targeting of poverty hotspots”, in Kharas, H., J. McArthur and I. Ohno (eds.), Leave No One Behind: Time for Specifics on the Sustainable Development Goals, Brookings Institution Press, Washington, DC, https://www.brookings.edu/books/leave-no-one-behind/.

[31] Collin, M. and D. Weil (2018), “The effect of increasing human capital investment on economic growth and poverty: A simulation exercise”, Policy Research Working Paper, No. 8590, World Bank Group, Washington, DC, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/786861537902769850/the-effect-of-increasing-human-capital-investment-on-economic-growth-and-poverty-a-simulation-exercise.

[46] Custer, S. et al. (2017), Beyond the Tyranny of Averages: Development Progress from the Bottom Up, AidData at William & Mary, Williamsburg, VA, https://www.aiddata.org/publications/beyond-the-tyranny-of-averages-development-progress-from-the-bottom-up.

[45] Doan, M. et al. (2023), “Counting people exposed to, vulnerable to, or at high risk from climate shocks”, Policy Research Working Paper, No. 10619, World Bank Group, Washington, DC, https://documents1.worldbank.org/curated/en/099602511292336760/pdf/IDU07639ca570f3cb048db09bf60fc2cc82df22d.pdf.

[39] European Commission (2023), The European Commission Inequality Marker (I-Marker), https://data.europa.eu/doi/10.2841/637400.

[35] G20 Brasil 2024 (2024), Task Force for a Global Alliance Against Hunger and Poverty (webpage), Presidency of the Republic of Brazil, Brasilia, https://www.g20.org/en/tracks/sherpa-track/hunger-and-poverty.

[22] Gentilini et al., U. (2022), “Social protection and jobs responses to COVID-19: A real-time review of country measures (version 16)”, COVID-19 Living Paper, World Bank Group, Washington, DC, https://documents1.worldbank.org/curated/en/110221643895832724/pdf/Social-Protection-and-Jobs-Responses-to-COVID-19-A-Real-Time-Review-of-Country-Measures.pdf.

[44] Georgieva, K., V. Gaspar and C. Pazarbasioglu (2022), “Poor and vulnerable countries need support to adapt to climate change”, IMF Blog, https://www.imf.org/en/Blogs/Articles/2022/03/23/blog032322-poor-and-vulnerable-countris-need-support-to-adapt-to-climate-change.

[42] Global Alliance of Territorial Communities (2023), Shandia, https://globalalliance.me/shandia/#top.

[13] Gulrajani, N. and R. Calleja (2019), “Understanding donor motivations”, Working Paper, No. 548, Overseas Development Institute, London, https://cisp.cachefly.net/assets/articles/attachments/77809_working_paper.pdf.

[26] Kenny, C., R. Dissanayake and M. Plant (2020), “What is the role of aid in middle-income countries?”, CGD Policy Paper, No. 201, Center for Global Development, Washington, DC, https://www.cgdev.org/publication/what-role-aid-middle-income-countries.

[11] Kenny, C. and G. Yang (2021), Why Do Some Donors Give More Aid to Poor Countries?, Center for Global Development, Washington, DC, https://www.cgdev.org/sites/default/files/Why-Some-Donors-Give-More-Poor-Countries.pdf.

[21] Manuel, M. et al. (2020), Financing the Reduction of Extreme Poverty Post COVID-19, Overseas Development Institute, London, https://odi.cdn.ngo/media/documents/reducing_poverty_post_covid_final.pdf.

[49] Manuel, M. et al. (2019), Subnational Investment in Human Capital, Overseas Development Institute, London, https://odi.org/en/publications/subnational-investment-in-human-capital/.

[14] Melonio, T., J. Naudet and R. Rioux (2022), Official Development Assistance at the Age of Consequences, Agence Française de Développement, Paris, https://www.afd.fr/en/ressources/official-development-assistance-age-consequences.

[4] OECD (2024), Aid (ODA) disbursements to countries and regions [DAC2A] (database), https://data-explorer.oecd.org/ (accessed on 30 June 2024).

[29] OECD (2024), Creditor Reporting System (database), https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=18&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_CRS%40DF_CRS&df[ag]=OECD.DCD.FSD&df[vs]=1.0&lo=5& (accessed on 30 June 2024).

[5] OECD (2024), Other official flows (OOF) and export credits [DAC2B] (database), https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=13&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC2%40DF_DAC2B&df[ag]=OECD.DCD.FSD&df[vs]=1.0&pd.

[8] OECD (2023), Climate Finance Provided and Mobilised by Developed Countries in 2013-2021: Aggregate Trends and Opportunities for Scaling Up Adaptation and Mobilised Private Finance,, Climate Finance and the USD 100 Billion Goal, OECD Publishing, Paris, https://doi.org/10.1787/e20d2bc7-en.

[9] OECD (2023), Climate-related development finance datasets - Recipient perspective (database), https://www.oecd.org/en/topics/climate-related-development-finance.html.

[2] OECD (2023), DAC High Level Meeting Communique, OECD Publishing, Paris, https://www.oecd.org/en/events/2023/11/dac-high-level-meeting-2023.html.

[3] OECD (2023), Development Co-operation Report 2023: Debating the Aid System, OECD Publishing, Paris, https://doi.org/10.1787/f6edc3c2-en.

[30] OECD (2023), Development Co-operation Report 2023: Debating the Aid System, OECD Publishing, Paris, https://doi.org/10.1787/f6edc3c2-en.

[37] OECD (2023), Development finance for gender equality and women’s empowerment (webpage), https://www.oecd.org/en/topics/gender-and-development.html.

[27] OECD (2023), Development Finance for Refugee Situations: Volumes and Trends, 2020-2021, Forced Displacement Series, OECD Publishing, Paris, https://doi.org/10.1787/cc2df199-en.

[25] OECD (2022), States of Fragility 2022, OECD Publishing, Paris, https://doi.org/10.1787/c7fedf5e-en.

[33] OECD (2022), Update on the DAC Community of Practice on Poverty and Inequalities, OECD Publishing, Paris, https://one.oecd.org/document/DCD/DAC/RD(2022)14/en/pdf.

[43] OECD (2021), OECD DAC Declaration on a new approach to align development co-operation with the goals of the Paris Agreement on Climate Change, OECD Publishing, Paris, https://one.oecd.org/document/DAC/CHAIR(2021)1/FINAL/en/pdf.

[12] OECD (2019), Development Co-operation Report 2019: A Fairer, Greener, Safer Tomorrow, OECD Publishing, Paris, https://doi.org/10.1787/9a58c83f-en.

[1] OECD (1960), The Development Assistance Committee’s Mandate, https://oecdgroups.oecd.org/Bodies/ShowBodyView.aspx?BodyID=869&BodyPID=15814.

[28] OECD DAC (2017), A New DAC in a Changing World: Setting Out a Path for the Future, OECD Publishing, Paris, https://one.oecd.org/document/DCD/DAC(2018)2/FINAL/en/pdf.

[34] OECD/Pathfinders for Peaceful, Just and Inclusive Societies (2023), “Improving the monitoring of SDG 16.3.3: Towards better access to justice”, OECD Public Governance Policy Papers, No. 38, OECD Publishing, Paris, https://doi.org/10.1787/c5fbed7e-en.

[32] Seery, E. and J. Seghers (2019), Hitting the Target: An Agenda for Aid in Times of Extreme Inequality, Oxfam, London, https://policy-practice.oxfam.org/resources/hitting-the-target-an-agenda-for-aid-in-times-of-extreme-inequality-620721/.

[40] Sightsavers (2024), View Our Latest Advocacy and Policy Data (webpage and database), https://www.sightsaversusa.org/policy-and-advocacy/.

[19] UN (2022), World Population Prospects 2022, United Nations Department of Economic and Social Affairs, New York, https://population.un.org/wpp/.

[41] UN Women (2022), Disability Inclusion Markers, https://www.unwomen.org/sites/default/files/2022-01/Brief-Disability-inclusion-markers-en.pdf.

[17] UNDP (2023), 2023 Global Multidimensional Poverty Index (MPI): Unstacking global poverty: Data for high impact action, United Nations Development Programme, New York, https://hdr.undp.org/content/2023-global-multidimensional-poverty-index-mpi#/indicies/MPI.

[38] UNDP and UN Women (2023), The Paths to Equal: Twin Indices on Women’s Empowerment and Gender Equality, United Nations Development Programme (UNDP)/UN Women, New York, https://hdr.undp.org/content/paths-equal.

[16] UNU-WIDER (2023), World Income Inequality Database (WIID). Version 28, https://doi.org/10.35188/UNU-WIDER/WIID-281123 (accessed on 30 June 2024).

[7] World Bank (2024), Foreign direct investment, net inflows (BoP, current US$) (database), https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD.

[6] World Bank (2024), Personal remittances, received (current US$) (database), https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT.

[24] World Bank (2024), The World Bank in Middle Income Countries (webpage), https://www.worldbank.org/en/country/mic/overview.

[18] World Bank (2024), World Bank Country and Lending Groups, World Bank Group, Washington D.C., https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

[15] World Bank (2024), World Bank Poverty and Inequality Platform (PIP) version 20240324, https://pip.worldbank.org/ (accessed on 30 June 2024).

[23] World Bank (2022), Poverty and Shared Prosperity 2022: Correcting Course, https://doi.org/10.1596/978-1-4648-1893-6.

[36] Yonzan, N., D. Gerszon Mahler and C. Lakner (2023), “Poverty is back to pre-COVID levels globally, but not for low-income countries”, World Bank Data Blog, https://blogs.worldbank.org/en/opendata/poverty-back-pre-covid-levels-globally-not-low-income-countries.

Notes

← 1. This development finance for climate and environment refers to concessional and developmental finance from official bilateral and multilateral sources.

← 2. See, for example, the following studies that analyse the relationship between ODA and poverty: https://www.cgdev.org/sites/default/files/some-unpleasant-ODA-arithmetic.pdf and https://www.cgdev.org/sites/default/files/Mitchell-Hughes-Aid-Allocations.pdf.

← 3. CPA is the portion of aid that providers can programme for individual countries or regions, and over which partner countries could have a significant say. CPA estimates are based on an updated, pilot methodology to the current methodology by the OECD used to publish statistics on CPA; further adjustments to this pilot methodology may be possible until the end of 2024. More details are available upon request at dac.contact@oecd.org.

← 4. This share is based on income groups in the year of disbursement and on the OECD DAC List of ODA-eligible recipients, available at https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/DAC-List-of-ODA-Recipients-for-reporting-2022-23-flows.pdf. These DAC income groups are mutually exclusive as presented; therefore, the categories of OLICs, LMICs and UMICs exclude LDCs.

← 5. This 2024 figure is based on projections. Among MICs, 50% of the world’s extreme poor are in LMICs and 5% in UMICs.

← 6. This indicator is defined as ODA grants to basic social services (basic health and education, water supply and sanitation, multisector aid for basic social services) and development food aid. For further detail, see https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-01.pdf.

← 7. The author thanks Nishant Yonzan and Daniel Gerszon-Mahler, Development Data Group, World Bank for sharing data on poverty rates, including projections up to 2030.

← 8. The sectors and themes in the proposed framework were identified based on extensive research by the Community of Practice on Poverty and Inequalities, desk reviews and consultations, and prior work across the Development Co-operation Directorate and DAC. The author thanks Julie Seghers, Julia Schnatz, Carolina Guerra, Isabel Davis, Danielle Mallon, Hector Moreno and José Ignacio Suarez Sarrazin for their earlier work that contributed to this framework.

← 9. For the purposes of this report, ODA to justice is defined using the following OECD DAC sector codes: legal and judicial development (15130), human rights (15160), ending violence against women and girls (15180), and labour rights (16070). This approach builds on existing measures of ODA to justice such as those found here: https://odi.org/en/publications/justice-aid-update-2023/..

← 10. For the purposes of this report, aid to food security is defined using the OECD DAC sector codes of school feeding (11250), basic nutrition (12240), food crop production (31161), agro-industries (32161), food security policy and administrative management (43071), household food security programmes (43072), food safety and quality (43073), food assistance (52010), and emergency food aid (72040), and any activity that targets nutrition as a principal or significant objective outside of these sectors.

← 11. In contrast to other OECD publications, to provide an indication of aid targeting, this analysis uses DAC income group classifications that vary over time, rather than using a fixed list based on the latest DAC List of ODA-eligible Recipients.

← 12. Estimates are available for all countries in 2019. A further subset, covering people exposed and vulnerable, is available for 75 countries representing 77% of the world’s population.

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment

Governance

Health

Industry, business and entrepreneurship

Regional, rural and urban development

Science, technology and innovation

Society

Taxation

Trade

Energy

Nuclear energy

Transport

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment

Governance

Health

Industry, business and entrepreneurship

Regional, rural and urban development

Science, technology and innovation

Society

Taxation

Trade

Energy

Nuclear energy

Transport

Countries A - C

Countries D - I

Countries J - M

Countries N - R

Countries S - T

Countries U - Z

Regional and global engagement

Countries

Countries A - C

Countries D - I

Countries J - M

Countries N - R

Countries S - T

Countries U - Z

Regional and global engagement

Publications

Publications

Featured publications

Data

Data

Featured data

News & Events

News & Events

Featured Events

About OECD

About

Engage with us

Work with us

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment