OECD Collaboration on ‘Joining Forces to Leave No One Behind’

We collaborated with the OECD’s Development Co-operation Directorate to produce data visualisations for the annual flagship report: OECD Development Co-operation Report 2018: Joining Forces to Leave No One Behind, published 11 December 2018.

Extreme Poverty

Historical look at poverty should bring optimism

The world has seen dramatic progress in poverty alleviation over the last few centuries, but particularly in very recent decades. In the 1800s almost everyone lived in extreme poverty. By 2000 this had fallen to just under one-third of the world population (29 percent). By 2015, this had fallen to one-in-ten people globally.

This chart can also be viewed as the absolute number of people living in extreme poverty by unticking the ‘relative’ box in the bottom-left of the chart.

Extreme poverty in the baseline (business-as-usual) scenario

The chart below shows the projected number of people in extreme poverty under the baseline (business-as-usual) scenario. This is based on the study presented in Nature by Crespo-Cuaresma et al. (2018).1 This work is also monitored/presented visually at the World Poverty Clock.

These estimates are presented from 2015 through to the end of the SDG period (2030/31). Clicking on any country will provide a time-series view of its projected trend over this period.

By 2030 it’s projected that the largest number of people in extreme poverty will be in Nigeria, at nearly 100 million. Second highest is the Democratic Republic of Congo at 60 million.

In the chart below this data is also shown as the share of the population living in extreme poverty in the baseline (business-as-usual) scenario. This is derived using the above data on the number of people in extreme poverty and UN Population Prospects (2017) medium fertility projections.

Using the ‘play’ button in the timeline below we can see how this is projected to change from 2015 to 2030. By clicking on a country, you can view its time series over this period. In the ‘chart’ tab below you can also view the time-series for the OECD’s Fragile States categories, or by region.

In the chart below we see the business-as-usual projections of extreme poverty aggregated by region. In the bottom-left of the chart you see a ‘relative’ tick-box; this will switch these figures to show their relative share of the total.

There are multiple key points here:

  1. By 2030, it’s projected that 446 million people will still live in extreme poverty. This is still a significant reduction from the 729 million in 2015 (approximately a 40 percent reduction).
  2. The total number of people in extreme poverty is expected to decline significantly in Asia (by approximately 85 percent from 292 million down to 38 million).
  3. The number of people in extreme poverty in Africa declines only slightly from 401 million in 2015 to 376 million in 2030 (a 6 percent reduction).
  4. Most people in world living in extreme poverty will be in Africa. In 2015 Africa accounted for 55 percent of the global total; by 2030 this is expected to rise to 84 percent.

In the chart below we also show the business-as-usual projections of extreme poverty aggregated by the OECD States of Fragility 2018 assessment. Here you can view it in absolute numbers of people in extreme poverty, but also switch to ‘relative’ view by ticking the box in the bottom-left of the chart; this shows the percentage of people in extreme poverty who live in fragile states.

It’s projected that by 2030, around 89 percent of people still living in extreme poverty will live in fragile (57 percent) or extremely fragile (32 percent) states.

Extreme poverty scenarios

The figures presented above are based on the baseline (business-as-usual) scenario. The chart below presents the results of other modelled scenarios. Crespo-Cuaresma et al. (2018) modelled multiple scenarios of extreme poverty based on the commonly used Shared Socioeconomic Pathways (SSP).

The projections of extreme poverty for 2030 under the five SSP scenarios are shown in the chart below. Using the “change country or region” button on the bottom-left of the chart you can view this data by country or region.  Two scenarios showed higher global poverty numbers than the baseline:

  1. a scenario based on low economic growth, educational attainment, high fertility rates and major climate change mitigation and adaptation challenges (this is shown in the next section);
  2. a scenario based on worldwide polarization with high income countries exhibiting relatively high growth rates of income, while developing economies present low levels of education, high fertility and economic stagnation.

Two scenarios produced lower global numbers of extreme poverty:

  1. a scenario based on low challenges for both climate change adaptation and mitigation resulting from income growth which does not rely heavily on natural resources and technological change, coupled with low fertility rate and high educational attainment.
  2. a scenario with high economic growth (and therefore low adaptation challenges) coupled with high demand for fossil energy from developing economies, but with high global CO2 emissions.

Poverty under high climate impact scenario

The future socioeconomic scenario (SSP3) which results in the greatest number of people in extreme poverty is one characterized by low economic growth coupled with low educational attainment levels and high population growth at the global level, which is exacerbated by high climate change mitigation and adaptation challenges.

In the chart below we present the estimated difference in the number of people in extreme poverty in SSP3 versus the baseline scenario. Here we see that for some countries — predominantly richer countries across Europe, North America and Oceania — this scenario would result in a decline of poverty headcounts. The magnitude of this decline is however several orders of magnitude lower than the increased poverty across most of the world’s countries. This is particularly true of some of the world’s most fragile countries including the Democratic Republic of Congo, Tanzania, Nigeria, Niger, and Afghanistan.

Overall, it’s projected that this scenario of heightened climate vulnerability (combined with high fertility, low educational attainment and growth) would result in an additional 60 million people in extreme poverty in 2030.

Poverty under worldwide polarization

The other scenario which results in higher numbers of extreme poverty relative to the baseline scenario is one of worldwide polarization. This is based on an assumption of high economic growth rates in today’s high-income countries with low levels of education, high fertility and economic stagnation in low-to-middle income countries. We may envisage this scenario could evolve from domestic protectionism, reduced trade and lower international investment.

This would result in reductions in extreme poverty across most high-income countries (including South Africa, Brazil and Latin America). However, poverty numbers increase across today’s lowest income and and fragile states, including the Democratic Republic of Congo, Nigeria, Tanzania, Niger, Uganda and Afghanistan.

Overall, it’s projected that this scenario would result in an additional 50 million people in extreme poverty in 2030.

Is extreme poverty a good indicator of multidimensional poverty?

Does extreme poverty, as measured by the international poverty line of $1.90 per person per day provide a good indication of deprivation?

In the chart below we have plotted the poverty rate as defined by the international poverty line (y-axis) versus the poverty share as measured by multidimensional poverty. The Multidimensional Poverty Index (MPI), is derived by weighing ten indicators of deprivation in the context of education, health and living standards. Individuals are considered poor if deprived in at least one third of the weighted indicators.

Here we notice two key points:

  1. there is an overall positive correlation that countries with higher extreme poverty rates based on income also have higher rates of multidimensional poverty.
  2. there can be large differences in extreme poverty and multidimensional poverty rates. The grey line below is a line of parity; countries which lie above the line have higher rates of ‘poverty’ by the extreme poverty income measure; countries below the line have higher rates of ‘poverty’ by the multidimensional poverty measure. Here we see that most (but not all) countries have higher measures of poverty by the multidimensional metric; in some countries this can be many times higher than the international poverty line would suggest (although admittedly the extreme poverty line is acknowledged to be set at a very low level of income).

Hunger

In the chart below we have visualized the latest statistics on undernourishment from the UN FAO.2 By region and at the global level, these estimates are available for the year 2017. At national levels, this extends only to the year 2016. National-level data is reported as “3-year averages”; for visualization we have taken the mid-year of these periods (e.g. “2014-2016” we have allocated to “2015”).

In the chart below we show the data of some key regions (these can be added using the “add country/region” button in bottom-left) in addition to the aggregate groupings of the OECD States of Fragility 2018 list. A map and timeline of national data can also be seen using the “map” tab below.

This data is shown as the share of people who are undernourished (top chart) and the total number (bottom chart). Here we see that despite overall progress in many countries over this period, there has been a global increase over the past 3 years. This increase has been dominated by a rise in undernourishment in Sub-Saharan Africa. In its 2017 and 2018 reports, The State of Food Security and Nutrition in the World, the UN FAO has suggested that both a rise in conflict and political instability, combined with the climatic impacts of the El Nino contributed to this increase.3,4

Aid

Country Programmable Aid (CPA) received

The chart below shows country programmable aid (CPA) received from all donors. The chart is based on data supplied by the OECD, extracted from the OECD Statistics on Development and official development assistance (ODA). The measure — Country Programmable Aid — is the portion of aid that donors programme at country or regional level (detailed explanation available at the link:https://data.oecd.org/oda/country-programmable-aid-cpa.htm). Data from 2000-2016 is based on estimated figures; those from 2017-2019 are projections based on countries’ Forward Spending Plans.

Clicking on a given country shows its time series over time.

Humanitarian and food aid received

The charts below presents the data on humanitarian and food aid received from 2002 to 2016.

Clicking on a given country shows its time series over time.

Aid received directly by state of fragility

In the chart below we show the total aid received directly (the sum of CPA, humanitarian and food aid) aggregated by the OECD States of Fragility 2018 groupings. This is shown from 2000 to 2016. By clicking on the ‘Relative’ box, this chart is displayed as the share of total aid received directly by each group.

Aid received per capita

In the charts below we have calculated total aid received (the sum of CPA, humanitarian and food aid) per capita, by combining OECD aid data provided with UN World Population Prospects (2017) estimates of population. We have also calculated this on a regional basis, for OECD fragility groupings, Least Developed countries (LDCs) and Small Island Developing States (SIDS). This is shown in map form with a timeline from 2000 to 2016 (and extension to 2019 based on Forward Spending Plans). Clicking on a country shows its time series form.

Alternatively time series are available by clicking the ‘Chart’ tab (or as shown in the second chart below).

In the third chart below we have created a scatterplot of aid received per capita versus GDP per capita. Both axes are currently on a log scale, but both (or one) can be changed to linear simply by clicking the ‘Log’ dial. The size of the bubble indicates the estimated number of people in extreme poverty (as defined by the international poverty line of $1.90 per person per day). Bubbles are coloured by World Bank income group; hovering over any of the income groups in the legend highlights all relevant countries in the chart. The timeline function is available from 2000 to 2016 at the bottom of the chart.

Under-aided countries

Countries defined as under-aided by the OECD are those which are are “underfunded” by need relative to others. This is assessed on the basis of multiple aid allocation models: the Egalitarian, Performance-based allocation (PBA), UNDP, and Collier-Dollar poverty allocation (CD) model. The top under-aided countries in a given year are defined here as those identified as underfunded on the basis of 3 or more of the allocation models.

In the charts below we show the top under-aided countries in any given year from 2006 to 2016 (this can be seen through time using the timeline in the first chart below). The second chart below shows the number of years a country has been identified as under-aided (according to at least 3 of the aid allocation models) over the period from 2006 to 2016. In 2016 all under-aided countries were in Africa, all are Least Developed Countries (LDCs), 1 is a Small Island Developing State (SIDS), 1 extremely fragile, 3 fragile, 1 not defined as fragile. Note that since all under-aided countries are in Africa, this map can be zoomed to show Africa only using the dial/cog button in the bottom right of the map chart.