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Investments to end poverty 2018: Appendix 2


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Data and analysis in the Investments to End Poverty 2018 report is based on a variety of sources, including estimates developed by Development Initiatives (DI). This annex outlines all the data sources used to inform our analysis in different sections of the report and provides more detailed methodology notes compared with those given in the notes within chapters.

Developing countries and regions

This report uses the Organisation for Economic Co-operation and Development (OECD)’s DAC List of ODA Recipients, effective as at 1 January 2015 for reporting on 2014, 2015, 2016 and 2017, as the definition of ‘developing countries’, which includes 146 countries.

Regional data on ODA is based on the OECD’s regional classifications, which group developing countries into nine regions: Far East Asia, Europe, Middle East, North and Central America, North of Sahara, Oceania, South America, South and Central Asia, and South of Sahara.

Regional data on poverty is based on the PovcalNet regional classifications which group developing countries into six regions: sub-Saharan Africa, South Asia, East Asia and the Pacific, Latin America and the Caribbean, Middle East and North Africa, Europe and Central Asia.

Country and region naming conventions used do not reflect a political position of Development Initiatives.

Poverty data

Poverty data used in this report refers to the extreme poverty line at PPP$1.90 a day and are based on the latest data from World Bank PovcalNet (2011 prices). The most recent year of available data is 2013.

Poorest 20%

When the ‘poorest 20%’ is used in the report, it refers to the poorest 20% of people in the world, who we have calculated live on less than PPP$2.56 a day (2011 prices). This includes everyone – men, women and children – currently living below the international poverty line plus the people who are most vulnerable to falling back into extreme poverty. Data is based on latest available data from World Bank PovcalNet, selected Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and China Family Panel Studies (CFPS).

Resource flows data

Domestic public resources

Domestic public resources are estimated by non-grant government revenue data sourced from International Monetary Fund (IMF) Article IV staff and programme review reports (various) and the IMF World Economic Outlook database. Grants are excluded to avoid double counting with official development assistance (ODA).

Non-grant government revenue per person in the report is shown in PPP$ (2011 prices) to indicate purchasing power of the revenue in each respective country on comparable terms. Data is rendered 2011 prices, which is the most recent available benchmark year from the International Comparison Program.

Domestic commercial resources

Domestic commercial resources are estimated using domestic credit to the private sector data, which refers to financial resources provided to the private sector by financial corporations (such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable) that establish a claim for repayment. To calculate this, we apply the World Bank World Development Indicator ‘Domestic credit to the private sector (% of GDP)’ to the gross domestic product (GDP) of each respective country.

International official resources

Official development assistance (ODA)

Data on ODA is sourced from the OECD Development Assistance Committee (DAC) aggregate tables and Creditor Reporting System (CRS). ODA figures reported in the report are inclusive of debt relief unless expressly stated otherwise. Data is shown in gross terms unless otherwise stated (net data is used for the measurement of ODA against certain targets such as the 0.7% ODA of gross national income (GNI) target). The report uses data on disbursements unless otherwise stated. ODA data shown in report is from the April and June 2018 OECD DAC online database updates. Unless otherwise specified, ODA refers to figures from DAC and multilateral donors only.

Where 'non-transfer ODA' is used in the report, it refers to aid activities that do not leave the donor country. This is constructed of debt relief, administrative costs, students within donor countries, refugees within donor countries, in-donor development awareness and subsidies paid to donor-country banks. The CRS fields ‘aid-type’ and ‘finance-type’ are used to categorise the non-transfer modality of ODA.

Non-country-specific ODA includes ODA that does not have a specified single country recipient. This can include ‘developing countries unspecified’, which refers to ODA that benefits several regions or non-country programmable aid such as administrative costs, refugees in donor country and in-donor research costs. This category further includes ODA to ‘regional’ recipients (e.g. South of Sahara, Regional) which refers to ODA that benefits several recipient countries within the specified region. Where 'country-allocable ODA' is used, it refers to ODA reported that is intended for a specific country recipient.

The channel of delivery of ODA refers to the first implementing partner of the ODA disbursement, which has implementing responsibility over the funds.

Development cooperation from other government providers

This includes data on south–south cooperation and on ODA from non-DAC donors that report to the OECD DAC. Data was sourced from OECD DAC1, JICA Research Institute, Government of India Union Budget, South Africa National Budget, ABC (Brazilian Cooperation Agency) and IPEA (Institute of Applied Economic Research), Mexican Agency for International Development Cooperation, and other national sources.

Other official flows (OOFs)

Data was taken from the OECD DAC aggregate tables and CRS. As of the December 2018 update, comprehensive total gross OOFs are no longer reported by the OECD DAC as a single combined flow; data on OOF disbursements used in aggregated analysis in this report includes the sum of OOF grants and other long-term amounts extended (i.e. loans) from all donors. OOFs broken down by sector are sourced from the OECD CRS.

Export credits

Data refers to officially supported export credits and was taken from OECD DAC aggregate tables.

Military and security spending

Estimates were collected from the Stockholm International Peace Research Institute (SIPRI). The SIPRI definition of military expenditure aims to include all spending on current military forces and activities.

Specifically, the SIPRI definition of military expenditure includes current and capital spending on:

  • the armed forces, including peace keeping forces
  • defence ministries and other government agencies engaged in defence projects
  • paramilitary forces when judged to be trained, equipped and available for military operations
  • military space activities.


Data on peacekeeping budgets and funding snapshots is sourced from SIPRI and refers to the cost of bilateral and multilateral peacekeeping operations, including those mandated by the UN.

Official long-term debt

Data is based on multiple indicators sourced from the World Bank DataBank. It refers to public and publicly guaranteed debt from official creditors, including loans from both international organisations (multilateral loans) and governments (bilateral loans). Data on ODA loans and OOF loans as reported to the OECD DAC database is subtracted to avoid double counting. Negatives are set to zero at the country level.

International commercial resources

Private finance mobilised via blending

Data was provided by the OECD, in supplement to data available from: Benn J., et al, 2017. Amounts mobilised from the private sector by official development finance interventions: Guarantees, syndicated loans, shares in collective investment vehicles, direct investment in companies, credit lines. OECD Development Co-operation Working Papers, No. 36. OECD Publishing, Paris. In the absence of comprehensive data on the amounts invested in blended finance projects by donors, this data is used as proxy for blended finance volumes. The source provides data from 2012 to 2015 only. 2015 data is used as a proxy for 2016 in cases of static analysis of resource flows, that is those which focus on 2016 data for other flows in the report. Data on private finance mobilised via blending is not shown in historical trends analysis.

Foreign direct investment (FDI)

Data on inward and outward FDI flows was taken from UNCTADstat. Data on FDI shown at the subnational or sectoral level was taken from fDi Markets from Financial Times Ltd. Data on outflows of profits on FDI was sourced from the World Bank DataBank.

Commercial long-term debt

Data is based on multiple indicators sourced from the World Bank DataBank. It refers to lending by commercial actors and includes private loans that are public and publicly guaranteed (PPG) – bonds, commercial banks and other private – and private non-guaranteed (PNG) loans – bonds and commercial banks. Private non-guaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Public and publicly guaranteed debt from private creditors includes: bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; other private credits from manufacturers, exporters, and other suppliers of goods; and bank credits covered by a guarantee of an export credit agency.

Short-term debt

Data is sourced from the World Bank DataBank. Short-term external debt is defined as debt that has an original maturity of one year or less. Data is presented in net terms at the source. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Negative flows are set to zero at the country level.

Portfolio equity

Data refers to cross-border transactions and positions involving equity securities other than those recorded as direct investment and including shares, stocks, depository receipts, and direct purchases of shares in local stock markets by foreign investors. Negative flows are set to zero at the country level. Data is taken from the World Bank World Development Indicators named from indicator ‘Portfolio equity, net inflows’.

International private resources


Data refers to migrant remittance inflows and outflows and is sourced from the World Bank Migration and Remittances Factbook database.

International tourism receipts and expenditures

Data is sourced from the World Bank World Development Indicators. Tourism is considered an export industry, but the flow being captured by the data is spending by individuals (non-residents) in the country of destination, thus similar in nature to remittances. It is therefore considered a ‘private’ source of financing for the purposes of the report. While tourism can have a detrimental impact on local economies and the environment, its positive potential in achieving sustainable development outcomes has been recognised in the context of the Sustainable Development Goals (SDGs), especially in relation to job creation and the promotion of local culture and products (see SDGs 8.9 and 12.B), particularly in Small Island Developing States (see SDG 14.7). Data on international tourism spending by individuals has thus been included in Chapter 3 analysis to reflect a more comprehensive picture of the different sources of financing that can contribute to SDG achievement in developing countries.

Private development assistance

Data is sourced from the Hudson Institute Index of Global Philanthropy and Remittances 2016 and OECD DAC.

Overlaps between international resource flows

It is known that there are overlaps in the flows captured by data series estimating different resource flows. Where possible, these overlaps have been quantified to avoid any double counting between series. Loans reported as ODA and OOFs were subtracted from disbursements of long-term loans from official sources at the recipient country level. Data on private development assistance is based on non-official sources of income to avoid overlaps with ODA. Estimates of climate change finance, development finance institutions (DFIs) and peacekeeping are not added to international flows on the assumption that these flows are captured entirely in data on other flows such as ODA, OOFs, private finance mobilised via blending, loans and FDI. It is known that there are other potential overlaps between international resource flows, but there is insufficient data available to quantify them. Domestic tax obtained from international resource flows such as tourism expenditure and expenditure of remittances have not been quantified as an overlap between domestic and international flows.

Other data

Population, GNI and GDP

Data on population, GNI and GDP was sourced from the World Bank World Development Indicators, IMF World Economic Outlook and OECD. Where possible, combinations of data were taken from the same source for consistency.

Countries being left behind

Countries being left behind refers to the 30 countries identified to be most at risk by poverty, human development, and climate and fragility models and measures.

The 30 countries at risk of being left behind includes: Afghanistan, Benin, Burundi, Central African Republic, Chad, Congo, Democratic Republic of the Congo, Eritrea, Gambia, Guinea, Guinea-Bissau, Haiti, Lesotho, Liberia, Madagascar, Malawi, Mali, Micronesia, Mozambique, Niger, Nigeria, Papua New Guinea, Somalia, South Sudan, Sudan, Syria, Togo, Uganda, Yemen, Zambia.

For more information see:


Constant prices

Our trends analyses on financial flows up to 2016 are in US$ constant prices (base year 2016) unless otherwise stated. We use data from the OECD DAC, the IMF’s World Economic Outlook and the World Bank World Development Indicators to construct deflators to convert financial data from current to constant prices.

Sector definitions

Sectoral ODA analysis is based on our own sector groupings, which are closely aligned with, but not identical to, the OECD DAC’s sectors. These groups are built by aggregating figures reported under different OECD DAC sectors.

‘Agriculture and food security’ includes the agriculture, forestry, fishing and developmental food aid sectors. ‘Business and industry’ includes figures reported to banking and financial services, business and other services, industry, mineral resources and mining, construction, tourism, and trade policy and regulation. ‘Debt relief’ covers debt forgiveness, rescheduling and refinancing, and other actions related to debt. ‘Education’ includes all identifiable general education components from policy and administration, to provision at primary, secondary and tertiary level (including multi-sector and vocational training). ‘Environment’ includes multi-sector projects related to general environmental protection. ‘General budget support’ includes figures specifically reported as general budget support. ‘Governance and security’ includes a wide range of activities including government and civil society, and conflict, peace and security. ‘Health’ includes general health activities (including medical services, medical research and the management of health policy), basic healthcare interventions (such as basic nutrition and infectious disease control), and population policy and reproductive healthcare (including HIV/AIDS programmes). ‘Humanitarian aid’ includes reconstruction, relief and rehabilitation, prevention and preparedness, and emergency. ‘Infrastructure’ covers transport and storage, energy generation, and supply and communications. ‘Other social services’ includes social and welfare services, policies related to employment and housing, statistical capacity building, and other social services. ‘Water and sanitation’ includes water supply, basic drinking and sanitation facilities, waste management, and water and sanitation policy, administration and education. These sector classifications are used in sectoral analysis of ODA throughout Chapter 2 of the report. Other sector figures reported outside of the sectors listed above include the administrative costs of donors, multisector ODA, support for refugees in the donor country and unallocated or unspecified flows. These sector classifications are included in other analysis using total ODA throughout the report.

Sectoral analysis is not possible across all flows beyond ODA due to gaps in data disaggregation. Where sectoral data is available (for OOFs, private finance mobilised via blending and FDI), analysis has been aligned to the same system as ODA as much as possible, but it is not fully consistent given the different sector classifications used for FDI data. Sectoral analysis in Chapter 3 thus relies on aggregated sectors that make the different classifications comparable. For FDI, that reported under ‘social sectors’ in Figure 3.14 only includes health-related investments (reported under the industrial cluster ‘life sciences’). This is partly due to education being considered as a cross-cutting business activity as opposed to an industry; findings may thus represent an underestimation of actual FDI to social sectors. For ODA, OOFs and private finance mobilised via blending the following was used for sector alignment: ‘social sectors’ are constructed from combining education, health, governance and security, water and sanitation and other social services. Agriculture and food security, business and industry, infrastructure, and environment are aligned to the respective sectors expanded upon in the previous paragraph. ‘Other’ combines general budget support, debt relief, humanitarian aid, and other sectors (as listed in the previous paragraph).

Projections and scenarios to 2030


To estimate the poverty headcounts in 2030, DI took the current distributions of incomes or consumptions from the World Bank’s PovcalNet April 2018 update. These distributions were projected forward based on the average GDP growth rates in constant prices published by the IMF’s World Economic Outlook (WEO) in Spring 2018. The average growth rates estimated by the WEO from 2013 to 2023 were used to forecast income growth rates to 2030. Populations with a forecasted income below 2011 PPP$1.90 were considered in poverty. The main figures used are based on an assumption of growth being evenly distributed across the population.

Non-grant government revenue

The scenario for estimating non-grant government revenue up to 2030 is calculated by applying forward-looking data on real GDP growth (annual percentage change) to non-grant government revenue data. Growth rates for the relevant year and country are applied to the non-grant government revenue data from 2017 onwards. The real GDP growth projections are available up to 2023. For all years post-2023 the 2023 growth is applied.


Projections of GNI are based on applying the average annual growth rate of GNI between 2012 and 2017 to each successive year from 2017 up to 2030. This is then used to calculate the level of ODA for each year under the scenario that the ODA/GNI target remains the same in 2017 for each year up to 2030. In a scenario where donors reach the 0.7% target by 2030, the level of ODA is calculated by applying the increasing (or maintained in the case the target has been met before 2030) ODA/GNI percentage for each successive year up to 2030.

Scenarios estimating proportion of ODA and non-ODA flows to countries with greater than 20% of people living in extreme poverty

The forward-looking scenario of estimating the proportion of ODA flows and non-ODA flows to countries with greater than 20% of people living in extreme poverty in 2030 used in Figure 5.2 are based on applying the 2016 share of flows going to countries forecasted to have poverty rates greater than 20% by 2030. Poverty forecasts are calculated using the methodology described previously for poverty headcounts in 2030 divided by estimated 2030 population data.

Scenarios estimating international funding to education

Scenarios estimating net ODA to education from 2017 to 2030 used in Figure 5.3 are based on the ODA projections derived from the above ODA and GNI projection methodology. These are applied for each year to the 2016 proportion of net ODA to education to total ODA in both countries identified as being left behind and other developing countries. The scenario estimating FDI to 2030 used in Figure 5.3 is based on applying the average annual growth rate of FDI over 2012–2016 to values of the flow in 2017 and to each successive year beyond this. This is then applied to the share of FDI to education over total FDI (2012–2016) in both countries being left behind and other developing countries. The scenario of private finance mobilised via blending up to 2030 used in Figure 5.3 is based on applying estimated growth rates for the flow based on the average annual growth rate for European DFIs between 2005 and 2017 (of 11%) as a proxy for forward growth rates of private finance mobilised via blending. This growth rate was used due to the data on private finance mobilised via blending not being sufficient to calculate a growth rate estimate. This growth rate is applied to each successive year of data from 2015 onwards up to 2030 and then applied to the share to education over total (2012–2015) in both countries being left behind and other developing countries.

Non-ODA inflows per capita to countries being left behind and other developing countries

The scenario for non-ODA inflows per capita to countries being left behind and other developing countries referenced in Chapter 5 is based on dividing the estimated combined value of non-ODA flows in 2030 by the estimated 2030 combined population for each respective group of countries. Non-ODA flows in 2030 are calculated by applying the mid-point growth rate between the 2002–2016 annualised rate and the average of three 4-year growth rates (2002–2006; 2007–2011 and 2012–2016) to the 2016 value for each flow and respective country grouping from 2017 to 2030. One exception is private finance mobilised via blending which uses a growth rate based on the average annual growth rate for European DFIs between 2005 and 2017, as referenced previously. This is applied to data on private finance mobilised via blending from 2015 to 2030 for each respective group of countries.

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