Data for Development Profiles

Methodology

How was ODA for data and statistics identified?

General remarks

The profiles use data on providers’ official development assistance (ODA) to data, statistics and statistical capacity development extracted from the Creditor Reporting System (CRS), the official source of information on aid flows maintained by the OECD, for the years 2010-19. The data are collected at the level of projects. This annex explains how information on providers’ support to statistics and statistical capacity building was extracted.

Reporters to the OECD’s CRS can classify ODA activities in support of “statistical capacity building” using the designated purpose code (16062). However, extracting only these projects for the purpose of the Data for Development (D4D) profiles would result in an incomplete picture of the full range of activities members of the Development Assistance Committee (DAC) (OECD, 2019[1]) implement in support of data and statistics in developing countries.

In addition to projects that were recorded under the purpose code for statistical capacity building, additional projects were identified by scanning project titles for specific terms indicative of support to data, statistics or statistical capacity building. Descriptions in project titles were first transformed to lower case letters and then classified as being in support of data and statistics if they contained any of the terms in Table 1.

Table 1. Search terms used to identify provider support to statistics and data from project titles in the CRS database

English	French	Spanish	Portuguese
statisti		estadisti, estadísti	estatísti
national account	comptes nationaux	cuentas nacionales	contas nacionais
price index	indice des prix	indice de precios, índice de precios	índice de preço, indice de preco
production index	indice de production	índice de produccion, indice de produccion	índice de produção, indice de producao
survey	enquête, enquete	enquesta	inquérito, inquerito
census	recensement	censo
information system	système d’information, systeme d’information	sistema de información, sistema de informacion	sistema de informação, sistema de informacao
birth registr	enregistrement des naiss	inscripción del naci, inscripcion del naci	registo dos nasci
death registr	enregistrement des déc, enregistrement des dec	inscripción del defunc, inscripcion del defunc	registo do óbito, registo do obito
civil registr, crvs	registre civil	registro civil	registo civil
land registr	enregistrement fonc, enregestriment des terrai	inscripción de tierra, inscripcion de tierra, registro de tierra	registo de terren, registo de propriedad
cadaster	cadastre	catastro	cadastro
business registr	registre des entrepr, registre du commerce	registro mercantil	registo das empresas, registos comerciais
database	base de données, base de donnees	base de datos	banco de dados
big data	mégadonnées, megadonnees	datos masivos	megadados
data for decisions	données pour les décisions, donnees pour les decisions	datos para decisiones	dados para decisões, dados para decisoes
data science	science des données, science des donnees	cienca de datos	ciência de dados, ciencia de dados
data for development	données pour le développement, donnees pour le developpement	datos para el desarrollo	dados para desenvolvimento
data journalism	journalisme de données, journalisme de donnees	periodismo de datos	jornalismo de dados
data for education	données pour l’éducation, donnees pour l’education	datos para la educación, datos para la educacion	dados para educação, dados para educacao
education data	données sur l’éducation, donnees sur l’education	datos educativos	dados educacionais
data for health	données pour la santé, donnees pour la sante	datos para la salud	dados para saúde, dados para saude
peacebuilding data
global data
global pulse
health data	données de santé, donnees de sante	datos de salud	dados de saúde, dados de saude
refugee data	données sur les réfugiés, donnees sur les refugies	datos de refugiados	dados de refugiados
migration data	données de migration, donnees de migration	datos de migración, datos de migracion	dados de migração, dados de migracao
data collection	collecte de données, collecte de donnees, collecte des données, collecte des donnees, rassemblement des données, rassemblement des donnees	recopilación de datos, recopilacion de datos, colección de datos, coleccion de datos, compilación de datos, compilacion de datos	regocida de datos
action through data
data project	projet de données, projet de donnees	proyecto de datos	projeto de dados
open government data	données publiques ouvertes, donnees publiques ouvertes
open data	données ouvertes, donnees ouvertes	datos abiertos	dados abertos
openstreetmap
“ ophi”, “ophi ” (note spaces!)
satellite data	données satellites, donnees satellites	datos satelitales	dados de satélite, dados de satelite

In a second step, the resulting projects were curated manually and some projects were subsequently removed. Examples include projects in support of surveys that are arguably not part of official statistics (e.g. surveys of unexploded ordnance and geological surveys) and projects with project titles citing evidence from surveys or information systems but which did not by themselves support these activities.

Other sources

In addition to the two steps described above, inclusion of all projects with the designated purpose code and text search and manual curation, additional data was spliced in for two DAC members, Japan and Korea.

Japan’s support to IMF capacity development

In the case of Japan, additional ODA was considered that would not have been included based solely on the method above, namely, Japan’s support to statistics in the context of its partnership with the International Monetary Fund (IMF) in the area of economic statistics. In 1990, Japan became the first partner to support IMF capacity development. Having contributed USD 474 million for capacity development since financial year (FY) 1990, it continues to be the single largest contributor today. In the period FY2013–17, Japan alone was responsible for 22 percent of external financing for IMF capacity development (IMF, 2017[2]).

The vehicle for Japan’s support to the IMF’s capacity development operations is the Japan Subaccount (JSA) of the Framework Administered Account for Selected Fund Activities. While the IMF’s capacity development operations entail various core areas, including fiscal policy and management, monetary policy and financial systems and legislative frameworks. However, a key area is also macroeconomic and financial statistics, including multisector statistical issues, balance of payments and other external sector statistics, government finance statistics, monetary and financial statistics and financial soundness indicators, national accounts and price statistics and data dissemination standards. In the period FY2010–20, Japan’s annual commitments for macroeconomic statistics averaged USD 4.3 million per year (seeTable 2)

Japan’s contributions to the JSA are captured only in aggregate and only since 2013. To capture Japan’s support to statistics via this channel, information provided by the IMF about JSA annual commitments disaggregated by topic was incorporated as follows: first, commitments reported by IMF financial years were matched to calendar years. As fiscal year t includes the second half of calendar year t-1 and the first half of calendar year t, the commitment in calendar year t was assumed to be equal to the average commitments in fiscal years t and t+1. Second, nominal dollar terms were deflated using the deflator used for the calculation or constant price aid flows in the CRS database. Third, the resulting series was appended to the CRS database with attributes gathered from Japan’s (aggregate) contributions via the JSA.

Table 2. Japan’s contribution to capacity development for macroeconomic statistics via the IMF, 2010-19

a) Original data reported by the IMF	FY10	FY11	FY12	FY13	FY14	FY15	FY16	FY17	FY18	FY19	FY20	Average FY10-20	Total FY2010-20
Macroeconomic statistics (nominal)	1.3	1.7	4.7	7.1	8.4	4.5	4.1	3.1	3.5	2.4	2.4	4.3	43.2
Total (nominal)	14.6	22.9	27.3	27.2	30.1	24.1	21.5	20	22.4	24.9	23.4	23.3	258.4
Share macroeconomic statistics	8.9%	7.4%	17.2%	26.1%	27.9%	18.7%	19.1%	15.5%	15.6%	9.6%	10.3%	17.4%	16.7%
b) Calendar years	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019		Average 2010-19	Total 2010-19
Macroeconomic statistics (nominal)	1.5	3.2	5.9	7.75	6.45	4.3	3.6	3.3	2.95	3.0		4.2	42.0
Total (nominal)	18.75	25.1	27.25	28.65	27.1	22.8	20.75	21.2	23.65	24.15		24.0	239.4
Share macroeconomic statistics	8.0%	12.7%	21.7%	27.1%	23.8%	18.9%	17.3%	15.6%	12.5%	12.5%		17.0%	17.5%
c) 2018 prices	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019		Average 2010-19	Total 2010-19
Deflator	124.57	134.86	133.65	108.94	102.20	91.31	101.82	98.53	100.00	101.86
Macroeconomic statistics (2018 prices)	1.2	2.4	4.4	7.1	6.3	4.7	3.5	3.3	3.0	3.0		3.9	38.9

Notes: IMF: International Monetary Fund.

Source: Authors’ adaptation based on IMF (2020[3]), Japan-IMF Partnership on Capacity Development: Annual Report 2020, https://www.imf.org/external/pubs/ft/ta/index.asp; IMF (2017[2]), Japan-IMF Partnership on Capacity Development: Annual Report Financial Year 2017, https://www.imf.org/external/pubs/ft/ta/2017/jaa/eng/pdf/jsa2017.pdf and IMF (2014[4]) Japan Subaccount under the IMF Framework Administered Account for Selected Fund Activities: Annual Report Fiscal Year 2014, https://www.imf.org/external/pubs/ft/ta/2014/jaa/eng/index.htm. Data on deflators used for aid flow data in the OECD Creditor Reporting System database are available from https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/informationnoteonthedacdeflators.htm.

Korea’s partnerships with the World Bank and the IMF

Korea also provided support to statistical capacity building that was not initially captured based on the text search described above: Korea’s Ministry of Economy and Finance (MOEF) contributed to two data- and statistics-related initiatives, the World Bank’s Trust Fund for Statistical Capacity Building (USD 3 million committed in 2015) and the International Monetary Fund’s Data for Development (D4D) thematic fund (USD 1.62 million in 2018). However, as these funds were classified as core support to these two institutions or part of larger funding vehicles, they were not initially identified. Instead, they were spliced in as described in Table 3.

Table 3. Approximate disbursement of Korea’s contribution for statistical capacity development via the World Bank’s TFSCB and the IMF’s D4D fund

	2015	2016	2017	2018	2019
Nominal
Korea-World Bank Partnership Framework (TFSCB)	1.0	0.5	0.5	0.5	0.5
Korea-IMF Capacity Development Partnership (D4D Fund)				1.65
b) 2018 prices
Deflator	92.84	92.30	96.85	100.00	93.84
Korea-World Bank Partnership Framework (real)	1.08	0.54	0.52	0.50	0.53
Korea-IMF Capacity Development Partnership (D4D Fund)	0	0	0	1.65	0

Notes: TFSCB: Trust Fund for Statistical Capacity Building; D4D: Data for Development.

Source: Author’s approximation based on commitment data supplied by DAC member.

The World Bank reports that Korea committed USD 3 million in 2015 in support of the Trust Fund for Statistical Capacity Building (TFSCB) and released the first tranche of USD 1 million in the same year. It is assumed that the remaining USD 2 million were released over the four subsequent years. In the case of the commitment made to the IMF’s D4D fund, USD 1.65 million that were committed in 2018 are assumed to also have been disbursed in that year.

Results

Figure 1 shows that the ODA disbursements to data and statistics identified using key word searches accounts for the majority of total ODA to data and statistics between 2010 and 2019 and that its share increased over time: it accounted for 70% of total ODA to data and statistics in 2019, up from 54% in 2010. Over the same time period, the share captured through the dedicated purpose code for statistical capacity building decreased from 45% in 2010 to 29% in 2019. This is driven by both increasing ODA classified not as statistical capacity building and a moderate decrease in ODA thus-classified. “Other sources”, disbursements spliced in for Japan and Korea in the context of funding vehicles with the IMF and the World Bank, play a small role in the DAC total throughout.

Figure 1. DAC members’ total ODA to data and statistics by source

USD millions (2018 prices), 2010-19

Source: Author’s calculations based on OECD (2020[5]), Creditor Reporting System (CRS) Aid Activity Database, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

Statlink

Additional projects identified were recorded under a wide range of purpose codes. The most important ones in terms of total disbursements of DAC members between 2010 and 2019 were public sector policy and administrative management (9.3%) and population policy and administrative management (7.6%) (Table 4). Until recently, the clarifications for reporters of the respective purpose codes often used statistical concepts (OECD, 2019[1]). For instance, they described “public sector policy and administrative management” as “[i]nstitution-building assistance to strengthen core public sector management systems and capacities”, including “monitoring and evaluation”, which may well involve strengthening of public sector statistics and data collection or analysis. “Population policy and administrative management” was described, until recently, as “[p]opulation/development policies; census work, vital registration; migration data; demographic research/analysis; reproductive health research; unspecified population activities” (emphasis added).

In addition, USAID’s funding of the Demographic and Health Surveys is recorded under a wide variety of purpose codes. But the largest portion falls under purpose codes for “reproductive healthcare” (13020), “family planning” (13030), “STD control including HIV/AIDS” (13040), and “malaria control” (12262). These purpose codes were also prominent among additional projects identified.

Table 4. Additional projects identified in support of statistics and data by purpose code

Share of of DAC members gross disbursements (2018 prices) to data and statistics identified via text search, 2010-19

Purpose code	Purpose name	Share
15110	Public sector policy and administrative management	9.3%
13010	Population policy and administrative management	7.6%
13040	STD control including HIV/AIDS	7.5%
13030	Family planning	5.9%
13020	Reproductive health care	4.8%
12110	Health policy and administrative management	4.6%
43010	Multisector aid	3.3%
12262	Malaria control	3.3%
31110	Agricultural policy and administrative management	3.1%
16010	Social Protection	3.1%
41010	Environmental policy and administrative management	2.5%
11220	Primary education	2.2%
22040	Information and communication technology (ICT)	2.1%
15111	Public finance management (PFM)	2.0%
15160	Human rights	1.6%
15114	Domestic revenue mobilisation	1.5%
11420	Higher education	1.4%
31210	Forestry policy and administrative management	1.4%
11110	Education policy and administrative management	1.4%
12240	Basic nutrition	1.3%
	Other	30.0%

Source: Author’s calculations based on OECD (2020[5]), Creditor Reporting System (CRS) Aid Activity Database, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

DAC members differ widely in the extent to which their ODA to data and statistics between 2010 and 2019 was recorded under the designated purpose code for statistical capacity building or some other purpose code ( Figure 2). In relative terms, support not classified under this purpose code was particularly important for Hungary, the Slovak Republic, the United States, Switzerland, Finland, Denmark, Ireland, Korea, France, Belgium and Japan. It was less important in relative terms for Iceland, Portugal, Poland, the United Kingdom, Sweden and Luxembourg. In absolute terms, it was important for the United States (total of USD 564.9 million in 2018 prices over the 2010-19 time period), the European Union (USD 309.8 million), Canada (USD 151.4 million), Korea (USD 149.5 million), the United Kingdom (USD 135.9 million) and Japan 109.6 million).

Figure 2. ODA to data and statistics by source

Share of gross disbursements, 2010-19

Source: Author’s calculations based on OECD (2020[5]), Creditor Reporting System (CRS) Aid Activity Database, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

Statlink

How was providers’ ODA to data and statistics matched to statistical domains?

The profiles report ODA to data and statistics by statistical domains. To do so, aid flow data were matched to different statistical domains (e.g. health statistics or economic statistics) based on a three-step procedure:

Matching purpose codes to statistical domains

In a first step, purpose codes were matched to one of eight domains with one residual category (Table 5). This matching is not exhaustive: over the 2010-19 time period about 24% of total ODA to data and statistics are classified in a non-informative, residual “Other”-category (Figure 3). Hence, further refinements were applied.

Table 5. Matching of purpose codes from the OECD Creditor Reporting System database to statistical domains

Domain	Purpose codes	Headline category / examples
General statistical capacity development	16062	Statistical capacity building
Population statistics	13000-13999	Population policies/programmes & reproductive health
Education statistics	11000-11999	Education
Health statistics	12000-12999	Health
Economic statistics	15110-15125; 15142; 21000-21999; 23000-25999; 32000-39999	Public sector policy and administrative management; public finance management; decentralisation and support to subnational government; anti-corruption organisations and institutions; domestic revenue mobilisation; public procurement; macroeconomic policy; transport energy; banking & financial services; business & other services; industry, mining, construction; trade polices & regulations; tourism
Agriculture statistics	31000-31999 43071-43073	Agriculture, forestry, fishing; food security, safety and quality
Gender statistics	15170-15180	Women's rights organisations and movements, and government institutions; ending violence against women and girls
Environmental statistics	14050; 14015; 41000-41999; 43040-43049	Waste management/disposal; water resource conservation; general environmental protection; rural development

Source: Authors’ elaboration.

Matching based on text mining of project titles

In a second step (on top of step 1), certain key words in project titles are assigned to domains. Examples include “health management information system” or “education management information system” that are matched to health and education statistics, respectively; “civil registr”, “birth registr”, “crvs”, “housing census” and “population census” to population statistics; “business registr” and “national accounts” to economic statistics; and so on.

Matching based on implementing agency

In a third step, on top of steps 1 and 2, specific domains were matched based on channels only if it was classified in the “General statistical capacity” or “Other” categories after steps one and two. For instance, it was assumed that all support channelled through the IMF had the express purpose of strengthening economic statistics, that all support channelled through the United Nations Entity for Gender Equality and the Empowerment of Women (UN WOMEN) would aim to strengthen gender statistics, and so on (Table 6).

Table 6. Matching of implementers to statistical domains

Statistical domain	Implementer
Population	IOM, UNFPA, UNHCR
Education	GPE, UNESCO
Health	GAVI, Global Fund, Pan-American Health Organization, UNAIDS, WHO
Economic statistics	IMF, UNIDO
Agriculture	FAO, IFAD, International Livestock Research Institute
Gender statistics	UN Women
Environmental statistics	GEF, Green Climate Fund, IPCC, UNEP, UNFCCC

Note: FAO: Food and Agriculture Organization; GAVI: Gavi, the Vaccine Alliance; GEF: Global Environment Facility; GPE: Global Partnership for Education; IFAD: International Fund or Agricultural Development; IMF: International Monetary Fund; IPCC: Intergovernmental Panel on Climate Change; UNEP: United Nations Environment Programme; UNESCO: United Nations Educational, Scientific and Cultural Organization; UNFCCC: United Nations Framework Convention on Climate Change; UNIDO: United Nations Industrial Development Organization; UN Women: United Nations Entity for Gender Equality and the Empowerment of Women; WHO: World Health Organization

Results

The results of this procedure and the effect of each step are displayed in Figure 3. As one would expect, both the shares of the “General statistical capacity” and the “Other” category decrease with each step. The share matched to “General statistical capacity” decreases from 34% to 31% after step two to 27% after step three; the share matched to the “Other” category decreases from 24% to 19% to 16%. There is also a very significant increase in going from step 1 to step 2 in the share of ODA to data and statistics classified as being in support of health data and statistics, from 8% to 17%. The share of population statistics increases significantly in going from step 2 to step 3, from 13% to 17%. This is the result of matching activities to population data and statistics implemented by UNFPA, IOM and UNHCR that had not been matched previously based on their purpose codes or through key words.

Figure 3. Effect of subsequent matching steps on overall shares of statistical domains

Gross disbursements of all DAC members, 2010-19

Source: Author’s calculations based on OECD (2020[5]), Creditor Reporting System (CRS) Aid Activity Database, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

Statlink

What are the policy markers and policy objectives?

Data analysis in the sections on DAC members’ thematic focus in the profiles rely on the DAC system of policy markers, a feature of the OECD aid flow data. The policy marker system facilitates monitoring and comparison of members’ activities in support of gender equality; aid to environment; participatory development/good governance (PD/GG); reproductive, maternal, newborn and child health (RMNCH); disaster risk reduction (DRR); nutrition; and inclusion and empowerment of persons with disabilities. Data collection is based on a marking system with three values:

1. Principal (primary) objective: the objective is fundamental in the design and impact of the activity. The DAC’s reporting directive suggest reporters ask whether the activity would have been undertaken without this objective.

2. Partial/significant (secondary) objective: the objective, although important, is not one of the principal reasons for undertaking the activity.

3. Not targeted to the policy objective: the score not targeted means that the activity has been screened against, but was found not be targeted to the policy objective.

Finally, some activities in the data have not been screened. See OECD (2020[6]) for more details.

What is “country-allocable” support?

In interpreting the analysis presented in the profiles on the allocation of DAC members’ ODA by recipient country and region, it is important to keep in mind that not all ODA is allocable by region or country and that this share differs across providers. For instance, ODA may be provided in the form of earmarked funding to programmes implemented by international organisations working in several countries or even several regions, in which case it will often not be allocable by country nor by region. Similarly, aid may be provided to regional organisations or earmarked for regional programmes and projects, in which case it will be allocable by region, but not by country. Figure 4 provides a breakdown of the share of DAC members’ ODA to data and statistics by allocability.

Figure 4. Allocability of ODA to data and statistics by region and by recipient country

Share of gross disbursements, 2010-19

Source: Author’s calculations based on OECD (2020[5]), Creditor Reporting System (CRS) Aid Activity Database, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

Statlink

Among DAC members profiled in this publication, the share of support that is allocable at the country level varies from only 99% for Poland to 41% for Australia. Australia (59%) and the United Kingdom (51%) allocated at least 50% of their ODA to data and statistics in a way that it cannot be allocated at the country level. Australia (28%), along with Canada (31%), also stands out for a large share of ODA to data and statistics that can be allocated to a specific region, but not to a specific country.

Various global or regional initiatives, which do not earmark funding by country, account for the relatively lower share of country allocable support, of which the following provide examples:

• The United Kingdom provided core funding to a wide range of multilateral organisations. Among the largest programmes were its support of the World Bank’s Trust Fund for Statistical Capacity Building and its Statistics for Results Facility Catalytic Fund, both global initiatives.

• Australia is the main contributor to the Bloomberg Philanthropies’ Data for Health Initiative (Asia region), the Ten-Year Pacific Statistics Strategy (Oceania region) as well as core funding for UN WOMEN (global), greatly explaining the large share that cannot be allocated by country.

• Canada supports the Project for the Regional Advancement of Statistics in the Caribbean, a regional initiative.

How are countries classified by income group and by fragility status?

The sections on DAC members’ geographic focus indluce information on bilateral ODA to data and statistics by country income group (low-, lower middle-, upper middle- and high-income) and fragility status. In the case of country income group, the World Bank’s taxonomy based on GNI per capita is used, specifically the classifications as applied in fiscal year 2020 (World Bank, n.d.[7]). Note that the classification used in a given fiscal year is based on data on GNI per capita two years prior, in this case, 2018. The country income group classifications are fixed over time in the profiles, i.e. a country that was classified as a low-income country in fiscal year 2020, based on data from 2018, will be classified as low-income over the entire period 2010-19 that is analysed in the profiles. See Table 7 for a list of countries in each category.

Table 7. Country and territory classifications used in the Data for Development Profiles

Country-income groups and state fragility, 2018-19

Classification	Countries and territories
Low-income (31 countries and territories)	Afghanistan, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic People's Republic of Korea, Democratic Republic of the Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Rwanda, Sierra Leone, Somalia, South Sudan, Syrian Arab Republic, Tajikistan, Tanzania, Togo, Uganda, Yemen
Lower middle-income (47 countries and territories)	Angola, Bangladesh, Bhutan, Bolivia, Cabo Verde, Cambodia, Cameroon, Comoros, Congo, Côte d'Ivoire, Djibouti, Egypt, El Salvador, Eswatini, Ghana, Honduras, India, Indonesia, Kenya, Kiribati, Kyrgyzstan, Lao People's Democratic Republic, Lesotho, Mauritania, Micronesia, Moldova, Mongolia, Morocco, Myanmar, Nicaragua, Nigeria, Pakistan, Palestinian Authority, Papua New Guinea, Philippines, Sao Tome and Principe, Senegal, Solomon Islands, Sudan, Timor-Leste, Tunisia, Ukraine, Uzbekistan, Vanuatu, Viet Nam, Zambia, Zimbabwe
Upper middle-income (56 countries and territories)	Albania, Algeria, Argentina, Armenia, Azerbaijan, Belarus, Belize, Bosnia and Herzegovina, Botswana, Brazil, China (People's Republic of), Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, Equatorial Guinea, Fiji, Gabon, Georgia, Grenada, Guatemala, Guyana, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kosovo, Lebanon, Libya, Malaysia, Maldives, Marshall Islands, Mauritius, Mexico, Montenegro, Namibia, Nauru, North Macedonia, Paraguay, Peru, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Serbia, South Africa, Sri Lanka, Suriname, Thailand, Tonga, Turkey, Turkmenistan, Tuvalu, Venezuela
High-income (36 countries and territories)	Antigua and Barbuda, Aruba, Bahamas, Bahrain, Barbados, Bermuda, British Virgin Islands, Brunei, Darussalam, Cayman Islands, Chile, Chinese Taipei, Croatia, Cyprus1, French Polynesia, Gibraltar, Hong Kong (China), Israel, Korea, Kuwait, Macau (China), Malta, New Caledonia, Northern Mariana Islands, Oman, Palau, Panama, Qatar, Saint Kitts and Nevis, Saudi Arabia, Seychelles, Singapore, Slovenia, Trinidad and Tobago, Turks and Caicos Islands, United Arab Emirates, Uruguay
Fragile contexts (57 countries and territories)	Afghanistan, Angola, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Congo, Côte d'Ivoire, Democratic People's Republic of Korea, Democratic Republic of the Congo, Djibouti, Egypt, Equatorial, Guinea, Eritrea, Eswatini, Ethiopia, Gambia, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, Iran, Iraq, Kenya, Lao People's Democratic Republic, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Nigeria, Pakistan, Palestinian Authority, Papua New Guinea, Rwanda, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sudan, Syrian Arab Republic, Tajikistan, Tanzania, Timor-Leste, Uganda, Venezuela, Yemen, Zambia, Zimbabwe

Note: See text.

The profiles follow the OECD’s classifications of fragile contexts (OECD, 2018[8]). As the period analysed in the profiles covers the years 2010-19, the 2018-19 classifications of state fragility are used throughout.2 In other words, a country classified as fragile in the 2018-19 reporting period is treated as fragile in all years. Countries classified as fragile for the purpose of the profiles are also listed in Table 7.

References

[3] IMF (2020), Japan-IMF Partnership on Capacity Development: Annual Report 2020, International Monetary Fund, Washington DC.

[2] IMF (2017), Japan-IMF Partnership on Capacity Development: Annual Report Financial Year 2017, International Monetary Fund, Washington DC, https://www.imf.org/external/pubs/ft/ta/2017/jaa/eng/pdf/jsa2017.pdf.

[4] IMF (2014), Japan Subaccount under the IMF Framework Administered Account for Selected Fund Activities: Annual Report Fiscal Year 2014, International Monetary Fund, Washington DC, https://www.imf.org/external/pubs/ft/ta/2014/jaa/eng/index.htm.

[6] OECD (2020), Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and teh Annual DAC Questionnaire, https://one.oecd.org/document/DCD/DAC/STAT(2020)44/FINAL/en/pdf?_ga=2.143866557.983539219.1621431948-99641878.1560272537 (accessed on 19 May 2021).

[5] OECD (2020), Creditor Reporting System (CRS) Aid Activity Database, OECD, Paris, https://stats.oecd.org/Index.aspx?DataSetCode=CRS1.

[1] OECD (2019), “Proposing a new approach to measure support to statistics and data in the OECD Creditor Reporting System”, OECD, Paris, http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DCD/DAC/STAT(2019)26&docLanguage=En.

[8] OECD (2018), States of Fragility 2018, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264302075-en.

[7] World Bank (n.d.), World Bank Country and Lending Groups, https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 20 May 2021).

Notes

← 1.  Note by Turkey

The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

Note by all the European Union Member States of the OECD and the European Union

The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

← 2.  In 2020, four contexts (Cambodia, Lesotho, Nicaragua and Togo) moved onto the framework and five contexts (Egypt, Malawi, Nepal, Rwanda and Timor-Leste) moved off.