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.

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.

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.

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.

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.

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.

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.

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.

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).

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:

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.

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.

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).

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.

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

  3. 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.

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.

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.

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.

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.


[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,

[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,

[6] OECD (2020), Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and teh Annual DAC Questionnaire, (accessed on 19 May 2021).

[5] OECD (2020), Creditor Reporting System (CRS) Aid Activity Database, OECD, Paris,

[1] OECD (2019), “Proposing a new approach to measure support to statistics and data in the OECD Creditor Reporting System”, OECD, Paris,

[8] OECD (2018), States of Fragility 2018, OECD Publishing, Paris,

[7] World Bank (n.d.), World Bank Country and Lending Groups, (accessed on 20 May 2021).


← 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.

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