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Behind the scenes of

Surge financing for COVID-19 is disguising a downward trend in health aid

This page describes the methodology and data used for ONE's data story on the effects of COVID-19 aid for overall Health ODA.

TL;DR

This story looks at the trend in health ODA from 2008 to 2022, the year with the latest data. It compares headline Health ODA spending with health ODA excluding COVID-19 related aid.

All data is presented in 2022 US dollars, in constant prices and exchange rates. This analysis is based in gross ODA disbursements to all developing countries (which, per OECD definitions, may include in-donor spending).

The analysis which looks at specific donors (e.g the US, UK, France, etc.) is based on bilateral plus imputed multilateral health aid (as gross disbursements). While the OECD does not provide sector-specific multilateral imputations, ONE has been calculating and releasing this data since 2019.

The troubling hidden trend in health aid

When COVID-19 hit, donor assistance for health surged to help countries respond to the pandemic. But this surge financing has disguised a concerning trend in official development assistance (ODA) for health: excluding COVID-19 funding, health official development assistance (ODA) for health reached a 13 year low in 2021 and only rebounded slightly in 2022.

The following chart compares gross ODA disbursements for all health sectors against the same data excluding activities related to COVID-19.

Health definition

ONE's definition of 'Health' ODA includes all purpose codes under DAC Code 120-123 (Health), plus all purpose codes under DAC Code 130 (Population Policies/Programmes & Reproductive Health):

COVID-19 related flows can be logged in a few different ways:

  1. Donors can flag that a project/activity is related to COVID-19 by using a keyword. For this analysis, we identify all projects/activities that include the string covid in the keyword field.
  2. Projects / activities can be reported as "COVID-19 control", which is purpose code 12281, within Basic Health (122).
  3. Financing can be provided by the COVID-19 Response and Recovery Multi-Partner Trust Fund (donor code 1047)

This system has quite significant limitations. According to the OECD, the purpose code "includes actions in immunisation, testing, pandemic prevention, treatment and post-recovery therapies." In contrast, the keyword "captures providers' response to the pandemic across other sectors and non-sector aid" (OECD 2024). However, the keyword can be used within the health sector, and donors were instructed to use it together with the purpose code when reporting vaccine donations.

When studying Health financing for COVID-19 related financing, we consider the projects/activities that fit into one of the 3 categories outlined above. Keywords do not indicate what percentage of a project was used for COVID-19 related objectives. It is therefore likely that this methodology represents an upper-bound estimate for COVID-19 related finance. But we take the data as it is reported by donors to the OECD DAC.

Health aid is declining across many major donors

When COVID-19 funding is excluded, health funding from many major donors has not rebounded to pre-pandemic levels.

When excluding COVID-19 funding, health ODA in 2022 remains below pre-pandemic levels in the UK, Canada, EU Institutions, France, and the Netherlands. It also declined between 2021 and 2022 in the US, UK, Germany, France, and Italy.

Calculating total health aid (from a donor's perspective)

Aid flows can be considered from two perspectives:

  • Amounts provided by specific donors (i.e the donor perspective)
  • Amounts received by recipient countries (i.e the recipient perspective)

The donor perspective is important when studying the final use of a donors' ODA spending in a given year. A portion of the funding they provide is 'bilateral', and a portion goes to core funding to multilateral institutions (who in turn provide financing to developing countries).

Getting a complete picture of the final purpose and destination of specific donors' spending means studying where and how multilateral financing is spent. This is known as "imputed multilateral aid".

Imputed multilateral aid are estimates produced by mapping money provided to specific countries (or sectors) back to bilateral donors, based on the amounts that they provide as core funding to multilateral institutions.

The OECD DAC does not publish multilateral imputations by sector. ONE has been producing and releasing this data since 2019. More information on our methodology can be found here.

In short:

  1. Using data from the Creditor Reporting System, we calculate each multilateral agency's total flows to a given sector as a share of its total spending (core resources only).
  2. Using data from the Members Total Use of the Multilateral System database, we calculate the bilateral donor’s core ODA contributions to each multilateral agency (for year n)
  3. For each donor, sectoral imputed multilateral aid figures are calculated by multiplying the contributions made to each multilateral organisation (obtained through step 2) by the sector spending shares for each specific multilateral organisation (obtained through step 1).

Sources and other data notes

This analysis uses data from the OECD DAC databases:

  • Creditor Reporting System (CRS), last accessed October 2024
  • Members total use of the multilateral system database, last accessed October 2024

Figures are converted to 2022 constant prices using the OECD DAC deflators.

For more information on ONE's methodology for multilateral sector imputations, please see this document.

All of ONE's research and analysis is open source. Code to reproduce this analysis can be found in this GitHub repository.

For more of ONE's ODA analysis:

ONE develops several python packages and tools to work with OECD DAC data, including:

  • oda-reader: a python package to access OECD DAC data from the data-explorer API.
  • oda-data: a python package to reproduce all of ONE's ODA analysis with a few lines of code
  • pydeflate: a python package to convert flows to constant prices and exchange rates, using IMF, World Bank, or DAC data price deflators and exchange rates data.

Please reach out if you would like to get started with any of these tools.