secondary school-age population in each host country.<sup>1</sup> These population estimates are then combined with refugee gross enrollment rates (GERs), compiled by UNHCR (2023c), to estimate the refugee population accessing education in each country. Host country GERs are taken from UNESCO’s Institute for Statistics, which provides national GERs for primary and secondary education only (UNESCO, n.d.).

# Financing data

possible. To improve the quality of data on financing for refugee education, the authors have provided more detailed estimates on all major sources of education financing through either collecting new primary data or re-examining existing data to produce more accurate financing information. The methods used to produce these estimates are explained below.

# (i) Bilateral and multilateral ODA for refugee education

The analysis of ODA presented in this paper is based on data from the OECD’s 2023 _Ad hoc Survey on Development Finance for Refugee Situations in Low- and Middle-Income Countries, 2020-21_ .<sup>2</sup> Unlike its predecessors, the 2020-21 refugee financing survey captured ODA by area/sector, making it possible to estimate ODA for refugee education for the first time. However, the OECD’s classification only allows financing to be categorized into one area/sector, limiting the accuracy of its sector level analysis. As such, this paper presents new estimates for refugee education financing for all LICs and MICs by reviewing the descriptors in each line of ODA to ensure that all financing for education purposes are categorized as such. For example, in several instances financing was classified in the OECD dataset as ‘emergency response’, but was used entirely for education purposes, and as such should be incorporated into refugee education financing estimates. To ensure that all education financing was captured in this paper and the accompanying dataset, ‘education identifiers’<sup>3</sup> were used to detect potential misallocations. Each potential misallocation was then reviewed in detail, with only financing allocated entirely for education purposes categorized as 'education' financing.<sup>4</sup> _situations_ in LICs and MICs. It thus provides estimates of DAC members’ and non-DAC providers’ ODA contributions to both refugees and host communities in the refugee-hosting recipient countries. As such the ODA recorded in this dataset is likely to inflate financing for refugee

> 1 For countries where data on refugee populations by age were not available, it was assumed that the number of primary and secondary school-age refugees in these countries were equal to 19 percent and 13 percent, respectively, of the total refugee populations of these countries, in line with the averages in all other LICs and MICs.

> 2 countries), 6 multilateral development banks and 14 UN agencies (core contributions) for the financial year 2020-21. Recipients of official assistance are recorded as the first implementing partner only to avoid duplication.

> 3 OECD dataset for the key words ‘education’, ‘school’, ‘teach’, and ‘classroom’. If any of these key words were identified, then each allocation’s descriptives were reviewed to determine whether financing was for refugee education, with only financing that was allocated entirely for education purposes included as such. Categories of spending captured include direct expenditure on education provision, such as teacher salaries and textbooks, as well as indirect sources of financing for education, such as School Feeding Programmes and Cash Transfers for Education.

> 4 ODA for refugee education analyzed in this paper with no disaggregation by education level possible.

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