Criteria

Multiple criteria are currently guiding the allocation of DAH, and many others have been proposed. For any assessment, discussion or choice of criteria, it is crucial to understand how the basic candidate criteria influence the distribution of DAH. Even if no criterion is sufficient alone, the distributional implications of single criteria indicate how each of those criteria affects the overall distribution if part of a set. Examination of single criteria is thus also a useful first step to examining packages of criteria.

Against this background, we examined 11 criteria. The selection of criteria was based on a review of indicators and criteria currently used by major funders and criteria proposed in the literature (Anderson, Reference Anderson2008; Guillaumont, Reference Guillaumont2008; Ottersen et al., Reference Thomas2017). We specifically sought to include criteria that related to different kinds of needs, to effectiveness, or to inequality. Similarly, the operationalisation of each of criteria examined was motivated by allocation criteria currently used or previously proposed, but the criteria were streamlined and simplified to facilitate comparison and intuitive understanding.

The nature of each criterion is best appreciated if it is seen as a combination of a metric and a prioritisation rule. The metric is the country characteristic that the criterion is directly concerned with, for example, level of GNIpc. The prioritisation rule specifies how the amount of DAH changes when a country’s value of a given metric increases. Key properties of the criteria are summarised in Table 1.

Table 1

Properties of the examined criteria

Note: IHME=Institute for Health Metrics and Evaluation. UNDP=United Nations Development Programme.

Missing data: GNIpc: countries for which data for years other than 2011 used were Djibouti (2009), Iran (2012), Libya (2009) and Syria (2010). Sufficiently recent estimates were unavailable for Myanmar, North Korea and Somalia. LE: estimates were unavailable for Dominica and Marshall Islands. DALYr: estimate was unavailable for South Sudan. GHEpc: estimates were unavailable for North Korea, occupied Palestinian Territory, Somalia and Zimbabwe. GHEpc gap: estimates were unavailable for North Korea, occupied Palestinian Territory, Somalia and Zimbabwe. 5% gap: countries for which data for years other than 2011 were used were Djibouti (2009), Iran (2012), Libya (2009) and Syria (2010). Sufficiently recent estimates were unavailable for Myanmar, North Korea and Somalia. IRAI: for Azerbaijan, the value for 2010 was used. The estimate for Sudan was also used for South Sudan. Estimates were unavailable for 59 countries. Gini: for 64 countries, estimates for years between 2005 and 2012 other than 2011 were used. The 2009 estimate for Sudan was also used for South Sudan. Suffciently recent estimates were unavailable for 35 countries. LEi: for Kiribati, Seychelles, South Sudan and Sudan estimates for 2013 were used. Estimates for Dominica and Marshall Islands were unavailable.

Criteria related to need

We examined six needs-based criteria. Three of these are linked to health outcomes. According to the U5MR criterion, DAH increases with U5MR. This is a key indicator of severe deprivation in health, and it is part of the allocation formula used by United Nations Children’s Fund (UNICEF) (Ottersen et al., Reference Thomas2017). According to the life expectancy (LE) criterion, DAH decreases with life expectancy at birth. Life expectancy is an indicator of health deprivation that is sensitive to mortality in both children and adults, but not directly sensitive to morbidity. Life expectancy is part of the HDI, which is used by the Luxembourg Agency for Development Cooperation to determine country eligibility for aid (Ottersen et al., Reference Thomas2017). According to the disability-adjusted life year rate (DALYr) criterion, DAH increases with DALYr, which is the number of DALYs per 100,000 individuals. DALYr is an indicator of health deprivation in terms of length of life as well as quality of life. The Global Fund is among the funders that use disease burden to guide their decisions on country eligibility and allocation of DAH (Ottersen et al., Reference Thomas2017).

The three remaining needs-based criteria are primarily linked to countries’ capacity to address health needs. According to the GNIpc criterion, DAH decreases with GNIpc. This is an indicator widely used determine countries’ eligibility for aid and how much aid they are to be offered (Ottersen et al., Reference Thomas2017). In the context of DAH, GNIpc is particularly relevant as an indicator of domestic capacity to address health needs.

According to the criterion linked to government health expenditure per capita (GHEpc), DAH decreases with GHEpc. This is an indicator of the short-term capacity to ensure priority services for the entire population.Footnote ¹ According to criterion linked to GHEpc gap from $86, DAH increases with the shortfall of GHEpc from $86. This gap is supposed to indicate the lack of resources for ensuring priority services for everyone in the context of low-income countries (LICs). The reference level of $86 (in both 2012 and 2015 terms) is a recently updated figure of the estimate for total health expenditure per capita of the Task Force for Innovative International Financing for Health Systems (McIntyre et al., Reference Vázquez2017). In this study, however, $86 was taken to represent the minimum level of GHEpc, because it was believed that for $86 to ensure priority services to everyone, it needs to come from mandatory, prepaid, pooled funds rather than from private spending. According to the criterion linked to conditional GHEpc gap from $86 (5% gap), DAH increases with the shortfall of GHEpc from $86 that would exist if GHE in the country represented 5% of GNI. A GHE/GNIFootnote ² ratio of at least 5% has been suggested as a useful target for government health expenditure (McIntyre et al., Reference Vázquez2017). The 5% gap can be seen as a indication of the lack of capacity to meet health needs, since any such gap suggests that countries are unable to reach the $86 target even if they spent 5% of GNI on publicly financed health services. It has been suggested that the primary role of DAH is to reduce this gap (Røttingen et al., Reference Ottersen, Moon and Røttingen2014).

For most need-based criteria, rank-dependent weights were applied (see Table 1). This was done by first ranking all countries from best off to worst off in terms of the indicator in question. The worst-off country was assigned a weight of two and the best-off country a weight of one. The weights assigned to each country in between decreased proportionally with its rank. This adjustment was made in order to incorporate the widespread belief that the worse off should have some special priority and to do so in a simple manner that retained an intuitive grasp of how the criteria work.

Criteria related to effectiveness

We examined two criteria related to effectiveness. According to the criterion linked to the International Development Association (IDA) Resource Allocation Index (IRAI), DAH increases with the IRAI. This measure is developed by the IDA and based on the Country Policy and Institutional Assessment (CPIA), which evaluates performance in terms of the quality of country’s policy and institutional framework (International Development Association, 2013). As many believe good governance improves the effectiveness of aid, IRAI is typically seen as an indicator of expected aid effectiveness.

According to the criterion linked to reduction in U5MR (rU5MR), DAH increases with recent reduction in U5MR. Reduction in U5MR can be seen as an indicator of demonstrated effectiveness and possibly an indicator of expected aid effectiveness.

Other criteria

We examined three criteria that does not clearly fall into the need or effectiveness categories, although inequality measures are often linked to need. According to the Gini index (Gini) criterion, DAH increases with the Gini index for income. A high value indicates greater inequality and more specifically that many people fall below the average level of income, that some people fall far below that average, or some combination. Income inequality is relevant for DAH primarily due to the correlation between income and health and due to the fact that low income can make health services unaffordable.

According to the criterion linked to inequality in life expectancy (LEi), DAH increases with LEi. This criterion is relevant for mainly the same reasons as the Gini criterion, but refers more directly to inequalities in health.

Countries and country categories

Our initial sample of countries included all low- and middle-income countries (LMICs) that received DAH in 2011 (n=138) (Institute for Health Metrics and Evaluation [IHME], 2014). In the presentation of results, we concentrate on five focus countries for illustrative purposes: Ethiopia, India, Ghana, Nigeria and South Africa. The five countries were chosen with aim of creating a diverse set of countries well suited for highlighting the distributional implications of different allocation criteria. Table 2 exhibits key characteristics of these countries, including several of the indicators examined in this study. As shown, the countries vary in multiple dimensions, including in population size, national income (GNIpc), health spending (GHEpc), health outcomes (U5MR and LE), improvement in health outcomes (cU5MR), inequality (Gini), and DAH received in 2011.

Table 2

Characteristics of focus countries

Note: GNIpc=gross national income per capita; GHEpc=government health expenditure per capita; U5MR=under-five mortality rate; LE=life expectancy; cU5MR=reduction in under-five mortality rate; DAH=development assistance for health; DAHpc=DAH per capita; LIC=low-income country; LMIC=low- and middle-income country; UMIC=upper-middle-income country.

Data sources: data on DAH and DAHpc from Institute for Health Metrics and Evaluation and for 2011. Other data from World Bank and for 2011. World Bank income class for fiscal year 2015.

Implications for four categories of countries were also examined. Three of these were World Bank income classes for the fiscal year 2015 (Word Bank, Reference Sumner2015). According to this classification, countries with GNIpc ≤$1045 in 2013 are classified as LICs; countries with GNIpc $1046–$4125 and $4126–$12 745 as LMICs and upper-middle-income countries (UMICs), respectively; and countries with GNIpc ≥$12,746 as high-income countries. The fourth category examined comprised the 20% of countries with the lowest life expectancy. Implications across income classes were examined because much of the debate on the allocation of DAH and aid more generally is framed in terms of the role of MICs compared with that of LICs. Similarly, implications for the countries with the lowest life expectancy were studied because concerns for countries with profound health needs also figure prominently in the current debate.

Simulation procedure

For each criterion, the question was the following: how will the total amount of DAH currently available be distributed across countries and country categories if only this criterion is applied? For each criterion, we also examined how the distribution differed from an income baseline, that is the distribution based on a GNIpc criterion alone. Given the prominent role of GNIpc in the allocation of aid (Ottersen et al., Reference Thomas2017), it is useful to directly compare the shares of DAH resulting from each criterion with the distribution that follows from a GNIpc criterion alone.

Obviously, no single funder or other actor is in position to determine the allocation criteria for all DAH worldwide. Funders also have different goals and mandates. However, examining how different criteria would allocate the total amount helps demonstrate how these criteria can affect the overall distribution of DAH, and this can inform funders’ choice of criteria. The overall distribution of DAH is also relevant for debates in the wider global community, where broad patterns of DAH are discussed.

The model used to simulate distributions relied heavily on proportional relationships in order to facilitate an intuitive understanding of the criteria and their implications. The scales of the 11 indicators were normalised so that the lowest and highest observed value for every indicator were 1 and 10, respectively. For criteria according to which DAH increases with the metric, the following procedure was used. We posited that DAH for a given country i (D _i ) increases proportionally with DAH per capita (DAHpc) (d _i ) and population size s _i . We further assumed that d _i increases proportionally with a constant a that applied for every country, the rank-dependent weight for the country (w _i ), and the level of the metric for the country (m _i ).

(1) $$D_{i} \,{\equals}\,d_{i} s_{i} \,{\equals}\,aw_{i} m_{i} s_{i} $$

We imposed the constraint that the sum of DAH across all countries n must equal total DAH currently available. This was defined as the total amount of DAH in 2011 that could be attributed to specific countries (IHME, 2014). That total was $14,582,799,981.

(2) $$\mathop \sum\limits_i^N D_{i} \,{\equals}\mathop \sum\limits_i^N aw_{i} m_{i} s_{i} \,{\equals}\,a\mathop \sum\limits_i^N w_{i} m_{i} s_{i} \,{\equals}\,14,582,799,981\,{\rm \,\$\,US}$$

We solved (2) for each criterion to find a. With a identified, we calculated the distribution of DAH, that is the amount of DAH allocated to each country i, by using (1).

For criteria according to which DAH decreases with the metric, such as the GNIpc criterion, m _i in (1) and (2) was replaced by $m_{i}^{{{\minus}1}} $ . When the estimate for m _i for a given indicator and given country was unavailable, DAH actually received in 2011 was used to calculate a and overall DAH for country categories.