The Economic Transition of Health in Africa: A Call for Progressive Pragmatism to Shape the Future of Health Financing

Abstract

Abstract—The new financing landscape for the Sustainable Development Goals has a larger emphasis on domestic resource mobilization. But, given the significant role of donor assistance for health, the fungibility of government health spending, and the downward revision of global growth, this article looks at what is possible with regard to a country's own ability to finance priority health services. Using cross-sectional and longitudinal economic and health spending data, we employ a global multilevel model with regional and country random effects to develop gross domestic product (GDP) projections that inform a dynamic panel data model to forecast health spending. We then assess sub-Saharan African countries' abilities to afford to finance their own essential health needs and find that there are countries that will still rely on high out-of-pocket or donor spending to finance an essential package of health services. To address this, we discuss policy opportunities for each set of countries over the next 15 years. This longer-term view of the economic transition of health in Africa stresses the imperative of engaging policy now to prioritize customized strategies and institutional arrangements to increase domestic financing, improve value for money, and ensure fairer and sustainable health financing. We address the need for rhetoric on UHC to incorporate “progressive pragmatism,” a proactive joint approach by developing country governments and their development partners to ensure that policies designed to achieve universal health coverage align with the economic reality of available domestic and donor financing.

Keywords:

• Africa
• economic transition
• health financing
• universal health coverage

DISCLAIMER

The opinions expressed in this article are the author's own and do not reflect the view of the United States Agency for International Development or the US government.

DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST

There are no conflicts of interest.

Notes

[a] The IMF identified ten countries based on 2006 to 2010 data of government revenues coming from natural resources, defined as having over 20% of their government revenues sourced from natural resources. These static data does not provide time-varying information needed to compute the effects of resource dependence on health spending. Longitudinal data on natural resource rents as a percentage of GDP approximates fiscal dependence on resources. Using a cutoff point of 15% of GDP, we develop a resource dummy variable to estimate the health spending behavior of resource-rich countries. On average, fiscally dependent resource-rich countries are richer, but they spend more out of pocket, rely less on donor assistance, and have worse voice and accountability and government effectiveness indicators than their non-resource-rich counterparts.

[b] The lag health expenditure variable captured the explanatory power, resulting in nonsignificant and inconsistent impact of these explanatory variables. Results are available upon request.

[c] We chose a random effect model specification because we are interested in the population-level regional effects, and the random effects model explicitly allows us to generate the region-specific means ($\alpha$ + ${\mu }_r$) distribution. This distribution would not be specified under a fixed effect model (see Ref. 22). We also performed a Hausman test that could not reject the equality between fixed and random effects coefficients.

[d] For resource-rich countries, we estimated a dynamic panel data model with a resource dummy interaction with log per capita GDP. Relative to non-resource-rich countries, income elasticity of total and government health spending is less and the income elasticity of out-of-pocket spending is slightly larger.

[e] We included tax revenues as a percentage of GDP in the analysis. Note that this resulted in a 56% loss in the sample data given the limited availability of longitudinal and cross-sectional tax revenue data. Two-sample t testing found that countries without data on tax revenue as a percentage of GDP tended to have smaller populations. Average per capita GDP and per capita total health spending tended to be higher for countries with data relative to countries without data, but these differences were not statistically significant. Including this proxy for fiscal space in the analysis did not alter the estimates for our key parameters of interest. In addition, this variable appeared significantly different from zero with a positive coefficient in most specifications, suggesting that health expenditure increases as fiscal space expands. This is consistent with a recent study conducted by the United States Agency for International Development that found that as fiscal space increases, government spending on health increases, particularly in low-income countries. However, when tax revenue as a percentage of GDP was interacted with per capita GDP or income group (LIC, LMIC, upper-middle-income country), the interaction was positive but not statistically significant. This suggests that the income elasticity of health is not necessarily higher when a country's fiscal effort is higher.