The GHED was updated in December 2017 to disaggregate expenditures by financing source. The update furthermore disaggregated expenditures by financing arrangement and separated out recurrent (or current) expenditure from capital expenditure. Unless clearly stated as capital expenditures, the health expenditure indicators include only current expenditure. Current expenditures are made up of expenditures on resources that are consumed within one year, including wages, goods, and services. Capital expenditures are expenditures on assets such as buildings and equipment that have a working life of one year or longer. This distinction is intended to make current expenditure estimates more comparable year on year, as capital expenditures tend to be “lumpy.” If capital expenditures and current expenditures were combined, an increase or fall in health expenditure could be due in part to the timing of infrastructure projects.

The indicator current health expenditure (CHE) consists of domestic general government expenditure (GGHE-D), health expenditure from external sources (EXT), and domestic private health expenditure (PVT-D) ( Figure 1). One point that is unclear in the GHED is how well external assistance is disaggregated between capital and current expenditure.

Figure 1 provides a breakdown of the sources of expenditure data, with the terminology used in the GHED. Further information is provided in WHO’s Technical brief ⁴ on the Indicators published on the World Health Organization’s Global Health Expenditure Database.

The GHED database contains 192 countries. We used two criteria to decide which countries would be included in our analysis:

Once countries with population less than 600,000 in year 2015 were eliminated, the sample size for our study was reduced from 192 to 159. After eliminating countries with missing values, the sample size further dropped from 159 to 125. Table 1 lists the countries included in the analysis by income group, and also lists the countries excluded and the corresponding rationale.

For grouping countries in the analysis, we used the World Bank analytical income classification system, which groups countries into low-income countries (LICs), lower-middle-income countries (LMICs), upper-middle-income countries (UMICs), and high-income countries (HICs). For our analysis, we classified each country as a LIC, LMIC, UMIC, or HIC at the start of the period of interest to us, in this case the year 2000, and held the groups constant from 2000 to 2015. Given the lags in data availability, the World Bank uses calendar year 2000 income data for its analytical income classification in its fiscal year (FY) 2002 (FY02). For this paper, we use the classification for FY02, based on 2000 income data.

As context, over the 15-year period, there was significant movement of countries across income categories. Every year, the World Bank updates the income thresholds in order to keep them constant in real terms and reclassifies countries based on their most recent Gross National Income (GNI) per capita. The total number of economies classified grew from 205 to 218 over the study period 2000–2015 due to the inclusion of newly independent states (Kosovo, Montenegro, Serbia, South Sudan, and Timor-Leste) as well as several small economies that had not previously been classified. Due to income growth, many countries changed income classification from 2000 to 2015. A total of 33 countries moved from LIC to LMIC or UMIC, and one (new) country, South Sudan was not classified until year 2011. The largest country that moved out of the LIC category was India. Another 31 countries moved from LMIC to UMIC, the largest of which was China, while 18 countries moved from UMIC to HIC. The changes in terms of income classification of the world’s population are more dramatic than the changes in terms of number of countries. About 41% of the world’s population was living in LICs in 2000. This proportion fell to 8% by 2015 ( Appendix 1 ⁵ ). In 2000, 11% of the world’s population lived in UMICs, which rose to 35% by 2015.

According to their 2015 GNI p.c., only 31 countries were still in the LIC category by 2015, compared to 63 at the start of the study period in 2000 ( Appendix 2 ⁵ ). The rationale for defining income groups at the start of the period is so that the findings might be useful for projections. In doing projections, we can only know a country’s income status at the start of the projection period, not at the end of the projection period.

We have created two additional income groups, namely, LICs without India (as India was classified as an LIC at the start of the period) and LMICs without China (as China was classified as an LMIC at the start of the period), to study the changes in health expenditures across the respective income groups excluding these two large economies and also to study changes in these two economies individually. While our study mostly examines the results for the different income groups and India and China, the GHED data can be used to examine health expenditure estimates for every individual country using our methodology. Country level results will vary from the respective income groups. Some of the variations observed for India and China when taken together with and separately from the LIC and LMIC groups respectively are discussed in the results and discussion sections.

Overall, the data on financing sources indicators are most complete for LICs and MICs. For the indicator GGHE-D as a percent of CHE, about 90% of LICs have data for all the years 2000 to 2015 and about 92% of MICs have a complete set of data. In contrast, only about 65% of HICs have complete data for the period 2000 to 2015.

The accuracy of data reported in the GHED varies on a country-to-country basis. National Health Account exercises form the core of a solid understanding of sources of financing at the country level. Some countries have completed national health accounts (NHA) exercises multiple times, and others have yet to conduct them. Most countries publish expenditure data, but others only provide budgeted data. For cases where NHA data is not regularly available, WHO has filled missing data gaps by using government budget data, interpolations and data from international sources such as the Organization for Economic Cooperation and Development’s Development Assistance Committee database, or through in-country consultations. Inevitably, some country data are much stronger than other country data.

This study first provides a general overview on changes in sources of financing, and then it more deeply analyzes the changes in GGHE-D. The GHED database contains several other important macro indicators, including GGE and GDP. For the purposes of the analysis, we primarily compare the growth in GGHE-D to the growth in GDP, the growth in GGHE-D to the growth in GGE, and the change in the share of GGHE-D to GGE. The GGE is intended to capture all government (federal, state, local) current expenditure. Our study aims to answer three main questions based on the analysis of these key GHED indicators:

To answer these three key questions, we looked at aggregate health expenditure estimates and ratios for the income groups defined above in sub-section (iii) and in two large economies, China and India. In this study, estimates are calculated for each income group as a unit. Our approach differs from the methodology used by WHO in their study New Perspectives on Global Health Spending for Universal Health Coverage; in that study, the WHO reports country-weighted averages. With country-weighted averages, a country such as India (1.3 billion population in 2015) is given the same weight in the LIC group as Comoros (800,000 population in 2015). In our method, a country with large expenditures would have greater weight than a country with smaller expenditures.

In order to aid comparison of our results with the WHO study and highlight the differences in methodologies of the two studies, some figures and tables in our study are intentionally designed to be similar to the ones used in the WHO study ( Figure 4– Figure 6 and Appendix 3 ⁵ ).

Figure 2 is a flowchart that summarizes data sources, country inclusion criteria, country classification by income, and calculation of key metrics used in this study. It includes the formulae used for calculating absolute values and ratios. For a given income group i, the estimated health expenditure value for year j is the summation of reported values for all countries in that income category included in our analysis. Ratios are then calculated by simple division of the particular estimated values. The formula used for calculating annualized rate of growth is available under Table 3 and Table 4. The database and calculations used in this study can be viewed on OSF ⁵ .

In addition to comparing trends in health expenditure estimates over the 2000 and 2015 time period, we have also conducted a decomposition analysis using the data available within the GHED to identify the key drivers of growth in general government health expenditures. This analysis is reported by income groups and separately for India and China.

We have looked at three key drivers that impacted the change in GGHE-D during the period 2000 and 2015 (ΔGGHE-D) ( Figure 3):

The formulae used in the decomposition analysis are shown below:

While the results of the decomposition analysis can vary from an individual country to country basis, the reporting by income groups helps us identify whether countries in general have been creating more fiscal space for health by reallocating government budgets towards health, strengthening tax bases, or whether growing government health expenditures are mostly spurred by economic growth alone.

In this sub-section, we present the results of our analysis on the change in the composition of CHE from 2000–2015 from the five main sources of health financing

Figure 4 shows the changes in composition of CHE between 2000 and 2015 for income groups while Appendix 3 ⁵ shows the trend over time during this period. We also show China and India separately given their very large population size.

During this period, GGHE-D as a share of current health expenditure rose for all groups, and OOP spending as a share of current health expenditure—a key indicator of financial protection—fell across all income groups as well as China and India if examined separately. However, the increase (for GGHE-D) and decrease (for OOP) was negligible in the LIC group with only an increase in GGHE-D/CHE from 28% to 29% and a fall in OOP/CHE from 60% to 58% over the period 2000–2015. The increase of GGHE-D as a share of CHE was most pronounced among the LMICs, from 41% to 59% along with a fall in the share of OOP expenses from 47% to 34% of CHE.

Examined individually, given their large size, China and India are in stark contrast in terms of improvement in share of GGHE-D over this period. While both India and China started with GGHE-D/CHE proportions of around 20% in 2000, China’s GGHE-D share of CHE increased three times to 60% by year 2015. By contrast, India’s GGHE-D share increased to around 30% in 2011, but fell to 26% of CHE in 2015. Along with government expenditure, China also saw an increase in voluntary insurance payments that reduced the share OOP expenses by half, which was not seen in the case of India.

Another way to examine changes in sources of expenditure is by changes over time on a per capita basis. Table 2 shows total current health expenditure per capita and per capita values from different financing sources across the income groups for 2000 and 2015. Current health expenditure per capita is the sum of GGHE-D per capita, EXT per capita, and private expenditure from domestic sources (PVT-D) per capita. The table also shows OOP expenditure per capita, typically the largest component of private expenditure from domestic sources. These numbers are expressed in constant 2010 US$ so they can be compared in real terms.

Growth in overall current health expenditure per capita was fastest in China, where it increased fourfold. Since China started the period in the LMIC category, its fast growth had a large impact on this group of countries as a whole; across this group, expenditure per capita increased almost threefold. Health expenditure across the LIC group as a whole doubled from US$ 32 to US$ 69 per capita during this period. Growth was slowest in UMICs and HICs, where there was only 50–60% growth in the overall current health expenditure on a per capita basis. EXT per capita in LICs was only US$ 1.3 in year 2000, and grew to US$ 4 per capita by year 2015. EXT per capita in UMICs was higher than in LICs.

Annualized growth rates are useful to readily see how rapidly different sources of finance are growing relative to each other as well as relative to growth in population, GDP, and overall government expenditure. Some differentials in annualized growth rates are large, others in Table 3 may seem small, but over a 15-year time period they can result in significant changes in relative shares.

Population growth during this period was highest in LICs, at 1.8% per year compared to the lowest growth rate of 0.7% in HICs. GDP growth was highest in the LMIC group, at 9.5%, and lowest in HICs, at 1.6%. However, China’s extraordinary annualized growth of 9.7% over the period impacts the average for LMICs as a whole, as China was a LMIC at the start of the period. With China included, LMICs grew at 7.2% per year as a group, but when China is excluded, the growth rate was 4.2% per year. India also raised the overall growth rate of LICs as a whole, as India was a LIC at the start of the period. India’s fast growth of 7.4% per year in GDP pulled up the average for LICs to 6.3% per year. When India is excluded from the group, the growth rate for LICs was 5.5% per year. Economic growth in and of itself raises tax revenue, permitting more government expenditure on health.

Typically, as shown in a 2015 IMF report, as economies develop, the tax base expands and tax administration improves, and tax revenue as a share of GDP increases over time. Some countries that are highly dependent on natural resources, with undiversified economies, may be an important exception to this trend. In addition, a 2011 report from the IMF Fiscal Affairs Department found that many resource-rich countries struggle with greater volatility in tax receipts compared to other countries. In addition to tax revenue from economic growth, tax revenue from an expanded tax base can provide a source for increased domestic government health expenditures. The GHED does not have data on tax revenue, but it does have data on GGE. Tax revenue and GGE are distinct concepts as expenditure can exceed revenue if governments run deficits. In all regions, GGE growth is faster than growth in GDP both in absolute and per capita terms, constant US$. The differential is largest in China, with GGE growing by almost 15% per year on average, exceeding GDP of about 10% per year.

Table 4 presents ratios of key indicators. The ratio in the first column is the annualized growth in GGHE-D relative to GDP. In all cases, it is positive, but it is highest for China at 8% and lowest for India at 0.9%. The growth in GGHE-D relative to GGE is positive in all groups, meaning that as GGE grows, either the share going to GGHE-D grows faster or governments are allocating a higher share to health. This growth rate is highest for China.

The growth in GGHE-D relative to current health expenditure is relatively low but positive, except in the low-income group when India is excluded, where it is weakly negative. As mentioned previously, the growth of out-of-pocket spending relative to current health expenditure is negative in all groups, except for low-income groups once India is excluded, where it is close to zero.

Even among HICs, GGHE-D is growing faster than GDP and faster than GGE. Many HICs have achieved UHC or close to it, but demands for health, changes in real costs, technological change, and difficulties in cost containment can mean expenditures continue to rise faster than income growth. These challenges have been long-discussed, including by William Baumol in the early 1990s ² .

Targets have been set for certain expenditure indicators in international forums and in international reports. For example, in April 2001, the heads of state of African Union countries pledged to allocate at least 15% of government budgets to health. Less often quoted from the Abuja meeting, but also important, was the call on donor countries to meet the target of at least 0.7% of GNP to official development assistance to developing countries (although the proportion to health was left ambiguous) ⁶ . The “Abuja” target of 15% has been widely referenced since this meeting in discussions about resource mobilization. Considerable emphasis is given to the policy lever of reallocation of overall government expenditures towards health in domestic resource mobilization discussions. As an example of another target, the 2010 World Health report suggested that over the long term, the level of combined government spending and compulsory insurance (which we include in public spending in our analysis) needs to reach 5 to 6% of GDP to achieve UHC ³ .

Figure 5 shows government expenditure on health (from domestic and external sources) as a share of government expenditure. While some individual countries reached the target, the LICs and LMICs income groups are very far from the Abuja target in 2015 (i.e., allocation of 15% of the government budget to health). As shown in Figure 5, between 2000 and 2015, this share increased from 4.8% to 5.2% for LICs and 8.6% to 10.4% for LMICs. The share for HICs (not shown in Figure 5) was over 20% in 2015. However, in many cases in HICs, cost containment, not resource mobilization, is high on the policy agenda, given what may appear to be unsustainable growth in government expenditures on health, both in absolute terms and as a share of government expenditure. It should also be noted that the GHED provides data on total capital health expenditure and does not disaggregate public and private sources of capital expenditure. The GGHE/GGE somewhat underestimates what the number would be if public capital expenditure on health were included in the numerator. However, if all capital expenditure were added to the numerator, the finding still holds that LICs, LMICs, and UMICs are well below the Abuja target in 2015.

China, when it is disaggregated from the LMICs as a group, is rapidly increasing the share of health in its government expenditures. Health expenditures expanded from 6.2% to 10.1% from 2000 to 2015. This ratio has stagnated in LICs and UMICs. It has climbed in LMICs as a group, in part because of China’s impact on the estimates for this group.

Similarly, apart from HICs, no other income group has met the target suggested in the 2010 World Health Report of combined government spending and compulsory insurance reaching 5-6% of GDP. However, this ratio is growing in most income groups. GGHE as a share of GDP rose slightly in LICs from 1.1% to 1.3% between 2000 and 2015. India brings down the estimate for LICs. After removing India, this share increases to 1.5% for LICs but is still much lower than the recommended level ( Figure 6).

Our study found some outlier countries where the level of level of general government health expenditure as a share of both GDP and general government expenditures seemed very low or very high. In some cases, the change (increase or decrease) in GGHE/GDP and GGHE/GGE between 2000 and 2015 was very dramatic. These ‘outlier’ countries are listed in Appendix 4 ⁵ . On the one hand, the performance of these countries may have varied greatly from the average values due to unique country experiences. On the other hand, data outliers can also point out possible data errors.

With the GHED database, we can identify how much of the growth in GGHE-D, in real terms, from 2000 to 2015 is due to (i) economic growth alone, (ii) expansion in public spending measured by GGE/GDP, (iii) reallocation of GGE towards health measured by GGHE-D/GGE and (iv) interactions across these factors.

Figure 7 shows the decomposition of GGHE-D growth between 2000 and 2015. Economic growth is the largest driver of increases in GGHE-D in LICs, LMICs, UMICs, and India. An illustration of the decomposition of the growth in GGHE-D for LICs is shown in Table 5. In HICs, shifts in public spending towards health are also big drivers.

Between 2000 and 2015, China experienced rapid economic growth with a 300% expansion of the GDP, which likely fueled rapid expansion of government budgets with GGE/GDP growing by more than 6 times during this period. This high growth and budget expansion, along with rapid prioritization of health is reflected in the high value of the interactions in the GGHE-D decomposition for China shown in Figure 7. Once China is separated from the LMIC country group, the interaction effect on GGHE-D growth diminishes greatly for the LMIC without China group.

This study was possible because of the major changes to the WHO GHED database in the December 2017 release. Our study reports on what the data currently show, while recognizing that the data, inevitably, contain errors and will change and improve over time. With the exception of China and India, our study reports on income group results, rather than individual country results, because the purpose was to understand broad trends. We used GGE/GDP as an indicator of the tax base relative to GDP, because this data was available in the WHO GHED database, but it is only a proxy for the tax base, as expenditures (GGE) can, in some years, exceed revenue. Our approach for disaggregating the sources of growth of GGHE-D into economic growth, strengthened tax base, and reallocation within the government budget is new to this study.

Our study has focused on resource generation and not on the important issue of improving efficiency. Funds for priority health activities can also be made available from improvements in allocative efficiency (spending money on the right things) and technical efficiency (spending money to achieve results at lowest cost). Considerations of how to generate fiscal space for health at a country level ideally need both elements, revenue generation and efficiency measures.

WHO’s study New Perspectives on Global Spending for Universal Health Coverage draws on the same database as this study. However, because of differences in methodological approaches, some of the findings are different. The WHO study classified countries into income groups by their income status at the end of the 2000 to 2015 period, and we chose to classify countries into income groups by their income status at the start of the period. More importantly, the WHO study used country-weighted averages of indicators, while we calculated indicators for the income group as a whole, so larger countries have a larger weight in our results, and smaller countries have a smaller weight. As an example of how the results differ because of these two differences in methodology, the WHO study found that OOP expenditures as a share of CHE fell from 46% to 38% over the period for LICs, while we found only a slight drop, from 60 to 58% (and the numbers are much higher). Differences in results between the two studies are not surprising, given that the WHO study used country-weighted averages while we calculated results for the income group as a whole, and also because countries were classified into income groups at different points in time. India is included in our LIC group because it was classified that way in 2000, while India is not in the WHO LIC definition, because it was no longer an LIC at the end of the period. India has a large weight in our calculations because of its large size, but India would be given the same weight as any other country in the WHO results. In terms of broad conclusions, there is more similarity across the two studies. The WHO study, our study, and others ^{7, 8} stress the importance of economic growth in driving GGHE-D growth relative to reallocation within the government budget. Looking forward, given the importance of economic growth in domestic resource mobilization, there is cause for concern in the poorer countries with slow or no growth projections and in conflict/fragile countries. Even in the poorer countries with rapid economic growth projections, it will take time to increase government budgets significantly given low starting points. External assistance for health needs to focus as much as possible on the poorest countries, and LICs and LMICs with unfavorable economic prospects.

Source data examined in this study, available in csv, xlsx and txt formats, have been collated on OSF. DOI: https://doi.org/10.17605/OSF.IO/WM8ZP ⁵ .

Appendices associated with this study are available on OSF. DOI: https://doi.org/10.17605/OSF.IO/WM8ZP ⁵ .

Appendix 1. Change in composition of population across income groups between 2000 and 2015.

Appendix 2. Number of countries in World Bank analytical income classification categories based on GNI p.c. in 2000 and 2015.

Appendix 3. Trends in change in current health expenditure by country income group over 2000–2015.

Appendix 4. Outlier countries.

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).