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The Economics of Public Health Care Reform in Advanced and Emerging Economies
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CHAPTER 2: Public Health Care Spending: Past Trends

Author(s):
David Coady, Benedict Clements, and Sanjeev Gupta
Published Date:
June 2012
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Author(s)
David Coady and Kenichiro Kashiwase 

This chapter analyzes the health care spending trends of 27 advanced economies and 23 emerging economies over the past four decades. Total health expenditures have risen sharply during this period, particularly in advanced economies (Figure 2.1).1 Since 1970, real per capita total health spending has increased fourfold in advanced economies, while spending as a share of GDP has increased from 6 percent to almost 12 percent.2 Two-thirds of this increase has been due to greater public health spending, whose share of total health spending rose from 55 percent to 60 percent. In the emerging economies, the increase in total health spending has been more moderate over the same period—from below 3 percent of GDP to about 5 percent—and public spending on health has increased from around 1½ to 2½ percent of GDP, about the same as the increase in private spending.

Figure 2.1Total, Private, and Public Health Expenditures, 1970–2008

(Percent of GDP)

Sources: Organization for Economic Cooperation and Development, OECD Health Data; World Health Organization; Sivard (1974–96); and IMF staff estimates.

Note: Average spending is weighted on the basis of GDP at purchasing power parity. For advanced economies without 2008 data (five countries), 2006 or 2007 data were used. The final year for spending data for the emerging economies is 2007.

The remainder of this chapter is structured as follows. First, it offers an overview of trends in public health spending in advanced economies. This is followed by a similar assessment of the drivers of public health outlays in emerging economies, and a discussion of the relationship between health outcomes and health system efficiency. Finally, appendices are provided that describe, respectively, data sources and differing approaches to measuring the efficiency of health spending.

TRENDS IN ADVANCED ECONOMIES

Public health spending in advanced economies has been characterized by short periods of accelerated growth followed by periods of cost containment (Figure 2.1). The rapid increase in spending during 1971–75 (rising by 1 percentage point of GDP) reflected the expansion of health insurance coverage in most countries. This was followed by a longer period of relative cost control when many countries introduced health reforms as part of broader fiscal consolidation efforts. Public health spending increased by less than 1 percentage point of GDP over the 15-year period from 1975 to 1990. Expenditures again began to accelerate in the early 1990s, before another period of containment in the second half of that decade. The slowdown in spending growth reflected reforms in both the United States and Europe as part of a broader restraint in total government spending. The growth of public health spending picked up after 2000, with outlays rising by 1 percentage point (to 7 percent of GDP) by 2008. This reflected a more widespread increase in total government spending over the 2000–08 period of 2 percentage points of GDP, following a period of spending containment in the 1990s (IMF, 2010).

The literature has identified income, aging, technology, and health policies as the key factors behind rising public-spending-to-GDP ratios. On the demand side, health care spending tends to rise as a share of GDP as countries develop. In addition, elderly people consume on average more health services than their younger counterparts. On the supply side, technological change has expanded the scope of what is medically possible by improving treatments and diagnostics. This expanding scope has increased the cost of medical services, reflecting improvements in quality (e.g., the diffusion of angioplasty and the use of MRIs instead of X-rays). Additionally, health costs have been driven upward by the relatively low productivity growth of services relative to other sectors of the economy (the so-called Baumol effect).3 Among these drivers, nondemographic factors dominate. On average, approximately one-fourth of the increase in public-spending-to-GDP ratios is explained by changes in the age distribution of the population (“aging”). The rest—known as excess cost growth—is attributable to the combined effect of nondemographic factors, including rising incomes, technological advances, the Baumol effect, and health policies and institutions.4 Of course, positive excess cost growth should not be interpreted to mean that the costs of public spending have exceeded its benefits, because technological advancements—the main driver of higher health care costs—have yielded enormous improvements in health status and well-being (Cutler and McClellan, 2001). In any case, the benefits of higher health spending would also need to be weighed against their costs, which is a task that is beyond the scope of this chapter.

The magnitude of increases in the ratio of public health spending to GDP has varied substantially across countries over the last three decades, and this has led to some convergence in this ratio. The ratio increased in virtually all advanced economies during this period (Figure 2.2). In 1980, the gap between the lowest-spending country (Greece) and the highest-spending country (Sweden) was 5 percentage points of GDP. By 2008, spending ranged from 5½ percent of GDP (Australia) to 8.7 percent (France)—a markedly lower spread than in 1980. On average, spending increased more rapidly in countries with low initial spending ratios (the correlation coefficient between increases in the spending ratio and the initial ratio is −0.8; Figure 2.2, right panel). The biggest increases occurred in the United States (3.8 percentage points), Portugal (3.4 percentage points), and New Zealand (2.7 percentage points), while the smallest increases were in Sweden (−0.7 percentage points), Ireland (0.0 percentage points), and Denmark (0.1 percentage points). Since 2000, 11 countries have experienced an increase in their public health spending ratio by 1 percentage point or greater: Canada, Denmark, Finland, Greece, Ireland, Italy, the Netherlands, New Zealand, Luxembourg, the United Kingdom, and the United States (Appendix Figure 2.1). The countries with the smallest increases (0.2 percentage point of GDP or less) were the Czech Republic, Germany, and Norway.

Figure 2.2Public Health Spending in Advanced Economies, 1980 versus 2008

(Percent of GDP)

Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.

Note: The figures and averages exclude the Republic of Korea, where spending as a share of GDP increased from 0.8 percent in 1980 to 3.6 percent in 2008. Data for 2008 refer to 2008 or latest year available. Averages are unweighted. AUS = Australia; AUT = Austria; CAN = Canada; DEU = Germany; ESP = Spain; FIN = Finland; FRA = France; GBR = United Kingdom; GRC = Greece; IRL = Ireland; ISL = Iceland; JPN = Japan; LUX = Luxembourg; NLD = Netherlands; NOR = Norway; NZL = New Zealand; PRT = Portugal; SWE = Sweden; USA = United States.

The low correlation between initial per capita GDP in 1980 and the increase in spending ratios over 1980–2008 indicates that income convergence was not a key factor. Furthermore, changes in relative age structures have been slow and are also unlikely to explain this convergence. Indeed, controlling for income and demographics, regression analysis indicates that countries with below-mean ratios of spending to GDP had significantly higher spending growth. This suggests that convergence was driven by “imitation” effects, borrowing from other countries some features of the public health system that seemed appealing. This led, for example, to the provision of health services previously not covered. Of course, such imitation required changes in health institutions and policies, including changes in the factors that determine the diffusion of technology. This in turn raises the question whether the rates of increase in spending that were observed during the convergence period will continue in the future (see Box 3.1).

TRENDS IN EMERGING ECONOMIES

Spending levels and increases have been substantially lower in all the emerging economies. During 1971–95, public health spending increased by ½ percentage point of GDP, to 2 percent. Spending accelerated after that, with an additional ½ percentage point of GDP in the following decade.5 Public-spending ratios are substantially higher in emerging Europe and Latin America than in emerging Asia, with no evidence of convergence in ratios across emerging economies over time (Figure 2.3). Since 1995, the largest increases in spending have been in Romania, Saudi Arabia, Thailand, and Turkey (by 1–1½ percentage points of GDP), while spending ratios have fallen in Estonia, Hungary, India, Latvia, Russia, and Ukraine. Since 2000—when average public-health-spending-to-GDP ratios started to rise—only six countries have had increases of more than ½ percentage point (Brazil, Bulgaria, Chile, Poland, Thailand, and Ukraine—see Appendix Figure 2.2).

Figure 2.3Public Health Spending in Emerging Economies, 1995 versus 2007

(Percent of GDP)

Sources: World Health Organization; and IMF staff estimates.

Note: Averages are unweighted. ARG = Argentina; BGR = Bulgaria; BRA = Brazil; CHL = Chile; CHN = China; EST = Estonia; HUN = Hungary; IDN = Indonesia; IND = India; LTU = Lithuania; LVA = Latvia; MEX = Mexico; MYS = Malaysia; PAK = Pakistan; PHL = Philippines; POL = Poland; ROM = Romania; RUS = Russian Federation; SAU = Saudi Arabia; THA = Thailand; TUR = Turkey; UKR = Ukraine; ZAF = South Africa.

The modest increases in public-health-spending-to-GDP ratios reflect the low priority given to this sector compared with other needs. Public health spending has remained at low levels even in countries where the constraints on higher spending—such as revenue-to-GDP ratios—have eased. For example, between 2000 and 2007, revenue-to-GDP ratios rose in the emerging economies in the sample (excluding Turkey) by 3½ percentage points of GDP, while public health outlays rose by about ½ percentage point. Developing economies allocated half as much of their spending to health as they allocated to education during the 1987–2007 period (Arze del Granado, Gupta, and Hajdenberg, 2010). By contrast, in advanced economies the shares have been approximately equal. Demand-side factors have also kept both total spending and public health spending low, including lower per capita incomes and differences in demographics, such as lower age-dependency ratios. Additionally, many emerging economies have not yet completed an epidemiological transition—from infectious to chronic diseases such as cancer, diabetes, and heart disease—that typically occurs with economic development and raises health care costs.6

HEALTH OUTCOMES AND HEALTH SYSTEM EFFICIENCY

Health outcomes vary widely in both advanced and emerging economies. In advanced economies, life expectancy (at birth) averages about 80 years but ranges from a low of 74 years in the Slovak Republic to 83 years in Japan (Joumard, Andre, and Nicq, 2010). The ranking of advanced economies on other health indicators related to longevity, such as life expectancy at age 65 and health-adjusted life expectancy, is similar to that for life expectancy at birth. Infant mortality rates also vary, ranging from a low of three (deaths per thousand) or less in Iceland, Luxembourg, and Sweden to more than five in Canada, the Slovak Republic, and the United States. At 71 years, average life expectancy in emerging economies is about nine years lower than in advanced economies. Among emerging economies, both life expectancy and infant mortality rates are more favorable in emerging Europe, on average, than in other regions. Life expectancy ranges from 52 years in South Africa to 79 years in Chile.

Inefficiencies in public health spending are large. While higher spending can help, improving the efficiency of these outlays is even more critical for improving health outcomes. This can be illustrated by examining the gains from reducing the “efficiency gap” for countries, which provides an estimate of the difference between the life expectancy they achieve—taking account also of the effects of socioeconomic and lifestyle factors—and that of the best-performing country at similar levels of spending.7 Cutting the efficiency gap of member countries of the Organization for Economic Cooperation and Development (OECD) in half, for example, would increase life expectancy by over one year. Achieving this same gain in life expectancy through higher spending, by contrast, would require a spending increase of over 30 percent. Countries where spending has been identified as the most efficient include Australia, the Republic of Korea, and Switzerland, while Hungary, the Slovak Republic, and the United States are among the least efficient. In developing and emerging countries, health spending is also an important determinant of health outcomes (Baldacci and others, 2008). As in the advanced economies, the efficiency of their outlays varies widely (Gupta and Verhoeven, 2001; Gupta and others, 2008), again suggesting ample room to improve health outcomes without raising spending.

CONCLUSION

Total health spending in advanced economies has risen by 6 percentage points of GDP since 1970, with two-thirds of the increase due to higher public sector spending. Public spending increases have been propelled by rising incomes, technology, aging, and public health policies. These outlays experienced distinct periods of accelerated growth, followed by episodes of cost containment. Public spending increases have been more modest in emerging economies than in advanced economies, rising by 1 percentage point of GDP since 1970. This has reflected the lower priority given to health spending relative to other spending needs. There has been some convergence in public health spending ratios in advanced economies over the past several decades, while emerging markets’ spending shares do not indicate such a pattern.

Inefficiencies in public health spending are large. This suggests that considerable improvements in health outcomes are possible by tackling these inefficiencies in both advanced and emerging economies.

APPENDIX 2.1. DATA SOURCES

The data for advanced economies are drawn from the OECD’s Health Database. For most countries, data on health expenditure (total, public, and private), as a percentage of GDP and in real per capita terms, are available. The availability of data in earlier years varies, and for most countries, the most complete set of data is available for 2008. The OECD data are subject to a number of structural breaks. To address these and allow for a consistent comparison of spending trends over time, we follow the procedure of Joumard and others (2008). For a year in which a structural break is identified, the average growth rate of real spending over the preceding five years is used to project spending growth in that year.8 In effect, this predicts spending in the year of the break, based on trend spending increases. The series are interpolated backward in time, based on the growth of spending in the unadjusted series. These adjusted data are used for all charts and tables showing developments in spending over time.

Appendix Table 2.1 provides summary statistics for public health spending for selected OECD countries between 1960 and 2008, unadjusted for these structural breaks. In some cases, data from the adjacent year were used when data were not available. For 1970, the data for Australia refer to 1969 and for the Netherlands to 1972. For the Netherlands, current public spending is used for data from 2003 onward, and for Belgium the entire series refers to current (rather than total) public health spending. Appendix Table 2.2 presents the data adjusting for the structural breaks. These data are also used in the figures and charts in the text. In both tables, Columns 2–8 show public health spending as a share of GDP for selected years, and columns 9–12 in Appendix Table 2.2 show the increase in this ratio over selected years to 2008. For the emerging economies, public expenditure data are derived from the World Health Organization (WHO). Ratios to GDP are calculated on the basis of data from the International Monetary Fund’s World Economic Outlook database. For data from 1970 to 1994, public health spending from Sivard (various years) as a share of GDP was used. It was assumed that private spending was a constant share of total spending over the 1970–95 period.

APPENDIX TABLE 2.1Unadjusted Public Expenditure on Health: Advanced Economies, 1960–2008(Percent of GDP, unadjusted for structural breaks)
1960197019801990200020072008
Australia1.82.33.84.45.45.7
Austria3.03.35.16.17.67.98.1
Belgium5.76.17.37.4
Canada2.34.85.36.66.27.17.3
Czech Republic4.65.95.85.9
Denmark6.67.96.96.88.2
Finland2.14.15.06.35.16.16.2
France2.44.15.66.48.08.68.7
Germany4.46.66.38.28.08.1
Greece2.33.33.54.75.8
Iceland2.03.15.56.87.77.57.6
Ireland2.84.16.84.44.65.86.7
Italy6.15.86.67.0
Japan1.83.24.74.66.26.6
Korea, Republic of0.81.52.23.53.6
Luxembourg2.84.85.05.26.6
Netherlands4.15.15.45.07.37.4
New Zealand4.25.25.76.07.27.9
Norway2.24.05.96.36.97.57.2
Portugal1.53.43.86.47.1
Slovak Republic4.95.25.4
Slovenia6.15.66.0
Spain0.92.34.25.15.26.16.5
Sweden5.88.27.47.07.47.7
Switzerland4.35.66.36.3
United Kingdom3.33.95.04.95.66.97.2
United States1.22.63.74.85.87.17.4
Average
Weighted1.73.34.65.26.16.97.3
Unweighted2.23.75.05.35.96.76.9
Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.Note: See discussion in text for description of data for 1970. For Luxembourg and Portugal data, 2007 refers to 2006.
Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.Note: See discussion in text for description of data for 1970. For Luxembourg and Portugal data, 2007 refers to 2006.
APPENDIX 2.2. MEASURING THE EFFICIENCY OF PUBLIC HEALTH SPENDING

Efficiency studies provide important insights for health care reform. These studies generally find that there are substantial inefficiencies in many countries, as measured by the relationship between spending inputs and health outcomes. This implies that achieving better health outcomes is possible by addressing these inefficiencies, even if spending does not increase.

APPENDIX TABLE 2.2Adjusted Public Expenditure on Health: Advanced Economies, 1960–2008(Percent of GDP, adjusted for structural breaks)
1960197019801990200020072008Changes (Percentage points)a
1960–1970–1980–1990–
2008200820082008
Australia1.83.03.84.45.45.73.92.72.01.3
Austria3.53.96.16.17.67.98.14.54.22.01.9
Belgium6.26.57.37.41.3
Canada2.44.95.16.36.27.17.34.92.42.20.9
Czech Republic3.95.95.85.91.9
Denmark6.98.17.27.18.21.40.11.1
Finland1.73.34.15.15.16.16.24.52.92.21.1
France2.84.76.57.48.08.68.75.94.02.21.3
Germany4.46.66.38.28.08.13.71.51.8
Greece2.33.33.54.75.83.52.62.3
Iceland2.02.85.16.27.17.57.65.54.72.51.3
Ireland3.04.56.74.44.65.86.73.72.20.02.4
Italy6.15.86.67.00.9
Japan1.83.34.84.76.26.64.83.41.91.9
Korea, Rep. of0.81.52.23.53.62.82.1
Luxembourg2.64.64.75.26.6
Netherlands4.25.35.55.27.37.43.22.21.9
New Zealand4.25.25.76.07.27.93.72.72.2
Norway2.44.36.36.76.97.57.24.82.90.90.4
Portugal1.63.74.16.47.15.63.43.0
Slovak Republic4.95.25.4
Slovenia6.15.66.0
Spain1.02.64.85.95.86.16.55.53.91.70.6
Sweden5.98.37.57.27.47.71.8−0.70.2
Switzerland4.05.66.36.32.3
United Kingdom3.13.64.64.65.66.97.24.13.52.52.6
United States1.22.63.74.85.87.17.46.24.93.82.7
Average
Weighted1.73.44.75.26.17.07.35.64.12.82.1
Unweighted2.23.85.15.36.06.76.94.93.41.91.6
Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.Note: See text for a description of the methodology for adjusting for structural breaks and for a description of the data for 1970. For Luxembourg and Portugal data, 2007 refers to 2006. The averages for given years (e.g., 1960, 1970) reflect different sample sizes, and comparisons should thus be made with caution.

For comparisons of changes up to 2008, the most recent year with available data is used (in some cases, 2007).

Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.Note: See text for a description of the methodology for adjusting for structural breaks and for a description of the data for 1970. For Luxembourg and Portugal data, 2007 refers to 2006. The averages for given years (e.g., 1960, 1970) reflect different sample sizes, and comparisons should thus be made with caution.

For comparisons of changes up to 2008, the most recent year with available data is used (in some cases, 2007).

Overview of Different Approaches

Nonparametric methods

Under a nonparametric technique such as data envelopment analysis (DEA), the first step in assessing efficiency is to create a production frontier that links spending inputs and health outcomes (e.g., public health spending per capita and life expectancy). The production frontier indicates the combinations of spending inputs and Adjusted Public Expenditure on Health: Advanced Economies, 1960–2008 (Percent of GDP, adjusted for structural breaks) outputs that are equally efficient. The distance of countries from the frontier is the measure of their inefficiency. Free disposable hull (FDH) analysis is similar to DEA but less restrictive (see Gupta and Verhoeven, 2001, for further discussion).

Appendix Figure 2.1Public Health Spending in Advanced Economies

(Percent of GDP, adjusted for structural breaks)

Sources: Organization for Economic Cooperation and Development, OECD Health Data; and IMF staff estimates.

The major advantage of nonparametric techniques is that no assumption is made about the functional form of the relationship between spending inputs and outputs. The drawback is that the frontier is formed by the outliers that establish “best practices,” with a large risk of measurement error.

Parametric methods

Under a regression (REG) approach, researchers typically take advantage of the panel structure of data (e.g., WHO or OECD health data) to utilize a large number of observations (e.g., Evans and others, 2000; WHO, 2000). This approach allows for the inclusion of a large number of explanatory variables. Efficiency is typically measured in terms of the size of the country fixed effect in the equation. Under stochastic frontier analysis (SFA), regression analysis is used to estimate the production frontier, and the efficiency of spending is measured using the residuals from the estimated equation. The disadvantage of these techniques is that a functional form of the relationship between spending inputs and outputs must be assumed.

Appendix Figure 2.2Public Health Spending in Emerging Economies

(Percent of GDP)

Sources: World Health Organization; and IMF staff estimates.

Empirical Findings

Nonparametric methods

Joumard and others (2008) and Joumard, Andre, and Nicq (2010) take into account three variables as inputs in explaining cross-country differences in health status in the OECD: health care spending per capita, a proxy for economic status (derived from the Program for International Student Assessment education survey), and a lifestyle variable. The main findings are that population health status could be improved significantly in most OECD countries by raising the efficiency of spending, and that increasing per capita health spending would have smaller effects on life expectancy than improvements in efficiency.

A large number of studies, including those by staff in the IMF’s Fiscal Affairs Department, have used DEA and FDH to evaluate the efficiency of education and health care expenditure (Gupta and Verhoeven, 2001; Hauner, 2007; Mattina and Gunnarsson, 2007; Verhoeven, Gunnarsson and Carcillo, 2007; and Gupta and others, 2008). These studies all conclude that there is considerable inefficiency in health spending in many countries.

Parametric methods

Joumard and others (2008) and Joumard, Andre, and Nicq (2010) estimate a panel regression and find that health care spending, lifestyle, and socioeconomic factors are all important determinants of population health status. Importantly, the ranking of countries (in terms of efficiency) is similar to that obtained using DEA. Evans and others (2000) and WHO (2000) estimate a fixed-effects model by using data from 191 countries from 1993 to 1997. Hollingsworth and Wildman (2003) reexamine WHO’s study by using both a time-variant fixed-effects model and DEA. They find that non-OECD countries show more variation in efficiency than OECD countries. Using the same WHO data, Self and Grabowski (2003) find that the comparatively higher life expectancy in wealthier countries is not a result of greater public health expenditures. In middle-income and less-developed countries, however, there is some evidence that public spending does improve health outcomes. Hollingsworth and Wildman (2003) implement an SFA and compare its results with DEA and REG. They find a high degree of correlation in efficiency measures across methods, as in Joumard and others (2008) and Joumard, Andre, and Nicq (2010).

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1The advanced economies in this study comprise 27 countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, the Republic of Korea, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and the United States. The 23 emerging economies are Argentina, Brazil, Bulgaria, Chile, China, Estonia, Hungary, India, Indonesia, Latvia, Lithuania, Malaysia, Mexico, Pakistan, the Philippines, Poland, Romania, Russia, Saudi Arabia, South Africa, Thailand, Turkey, and Ukraine.
2All country group averages are weighted on the basis of GDP at purchasing power parity, unless otherwise noted. The public health spending data have been adjusted for structural breaks to ensure comparability over time (see Appendix 2.1).
3The Baumol effect refers to the rising unit labor costs in sectors where it is difficult to achieve productivity gains, usually in services. Because salaries rise in these sectors in line with economy-wide averages, while productivity does not, unit labor costs rise in relative terms. For evidence of the Baumol effect in health spending, see Pomp and Vujic (2008).
4The precise breakdown of the role of these different factors in driving health spending has varied across studies, as very few consider all of these factors simultaneously. The literature has primarily focused on the drivers of total health spending, rather than public health outlays. In Smith, Newhouse, and Freeland (2009), the residual for technological advances explains between one-third and one-half of the increase in total health spending over 1960–2007 for the United States, depending on assumptions about income elasticity and medical care productivity. The remainder is due to changes in income, the Baumol effect, the rise of insurance coverage, and demographics.
5Public health spending in low-income countries, at 2 percent of GDP, is broadly similar to that in emerging economies.
6The number of premature deaths from noncommunicable diseases (NCDs) is increasing rapidly. NCDs are the biggest cause of death worldwide. In September 2011, the UN General Assembly held a High-Level Summit to discuss for the first time the prevention and control of NCDs.
7See Appendix 2.2 for a discussion of approaches to measuring the efficiency of health spending. The efficiency results cited here from Joumard, Andre, and Nicq (2010) control for the effects of these nonspending inputs on life expectancy. Still, the limitations of this analysis should be kept in mind, since health spending that leads to improvements in the quality of life but does not affect life expectancy will be measured as inefficient under this approach.Although life expectancy is only one dimension of health status, it is highly correlated with other widely used health status indicators (Joumard, Andre, and Nicq, 2010).
8In the case of Germany, no adjustment was made for the series break in 1991. In France, to address the large structural break in 1995, spending in that year, as a share of GDP, was set equal to the level of 1996. The series was then adjusted in earlier years to be consistent with the new, higher level.

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