The influence of health care financing systems on health outcomes : Are nations with a greater share of public health care healthier?

B.A. Thesis / Bachelorarbeit

The influence of health care financing systems on health outcomes

Are nations with a greater share of public health care healthier?

Universität Konstanz Fachbereich Politik- und Verwaltungswissenschaft

Erstgutachter: PD Dr. Sven Jochem Zweitgutachter: Prof. Dr. Christoph Knill

Verfasser: Marian Schmidt Matrikel-Nr.: 01/637896 Adresse: Rauhgasse 1

78464 Konstanz

Bachelor Politik- und Verwaltungswissenschaft 5. Fachsemester

Abgabedatum: 24. März 2010

Abstract

This thesis investigates the relationship between health care financing systems and cross-national health outcomes. For this purpose, health care financing systems are defined along three dimensions: a) total expenditures on health, b) public-private mix, and c) tax-financing vs. social insurance contributions. Based on various theoretical foundations, it is argued that public health care will increase equality of health care utilization. A panel analysis of the 30 OECD member states during the time period 1999 to 2005 finds that among these three dimensions the share of taxes in public health care expenditure is most strongly associated with higher life expectancy and lower premature mortality. Controls for other important health inputs like environment, education, lifestyle, demographics, and income have been included. Findings on the impact of a higher share of publicly financed health care differ for the two outcome indicators but suggest a positive correlation, while total expenditure on health has no systematic influence in this sample.¹

Zusammenfassung

Ziel dieser Bachelorarbeit ist es, den Zusammenhang zwischen der Finanzierung nationaler Gesundheitssysteme und Gesundheitsindikatoren auf Staatenebene zu untersuchen.

Gesundheitssysteme werden anhand von drei Dimensionen analysiert: a) die Gesamtgesundheitsausgaben, b) das Verhältnis von öffentlichen zu privaten Ausgaben und c) die Zusammensetzung öffentlicher Ausgaben aus Steuereinnahmen gegenüber Sozialversicherungsbeiträgen. Auf theoretischer Ebene wird argumentiert, dass öffentliche Gesundheitssysteme zu geringeren Nutzungsungleichheiten zwischen verschiedenen Einkommensschichten führen und daher bessere Ergebnisse erzielen. Eine Panel-Analyse der 30 OECD Mitgliedsländer von 1999 bis 2005 ergibt, dass, unter Kontrolle von anderen wichtigen Einflussfaktoren auf Gesundheit, der Anteil an Steuern den stärksten Einfluss auf höhere Lebenserwartung und geringere Mortalitätsraten hat. Die Ergebnisse von öffentlichen Gesundheitsausgaben im Allgemeinen variieren mit der Wahl der abhängigen Variablen, lassen aber in der Tendenz einen positiven Zusammenhang vermuten. Die Gesamtgesundheitsausgaben hingegen zeigten für unsere Daten keinen systematischen Einfluss auf Gesundheitsindikatoren.

1 I would like to thank Ashley M. Smith, UC Berkeley, and Reinhard Zschoche, ETH Zürich, for their valuable feedback on this thesis.

Table of Contents

Abstract...... II Zusammenfassung ... II Index of Tables ... IV Index of Figures... IV

1. Introduction ... 1

2. Theory ... 3

2.1. Conceptual framework for analyzing health outcomes ... 3

2.1.1. Definitions, health outcomes and health status ... 3

2.1.2. Determinants of health status ... 3

2.1.3. Health care financing ... 6

2.2. Theoretical links between health care financing and health outcomes ... 7

2.2.1. Effects on health care utilization ... 7

2.2.2. Cost containment in public systems ... 8

2.2.3. Externalities of private financing ... 9

2.2.4. Analysis of marginal returns ... 10

2.2.5. Further arguments ... 11

2.3. Empirical findings in the literature ... 11

2.4. Hypotheses ... 14

3. Research Design ... 15

3.1. Cases ... 15

3.2. Model ... 15

3.2.1. Panel methods ... 15

3.2.2. Time lags ... 16

3.3. Data ... 17

3.3.1. Indicators ... 17

3.3.2. Data sources ... 22

3.3.3. Missing data ... 22

4. Results ... 23

4.1. Descriptive Statistics ... 23

4.2. Results of panel analysis ... 26

4.2.1. Life expectancy ... 26

4.2.2. PYLL ... 27

4.2.3. Lagged model ... 29

4.2.4. Robustness tests ... 29

5. Discussion ... 31

5.1. Assessment of hypotheses ... 31

5.2. Limitations of results ... 31

5.3. Fields of further research ... 33

5.4. Policy implications ... 34

6. Conclusion ... 35

References ... 36

Appendix ... 40

Additional tables ... 40

Additional regression models ... 44

Stata files ... 49

Index of Tables

Table 1: Individual and macro-level inputs for health ... 4

Table 2: Table of variable names, summary statistics for 1999-2005 and sources ... 23

Table 3: Estimation results for analysis using life expectancy as health outcome indicator ... 26

Table 4: Standardized beta coefficients for panel analysis using life expectancy as health outcome indicator 27 Table 5: Estimation results for panel analysis using PYLL as health outcome indicator ... 28

Table 6: Standardized beta coefficients for panel analysis using PYLL as health outcome indicator ... 29

Table 7: Health outcome indicators for all OECD countries 2005 – Differences to 1999 in parentheses ... 40

Table 8: Health care financing system indicators for all OECD countries 2005 – Differences to 1999 ... 41

Table 9: Health care expenditure by source and coverage rates for all OECD countries 2005 ... 42

Table 10: Overview over missing observations and imputed values ... 43

Table 11: Standardized beta coefficients for panel models using lagged health inputs ... 44

Table 12: Robustness tests for panel analysis using life expectancy as health outcome indicator ... 45

Table 13: Robustness tests for panel analysis using PYLL as health outcome indicator ... 47

Index of Figures

Figure 2: Diagram macro- and micro-level propositions: How HC financing influences health outcomes ... 5

Figure 3: Life expectancy at birth and PYLL per 100,000 all causes of mortality – OECD countries 1990-2005 .. 24

Figure 4: Health care financing per capita in international Dollar by sources for all OECD countries 2005 ... 25

Figure 5: Scatter plot TEH and life expectancy (r=0.59, n=30) for 2005 ... 25

1. Introduction

The debate on health care reform is ongoing in almost every developed country. Although it tends to arise only periodically as a priority on political agenda, the focus on health care reform inevitably returns. Steadily rising health expenditures and burgeoning national budget deficits force governments to evaluate cost-containment strategies that mitigate adverse effects on their respective population’s health. Currently, one observes a period of particularly intensive debate marked by an ideological divide on the role of free markets versus state intervention. The United States, one of the few developed nations to rely primarily on private health care delivery for its citizens under the age of 65, has recently taken steps towards more public options. Yet, it is not the only country considering major changes to its present health care system. With the introduction of co-payment schemes in the last decade, many European countries with largely public, universal health care systems have already taken steps in the other direction towards increased privatization.

Given the multitude of financing schemes and varying levels of private health care spending across countries, the common question then is to determine the optimal mix of expenditure on a public- private healthcare spectrum. Since this question of “Health care, quo vadis?” will arise repeatedly in the future, it would be helpful to have more objective guidance and not leave the answer simply to ideological judgments. Can empirical evidence on health outcomes answer the question of whether it is better to rely upon a greater share of public or private financing? This question must be examined in a multidisciplinary approach that includes medical, social, economic, and institutional factors.

Figure 1: Per capita total expenditure on health (TEH) for OECD countries 1995 to 2005

WHO (2009) data

The objective of this thesis is to investigate the relationship between health care financing systems and cross-national health outcomes. In doing so, the theoretical causalities at work on the micro and macro levels will be described in order to observe the impact of health care financing on individual health care utilization and well-being, as well as on aggregated national health. To find support or

0 1000 2000 3000 4000 5000 6000 7000

1995 2000 2005

TEH in international $

minimum OECD mean maximum

counterevidence for the specific theoretical assumptions, health outcomes of the member states of the Organisation for Economic Co-operation and Development (OECD) will be examined. A panel analysis of the 30 member states over a period of seven years from 1999 to 2005 will enable us to draw some conclusions and answer the research question.

Upon the initial glance, this study seems to have limited connection to political science as it borrows models from economic theory on health, which is far more elaborate than in political science.

Nevertheless, our research question touches the central point of policy analysis: Do policy outputs truly make a difference on policy outcomes? More specifically, does a particular financing system, developed through a multitude of policies over time, truly make a difference on the policy goal of improved health? This question is central in the phase of policy evaluation, but its answer will also influence other phases of the policy cycle, particularly policy formulation and decision.²

This thesis will be organized as follows: After the introduction in Section 2, the conceptual framework of this study will be defined and the hypotheses will be presented. Section 3 gives a detailed description of the applied research design. Results from the data analysis will be presented in Section 4. Then the limitations and prospects of these findings will be discussed in Section 5, before summarizing conclusions in Section 6.

2 For further information on the stages approach to the policy process see Windhoff-Héritier (1987)

2. Theory

2.1.Conceptual framework for analyzing health outcomes

The World Health Organization (WHO) has defined three central goals for health care systems. First and foremost, they should improve health. Second, they should be responsive to the demand of the population. And third, they should protect against financial losses in case of sickness (WHO 2000, p.8).

This thesis will focus on the first point: health outcomes. This concept is not only defined by health status but also includes quality indicators measuring the performance of the medical system. In order to concentrate on health status as the main aspect of health outcomes we will apply a definition by Jee & Or (Jee & Or 1999; Hurst & Jee-Hughes 2001). Hence, health outcomes are “changes in health status brought about by health care – or health system – activities” (Jee & Or 1999, p.6). By using this rather narrow definition, the quality dimension of health care is excluded, thereby leaving mortality and morbidity figures as health outcome indicators. Consequently, we will use the terms health outcomes and health status synonymously throughout this thesis, defining them by indicators on national mortality, which are further delineated in Section 3.3.1.

Health care financing is the process of collecting revenue, regardless of whether from individuals, groups or firms, to pay for the operation of the health care system. We do not differentiate between the terms health/ health care financing, spending, or expenditure. However, it should be clear that the funding of health care is conceptually different because it concentrates on the question of how to allocate revenues to providers within the health care sector, i.e. investigating reimbursement schemes (Santerre & Neun 2000, p.33).

Public financing of health care is defined as “financing via taxes and compulsory health insurance premiums” (Leu 1986, p.44). Out-of-pocket payments are health expenditures paid directly by individuals consisting of co-payments, co-insurance, deductibles, and spending on self-medications (Calikoglu 2009, p.69). Although public financing of health care represents the majority of total expenditure on health (see Section 4.1), occasionally the term private or privately financed health care system will be used throughout this thesis in the sense of ‘to a high relatively high share privately financed system’ (with a higher amount of direct payments and private insurance premiums than in other countries).

2.1.2. Determinants of health status

Independent from our background as a researcher, health always must be viewed as something influenced by a multitude of factors on the individual level. Whether or not we see health as a result

of a production function (as in economic theory), it is always the same clusters of indicators that have to be analyzed when we are interested in the question: Why are people more or less healthy?

Typically in recent research health is seen as a commodity produced by different inputs. Thereby Grossman’s original investment model is lifted from a rather individual perspective to the macro- level (Grossman 2000). Nixon & Ulmann (2006, p.8) find that the distinction between the individual Grossman model and a production function approach has become somewhat blurred, because the relevant inputs in both models are mostly the same.

The inputs can be categorized into health care inputs (HC) and other non-medical inputs (NM).

The non-medical inputs on the individual level are environmental factors, lifestyle, education, age, income and genetics (Arah et al. 2005, p.82; Berger & Messer 2002, p.2108). Since we want to investigate health outcomes on a national level, we must translate these inputs into macro-level variables (see Table 1).

Table 1: Individual and macro-level inputs for health

Input Individual level Macro-level

HC Health care utilization Health care system

ENV Exposure to environmental

health risks Aggregate environmental health risks for the population

STYLE ^Lifestyle Aggregate lifestyle indicators (e.g. on drug use, physical exercise, overweight)

EDU ^Education Aggregate skills and knowledge

of the population

DEMO ^Age Demographic structure of the

population

ECON Income, wealth Aggregate economic endowment of the population

Others (ε) ^Genetics Distal factors – Social, cultural or political factors that influence the individual’s genetics, lifestyle or

education

We cannot, however, numerically observe genetic variation amongst nations. This variation will be captured in the error term of the regression model (ε). The macro-level production function is therefore:

Thus, health outcomes will depend on these seven factors. While the influence of health care financing (HF) will be explained in detail below, the other determinants are now briefly examined.

The natural environment (ENV) indicates exposure to external health risks. For example, pollution might cause respiratory illness, or radiation can cause leukemia. Lifestyle (STYLE) choices often have a large impact on individual health. Smoking causes lung cancer, alcohol consumption liver failure, and an unhealthy diet or prolonged stress tends to result in cardiac diseases. Following Grossman’s model, education (EDU) will allow people to produce health more efficiently, because they are better informed and follow medical advice more strictly (Grossman 2000). Furthermore, education can influence other inputs like environment, lifestyle, and income.³

However, it is still necessary to theoretically explain the links between macro-level data and micro- level processes, which are crucial for health. Therefore, we apply Coleman’s multilevel model of propositions and explain the theoretical linkages at both levels (Coleman 1994, p.8).

Since older age increases the probability of illness and eventually dying, it is an important input for health. On the macro-level this influence is captured by the demographic structure (DEMO) of society. Economic endowment (ECON), i.e. wealth and income, enables people to purchase products for healthier life, as well as medical services not covered by insurance.

Figure 2: Diagram macro- and micro-level propositions: How health care financing influences health outcomes

3 Education influences job quality (occupations of better educated individuals are physically less demanding and have a healthier environment), healthier lifestyle (people are more informed about the negative impact of smoking, unhealthy diet, etc), and income (jobs are better paid)(Or 2000, p.60).

Macro-level

Micro-level

Health care financing system

National health status

Individual health care coverage

Individual health status

Probability of seeing a doctor

Intensity of health care utilization

Allocation of financial resources

Regulation of access

Aggregation of individual health outcomes

2.1.3. Health care financing

One important feature, possibly even the core of health care systems, is how revenues are collected.

Financing arrangements determine partly or entirely individual access to the system, who bears the burden of paying for health care and to what extent, and how well a country can control its health care expenditures. Health care can be financed by direct payments⁴

When analyzing health systems in general, one usually speaks about three different systems: the Beveridge model (National Health Systems - NHS) with the United Kingdom system as its prototype, the Bismarckian model (Social Insurance Systems - SIS) with the German system as its prototype, and private systems with the United States and Switzerland as the major examples in the OECD. What differentiates the three systems in terms of health care financing?

, private insurance premiums, social insurance contributions, or taxes for public insurance (Thomson et al. 2009; Honekamp &

Possenriede 2008). The choice of the financing methods has important implications for equity in terms of access, utilization, and income (Honekamp & Possenriede 2008, p.405).

For our purpose of analyzing health care financing, we develop a framework consisting of three dimensions, ordered by specificity:

o Level A) Extensiveness of health care financing – describes the total amount of financial resources allocated to health care, independent of source.

o Level B) Public-private mix of health care financing – describes to what extent health care is financed through private (direct payments or private insurance premiums) or public sources (social insurance contributions or general taxes).

o Level C) Source of public financing – distinguishes between financing by taxes (mostly present in Beveridge systems or NHS⁵

Examining these three dimensions, we see that countries are not necessarily clustered into three distinct categories, but that they rather form a continuous spectrum of 30 different systems within the three prototypical systems. This is an advantage in analysis considering that there are indeed countries like Portugal which have a hybrid NHS-SIS system (Elola et al. 1995, p.1398). It makes a classification into distinct groups unnecessary.

) and social insurance contributions (levied by Bismarckian systems or SIS).

4 This includes out-of-pocket payments which are also part of co-payment arrangements in both public and private systems.

5 Not every tax-financed system is automatically an NHS. Canada for example has a tax-financed health care system but mostly relies on private health care delivery like Germany.

2.2.Theoretical links between health care financing and health outcomes

Our research question investigates the classic dilemma on the extent to which the state should intervene in certain markets. When health care, as most researchers suggestis a market that cannot be left unregulated, it is a comprehensible conclusion to expect government involvement producing better health outcomes (Santerre & Neun 2000, chap.9). We will now explain why state intervention via publicly financed health care systems is expected to have a positive influence on population health.

Our main argument is that public health care will increase equality of health care utilization. Only when all groups of society, independent of their socio-economic status, have access to universal health care, a nation will perform well on macro-indicators where the health status of every individual is equally weighted. Our assumption is supported by Self and Grabowski who argue that the poor health status of underprivileged groups in society suggest that health inequalities can be a mediating variable between public health expenditure and national health status (Self & Grabowski 2003, p.838).

As already implied in the aforementioned definition, this study does not focus on outcome measures that capture “quality of health care”, as discussed by Wendt (2009), but rather on health outcome measures on the macro-level, i.e. national health status (Wendt 2009, p.435). This is because our theoretic considerations suggest that worse health outcomes are caused by inequalities in health care utilization, assumedly related to the health care financing system. So-called ‘input indicators’

like physician density or per-capita hospital beds do not adequately capture adverse health outcomes caused by inequalities of utilization.⁶

Although Navarro et al. (2006) argue that the effect of health policies is mediated by the redistributive characteristics of the system, we cannot agree to this conclusion. First, the causal links are far from obvious, especially considering the multitude of policy fields where redistribution can take place. Second, the applied methods in their research, i.e. simple correlations, do not satisfactorily show any empirical link between politics or redistributive policies and health outcomes.

2.2.1. Effects on health care utilization

In addition to the assumption that we expect health care utilization to grow if national expenditure on health grows (Level A), it is especially the public-private mix that has serious implications for the extent to which people can use health care services.

6 For example, we consider two countries with an equal number of physicians per capita. In country A all of the physicians are located in the capital; in country B they are equally distributed over the country. We certainly expect national health outcomes to be much better in country B.

It is argued that public systems (Level B) suffer extensively from moral hazard.⁷

Theoretical research on the effects of different public health care systems (Level C) is less pronounced. Here, health care utilization is rather determined by mode of health care provision. Or et al. (2008) find that “social inequalities in specialist use are less strong in countries with NHS and where doctors work as gatekeepers” (Or et al. 2008, p.13). Since all NHS systems are tax-financed, this might lead to the assumption that tax-financed systems cause more equal health care utilization and therefore better outcomes. In addition, all tax-financed systems achieve universal coverage, whereas most social insurance systems do not achieve 100% coverage rates

The implied overconsumption of health care services occurs if medical expenditures are insured (Leu 1986, p.44).

This may lead to rising costs in public systems and thus negatively affect health outcomes if scarce resources are overused. However, this is also the case with private health insurance and may only adversely affect public health care if a public system increases the total number of insured. A systematic difference in the influence of public or private systems on moral hazard is not expected (Leu 1986, p.44). Underutilization, on the other hand, is a common result in private systems due to high barriers of access. These are foremost established by direct payments required from individuals.

Out-of-pocket payments are correlated with access to health care and usually decrease health care utilization of lower income groups (Wendt 2009, p.434). In addition to decreasing health care utilization amongst lower income groups, co-payments can widen the inequality gap by increasing utilization among upper income groups (Tuohy et al. 2004, p.378).

8

2.2.2. Cost containment in public systems

. Greater coverage is directly linked to less health inequality.

There are several reasons to assume that publicly and privately financed systems differ in their ability to contain total health care expenditure. First, private systems are characterized by higher administration cost, due to more complicated billing procedures in a system of multiple insurers and less economy of scale, as compared to that of a single public insurer. Spithoven (2009) shows that health care administration expenditure per capita is 5 times higher in the US than in Canada (Spithoven 2009, p.9).

Second, in private systems costs are higher for determining applicants’ risks and insurance premiums, as well as for advertisement (Spithoven 2009, p.9). These procedures are unnecessary in public systems.

7 Moral hazard is a „situation where individuals alter their behaviour after they have purchased medical insurance because they are no longer liable fort he full cost of their actions“ (Santerre & Neun 2000, p.620).

8 See Appendix Table 9

Third, a majority of public systems has implemented gatekeeper models (Macinko et al. 2003, p.859).

Gatekeeper models reduce health care expenditure significantly due to better coordination and are even expected to directly improve prevention and cure through earlier detection of diseases (Wagstaff 2009, p.11). This is because sick people only have one general practitioner to consult, who is well informed about the patient’s history and thus can directly refer patients to the right specialist.

Simultaneously, the total number of specialist consultations decreases, because specialists cannot be consulted if no need was indicated by the gatekeeper (Spithoven 2009, p.13).

Fourth, public systems have the advantage of greater interventional power. In this monopsony setting, the state as only consumer of health services has price-setting power. It is therefore able to force prices below the market equilibrium, which will also cause supply to decline (Santerre & Neun 2000, p.263). However, rationing is not always considered to be deleterious in health research because in rationing settings only severe cases will get treated and overconsumption is avoided.

Finally, we also want to present an argument against the cost containment abilities of public systems.

Kling (2006) explains that the guaranteed absorption of costs by the public decreases incentives for cost-saving technological development, which will result in higher expenditure in public systems.

All in all, one should expect that a higher share of public financing in health care can stabilize health care costs (Wendt 2009, p.434).

When looking at the third dimension of health care financing, we observe that tax-financed systems, especially in the presence of NHS, make greater use of gatekeeper models. In the data of Macinko et al. (2003) tax-financed systems except those of Greece and Sweden had implemented a gatekeeper.

This is expected to improve health outcomes and lower health care spending for the aforementioned reasons (Wagstaff 2009, p.11).

2.2.3. Externalities of private financing

Generally, it is not expected that private health care consumption per se has a direct negative impact on health outcomes (Glied 2008, p.6). However, completely private systems and mixed systems of public and private financing have higher total expenditures on health than completely publicly financed systems (Glied 2008, p.8). This can be explained by the destruction of the earlier depicted monopsony setting in completely public systems. As soon as private insurance becomes available in previously public systems, its advantage of price-setting power to keep prices low is eliminated. A shift of resources to the private sector, which generally has higher prices for better quality or offers additional services, will reduce availability of public services by quantity restrictions. This problem is exacerbated by the difference between monopsonist prices and higher private sector prices. Fewer health care providers will work less in the public sector or will adjust prices, i.e. raising prices in the

public sector to those of the private sector. On one hand, we consequently see higher consumption and higher prices. Although we have a welfare gain in economic terms, raising costs will shift consumption towards higher incomes, which will lead to more inequality (Glied 2008, pp.9-11). On the other hand, parallel private insurance will lead to longer public sector waiting lists (Tuohy et al.

2004, p.391).

In the case of complementary private insurance, public health expenditure will increase because people are using more public services in the presence of complementary insurance (e.g. when prescription drugs are covered by additional private insurance, a consumer will contact the publicly financed doctor more often to acquire the prescription) (Glied 2008, p.11). Substitutive private insurance overrides the gatekeeper functions implemented in the public system like waiting lists, increases use of public services, and raises public health expenditure (Glied 2008, p.12).

Finally, in the case that private insurance can replace public insurance, as it is the case for high income earners in Germany, healthier people are more likely to opt out the public system, thereby creating the phenomenon of adverse selection. At the same time, private insurers are likely to

“cream-skim”, or attract healthier people. As a result, on average sicker people are left in the public system, which causes higher per-capita expenditure and simultaneous decreases in public system revenue as usually high income earners will opt out of the public system (Glied 2008, p.13).

In summary, the presence of private insurance makes non-purchasers relatively worse off. Not only will satisfaction with the public system decrease, but the costs of the public system will also increase.⁹

2.2.4. Analysis of marginal returns

When equality is seen as a public good, private finance will have a negative impact because private markets underproduce public goods. The presence of private health insurance will cause externalities and by increasing costs, adversely affect existing public health systems.

Although it is debated why total expenditure on health is soaring in developed nations over the last few decades, technological advancement and widespread usage of expensive medical procedures like MRIs are certainly one factor (Kling 2006). Generally, rising health care spending should produce, despite growing budget constraints, better health outcomes. But what if we see that this additional investment in health is not equally used among different groups of the population? Higher inequality reduces average health because health spending is subject to diminishing returns in the production of health (Wagstaff 2009, p.10). What do diminishing returns mean in practice? In health economics medical treatments are generally evaluated on their cost-effectiveness efficiency. Given a constant

9 Additional increases in costs are for example also caused by the need to regulate the private health insurance sector (Tuohy et al. 2004, p.372).

budget of 1 million US dollars, is it more efficient from a macro-level perspective to allocate them on regular preventive cancer screenings for 1000 people or on neurosurgery for one diseased patient?

The first option will probably increase the population’s life expectancy more significantly. Investment in high technology care will assumingly only profit a few individuals, while basic medical services usually underutilized by lower income groups can improve health outcomes also with regard to macro-level indicators (Glied 2008, p.6).

Thus we can make two conclusions. First, health care expenditure is subject to diminishing returns and second, a more equal distribution of health care expenditure will result in better macro-level outcomes. We posit that publicly financed health care systems, where service provision is independent from the individual’s ability to pay, will cause less health inequalities than private insurance.

2.2.5. Further arguments

We want to conclude the theoretical framework by presenting further arguments in the discussion of public and private health care financing in order to draw a complete picture.

So far, considerations about fairness have been left out. While they are usually unimportant from an economic perspective, they intensely influence the political debate. Public financing of health care services implicates redistribution of financial burden from healthy to sick and from rich to poor (Honekamp & Possenriede 2008). On the other hand, we generally observe the highest rates of non- insurance for minorities with low income and for the poor in private systems like the US (Spithoven 2009, p.16). In this case, the weakest members of society suffer under private insurance systems, which raises questions about fairness.

Arguments against public health care often include a) a lower willingness to pay for a tax-based system, b) less effective remuneration schemes, c) less competition among providers leading to higher cost, d) less innovation. These arguments can be retraced in Wagstaff’s article (Wagstaff 2009).

2.3.Empirical findings in the literature

Quantitative researchers have always been interested in the determinants of cross-national health outcomes. In case of the industrialized countries, there have been three broad fields of research.

First, levels of mortality or life expectancy have been linked to socio-economic development, a hypothesis that has seen widespread support (Spijker 2003). However, when comparing nations of similar development level, this finding tends to become spurious and has been refined by examining figures of total health expenditure (Or 2000, p.56).¹⁰

10 In general, GDP per capita and heath expenditure per capita correlate significantly. For our data r=0.911.

Second, since the 1980s researchers have

focused on the impact of income inequality as a determinant of health outcomes. A meta-analysis of such studies by Lynch et al. (2004, p.81) found results inconclusive. Third, only recently have health care systems been investigated on a comparative level. These approaches are shortly described below. Thereby, we will ignore literature that only reassesses Esping-Andersen’s welfare state typology in terms of health system performance (Bambra 2005; Eikemo et al. 2008; Bambra 2005;

Bambra 2009).

A positive influence of public health care expenditure on health, measured by Potential Years Life Lost (PYLL)¹¹, has been found by Or (2000) for 21 OECD countries over 23 years in a fixed-effects panel analysis. While the long-term effect of public financing in a single country is small, it is a significant determinant in between-countries health differences. In an analysis of Spanish regions Rivera (2004) shows that self-assessed health status and morbidity indicators are better in regions with a higher share of public health expenditure. Arah et al. (2005), investigating 18 OECD countries from 1970-1999, also with a fixed-effects model, find that public health expenditure is one of the amenable factors that can reduce mortality and premature death. However, they do not control for other sources of health care spending.¹²

In contrast, there are several studies finding no or negative influence of public health care financing on outcomes. In a sample of 20 OECD countries for the time period of 1960-1992 Berger & Messer (2002) find that the share of public health expenditure has a negative impact on health, increasing mortality. Although this relationship does not hold for all single years of the panel, they conclude that less efficient health care provision causes the worse performance of public systems (Berger &

Messer 2002, p.2111). Self & Grabowski (2003) show in a cross-country analysis for 191 states that the squared share of public health expenditure negatively influences Disability Adjusted Life Expectancy (DALE) . Their model however is flawed by inappropriate time lags that were determined only by data availability and an insufficient approximation for environmental factors by urbanization rates. From Grubaugh's & Santerre's (1994) findings, we can indirectly support the negative impact thesis. They conclude that the mainly privately financed system of the U.S. would have a much higher infant mortality rate if its health system was more similar to other OECD countries, which have a higher share of public health expenditure.

An explicit analysis of the Level C of health care financing (tax-financed vs. social insurance) has so far only been undertaken by Elola et al. (1995), Or et al. (2009), and Wagstaff (2009). While using descriptive statistics and exploring only 5 countries, Or et al. (2009) find that neither Beveridge (NHS)

11 A detailed description of different health outcome measures can be found in section 3.3.1.

12Conley & Springer (2001) support these findings by showing that public health care spending reduces infant mortality.

systems nor Bismarckian (SIS) systems perform better in a general sense. While NHS are better in cost-containment and affordability, advantages of SIS are access and availability of services. No distinction between the two is possible in terms of health outcomes (Or et al. 2009, p.4). In a cross- national comparison of 17 Western European countries, data of Elola et al. (1995) show that National Health Systems have an 11-13 % lower infant mortality rate than that of Social Insurance Systems, whereas in general the public share of health care expenditure has no significant impact. They explain their findings with the causal factor that NHS “have more direct control over expenditures, more equitable distribution of resources and allocative efficiency, lower out-of-pocket expenses, and lower administrative costs than social security systems” (Elola et al. 1995, p.1401). However, it has to be mentioned that they included only a few control variables and only data for a single year, which makes the findings less validate. In accordance with the other authors Wagstaff (2009) demonstrates slight empirical advantages for tax-financed health care systems. They have a 3.5 % lower expenditure level than social insurance systems and lower premature mortality from breast cancer.

The breast cancer rate is 5-6 % higher in social insurance systems. Nevertheless, he did not find generalizable differences between SIS and tax-financed systems in terms of health outcomes for 29 OECD countries from 1960-2006. He, however, did not control for any non-medical inputs of health.

In summary, there is still a considerable gap in research regarding the investigation of health care systems and their influences on health outcomes, although when it comes to policy learning reliable results are important premises for improving existing systems in times of major reform need (Wendt 2009, p.443). Mixed results across different studies are certainly influenced by recurrent shortcomings. These are missing control variables, inappropriate statistical methods, non-application of time lags, and unbalanced samples, where observations are chosen by data availability and no statistical correction is done (Conley & Springer 2001, p.774). Therefore, we try to contribute to the existing research in several ways. First, our theoretical framework will discuss all of the health care inputs and choose of the most reliable indicators available at this time. Second, we develop a new framework for analyzing health care financing systems to scrutinize the relationship between health expenditure and health outcomes more closely. It is therefore expected that the influence of total expenditure on health (Level A) will diminish as soon as we control for the share of public expenditure and taxes. Third, we use modern panel model methodology and analyze all OECD countries. Finally we try to integrate time lags for independent variables to reproduce theoretical relationships more accurately.

2.4.Hypotheses

According to the theoretical assumptions explained previously and the decomposition of our concept of “health care financing systems” in section 2.1.3, the following hypotheses are derived:

H1: The amount of total expenditure on health per capita is positively associated with health outcomes, but its impact will diminish as soon as we control for other dimensions of health care financing, namely the public-private mix and the share of taxes.

H2: The share of public expenditure in total expenditure on health is positively associated with health outcomes.  Publicly financed health systems perform better than private ones.

H3: The share of taxes in public expenditure on health is positively associated with health outcomes.  Tax-financed health systems perform better than social insurance systems.

3. Research Design 3.1.Cases

In this study, we investigate health care systems of all 30 OECD countries¹³ over a time period of seven years from 1999 to 2005. Some data of lagged variables might reach back until 1989. This sample has been chosen for several reasons. Mainly, this research wants to contribute evidence to the ongoing debate about health care in developed countries. The OECD is a union of nations with common economic background, intense cooperation, and common benchmark of policy evaluation.

Also, it provides standardized and, to a certain extent, reviewed¹⁴

3.2. Model

data on most socio-economic and medical variables of interest to us. The time frame was chosen to include the most recent but still reliable data available. Also, we did not include the year 2006 in order to avoid unnecessary missing values. In order to be able to lag certain health inputs, such as data on smoking up to 10 years, we could not extend the time-frame further to the past as some countries in the sample did not exist before 1990. Furthermore, we see that missing values become more frequent in earlier datasets.

3.2.1. Panel methods

To empirically test our research question, time-series cross-section data are used. Because pooling observations and running simple Ordinary Least Squares (OLS) regression is not an option for analyzing serially correlated and heteroskedastic country metadata, we must choose from a wide variety of different modeling techniques for panel data (Baltagi 2008). The studies that have been reviewed for this research usually apply standard fixed-effects (FE) models controlling for first-term autocorrelation (AR 1) (Spijker 2003; Spijker 2005; Macinko et al. 2003). Robustness tests are sometimes done by Feasible Generally Least Square (FGLS) estimation (Conley & Springer 2001;

Nixon & Ulmann 2006). Beck & Katz (2004) have pointed out in an evaluation of different approaches to analyze panel data that fixed effects largely diminish the impact of slowly changing variables, such as most of our input factors, to illustrate their impact on the dependent variable. They conclude that

“obviously a fixed effects analysis should not conclude anything about the inter-unit effects of the independent variables, since such effects have been removed” (N. Beck & Katz 2004, p.6). However, we expect cross-country differences being an important factor in analyzing health systems.

Statistical literature has extensively discussed appropriate estimation methods for panel analysis.

Since Beck & Katz (1995) published their widely cited paper on how to handle panel data, it is

13As of 2009. Chile joined the OECD in January 2010 as the first new member state since 2000.

14About the limitations concerning data collection and measurement, see section 3.3 and 5.2. It has to be mentioned that data on most cross-country variables will be subject to national desirability. We can, and especially after the recent fraud on economic indicators in the European Union, never should have too much trust in those figures.

theoretically accepted that in panel analyses with higher I (i.e. more cross-section observation) than t (time-sections observed) OLS regression using panel corrected standard errors (PCSE) should be used. However, this procedure has not been applied in any of the reviewed studies.¹⁵

Data has been tested and later corrected for serial correlation (AR 1) and heteroskedasticity.

In order to guarantee comparability with the existing literature, we will estimate coefficients with both OLS estimation using panel-corrected standard errors and FGLS estimation.

16

3.2.2. Time lags

OLS estimation with PCSE gives two possibilities to correct for serial correlation, either assuming that the AR1 correction term is constant for all countries or that each country has a specific PSAR1 term. We will generally use AR1 correction because Beck and Katz argue that PSAR1 correction is too conservative (Stata Corporation 2009, p.378). For robustness testing, however, we will also present the latter model.

Incorporating time lags between independent variables (determinants) and dependent variables (health outcome measures) is an unusual procedure, although it is indispensable from a theoretical perspective. For example, it has already been explained that more educated people might either have less physically demanding jobs or a better understanding of the consequences of unhealthy living. If today, we were educating the whole population of a given country equally well, when will we observe a difference in mortality indicators? This could be centuries from now on, when a potentially heavy smoker does not die from lung cancer at age 55 and raises the probability to survive every single year, thus also raising life expectancy on the aggregate level.¹⁷

Nevertheless, applying time lags to regression models is still rare in the empirical literature. Lynch et al. (2004, p.79) have found that only 12 out of 98 studies investigating the effects of income inequality on population health modeled the relationship with time lags. In a meta-analysis conducted by (Nixon & Ulmann 2006, p.9) only 4 of 32 articles used this theoretically necessary procedure. The reluctance to apply time lags is easily explained. First, time lags require the collection of considerably older data, which is often unavailable. Second, statistical methods to determine

Time-lags of independent variables are not only necessary from a theoretical perspective, but also help to prevent endogeneity problems (Self & Grabowski 2003, p.839).

15 An empirical application of this method can be found at Stadelmann-Steffen et al. (2008), explaining public education expenditure in the 26 Swiss cantons.

16 Results of the adjusted Lagrange Multiplier test indicated that random effects can be rejected while our data is serially correlated. Therefore, correction for first term autocorrelation (AR1) and heteroskedasticity is necessary.

17 Further details on the calculation of life expectancies can be found in an article from Beck et al. (1982). Of course our hypothetical event would only increase life expectancy if there were millions of potential heavy smokers that quit smoking due to better education.

appropriate time lags are still underdeveloped (Arah et al. 2005, p.86) and auxiliary approaches as done by Spijker (2005) proved to be questionable from a methodological perspective when applied to our dataset ¹⁸

These reasons also hindered us from creating a satisfying time lagged model. Nonetheless, attempts to create such a model restricted to data availability and its limitations will be presented in Section 4.2.3. Thereby, the models used were compared to lags tested by (Spijker 2005).

.

3.3.Data

3.3.1. Indicators

Subsequently, a detailed discussion of the indicators chosen to approximate the input variables presented in our theoretical framework, as well as of the health outcome measures, will follow. This should be emphasized as in published research we sometimes observe the strategy where indicators are chosen according to convenience, data availability or common sense in the field. This practice perpetuates potential misspecifications. Although there are of course limitations on measurability of certain latent variables; however, it should be obvious that measuring health through infant mortality rates is outdated for the sample of developed countries. On the contrary, common practice should be that indicators are selected following a theoretically backed operationalization of the research question (Schnell et al. 2005, pp.128-137). Even if we will never be able to perfectly model processes in the social sciences, we should at least try to define our concepts, justify our research design, and reveal its limitations as well as possible.

Health outcomes – dependent variable

The pool of possible measures for health status is almost endless and yet “development of comparable indicators of health outcomes on the international level is in its infancy” (Jee & Or 1999, p.10). Since we earlier defined health outcomes through aggregate health status measurements, there is a choice left amongst mortality, morbidity, and self-assessed health indicators. Jee & Or (1999, p.7) see mortality figures as the established way of measuring health outcomes in research.

Although, there are new, more elaborate indicators that include information about non-fatal diseases, disability and the quality of care¹⁹

18 The author tested a range of different lags of each variable and took the one with the highest t-value when the coefficent had the desired direction or the lowest t-value when coefficients generally showed the wrong direction. For our dataset we observed t-values to vary considerably between years so that chosing the most desired one seems arbitrary from a methodological perspective. Moreover, when including the all „best“ time- lags into a complete model, t-values changed again and results were less significant than without time lags.

, reliable and cross-nationally standardized data are to be found in mortality rates. Rivera (2004) founds her criticism of mortality indicators mostly on the low sensitivity of these numbers. We place greater weight on the first rationale in favor of mortality rates

19 E.g. health expectancies or DALYs (Jee & Or 1999, p.8).

than on the concerns by Rivera. It is preferable to have more reliable data that are less sensitive than the other way round.

Life expectancy measures the average length of life for a future generation, calculated by numbers of deaths per age-group and total population. It is one of the most widely available measures of health status (Jee & Or 1999, p.14).²⁰ A second approach in measuring aggregate mortality is the Potential Years of Life Lost (PYLL). It provides information on premature mortality by cause of death (Jee & Or 1999, p.17). While adding up the year difference between 70 and the actual age of a person that died earlier, it gives a higher weight to early deaths in life because deaths before age 70 are assumed to be preventable (Or 2000, pp.55-56).²¹

Very promising is the idea to measure Health Expectancies (HE) integrating both mortality and morbidity in a single index, but international comparison is still difficult due to lack of standardization in disability data (Jee & Or 1999, p.8). Another prevalent concept is the usage of mortality from amenable diseases, i.e. preventable or curable conditions (Mackenbach 1991; Nolte & McKee 2003;

Nolte & McKee 2008). Although we would have been interested in confirming our results with this indicator, we could not find freely available time-series data on this indicator.

After these considerations and in congruence with the vast majority of we will use two indicators for our dependent variable studies (Nixon & Ulmann 2006, p.8).²²

Once again, it should be mentioned that the greatest limitation of all mortality figures is that they do not provide information on quality of life and non-fatal diseases (Jee & Or 1999, p.17).

First, life expectancy at birth for both sexes (y_life_pop) and second, Potential Years Life Lost (y_pyll_popi) for all causes of death and both sexes. Data on PYLL is not available for Belgium and Turkey in the period between 1999 and 2005 – these two cases will therefore be excluded in the models using PYLL as dependent variable.

Health care financing system – independent variable of interest

According to our framework of health care financing as developed in Section 2.1.3, we must determine indicators resembling our three levels of health care financing. First, it should be considered that all figures need to be adjusted by purchasing power parities (PPPs) to account for different price levels across countries. This adjustment, however, comes with limitations considering the representation of medical products in the consumption basket (Or 2000, p.57). Although some scholars suggest using adjustment for health care prices for the aforementioned reasons, an

20 For further information about the mathematical determination of life expectancy rates see Beck et al. (1982).

21 The age limit of 70 is an arbitrary choice by the OECD for calculation PYLL figures, so it also applies to the data used in this study.

22 We will estimate always two models and thus use the method of multiple indicators, recommended by Schnell et al. (2005, p.134).

adjustment only for PPPs might constitute a better strategy, since prices for health related products and services are probably influenced by the health system, e.g. by market structures, governmental price setting power, number of suppliers.²³

Level A of health care financing system will be measured by total expenditure on health (TEH) per capita in international dollars, adjusted by PPP (hex_tot_pc_who). They are subject to limitations caused by differences in national accounting practices (see Section 5.2).

Measuring health care expenditure as share of GDP can be inadequate for single years because it will be dependent on business cycles, i.e. when the economy expands and health expenditure only increases moderately. Then this indicator will mistakenly suggest that actual health expenditure decreased (Calikoglu 2009, p.63).

Level B of health care financing system will be quantified as the share of public expenditure on health (PUB) in total health expenditure (hex_pub_teh_who). Not using per capita public expenditure but a relative number reduces collinearity between our three different indicator levels since states with higher TEH also tend to have higher public expenditure (Berger & Messer 2002; Arah et al. 2005, p.83). Figures on public health spending generally ignore tax expenditure on health. Therefore, expenditure levels might be underestimated in countries that extensively rely on tax credits, such as the United States. In order to confirm our theoretical explanation that the positive effect of public health expenditure works through less negative externalities caused by private insurance and better access to health care in the absence of direct payment, we will replace our indicator by measures of out-of-pocket payments in percentage of TEH (hex_out_teh_who) in single models.²⁴

Level C of health care financing will be represented by tax revenues as a percentage of PUB (hex_tax_pub_who). Thereby, we can separate the effect that differences in the composition of public health expenditure have from general effects of public health care spending on health outcomes. Unlike published research so far, we do not code tax-financed systems as a dummy variable (Or et al. 2008, p.10; Wagstaff 2009), but try to apply a continuous measure. However, it should be noted that WHO and OECD classify all revenues channeled through social insurance budgets as social insurance contribution, even if this budget is subsidized by general government revenues (taxes). This is why some of the social insurance systems may be more mixed regarding public finance and the share of taxes is underestimated in these cases (Thomson et al. 2009, p.30).

23 Evidence is provided by Spithoven (2009) who shows that wages of doctors and pharmaceutical prices in comparison to the general price level vary considerably between countries.

24 This procedure is in accordance with Or et al. (2008, p.10) who see share of public health expenditure in TEH and out-of-pocket payments in percentage of TEH as the two characteristics of the public-private mix in health care financing.

Environment – control variable

Environmental influences which affect health on an individual level are especially hard to determine, since they are difficult to capture with macro-level indicators. For example does it make a difference as to whether people are living next to coal-burning power plant or 10 kilometers away? National pollution rates are therefore no good proxies for the individual exposure to environmental factors, since individual affection by pollution is heavily influenced by proximity to its source. However, there are no better data than air pollution indicators available. Nevertheless, we tried to improve the state of research by including an index of sulphur oxides (SOx), nitrogen oxides (NOx), and carbon monoxide (CO) emissions in kilogram per capita, instead of observing only one indicator. Therefore, the three single emission figures have been standardized and summed up to an additive index (env_air_index).

Lifestyle – control variable

Lifestyle as a latent variable has many different aspects that can be measured by different indicators.

Unfortunately, cross-country data is only available on nutritional and drug consumption factors.²⁵ Because unhealthy lifestyle accounts for 40% of disease burden and over 50% of mortality in industrialized countries (Arah et al. 2005, p.87), it is still important to capture these influences as good as possible. Nutrition can be approximated by fat, sugar, or calorie intake. Data were not available for years 2004, 2005 for all countries and recent years for Luxembourg, thereby eliminating the possibility of controlling for fat intake. It has been proposed to use data direct on obesity instead of fat intake (Comanor et al. 2006). However, data on obesity is not comparable between countries because of differing definitions of overweight across countries and because data are only available for single years (OECD 2009). Furthermore, fruit and vegetable consumption has been proposed as a lifestyle measurement and applied for example by Arah et al. (2005) but it was insignificant in most studies and time-series data from OECD are incomplete. Hence no control for nutrition has been introduced.²⁶ To control for the influences of smoking most researchers rely on more widely available data measuring tobacco consumption per capita. We assume that the actual number of daily smokers is a better indicator (Macinko et al. 2003, p.855; Arah et al. 2005, p.86). However, data on daily smokers provided by the OECD is subject to an extensive amount of missing values. Although they have been filled, it must be mentioned that approximation of the percentage of smokers is very crude in our dataset.²⁷

25 Other factors are for example physical exercise, hygiene or avoidance of other health risks (Or 2000, p.58).

Alcohol consumption is usually measured in per capita terms. More

26 However, we ran a regression model introducing fat intake, for all countries but Luxembourg. Estimation of other coefficient was not worringly influenced, allowing the conclusion that omitting the fat intake variable did not cause much bias.

27 The number of daily smokers has been chosen over tobacco consumption also because data for the latter indicator was missing for several countries in the complete time-series, while data on smokers (at least one data point) was available for all countries and missing values could be imputed.

appropriate would be to calculate the “share of population that consumes more than 1 drink a day”, because relationship between alcohol and health is U-shaped (Macinko et al. 2003, p.856).

Unfortunately, adequate data are not available.

This study will include two indicators to control for lifestyle health inputs. Smoking is represented by percentage of daily smokers in the population (style_tob_smoi), and alcohol by consumption in liters per capita (for the population older than 15) (style_alci).

Education – control variables

How to measure education for our purpose of health production is still debated. Against the background of incomplete attainment data provided by the OECD, it will be interesting if new indicators as developed in the PISA study can measure education more effectively in the future.

Alternatively, taking the share of white-collar workers instead of standard educational indicators as done by Or (2000) seems to be a good approach. However, data was not available at the stated OECD sources. Moreover, it also seems to be unlikely that any figures can be a valid measure because classifying modern occupations into the traditional blue or white collar pattern becomes increasingly difficult for modern jobs.

Today, attainment rates by level of schooling and the length of education are available. It is important to mention that secondary attainment rates are not suitable for developed nations because a high share of population has received post-secondary education. For this reason, we should always look at the share of population with the highest possible level of education. That is why for recent data we will use tertiary attainment rates by OECD (edu_ter) and for the lagged models we use post-secondary attainment rates by Barro & Lee (2000) (edu_postsec_BLi).

Demographics – control variable

There are two basic indicators for including a nation’s age structure into analysis: the dependency ratio and the share of population aged 65 and above. Generally, the dependency ratio might be the better choice because health care costs are higher for very young and very old. The included age dependency ratio measures the number of persons aged under 15 and over 65 as a proportion of persons aged 15-65 (demo_age).

GDP – control variable

Income is theoretically not a direct input of health but works through inputs like health care utilization, education, or lifestyle (Evans et al. 2000, p.13). Nevertheless, to include some approximation for individual income and wealth (ECON input) we will control for it by using Purchasing Power Parities adjusted GDP in US$ per capita (econ_gdp_pc).

3.3.2. Data sources

Data that is referenced as OECD has been obtained from the OECD Health Data 2009, available at environmental, lifestyle, demographic and economic input factors. OECD data are regarded as reliable and are available for long time periods (Calikoglu 2009, p.64).

Further, we took data from the World Health Organization Statistical Information System (WHOSIS), available at because OECD indicators were not available for every country-year-observation. Therefore, all the relevant variables to measure our concept of health financing system will be derived from WHO data.

Finally, to obtain earlier data on educational indicators, which are only systematically collected by the OECD from 1998 onwards, we used educational attainment rates from Barro & Lee (2000) available from the Harvard Center for International Development (CID) at

3.3.3. Missing data

The raw data set with merged OECD, WHO, and CID figures contained some missing observations.

Since merely dropping these observations from the panel model would not only result in too few data points but also cause a serious selection bias, missing data has been partly imputed. This procedure has been carefully undertaken in order not to distort the numbers. Therefore, some single missing values had to be imputed so as not to lose observations. Usually arithmetic means have been used for single missing values, and linear regression if more consecutive values were missing.²⁸ All cases of data manipulation are disclosed in the respective Stata file (2_modify_variables.do) and summarized in Table 10 in the Appendix.

28 The only case of very fragmentary data is the educational indicators of Barro & Lee (2000) because data were collected in 5 year intervals. For every interval, a separate linear regression to impute values has been used.