Indicators

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of certain latent variables; however, it should be obvious that measuring health through infant mortality rates is outdated for the sample of developed countries.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 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).