Abstract

Throughout the world there exists a notable relationship between income inequality and health. Political factors and circumstances, which arguably affect the distribution of income, may also be related to population health. This paper explores the geographic dimensions of these relationships in an attempt to determine whether or not levels of democracy complement, influence or confound the association between income inequality and health.

This paper was prepared for presentation at the conference, Responding to Globalization: Societies, Groups and Individuals, sponsored by the Globalization and Democracy Research and Training Program, Program on Political and Economic Change, Institute of Behavioral Science, University of Colorado at Boulder, 4-7 April 2002. The data used in this paper are available via ftp at pacific.ssnet.ucla.edu/pub/tmp/shinm.

Introduction

Income inequality is one of the predominant features of global society. Moreover, income inequalities between and within countries are increasing (Milanovic 1999; Prichitt 1997; Jones 1997). One of the more perplexing social costs related to income inequality is its impact upon health, or more specifically, the finding that high levels of income inequality are associated with lower life expectancy, and higher infant and childhood mortality rates throughout the world (Rodgers 1979; Waldmann1992). The relationship between income inequality and health, referred to as the ‘relative income hypothesis’, remains robust even when absolute levels of income are controlled and when making comparisons between and within countries (e.g., Kennedy et al. 1996; Ben-Shlomo et al. 1996).

One factor that is possibly related to both income inequality and health is democracy. There is a long tradition of research on the relationship between economic development, inequality and democracy (e.g., Lipset 1959; Muller 1995; Midlarsky 1997), and it is plausible that health is implicated in this relationship. Determining whether or not and to what extent democracy tempers or fortifies this association between income inequality and health is the primary objective of this research. A geographic perspective is used to explore and examine the relative income hypothesis at the global scale. I contend that situating analyses of the relative income hypothesis within such a geographic framework will help to clarify the nature of the linkages between income inequality, democracy and health.

Theoretical considerations

Over the course of the last century, life expectancy and levels of economic prosperity increased dramatically and concurrently around the world. Increases in life expectancy can, in part, be attributed to the reduction of death rates among the young, or in other words, more and more people are now reaching older ages than ever before. Though increases in absolute income are undoubtedly linked to health, especially in underdeveloped countries, it is interesting to note that the beneficial returns on health appear to diminish after a per capita income of approximately $5,000 is achieved. In fact, after this $5,000 threshold is surpassed, the relationship between life expectancy and wealth becomes quite unclear. For example, life expectancy in the United States, the richest country in the world in terms of per capita income at the turn of the new century, falls short of that for several countries such as Japan and Italy, and infant mortality rates in America are higher than those in the Czech Republic (World Bank 2001). It may appear that the limits to human longevity have been reached in many developed countries, but the rate of increase in life expectancy (i.e., 2-3 years per decade) remained remarkably stable over the course of the twentieth century (Wilkinson 1994; 1996).

Country to country variations in life expectancy and infant mortality are frequently understood in terms of the epidemiological transition model (see Omran 1971; 1983; Wilkinson 1994). This model documents a given society’s pathway to the reduction of death rates, particularly those deaths associated with communicable diseases. Demographic characteristics of countries that have yet to complete the epidemiological transition include: high levels of mortality primarily due to infectious diseases, high levels of fertility and a predominantly young population which is especially vulnerable to infectious disease. Once the detrimental effects of communicable disease can be overcome by a society, mortality and fertility rates gradually decrease and stabilize, the population ages and non-communicable diseases such as heart disease, stroke, obesity and cancer become more common as causes of death (Wilkinson 1996).

Improvements in agricultural production, transportation and access to health care are all factors that can undoubtedly help a country complete this critical transition. The transformation of various social and economic practices, political structures and cultural, traditions, such as the active and passive repression of women, children and other groups in many societies is also recognized to be important to population health. Countries that have successfully completed the epidemiological transition often experience similar patterns and trends, for example, periods of massive rural to urban migration and the emergence of a distinct urban hierarchy (Omran 1971; 1983). However, as Jones and Moon (1992) and Picheral (1989) point out, the geographic and temporal manifestations of the epidemiological transition are not necessarily identical in different places nor are they consistent over time.

The epidemiological transition marks an important stage in the process of economic development for a society, and once the transition is completed, increases in absolute income impact health very little (Wilkinson 1994; 1996). Another striking post-transition health trend is that diseases once more commonly diagnosed in the affluent (e.g., heart disease, obesity, cancer, etc.) become more prevalent in the under-classes and the poor (Marmot et al. 1978; Wing 1988). Moreover, a significant body of research shows that population health becomes more a function of relative deprivation than of absolute deprivation after the epidemiological transition (Waldmann 1992; Wilkinson 1992). In other words, increases in absolute income are arguably less important than disparities in income levels insofar as population health is concerned. Evidence that supports this relationship, which is referred to as ‘the relative income hypothesis’ exists when making: i) international comparisons (e.g., Rodgers 1979; Wilkinson 1996; LeGrand 1987; van Doorslaer et al. 1997), ii) comparisons between less developed countries (e.g., Rodgers 1979; Flegg 1982), iii) comparisons between developed economies (e.g., Duleep 1995; Wennemo 1993), and iv) it is also present within societies (e.g., Kaplan et al. 1996; Walberg et al. 1998; Merva and Fowles 1993; Kahn et al. 1998; Chiang 1999; Hales et al. 1999). Though there are multiple scales from which the relative income hypothesis can be examined, this research attempts to clarify the nature of this relationship at the global level.

Several explanations have been posited with regard to how income inequality affects health, and range from the argument that inequality leads to problematic variations in access to health care, education and social services to the argument that income polarization strains the beneficial components of social capital, which in turn adversely affects population health (e.g., Rodgers 1979; 1996; Berkman 1996; Kawachi et al. 1997). The relationship between income inequality and health has also been subject to much scrutiny and skepticism. Concerns about the relative income hypothesis range from methodological issues about data measurement to demands to identify more clearly the pathway between income inequality and health. Such concerns will be addressed and discussed further as they arise within the scope of this analysis.

One dimension of the relative income hypothesis that has not thoroughly been examined relates to those factors that are associated with and possibly affect the distribution of income within a country. In other words, does income inequality itself impact population health, or is income inequality a possible surrogate for other political or economic factors? It is not difficult to envisage that the distribution of income within a society may indeed be a function of political considerations, such as levels of democracy. Conversely, a substantial body of research exists with respect to the economic determinants of democracy (e.g., Lipset 1959; Muller 1988; 1995). Economic development arguably facilitates democracy through increases in education and the enlargement of the middle classes (Lipset 1959). The former arguably promotes socio-political tolerance while the latter helps to temper social conflict by reducing the size of the population that is attracted to anti-democratic ideologies and anti-system parties. Though strong evidence exists to support the relationship between economic prosperity and democracy in certain geographic regions and over certain periods of time, several exceptions illustrate well the complexity underlying this relationship (Huntington 1991: 59-72; O’Loughlin et al. 1998).

The view that democracy alleviates social disparities and income inequalities stems from the argument that political enfranchisement in the form of suffrage empowers those who are on the margins of society (Lipset 1959; Lenski 1966; Muller 1995). Once granted a political voice, the marginalized and the disadvantaged will attempt to expropriate and redistribute wealth from the advantaged through, for example, support for more radical parties. The assertion that inequality, and income inequality in particular, is perceived as unjust and elicits a political response is disputed. Bollen and Jackman (1995) argue that there is little substantive evidence to suggest that income inequality is a suitable proxy for political resentment, and reiterate that no significant relationship exists between income inequality and democracy, and vice versa (Bollen and Jackman 1985; 1995).

Paralleling the possible relationship between income inequality and democracy is that between democracy and health. The logic that underpins the relationship between health and democracy is similar, and may be related, to that discussed above. If democracy indeed reduces socio-economic inequalities, it may also improve population health. Within egalitarian and democratic societies, healthcare is arguably more easily obtained and there are more opportunities to improve socio-economic status, which in turn provides greater access to the means by which health can be improved (e.g., food, shelter, medicine, healthcare, etc.). Furthermore, at the regional and global scales, democracies tend to be pacific with each other, thus reducing the deleterious effects of war (Russett 1993; Ray 1993). War not only inflates mortality rates through battlefield and collateral deaths in the short term, but war can also have several health consequences over the long term (Ghobarah 2001; Murray 2002). War creates refugees, destroys vital social and health infrastructures and dismantles local, national and regional economies to the extent that both direct and indirect victims of war (e.g., successive generations) become susceptible to malnourishment, infectious disease and premature death.

Peaceful inter-state relations are typically necessary for mutually beneficial interactions and exchanges such as trade to develop and grow, which subsequently can contribute to and improve economic development and likewise population health. It is understood, however, that becoming democratic often entails violence and conflict. Though relatively smooth democratic transitions diminish the likelihood of war, the more irregular the transition to democracy, the greater the chance of warfare (Ward and Gleditsch 1998; Gleditsch and Ward 2000). It is also possible that very high levels of wealth and democracy create diseconomies of affluence and freedom that may negatively impact population health (Rodgers 1979: 343). The crisis of expensive, incomplete and spotty health care in the United States, especially for the less privileged, may be related to American “hyperdemocracy” where the profit motive coupled with the freedom of choice, and their preservation, takes precedence over the common good and the provision of universal health care (Heclo 1999). Notwithstanding the debate surrounding the relation between democracy and income inequality, the possible role of democracy within the framework of the relative income hypothesis merits further investigation.

A causal model of the relationships between income, income inequality, democracy and health is provided in Figure 1.

Figure 1. Relationships between income, income inequality, democracy and health.

It is widely accepted that economic prosperity improves population health, and as discussed previously, a positive relationship is expected between democracy and health, with levels of democracy a function of absolute income. Income inequality is also believed to be a function of income, but this relationship is believed to take the form of an inverted-U. According to Kuznets (1955), higher incomes reduce inequalities in developed countries, but in less developed countries, the early stages of economic development can exacerbate inequalities. The nature of the relationship between income inequality and democracy remains in question (see Muller 1995; Bollen and Jackman 1995), and it is plausible that this association is bi-directional. Finally, a negative relationship is expected to exist between income inequality and health.

Data and methodology

A robust statistical relationship has been found to exist between income inequality and a variety of indicators of population health, such as life expectancy, infant mortality rates and childhood mortality rates at the international level (e.g., Rodgers 1979;Waldmann 1992). Life expectancy, and somewhat paradoxically, mortality rates have proved to be concise and reliable indicators for population health in light of the difficulties in determining, defining and gauging the concept of health (Wilkinson 1996: 55-56). Within the scope of this analysis, 1999 life expectancy data are used to measure health and are taken from the 2001 World Development Indicators published by the World Bank. Life expectancy at birth is defined as “the number of years a newborn infant would live if prevailing patterns of mortality were to stay the same throughout its life” (World Bank 2001: 117).

Income inequality data in the form of the Gini index are also drawn from the 2001 WDIs. Derived from the Lorenz curve that portrays graphically the cumulative shares of income at successive income intervals, the value of the Gini index falls between zero, which indicates perfect equality, and 100, which indicates perfect inequality (see Sen 1973; Amiel and Cowell 2000). Though other measures of inequality exist, and are evaluated with respect to the relative income hypothesis elsewhere (e.g., Kawachi and Kennedy 1997), the Gini index is the statistic used most frequently and is strongly correlated with other such measures. Note that use of the Gini index presents several comparability issues (Deininger and Squire 1996; Milanovic 1999). Inequality measures such as the Gini index are often based upon household surveys that are difficult to adjust and standardize over time and between countries. In order to provide the most complete set of inequality measures, the World Bank reports Gini index values based on both income and consumption expenditures, the former tending to capture greater inequalities (World Bank 2001: 73). Additionally, variations in household size and levels of income preclude strict country-to-country comparisons. Nevertheless, recent efforts to overcome such comparability issues make these data, at the very least, as accurate and as valid as those used in previous analyses examining the relationship between income inequality and health.

The measure for democracy is taken from the Polity 98D database (November 2000 version) compiled by Kristian Gleditsch (Gleditsch 2002). Democracy scores are extracted for 1998 and range from zero to ten. These data are based upon the comprehensive Polity III database that provides comparable data about the political characteristics of countries since 1800. The democracy scores used are derived from a combination of measures such as the extent of constraints placed upon the chief executive and the competitiveness of political participation (for details, see Gleditsch and Ward 1997). Though the use of a single year’s data fails to capture whether or not a polity is experiencing a political transition, change or trend that could possibly affect population health, the scores provide a very useful proxy of democracy within subsequent statistical analyses. Finally, 1992 GDP per capita data, in constant 1985 U.S. dollars, are extracted from the Penn World Tables, Mark 5.6a (Summers and Heston 1991). Controlling for the relationship between absolute income and health, which tends to diminish as income increases, will elucidate the possible associations between democracy, income inequality and health. Though more recent data on per capita GDP are available, 1992 values are used to capture the possible lagged effects of income upon health.

The complete data set contains a total of 88 countries, and is provided in Appendix A. Inconsistencies between data sources force the exclusion of several countries, but a sufficiently large and geographically diverse pool of observations remains for examination.

Summary statistics for the four variables of interest are provided in Table 1 and scatterplots and the correlation matrix is provided in Figure 2.

	mean	σ	minimum	maximum
life expectancy, 1999	65.93	12.48	37 Sierra Leone	81 Japan
real GDP/capita, 1992	$4931.32	$4991.60	$274.00 Ethiopia	$17,945.00 United States
Gini index	39.99	10.71	19.5 Slovakia	62.9 Sierra Leone
Democracy score, 1998	6.69	3.51	0 9 countries	10 25 countries

Table 1. Summary statistics for the relative income data set.

	lifex99	rgdp92	gini	dem98
lifex99	1.00	0.67	-0.42	0.61
rgdp92	0.67	1.00	-0.48	0.58
gini	-0.42	-0.48	1.00	-0.21
dem98	0.61	0.58	-0.21	1.00

Figure 2. Scatterplot and correlation matrix for the relative income data set.

All correlations between the variables of interest are statistically significant (i.e., p < 0.05), with the strongest relationship between per capita GDP and life expectancy, and the weakest relationship between democracy and the Gini inequality index. Note also the curvilinear relationships between income and life expectancy, and income and democracy, with the former more apparent.

Most examinations of the relative income hypothesis utilize ordinary least squares (OLS) regression, and follow the general model specification,

y = α + βX + ε (1)

where y is life expectancy or some other measure of health, α is the intercept term, X is the matrix of independent variables (i.e., per capita GDP, Gini index, etc.), and ε is the error term is distributed normally and independently (i.e., ε_i ~ NID(0,σ ²)). A series of models based upon Rodger’s (1979) original work (i.e., equations 1-4), which is the basis for much research on the relative income hypothesis (see Kawachi et al. 1999), are estimated and serve as benchmarks for comparison to subsequent equations that include the democracy variable (i.e., equations 5-9) in Table 2.

dependent variable: life expectancy at birth, 1999

constant

75.88

-13.05

83.32

-9.39

51.43

68.55

-8.80

75.73

-3.89

-17,132

(1,179)

-16,027

(1,188)

-14,593

(1,263)

-13,699

(1,248)

ln(gdp)

9.91

(0.65)

9.69

(0.73)

9.00

(0.82)

8.68

(0.90)

Gini index

-0.20

(0.07)

-0.05

(0.07)

-0.19

(0.06)

-0.06

(0.07)

democracy

2.18

(0.30)

0.88

(0.22)

0.44

(0.25)

0.83

(0.21)

0.46

(0.25)

adj. R²

0.71

0.73

0.72

0.37

0.75

0.73

0.77

0.73

F-statistic

211.30

232.14

119.65

115.52

51.10

131.63

120.43

98.53

80.26

Table 2. OLS estimates of the relative income hypothesis, standard errors in parentheses.

Estimates in boldface are significant at p < 0.01 level.

Results from equations 1 through 4 are more or less in line with previous research findings (e.g., Rodgers 1979; Gravelle et al. 2002), even though a newly constructed data set is used. Across all estimated equations, only the absolute measures of income are consistently significant, and the initial equations support the argument that life expectancy increases with income, but at a diminishing rate. As hypothesized, the Gini index is negatively related to life expectancy, but is only significant when the reciprocal of per capita GDP, rather than logged income, is used. Note that the equations with the best fit, or highest adjusted R² values, include the democracy variable. A rather strong positive relationship exists between the level of democracy and life expectancy, and the inclusion of democracy only slightly diminishes the strength of the association between the Gini index and health (i.e, comparing equations 3 and 8). Moreover, in equations using the reciprocal of per captia GDP, the values of the constant terms fall within a reasonable range for life expectancy, especially the constant in equation 6 that is slightly greater than the average life expectancy for the entire data set. Regression diagnostic tests indicate that the error terms from all estimated equations in Table 2 are contaminated by a significant amount of heteroskedasticity, the consequences of which are incorrect parameter and model inferences. Heteroskedasticity is often the by-product of the geographic distribution of data values, which is seldom ran

Abstract

Introduction

Theoretical considerations

Data and methodology

Sierra Leone

Japan

Ethiopia

United States

Slovakia

Sierra Leone

F-statistic