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1、Inequalities in Income and Inequalities in Health0. Introduction.In much of applied welfare economics, incomereal income, consumption, or consumption perequivalentserves as our measure of individual welfare. But the goods and services provided byincome are not all that there is to well-being. Health
2、 is not only instrumental in enabling peopleto earn a living, and to enjoy the fruits of their labors, but is an important element of well-beingin it own right. Health status is correlated with income, both for individuals within nations, andacross nations in aggregate. But the correlation is far fr
3、om perfect so that looking at health leadsto different assessments of well-being than come from looking only at income. Much the samecan be said for education, and such considerations have led to simple measures such as the UNsHuman Development Index, a composite of life-expectancy, income, and lite
4、racy. In this paper, Iconsider only two of the three components, health and income. In particular, I am concerned withwhat it means to talk about inequality in health, and whether, according to some useful definitionof the concept, health inequality in the United States is rising in tandem with the
5、rise in incomeinequality. I also investigate the possibility that income inequality itself is a health hazard, ahypothesis advocated by Richard Wilkinson (1996).The first section of the paper is concerned with the concept of health inequality, with alter-native definitions, and with the evidence tha
6、t health inequalities in the United States are in-creasing. The second section turns to the Wilkinson hypothesis. In an attempt to develop itstheoretical underpinnings, I present two simple models in which there is a link between aggregateinequality and individual health, in only one of which is ine
7、quality a fundamental risk factor inits own right. It is widely understood that a nonlinear (typically concave) relationship betweenhealth and income at the individual level will generate an aggregate relationship in which average1health depends (negatively) on the degree of inequality. But I also s
8、how that if health statusdepends not on income, but on income relative to the incomes of members of some referencegroup, then the relationship between income and health, unconditional on information about thereference groupand such information is typically unavailabledepends on the relative size ofw
9、ithin- and between-group inequality. The model can readily be extended to incorporate a morefundamental role for inequality in the determination of health status, for example by making eachindividuals health within the reference group depend on the shortfall of individual income fromthat of the rich
10、est person in the group. But I argue that such mechanisms can just as well generatea positive as a negative link between inequality and health. That individual health depends onrelative income is consistent has no direct implication for the relationship between inequality andaverage health. Section
11、3 presents some empirical evidence on mortality in the United States at ahitherto unexplored level of aggregation, that of a birth cohort. By matching cohort mortalityrates to cohort income and income inequality data from the Current Population Surveys, I can testwhether the rising income inequality
12、 in the 1980s played any role in the contemporaneous slow-down in mortality decline. Although the inequality hypothesis is hardly well-enough developedto permit a sharp and convincing test, I find little or no evidence of a direct link between incomeinequality and mortality. However, the cohort data
13、 suggest that increasing inequality increasesthe slope of the regression of mortality on income, a finding that is predicted by the theory if theincrease in income inequality acts to increase inequality within reference groups by more thaninequality between them. Section 4 concludes and discusses a
14、number of important caveats.21. Rising health inequality?A good way to approach health inequality is to start with income inequality, and to ask whetherthe theoretical and measurement structure of the latter can be transferred to the former. Measuresof income inequality are measures of dispersion of
15、 the (univariate) distribution of income acrosspersons. Questions of why such quantities are of interest, or whether some are of more interestthan others, can be answered through the theoretical apparatus developed by Anthony Atkinson(1970) and Amartya Sen (1973). Inequality aversion, or a preferenc
16、e for a more equal distributionis coded into a social welfare function according to which mean-preserving but equalizing trans-fers increase social welfare or, alternatively, one in which there is diminishing (social) marginalutility to income. As Atkinson showed, these formulations lead to an aggre
17、gate measure ofwelfare which can be thought of as the product of mean income and income equality, which isthe complement of inequality, and is a number between zero and one.Health promotes well-being, just as income promotes well-being, and some people havebetter health than others; statements like
18、A is healthier than B are meaningful in much the sameway as are statements such as A has more income than B. But an immediate problem is measure-ment; we have a cardinal measure of income, but no comparable measure of health status. Forpopulations, life-expectancy is a useful statistic with convenie
19、nt properties, but is much less use-ful at the individual level. Life expectancy at a given age in a given year is not the expected yearsof life of a person of that age in that year; period estimates of life expectancy are based on age-specific mortality rates at a fixed moment in time, rates that c
20、an be expected to change (typicallydecline) by the time the individual reaches those ages. The expected lifespan for each individualwould be a good tool for measuring well-being, but its calculation among the living requires an3assessment of health status with a 6view to assessing individual mortali
21、ty probabilities in thefuture. The measurement of health status is extremely difficult, except among the elderly amongwhom functional impairments are sufficiently widespread and apparent to allow useful calibra-tion. Self-reported measures of self-reported health status (my health is “poor,” “fair,”
22、 “good,”“very good” or “excellent”) have been shown to predict later health conditions, but estimates ofinequality based on such reports are typically not invariant to the wide range of equally reason-able ways of scoring or scaling the responses. Discussions of how to measure inequality usingqualit
23、ative data can be found in Deaton and Paxson (1998b) and Allison and Foster (1998).Suppose that there were no measurement problems, and that for each individual we couldreadily calculate a measure of life expectancy. One could imagine doing this long after everyonehas died, so that we have an actual
24、 distribution of years lived for a birth cohort, or we might beprepared to accept the period life-tables and calculate for each person alive now an expected ageat death. Inequality in years lived could then be calculated, as for 32 developed countries inJulian Le Grand (1987). But as Le Grand recogn
25、izes, the axioms that underlie inequality mea-surement are not obviously applicable in this context. For example, it is unclear how manypeople would assent to the proposition that society would be better-off if a fifty year-old died at49 instead of 50 in order to extend the life of a 45 year-old by
26、one year. Unlike income, wherepolicies for effecting transfers can readily be imagined, transfers of life are not readily linked tohealth policy, so that it is unclear that reduction in inequality in health outcomes is a worthy (orfeasible) target for policy.There is another literature on “health in
27、equalities” which defines inequality quite differently.In the public health, psychology, sociology, demography, and epidemiology literatures, inequali-4ties in health are taken to refer to the differences in health across different socio-economicgroups, typically defined by income, occupation, or ed
28、ucation. That mortality was higher formembers of lower status occupations was noted in Britain from the middle of the last century,and the failure of this “gradient” to vanish in response to the introduction of the National HealthService after the Second World War has been the impetus for an enormou
29、s amount of subsequentresearch as well as political acrimony, see Sally McIntyre (1997) for a fine review.Although much of the best work on the gradient is still done in Britain, particularly theWhitehall study run by Michael Marmot and his collaborators, socioeconomic differences inhealth status ar
30、e also well-established in the United States. For example, the National Longi-tudinal Mortality Study has merged subsequent death certificates back into Current Populationand Census data from the late 1970s until the mid-1980s, and has generated very large samplesfor examining differences in mortali
31、ty by income. Using these data, Rogot et al (1992) calculatelife expectancy by age for seven family income groups. Comparing the bottom income group,defined as those with less than $5,000 of family income in 1980, with the top group, which hadmore than $50,000, life expectancy for men at age 25 was
32、43.6 years at the bottom as opposed to53.6 years at the top. Although the absolute difference is smaller for older men, the proportionaldifference remains more or less constant, 26.2 versus 39.0 years at age 45, and 13.3 versus 17.2years at age 65. Family income differences are less important for wo
33、mens mortality than formens mortality, but the gradient is still apparent for women. Life-expectancy appears to increasemonotonically with income; excess mortality is not associated only with poverty. Nor can thegradient readily be removed by controlling for other factors, such as race or smoking be
34、havior.Causality almost certainly runs both ways, from health to incomefor example through the5effects of ill-health on employmentbut there is fairly wide agreement that the effects that runfrom income to health are a major part of the story. And while income almost certainly isstanding in for other
35、 factors such as education, both factors are separately important, Elo andPreston (1996).Most commentators see these health inequalities as deeply offensive, more so than the econo-mic and social inequalities to which they are related. Their elimination is seen as an urgent prior-ity for public heal
36、th policy, and some governments and international agencies have accepted thereduction of health inequalities as target. For example, the countries of the European Region ofthe World Health Organization unanimously adopted a resolution in 1984 that “by the year 2000the actual differences in health st
37、atus between countries and between groups within countriesshould be reduced by at least 25 percent.” Many people who are prepared to accept inequality inthe allocation of goods as a (possibly) necessary evil are not prepared to accept similar inequal-ities in health outcomes. However, since health i
38、nequalities appear to be much the same whetheror not health care is provided through the market, for example in the U.S. versus Britain, and tobe much the same whether diseases are treatable or not, the remedy is not the provision of accessto health care without reference to financial resources, Adl
39、er et al (1993). Indeed, the literaturemakes a good case that inequalities in mortality seem to have relatively little to do with betteraccess to health care by better-off people. Health status may not be much affected by health care,and health inequalities may be as deeply rooted and as intractable
40、 as the social inequalities withinwhich they are set.One issue in the current debate is the claim that health inequality is increasing, not in thesense that the univariate distribution of health status is widening, but in the sense that the gradi-6ent is steepening, so that the same difference in so
41、cio-economic status, income or education, isnow associated with a larger difference in the probability of death. (Univariate measures of healthinequality in terms of years lived have decreased with reductions in infant mortality; because theunivariate distribution is bimodal, reductions in the mass
42、at the lower mode are a powerful forcein reducing inequality.) Studies by Feldman et al (1989) and by Pappas et al (1993) have con-cluded that the differences in mortality across educational groups are larger than those in 1960documented in the pioneering study of Kitagawa and Hauser (1973). Deaton
43、and Paxson (1998a)find positive time trends in the variance of self-reported health status and in its correlation withincome, so that the gradient between (this measure of) health status and income has beentrending upward, something that is true for both men and women. The best estimates of thechang
44、es in the relationship between mortality and education come from Preston and Elo (1995),again using the NLMS. They confirm that for white males, moving from the bottom to the top ofthe education distribution reduced the standardized death rate in 197985 by three times as muchas it did in 1960. They
45、find no similar effect for females. Among the possible causes for thechanges, the authors point out that the changing distribution of heart disease over educationgroups accounts for much of the change, and argue that improvements in prevention and treat-ment may have diffused more rapidly among more
46、 educated men. Other potential causes are thewidening income difference associated with educational differencesthe increasing rate ofreturn to educationand the decrease in income differences between men and women.Both of these arguments point to a protective role for income that operates independent
47、ly ofeducation, and implicate in health inequalities the same factors (particularly the increase in thereturns to education) that are usually identified as contributing to increases in income inequality.7Such a mechanism provides a link between income inequality and both concepts of health in-equali
48、ty; growing income differences are associated both with an increase in spread in the uni-variate distribution of health, as well as with an increase in the gradient linking education andhealth. In the simplest model, we might write an earnings function as?y y? (S S) (1)iiiwhere y is the logarithm of
49、 income, and S years of schooling, coupled with a health statusequation?H H (y y?) (2)iiiwhere H is some measure of health status that is (negatively) linked to mortality. As the rate ofreturn to education increases with a fixed distribution of schooling, the distribution of incomewidens, as does the distribution of health. The slope of the health to education gradient is ,which also increases with increases in the rate of return to schooling, . This model works muchbetter for men than for women, for whom has also r