Educational Attainment

Education is usually assessed by the use of two standard questions that ask about the number of years of schooling completed and the educational credentials gained. The quality of education also may be relevant to health, but it is more difficult to assess accurately. An extensive literature has linked education to health outcomes, including mortality, morbidity, health behaviors, and functional limitations. The relationship between lower educational attainment and worse health outcomes occurs throughout the life course. For example, infants born to Caucasian mothers with fewer than 12 years of schooling are 2.4 times more likely to die before their first birthday than infants born to mothers with 16 or more years of education (NCHS, 1998). The pattern of association between maternal education and infant mortality has been described as a “gradient,” with higher mortality risk occurring with successively lower levels of educational attainment (NCHS, 1998). A similar pattern of educational disparities is apparent for all racial/ ethnic groups, including African American, Hispanic, American Indian, and Asian/Pacific Islander infants (NCHS, 1998). Steep educational gradients also are observed for children’s health (e.g., cigarette smoking, sedentarism and obesity, elevated blood lead levels), health in midlife (e.g., mortality rates between the ages of 25 and 64), and at older ages (the prevalence of activity limitations resulting from chronic conditions such as diabetes and hypertension) (NCHS, 1998).

An association between education and health in observational data does not necessarily imply causation. For example, an association between lower educational attainment and an increased risk of premature mortality during midlife (even in longitudinal study designs) may partly reflect the influence of reverse causation—that is, lower educational attainment in adulthood may have been the consequence of serious childhood illness that truncated the ability of a given individual to complete his/her desired years of schooling (and which independently placed that person at higher risk of premature mortality). Alternatively, the association between education and health may partly reflect confounding by a third variable, such as ability, which is a prior common cause of both educational attainment and health status. Although highly unlikely, in the extreme case, if the association between education and health is entirely accounted for by confounding bias, then improving the individual’s level of schooling would do nothing to improve his/her health chances.

The totality of the evidence suggests, nonetheless, that education is a causal variable in improving health. Natural policy experiments—such as the passage of compulsory schooling legislation at different times in different localities within the United States—suggest that higher levels of education are associated with better health (lower mortality) (Lleras-Muney, 2002). In addition, randomized trials of preschool education, such as the High/Scope Perry Preschool Project, indicate beneficial outcomes even in adolescence and adulthood, such as fewer teenage pregnancies, lower rates of high-school drop-out, and better earnings and employments prospects (which may independently improve health chances) (Parks, 2000; Reynolds et al., 2001). It is therefore likely that the association between schooling and health reflects both a causal effect of education on health, as well as an interaction between the level of schooling and inherited characteristics.

Several causal pathways have been hypothesized through which higher levels of schooling can improve health outcomes. They include the acquisition of knowledge and skills that promote health (e.g., the adoption of healthier behaviors); improved “health literacy” and the ability to navigate the health care system; higher status and prestige, as well as a greater sense of mastery and control, associated with a higher level of schooling (a psychosocial mechanism); as well as the indirect effects of education on earnings and employment prospects (Cutler and Lleras-Muney, 2006). Although it is not established which of these pathways matter more for health, they each are likely to contribute to the overall pattern of higher years of schooling being associated with better health status. Moreover, the evidence points to the importance of improving access to preschool education as a means of enhancing the health prospects of disadvantaged children (Acheson, 1998).

Income

The measurement of income is more complex than assessing educational attainment. Survey-based questions inquiring about income must minimally specify the following components: (a) time frame—for example monthly, annually, or over a lifetime (in general, the shorter the time frame for the assessment of income, the greater the measurement error); (b) sources, such as wages and salary, self-employment income, rent, interest and dividends, pensions and social security, unemployment benefits, alimony and near-cash sources such as food stamps; (c) unit of measurement, that is, whether income is assessed for the individual or the household (with appropriate adjustments for household size in the latter case); and (d) whether it is gross or disposable income (i.e., taking account of taxes and transfer payments). In addition to the higher rate of measurement error for income (as compared to educational attainment), this variable also is associated with higher refusal rates in surveys that are administered to the general population.

As with education, an extensive literature has documented the association between income and health. For example, even after controlling for educational attainment and occupational status, post-tax family income was associated with a 3.6-fold mortality risk among working-age adults in the Panel Study of Income Dynamics, comparing the top (>$70,000 in 1984 dollars) to the bottom (<$15,000) categories of income (Duncan et al., 2002). The association between income and mortality also has been described as a “gradient” (Adler et al., 1994). That is, the excess risks of poor health are not confined simply to individuals below the official poverty threshold of income. Rather, an individual’s chances of having good health (e.g., avoiding premature mortality) improve with each incremental rise in income (although the relationship is also steepest at lower levels of income and tends to flatten out beyond incomes that are about twice the median level).

Also, as with education, the causal direction of an association between income and health does not entirely run from income → health. That is, the relationship between the two variables is acknowledged to be dynamic and reciprocal. Ill health is a potent cause of job loss and reduction in income. Indeed, income as an indicator of SES is more susceptible to reverse causation than education, which tends to be completed in early adult life prior to the onset of major causes of morbidity and functional limitations.

Nonetheless, tests of the income/health relationship in different datasets suggest that lower income is likely to be a cause of worse health status. For example, children do not normally contribute to household incomes, yet their health is strongly associated with levels of household income in both the Panel Study of Income Dynamics and the National Health Interview Surveys (Case et al., 2002). Furthermore, the adverse health effects of lower income accumulate over children’s lives, so that the relationship between income and children’s health becomes more pronounced as children grow older (Case et al., 2002).

An alternative possibility is that the relationship between income and health is explained by a third variable—such as inherited ability—that is associated with both socioeconomic mobility and the adoption of health maintenance behaviors. However, even inherited ability is unlikely to entirely account for the income/health association. If inherited ability is the sole explanation for the income/health relationship, we would not expect to find any association between family income and health among children who are adopted soon after birth by nonbiological parents (assuming that adoptive parents do not get to choose the children they will adopt based on their background, including their socioeconomic circumstances). Yet, in the National Health Interview Survey, the impact of family income on child health has been found to be similar among children who were adopted by nonbiological parents compared to children who were reared by their biological parents (Case et al., 2002). Other types of tests of the income/health association—such as the use of instrumental variable estimation (Ettner, 1996) and the observation of natural experiments that resulted in exogenous increases in income (Costello et al., 2003)—similarly have led to the conclusion that the effect of higher incomes on improved health status is likely to be causal.

The causal pathways linking income to health are likely to be different from those linking education to health. Most obviously, income enables individuals to purchase various goods and services (e.g., nutrition, heating, health insurance) that are necessary for maintaining health. Additionally, secure incomes may provide individuals with a psychological sense of control and mastery over their environment. (See Chapter 4 for a detailed discussion of psychological factors and health.) That said, it has also been observed that higher incomes are associated with healthier behaviors (such as wearing seatbelts and refraining from smoking in homes) that do not, in themselves, cost money (Case and Paxson, 2002). Although the causal mechanisms underlying these relationships are not clear, it has been speculated that “the lack of adequate resources strips parents of the energy necessary to wrestle children into seat belts. Poorer parents may also smoke to buffer themselves from poverty-related stress and depression” (Case and Paxson, 2002).

Debate also exists in the literature concerning whether it is absolute income or relative income that matters for health (Kawachi and Kennedy, 2002). The absolute income theory posits that an individual’s level of wellbeing is determined by his/her own (absolute) level of income, and only his/her own income. Many definitions of poverty, for example, are based upon the concept of the failure to meet a minimal standard of living defined in absolute terms (e.g., the inability to afford food). By contrast, the relative income theory posits that individual health is determined by the relative distance (or gap) between a given individual’s income and that of others around him/her (Kawachi and Kennedy, 2002).

The concept of relative income has been operationalized in empirical research by measures of relative deprivation (at the individual level) as well as by aggregate measures of income inequality (at the community level). Measures of relative deprivation involve assessments of the income distance between individuals and their comparison (or reference) group—that is defined by others who are alike with respect to age group, occupational class, or community of residence. The causal mechanisms underlying the relationship between absolute income and health are linked to the ability to access material goods and services necessary for the maintenance of health. Relative income is hypothesized to be linked to health through psychosocial stresses generated by invidious social comparisons as well as by the inability to participate fully in society because of the failure to attain normative standards of consumption. Growing evidence has suggested an association between relative deprivation (measured among individuals) and poor health outcomes (Aberg Yngwe et al., 2003; Eibner et al., 2004). A related literature has attempted to link the societal distribution of income (as an aggregate index of relative deprivation) to individual health outcomes, although the findings in this area remain contested (Subramanian and Kawachi, 2004; Lynch et al., 2004).

Variables other than household income also may be useful for health research—such as assets including inherited wealth, savings, or ownership of homes or motor vehicles (Berkman and Macintyre, 1997). While income represents the flow of resources over a defined period, wealth captures the stock of assets (minus liabilities) at a given point in time, and thus indicates economic reserves. Measuring wealth is particularly salient for studies that involve subjects towards the end of the life course, a time when many individuals have retired and depend on their savings. In the Panel Study of Income Dynamics, for example, only a weak association was seen between post-tax family income and mortality among post-retirement-age subjects, while measures of wealth continued to indicate a strong association with mortality risk (Duncan et al., 2002).

Finally, measures of income, poverty, and deprivation have been extended to incorporate the dimension of place. Growing research, utilizing multilevel study designs, has conceptualized economic status as an attribute of neighborhoods (Kawachi and Berkman, 2003). These studies have revealed that residing in a disadvantaged (or high-poverty) neighborhood imposes an additional risk to health beyond the effects of individual SES. A recent Department of Housing and Urban Development randomized experiment in neighborhood mobility, the so-called Moving To Opportunity study, found results consistent with observational data: Moving from a poor to a wealthier neighborhood was associated with significant improvements in adult mental health and rates of obesity (Kling et al., 2004). Disadvantaged neighborhoods are often characterized by adverse physical, social, and service environments, including exposure to more air pollution via proximity to heavy traffic, a lack of local amenities such as grocery stores, health clinics, and safe venues for physical activity, and exposure to signs of social disorder (Kawachi and Berkman, 2003). In other words, the relevant social and cultural “environments” for the production of health include not only an individual’s immediate personal environment (e.g., his/ her family), but also the broader social contexts such as the community in which a person resides.

Occupational Status

The third standard component of SES that typically is measured by social scientists is occupational status, which summarizes the levels of prestige, authority, power, and other resources that are associated with differ ent positions in the labor market. Occupational status has the advantage over income of being a more permanent marker of access to economic resources.

Three main traditions can be discerned in the way in which different disciplines have approached the measurement of aspects of occupations relevant to health. In the traditional occupational health field, researchers have focused on the physical aspects of the job, such as exposure to chemical toxins or physical hazards of injury (Slote, 1987). In the fields of occupational health psychology and social epidemiology, researchers have focused on characterizing the psychosocial work environment, including measures of job security, psychological job demands and stress, and decision latitude (control over the work process) (Karasek and Theorell, 1990). Finally, the sociological tradition has tended to focus on occupational status, which includes both objective indicators (e.g., educational requirements associated with different jobs) as well as subjective indicators (e.g., the level of prestige associated with different jobs in the occupational hierarchy) (Berkman and Macintyre, 1997).

Several alternative approaches currently exist for the measurement of occupational status. For a detailed description, see Berkman and Macintyre (1997) as well as Lynch and Kaplan (2000). For example, the Edwards classification (U.S. Census Bureau, 1963) is a scheme based upon the conceptual distinction between manual and nonmanual occupations. The Edwards classification was used to demonstrate that individuals who grew up in manual (as compared to nonmanual) households during childhood and adolescence were at increased risk of developing heart disease in later adult life, independently of the individual’s own attained SES (Gliksman et al., 1995). An alternative and commonly used measure of occupational status is the Duncan Socioeconomic Index (SEI), which combines subjective ratings of occupational prestige with objective measures of education and incomes associated with each occupation. SEI scores, which range from 0 to 100, were originally constructed by Duncan (1961) using data from the 1947 National Opinion Research Center study, which provided public opinions about the relative prestige rankings of representative occupations. These prestige rankings were then combined with U.S. Census information on the levels of education and incomes associated with each Census-defined occupation. The resulting SEI scores have been updated several times (Burgard et al., 2003). In the Wisconsin Longitudinal Survey of men and women who graduated from Wisconsin high schools in 1957 (53 or 54 years old in 1992-1993), Duncan SEI scores were inversely associated with self-reported health, depression, psychological well-being, and smoking status (Marmot et al., 1997).

As is the case with both education and income, an association between occupational status and health may partly reflect reverse causation. That is, ill health (e.g., depression or alcoholism) is a major cause of downward occupational mobility, as well as a constraint on upward social mobility. An individual’s choice of occupation also may reflect unmeasured variables (such as ability) that simultaneously influence health status. Although the adverse health impact of job loss (e.g., through factory closure studies) is widely accepted (Kasl and Jones, 2000), fewer studies have convincingly demonstrated a causal effect of variables such as occupational prestige on health outcomes. As noted above, existing measures of occupational status such as the Duncan SEI combine measures of prestige with indicators of education and income that are thought to affect health independently. In addition, there are uncertainties regarding the optimal time point for measuring occupational status, especially since individuals change occupations over their life course. Job changes that occur earlier in people’s careers are often associated with upward social mobility, while late-career changes may be related to a diminished capacity to function within demanding occupations (Burgard et al., 2003). For this reason, the frequently used “final occupation”—that is the occupation of an individual at the time of death or at the onset of disease—may not be an optimal indicator of the occupational conditions experienced over the individual’s life course. Few studies have examined the health effects of occupational status over an individual’s entire life course (Burgard et al., 2003), although some evidence suggests that persistently low occupational status measured at multiple time points or downward status mobility over time may be associated with worse health outcomes (Williams, 1990).

The potential pathways linking occupational status to health outcomes are again distinct from those linking either education or income to health. First, higher status (and nonmanual) occupations are less likely to be associated with hazardous exposures to chemicals, toxins, and risks of physical injury. Higher status jobs also are more likely to be associated with a healthier psychosocial work environment (Karasek and Theorell, 1990), including higher levels of control (decision latitude) as well as a greater range of skill utilization (lack of monotony). A greater sense of control in turn implies improved ability to cope with daily stress, including a reduced likelihood of deleterious coping behaviors such as smoking or alcohol abuse. Undoubtedly, a major intervening pathway between occupational status and health is through the indirect effects of higher incomes and access to a wider range of resources such as powerful social connections.

In summary, there is good evidence linking each of the major indicators of SES to health outcomes. Together, education, income, and occupation mutually influence and interact with one another over the life course to shape the health outcomes of individuals at multiple levels of social organization (the family, neighborhoods, and beyond).