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).
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