Abstract
The positive associations between education and health and survival are well established, but whether the strength of these associations depends on gender is not. Is the beneficial influence of education on survival and on self-rated health conditioned by gender in the same way, in opposite ways, or not at all? Because women are otherwise disadvantaged in socioeconomic resources that are inputs to health, their health and survival may depend more on education than will men’s. To test this hypothesis, we use data from the National Health Interview Survey-Linked Mortality Files (NHIS-LMF). We find that education’s beneficial influence on feeling healthy and on survival are conditional on gender, but in opposite ways. Education has a larger effect on women’s self-rated health than on men’s, but a larger effect on men’s mortality. To further examine the mortality results, we examine specific causes of death. We find that the conditional effect is largest for deaths from lung cancer, respiratory disease, stroke, homicide, suicide, and accidents. Because women report worse health but men’s mortality is higher, education closes the gender gap in both health and mortality.
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Notes
Hypotheses derived from resource substitution on other disadvantaged statuses, such as race or parental education, are also somewhat inconclusive. In support of resource substitution, recent evidence shows that personal education has a larger influence on health among persons whose parents were poorly educated than among those from more advantaged family backgrounds (Ross and Mirowsky 2011), but, counter to resource substitution theory, that education has a smaller impact on health among blacks than whites (Williams 1997).
Ordered logistic regression is a proportional-odds model, in that it assumes the effect of a predictor on the log odds of reporting a higher category over all lower categories is the same across all possible higher-lower comparisons. This assumption of “parallel regression” was tested for education’s effect on self-rated health by running separate binary logistic regression models of all possible higher-lower categorical comparisons, and was found to be satisfactory.
All coefficients noted as significant at p < .001 (t > 3.29) in fact have t values greater than 3.7 \( \sqrt {{\ln (n)}} \), which has been suggested as a conservative test of significance.
The one exception is that compared with having a college degree, having a master’s degree or more does not continue to close the gender gap but rather re-opens it somewhat. Master’s degrees or higher do not benefit women’s health very much. However, caution should be taken in interpreting this because the education measure is truncated at master’s level and higher, and this category probably contains differences in the educational attainment of men and women, with the men more likely to have achieved an M.D., J.D., or Ph.D. and the women more likely to be in the master’s category. Other research has shown a leveling off in the health benefits of a master’s degree compared with a bachelor of arts degree, but a substantial health benefit to more advanced degrees (Ross and Mirowsky 1999), possibly because master’s degree fields such as social work or nursing (typically attained by women) do not translate into high-status jobs that pay well. These data do not allow us to make the distinction between master’s degrees and more-advanced degrees.
Age equivalents equal the coefficient of interest divided by the coefficient associated with age. To take an example from Table 2 in the prediction of poor self-rated health, the coefficient associated with each year of schooling is –0.161 for men and −0.172 for women (−0.161 − [−0.011]); the coefficient associated with each year of age is 0.047 for men and 0.039 for women (0.047 – 0.008). For women: –0.172 / 0.039 = –4.410, and for men: −0.161 / 0.047 = −3.425. Thus, the difference in age equivalents between 12 years and 16 years of schooling is 17.64 for women and 13.7 for men, a difference of 3.94 years. Age equivalents provide a standard of comparison in the study of health and mortality because age is consistently associated with declines in health and increases in mortality (Mirowsky 2005).
This speculative explanation refers to dangerous behaviors, not all risk factors. Some risk factors, such as a sedentary lifestyle or a high-fat diet, are not thought of as dangerous and destructive. Although a sedentary lifestyle, excess calories, and being overweight are major risk factors for heart disease, diabetes, stroke, and other fatal health problems, they are common and normative. (And men are not even at higher risk, given that they exercise more than women). In fact, this has been called the “default” American lifestyle (Mirowsky and Ross 2010). Future research on these ideas requires data with measures of health lifestyle, including dangerous behaviors—and, ideally, enough cases to examine specific causes of death. According to Pampel (2002), smoking is the behavior most destructive to health, accounting for a significant portion of men’s excess mortality and for the narrowing gender gap in mortality in recent years (as women’s cigarette use approaches men’s), so initial tests can be undertaken with recent years of the NHIS-LMF, which includes measures of smoking. However, we found conditional effects for homicide, suicide, and accidents, in addition to lung cancer and emphysema, which suggest that dangerous behaviors other than smoking may also be important. In fact, some of these dangerous behaviors influence mortality directly (from accidents, homicide, and to some extent suicide), not by way of poor health (Benjamins et al. 2004).
Most causes of death, like heart disease, are indirectly influenced by health and could have counteracting effects that cancel each other in the following way. Men’s advantaged status may give them the latitude to engage in dangerous behaviors, but obviously men’s position isn’t all bad for health. Their better jobs and higher incomes, for example, benefit health. Thus, there could be counteracting effects of risky lifestyle and socioeconomic advantage, with interactions in opposite directions. When destructive lifestyle does not predominate, the counteracting interactions could balance, producing null or nonsignificant interactions of education and sex on mortality. While speculative, this is consistent with what we find for cancer (other than lung cancer), infectious disease, and, especially, heart disease.
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Acknowledgments
This research was funded by a grant from the National Institute of Child Health and Human Development (RO1-HD 053696) to Robert Hummer, and a grant from the National Institute on Aging to Catherine Ross (RO1-AG035268).
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Ross, C.E., Masters, R.K. & Hummer, R.A. Education and the Gender Gaps in Health and Mortality. Demography 49, 1157–1183 (2012). https://doi.org/10.1007/s13524-012-0130-z
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DOI: https://doi.org/10.1007/s13524-012-0130-z