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Research

Research Studies in Pension

Factor Affecting Retirement Mortality (FARM)

This FARM site consists of

• an Introduction
• an Abstract
• a Bibliography of research papers
• a collection of Summaries of the research papers.

Use the Table of Summaries to link to the summaries either by author or by risk factor.

Abstract | Bibliography | Introduction | Table of Summaries

Education

Vaillant and Mukamal (2001) found that one of the most important predictors of successful aging was a high level of education.

Sorlie et al (1995) found that higher education level was associated with lower mortality in men and women. Similar results were obtained in Pappas et al (1993) and in Rogers et al (1999). Pappas et al (1993) found that death rates fell consistently with increasing levels of education. Adjustments for other variables reduced the risks but they remained significant. Rogers et al (1999) state that the continuous inverse relationship between education and mortality is robust to controls for age, sex, race, marital status, cigarette smoking, adequacy of housing and income. In contrast, Attanasio and Emmerson (2001) found that education was significant for morbidity, but not for mortality and Lantz et al (1998) found that education was related to mortality through its association with income.

Education is one possible measure of socioeconomic status. Deaton and Paxson (1999) show that at the individual level, both income and education are separately protective against mortality. However, Vaillant and Mukamal (2001) suggest that education is a more significant cause of differential mortality than other differences in socioeconomic status. In comparing college students and core–city youth over a long period, they found that the core–city men who had completed 16 or more years of education had very similar health to the college cohort. Preston and Elo (1995) also consider education to be advantageous relative to occupation and income. They state that information on educational attainment is available for people who are not currently in the labor force and its value is less influenced by health problems that develop in adulthood. Rogers et al (1999) also suggest that education may be the best measure as it is determined early in life and so can be assessed for all individuals. However, the Statistical Bulletin (1975) states that occupation, education and income are associated with health and longevity, but highlights that the interdependence of these factors mean that the effect of any one is affected by the presence of the others.

The level of education can also affect the cause of death. Kallan (1997) found that education affected every cause of death in the younger age group, but particularly those having a large behavioral component. Bucher and Ragland (1995) found that education was inversely related to blood pressure, cholesterol and smoking. It was also inversely associated with mortality from coronary heart disease, stroke and all causes. However, no significant association was found between death from non–lung cancer and education.

Deaton and Paxson (1999) comment that education affects mortality differently for men and women. For men, education influences mortality only through its association with income, whereas for women, education has a separate protective effect. Lantz et al (1998) found that the relationship between education and mortality and between income and mortality is stronger for females.

Sorlie et al (1995) found that the strongest relationships between higher education and lower mortality were in the under 65 age group, as did Deaton and Paxson (1999). Rogers et al (1999) state that not only does education affect mortality through its link to employment, income generation and information gathering, it also affects mortality by influencing health behavior and the use of health services. This would support Kallan's (1997) finding that education particularly affected the causes of death having a large behavioral component. In another paper, Rogers et al (1999) comment that those with less education and lower incomes were more likely to be smokers and less likely to quit. However, in their study, income and education did not alter the effects of smoking significantly. Lantz et al (1998) also found that the distribution of four behavioral risk factors (cigarette smoking, alcohol drinking, sedentary lifestyle and relative body weight) significantly varied by educational attainment. Those with the least education were significantly more likely to be current smokers, overweight and in the lowest quintile for physical activity. Bucher and Ragland (1995) also found those with less education had higher risk factors. They noted that those with less education were in the older age groups, were less tall and smoked more.

Some of the papers also identified increases in educational differentials in recent years. Pappas et al (1993) found that absolute death rates declined for people of all educational levels, but the reduction was greater for those with more education than for those with less, highlighting an increasing disparity in mortality rates for those of different education levels. Preston and Elo (1995) found that educational inequalities have widened for males but contracted for working–age females. For both sexes, inequality trends are more adverse for people aged 65+ than for those aged 25–64. They state that the reasons for these changes are not easily explained and are likely to be multi–factorial and some or all of the factors must be highly differentiated by sex.