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Using a variety of data sets from two countries, we examine possible explanations for the relationship between education and health behaviors, known as the education gradient. We show that income, health insurance, and family background can account for about 30 percent of the gradient. Knowledge and measures of cognitive ability explain an additional 30 percent. Social networks account for another 10 percent. Our proxies for discounting, risk aversion, or the value of future do not account for any of the education gradient, and neither do personality factors such as a sense of control of oneself or over one's life.In 1990, a 25 year-old male college graduate could expect to live another 54 years. A high school dropout of the same age could expect to live 8 years fewer (Richards and Barry, 1998). This enormous difference in life expectancy by education is true for every demographic group, is persistent -if not increasing -over time (Kitagawa and Hauser, 1973;Elo and Preston, 1996; Meara, Richards, and Cutler, 2008), and is present in other countries (Marmot, Shipley, and Rose, 1984 (the U.K.); Mustard, et al. 1997 (Canada); Kunst and Mackenbach, 1994 (northern European countries)). 1 A major reason for these differences in health outcomes is differences in health behaviors. 2 In the United States, smoking rates for the better educated are one-third the rate for the less educated. Obesity rates are half as high among the better educated (with a particularly pronounced gradient among women), as is heavy drinking. Mokdad et al. (2004) estimate that nearly half of all deaths in the United States are attributable to behavioral factors, most importantly smoking, excessive weight, and heavy alcohol intake. Any theory of health differences by education thus needs to explain differences in health behaviors by education. We search for explanations in this paper. 3 In standard economic models, people choose different consumption bundles because they face different constraints (for example, income or prices differ), because they have different beliefs about the impact of their actions, or because they have different tastes. We start by showing, © 2009 Elsevier B.V. All rights reserved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1 See Cutler and Lleras-Muney (2007) for additional references. 2 Observed health behaviors however do not explain all of the differences in health status by education or other SES measures. We do not focus on this issue in this paper. 3 Formal explanations for this phenomenon date from Grossman (1972) although there was less formal d...

There is a large and persistent association between education and health. In this paper, we review what is known about this link. We first document the facts about the relationship between education and health. The education 'gradient' is found for both health behaviors and health status, though the former does not fully explain the latter. The effect of education increases with increasing years of education, with no evidence of a sheepskin effect. Nor are there differences between blacks and whites, or men and women. Gradients in behavior are biggest at young ages, and decline after age 50 or 60. We then consider differing reasons why education might be related to health. The obvious economic explanations-education is related to income or occupational choice-explain only a part of the education effect. We suggest that increasing levels of education lead to different thinking and decision-making patterns. The monetary value of the return to education in terms of health is perhaps half of the return to education on earnings, so policies that impact educational attainment could have a large effect on population health.

Mortality rates in the United States fell more rapidly during the late nineteenth and early twentieth centuries than in any other period in American history. This decline coincided with an epidemiological transition and the disappearance of a mortality "penalty" associated with living in urban areas. There is little empirical evidence and much unresolved debate about what caused these improvements, however. In this article, we report the causal influence of clean water technologies--filtration and chlorination--on mortality in major cities during the early twentieth century. Plausibly exogenous variation in the timing and location of technology adoption was used to identify these effects, and the validity of this identifying assumption is examined in detail. We found that clean water was responsible for nearly half the total mortality reduction in major cities, three quarters of the infant mortality reduction, and nearly two thirds of the child mortality reduction. Rough calculations suggest that the social rate of return to these technologies was greater than 23 to 1, with a cost per person-year saved by clean water of about dollar 500 in 2003 dollars. Implications for developing countries are briefly considered.

Amid soaring health spending, there is growing interest in workplace disease prevention and wellness programs to improve health and lower costs. In a critical meta-analysis of the literature on costs and savings associated with such programs, we found that medical costs fall by about $3.27 for every dollar spent on wellness programs and that absenteeism costs fall by about $2.73 for every dollar spent. Although further exploration of the mechanisms at work and broader applicability of the findings is needed, this return on investment suggests that the wider adoption of such programs could prove beneficial for budgets and productivity as well as health outcomes.

In this paper we examine educational disparities in mortality and life expectancy among non-Hispanic blacks and whites in the 1980s and 1990s. Despite increased attention and substantial dollars directed to groups with low socioeconomic status, within race and gender groups, the educational gap in life expectancy is rising, mainly because of rising differentials among the elderly. S o c i a l D e t e r m i n a n t s

Spatial separation of racial and ethnic groups may theoretically have positive or negative effects on the economic performance of those groups. We examine the effects of segregation on outcomes for blacks in schooling, employment, and single parenthood. We find that blacks in more segregated areas have significantly worse outcomes than blacks in less segregated areas. We control for the endogeneity of location choice using instruments based on political factors, topographical features, and residence before adulthood. A one standard deviation decrease in segregation would eliminate one-third of the black-white differences in most of our outcomes.

Summary Aging is a dynamic process with trends in health status of older adults varying over time due to a range of factors. We examined reported trends in morbidity and mortality among older adults over the past two decades in order to determine patterns of ageing across the world. We found some evidence for compression of morbidity, i.e., less amount of time spent in worse health, when: a) studies were of a good quality based on evaluation criteria scores; b) a disability- or impairment-related measure of morbidity was used; c) studies were longitudinal or; d) studies were conducted in the United States and some other high income countries. Many studies reported evidence to the contrary, i.e., for an expansion of morbidity but with different methods these are not directly comparable. Expansion of morbidity was more common when trends in chronic disease prevalence were studied. Our secondary analysis of data from longitudinal ageing surveys present a similar picture. However, there are considerable variations across countries in patterns of limitations in functioning and within countries over time with no discernible explanations. Data from low income countries is very sparse and efforts to collect information on the health of older adults in less-developed regions of the world is urgently required. Studies focussing on refining measurement with a core set of domains of functioning and studying the impacts of these evolving patterns on the health care system and their economic implications are needed.