Understanding differences in health behaviors by education

Abstract

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...

Complementarity in dietary supplements and foods: are supplement users vegetable eaters?

“…The results indicate that parents consumed fewer supplements as they had more children, but overall family consumption still rose due to the children’s intake. Education and income contributed to higher supplement consumption, as was observed in previous studies [17–19]. The households with homemakers consumed fewer vitamins, but more ginseng and lactobacillus.…”

The health knowledge mechanism: evidence on the link between education and health lifestyle in the Philippines

“…The few studies that test for the relevance of knowledge as a mediating channel in explaining education effects on health decisions come to mixed results. While most of them do at least partially confirm the allocative efficiency predictions, in none of them health knowledge accounts for a considerably large share of education effects [14,41,48,49]. The evidence suggests that other mechanisms, such as differences in productive efficiency or omitted third factors, might drive the effects.…”

“…However, even under control for health knowledge, a large share of the education effect remains unexplained, suggesting that schooling may additionally have an impact on behavior through channels other than knowledge. Similarly, [14] find only a modest impact of knowledge on the education gradient in health behavior, using two measures of knowledge about the risks of smoking and drinking. They report a decrease of 17% of education effects on current smoking and no change in the coefficients for drinking once the knowledge measure is introduced in the model (See also [48]).…”

“…The main analysis above is repeated in a final step in which we additionally control for a set of other factors M in the baseline model [1], which have been proposed as alternative mediating channels in the literature [14,21,25,54]. Like before, we add knowledge K to the right-hand side of the equation to test for changes in the education coefficient.…”

“…As a further contribution to the literature, we try to overcome a major challenge in the testing of the allocative efficiency mechanism, which is the measurement of respondents' health knowledge. In previous studies, indicators tended to measure knowledge in a restricted and simplistic way, limiting attention to knowledge about one health topic, such as the risks of smoking, which might not well capture the multi-dimensionality and complexity of the construct [3,14,41,48]. Using self-collected data, we construct an extensive knowledge index, which is tailored to the local context.…”