Why Do College Graduates Behave More Healthfully Than Those Who Are Less Educated?

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<p class="first" id="P1">College graduates live much healthier lives than those with less education, but research has yet to document with certainty the sources of this disparity. This study examines why U.S. young adults who earn college degrees exhibit healthier behavior than those with less education. I use data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), which offers information on education and health behaviors across adolescence and young adulthood (N=14,265). Accounting for selection into college degree attainment substantially reduces the associations between college degree attainment and health behavior, but college degree attainment demonstrates a strong causal effect on young adult health. Financial, occupational, social, cognitive, and psychological resources explain less than half of the association between college degree attainment and health behavior. The healthier behaviors of college graduates are the result of sorting into educational attainment, embedding of human capital, and mechanisms other than socioeconomic and psychosocial resources. </p>

Related collections

Most cited references 32

Record: found
Abstract: found
Article: not found

Socioeconomic Disparities in Health Behaviors.

Fred Pampel, Patrick M. Krueger, Justin Denney (2010)

The inverse relationships between socioeconomic status (SES) and unhealthy behaviors such as tobacco use, physical inactivity, and poor nutrition have been well demonstrated empirically but encompass diverse underlying causal mechanisms. These mechanisms have special theoretical importance because disparities in health behaviors, unlike disparities in many other components of health, involve something more than the ability to use income to purchase good health. Based on a review of broad literatures in sociology, economics, and public health, we classify explanations of higher smoking, lower exercise, poorer diet, and excess weight among low-SES persons into nine broad groups that specify related but conceptually distinct mechanisms. The lack of clear support for any one explanation suggests that the literature on SES disparities in health and health behaviors can do more to design studies that better test for the importance of the varied mechanisms.

0 comments Cited 512 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Understanding differences in health behaviors by education.

David Cutler, Adriana Lleras-Muney (2010)

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. Copyright 2009 Elsevier B.V. All rights reserved.

0 comments Cited 405 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

Jared K Lunceford, Marie Davidian (2004)

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. 2004 John Wiley & Sons, Ltd.