Understanding differences in health behaviors by education
Introduction
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 et al., 2008), and is present in other countries (Marmot et al., 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, as others have as well, that income and price differences do not account for all of these behavioral differences. We estimate that access to material resources, such as gyms and smoking cessation methods, can account for at most 30 percent of the education gradient in health behaviors. Price differences work the other way. Many unhealthy behaviors are costly (smoking, drinking, and overeating), and evidence suggests that the less educated are more responsive to price than the better educated. As a result, we consider primarily differences in information and in tastes.
Some of the differences by education are indeed due to differences in specific factual knowledge—we estimate that knowledge of the harms of smoking and drinking accounts for about 10 percent of the education gradient in those behaviors. However, more important than specific knowledge is how one thinks. Our most striking finding, shown using US and UK data, is that a good deal of the education effect – about 20 percent – is associated with general cognitive ability. Furthermore this seems to be driven by the fact that education raises cognition which in turn improves behavior.
A lengthy literature suggests that education affects health because both are determined by individual taste differences, specifically in discounting, risk aversion, and the value of the future—which also affect health behaviors and thus health. Victor Fuchs (1982) was the first to test the theory empirically, finding limited support for it. We suspect that taste differences in childhood cannot explain all of the effect of schooling, since a number of studies show that exogenous variation in education influences health. For example, Lleras-Muney (2005) shows that adults affected by compulsory schooling laws when they were children are healthier than adults who left school earlier. Currie and Moretti (2003) show that women living in counties where college is more readily available have healthier babies than women living in other counties. However, education can increase the value of the future simply by raising earnings and can also change tastes.
Nevertheless, using a number of different measures of taste and health behaviors, we are unable to find a large impact of differences in discounting, value of the future, or risk aversion on the education gradient in health behaviors. Nor do we find much role for theories that stress the difficulty of translating intentions into actions, for example, that depression or lack of self-control inhibits appropriate action (Salovey et al., 1998). Such theories are uniformly unsupported in our data, with one exception: about 10 percent of the education gradient in health behaviors is a result of greater social and emotional support.
All told, we account for about two-thirds of the education gradient with information on material resources, cognition, and social interactions. However, it is worth noting that our results have several limitations. First, we lack the ability to make causal claims, especially because it is difficult to estimate models where multiple mechanisms are at play. Second, we recognize that in many cases the mechanisms we are testing require the use of proxies which can be very noisy, causing us to dismiss potentially important theories. Nevertheless we view this paper as an important systematic exploration of possible mechanisms, and as suggesting directions for future research.
The paper is structured as follows. We first discuss the data and empirical methods. The next section presents basic facts on the relation between education and health. The next two sections discuss the role of income and prices in mediating the education-behavior link. The fourth section considers other theories about why education and health might be related: the cognition theory; the future orientation theory; and the personality theory. These theories are then tested in the next three sections. We then turn to data from the U.K. The final section concludes.
Section snippets
Data and methods
In the course of our research, we use a number of different data sets. These include the National Health Interview Survey (NHIS), the National Longitudinal Survey of Youth (NLSY), the National Survey of Midlife Development in the United States (MIDUS), the Health and Retirement Study (HRS), the Survey on Smoking (SOS), and the National Childhood Development Study (NCDS) in the U.K. We use many data sets because no single source of data has information allowing us to test all the relevant
Education and health behaviors: the basic facts
We start by presenting some basic facts relating education and health behaviors, before discussing theories linking the two. Health behaviors are asked about in a number of surveys. Probably the most complete is the National Health Interview Survey (NHIS). In order to examine as many behaviors as possible, we use data from a number of NHIS years, 1990, 1991, 1994 and 2000.9 We group health behaviors into eight groups: smoking,
Education as command over resources
An obvious difference between better educated and less educated people is resources. Better educated people earn more than less educated people, and these differences in earnings could affect health. There are two channels for this. First, higher income allows people to purchase goods that improve health, for example, health insurance. In addition, higher income increases steady-state consumption, and thus raises the utility of living to an older age. We focus here on the impact of current
Prices
Differences in prices or in response to prices are a second potential reason for education-related differences in health behaviors. This shows up most clearly in behaviors involving the medical system. In surveys, lower income people regularly report that time and money are major impediments to seeking medical care.13 Even given health insurance, out-of-pocket costs may be greater for the poor than for
Knowledge
The next theory we explore is that education differences in behavior result from differences in what people know. Some information is almost always learned in school (advanced mathematics, for example). Other information could be more available to educated individuals because they read more. Still other information may be freely distributed, but believed more by the better educated. Most health information is of the latter type. Everyone has access to it, but not everyone internalizes it.
The
Utility function characteristics: discount rates, risk aversion and the value of the future
The most common economic explanation for different behaviors is tastes. In our framework, tastes take the form of differences in discount rates, the value of the future, or risk aversion. The source of differences in utility functions is not clear. Education may lead people to have lower discount rates (Becker and Mulligan, 1997): for example, if education raises future income, individuals have an incentive to invest in lowering their discount rate. Education may also lead people be more risk
Translating intentions into actions
Even when people know what they want to do, translating intensions into actions may be easier for the better educated. We noted above the example of smoking: the better educated are more successful at quitting smoking than the less educated, not because they try to quit more frequently or use different methods, but because they are more successful when they do try.30 This parallels Rosenzweig and Schultz (1989) results on the success of
Evidence from the United Kingdom
Our results to this point have focused on the United States. As noted earlier, education gradients are pervasive in the developed (and developing) world. Analyzing data from other countries can help determine if the results in the United States carry over in other settings.
Data from the National Child Development Study (NCDS) in the United Kingdom are available to address these issues. The NCDS is a study of everyone born in a given week in Great Britain in 1958. We use data from the 6th
Education and cognition: further results
One of our most interesting results is that a non-trivial share of the education gradient in health behaviors can be accounted for by cognition measures. Previous literature has considered whether the relationship between education and health (rather than health behaviors) is mediated by cognition, and finds mixed results. Most notably, Auld and Sidhu (2005) find that including test scores has a large effect on the education gradient in self-reported health status, whereas Grossman (1975) finds
Conclusion
Using a variety of data sets in two countries, we examine the relation between education and health behaviors. Education gradients in health behaviors are large; controlling for age, gender, and parental background, better educated people are less likely to smoke, less likely to be obese, less likely to be heavy drinkers, more likely to drive safely and live in a safe house, and more likely to use preventive care. Given the similarity across so many different behaviors, we focus on broad
Acknowledgements
We are grateful to Lisa Vura-Weiss, Tom Vogl and Rebecca Lowry for excellent research assistance, to Frank Sloan for generously sharing the Survey on Smoking data with us, to Chris Winship and Alan Block for help with data and programs, and the National Institutes on Aging for research support. This work greatly benefited from comments from Michael Grossman, Joe Newhouse and the seminar participants at Princeton, the NBER, Duke and Harvard.
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