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, 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 know 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, Rothman, and Rodin, 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.

I. 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 theories. For the US we have restricted our attention to the whites only because our earlier work showed larger education gradients among them (Cutler and Lleras-Muney 2008b) but the results presented here are not particularly sensitive to that choice. A lengthy data appendix discusses the surveys in more detail.

In all data sets we restrict the samples to individuals ages 25 and above (so education has been mostly completed)—but place no upper limit on age. The health behaviors we look at are self-reported. This is a limitation of our study, but we were unable to find data containing measured (rather than self reported) behaviors to test our theories.⁴ To the extent that biases in self reporting vary across behaviors, our use of multiple health behaviors mitigates this bias. Nevertheless it is worth noting that not much is known about whether biases in reporting vary systematically by education.

To document the effect of education on health behaviors, we estimate the following regression:

$$H_i={\beta }_0+{\beta }_{\mathit{1}}^{*}Educatio{n}_i+{\mathit{X}}_i\alpha +{\epsilon }_i$$ (1)

Where H_i is a health behavior of individual i, Education is measured as years of schooling in the US, and as a dummy for whether the individual passed any A level examinations in the UK.⁵ The basic regression controls for basic demographic characteristics (gender, age dummies and ethnicity) and all available parental background measures (which vary depending on the data we use). Ideally in this basic specification we would like to control for parent characteristics and all other variables that determine education but cannot be affected by it, such as genetic and health endowments at birth— we control for the variables that best seem to fit this criterion in each data set.⁶ The education gradient is given by β₁, the coefficient on education, and measures the effect of schooling on behavior, which could be thought of as causal if our baseline controls were exhaustive. We discuss below whether the best specification of education is linear or non-linear.

In testing a particular theory we then re-estimate equation (1) adding a set of explanatory variables Z:

$$H_i={\alpha }_0+{\alpha }_{\mathit{1}}^{*}Educatio{n}_i+{\mathit{X}}_i\alpha +Z_i\gamma +{\epsilon }_i.$$ (2)

We then report, for each health measure, the percent decline in the coefficient of education from adding each set of variables, 1 - α₁/β₁.

Many of our health measures are binary. To allow for comparability across outcomes, we estimate all models using linear probability, but our results are not very different if we instead use a non-linear model. Thus, the coefficients are the percentage point change in the relevant outcome. Since we have many outcomes, it is helpful to summarize them in a single number. We use three methods to form a summary. First we compute the average reduction of the gradient across outcomes for those outcomes with a statistically significant gradient in the baseline specification. Of course, not all behaviors contribute equally to health outcomes. Our second summary measure weights the different behaviors by their impact on mortality. The regression model, using the 1971–75 National Health and Nutrition Examination Survey Epidemiological Follow-up Study, is described in the Appendix. For comparability reasons, the behaviors are restricted to smoking, drinking, and obesity. The summary measure is the predicted change in 10 year mortality associated with each additional year of education.⁷ Finally, we report the average effect of education across outcomes using the methodology described in Kling, Liebman, and Katz (2007), which weights outcomes equally after standardizing them.⁸

II. 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, diet/exercise, alcohol use, illegal drugs, automobile safety, household safety, preventive care, and care for people with chronic diseases (diabetes or hypertension). Within each group, there are multiple measures of health behaviors. Because the NHIS surveys are large, our sample sizes are up to approximately 23,000.

Table 1 shows the health behaviors we analyze and the mean rates in the adult population. We do not remark upon each variable, but rather discuss a few in some depth. Current cigarette smoking is a central measure of poor health. Mokdad et al. (2004) estimate that cigarette smoking is the leading cause of preventable deaths in the country (accounting for 18 percent of all deaths). The first row shows that twenty-three percent of white adults in 2000 smoked cigarettes. The next columns relate cigarette smoking to years of education, entered linearly. We control for single year of age dummies, a dummy for females, and a dummy for Hispanic.

Table 1.

Health Behaviors for Whites over 25 National Health Interview Survey

				Demographic Controls		Adding Income			Adding Income and other Economic
Dependent Variable	Mean	N	Year	Years of Education (β)	std error	Years of Education (β)	std error	Reduction in Education Coefficient	Years of Education (β)	std error	Reduction in Education Coefficient
Smoking
Current smoker	23%	22141	2000	−0.030	(0.001) **	−0.022	(0.001) **	26%	−0.020	(0.001) **	33%
Former smoker	26%	22270	2000	0.004	(0.001) **	0.002	(0.001)	58%	0.001	(0.001)	79%
Ever smoked	49%	22156	2000	−0.026	(0.001) **	−0.021	(0.001) **	20%	−0.019	(0.001) **	25%
Number cigs a day (smokers)	17.7	4910	2000	−0.697	(0.068) **	−0.561	(0.071) **	19%	−0.444	(0.073) **	36%
Made serious attempt to quit °	64%	7603	1990	0.013	(0.002) **	0.011	(0.002) **	12%	0.011	(0.002) **	16%
Diet/Exercise
Body mass index (BMI)	26.7	21401	2000	−0.190	(0.014) **	−0.159	(0.015) **	16%	−0.139	(0.016) **	27%
Underweight (bmi<=18.5)	2%	21401	2000	−0.0005	(0.0004)	−0.0001	(0.0004)	85%	0.0000	(0.0004)	98%
Overweight (bmi>=25)	59%	21401	2000	−0.014	(0.001) **	−0.014	(0.001) **	0%	−0.013	(0.001) **	12%
Obese (bmi>=30)	22%	21401	2000	−0.014	(0.001) **	−0.011	(0.001) **	18%	−0.010	(0.001) **	28%
How often eat fruit or veggies per day	1.9	22285	2000	0.079	(0.004) **	0.067	(0.004) **	16%	0.067	(0.004) **	15%
Ever do vigorous activity	39%	22003	2000	0.039	(0.001) **	0.032	(0.001) **	18%	0.028	(0.001) **	28%
Ever do moderate activity	53%	21768	2000	0.037	(0.001) **	0.030	(0.001) **	17%	0.029	(0.001) **	21%
Alcohol
Had 12+ drinks in entire life	80%	22054	2000	0.021	(0.001) **	0.017	(0.001) **	19%	0.014	(0.001) **	33%
Drink at least once per month	47%	21803	2000	0.033	(0.001) **	0.025	(0.001) **	24%	0.020	(0.001) **	41%
Number of days had 5+ drinks past year- drinkers	10.8	13458	2000	−2.047	(0.157) **	−1.711	(0.167) **	16%	−1.754	(0.170) **	14%
Number of days had 5+ drinks past year- all	6.8	21663	2000	−0.848	(0.092) **	−0.703	(0.098) **	17%	−0.763	(0.100) **	10%
Average # drinks on days drank	2.3	13600	2000	−0.162	(0.012) **	−0.162	(0.012) **	0%	−0.144	(0.012) **	11%
Heavy drinker (average number of drinks>=5)	8%	13600	2000	−0.018	(0.001) **	−0.015	(0.001) **	12%	−0.015	(0.001) **	13%
Drove drunk past year °	11%	17121	1990	−0.003	(0.001) **	−0.002	(0.001) **	27%	−0.005	(0.001) **	−38%
Number of times drove drunk past year °	93%	17121	1990	−0.140	(0.036) **	−0.103	(0.038) **	27%	−0.119	(0.040) **	15%
Illegal Drugs
Ever used marijuana °	48%	13413	1991	0.015	(0.002) **	0.014	(0.002) **	9%	0.009	(0.002) **	41%
Used marijuana, past 12 months °	8%	13413	1991	−0.001	(0.001)	0.000	(0.001)	139%	−0.002	(0.001) **	−100%
Ever used cocaine °	16%	13174	1991	0.005	(0.001) **	0.005	(0.001) **	−14%	0.000	(0.001)	94%
Used cocaine, past 12 months °	2%	13174	1991	0.000	(0.000)	0.000	(0.001)	---	−0.001	(0.001)	---
Ever used any other illegal drug °	22%	13370	1991	0.003	(0.014) **	0.006	(0.002) **	−80%	0.001	(0.002) **	79%
Used other illegal drug, past 12 months °	5%	13176	1991	−0.002	(0.001) **	0.000	(0.001)	87%	−0.002	(0.001) **	20%
Automobile Safety
Always wear seat belt °	69%	29993	1990	0.033	(0.001) **	0.027	(0.001) **	19%	0.026	(0.001) **	23%
Never wear seat belt °	9%	29993	1990	−0.014	(0.001) **	−0.011	(0.001) **	20%	−0.011	(0.001) **	22%
Household Safety
Know poison control number °	65%	6838	1990	0.031	(0.002) **	0.026	(0.002) **	18%	0.027	(0.002) **	15%
1 + working smoke detectors °	80%	29021	1990	0.019	(0.001) **	0.012	(0.001) **	36%	0.012	(0.001) **	38%
House tested for radon °	4%	28440	1990	0.007	(0.000) **	0.005	(0.000) **	29%	0.005	(0.000) **	25%
Home paint ever tested for lead °	4%	9600	1991	0.000	(0.001)	0.001	(0.001)	---	−0.001	(0.001)	---
At least 1 firearm in household	42%	14207	1994	−0.011	(0.002) **	−0.019	(0.002) **	−73%	−0.012	(0.002) **	−9%
All firearms in household are locked (has firearms)	36%	5268	1994	−0.005	(0.003) **	−0.008	(0.003) **	−60%	−0.007	(0.003) **	−40%
All firearms in household are unloaded (has firearms)	81%	5262	1994	0.006	(0.002) **	0.003	(0.001) **	50%	0.004	(0.002) **	33%
Preventive Care-recommended population
Ever had mammogram-age 40+	87%	8169	2000	0.017	(0.001) **	0.013	(0.002) **	27%	0.010	(0.002) **	40%
Had mamogram w/in past 2 yrs	56%	8100	2000	0.026	(0.002) **	0.017	(0.002) **	34%	0.014	(0.002) **	45%
Ever had pap smear test	97%	11866	2000	0.009	(0.001) **	0.009	(0.001) **	7%	0.009	(0.001) **	1%
Had pap smear w/in past yr	62%	11748	2000	0.028	(0.002) **	0.019	(0.002) **	32%	0.015	(0.002) **	46%
Ever had colorectal screening-age 40+	31%	14302	2000	0.021	(0.001) **	0.019	(0.002) **	11%	0.018	(0.002) **	14%
Had colonoscopy w/in past yr	9%	14259	2000	0.007	(0.001) **	0.007	(0.001) **	11%	0.006	(0.001) **	17%
Ever been tested for hiv	30%	20853	2000	0.011	(0.001) **	0.011	(0.001) **	0%	0.011	(0.001) **	2%
Had an std other than hiv/aids, past 5 y	2%	11398	2000	0.000	(0.001)	0.001	(0.001)	---	0.000	(0.001)	---
Had flu shot past 12 mo	32%	22047	2000	0.014	(0.001) **	0.013	(0.001) **	11%	0.013	(0.001) **	11%
Ever had pneumonia vaccination	18%	21705	2000	0.005	(0.001) **	0.006	(0.001) **	−30%	0.006	(0.001) **	−25%
Ever had hepatitis b vaccine	19%	21118	2000	0.018	(0.001) **	0.017	(0.001) **	4%	0.017	(0.001) **	8%
Received all 3 hepatitis B shots	15%	20848	2000	0.015	(0.001) **	0.014	(0.001) **	6%	0.014	(0.001) **	7%
Among Diabetics
Are you now taking insulin	32%	1442	2000	−0.002	(0.004)	−0.003	(0.004)	−38%	−0.003	(0.005)	−36%
Are you now taking diabetic pills	66%	1443	2000	−0.006	(0.004)	−0.004	(0.004)	25%	−0.004	(0.005)	40%
Blood pressure high at last reading °	7%	28373	1990	−0.005	(0.001) **	−0.004	(0.001) **	24%	−0.004	(0.001) **	24%
Among hypertensives
Still have high bp °	47%	6899	1990	−0.012	(0.002) **	−0.010	(0.002) **	19%	−0.009	(0.002) **	25%
High bp is cured (vs controlled) °	26%	3537	1990	0.000	(0.003)	−0.001	(0.003)	---	−0.002	(0.003)	---
Average Reduction in Education Coefficient
Unweighted (outcomes w/significant gradients at baseline)								12%			22%
Mortality weighted	11%							24%			32%

Notes: Sample sizes are constant across columns. Demographic controls include a full set of dummies for age, gender, and Hispanic origin. Economic controls include family income, family size, major activity, region, MSA, marital status, and whether covered by health insurance.

Outcomes marked with ° came from waves of the NHIS that did not collect health insurance data, so health insurance is not included in these regressions. Self reports are from questions of the form "Has a doctor ever told you that you have …?" Unweighted average reduction in education coefficient is calculated for all behaviors where the education effect without controls is statistically significant. NHIS weights are used in all regressions and in calculating means.

^**(*) indicates statistically significant at the 5% (10%) level.

Each year of education is associated with a 3.0 percentage point lower probability of smoking. Put another way, a college grad is 12 percentage points less likely to smoke than a high school grad. Given that smoking is associated with 6 years shorter life expectancy (Cutler et al., 2000), this difference is immense.

Entering education linearly may not be right. One might imagine that some base level of education is important, and that additional education beyond that level would not reduce smoking. That is not correct, however. The first part of Figure 1 shows the relationship between exact years of education and smoking: the figure reports the marginal effect of an additional year of education for each level of education, estimated using a logit model. If anything, the story is the opposite of the ‘base education’ hypothesis; the impact of education is greater at higher levels of education, rather than lower levels of education (although there are few observations at the lower end of the education distribution and thus these estimates are imprecise). Overall the relationship appears to be linear above 10 years of schooling for all of the outcomes in Figure 1.

Figure 1. Effect of education on various health behaviors, by single year of schooling.

Note: Marginal effects from logit regressions on education, controlling for race and gender. The shaded areas are 95% confidence intervals for each coefficient. Exact years of education are not available in all surveys and were imputed as the middle of the education category. Years of education is top coded as 17.

Next to smoking, obesity is the leading behavioral cause of death. While all measures of excess weight are correlated, we focus particularly on obesity (defined as a Body Mass Index or BMI equal to or greater than 30). Twenty-two percent of the population in 2000 self-reported themselves to be obese.¹⁰ This too is negatively related to education; each year of additional schooling reduces the probability of being obese by 1.4 percent (Table 1). The shape by exact year of education is similar to that for smoking (Figure 1). Obesity declines particularly rapidly for people with more than 12 years of education.

Heavy drinking is similarly harmful to health. We focus on the probability that the person is a heavy drinker – defined as having an average of 5 or more drinks when a person drinks. Eight percent of people are heavy drinkers. Each additional year of education lowers this by 1.8 percent. Interestingly the better educated are more likely to drink but less likely to drink heavily.

Self-reported use of illegal drugs is relatively low; only 2 to 8 percent of people report using such drugs in the past year. Recent use of illegal drugs is generally unrelated to education (at least for marijuana and cocaine). But better educated people report they are more likely to have ever tried these drugs. Better educated people seem better at quitting bad habits, or at controlling their consumption. This shows up in cigarette smoking as well, where the gradient in current smoking is somewhat greater than the gradient in ever smoking.

Automobile safety is positively related to education; better educated people wear seat belts much more regularly than less educated people. The mean rate of always wearing a seat belt is 69 percent; each year of education adds 3.3 percent to the rate. The analysis of seat belt use is particularly interesting. Putting on a seat belt is as close to costless as a health behavior comes. Further, knowledge of the harms of non-seat belt use is also very high. But the gradient in health behaviors is still extremely large.

Household safety is similarly related to education. Better educated people keep dangerous objects such as handguns safe and know what to do when something does happen (for example, they know the poison control phone number).

Better educated people engage in more preventive and risk control behavior. Better educated women get mammograms and pap smears more regularly, better educated men and women get colorectal screening and other tests, and better educated people are more likely to get flu shots. Among those with hypertension, the better educated are more likely to have their blood pressure under control. Services involving medical care are the least clear of our education gradients to examine, since access to health care matters for receipt of these services. We thus focus more on the other behaviors. But, these data are worth remarking on because it does not appear that access to medical care is the big driver. Controlling for receipt of health insurance does not diminish these gradients to any large extent (the education coefficient on receipt of a mammogram is reduced by only 18 percent, for example, if we control for insurance in addition to age and ethnicity alone). This is consistent with the Rand Health Insurance Experiment (Newhouse et al., 1993); making medical care free increases use, but even when care is free, there is still significant underuse. Seeing a doctor may be like wearing a seat belt; it is something that better educated people do more regularly.

Table 1 makes clear that education is associated with an enormous range of positive health behaviors, the majority of health behaviors that we explore. The average predicted 10 year mortality rate is 11 percent, shown in the last row of the Table. Relative to this average, our results suggest that every year of education lowers the mortality risk by 0.3 percentage points, or 24 percent, through reduction in risky behaviors (drinking, smoking, and weight).

We have examined the education gradient in health behaviors using other data sets as well. Some of these results are presented later in the paper. In each case, there are large education differences across a variety of health behaviors and for somewhat different samples. Education differences in health behaviors are not specific to the United States. They are apparent in the U.K. as well. As documented later in the paper (Appendix Table 3), we analyze a sample of British men and women at ages 41–42. People who passed the A levels are 15 percent less likely to smoke than those who did not pass. Additionally those that passed A levels are 6 percent less likely to be obese, and are 3 percent less likely to be heavy drinkers.

Appendix Table 3.

Explanations for Health Differences in the NCDS. Summary statistics by Education Level

Variable	Did not pass A levels		Passed A levels
	N	Mean	N	Mean	Min	Max
Cognitive Measures
Age 7
Math (arithmetic)	7,128	4.78	2,973	6.39	0	10
Drawing (Draw-a-man test)	7,017	23.14	2,913	26.29	0	53
Age 11
Reading Comprehension	6,892	14.59	2,909	20.90	0	35
Math	6,892	14.28	2,907	25.21	0	40
Verbal	6,893	20.46	2,908	28.91	0	40
Non-verbal	6,893	19.67	2,908	26.29	0	40
Drawing (Copying designs)	6,881	8.23	2,901	8.83	0	12
Age 16
Reading Comprehension	5,963	23.86	2,639	30.54	0	35
Math	5,930	10.72	2,636	19.01	0	31
Life Satisfaction
Current (0=min; 10=max)	7,927	7.23	3,337	7.43	0	10
In ten years (0=min; 10=max)	7,906	8.03	3,332	8.11	0	10
Personality scales
efficacy 1 (never get what I want out of life=1)	7,904	0.26	3,328	0.15	0	1
efficacy 2 (usually have control over my life=1)	7,916	0.87	3,334	0.94	0	1
efficacy 3 (can run my life how I want=1)	7,916	0.94	3,331	0.96	0	1
Malaise Index (1=healthy; 24=unhealthy)	7,920	3.86	3,336	2.96	0	24
GHQ12 (1=low stress; 12=high stress)	7,927	1.83	3,338	1.88	0	12
Socialization
Mother is alive (percent)	7,692	0.76	3,280	0.82	0	1
Frequency sees mother (0=every day, 4=never)	6,169	1.67	2,756	2.08	0	4
Father is alive (percent)	7,756	0.57	3,305	0.64	0	1
Frequency sees father (0=every day, 4=never)	4,580	1.85	2,141	2.23	0	4
Frequency eat together as a family (1=daily, 5=never)	5,090	2.18	2,197	2.12	1	5
Frequency go out together as a family (1=daily, 5=never)	5,126	2.65	2,254	2.17	1	5
Frequency visit relatives as a family (1=daily, 5=never)	5,177	2.11	2,274	2.14	1	5
Frequency go on holiday as a family (1=weekly, 5=never)	5,106	3.83	2,260	3.50	1	5
Frequency go out alone or with friends (1=weekly, 4=never)	6,328	2.24	2,719	2.16	1	4
Frequency attends religious services (1=weekly, 4=never)	6,900	3.54	2,580	3.04	1	4

III. 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 income as a whole, and consider specifically the value of the future in a later section.

A number of studies suggest that both education and income are each associated with better health. Thus, it is clear that income does not account for all of the education relationship. But for our purposes, the magnitude of the covariance is important. We examine this by adding income to our basic regressions in Table 1. The NHIS asks about income in 9 categories (13 in 2000). We include dummy variables for each income bracket. There are endogeneity issues with income. Current income might be low because a person is sick, rather than the reverse—although the endogeneity problem is less clear for behaviors than for health. Nevertheless, we can interpret these variables as a sensitivity test for the potential role of income as a mediating factor.

The second columns in Table 1 report regressions including family income. Adding income accounts for some of the education effect. For example, the coefficient on years of education in the current smoking equation falls by 26 percent. The coefficient on body mass index falls by 16 percent (roughly the same as the fall in the coefficients on overweight and obese), and the coefficient on heavy drinking falls by 12 percent. The average decline (for outcomes with a significant gradient at baseline) is 12 percent. The mortality-weighted average is a decline of 24 percent. It is worth noting that our income measure includes both permanent and transitory income and further is measured with error. Thus, the reduction in education coefficients we observe might be too small.

The NHIS contains a number of other measures of economic status beyond current income, including major activity (whether individual is working, at home, in school, etc), whether the person is covered by health insurance,¹¹ geographic measures (region and urban location), family size, and marital status. These variables are likely to determine permanent income and in principle can be affected by educational attainment.

As with income, each of these variables may be endogenous. Sicker people (or those with poor risky behaviors) may be more or less likely to get insurance, depending on the operation of public and private insurance markets. In each case, the coefficients on those variables may not be the ‘true effect’, and furthermore, including these variables may bias the coefficient of education. Still, the results are an important sensitivity test: the results are suggestive about what the largest effect of “resources” broadly construed may be.

The last column in Table 1 adds these additional economic controls to the regressions (in addition to income). As a group, these variables do not add much beyond income. The additional reduction in the education coefficient is 7 percent in the smoking regression, 11 percent for obesity, and 1 percent for heavy drinking. All told, the effect of material resources in the NHIS accounts for 20 to 30 percent of the education effect. ¹² The reduction of 20–30 percent may be an underestimate of the true effect, because characteristics like permanent income are measured with error, or an overstatement, because we control for variables that are themselves influenced by education.

The NHIS does not have measures of wealth or family background. Further, measures of income in the NHIS are underreported, as in many surveys. To obtain better estimates of the possible effect of resources on the education gradient (beyond background), we repeated our analysis using the Health and Retirement Study, a sample of older adults. The economic data in the HRS are generally believed to be extremely accurate and HRS has family information as well, although only four health behaviors are asked about: smoking, diet/exercise, drinking, and preventive care.

Table 2 shows the HRS results. The first column shows results controlling for demographics and a large set of socioeconomic background measures: a dummy for father alive, father's age (current or at death), dummy for mother alive, mother's age (current or at death), father's education, mother's education, religion, self reported SES at age 16, self reported health at age 16, and dad's occupation at age 16. The HRS data show similar gradients to the NHIS data, though in some cases they are smaller. For example, smoking declines by 2 percentage points with each year of education, compared with 3 percentage points in the NHIS. In part, this reduction results from the fact we have added more extensive background controls as thus would be expected. If we used only the same basic demographics available in the NHIS, we would still find somewhat smaller gradients in the HRS (available upon request). Lower coefficients might also be due to selective mortality: lower educated individuals die younger and thus are less likely to be in the HRS. Although we do not know the reason, our finding that education gradients are smaller for older individuals has been noted elsewhere (see Cutler and Lleras-Muney 2008a for references).

Table 2.

Health Behaviors, Resources, and Risk Aversion Health and Retirement Study (wave 3), Whites

			Coefficient on Years of Education			Reduction in Education Coefficient
Dependent Variable	Mean	N	Demographic and Background Controls	Adding Economic Controls	Adding Risk Aversion (in addition to economic controls)	Economic Controls	Adding Risk Aversion and economic controls
Smoking
Current smoker	21%	5036	−0.020**	−0.018**	−0.018**	10%	0%
			(0.003)	(0.003)	(0.003)
Former smoker	41%	5036	0.000	−0.001	−0.001	N/A	N/A
			(0.003)	(0.003)	(0.003)
Ever smoked daily	63%	5217	−0.020**	−0.018**	−0.019**	10%	−5%
			(0.002)	(0.003)	(0.003)
Diet/Exercise
BMI	27.2	5144	−0.132**	−0.115**	−0.113**	13%	2%
			(0.031)	(0.031)	(0.031)
Underweight	2%	5144	0.001	0.001	0.001	0%	0%
			(0.001)	(0.001)	(0.001)
Overweight	65%	5144	−0.008**	−0.008**	−0.008**	0%	0%
			(0.003)	(0.003)	(0.003)
Obese	24%	5144	−0.009**	−0.007**	−0.007**	22%	0%
			(0.003)	(0.002)	(0.002)
Vigorous activity 3+ times/week	53%	5214	0.000	−0.004	−0.004	N/A	N/A
			(0.003)	(0.003)	(0.003)
Drinking
Current drinker	58%	5187	0.024**	0.018**	0.018**	25%	0%
			(0.003)	(0.003)	(0.003)
Heavy drinker (ever drinks>5 drinks--all persons)	2%	5187	−0.003**	−0.003**	−0.003**	0%	0%
			(0.001)	(0.001)	(0.001)
Preventive Care
Got flu shot	39%	5215	0.011**	0.011**	0.012**	0%	−9%
			(0.003)	(0.003)	(0.003)
Got mammogram (women)	73%	2864	0.025**	0.022**	0.022**	12%	0%
			(0.004)	(0.004)	(0.004)
Got pap smear (women)	68%	2858	0.020**	0.016**	0.016**	20%	0%
			(0.004)	(0.005)	(0.005)
Got prostate test (men)	67%	2348	0.027**	0.026**	0.026**	4%	0%
			(0.004)	(0.004)	(0.004)
Average Reduction in Education Coefficient
Unweighted standardized index, excluding preventive care		4936	0.012**	0.010**	0.011**	20%	−5%
			(0.002)	(0.002)	(0.002)
Unweighted percentages (outcomes w/significant gradients at baseline)						10%	−1%
Mortality weighted						17%	0%

Notes: Sample sizes are constant across columns. Data are from wave 3 of the HRS. Demographic controls include a full set of dummies for age, gender, and Hispanic origin. Socioeconomic background measures include dummy for father alive, father's age (current or at death), dummy for mother alive, mother's age (current or at death), father's education, mother's education, religion, self reported SES at age 16, self reported health at age 16, dad's occupation at age 16. Economic controls include total family income, total assets, number of individuals in the household, labor force status, region, MSA, marital status. Unweighted regression results use the methodology of Kling et al. (2007). Unweighted average reduction in education coefficient is calculated for all behaviors where the education effect without controls is statistically significant. HRS weights are used in all regressions and in calculating means. Standard errors are clustered at the person level.

^**(*) indicates statistically significant at the 5% (10%) level.

In the middle columns of the table, we include economic controls: labor force status, total family income, family size, assets, major activity, region, MSA, and marital status. The reduction in the education coefficient ranges from 0 percent for flu shots to 25 percent for current drinking. The average reduction in the education effect is 20 percent, and the mortality-weighted reduction is 17 percent.

In total, therefore, we estimate that material resources account for about 20 percent of the impact of higher education on health behaviors, assuming that all our measures can be thought of as material resources. This matches what we find in other data sets as well (see below). With the understanding that this estimate is likely too high (because of endogeneity), we conclude that there is a large share of the education effect still to be explained.

IV. 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 the rich – for example, their insurance might be less generous. Time prices to access care may be higher as well, if for example travel time is higher for the less educated.

A consideration of the behaviors in Table 1 suggests that price differences are unlikely to be the major explanation, however. While interacting with medical care or joining a gym costs money, other health-promoting behaviors save money: smoking, drinking, and overeating all cost more than their health-improving alternatives. It is possible that the better educated are more responsive to price than the less educated, explaining why they smoke less and are less obese. But that would not explain the findings for other behaviors which are costly but still show a favorable education gradient: having a radon detector or a smoke detector, for example. Still other behaviors have essentially no money or time cost, but still display very strong gradients: wearing a seat belt, for example.

More detailed analysis of the cigarette example shows that consideration of prices exacerbates the education differences. A number of studies show that less educated people have more elastic cigarette demand than do better educated people.¹⁴ Prices of cigarettes have increased substantially over time. Gruber (2001) shows that cigarette prices more than doubled in real terms between 1954 and 1999; counting the payments from tobacco companies to state governments enacted as part of the Master Settlement Agreement, real cigarette taxes are now at their highest level in the post-war era. Yet over the same time period, smoking rates among the better educated fell more than half, and smoking rates among the less educated declined by only one-third. For these reasons, we do not attribute any of the education gradient in health behaviors to prices.¹⁵

V. 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 possible importance of information is demonstrated by differences in how people learn about health news. Half of people with a high school degree or less get their information from a doctor, compared to one-third of those with at least some college.¹⁶ In contrast, 49 percent of people with some college report receiving their most useful health information from books, newspapers, or magazines, compared to 18 percent among the less educated.

A. Specific Health Knowledge

The 1990 NHIS asks people 12 questions about the health risks of smoking and 7 questions about drinking (see the Data Appendix). In the smoking section, respondents were asked whether smoking increased the chances of getting several diseases (emphysema, bladder cancer, cancer of the larynx or voice box, cancer of the esophagus, chronic bronchitis and lung cancer). For those under 45, the survey also asked respondents if smoking increased the chances of miscarriage, stillbirth, premature birth and low birth weight; and also whether they knew that smoking increases the risk of stroke for women using birth control. In the heart disease module individuals were asked if smoking increases chances of heart disease. Similarly, respondents were asked whether heavy drinking increased one’s chances of getting throat cancer, cirrhosis of the liver, and cancer of the mouth. For those under 45, the survey also asked respondents if heavy drinking increased the chances of miscarriage, mental retardation, low birth weight and birth defects.

These questions are important, though they do suffer a (typical) flaw – the answer in each case is yes. Still, not everyone knows this. Table 3 shows the share of questions that the average person answered correctly, separated by education group. About three-quarters of people do not answer all questions correctly (not reported in the table). This seems low, but the answers are much better on common conditions. For example, 96 percent of people believe that smoking is related to lung cancer, and 92 percent believe it is related to heart disease. On average, individuals get 81 percent of smoking questions correct and 67 percent of drinking questions correct. There are some differences in responses by education, but often these are not that large. For example, 91 percent of high school dropouts report that smoking causes lung cancer, compared to 97 percent of those with a college degree. For heart disease, there is a bigger difference: 84 percent of high school dropouts versus 96 percent of the college educated believe smoking is related to heart disease.

Table 3.

Explanations for Health Differences

			Mean by Education
Measure (Data set)	N	Mean (all)	<High School	High School	Some College	College +	Min	Max
Knowledge
Health Knowledge (NHIS)
Smoking questions (percent correct)	30,469	81%	74%	81%	83%	86%	0	1
Drinking questions (percent correct)	30,468	67%	62%	66%	69%	70%	0	1
AFQT (NLSY, 2002 weights)	4,709	52.7	17.8	41.4	58.4	72.8	1	99
Utility Function Parameters
Discounting (MIDUS)
Life satisfaction current (0=worst; 10=best)	2,561	7.7	7.6	7.8	7.4	7.8	0	10
Life satisfaction future (0=worst; 10=best)	2,561	8.3	7.8	8.4	8.2	8.5	0	10
Plan for the future (percent agree)	2,547	43%	32%	42%	41%	50%	0	1
Risk aversion (HRS) (1=least; 4=most)	5,217	3.3	3.3	3.4	3.3	3.2	1	4
Discounting (SOS)
Impulsivity Index (higher values correspond to more impulsive)	556	35.6	38.7	36.1	35.2	34.8	20	54
Financial tradeoff variables
Win $1k now vs. $1.5k in a year (percent prefer now)	561	62%	75%	71%	61%	53%	0	1
Win $20 now vs. $30 in a year (percent prefer now)	561	79%	92%	83%	78%	73%	0	1
Lose $1.5k in a year vs. $1k now (percent prefer in a year)	545	47%	53%	45%	51%	43%	0	1
Lose $30 in a year vs. $20 now (percent prefer in a year)	551	43%	53%	42%	42%	43%	0	1
Planning horizon for savings and spending (years)	564	6.93	5.47	5.29	6.57	8.62	0	20
Spent a great deal of time on financial planning (percent agree)	562	58%	45%	54%	55%	66%	0	1
Spent a great deal of time planning vacation (percent agree)	556	59%	52%	56%	60%	62%	0	1
Health discounting questions
Extra healthy days 1 year from now equal to 20 healthy days now	351	61.2	92.4	68.8	83.5	34.8	0	365
Extra healthy days 5 years from now equal to 20 healthy days now	344	79.7	101.6	77.7	103.3	58.1	0	365
Extra healthy days 10 years from now equal to 20 healthy days now	340	94.8	105.3	92.2	112.1	80.1	0	365
Extra healthy days 20 years from now equal to 20 healthy days now	330	105.5	92.3	101.5	128.7	90.7	0	365
Personality Scores
Self control, efficacy, depression (NLSY 2002 weights)
Rosenberg self-esteem score (1980) (0=min; 30=max)	4709	22.1	19.7	21.3	22.6	23.5	0	30
Rosenberg self-esteem score (1987) (0=min; 30=max)	4709	22.8	20.1	22.1	23.3	24.2	0	30
Pearlin score of self control (1992) (0=min; 28=max)	4709	21.8	19.9	21.5	22.1	22.4	0	28
Shy at age 6 (percent extremely or somewhat)	4709	57%	63%	61%	57%	52%	0	1
Shy as an adult (1985) (percent extremely or somewhat)	4709	26%	35%	26%	24%	23%	0	1
Rotter scale of control over life (1979) (1=internal; 16=external)	4709	8.7	9.3	9.0	8.6	8.2	1	16
Depression scale (1992) (0=minimum; 21=maximum)	4709	3.7	5.0	4.1	3.5	3.1	0	21
Depression scale (1994) (0=minimum; 21=maximum)	4709	3.4	4.6	3.8	3.4	2.5	0	21
Personality (MIDUS)
Depression scale (0=no; 7=maximum)	2,561	0.9	1.2	0.8	0.9	0.7	0	7
Generalized anxiety disorder (0=no; 10=maximum)	2,561	0.2	0.5	0.2	0.2	0.1	0	10
Positive affect (1=all of time; 5=none of time)	2,555	3.3	3.3	3.3	3.3	3.4	1	5
Negative affect (1=all of time; 5=none of time)	2,553	1.6	1.8	1.6	1.6	1.5	1	5
Control (1=lowest; 7=highest)	2,553	2.7	2.6	2.7	2.6	2.7	0	3
Depression scale (SOS, 0=no; 9=maximum)	632	2.2	3.4	2.4	2.3	1.6	0	9
Socialization (MIDUS)
Friends support (positive) scale (1=least; 4=most)	2,551	3.2	3.1	3.2	3.2	3.3	1	4
Friends strain (negative) scale (1=least; 4=most)	2,552	1.9	1.9	1.9	2.0	2.0	1	4
Family support (positive) scale (1=least; 4=most)	2,548	3.9	3.9	3.9	3.9	3.9	1	4
Family strain (negative) scale (1=least; 4=most)	2,545	2.1	2.1	2.1	2.2	2.1	1	4
Spouse/partner support (positive) scale (1=least; 4=most)	1,838	3.6	3.6	3.6	3.5	3.6	1	4
Spouse/partner strain (negative) scale (1=least; 4=most)	1,838	2.3	2.3	2.2	2.3	2.3	1	4
Social integration (3=min; 21=max)	2,550	13.8	12.9	13.7	13.6	14.5	3	21
Social contribution (3=min; 21=max)	2,550	15.2	13.1	14.4	15.4	17.2	3	21
Stress (MIDUS)
Worrying describes you (percent agree)	2,556	53%	59%	56%	51%	48%	0	1
All stress (answered yes to 3 stress questions)	1,816	7%	7%	6%	6%	8%	0	1
Any stress (answered yes to any stress question)	1,818	47%	36%	43%	51%	54%	0	1

Weights used in all means. The appendix has specific questions and coding information.

Table 4 examines how important knowledge differences are for smoking and drinking. The first columns in the table show the gradient in poor behaviors associated with education when controlling for socioeconomic factors and income but not knowledge. The coefficients are roughly similar to those reported in the last specification of Table 1, although from a decade earlier.

Table 4.

The Impact of Health Knowledge on Health Behaviors 1990 National Health Interview Survey, whites ages 25 and over

			Regression Coefficients Without Knowledge Questions	Regression Coefficients With Knowledge Questions
Dependent Variable	Mean	N	Years of Education	Years of Education	Percent Questions Correct	Reduction in Education Coefficient
Smoking
Current smoker	26%	29929	−0.021**	−0.018**	−0.318**	17%
			(0.001)	(0.001)	(0.012)
Former smoker	28%	29929	0.003**	0.001	0.156**	63%
			(0.001)	(0.001)	(0.013)
Made serious attempt to quit (smokers)	64%	7602	0.011**	0.008**	0.24**	28%
			(0.002)	(0.002)	(0.024)
Number cigs a day (smokers)	21.5	15388	−0.327**	−0.327**	0.056	0%
			(0.046)	(0.047)	(0.554)
Alcohol
Drink at least 12 drinks per year	73%	29869	0.010**	0.010**	−0.044**	−3%
			(0.001)	(0.001)	(0.009)
Heavy drinker (usually drinks>=5--all persons)	5%	30222	−0.005**	−0.005**	−0.011**	1%
			(0.0005)	(0.0005)	(0.005)
Number drinks when drinks (drank in last two weeks)	2.4	13845	−0.105**	−0.103**	−0.189**	1%
			(0.006)	(0.006)	(0.049)
Average Reduction in Education Coefficient
Unweighted standardized index		29836	0.022**	0.021**		5%
			(0.001)	(0.001)
Unweighted percentages (outcomes w/significant gradients at baseline)						18%
Mortality weighted						12%

Notes: The sample is aged 25 and older. Sample sizes are constant across columns. All regressions include a full set of age dummies, gender, Hispanic origin, family income, family size, major activity, region, MSA, and marital status. The smoking questions ask whether smoking increases a person's risk for 7 diseases, for 4 pregnancy complications, and for stroke incidence while on birth control. The drinking questions ask whether alcohol increases the risk for 3 diseases and 4 pregnancy complications. Unweighted regressions use the methodology of Kling et al. (2007).

^**(*) indicates statistical significance at the 5% (10%) level.

As the next columns show, people who answer more smoking questions correctly are less likely to smoke. Indeed, answering all questions correctly eliminates smoking. Similarly, people who answer drinking questions correctly are less likely to drink heavily. But knowledge has only a modest impact on the education gradient in smoking and little impact on the gradient in drinking. The coefficient on years of education in explaining current smoking declines by 17 percent with the knowledge questions included, while the coefficient for drinking is essentially unaffected. The average reduction is between 5 and 18 percent, depending on the metric. These results thus suggest that specific knowledge is a source, but not the major source, of differences in smoking and drinking. These results are in line with those found by Meara (2001) and interestingly with those reported by Kenkel (1991), who attempted to account for the possibility that health knowledge is endogenous.¹⁷

Cognitive dissonance suggests an important caveat to these findings: individuals may differ in the extent to which they report they know about what is harmful as a function of their habits (for example smokers might report they don’t know as much). In the case of smoking Viscusi (1992) suggests that both smokers and non-smokers vastly overestimate the risks of smoking (though other studies find different results, see Schoenbaum 1997 for example). Most importantly here, it is not known whether these biases differ by education.

One potential concern about the knowledge questions is that we do not know the extent to which the answers reflect the depth of individuals’ beliefs. People may know what the correct answer is without believing it that strongly. For decades, tobacco producers sought to portray the issue of smoking and cancer as an unresolved debate, rather than a scientific fact. This might have had a greater impact on the beliefs of the less educated, for whom the methods of science are less clear.¹⁸

We have only a single piece of evidence along these lines. We examined self-reported questions from the Motor Vehicle Occupant Safety Survey (MVOSS), which asks people about the value of wearing a seat belt (results available upon request).¹⁹ Respondents are asked to strongly agree, somewhat agree, somewhat disagree, or strongly disagree with two questions about seat belt use: “If I were in an accident, I would want to have my seat belt on,” and “Seat belts are just as likely to harm you as help you.” A claim that seat belts harm people in an accident is commonly expressed by those who oppose mandatory seat belt legislation, somewhat akin to the ‘debate’ about the harms of tobacco.

Answers to the question about wanting a seat belt in an accident are uniformly high; 89 to 97 percent of people strongly or somewhat agree that they would want a seat belt on if they were in an accident. But there is still residual doubt about the value of a seat belt that is much more common among the less educated. Fifty-five percent of people with less than a high school degree strongly or somewhat agree that seat belts are just as likely to harm as help them, compared to only 17 percent of those with a college degree.²⁰ These patterns suggest that superficially, individuals of all education levels have received the main public health message that one should wear a seat belt, and they report as much when asked. But uneducated individuals seem less certain of the validity of that information, and that becomes clear when the questions are asked slightly differently. Furthermore, we can “explain” a larger share of the effect of education on seat belt use when we include these alternative measures of “depth of knowledge”.

We cannot further examine this possibility here. We simply note that our results suggest that providing factual information alone may not be sufficient to make individuals change their behavior, and that differences in information alone are not sufficient to explain much of the education gradient in health behavior.

B. Conceptual Thinking

The tobacco and seat belt examples suggest that information processing, more than (or in addition to) exposure to knowledge, may be the key to explaining education gradients in behaviors. Similar arguments have been made to explain why education raises earnings in the labor market. Nelson and Phelps (1966) first hypothesized that “education is especially important to those functions requiring adaption to change” and that “the rate of return to education is greater the more technologically progressive is the economy.” This was echoed by Schultz (1975), who proposed that education enhances individuals’ “ability to deal with disequilibria” and Rosenzweig (1995), who argued that education improves individuals’ ability to “decipher” that information. All of these ideas can easily be applied in the context of health behaviors.

The existing literature provides some suggestions that cognitive ability is related to education gradients. For example more educated people are better able to use complex technologies/treatments than less educated individuals. Goldman and Smith (2002) document that the more educated are more likely to comply with HIV and diabetes treatments, which are extremely demanding. Rosenzweig and Schultz (1989) similarly show that contraceptive success rates are identical for all women for “easy” contraception methods such as the pill, but the rhythm method is much more effective among educated women. The more educated appear to be better at learning. Lleras-Muney and Lichtenberg (2005) find that, controlling for insurance, the more educated are more likely to use drugs more recently approved by the FDA, but this is only true for individuals who repeatedly purchase drugs for a given condition, so for those who have an opportunity to learn. Similarly Lakdawalla and Goldman (2005) and Case, Fertig and Paxson (2005) find that the health gradient is larger for chronic diseases, where learning is possible, than for acute diseases.

To examine the possibility that cognitive ability lies behind the education gradient in behavior, we turn to measures of general cognition.²¹ The NLSY administered the Armed Services Vocational Aptitude Battery (ASVAB) to all participants in 1979. The ASVAB is the basis for the Armed Forces Qualification Test (AFQT) but it contains many more dimensions than are scored in the AFQT. We include the test results for all 10 subjects, namely science, arithmetic, mathematical reasoning, word knowledge, paragraph comprehension, coding speed, numeric operations speed, auto and shop information, mechanical competence, and electronic information.²² Table 3 shows that those with a college degree or more scored much higher in the AFQT (73^rd percentile on average) compared to high school dropouts (18^th percentile).

Table 5 shows the relation between education, ASVAB scores, and a variety of health behaviors (smoking, diet, exercise, alcohol consumption, illegal drug use and preventive care). We use behaviors from relatively recent survey years, 1998 or 2002. The respondents thus range in age from the mid-30s to the mid-40s. Mean rates of favorable and poor health behaviors are shown in the first column; these percentages are close to those for the NHIS, particularly when restricted to the same ages.

Table 5.

The Impact of Cognitive Ability and Personality on Education Gradients National Longitudinal Survey of Youth 1979, Whites

				Coefficient on Years of Education				Reduction in Education Coefficient
						Addition to Economic and Family Background controls			Addition to Income and Family Background
Measure	Mean	N	Year	Demographic and family background Controls	Economics controls	ASVAB Scores	Personality Scales	Economic controls	ASVAB Scores	Personality Scales
Smoking
Current Smoker	27%	5052	1998	−0.049**	−0.047**	−0.039**	−0.045**	5%	15%	4%
				(0.003)	(0.003)	(0.003)	(0.003)
Former Smoker	21%	5053	1998	0.0028	0.0027	0.0003	0.0014	3%	86%	49%
				(0.003)	(0.003)	(0.003)	(0.003)
Diet/Exercise
BMI	27.53	4548	2002	−0.197**	−0.169**	−0.126**	−0.156**	14%	22%	7%
				(0.039)	(0.040)	(0.046)	(0.040)
Underweight	1%	4548	2002	−0.00106	−0.00067	−0.00087	−0.00094	37%	−19%	−25%
				(0.0008)	(0.0008)	(0.0009)	(0.0008)
Overweight	64%	4548	2002	−0.014**	−0.013**	−0.006	−0.013**	4%	51%	1%
				(0.003)	(0.003)	(0.004)	(0.003)
Obese	27%	4548	2002	−0.016**	−0.014**	−0.012**	−0.013**	17%	9%	3%
				(0.003)	(0.003)	(0.004)	(0.003)
Vigorous exercise	42%	3730	1998	0.032**	0.030**	0.029**	0.024**	8%	1%	17%
				(0.004)	(0.004)	(0.005)	(0.004)
Light exercise	79%	3729	1998	0.019**	0.017**	0.010**	0.013**	8%	38%	21%
				(0.004)	(0.003)	(0.004)	(0.003)
Alcohol
Current drinker	60%	4704	2002	0.016**	0.010**	−0.001	0.006*	40%	64%	24%
				(0.003)	(0.003)	(0.004)	(0.003)
Heavy drinker (mean # of drinks>=5--all population)	8%	4704	2002	−0.011**	−0.009**	−0.008**	−0.009**	16%	10%	−2%
				(0.002)	(0.002)	(0.002)	(0.002)
Frequency of heavy drinking past month (drinkers only)	97%	2751	2002	−0.141**	−0.132**	−0.106**	−0.126**	7%	18%	4%
				(0.019)	(0.019)	(0.023)	(0.020)
Number of drinks (drinkers only)	264%	2746	2002	−0.154**	−0.134**	−0.087**	−0.125**	13%	30%	6%
				(0.016)	(0.016)	(0.019)	(0.017)
Illegal Drugs
Never tried pot	34%	5036	1998	0.002	0.002	0.008**	0.003	−3%	−339%	−68%
				(0.003)	(0.003)	(0.003)	(0.003)
# times smoked pot in life>50	26%	5036	1998	−0.014**	−0.014**	−0.017**	−0.014**	3%	−27%	−4%
				(0.003)	(0.003)	(0.003)	(0.003)
Never tried cocaine	73%	5048	1998	0.000	0.000	0.007**	0.000	123%	1906%	117%
				(0.003)	(0.003)	(0.003)	(0.003)
# times used cocaine in life>50	7%	5048	1998	−0.006**	−0.005**	−0.008**	−0.006**	13%	−67%	−17%
				(0.002)	(0.002)	(0.002)	(0.002)
Preventive Care Use
Regular doctor visit last year	57%	4709	2002	0.005**	0.003	0.007	0.002	36%	−57%	35%
				(0.003)	(0.003)	(0.003)	(0.003)
OBGYN visit last year	58%	2424	2002	0.027**	0.021**	0.023**	0.021**	22%	−9%	−1%
				(0.004)	(0.005)	(0.006)	(0.005)
Other
Read food labels	46%	4709	2002	0.035**	0.034**	0.020**	0.031**	1%	40%	10%
				(0.003)	(0.003)	(0.004)	(0.003)
Average Reduction in Education Coefficient
Unweighted standardized index, excluding OBGYN visits, 2002			2002	0.033**	0.028**	0.020**	0.026**	14%	27%	7%
				(0.003)	(0.003)	(0.004)	(0.003)
Unweighted standardized index, 1998			1998	0.021**	0.020**	0.018**	0.018**	4%	10%	13%
				(0.003)	(0.003)	(0.004)	(0.003)
Unweighted percentages (outcomes w/significant gradients at baseline)								14%	9%	7%
Unweighted percentages, excluding illegal drugs (outcomes w/significant gradients at baseline)								15%	18%	10%
Mortality weighted								12%	15%	4%

Reading food labels is an indicator for whether the person always or often reads nutritional labels when buying food for the first time. Frequency of heavy drinking reports the number of times in the last month that the respondent had 6 or more drinks in a single occasion. Demographic controls include a full set of dummies for age, and gender. Economic controls include family income, family size, regio MSA, marital status. Background controls include whether respondent is American, whether mom is America, whether dad is American, familiy income in 1979, mother's education, father's education, whether lived with dad in 1979, whether the person had tried marijuana by 1979, whether the person had damaged property by 1979, whether the person had fought in school by 1979, and whether the person had been charged with a crime by 1980 and height. Personality scores include the Rosen self esteem score in 1980 and 1987, the Pearlin score of self control in 1992, the Rotter scale of control over one's life in 1979, whether the person considered themselves shy at age 6 and as an adult (in 1985), and history of depression (the CESD, measured in 1992 and 1994). Sample contains individuals with no missing education or AFQT. Indicator variables for missing controls are included whenever any other control is missing.

Unweighted regressions use the methodology in Kling et al. (2007). NLSY weights are used in all regressions and in calculating means.

^**(*) indicates statistical significance at the 5% (10%) level.

We first document education gradients and the effects of economic resources in this sample. The first column shows the impact of education on behavior including only demographic and family background controls. The impact of education on behavior is large, often times larger than the NHIS. For example, each year of education is associated with a 4.9 percent lower probability of smoking and a 1.6 percent lower chance of being obese. The next column includes economic resources. There is generally a significant impact of these variables on the education gradient. Using the mortality weights noted above we estimate that 12 percent of the education gradient in mortality is explained with economic controls (alternative averages yield similar results).

The third column includes the individual ASVAB scores, in addition to the income and family background. The additional impact of these controls is substantial, though it varies by outcome. ASVAB scores account for an additional 15 percent of the education gradient in smoking, 9 percent of the gradient in obesity, and 10 percent of the gradient in heavy drinking. The overall average reduction varies depending on whether the illegal drug use variable is included or not. Including test scores exacerbates the education gradients in illegal drug use. It is not clear why this is the case, and is not true with the British data (discussed below).²³ We also find that adding cognition increases the education gradient in preventive care. The reduction is about 20 percent without those variables but near zero (or negative) with those variables. Using the mortality weights, ASVAB scores explain 15 percent of the education effect. A central concern about these results is causality: is cognitive ability affected by education, or does cognitive ability lead people to become more educated? We return to this in Section IX.

While the estimates differ across specifications, our overall summary is that together knowledge and cognition account for 5 to 30 percent of the education gradient in behaviors, although cognition measures tend to increase education gradients in illegal drug use and preventive care, a puzzle which we do not resolve here.

VI. 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 averse. Alternatively, education may itself be the product of differences in utility functions (Fuchs, 1982), which may be distributed randomly, may be inherited, or may be a product of the early childhood environment.

Some preliminary evidence suggests that differences in utility functions cannot be the primary explanation for differences in health behaviors. Were the difference in health behaviors driven by fixed aspects of individuals, we would expect that health behaviors would be highly correlated across individuals: people who care about their health would maximize longevity in all ways. However, while almost all health behaviors are related to education, these behaviors are not particularly highly correlated at the individual level. Cutler and Glaeser (2005) show that the correlation between different health behaviors is generally about 0.1. Still, we can investigate this hypothesis more directly.

We start first with the value of the future. Probably the best measures of discounting and of the value of the future come from the National Survey of Midlife Development in the United States, or MIDUS, a sample of people aged 25–74 in the mid-1990s.²⁴ MIDUS has several measures of the value of the future. In an overall summary question about future expectations, individuals are asked “Looking ahead ten years into the future, what do you expect your life overall will be like at that time?”²⁵ The same question is asked about current situation, which we include as well. There are some questions that can also be used as proxies for discount rates. Individuals were asked whether they agreed with the following statement: “I live one day at a time and don't really think about the future.” We code those who strongly disagree as being able to plan for the future. Theory suggests that that people with higher future utilities or who are able to plan will invest more in health, and possibly that there will be an interaction between the two (those who value the future and are good at planning will invest even more in health).

Table 3 shows summary measures of these variables by education. High school dropouts are indeed less future oriented than those with more than a college degree, but there appears to be no difference between high school graduates and those with some college only. The more educated are equally satisfied with their current life as the least educated, and those with some college report the lowest current satisfaction. The relationship between education and future satisfaction is also not linear, being the highest among the college educated, followed by high school graduates, those with some college and high school dropouts. Although these satisfaction measures are not very highly correlated with education, Figure 2 shows that the ratio of future to current satisfaction is monotonically increasing in education—the more educated value the future more relative to the present.²⁶

Figure 2. Ratio of future to current satisfaction, by education.

Note: Data are from the MIDUS survey.

MIDUS asks about some measures of health, though not as many as dedicated health surveys. It includes smoking and weight, though not alcohol consumption. Questions are also asked about general health behavior, illegal drug use, and receipt of preventive care.

Table 6 shows results from the MIDUS survey. The first columns report means of the independent variables. Where we can compare, the means are close to the NHIS. Using just demographic and family background measures as controls (the first column of regression coefficients) the education coefficients are also similar, if anything slightly larger. Each year of education reduces smoking by 3.5 percent and obesity by 1.6 percent.

Table 6.

Discounting and the Value of the Future National Survey of Midlife Development in the United States, Whites, 1995–1996

			Coefficient on Years of Education					Reduction in Education Coefficient
					Addition to Income and Family Background				Addition to Income and Family Background
Dependent Variable	Mean	N	Basic Demographics and family background	Economic controls	Current and Future Life Satisfaction and Future Planning	Personality	Social integration	Economic controls	Current and Future Life Satisfaction and Future Planning	Personality	Social integration
Smoking
Current smoker	25%	2545	−0.035**	−0.032**	−0.032**	−0.032**	−0.029**	9%	1%	−1%	9%
			(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
Former smoker	29%	2546	−0.009*	−0.008	−0.008	−0.006	−0.008	12%	−2%	18%	−2%
			(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
Average # of cigs per day	26.1	1372	−1.013**	−0.955**	−0.949**	−0.955**	−0.945**	6%	1%	0%	1%
			(0.240)	(0.245)	(0.244)	(0.254)	(0.267)
Ever tried to quit smoking (if smoker)	83%	585	−0.006	−0.004	−0.005	−0.006	−0.004	31%	−11%	−26%	3%
			(0.011)	(0.012)	(0.012)	(0.012)	(0.012)
Diet/Exercise
BMI	26.5	2440	−0.148**	−0.101*	−0.097	−0.100	−0.080	32%	3%	1%	14%
			(0.059)	(0.059)	(0.059)	(0.059)	(0.062)
Underweight	3%	2440	0.00022	0.0027*	0.0028*	0.003**	0.003	−13%	−4%	4%	0%
			(0.0015)	(0.0016)	(0.0016)	(0.0015)	(0.0017)
Overweight	56%	2440	−0.009	−0.004	−0.003	−0.004	−0.002	56%	5%	−6%	24%
			(0.006)	(0.006)	(0.006)	(0.006)	(0.006)
Obese	21%	2440	−0.016**	−0.013**	−0.012**	−0.013**	−0.012**	18%	3%	2%	3%
			(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
# of times per month engages in vigorous exercise	5.9	2546	0.164**	0.114**	0.103*	0.113**	0.072**	30%	7%	1%	26%
			(0.055)	(0.057)	(0.056)	(0.057)	(0.060)
Lose 10 lbs due to lifestyle	22%	2466	−0.012**	−0.011**	−0.012**	−0.012**	−0.012**	10%	−4%	−5%	−3%
			(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
Illegal Drugs
Used cocaine, past 12 months	1%	2538	−0.001	−0.002*	−0.002*	−0.003*	−0.002	−77%	−8%	−23%	0%
			(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Used marijuana, past 12 months	6%	2536	0.000	−0.002	−0.003	−0.003	−0.003	−2100%	200%	−500%	−300%
			(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
Other illegal drug used, past 12 months	10%	2524	−0.004	−0.003	−0.004	−0.001	−0.001	26%	8%	37%	47%
			(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
Preventive Care
Take vitamin at least few times per week	48%	2546	0.024**	0.022**	0.022**	0.022**	0.020**	7%	1%	−1%	10%
			(0.006)	(0.006)	(0.006)	(0.006)	(0.006)
Had blood pressure test, past 12 months	67%	2516	0.006	0.003	0.004	0.002	0.003	46%	−9%	14%	−9%
			(0.005)	(0.006)	(0.006)	(0.006)	(0.006)
Doctor visit, past 12 months	69%	2496	0.011**	0.009*	0.009*	0.009	0.010	15%	3%	−2%	−3%
			(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
General Behavior
Work hard to stay healthy (1–7 scale, 1 is better)	2.4	2546	0.014	0.011	0.015	0.009	0.032**	20%	−27%	16%	−149%
			(0.015)	(0.015)	(0.015)	(0.015)	(0.015)
Effort put on health (0–10 scale, 10 is better)	7.1	2546	−0.008	−0.007	−0.014	−0.003	−0.034	17%	−103%	41%	−355%
			(0.023)	(0.024)	(0.024)	(0.024)	(0.025)
Average
Unweighted standardized index		2279	0.018**	0.015**	0.014**	0.015**	0.012**	14%	8%	1%	22%
			(0.004)	(0.004)	(0.004)	(0.004)	(0.004)
Unweighted percentages (outcomes w/significant gradients at baseline)								18%	1%	1%	7%
Mortality weighted								11%	1%	1%	7%

Note: Basic regressions include controls for age and gender. Economic measures include family size, family income, family income missing, major activity, marital status, and region. Family background measures include self reported health status at age 16, whether born in the US, whether speak English at home, dad born in the US, dad's employment status at age 16, dad's education, dummy for dad alive at time of survey and dad's health status if alive, head of the household when was 16, mom's employment status at age 16, mom's education, mom alive at time of survey and mom health status if alive, whether family was on welfare while growing up, whether family was better off than other while growing up. Personality measures include a depression scale, a generalized anxiety scale, a scale on sense of control, a positive affect scale and a negative affect scale and dummy variables whenever each scale is missing. Social integration measures include a scale of social integration, the scale of social contribution, a scale on positive relations with spouse, a scale on negative relations with spouse, a scale of positive relations with friends, a scale on negative relations with friends, and dummy variables whenever each scale is missing. Effort put into health: individuals were asked to rate from 0 to 10 "How much thought and effort do you put into your health these days?", were 10 is the highest. Work hard to stay healthy: individuals were asked how strongly they agreed with the statement "I work hard at trying to stay healthy" & 1 is coded as strongly agree. MIDUS weights are used in all regressions and in calculating means. Unweighted regressions use the methodology of Kling et al. (2007). Mortality weights assume no difference in drinking.

^**(*) indicates statistical significance at the 5% (10%) level.

The next columns show the impact of including economic resources. The impact is somewhat lower than the NHIS and NLSY. On average, 11 percent of education differences in behavior are attributable to economic resources.

The next column includes measures of current and future life satisfaction, the ability to plan for the future, and the interaction of planning and future life satisfaction, in addition to economic resources.²⁷ There is no significant impact of these variables on education gradients. Indeed, in some cases the addition of these variables actually increases the effect of education. For the major outcomes we consider, smoking and obesity, the changes are 2 percent or less.

The measures of discount rates in the MIDUS are not ideal. Indeed, it is not entirely clear that there is a single measure of discounting that applies to all settings. To investigate whether there is variation in the appropriate measure, we use data from the Survey on Smoking (SOS), a sample of 663 individuals between 50 and 70 years of age.²⁸ The SOS asks a variety of discounting questions (discussed below). The drawback of the SOS is the sample size and lack of many health questions (in addition to the fact that the sample is not nationally representative). For these reasons, we can only relate education to two outcomes – current smoker and obesity.

Table 7 shows the basic gradients in smoking and obesity in this sample. Education significantly lowers the likelihood of smoking and of being obese. Controlling for income (a dummy is used for each income category) lowers the smoking gradient by 9 percent and the obesity gradient by 21 percent.

Table 7.

Effect of discounting and other measures Survey of Smoking, Whites

	Coefficient on Years of Education
			Adding alternative Measures of Discounting, in addition to income
Outcome	Demographics	Adding income	Winning and losing questions	Planning horizon	Time spent on financial planing	Time spent planning vacation	Impulsivity index	Health discounting
Current smoker (mean=.38)	−0.0309****	−0.0280****	−0.0298****	−0.0265****	−0.0280****	−0.0276****	−0.0270****	−0.0256****
	[0.0079]	[0.0086]	[0.0087]	[0.0087]	[0.0087]	[0.0087]	[0.0087]	[0.0088]
% of base explained		9%	−6%	5%	0%	1%	3%	8%
Obese (mean=.32)	−0.0248***	−0.0197**	−0.0197**	−0.0202**	−0.0182**	−0.0200**	−0.0183**	−0.0216**
	[0.0075]	[0.0082]	[0.0083]	[0.0083]	[0.0083]	[0.0082]	[0.0083]	[0.0084]
% of base explained		21%	0%	−2%	6%	−1%	6%	−8%

Note: The sample size is 558 in all regressions. Demographic controls include dummies for male, married, hispanic and age. Income is a series of dummy variables.

^*significant at 5%;

^**significant at 1%

We then look at the effect of adding various financial discounting measures. For our first measure of financial discounting, we use responses to 4 questions of the form “would you rather win (lose) $x now or $y a year from now?” The mean responses to these questions by education level are reported in Table 3. On average, individuals are very impatient (64% prefer $1000 now to $1500 in a year), and more so when the stakes are small (80% prefer $20 now to $30 in a year). When the questions refer to losing amounts, individuals are very impatient, but less than for gains. More importantly, for all the questions, more educated individuals are on average more patient (with the exception of the last question) as predicted by Fuchs. However, Table 7 shows that adding these discounting questions as regressors increases the magnitude of the coefficient on education for smoking and has no effect on obesity.

A second measure of discounting is the planning horizon that people use. Respondents were asked “in planning your savings and spending, which of the following time periods is most important to you and your family? (choices are “the next few months, the next year, the next few years, the next 5–10 years, longer than 10 years”). The answers were converted into numbers using the middle of the category. Table 3 shows that more educated individuals have longer planning horizons. Controlling for this measure lowers the coefficient on education in the smoking regression by 5 percent but increases the coefficient of education in the obesity regression.

The third set of measures of discounting are the answer to the questions “I spent a great deal of time on financial planning” and “I spent a great deal of time planning vacations”. More educated individuals are more likely to report that they agree than less educated individuals (Table 3) although the differences are small, especially for vacations. Adding the answers to these questions (a dummy for each possible answer: strongly agree, agree, agree somewhat, disagree somewhat, disagree, or disagree strongly or missing) has very little impact on our two measures of health.

Discounting may also take the form of impulsivity and lack of self-control, as suggested by Ross and Mirowsky (1999). More impulsive individuals may be less able to undertake actions with current costs but future gains, even if they know what is in their long-term interest. Individuals were asked a series of 14 questions, such as “I make hasty decisions”, “I do things on impulse that I later regret,” etc. Answers ranged from “disagree strongly” to “agree strongly”. We score the questions on a 1–5 scale and sum them, with an index that ranges from 14 (not impulsive) to 70 (greater impulsivity). High school dropouts are more impulsive than college graduates (Table 3). Adding the impulsivity index lowers the coefficient on education, but only by 3 percent for smoking and 6 percent for obesity.

It is possible that individuals discount health differently from money. A subset of the respondents was asked questions about time preferences for health: “20 extra days in perfect health this year would be just as good as ? extra days in perfect health X years from now”, where X was 1, 5, 10 and 20. As with financial discounting, the more educated are more patient, and the differences are greater for tradeoffs in the near future. Adding these questions to our regression lowers the coefficient on education by about 8 percent for smoking but increases the effect of education on obesity by 8 percent.

Even included together, the impact of these variables is not substantial. When all the discount measures are included, the coefficient on education falls by about 8 percent for smoking and 1 percent for obesity.

Neither MIDUS nor NHIS have measures of risk aversion. To investigate the role of risk aversion we use data from the Health and Retirement Survey (HRS). The HRS in 2002 asked hypothetical questions that allow for categorization of individuals into 4 risk aversion categories (Barsky et al., 1997). Respondents are first asked if they would risk taking a new job, given that family income is guaranteed now. The new job offers a chance to increase income but also carries the risk of loss of income. If the respondent says he/she would take the risk, the same scenario is presented, but with riskier odds. Risk aversion is scored on a 1 to 4 basis, from least to most risk averse (see the Appendix). Table 3 shows that education is not monotonically related to risk aversion; those with a high school degree are the most risk averse. This already suggests risk aversion is not a very promising factor in accounting for the education gradient.

More formal models are presented in Table 2. The addition of the risk aversion categories, shown in the last column of regressions, has virtually no impact on the education coefficient. The overall impact is within 1 percent. Indeed, the categories for risk aversion are not very consistently related to health behaviors. It may be that this measure of risk aversion is not ideal, but we do not have a way of testing this.²⁹

All told, we attribute very little of the education gradient in health behaviors to utility function characteristics.

VII. 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’s (1989) results on the success of contraceptive use. Many of these aspects of education were stressed by Grossman (1972); in his formulation, education allows inputs to be combined more productively.

One reason this might be the case is time constraints. The daily hassles of life (cooking, errands, children, etc.) may involve more intensive effort by the less educated, and hence leave them less time for health planning or the mental energy to devote to behavioral change. To test this theory, we looked at behaviors before and after retirement.³¹ If time constraints are a major issue, behavioral differences by education ought to decline after retirement, when leisure time increases. Results from the HRS (not shown) suggest this is not the case, however. The behavior of the more and the less educated does not change differentially after retirement, and in some cases the gradient increases.

Beyond time constraints, it may be that individuals differ in their psychological capacity to make behavioral changes. In many psychological theories, individuals need to be ‘ready’ to change, and feel able to do so. Depression or other psychological distress may hinder behavioral changes. Similarly, social integration and reinforcement may be helpful.

The NLSY asks a battery of questions about personality traits and sense of control. These include two self esteem scores (the Rosenberg self-esteem score, measured in 1980 and 1987), a score about one’s self-control (the Pearlin score, measured in 1992), a score about a sense of control over one’s life (the Rotter scale, measured in 1979), depression (the CES-D, administered in 1992 and 1994), and two indicators for whether the person is shy (one at age 6 and one in 1985). The Appendix discusses the questionnaires in more detail. Table 3 shows the mean of these variables by education. In general, there are differences in these measures across education groups, particularly in depression scales.

Table 5 shows the impact of adding the personality scales in the NLSY (in addition to economic resources). The impacts on exercise and regular doctor visits are among the largest effects (17 to 35 percent). But personality measures actually increase the gradient in illegal drug use measures and have minimal effects on smoking, drinking, and obesity. The average reduction in the education coefficient is 4 percent using the mortality weights (though a bit larger – as much as 13 percent – using other measures). This table suggests personality might matter for some outcomes. We explore this issue further with other data sets.

Some authors have posited that stress, depression, and anxiety are the mediating factor in behavioral changes (Salovey, Rothman, and Rodin, 1998). Individuals suffering from these conditions may not think their future will be very good or may not be able mentally to make behavioral changes. We have already included some of these measures in the previous NLSY analysis. But we have additional measures in other data sets. The MIDUS survey has several measures of whether individuals are under stress and whether they worry a lot. Table 3 shows that the less educated are under more stress than the better educated, but that extreme stress (answering yes to all three questions about stress) is relatively constant across education groups. This survey also contains a depression scale, an anxiety scale, a scale for sense of control, a scale for positive affect, and a scale for negative affect (the appendix shows how these are constructed). Table 6 shows that controlling for all of these measures (personality and stress) has no significant effect on the education gradients (again with a few exceptions); the overall change is essentially zero.³²

Beyond individual attributes, we consider measures of social integration. The MIDUS asks a variety of questions about social integration, including scales for social ties, social contributions, positive and negative relations with spouse, and positive and negative relations with friends (see the appendix). These social measures pick up a number of different traits. Some part reflects individual personality—some individuals are more social than others. These measures also represent resources. Family and friends can be sources of information or reinforcement about behaviors. They can provide help in times of need or alternatively be the source of one’s troubles. They might also pick up other aspects of the environment such as the ability to meet other people easily. The questions in the MIDUS survey attempt to capture the extent of an individual’s social connections and the quality of these connections, both of which might matter. Interestingly many of these variables do not show steep education gradients, except for the extent to which individuals feel they are socially integrated and that they contribute to society (Table 3).

The final column of table 6 shows the impact of social integration on education gradients in behaviors in the MIDUS. There is a modest impact of these social integration measures. The coefficient on current smoking falls by 9 percent when social integration measures are added, and the coefficient on obesity falls by 3 percent. The average effect, shown in the last rows of the table, is 7 to 22 percent.

Overall we find that the vast bulk of personality measures relating to sense of control, stress, and psychological impairment account for very little of the education gradient. On the other hand our measures of social integration do account for a part of the gradient, though it is not entirely clear why they matter.³³

VIII. 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 6^th interview wave, conducted in 1999–2000, when the participants were 41–42 years old. Nearly 6,500 people are surveyed. Years of schooling is a less meaningful measure in the U.K. than it is in the U.S. We form a dichotomous variable for whether the person passed the A levels, roughly equivalent to a college degree in the U.S.

The NCDS contains a number of health measures, detailed in the first column of Table 8. The four biggest risk factors are all asked about: smoking, drinking, diet/exercise, and illegal drug use. On many measures, people in the U.K. are comparable to the U.S. Smoking rates are similar, though a bit higher in the U.K., while obesity rates are somewhat lower. Because of its longitudinal nature, the NCDS has a large set of income and background controls. These include height at age 15, birth weight, SES of father at birth, age 7, 11, and 16, marital status of mother at birth, mother’s and father’s birthplace, own birthplace, and mother’s and father’s education. Because these were collected during earlier waves, they are less likely to be misreported than in surveys such as the HRS, which asks respondents about these measures retrospectively.

Table 8.

Effect of Test Scores on the Education Gradient in the UK National Child Development Study (Wave 6)

			Coefficient on Passing A Level							Percent of Education Coefficient Explained By
					Addition to Income and Background Controls						Addition to Income and Background Controls
Behavior	Mean	N	Demographics and background	Economic Controls	Cognitive Ability	Current and future satisfaction	Personality	Social Integration	All Factors	Economic Controls	Cognitive Ability	Current and future satisfaction	Personality	Social Integration	Adding All factors
Smoking
Current smoker	29%	6499	−0.119**	−0.094**	−0.040**	−0.092**	−0.091**	−0.077**	−0.033**	21%	45%	2%	3%	14%	51%
			(0.013)	(0.014)	(0.015)	(0.014)	(0.014)	(0.014)	(0.015)
Former smoker	25%	6493	−0.023*	−0.020	−0.013	−0.022	−0.019	−0.028*	−0.020	13%	30%	−9%	4%	−35%	0%
			(0.013)	(0.014)	(0.015)	(0.014)	(0.014)	(0.014)	(0.015)
Quit smoking (ever smoked only)	46%	3492	0.100**	0.084**	0.043*	0.080**	0.083**	0.062**	0.031	16%	41%	4%	1%	22%	53%
			(0.021)	(0.022)	(0.024)	(0.022)	(0.022)	(0.023)	(0.024)
Number of cigarettes smoked	17.0	1599	−1.556**	−1.400**	−1.391**	−1.562**	−1.417**	−1.106**	−1.118*	10%	1%	−10%	−1%	19%	14%
			(0.586)	(0.613)	(0.657)	(0.610)	(0.604)	(0.630)	(0.668)
Diet/Exercise
BMI	25.8	6303	−0.641**	−0.751**	−0.664**	−0.733**	−0.723**	−0.638**	−0.572**	−17%	14%	3%	4%	18%	28%
			(0.133)	(0.144)	(0.158)	(0.145)	(0.145)	(0.149)	(0.161)
Underweight	1%	6303	0.004	0.005	0.006*	0.005*	0.005	0.005	0.005	−25%	−25%	0%	0%	0%	0%
			(0.003)	(0.003)	(0.004)	(0.003)	(0.003)	(0.003)	(0.004)
Overweight	52%	6303	−0.073**	−0.079**	−0.081**	−0.079**	−0.075**	−0.068**	−0.068**	−10%	−1%	1%	7%	16%	16%
			(0.015)	(0.016)	(0.017)	(0.016)	(0.016)	(0.016)	(0.018)
Obese	15%	6303	−0.039**	−0.040**	−0.033**	−0.040**	−0.039**	−0.032**	−0.03**	−3%	18%	0%	3%	21%	26%
			(0.011)	(0.012)	(0.013)	(0.012)	(0.012)	(0.012)	(0.013)
Exercise regularly	75%	6498	0.091**	0.063**	0.046**	0.064**	0.062**	0.052**	0.044**	31%	19%	−1%	1%	12%	21%
			(0.013)	(0.014)	(0.015)	(0.014)	(0.014)	(0.014)	(0.015)
Eat fruit every day	53%	6505	0.107**	0.098**	0.086**	0.101**	0.096**	0.075**	0.076**	8%	11%	−3%	2%	21%	21%
			(0.014)	(0.016)	(0.017)	(0.016)	(0.016)	(0.016)	(0.017)
Eat vegetables every day	17%	6505	0.025**	0.010	0.030**	0.016	0.011	0.003	0.026**	60%	−80%	−24%	−4%	28%	−72%
			(0.011)	(0.012)	(0.013)	(0.012)	(0.012)	(0.012)	(0.013)
Drinking
Drinker	95%	6499	0.010*	0.005	−0.004	0.003	0.004	0.007	−0.001	50%	90%	20%	10%	−20%	60%
			(0.006)	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)
Heavy drinker	12%	6499	−0.027**	−0.016	−0.02*	−0.014	−0.015	−0.005	−0.009	41%	−15%	7%	4%	41%	26%
			(0.009)	(0.010)	(0.011)	(0.010)	[0.010]	(0.010)	(0.011)
Number of drinks in week	19.5	5008	−3.394**	−2.348**	−2.044**	−2.224**	−2.174**	−1.381*	−1.136	31%	9%	4%	5%	28%	36%
			(0.716)	(0.775)	(0.850)	(0.777)	(0.776)	(0.784)	(0.848)
Illegal Drugs
Illegal drugs in last 12 months	8%	6446	0.003	0.007	0.007	0.007	0.006	0.005	0.004	−133%	0%	0%	0%	67%	100%
			(0.008)	(0.008)	(0.009)	(0.008)	(0.008)	(0.008)	(0.009)
Ever tried illegal drugs	33%	6446	0.072**	0.066**	0.048**	0.062**	0.069**	0.052**	0.038**	8%	25%	6%	−4%	19%	39%
			(0.013)	(0.014)	(0.015)	(0.014)	(0.014)	(0.015)	(0.015)
Average
Unweighted standardized index		6505	0.070**	0.058**	0.046**	0.059**	0.055**	0.49**	0.044**	17%	17%	−2%	4%	12%	20%
			(0.009)	(0.009)	(0.010)	(0.009)	(0.010)	(0.009)	(0.010)
Unweighted percentages (outcomes w/significant gradients at baseline)										19%	15%	0%	2%	15%	23%
Mortality weighted										24%	44%	2%	2%	15%	48%

Note: The sample is people who took cognitive tests at all ages. Demographic controls include age, sex, race, and ethnic dummies. Parental and background measures include height at age 16, birth weight, SES of dad at birth age 7, age 11 and age 16, marital status of mom at birth, mother and father's age at birth, mother and father's birthplace, own birthplace, and mom and dad's education. Economic controls include family income, family size, region or residence, employment status, marital status and current SES. Three cognitive tests are included: at age 7 (math and drawing), age 11 (reading, math, verbal, non-verbal, and drawing), and age 16 (math and reading comprehension). Current life satisfaction is measured by a 10 point scale on a question of how good life has turn out so far. Future life satisfaction is a 10 scale measure on a question on where you expect to be in 10 years. Personality measures include 3 measures of efficacy based on answers to three questions (never get what I want out of life, usually have control over my life, can run my life how I want), the GHQ12 score (designed to measure short-term changes in mental health including depression, anxiety, social dysfunction and somatic symptoms), and the malaise score (psychiatric morbidity index ranging from 1–12).

Social integration measures include: parents alive, see parents, frequency eat together as with family, frequency visit relatives with family, frequency go out together as family, frequency spend holidays together as family, frequency go out alone or with friends, frequency attends religious service. Missing variables were included as zeros, with dummies identifying missing data. Health outcomes are measured at wave 6. Unweighted regressions use the methodology of Kling et al. (2007).

^**(*) indicates statistical significance at the 5% (10%) level.

The first set of regression results relates behaviors to demographic and background controls only. As in the US, more education is associated with better health behaviors in the U.K. (though our measures of education are not quite comparable). Passing the A levels is associated with a 12 percent lower probability of smoking and a 4 percent lower probability of being obese. As in the U.S. more educated individuals are more likely to drink (1 percent), but less likely to be heavy drinkers (3 percent). The next column shows the impact of adding economic controls. As in the U.S., these controls have a significant impact on the education gradient in behaviors. The impact of education on current smoking falls by 21 percent, but the impact of education on weight measures increases. The average reduction is between 17 and 24 percent, depending on the measure used. This degree of explanatory power is somewhat greater than in the U.S. but not much.

The NCDS has a number of tests of cognitive ability. Cognitive tests were administered at age 7 (math and drawing), age 11 (reading, math, verbal, non-verbal, and drawing), and age 16 (math and reading comprehension). The next column of the table includes the results of all these cognitive tests. As in the U.S., scores on cognitive tests predict a significant part of the education gradient. Controlling for cognitive ability reduces the impact of education on current smoking by 45 percent and the impact on obesity by 18 percent. The share of the education effect that is attributable to cognitive ability ranges between 15 and 44 percent.

The NCDS has measures of current and expected future life satisfaction (each is a scale from 1–10 where 10 is the highest; see the appendix), although there are no measures of discount rates. The next column shows that life satisfaction does not affect the education gradient. The average decline is 1 to 2 percent, roughly the same as in the U.S.

The NCDS also has several personality measures. There are three measures of self-efficacy: whether the respondent gets what they want out of life, how much control they have over life, and whether they can run their life how they want. These variables are most related to the self-esteem and self-control measures in the NLSY. The survey also contains two scales that measure mental health and stress: the Malaise index and the General Health Questionnaire (GHQ12). The impact of adding these variables is shown in the next column of the table. Relative to economic and background controls only, personality controls have a negligible impact on the education gradient in behaviors. The overall effect is about 2 percent change in any of the average measures.

Finally, the NCDS has a variety of measures of social integration: whether the respondent’s parents are alive, whether the respondent sees their parents, and whether they frequently eat together as a family, visit relatives, go out as a family, spend holidays as a family, go out alone or with friends, and attend religious services. These differ in nature from those in the MIDUS: they capture frequency of interactions, but not their quality. The next column of the table presents the results from adding these measures. Again we find that social measures have an impact on the education gradient in behaviors, reducing the coefficient by about 15 percent (in comparison to the 7 percent in the U.S.).

The final column of the table shows the combined impact of cognitive ability, future valuation, personality factors, and social integration on the education gradient in behavior. The cumulative impact is 48 percent using the weighted measure and less with the unweighted ones. Along with the 24 percent of the education gradient that is attributable to economic and background factors, we can account for up to 72 percent of the education gradient in health behaviors. Overall these results from the U.K. are remarkably similar to those from the U.S. data.

IX. 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 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 that it does not.

Causality is a central issue in this debate. It may be that education leads to greater intelligence (by this we mean better decision making abilities), and that intelligence matters for outcomes—we term this the learning channel. An equally plausible hypothesis is that people who are more intelligent go on to more education, and education matters for outcomes. Alternatively, there may be some third factor that influences both education and cognitive ability and also determines health behaviors. Of course these mechanisms are not mutually exclusive. To trace out these pathways one would need to estimate causal effects of education and cognition on health (or health behaviors), as well as causal effects of cognition on education and vice-versa. The studies we know of cannot establish all of these, nor can we.³⁴

In this section we focus instead on whether there is any evidence for the learning channel: the idea that education is causally related to health because of its impact on cognition. Some previous work supports this idea. For example, several studies point out that education seems to have a causal effect on health (as discussed in the introduction). In addition, other studies find causal evidence that schooling increases AFQT (or other measures of cognition). For example, Hansen, Heckman and Mullen (2004) find that that one year of schooling increases AFQT scores between 2 and 4 percentage points (see also Neal and Johnson, 1996, and Winship and Korenman, 1997).³⁵ Finally cognition is associated with better health and health behaviors (Gottfredson and Deary, 2004), although we know of no causal evidence. Together this evidence points to a causal effect of education on health and health behaviors through increases in cognition.

We can present some additional, albeit imperfect, evidence that is consistent with the learning channel using our data sets. In particular, both the NLSY and the NCDS have test scores taken at different ages. A finding that cognitive ability at later ages is more important in mediating the education effect would suggest that education influences later life cognitive ability, which in turn explains differences in health behavior. If cognitive behavior at younger ages were more important, in contrast, it would suggest that early cognitive ability influences education and health behaviors.

Table 9 presents the results using a small subsample of the NLSY for which early test score measures are available.³⁶ For most outcomes the effect of including late IQ is much larger than that of early IQ. Overall, late IQ (controlling for early IQ) reduces the effect of education by 8 to 32 percent, whereas controlling for early IQ alone has no effect on average.

Table 9.

The Effect of Test Scores on the Education Gradient National Longitudinal Survey of Youth 1979, Whites

		Coefficient on Years of Education				Reduction of education coefficient in addition to income and family background
				Addition of income and family background
Measure	N	Demographic family background Controls	Economic Controls	add early IQ	ASVAB Scores and early IQ	adding early IQ	adding early and late IQ
Smoking
Current Smoker	1007	−0.056**	−0.056**	−0.057**	−0.048**	−2%	14%
		[0.006]	[0.006]	[0.007]	[0.007]
Former smoker	1007	0.00981	0.011	0.009	0.009	28%	28%
		[0.006]	[0.006]	[0.007]	[0.008]
Diet/Exercise
BMI	924	−0.182**	−0.099	−0.120	−0.035	−12%	35%
		[0.090]	[0.094]	[0.101]	[0.113]
Underweight	924	−0.001	−0.000	0.001	0.002	109%	136%
		[0.002]	[0.002]	[0.002]	[0.002]
Overweight	924	−0.008**	−0.003	−0.005	−0.001	−24%	29%
		[0.007]	[0.008]	[0.008]	[0.009]
Obese	924	−0.015**	−0.008	−0.008	−0.002	3%	39%
		[0.007]	[0.007]	[0.008]	[0.009]
Vigorous exercise	707	0.020**	0.017	0.016	0.009	3%	40%
		[0.010]	[0.010]	[0.010]	[0.012]
Light exercise	707	0.008	0.007	0.009	0.008	−33%	−14%
		[0.008]	[0.008]	[0.009]	[0.009]
Alcohol
Current drinker	947	0.010	0.006	0.002	−0.004	36%	100%
		[0.007]	[0.008]	[0.008]	[0.009]
Heavy drinker (mean # of drinks>=5--all population)	947	−0.015**	−0.013**	−0.013**	−0.011**	−5%	10%
		[0.004]	[0.004]	[0.004]	[0.005]
Frequency of heavy drinking past month (drinkers only)	587	−0.20**	−0.187**	−0.153**	−0.134**	17%	27%
		[0.044]	[0.046]	[0.050]	[0.056]
Number of drinks (drinkers only)	583	−0.180**	−0.151**	−0.143**	−0.104**	5%	26%
		[0.035]	[0.036]	[0.038]	[0.044]
Preventive Care Use
Regular doctor visit last year	947	0.001	0.000	0.010	0.023	−774%	−1671%
		[0.008]	[0.008]	[0.009]	[0.010]
OBGYN visit last year	487	0.017	0.00637	0.009	0.007	−17%	−4%
		[0.011]	[0.012]	[0.012]	[0.014]
Other
Read food labels	947	0.031**	0.032**	0.025**	0.020**	25%	42%
		[0.008]	[0.008]	[0.009]	[0.010]
Average Reduction in Education Coefficient
Unweighted standardized index, excluding OBGYN visits, 2002		0.030**	0.021**	0.020**	0.015*	2%	21%
		[0.007]	[0.007]	[0.007]	[0.008]
Unweighted standardized index, 1998		0.029**	0.030**	0.034**	0.027**	−13%	8%
		[0.007]	[0.007]	[0.008]	[0.009]
Unweighted percentages (outcomes w/significant gradients at baseline)						1%	32%
Mortality weighted						3%	24%

Sample is identical to sample in Table 5 but is further restricted to those who have a early IQ test score. Reading food labels is an indicator for whether the person always or often reads nutritional labels when buying food for the first time. Frequency of heavy drinking reports the number of times in the last month that the respondent had 6 or more drinks in a single occasion. Demographic controls include a full set of dummies for age, and gender. Economic controls include family income, family size, region, MSA, marital status. Background controls include whether respondent is American, whether mom is American, whether dad is American, familiy income in 1979, mother's education, father's education, whether lived with dad in 1979, whether the person had tried marijuana by 1979, whether the person had damaged property by 1979, whether the person had fought in school by 1979, and whether the person had been charged with a crime by 1980 and height. When early IQ is controlled for, we also include dummies for the year in which the test was taken, the type of test it was and indicators for whether this information is missing. Sample contains individuals with no missing education or AFQT. Indicator variables for missing controls are included whenever any other control is missing.

We can repeat this exercise using the British data as well, which has test scores for all individuals in the sample at ages 7, 11 and 16. These data are better suited for this exercise because of the larger sample, the fact that all individuals were administered the same test and that the tests are available at 3 different ages rather than 2. Table 10 shows the results. The pattern of the cognitive test scores again suggests that education is causally related to behaviors, rather than the reverse. Adding cognitive test scores at age 7 often increases education gradients and on average has no effect. Conditional on test scores at age 7 and background measures, adding test scores at age 11 reduces the effect of schooling on average by 14–23 percent. But together test performance at age 11 and 16 reduce the coefficient on A levels by 22–45 percent relative to its size when income, background and test performance at age 7 are accounted for. To the extent that performance in these test reflects learning in school, the results suggests that what is learned from age 7 to 11, and then from age 11 to 16 accounts for a significant portion of the education gradient.

Table 10.

Effect of Test Scores on the Education Gradient in the UK National Child Development Study (Wave 6)

	Coefficient on Passing A Levels
		Addition to Income and Background Controls			Reduction in Coefficient on Passing A Levels
Behavior	Income and Background	test age 7	tests age 7 and 11	tests age 7, 11 and 16	age 7	age 11 (relative to age 7)	age 11 and age 16 (relative to age 7)
Smoking
Current smoker	−0.094***	−0.094***	−0.073***	−0.040***	0%	22%	57%
	[0.014]	[0.014]	[0.015]	[0.015]
Former smoker	−0.02	−0.027*	−0.026*	−0.013	−35%	4%	52%
	[0.014]	[0.014]	[0.015]	[0.015]
Quit smoking (ever smoked only)	0.084***	0.078***	0.060**	0.043*	7%	23%	45%
	[0.022]	[0.023]	[0.024]	[0.024]
Number of cigarettes smoked	−1.400**	−1.465**	−1.503**	−1.391**	−5%	−3%	5%
	[0.613]	[0.621]	[0.644]	[0.657]
Diet/Exercise
BMI	−0.751***	−0.690***	−0.614***	−0.664***	8%	11%	4%
	[0.144]	[0.147]	[0.154]	[0.158]
Underweight	0.005	0.006*	0.006	0.006*	−20%	0%	0%
	[0.003]	[0.003]	[0.003]	[0.004]
Overweight	−0.080***	−0.080***	−0.077***	−0.081***	0%	4%	−1%
	[0.016]	[0.016]	[0.017]	[0.017]
Obese	−0.040***	−0.034***	−0.029**	−0.033***	15%	15%	3%
	[0.012]	[0.012]	[0.012]	[0.013]
Exercise regularly	0.063***	0.061***	0.054***	0.046***	3%	11%	25%
	[0.014]	[0.014]	[0.015]	[0.015]
Eat fruit every day	0.098***	0.095***	0.096***	0.086***	3%	−1%	9%
	[0.016]	[0.016]	[0.017]	[0.017]
Eat vegetables every day	0.01	0.014	0.024*	0.030**	−40%	−71%	−114%
	[0.012]	[0.012]	[0.013]	[0.013]
Drinking
Drinker	0.005	0.003	−0.001	−0.004	40%	133%	233%
	[0.007]	[0.007]	[0.007]	[0.007]
Heavy drinker	−0.016	−0.024**	−0.026**	−0.020*	−50%	−8%	17%
	[0.010]	[0.010]	[0.011]	[0.011]
Number of drinks in week	−2.348***	−2.916***	−2.633***	−2.044**	−24%	10%	30%
	[0.775]	[0.787]	[0.829]	[0.850]
Illegal Drugs
Illegal drugs in last 12 months	0.007	0.002	−0.001	0.007	71%	150%	−250%
	[0.008]	[0.008]	[0.009]	[0.009]
Ever tried illegal drugs	0.066***	0.048***	0.021	0.048***	27%	56%	0%
	[0.014]	[0.014]	[0.015]	[0.015]
Average
Unweighted standardized index	0.058**	0.060**	0.059**	0.046**	−3%	0%	23%
	(0.009)	(0.009)	(0.010)	(0.010)
Unweighted percentages (outcomes w/significant gradients at baseline)					−3%	14%	22%
Mortality weighted					1%	23%	45%

Note: The sample is people who took cognitive tests at all ages. Demographic and income controls include age, sex, race, and ethnic dummies, family income, family size, region or residence, employment status, marital status and current SES. Parental and background measures include height at age 16, birth weight, SES of dad at birth age 7, age 11 and age16, marital status of mom at birth, mother and father's age at birth, mother and father's birthplace, own birthplace, and mom and dad's education. Three cognitive sets of tests are included: at age 7 (math and drawing), age 11 (reading, math, verbal, non-verbal, and drawing), and age 16 (math and reading comprehension).

Unweighted regressions use the methodology of Kling et al. (2007).

^**(*) indicates statistical significance at the 5% (10%) level.

Finally we examine the types of cognitive abilities that appear to “explain” the effect of education on behaviors. Using the 1992 HRS we investigated how different commonly used measures of cognition among adults and the elderly affect the education gradient in behaviors.³⁷ Table 11 shows the results. We find that indicators of higher level processing (such as scores on the WAIS test³⁸ or self reports of one’s ability to read a map, follow instructions or use computers) reduce the education gradient, whereas memory measures (the ability to recall a list of words for example) do not appear to account for any of the education gradient.³⁹

Table 11.

Health Behaviors, education and cognition HRS Wave 1 (1992), Whites

		Coefficient on Years of Education					Reduction in Education Coefficient
Behavior	Mean	Basic demographic and background controls	Include objective cognitive ability measures	Include subjective cognitive ability measures	Include objective memory measures	Include all cognitive measures	Objective cognitive ability	Subjective cognitive ability	Memory measures	All cognitive measures
Smoking
Currently smokes	25%	−0.025***	−0.018***	−0.021***	−0.025***	−0.016***	28%	16%	0%	36%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Ever smoker	64%	−0.022***	−0.019***	−0.022***	−0.022***	−0.019***	14%	0%	0%	14%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Diet/Exercise
BMI	26.74	−0.158***	−0.128***	−0.149***	−0.151***	−0.120***	19%	6%	4%	24%
		[0.030]	[0.034]	[0.033]	[0.031]	[0.035]
Underweight	1%	0.000	0.000	0.000	0.000	0.001	N/A	N/A	N/A	N/A
		[0.001]	[0.001]	[0.001]	[0.001]	[0.001]
Overweight	60%	−0.012***	−0.009***	−0.014***	−0.011***	−0.010***	25%	−17%	8%	17%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Obese	21%	−0.009***	−0.008***	−0.008***	−0.009***	−0.006**	11%	11%	0%	33%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Vigorous exercise	26%	0.026***	0.023***	0.022***	0.025***	0.020***	12%	15%	4%	23%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Alcohol
Drinks	67%	0.028***	0.022***	0.021***	0.025***	0.017***	21%	25%	11%	39%
		[0.003]	[0.003]	[0.003]	[0.003]	[0.003]
Heavy drinker (+ than 3 drink a day)	5%	−0.005***	−0.003*	−0.002	−0.004**	−0.002	40%	60%	20%	60%
		[0.002]	[0.002]	[0.002]	[0.002]	[0.002]
Average Reduction in Education Coefficient
Unweighted percentages (outcomes w/significant gradients at baseline)							21%	15%	6%	31%
Mortality weighted							22%	20%	3%	39%

Data: Wave 1 HRS (1992). Objective cognitive ability measures include WAIS score and interviewer report of whether the respondent understood the survey questions. Subjective cognitive ability measures include wheterh the person has probelms using a computer, using a calculator, reading maps, or using a microwave after reading instructions. Memory measures include word recall immediate and after 10 minutes, and interviewer report of difficulty remembering questions. Demographic controls include gender, ethinicity dummies (6), birth year dummies, mother's education, father's education, marital status dummies, region of residence dummies and a dummy for whether the respondent was born in the US. Sample: dropped individuals with missing education, race, birth year, mother's education, father's education. Also dropped individuals with any cognitive measure missing. N=5,488. Survey weights used in calculating means and in regressions. Robust standard errors in brackets.

Similarly we also found that vocabulary and spelling test scores at age 16 in the British Cohort Survey (results available upon request) did not impact education gradients, while math scores did. In the NLSY, most components of the ASVAB test scores (math, science, verbal, speed, or vocational) account for about an equal reduction in the education gradient, but the effects are quite heterogenous depending on the outcome of interest (results available upon request). Overall it would appear that measures of abstract thinking, rather than memory-based or knowledge-based questions, are more important in explaining the education gradient.

X. 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 explanations for health behaviors, rather than explanations specific to any particular behavior.

With a number of different theories, we are able to account for a good share of the education gradient. Table 12 summarizes our quantitative results. Resources are an important first factor. Income, health insurance, and other economic indicators account for 11 to 32 percent of the education gradient in behavior; a consensus estimate is perhaps 20 percent.

Table 12.

Share of Education Gradient Explainable by Different Factors

	Explanatory Power
Factor	NHIS	HRS	NLSY	MIDUS	NCDS	Approximate Summary
Economic resources	32%	17%	12%	11%	24%	20%
Additional reduction when add:
Specific knowledge	12%	NA	NA	NA	NA	12%
Cognitive ability	NA	NA	15%	NA	44%	30%
Tastes	NA	0%	NA	1%	2%	1%
Personality	4%	NA	4%	1%	2%	3%
Social integration	NA	NA	NA	7%	15%	11%

Note: Based on the results in the previous tables. The table reports mortality weighted reductions (see text for explanation)

Our most surprising result is that education seems to influence cognitive ability, and cognitive ability in turn leads to healthier behaviors. As best we can tell, the impact of cognitive ability is not so much what one knows, but how one processes information. Everyone ‘knows’ that smoking is bad and seat belts are useful, but the better educated may understand it better. We estimate that cognitive ability is about as important as resources in accounting for health behaviors; a guess is about 30 percent. Specific knowledge by contrast accounts for about 12 percent of the gradient.

Many economic theories stress the role of tastes in accounting for behavioral differences: better educated people will have lower discount rates or risk aversion than the less educated. Our proxies for these taste parameters are possibly measured with error, though we attempted to obtain the best measures available. Nevertheless none of our proxies for discounting, risk aversion, or the value of future explain any of the education gradient in health behaviors.

The theory that is most difficult to test is the translation theory: more and less educated people each want to improve health behaviors, but carrying out these intentions is difficult. Our data do not support the hypothesis that self-esteem, sense of control, stress, depression, or anxiety are important mediating factors in the education gradient. But the social environment does appear to be somewhat healthier for the better educated. In both the U.S. and U.K., the degree of social integration accounts for about 11 percent of the education gradient in behavior.

All told, our different theories account for 60 to 80 percent of the education gradient. This is a very high share, given the magnitude of these effects and the persistent inability of previous research to make sense of these gradients. The explanation for the remaining one-quarter to one-third of the education gradient is a topic for future research. Our results suggest several possible candidates. First, measurement error in the various proxies we use may explain why in some data sets some mechanisms matter more than in others—in the data sets where income and background are better measured, they account for a larger share of the gradient, and the same is true for cognition. However, regardless of how many different proxies for personality or discounting we had, we did not find these mattered.

Another possibility is that there are important peer effects. The existence of peer effects cannot explain why educated groups adopt better behaviors than uneducated groups to begin with, but peer effects can magnify the effects of education. Finally we did not explore the possibility of interactions between our different mechanisms. It is possible that cognition matters only when individuals have knowledge, or that income matters less (or more) for those who are well-integrated in society.

Appendix Table 1.

Logistic Equation for 10 Year Mortality, NHANES I

Independent Variable	Coefficient	Std Error
Black	0.489	(0.124) **
Other race	−1.409	(0.901)
Married	−0.427	(0.115) **
Smoking
Current smoker	0.753	(0.114) **
Former smoker	0.209	(0.131)
Drinking
Heavy drinker	0.040	(0.161)
Light drinker	−0.299	(0.113) **
Weight
Underweight	0.864	(0.226) **
Overweight	−0.231	(0.113) **
Obese	0.624	(0.139) **
N	6,647

Note: The equation includes 10 year age-sex dummy variables, which are not reported.

Appendix Table 2.

The Impact of Education, Depression, and Anxiety on Health Behaviors National Health Interview Survey 2000, Whites

		Demographics and economic controls		Depression and anxiety scales
Dependent Variable	N	Years of Education (β)	std error	Years of Education (β)	std error	Percent Reduction
Smoking
Current Smoker	22204	−0.022	(0.001) **	−0.021	(0.001) **	4%
Former smoker	22204	0.001	(0.001) **	0.001	(0.001) **	10%
Ever Smoked	22219	−0.020	(0.001) **	−0.020	(0.001) **	4%
Number cigs a day (smokers)	4928	−0.455	(0.072) **	−0.437	(0.071) **	4%
Diet / Exercise
BMI	21463	−0.132	(0.015) **	−0.125	(0.015) **	6%
Underweight	21463	0.000	(0.000)	0.000	(0.000)	47%
Overweight	21463	−0.012	(0.001) **	−0.012	(0.001) **	3%
Obese	21463	−0.009	(0.001) **	−0.009	(0.001) **	7%
Ever do vigorous activity	22065	0.029	(0.001) **	0.029	(0.001) **	1%
Ever do moderate activity	21830	0.029	(0.001) **	0.029	(0.001) **	2%
How often eat fruits/vegetables in one day	22350	0.067	(0.004) **	0.064	(0.004) **	4%
Alcohol
Drink at least once per month	21864	0.019	(0.001) **	0.019	(0.001) **	2%
Abstains from drinking	22051	−0.014	(0.001) **	−0.014	(0.001) **	−1%
Ever had more than 12 drinks in one year	22109	0.016	(0.001) **	0.016	(0.001) **	1%
Had 12+ drinks in entire life	22116	0.014	(0.001) **	0.014	(0.001) **	−1%
Number drinks when drinks (drinkers)	13633	−0.149	(0.012) **	−0.143	(0.011) **	4%
Heavy drinker (average number of drinks>=5)	22350	−0.029	(0.001) **	−0.028	(0.001) **	2%
Number of days had 5+ drinks in past year (all)	21724	−0.826	(0.099) **	−0.788	(0.099) **	5%
Number of days had 5+ drinks in past year	13491	−1.871	(0.169) **	−1.797	(0.169) **	4%
Preventive care use
Ever had a mammogram	8191	0.011	(0.002) **	0.012	(0.002) **	−4%
Had mammogram past 2 years	8121	0.016	(0.002) **	0.015	(0.002) **	4%
Ever had a pap smear	11893	0.010	(0.001) **	0.010	(0.001) **	−2%
Had pap smear in last year	11772	0.017	(0.002) **	0.017	(0.002) **	1%
Ever had colorectal screening	14341	0.019	(0.002) **	0.020	(0.001) **	−5%
Had colorectal screening in last year	14297	0.006	(0.001) **	0.007	(0.001) **	−5%
Ever had HIV test	20908	0.011	(0.001) **	0.012	(0.001) **	−7%
Had flu shot in last year	22109	0.014	(0.001) **	0.014	(0.001) **	−2%
Ever had pneumonia vaccine	21764	0.006	(0.001) **	0.007	(0.001) **	−6%
Ever had Hepatitis B vaccine	21174	0.017	(0.001) **	0.017	(0.001) **	−1%
Had all 3 Hepatitis B vaccines	20903	0.014	(0.001) **	0.014	(0.001) **	0%
Average effect
Outcomes, not including gender-, smoker-, or
drinker-specific questions	18225	0.023	(0.001) **	0.022	(0.001) **	1%
Average % reduction (significant outcomes)						3%
Mortality weighted						4%

Notes: Sample sizes are constant across columns. All regressions include a full set of age dummies, gender, Hispanic status, marital status dummies, income, family size, labor force status, region dummies and urban status. Note: # of drinks when drinks in this table is on a different scale than in Table 3.

Appendix Table 4.

National Longitudinal Survey of Youth results without income (Compared to Table 5), Whites

	Coefficient on Years of Education				Reduction in Education Coefficient
		Addition to Family Background			Addition to Family Background and demographic controls
Measure	Family background and demographic controls	Current Income	ASVAB Scores	Personality Scales	Income	ASVAB Scores	Personality Scales
Smoking
Current Smoker	−0.049**	−0.047**	−0.041**	−0.046	5%	17%	6%
	(0.003)	(0.003)	(0.003)	(0.003)
Former smoker	0.0028	0.0027	0.00003	0.001	3%	99%	60%
	(0.003)	(0.003)	(0.003)	(0.003)
Diet/Exercise
BMI	−0.197**	−0.169**	−0.148**	−0.175**	14%	25%	11%
	(0.039)	(0.040)	(0.050)	(0.040)
Underweight	−0.00106	−0.00067	−0.001	−0.001	37%	−4%	−21%
	(0.0008)	(0.0008)	(0.0009)	(0.0008)
Overweight	−0.014**	−0.013**	−0.007*	−0.013**	4%	52%	2%
	(0.003)	(0.003)	(0.004)	(0.003)
Obese	−0.016**	−0.014**	−0.014**	−0.015**	17%	13%	7%
	(0.003)	(0.003)	(0.004)	(0.003)
Vigorous exercise	0.032**	0.030**	0.033**	0.026**	8%	−1%	19%
	(0.004)	(0.004)	(0.004)	(0.004)
Light exercise	0.019**	0.017**	0.012**	0.014**	8%	38%	25%
	(0.004)	(0.003)	(0.004)	(0.003)
Alcohol
Current drinker	0.016**	0.010**	0.004	0.011**	40%	75%	33%
	(0.003)	(0.003)	(0.004)	(0.003)
Heavy drinker (mean # of drinks>=5--all population)	−0.011**	−0.009**	−0.009**	−0.011**	16%	15%	1%
	(0.002)	(0.002)	(0.002)	(0.002)
Frequency of heavy drinking past month (drinkers only)	−0.141**	−0.132**	−0.113**	−0.132**	7%	20%	6%
	(0.019)	(0.019)	(0.022)	(0.019)
Number of drinks (drinkers only)	−0.154**	−0.134**	−0.103**	−0.139**	13%	33%	9%
	(0.016)	(0.016)	(0.019)	(0.016)
Illegal Drugs
Never tried pot	0.002	0.002	0.008**	0.003	−3%	−374%	−81%
	(0.003)	(0.003)	(0.003)	(0.003)
# times smoked pot in life>50	−0.014**	−0.014**	−0.017**	−0.014**	3%	−26%	−3%
	(0.003)	(0.003)	(0.003)	(0.003)
Never tried cocaine	0.000	0.000	0.006*	0.000	123%	1751%	97%
	(0.003)	(0.003)	(0.003)	(0.003)
# times used cocaine in life>50	−0.006**	−0.005**	−0.009**	−0.006**	13%	−61%	−14%
	(0.002)	(0.002)	(0.002)	(0.002)
Preventive Care Use
Regular doctor visit last year	0.005**	0.003	0.008*	0.003	36%	−45%	48%
	(0.003)	(0.003)	(0.004)	(0.003)
OBGYN visit last year	0.027**	0.021**	0.028**	0.026**	22%	−4%	1%
	(0.004)	(0.005)	(0.005)	(0.005)
Other
Read food labels	0.035**	0.034**	0.02041**	0.031**	1%	41%	11%
	(0.003)	(0.003)	(0.004)	(0.003)

Reading food labels is an indicator for whether the person always or often reads nutritional labels when buying food for the first time. Frequency of heavy drinking reports the number of times in the last month that the respondent had 6 or more drinks in a single occasion. Demographic controls include a full set of dummies for age and gender. Family background controls include family size, region, MSA, marital status, and socioeconomic background (whether respondent is American, whether mom is America, whether dad is American, familiy income in 1979, mother's education, father's education, whether lived with dad in 1979, whether the person had tried marijuana by 1979, whether the person had damaged property by 1979, whether the person had fought in school by 1979, and whether the person had been charged with a crime by 1980 and height). Personality scores include the Rosen self esteem score in 1980 and 1987, the Pearlin score of self control in 1992, the Rotter scale of control over one's life in 1979, whether the person considered themselves shy at age 6 and as an adult (in 1985), and history of depression (the CESD, measured in 1992 and 1994). Sample contains individuals with no missing education or AFQT. Indicator variables for missing controls are included whenever any other control is missing.

Appendix: Data details and variable construction

Data Sets used

Dataset	Analysis Years	Samples
National Health Interview Survey (NHIS)	1990, 1991, 1992, 1994, 2000	Whites aged 25 and older.
National Longitudinal Survey of Youth 1979 (NLSY79)	1998, 2002	All white respondents with non- missing dependent and independent variables.
National Survey of Midlife Development in the United States (MIDUS)	1995–1996	All white respondents with non- missing dependent and independent variables.
Health and Retirement Survey (HRS)—Rand version.	1996	All white respondents aged 55–64 in wave 3 who did not have a proxy interviews, and with no missing values for risk questions.
Survey on Smoking (SOS)	2005	Adults ages 50 to 70 who were current, former or never smokers
National Child Development Survey (NCDS)	Wave 6, year 2000	All respondents

Data Bibliography

Brim, Orville G., Paul B. Baltes, Larry L. Bumpass, Paul D. Cleary, David L. Featherman, William R. Hazzard, Ronald C. Kessler, Margie E. Lachman, Hazel Rose Markus, Michael G. Marmot, Alice S. Rossi, Carol D. Ryff, and Richard A. Shweder. NATIONAL SURVEY OF MIDLIFE DEVELOPMENT IN THE UNITED STATES (MIDUS), 1995–1996 [Computer file]. 2nd ICPSR version. Ann Arbor, MI: DataStat, Inc./Boston, MA: Harvard Medical School, Dept. of Health Care Policy [producers], 1996. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2003.

Economic and Social Data Service, “SN 5560 -National Child Development Study Response Dataset, 1958–2005”, http://www.esds.ac.uk/findingData/snDescription.asp?sn=5560, accessed August 20, 2007.

Leacock, Cindi, “Getting Started with the Health and Retirement Study”, Ann Arbor: University of Michigan, 1996.

U.S. Dept. of Health and Human Services, National Center for Health Statistics. NATIONAL HEALTH INTERVIEW SURVEY, 1987: CANCER CONTROL STUDY [Computer file]. Washington, DC: U.S. Dept. of Commerce, Bureau of the Census, and Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producers], 1989. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1990.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1990: HEALTH PROMOTION AND DISEASE PREVENTION (HPDP) SAMPLE PERSON SUPPLEMENT [Computer file]. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1990. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor], 1993.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1991: AIDS KNOWLEDGE AND ATTITUDES SUPPLEMENT [Computer file]. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1992. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor], 1993.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1991: HEALTH PROMOTION AND DISEASE PREVENTION SUPPLEMENT [Computer file]. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1992. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1993.

---------. NATIONALHEALTH INTERVIEW SURVEY, 1992: CANCER CONTROL SUPPLEMENT [Computer file]. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1993. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor], 1994.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1994 : YEAR 2000 OBJECTIVES SUPPLEMENT [Computer file]. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1996. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1997.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1998 [Computer file]. ICPSR version. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1999. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2001.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 2000 [Computer file].2nd ICPSR version. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 2000. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2002.

---------. NATIONAL HEALTH INTERVIEW SURVEY, 1991: DRUG AND ALCOHOL USE SUPPLEMENT [Computer file]. ICPSR version. Hyattsville, MD: U.S. Dept. of Health and Human Services, National Center for Health Statistics [producer], 1991. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2002.

U.S. Department of Labor, Bureau of Labor Statistics, “The NLSY79”, http://www.bls.gov/nls/handbook/2005/nlshc3.pdf, accessed August 20, 2007.

Specific Questions Used in the Analysis

National Health Interview Survey (NHIS)

Knowledge of the harms of smoking and drinking

The 1987 NHIS asks three types of questions about smoking knowledge.

1. Tell me if you think cigarette smoking definitely increases, probably increases, probably does not increase, or definitely does not increase a person’s chance of getting to following problems-

   1. Emphysema
   2. Bladder cancer
   3. Cancer of the larynx or voice box
   4. Cancer of the esophagus
   5. Chronic bronchitis
   6. Lung cancer
2. [asked if < 45 years old] Does cigarette smoking during pregnancy definitely increase, probably increase, probably not, or definitely not increase the chances of-

   1. Miscarriage
   2. Stillbirth
   3. Premature birth
   4. Low birth weight

3. [asked if < 45 years old] If a woman takes birth control pills, is she more likely to have stroke if she smokes than if she does not smoke?

The correct answer to each question is yes.

In addition we use the answer to the following question: “I am going to read a list of things which may or may not affect a person’s chances of getting heart disease. After each one, tell me if you think it definitively increases, probably increases, probably does not, or definitively does not increase a person’s chances of getting heart disease—First cigarette smoking?”

Individuals who answered definitively or probably were coded as answered correctly.

The percent of questions answered correctly is calculated using all 12 questions for people aged 44 and lower, and for the first 7 questions for people aged 45 and older.

Two questions are asked about the harms of drinking:

1. Tell me if you think heavy alcohol drinking definitely increases, probably increases, probably does not increase, or definitely does not increase a person’s chance of getting to following problems-

   1. throat cancer
   2. cirrhosis of the liver
   3. cancer of the mouth.
2. [if age < 45] Does heavy drinking during pregnancy definitely increase, probably increase, probably not, or definitely not increase the chance of-

   1. Miscarriage
   2. mental retardation of the newborn
   3. low birth weight of the newborn
   4. birth defects.

The correct answer to each question is yes. The percent of answers that are correct is calculated for all 7 drinking questions for those age 44 and younger, and for the first three questions for paged aged 45 and older.

Depression and anxiety scales

ANXIETY: Anxiety scale is based on the following questions:

• NERVOUS: During the PAST 30 DAYS, how often did you feel…. nervous?

• RESTLESS: During the PAST 30 DAYS, how often did you feel…. restless or fidgety?

Answers were coded as follows: ALL of the time=4, MOST of the time=3 SOME of the time=2, A LITTLE of the time=1, NONE of the time=0

Anxiety scale=Nervous+Restless

DEPRESSION: the depression scale is based on the following questions:

• SAD: During the PAST 30 DAYS, how often did you feel…. so sad that nothing could cheer you up?

• HOPELESS: During the PAST 30 DAYS, how often did you feel…. hopeless?

• EFFORT: During the PAST 30 DAYS, how often did you feel…. that everything was an effort?

• WORTHLESS: During the PAST 30 DAYS, how often did you feel…. worthless?

Answers were coded as follows: ALL of the time=4, MOST of the time=3 SOME of the time=2, A LITTLE of the time=1, NONE of the time=0

Depression scale=SAD+HOPELESS+EFFORT+WORTHLESS

National Longitudinal Survey of Youth (NLSY)

Rosenberg Self-Esteem Scale (measured in 1980 and 1987)

Now I’m going to read a list of opinions people have about themselves. After I read each one I want you to tell me how much you agree or disagree with these opinions.

• I feel that I'm a person of worth, at least on an equal basis with others

• I feel that I have a number of good qualities

• All in all, I am inclined to feel that I am a failure

• I am able to do things as well as most other people.

• I feel I do not have much to be proud of

• I take a positive attitude towards myself

• On the whole, I am satisfied with myself

• I wish I could have more respect for myself

• I certainly feel useless at times

• At times, I think I am no good at all

Pearlin Mastery Scale (1992)

“The next series of sentences describes the way some people feel about how much control they have over their lives. After each statement, please tell me whether you strongly disagree, disagree, agree or strongly agree….

• There is really no way I can solve some of the problems I have…

• Sometimes I feel that I'm being pushed around in life…

• I have little control over the things that happen to me…

• I can do just about anything I really set my mind to…

• I often feel helpless in dealing with problems of life

• What happens to me in the future mostly depends on me…

• There is little I can do to change many of the important things in my life…”

The scale is constructed by adding together the responses from each item; thus, a range of 7 to 28 is possible.

Rotter Scale (1979)

We would like to find out whether people’s outlook on life has any effect on the kind of jobs they have, the way they look for work, how much they work, and matters of that kind. On each of these cards is a pair of statements numbered 1 and 2. (hand respondent card booklet 2.) For each pair, please select one statement which is closer to your opinion. In addition, tell me whether the statement you select is much closer to your opinion or slightly closer.

• What happens to me is my own doing…sometimes i feel that i don't have enough control over the direction my life is taking…

• When I make plans, I am almost certain that I can make them work…or it is not always wise to plan too far ahead, because many things turn out to be a matter of good or bad fortune anyhow…

• In my case, getting what I want has little or nothing to do with luck…many times we might just as well decide what to do by flipping a coin…

• Many times I feel that I have little influence over the things that happen to me…or it is impossible for me to believe that chance or luck plays an important role in my life…

Respondents were asked to select one of each of the paired statements and decide if the selected statement was much closer or slightly closer to their opinion of themselves. The following shows how the scale is constructed:

Internal Control Item		External Control Item
Much closer	Slightly closer	Slightly closer	Much closer
1	2	3	4

Each of the four paired items is constructed in the same manner as the above example. The values for each item are then summed. The maximum possible score is 16, indicating high external control, while the minimum possible score is four, indicating high internal control.

Depression scale CESD

1992:

“Now I am going to read a list of the ways you might have felt or behaved recently.

After each statement, please tell me how often you felt this way during the past week…

1. I was bothered by things that usually don't bother me

2. I did not feel like eating; my appetite was poor

3. I felt that I couldn't shake off the blues even with help from my family and friends

4. I felt that I was just as good as other people

5. I had trouble keeping my mind on what I was doing

6. I felt depressed

7. I felt that everything I did was an effort

8. I felt hopeful about the future

9. I thought my life had been a failure

10. I felt fearful

11. My sleep was restless

12. I was happy

13. I talked less than usual

14. I felt lonely

15. People were unfriendly

16. I enjoyed life

17. I had crying spells

18. I felt sad

19. I felt that people dislike me

20. I could not get "going"…

1994: only asked questions 2, 5, 6, 7, 11, 18, 20.

We compute depression using the identical set of questions in both years.

The answers (after positives are reversed) are coded as

• 0Rarely or none of the time (Less than 1 day)
• 1Some of a Little of the Time (1–2 days)
• 2Occasionally or a Moderate Amount of the Time (3–4 days)
• 3Most or All of the Time (5–7 days)

The scale is constructed by summing over answers. For the full scale 60 is the maximum possible score.

Shy (1985)

Age 6: “Thinking about when you were 6 years old, would you describe yourself as: 1 extremely shy 2 somewhat shy 3 somewhat outgoing 4 extremely outgoing”

Adult: “Thinking about yourself as an adult, would you describe yourself as: 1 extremely shy 2 somewhat shy 3 somewhat outgoing 4 extremely outgoing”

National Survey of Midlife Development in the United States (MIDUS)

The MIDUS survey asks several questions about the value of the present and future and discount rates:

Value of the future: “Looking ahead ten years into the future, what do you expect your life overall will be like at that time?” Coded from 0 (the worst possible) to 10 (the best possible).

Value of the present: “How would you rate your life overall these days?” Coded from 0 (the worst possible) to 10 (the best possible).

Discount rates: “I live one day at a time and don't really think about the future.”

Scales

1. Depression scale: Depression [DEPCON]: (continuous variable based on [DEP] and [DEP2]) : ranging from 0 to 7 : = 0, if a respondent was diagnosed as negative for both depressed affect and anhedonia: i.e., DEPX=0 and DEPZ=0.

Depressed Affect

[DEP]: (continuous variable based on 7 items)

(During two weeks in past 12 months, when you felt sad, blue, or depressed, did you)

• A60. “lose interest in most things?”

• A61. “feel more tired out or low on energy than is usual?”

• A62. “lose your appetite?”

• A63. “have more trouble falling asleep than usual?”

• A64. “have a lot more trouble concentrating than usual?”

• A65. “feel down on yourself, no good, or worthless?”

• A66. “think a lot about death?”

Scaling: [DEP] was constructed by taking the number of “Yes” responses to the items.

[DEPX]: (dummy variable based on QA58, QA59, and [DEP]) = 1 if : The feeling of being sad, blue, or depressed lasted “All day long” or “Most of the day” (A58), AND You feel this way “Everyday” or “Almost every day” (A59), AND [DEP] is greater than or equal to “4.” = 0, otherwise.

Anhedonia

[DEP2]: (continuous variable based on 7 items)

(During two weeks in past 12 months, when you lost interest in most things, did you)

• A72. “feel more tired out or low on energy than is usual”

• A73. “lose your appetite”

• A74. “have more trouble falling asleep than usual”

• A75. “have a lot more trouble concentrating than usual”

• A76. “feel down on yourself, no good, or worthless”

• A77, “think a lot about death”.

Scaling: [DEP2] was constructed by taking the number of “Yes” responses to the items. [DEPZ]: (dummy variable based on QA70, QA71, and [DEP2]) = 1 if: The loss of interest in most things lasted “All day long” or “Most of the day” (A70), AND You feel this way “Everyday” or “Almost every day” (A71), AND [DEP2] is greater than or equal to “4.” = 0 otherwise.

2. Anxiety Disorder: [GADCON]: (continuous variable based on 10 items) How often - over the past 12 months-, you)

1. “were restless because of your worry”

2. “were keyed up, on edge, or had a lot of nervous energy”

3. “were irritable because of your worry”

4. “had trouble falling asleep”

5. “had trouble staying asleep because of your worry”

6. “had trouble keeping your mind on what you were doing”

7. “had trouble remembering things because of your worry”

8. “were low on energy”

9. “tired easily because of your worry”

10. “had sore or arching muscles because of tension”

- A respondent answered s/he worries “A lot more” than most people (A80a), AND worried “Every day, Just about every day, or Most days” (A81), AND worries about “More than one thing” (A82), OR has different worries “At the same time” (A82a) Coding: 1 most days; 2 about half the days; 3 less than half the days; 4 never Scaling: [GADCON] was constructed by taking the number of “Most days” responses to the items.

3. Positive Affect [GOODMOOD]:

(During the past 30 days, how much of the time did you feel…)

1. “cheerful?”

2. “in good spirits?”

3. “extremely happy?”

4. “calm and peaceful?”

5. “satisfied?”

6. “full of life?”

Coding: 1 All of the time; 2 Most of the time; 3 Some of the time; 4 A little of the time; 5 none of the time.

4. Negative Affect [BADMOOD]:

(During the past 30 days, how much of the time did you feel…)

1. “so sad nothing could cheer you up?”

2. “nervous?”

3. “restless or fidgety?”

4. “hopeless?”

5. “that everything was an effort?”

6. “worthless?”

Coding: 1 All of the time; 2 Most of the time; 3 Some of the time; 4 A little of the time; 5 none of the time.

5. Perceived Control (combined scale):

Items: 12-item scale combining the 4 “personal mastery” items and the 8 “perceived constraints” items.

Scaling: Scale score can be constructed by calculating the mean of the 12 items. Items from “personal mastery” need to be reverse-coded so that higher scores represent higher levels of the overall perceived control.

Personal Mastery [MASTERY]:

• u. “I can do just about anything I really set my mind to.”

• x. “When I really want to do something, I usually find a way to succeed at it.”

• z. “Whether or not I am able to get what I want is in my own hands.”

• dd. “What happens to me in the future mostly depends on me.”

Perceived Constraints [CONSTRNT]:

• s. “There is little I can do to change the important things in my life.”

• t. “I often feel helpless in dealing with the problems of life.”

• v. “Other people determine most of what I can and cannot do.”

• w. “What happens in my life is often beyond my control.”

• y. “There are many things that interfere with what I want to do.”

• aa. “I have little control over the things that happen to me.”

• bb. “There is really no way I can solve the problems I have.”

• cc. “I sometimes feel I am being pushed around in my life.”

Coding: 1 Strongly agree; 2 Somewhat agree; 3 A little agree; 4 Don’t know; 5 A little disagree; 6 Somewhat disagree; 7 Strongly disagree.

Scaling: [MASTERY] and [CONSTRNT] were constructed by calculating the mean across each set of items. Items were recoded so that high scores reflect higher standing in each dimension. The scales can be constructed by calculating the sum of the reverse-coded values of the items.

6. Social Integration [SWBSI]:

• b. “I don’t feel I belong to anything I’d call a community.”

• f. “I feel close to other people in my community.”

• k. “My community is a source of comfort.”

7. Social Contribution [SWBSC]:

• d. “I have something valuable to give to the world.”

• g. “My daily activities do not create anything worthwhile for my community.”

• o. “I have nothing important to contribute to society.”

8. Family Support [KINPOS]:

• M2. “Not including your spouse or partner, how much do members of your family really care about you?”

• M3. “How much do they understand the way you feel about things?”

• M4. “How much can you rely on them for help if you have a serious problem?”

• M5. “How much can you open up to them if you need to talk about your worries?”

Coding: 1 A lot; 2 Some; 3 A little; 4 Not at all.

Scaling: [KINPOS] was constructed by calculating the mean of four items. Items were recoded so that higher scores reflect higher support. The scale can be constructed by calculating the sum of reverse-coded values of the items.

9. Family Strain [KINNEG]:

• M6. “Not including your spouse or partner, how often do members of your family make too many demands on you?”

• M7. “How often do they criticize you?”

• M8. “How often do they let you down when you are counting on them?”

• M9. “How often do they get on your nerves?”

Coding: 1 Often; 2 Sometimes; 3 Rarely; 4 Never.

Scaling: [KINNEG] was constructed by calculating the mean of four items. Items were recoded so that higher scores reflect higher strain. The scale can be constructed by calculating the sum of reverse-coded values of the items.

10. Friend Support [FDSPOS]:

• M11. “How much do your friends really care about you?”

• M12. “How much do they understand the way you feel about things?”

• M13. “How much can you rely on them for help if you have a serious problem?”

• M14. “How much can you open up to them if you need to talk about your worries?”

Coding: 1 A lot; 2 Some; 3 A little; 4 Not at all.

Scaling: [FDSPOS] was constructed by calculating the mean of four items. Items were recoded so that higher scores reflect higher support. The scale can be constructed by calculating the sum of reverse-coded values of the items.

11. Friend Strain [FDSNEG]:

• M15. “How often do your friends make too many demands on you?”

• M16. “How often do they criticize you?”

• M17. “How often do they let you down when you are counting on them?”

• M18. “How often do they get on your nerves?”

Coding: 1 Often; 2 Sometimes; 3 Rarely; 4 Never.

Scaling: [KDSNEG] was constructed by calculating the mean of four items. Items were recoded so that higher scores reflect higher strain. The scale can be constructed by calculating the sum of reverse-coded values of the items.

12. Spouse/ Partner Support (Marital Empathy Scale) [SPEMP]:

• P11. “How much does your spouse or partner really care about you?”

• P12. “How much does he or she understand the way you feel about things?”

• P13. “How much does he or she appreciate you?”

• P14. “How much do you rely on him or her for help if you have a serious problem?”

• P15. “How much can you open up to him or her if you need to talk about your worries?”

• P16. “How much can you relax and be yourself around him or her?”

Coding: 1 A lot; 2 Some; 3 A little; 4; not at all.

Scaling: [SPEMP] was constructed by calculating the mean of six items. Items were recoded so that higher scores reflect higher support. The scale can be constructed by calculating the sum of reverse-coded values of the items.

13. Spouse/Partner Strain [SPCRIT]:

• P17. “How often does your spouse or partner make too many demands on you?”

• P18. “How often does he or she argue with you?”

• P19. “How often does he or she make you feel tense?”

• P20. “How often does he or she criticize you?”

• P21. “How often does he or she let you down when you are counting on him or her?”

• P22. “How often does he or she get on your nerves?”

Coding: 1 Often; 2 Sometimes; 3 Rarely; 4 Never.

Scaling: [SPCRIT] was constructed by calculating the mean of six items. Items were recoded so that higher scores reflect higher strain. The scale can be constructed by calculating the sum of reverse-coded values of the items.

Health and Retirement Study (HRS)

The HRS has a measure of risk aversion based on job choice. R is asked if he/she would risk taking a new job, given that family income is guaranteed now. The new job offers a chance to increase income but also carries the risk of loss of income. If R says he/she would take the risk, the same scenario but with riskier odds is presented. If R says he/she would not take the risk, the same scenario with less risky odds is asked.

The question wording is: Now I have another kind of question. Suppose that you are the only income earner in the family, and you have a good job guaranteed to give you your current (family) income every year for life. You are given the opportunity to take a new and equally good job, with a 50-50 chance it will double your (family) income and a 50-50 chance that it will cut your (family) income by a third. Would you take the new job?

If yes, then: Suppose the chances were 50-50 that it would double your (family) income, and 50-50 that it would cut it in half. Would you still take the new job?

If no, then: Suppose the chances were 50-50 that it would double your (family) income and 50-50 that it would cut it by 20 percent. Would you then take the new job?

This variable is set using the following:

1. R takes first risk and second (more risky) job=1, least risk-averse.

2. R takes the first risk but not the second=2, 2nd least risk averse.

3. R doesn’t take the first risk but does take the second (less risky) job=3, 2nd most risk averse.

4. R takes neither risk=4, most risk averse.

Survey on Smoking (SOS)

Impulsivity index

It is constructed using the answer to the following questions:

For each statement, please indicate whether you disagree strongly, disagree, neither agree nor disagree, agree, or agree strongly.

1. I rarely make hasty decisions

2. I never seem to be able to get organized

3. I fly off the handle

4. There are so many little jobs that need to be done that I sometimes just ignore them all

5. I control my temper

6. I do things on impulse that I later regret

7. I control my angry feelings

8. I often worry about things that might go wrong

National Child Development Study (NCDS)

GHQ12--General Health Questionnaire

The General Health Questionnaire (GHQ) is a self-administered screening test, designed to identify short-term changes in mental health (depression, anxiety, social dysfunction and somatic symptoms). It is a pure state measure, responding to how much a subject feels that their present state "over the past few weeks" is unlike their usual state. The 12 questions are

• GHQ1 (SC)…can concentrate on what you are doing?

• GHQ2 (SC)…lost much sleep over worry?

• GHQ3 (SC)…felt you were playing a useful part in things?

• GHQ4 (SC)…felt capable of making decisions?

• GHQ5 (SC)…felt constantly under strain?

• GHQ6 (SC)…felt could not overcome difficulties?

• GHQ7 (SC)…been able to enjoy normal activities?

• GHQ8 (SC)…been able to face up to your problems?

• GHQ9 (SC)…been feeling unhappy and depressed?

• GHQ10 (SC)…been losing confidence in yourself?

• GHQ11 (SC)…been thinking yourself as worthless?

• GHQ12 (SC)…been feeling reasonably happy?

The final score is equal to the sum of the answers for each question (thus the score goes from 0 to 12). Questions are coded as 1 if individuals answered that their present state is unlike their usual state.

Malaise Inventory

The Malaise Inventory provides a measure of for assessing psychiatric morbidity, developed by the Rutter and others at the Institute of Psychiatry from the Cornell Medical Index (Rutter M, Tizard J, and Whitemore K (1970) Education, Health and Behaviour. London). The 24 variables included on the dataset are identified below:

• MAL01 (SC) Do you often have backache?

• MAL02 (SC) Do you feel tired most of the time?

• MAL03 (SC) Do you often feel miserable or depressed?

• MAL04 (SC) Do you often have bad headaches?

• MAL05 (SC) Do you often get worried about things?

• MAL06 (SC) Usually have difficulty falling or staying asleep?

• MAL07 (SC) Usually wake unnecessarily early in morning?

• MAL08 (SC) Do you wear yourself out worrying about health?

• MAL09 (SC) Do you often get into a violent rage?

• MAL10 (SC) Do people often annoy and irritate you?

• MAL11 (SC) Have you had twitching of face/neck/shoulders?

• MAL12 (SC) Often suddenly become scared for no reason?

• MAL13 (SC) Often scared to be alone without friends near?

• MAL14 (SC) Are you easily upset or irritated?

• MAL15 (SC) Are you frightened of going out alone?

• MAL16 (SC) Are you constantly keyed up and jittery?

• MAL17 (SC) Do you suffer from indigestion?

• MAL18 (SC) Do you suffer from an upset stomach?

• MAL19 (SC) Is your appetite poor?

• MAL20 (SC) Does every little thing get on your nerves?

• MAL21 (SC) Does your heart often race like mad?

• MAL22 (SC) Often have bad pains in your eyes?

• MAL23 (SC) Troubled with rheumatism or fibrositis?

• MAL24 (SC) Have you ever had a nervous breakdown?

Malaise Index is calculated as the sum of the positive responses to the 24 items

The value of the present and future

Lifesat1: “Here is a scale from 0–10 where '0' means that you are completely dissatisfied and '10' means that you are completely satisfied. Please enter the number which corresponds with how satisfied or dissatisfied you are about the way you life has turned out so far.”

lifesat2: “Please use the scale once more to show how you expect to be in ten years time.”

Efficacy Measures

The last few questions are to do with how you feel about your life so far. Please enter the number next to the statement which is most true for you.

EFFICAC1

• (1) "I never really seem to get what I want out of life"

• (0) "I usually get what I want out of life"

EFFICAC2

• (1) "I usually have a free choice and control over my life"

• (0) "Whatever I do has no real effect on what happens to me"

EFFICAC3

• (1) "Usually I can run my life more or less as I want to"

• (0) "I usually find life’s problems just too much for me"