The social gradient in self-reported health: gender, age and
race/ethnic specific results
March 2003
Jacob Nielsen Arendt
M. Sc., Ph. d.
AKF, Institute of Local Government Studies, Nyropsgade 37, 1602 Copenhagen V, Denmark,
Phone: +45 33 14 59 49 + 75. Fax: +45 33 15 28 75. E-mail: [email protected]
Key words: Self-reported Health, Socio-economic status, Demographic heterogeneity
The social gradient in self-reported health: gender, age and
race/ethnic specific results
2
ABSTRACT
New evidence on social gradients in self-reported health is provided by exploration of the
relative importance of education and income for gender, age and race specific groups on US
data. We stress that the educational gradient exist simultaneously with an income gradient, but
dominate this in all age groups from 25-34 to 65+. In contrast to many earlier results, education
gradients are larger for women than for men, and income gradients the same. Education
gradients also exist in populations of non-whites only, but are different in shape and size from
gradients among whites. Differences in education and income between whites and non-whites
explain only 33-50% of their differences in self-reported health. Relative gradients are smaller
for the oldest and largest for the youngest age group.
3
INTRODUCTION
That socioeconomic status (henceforth SES) is related to mortality was noted as early as in the
mid-1800s in Britain (Macintyre 1997). Despite tremendous decreases in mortality over the 20th
century, differences in mortality related to SES, education in particular, seem to be persistently
large (Duuleep 1989; Kitagawa and Hauser 1968; Rogot et al. 1992) and some evidence
indicates that SES related differences have widened over the past decades (Feldman et al. 1989,
Pappas et al. 1993).
This paper has two main aims. The first aim is to explore the heterogeneity of the gradient within
subgroups of the US population. In spite of an increasing understanding of determinants of the
gradient, for instance through exploration of psychosocial pathways (e.g. Marmot et al. 1998),
there is still considerable uncertainty about the size and importance of the gradient for different
population groups. To be able to take action towards reducing the gradient, this is important
knowledge which complements research on deeper determinants of the gradient.
The second aim is motivated by the fact that evidence on SES related differences in health based
on other health measures than mortality risk, is dispersed. This does not make it less important,
as many differences in health are more related to quality rather than quantity aspects of life.
In this paper we shed light on these issues by providing new evidence on SES related health
gradients within different population groups using self-reported health status (henceforth SRH)
from the 1999 March Supplement to the Current Population Survey (CPS), provided by the U.S.
Census Bureau.
The population groups we focus on are gender, age and race/ethnicity. Attention is paid to which
of education or income is the most relevant marker of SES for these groups, exploring different
statistical specifications as well reviewing related findings. The paper is organized as follows. In
the next section we review main empirical findings. The third section contains a description of
4
our data followed by a section containing the empirical results. The final section concludes the
paper.
PREVIOUS FINDINGS
Numerous studies of SES related differences in health exist, see e.g. Macintyre (1997) or Preston
and Taubman (1994) for reviews. However, few studies examine results with simultaneous SES
indicators or interactions with demographics. When both income and education are included as
determinants of health, they have been found to have simultaneous large effects (studies using
SRH: Kawachi et al. (1999); Marmot et al. (1998); Soobader and LeClere (1999); Taubman and
Rosen (1982). Studies using mortality: e.g. Deaton and Paxson (1999); Elo and Preston (1996)).
Among the SRH studies only Taubman and Rosen (1982) and Marmot et al. (1998) provide
discussions of education effects, the former in a sample of people aged 58-63 only.
With respect to demographic differences in SES gradients, studies using mortality data have
reported a tendency for education and income gradients to be larger for men than for women
(Deaton and Paxson 1999; Elo and Preston 1996; Pappas et al. 1993; Rogot et al. 1992). In
addition, both education and income gradients are often found to diminish across age groups
(Christenson and Johnson (1995) find a continued decrease from age 25 in education gradients;
Deaton and Paxson (1999) find that income effects peak for age group 40-54. Elo and Preston
(1996), Feldman et al. (1989) and Rogot et al. (1992) all find that education gradients decrease
after the age of 65). Lauderdale (2002) on the other hand finds increasing educational gradients
with age within cohorts, but specualte that they are explained by period effects.
The evidence is more mixed on gradients in SRH, but a picture is emerging that resembles
findings on mortality. A majority of studies find that male health gradients are larger than female
health gradients (Arber 1997; Cavelaars et al. 1998; Marmot et al. 1998). Preston and Taubman
(1994) present evidence that the education gradient in SRH remains constant until age 65 in the
5
US, after which it decreases. Deaton and Paxson (1998) find that the correlation between
income and SRH peaks around age 60 in the US.
A good deal of attention has also been given to the role that race or ethnicity plays in the
relationship between SES and health, see for instance Deaton and Paxson (1998) or the reviews
and thorough discussions in House and Williams (2000) and Smith and Kington (1997). The
latter mention a number of studies, where income differences explain large parts, sometimes all,
of the differences in health among whites and blacks. Race has also been mentioned as an
important confounder in the literature studying possible effects of income inequality on health,
see e.g. Deaton and Lutbotsky (2002).
METHODS
We use the March 1999 Supplement to the CPS data published by the U.S. Census Bureau. This
is a survey data set, which covers information on 132,324 individual and 65,377 household
records, forming a representative sample of the civilian non-institutionalized population of U.S
aged above 151. We use both education and income as markers for SES, as they might convey
different relationships. In the CPS data, education is reported as highest grade completed, and
income as household income, which refers to the year prior to collection of other individual
characteristics. Because we use household income in individual analyses, we divide household
income by an adult equivalence scale, counting individuals below 18 years as half of those aged
18 or more. We compared this equivalence scale with others, allowing for economies of scales in
household size, and the results did not depend to a large degree on the scale used.
The health measure applied in our analyses is the answer to the question: would you say that
your health in general is excellent, very good, good, fair or poor? Observations for individuals
under 25 are deleted (23.831 men and 24.273 women) to focus on individuals who have finished
1
Documentation is found at: http://www.bls.census.gov/cps/ads/1999/smethdoc.htm (October 2002).
6
their education. In addition, 17 observations with no household weight are deleted. We end up
with 39.584 men and 44.615 women2. We will sometimes refer to SRH as health out of
linguistic convenience. It has been argued that the use of self-rated health, rather than mortality
or measures of morbidity, might pose problems. Particular attention has been paid to whether
people out of work underreport their health to justify that they do not have a job, but the
evidence is mixed (e.g. Butler et al. 1987, Dwyer and Mitchell 1999). A more profound problem
is perhaps what is measured by the variable. This is studied e.g. by Idler (1994). She and several
others have found that the incidence of poor or fair health is a good predictor of mortality and
functional disability and seems to be a good summary measure of overall health status.
Moreover, using SRH rather than, say, mortality, we might capture determinants of poor health
at an earlier life stage as well as identify important health-gradients, which are not mortal. Some
of the problems of using SRH is sought solved in Sadana et al. (2002), but their approach still
needs to stand further testing.
The statistical analyses are conducted by means of probit estimations controlling for age, race,
marital status, whether living in inner city areas and US state of living. The probit model is
estimated with maximum likelihood using STATA 6.0. For categorical variables, we present
differences in the predicted probabilities between given groups and reference groups, and
marginal effects on predicted probabilities for other variables. They are all evaluated at means of
explanatory variables, and if not marked with stars (*), their corresponding probit coefficients
are significant at a 1% level. The probit model, variable selection and specification of functional
form with respect to the relationship between education, age, income and health are described in
appendix B.
2
In previous analyses, mainly to limit reverse causality from income and prior health, we
focused on individuals in work, who have not left a job due to prior health problems. These
selections may however substantially distort both education and income gradients. The results
for this sub sample are available upon request.
7
RESULTS
In all analyses, the final CPS March supplemental weight is used as population weight. The
means and standard deviations of important variables in our data are reported in Table 1 for men
and women separately. The most frequent health outcome is the second best outcome, ‘very
good health’. 15% of all men and 16.8% of all women report having fair or poor health. The
columns labelled “Share PH”, show the share with poor or fair health, which we henceforth just
refer to as poor health. They illustrate that there are substantial differences in poor health across
education groups, which range from 37% for men and 42% for women with only elementary
schooling to 7% for those with a post college degree. The rows just below illustrate that the
share with poor health varies across income levels as well, the ratio of those with poor health
being six times higher for those below the 2nd income decile, when compared to those above the
8th decile. The other rows show the size of other individual groups, as well as their share with
poor health, which varies substantially across for instance racial/ethnic groups.
Overall gender specific gradients in SRH
The top of Table 2 shows the results for men. Results for all age and racial groups are shown in
the last column. The rows show that differences in poor health probabilities between those with
some high school or with a high school diploma, which is the reference category, and those with
other educational levels, are significant at a 1% level. The poor health difference between those
with some high school and those without is 6.1 percentage point and the advantage of a post
college degree is 5.2 point3. A test of equality between education levels is rejected, that is, a
graded relationship between education and health exist throughout the education distribution.
We found that the log of household income gave a good fit, see appendix B, which reveals that
the change in health with an associated change in income, phases out as the initial income level
3
Without any controls but age, these two effects are 10.2 and 8.8 point respectively.
8
increases (sometimes referred to as “the Preston Curve”, see Preston (1975)). Increasing mean
income by one standard deviation of log income, reduces the probability of poor health by 0.044
percentage point. The mean log income corresponds to 18,948$ and one standard deviation
above the mean to 44,696$. The estimated income effect corresponds to the effect of 8.8 years of
age (the income effect divided by the age effect). Comparatively, the effect of not having
completed high school and that of having completed more than a college degree corresponds to
11.1 and 9.7 years of age respectively, when compared to high school graduates. The included
demographic variables as well as US states are highly significant. For later reference we stress
that Asian and black men have approximately a 6 point higher probability of poor health than
white men.
Turning to the education and income gradients for women shown in the bottom of the table, we
first note that the education groups included are different than those included for men. This is
because a better fit is obtained when those with some college are included in the reference
group, and those with some high school are not. Educational differences in SRH are all
significant and compared to the results for men, the overall gradient (no high school versus post
college) is larger. This is due to an especially high probability of poor health for those who have
no high school, which is 11.9 point higher than the probability of poor health for the reference
group. The absolute advantage of those with a post college degree compared to the reference
group is comparable to that observed for men4. Demographic groups have a significant effect on
SRH, again stressing that Blacks and Indians have a much higher higher probability of poor
health than white women. The income effect is close to that for men. Calibrating results to age
effects, one standard deviation of log income corresponds to 9.1 years of age, whereas no high
4
With age as the only control, the education related differences in SHR are 16.7 and 7.9 point
for no high school and post college respectively.
9
school and a post college degree correspond to 24.6 and 9.7 years of age respectively, relative to
the reference group.
Age specific gradients in SRH
In this section we compare health gradients across age groups. The age groups used are 25-34,
35-44, 45-54, 55-64 and 65+, and results are presented in the first five columns of Table 2. The
large number of observations in the CPS data implies that we obtain reasonable sample sizes for
each group. Nevertheless, many of the, especially demographic, variables are insignificant. For
clarity these results are not discussed in detail, since we will focus on the relative effects of
education and income between the age groups.
To obtain an overview of the relative effects across age groups, we have gathered them in Table
3. The numbers presented are the absolute effects from Table 2 divided by the predicted
probability of poor health evaluated at mean covariates. The top of the table shows relative
education and income effects for men. The income effects are for one additional standard
deviation of log income and the education effects are compared to the reference categories used
in Table 2. As an example of how to read the numbers, the no high school-high school
differential for men of age 25-34 years is 1.031. This is obtained as the absolute differential in
the probability of poor health between these education groups, reported in Table 2 to be 0.033,
divided by the predicted probability in this age group, reported in Table 2 to be 0.032. The
effects vary a lot, many with no clear pattern, but the no high school and the income effects seem
to be decreasing across age groups (at least after age 35-44). When looking at overall
educational differences, calculated as the relative difference between those with no high school
diploma and those with a post college degree, these are also decreasing across age groups.
The lower part of the table shows the relative effects for women. For women, the table shows
that the relative effect of having a post college degree as well as the relative income effect, seem
10
to be decreasing across age groups, again at least after age 35-44 with respect to income. For
women, the overall educational gradient is not decreasing throughout all age groups, as was the
case for men, but it is highest for the youngest age group, those aged between 25 and 34, and
lowest for oldest, aged more than 64.
Race specific gradients in SRH
Our results in the previous section did indicate that there are substantial health differences
between racial or ethnic groups, even when education and income is accounted for. We exploit
that the CPS sample is among the few US data sets, which is large enough to allow for race
specific investigations of gradients in health. The variable “race” in the CPS sample identifies
whites, blacks, American Indians/Eskimo and Asians/Pacific islander. We explore education and
income gradients for the three non-white racial groups, estimating the same probit model as in
the previous section. Results are presented in Table 4. The table shows that non-white men with
no education have particularly poor health, and that the overall the relative (to race specific
probability of poor health) education and income gradients are of roughly similar magnitude for
non-white men as for white. However, absolute differences in SRH between educational levels
above high school are small, and only the high school-no high school differential is significant.
For women, there are significant gradients both with respect to education and income, and both
in absolute and relative terms, they are larger than the corresponding gradients for whites.
Calibrated to age effects, one standard deviation of log income corresponds to 8.5 and 9.0 years
of age for men and women respectively, whereas, relative to education reference groups, the no
high school health effect corresponds to 11.0 and 17.2 years of age and that of a post college
degree to 4.3 and 10.2 years of age, also for men and women respectively.
Because a large part of non-whites have less education and earn less than whites, it is interesting
to explore how large a part of racial differences in health is explained by income and education
differences. As in Deaton and Paxson (1998), we analyse this using the conditional distribution
11
of poor health for non-whites given their characteristics, and replace education and income
means for non-whites by that for whites. The predicted probability of poor health is 0.16
evaluated at means for non-white men, and 0.10 for white men. When replacing non-whites
education and income with that of whites, the probability of poor health for non-whites
decreases to 0.14. For women, the probability of poor health is 0.171 for non-whites and 0.123
for whites. Replacing non-whites education and income by that of whites yields a probability of
poor health for non-whites of 0.147. Therefore around a third of the difference in poor health
probabilities between whites and non-whites for men and half for women can be explained by
differences in education and income.
DISCUSSION
This study documents a number of important findings that add to the description of the well
established gradient in health. Indeed, we find that large education and income related
differences in health exist simultaneously and that both are graded. In particular, differences in
health between those with and those without a high school diploma are pervasive. This result
persists throughout the analyses.
The latter result is the main reason for two key findings. The first is that the overall education
gradients (no high school versus more than college) seem to dominate income gradients
(measured as the difference in SRH related to a one standard deviation difference in income) in
the total sample, as well as in any age group from age 25 to 65+. Furthermore, overall
educational differences in health are larger for women than for men, particularly because many
non-educated women have poor health. In addition, the income gradients are of similar size for
women and men. These findings are in contrast to common findings.
With respect to age specific gradients, income gradients seem to shrink after age 35. Overall
relative education gradients are also shrinking across age groups for men, and are smallest in the
12
oldest age group (65+) for women. The reasons for the decrease are however different for men
and women, namely mainly due to a decrease in the high school “effect” for men, and a decrease
in the post college “effect” for women. SES gradients may change over the life cycle either
because past experiences of disadvantages accumulate or because the impact of SES on health
changes. We must however have in mind that we cannot distinguish these lifecycle explanations
from cohort specific differences. In particular, recent findings suggest that SES gradients in
health have increased for new cohorts over the last decades (Feldman et al. 1989; Pappas et al.
1993), which would imply that gradients in a cross-section would decrease with age.
However, the fact that income gradients are “decreasing” with age for both men and women
makes it unlikely, but not impossible, that it reflects that gradients have been increasing over the
past decades, because this has mainly been observed for men (Feldman et al. (1989); Pappas et
al. (1993)). However, there is yet no consensus on how education gradients develop with time
and age and no matter what scenario is true (age, period or cohort), the large relative gradients
for young persons are alarming, and in fact the more so if they reflect that gradients are larger
for new cohorts.
Finally, we find no indication of the results being confounded by racial differences, since the
SES gradients exist also within non-white groups, although they are differently shaped. Both
education and income gradients are larger for non-white women than for white women. Our
results suggest that differences in education and income between whites and non-whites explain
33-50 % of health differences between these groups, which is somewhat less than what has been
found in many previous studies (see e.g. Smith and Kington 1997).
When it comes to interpretation of these differences, it is important to have in mind that the
results do not convey anything about causality. SES related differences in health may be due
causality in both directions as well as to a third unmenasured variable common to both the
determination of SES and health (indirect selection). This is still highly debated as seen e.g. in
13
the review of findings on life course explanations in Wadsworth (1999) and the findings on the
importance of childhood conditions in Case et al. (2002). Neither do the results convey anything
about what is measured by these variables. Both education and income may stand proxy for
something else than, say, health knowledge or economic resources, such as perception of control
or social rank as suggested by e.g. Marmot (1994) and discussed e.g. in Deaton (2001).
CONCLUSION
This study is among the first to systematically explore simultaneous education and income
gradients in health across demographic groups, using a health measure complementary to
mortality, self-reported health. Of particular importance is that the gradient exist within all
considered demographic groups, but that it differs in shape and size. Our findings suggest that
education is a particular important marker of SES, but that the income gradient is more robust
across demograhpic groups. Relative gradients are particularly large among young people,
women and non-white women.
14
ACKNOWLEDGEMENTS
Grants from the Danish Research Academy and The Fulbright Commission are gratefully
acknowledged. The grants made a visit to Princeton University, where this work was initiated,
possible. The hospitality of Princeton is gratefully appreciated. The author is indebted to Angus
Deaton for his very helpful comments. Thanks to Douglas Miller for initial data guidance, and to
Karsten Albaek, participants at iHEA 2001 in York, and at seminars at Institute of Economics
and Institute of Preventive Medicine at University of Copenhagen for helpful comments.
15
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19
APPENDIX A: EDUCATION AND STATE DUMMIES
The education classifications correspond to the following categories of the CPS variable for
educational attainment (A-HGA): Elementary: <34, Some High School: 34-38, High School
Diploma : 39, Some College: 40, College Degree: 41-42, Bachelor Degree: 43, Masters or other
Professional Degree: 44+. All US states are included, and Washington D.C. is counted
separately.
APPENDIX B: PROBIT SPECIFICATION
If H is indicator variable taking value one for persons in poor health, and zero otherwise, and X
are explanatory variables, the probit model of the probability of poor health is given as follows:
P( H  1| X )  ( X  )
where is the cumulative density function of the standard normal distribution, and are
unknown parameters to be estimated. For categorical variables, the effects presented in Tables
2-4 are given as:
P( H  1| X 1 , X 2  1)  P( H  1| X 1 , X 2  0)
where X1 are variables excluding the dummy variable, X2, and a bar denotes a population
average. For income and age, we present marginal effects, given as:
P( H  1| X )
  2 ( X  )
X 2
where  is the standard normal density. We estimated several probit models with different
education groups and with different age and income specifications. For men, the probability of
poor health does not differ significantly between those with high school and those with some
high school nor between the groups with higher education than a college degree. The latter also
holds for women, whereas large differences in health are found between women with some high
20
school and no high school at all. This lead us to collapse some education groups. Age is found to
affect the probability of poor health linearly. A fourth order polynomium of income captures
important variation in poor health, but within the 5th and 95th percentile of income, it can hardly
be distinguished from the fit obtained when using log income (249 income observations are not
positive and are excluded), and the latter is therefore chosen. In previous analyses we supported
the specification of age and income effects by non-parametric loess estimates (see Fan 1992).
The results are available from the author upon request. Number of persons in the household and
an indicator for living alone were excluded for men, because they were insignificant. Similarly,
an indicator for being asian and an indicator for living in an inner city areas are excluded for
women.
21
TABLE 1. MEANS AND STANDARD ERRORS OF VARIABLES FROM CPS, 1999.
Excellent Health
Very Good Health
Good Health
Fair Health
Poor Health
Mean
0.281
0.325
0.244
0.101
0.049
Men
Std.Err Share PH
0.003
0.003
0.002
0.002
0.001
Mean
0.244
0.321
0.268
0.115
0.053
Women
Std.Err
0.002
0.002
0.002
0.002
0.001
0.308
0.119
0.059
23,918
0.095
0.200
0.211
124
0.002
0.002
0.002
0.320
0.141
0.068
0.008
0.014
0.001
0.002
0.003
0.002
0.001
0.002
0.001
0.415
0.360
0.171
0.120
0.109
0.071
0.070
0.088
0.002
0.002
0.002
0.002
0.002
0.003
0.002
0.002
0.002
0.002
0.001
0.000
0.056
0.087
0.141
0.231
0.352
0.130
0.267
0.182
0.160
0.229
0.126
0.232
HH-Income/ae
HH-Inc (ae) < 2.decile
HH-inc (ae) in 5/6.decile
HH-Inc (ae) > 8.decile
26,567
0.192
0.200
0.209
147
0.002
0.002
0.002
Number of prs. in HH
Education (years)
Elementary Schooling
Some High School
High School
Some College
College
Bachelor
Post Bachelor
2.933
13.110
0.038
0.128
0.318
0.171
0.070
0.179
0.096
0.008
0.017
0.001
0.002
0.003
0.002
0.002
0.002
0.002
0.366
0.317
0.155
0.125
0.091
0.066
0.065
2.840
12.896
0.035
0.131
0.347
0.175
0.080
0.162
0.070
Age
25 <= Age < 34
35 <= Age < 44
45 <= Age < 54
45 <= Age < 54
65 <= Age
Married
Lives Alone
Lives in Central City
White
Black
Asian
Indian
47.503
0.205
0.242
0.192
0.122
0.167
0.672
0.125
0.240
0.849
0.105
0.037
0.008
0.087
0.002
0.003
0.002
0.002
0.002
0.003
0.002
0.002
0.002
0.001
0.001
0.000
0.048
0.085
0.131
0.212
0.355
0.138
0.202
0.164
0.141
0.224
0.132
0.193
49.157
0.192
0.224
0.183
0.120
0.206
0.602
0.165
0.243
0.833
0.121
0.039
0.007
Observations (N)
39,584
Share PH
44,615
Notes : Population weighted statistics, using the CPS march supplement weight. "Share PH" is the share with poor or fair
health. HH-income/ae is household income per adult equivalent, see text. See the appendix for definition of education codes.
22
TABLE 2. MARGINAL EFFECTS ON THE PROBABILITY OF POOR HEALTH.
Men
Variable
Constant
No High School
Some College
College
Post College
Age
Log HH-Income
25-34
-3.295
0.033
-0.006***
-0.015**
-0.017
0.005
-0.016
35-44
-2.587
0.045
0.002***
-0.016***
-0.027
0.003
-0.035
Age
45-54
-2.838
0.068
-0.008***
-0.006***
-0.035
0.008
-0.053
55-64
-2.137
0.079
-0.051
-0.064*
-0.111
0.006*
-0.065
65+
-1.545
0.101
-0.032***
-0.036***
-0.122
0.008
-0.070
-1.796
0.061
-0.013
-0.023
-0.052
0.005
-0.044
-0.025
0.023
0.014***
0.034***
0.003***
-0.058
0.040
0.033**
-0.006***
-0.003***
-0.075
0.047
0.025***
0.094*
0.008
-0.070
0.048**
0.058***
0.087***
0.016***
-0.055
0.031***
0.002***
-0.109***
0.015***
-0.054
0.041
0.025*
0.049*
0.009**
0.393
7,848
0.032
0.499
9,270
0.063
0.011
7,743
0.105
0.000
4,839
0.176
0.000
6,596
0.342
0.000
39,335
0.108
Women
Variable
Constant
No High School
Some High School
College
Post College
Age
Log HH-Income
25-34
-2.219
0.026***
0.025
-0.006***
-0.029
0.003
-0.017
35-44
-2.555
0.036*
0.053
-0.017
-0.035
0.005
-0.036
Age
45-54
-2.142
0.123
0.108
-0.014***
-0.046
0.005
-0.049
55-64
-2.580
0.210
0.156
-0.036*
-0.070
0.008
-0.069
65+
-2.056
0.183
0.133
-0.037*
-0.044*
0.007
-0.065
Married
Lives Alone
Number of Prs in HH.
Black
Asian
Indian
Lives in Inner City Area
-0.019
-0.011***
-0.003**
0.013***
0.007***
0.120
-0.011*
-0.017*
0.006***
-0.011
0.019*
0.010***
0.054***
-0.000***
-0.038
0.006***
-0.008*
0.043
0.013***
-0.001***
0.010***
-0.029**
0.024***
-0.005***
0.038**
0.012***
0.074***
0.025***
-0.027*
-0.050*
0.007
0.094
-0.113*
0.121***
0.004***
-0.030
-0.031
-0.011
0.043
0.232
9,558
0.046
0.117
10,942
0.070
0.016
8,906
0.122
0.000
5,775
0.210
0.000
9,112
0.341
0.000
44,293
0.129
Married
Black
Asian
Indian
Lives in Inner City Area
US state dummies (p-value)
No. Observations:
Predicted Prob. at Means
US state dummies (p-value)
No. Observations:
Predicted Prob. at Means
All
All
Notes : Marginal effects on predicted probabilities of poor health from Probit estimations. For discrete variables,
the difference to reference groups is presented. The reference group for education for men is some high school
or high school, and for women it is high school or college. The reference group for race is white. US state dummies
includes DC, and the p-value of the F-test of their joint significance is presented. Income is standardized. Unmarked
effects have p<0.01, * denotes 0.01<p< 0.05, ** that 0.05<p<0.1 and *** that p>0.1.
23
-1.411
0.119
0.085
-0.021
-0.047
0.005
-0.044
0.089
TABLE 3. RELATIVE EDUCATION AND INCOME EFFECTS ON THE
PROBABILITY OF POOR HEALTH ACROSS AGE GROUPS.
Men
No High School
Some College
College
Post College
Income
Overall Education Gradient
Women
No High School
Some High School
College
Post College
Income
Overall Education Gradient
25-34
1,031
-0,188
-0,469
-0,531
-0,563
35-44
0,714
0,032
-0,254
-0,429
-0,635
Age
45-54
0,648
-0,076
-0,057
-0,333
-0,590
1,563
1,143
0,981
1,080
0,652
0,565
0,543
-0,130
-0,630
-0,413
0,514
0,757
-0,243
-0,500
-0,586
1,008
0,885
-0,115
-0,377
-0,451
1,000
0,743
-0,171
-0,333
-0,371
0,537
0,390
-0,109
-0,129
-0,214
1,719
1,127
1,610
1,591
0,664
55-64
0,449
-0,290
-0,364
-0,631
-0,426
65+
0,295
-0,094
-0,105
-0,357
-0,240
Notes : Absolute effects from Table 2 divided by age specific predicted probabilities also in Table 2.
The overall education gradient is the No High School versus Post College differential.
24
TABLE 4. MARGINAL EFFECTS ON THE PROBABILITY OF POOR HEALTH,
FOR BLACKS, ASIANS AND INDIANS.
Men
Variable
Constant
No High School
Some College
College
Post College
Age
Log HH-Income
-2.635
0.075
0.003***
-0.008***
-0.029***
0.007
-0.044
0.210
0.178
0.062
0.220
44.839
2.730
Married
Indian
Asian
Lives in Inner City Area
-0.065
-0.001***
-0.051
0.023***
0.542
0.055
0.255
0.447
US state dummies (p-value)
No. Observations:
Predicted Prob. at Means
Mean
Women
Variable
Constant
-1.914
No High School
0.107
Some High School
0.086
College
-0.019***
Post College
-0.064
Age
0.006
Log HH-Income
-0.056
Married
Lives Alone
Number of prs in HH
Indian
Asian
0.015
4,863
0.159
-0.004***
0.017***
-0.013
0.041***
-0.051
0.000
6,412
0.163
Notes : The reference group for race is blacks. See also comments to table 1 and 2.
25
Mean
0.046
0.168
0.251
0.261
45.842
2.543
0.453
0.152
3.149
0.044
0.230