Understanding whether the gradient in children's health becomes steeper with age is an important first step in uncovering the mechanisms that connect economic and health status, and in recommending sensible interventions to protect children's health. To that end, this paper examines why two sets of authors, Chen et al (2006) and Case et al (2002), using data from the same source, reach markedly different conclusions about income-health gradients in childhood. We find that differences can be explained primarily by the inclusion (exclusion) of a handful of younger adults living independently.

Anne Case

Christina Paxson

Tom Vogl

English

Research Papers in Economics

NBER WORKING PAPER SERIES
SOCIOECONOMIC STATUS AND HEALTH IN CHILDHOOD:
A COMMENT ON CHEN, MARTIN, AND MATTHEWS (2006)
Anne Case
Christina Paxson
Tom Vogl
Working Paper 12267
http://www.nber.org/papers/w12267
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
May 2006
The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National
Bureau of Economic Research.
©2006 by Anne Case, Christina Paxson and Tom Vogl.  All rights reserved. Short sections of text, not to
exceed two paragraphs, may be quoted without explicit permission provided that full credit, including ©
notice, is given to the source.Socioeconomic Status and Health in Childhood: A Comment on Chen, Martin and Matthews
(2006)
Anne Case, Christina Paxson and Tom Vogl
NBER Working Paper No. 12267
May 2006
JEL No. I1
ABSTRACT
Understanding whether the gradient in children's health becomes steeper with age is an important
first  step  in  uncovering  the  mechanisms  that  connect  economic  and  health  status,  and  in
recommending sensible interventions to protect children's health. To that end, this paper examines
why two sets of authors, Chen et al (2006) and Case et al (2002), using data from the same source,
reach markedly different conclusions about  income-health gradients in childhood. We find that
differences can be explained primarily by the inclusion (exclusion) of a handful of younger adults
living independently.
Anne Case
367 Wallace Hall
Princeton University
Princeton, NJ 08544
and NBER
accase@princeton.edu
Christina Paxson
316 Wallace Hall
Princeton University
Princeton, NJ 08544
and NBER
cpaxson@princeton.edu
Tom Vogl
Center for Health and Wellbeing
Wallace Hall
Princeton University
Princeton, NJ 08544
tvogl@princeton.edu 
Introduction  
The relationship between socioeconomic status (SES) and health in childhood is an active 
and important area of research, one that may improve our understanding of the origins of 
socioeconomic gradients in adult health, and mechanisms through which the 
intergenerational transmission of poverty takes place. For these reasons, we read with 
interest the recent article “Socioeconomic status and health: Do gradients differ within 
childhood and adolescence?” (Chen, Martin and Matthews, 2006). These authors find 
that, although assessments of child health differ significantly between richer and poorer 
households, these differences do not increase as children age. This result stands in sharp 
contrast to recent findings that the relationship between household income and children’s 
health becomes more pronounced as children age, with no diminution of the gradient in 
adolescence (Case, Lubotsky and Paxson, 2003). The difference in findings between 
Chen, Martin and Matthews (CMM) and Case, Lubotsky and Paxson (CLP) are 
surprising, given that the two sets of authors rely on the same data source – the National 
Health Interview Survey (NHIS) – and choose the same socioeconomic and health 
variables for their analyses.  
  Understanding whether the gradient in children’s health becomes steeper with age 
is an important first step in uncovering the mechanisms that connect economic and health 
status, and in recommending sensible interventions to protect children’s health. To that 
end, this paper examines why these authors reach such different conclusions about the 
income-health gradient in childhood. We find these differences can be explained 
primarily by the inclusion, as if they were dependent children, of younger adults living 
independently.  
  2 
Methods 
Sample choice 
CLP analyzed 10 years of NHIS data for approximately 230,000 children aged 0 to 17, 
surveyed between 1986 and 1995. CMM analyzed data from the 1994 round of the NHIS, 
for approximately 29,000 children aged 0 to 18. CMM note that, in most cases, a proxy 
adult respondent (typically the child’s mother) reported on the health of persons under 
age 19. For simplicity, they refer to these responses as ‘parental reports.’  
  From this description, it is clear that the samples analyzed varied in two important 
ways. CLP worked with a data set that was an order of magnitude larger. In addition, 
CLP excluded 18 year olds from their analysis, while CMM did not. CLP noted that they 
chose not to include 18 year olds because they were concerned about the living 
arrangements of college-aged individuals, and whether these respondents would report 
their current incomes or the incomes of the families in which they were raised.  
  Data from the 1994 NHIS suggest it may be problematic to include 18 year olds 
in such analyses. In the NHIS, all relationships are recorded relative to a reference 
person, who is the person who rents or owns the housing unit surveyed. While, overall, 
less than one percent of children in 1994 reported themselves to be the household 
reference person, two percent of 17 year olds and 17 percent of 18 year olds did so. (In 
what follows, we will refer to a young adult who is not the household reference person as 
‘dependent’ and one who is the household reference person as ‘independent.’)  Nearly 80 
percent of  independent 18 year olds have completed at least 12 years of schooling (in 
contrast to 50 percent of the dependent 18 year olds), suggesting that a non-trivial 
  3fraction of 18 year olds living away from their families of origin may be enrolled in 
college. The current household income of such 18 year olds will be a poor guide to the 
socioeconomic status of the households in which they were raised, and may be an 
inaccurate estimate of their access to resources. Indeed, only 2 percent of dependent 18 
year olds reported household incomes of less than $5,000, while fully 70 percent of 
independent 18 year olds did so. Moreover, if the healthiest and highest SES 18 year olds 
were more likely to have left for college, their systematic absence in their households of 
origin and their presence in newly formed (apparently low income) households will bias 
estimates of the impact of household socioeconomic status on health outcomes, at least to 
some degree.   
  
Variable construction 
CLP and CMM also differ in their construction of the household income variable. For all 
years from 1986 to 1995, the NHIS reports information on total household income for 27 
income categories, coded from 0 to 26—corresponding to $1000 intervals for incomes 
between $0 and $20,000, and to $5,000 intervals for incomes between $20,000 and 
$50,000. The highest income category is “$50,000 or more,” so all household incomes 
above $50,000 are top coded. 
  CLP assigned incomes to these income categories using data from the 1986-1995 
March Current Population Surveys (CPS). Specifically, they calculated, for each income 
category in each year, the mean total household income in the CPS for households whose 
head’s education matched that of the reference person in the household and whose 
income fell into that income category. (See the Appendix to CLP for details.) CLP’s 
  4specifications generally used the logarithm of household income as an explanatory 
variable. Implicit in this specification is the assumption that, in wealthy and poor 
households alike, a one-percent increase in household income has the same marginal 
effect on the health of children of a given age. However, CLP note that their results are 
robust to changes in functional form. In particular, their finding that the gradient in 
children’s health becomes steeper with each year of age continues to hold when 
household income is used in place of the logarithm of household income. 
  CMM chose to measure household income as the categorical income variable 
published in the NHIS. That is, their income variable consists of integer values from 0 to 
26. Implicit in this choice is an assumption that a $1000 increase in income in poorer 
households (i.e., a one-unit change in the NHIS’s categorical income variable for 
households with incomes below $20,000) has the same marginal effect on children’s 
health as does a $5,000 increase in income in wealthier households (i.e., a one-unit 
change for households with incomes above $20,000 annually). This specification has the 
very odd implication that the effect of an additional dollar is five times as large for 
households with incomes of $19,999 than it is for households with incomes of $20,000.  
  
Data analysis  
We re-analyzed the NHIS data using STATA/SE 9.2. Following CMM, we first ran 
logistic regressions using 1994 data only, in which an indicator that a child is reported in 
fair or poor health is regressed on the income measure used by CMM, age, an income-age 
interaction, and controls for race (white, black, other) and sex.  The analysis is repeated, 
restricting the sample to dependent children (that is, removing 232 children – primarily 
  517 and 18 year olds – reported to be the household reference person). We then estimate 
this model using 10 years of NHIS data from 1986-95. Finally, we repeat these analyses 
using the logarithm of income in place of the CMM measure. All results were weighted 
using sampling weights. In all cases, we report odds ratios (ORs) so that our results can 
be directly compared to those of CMM. 
 
Results 
Table 1 presents the results of logistic regressions using the CMM categorical income 
measure in columns 1 to 4, and using the CLP log income measure in columns 5 to 7. 
Column 1 reproduces CMM’s estimate of the impact of household income on the log 
odds of reporting a child in fair or poor health, and column 2 produces results reported in 
CMM that they find no significant age-income interaction term. The lack of a significant 
age-income interaction term is also seen in column 5, where the categorical income 
measure has been replaced by log income. However, once the 232 independent young 
adults are removed from the sample (column 6), the age-income interaction using log 
income becomes statistically significant (P<.05). For both income specifications using 10 
years of NHIS data (columns 4 and 7), once young adults living independently are 
removed from the sample, we find age-income interactions that are statistically 
significant (P’s<.001). Furthermore, the estimates imply substantial changes in the 
relationship between income and health as children become older. The results in column 
7 suggest that a change in household income from the 75
th to the 25
th percentile of the 
1986-95 income distribution (from about $37,000 to about $13,000) at age 3 increases the 
probability of being in fair or poor health from 1.7 percent to 2.7 percent. The same 
  6change in household income at age 15 increases the probability from 1.8 percent to 3.3 
percent. 
  Table 1 suggests that the inclusion of a small number of young adults living on 
their own changes the estimated age-income interaction term dramatically. This can also 
be seen in Figure 1, which plots the coefficients on log income from logistic regressions 
run separately for each age, from 0 to 18, for all children observed in the NHIS from 
1986 to 1995. The lightly shaded diamonds represent the odds ratios for log income from 
age-specific regressions run for all children, while the darkly shaded circles correspond to 
the odds ratios for dependent children only. These show convincingly that income is 
increasingly protective with age when the newly-independent children are excluded. Note 
that, from age 0 to age 13, there are no differences in the estimated coefficients for the 
two samples—dependents only and all children—because no children below age 14 are 
reported to be living on their own. The low incomes of college-aged young adults living 
independently have a very large effect on the estimated relationship between income and 
health status in the NHIS for 17 and 18 year olds. Similar results are found using ordered 
probits of health status, measured on a five-point scale. 
 
Discussion   
The differences in the age patterns observed for income-health gradients between CMM 
and CLP are not driven by CMM’s use of a categorical income variable. Both the CLP 
and the CMM age-income interactions terms are insignificant when young adults living 
independently are included in the analysis. Both become highly significant and imply 
  7large changes in the gradient with age when the analysis is restricted to dependent 
children and 10 years of NHIS data are used.  
  Neither are the differences between the CLP and CMM results due to the much 
larger sample size used by CLP. The age-income interaction term becomes significant 
using CMM’s income measure, but restricting the analysis to dependent children, if one 
combines only two years of NHIS data – 1994 and 1995, for example (P<.001). Rather, 
the differences between the CLP and CMM results are driven largely by CMM’s 
inclusion of independent 18 year olds. We believe it is inappropriate to include these 
young adults in an analysis of the impact of socioeconomic status on health in childhood 
and adolescence, as the “socioeconomic status” markers available for these independent 
college-aged individuals are very possibly a poor reflection of the socioeconomic status 
they enjoyed for much (or most) of their lives.  
  Alternative measures of socioeconomic status in a child’s household of origin 
face the same problem. CMM construct and use a variable measuring the years of 
education of a parent (or responsible adult) in each child’s household. However, creation 
of this variable is made problematic by the 17 and 18 year olds who report themselves as 
the household reference person. For these young adults, the education of the “responsible 
adult” defaults to their own education (in most cases, 12 years of completed schooling). 
This adds measurement error to a variable intended to capture long-term effects of 
parents’ education, and may bias all estimated coefficients.   
  Young adults are an important group, deserving of attention. However, we will 
learn more by following them longitudinally (out of their households of origin and into 
  8adulthood – see, for example, Case, Fertig and Paxson, 2005) than we will learn looking 
at them in cross-sectional studies, such as the NHIS.  
 
References  
Case, A., Fertig, A., & Paxson, C. (2005). The lasting impact of childhood health and 
circumstance. Journal of Health Economics, 24, 365-389. 
 
Case, A., Lubotsky, D., & Paxson, C. (2002). Economic status and health in childhood: 
The origins of the gradient. American Economic Review, 92, 1308-1334. 
 
Chen, E., Martin, A. D., & Matthews, K. A. (2006). Socioeconomic status and health: Do 
gradients differ within childhood and adolescence? Social Science & Medicine, 62, 2161-
2170. 
 
 
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 0  5  10  15  20 
 Age
Dependents only All observations
Note: The dependent variable equals 1 if the child is in fair/poor health, 0 otherwise.  
Odds ratios are adjusted for race, sex, and survey year.  All specifications include 
survey weights. 
Figure 1: Age patterns in the fair/poor health gradient, NHIS 1986-1995 
 
  10Table 1 
Exploring income-age interactions using data from the National Health Interview Survey 
 
  Chen et al income measure  Log income 
  (1) (2) (3) (4)  (5) (6) (7) 
Income 0.935 
(0.004)** 
0.935 
(0.008)** 
0.941 
(0.008)** 
0.940 
(0.003)** 
0.636 
(0.029)**
0.683 
(0.034)** 
0.682 
(0.012)**
Age × 
Income 
 1.000 
(0.001) 
0.999 
(0.008) 
0.998 
(0.0003)**
1.004 
(0.004) 
0.989 
(0.005)* 
0.987 
(0.002)**
Sample  All All  Dependents Dependents All  Dependents  Dependents
Years  1994 1994 1994  1986-95 1994 1994  1986-95 
Observations  27833 27833 27601  274623  27833 27601  274623 
 
Note: Odds ratios from logistic regression models, robust standard errors in parentheses. 
The dependent variable equals 1 if the child is in fair or poor health, 0 otherwise. Log 
income is calculated using the mean of each income bracket in the March Current 
Population Surveys. All models include controls for age, race, and sex, and are weighted 
using survey weights. * significant at 5%. ** significant at 1%. 
 
 
  11

Socioeconomic Status and Health in Childhood: A Comment on Chen, Martin and Matthews (2006)

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