ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series
No.E12-2

Parental Income and Child Health in Japanby
Sayaka Nakamura

Revised version as of April 2013

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Parental Income and Child Health in Japan*

Sayaka NakamuraaaSchool of Economics, Nagoya University

Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
nakamuras@soec.nagoya-u.ac.jp
Phone & Fax: +81-52-789-5961

Keywords: child health, income gradient, parental income
Total word count: 6745 words
Body text: 4167 words
Number of tables: 9*

I gratefully acknowledge financial support from a Health Labour Sciences Research

Grant for Research on Policy Planning and Evaluation. I have no conflicts of interest to
declare. I obtained an official permission to utilize Comprehensive Survey of Living
Conditions (CSLC) from Statistics and Information Department of the Ministry of
Health, Labour and Welfare on October 23, 2012 (Permission Number 1023-2). I am
grateful to Shunichiro Bessho, Shiko Maruyama and Kazumitsu Nawata for theirhelpful comments. Any errors are mine.
1

Abstract
Previous studies have consistently found evidence of an income gradient in healthamong children in various countries, and studies in Anglo-Saxon countries have foundthat this gradient increases with child age. Using nationally representative
individual-level data, I examine the association between child health and parental
income in Japan. Japan has a child poverty rate that is similar to the rate of many
countries that have been studied previously, but Japan outperforms those countries onmost health indicators. I find that an income gradient exists in child health in Japan, butthat it is less consistent than the gradient found in other countries or among Japanese
adults. Moreover, I find no evidence that the gradient increases with child age, a resultthat is consistent with a prior study of German children. The fact that children in
low-income families have relatively modest and non-accumulating health disadvantages
may contribute to the overall health of the Japanese population. Nevertheless, there is astatistically significant negative association between parental income and the incidencesof asthma, hearing problems, and dental symptoms in children, implying that future
efforts to improve the health of underprivileged children should focus on the prevention
and control of these diseases. (197 words)

2

1. Introduction
The literature has established that there is a strong positive association between
socioeconomic status (SES) and health. The economic literature has offered three
explanations for this gradient: a causal effect of SES on health, a causal effect of healthon SES, or a third factor, such as innate abilities that affect both SES and health (Fuchs
2004). To better understand the complex relationship between SES and health, Case etal. (2002) conducted a seminal study in which they examined the relationship between
parental income and child health. A focus on the effect of parental income on childhealth offers the advantage of reducing concerns about reverse causality. Based on US
data, these authors found that an income gradient existed from early childhood, and thatthe gradient grew as children aged. Replication studies in Canada (Currie and Stabile
2003), the UK (Case et al. 2008), and Australia (Khanam et al. 2009) also foundevidence of a positive and increasing income gradient in child health, although thefindings from the UK were mixed, especially for older children (Currie et al. 2007,
Propper et al. 2007, West and Sweeting 2004). Nevertheless, to date, evidence insupport of an increasing gradient is limited to Anglo-Saxon countries. Studies from
Germany (Reinhold and Jürges 2012) and Indonesia (Cameron and Williams 2009)
found a positive income gradient for child health, but they did not find that the gradient3

increased. The finding from Germany is particularly notable because Germany is similar
to the aforementioned Anglo-Saxon countries in terms of per-capita GDP.
This study examines the association between parental income and child healthin Japan. Japan is similar to the aforementioned Anglo-Saxon countries in terms of
per-capita GDP and income inequality. In the late 2000s, the relative poverty rate amongchildren in Japan was similar to the child poverty rates in Australia, Canada, and the UK,
but it was significantly higher than the rate in Germany, as shown in Table I. Table I
also shows that the change in the poverty rate between the mid-1980s and the late 2000s
was small in each of these countries.1 Japan differs significantly from the previously
studied countries, however, in its high rankings for most health indicators. Indeed, atleast until it was hit by the Tohoku earthquake in 2011, Japan had the highest longevity
in the world. Japan also has one of the lowest infant mortality rates in the world and thesecond-lowest obesity rate among the OECD countries (OECD 2011, WHO 2011).

1

Studies that compare Japan with the rest of the world in terms of income inequality inthe 1990s reach different conclusions. According to the OECD (2008), the Gini index
and the relative poverty rate in Japan were consistently well above the OECD average
from the mid-1980s to the mid-2000s. However, based on Japanese data collected in
1993, the World Bank (2003) found that Japan had one of the lowest levels of income
inequality in the world.
4

[Insert Table I here]
Because the tax burden is high and the transfer payments to low-income
households with children are small, Japan is the only OECD country in which taxes andtransfers have consistently increased the child poverty rate since the 1980s (Jones 2007).
Until recently, little attention has been paid to child poverty in Japan, and policy
development has been slow. Japan has universal health insurance, but unlike Australia,
Canada, Germany and the UK, out-of-pocket health care expenses for children are notfully waived. Until the coinsurance rate for children 3 years old or younger was reduced
to 20% in 2008, the coinsurance rate was 30% for all individuals younger than 70 years
old, although some municipalities and prefectures provide additional subsidies forchildren’s medical expenditures.
At the same time, Japan has unique and long-established school-based
programs that may help to reduce socioeconomic disparities in child health. In particular,
the law requires that elementary and middle school children receive annual medical
examinations from physicians during school time. In addition, inexpensive school meals
that meet strict nutritional standards are provided at approximately 98% of elementary
schools and 77% of middle schools (MEXT 2012). For pre-school children from
low-income families with two working parents or a single parent, there are licensed5

(including public) nursery schools that offer free or low-cost care and meet strict quality
requirements for staffing, space, and safety equipment. These nursery schools arelegally required to provide a nutritionally adequate lunch every school day and medical
examinations by a physician at least twice a year.
Epidemiological studies have established that there is a strong association
between income and health among Japanese adults (Kagamimori et al. 2009).
Specifically, there is a statistically significant positive relationship between income andself-rated health (Shibuya et al. 2002) and between income and the majority ofsubjective symptoms (Fukuda and Hiyoshi 2012).2 However, there are a limited
number of empirical studies on parental income and child health in Japan. Aida et al.
(2008) and Komamura (2009) reported a positive association between average income
and child caries using municipality-level data. Based on panel data, Abe (2011) foundthat hospitalization rates decrease and the use of outpatient care increases with parental
income.3 The findings have been mixed regarding the socioeconomic gradient in the2Studies that have compared the socioeconomic gradient in adult health in Japan to thegradients in Britain and Finland have reported mixed results (Martikainen et al. 2004,
Sekine et al. 2009).
3

Similar to this paper, Abe (2011) regressed the self-rated health of children on

household income and found a positive association between them. The result is difficult
6

birth weight of Japanese babies (Kohara and Ohtake 2012, Teramoto et al. 2006, Qiu etal. 2011).
This study yields three major findings. First, a positive association between
parental income and child health exists in Japan, but the association is limited to
specific diseases, and it is less consistent than the relationships found in other countries
and among Japanese adults. Second, this study finds no evidence that the income
gradient in child health increases with child age. The first and second findings imply
that the health disadvantages of low-income children are relatively modest and do notaccumulate over time, which may contribute to the overall health of the Japanese
population. Third, this study identifies common childhood diseases that have astatistically significant association with parental income. These findings have important
policy implications for improving the health of underprivileged children.
2. Data
I use individual-level data from the Comprehensive Survey of Living
Conditions (CSLC), a survey of a nationally representative sample conducted by theto interpret, however, because the regression model did not control for household size,
which varies greatly among Japanese families because of the large number ofthree-generation households.
7

Ministry of Health, Labour and Welfare (MHLW).4 The CSLC is a pooled
cross-sectional survey that utilizes self-administered questionnaires to assess the health,basic demographics, and socioeconomic characteristics of household members. Healthinformation is collected in the large-scale CSLC every three years. The sample unit
areas are cluster sampled from all 47 Japanese prefectures, and all households within thesampled areas are asked to participate in the household and health surveys. The income
survey is conducted on a stratified random sub-sample of these households. For thisanalysis, I use the combined questionnaires for household demographics, health, andincome in the large-scale surveys of the CSLC from 1998, 2001, 2004, and 2007. The
numbers of surveyed households in 1998, 2001, 2004, and 2007 were 30,506, 30,386,
25,091, and 23,513, respectively, and the response rates were 79%, 79%, 70%, and 68%,
respectively. The selection of the survey waves is based on the long-term increasing
trends in both the child poverty rate and the socioeconomic gradient in adult health inJapan (Jones 2007, Kondo et al. 2008).
The subjects of this study are children aged 15 years or younger living in
nuclear families. Children who share households with their grandparents are excluded,

4

I obtained permission to use the questionnaire-level data from the MHLW on October23, 2012.
8

primarily because the economic circumstances of elderly individuals depend on wealth(rather than current income), but little information on wealth is collected in the survey.
Although support given to children by their grandparents is an important subject, it
complicates the analysis significantly and thus is beyond the scope of this paper. I also
exclude a small number of children who (1) do not have a mother in the household; (2)have more than two siblings; (3) share a household with adult siblings; or (4) havemissing data for essential variables, such as age, sex, or parental income. Excludingchildren with adult siblings in the household (3) avoids the complication of the presenceof income-earning individuals other than the parents in the household. Unfortunately,
the CSLC does not collect information on whether children are adopted or living with a
stepparent, but adoptions of minor children and remarriages of parents with young
children are both extremely rare in contemporary Japan (Moriguchi 2010, Inaba 2011).
The CSLC questionnaire on health asks each household member about his or
her own health. For children younger than 12 years old, guardians (including parents)
are instructed to help the children respond to the survey questions. Children 6 years old
or older are asked about their general health, including their subjective health,impairment in daily life, and the number of days they have spent in bed. Subjective
health is a categorical variable assessed using the question, “How is your current health9status?” It is measured on an ordinal scale: 1 - excellent, 2 - good, 3 - fair, 4 - poor, 5 extremely poor. Impairment in daily life is a binary dummy variable that is measured
using the question, “Do you have any influence of ill health in your daily life?” The
number of days spent in bed is a categorical variable that is measured using the question,“How many days did you spend all day in bed during the past month?” It is measured
on an ordinal scale: 1 - none, 2 - 2-3 days, 3 - 4-6 days, 4 - 7-14 days, 5 - 15 days or
more.
All of the children are asked about their current symptoms and the presence of
any ongoing medical treatment. The questionnaire asks if they have experienced any
symptoms due to sickness or injury in the last few days; if the children answer yes, they
are then instructed to choose all the symptoms they have experienced from the list
provided. The list includes 41 symptoms classified into 12 broad categories. In addition
to these broad categories, I examine two specific symptoms, difficulty hearing andwheezing, because previous studies have found a significant socioeconomic gradientrelated to hearing impairment and asthma in children (Boss et al. 2011, Currie 2009).
Similarly, respondents are asked if they are currently receiving any outpatient treatment
at medical institutions for illnesses or injuries. If they answer yes, they are given a listof conditions and instructed to indicate all the conditions for which they are currently

10receiving outpatient treatment.5 The lists differ slightly in different survey years, but atleast 40 diagnoses are listed in each wave of the survey, and the diagnoses (except thoserelated to obstetrics and gynecology) are classified into 15 large categories. In addition
to these categories, I examine the common cold (acute nasopharyngitis), hay fever(allergic rhinitis), and asthma because of the high prevalence of these conditions andprevious findings that have shown a large socioeconomic gradient in child asthma(Currie 2009).
As a measure of income, I use pre-tax parental income for the year prior to thesurvey, following the methodology used by Case et al. (2002). I do not control for theoverall consumer price level because it changed only slightly. The Consumer Price
Index steadily declined from 1997 to 2003, dropping a total of 2.3% over that period,
and remained nearly unchanged from 2003 to 2006, according to the Annual Report on
the Consumer Price Index (CPI) published by the Statistics Bureau of the Ministry ofInternal Affairs and Communications.
5

Medical institutions include treatment places for massage, acupuncture, moxibustion,or Judo therapy in addition to hospitals and dental or medical clinics. Information about
the type of medical institution is only available in the 1998 wave of the survey.
However, the use of these treatment places is uncommon; less than 0.6% of the children
in the 1998 sample were treated at them.
11

The summary statistics for the sample are shown in Tables II and III. Although
information on subjective general health is collected for individuals 6 years old or older,
it is missing for many 6-year-olds. Thus, I limit the sample to 7- to 15-year-olds in theanalysis of the general health variables. Overall, the sample of children aged 0-15 andthe sample of children aged 7-15 are similar in demographics and family background.
The sample size declines over the years because of the increase in non-response rates
and the decline in the birth rate. Among 7- to 15-year-olds, 3% rate their health status as“poor” or “extremely poor”, 4% believe that ill health influences their daily life, and 7%
spent at least one day in bed during the past month. Among 0- to 15-year-olds, 24%
report at least one symptom, and the majority of these children have respiratory
symptoms. Of the 0- to 15-year-olds, 18% are receiving outpatient care, mostcommonly for diseases of the respiratory system.
[Insert Tables II and III here]
3. Estimation Methods and Results
3.1. Regression of General Health Variables
First, I estimate base regression models of general health variables using thedata for children aged 7-15. As explanatory variables, I include dummy variables forage, sex, year, number of siblings, each parent's age, each parent’s working status and12the father's presence in the household in addition to log parental income.6 Following
previous studies, I estimate an ordered probit model of subjective health. In addition, I
estimate a binary probit model in which the dependent variable takes the value of one if
subjective health is rated “poor” or “extremely poor”. This follows the suggestion of
Etilé and Milcent (2006) to use binary categories (poor/non-poor health) to reduce thebias that arises from reporting heterogeneity.7 I estimate an ordered probit model of thenumber of days in bed and a binary probit model of whether children report impairment
in daily life. I use data from all four survey waves in the regression of subjective healthand impairment in daily life. I use data from the 1998, 2001, and 2004 waves in the
regression of the number of days in bed because the data for this variable areunavailable for 2007.8
Table IV shows the results of the base regression models for the general health6

Unfortunately, the CSLC does not collect information regarding education in thesurvey waves used in this study.
7

In addition, Jones and Schurer (2011) found that controlling for unobserved

heterogeneity eliminates the association between income and very good reported health,but it does not eliminate the association between income and poorer reported healthstatus.
8

Observations with missing dependent variables are excluded in each regression

analysis.
13

variables. Consistent with previous studies, the coefficient of log parental income issignificantly negative in the ordered probit regression of subjective health, as shown in
the far left column; however, the results are weaker for the other measures of general
health. The coefficient of log parental income is not statistically significant in the
regression for “poor” or “extremely poor” subjective health, is negative but only
marginally significant in the regression of the number of days in bed, and is not
significant in the regression of impairment in daily life.9[Insert Table IV here]
To examine the possibility that the income gradient in child health increases aschildren age, I conduct a sub-sample regression analysis of general health variables bychild age, dividing the sample into three age groups: 7- to 9-year-olds, 10- to
12-year-olds, and 13- to 15-year-olds. This categorization roughly corresponds to theage groups in Japanese schools; Japanese children begin elementary school at the age of
6 and begin middle school at the age of 12. Table V shows the estimated coefficients oflog parental income by age group. The results do not support the hypothesis that
disparities in child health associated with parental income increase as children age. The

9There is little change in the results when logged income is replaced with income

dummies.
14

results of the ordered probit regression of subjective health show that the coefficientwith the greatest absolute value corresponds to the youngest age group. There are no
consistent age-related trends in the regression of “poor” or “extremely poor” subjective
health, the number of days spent in bed, or impairment in daily life.
[Insert Table V here]

3.2. Self-reported Symptoms
Next, I estimate binary probit models of self-reported symptoms using datafrom all of the children aged 0-15 in all four survey waves. On the same set of
explanatory variables as above, I regress dummy variables that take the value of one if achild reports any symptom, difficulty hearing, wheezing, or any symptom in each of thecategories listed in the questionnaire.10 Table VI shows the estimated coefficients andthe marginal effects of log parental income for the regression of self-reported symptoms.
The results vary significantly among the symptoms. On one hand, the coefficients of log
parental income are negative and marginally significant for dental symptoms andsymptoms of the ear and respiratory system, and they are significantly negative for10

I did not conduct a regression analysis of symptom categories with less than 100

reports in the sample.
15difficulty hearing and wheezing. On the other hand, the coefficients are significantly
positive for injury and skin-related symptoms, and they are insignificant for the othersymptom categories. The coefficient of log parental income is not statisticallysignificant in the regression of any reported symptoms.
[Insert Table VI here]
To re-examine the possibility that the income gradient in child health increases
with child age, I conduct a sub-sample regression analysis of subjective symptoms bychild age. I limit my analysis to symptoms reported by at least 500 children and divide
the sample into five age groups: 0- to 3-year-olds, 4- to 6-year-olds, 7- to 9-year-olds,
10- to 12-year-olds, and 13- to 15-year-olds. Table VII shows the estimated coefficientsof log parental income by age group. Again, the results do not support the hypothesisthat the gradient increases with child age. The strong negative association between
parental income and wheezing appears to decrease as children grow older. It is difficult
to find any consistent patterns of age-related changes in the income gradient for theother dependent variables. For these dependent variables, the coefficients of log parental
income are either more negative or less positive for the 4-6 age group than for the 0-3
age group, but they are either less negative or more positive for the 7-9 age group thanfor the 4-6 age group.

16[Insert Table VII here]
3.3. Ongoing Outpatient Treatment
Finally, I estimate binary probit models of ongoing outpatient treatment using
the data from the full sample of children 15 years or younger from all four survey waves.
On the same set of explanatory variables as above, I regress dummy variables that takethe value of one if a child is receiving outpatient treatment for any diagnosis, for any
diagnosis in each of the categories listed in the questionnaire, or for each of the threemost common respiratory diagnoses (the common cold, hay fever, and asthma).11
Because the regression does not control for health status, the coefficients of log income
reflect the income gradient not only in child health but also in care-seeking behavior.
Self-reported symptoms and general health indicators could be included as explanatory
variables to estimate the association between parental income and children’s use of
outpatient care conditional on reported health and symptoms. I do not take this approach
because current health care utilization could inversely affect current symptoms. In
addition, both the reporting of symptoms and care-seeking behavior may be affected byparental health knowledge and awareness.

11

I did not conduct a regression analysis for diagnosis categories with less than 100

reports in the sample.
17

Table VIII shows the estimated coefficients and the marginal effects of log
parental income. The coefficients are significantly positive in the regression of
outpatient treatment for any diagnosis. The coefficients are also significantly positivefor hay fever and for diagnoses related to the skin and subcutaneous tissue, but they are
insignificant for the other categories of diagnoses that were examined.
[Insert Table VIII here]
To explore possible changes in the relationship between parental income andoutpatient care utilization that may occur as children grow older, I conduct a sub-sample
regression analysis by child age. I limit my analysis to the diagnosis categories with atleast 500 outpatients and divide the sample into the same five age groups that were
described in the previous section. The estimated coefficients of log parental income byage group are shown in Table IX. In the regression of outpatient care utilization for any
diagnosis, the coefficient and the marginal effect clearly increase with child age. The
results for the individual diagnosis categories also demonstrate the pattern of anincreasing positive association between parental income and outpatient care utilization.
[Insert Table IX here]
4. Discussion and Conclusion
There are several major findings of this study. First, there is a positive and18

significant parental income gradient in child health in Japan, but the gradient is lessconsistent than the gradient found in other countries and among Japanese adults. It isdifficult to directly compare the results of this study with the results of previous studies
in other countries because of the differences in survey methodology and possible
heterogeneity in reporting.12 Nevertheless, this study does not find a strong negative
association between parental income and several health-related variables (bed days,
impairment in daily life, subjectively rated “poor” or “extremely poor” health, themajority of self-reported symptoms and the majority of diseases receiving treatment), a
result that clearly differs from previous studies that have found a significant negative
association between parental income and various measures of child health (Cameronand Williams 2009, Case et al. 2002, Propper et al. 2007). Moreover, in contrast to
Fukuda and Hiyoshi’s (2012) findings, which show a significant income gradient among
Japanese adults in all categories of symptoms other than rhinopathy and dermopathy, I
find no evidence of an income gradient among children in symptoms related to the eyesor the digestive system, musculoskeletal symptoms, or injuries.

12“Anchoring vignettes” and objective health information such as blood test results andphysician-assessed health could be used to reduce these concerns (Bago d’Uva et al.
2008). Unfortunately, the CSLC does not collect such data.
19

Second, this study finds no evidence of an increasing income gradient in childhealth with child age, similar to the findings of studies conducted in Germany (Reinhold
and Jürges 2012) and Indonesia (Cameron and Williams 2009). The results from theregression of subjective health, based on a sample of 7- to 15-year-old children, showthat the gradient does not increase with age among Japanese children in this age range,
in contrast to the results of previous studies in the US (Case et al. 2002), Canada (Currie
and Stabile 2003), and the UK (Case et al. 2008). Additionally, the results from theregression of subjective symptoms and the regression of diseases receiving outpatient
treatment, based on a sample of 0- to 15-year-old Japanese children, show no support
for an increasing gradient in any symptoms or diagnoses, in contrast to previous
findings from the US (Case et al. 2002).
Third, although parental income is not associated with the majority of thecommon diseases of children, significant income gradients are associated with somediseases. Consistent with previous studies, there is a significant negative association
between parental income and hearing difficulty, wheezing, and dental symptoms (Aidaet al. 2008, Boss et al. 2011, Currie 2009, Komamura 2009). At the same time,
outpatient treatments for asthma, ear-related diagnoses, and dental diagnoses are notsignificantly related to parental income, suggesting underutilization of outpatient care20

by low-income children with these symptoms. Because childhood asthma, hearing
impairments and dental diseases may have serious negative impacts on educational andlabor market outcomes (Currie 2009, Glied and Neidell 2010), future efforts to improve
the health of underprivileged children should focus on the prevention and control ofthese diseases.
The income gradient in child health in Japan is less consistent and lesscumulative than in previously studied countries, implying that the health disadvantagesof low-income Japanese children are comparatively small despite the high child poverty
rate and the lack of comprehensive public support for low-income households withchildren. The relatively small health disadvantages of children from low-income
families may contribute to the overall health of the Japanese population. Further studies
are needed to explore the role of schools and nurseries in reducing the socioeconomic
gradient in child health and the effect of parental SES on children's health behavior andhealth care utilization.

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West P, Sweeting H, 2004. “Evidence on equalisation in health in youth from the Westof Scotland,” Social Science & Medicine, 59(1): 13-27.
World Bank, 2003. World Development Indicators, Washington, DC: The World Bank.

26

WHO (World Health Organization), 2012. World Health Statistics 2012, Geneva: World
Health Organization.

27

Table I. The relative poverty rate among children in the late 2000s in Japan and in previously
studied countries
Average annual change in
Relative poverty rate among

the relative poverty ratechildren, late 2000s

between mid-1980s and late
2000s

Japan

14.2

1.3

Australia

14.0

1.9

Canada14.8-0.2

Germany

8.3

2.1

United Kingdom

13.2

2.2

United States

21.6

-0.1

OECD average

12.3

1.0

Source: OECD (2011).

28

Table II. Summary statistics for demographic and family characteristics and subjective
general health ratings
Age 0-15
MeanN

Age 7-15
S.D.

29263

Mean

S.D.

16313

Survey year
1998

0.31

0.46

0.31 0.46

2001

0.28

0.45

0.29 0.45

2004

0.21

0.41

0.20 0.40

2007

0.20

0.40

0.20 0.40

Age (in years)

7.46

4.54

10.93 2.58

Male

0.51

0.50

0.51 0.50

Father in household

0.94

0.23

0.92 0.26

Parental income (in million yen)

634

406

687

449

Father's income

544

367

576

408

Mother's income

90

168

111

187

1 sibling

0.54

0.50

0.54 0.50

2 siblings

0.23

0.42

0.25 0.43

Father's age (in years)

36.8

11.0

39.2 12.4

Mother's age (in years)

36.5

5.9

39.7

Father works

0.94

0.25

0.91 0.28

Mother works

0.50

0.50

0.60 0.49

Excellent--

0.48 0.50

Good

-

-

0.19 0.40

Fair

-

-

0.30 0.46

Poor

-

-

0.03 0.16

Extremely poor--

0.00 0.04

Unknown--

0.03 0.17

4.8

Subjective health

Impairment in daily life

0.04 0.04

Days in bed
None

-

-

0.93 0.25

1-3 days--

0.06 0.24

4-6 days--0.01 0.09

7-14 days--

0.00 0.05

15 days or more--

0.00 0.03

Unknown--

0.03 0.16

29

Table III. Summary statistics for subjective symptoms and diagnoses (age 0-15, N=29263)
Mean

S.D.

Subjective symptoms
Any symptom

0.24

0.43

Systemic/neurological

0.05

0.23

Eyes

0.01

0.09

Ears

0.01

0.09

0.01

0.08

Chest

0.00

0.04

Respiratory system

0.14

0.35

Wheezing

0.03

0.16

Digestive system

0.03

0.17

Dental

0.01

0.12

Skin

0.06

0.23

Musculoskeletal

0.01

0.11

Limbs

0.00

0.06

Genitourinary system

0.00

0.06

Injury0.02

0.15

Any diagnosis

0.18

0.39

Endocrine/metabolic disorders

0.00

0.04

Mental and behavioral disorders

0.00

0.04

Diseases of the eye

0.00

0.06

Diseases of the ear

0.01

0.12

Diseases of the circulatory system

0.00

0.06

Diseases of the respiratory system

0.09

0.28

Acute nasopharyngitis (common cold)

0.03

0.16

Allergic rhinitis (hay fever)

0.03

0.16

Asthma0.02

0.15

Diseases of the digestive system

0.00

0.05

Diseases of the teeth

0.03

0.18

Diseases of the skin and subcutaneous tissue

0.04

0.19

Diseases of the musculoskeletal system

0.00

0.05

Diseases of the genitourinary system

0.00

0.04

Injury/trauma

0.01

0.10

Anemia/diseases of the blood

0.00

0.03

Malignant neoplasm (cancer)

0.00

0.02

Other diseases

0.02

0.14

Difficulty hearing

Receive outpatient care for30

Table IV. Regression of subjective general health (base model)
Dependent variable

Model

Subjective (ill) health# of days in

Impairment

bed

in daily life

Ordered

BinaryOrdered

BinaryprobitprobitprobitprobitLog parental income-0.076***-0.002-0.059*0.012

Year 2001

0.050*

0.007

0.297***

0.145***

Year 2004-0.055*-0.0050.218***

0.120**

Year 2007-0.018-0.004

-

0.088

Age 8

-0.011

0.057-0.019

0.078

Age 9

0.024

-0.052-0.140**0.053

Age 10

0.008

0.04

-0.0810.133*

Age 11

0.028

0.007

-0.127*0.170**

Age 120.168***

0.159*-0.145**0.135*

Age 130.191***

0.237***-0.122*0.185**

Age 140.238***

0.144-0.052

0.195**

Age 150.275***

0.325***-0.207***0.240***

Male-0.014-0.01-0.006

0.112***

1 sibling-0.067**-0.029

-0.05

0.032

2 siblings-0.109***-0.109*-0.069-0.059Father in household-0.124-0.189

0.316

0.023

Father's age-0.002-0.005-0.011**-0.002

Mother's age

0.011***

0.006

0.002

0.005

Father works

0.311***

0.381

0.074

-0.1

Mother works-0.090***-0.068-0.014-0.063

N

15820

15820

12763

15975

-17904.0-2022.1-3676.6-2841.6

256.83646.12587.996

48.37

ll
2

chi

Notes: The reference categories are the survey year 1998 and 7 years of age. In the ordered
probit regression of subjective health, the explanatory variable is defined as 1 - excellent, 2 good, 3 - fair, 4 - poor, 5 - extremely poor. In the binary probit regression of subjective health,the explanatory variable equals 1 for “poor” or “extremely poor” and 0 otherwise. The
number of days in bed is categorized into the following: 0 days, 1-3 days, 4-6 days, 7-14 days,
and more than 15 days. The number of days in bed is unavailable for 2007. Observations withmissing dependent variables are excluded in each regression analysis.

31

Table V. Coefficients of log parental income in a sub-sample regression of subjective general
health by age group# of days in

Impairment inbed

daily life

Binary probit

Ordered probit

Binary probit-0.091***

0.003-0.064

0.088

10-12

-0.067**0.077-0.045-0.03313-15-0.075***-0.063-0.0640.001

Dependent variable

Subjective (ill) healthModel

Ordered probit

7-9

Age group

Notes: The other explanatory variables include the following: dummy variables for age, sex,
survey year, number of siblings, each parent's age and working status, and the father's
presence in the household. In the ordered probit regression of subjective health, theexplanatory variable is defined using the following scale: 1 - excellent, 2 - good; 3 - fair, 4 poor, 5 - extremely poor. In the binary probit regression of subjective health, the explanatory
variable equals 1 for “poor” or “extremely poor” and 0 otherwise. The number of days in bed
is categorized into the following: 0 days, 1-3 days, 4-6 days, 7-14 days, and more than 15
days. The number of days in bed is unavailable for 2007.
Table VI: Coefficients and marginal effects of log parental income in a binary probit ofsubjective symptoms (N=29263)
Coefficient

Marginal effectAny symptom-0.004-0.001

Systemic/neurological-0.014-0.002

Eyes

0.018

0.000

Ears

-0.090**-0.002*

Difficulty hearing-0.106**-0.002**

Respiratory system-0.038*-0.008*Wheezing-0.120***-0.007***

Digestive system-0.0030.000

Dental-0.063*-0.002*

Skin

0.053**

0.006**

Musculoskeletal

0.007

0.000

Injury0.085**

0.005**

Notes: The other explanatory variables include the following: dummy variables for age, sex,
survey year, number of siblings, each parent's age and working status, and the father's
presence in the household.

32

Table VII: Coefficients and marginal effects of log parental income in a sub-sample analysis
of subjective symptoms
Age

Any symptom

Systemic/neurological

Respiratory system

Wheezing

Digestive system

Skin

Injury

0-3

4-6

7-9

10-12

13-15

Coefficient-0.026-0.0580.038-0.002

0.027

Marginal effect-0.009-0.0190.011-0.001

0.008

Coefficient-0.013-0.108*0.095-0.010-0.021Marginal effect-0.002-0.010*0.008-0.001-0.003

Coefficient-0.039-0.058-0.046-0.0650.024

Marginal effect-0.011-0.015-0.009-0.011

0.004

Coefficient-0.133**-0.115*-0.174**-0.051-0.088Marginal effect-0.013**-0.008*-0.009**-0.002-0.002

Coefficient

0.024-0.058

0.094

0.080

-0.099*Marginal effect

0.002-0.004

0.005

0.005

-0.007*

Coefficient

0.056

0.008

0.077

0.026

0.103*

Marginal effect

0.008

0.001

0.009

0.002

0.008*

Coefficient

0.140-0.0610.210***

0.085

0.051

Marginal effect

0.004-0.0030.014***

0.005

0.004

Notes: The binary probit model is used for the estimation. The other explanatory variables
include the following: dummy variables for age, sex, survey year, number of siblings, eachparent's age and working status, and the father's presence in the household.

33

Table VIII: Coefficients and marginal effects of log parental income in a binary probit ofoutpatient care (N=29263)
Coefficient

Marginal effectAny diagnosis

0.051***

0.014***

Eyes

0.117*

0.001

Ears

0.000

0.000

Circulatory system

0.012

0.000

Respiratory system

0.018

0.003

Common cold

0.001

0.000

Hay fever0.064*

0.004*

Asthma-0.003

0.000

Teeth

0.023

0.002

Skin and subcutaneous tissue

0.073**

0.006**

Injury/trauma

0.028

0.001

Notes: The other explanatory variables include the following: dummy variables for age, sex,
survey year, number of siblings, each parent's age and working status, and the father's
presence in the household.

34

Table IX: Coefficients and marginal effects of log parental income in a sub-sample analysis of
outpatient treatment
Age
10-12

13-15

0.041

0.121***

0.149***-0.0140.011

0.029***

0.033***-0.029-0.066

0.029

0.084

0.108*

Marginal effect-0.005-0.011

0.004

0.009

0.009*

Coefficient

0.044

-0.07

-0.077-0.0100.093

Marginal effect

0.005-0.005-0.003

0.000

0.002

Coefficient

0.064

0.009

0.068

0.039

0.137**

Marginal effect

0.002

0.001

0.006

0.003

0.008*

Coefficient-0.144**-0.017

0.086

0.045

0.065

Marginal effect-0.007**-0.001

0.006

0.002

0.002

Coefficient

0.034-0.028-0.013

0.100

0.051

Marginal effect

0.001-0.003-0.001

0.007

0.003

Skin and subcutaneous

Coefficient

0.010-0.0280.155**

0.101

0.213***

tissueMarginal effect

0.001-0.0030.012**

0.007

0.012***

Any diagnosis

Respiratory system

Common cold

Hay feverAsthma

Teeth

0-3

4-6

Coefficient-0.002-0.047

Marginal effect-0.001

Coefficient

7-9

Notes: The binary probit model is used for the estimation. The other explanatory variables
include the following: dummy variables for age, sex, survey year, number of siblings, eachparent's age and working status, and the father's presence in the household.

35