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Bulletin of Indonesian Economic Studies

ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20

FAMILY SIZE, UNWANTEDNESS, AND CHILD HEALTH
AND HEALTH CARE UTILISATION IN INDONESIA
Eric R. Jensen & Dennis A. Ahlburg
To cite this article: Eric R. Jensen & Dennis A. Ahlburg (2002) FAMILY SIZE, UNWANTEDNESS,
AND CHILD HEALTH AND HEALTH CARE UTILISATION IN INDONESIA, Bulletin of Indonesian
Economic Studies, 38:1, 43-59, DOI: 10.1080/000749102753620275
To link to this article: http://dx.doi.org/10.1080/000749102753620275

Published online: 17 Jun 2010.

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Date: 19 January 2016, At: 20:23

Bulletin of Indonesian Economic Studies, Vol. 38, No. 1, 2002: 43–59

FAMILY SIZE, UNWANTEDNESS, AND CHILD HEALTH
AND HEALTH CARE UTILISATION IN INDONESIA
Eric R. Jensen

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College of William and Mary, Williamsburg
Dennis A. Ahlburg*
University of Minnesota and University of Southampton
This paper presents and estimates a model of the determinants of child health and
health care utilisation in Indonesia. In particular, it estimates the impact of

unwantedness and number of siblings on health outcomes and treatment. It finds
evidence that children who are unwanted at birth are more likely than other children to become ill and less likely to receive treatment for illnesses. No evidence is
found that children from larger families suffer adverse health consequences.

Since at least the 1970s, the general trend
in Indonesia has been one of declining
family size and improvements in the
overall well-being of members of the
population. Life expectancies are longer,
earnings are higher, and educational attainment has increased—by virtually
any measure of human welfare, the average Indonesian is better off than his
or her parents. Certainly, over time,
health and educational infrastructures
have expanded dramatically. An underlying process of ‘modernisation’ brought
with it preferences for both smaller family sizes and children of higher quality
(e.g. with better health and education).
With worsening economic and political
conditions at the turn of the 21st century,
there was widespread concern that these
gains could be wiped out (Booth 1999;

Jones, Hull and Ahlburg 2000). However, it appears that, despite significant
economic shocks, education and health
have so far not suffered large reversals.

Our aim in this paper is to examine,
at the family level, the impact on child
health and health care decisions of the
number of children and whether a child
was wanted at birth by its parents. Our
premise is that family resources are finite, and therefore that allocative choices
must be made. These choices, or their
consequences, may be observable in survey data on child health. To examine this
contention, we investigate diarrhoeal
and respiratory disease incidence in children, and curative care provision to children for either illness. If fertility is
imperfectly controlled, unwanted births
are likely to occur, and fewer family resources per child are available than is
desired by the parents. Therefore, many
choices requiring resource commitments
by parents, including our measures of
child health, will be affected by the occurrence of unwanted births. We view

the occurrence of an unwanted birth as
a largely exogenous shock occurring

ISSN 0007-4918 print/ISSN 1472-7234 online/02/010043-17

© 2002 Indonesia Project ANU

44

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outside the parents’ decision making
calculus. In Indonesia, the proportion of
unwanted births is low, so for a parent
to declare that a child is unwanted is a
strong signal. Thus, Indonesia is a good
country in which to test for the impact
of unwantedness on child health.
CHILD WELFARE AND
RESOURCE ALLOCATION

Child Health and Education
as Indicators of Well-Being
Educational attainment and child health
are two broad categories of indicators
that have been used in the past to assess
the human capital improvements accompanying development (Kelley 1996;
Cassen 1994). Literacy and numeracy are
important in moving from traditional,
largely agrarian economies to those
based on modern manufacturing. Returns to ed uc ation in the form o f
increased earnings provide clear quantification of the value of attaining such
education. On the other hand, well over
a decade passes before a newborn child
completes his or her education. This
makes it difficult to address directly the
impacts of fertility upon family-level
resource allocation and, through this
mechanism, subsequently upon educational attainment. 1
Evidence from the Asian economies,
most notably Japan, shows the impact

of childhood nutrition on adult physical stature. When severe, malnourishment in childhood can cause diminished
intellectual function in adulthood, and
also may harm the performance of children in school. Child malnutrition is
problematic in Asia, which is home to
75% of the malnourished children in the
world (Ahlburg and Flint 2001). Ill
health and poor growth in childhood are
also related to adult morbidity and
mortality (Fogel 1994), and to lower
productivity and poorer labour market
outcomes (Strauss and Thomas 1998).

Eric R. Jensen and Dennis A. Ahlburg

Child health is thus an important measure of both current and future wellbeing. Parents’ willingness and ability
to commit resources to their children’s
health is therefore of interest. Empirically, examining relationships between
fertility or number of siblings and child
health is an appealing way to get at underlying resource allocation decisions,
because observable consequences of

these decisions may begin to appear almost immediately after a child is born.
The increase in mean adult heights
observed in postwar Japan shows that
resources play an important role in human capital outcomes.2 Families, on average, provided their children with
more protein and calories than in earlier times because they could afford to
do so. This was true in such a large proportion of families that the overall impa ct in the entire po pulatio n was
significant. Incomes per family member
rose both because family incomes increased with rising productivity and
because the average family size fell. Historically, these phenomena are deeply
intertwined, and inferring a causal role
for fertility decline is difficult. Certainly,
in present-day Japan, the overwhelming majority of parents would be able
to provide adequate nutrition to numbers of children much greater than the
numbers they currently bear. However,
during the post-Meiji fertility decline in
Japan, incomes were much lower than
their present level. Plausibly, the tradeoffs between numbers of children and
the nutrition (and subsequent health
conditions) these children enjoyed were
starker than currently is the case. In this

sense, the historical trade-off between
number of children and their health status was much like the present-day
trade-off between number of children
and education, or between number of
children and size of bequests, in many
Asian societies.

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Family Size, Unwantedness, and Child Health and Health Care Utilisation

Inferring Resources Devoted
to Child Health
One major problem in inferring resources devoted to child health is empirical. Child health, and indeed the
broader concept of child well-being, has
many dimensions, and in this sense is
difficult to measure. The tie between resources devoted to child health and actual health outcomes is a ‘noisy’ one.
Genetic endowments and chance play
an important role in morbidity, for example. Very well nourished, well fed
and clean children still get diarrhoeal

and respiratory infections and, conversely, poorly nourished children are
not always ill. The presumption underlying our analysis of child morbidity is
that the relative frequency of illness declines with increases in resources committed to c hildren. Other in direct
measures of child welfare are available,
but are equally imperfect. For example,
weight-for-height measurements are
taken as part of some surveys (although
not in the survey we use for this paper).
Just as with health outcomes, the tie between inputs, in the form of nutrition
and so forth, and anthropometric outcomes depends on a range of unobs erved fa cto rs , includ ing geneti c,
metabolic and other factors.
An additional confounding factor is
theoretical in nature, and leads to statistical problems of identification. Parents
are making choices about a host of factors simultaneously. In the broadest of
terms, they are making decisions about
numbers of children, resource commitment per child, and non-child expenditures. Som e parents ma y choose
relatively more children, with relatively
less committed per child, than other
parents. As Montgomery and Lloyd
(1996a) point out, the simple finding of

an inverse relationship between fertility
and child well-being therefore does not,
of itself, constitute justification for

45

policy. It is completely consistent with
standard economic models of family formation (e.g. Becker and Lewis 1974) in
which parents with a taste for lower
quality per child choose to have more
children, because the price per child is
lower than that for children of higher
quality. To show that fertility affects
child well-being, what is needed is evidence that changes the in number of
children affect quality per child independently of the underlying variation in
tastes, income or prices generating the
initial distribution in the number of children. Such independent effects, by the
nature of the quality–quantity interaction in children, are inherently difficult
to tease out, rendering statistical identification of structural quantity and quality equations difficult.
Little work has been done on estimating the quantity–quality trade-off using

child health as a measure of quality.
Some work on the impact of fertility on
educational attainment has focused on
the quantity–quality trade-off, branching in two directions toward analysis of
the within-household impacts of large
family sizes.
One path focuses on average wellbeing within families, examining, for
example, differences in average educational attainment of children as a function of number of siblings. One of the
closest relationships between the number of siblings and average educational
attainment by family members is reported in the work of Knodel, Havanon
and Sittitrai (1990) for Thailand. Other
studies based on essentially similar conceptions of within-family allocation include those by Bauer et al. (1992) for the
Philippines; Behrman and Wolfe (1987)
for Nicaragua; and Rosenzweig and
Wolpin (1980) for India. All of these authors find that children from large families receive less education than do
children from small families. The effects

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46

are often small, and again, causality is
difficult to infer. It may be that increasing competition among siblings for finite family resources decreases average
access to education. This is the ‘resource
dilution’ model associated with Judith
Blake (1981).
A second approach acknowledges
that parents with a preference for larger
families may be those who see less need
to educate their children, so that the correlation between family size and children’s educational attainment reflects
tastes and is not causal (Montgomery
and Lloyd 1996a; Becker and Lewis
1974). The unsuitability of empirical
models that do not account for the
endogeneity of fertility in parents’ decisions lies at the heart of reviewers’ criticisms of much of the work in the field
(e.g. King 1987; Kelley 1996). Models
that pay careful attention to statistical
identification tend to find small effects
of family size on household resource allocation. For example, in an attempt to
examine the impact of the purely exogenous component of fertility, Rosenzweig and Wolpin (1980) use a sample
of twin births, and find a small impact
of exogenous fertility on subsequent
educational attainment. 3 Behrman and
Wolfe (1987) use a sample of adult sisters toward similar statistical ends. Work
to date has generated little firm support
for the notion that negative within-family consequences of family size on the
welfare of family members are important, at least when measuring welfare by
educational attainment.
In considering the decision making
process of parents, we have thus far
spent little time discussing how their
decisions translate into actual fertility.
Conception carries with it a substantial
element of randomness, and therefore so
does contraception im perfectly employed.4 Desired births may not happen,

Eric R. Jensen and Dennis A. Ahlburg

undesired births may occur, or births
may come earlier or later than desired.
The occurrence of an unwanted birth
represents the exogenous impact of fertility. Unless one makes the heroic
assumption that parents anticipate (perfectly) not only the likelihood, but the
actual occurrence, of contraceptive failure, the unplanned nature of an unwanted birth implies that the event is
independent of the parents’ decision
making calculus. Therefore, a ‘pure’
causal impact of unwanted births on
meas ure s of child quality ma y be
estimable. For instance, in Thailand,
Frenzen and Hogan (1982, cited in
Montgomery and Lloyd 1996b), found
that children wanted by both parents
have a significantly higher probability
of surviving their first year than do children wanted by only one or neither parent. The impact may be felt by the child
in question, or it may be distributed over
a larger group of children.
In considering within-family resource
allocation, a more fully developed
strand in the literature examines differential allocations of family resources on
the basis of an indirect measure of wantedness: the child’s sex. This strand, in a
sense, is a logical extension of the work
by Rosenzweig and Wolpin, in that the
birth o f a gir l is o uts ide the preconception decision making calculus of
parents. Work by Chen et al. (1981),
Simmons et al. (1982) and Dasgupta
(1987) has shown that South Asian girls
receive less food than their male siblings,
and are less lik ely to survive their
childhood. The Simmons et al. work is
noteworthy in demonstrating the relationship between sibling competition for
resources and the impact of an unwanted daughter’s birth. Rosenzweig
and Schultz (1982) tie this to unfavourable labour market outcomes for some
Indian girls. Consistently in the litera-

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Family Size, Unwantedness, and Child Health and Health Care Utilisation

ture for South Asia, one sees a relationship between the sex of a birth and resource allocation, as measured by food,
education or other costly resources devoted to the child.
Montgomery and Lloyd (1996b) cite
a Finnish study by Myhrman et al. (1995),
in which mothers interviewed in their
sixth or seventh month of pregnancy
were asked whether the pregnancy was
wanted, mistimed or unwanted. The
timing of the questions guarantees that
the responses are not influenced by the
characteristics of the child, because the
wantedness information was collected
before the child was born, and the consequences were measured on a followup survey done more than two decades
later. Myhrman et al. found that lower
educational attainment for unwanted
daughters occurs for any number of siblings, while for unwanted sons it occurs
only if there are two or more siblings.
This shows a strong element of parental
choice, coupled with a resource constrain t that appears to bind as the
number of siblings increases, albeit with
differing strengths for sons and daughters. That resource constraints (measured by number of siblings, all else
constant) bind less tightly for sons than
for daughters shows that parents are
able to compensate, to some degree, for
an increasing number of siblings. On the
other hand, the fact that number of siblings matters, even for sons, implies that
resource constraints are increasingly
important as the number of siblings
grows.
Wantedness of births plays a key role
in our analysis. Mothers in Indonesia’s
D em ographic and Health Surveys
(DHS) are asked specifically about wantedness at the time of conception for each
live birth in a period of three to five years
preceding the survey. Because they are
asked retrospectively, responses to these

47

questions are often thought to be subject to post-hoc rationalisation. The direction of such rationalisation is not clear,
however. Knodel and Prachuabmoh
(1973), for example, believe their Thai
data understate the degree of unwantedness, as mothers are reluctant to say
that a given child was in fact unwanted.
Rosenzweig and Wolpin (1993), on the
other hand, claim that their US data
show the opposite. On the basis of an
undesirable outcome such as an unhealthy baby, Rosenzweig and Wolpin
claim that some women (perhaps nearly
one-fourth) who, prior to conceiving,
said that they wanted a birth changed
their post-partum response to ‘unwanted’. It seems prudent to take both
arguments into account by allowing
wantedness (potentially) to be endogenous, that is, dependent upon characteristics of mother, of siblings, and of the
reference child, and we do so in our
empirical work.
THE MODEL
We model two measures of child wellbeing as functions of child, family and
community characteristics: probability
of illness with either diarrhoea or fever/
cough; and use of curative care for these
conditions. Pragmatic concerns dictate
this strategy, as diarrhoea and respiratory infections are the two illnesses most
readily observed in survey data. However, they are also of policy interest, as
these two disease categories account for
roughly one-third of infant and child
mortality in the developing world. We
construct a model based upon the concept of a child-specific index of ‘child
value’, or parents’ willingness to commit resources to a particular child. This
index is posited to be a function of exogenous individual, household and
community variables. Household resource commitments are measured

48

Eric R. Jensen and Dennis A. Ahlburg

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directly by use of health care, with associated monetary, time and other costs;
and indirectly by the incidence of
morbidity.
Define Z to be the index value for a
given child, where, for X, a vector of family and child-specific variables, such as
age, educational attainment and wealth
holding, w, a scalar index of wantedness,
and s, a scalar count of number of siblings,
Z = f(X, w, s)

(1)

and define A to be a vector of variables
measuring family access to health care,
and R to be a vector measuring risks of
illness. Then the following conditions
characterise the incidence of illness and
subsequent use of curative care for living children:
Illness observed : I = 1
if Z *1 ³ Z | X, w, s, R

(2)

Treatment observed : T = 1
if Z *2 £ Z | X, w, s, A, I = 1

(3)

where Z* denotes unobserved threshold
variables. These are the usual thresholds
underlying discrete choice models
which, although they are not observed
directly, carry observable consequences.
In this setting, illness occurs if the index
of child value, conditioned on child and
family specific covariates and risks of
illn ess, fa lls below an unobserved
threshold value, and curative treatment
occurs if child value, conditioned on access, covariates and illness, exceeds a
minimum (unobserved) threshold. The
presumption is that, all else constant,
wantedness is associated with decreased
probability of illness and increased probability of curative treatment, while
number of siblings is assumed to work
in the opposite direction. Family-level
covariates associated with increased

wealth, income or socio-economic status are expected to exert a similar effect
to wantedness, and increases in accessibility and risk are presumed to increase
the probability of treatment and illness,
respectively.5
Wantedness responses, because they
are given after the birth has occurred,
may be subject to the sort of post-hoc
rationalisation we have dis cus sed
previously, and we therefore model
wantedness as a function of family characteristics and number of sib lings.
Number of siblings is a reflection of past
values of child value indices. These values are likely to be highly correlated
with current values. Therefore, to complete the model, we have:
w = g(Xw ,s)

(4)

s = h( Z -T )

(5)

where Xw is a vector of variables measuring family-specific considerations,
including characteristics of the child,
such as sex, birth weight and nonsingleton status. Number of siblings
is a function of Z –T, notational shorthand for the set of past values of the
index of child value Z .
An important part of the Rosenzweig
and Wolpin (1993) study is their claim
that wantedness responses are subject to
post-birth adjustment, conditional on
child characteristics (including, but not
limited to, child morbidity). Because
DHS surveys only have information
about child morbidity in the immediate
pre-survey period, it is not possible to
subject the morbidity aspect of the
Rosenzweig and Wolpin finding to full
scrutiny. It is po ssible to ex amine
whether recent or current illness has an
impact on wantedness responses, however. To do so, one would specify wantedness as an endogenous structural
determinant of equations (2) and (3).

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Family Size, Unwantedness, and Child Health and Health Care Utilisation

Our measure of health, limited as it is to
the immediate pre-survey period, is a
somewhat ‘noisy’ measure of child endowments. A failure of this test to reject
the hypothesis of exogeneity lends support to the notion that our outcome
measures are not determinants of wantedness, but such a test is less powerful
than one might like in examining the
contention that child endo wments
(broadly defined) do not affect wantedness. Similarly, it is feasible to allow for
endogeneity of number of siblings, 6 and
to test statistically for such a possibility.
Comparable caveats apply, as the test
specifically relates to the impact of illness in the immediate pre-survey period
(or treatment for this illness) upon
number of siblings, and not the impact
of some more general measure of child
endowments upon number of siblings.
A birth generates an increase in the
number of siblings, and each birth must
be classified as either wanted or unwanted. Therefore, wanted births carry
with them only a (relative ly pure)
number of siblings effect, while unwanted births are accompanied by the
differential impacts upon child wellbeing of an unwanted birth into the family and of the accompanying increase in
number of siblings. Except where occasionally contaminated by unwanted
births, number of siblings is a reflection
of parents’ desires. Unwanted births, on
the other hand, are reflective of exogenous shocks to the family-formation
process, and therefore are expected to
generate larger impacts on subsequent
resource allocations.
The within-family mechanism
through which unwantedness operates
could be one in which per capita resource declines are spread more or less
evenly over household members, or, as
seems more likely given models of
within-household allocation (Simmons
et al. 1982; Rosenzweig and Schultz

49

1982), unequally according to preferences or past investments. Children who
are older or otherwise relatively favoured are less likely to feel the consequences of the birth of an unwanted
younger sibling, therefore concentrating
the observable response on younger
children, particularly the unwanted
birth itself. If the resource pressures accompanying an unwanted birth are
spread evenly over all children and one
examines—as we do—the consequences
only for the child in question of his or
her unwantedness, the effect will be to
understate the apparent effect of unwantedness. Since the impact on only
one child is included in our analysis, the
estimated impacts of unwanted status
on the most recent birth presented below are low er bounds to the tota l
intrahousehold allocative response to
unwantedness.7
DATA AND SETTING
The data come from the 1991 Indonesian
Demographic and Health Survey (Indonesia Central Bureau of Statistics, State
Ministry of Population and Ministry of
Health and Macro International 1992).
The survey uses interviews with 22,909
ever-married women, of whom 21,109
were married at the time of the survey,
and reports births of 14,393 children in
the five years preceding the interview
date. The estimated total fertility rate for
15–44 year-olds was 2.99.
For births in the five years preceding
the survey, detailed information on
health was collected. Mothers were
asked if their children had experienced
diarrhoea or cough/fever in the two
weeks preceding the survey, as well as
what treatment the children were given.
Treatments can consist of commodities,
advice, or some combination of both. As
in many developing countries, there is
an active traditional sector in Indonesia
providing health care. We are unable to

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50

differentiate among various folk cures
in many instances. For example, ‘herbs’
as a treatment for an illness is difficult
to assess in terms of effectiveness. We
have therefore focused on treatment
supplied by the modern sector. The presumption is that modern methods cost
at least as much as traditional methods
and are less accessible. Therefore, use
of modern-sector treatments reflects
greater willingness on the part of parents to seek out effective care and
commit resources than does use of traditional methods.8
The DHS question on wantedness
comes in a section of the questionnaire
extracting detailed information on recent births. The mother is asked whether
she wanted the current birth at the time
she became pregnant, whether she
wanted the birth but would have preferred that it had come later, or whether
she would have preferred that the birth
had not occurred at all. In our regression analyses, we classify a birth as
‘wanted’ if the mother reports that it was
wanted either at the time of conception
or later. Of all live births, 95% were classified in this way in the survey.
RESULTS
We first discuss the impacts of areal
(community-level and provincial-level),
family, and individual-level determinants, including wantedness and number of siblings, on child morbidity. We
then examine the impact of a similar set
of determinants on curative care. We
employ a linear specification for number of siblings in the equations for morbidity and treatment. We test for the
potential endogeneity of wantedness
and number of siblings on both morbidity and treatment equations, again
based on residuals from reduced-form
equations on wantedness and number
of siblings.

Eric R. Jensen and Dennis A. Ahlburg

Ideally, one would estimate the parameters of the illness/treatment sequence
jointly. That is, if the underlying issue is
one of the resource commitments of parents, then the susceptibility of children
to illness and their subsequent use of
care, conditional on illness, are two
manifestations of an unobserved resource allocation decision. Given the binary outcomes of the two measures, a
bivariate probit model of sample selection is most efficient. However, the
process is sufficiently ‘noisy’ that the bivariate likelihood function does not converge reliably.9 We therefore estimate the
determinants of treatment in two ways.
We estimate a univariate probit for the
probability of receiving treatment, ignoring that the child must first be ill before
receiving treatment. As an alternative,
we estimate the treatment equation as a
lin ear probability with a two-stage
Heckman sample selectivity correction. 10,11 When transformed to derivatives evaluated at sample means, the
simple probit gives virtually identical
results to the Heckman estimates, and
subsequent discussion of the estimates
applies to either formulation. In either
instance, we estimate a univariate probit
transformation of the determinants of
morbidity.
Table 1 presents descriptive statistics.
Many variables are familiar, but some
bear further explanation. The first is our
measure of permanent income or wealth.
DHS surveys do not collect direct data
on income or wealth. Instead, they use
a collection of questions about asset
ownership (vehicles and appliances),
housing quality (roof and floor materials and plumbing) and access to fresh
water. Using factor analysis, we have
com bined the responses to many of
these questions into two factors. This
makes the subsequent regression results
less cluttered, while allowing us to con-

Family Size, Unwantedness, and Child Health and Health Care Utilisation

51

TABLE 1 Description of Variables Used in the Analysis, Indonesia, 1991

Variable

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Fever/cough
Fever care
Diarrhoea
Diarrhoea care
Wanted birth
Siblings alive
Male
Child’s age
Mother’s age
Mother’s education
Husband’s education
Assets 1
Assets 2
Water
Type of toilet
Travel time

a

Definition

Child ill with fever or cough in the last two weeks
Modern advice sought or modern care given for
fever/cough
Child with diarrhoea in the last two weeks
Modern advice sought or modern care given for
diarrhoea
Dummy for birth wantedness: 1 if the birth was
wanted, then or later
Number of siblings alive at time of birth
Dummy for male: 1 if birth is male
Child’s age in months
Age in years of mother at child’s birth
Mother’s education in years
Education of mother’s current husband in years
Factor score based on asset ownershipa
Factor score based on asset ownershipa
Dummy for access to piped or well water for
drinking: 1 if yes
Dummy for flush or pit toilet access: 1 if yes
Mean travel time to family planning service
provision point in minutes

Mean

Standard
Deviation

0.35

0.48

0.83
0.10

0.38
0.30

0.78

0.42

0.95
1.58
0.52
29.24
29.09
6.22
7.40
–0.07
–0.08

0.22
1.70
0.50
16.92
6.15
5.29
5.47
0.98
0.98

0.09
0.18

0.29
0.39

32.16

7.41

See text for a more complete description of the constructed variables.

trol for variations in a fairly large number of asset variables. ‘Ownership of television or refrigerator ’ and housing
attributes ‘non-dirt floor ’ and ‘in-house
electricity’ load on the first factor, while
‘ownership of automobile or stove’ and
‘numbers of rooms for sleeping’ load on
the second. Taken together, these variables capture asset ownership, and as
such are proxies for permanent income.
There are three variables constructed as
provincial-level means: the mean incidence of fever/cough and diarrhoea,
and the mean travel time to health facilities. These are constructed using responses for children of every eligible
respondent in the province except the
reference birth, and therefore are indica-

tive of the community-level conditions
faced by the reference birth. The variables for water and type of toilet are
household-specific. Since we are examining care given to living children, the
sample is restricted to currently living
children.12
Two sets of results are discussed here.
The first set relates to the impact of areal,
family and individual-level determinants, including wantedness and number of siblings, on child morbidity. The
second relates to the impact of a similar
set of determinants on curative child
health care. The results are based upon
model specifications that employ actual
values of wantedness and number of siblings rather than their instruments.

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52

Because of the possible endogeneity of
these variables, we generated instruments for wantedness and number of
siblings by using reduced-form equations to predict their values. 13 We then
used the residuals from these equations
as regressors in our structural equations
for morbidity and health care, in order
to carry out Hausman tests for exogeneity of wantedness and number of siblings in each of the structural equations.
In no cases were we able to reject the null
hypothesis of exogeneity for either
wantedness or number of siblings. In
other words, we have no evidence in this
sample that illness in the last two weeks
or health care provision to children affects post-hoc wantedness or number of
siblings responses.14 Therefore, the results we present do not employ instrumental variables for wantedness or
number of siblings.
An additional question of model
specification is the manner in which
number of siblings might influence allocations to child ren. Kelley (1996)
claims that failure to include potential
non-linearities in studies of the effect of
number of siblings on educational attainment of children is an error leading
to overstatement of the impact of the
number of siblings on resources allocated per child. We find no evidence for
such scale effects on the incidence of
morbidity or allocation of health care. To
test the proposition, we specified a variable that equalled 1 for large families
and 0 otherwise, where ‘large’ was defined as ‘having six or more living children’. 15 We used this ‘large family’
dummy variable, interacted with the full
set of covariates, to perform Wald tests
on jointly restricting the coefficients
across values of the large family dummy,
and were unable to reject the null
hypothesis that they were the same. Individually, the signs and rough magnitudes of family size coefficients and their

Eric R. Jensen and Dennis A. Ahlburg

associated statistical significance levels
also were the same. In other words, we
have no statistical grounds to support
the contention that the impact of an additional child differed between small
and large families. Therefore, we present
estimates that include the impact of family size (alone and untransformed) on
illness and treatment.
Child Morbidity
Diarrhoea. Table 2 shows that wantedness at birth plays an important role in
reducing morbidity in Indonesia. The
impact of wantedness is to decrease the
chance of contracting diarrhoea by approximately 50% compared to the overall prevalence level of diarrhoea, a very
large and statistically significant effect.16 Asset availability, as measured by
the constructed factors, shows no impact on diarrhoea incidence. Mother’s
education, access to flush toilets and the
mean provincial prevalence of diarrhoea are all statistically significant and
largely operate in expected fashion:
children of more educated mothers,
with access to flush toilets, and living
in areas where diarrhoea prevalence is
lower, experience less diarrhoeal disease, all else constant. Children from an
area with a prevalence rate one point
higher than another area have a predicted probability of contracting diarrhoea seven-tenths of a point higher
than children from the latter area. This
is comparable to the diarrhoea morbidity differential experienced by children
whose mother ’s educational attainments differ by three and one-half
years, roughly half the diarrhoea morbidity reduction attributable to having
access to flush toilets, and approximately one-seventh of the diarrhoeal
morbidity impact of wantedness.
Higher numbers of siblings are associated with a small but statistically significant decrease in the probability that a

Family Size, Unwantedness, and Child Health and Health Care Utilisation

53

TABLE 2 Coefficients of Probit Models of Child Morbidity, Indonesia, 1991

Probit Partial Derivative (p-value)
Variablea

Wanted birth

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Number of siblings
Male
Child’s age
Child’s age squared
Mother ’s education
Husband’s education
Assets 1
Assets 2
Urban
Water
Toilet
Provincial mean prevalence
Sample size
a

Diarrhoea

Fever/Cough

–0.047
(0.00)
–0.004
(0.01)
0.009
(0.08)
0.012
(0.10)
–0.008
(0.00)
–0.002
(0.00)
0.001
(0.03)
0.006
(0.14)
–0.002
(0.41)
0.001
(0.94)
0.011
(0.25)
–0.016
(0.06)
0.754
(0.00)

–0.083
(0.00)
–0.011
(0.00)
0.018
(0.05)
0.022
(0.08)
–0.103
(0.00)
–0.000
(0.94)
0.001
(0.15)
0.019
(0.01)
0.001
(0.81)
–0.019
(0.17)
0.027
(0.10)
–0.038
(0.01)
1.02
(0.00)

13,118

13,231

See table 1 for variable definitions.

child will contract diarrhoea. The estimated decline in diarrhoeal morbidity
is approximately 4% per sibling. Two
possible explanations suggest themselves. One conjecture is that this is not
due to enhanced resistance to disease of
children with large numbers of siblings
(in part because there are few large families in the data) but, rather, that children
with few or no siblings have higher
morbidity. This draws on the wellknown claim of John Bongaarts (1987)

that family planning programs, when
successful, increase mean infant and
child mortality rates; this occurs because, as fertility falls, the proportion of
births that are first births increases and,
for physiological reasons, first births are
at higher risk of mortality. Unfortunately, family sizes are so small that
there are not enough cases to allow us
to say anything definite on this point,
other than that a smaller effect persists
when one-child families are removed

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54

from the sample. In the 1991 Indonesia
sample, median number of siblings was
1, and mean number of siblings was
1.58. Of the children in the sample, 33%
were first births, and 59% had either no
siblings or one sibling. A second possibility is a learning effect. Mothers learn
from their experiences with their first
child, and so mothers who have more
children are better at keeping them
healthy.
Respiratory Infection. The pattern for
respiratory in fections is much the
same. Wanted births are roughly 13%
less likely to contract respiratory infections than are unwanted births.
One measure of asset ownership is statistically significant but has an estimated coefficient of the wrong sign.
It is of little practical impo rtance,
however: a one-standard deviation
increase in wealth, which when compared to the mean implies leapfroggi ng o v er a b o ut o ne -thi rd o f th e
income distribution, would yield an
increase of only 5% in fever/cough
m o r b i d i ty. A s w a s th e c a s e w it h
diarrhoeal disease, respiratory illness
is more likely to occur in areas where
its prevalence is high. Access to a private flush toilet decreases the probability of contracting a respiratory
illness. It is doubtful that this represents so direct a link in disease reduction: perhaps this variable is acting as
some sort of proxy for housing quality. Boys are (barely) statistically more
likely to contract respiratory illnesses
than girls. As was the case for diarrho ea, chi ldren w ith man y l ivi ng
siblings are less likely to contract respiratory disease than are those from
smaller families. Once again, the magnitude is small, with each additional
sibling accounting for a drop of 3% in
the probability of illness. The apparent beneficial impact of siblings is
swamped if that child is unwanted.

Eric R. Jensen and Dennis A. Ahlburg

Curative Health Care
Of ill children, 78% of those with diarrhoea and 83% of those with fever/
cough received modern sector treatment. In the Philippines, Costello and
Lleno (1995) found a preference for
(typically incorrect) antibiotic-based
treatm ent regimens fo r diar rhoea.
There is some evidence that this tendency also prevails in Indonesia, with
only about 20% of children ill with
diarrhoea receiving oral rehydration
salts (ORS). It therefore seems unlikely
that the relatively high treatment rates
in Indonesia are attributable to adherence to the ORS protocol.
The marginal effect of being wanted
was to increase the probability of being treated for diarrhoea by roughly
0.13 (table 3). Alternatively, compared
to the mean probability of treatment
of diarrhoea of 0.83 (table 1), a wanted
child was 17% more likely to receive
treatm ent th an wa s an un wan ted
child. Children from larger families
were less likely to receive treatment,
although the latter variable was not
statistically significant. These impacts
were very similar for both estimation
approaches. However, the size of the
impact of these variables on treatment
for fever/cough was not stable across
d i ffe re n t e s ti m a ti o n a pp r o ac h e s .
Household assets and prenatal care
did have statistically significant and
consistent im pacts. Mother ’s use of
prena ta l care inc rea sed trea tment
probability by 13%. Both asset ownership factors had positive, statistically
significant coefficients, with a simultaneous one standard deviation change
accounting for an increase of 8% in
treatment probability. None of the
other variables could account for a deviation of more than 1% from mean
treatment probability.
The larger and more consistent impact of wantedness on diarrhoea treat-

Family Size, Unwantedness, and Child Health and Health Care Utilisation

55

TABLE 3 Coefficients of Models of Treatment for Child Morbidity, Indonesia, 1991

Treatment for Diarrhoea

Variablea

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Wanted birth
Siblings alive
Male
Child’s age
Child’s age squared
Mother ’s age
Mother ’s education
Husband’s education
Assets 1
Assets 2
Prenatal care
Travel time
Urban
Outer islands
Java/Bali
Constant
Selection coefficient
Sample size
a

Treatment for Fever/Cough

Heckman
Coefficient
(p-value)

Probit Partial
Derivative
(p-value)

Heckman
Coefficient
(p-value)

Probit Partial
Derivative
(p-value)

0.127
(0.05)
–0.002
(0.83)
0.001
(0.97)
0.108
(0.03)
–0.016
(0.16)
0.001
(0.68)
0.003
(0.48)
0.004
(0.20)
0.030
(0.15)
0.047
(0.01)
0.054
(0.10)
–0.005
(0.01)
–0.011
(0.80)
–0.057
(0.13)
0.031
(0.45)
0.840
(0.00)
–0.137
(0.12)

0.137
(0.04)
–0.005
(0.66)
0.014
(0.65)
0.135
(0.01)
–0.026
(0.01)
0.001
(0.68)
–0.001
(0.78)
0.005
(0.16)
0.046
(0.06)
0.055
(0.00)
0.046
(0.18)
0.009
(0.01)
0.009
(0.85)
–0.052
(0.19)
0.066
(0.14)

0.218
(0.60)
0.018
(0.37)
0.011
(0.31)
0.089
(0.00)
–0.010
(0.01)
–0.003
(0.07)
0.002
(0.12)
0.002
(0.03)
0.037
(0.00)
0.029
(0.00)
0.053
(0.00)
–0.000
(0.93)
0.053
(0.00)
0.013
(0.27)
0.046
(0.18)
0.682
(0.14)
–0.279
(0.00)

–1.49
(0.01)
–0.063
(0.01)
0.008
(0.53)
0.091
(0.00)
–0.014
(0.00)
–0.002
(0.19)
0.004
(0.01)
0.003
(0.05)
0.060
(0.00)
0.292
(0.00)
0.079
(0.00)
–0.001
(0.192)
0.025
(0.208)
0.003
(0.83)
–0.085
(0.07)

989

4,665

See table 1 for variable definitions.

ment than on fever/cough treatment is
puzzling, since diarrhoea treatment is,
at least in theory, somewhat cheaper and
easier to obtain. Possibly, parents take

fever/cough more seriously than diarrhoea (so that costs play a less important role in assessing treatment choices).
If so, one might expect fever/cough to

56

Eric R. Jensen and Dennis A. Ahlburg

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be more likely to be treated in any circumstance, but in overall treatment levels clear differentials by disease are not
evident in Indonesia.
DISCUSSION
Our aim in this paper has been to demonstrate the impact of within-family resource pres sures on allocations to
children. We use one measure of such
pressures, number of siblings, which is
potentially endogenous to the overall
decision making process of the family.
Our second measure, unwantedness at
conception, is more a reflection of exogenous shocks outside the decision making calculus of the parents. We focus on
the impacts upon measures of child
health, because human capital improvements appear to account for an important share of the economic growth that
has occurred in many societies. Such
human capital increases come from a
willingness to commit resources to
children.
Incomes are low in Indonesia, and
low incomes can imply harsh tradeoffs between numbers and quality of

children, and differential treatment of
children. These trade-offs can take the
form of reductions in calories, protein
or other measures of nutrition that can
generate observable implications for
child morbidity. Indeed we find that
unwantedness leads to increases in
morbidity and decreases in health care
treatment. The morbidity implications
are quite large in a policy context. We
do not find a consistent impact on
treatment probabilities for number of
siblings. It appears that, in Indonesia,
large families who face pressing resource constraints make other sacrifices to protect the health of their
children. However, children who are
unwanted, a quite rare phenomenon
in Indonesia, are disadvantaged, and
are more likely than other children to
become ill, and less likely to receive
treatment when ill. To the extent that
continuing economic and political instability in Indonesia leads to an increase in unwanted births, a larger
proportion of children may suffer the
adverse treatment identified in this
paper.

NOTES
*

1

2

3

The authors thank Ron Lee, Andy Mason, Jerry Russo, Amy Tsui, and seminar
participants at the East–West Center, College of William and Mary and World
Bank for helpful comm ents, and Jeff
Brown, Shi-Jen He and Lixia Xu for capable research assistance.
One attempt to address this is a study for
Finland by Myhrman et al. (1995), which
uses data from a baseline survey and two
revisits, the latter of which came 24 years
after the initial survey.
See Steckel (1995) for a survey of the literature on the relationship between
childhood nutrition and adult physical
stature.
Given the extremely small number of
multiple births in the survey these autho rs e m ploy, the f in d ing of n on -

4

5

significance should be interpreted with
caution.
Trussell and Kost (1987) provide estimates of annual contraceptive failure
rates, largely for the US, that are typically
an order of magnitude greater than their
theoretical minima.
The model is one of resource allocation
to living children. Clearly, prior sibling
mortality is relevant in selecting the sample of children for whom the allocation
decisions are being made, and non-random prior mortality has the potential to
bias empirical results based on this
model. However, in our initial empirical
work, we found no evidence of mortality selectivity. In related work, Pitt (1997)
found only very small impacts of a failure to include the self-selectivity of fer-

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Family Size, Unwantedness, and Child Health and Health Care Utilisation
tility in models of child mortality. Therefore, we treat survival to selection in our
sample as exogenously determined.
6 One reason that this might be desirable
is that past fertility contributes to the current number of siblings, and the past and
current determinants of fertility, especially in survey data, may be difficult to
separate.
7 It would be desirable to incorporate some
sort of family-level effect in this model.
However, data constraints make such effects impossible to estimate using DHSstyle data sets, as there are very few
multiple-birth households reporting the
data we require.
8 A reviewer offered an alternative interpretation: more ‘traditional’ parents always seek out modern care as a ‘last
resort’. If this is true, use of traditional
care does not necessarily indicate less determination to seek out effective care; it
rather reflects more traditional ways of
viewing the world.
9 Pitt (1997) reports similar difficulties in a
model of fertility and mortality.
10 The linear probability model yields consistent estimators, even with the Heckman l as a covariate. However, it is
inefficient, heteroscedastic (in known
fashion), and can yield predictions outside the unit interval (see, for example,
Van de Ven and Van Praag 1981).
11 For each illness, we first estimate the
structural equation for morbidity, and
then use the estimated parameters in constructing the inverse Mills ratio term for
the treatment equation.
12 If morbidity is a reflection of choice, then
mortality might be a reflection of the
same phenomenon. Therefore, in an
analysis not reported here, we examined
the possibility of selectivity bias induced

13

14

15

16

57

by excluding dead children. Using a
two-stage Heckman approach in models of morbidity, we were consistently
and robustly far from being able to reject the null hypothesis of random
mortality. In other words, had the children who died in fact survived, we
would not predict the incidence of disease to be any greater among them
than among the actual survivors.
Reduced forms for these variables are
estimated and used to generate instruments for subsequent Hausman tests.
All predetermined variables from the
structural equations for illness and
treatment, as well as years since first
marriage, whether parents expected to
receive financial suppor t from their
children or to live with them in retirement, birth w eight categories , and
whether the reference birth was one of
a multiple birth, are used in estimating the reduced-form equations.
Note that child health and the provision of care are indicators of quality,
w hic h in the B e ck e r –L e w is ( 19 7 4)
framework determine the price per
child. It is therefore possible for current health to play a causal role in the
determination of number of siblings,
although we find no empirical support
for this notion.
This value was chosen becau