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The Determinants of Birthweight: Addressing Potential

Sample Selection Bias from Babies Who Are Not Weighed at

Birth!

Heni Wahyuni1

Introduction

The empirical literature reviewed with regard to the infant health production function has

focused on the issues relating to endogeneity and sample selection biases, caused by unobserved

health heterogeneity and the pregnancy-resolution decision (Liu 1998; ROllS, Jewell & Brown

2004). The first bias relates to endogeneity of prenatal care, while the second, in existing studies,

arises from a given woman's decision to abort or continue her pregnancy. Specifically,

unobservable factors that may influence a woman's decision to proceed with the pregnancy or

abort are factors that are also likely to influence her use of prenatal care and birth outcomes,

particularly birthweight. Sample selection bias relating to the decision to abort is unlikely to be a

problem in Indonesia, where abortion is socially unacceptable and only conducted for medical

reasons. There is a potential for selection bias, however, due to non-random missing information

on birthweight.

The potential sample selection bias that arises from birthweight being missing for some babies

(those not weighed at birth) is a common issue in developing countries and generally does not

occur in studies of birthweight in developed countries. If the birth weight information in the

sample is not missing at random, however, the analysis of the determinants of birthweight

(without considering unreported birthweight) will be biased (Heckman 1979). This represents a

possible sample selection issue, given that the data on a key variable (birthweight) are available

1 This paper has been presented at the National Seminar of the 60th anniversary of the Faculty of Economics andBusiness UGM Seminar, Balancing Indonesian Economy: Governance and Accountability, Ethics, and Strategytoward Inclusive Growth, 19 September 2015.2 Heni Wahyuni is a lecturer and researcher at Faculty of Economics and Business, Universitas Gadjah Mada. E-mail: hwahyuni@ugm.ac.id

only for a subset of the population, who are not weighed at birth. This is often referred to as

incidental truncation (Wooldridge 2002, p. 552).

Relatively few studies, however, have investigated the potential of sample selection bias from

unweighed babies in the relationship between prenatal care and infant health in developing

countries, including Indonesia. Among those few studies, two significant studies have considered

this issue (Habibov & Fan 2011; Mwabu 2009). Habibov and Fan (2011) used 73 percent of all

live births with birthweight in Azerbaijan to analyze the effect of prenatal care on birthweight.

Mwabu (2009) reports that only 17 percent of babies delivered at home and 75 percent of babies

delivered at modem facilities in Kenya have a reported birthweight. Those two studies tested the

potential bias, due to unweighed babies, and found no evidence of selection bias in their data.

One study has examined the impact of the village midwife program on birthweight in Indonesia

(Frankenberg & Thomas 2001), but it does not take into account the selection problem, arising

due to some babies not being weighed at birth.

This study will use the Indonesia Family Life Survey (lFLS) data, which are IFLS3 and IFLS4

data. The focus of the study is to test whether there is a sample selection bias on the determinants

of birthweight. Specifically, birthweight is the outcome of interest, but I will observe this

outcome, conditional upon whether or not the baby has been weighed at birth. The IFLS data for

live-birth babies, born during 2002-08, inclusively, indicates that approximately II percent of

babies were not weighed. It is not appropriate to eliminate unweighed babies from the sample

and only include an analysis of the pregnancy outcome (birthweight) of the subset of mothers

whose babies were weighed, unless the birthweight and whether or not the baby was weighed are

independent. Otherwise, it could lead to biased estimates. Furthermore, in Indonesia, the IFLS

data shows that babies not weighed at birth are more likely to be born at home or in the office of

midwives with a traditional birth assistant, as well as to low-income and less educated mothers.

Previous studies, for example Mwabu (2009), use various instruments such as money prices,

time prices, household assets and income, environmental factors (rainfall), interaction terms

between land and mean long-term rainfall, and between cattle and mean long-term rainfall.

However, these are not available in the IFLS data.

2

A Modeling Framework

Heckman Sample Selection Model

In an attempt to analyze the potential selection problem from unweighed babies, I have used a

Heckman selection model. The model consists of two equations. The selection equation (I)

represents whether the baby was weighed and the outcome equation (2) relates to birthweight,

(1)

_ {1 ifzi > 0Zi - O·f * - 0l Zi -

f xdJ + Eiifzi > 0Yi = l-unobservedifz; = 0 (2)

where z; is a latent variable measuring the propensity of a baby to be weighed at birth; Wi is a

vector of factors known to influence a baby's to be weighed at birth that includes prenatal care

usage, as well as the age of the mother when pregnancy ended, per capita expenditure, years of

schooling, household characteristics (HH index), health condition (general health and 8MI),

whether she smoked before or during pregnancy, and whether she experienced any complication

during the pregnancy, baby-specific characteristics (singleton or multiple birth, gender, order of

pregnancy, year of birth), and the cost of the delivery; Ui contains any unmeasured factors in

equation (I).

We do not actually observe z;. All we observe is a dichotomous variable z., where it is equal to

I if the baby was weighed and zero, otherwise; however, there is only a selected (censored)

sample for the birthweight equation, Yi.

The dependent variable in the selection equation (1) is a dummy variable, indicating whether the

baby was weighed or not and the dependent variable in the outcome equation (2) is the baby's

birthweight (in kilograms). The independent variables in the selection equation (1) are also

included, as independent variables (xa, in the outcome equation (2). In order for the model to be

identified, the selection equation should also have at least one independent variable that is not

3

included in the outcome equation. Otherwise, the model is identified only by functional form,

and the coefficients have no structural interpretation (see identification section).

The disturbance term is assumed to be normal with:

u(-N(O,l)

Er-N(O,l)

When p "* 0, standard OLS techniques applied to the birthweight equation will yield biased

results. If p = 0, then there is no selection problem and the standard OLS model is appropriate. It

is important to test if there is a potential selection problem - if babies that were not weighed at

birth to mothers who have different characteristics from those mothers whose babies were

weighed. The null hypothesis of no selection bias is Ho:P = O. The Heckman selection model is

used to determine whether there is a selection problem or not. The sample is split between babies

that were or were not weighed directly after delivery.

Identification

4

!

1

Identification is very important in the system of equations. As explained previously, most

unweighed babies were delivered at home with midwives or traditional birth attendants. One

reason for mothers to choose to deliver the baby at home is because it may be less costly than

doing so in a modem health facility with professional birth attendants. Any variable, therefore,

that represents the cost of delivery may potentially become an identifier for the selection ~f

whether the baby is delivered at home or at a modem facility. It also can be said that this

identifier is an exclusion restriction in the selection equation, which is whether the baby was

weighed or not. In this analysis, the cost of delivery as an identifier will be applied in the

selection equation and it will be excluded from the birthweight equation, since it is unlikely that

any measure of delivery cost will influence birthweight.

Data

The sample for the empirical analysis is restricted to live births for pregnancies which ended

between the years 2002 and 2008, inclusively, for ever-married women in IFLS4 (2007-08). A

pooled cross-section approach is used, but it utilizes the panel nature of the IFLS data to provide

information on a range of explanatory variables. After excluding observations with missing

responses;' the sample consists of 4,436 live births in which the birthweight is observed (the

baby is weighed). Including live births in which the birthweight is not observed (the baby is not

weighed), the number of observations is 5,023.

Descriptive Statistics

Table I presents the summary statistics for the sample, used in the empirical analysis, separately

tor mothers of babies that were weighed and not weighed. The average number of prenatal care

visits for mothers of babies weighed is 9.19, which is much greater than the 6.13 visits for those

unweighed. Similarly, the percentage of mothers who followed the WHO recommendation for

prenatal care visits is much higher for the weighed babies, approximately 84.49 percent,

compared with only about 54.86 percent for unweighed babies. The household index, as an

indicator of economic background, is higher for mothers of babies that were weighed than babies

that were unweighed. The average number of years of schooling was about 12 years or senior-

high school level for mothers whose babies were weighed, but only about 7 years or graduated

from primary school for those whose babies were not weighed. The lower level of maternal

education relating to unweighed babies may indicate a limited knowledge about health, which

may negatively impact pregnancy outcomes. Moreover, more than 55 percent of mothers whose

babies were weighed lived in urban areas, compared to approximately 17 percent of those with

unweighed babies. The average age at the end of the pregnancy was about the same for both

groups: 27 years of age. Similarly, there was not much difference in the 13M!of mothers and the

general health condition of the mothers. There were very low rates of smoking behavior and

pregnancy complications during pregnancy for both categories of mothers.

3 Responses were coded as missing are defined as the responses with an illogical answer; the surveyor could notmeet the respondents, such as in the case of 8M! (height &weight measurement) or when the respondent refused toanswer.

5

27.7143.17

1.2450.9794.2115.8955.95

892,835.304436

The inclusion of unweighed babies is important because not only are there a significant

percentage (approximately II percent) of babies not weighed in the sample, but the observed

characteristics of mothers of babies not weighed are different from those whose babies were

weighed, thus increasing a potential for bias. More specifically, these mothers are from a lower

socioeconomic background, compared to those whose babies were weighed, in terms of per

capita expenditure, years of education, owning a television, using electricity, having good

drinking water (the household index), and living in rural areas.

Table 1: Statistics Descriptive of Variables Per Category of Birthweight and Definitions

(in percent, unless otherwise indicated)

Variable Birthweight__________________________________________________ ~N~o~t_w~e~ig=h~ed~VVeighed

6.13 9.1954.86 84.493.71 5.457.29 11.99

27.47 27.7121.45 22.5387.56 88.8955.54 64.2957.92 29.13

Total number of prenatal care visit (visits)WHO recommendation"Household indexYears of education (years)Age of mother (years)Body mass index (index)Healthy mother (general health condition)First birthPer capita expenditure below 25th percentile of population levelPer capita expenditure between 25th and 50th percentile of populationlevelPer capita expenditure above 50th percentile of population levelSmoking behaviorMale babySingleton babyHaving pregnancy complicationLiving in urban areaCost of delivery of the baby (rupiahs)Number of observations

Source: IFLS3 (2000) and IFLS4 (2007-08).

24.1917.890.68

52.4792.8414.3117.38

126,338.20587

4 WHO recommends that the minimum number of prenatal care visits during pregnancy be four; with at least onevisit in the first trimester of pregnancy, at least once in the second trimester, and at least twice in the third trimester(World Health Organization 2005).

6

The delivery expense is the variable that differentiates mothers whose babies were and were not

weighed at birth. In general, difference in delivery cost is large between the two groups of

mother: 892,835.30 rupiahs (about A$100) for weighed babies and 126,338.20 rupiahs (A$14)

for unweighed babies. This is a key variable that will be included in the selection equation

(selection for weighed or not weighed), but will not be included in the outcome equation for

birthweight (in kilograms). Thus, it is assumed that the cost of delivery is an instrument in the

selection equation that will not influence birthweight.

Results and Discussion

Estimation results of the Heckman model with the selection problem are provided in Table 2.

The selection equation is the equation that represents whether the baby is weighed or not

weighed immediately following delivery. The estimate for rho indicates a weak correlation

between the selection and the birth outcome (jj = -0.0614). The negative estimate of rho may

appear counterintuitive; however, it is not statistically significant. The associated Wald-test of

independence of equations is not statistically significant (chi2 = 0.63) with p-value = 0.4289.

This suggests that the Heckman model with selection may not be appropriate in this case or, in

other words, there is no sample selection bias problem due to unweighed babies.

Although the Heckman model is not appropriate in this case, it is interesting to observe the

results of the selection equation. The estimated coefficient of money spent on delivery, as a key

variable, is significant in explaining whether the baby was weighed or not. Birthweights also are

more likely to be recorded for mothers who have more years of schooling, are from households

with a higher household socioeconomic index with per capita expenditure between 25th and so"percentiles of the population level. However, as there is no evidence of a selection bias, the

analysis can be continued on a single structural equation for birthweight, using Ordinary Least

Squares (OLS) regression.

7

I

,J

Variable Selection

Table 2: Heckman Selection Model Cor Weighed and Not Weighed Babies

Outcome

Coefficient (SE)Coefficient (SE)Total number of prenatal care visitTotal number of prenatal care visit squared

Household indexYears of education

Age of mother less than 25 yrs (baseline)Age of mother between 25-34 yrs

Age of mother 35 and olderBody mass indexBody mass index squared

Dummy if Body mass index is imputedHealthy mother (general health status)First birthPer capita expenditure below 25th percentile ofpopulation level (baseline)Per capita expenditure between 25th and so"percentile of population levelPer capita expenditure above 50th percentile ofpopulation level

Smoking behaviorMale baby

Singleton babyHaving pregnancy complicationYear of baby's birth

Cost of delivery of the babyConstant

o.oi 17 (0.0059)**

-0.0002 (0.0002)-0.0064 (0.0072)

0.0017 (0.0022)

0.0452 (0.0198)**

0.0832 (0.0304)***

0.0435 (0.0126)***-0.0006 (0.0002)**-0.0810 (0.1691)

-0.0098 (0.0296)

-0.0735 (0.0180)***

-0.0203 (0.0242)

-0.0349 (0.0237)

0.0931 (0.0948)

0.0838 (0.0169)"*

0.2911 (0.0489)"*0.0365 (0.0250)

-0.0158 (0.0051)"*

2.2066 (0.1719)***

0.1290 (0.0135)···-0.0034 (0.0005)··*

0.1848 (0.0229)**·

0.0497 (0.0075)···

0.1166 (0.0683)·

0.1391 (0.0997)

0.0145 (0.0638)0.0003 (0.0013)5.2062 (0.1852)···

0.0573 (0.0923)0.1096 (0.063)·

0.1377 (0.0722)*

0.0977 (0.0781)0.5679 (0.3416)*

0.0003 (0.0544)0.102 (0.1392)

-0.0456 (0.0886)-0.0009 (0.0181)

0.0000015 (0.0000005)"*-2.1115 (0.7648)*"

RhoWald test of independent equation (chi2)=0.63Probability> chi2 = 0.4289

Number of observation 4436 587

-0.0614 (0.0775)

••• Significant at a ) percent level; ** at a 5 percent level; * at a )0 percent level.

Additional (Sensitivity) Analysis

Variables, such as years of education, BMI, general health status, household index, and per

capita expenditure have missing information. Observations with missing information were

excluded from the regression analyses. As a sensitivity analysis, the model was re-estimated,

using imputed values for the missing data. The median value for continuous variables and zero

values for dummy variables were applied to replace the missing values. Dummy variables,

8

In this study, I use the Heckman selection model to test whether there is a potential selection bias

from babies that are not weighed. The results show that I did not find evidence of a selection

problem from some babies not having been weighed in Indonesia. The analyses, therefore, can

be continued on the sub-sample of live-birth babies with reported birthweight.

iildicnting that the data were missing, were included as additional explanatory variables. The

results of these sensitivity analyses were qualitatively the same as those reported. The estimate

lor rho indicates a weak correlation between selection and birth outcome (jj = -0.0718814 ).

The associated Wald-test of independence of equations is not statistically significant (chi ' =

0:.)'-;) with p-value = 0.3297. This indicates that the reported outcome equations are robust to

»ussingness and the evidence of the sample selection problem has not been found.

'Chapter Conclusion

This study has analyzed the potential selection problem that arises when some babies are not

'wcip,hed immediately after delivery in Indonesia. In other countries, there is a sample selection

oi[..; because some pregnancies do not end in live birth due to abortion decisions (pregnancy-

resolution bias), which are not random. In Indonesia, religious and cultural views indicate that

the pregnancy-resolution decision is less of a concern, but there is a similar sample selection

issue for unweighed babies.

One limitation has been noted in the analysis. The cost of delivery here is reported by the

women. It is difficult to use the cost of delivery that is measured at the community level because

the data on the community level is based on the sample of communities from IFLS 1993

(IFLSI). However, I use the data from IFLS 2007-08. There are many missing community data

for new respondents (not panel respondents) in IFLS 2007. Therefore, it is difficult to use any

variables from the community module and so the analysis uses the information of cost of

delivery that is reported by individual mothers,

9

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Reference

Frankenberg, E. & Thomas, D. 2001, 'Women's health and pregnancy outcomes: do servicesmake a difference?', Demography, vol. 38, no. 2, pp. 253-65.

Habibov, N.N. & Fan, L. 2011, 'Does prenatal healthcare improve child birthweight outcomes inAzerbaijan? Results of the national demographic and health survey', Economics andHuman Biology, vol. 9, no. 1, pp. 56-65.

Heckman, J.1. 1979, 'Sample selection bias as a specification error', Econometrica, vol. 47, no.1, pp. 153 - 61.

Liu, G.G. 1998, 'Birth outcomes and the effectiveness of prenatal care', Health ServicesResearch, vol. 32, no. 6, pp. 805-23.

Mwabu, G. 2009, 'The production of child in Kenya: a structural model of birth weight', Journalof African Economies, vol. 18, no. 2, pp. 212-60.

Rous, J.1., Jewell, R.T. & Brown, R.W. 2004, 'The effect of prenatal care on birthweight: a full-information maximum likelihood approach', Health Economics, vol. 13, no. 3, pp. 251-64.

Wooldridge, J.M. 2002, Econometric analysis of cross section and panel data, The MIT PressCambridge, Massachusetts, London.

World Health Organization 2005, The World Health Report 2005: Make every mother and childcount, World Health Organization, Geneva.

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Faculty of Economics and Business.UNIVERSITAS GADJAH MADA . -. .

This is certify that

Heni Wahyuni, Ph.D.

INNOVAIIINIIRACII NSPIR I

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1955-2015

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