An econometric analysis using STATA

1. Low infant birth weight and smoking by mother
during pregnancy: An Irish perspective
Soumyadeep Mukhopadhyay, 17235308
1st Semester, MEconSc (NREP), NUI Galway
E-mail: soumya_m@ymail.com
4th
January, 2018
Abstract
Numerous studies around the world acknowledges that low birth weight
(LBW) of the infant has a correlation with smoking behaviour by the
mother during pregnancy. This report explores a dataset collected through
a cross-sectional study in Ireland. A total of 4 econometric models with in-
creasing relevance were constructed and discussed. According to the most
conservative model which analyses 3569 samples, with control on num-
ber of child births, mother’s BMI, ethnicity, and family structure, daily
smoking is predicted to have an infant weighing 244 gm lesser than the
mean of ~3.5 kg. Also, a model using a smaller sample of 459 smoking
mothers predicted that for each cigarette smoked per day by the preg-
nant mother, the child loses 60 gm of weight, ceteris paribus. There is
overwhelming evidence on correlation between LBW and smoking from
the dataset. Therefore, the policy should discourage pregnant mothers
to smoke. This view has been supported by citing various studies in the
conclusion. However, to determine causal relationship, targeted epidemi-
ological study needs to be undertaken.
Disclaimer: This is an assignment submitted to Dr John Cullinan
for EC506 Econometrics course
1 Introduction
It is widely acknowledged that there exists an inverse relationship between birth
weight of the o¤spring and smoking by the mother during pregnancy. Under-
standing the e¤ect of smoking on infant’s birth weight is important for at least
two reasons. Firstly, to spread awareness among the public regarding this prob-
lem to build up public opinion and secondly, introducing appropriate policy to
tackle the problem.
A number of researchers have commented on this issue since 1950s. Simp-
son (1957) was the …rst to notice this correlation. Roberts (1969) con…rmed
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2. Smoking vs LBW in Ireland
this e¤ect, showing a mean reduction in birth weight varying between 150 and
250 gm. In the 1958 British Perinatal Mortality Survey (Butler and Alberman,
1969, and Butler et al, 1972), birth weight was found to be reduced by 170 gm
in the o¤spring of smokers, with a corresponding rise in late foetal and neonatal
mortality. However, mothers who smoked in pregnancy tended to come from
poorer social backgrounds, were older, and were of higher parity than non-
smokers. This raised the possibility that smoking ‘caused’neither the lowering
of birth weight nor the increase in perinatal mortality, but was rather an index
of a particular type of mother, a viewpoint advanced by Yerushalmy (1964).
In a 1987 review paper, factors with well-established direct causal impacts on
intra-uterine growth were listed. These factors include infant sex, racial/ethnic
origin, maternal height, pre-pregnancy weight, paternal weight and height, ma-
ternal birth weight, parity, history of prior low-birth-weight infants, gestational
weight gain and caloric intake, general morbidity and episodic illness, malaria,
cigarette smoking, alcohol consumption, and tobacco chewing. It was observed
that cigarette smoking was more prevalent in developed nations (Kramer, 1987).
Many more publications have also found signi…cant association between ciga-
rette smoking and low birth weights, but it is yet to be concluded that smok-
ing causes low birth weight since many other factors can in‡uence the birth
weight of the infant (US General, 1990, Windham, 2000, Blake et al, 2000).
This report will attempt to investigate whether there is any relationship
between smoking during pregnancy and low birth weight (LBW) in Ireland.
2 Data Description
The data used in this analysis has been used as provided by Dr John Cullinan
in the form of the …le ‘birthweight1.dta’. There is a total of 4022 observations
in the dataset and 15 variables. Out of these 15 variables, there are 3 numerical
and continuous data, viz. age of main carer (age), Study child’s birth weight in
kilograms (bweight) and Primary Caregiver’s BMI (PCBMI). The rest 12 are
categorical data. The data description has been provided in the following Figure
1. The normal distribution of the three numerical data were checked using the
histogram as shown in Figure 2. The Figure 5 in Appendix describes the detailed
summary of the 3 numerical variables. From these …gures, it is clear that the
PCBMI had a severe skewness. In order to improve the data distribution and
reduce the skewness, logarithmic transformation was performed on PCBMI and
the resulting histogram and skewness have been represented side by side with
PCBMI in Figures 2 and 5. On taking log, the variance decreased from 24.6
to 0.03, standard deviation decreased from 4.9 to 0.177 and skewness decreased
from 1.45 to 0.72. Although all 4022 observations on age are available, there
are few missing values for birth weight and PCBMI.
The Figures 6 and 7 in the appendix tabulate the rest of the 12 categorical
variables. These describe the categories under each variable, their frequency and
percentages. Knowing the categories is critical for our analysis since most of the
dataset is made up of categorical variables. In the subsequent data analysis in
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3. Smoking vs LBW in Ireland
STATA, the ‘i.’command was used for working with these categorical variables.
It should be noted that the variable cigs (number of cigarettes the pregnant
mother smoked per day) was not considered in most part of the data analysis
because of two reasons, viz. (i) it is highly multicollinear with ther main variable
of interest i.e. smoking and (ii) only 496 observations are available in the dataset
of 4022- inclusion of this variable will increase R
2
, but in the process, it will
ignore 3500 observations from the dataset.
Figure 1: Data Description
3 Data Analysis
3.1 Econometric models
Four di¤erent multiple regression models have been speci…ed containing the
independent variable of smoking. The dependent variable in each model is
birth weight of the infant, measured in kg. Variables are smoking behaviour
of the mother during pregnancy (smoke), study child single birth, twin, triplet
(nbirths), log of Primary Caregiver’s BMI (logPCBMI), ethnic or cultural back-
ground of the child (ethnicity) and Category Household Type (hhtype4). Figure
3 shows the results. Figure 8 in Appendix shows the scatter plots among the
numeric and continuous variables. Also, the predicted value vs observed value
of birth weight (depending on Model 4) has been depicted in one these scatter
plots and that shows that the data points being concentrated around the 45
degree line which indicates a fairly good predictive power of the model 4.
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4. Smoking vs LBW in Ireland
Figure 2: Histograms and k-density of the numerical data and log of PCBMI
An additional Model 5 has been shown in Figure 4 where the variable ‘smok-
ing’has been replaced with the variable ‘cigs’. The purpose of Model 5 is to
predict the e¤ect of number of cigarettes per day on the infant birth weight.
Both ‘smoking’and ‘cigs’could not be included in the model because of perfect
collinearity as explained in ‘Data description’section.
3.2 Tests for CLRM Assumptions
The models were analyzed for heteroskedascticity, omitted variable bias, spec-
i…cation error, multicollinearity and normality for ful…lling Classical Linear
Regression Models (CLRM) assumptions. The four models along with their
heteroskedascticity, omitted variable bias, speci…cation error and multicollinear-
ity tests have been attached in the Appendix (Figures 9 to 14). Heteroskedasc-
ticity is tested using ‘estat hettest’ (desirable value to be homoskedastic is p
value>0.05 and low chi2), omitted variable bias is tested by ‘ovtest’ (desirable
value is p>0.05 which means no more variables are needed), speci…cation error
is tested by ‘linktest’ (desirable value is hatsq P > jtj > 0:05 which means cor-
rect speci…cation), multicollinearity is tested by ‘vif’ (desirable value is vif<10
and 1/vif>0.10 which means no multicollinearity exists). Thus, all the 4 models
discussed here follow CLRM assumptions.
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5. Smoking vs LBW in Ireland
Figure 3: Set of 4 econometric models
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6. Smoking vs LBW in Ireland
Figure 4: Model 5. This resembles Model 4 where the variable ’smoking’ is
replaced by ’cigs’
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7. Smoking vs LBW in Ireland
4 Results and Discussion
In Model 1, the infant’s birth weight is regressed on smoking behaviour of
the mother. The magnitudes of the coe¢ cient as well as the absolute values of
t-statistics, both are very signi…cant. The value of beta coe¢ cient on pregnant
mothers who smoke occasionally (MOcc) is -0.151 and this indicates that MOcc
will have an infant weighing 151 gm less than the infant of a pregnant woman
who does not smoke (MNo- base case scenario). Similarly, the value of beta co-
e¢ cient on pregnant mothers who smoke daily (MD) is -0.253 and this indicates
that MD will have an infant weighing 253 gm less with respect to the infant of
MNo. This indicates a direct correlation between more frequency of smoking
and lowering of birth weight. However, there is chance to be critical about this
model due to the overall low R2 of only 0.0225 i.e. only 2.25% of the variations
in the observations were described by this model which is not very good. The
details of this model has been shown in Figure 9 along with their CLRM tests.
Therefore, other variables need to be added in this model.
In Model 2, the infant’s birth weight was regressed on smoking behaviour of
the mother, number of infant birth (single, twin or triplet) and log of mother’s
measured BMI (logPCBMI). Literature indicates both these factors to have
signi…cant e¤ect on infant’s birth weight. Gedda et al (1981) showed that low
birth weight in twins is a di¤erent condition from low birth weight in singletons
and should be dealt with independently (Gedda et al, 1981). In another study,
the birth weight reducing e¤ect of maternal smoking was found to be of the
same magnitude among twins and singletons but when gestational duration
was taken into consideration, this di¤erence was less pronounced. The e¤ect
of maternal smoking on gestational duration was stronger among singletons
than twins (Rydhstroem & Källén, 1996). Maternal BMI was found to be of
signi…cance to explain trends in infants’ birth weight over time, but not of
sole importance (Brynhildsen et al., 2009). Therefore, the mother’s BMI was
included in the model as well. A logarithmic form of BMI was included due
to it’s better normal distribution. This model 2 was found to have a higher
R2 of 0.1106 which improved considerably from model 1, indicating that no
irrelevant terms were added. However, it should be noted that compared to
3894 observations in Model 1, there were 3573 observations in Model 2 and this
number can be considered to be ok for …tting the data and making predictions.
On closer inspection, it was observed that MOcc is predicted to have an infant
birth weight of 159 gm lesser than MNo and MD is predicted to have an infant
weighing 257 gm lesser than MNo keeping other factors constant. These …gure
are even more concerning than Model 1. However, it was also observed that
birth weight of the infant is predicted to decrease for birth of twins or triplets
by 896 gm and 1.4 kg, respectively when smoking and logPCBMI are controlled.
Also, for an increase in every point of PCBMI, there is an increase of 29.7% in
predicted weight of the infant ceteris paribus, which is a lot. When a smoking
mother with a twin or triplet is considered, the model predicts an even alarming
decrease in the birth weight, only intensifying the e¤ect, considering the average
weight of an infant is only 3.5 kg. All of these variables had jtj > 1:96 signifying
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8. Smoking vs LBW in Ireland
high statistical signi…cance in the 95% level. The details of this model has been
shown in Figure 10 along with their CLRM tests. Although this model seems
to be considering all of the relevant variables from ovtest, we still look towards
improving the model with a higher R2.
In Model 3, another new variable was included in the form of ethnicity. In
a study in USA, Fulda et al. (2014), observed paternal race/ethnicity to be an
important predictor of very low birth weight among Caucasian and Hispanic
mothers. Barron (1983) however proposed that the variation of birth weight
according to ethnicity is due to di¤erent dietary habits among di¤erent races.
Other studies also indicate birth weight to be correlated with ethnicity (Seed et
al., 2000 & Kelly et al., 2008). Therefore, ethnicity was included in this Model
3 and it was found to increase the R2 to 0.1167 from Model 2. This indicates
that ethnicity is a relevant variable. The details of this model has been shown
in Figures 11 and 12 along with their CLRM tests. Ceteris paribus, MOcc is
predicted to have an infant birth weight of 165 gm lesser than MNo and MD
is predicted to have an infant weighing 268 gm lesser than MNo. These …gures
are very signi…cant. Similar to Model 2, twins, triplets and logPCBMI were
observed to have high impact on the birth weight ceteris paribus. Ethnicity was
found to have very signi…cant e¤ect on birth weight. Ceteris paribus, almost
all other ethnicity e.g. other white (77 gm lighter), African (224 gm lighter),
other Blacks (490 gm lighter), Chinese (195 gm lighter) and other Asian (341
gm lighter) infants were predicted to have lower birth weight compared to Irish
infants. However, the number of observations for some of these categories were
very few. the main takeaway from this observation is that in general, the Irish
infants were heavier than the other ethnic babies. This observation matches
with the deductions in other study in UK by Kelly et al. (2008) which stated
that Indian, Pakistani and Bangladeshi infants were 280–350 gm lighter, and
2.5 times more likely to be low birth weight compared with White infants while
Black Caribbean infants were 150 gm and Black African infants 70 gm lighter
compared with White infants. The reason was predicted to be socioeconomic
factors. In the Model 3, all of the variables had jtj > 1:96, signifying high
statistical signi…cances.
Finally, in order to obtain an even more accurtae model, new variables were
added in Model 3 and various statistical tests were performed until it was found
that family structure of the household determined by single and couple parents
having various number of kids (hhtype4) had considerable in‡uence on the infant
birth weight. This variable was chosen to be included in the Model 4 because
of it’s importance highlighted in various studies (McLanahan & Sandefur, 1994;
Ryan, 2012; Golombok et al., 1997; Albrecht, 1994). On it’s inclusion, the
R2 increased to 0.1473 from 0.1167 in Model 3. The number of observations
were 3569 which was satisfactory. Ceteris paribus, MOcc is predicted to have an
infant birth weight of 147 gm lesser than MNo and MD is predicted to have an
infant weighing 244 gm lesser than MNo. These …gures are notably lower than
all the above Models 1, 2 and 3. Similar to Model 2, twins, triplets, logPCBMI
and ethnicity were observed to have high impact on the birth weight ceteris
paribus. Focusing on hhtype4, the base case scenario was single parent with
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9. Smoking vs LBW in Ireland
1 or 2 children. With respect to this scenario, single parent with 3 or more
children was predicted to have an infant weighing 111 gm heavier, a couple with
1 or 2 children was predicted to have an infant weighing 23 gm heavier, and a
couple with 3 or more children was predicted to have an infant weighing 234
gm heavier, ceteris paribus. This indicates that with more number of kids, the
parents are more likely to learn the process of taking care of the foetus. Also,
couples were more likely to have a heavier child than a single parent, indicating
the role of …nancial and social struggles and highlighting importance of family
structure. In the Model 4, all of the variables had jtj > 1:96, signifying high
statistical signi…cances. The details of this model has been shown in Figures 13
and 14 along with their CLRM tests.
An additional Model 5 has been illustrated in Figure 4. This resembles
Model 4, only the categorical variable ’smoking’was replaced with numerical
variable ’cigs’. This model reveals that for each cigarette smoked per day by
the pregnant mother, the child loses 60 gm of weight, ceteris paribus. However,
this interpretation should not be taken too literally because the sample size is
only 459 and the R2 is a moderate 0.112.
5 Conclusion
The analysis in this paper shows that smoking by pregnant mothers de…nitly
have a negative e¤ect on infant birthweight. The e¤ect remains statistically sig-
ni…cant even after controlling for a range of di¤erent in‡uencing factors such as
number of child births, mother’s BMI, ethnicity, and family structure. This con-
clusion matches with several researchers who made similar observations. Some
of the most relevant studies are mentioned here. Magee et al. (2004) analyzed
a sample of 79,904 birth certi…cates in Massachusetts for 1998, and concluded
that the amount of low birth weight (LBW) attributable to smoking was 6.4% in
the sample. Among those who smoked, LBW was 58% more likely than among
nonsmokers, and 60% of the overall population e¤ect of smoking on LBW was
noted among light smokers. A study by Tyrrell et al. (2012) strengthens the ev-
idence that smoking during pregnancy is related to lower o¤spring birth weight
and suggests that population interventions that e¤ectively reduce smoking in
pregnant women would result in a reduced prevalence of low birth weight. Ko
et al. (2014) found that maternal smoking was responsible for increased inci-
dences of LBW and preterm delivery of babies. They recommended smoking
cessation/reduction to pregnant women to reduce morbidities in their neonates.
Agrawal et al. (2014) also concluded that maternal smoking during pregnancy
was associated with decreased birth weight, low scholastic achievement, regular
smoking and attention de…cit hyperactivity disorder.
However, the conclusions above are subject to a number of considerations. A
number of di¤erent factors e.g. number of child births, mother’s BMI, ethnicity,
and family structure were found to have in‡uence on the birth weight, ceteris
paribus. The cause of smoking among the pregnant mothers need to be addressed
from psychological point of view. The mental condition of the pregnant mothers
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10. Smoking vs LBW in Ireland
is not clear from the dataset. Therefore, it is not clear whether more anxious
mothers smoke more and thereby the overall health is a¤ected by anxiety and
insecurity resulting in lower birth weight. Smoking may be a manifestation of
the deep seated anxiety and insecurity among the mothers. Single mothers have
decreased infant weight strengthens this theory.
Therefore, casually speaking, low birth weight among infants is strongly
associated with smoking behaviour of mother’s during pregnancy. Even keeping
several important factors under control, it is safe to predict that women who
smoke daily during pregnancy have children that are 244 gm lighter on average
than children of women who don’t smoke during pregnancy.
However, it should be considered that causal relationship can be estab-
lished by a di¤erent methodology. Common frameworks for causal inference
are structural equation modeling and the Rubin causal model. Epidemiological
studies need to be employed forcollecting and measuring evidence of risk factors
and e¤ect to measure association between smoking and low birth weigh. The
study should be able to answer three types of causal queries: (1) the e¤ects of
potential interventions, (2) probabilities of counterfactuals, and (3) direct and
indirect e¤ects (also known as “mediation”) (Pearl, 2010).
The fact that there are so strong evidences on the correlation between
frequency of smoking and low birth weight implies that the policy should dis-
courage pregnant mothers to smoke. The statisticla data from Ireland and all
around the world con…rms this. A strong anti-smoking campaign should be
launched to make the people aware of the danger of smoking while pregnant.
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13. Smoking vs LBW in Ireland
Appendix
Figure 5: Detailed summary of the numerical data and log of PCBMI
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14. Smoking vs LBW in Ireland
Figure 6: Tabular summary of the categorical variables (1)
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15. Smoking vs LBW in Ireland
Figure 7: Tabular summary of the categorical variables (2)
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16. Smoking vs LBW in Ireland
Figure 8: Scatter plots
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17. Smoking vs LBW in Ireland
Figure 9: CLRM tests for Model 1
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18. Smoking vs LBW in Ireland
Figure 10: CLRM tests for Model 2
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19. Smoking vs LBW in Ireland
Figure 11: CLRM tests for Model 3
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20. Smoking vs LBW in Ireland
Figure 12: CLRM tests for Model 3 (contd)
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21. Smoking vs LBW in Ireland
Figure 13: CLRM tests for Model 4
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22. Smoking vs LBW in Ireland
Figure 14: CLRM tests for Model 4 (Contd)
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