Maternal Smoking During Pregnancy and Child Birth
Weight
Emma Tominey1
October 2006
Abstract
Birth weight (BW) has predictive powers for an array of later lifetime outcomes, so that much
of what an individual achieves in life is determined at birth. We know that maternal smoking
during pregnancy will result in lower BW of their child, owing to a lack of oxygen. However,
this relationship may only represent a correlation, as birth weight and the incidence of smoking
during pregnancy could be driven by “risky behaviour” of the mother. Therefore, estimating the
e¤ect of smoking during pregnancy whilst ignoring the fact that smoking is an endogenous decision,
would result in omitted variable bias. We utilise a dataset which provides much information on
the mothers, and argue this allows us to identify the causal impact of smoking during pregnancy
upon child BW. Further, we evaluate the dynamic process of this relationship. In particular, how
does quantity smoked and quitting during the pregnancy change outcomes, can high socio-economic
status cushion the child from the harm. The results suggest that smoking during pregnancy will
causally lower child BW by 5% on average, rising to 6% at the bottom of the birth weight distribution. To give this some magnitude, one third of unhealthy low birth weight babies could have been
born healthy, had their mothers refrained from smoking. Additionally, we …nd that relatively more
educated mothers respond more successfully to experience from previous pregnancies than the less
educated mothers.
JEL Code: I12, I18, J13, J24.
I would like to thank Richard Blundell, Steve Machin and Paul Gregg for continued advice,
participants at a UCL internal seminar for useful comments and Ximena Quintanilla for useful
comments. Thanks additionally to ESRC and DfES for funding.
Address for correspondence:
Emma Tominey
Department of Economics
University College London
Gower Street, London
[email protected]
1 Department
of Economics, University College London.
1
1
Introduction
The weight of a child at birth had been the focus of a multitude of studies, examining both the
determinants of birth weight and the return to birth weight. For example, Currie & Moretti (2003)
…nd maternal education can reduce the chance of giving birth to a very low birth weight child2 and
Black et al (2005) …nd that an increase in the weight of a child at birth will increase the probability
of completing high school, raise earnings and increase adult height. If birth weight does have large
predictive power for later lifetime outcomes, then policy makers could use birth weight as a tool for
raising not just health, but economic prospects for individuals. For example, governments could
aim to raise birth weight, through targeted intervention to increase the nutritional intake in the
womb, or by promoting smoking cessation for pregnant mothers, so as to improve lifetime outcomes
for the foetus.
There have been studies which have examined the impact on child outcomes of government
policies which increase nutrition to pregnant mothers3 . The aim of this paper is to analyse the
impact another mechanism of raising child birth weight. We aim to estimate the causal consequences
from smoking during pregnancy upon child birth weight.
Smoking during pregnancy may have several health e¤ects upon the foetus, including lowering
the birth weight. From a policy perspective, the causal impact of smoking is of interest, so that we
can identify the direct bene…t to child birth weight from smoking cessation, for a current pregnant
smoker. The econometric di¢ culty with identifying the causal e¤ect is omitted variable bias in
the form of health attitudes, high discount rates, or more generally, “risky behaviour”, which will
confound the impact of smoking during pregnancy. A worse case scenario is that if risky behaviour
drives factors other than smoking during pregnancy, then cessation of smoking may have no impact
upon child health.
One contribution of this paper is to use the wealth of information on cohort members of the UK
National Child Development Survey (NCDS) to isolate the policy parameter of interest. Another
is to understand the dynamic process of the harm from smoking throughout the nine months of
pregnancy and to look closely at non-linearity in the impact across the socio-economic status (SES)
of the mother at the time of birth. We may expect, for example, that high SES mothers are able to
cushion the child against the harm from smoking during pregnancy, by taking advantage of a better
environment for pregnancy, better health care etc. This study will therefore contribute towards the
policy debate of the harm of maternal smoking and aim to understand whether government policies
will have any e¤ect upon child outcomes, which mothers should be targeted and the optimal timing
of policy intervention.
A brief summary of the result is that smoking during pregnancy does indeed harm the child,
by reducing birth weight by 4.8% on average. This is after controlling for many mother, child and
health behavioural level variables. We observe extensive heterogeneity in this e¤ect however. The
harm from smoking during pregnancy is largest for very young and old mothers, for less educated
mothers and for those children born in the lowest decile of the birth weight distribution. However,
once we control for the quantity of cigarettes consumed, the variation across SES disappears. We
give some magnitude to these results, by calculating the that one third of children classi…ed as low
birth weight (<2500g), would have been born healthy had their mothers refrained from smoking.
A surprising result is that, contrary to beliefs that the greatest harm to the baby will be borne
during the …rst trimester of pregnancy (weeks 1-12), we …nd a negligible e¤ect to smoking during
pregnancy if the mother quits smoking by the …fth month of the gestation period. This result is
intuitive given our chosen child outcome of birth weight, because it is during the …nal 20 weeks that
2A
very low birth weight child (weighing less than 1500g) is prone to developmental and health problems
Behrman et al (2005), Attanasio & Vera-Hernandez (2004), Dubois & Ligon (2005).
3 See
2
90% of the growth of the child occurs. This could be seen as a positive result, as it suggests that
government policy could be e¤ective if aimed at mothers who are already pregnant - it is never too
late to quit. On the other hand, our future research will analyse the e¤ect of smoking upon other
child outcomes. For example, the brain develops during the …rst trimester of pregnancy. Therefore,
we may see an e¤ect of smoking during pregnancy which is more important in the early period of
pregnancy for test score outcomes of the children. Finally, our evidence suggests that the mothers
with relatively more education respond to experience accrued during previous pregnancies, more
successfully than the less educated mothers.
The paper is structured as follows. Section 2 motivates the paper, explaining why it is important
to understand the impact of smoking during pregnancy upon child birth weight. Section 3 details
the methodology and section 4 the dataset used in this paper. Section 5 reports and discusses the
results. Section 6 performs a robustness test, comparing outcomes of the NCDS with outcomes
from a Norwegian dataset, which measures not just maternal, but paternal birth weight. Finally,
section 7 concludes.
2
2.1
Motivation
Is child birth weight an important outcome.
measure of human capital
Birth weight as a
We have chosen primarily birth weight as an outcome of smoking during pregnancy. However,
birth weight may not be important for any aspect of a child’s life, in which case we can attach no
importance to …nding a harm on birth weight. In economics, there have been several studies which
suggest that birth weight is a determinant for many later life outcomes. Behrman & Rosenzweig
(2004) identify the return to birth weight by estimating the impact of variance in birth weight
across monozygotic female twins4 upon various adult outcomes, using a twin study of Minnesota.
The authors …nd that foetal growth signi…cantly drives adult schooling, height and wage rates. An
increase in birth weight by one pound will on average, raise years of schooling, height and the wage
rate by 1/3 of a year, 0.6 inches and 7% respectively.
Black, Devereux, Salvanes (2005) utilise a Norwegian dataset recording all births between 19671997 to examine the impact of birth weight on short-run mortality rates, long-run educational
outcomes and wages. Also exploiting within twin variation in birth weight, the evidence suggests
that a 10% increase in birth weight raises the probability of completing high school by 1%, raises
earnings at 21 by 1%, reduces 1 year mortality rates by 5.6 births per 1000, raises height by 0.57cm
and increases BMI by 0.1. Using OLS in the NCDS, Currie and Hyson (1999) estimate the e¤ect of
low birth weight upon employment prospects, quali…cations and self-reported health. They interact
socio-economic status to see how the e¤ect varies with SES. Results show that health shocks have
stronger e¤ect on employment prospects and self-reported health than wages. Surprisingly, this
e¤ect is homogenous across the distribution of SES.
Almond, Chay, Lee (2004) examine speci…cally the cost of low birth weight5 , arguing that it is
low birth weight that is dangerous to child health and development. Using a mother …xed e¤ects
approach from data on twin births, the authors …nd signi…cant impact of low birth weight upon
4 The variation in birth weight across twins is due to positioning of the foetus in the womb, which is considered
by authors to be exogenous
5 De…ned as birth weight less than 2500g
3
short-run mortality rates, hospital costs, ventilation use. Finally, Royer (2005) employs a twin …xed
e¤ect study using birth records from California. An increase in birth weight by 1kg raises education
attainment by 0.12-0.16 of a year.
The above studies use di¤erent units of measurement to assess the return to birth weight, and
therefore generate estimates which are di¤erent in magnitude. However, the sign and signi…cance
of the e¤ects observed is common to the studies. A conclusion to draw is that the weight at birth is
very important for outcomes of an individual, for their working lifetime. Therefore, we feel con…dent
that by analysing the impact of maternal smoking during pregnancy upon child birth weight, we
are estimating an e¤ect that will have important and long lasting consequences for the child.
2.2
Born with a silver spoon: Smoking during pregnancy is becoming
focused upon disadvantaged households.
Evidence suggests that UK households are becoming “polarised”in terms of joblessness6 . It is also
true that polarisation of households is occurring by health status. This can be seen in Table 1, which
compares the characteristics of households with mothers who do and do not smoke during pregnancy
over the last thirty years in the UK. Comparing across the 1970 British Cohort Study7 and the
2000 Millennium Cohort Study8 , results show that the incidence of smoking during pregnancy has
declined from 46% of births to 16%9 . However, although smoking during pregnancy has declined,
it is focused on a group of “disadvantaged”(young and lower educated) mothers. In 2000, mothers
smoking displayed increased heterogeneity in terms of age of pregnancy, with non-smoking mothers
on average 2.6 years older when they gave birth than non-smoking mothers. This compares to just
1 year age di¤erence on average in 197010 . Smoking mothers tend to have higher education in both
years of observation, but more so in 2000, as the di¤erence has risen from 0.54 years to 1.29 years11 .
Therefore, smoking during pregnancy in the UK tends to be focused upon households which are
already de…ned by low SES.
Table 1
Polarisation of households by socio-economic and health status
1970
2000
% mothers smoked during pregnancy
46%
16%
Age pregnancy: difference for non- and smoking mothers
1 year
2.6 years
Education mother at birth: difference for non- and smoking 0.54 years 1.29 years
mothers
Data Source: 1970 British Cohort Study and 2000 Millennium Cohort Study
6 see
Gregg & Wadsworth (2001) and Gregg & Wadsworth (2004).
live births in Britain during one week April 1970.
8 All live births in UK between 1 September 2000 – 31 August 2001
9 these are statistically di¤erent to each other: jtj stat for null of same mean is 217.39
1 0 in both cases, can reject the null that the two groups have the same mean. jtj stats are 6.86 (1970) and 14.61
(2000)
1 1 in both cases, cannot reject the null that the two groups have the same mean. jtj stats are 0.68 (1970) and 1.28
(2000)
7 All
4
What are the implications of this …nding? Many studies have observed intergenerational transmission of economic status12 , so that children born in low SES households are likely to remain low
SES during adulthood. Furthermore, Currie & Moretti (2005) show evidence of intergenerational
persistence in birth weight status. Using records from Californian births 1989-2001, the authors
are able to link mothers to o¤spring and siblings. Women whose mothers were LBW are 50% more
likely to be LBW themselves, even with controls and grandmother …xed e¤ects.
The table above suggests that persistence in SES and health outcomes estimated in the intergenerational transmission literature will be exacerbated by maternal smoking. Over time, smoking
during pregnancy falls increasingly upon low SES households. Therefore, if we do …nd a signi…cant
harm from smoking during pregnancy upon child outcomes, then the incidence of maternal smoking
will have large implications for the transmission on inequalities from one generation to the next.
This will strengthen the need for government intervention into programmes to promote smoking
cessation for pregnant mothers. An analysis of the impact of smoking during pregnancy upon child
outcomes is an important topic, as the …nding may have large consequences over and above the
health of the child at birth.
2.3
Smoking during pregnancy may reduce child birth weight
We now turn to a description of how exactly smoking during pregnancy may harm birth weight of
the foetus. A literature review by Tuormaa et al (1995) describes two mechanisms through which
smoking during pregnancy will lead to foetal hypoxia (low levels of oxygen). Firstly, the ‡ow of
blood between the uterus and the placenta is slowed by nicotine13 , which restricts the supply of
nutrients and oxygen. Secondly, carbon monoxide from the smoke combines with oxygen carrying
haemoglobin to form carboxyhaemoglobin, thus further restricting the supply of oxygen.
A consequence is lower foetal growth which may lower birth weight. There is a large epidemiological literature examining the impact of maternal smoking during pregnancy upon birth weight.
Sexton & Hebal (1984) conducted a random experiment of smoking intervention, …nding that smoking cessation during pregnancy increases birth weight by 92g14 . Steyn et al (2006) collected data
on births within a 7 week period in 1990 in the Greater Johannesburg and Sowento area of South
Africa. Controlling for mother age, race, employment child sex, parity and gestation, they estimate
the babies born to non-smoking mothers are on average 137g heavier then smoking babies. A report
in Morbidity and Mortality Weekly (1990) found that, low birth weight status of babies born in
Ohio was twice as common in children born to smoking mothers than non-smoking mothers.
The link between smoking during pregnancy and birth weight has also been investigated in the
economic literature, in which study designs incorporate smoking as an endogenous choice of the
pregnant mother. That is, identifying the causal impact of smoking during pregnancy upon birth
weight must take account of the fact that the decision to smoke may be correlated with other
factors which drive birth weight, independently of smoking habits15 . To overcome the potential
endogeneity and estimate a causal parameter, economists have used instrumental variables technique
and propensity score matching.
1 2 See
Gregg & Machin (1998), Carneiro, Meghir & Parey (2006), Plug (2006)
activates the adrenergic discharge, which constricts blood vessels and reduces uteroplacental blood ‡ow
1 4 To give this some magnitude, the mean birth weight in our dataset is 3400g
1 5 The endogenous smoking behaviour of the mother is explored in section 3.
1 3 Nicotine
5
2.3.1
Instrumental Variables
The instrument used in the literature is variation in the taxation on cigarettes, both across states
in USA and across time. In a basic framework, the decision of the mother to smoke will optimally
trade o¤ the bene…ts and costs of smoking. An increase in taxation on cigarettes raises the cost of
smoking, so we may expect mothers to smoke fewer cigarettes.
Evans & Ringel (1997) exploit within state variation in taxes on cigarettes between 1989-1992.
With a dataset containing 10.5 million births, they show …rstly that the taxes lower the probability
a mother will smoke, although it does not change the incidence of smoking for those who already
smoke. The estimates here show that maternal smoking lowers birth weight by 356-594 grams, (or
10.6-17.6% of average birth weight) which is higher than OLS estimates and far higher than any
of the above estimates of the e¤ect of smoking upon child birth weight. A reason the authors give
for the higher impact with IV, is that they estimate a local average treatment e¤ect. That is, the
instrument drives the smoking behaviour only of the marginal smokers, who are induced to quit
smoking by the policy change. This means that, compared to the average, these mothers will have
a higher marginal bene…t from smoking.
In a similar vein, Lien & Evans (2005) exploit the introduction of large one-o¤ tax hikes in
four US states, between 1992-1994. The counterfactual for states was identi…ed using matching
methods, then a di¤erence-in-di¤erence approach adopted. They show that the change of taxation
did change the smoking habits of pregnant mothers, hence, assuming that the taxes are exogenous
to child health, the policy change is a valid instrument. The authors estimate that smoking reduces
birth weight of the child by 189 grams (or 5.6% of the average birth weight across the 4 states),
which was of a similar magnitude to the OLS estimation.
A problem with the instrumental variables methodology is that, even if we do observe mothers
smoking fewer cigarettes, the quantity of nicotine inhaled and, therefore the harm on the child,
may remain constant. Adda & Cornaglia (2005) explain that, whilst individuals may respond to
the higher taxes by cutting down the number of cigarettes they consume, they can top up the level
of nicotine by inhaling with a greater intensity and smoking the cigarette right down to the …lter.
Both of these changes cause discomfort to the smoker. Inhaling more deeply will increase nicotine
levels in the blood very quickly, which may cause nausea and the taste of the cigarette is bad near
to the …lter. Hence the smokers adjust to the lower quantity of cigarettes by employing strategies
which otherwise would not be favourable, in order to raise nicotine levels. Using measures of cotinine
concentration in saliva of participants of the National Health and Nutrition Examination Survey
of 20,000 individuals, the authors estimate the impact of an increase in cigarette taxation upon
the dosage of nicotine. Nicotine is removed from the body by a metabolism process which changes
nicotine into cotinine. There is a problem in measuring the quantity of nicotine in the blood of an
individual in order to infer smoking behaviour, as nicotine remains in the body for just 20 hours.
However, cotinine can be measured for approximately 20 days. Therefore, the authors measure
cotinine concentration in saliva samples of the participants, allowing a more accurate measurement
of the amount of nicotine inhaled during smoking. They show that whilst taxes reduce the quantity
smoked, cigarettes are smoked with a greater intensity, leaving nicotine levels higher than that
suggested by their cigarette consumption. Therefore the instrument used in the above papers is
hard to interpret, if there is not a clear cut change in the dosage. Additionally, individuals may
respond to a tax hike by crossing the border to a neighbouring state, in order to buy cigarettes at
a cheaper price and also that individuals may choose to live in a state depending upon state level
policies which include taxes on cigarettes. These factors cast doubt over the validity of variation
in taxation on cigarettes as an appropriate instrument. This would explain, for example, why the
OLS estimates in Lien and Evan’s paper were of similar magnitude to IV estimates.
6
Additionally, these papers are not able to observe paternal smoking habits. If the changes in
taxes cause fathers to reduce smoking, the IV estimates will overestimate the e¤ect of maternal
smoking during pregnancy. Paternal smoking habits may be important in their own right, as driving
passive smoking and also the smoking behaviour of the mother. Our dataset allows us to control
for paternal smoking habits, which turn out to be an important factor of our study design.
2.3.2
Propensity Score Matching
Almond, Chay & Lee use a propensity score matching approach to investigate the e¤ect of smoking
during pregnancy upon the probability of having a low birth weight (LBW) baby. LBW babies,
weighing less than 2500g, are susceptible to infant death, short- and long-term health problems
and, as the authors note, raise large hospital costs. Therefore it is important to understand how
intervention to stop mothers smoking during pregnancy can improve outcomes, so lowering the tax
burden of low birth weight babies. They use birth records from Pennsylvania between 1989-1991,
which provided in-depth information on variables such as maternal smoking and other mother health
variables and …nd that smoking during pregnancy increases the incidence of LBW by 3-4%. The
relationship is non- linear in birth weight, as they …nd no e¤ect for births below 1500g. Smoking
during pregnancy is an endogenous decision of the mother, driven by variables which are arguably
unobservable. Therefore, the adoption of propensity score matching, which assumes selection on
observable characteristics may not be su¢ cient to identify a causal impact from smoking.
In this study, we estimate the reduction to birth weight from maternal smoking during pregnancy. We contribute to the existing literature in three ways. Firstly, do consider smoking to be
an endogenous choice and overcome this endogeneity by utilising a dataset which follows cohort
members from birth, up to the age of 42. This gives us information on birth outcomes of the cohort
members plus their children, but more importantly on many proxies for “risky behaviour” of the
participants, so that we can isolate a causal impact for the entire sample. Secondly, the details
on the smoking behaviour of the participants are very detailed, listing the number of cigarettes
smoked, the number of months the mother smoked for, for each birth of the mother up to the age
of 42. Therefore we do not just estimate the harm from smoking, but aim to understand how this
harm accumulates. This is an important contribution, as there is a time delay between becoming
pregnant and learning of the pregnancy. We want to understand if all of the harm from smoking
occurs during this time lag, or if the mother can still recover some of the harm by quitting during
the pregnancy. Forthly, unlike any of the aforementioned studies assessing how maternal smoking
harms the foetus, we are able to control for the birth weight of the mother – a variable which we
argue proxies for the genetics, or endowment element, of child birth weight. To the extent that
maternal smoking is correlated with maternal birth weight, this will enable a more precise estimate
of the harm from maternal smoking. As smoking is focused upon lower classes in society, and birth
weight is correlated with social class, there may be a correlation between maternal birth weight
and smoking habits. Finally, we control for the smoking habits of the mother’s partner. This
could a¤ect child birth weight not only through a passive smoking mechanism, but also by driving
maternal smoking habits. To the extent that individuals partner with others of similar smoking
habits, we can use this variable as a replacement variable, to absorb the endogeneity inherent in
the decision to smoke during pregnancy.
Unlike Almond, Chay & Lee, we do not focus on LBW as the speci…c outcome, as the occurrence
of LBW is so rare that the sample size is very small. We do, however, estimate the impact of smoking
not just at the mean, but across the distribution of child birth weight using the quantile regression
approach, to understand the non-linear relationship between smoking and child birth weight.
7
3
Methodology
A simple production function for birth weight (bw) of child c, of mother m, is detailed below.
lbwcm =
1
+
2 Em
+
3 Xcm
+
4 Hcm
+ Scm + ucm
S is a dummy variable which takes the value 1 if the mother smokes during pregnancy and 0
otherwise.
is therefore the parameter of interest in this model. The birth weight of a child is
partially genetically determined. In our model the endowment (E) of the mother forms the genetic
component of birth weight of their child. We proxy for the endowment element of child birth weight
by using the birth weight of the mother, information which has not previously been included as
an input into the child production function when assessing the harm from maternal smoking. It
is an important input for three reasons. Firstly, Conley & Bennet (2000) report the impact of
maternal birth weight upon the probability of having a low birth weight (LBW) child. Using PSID
sibling data between 1968-92, the authors …nd LBW of the mother increases the probability of the
child being LBW by a factor of four. Secondly, it is important to control for endowments when
the outcome is child birth weight as, low birth weight may not be a negative outcome, rather a
genetic trait. It is the birth weight of a child, conditional upon its endowment, that is in‡uential
from a policy perspective, so therefore of interest here. Finally, as smoking is focused upon lower
SES individuals and birth weight is correlated with social class, there may be a correlation between
maternal birth weight and smoking habits, which would lead to omitted variable bias in our estimate
of . We overcome this potential bias with the inclusion of maternal birth weight as an input in
the child production function.
We control additionally for mother speci…c characteristics (X) which determine child birth
weight. Health behaviour of the mother (H), or “riskiness” may be driven by high discount rates,
and represents the source of endogeneity of smoking during pregnancy. u is a child level error term
This model describes a linear relationship between inputs and birth weight outcome. We will
relax this assumption, and allow for quadratic and interaction terms, to explore a more ‡exible
relationship between inputs and child outcomes.
An econometric issue inherent in this study concerns the correlation between H and S. We
describe this relationship below.
Scm
Xcm
Hcm
=
1+
0
Xcm
0
Hcm
0
2 Xcm
+
0
3 Hcm
+ "cm
In this model, smoking of the mother m, during pregnancy of child c16 is a function of mother
speci…c characteristics, healthy behaviour and a child-mother speci…c latent unobserved term ". The
mother and health behaviour variables which drive child birth weight are a subset of those which
drive smoking behaviour. Substituting equation (2) into equation (1), our parameter of interest, ,
will be biased if
E("cm jEcm ; Xcm ; Hcm ) 6= 0
There are two potential sources of endogeneity. On the one hand, if health behaviour (or “risky
attitudes”) drives smoking habits during pregnancy as well as birth weight, we will overestimate the
impact of smoking during pregnancy upon child birth weight. In this paper, we carefully construct
OLS estimation, to ensure that
1 6 We
allow behaviour to vary across pregnancies
8
E("cm jEcm ; Xcm ; Hcm ) = 0
We run OLS regressions with controls for inputs in the production function of child birth weight,
including a set of variables which drive health behaviour. We therefore assume that we control for
health behaviour and mother characteristics su¢ ciently to ensure the error term from equation (2)
is uncorrelated with the covariates of the OLS equation. With the NCDS, there is a wealth of
information on the cohort members, throughout their lifetime, to ensure that the assumption is
not heroic. In the next section, we discuss the various measures used to absorb these potential
confounding factors in the estimate of smoking during pregnancy.
Rosenzweig & Schlutz (1983) describe another form of endogeneity of health inputs in the child
health production function. Individuals have superior information on the expected health of their
children, which may cause adverse selection in health care during pregnancy. For example, the
result would be that mothers refrain from smoking if expected health of the foetus is low. To
overcome the endogeneity, the authors use instrumental variables technique. They argue that the
price of health care locally, government health expenditure, race, education, parental income are
instruments which drive health inputs, but are exogenous to child outcomes. In the NCDS, we
do not have access to prices of local technology for child birth weight production, however we do
control for the other parental speci…c variables, such as race and education.
3.1
Interesting Extensions
We may expect to observe non-linearity in the relationship between maternal smoking and birth
weight, along three dimensions. It will be important to look at whether giving up smoking during
the pregnancy can bene…t the child, and if so, whether there is an optimal date for quitting, or a
point at which the damage cannot be undone. MacCarthur & Knox (1988) examine whether there
is heterogeneity in the impact depending on when mothers give up smoking and …nd that giving up
in the …rst 6 weeks of gestation has no further bene…t than giving up in the …rst 16 weeks. However,
the mean adjusted birth weight for all groups of maternal smokers are higher than for persistent
smokers, which questions the validity of this …nding. We are able to evaluate at which month of
pregnancy the harm from smoking is the greatest, as we are able to categorise mothers in the study
into those who never smoked during pregnancy, who smoked for the …rst 5 months and quit and
those who smoked consistently for the entire pregnancy.
Our model becomes:
lbwcm =
1
+
2 Em
+
3 Xcm
+
4 Hcm
+
3
i=1 Di;cm i Si;cm
+ ucm
where D is an indicator of smoking duration during pregnancy,itakes the value 1 if the mother
never smokes, 2 if she smokes for up to 5 months and 3 if she smokes for the duration of the
pregnancy.
It is often thought that the …rst few months of pregnancy, in which the foetus develops its
skeleton and organs, are the most important in terms of maternal behaviour. However, using birth
weight as the outcome, we may observe a di¤erent e¤ect, as it is during the …nal 20 weeks that the
baby gains most of its body weight. We think that this is a big contribution to the policy debate
of maternal smoking, as there is generally a time lag between the mother becoming pregnant and
learning of their pregnancy. If they smoke during this period, there may be no point in them
quitting smoking, if the harm has already been imposed on their foetus. However, if it is the
9
case that smoking is more important in the later stages of pregnancy, then programmes aimed at
smoking cessation for pregnant mothers could still be e¤ective in improving the outcomes for the
foetus. By observing both the quantity of cigarettes consumed and a change in smoking behaviour
during pregnancy, we are able to understand timing of optimal policy intervention into smoking
cessation programmes.
Secondly, we also examine potential non-linearity in the distribution of child birth weight using
quantile regression approach. We do this because there is an important threshold in the birth weight
of a child: LBW, which determine the future health and development of a child. By adopting a
quantile regression approach, we estimate heterogeneity in the impact of maternal smoking during
pregnancy, which will help us to understand whether smoking can force the chid into the dangerous
LBW status. Let denote a quantile of the child birth weight distribution. We estimate the
following model.
lbwcm =
1
+
2 Em
+
3 Xcm
+
4 Hcm
+
Scm + ucm
Finally, we estimate the relative impact of smoking depending upon the quantity of cigarettes
consumed. Therefore, we answer the question of whether it is the …rst cigarette that causes the
greatest marginal harm and whether the harm is increasing linearly across quantity consumed. We
group the quantity smoked during pregnancy by zero (i=1), 2-19 per day (i=2) and 20+ (i=3),
estimating the model described in equation 3.
3.2
Passive Smoking
It is interesting to see the impact on child birth weight from living with a smoker, independently
from the impact of maternal smoking. The UK government emphasise in their advice to pregnant
mothers that it is important that their partners quit smoking. We may expect passive smoking
to drive child birth weight, through a direct and an indirect channel. Firstly, through the indirect
channels, we may expect an interaction with maternal smoking behaviour and living with a smoker.
The event of living with a smoker may drive motivation for the mother to quit smoking. It may be
correlated with the mother’s smoking behaviour, if there is assortative mating by smoking habits17 ,
so again we expect a correlation between the impact of maternal smoking and partner’s smoking on
the child birth weight. Note also that, to the extent that partners do assortatively mate by “healthy
behaviour”, control for the mother living with a smoker will in part absorb the endogeneity inherent
in the mother’s smoking status during pregnancy.
Secondly, we may confound unobservable traits of the partner, which drive child birth weight,
such as risky behaviour, high discount rates, with the passive smoking e¤ect. Passive smoking, can
be measured in pregnant mothers by the concentration of cotinine. Kharrazi et al (2001) found
the harm for children born to non-smoker mothers is 106g on average. Note however that for the
reasons mentioned above, this may not be the causal e¤ect of passive smoking. In fact, Jarvis et al
(2001) have found that the cotinine concentration for passive smokers is 0.6-0.7% that of smokers,
suggesting that this “passive smoking” e¤ect measured in Karrazi et al is entwined with other
observable traits of the partner.
1 7 53%
of smoking mothers live with a smoker, compared to 22% of non-smoking mothers
10
3.3
Smoking During Pregnancy: Other Health E¤ects
It will be important for our identi…cation strategy to separate the harm from smoking upon child
birth weight from to other health problems that are imposed on the foetus from smoking. Tuormaa
et al list some of the other e¤ects from maternal smoking as reduce gestation, placenta previa and
placental abruption18 . The latter two conditions may lead to premature births, which will a¤ect
birth weight. Hence there are other outcomes from smoking which are potentially tied in with child
birth weight. In the future, we plan to extend our analysis by considering the e¤ect of smoking
during pregnancy upon other child outcomes, such as gestation. At this stage, in order to isolate the
impact upon smoking on birth weight independently from these other outcomes, we will control for
the gestation of pregnancy and also for a dummy variable which equals 1 if there were complications
during pregnancy and 0 otherwise.
4
Dataset
We use the UK National Child Development Study, a longitudinal panel data set, whose participants
are the cohort of children born in the UK between 3-9 March 1958. The most recent period of
observation was in 2001, which gives over 40 years of information on the cohort members. It is the
information on children of the cohort members which we exploit for this study, for which there is
in-depth pregnancy information
Studies of this kind may be prone to bias from sample selection, as we only observe births
conditional upon the mothers having given birth within our period of observation. The age of
the mother at birth is non-random, correlated with characteristics such as education and income.
However, we believe that by the age of 42, the majority of births will have been realised. Therefore,
we do not believe there to be a strong bias from sample selection.
The birth outcomes of the children of the cohort members are very detailed and include birth
weight and gender of the child (boys tend to be larger than girls at birth). We control additionally
for the length of the gestation period and whether or not there were complications during the birth.
In the …nal period of observation in the NCDS, the pregnancy questions not only asked about
the incidence of complications during birth, but additionally the mothers gave information on any
complications. This is so that we can isolate the e¤ect of smoking during pregnancy upon child
birth weight, independently from the e¤ect on other birth outcomes, as discussed above. Finally, we
include in all regressions dummy variables for the year of birth, to absorb potential cohort e¤ects.
Children are born between 1973-200019 .
Given that the cohort members have been tracked in the NCDS since birth, we are able to
incorporate a wealth of information on the mothers which will i) drive their smoking behaviour and
ii) form important inputs into the child birth weight production function. This includes a proxy for
the endowment, or genetic component of child birth weight, which is the mother’s birth weight. To
my knowledge, no other study looking at the impact of maternal smoking upon child birth weight
has controlled directly for birth weight of the mother. However, it is very important to control
for maternal birth weight in the child production function, as discussed above. To the extent that
genetics are correlated with maternal smoking, it will enable an estimate closer to the true causal
parameter. As smoking is focused upon lower classes in society, and birth weight is correlated with
social class, there may be a correlation between maternal birth weight and smoking habits.
1 8 See
1 9 The
Appendix 1 for precise details of other outcomes
distribution of child year of birth is shown in Appendix 2.
11
Another mother level variable which forms an input in the child birth weight production function
is the age of mother at birth. Royer (2004) …nds poorer child health outcomes for young and old
mothers, relative to other mothers. We may expect young mothers to be less experienced and
educated about health, which may lower the birth weight of their children. There is a risk associated
with pregnancy outcomes of older aged mothers. “..they are more likely to get high blood pressure,
diabetes, develop problems with the placenta and then need a Caesarean section”20 .
We control additionally for marital status, ethnicity and education of the mother. The number
of previous births is controlled for, as a version of birth order as, according to Black et al (2005),
through the trade-o¤ between child quantity and quality, birth order is negatively correlated with
educational achievement. If this is true for child birth weight, we would expect to see a negative
impact from the number of previous births upon child birth weight21 .
In terms of health behaviour or the attitude towards health of the mothers, we want to control
for inherent attitudes, rather than those at the time of pregnancy. The health variables cannot be
measured at the time of pregnancy, as the mother may change behaviour during pregnancy, in which
case we will not su¢ ciently control for the endogenous smoking choice of the mother. However, we
do want to allow for these attitudes to change over time. Therefore, we proxy health behaviour
of the mother by whether they exercise regularly and drink over the recommended allowance of
alcohol, at the period of observation most recent to the birth of the child. This ensures that we
satisfy both criteria of capturing attitudes towards health which are less transient than behaviour
during pregnancy, but allows for time variance in attitudes towards risky behaviour. Furthermore,
it allows for the possibility that previously unhealthy mothers …nd it harder to give up smoking
during pregnancy. Both of the variables used are measured throughout the observations of the
cohort members. As mentioned above, when we take into account whether the mother lives with a
smoker, this may control for the health attitude of the mother, if partners living together behave
similarly along this dimension.
An advantage of the panel data nature of the NCDS over other datasets with pregnancy information, is that it allows us to observe in-depth smoking habits of pregnant mothers. We observe
the incidence of smoking during pregnancy , the number smoked at each period of observation and
information on any change of habits during pregnancy22 . Thus we can piece together information
on if and for how long the mother smoked, and the dosage for mothers who do not change their
habit23 .
Our sample includes 7685 female cohort member mothers and 3663 children. Raw statistics are
reported in the section below.
5
5.1
Results
Descriptive Statistics
Summary statistics are reported in Table 2. The total number of children born to female cohort
members is 7685. Of these, 67% did not smoke during pregnancy, 6% smoked for the …rst …ve
months, then quit and 27% smoked consistently for the duration of the pregnancy. We can see that
2 0 Dr Antinori (2006) BBC News “Doctor defends IVF for woman, 63”, http://news.bbc.co.uk/go/pr/fr//1/hi/health/4971930.stm
2 1 We did exchange the number of previous births control for birth order, which led us to the same conclusions.
2 2 Klebano¤ (1998) compared serum cotinine concentration to self-reported smoking and conclude that women were
honest in their reports, but cotinine concentration was a better measure of the dose.
2 3 82% of smoking mothers reportedly did not change their smoking habit during pregnancy.
12
the birth weight outcome is strictly decreasing in the duration of smoking during pregnancy. The
mean birth weight for non-smoking births is 239g heavier than the mean for consistently smoking
births. Smoking during pregnancy is not random across observable mother characteristics however,
as smoking mothers give birth at an earlier age, are much more likely to live with a smoker, have
more children and leave school at an earlier age. They also smoke more cigarettes before pregnancy,
which is not surprising. Some mothers who do not smoke during pregnancy, do smoke before hand,
as the mean number of cigarettes smoked by this group of mothers is about 1 per day. Smoking
mothers are also less likely to play sport regularly. All in all, these statistics point to the fact that
mothers smoking during pregnancy tend to be have less advantageous outcomes. It is interesting
that there is a greater variance in birth weight for smokers, relative to non smokers, as shown by
the …gures in parentheses. However, the age of the mother at birth and the age left school inputs
are more similar for smoking mothers than non smoking mothers.
As with most datasets, in the NCDS there is a degree of missing information for certain variables.
This means, that in selecting a balanced panel, I drop some observations. In order to check that the
sample selected is not di¤erent to the original sample, I follow the technique in Gregg et al (2006)
and set the mean and standard deviation of variables in the original sample to 0 and 1 respectively,
then summarise these variables in the …nal dataset. The means and standard deviations for the
main variables are reported in Table 3. We can see that the means for the variables are very close
to zero, for all variables except whether the mother plays sport regularly –mothers in the balanced
panel are more likely than in the original to play often. The balanced panel is not dissimilar, in
terms of the underlying distribution of characteristics, to the original sample.
5.2
OLS
Table 4 reports the full results of the OLS regressions on the log child birth weight in grams, where
we cluster at the level of the mother to take account of mother speci…c birth weight e¤ects. In column
1, the impact of maternal smoking during pregnancy, controlling only for year of birth dummies,
reduces birth weight by 6.4%. From column 2, controlling for mother birth weight reduces this
e¤ect only slightly, to 6.1%. There is a raw elasticity of intergenerational birth weight of 0.2, which
is signi…cantly di¤erent to zero. Part of the e¤ect of smoking during pregnancy upon birth weight
may be through a reduced gestation, so in column 3 we control for the gestation of the pregnancy.
This does reduce the impact of maternal smoking during pregnancy, to 5.4%, and has an e¤ect
of one week gestation increasing child birth weight by 4.9%. This e¤ect is robust to inclusion of
other covariates in the remaining columns and to year of birth dummies. We can see that gestation
period and birth weight are strongly related, as inclusion of the former increases the goodness of
…t to 0.39 from 0.06. Controlling for the gestation period reduces the birth weight elasticity to
0.174, therefore part of the intergenerational transmission of birth weight comes through gestation.
This result is also robust to inclusion of remaining variables. This estimate of the intergenerational
elasticity of birth weight is consistent with the estimate in Currie and Moretti (2005), who …nd
elasticity of 0.17-0.205.
It is important to identify the impact from smoking during pregnancy independently from the
impact smoking may have upon other health outcomes at the time of birth, which may be are
correlated with birth weight, as discussed in section 2. Therefore, column 4 includes a dummy
variable which equals one if there was a complication during the pregnancy and zero otherwise. We
can see that the e¤ect of smoking upon birth weight is unchanged. Something going wrong during
pregnancy signi…cantly reduces the birth weight of the child by 4.4%.
Looking across the next two columns, to column 5&6, we control …rstly for mother level characteristics at the time of birth and then for time varying maternal attitudes towards health risks.
13
Inclusion of all of these controls changes the impact of maternal smoking by only 3 percentage
points; the impact is robust to inclusion of many variables. We see that boys are 3.7% heavier
at birth than girls, that older mothers have heavier babies than younger mothers and that this
increase is diminishing across the age of the mother. Mothers postponing birth by one year in this
dataset tend to have babies 0.7% heavier than mothers not waiting. There is evidently a birth order
e¤ect, as babies born to mothers with previous births are healthier, in terms of their birth weight.
This could indicate that mothers gain experience from each pregnancy, that enables their child to
be more healthy. Ethnicity is important for the birth weight of children. Relative to Caucasian,
Black, and the groups of Asian children are signi…cantly lighter at birth. This may represent raw
ethnicity di¤erences, or may represent an income e¤ect. In terms of the health behaviour of the
mother, there is no signi…cant impact of exercising regularly or drinking over the weekly recommended limit on child birth weight24 . Mother’s education and marital status are insigni…cant. This
is consistent with much of the literature on the intergenerational persistence of education, which
…nds that once paternal education is controlled for, maternal education can have no e¤ect on the
educational achievement of their o¤spring25 .
In the …nal column of Table 4, we control for a dummy variable which equals one if the mother
lives with a smoker and zero otherwise. As predicted, there is a correlation between this variable
and maternal smoking, possibly through the channels of assortative mating and motivation factors
(it is harder for the mother to quit is her partner smokes). Hence the impact of maternal smoking
falls to 4.8% (although the result is not signi…cantly di¤erent to the estimate in any of the other
columns). To the extent that partners assortatively mate by health behaviour, inclusion of the
variable indicating whether the mother lived with a smoker, works as an additional control for the
endogeneity inherent in the decision to smoke during pregnancy by the mother.
The estimate of living with a smoker itself has a very large e¤ect on birth weight of the child,
reducing it by on average 1.3%. Referring to the paper by Jarvis et al, the cotinine concentration
from passive smoking was found to be 0.6-0.7% that of smokers, thus as discussed above, this e¤ect
is the sum of passive smoking plus other traits of the partner which we have not controlled for, such
as attitudes towards health or risky behaviour which drive birth weight of the child. Therefore, this
is not a causal passive smoking e¤ect, but simply the total e¤ect of living with a smoker.
5.3
Heterogeneity in the E¤ect
Although we have controlled for the education and age of the mother at the birth of the child, we
are interested in whether the impact of maternal smoking during pregnancy displays heterogeneity
across the distribution of these variables. We may expect this to be the case because education
and age at birth can shift the budget constraint faced by a pregnant mother, both in terms of time
(from opportunity cost of education) and …nancial constraints.
Table 5 shows the impact of maternal smoking during pregnancy, controlling for mother, child
and health variables, across the distribution of maternal education. We de…ne low education mothers
as those attaining the compulsory years of education only26 (64% of sample), medium education
group mothers stay on until age 17 or 18 (22% of sample) and high education mothers leave school
after age 18 (14% of sample).
The impact of maternal smoking during pregnancy is signi…cant for each group, but the largest
by far for low educated mothers, at 5%, compared to 3.7% for medium educated mothers and 4.0%
2 4 I tried other speci…cations for drinking over the limit, such as including squared term, or nonlinear term for units
drunken, to see if there is a non-linear relationship. However, the e¤ect in these speci…cations was not signi…cant.
2 5 See Plug (2006) for a review of this literature.
2 6 For cohort members of the NCDS, the compulsory education is age 16
14
for high educated mothers27 . Low educated mothers are likely to have higher monetary constraints
relative to mothers with higher levels of education, as the return to education is prominent and
high. Furthermore, if mothers assortatively match by education levels, the …nancial constraints are
exacerbated28 . For the most educated women, their opportunity cost of having children is highest.
However, with the option of hiring carers for children, these mothers may substitute their time
investment for monetary investment, so that they can spend more hours in the labour market.
The result may be due to the fact that the quantity of cigarettes consumed is inversely related to
education. We explore whether we pick up purely a quantity e¤ect, rather than a di¤ereing e¤ect
across socio-economic status in section 5.3.1.
Looking at the other covariates, the intergenerational e¤ect of birth weight varies between 0.140.19, and is strongest for mothers of medium education. It is interesting that complications during
birth are most harmful for low educated mothers and insigni…cant for highly educated mothers.
This may be due to di¤ering quality of prenatal care across maternal education. We investigated
whether this e¤ect is due simply to the fact that more educated mothers had children at a later
date than others, hence technology for dealing with complications during births may have been
of higher quality for the …nal column of mothers. However, inclusion of interactions with dummy
variables for the year of birth of the children and education led to no change in this …nding.
Notice that the number of previous births has a nonlinear e¤ect upon child birth weight across
the distribution of mother education. One reason we expected the number of previous births to
play a role is because mothers gain experience with each pregnancy. Therefore, this evidence points
to the fact that more educated mothers learn more about their pregnancy than lower educated
mothers. In other words, this suggests that investment in the human capital of the mother and the
child are complementary, so that investment in child human capital is more e¢ cient the greater the
investment in maternal human capital.
Low educated mothers exercising regularly tend to have healthier babies, that can partly counteract the e¤ect of smoking. However, for medium or high educated mothers, this is not the case.
Living with a smoker is most harmful for the lowest education category of mothers. This could
be picking up an e¤ect of family income for, if lower educated families live in smaller houses for
example, then we may expect a larger e¤ect of passive smoking.
In Table 6 we stratify the sample by the age at birth of the mother. As the age of birth and
education of the mother are tradeo¤s, we may expect a di¤erential impact of smoking by age of
birth due to credit constraints, as above. Furthermore, the foetus of young and older mothers are at
greater risks than those of other mothers, which again may lead to an interaction between the e¤ect
of smoking during pregnancy with the age of birth. The results in Table 6 separate the e¤ect of
smoking during pregnancy upon child BW by age, where very young mothers are younger then 17,
young mothers are younger than 22, the second youngest category include mothers aged 22-25 at
birth, third sample includes mothers aged 25-31 at birth of the child and …nally the oldest mothers
are aged 31+. The age categories from young to oldest mothers represent quartiles in the maternal
age at birth distribution.
Looking at the …rst column of very young mothers, aged less than 17, the impact of smoking
during pregnancy is very large, at 16%. This is signi…cant, despite the very small sample size of
48 children. We see a large impact of smoking on birth weight in for the oldest mothers, at 6.8%.
After the age of 32, fertility declines for women, and the pregnancies are prone to more negative
outcomes, as discussed in section 4. It seems to be the case therefore that the children of older
mothers are more sensitive to adverse behaviour of the mother during pregnancy. The e¤ect is
large for young mothers, higher than for mothers in the middle age groups. Mothers in the middle
categories of age distribution at birth experience an impact of smoking of approximately 4%. This
2 7 The
di¤erences are statistically signi…cant only for the comparison of low educated and medium educated mothers.
work will investigate the role of assortative mating upon child outcomes.
2 8 Future
15
and the previous table suggest there are large heterogeneities in the impact of maternal smoking
upon birth weight of the child.
Cutting the sample by the age of mother at birth, we see di¤erent e¤ects of complications
during birth. We cannot reject zero e¤ect for very young mothers (there are only 8 mothers in
this sample who experienced complications) or old mothers (244 or 11% of mothers experienced
complications, so the lack of signi…cance is more indicative of no real e¤ect for this group, than
to a lack of information in the very young mothers group). Living with a smoker is harmful for
the young mothers and those in aged 25-31, lowering birth weight on average by 2.9% and 1.1%
respectively, but is insigni…cant for other groups of mothers.
The results above suggest that the harm from smoking during pregnancy varies across socioeconomic status. This is an important …nding, as suggests that government policy may reduce the
harm through intervention. However, this …nding may be misleading, if in fact the relatively low
educated and older mothers smoke a relatively greater quantity of cigarettes during the pregnancy.
We explore this suggestion in section 5.4.3, by looking …rstly at the number of cigarettes smoked by
mothers across di¤erent socio-economic groups, then by regression analysis. The results are con…ned
to the later section because, as described below, we can only measure the quantity consumed for
mothers who do not change their smoking habits during pregnancy and we want to thoroughly
explore the implications of this selection.
5.4
5.4.1
Non-Linearity in the E¤ect
Quitting Smoking
The next stage is to examine possible non-linearity in the e¤ect of maternal smoking during pregnancy, as scienti…c studies suggest that if mothers give up smoking, the month they quit may be
important for the health of their child. The detailed information in the NCDS allows us to isolate
the e¤ect for smoking during the …rst 5 months and the entire 9 months of pregnancy, relative
to not smoking. The results are displayed in Table 7. Similarly to Table 4, we …rst estimate the
raw relationship between maternal smoking habits and child birth weight (controlling only for year
of child birth dummies), then additively include controls for maternal birth weight, gestation and
complications during pregnancy, maternal characteristics, year of birth dummies, health attitudes
of the mother and whether or not the mother lived with a smoker. In all columns, for all speci…cations, there is a negligible birth weight e¤ect of smoking during the …rst …ve months, relative to
non-smokers during pregnancy. On the other hand, there is a large impact of smoking consistently
for the full 9 months of pregnancy, relative to not smoking. In column 6, once we have controlled
for the entire set of controls, mothers smoking for the entire pregnancy tend to give birth to babies
6% lighter than mothers who do not smoke. This is an strong result and suggests that policy aimed
at stopping the mother smoking during pregnancy should target mothers throughout the entire
gestation period, as there would be a substantial health bene…t to the child more than half way
through the pregnancy.
Although the result sounds surprising at …rst, it is logical if we consider the stages of growth
and development of the foetus during the pregnancy. In the …rst trimester of pregnancy (weeks
1-12), the baby develops facial features, limbs, heart and organs including brain. During the second
trimester (weeks 13-28) the baby strengthens and grows. 90% of weight growth occurs from week
20 onwards. Therefore, the result we …nd may change if we were to consider another outcome,
other than child birth weight. The next step of our analysis will be to incorporate di¤erent child
outcomes, such as test scores later in life and child mental health indicators. As the development of
16
the brain is earlier in the period of gestation, we may see the opposite results, that smoking during
pregnancy is most harmful during the early stages of pregnancy.
Of course, it is important to note that the group of quitters is possibly an endogenous one,
with unobserved heterogeneity that not only led to them quitting smoking during pregnancy, but
taking other precautions to ensure the foetus is healthy. Table 1 suggests that they do tend to be
a bit healthier, as they smoke fewer cigarettes before the pregnancy and are more likely to play
sport regularly. However, this is still an interesting result with policy implications for cessation
programmes.
5.4.2
Non-linearity Across the Child Birth Weight Distribution
There are important thresholds de…ned to determine whether the birth weight of a child is dangerously light or heavy. A low birth weight (LBW) child weighs less than 2500g and a high birth weight
(HBW) child weighs more than 4500g. There are health and development consequences of LBW
or HBW, in terms of development and later health outcomes, we extend the analysis by looking
not at the mean e¤ect of smoking during pregnancy, but at estimate the impact across the child
birth weight distribution. In this section, it is worth noting once more that we control for the birth
weight of the mother, to factor out the variation in birth weight that may be genetic, rather than
dangerous for the child. The results are reported in Table 8 and the estimated coe¢ cients displayed
in Figure 1. In the table, each column displays the impact of maternal smoking during pregnancy
for a di¤erent percentile of the child birth weight distribution. The …rst column, the bottom decile,
roughly represents babies classi…ed as LBW (birth weight is 2636g at the 10th percentile). There
is non-linearity in the e¤ect across the distribution of child birth weight, with the low birth weight
children bearing the burden of smoking: the harm is 6% at this decile. We may have expected no
e¤ect of maternal smoking during pregnancy for the low birth weight children, as they are at the
bottom of the birth weight distribution, which may mean that they cannot fall lower. These results
show that to the contrary, smoking during pregnancy is especially harmful for children who are “at
risk”already. This …nding is particularly relevant, given our account in Section 2.2 that smoking is
more and more concentrated on the less a- uent households. As 69% of LBW children were born
to mothers with low education (as classi…ed above), just 19% to medium educated mothers and
12% to highly educated mothers, the e¤ect of smoking relevant to the type of mothers who remain
smokers in current periods will be the e¤ect at the 10th percentile. The e¤ect is decreasing across
the distribution, falling to 2.9% for children born in the 90th percentile.
17
Figure 1: Quantile regression estimates
Quantile Regression Results and 95%
Confidence Intervals
0
0.1
0.25
0.5
0.75
0.9
-0.02
-0.04
-0.06
-0.08
Quantile of Child Birth Weight Distribution
The children in the bottom decile of child birth weight are not just most sensitive to smoking
during pregnancy, but to the other maternal inputs and outcomes during pregnancy. The transmission of birth weight between the mother and child is stronger for these children, the harm from
living with a smoker is highest for the low birth weight children, where it is 1.9%, compared to
8-11% for other children. Gestation has an e¤ect on child birth weight which is also decreasing
systematically across the distribution. It is 5.8% for the LBW children compared to 3% for children born in the upper decile. This means that, if the most risky children are most susceptible
to the pregnancy environment, that it may be possible to identify and target intervention at these
particular children, to shift up the child birth weight distribution.
5.4.3
Non-Linearity in Quantity Consumed
Examining how the harm to birth weight di¤ers across quantities of cigarettes smoked, England et
al (2001) …nd “The sharpest decline in birth weight occurred at low levels of smoking; no further
declines in birth weight were seen at higher levels of exposure.” In our unique dataset, we can
observe the number of cigarettes smoked by the cohort members. This allows us to assess whether
the relationship between the quantity of cigarettes smoked and the harm on child birth weight. We
address the question, does the …rst cigarette in‡ict the harm? This is important, given that mothers
18
rarely learn about their pregnancy at the start, so may decide whether to continue to smoke during
pregnancy having already smoked for a period of the gestation.
In the NCDS, the information on quantity smoked does not refer to the behaviour during
pregnancy, but is asked of cohort members at each period of observation. In order to assign the
number of cigarettes consumed during pregnancy, we have to select the sample of mothers whose
habits regarding the quantity consumed, do not change during pregnancy. Therefore, for this part
of the analysis, we drop mothers who cut down or give up during pregnancy relative to their usual
habits (589 and 470 mothers respectively). What this means, is that we select out the relatively
more healthy smokers, who adjust their behaviour when they learn about their pregnancy.
Table 9i shows the new sample statistics for the NCDS. The remaining smokers give birth to
babies of slightly higher birth weight, tend to be younger, less educated, have fewer children and
smoke more during pregnancy. However, these di¤erences are very small.
Table 9ii displays the quantity of cigarettes consumed by the new sample. We can see that the
highest proportion of smoking mothers - 15% - tend to smoke between 2-19 cigarettes per day. 4%
and 12% of smokers smoke 1 cigarette and 20+ per day respectively.
The results are reported in Table 9iii. The coe¢ cients reported are conditional on all sets of
controls, for pregnancy, mother and health variables. Firstly, column 1 reports the harm from
smoking during pregnancy, using the original sample of all mothers. This column is identical to
column 7 in Table 4, and comparable to the speci…cation in column 2, Table 9iii, but with the
restricted sample. We can see that the harm from smoking during pregnancy upon child birth
weight is lower in column 1 than 2. That is, restricting the sample to mothers who do not change
their smoking habits during pregnancy, leads to a higher estimate of the harm from smoking. This
could be due to two explanations. Firstly, may we fail to control for unobserved heterogeneity that
simultaneously drives the mothers to be included in the sample of column 2 by not changing their
habits, and reduces child birth weight. The second explanation is that the mothers in the new
sample smoke a greater quantity of cigarettes than those whose habits change.
We can assess which explanation is more likely, by looking at the average number of cigarettes
smoked per day for the treatment group of smoking mothers in the two samples, by comparing
Tables 2 and 9i. In Table 9i, we have dropped the group of mothers who quit during the …rst 5
months of pregnancy, who smoke on average 11.64 cigarettes per day, compared to the 15.52 smoked
by the new treatment group described in Table 9i. Additionally, because Table 9i also drops those
who do smoke for the entire pregnancy, but change their habits, the average number of cigarettes
smoked in the treatment group of consistent smokers is 15.52, compared to 15.13 in the original
sample. This suggests that the …rst explanation of simultaneous causation may not be driving the
results, as the quantity smoked during pregnancy is di¤erent across the samples.
More formally, by controlling for the quantity smoked during pregnancy in column 3 we can
assess the relative contribution of the competing explanations. The harm from smoking during
pregnancy using the sample of mothers who do not change their habits is aligned with the estimate
in the original speci…cation of Table 4, now that we have controlled for quantity smoked. We see
that each cigarette smoked, reduces the child’s birth weight by 0.1%, and smoking during pregnancy
by 4.7%.
In column 4, we allow the harm from smoking during pregnancy to become non-linear in the
quantity smoked. We do this by creating a set of dummy variables which exclusively describe the
smoking behaviour of the mothers in this sample during pregnancy. The excluded dummy variable
takes the value 1 if the mother never smokes and 0 otherwise and is the comparison group. We look
at the harm from smoking 1 cigarette per day, 2-19 cigarettes per day and 20+ per day, relative to
mothers who refrain from smoking for the entire pregnancy.
We see that the harm from smoking during pregnancy is accumulated when mothers smoke
more than 1 cigarette per day, but negligible for mothers smoking just 1 cigarette throughout the
19
pregnancy. However, mothers smoking 2-19 cigarettes per day will, conditional upon controls, tend
to have a child 3.9% lighter than mothers not smoking. This number is 1.6 percentage points higher
for mothers with a habit of 20 cigarettes per day, at 5.5%. There is a non-linear e¤ect of smoking
during pregnancy, in that a very small dosage in‡icts no real harm. The trend is increasing in the
quantity smoked.
5.4.4
SES vs Quantity Smoked
We observed above heterogeneity in the harm from smoking during pregnancy upon child birth
weight, across SES. We want to understand whether this stems purely from SES itself, so that
mothers with more relaxed budget constraints during pregnancy are able to cushion their unborn
child from the harm from smoking. On the other hand, the …nding may be driven by a correlation
between quantity consumed during pregnancy and SES.
We …rstly assess this correlation by detailing the quantity of cigarettes consumed during pregnancy, across SES, in Table 9iv. There is a clear negative correlation between the education of the
mother and the quantity she consumes during pregnancy, given that she smokes. The low educated
mothers smoke a median of 15 per day, but the highest education category mothers smoke 1/3 of
this, at the median. However, the same picture does not emerge across the distribution of maternal
age at birth. There is no di¤erence in the median mother from the second quartile of education
upwards, all whom smoke 15 per day. This suggests that there is more than just a quantity effect which explains why we observe heterogeneity across socio-economic status. We turn now to
regression analysis.
Table 9v strati…es the results on quantity smoked by the age the mother left school and table
9vi by the age of the mother at birth.
Table 9v strati…es the results on quantity smoked by the age the mother left school and table
9vi by the age of the mother at birth. All regression results in these tables control for all pregnancy,
mother and health covariates. Looking at Table 9v, we see that as above, there is an insigni…cant
harm from smoking 1 cigarette per day, compared to not smoking. We …nd no signi…cant harm
from smoking for the high educated mothers, now that we have controlled for quantity consumed.
Only 6% of highly educated mothers smoke in the new sample and, as we saw above, these mothers
smoke few cigarettes on average. This suggests that we do not have accurate information on these
mothers to identify an e¤ect. For the two other categories, the harm from smoking 2-19 cigarettes
per day is similar, and statistically the mean is the same. However, low educated mothers have a
harm of 5.6% to child birth weight, relative to non-smokers. Again, it is impossible to rule out the
likelihood with, with such a small sample of smoking mothers, there are too few in the category
of smoking 20+ per day to provide adequate information to estimate the signi…cant e¤ect29 . This
means unfortunately, that we cannot conclude whether SES or quantity is driving results.
There is a di¤erent picture however, when we stratify the sample by the age of the mother at
birth. For mothers in the oldest age category, there is a large harm from smoking 1 cigarette per
day, relative to not smoking during pregnancy. For all other groups, the harm is increasing in the
quantity smoked, with the birth weight of young mothers 8% lower, if mothers smoke 20+ per day
during pregnancy. We do detect heterogeneity across this distribution, as smoking 2-19 per day
causes the greatest harm to young mothers, which is 1.9 percentage point higher than the second
largest estimate, for mothers aged 25-31. In summary, although our investigation into whether
SES is driving heterogeneous estimates of the harm from smoking, or it is a result of di¤erent
2 9 Only
4% of medium educated, smoking mothers, smoke 20+ cigarettess per day
20
quantity consumed, is inconclusive for education of the mothers, we can conclude that across the
age distribution of mothers, SES is an important factor.
5.5
Magnitude of the E¤ect
We have estimated the harm from a mother smoking during pregnancy upon the health of her child
at birth, as ranging between 3.7% to 16%. We now aim to give some magnitude to these …gures,
to understand the implications of maternal smoking. We calculate how many babies could have
been born in a healthy state, had their mothers not smoked during pregnancy. There exists a birth
weight threshold, below which the child is de…ned as unhealthy at birth. Low birth weight (LBW)
babies are classi…ed as such, if born weighing less than 2500g. There are implications for the health
and development of the baby (reference), and also for hospital costs of LBW status (see Almond,
Chay, Lee, 2004).
In our sample of births, altogether 525 babies are LBW (6.8%). Of these, about half (259
mothers) of the mothers smoked during pregnancy. This is a far greater proportion than mothers
smoking in the total sample - 34%.
We interpret our estimates from the analysis above, as a causal e¤ect of smoking, which shifts
the child’s birth weight down in the distribution of child birth weight. The number of children born
to smoking mothers and classi…ed as LBW, who would otherwise have been born above the 2500g
threshold had their mothers refrained from smoking, is our measure of the magnitude of smoking
during pregnancy. The results are shown in Table 10 below.
On average, smoking during pregnancy was estimated to reduce child birth weight by 4.8%. This
means that born to smoking mothers between the weight of 2380 and 2500 (4.8% below the LBW
threshold) were classi…ed as LBW and, according to our results could have been healthy children
had their mothers not smoked. 79 babies were born within this margin, to smoking mothers, which
is only 3% of smoking mothers, 1% of the total sample but 30.5% of the sample of LBW babies
born to smoking mothers.
However, the average treatment e¤ect is not the appropriate measure of the harm from smoking, as quantile regression analysis revealed the harm is non-linear across the child birth weight
distribution. As mentioned above, section 2.2 shows that the characteristics of smoking mothers in
2000 are less educated, youner mothers. Almost 70% of the LBW children were born to low educated mothers. Therefore, it is the LBW children who are generally born to the smoking mothers
to current births and who will be targets of any government intervention into maternal smoking
during pregnancy. Children born in the bottom decile weigh less than 2637g, therefore, our next
step uses the treatment e¤ect calculated for the bottom decile of birth weight babies, to estimate
the magnitude of the e¤ect. Our estimate above was that smoking during pregnancy reduces child
birth weight by 6% at the 90th percentile. 92 babies of smoking mothers were born within the
range 2350-2500g. This describes 3.6% of smoking mothers who, assuming this is a causal e¤ect,
would have been born as healthy babies had their mothers refrained from smoking. Of LBW babies
born to smoking mothers, this represents 35.5%, or over a third of LBW babies which could have
been classi…ed as healthy.
6
Robustness check: Exclusion of Paternal birth weight
In Section 3, a child birth weight production function was described in terms of maternal inputs,
including the “endowment” or genetic input into child birth weight. Using the NCDS, we are able
21
to proxy for this endowment, using the birth weight of the mother. That is, we can capture the
extent to which small mothers give birth to small children, independently to choices of the mother
during pregnancy. However, paternal birth weight will clearly be important in the determination
of child birth weight. Unfortunately in our dataset, we do not have access to the birth weight of
partners of the cohort members.
The lack of information on paternal birth weight in the NCDS may have consequences for
the estimates of intergenerational transmission of birth weight from the mother to the child30 .
According to the evidence discussed above, birth weight has strong predictive power for health
variables (BMI, height) and economic outcomes (wages, educational achievement):
Xm
Xf
= f (bwm )
= f (bwf )
X denotes the outcomes for the mother m and father f as a function of their birth weight, bw.
If partners positively assortatively mate by these variables, then there will be a positive correlation between birth weight of the mother and the father.
E(bwm ; bwf ) > 0
Therefore, omitting paternal birth weight from the child birth weight production function will
result in an overestimate of the elasticity between mother and child birth weight.
Furthermore, the parameter of interest in this study, the coe¢ cient on maternal smoking during
pregnancy, may be prone to bias if partners assortatively mate31 according to characteristics which
are correlated with the decision of the mother to smoke during pregnancy. Formally there may be
a bias if
E(bwcf Scm ) 6= 0
For example, Burdett & Coles (1999) describe individuals as endowed with “charm”, which is
observable to the opposite sex. This charm can represent a plethora of characteristics, such as
looks, wealth, attitudes towards risk etc. In their model, partnerships are formed according to this
endowment. It may be that body mass index (BMI) is an element of charm upon which partners
assortatively mate. Smoking habits and BMI may well be correlated, as both are at least partly
driven by discount rates, or attitudes towards healthy living. In this case, it is certainly not true
that
E(bwcf Scm ) 6= 0
as we know that birth weight is a driver of BMI.
6.1
Data
We are able to directly test the extent to which exclusion of the father’s birth weight creates a
bias in our estimated coe¢ cients of maternal inputs in the child production function. We utilise
3 0 Note that although we obtain similar estimates to those of Currie & Moretti (2005) of the intergenerational
transmission of birth weight, their paper did not have information on paternal birth weight either.
3 1 Assortative mating refers to the extent to which husbands and wives are similar across a range of characteristics.
22
a unique Norwegian dataset32 of birth records, which contains all births in Norway between 1967
and 2003. This means that we can create a dataset of child births between 1999-2003 and link
this to birth weight data for both the mother and the father. The birth information available is
birth weight, gestation of pregnancy, other outcomes such as head size at birth. We also observe
for the child dataset, whether the mothers smoked during pregnancy, the maternal age at birth and
whether the mother was married at the time of birth.
We combine the birth information with administrative data, on educational attainment and
wages. These provide a set of maternal and paternal controls. This enables us to recreate our
analysis used for the UK dataset, for Norway and hence to assess the extent of bias in the NCDS
results. The Norwegian data lacks information on some of the variables, such as sporting activity,
alcohol consumption and living with a smoker. The set of controls that are comparable across the
two data sets, which we use as regressors, are the mother’s age at birth, age squared, marital status,
education, gestation, the number of previous births and year of birth dummy variables.
6.2
Censored Data
The oldest mothers in our dataset, by de…nition, are aged 37. We know that birth outcomes for
younger mothers and older mothers are relatively less advantageous (see Royer 2004). Consequently,
our data under-represent children born in the lower distribution of birth weight.
We look further into this using the complete Norwegian dataset. For the purpose of comparison
of results between Norway and the UK, we take children born in this period and match their mothers
using the mother’s birth records recorded between 1967-1998. However, for this section, we require
the age of mothers for whom we are not able to match birth records. Maternal age at birth is
not directly reported in the birth records dataset between 1999-2003. Therefore, I take all births
between the years 1967-1998, where a mother age at birth variable is recorded and calculate the
proportion of births for which the mother is aged over 37.
Shown below in Figure 2 is a plot of maternal age for the entire period of births between 19671998, with a line indicating age 37, where our matched data is censored. 3.3% of births (or 60947
out of the 186938) are recorded by mothers aged 37+ in this period. This is a very small number
and suggests that the bias from censoring the data will be relatively small.
3 2 Many
thanks to Kjell Salvanes for providing access to the Norwegian dataset.
23
Figure 2: Distribution of maternal age at birth
6.3
6.3.1
Results
Descriptive Statistics
The summary statistics for the Norwegian dataset are reported in Table 11i, which compares to
Table 2 for the NCDS. There are over 120,000 children level observations in the dataset. A larger
number of mothers smoke during pregnancy in the Norwegian dataset, than in the NCDS dataset:
40% compared to 34%. Raw child birth weight is highest for children of mothers who did not smoke
during pregnancy or who quit during pregnancy, compared to mothers who reported smoking at
the end of the pregnancy. Birth weight on average, is higher in this sample, relative to the NCDS.
This could be because birth weight increases slowly over time We take this into account in the
regression analysis, by controlling for year of birth. The oldest and most educated mothers at birth
in the dataset are those who never smoke. Interestingly, in Norway, mothers who smoke at the end
of the pregnancy term tend to be older and more educated than those who smoke just at the start.
The average number of cigarettes smoked in Norway is about half the quantity of cigarettes
smoked in the UK. Number of children is lower in the Norwegian dataset, because we do not
include all children of parents, but only those born between 1998-2003, whereas the NCDS dataset
contains a family size closer or equal to the completed family (for births of cohort members up to
age 42). Mothers who report smoking at the end of the pregnancy, tend to smoke slightly more, on
average, than they smoke at the beginning.
24
6.3.2
Regression Analysis
As noted above, the extent to which the NCDS estimates of the elasticity between maternal and child
birth weight and the harm from maternal smoking during pregnancy are biased by omitting paternal
birth weight, is determined by the correlation between paternal and maternal birth weight. This
raw correlation in the Norwegian dataset is close to zero, at 0.0148. The parents in Norway do not
seem to assortatively mate by birth weight. We can con…rm this …nding in the regression results,
looking at the unconditional and conditional correlations. Table 11ii reports the raw regression
results of the harm of smoking upon child log birth weight. In column 1, 2 and 3, the child birth
weight production function is reported for the Norwegian dataset and, in column 4 the results for
the same speci…cation are reported for the UK using the NCDS.
From column 1, the raw elasticity between maternal and child birth weight in Norway is 0.17.
Looking across to column 2, the elasticity between paternal and child birth weight is lower at 0.96.
Both of these results are statistically signi…cant. In column 3, we assess the correlation between
paternal and maternal birth weight, by including both terms in the regression. We can see that
both elasticities fall, but only very slightly. We can conclude that in Norway, there does not appear
to be a bias in the estimation of the intergenerational correlation between maternal and child birth
weight. In column 4, we report the equivalent results, for the NCDS dataset, which shows that the
elasticity between maternal and child birth weight is higher in the UK at 0.2.
What does the lack of correlation mean? Mothers and fathers do not seem to assortatively
mate on birth weight. If birth weight did not determine later outcomes in life, this would not be
surprising. However, it is a surprising …nding given that many studies relate birth weight to later
outcomes, such as educational attainment. We look at the correlations between other mother and
father characteristics, in the table below. Correlations between child birth outcomes of husbands
and wives are all very small and close to zero, suggesting that partners tend to mate randomly
across these variables. However, there seems to be evidence of assortative mating by later lifetime
outcomes, such as education and age, with correlations at 0.50 and 0.68 respectively. It must
be then, that the element of birth weight which drives education, is unrelated to the element of
education upon which partners mate. We think that this is an interesting …nding and in future
research, we plan to look more closely at how partners mate, and the impact of this pairing upon
child outcomes.
Opposites attract?
Outcome
Child birth outcomes
Birth weight
Height at birth
Gestation
Head size at birth
Birth order
Later lifetime outcomes
Years of education
Age
Correlation
0.0148
0.0446
0.0035
0.0456
0.0544
0.500
0.6809
Source: Norwegian birth records
25
We now turn to the question of possible correlation between paternal birth weight and maternal
smoking status during pregnancy, by running regressions of smoking during pregnancy upon child
log birth weight, with the relevant controls, in Table 11iii. Columns 1&2 report results for Norway
and column 3 the comparative results for the UK.
From columns 1&2, we can see the conditional elasticity between maternal and child birth weight
is 0.114 and for fathers is 0.088. Note that the omission of paternal birth weight has no signi…cant
e¤ect upon the remaining estimated covariates. Therefore, we can conclude that in Norway, there
is no bias in the estimate of the harm from smoking during pregnancy upon child birth weight from
omission of paternal birth weight. We now ask whether we can conclude from this table that the
same can be said about the UK, by addressing the comparability of the Norwegian and the UK
results.
There are di¤erences in estimated coe¢ cients comparing the …rst 2 columns with the …nal
column. The conditional elasticity between maternal and child birth weight is 0.114 in Norway
and 0.167 in the UK. Looking at the other regressors, it appears that children in the UK are more
sensitive to many environmental factors at the time of birth, such as smoking, gender composition
and age at birth. Whilst the incidence of smoking during pregnancy has the impact of reducing
child birth weight by 5.5% in the UK33 , but by 2.1% in Norway.
The lack of similarity in results between Norway and UK in Table 11iii suggests that there must
be di¤erences between the two countries. The di¤erence in the estimates of the harm from smoking
may be due to the fact that that smoking mothers tend to smoke more cigarettes in the UK than in
Norway. We consider this carefully in the next section. An explanation for the …nding that the UK
children are more sensitive to environmental factors around the pregnancy term, is that health care
is more standardised in Norway relative to the UK. For example, Norwegian children’s health may
be protected from socio-economic status such as maternal age at birth, whereas it is an important
input for UK children. If this is the case, then the conclusions we draw from the Norwegian data,
that omitting paternal birth weight does not create a bias in estimates of maternal inputs, may not
be generalised to the results of the NCDS. We look more carefully at the assumptions necessary to
generalise the …nding in section 6.3.3.2.
6.3.3
Comparability of Results in Norway and UK
Quantity Consumed A possible explanation for the di¤erence in the harm from smoking between Norway and the UK is that, as we saw in Table 2 and Table 11i, mothers smoking in Norway
tend to smoke in smaller quantities relative to UK mothers. On average, UK mothers smoking during pregnancy consume 14.5 cigarettes per day, but for Norwegian smoking mothers, the number
is much lower, at 6.7. Therefore, we extend the analysis by examining speci…cally how dosage of
cigarettes harms child health.
For NCDS, the sample used for analysis is, as above, described by Table 9i. Recall that we cut
the sample, in order to measure dosage, to mothers who do not change their smoking habits during
pregnancy compared to the observation period preceding the pregnancy.
We look at the dosage smoked during pregnancy in Table 11iv, which shows that 38% and
29% of mothers smoke more than 1 cigarette per day at the start and the end of the pregnancy
respectively, in Norway. In the UK, 36% and 27% of mothers smoke more than 1 cigarette per day
in the year prior to observation and during pregnancy given that they don’t change their habits,
respectively. The distribution of quantity smoked in Norway at the end of the pregnancy, is quite
3 3 Note that this number is larger than the …nal column estimate of Table 4, owing to exclusion of come covariates
such as living with a smoker.
26
similar to that of the UK, during the pregnancy and the distribution of quantity smoked at the
start of pregnancy are also similar across the countries. UK mothers are however more likely to
smoke 20+ cigarettes per day, as opposed to 2-19.
Table 11v reports the e¤ect of number of cigarettes smoked during pregnancy upon child log
birth weight. Results for Norway and UK are shown in columns 1 & 2 respectively. The table
reveals a non-linear e¤ect on birth weight by the quantity smoked during pregnancy. Smoking 2-19
cigarettes per day will lower child birth weight by 4.4% and 4.7% in Norway and UK respectively,
relative to children born to mothers who did not smoke during pregnancy. These …gures are not
statistically di¤erent to each other. The penalty from smoking 20+ cigarettes per day is much
higher, at around 6%. Comparing within country estimates, the e¤ect of smoking 2-19 cigarettes
per day is statistically di¤erent to the e¤ect from smoking 20+ cigarettes per day, for both countries.
The results are now aligned for UK and Norwegian harm on child health from smoking, if parents
smoke more than 1 cigarette per day. For parents smoking 1 cigarette per day, we …nd for Norway
a penalty of 2.1% relative to non smokers, but for UK mothers, the result is insigni…cantly di¤erent
to zero.
The alignment of results analysing the harm from smoking in the UK and Norway is important
as it suggests that our robustness check is valid, the datasets are comparable.
Generalising Results The conclusion that the estimates of intergenerational birth weight term
and the harm from smoking upon child health are unbiased from omission of paternal birth weight,
will apply to the UK as well as to Norway if:
j
j
i
i
i) E(bwcf
Scm)
= E(bwcf
Scm)
and
j
i
i
j
ii) E bwcf
bwcm
= E bwcf
bwcm
where i and j denote Norway and UK respectively.
We can assess i) by taking the a sample of male cohort members from the NCDS dataset, and
the smoking behaviour of their wives. The correlation between paternal birth weight and maternal
smoking for this sample is -0.0160 in the UK, and very similar at -0.0185 in Norway. This suggests
that the bias from omitting paternal birth weight, for the estimate of the harm from smoking during
pregnancy in the UK is negligible.
We cannot assess the correlation between father and mother birth weight in ii) directly without
observation of both maternal and paternal birth weight in the NCDS. Instead, we calculate raw
correlations between educational attainment of partners in the NCDS and compare this to the
Norwegian dataset. The correlation between father and mother’s years of schooling in Norway is
0.5 and in UK is 0.55, which again is very similar. Of course, we can not conclude from this that
assortative mating by birth weight is the same in the UK as in Norway. However the …nding that
assortative mating by educational background is similar in the two countries suggests assumption
ii) is possible.
6.3.4
Summary of Results
A summary for the results from this section follows.
There exists no bias in Norway from the exclusion of paternal birth weight. Our evidence suggests no correlation between birth weight of the mother and father in Norway and more importantly
for this paper, no correlation between paternal birth weight and maternal smoking during pregnancy in Norway. We believe that the latter result can be generalised to the UK, as the correlation
27
between paternal birth weight and maternal smoking was estimated in the NCDS to be very similar
to that from the Norwegian data. With regard to the correlation between paternal and maternal
birth weight, as we have no resources in the NCDS to compare maternal and paternal birth weight,
we have to rely on the assumption that …nding correlations between assortative mating by education
were similar across the countries, suggest that it may also be similar along birth weight.
The intergenerational transmission of birth weight is larger in the UK than in Norway and the
harm from smoking is lower in Norway than UK. The latter is explained by di¤erences in quantities
smoked in the two countries; Norwegian mothers tend to smoke less than UK mothers. Although,
in Norway we detected harm from smoking 1 cigarette, whereas in UK, the harm is essentially zero.
The conclusion from this section is that we can conclude that our estimates of the harm from
smoking during pregnancy are robust to omission of paternal birth weight.
7
Conclusion
We have used a dataset very rich in information about mothers and children, in order to estimate
the harm on child birth weight from smoking during pregnancy. We take this a step further,
and assess which group of mothers experience the greatest burden and whether their behaviour
during pregnancy can change the e¤ect. We …nd that for very young and young mothers, their
decision to smoke will cause their child to be born weighing 16% and 5.5% lower, relative to that of
non-smoking mothers, a result which was not explained by di¤erences in the number of cigarettes
smoked. Furthermore, the low educated mothers have a greater harm than others, possibly due
to inexperience during the pregnancy, possibly due to binding income constraints. That maternal
smoking during pregnancy tends to fall upon younger and less educated mothers in 2000, means
that these results are particularly relevant to smokers today. We found a negligible e¤ect of
maternal smoking during the pregnancy, if the mother quits by month …ve, which may suggest
that a cumulative e¤ect of smoking is important, or just that this group of mothers changing their
behaviour during pregnancy are endogenous. The harm from smoking was found to be non-linear,
depending upon the quantity consumed and the position in the child birth weight distribution.
Giving some magnitude to these results, we …nd that up to one third of children born low birth
weight to smoking mothers, could have been classi…ed as healthy, had their mothers not smoked.
In terms of policy aimed at equalising inequality by child health status, the nonlinear e¤ect by
socio-economic status should be perceived as reassuring, as it shows that the harm from maternal
smoking can be manipulated by the environment during pregnancy and reduced.
28
29
2104
7685
Consistently
Total Sample
100
27.38
6.12
66.51
3397.40
(540.12)
3377.46
(568.17)
3158.33
(583.59)
3330.73
(564.05)
% of sample Birth weight
(grams)
Standard deviation is shown in parentheses
470
5111
Number of
observations
At the start
Never
Smoking during
pregnancy
Table 2
NCDS Summary Statistics
28.97
(5.57)
25.71
(4.24)
25.48
(5.11)
27.81
(5.61)
Mother age
2.62
(1.12)
2.86
(1.27)
3.13
(1.32)
2.78
(1.22)
Number of
children
17.35
(2.19)
16.68
(1.52)
16.32
(1.21)
17
(1.99)
Age left
school
# cigs smoked
prior to
pregnancy
0.96
(3.79)
11.64
(7.49)
15.13
(8.49)
5.49
(8.61)
Table 3
NCDS Balanced Panel Sample
Variable
No. Obs
Mean
Birth weight child
Birth weight mother
Has mum had any children
Did mother smoke during pregnancy
7718
7718
7718
7718
0.033
-0.003
0.010
-0.070
Standard
Deviation
0.981
1.007
0.000
0.978
Mother didn't smoke during pregnancy
Mother quit by month 5
7718
7718
0.070
-0.003
0.978
0.994
Mother smoked for the entire pregnancy
Sex of child
Does mother play sport regularly
Age mother left school
Number of children
Non white marker
Ethnicity
7718
7718
7718
7718
7718
7718
7718
-0.072
0.000
0.116
0.034
-0.018
-0.006
-0.006
0.967
1.000
0.970
1.017
0.930
0.967
0.947
Number of cigarettes smoked in period of observation
preceding childbirth
7718
-0.033
0.983
Was mother a lone parent during pregnancy
Gestation
7718
7718
0.053
0.011
1.119
0.970
Did anything go wrong during pregnancy?
7718
-0.009
0.989
30
Table 4: Full OLS regression results
Dependent variable is log child birth weight
Did mother
smoke during
pregnancy
(1)
-0.064
(2)
-0.061
(3)
-0.054
(4)
-0.054
(5)
-0.051
(6)
-0.051
(7)
-0.048
(0.006)***
(0.006)***
0.199
(0.004)***
0.174
(0.004)***
0.173
(0.005)***
0.171
(0.005)***
0.171
(0.005)***
0.171
(0.017)***
(0.014)***
0.049
(0.002)***
(0.014)***
0.048
(0.002)***
-0.044
(0.014)***
0.048
(0.002)***
-0.044
(0.014)***
0.048
(0.002)***
-0.044
(0.007)***
(0.007)***
0.037
(0.003)***
0.000
(0.007)***
0.037
(0.003)***
0.000
(0.014)***
0.048
(0.002)***
-0.044
(0.007)***
(0.007)***
0.037
(0.003)***
0.000
(0.001)
0.007
(0.001)
0.007
(0.001)
0.001
(0.003)**
-0.000
(0.003)*
-0.000
(0.032)
-0.000
(0.000)**
0.003
(0.000)*
0.003
(0.001)
0.002
(0.005)
0.016
(0.005)
0.016
(0.005)
0.017
(0.002)***
-0.063
(0.027)**
-0.096
(0.031)***
-0.186
(0.009)***
0.021
(0.068)
(0.002)***
-0.063
(0.027)**
-0.093
(0.031)***
-0.183
(0.010)***
0.020
(0.068)
0.008
(0.002)***
-0.063
(0.027)**
-0.095
(0.031)***
-0.227
(0.039)***
0.024
(0.067)
0.005
(0.005)
0.003
(0.005)
0.004
(0.005)
(0.005)
-0.013
Log birth weight
mother
Child gestation
Complications at
birth
Male
Age mother left
school
Age when
mother had child
Mother age child
squared
Married when
had baby
Number of
previous births
of mother
Black
Indian/Pakistani
Other Asian
Mixed race
Exercise
regularly
Over alcohol
recommended
limit per week
(14 units fem, 21
male)
Live with
smoker
Constant
R-squared
N=7686
4.774
(0.003)***
0.03
3.833
(0.082)***
0.06
2.017
(0.087)***
0.39
Additional controls for year of birth dummies in columns 5,6,7 31
2.072
(0.087)***
0.40
1.913
(0.098)***
0.42
1.922
(0.100)***
0.42
(0.005)***
1.958
(0.439)***
0.42
32
Table 5: Stratify sample by education of mother
Dependent variable is log child birth weight
Did mother smoke during
pregnancy
Log birth weight mother
Child sex
Child gestation
Complications at birth
Age mother left school
Age Mother had child
Mother age squared
Married when had baby
Black
Indian/Pakistani
Other Asian
Mixed race
Live with smoker
Number of previous births
of mother
Exercise regularly
Drink over alcohol
recommended limit per
week (14 units fem, 21
male)
Constant
R-squared
% smokers
Low education (<=16)
Med edu (17,18)
High Edu (19+)
-0.050
-0.037
-0.040
(0.005)***
0.166
(0.016)***
0.036
(0.004)***
0.047
(0.002)***
-0.056
(0.009)***
0.005
(0.011)
-0.008
(0.039)
0.000
(0.001)
-0.000
(0.006)
-0.091
(0.030)***
-0.089
(0.059)
-0.223
(0.040)***
0.072
(0.069)
-0.012
(0.006)**
0.014
(0.011)***
0.192
(0.034)***
0.036
(0.007)***
0.047
(0.003)***
-0.031
(0.015)**
0.000
(0.009)
0.003
(0.078)
-0.000
(0.001)
-0.000
(0.010)
-0.051
(0.076)
-0.117
(0.015)***
0.000
(0.000)
-0.024
(0.129)
-0.014
(0.011)
0.026
(0.020)**
0.153
(0.038)***
0.041
(0.008)***
0.055
(0.004)***
-0.009
(0.017)
-0.000
(0.003)
0.059
(0.112)
-0.001
(0.002)
0.012
(0.011)
-0.001
(0.045)
-0.107
(0.016)***
0.000
(0.000)
-0.080
(0.019)***
-0.006
(0.017)
0.020
(0.002)***
0.011
(0.006)*
0.006
(0.005)***
-0.010
(0.010)
-0.010
(0.005)***
-0.006
(0.014)
0.010
(0.006)
2.101
(0.538)***
N=4948
0.43
42
(0.010)
1.773
(1.085)
N=1672
0.38
22
(0.012)
1.270
(1.530)
N=1066
0.48
11
Additional control for year of birth dummy variables
33
34
Table 6: Stratify sample by age of mother at birth
Dependent variable is log child birth weight
Did mother smoke
during pregnancy
Log birth weight mother
Child gestation
Complications at birth
Child sex
Age mother left school
Age Mother had child
Mother age squared
Married when had
baby
Black
Indian/Pakistani
Other Asian
Mixed race
Live with smoker
Number of previous
births of mother
Exercise regularly
Drink over alcohol
recommended limit
per week (14 units
fem, 21 male)
Constant
R-squared
% smokers
V young
mothers <17
Young mothers
<22
Mothers 22-25
Mothers 25-31
Oldest mothers
31+
-0.161
-0.054
-0.041
-0.037
-0.068
(0.085)*
0.326
(0.183)*
0.023
(0.019)
-0.207
(0.135)
0.088
(0.078)
-0.020
(0.088)
-5.528
(5.697)
0.173
(0.176)
-0.172
(0.010)***
0.176
(0.033)***
0.041
(0.004)***
-0.092
(0.019)***
0.037
(0.010)***
0.007
(0.009)
0.031
(0.282)
-0.001
(0.007)
-0.015
(0.009)***
0.194
(0.025)***
0.044
(0.003)***
-0.037
(0.015)**
0.036
(0.008)***
0.001
(0.003)
0.598
(0.486)
-0.012
(0.010)
0.011
(0.007)***
0.176
(0.019)***
0.050
(0.002)***
-0.039
(0.010)***
0.032
(0.005)***
0.001
(0.001)
0.066
(0.159)
-0.001
(0.003)
-0.002
(0.010)***
0.142
(0.024)***
0.052
(0.002)***
-0.019
(0.012)
0.043
(0.006)***
-0.001
(0.002)
-0.049
(0.161)
0.001
(0.002)
0.008
(0.101)*
0.000
(0.000)
0.000
(0.000)
-0.196
(0.127)
0.190
(0.100)*
-0.009
(0.094)
0.123
(0.016)
-0.098
(0.029)***
0.000
(0.000)
-0.217
(0.047)***
0.085
(0.077)
-0.029
(0.011)***
0.019
(0.011)
-0.007
(0.024)
-0.154
(0.041)***
0.000
(0.000)
0.159
(0.102)
-0.009
(0.010)
0.016
(0.006)
-0.098
(0.031)***
-0.158
(0.059)***
0.000
(0.000)
-0.074
(0.090)
-0.011
(0.007)*
0.014
(0.008)
-0.045
(0.035)
-0.026
(0.055)
0.000
(0.000)
-0.096
(0.106)
-0.003
(0.009)
0.018
(0.081)
0.128
(0.069)*
-0.120
(0.008)**
0.015
(0.013)
0.011
(0.004)***
0.005
(0.009)
0.010
(0.003)***
0.008
(0.007)
-0.011
(0.003)***
-0.005
(0.009)
0.008
(0.083)
46.521
(46.160)
N=48
0.55
56
(0.011)
1.860
(2.800)
N=1207
0.43
57
(0.011)
-5.126
(5.760)
N=1452
0.35
45
(0.009)
1.140
(2.216)
N=2826
0.43
30
(0.008)
2.838
(2.807)
N=2201
0.45
17
Additional control for year of birth dummy variables
35
Table 7: Quitting smoking
Dependent variable is log child birth weight
Mother smoked
first 5 months
Mother smoked
entire pregnancy
Log birth weight
mother
Constant
R-squared
(1)
-0.008
(2)
-0.005
(3)
-0.007
(4)
-0.006
(5)
-0.002
(6)
0.000
(0.010)
-0.077
(0.010)
-0.074
(0.007)
-0.064
(0.007)
-0.064
(0.007)
-0.063
(0.007)
-0.060
(0.006)*** (0.006)*** (0.005)*** (0.005)*** (0.005)*** (0.005)***
0.198
0.174
0.173
0.171
0.171
(0.017)*** (0.014)*** (0.014)*** (0.014)*** (0.014)***
4.774
3.836
2.023
2.079
1.940
2.035
(0.003)*** (0.081)*** (0.087)*** (0.087)*** (0.099)*** (0.437)***
N=7716
N=7716
N=7686
N=7686
N=7686
N=7686
0.03
0.06
0.40
0.40
0.42
0.43
Additional controls for gestation, complications during birth, gender child, age mother left school, age mother at birth, age mother squared, marital status
at birth, ethniticy, number of previous births, exercise, alcohol consumption over recommended limit and year of birth dummy variables
36
Table 8. Quantile Regression Analysis
Dependent variable is log child birth weight
Did mother smoke
during pregnancy
Log birth weight
mother
Live with smoker
Gestation
Complications at
birth
Constant
Obs
Birth Weight
0.1
-0.060
0.25
-0.056
0.5
-0.048
0.75
-0.039
0.9
-0.029
(0.006)***
0.188
(0.004)***
0.180
(0.005)***
0.170
(0.005)***
0.163
(0.005)***
0.157
(0.017)***
-0.019
(0.008)**
0.058
(0.001)***
-0.076
(0.015)***
-0.011
(0.005)**
0.054
(0.001)***
-0.042
(0.013)***
-0.008
(0.007)
0.048
(0.001)***
-0.035
(0.010)***
-0.011
(0.005)**
0.037
(0.002)***
-0.030
(0.013)***
-0.011
(0.006)*
0.030
(0.002)***
-0.019
(0.013)***
0.849
(0.596)
7686
2236.5g
(0.010)***
1.770
(0.367)***
7686
3005.0g
(0.008)***
2.279
(0.443)***
7686
3373.6g
(0.007)***
2.709
(0.494)***
7686
3713.8g
(0.007)***
2.897
(0.442)***
7686
3997.3
Additional controls for gender child, age mother left school, age mother at birth, age mother squared, marital status at birth, ethniticy, number of
previous births, exercise, alcohol consumption over recommended limit and year of birth dummy variables
37
38
2075
6529
Consistently
Total Sample
100
32
68
During
pregnancy
1
304
4.66%
0
4454
68.22%
15.30%
2-19
999
3397.41
540.12
3177.80
567.45
3347.20
554.18
% of sample Birth weight
(grams)
Table 9ii: How much do they smoke?
4454
Number of
observations
Never
Smoking during
pregnancy
20+
772
28.97
5.57
24.53
4.59
27.96
5.67
Mother age
11.82%
Table 9i: Quantity of cigarettes consumed
NCDS Summary Statistics: restricted sample
2.62
1.13
3.26
1.35
2.77
1.22
Number of
children
17.35
2.18
16.22
1.12
17.09
2.05
Age left
school
# cigs smo
during
pregnancy
0
0
15.52
8.94
4.29
8.17
Table 9iii: Quantity of cigarettes consumed
Dependent variable is log child birth weight
Did CM smoke
during
pregnancy
(1)
Full sample
-0.048
(2)
-0.056
(0.005)***
(0.006)***
amount smoked
yr before preg
(3)
Restricted sample
-0.047
(4)
(0.008)***
-0.001
(0.000)*
smoked 1 per
day
0.003
(0.011)
-0.039
smoked 2-19 per
day
(0.007)***
-0.055
smoked 20+ per
day
log birth weight
parent
Constant
R-squared
0.171
0.165
0.165
(0.008)***
0.167
(0.014)***
1.958
(0.439)***
N=7686
0.42
(0.015)***
1.795
(0.125)***
N=6626
0.43
(0.015)***
1.801
(0.126)***
N=6626
0.43
(0.015)***
1.792
(0.129)***
N=6626
0.43
Additional controls for gestation, complications during birth, living with a smoker, gender child, age mother left school, age
mother at birth, age mother squared, marital status at birth, ethniticy, number of previous births, exercise, alcohol consumption
over recommended limit and year of birth dummy variables.
39
Table 9iv. Quantity vs SES
Socio-Economics Status
Education
Low education
Medium education
High education
Age mother
Young
2nd quartile
3rd quartile
Oldest
Number of
smokers
Quantity Smoked During Pregnancy
Median
Mean
Standard
Deviation
1759
252
64
15
10
5.5
14.29
10.75
9.11
9.17
8.52
7.99
839
583
406
247
10
15
15
15
11.49
14.89
15.69
15.14
9.70
8.48
8.69
7.85
Table 9v. Quantity vs SES: Maternal Education
Low education
(<=16)
smoked 1 per day
smoked 2-19 per day
smoked 20+ per day
Constant
R-squared
% smokers
0.009
(0.013)
-0.042
(0.008)***
-0.056
(0.009)***
2.015
(0.563)***
N=4113
0.44
43
Med edu (17,18)
-0.024
(0.029)
-0.044
(0.015)***
-0.011
(0.024)
2.702
(1.053)**
N=1442
0.40
17
40
High Edu (19+)
0.027
(0.082)
0.027
(0.031)
-0.050
(0.040)
1.595
(1.705)
N=974
0.53
6
Table 9vi. Quantity vs SES: Maternal Age
smoked 1 per
day
smoked 2-19
per day
smoked 20+
per day
Constant
R-squared
% smokers
Young
mothers <22
Mothers 2225
Mothers 2531
Oldest
mothers 31+
-0.019
0.010
0.043
-0.138
(0.016)
-0.061
(0.029)
-0.036
(0.030)
-0.042
(0.039)***
-0.033
(0.016)***
-0.080
(0.013)***
-0.029
(0.012)***
-0.062
(0.015)**
-0.070
(0.017)***
0.829
(3.095)
N=1010
0.43
83
(0.015)*
-4.565
(6.458)
N=1227
0.36
48
(0.014)***
-0.382
(2.473)
N=2331
0.41
17
(0.018)***
3.372
(3.114)
N=1961
0.50
13
Table 10. Calculating the Magnitude of the Harm
from Smoking During Pregnancy
Estimated
effect
Birth weight
range affected
Percentage of
total sample
Percentage of
LBW sample
2380-2500
Number of
babies born
to smoking
mothers
79 babies
Mean effect:
4.8%
90th percentile
effect: 6%
3%
30.5%
2350-2500
92
3.6%
35.5%
41
42
8,144
40,567
120,090
At the start
At the end
Total Sample
100.00
33.78
6.78
59.44
% of sample
Standard deviation is shown in parentheses
71,379
Number of
observations
Never
Smoking during
pregnancy
3560.71
(534.46)
3529.12
(557.50)
3459.04
(567.50)
3524.223
(549.43)
Birth weight
(grams)
Table 11i
Norwegian Data Summary Statistics
27.95
(3.55)
26.43
(4.02)
27.22
(3.88)
27.60
(3.73)
Mother age
1.64
(0.79)
1.71
(0.78)
1.90
(0.85)
1.86
(0.811)
Number of
children
13.26
(2.19)
12.11
(1.96)
12.27
(2.19)
12.84
(2.24)
Years
education
# cigs
smoked start
pregnancy
0.044
(0.64)
6.63
(5.10)
6.67
(5.90)
3.27
(5.21)
Table 11ii: Comparison of Norway and NCDS
Dependent variable is log child birth weight
1
Mother log birth
weight
2
Norway
0.170
0.169
(0.004)***
0.096
(0.004)***
0.093
(0.004)***
7.368
(0.032)***
N=120090
0.01
(0.004)***
6.018
(0.046)***
N=120090
0.03
Father log birth
weight
Constant
R-squared
3
6.769
(0.034)***
N=120090
0.02
43
4
NCDS
0.205
(0.019)***
6.435
(0.153)***
N=7737
0.03
Table 11iii: Comparison of Norway and NCDS
Dependent variable is log child birth weight
1
2
Norway
Maternal log birth weight
0.115
(0.003)***
Paternal birth weight
Did mother smoke during pregnancy
-0.022
(0.001)***
-0.021
(0.001)***
0.000
(0.001)
-0.000
(0.000)
0.010
(0.002)***
0.001
(0.000)***
0.065
(0.001)***
0.015
(0.001)***
4.682
(0.033)***
N=120090
0.51
Male child
Mother’s age at birth
Mother age at birth squared
Married at birth
Mother’s education
Gestation length
Number previous births
Constant
R-squared
Additional controls for year of birth dummy variables
44
0.114
(0.003)***
0.088
(0.003)***
-0.021
(0.001)***
0.021
(0.001)***
-0.000
(0.001)
-0.000
(0.000)
0.010
(0.002)***
0.000
(0.000)*
0.064
(0.001)***
0.015
(0.001)***
3.983
(0.039)***
N=120090
0.51
3
NCDS
0.167
(0.015)***
-0.055
(0.005)***
0.038
(0.004)***
0.008
(0.003)**
-0.000
(0.000)**
0.003
(0.005)
-0.000
(0.001)
0.057
(0.002)***
0.018
(0.002)***
4.332
(0.144)***
N=7737
0.45
Table 11iv: Comparison of Norway and NCDS
How many do they smoke?
Norway
Start of
pregnancy
End of
pregnancy
UK
Period
preceding
observation
During
pregnancy
% smoke
0
1
2-19
20+
33,982
686
19,854
1,522
60.63%
33,070
1.22%
614
35.43%
13,662
2.72%
355
69.33%
1.29%
28.64%
0.74%
4,604
317
1,683
1,081
59.91%
4454
4.12%
304
21.90%
999
14.07%
772
68.22%
4.66%
15.30%
11.82%
45
Table 11v: Comparison of Norway and NCDS
Dependent variable is log child birth weight
1
Norway
0.115
(0.004)***
0.090
(0.004)***
-0.021
(0.005)***
-0.044
(0.002)***
-0.058
(0.008)***
N=47701
0.52
Maternal log birth weight
Paternal log birth weight
smoked 1 per day
smoked 2-19 per day
smoked 20+ per day
R-squared
2
NCDS
0.168
(0.015)***
-0.000
(0.012)
-0.047
(0.007)***
-0.062
(0.008)***
N=6529
0.43
Additional controls for gestation, gender child, age mother left school, age mother at birth, age mother squared, marital status at birth, number of
previous births and year of birth dummy variables
46
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48
Appendix 1.
Smoking during pregnancy: other e¤ects
Smoking during pregnancy may reduce child birth weight. Additionally, there may be other
health problems. Tuormaa et al (1995) lists the other e¤ects as:
reduce gestation and prematurity
increase miscarriages
placenta previa (abnormal location of the placenta in the lower part of the uterus, near or
over the cervix. This can cause bleeding late in pregnancy and caesarean may be required)
placental abruption (placenta tears away from the uterus before the baby is born. The
doctor may have to induce labour if the abruption is severe)
premature rupture of membranes (preterm premature rupture of the membranes (PPROM)
is associated with 30-40% of preterm deliveries and is the leading identi…able cause of preterm delivery. The 3 most common risk factors for PPROM are smoking, previous preterm delivery, and
vaginal bleeding at any time during the index pregnancy. The result, is premature deliveries)
sudden infant death syndrome (Sudden Infant Death Syndrome (SIDS) is the diagnosis
given for the sudden death of an infant under one year of age that remains unexplained after a
complete investigation, which includes an autopsy, examination of the death scene, and review of
the symptoms or illnesses the infant had prior to dying and any other pertinent medical history.
Because most cases of SIDS occur when a baby is sleeping in a crib, SIDS is also commonly known
as crib death).
increased perinatal mortality rates (stillbirths 0.4% of all births) plus neonatal deaths
(from 28 weeks gestation to 7 days post birth) (England 2001).
49
Appendix 2.
Year of birth of children year of birth of cohort members.
50