A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight: Evidence from Two Samples

Abstract

There is a large literature showing the detrimental effects of prenatal smoking on birth and childhood health outcomes. It is somewhat unclear though, whether these effects are causal or reflect other characteristics and choices by mothers who choose to smoke that may also affect child health outcomes or biased reporting of smoking. In this paper we use genetic markers that predict smoking behaviors as instruments to address the endogeneity of smoking choices in the production of birth and childhood health outcomes. Our results indicate that prenatal smoking produces more dramatic declines in birth weight than estimates that ignore the endogeneity of prenatal smoking, which is consistent with previous studies with non-genetic instruments. We use data from two distinct samples from Norway and the United States with different measured instruments and find nearly identical results. The study provides a novel application that can be extended to study several behavioral impacts on health and social and economic outcomes.

Full text

A Genetic Instrumental Variables Analysis of the Effects of
Prenatal Smoking on Birth Weight: Evidence from Two Samples
George Wehby, Ph.D.*,
Assistant Professor of Health Economics, Dept. of Health Management and Policy, College of
Public Health, University of Iowa, 200 Hawkins Drive, E205 GH, Iowa City, IA 52242 USA, Phone:
319-384-5133; Fax: 319-384-5125
Jason M. Fletcher, PhD*,
Assistant Professor of Public Health, Division of Health Policy and Administration Department of
Epidemiology and Public Health Yale University, 60 College St, #303; New Haven, CT 06520
Steven F. Lehrer, Ph.D.,
Queen’s University, School of Policy Studies, Kingston, OntarioCanada, K7L 3N6
Lina M. Moreno, PhD., DDS.,
Assistant Professor, University of Iowa, N401 DSB, Iowa City, IA, 52242, USA
Jeffrey C. Murray, MD.,
University of Iowa, Dept of Pediatrics, Iowa City, IA 52242, USA, Phone 1 319 335 6897
Allen Wilcox, MD, PhD., and
National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina,
USA
Rolv T. Lie, PhD.
University of Bergen, Bergen, Norway
George Wehby: george-wehby@uiowa.edu; Jason M. Fletcher: jason.fletcher@yale.edu; Steven F. Lehrer:
lehrers@queensu.ca; Lina M. Moreno: lina-moreno@uiowa.edu; Jeffrey C. Murray: jeff-murray@uiowa.edu; Allen Wilcox:
wilcox@niehs.nih.gov; Rolv T. Lie: Rolv.Lie@smis.uib.no
Abstract
There is a large literature showing the detrimental effects of prenatal smoking on birth and
childhood health outcomes. It is somewhat unclear, though, whether these effects are causal or
reflect other characteristics and choices by mothers who choose to smoke that may also affect
child health outcomes or biased reporting of smoking. In this paper, we use genetic markers that
predict smoking behaviors as instruments in order to address the endogeneity of smoking choices
in the production of birth and childhood health outcomes. Our results indicate that prenatal
smoking produces more dramatic declines in birth weight than estimates that ignore the
endogeneity of prenatal smoking, which is consistent with previous studies with non-genetic
instruments. We use data from two distinct samples from Norway and the US with different
measured instruments and find nearly identical results. The study provides a novel application that
can be extended to study several behavioral impacts on health, social and economic outcomes.
Correspondence to: George Wehby, george-wehby@uiowa.edu; Jason M. Fletcher, jason.fletcher@yale.edu.
*George Wehby and Jason Fletcher contributed equally to this paper. For questions on the Norway sample analysis, contact George
Wehby (george-wehby@uiowa.edu). For questions on the Add Health analysis, contact Jason Fletcher (jason.fletcher@yale.edu).
The authors do not have any conflicts of interest in this work.
NIH Public Access
Author Manuscript
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Published in final edited form as:
Biodemography Soc Biol
. 2011 ; 57(1): 3–32.
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Keywords
Smoking; Birth Weight; Infant Health; Instrumental Variables; Genetic Instruments; Mendelian
Randomization
I. Introduction
Maternal health behaviors at conception and during pregnancy are important determinants of
fetal growth and child development. Maternal smoking is one of the most commonly studied
behavioral risk factors that affect fetal/child development and is often considered the single
most important, modifiable factor affecting birth outcomes (Kramer 1987). Prenatal and
postnatal exposures to cigarette smoking are leading causes of child mortality and morbidity
(DiFranza and Lew 1996; Ebrahim, Floyd, Merritt, et al. 2000). Prenatal smoking has also
been linked to low fetal growth, low birth weight, premature births, and sudden infant death
syndrome (Schoendorf and Kiely 1992), and has been shown to increase the risk of
admission to neonatal intensive care, increasing healthcare costs of the birth by $700
(Adams et al. 2002).
Several observational studies have found that prenatal maternal smoking decreased birth
weight by about 250 grams (Evans and Ringel 1999; Rosenzweig 1983). Low birth weight is
an important predictor of child neurodevelopment and future health and socioeconomic
status (Anderson and Doyle 2003; Boardman et al. 2002; Mervis et al. 1995; Saigal et al.
2001; Victora et al. 2008; Wolf, Smit, and de Groot 2001), suggesting that fetal exposure to
smoking may reduce long term health and human capital through the impact of smoking on
birth outcomes. Indeed, researchers have found that maternal smoking during pregnancy is
associated with greater child’s behavioral risks including developing behavioral problems
later in childhood (Weitzman, Gortmaker, and Sobol 1992), participating in criminal
behavior, and lifetime nicotine dependence (Buka, Shenassa, and Niaura 2003). Maternal
smoking during pregnancy has also been associated with increased risks of language
problems, hyperactivity, fearfulness, and not getting along with peers (Faden and Graubard
2000).
The prevalence of prenatal smoking has fallen over time but is still high with a substantial
number of children exposed to tobacco pre and postnatally worldwide. For example, 13.8%
of women smoked during pregnancy in 2005 in the US(Tong et al. 2009). In Norway, the
rate of smoking during pregnancy was 11 % in 2004, compared to about 21% in 1994–1995
(Eriksson et al. 1998; Kvalvik, Skjaerven, and Haug 2008).
Discovering which behavioral factors have the greatest negative effect on fetal and child
development will aid policymakers in the development of interventions to reduce these
negative effects (Heckman 2000; Heckman 2008). Given that maternal health behaviors
during the prenatal period are likely to influence multiple child physical and neurological
outcomes (such as birth weight and neurological development), developing interventions
that address these health behaviors is likely to have large returns in child and future health
and to be more cost-effective in enhancing child health than specific interventions that target
child developmental problems post occurrence.
The commonly reported harmful effects of prenatal smoking on fetal growth and child
health may occur via various biological pathways, including cell damage and changes to the
placenta and a reduction in oxygen availability (hypoxia) to the fetus (Walsh 1994).
However, endogenous maternal selection into smoking and biased reporting of smoking
behaviors complicate the estimation of the causal effects of smoking on birth
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outcomes(Brachet 2005). Specifically, mothers who smoke during pregnancy are also likely
to self-select into smoking based on their preferences for health and risk taking and their
perceptions of fetal health endowments. These factors, typically unobserved in available
data samples, are related to fetal health through other pathways besides smoking. For
example, women who smoke during pregnancy may adopt other unhealthy behaviors that
may also have adverse effects on the fetus (e.g. poorer nutrition or reduced prenatal care),
but may also be less likely to have a family history of poor birth outcomes. Therefore, the
actual contribution of smoking to child health, independently of the confounding pathways
that correlate with both smoking and child health, and the direction of the potential net bias
in estimating the effects of smoking on birth outcomes without accounting for non-random
self-selection into smoking, is theoretically ambiguous and an open question.
Several papers have previously evaluated the effects of smoking on birth weight using a
myriad of statistical and econometric methods. Most commonly, researchers employ
statistical models that attempt to adjust directly for a variety of observable characteristics
that may proxy for the relevant unobservable factors. These factors include other maternal
behaviors besides smoking, measures of pregnancy wantedness and maternal health
(Reichman et al. 2006). Others have used propensity score matching strategies that are also
limited to observable characteristics and found similar results as more traditional
specifications (Almond, Chay, and Lee 2005).
Several authors have used experimental or quasi-experimental designs to attempt to estimate
causal effects of prenatal smoking on birth outcomes. Permutt and Hebel (1989) use a
smoking cessation intervention to introduce random variation in smoking status and find
large effects of smoking cessation on birth weight (15 grams per cigarette vs. 2 grams using
OLS and an overall effect of 400 grams)(Permutt and Hebel 1989). Evans and Ringel (1999)
use state level cigarette taxes in an instrumental variable (IV) strategy and find no
statistically discernable difference between 2SLS and baseline estimates(Evans and Ringel
1999). However, the 2SLS effect estimate is 350–600 gram decrease in birth weight across
several 2SLS models versus 230–250g across OLS models. Interestingly, the 2SLS
estimates in both Permutt and Hebel and Evans and Ringel are larger than their OLS
estimates. This is consistent with other studies that use IV estimation of smoking effects on
birth weight, which also find generally larger adverse smoking effects using IV than those
found with classical single-equation models (see also (Grossman and Joyce 1990; Lien
2005; Rosenzweig 1983)).
In this paper, we employ an IV model with genetic risk factors for smoking as instruments to
shed more light on the causal link between maternal smoking during pregnancy and infant
birth weight.1 The goal is to identify the effects of smoking using a previously unexplored
source of variation in smoking that is due to individual-level differences in genetic risk
factors that predispose for smoking, in order to account for unobserved factors that are
related to the choice of smoking and to birth outcomes. One strength of utilizing genetic
variants as instruments is that these variants are inherited at conception and, therefore,
cannot be reversely affected by smoking or other behaviors. Another advantage is that
confounding factors for smoking and birth weight, such as state-level health measures, are
unlikely to be correlated with genetic variants compared to other instruments, such as tax
rates or smoking policies (Lawlor et al. 2008; Smith et al. 2007). Similar to other
instruments, there are also challenges in using genetic instruments, which we describe in
detail in the instrument validity section below.
1The use of genetic instruments is sometimes referred to as “Mendelian Randomization” in epidemiology, but the approach is a
standard instrumental variable application with genetic instruments (Wehby, Ohsfeldt, and Murray 2008).
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Previous IV estimations of the effects of smoking have mainly utilized taxes or changes in
smoking policies between states as instruments. One limitation in these analyses is that these
are aggregate level measures that only utilize area-level variation in smoking and ignore
within area variation due to individual-level factors. Our primary contribution is that
variation in genetic markers occurs within the individual at the molecular level. Thus, the
study uses a different source of variation to identify impacts. It is of interest to use different
instrument sets in order to potentially estimate different local average treatment effects
(LATE). We describe below how we select the genetic instruments for this analysis.
We focus our attention on birth weight as the measure of infant health, similar to most of the
previous studies. Low birth weight is generally considered to be an important predictor of
other health and human capital outcomes later in life. From a policy perspective, the study
findings are important for developing health policies that target pharmacotherapy or raise
awareness for smoking cessation and public taxation policies that aim at reducing the
negative externalities of smoking, given that accurate estimation of the effects of smoking
on child health is needed in order to assess the cost-effectiveness of such policies.
There is a broad range of scientific evidence that supports the use of genetic markers as
instruments for smoking. Many twin and adoption studies have demonstrated that genetic
heritability is at least 50% for both smoking initiation and smoking persistence (Carmelli et
al. 1992; Heath and Martin 1993; Lessov et al. 2004; Maes et al. 2004; Sullivan and Kendler
1999). Researchers have also identified several variants in nicotine, detoxification, and
neurotransmitter genes to be significantly correlated with smoking behaviors including
through candidate gene, genome-wide association studies (GWAS), and meta-
analyses(Berrettini et al. 2008; Beuten 2005; Caporaso et al. 2009; Freathy et al. 2009; Li et
al. 2009; Liu et al. 2010; Tyndale 2003; Vink, Staphorsius, and Boomsma 2009). Specific
genetic risk factors have also been related to quitting and intensity of smoking during
pregnancy (Freathy et al. 2009). Further, several studies have shown that the success of a
treatment for smoking cessation may vary by neurotransmitter and detoxification pathway
genes(Kortmann et al. 2009). The genetic variants that are employed in this study and
described below in detail are in candidate genes for smoking.
The remaining sections are designed as follows: Section II describes the data sources and the
model used to identify the impact of prenatal smoking on birth weight, as well as a
description of the study measures. Section III presents our empirical results. Section IV
discusses the study findings and implications for public policy and future research studies.
II. Data and Methodology
A. Study Samples
The study uses two independent samples from Norway and the US and conducts all analyses
separately for each sample. The goal is to compare the results between samples that differ in
their populations, smoking rates, and the genetic instruments used to identify the effects of
smoking in order to gauge the result sensitivity to these factors. The idea is that observing
similar findings under these different factors suggests that the effect is generally insensitive
to differences in these factors. We describe below each data source in detail.
A.1 Norwegian Sample—The Norwegian sample includes children born without birth
defects who were enrolled as a control group in a study of oral clefts in Norway. The study
is a joint collaboration between the National Institute for Environmental Health Sciences
(NIEHS.) and researchers at the University of Bergen, and involved a population survey of
infants born with oral clefts in Norway in 1996 through 2001 and their parents as well as a
randomly selected control sample of infants born without oral clefts in the same period
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(NIEHS. 2009), with a total sample of 574 cleft cases and 763 non-cleft cases recruited. The
study goal is to identify behavioral, environmental and genetic risk factors for oral clefts,
which are common and burdensome birth defects. DNA samples from parents and children
and extensive data on maternal behaviors and household factors and socioeconomics were
collected. Behavioral data were self-reported by the mothers through self-administered
questionnaires, which were completed between 3–4 months on average post delivery. The
DNA samples have been genotyped for a large list of single nucleotide polymorphisms
(SNPs), which are DNA base-pair variants, including those in genes that are related to
nicotine dependence and detoxification. The data has been used in several studies(Jugessur
et al. 2009; Lie et al. 2008).2
Our study uses the sample of babies without oral clefts (control sample) to identify the
effects of smoking on birth weight, using a set of SNPs that are involved in neurotransmitter
or detoxification pathways and that are predictive of smoking in this sample. We limit the
study to the control sample given that it represents a random sample of the population of
births without oral clefts and in order to provide a more comparable sample to the Add
Health sample, which is a general birth sample. We describe the SNP selection process
below. The analytical sample includes 507 children with complete data on all the model
variables, including genetic variants. Out of the 763 unaffected children, 592 samples had
been genotyped (Jugessur et al. 2009).3
For the analyses using the Norway sample, we measure smoking by smoking participation
and the number of cigarettes smoked per day during the first trimester.4 We evaluate the
effects of any smoking participation during pregnancy given that less than daily smoking
exposure may still have adverse effects on fetal development.5 We also study the number of
cigarettes in order to evaluate the smoking intensity effects.
The Norway dataset provides data on several inputs and risk factors that are relevant for
infant health production including behavioral inputs (alcohol drinking, multivitamin use,
maternal calorie intake and BMI during pregnancy; pregnancy wantedness), and maternal
socioeconomic and demographic characteristics. Table 1 presents the distribution of the
study variables for the Norway sample with complete data on all model variables. Table A1
in the Appendix compares the distributions of study variables between the study samples
and the excluded samples due to incomplete data. As can be seen, there is no systematic
differences between the analytical and excluded samples, suggesting random data loss.
The average birth weight for the analytical Norway sample is 3647 grams. The mean
population birth weight in Norway between 1999 and 2004 was around 3566
grams(Kvalvik, Skjaerven, and Haug 2008). The average rate of any first trimester smoking
participation in the analytical sample is 31.7%. About 21% of the sample smoked 1 cigarette
or more per day during the first trimester. Eriksson et al. (1998) reported that about 21% of
pregnant women in Norway reported daily smoking around 18 weeks of pregnancy in 1994–
1995 based on a multisite sample(Eriksson et al. 1998). Using data on the whole birth
population, Kvalvik et al, (2008) reported that about 17.3% of pregnant women reported
2See study website http://www.niehs.nih.gov/research/atniehs/labs/epi/studies/ncl/publications.cfm
3The genotyped sample did not include samples of 171 control children due to inadequate and/or low quality DNA samples. The
analytical sample excludes 85 cases from the genotyping sample due to missing data on the study variables, including genotypic data.
The data loss is not correlated with any characteristics that are related to smoking and birth weight as described below, and is therefore
thought to be random and not systematic.
4The first trimester is a critical period for fetal development and maternal exposures during this period are likely to have large effects
on fetal growth and birth weight. The majority (about 74%) of mothers who smoked at pregnancy continued to do so during the first
trimester.
5In a sensitivity analysis, we estimated the impact of daily smoking (i.e. a minimum of 1 cigarette per day) during the first trimester
on birth weight.
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daily smoking at the end of the pregnancy in 1999–2001. The Norway sample includes
births in 1995–2002. Therefore, the sample smoking participation rates are comparable to
other study estimates for Norway.6
A2. US Sample—The US data sample is from the restricted version of the National
Longitudinal Study of Adolescent Health (Add Health). Add health was initially designed as
a school-based study of the health-related behaviors of a nationally representative sample of
adolescents who were in grades 7 to 12 in 1994/5. A large number of these adolescents have
subsequently been followed and interviewed two additional times in both 1995–6, and
2001–2. There are approximately 6,700 records of completed pregnancies by the time of the
wave 3 survey, when the 15,000 respondents were on average 22 years old. Approximately
4,500 completed pregnancies are reported by females. Nearly 2,900 pregnancies resulted in
a live birth, and nearly 2,100 pregnancies are a first birth.7 Nearly 1,850 pregnancies were
reported as the first pregnancy of the relationship. We have information on birth weight and
child gender for approximately 1,700 of these births. Because the DNA sample currently
includes only 2,500 of the original 15,000 respondents in Add Health, our sample size that
has genetic marker information available is approximately 300 mothers.
For the analyses using the Add Health sample, we measure smoking by smoking
participation during pregnancy.8 Table 2 represents the distribution of the study variables
used in the analytical sample of the Add Health sample. Table A2 in the Appendix compares
the distributions of the model variables between the analytical and the overall sample. As
can be seen, the analytical sample is representative of the overall sample and data loss is
thought to be random.
The average birth weight in the Add Health sample is over 3200 grams (approximately 7.2
pounds). Seventeen percent of the sample reports smoking during pregnancy (2.5 cigarettes
a day on average). Ebrahim et al. (2000) shows using the BRFSS survey that the prevalence
of pregnant women who smoked between 1987 and 1996 fell from 16.3% to 11.8%
(Ebrahim, Floyd, Merritt, et al. 2000). Ventura et al. (2003) shows using recent birth
certificate data that the proportion of mothers who smoke prenatally is 15% for 15–17 year
olds, 19% for 18–19 year olds and 16.8% for 20–24 year olds(Ventura et al. 2003). The
CDC (2004) finds that approximately 11.4% of all US women report smoking during
pregnancy in the early 1990s and 2000s(CDC 2004). Fewer than 40% of the births in the
Add Health sample were reported to be wanted at the time of the pregnancy.
B. Empirical model
A well established empirical literature in economics has analyzed the effects of prenatal
investments on birth outcomes. These studies generally estimate birth weight production
functions and examine the impacts of various inputs including, pregnancy-specific
behavioral investments such as parental smoking and prenatal care (Grossman and Joyce
1990; Rosenzweig 1983; Wehby et al. 2009). Following this analytical framework, the birth
weight production function is specified as:
(1)
6The smoking rates in the Norway sample reported at the first prenatal visit at about 10.3 weeks of gestation on average (available
through the Medical Birth Registry) were lower than first trimester smoking rates reported post delivery in the NCL survey,
suggesting that smoking status later in pregnancy may not accurately reflect first trimester smoking due to quitting during pregnancy
(Lie et al. 2008).
7We identify first births by examining whether the reported age of the mother at the time the pregnancy ended was the lowest age of
all observations for that mother.
8The Add Health sample had no data on number of cigarettes smoked—only number of packs smoked.
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where Smoke is an indicator of maternal smoking during pregnancy and X is a vector of
other inputs to the production process.
It is well known that OLS estimation of equation (1) would yield biased estimates of β1
since the mother’s decision whether or not to smoke reflects behavioral choices which are
likely based on her perceptions of fetal health risks and anticipations of child health
outcomes, including birth weight.9 Therefore, self-selection of risk behaviors is likely
correlated with unobserved characteristics that also affect child health in equation (1).
To overcome the endogeneity problem we consider an instrumental variable (IV) analysis
which exploits exogenous variation in smoking generated from a set of instruments to
identify its impact on birth weight. Specifically, we use sets of genetic variants in candidate
genes for smoking as the instruments.10 These variants are statistically valid instruments if
they are significantly correlated with smoking, and are otherwise unrelated to birth weight
through unobserved characteristics.
We estimate the IV model using 2SLS, which estimates the following equation by OLS prior
to equation (1), with Z representing a vector of the genetic instruments:
(Ebrahim, Floyd, Merritt, et al.; Ebrahim, Floyd, Merritt II, et al. 2000)
The 2SLS model estimates the local average treatment effect of smoking on birth weight
under well-known conditions(Angrist, Imbens, and Rubin 1996; Imbens and Angrist 1994).
We conduct a Hausman test to determine if researchers should treat maternal smoking as
endogenous (Hausman 1978). We describe below the genetic instruments.
B.1 Instrument selection in Norwegian Sample—In order to identify candidate
genetic instruments, we evaluate 65 SNPs in neurotransmitter and detoxification genes that
are considered to be candidate genes for nicotine dependence by the NICSNP Nicotine
Project11 and that have been genotyped in this sample12. We assess the SNP correlations
9These expectations are based on maternal perceptions of the biologic and environmental risk factors that contribute to maternal and
child’s health (health. endowments). However, maternal risk perceptions and health endowments are inadequately measured in
typically available datasets for birth outcomes studies, including the samples used in this study. The net bias in estimating the effects
of smoking on birth weight by estimating equation (1) via OLS is a function of the average positive and negative biases in an available
sample and cannot be signed a priori. For example, a potential mother may choose to smoke prior to pregnancy in part due to her
perceptions of her health risks and how these risks might be affected by smoking. During pregnancy, the mother will decide to
continue to smoke or not in part due to her perceptions of her health risks during pregnancy, of fetal health risks, and of the effect of
smoking on these risks. Thus, a woman may decide to smoke during pregnancy in part because she considers herself to be healthy and
considers that continuing to smoke will have no adverse effects on her and her child’s health. In this case, unobservable indicators of
health endowments that are positively correlated with both smoking and child health (such as no history of low birth weight in the
family or in previous pregnancies, history of smoking without health problems in the family, and others) may result in an
underestimation (positive bias) of the negative effects of smoking on birth weight. On the other hand, mothers who smoke are likely to
have, on average, stronger preferences for current versus future consumption and are therefore more likely to engage in other
unhealthy behaviors besides smoking, such as poor nutrition, lack of exercise, drug use, overall risk taking, and others. These factors
likely have negative effects on birth weight. If some of the health behaviors correlated with smoking and birth weight are unobserved,
as is typically the case in available datasets, the effects of smoking on birth weight may be overestimated (negative bias). The net bias
in estimating the effects of smoking on birth weight is a function of the average positive and negative biases in an available sample
and cannot be signed a priori.
10A few studies (Evans and Ringel 1999; Rosenzweig 1983) among others have used an IV strategy to account for self-selection into
prenatal smoking. As we discuss above, the main contribution is to exploit a new source of variation generated by genetic instruments
that vary at the individual level as opposed to group or state level instruments of tax rates.
11http://zork.wustl.edu/nida/Results/data1.html
12These were GSTM1, UGT1A7, NAT1, NAT2, CYP2E1, CHRNA4, GSTT1, CYP2D6, GABBR2, GABRB3, DDC, GAD1, and KCNJ2.
We also consider SNPs in the gene ACTN1 which has been identified in a recent GWA study of smoking and that have been
genotyped in this sample (Caporaso et al. 2009).
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with smoking using chi-square and ANOVA tests for smoking participation indicator and
number of cigarettes, respectively.
SNPs in NAT2, CYP2D6, GABBR2, GABRB3, and ACTH1havea significant or marginally
significant association with the smoking measures and are used as instruments.13 Due to
potential interactive effects between smoking and detoxification pathways genes on birth
outcomes (Shi, Wehby, and Murray 2008), we estimate sensitivity analysis models that
excluded NAT2 and CYP2D6 variants as instruments. The instruments include two binary
indicators that represent the three genotypes of each of the relevant SNP in order to avoid
any restrictive assumptions about the recessive or dominant effects of these genes and to
allow for gene dosage effects.
B.2 Instrument selection in Add Health Sample—While the Norwegian data
includes several candidate SNPS, the Add Health sample only contains six genetic markers
that are in candidate genes for smoking, including the dopamine transporter (DAT), the
dopamine D4 receptor (DRD4), the serotonin transporter (5HTT), monoamine oxidase A
(MAOA), the dopamine D2 receptor (DRD2) and the cytochrome P4502A6 (CYP2A6) gene.
14 Gene-gene interactions are likely important and a growing body of evidence is consistent
with this notion. For example, Skowronek et al. (2006) suggest an interactive effect between
DRD4 and 5-HTTLPR in predicting tobacco use(Skowronek et al. 2006). After examining
the first stage properties of the genetic markers and consulting the medical literature, we use
combinations between the DRD2 and MAOA, the DRD4 and 5HTT, and the MAOA and
5HTT genes as instruments for the analysis, which are described in Table 2.1516
B.3 Instrument Validity and Alternative Estimations—Similar to other instruments,
genetic instruments may suffer from certain limitations that should be acknowledged. One
challenge in using genetic instruments is that they may correlate with other “physically
near” genetic variants on the same chromosome that in turn are correlated with the
unobserved confounders that impact the outcome (Lawlor et al. 2008).17 However, we are
aware of no such effects for the specific instruments employed in this study. Indeed, work
by Fletcher and Lehrer (2009) find no evidence of linkage disequilibrium for the genetic
variants used in our study. Another potential limitation is that genetic variants may also
influence the study outcome through other pathways besides the endogenous variable due to
13rs1041983 (NAT2), rs1930139 (GABBR2), rs1432007 (GABRB3), and rs4906908 (GABRB3) are correlated with smoking
participation and are used as instruments. rs1041983 (NAT2), rs721398 (NAT2), rs1432007 (GABRB3), rs2059574 (GABRB3),
rs5758589 (CYP2D6), rs2268973 (ACTN1) are correlated with the number of cigarettes and used as instruments. NAT2 and CYP2D6
are genes of detoxification pathways and have been implicated in several types of cancer including breast, prostate, bladder cancer,
and others (Abdel-Rahman et al. 2000; Sanderson, Salanti, and Higgins 2007)) especially when combined with smoking and alcohol,
though results are generally inconsistent across studies. Given that the study is limited to women of childbearing age and is focused on
birth outcomes, it is unlikely that these variants affected the studied outcomes through their effect on cancer risks. GABBR2 and
GABRB3 are genes that code receptors for the neurotransmitter GABA, which are involved in neurological inhibition. GABBR2 has
been implicated in smoking behaviors Previous studies of smoking genetics that included GABRB3 did not report significant results t
in coding for cytoskeletal proteins and has overall no well documented disease associations and functions, but a SNP in ACTN1 has
recently been found in a GWA study to be significantly related to a threshold indicator of number of cigarettes per day(Caporaso et al.
2009).
14All of these genes except for DRD4 are considered to be candidate genes for smoking by the NICSNP Nicotine Project. However,
DRD4 have been linked in previous studies to smoking behaviors (Laucht et al, 2008; Hutchison et al. 2002). Comings et al. (1996)
find an association between DRD2 and smoking behavior, while Jin et al. (2006) find an association between MAOA and smoking
behaviors, and Gerra et al. (2005) find an association between 5-HTT and smoking behaviors(Comings 1996; Gerra et al. 2005).
15There is no consistent evidence in the literature for interactive effects between the genetic variants used as instruments in the
analysis of the Norway sample. Therefore, we do not include interaction terms between these variants in the Norway sample analysis.
Using binary indicators as instruments for the main effects of these genetic variants as done in the Norway data model is suboptimal as
these indicators have insignificant effects, which weakens the first stage.
16Four new SNPs have recently been added to the Add Health data, but they were weaker predictors of smoking behaviors of
pregnant women in our sample than those we use in this paper.
17This may occur due to the correlations between alleles that are tightly linked within a certain genomic area on a certain
chromosome (referred to as linkage-disequilibrium).
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the multi-functionality of certain genes and alleles(Lawlor et al. 2008). For example, it is
possible that one or more of the employed instruments may impact birth weight through
impacting unobserved behaviors or risk factors that are also related to birth weight.
However, there is no current evidence that the specific employed instruments in this study
are related to birth weight through such unobserved pathways.
There is no way to fully test that instruments, of any type, are truly exogenous due to the
role of unobservables(Wooldridge 2002). Therefore, it is important to appeal to the
theoretical strengths and genetics literature that motivate our use of genetic variants as
instrumental variables. However, in order to further validate the extent to which the set of
genetic instruments fit the IV assumptions, we employ the standard over-identification
statistical test, which is a partial test of the extent to which instruments are truly excludable
from the birth weight function. We also describe the sensitivity of our results to alternative
instruments specifications as described above.18
One limitation that may be common with genetic instruments and that is relevant for our
study is that instruments may have “weak” statistically significant effects on behaviors do to
the complex etiology of behaviors that may involve multiple genetic and non-genetic risk
factors. Instruments are generally considered to be weak if they have a joint F-statistic in
equation (Ebrahim, Floyd, Merritt, et al.) less than 10 (Staiger and Stock 1997). In our
analysis the F-statistics range from 3.3 to 4.4. In this case, weak-instrument robust
confidence bounds are needed for accurate inference. Therefore, in addition to standard
inference using the usual asymptotic standard errors, we estimate 95% confidence bounds
that are robust for weak instruments using the conditional likelihood ratio (CLR) statistic,
which has more statistical power than other tests (Finaly and Magnusson 2009; Andrews,
Moreira, and Stock 2006). Furthermore, we also re-estimate the IV model using limited
information maximum likelihood (LIML), which has been suggested to provide less biased
estimates with weak-instruments compared to 2SLS (Stock, Wright, and Yogo 2002).
Assuming that the instruments are exogenous (unrelated to unobserved confounders), weak-
instruments tend to over-reject the over-identification restrictions (Hahn and Hausman
2003). Therefore, failing to reject the over-identification restrictions if instruments are
exogenous is unlikely to be a result of weak instruments.
III. Results
A. Norway Sample
Table 3 below reports the OLS and the 2SLS coefficients of the birth weight production
function in the Norway Sample.19 Under OLS, smoking participation in the first trimester
reduces birth weight by about 162 grams. The number of cigarettes smoked reduces birth
weight by about 12.7 grams per cigarette (marginally significant).
Under 2SLS, smoking participation has a marginally significant and larger effect (in
absolute value) on birth weight than under the OLS model. Smoking participation reduces
birth weight by about 523 grams (p=0.075). However, the effect is not significant based on
the 95% weak-instrument robust confidence bounds. A larger smoking effect (in absolute
value) is observed under LIML, with a 614 gram decrease in birth weight (p=0.098), but the
effect is insignificant based on the weak-instrument robust confidence bounds.20
18See Fletcher and Lehrer (2009), Ding et al. (2006, 2009) and Norton and Han (2008) for other applications that use genetic markers
as instruments. These studies use a similar approach to evaluate the instrument validity (Ding 2006; Ding et al. 2009; Fletcher and
Lehrer 2009; Norton and Han 2008).
19Table A3 in the Appendix reports the full regression results along with the tests of the IV assumption and Table A4 reports the
coefficients of the first stages of the 2SLS models.
20The exogeneity of smoking participation is not rejected based on a Hausman test (0.189)
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Similar to smoking participation, the number of cigarettes has a larger and significant effect
under 2SLS, reducing birth weight by about 46 grams (p=0.04).21 The effect is significant
based on the weak-instrument robust confidence bounds. The LIML cigarette effect is
slightly larger than the 2SLS estimate, with a 52 gram birth weight decrease per cigarette
(p=0.053), and the effect is significant using the weak-instrument robust confidence bounds.
The instruments have significant F statistics of 3.3 for smoking participation and 3.99 for the
number of cigarettes. Over-identification tests fail to reject the validity of the instruments,
with p values ranging from 0.5 to 0.8.
Table A5 in the Appendix reports the sensitivity analysis models in the Norway sample
described above. The 2SLS estimates of smoking participation and cigarette effects on birth
weight are larger (in absolute value) when the detoxification gene instruments are excluded.
The same pattern of differences between OLS and 2SLS estimates is observed when daily
smoking participation (smoking at least one cigarette per day) is used as the smoking
measure, with a decrease of 612 grams in birth weight under the 2SLS model (p=0.056). The
over-identification restrictions cannot be rejected under these sensitivity analyses.
B. Add Health Sample
Table 3 presents the OLS and 2SLS estimates of the effects of smoking in the Add Health
sample.22 Under OLS, mother’s report of smoking during pregnancy reduces birth weight
by over 150 grams. The 2SLS results suggest larger effects of smoking on birth weight than
those indicated in our baseline specifications. Maternal prenatal smoking reduces birth
weight by 587 grams (p-value<0.15).23 The instruments have a first stage F-statistic of 4.4,
and the over-identification test does not reject the excludability of the instruments from the
birth weight function (p-value < 0.63).24 A larger smoking effect (in absolute value) is
observed under LIML, with a 617 gram decrease in birth weight (p=0.162) and an over-
identification test of p=0.632.
VI. Discussion and Conclusion
The study provides the first empirical estimation of the effects of prenatal smoking on birth
weight using a source of variation in smoking that is due to individual-level genetic
instruments, unlike previous studies which mostly utilize area-level instruments. The
findings suggest that standard OLS estimates may significantly underestimate the harmful
effects of maternal smoking during pregnancy on birth weight. Specifically, smoking may
reduce birth weight by as much as three times more than what is estimated using OLS.
It is important to acknowledge that the instruments are considered “weak” which
complicates IV inference. However, the significant weak-instrument robust confidence
bounds for cigarettes in the Norway sample and the similarity of the IV smoking
participation effects between the two study samples even with different populations,
smoking rates, and genetic instruments, provides support for the study results. Also, it is
important to acknowledge that even if the instruments satisfy the over-identification tests at
21The exogeneity of cigaretes is not rejected based on a Hausman test (p=0.107).
22Table A6 reports the full OLS and 2SLS regression results.
23The Add Health Wave IV data have recently been released. At a reviewer’s request, we attempted to increase our sample by
including the births that occurred between the two waves of data. While we were able to double our sample, the instruments were
somewhat weaker in the larger sample, (F-stat <2). The point estimates were nearly identical to those in the tables (−570 in the large
sample vs. −586 in this paper). The reason for the weaker instruments in the large sample are unknown but could be related to the
composition of the new births, which are from older, more advantaged mothers. It appears that these genetic variants play a smaller
role in the smoking decisions of these mothers.
24First-stage results are available in Appendix Table A7.
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relatively high p-values, which provide statistical support excluding the instruments from
the birth weight function, it is still possible that the instruments may be related to birth
weight through other unobserved factors. If so, the IV estimates would be biased as they
would be reflecting the effects of other factors besides smoking. However, there are some
safeguards that provide assurance against a major potential bias. First, there is no apriori
consistent evidence in the literature that the instruments we employ in the Norwegian data
are not exogenous and that they affect birth weight through unobserved pathways. Second,
we control for several behavioral and human capital factors that are expected to serve as
good proxies for potential unobserved behavioral effects on birth weight that are related to
the instrument. Third, we re-estimate the Add Health model adjusting for alcohol use during
pregnancy and ADHD status, which have been associated with the instruments we employ
in Add Health, and find no qualitative difference in our findings (results available upon
request). Fourth, we also re-estimate the Add Health model adjusting for twin-birth status,
given that the Add Health sample includes a higher-than-average proportion of twins. Both
the OLS and 2SLS smoking effects are virtually unaffected by this adjustment25. Finally,
the result insensitivity to alternative smoking measures and instrument specifications that are
described above also provides support for the stability of the 2SLS estimates. Therefore,
while it is still possible that the study findings are biased due to weak-instruments and the
potential of the instruments may be related to birth weight through other factors besides
smoking, the set of results as a whole provides are quite suggestive. As further knowledge
becomes available on the functions of the genes that play a role in smoking, future studies
become feasible to further evaluate these issues in selecting the instruments for smoking.
There is currently limited knowledge of what constitutes a causal genetic variant for
smoking and several efforts are currently underway to identify causal variants. This study
uses existing genotypic data which provides a cost-effective approach to study the smoking
effects on birth weight using genetic instruments but imposes the limitation of being
restricted to a certain set of candidate instruments. As discussed above, we select the
instruments from variants in several genes that are commonly considered to be candidate
genes for smoking, and some of which have been reported in more than one study to be
associated with smoking (e.g. GABBR2). However, we are unable to evaluate other genes
and variants as candidate instruments. Identifying causal variants for smoking behaviors will
enable future studies to evaluate their utility as instruments for studying the effects of
smoking on health outcomes. Further, this emphasizes the importance of replicating our
study in future studies that use different samples and/or different variants.
The underestimation of OLS smoking effects is consistent with most previous IV studies
that also find larger harmful smoking effects on birth weight under IV estimation (Evans and
Ringel 1999; Grossman and Joyce 1990; Lien 2005; Permutt and Hebel 1989; Rosenzweig
1983). This may result from “favorable” self-selection into smoking based on unobservable
factors that also affect birth weight. For example, a favorable history of pregnancy outcomes
and infant health in the immediate and extended family of the mother may increase the
propensity of the mother to smoke during pregnancy with the study infant, ceteris paribus,
but may also improve the infant’s birth weight through correlated unobserved genetic or
social health endowments. Moreover, mothers whose parents smoked and had favorable
health and pregnancy outcomes may be more likely to smoke themselves during pregnancy
than mothers whose parents smoked and had unfavorable health and pregnancy outcomes. It
is expected that the unobserved genetic or social endowments that contribute to better health
and pregnancy outcomes in the first group of families may also contribute to improving the
25In the Add Health models that adjust for twin-birth status, the smoking coefficient is −144 (marginally significant), −551 and −571
under OLS, 2SLS and LIML, respectively. The instrument effects on smoking are also virtually unaffected (F-statistic = 4.22). Further
detailed results are available upon request.
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birth weight of infants of mothers in this group compared to the second group. Unobserved
maternal health characteristics may also contribute to favorable selection into smoking,
conditional on the other determinants of smoking. Specifically, healthier mothers may be
more likely to smoke during pregnancy compared to less healthy mothers due to their
perceptions of low health risks. The underestimation of smoking effects on birth weight in
OLS is similar to the underestimation of prenatal care effectiveness due to adverse maternal
self-selection into prenatal care (Rosenzweig 1983; Wehby et al. 2009).
Biased maternal reporting of prenatal smoking status post delivery based on observed infant
health and birth weight may also contribute to underestimation of the adverse smoking
effects on birth weight. Specifically, mothers of infants with poorer birth outcomes may
underreport their prenatal smoking status due to feelings of guilt, which would result in a
positive bias in the OLS smoking effects. In both the Norway and Add Health samples,
mothers reported their prenatal smoking status after delivery. However in the Norway
sample, all mothers who report smoking at the time of their first prenatal visit also report
smoking during the first trimester in the post-delivery survey (Lie et al. 2008). This provides
some assurance against significant underreporting of smoking in the Norway sample based
on observed birth weight.26
Other unobserved health factors such as maternal preferences for child health and risk taking
which result in correlations between several maternal health and risk behaviors may result in
a positive bias in OLS smoking effects. For instance, smoking mothers may also adopt less
healthy behaviors during pregnancy (drug use, poor nutrition, less exercise, more stress).
The net OLS bias is a function of the contribution of all unobserved factors, which may
contribute differently to this bias, as described above. The study results suggest that
conditional on the observed maternal behaviors and other production inputs that may
influence the infant’s birth weight, the net effect of unobservable factors results in a positive
bias in OLS estimates.
We must also be aware of the issues of population stratification in studies that use genetic
variants as instruments. While the Norwegian population is quite homogenous, the Add
Health data comes from a representative sample of the US. In order to attempt to control for
issues of population stratification, we adjusted the analyses for racial/ethnic group
membership. Our sample size in the Add Health is far too small to report separate estimates
for each racial group (65 blacks and 57 Hispanics), so we conducted auxiliary analyses for
the sample of Caucasian mothers in our sample. The estimates were somewhat larger (−660
grams), statistically significant at the 10% level and had a first stage F-statistic of 5.2.
Therefore, we place more weight on the results for Caucasian mothers in both samples and
exercise caution in extrapolating the results to other populations until further evidence is
available.
The used instruments represent variants in genes that are candidates for contributing to the
etiology of smoking. Other genetic variants have been identified that also contribute to
smoking behaviors. Unfortunately, many of these have not been measured yet in the
available data samples for this study. In the future, we hope to replicate this study with other
genetic instruments for smoking when they are measured. Studies using genetic instruments
in other samples are also needed in order to gauge the sensitivity of the results to alternative
samples and genetic specifications. The study supports the utility of employing genetic
26The rates of smoking reported post delivery in the Norway sample were higher than those reported during the first prenatal visit.
This may be due to some mothers stopping smoking before their first prenatal visit (around 10 weeks of pregnancy on average in this
sample) or due to underreporting of smoking during prenatal visits) (Lie et al. 2008). Therefore, biased reporting of smoking cannot be
completely ruled out as a potential contributor to underestimation of smoking effects by OLS in the Norway sample.
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instruments to identify the effects of maternal smoking on infant health. Other maternal risk
behaviors and health characteristics including alcohol use and obesity are also influenced by
genetic factors, and previous studies have identified specific genetic variants that may
contribute to their etiology. Therefore, genotyping these genetic variants in relevant data
samples with maternal DNA is a major research priority in order evaluate the utility of these
variants as instruments for maternal alcohol use and weight and in order to obtain accurate
estimates of the effects of these risk behaviors on infant health outcomes.
The study results have major implications for developing public health policies to reduce the
negative externalities of maternal smoking on child health. The study highlights the need for
further public health efforts to increase the awareness of women of childbearing age about
the harmful effects of smoking, which the current study suggests exceed previous findings.
Further, the study suggests that taxation rates and smoking ban policies need to take into
account a larger negative effect of smoking on child health that may also be carried into
larger health risks and poorer socioeconomic outcomes during adulthood. Finally, the study
supports the development of additional policy interventions that enhance early life
investments in health beginning at conception and reduce maternal risk behaviors as these
may have large individual and social health and economic returns.
Acknowledgments
This research uses data from the Norway Facial Cleft Study (NCL) and Add Health. The research using the NCL
sample was supported by NIDCR grants R03 DE018394 and R01 DE020895. Add Health is a program project
designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921
from the National Institute of Child Health and Human Development, with cooperative funding from 17 other
agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original
design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population
Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). We thank Jeremy Green for
helpful comments and research assistance.
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APPENDIX
Table A1
Distribution of study variables in the NCL analytical and excluded samples due to
incomplete data
Variable
Mean (SD)
Analytical sample Excluded Samplee
Birth weight 3646.64** (589.76) 3540.04 (585.64)
Smoking 0.32 (0.47) 0.32 (0.47)
Daily smoking 0.27 (0.44) 0.29 (0.45)
Cigarettes 1.83 (3.79) 2.36 (4.61)
Binge drinking in the first trimestera0.05 (0.21) 0.06 (0.23)
Moderate drinking in the first trimestera0.09 (0.29) 0.09 (0.28)
Low drinking in the first trimestera0.18 (0.39) 0.14 (0.34)
Binge drinking before pregnancya0.38 (0.49) 0.4 (0.49)
Moderate drinking before pregnancya0.4 (0.49) 0.41 (0.49)
Low drinking before pregnancya0.13* (0.34) 0.09 (0.29)
Multivitamin use 0.38 (0.49) 0.34 (0.47)
Underweightb0.03 (0.17) 0.05 (0.22)
Overweightb0.19 (0.39) 0.18 (0.38)
Obeseb0.07 (0.25) 0.08 (0.26)
Calories 2281.19 (751.15) 2300.01 (928.38)
Maternal age 29.43 (4.7) 28.84 (4.91)
Married 0.53 (0.5) 0.53 (0.5)
Less than high schoolc0.09*** (0.28) 0.17 (0.37)
High schoolc0.27 (0.44) 0.3 (0.46)
Technical collegec0.2 (0.4) 0.21 (0.41)
Universityc0.07 (0.26) 0.04 (0.2)
Maternal employment 0.86 (0.35) 0.83 (0.37)
Very low maternal incomed0.34* (0.47) 0.41 (0.49)
Moderate maternal incomed0.25 (0.43) 0.2 (0.4)
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Variable
Mean (SD)
Analytical sample Excluded Samplee
High maternal incomed0.15 (0.35) 0.13 (0.34)
Pregnancy planning 0.74 (0.44) 0.71 (0.45)
rs1041983_ C/T 0.43 (0.5) 0.45 (0.5)
rs1041983_ C/C 0.49 (0.5) 0.36 (0.49)
rs1930139_ A/G 0.32 (0.47) 0.26 (0.44)
rs1930139_ G/G 0.05 (0.21) 0.11 (0.31)
rs721398_C/T 0.39 (0.49) 0.49 (0.51)
rs721398_C/C 0.07 (0.26) 0.07 (0.26)
rs5758589_A/G 0.49 (0.5) 0.5 (0.51)
rs5758589_G/G 0.25 (0.44) 0.24 (0.43)
rs1432007_A/G 0.52 (0.5) 0.56 (0.5)
rs1432007_G/G 0.26 (0.44) 0.24 (0.43)
rs2059574_A/T 0.47 (0.5) 0.52 (0.51)
rs2059574_T/T 0.34 (0.47) 0.36 (0.49)
rs2268973_A/G 0.4 (0.49) 0.36 (0.48)
rs2268973_G/G 0.52 (0.5) 0.48 (0.51)
aThe reference category is no drinking.
bThe reference category is normal weight (18.5≤BMI<25).
cThe reference category is 2–4 years of college.
dThe reference category is 151,000–200,000 kr.
eThe distribution in the excluded sample for a certain variable is based on the observations that had data for that variable
(these observations did not have complete data on all study variables). In the Norway sample, About 250 observations had
data on non-genetic variables except for calories (about 200 observations). About 40–45 observations had data on the
genetic instruments.
*, **, and *** indicate significant differences between the analytical and excluded
samples at p<0.1, 0.05 and 0.01 respectively.
Table A2
Distribution of study variables in the Add Health analytical and excluded samples due to
incomplete data
Variable
Mean (SD)
Analytical sample Excluded Samplea
Birth weight (grams) 3246 (556) 3260 (568)
Smoke During Pregnancy 0.17 (0.38) 0.17 (0.38)
Age (wave 1) 17.43 (1.64) 17.43 (1.6)
Male baby 0.51 (0.5) 0.52 (0.5)
Married at Birth 0.28 (0.45) 0.26 (0.44)
Black 0.21 (0.41) 0.3 (0.46)
Hispanic 0.19 (0.39) 0.16 (0.37)
Family Income (during adolescence) 36.0 (30.78) 35.58 (33.62)
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Variable
Mean (SD)
Analytical sample Excluded Samplea
Grandmother Education 12.45 (2.04) 12.56 (2.01)
No Health Insurance (wave 3) 0.25 (0.44) 0.22 (0.41)
Medicaid (wave 3) 0.26 (0.44) 0.26 (0.44)
Want Child Before Pregnancy 0.38 (0.49) 0.37 (0.48)
aN = 1691.
Table A3
Birth Weight Function Coefficients in Norway Sample
OLS Smoking OLS Cigarettes 2SLS Smoking 2SLS Cigarettes
Smoking −161.7
**
(63.74) −522.5
*
(293.4)
Cigarettes −12.66 (8.256) −46.21
**
(22.85)
Binge drinking in the first
trimester 59.55 (137.2) 47.33 (137.8) 117.5 (147.7) 83.72 (148.8)
Moderate drinking in the first
trimester 126.8 (91.89) 111.4 (91.35) 221.1** (112.8) 182.7* (103.2)
Low drinking in the first trimester 96.64 (73.96) 84.51 (75.52) 156.3* (89.16) 123.3 (80.79)
Binge drinking before pregnancy −176.4 (112.3) −210.0
*
(111.8) −71.88 (128.0) −174.7 (112.6)
Moderate drinking before
pregnancy −69.72 (108.4) −92.46 (108.9) −34.73 (108.2) −111.2 (111.1)
Low drinking before pregnancy 99.63 (119.3) 79.95 (119.6) 117.1 (117.4) 48.52 (122.3)
Multivitamin use −90.13
*
(53.56) −91.43
*
(53.74) −101.5
*
(55.86) −108.4
**
(54.57)
Underweight −257.6
**
(101.6) −284.3
***
(100.6) −197.5 (123.4) −283.9
***
(103.8)
Overweight 107.9* (61.65) 107.2* (62.10) 116.5* (61.90) 115.7* (60.94)
Obese 77.52 (131.2) 67.65 (129.4) 108.3 (139.5) 77.98 (129.2)
Calories 0.0115 (0.0358) 0.00693 (0.0352) 0.0273 (0.0394) 0.0136 (0.0352)
Maternal age 10.65 (6.572) 10.35 (6.549) 12.28* (6.787) 11.47* (6.557)
Married −68.63 (57.55) −68.58 (57.38) −93.70 (62.87) −98.21 (62.44)
Less than high school −2.996 (101.7) −12.19 (103.7) 87.71 (129.6) 71.16 (122.6)
High school 62.46 (68.28) 50.23 (68.10) 105.6 (72.63) 69.02 (66.82)
Technical college −92.98 (78.27) −104.0 (78.33) −38.16 (92.93) −68.29 (81.38)
University 0.125 (105.9) 14.41 (107.9) −9.110 (107.3) 41.32 (116.4)
Maternal employment 4.716 (81.44) 16.67 (81.98) −49.57 (93.47) −16.11 (85.36)
Very low maternal income 98.28 (69.89) 96.38 (69.72) 110.1 (71.17) 105.4 (69.56)
Moderate maternal income −48.59 (79.60) −49.23 (79.42) −30.41 (78.32) −29.36 (77.50)
High maternal income −86.23 (101.5) −83.38 (103.0) −94.69 (102.9) −85.87 (105.2)
Pregnancy planning 56.70 (61.52) 58.24 (62.15) 40.61 (61.70) 43.21 (61.58)
Constant 3408.6*** (263.5) 3424.8*** (263.9) 3394.8*** (265.9) 3451.1*** (261.2)
Instrument F statistic [df] 3.33*** [8, 476] 3.99*** [12, 472]
Over-identification Chisquare [df] 6.0 [7] 8.66 [11]
Wu-Hausman F Statistic [df] 1.66 [1, 482] 2.53 [1, 482]
Heteroscedasticity-robust asymptotic standard errors are in parentheses.
*p< 0.1,
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**p< 0.05,
***p< 0.01
Table A4
First Stage Regression Estimates in the Norway sample
Smoking Cigarettes
Binge drinking in the first trimester 0.172* (0.0960) 0.983 (0.776)
Moderate drinking in the first trimester 0.258*** (0.0691) 2.320*** (0.562)
Low drinking in the first trimester 0.171*** (0.0529) 1.233*** (0.426)
Binge drinking before pregnancy 0.300*** (0.0767) 1.122* (0.620)
Moderate drinking before pregnancy 0.101 (0.0757) −0.773 (0.614)
Low drinking before pregnancy 0.0192 (0.0860) −1.204
*
(0.694)
Multivitamin use −0.0424 (0.0402) −0.605
*
(0.324)
Underweight 0.164 (0.114) 0.138 (0.922)
Overweight 0.00861 (0.0496) 0.214 (0.401)
Obese 0.0749 (0.0782) 0.271 (0.632)
Calories 0.00004 (0.00003) 0.0001 (0.0002)
Maternal age 0.00504 (0.00459) 0.0516 (0.0373)
Married −0.0661 (0.0413) −0.891
***
(0.334)
Less than high school 0.216*** (0.0786) 2.172*** (0.636)
High school 0.109** (0.0521) 0.507 (0.418)
Technical college 0.133** (0.0589) 0.953** (0.473)
University −0.0235 (0.0842) 0.862 (0.681)
Maternal employment −0.142
**
(0.0624) −0.915
*
(0.506)
Very low maternal income 0.0410 (0.0524) 0.466 (0.425)
Moderate maternal income 0.0326 (0.0570) 0.342 (0.462)
High maternal income −0.0245 (0.0709) −0.166 (0.570)
Pregnancy planning −0.0517 (0.0458) −0.388 (0.371)
rs1041983_C/T −0.109 (0.0717) −1.029 (1.355)
rs1041983_C/C −0.00760 (0.0713) 0.312 (1.499)
rs1930139_A/G 0.0782* (0.0415)
rs1930139_G/G 0.0839 (0.0908)
rs1432007_A/G −0.0973
*
(0.0497) −0.955
**
(0.404)
rs1432007_G/G −0.0738 (0.0565) −0.469 (0.463)
rs2268973_A/G −0.157
**
(0.0746) −0.563 (0.607)
rs2268973_G/G −0.204
***
(0.0725) −1.507
**
(0.591)
rs721398_C/T 1.077 (0.737)
rs721398_C/C 1.136 (1.593)
rs5758589_A/G −0.116 (0.383)
rs5758589_G/G −0.962
**
(0.436)
rs2059574_A/T 0.703* (0.414)
rs2059574_T/T −0.618 (0.433)
Constant 0.226 (0.207) 2.164 (2.112)
Heteroscedasticity-robust asymptotic standard errors are in parentheses.
*p< 0.1,
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**p< 0.05,
***p< 0.01
Table A5
Sensitivity Analysis Models in the Norway sample
Smoking 2SLSaCigarettes 2SLSaDaily smoking 2LSbDaily smoking OLSb
Smoking −676.3
*
(358.0)
Cigarettes −57.45
**
(28.85)
Daily smoking −107.1 (68.07) −612.2
*
(320.5)
Binge drinking in the first
trimester 142.2 (159.9) 95.91 (156.2) 45.53 (136.1) 101.8 (150.0)
Moderate drinking in the first
trimester 261.4** (126.0) 206.5* (110.0) 109.0 (92.90) 224.6** (112.5)
Low drinking in the first trimester 181.8* (96.85) 136.2 (84.30) 86.44 (76.24) 164.5* (91.70)
Binge drinking before pregnancy −27.30 (142.9) −162.9 (116.0) −199.2
*
(112.1) −85.61 (121.6)
Moderate drinking before
pregnancy −19.81 (112.2) −117.5 (113.5) −82.87 (108.1) −70.96 (107.3)
Low drinking before pregnancy 124.5 (119.8) 37.98 (125.5) 91.85 (119.2) 92.06 (118.3)
Multivitamin use −106.4
*
(57.86) −114.1
**
(56.65) −86.88 (54.01) −95.63
*
(57.28)
Underweight −171.9 (135.6) −283.7
***
(106.4) −275.1
***
(101.3) −231.1
*
(124.3)
Overweight 120.2* (64.43) 118.5* (61.75) 104.4* (61.94) 106.2* (62.30)
Obese 121.4 (145.7) 81.44 (130.6) 72.68 (130.2) 114.8 (143.5)
Calories 0.0341 (0.0416) 0.0159 (0.0355) 0.00818 (0.0354) 0.0260 (0.0394)
Maternal age 12.97* (7.008) 11.85* (6.621) 9.970 (6.604) 10.18 (6.861)
Married −104.4 (66.40) −108.1
*
(65.53) −66.77 (57.59) −111.0 (68.41)
Less than high school 126.4 (144.2) 99.09 (132.7) −17.47 (102.5) 106.0 (136.9)
High school 124.0 (77.86) 75.31 (67.78) 51.10 (68.48) 88.63 (73.30)
Technical college −14.79 (101.2) −56.31 (85.20) −97.79 (79.61) −4.656 (103.6)
University −13.05 (111.2) 50.33 (122.0) 1.775 (106.2) −9.955 (108.7)
Maternal employment −72.71 (101.2) −27.09 (88.11) 15.68 (81.90) −47.34 (93.51)
Very low maternal 115.2 (75.38) 108.4 (70.80) 92.13 (69.99) 88.13 (74.23)
Moderate maternal −22.66 (81.51) −22.69 (78.59) −53.50 (79.90) −38.26 (80.82)
High maternal income −98.30 (105.1) −86.70 (107.3) −88.63 (102.4) −117.8 (112.3)
Pregnancy planning 33.75 (63.02) 38.17 (62.87) 59.28 (61.87) 37.43 (63.83)
Constant 3388.9*** (274.0) 3459.9*** (265.3) 3429.7*** (265.0) 3500.0*** (278.6)
F statistic [df] 2.88*** [6, 478] 4.52*** [6, 478] 3.18*** [8, 478]
Over-identification Chisquare [df] 4.59 [5] 7.1 [5] 4.74 [7]
Wu-Hausman F Statistic [df] 2.3 [1, 482] 2.98 [1, 482] 2.76* [1, 482]
Heteroscedasticity-robust asymptotic standard errors are in parentheses.
*p< 0.1,
**p< 0.05,
***p< 0.01
aThe detoxification gene (NAT2 and CYP2D6) instruments are excluded.
bWomen who smoked less than one cigarette per day on average are considered non-smokers (daily smoking defined as
smoking 1 cigarette per day or more).
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Table A6
Birth Weight Function Coefficients in Add Health Sample
OLS 2SLS
Smoke During Pregnancy −154.907
*
(88.511) −586.857 (409.118)
Age (wave 1) 0.035 (22.339) 3.286 (22.230)
Male Baby 120.112* (65.475) 114.881* (65.743)
Married at Time of Birth 67.252 (76.857) 7.854 (97.790)
Black −81.584 (80.032) −206.224 (126.365)
Hispanic 96.933 (103.675) −8.166 (136.918)
Family Income 2.221*** (0.730) 2.176*** (0.669)
Maternal Education 3.820 (22.537) −1.340 (23.379)
No Health Insurance (wave 3) 105.727 (74.230) 148.051* (83.208)
Medicaid (wave 3) 31.645 (90.822) 58.944 (92.977)
Want Child −34.866 (70.966) −48.549 (71.466)
Constant 3,040.608*** (572.485) 3,184.209*** (636.250)
Observations 307 302
F-statistic [df] 4.353*** [3, 301]
Over-identification Chisquare [df] 0.926 [3]
Wu-Hausman F Statistic [df] 0.82 [1, 289]
Heteroscedasticity-robust asymptotic standard errors are in parentheses.
***p<0.01
**p<0.05
*p<0.1
Table A7
First Stage Regression Estimations in the Add Health sample
Age −0.012 (0.013)
Male Baby −0.005 (0.042)
Married at Time of Birth −0.119
**
(0.049)
Black −0.233
***
(0.046)
Hispanic −0.238
***
(0.048)
Family Income ($1000s) 0.002 (0.007)
Maternal Education −0.017
*
(0.010)
No Health Insurance (wave 3) 0.097* (0.056)
Medicaid (wave 3) 0.078 (0.052)
Want Child −0.016 (0.044)
A1A2 × MAOA4R1 −0.101
**
(0.048)
SL × DRD4R1 0.138* (0.070)
SL × MAOAR2 −0.150
***
(0.053)
Constant 0.713** (0.280)
Heteroscedasticity-robust asymptotic standard errors are in parentheses.
*p< 0.1,
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**p< 0.05,
***p< 0.01
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Table 1
Distribution of Study Variables in the Norway sample (N=507)
Variable Definition Mean (SD)
Birth weight Infant’s birth weight in grams 3646.64 (589.76)
Smoking Binary (0,1) indicator for smoking in the first trimester 0.32 (0.47)
Daily smoking Binary (0,1) indicator for daily smoking in the first trimester 0.27 (0.44)
Cigarettes Number of cigarettes per day smoked in the first trimester 1.83 (3.79)
Binge drinking in the first trimesteraBinary (0,1) indicator for ≥ 4 drinks per drinking occasion in the first trimester 0.05 (0.21)
Moderate drinking in the first trimester
aBinary (0,1) indicator for 2–3 drinks per occasion in the first trimester 0.09 (0.29)
Low drinking in the first trimester aBinary (0,1) indicator for 1 drink per occasion in the first trimester 0.18 (0.39)
Binge drinking before pregnancy aBinary (0,1) indicator for ≥ 4 drinks per drinking occasion in the last few years
before pregnancy 0.38 (0.49)
Moderate drinking before pregnancy aBinary (0,1) indicator for having 2–3 drinks per occasion in the last few years
before pregnancy 0.4 (0.49)
Low drinking before pregnancy aBinary (0,1) indicator for having 1 drink per occasion in the last few years
before pregnancy 0.13 (0.34)
Multivitamin use Binary (0,1) indicator for using multivitamins before and during the first two
months of pregnancy 0.38 (0.49)
UnderweightbBinary (0,1) indicator for maternal BMI prior to pregnancy of less than 18.5 0.03 (0.17)
OverweightbBinary (0,1) indicator for maternal BMI prior to pregnancy between 25 and
29.9 inclusive 0.19 (0.39)
ObesebBinary (0,1) indicator for maternal BMI prior to pregnancy of greater than 29.9 0.07 (0.25)
Calories Average daily calorie intake during the first trimester 2281.19 (751.15)
Maternal age Maternal age in years 29.43 (4.7)
Married Binary (0,1) indicator for married mothers 0.53 (0.5)
Less than high schoolcBinary (0,1) indicator for maternal education of less than completed high
school 0.09 (0.28)
High schoolcBinary (0,1) indicator for maternal education of completed high school 0.27 (0.44)
Technical collegecBinary (0,1) indicator for mother attending technical college 0.2 (0.4)
UniversitycBinary (0,1) indicator for mother completing university education 0.07 (0.26)
Maternal employment Binary (0,1) indicator for maternal employment in the first trimester 0.86 (0.35)
Very low maternal incomedBinary (0,1) indicator for current gross maternal yearly income of less than
150,000 kr 0.34 (0.47)
Moderate maternal incomedBinary (0,1) indicator for current gross maternal yearly income between
201,000 and 250,000 kr inclusive 0.25 (0.43)
High maternal incomedBinary (0,1) indicator for current gross maternal yearly income of 251,000 kr
or more 0.15 (0.35)
Pregnancy planning Binary (0,1) indicator for mothers who were planning to become pregnant 0.74 (0.44)
rs1041983_C/T Binary (0,1) indicator of C/Tgenotype of rs1041983 0.43 (0.5)
rs1041983_C/C Binary (0,1) indicator of C/Cgenotype of rs1041983 0.49 (0.5)
rs1930139_A/G Binary (0,1) indicator of A/G genotype of rs1930139 0.32 (0.47)
rs1930139_G/G Binary (0,1) indicator of G/G genotype of rs1930139 0.05 (0.21)
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Variable Definition Mean (SD)
rs721398_C/T Binary (0,1) indicator of C/T genotype ofrs721398 0.39 (0.49)
rs721398_C/C Binary (0,1) indicator of C/C genotype of rs721398 0.07 (0.26)
rs5758589_A/G Binary (0,1) indicator of AB genotype of rs5758589 0.49 (0.5)
rs5758589_G/G Binary (0,1) indicator of BB genotype of rs5758589 0.25 (0.44)
rs1432007_A/G Binary (0,1) indicator of AB genotype of rs1432007 0.52 (0.5)
rs1432007_G/G Binary (0,1) indicator of BB genotype of rs1432007 0.26 (0.44)
rs2059574_A/T Binary (0,1) indicator of A/T genotype of rs2059574 0.47 (0.5)
rs2059574_T/T Binary (0,1) indicator of T/T genotype of rs2059574 0.34 (0.47)
rs2268973_A/G Binary (0,1) indicator of A/G genotype of rs2268973 0.4 (0.49)
rs2268973_G/G Binary (0,1) indicator of G/G genotype of rs2268973 0.52 (0.5)
aThe reference category is no drinking;
bThe reference category is normal weight (18.5≤BMI<25);
cThe reference category is 2–4 years of college;
dThe reference category is 151,000–200,000 kr.
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Table 2
Distribution of Study Variables in the Add Health sample(N=307)
Variable Definition Mean (SD)
Birth weight Infant’s birth weight in grams 3246 (556)
Smoke During Pregnancy Binary (0,1) indicator for smoking during the pregnancy 0.17 (0.38)
Age Maternal age in years at wave 1 of the survey 17.43 (1.64)
Male baby Binary (0,1) indicator for male gender of baby 0.51 (0.5)
Married at Birth Binary (0,1) indicator for whether the mother was married at the time of birth 0.28 (0.45)
Black Binary (0,1) indicator for whether the mother was black 0.21 (0.41)
Hispanic Binary (0,1) indicator for whether the mother was Hispanic 0.19 (0.39)
Family Income Income is $1000s of the mother’s family during adolescence (wave 1 of the survey) 36.0 (30.78)
Grandmother Education Years of completed schooling by the mother’s mother reported at wave 1 of the survey 12.45 (2.04)
No Health Insurance (wave 3) Binary (0,1) indicator for whether the mother reported having no health insurance at the time
of birth 0.25 (0.44)
Medicaid (wave 3) Binary (0,1) indicator for whether the mother reported Medicaid status at the time of birth 0.26 (0.44)
Want Child Before Pregnancy Binary (0,1) indicator for whether the mother reported wanting to have a child at the time of
the pregnancy 0.38 (0.49)
A1A2 × MAOA4R1 Binary (0,1) indicator for having the combination of heterozygous DRD2 gene (43%) and one,
4-repeats of the MAOA gene (41%) 0.2 (0.4)
SL × DRD4R1 Binary (0,1) indicator for having the combination of short/long allele of the 5-HTTLPR gene
(43%)and one, 7-repeats of the DRD4 gene (38%) 0.14 (0.35)
SL × MAOAR2 Binary (0,1) indicator for having the combination of short/long allele of the 5-
HTTLPR(43%)and two, 4- repeats of the MAOA gene (36%) 0.18 (0.38)
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Table 3
Effects of Smoking on Birth Weight
Smoking Indicator OLS 2SLS LIML
Norway Sample
Smoking participation −161.7
**
(63.74) −522.5
*
(293.4)
[−1641.4,228.1] −614.4
*
(371.5)
[−1634.95, 219.5]
Number of cigarettes −12.66 (8.26) −46.21
**
(22.85)
[−110.4, −5.0] −52.3
*
(27.02)
[−110.5, −5.27]
Add Health Sample
Smoking participation −154.907
*
(88.511) −586.857 (409.118)
[−2134, 215] −616.7 (440.255)
[−2129, 191]
Heteroscedasticity-robust asymptotic standard errors are in parentheses. 95% weak-instrument robust confidence bounds for 2SLS and LIML are in
brackets.
Each set of rows is from a separate regression
*p< 0.1,
**p< 0.05.
Biodemography Soc Biol. Author manuscript; available in PMC 2012 January 12.