PRELIMINARY AND INCOMPLETE – PLEASE DO NOT CITE
New Evidence on Minimum Drinking Age Laws, Pregnancy and Birth Outcomes
Alan Barreca
Department of Economics
Tulane University
206 Tilton Hall
New Orleans, LA
[email protected]
Marianne E. Page
Department of Economics
University of California Davis
One Shields Avenue
Davis, CA
[email protected]
March 2012
Abstract
Do restrictive alcohol policies affect birth outcomes? In this paper we re-evaluate the
relationship between minimum drinking age laws and infant health outcomes by examining the
sensitivity of current findings to the inclusion of additional, and economically significant,
controls. We also implement a new differences-in-differences design that compares differences
in health across infants whose mothers who turned 21 during vs. after pregnancy, to differences
in health across infants whose mothers turned 20 during the same stages of pregnancy. Our
estimates provide little support for the hypothesis that alcohol policies affect birth outcomes.
We would like to thank seminar participants at UC Merced for their comments. We gratefully
acknowledge funding support from National Institutes of Health (R01AA017990-01). We are solely
responsible for the views expressed in the article. Barreca is an Assistant Professor at Tulane University
and can be contacted at [email protected]. Page is a Professor at University of California, Davis and
can be contacted at [email protected].
I. Introduction
Do restrictive alcohol policies affect birth outcomes? Several studies have found evidence that
minimum drinking age laws affect the amount of alcohol young women consume and their
probability of engaging in risky sexual activity.1 Taken together with evidence on the negative
correlation between prenatal alcohol consumption and birth defects2, it would seem that the
answer to the question must be Yes. Yet surprisingly little is known about whether laws that
limit young adults’ consumption of alcohol improve birth outcomes. The answer to this question
is important since a substantial portion of young adults drink alcohol. Carpenter and Dobkin
(2009), for example, find that a third of young adults drink heavily, and Fertig and Watson
(2009) find that approximately 33% of pregnant women between the ages of 18 and 22 consume
alcohol. Given the mounting evidence that health, even at the very beginning of life, affects
markers of later socioeconomic success (Almond and Currie, 2010), MLDA laws may be an
important tool for improving children’s life chances.3
The goal of this paper is to test whether alcohol policies affect birth outcomes. To date,
the most convincing research design that has been used to estimate causal effects on birth
outcomes exploits cross-state changes in U.S. minimum legal drinking age (MLDA) laws that
occurred during the 1970s and 1980s. Prior to the 1970s, most states maintained a legal drinking
age (MLDA) of 21, but during the early 1970s a number of states reduced their MLDA, and in
many states the new drinking age was as low as 18. A subsequent rise in the number of alcohol
related fatalities induced the majority of these states to raise their minimum drinking age to 21
again during the 1970s and 1980s. With the passage of the Uniform Drinking Act, all states had
adopted an MLDA of 21 by 1988.4
Using this policy variation, Fertig and Watson (2009) (hereafter FW) find that, among
young mothers, lenient drinking laws are associated with higher rates of low birthweight and
1
See, for example, Cook and Mare (2002); Cooper (2002); Grossman and Markowitz (2002); Kaestner (2000);
Kaestner and Joyce (2000); Markowitz et. al. (2005); Rashad and Kaestner (2004); Rees et. al. (2001); and Sen
(2002).
2
A non-exhaustive list of recent studies includes Albertsen et al., 2004; Berkowitz et. al, 1982; Jaddoe et al, 2007;
Kesmodel et. al, 2000; McDonald, et. al, 1992; Mills et. al, 1984; Shu et. al, 1995; Whitehead and Lipscomb, 2003;
Windham et. al, 1995.
3
In doing so, they may also substantially reduce medical costs. Almond, Chay and Lee (2005), for example,
estimate that increasing the birthweight of singleton births that are below 2500 grams, until they met the low
birthweight threshold would produce an average cost savings of $10,000 per child.
4
Although the law mandating a MLDA of 21 was passed in 1984, the law was not enforced until late in 1986. The
law withheld a portion of a state's Federal highway funds if the state did not enact a MLDA of 21 by October 1,
1986. (Distilled Spirit Council of the United States 1996)
2
premature birth. They note that this result is partly due to selection effects. In particular, they
find that a lower drinking age is associated with lower levels of education among births to white
mothers and the absence of paternal information on the birth certificates associated with black
mothers. This selection effect is also documented by Dee (2001), who finds that increasing the
MLDA to age 21 reduced childbearing rates among black teens by roughly 6 percent, but had no
statistically significant effect on whites.
As Dee notes, however, there are limitations to using changes in the MLDA to identify
causal effects; namely, factors underlying the policy changes may not be completely eliminated
even when using state level changes in the MLDA. States that initially had low MLDAs and
were required to raise the MLDA to 21 when the Uniform Drinking Act was passed in 1984, are
arguably different from states that consistently maintained an MLDA of 21. The speed with
which states complied with changes in the federal MLDA during the late 1980s is also likely
nonrandom.
In this study, we explore the robustness of FW’s findings to the inclusion of an
alternative set of controls that better address the endogeneity of the MLDA changes and possible
omitted variables bias. We show that there is no consistently meaningful relationship between
the MLDA laws and birth outcomes once key control variables, i.e. age specific time trends and
state-by-age fixed effects, are included. We also make note of the fact that 12 states consistently
maintained a MLDA of 21 during our sample period,7 and we document the importance of our
preferred control variables by examining their economic and statistical significance in these 12
“placebo” states. The control variables turn out to have predictive power in the states where
MLDA laws did not change, which suggests that previous evidence on the relationship between
MLDA laws and birth outcomes is likely driven by omitted variables.
We also introduce a complementary difference-in-differences estimation strategy that
does not rely on policy changes for identification. For this analysis, we use restricted data on the
universe of births that occurred in California between 1989 and 2004. This research design
compares differences in birth outcomes across two samples of births: births to mothers who turn
20 sometime within a year of conceiving (the control group), and births to mothers who turn 21
7
These 12 states include: Arkansas, California, Indiana, Kentucky, Missouri, Nevada, New Mexico, North Dakota,
Oregon, Pennsylvania, Utah, and Washington. Note that many of these states are geographically clustered. For
example, California, Oregon, Nevada, and Washington, and are thus less likely to be affected by MLDA law
changes in treated states.
3
within a year of conceiving (the treatment group). Within each sample, we group births
according to the four-week bin during which the mother experienced a birthday, and take first
differences in birth outcomes between those whose mothers turned 20(21) in a given 4-week bin
and those whose mothers turned 20(21) in a post-partum bin (i.e. weeks 45-48). We then
estimate the impact of gaining legal access to alcohol by estimating a “second difference” that
compares the differences across the treatment and control groups.
One advantage of this difference-in-difference identification strategy is that we can test
whether there are non-linear effects associated with gaining access to alcohol during particular
stages of pregnancy. For example, it may be that gaining access to alcohol is particularly harmful
during the first 8 weeks (or so) of pregnancy. 8 Convincing human studies on the impact of prenatal alcohol exposure are hard to come by, but animal studies suggest that prenatal exposure to
“teratogens”, or foreign substances like alcohol, matter most during the early part of pregnancy
(Tough et al. 2006). In addition, some women do not realize they are pregnant during the early
weeks of pregnancy, which might lead to risky behaviors (e.g. binge drinking) that they might
have avoided had they been cognizant of the pregnancy (Kesmodel 2001; Tough et al. 2006).
Alcohol consumption might also lead to negative selection (or culling) during the early part of
pregnancy, something we can test by examining impacts on the fraction of births that are female
since male fetuses are less likely to survive in utero health shocks. Our D-in-D approach allows
us to explicitly examine whether access to alcohol during this period matters differently than
access at later stages. Further, the women in our D-in-D sample could not legally drink prior to
conception, which helps mitigate alcohol-related selection into pregnancy.
Similar to the analysis using policy changes, however, our difference-in-difference
identification strategy yields, at best, weak evidence that pregnant mothers’ legal access to
alcohol affects their infants’ health outcomes. Most of the relevant coefficient estimates are not
statistically distinguishable from zero.10 In cases where they are both economically and
statistically significant, other aspects of the analysis produce red flags. One might be concerned
that our lack of evidence results from measurement error in mothers’ reports of their estimated
date of last menses; we dismiss this concern by dropping mothers who have a date of menses that
8
As a matter of language, it is common to count weeks as the time from the last menses. The actual conception
usually occurs 14 days after the last menses (Niebyl and Simpson 2001).
10
Point estimates are sometimes large but too imprecisely estimated to be able to reject the null hypothesis of no
effect.
4
is most likely to be measured with error. Furthermore, we find some evidence of “flattening” in
the age-outcome profile among women who turn 21 during pregnancy (compared to women who
turn 20), suggesting our estimates are biased away from zero by positive selection. Thus, our
estimates may be upper bound estimates of the true causal effects.
Taken as a whole, our analyses provide little support for the hypothesis that alcohol
policies affect birth outcomes. However, we cannot rule out the possibility that alcohol
consumption during pregnancy has a detrimental effect on infant health. It may be that young
mothers find non-legal ways of obtaining alcohol and that the laws have little impact on their
drinking behavior. Alternatively, MLDA laws may affect maternal consumption of alcohol and
subsequently affect infant health in ways that cannot be observed in existing data. At the same
time, Armstrong (2003) makes a compelling case that, given the quality of existing research,
Americans may have jumped too quickly to the conclusion that consumption of alcohol during
pregnancy in any amount has devastating consequences. Although the relationship between
alcohol access and infant outcomes is intuitive, our research indicates that the causal link is far
from established.
In the next section we provide background on minimum legal drinking age laws.
Sections II and III describe our data and empirical methodologies. Our results are described in
Section IV, with further interpretation and conclusions in Section V.
II. Data
The data on MLDA laws come from the Distilled Spirits Council of the United States.
We have information on the month and year that the MLDAs changed for each state, between
1978 and 1988. We match the MLDA data with birth outcome data by mother's estimated age at
conception and the month the child was conceived.11 Treatment is assigned based on whether the
mother's age was greater than or equal to the MLDA in her state of residence at the time her
child was conceived. Figure 1 illustrates the changes in the MLDA between 1977 and 1989. For
example, the number of states with a MLDA of 21 increased from 12 in 1977 to 23 in 1985, and
51 (including the District of Columbia) by 1988.
11
Following Fertig and Watson, we assume that the mother’s age at conception was one year less than her age at
birth for all gestational lengths over 26 weeks.
5
The birth-outcome data used in our state-year MLDA analyses come from the National
Center for Health Statistics (NCHS) public-use Natality Files. The NCHS data are derived from
information reported on birth certificates and include the near universe of all U.S. births that
occurred in the 1970s and 1980s.12 With respect to birth outcomes, the NCHS data have
information on the child's gender, birth weight, length of gestation, presence of congenital
anomalies, and five-minute APGAR score. Birth weight and APGAR score are commonly used
indicators of infant health at birth and have been linked to a number of long-term outcomes (e.g.
Black, Devereux and Salvanes, 2007; Behrman and Rosenzweig, 2004; Oreopolous and Stabile,
2008).13 14 In addition, we construct dichotomous indicator variables for whether the child's birth
weight is below 2500 grams ("low birth weight") and whether the length of gestation was under
37 weeks ("premature birth"). We include the fraction of births that are female as a dependent
variable because evolutionary theory suggests that males are more sensitive than females to in
utero health shocks (Trivers and Willard, 1973) and because several recent studies have found
evidence that stressors during pregnancy affect the probability of bearing a male child (Cagnacci
et al., 2004; Almond et. al., 2007; Nilsson, 2008; Sanders and Stoecker, 2011).
To discern whether the mother was of legal drinking age during her pregnancy, we rely
on information in the NCHS data on the mother’s age, the mother's state of residence, and the
month of the child's birth. A limitation of the data is that we observe the mother's age (in whole
years) at the time of child's birth but not at the time of conception so we cannot tell whether the
mother was the same age during the majority of her pregnancy. To be consistent with previous
research, we assume the mother's age at conception is one year less than her age at the child's
birth.15 We assume a mother is eligible to drink if her age at conception is at or above the MLDA
in her state of residence.
Our NCHS analyses are restricted to mothers who are between 14 and 24 years of age at
the time of conception and are U.S. residents.16 Women over the age of 21 are legally able to
drink throughout the entire period under study, so the MLDA laws should not causally affect
12
The vast majority of states reported 100-percent samples. The remaining states report 50-percent samples.
APGAR scores are on a 10-point scale based on five categories of infant health.
14
We are the first to explore the effects of changing MLDA laws on infants’ APGAR score. The Vital Statistics
began collecting APGAR scores in 1978 but at that time they were only reported in 38 states. The number of states
reporting the APGAR score increased gradually through 1989 when it was reported in 47 states.
15
For gestational lengths less than 26 weeks, we assme the mother’s age at delivery and conception are equal.
16
Note that we assign treatment based on the state of residence to avoid contamination of our estimates by
endogenous short-term migration responses that are correlated with the MLDA.
13
6
these women (assuming there are no intertemporal effects or spillovers.) As such, changes in the
health outcomes of infants born to these women can be used to control for within-state changes
in birth outcomes that may be spuriously correlated with MLDA changes, or for any effects of
the MLDA that are common to all age groups. For example, the MLDA policy changes may be
endogenous to the changes in health conditions that affect infants born to women of all ages.
For our difference-in-differences analyses we need information on mother’s exact date of
birth, which is not available in the NCHS public-use Natality Files, but is available from the
California Department of Public Health’s (CDPH) restricted-use Birth Cohort Files and Birth
Statistical Master Files. These files contain information that is provided on birth certificates for
all births that took place in California between 1989 and 2004. These data have information on
all of the birth outcomes mentioned above except for the APGAR score, which is not reported.
We can infer the exact age of the mother at any point during her pregnancy because we have the
mother's exact date of birth, the child's exact date of birth, and the date of the mother’s last
menses. Importantly, these data are linked to infant mortality records so we can estimate impacts
on the probability the child died within one year of birth. We restrict our sample to births among
mothers who were themselves born in California, and for whom last normal menses is reported.17
An advantage of these datasets is that they include information on several million births.
Our NCHS sample has over 19 million births to women ages 14-24, and our CDPH samples have
close to 300 thousand observations for those mothers who were between 19 and 20 at the time of
conception. Most human studies on the relationship between maternal alcohol consumption and
fetal health rely on data with only a few hundred to a few thousand observations. Both datasets
include information on mother’s race and education, which allows us to test for effects on the
composition of women who give birth. Summary statistics for our samples of births are
presented in Table 1 and Table 2.
III. Estimation Approach
A. MLDA Law Changes
Our first identification strategy exploits variation in the MLDA laws across states and
over time. Because the MLDA laws vary across states, years and cohorts we conduct our
17
We restrict our sample to mothers who were born in California because we do not want our estimates to reflect
migration, in the unlikely event that women migrate in anticipation of turning 21.
7
analyses at the state-year-age cohort level. We begin by estimating the following regression via
ordinary least squares:
(1) Yast = !1 MLDA18st + !2 MLDA18st * age14-17ast + !3 MLDA18st * age18-20ast
+ "s + #t + $a + %s * t + &cst
where Yast is an average outcome for infants born to mothers of age a residing in state s at the
time of conception t,18 MLDA18st is an indicator for whether the drinking age in the mother’s
state of residence s was 18 at time t, MLDA18st is interacted with age14-17ast and age18-20ast to
allow the effects of the MLDA to vary for mothers who were 14 to 17 and 18 to 20 years old,
respectively, "s are state fixed effects; #t are year-month fixed effects; $a are age fixed effects;
and %s * t are state-specific linear time trends. The inclusion of state specific time trends allows
us to account for the possibility that variation in the MLDA is correlated with unobserved factors
that vary by state and year that might affect infant health. To account for the possibility that the
error term & is correlated within states, we cluster our standard error estimates at the state level.
We focus on whether the state had an MLDA of 18 in order to be consistent with the
existing literature. Dee (2001) shows that an indicator for whether the state has an MLDA of 18
sufficiently captures the variation in youth’s drinking behavior, and FW also adopt this approach.
We have run similar models where we replace the MLDA18 dummy and its interactions with
indicators for whether the mother could legally drink at the time of conception. These models
produce similar, though smaller, estimated relationships. One interpretation of the smaller
estimates is that, in addition to providing legal access to alcohol for those 18 and over, an MLDA
of 18 also provides women under the age 18 greater access to alcohol, possibly through their 18year-old peers. An implication of this is that women under age 18 cannot serve as a reliable
control group. Women who are over 21 can arguably serve as an adequate control group,
however, since the MLDA varies between 18 and 21.19
Equation (1) is qualitatively similar to the model to that FW use and produces very
similar estimates.20 After estimating equation (1) we systematically add two important sets of
18
The month the mother conceived the child can be inferred by using publicly available information on length of
gestation; in the cases where gestation is not reported, we assume gestation began 40 weeks prior to the date of the
child's birth.
19
There is some possibility that exposure to an MLDA of 18 has long-term effects on fertility decisions and birth
outcomes, which would potentially bias our results downward.
20
FW rely on individual level data and control for infant sex and plurality. We disagree with controlling for these
characteristics since they are potentially endogenous to changes in the MLDA.
8
controls that have not been presented in previous studies.21 First, we add age-specific linear
trends. The inclusion of these trends allows us to account for the possibility that the estimated !
coefficients are capturing a convergence in birth outcomes among mothers of different ages over
time. Second, we also include state-specific age fixed effects. This allows us to control for the
possibility that within state differences in birth outcomes between mothers of different ages are
correlated with states’ MLDA policies. This might be the case if states differ in the extent to
which they protect teenagers, or the extent to which they regulate teenagers’ behavior. We show
that including these controls substantively affects the estimated impact of alcohol access on birth
outcomes.
B. Difference-in-Differences Analysis
Our second identification strategy relies on restricted CDPH natality data covering births
that occurred in California between 1989 and 2004, a period when the MLDA was fixed at 21 in
California and all other states. We exploit the fact that the CDPH data have information on the
child’s date of birth, the mother’s date of birth, and the date of the mother’s last menses, to set up
a difference-in-differences (D-in-D) style identification strategy. Specifically, we compare: a)
the difference in birth outcomes among women who turned 21 during pregnancy to birth
outcomes among women who turned 21 just after giving birth to b) the difference in birth
outcomes among women who turned 20 during their pregnancy to birth outcomes among
women who turned 20 just after giving birth. This approach has several nice features. First, it
allows us to estimate the impact of the MLDA law in a more current context---the MLDA has
been 21 for over two decades. Second, it allows us to estimate the impact of gaining access to
alcohol at different points during pregnancy. Third, none of the women in our sample had legal
access to alcohol before they turned 21, which mitigates the possibility of alcohol-consumptionrelated selection into fertility.
In order to implement this identification strategy we create two samples. The first sample
consists of births to mothers who were 19 at the time of their last menses, and the second sample
consists of births to mothers who were 20 at the time of their last menses. The first group is our
control group, and the second group is our treatment group. As a starting point, we take each
21
FW discuss the robustness of their results to the inclusion of the age-specific trends, but this robustness check is
not presented in the published manuscript.
9
sample and group the observations according to the four-week period during which the mother
experienced a birthday. We assign an indicator variable for each 4-week period and run the
following regressions:
(2) Yiat = DUM21ia + 't (t WEEKt+ 't !t (WEEKt * DUM21ai) + ) Xiat + &it
where Y is a measure of health (birthweight, gestation, etc.) for an infant born to mother i, who
was age a and experienced a birthday in trimester t. DUM21 is an indicator variable that
identifies whether the mother was 20 years old at the time of her last menses (and subsequently
turned 21). The inclusion of this variable helps control for the fact that infants born to 20 year
olds are healthier, on average, than infants born to 19 year olds. WEEKt is a vector of 11 dummy
variables that indicate whether the mother turned 20 (21) during one of 11 four-week bins (1-4,
5-8, …, 41-44). X is a vector of controls including year-month (of last menses) fixed effects and
county fixed effects.22 We cluster our standard error estimates&) on the county of residence in
order to account for the possibility, however small, that some counties have policies or norms
that have differential effects on women of different ages.
Our omitted category is infants whose mothers experienced a birthday during weeks 4548, which is nearly always postpartum. As Appendix Figure A1 illustrates, only 2 percent of all
pregnancies ever reach 45 weeks. Further, given less than 25% of pregnancies ever reach 41
weeks, we can use the ! estimate on weeks 41-44 as a useful placebo check. Note that our
control group does not include births to women who have a birthday in weeks 49-52 of the
pregnancy since these women are likely to have had a birthday just prior to conception, which
might affect the composition of mothers in our sample. If the types of mothers who conceive
just after their birthday are different from those who conceive at other times, then including
births to these women may affect the magnitude of the estimates and their interpretation.23 Thus,
( captures the first difference between the outcomes of infants whose mothers experienced a
birthday in the four-week-bin t, and infants whose mothers experienced a birthday in the
postpartum period (i.e. weeks 45-48).
The coefficient vector ! reflects the impact of turning 21 at different stages of the
pregnancy. After accounting for fixed differences in birth outcomes to women who conceived at
age 19 and age 20, we assume that the counterfactual outcome for women who turned 21 during
22
The inclusion of these controls does not affect the point estimates or the precision of said point estimates.
The results of regression discontinuity analyses not shown (but available from the authors), indicate notable
increases in fertility right after a woman’s birthday.
23
10
period t of their pregnancy would have been equivalent to turning 20 during the same period.
The detrimental effect of alcohol on fetal development has been difficult to pinpoint and there
are several reasons one might expect any or all of the coefficients to be important. Much of the
medical literature suggests that exposure to teratrogens, like alcohol, is most harmful during days
31 and 71, or when the heart and central nervous system are developing (Niebyl and Simpson
2007). Further, we might expect drinking intensity to be greatest during the early part of
pregnancy, roughly weeks 3 through 8, when women may not know they are pregnant
(Kesmodel 2001; Tough et al. 2006). This suggests that the magnitude of the coefficient on
weeks 5-8, in particular, may dominate the magnitude of the other coefficients. On the other
hand, some animal studies suggest that the third trimester is a particularly important period for
brain development (Marcussen et al. 1994). It is also the period associated with the largest
weight gain. If calories from alcohol are substituted for calories containing more nutrition, then
access to alcohol during the third trimester might be more important. Previous studies (e.g.
Almond et. al, 2010; Painter et. al, 2005) have found that nutritional shocks during the third
trimester have the largest impact on birthweight.
In sum, pinpointing the detrimental effect of alcohol with respect to a specific period of
gestation remains an open and important research endeavor. While our estimates can address this
question to a certain extent, it should be noted that our treatment captures the effects of gaining
access to alcohol from at a given point in the pregnancy onwards. Thus, distinguishing from an
acute episode of drinking and the cumulative effects of drinking throughout pregnancy is
impossible.
The D-in-D analyses are an excellent complement to analyses based on state-year policy
changes because of the different assumptions that are necessary for clean identification. Using
state/year variation in MLDAs relies (perhaps questionably) on the assumption that policy
changes are exogenous. On the other hand, underlying the D-in-D analyses are functional form
assumptions regarding the relationship between mother’s age and infant health.24 Both
identification strategies are susceptible to error-in-variables bias. In the case of the D-in-D
strategy measurement error problems may result if a significant number of women inaccurately
report the date of their last menses. When we use state-year level policy changes we only have
24
We examine the robustness of our estimates to a variety of alternative assumptions about this relationship.
11
information on the mother’s age at the time of the birth and are forced to make some
assumptions about her age at conception.
The two empirical approaches also pick up different local average treatment effects. The
D-in-D design identifies the effect of maternal alcohol consumption on infants whose mothers
are close to their 21rst birthday, whereas using variation in state level MLDA’s over time
identifies the effect on infants born to mothers who are significantly younger. One might expect
the impact of alcohol access to differ across these two groups. For example, the same amount of
alcohol may affect babies born to teenage girls differently than babies born to older mothers
because of other differences in maternal health. Infants born to young mothers tend to be less
healthy than other infants so they may be more susceptible to the impacts of alcohol. Older
women may also be less likely to drink (or less likely to drink heavily) during pregnancy than
younger women, regardless of legality, because they are either better informed or more
responsible. Finally, it is important to keep in mind that our two approaches use data from
different locations and time periods: our NCHS analyses are based on the 11 years of data
covering all births between 1978 and 1988, whereas our CDPH analyses are based on all births
that took place in California between 1989 and 2004. If there are differences in the impact of
MLDA laws on maternal consumption across states or over time then this could also generate
differences in the local average treatment effects.
IV. Results
A. Estimates based on State/Year Variation in MLDA Laws
Table 3 presents the estimated impact of the MLDA laws using variation in these laws
across states and over time. We present results for three specifications: the first specification
includes dummy variables that control for state of birth, month and year of birth, and mother’s
age, along with state-specific time trends. This specification is nearly identical to that used by
FW and produces estimates that are nearly identical.25 The second specification adds an agespecific linear trend to the set of control variables. And our third specification adds state by age
interactions. We present the estimates for all races (Panel A), white mothers only (Panel B), and
black mother only (Panel C).
25
For example, the three key coefficients in Panel A column (1) are -0.18, 0.49, and 0.24. FW’s analogous
coefficients are -0.17, 0.50, and 0.26, respectively.
12
The pattern across the specifications is striking. When age trends and state*age
interactions are omitted from the regression, we find that lower legal drinking ages are associated
with worse birth outcomes for both the affected group, and among women who are younger than
the MLDA. The coefficient estimates suggest that among 18-20 year old mothers an MLDA of
18 increases the probability of low birthweight, relative to 21-24 year olds, by 0.24 percentage
points. The probability of a premature birth also increases by 0.16 percentage points. To put
these estimates into perspective, about 8 percent of infants born to 18-20 year old mothers are
born weighing less than 2500 grams, and about 12 percent are born before 37 weeks. The
estimates, therefore, appear to be economically as well as statistically significant.
Note, however, that the estimated impact of the MLDA on births to the older age group is
also substantive, and in the opposite direction. Among women 21-24 years old, who are too old
to be materially affected by a change in the minimum legal drinking age26, an MLDA of 18 is
associated with a 0.18 percentage point lower probability of having a low birthweight, and a 0.26
percentage point lower probability of a premature birth. If the state minimum drinking age were
truly exogenous we would expect these estimated coefficients to be near zero. The fact that
these coefficient estimates are opposite in sign to the estimated impacts on younger mothers,
together with the fact that the fraction of states with an MLDA of 18 was declining over this
period, suggests that the estimated effects among younger women may be biased by differential
trends across age groups.
In order to investigate this possibility, we add age-specific trends to the regression, and
display the results in columns 2 and 5. Including age trends cuts the estimated MLDA
coefficients dramatically, and substantively reduces their statistical significance. Columns 3 and
6 show what happens when we add state*age interactions: the estimated coefficients fall even
further, and when the dependent variable is an indicator for premature birth the estimated
coefficient on the MLDA is no longer statistically different from zero.
FW acknowledge that adding age trends reduces the estimated impact of the MLDA laws
but they argue that omitting these trends is reasonable given the strong degree of colinearity
between the trends and the MLDAs. Their argument is that the addition of age specific time
trends absorbs important identifying variation in the MLDA laws. In general, we find that the
standard error does increase with the addition of the age-specific time trends, although only
26
It is possible that 21-24 year olds may have been affected by the MLDA law change earlier in life.
13
slightly. For example, the standard error on “MLDA is 18 x mother is 14-17” for the low
birthweight outcome goes from 0.157 to 0.183 (columns 1 and 2, respectively). However, when
both age-specific trends and state-by-age fixed effects are included, the standard error estimates
are actually smaller. In the previous example, the standard error is now 0.080 (column 3). Thus,
we can rule out the possibility that our model is absorbing a substantial portion of the identifying
variation.
More plausibly, the estimates in columns 1 and 4 are being identified from: (a) changes in
birth outcomes over time that differentially affect mothers of different ages, and (b) differential
age-outcome profiles for treated and untreated states. Figure 2 begins to investigate possibility
(a) by plotting differences in outcomes between 14-17 years olds and 21-24 year olds over time,
separately for states that did (“treatment states”), and did not (“placebo states”), experience
changes in their MLDA laws between 1978 and 1988. As Panel A.1 in Figure 2 illustrates,
relative to infants born to 21-24-year-old mothers, there are notable decrease in low birthweight
rates born to 14-17 year old mothers over time. Panel B.1 illustrates that 18-20 year olds have
also had improved outcomes, relative to 21-24 year olds, over time. Also, 14-17 year olds and
18-20 year olds, respectively, saw an improvement in pre-term delivery rates relative to 21-24
year olds (Panels A.2 and B.2.) Again, the convergence in pre-term delivery rates is apparent in
both the “treatment” and “placebo” states, which strongly suggests that the patterns are driven by
factors that are unrelated to the changes in the MLDA. These illustrations suggest that failing to
control for age-specific trends will result in overstating the benefits of the MLDA changes.
Figure 3 tests the possibility that treated and control states have differential age-outcome
profiles. Panel A illustrates that younger ages had worse birth outcomes compared to older ages
in 1978. However, younger mothers were relatively worse off in treated states compared to
control states. Panel B demonstrates that the younger age groups are were still worse off in 1988
in treated states. Thus, failing to control for state-by-age fixed effects would lead to an
overestimate of the effects of an MLDA of 18 on birth outcomes.
Table 4 shows the relationship between alcohol policies and all of the other birth
outcomes that we are able to observe in the NCHS files, once we include the full set of control
variables. When the full spectra of outcomes are considered, we cannot reject the hypothesis that
higher MLDAs have no impact on birth outcomes. Nearly all of the coefficient estimates on the
MLDA interactions are small and statistically insignificant when our preferred controls are
14
included. The patterns are similar for both white and black births. An exception to this pattern is
that an MLDA of 18 is associated with a higher fraction of births that are female among the 1820 year old group, especially for the sample of black mothers (Panel C). Taken with the fact that
we see a statistically significant decrease in the probability of an Apgar score less than or equal
to 5, the female results may be indicative of higher rates of fetal deaths, based on the TriversWillard hypothesis, and positive selection.
Previous studies have also found evidence that MLDA laws affect selection into
motherhood, particularly among blacks (FW, 2009; Dee, 2001). Table 5 explores this possibility
in the presence of age specific trends and state*age fixed effects. The coefficients on MLDA is
18 x mother is 14-17 suggest that an MLDA of 18 increases the fraction of births to black
mothers by 0.8 percentage points, decreases the fraction of women without a high-school degree
by 0.2 percentage points, and the fraction of births to where the father’s information is missing
by 1.1 percentage points. The estimated relationships for mothers 18-20 is qualitatively similar,
but smaller in magnitude. Although only the estimate on the probability the mother is black is
statistically significant, on the whole these estimates suggest that an MLDA of 18 increases the
fraction of births to more economically disadvantaged groups. Further, these results are in
accordance with Dee (2001), who finds that less restrictive MLDAs increased childbearing
among blacks. All of this suggests that our estimates are potentially biased towards finding
negative effects of a MLDA of 18, which is contrary to our finding that a MLDA of 18 has no
observable impact on birth outcomes.
B. Difference-in-Difference Results
We further investigate the relationship between MLDA laws and infant health outcomes
by employing the D-in-D design described in Section IV.B. Figure 4 shows the ! coefficients
and 95% confidence intervals that are produced when we estimate equation (2). The full set of
coefficient and standard error estimates are available in Appendix Table A.1. Each panel of
Figure 4 focuses on a different outcome, and presents the impact of turning 21 during each of the
four-week bins.
15
Very few of the estimated coefficients are statistically different from zero.27 We find no
statistically significant evidence that turning 21 during pregnancy affects the probability of being
born prematurely, the probability of having abnormal conditions, or the probability of being
female. The analyses produce weak evidence that turning 21 during weeks 13-16 affects
birthweight outcomes but these are the only coefficient estimates that are statistically different
from zero at conventional levels. Importantly, we do not observe any sharp change in outcomes
during the first trimester or during weeks 5-8, when we might expect turning 21 to have the
largest effects. Even though we have been able to harness a sample that includes some 270,000
observations, the confidence intervals are too large to reject the null hypothesis.
At first glance there does appear to be evidence that the probability of dying within the
first year is adversely affected by changes in mothers’ access to alcohol. The estimated impacts
of turning 21 on infant mortality are positive, statistically different from zero and of substantive
magnitude: representing an increase of approximately 3-4 deaths per 1000 births. Across the full
sample, about 8 out of every 1000 infants die within the first year of life, so the magnitude of
these estimates is non-trivial. Notice, however, that these large effects are present even for
mothers who turned 21 during weeks 41-44 of their pregnancy. This is surprising because over
75 percent of the mothers in our sample have already given birth by week 41. Thus, if legal
access to alcohol was driving the estimated effects on infant mortality, we would expect the
coefficient estimate associated with weeks 41-44 to be smaller than the estimates that are
associated with turning 21 earlier. This suggests that the estimated coefficients are driven by
some unobservable variable or noise that is affecting the control group (weeks 45-48).
The key identifying assumption with the difference-in-difference identification strategy is
that in the absence of alcohol access, the age-outcome profiles would have been the same for
women in our treatment and control groups. Equation (2) includes an age intercept (DUM21),
but otherwise we assume that in the absence of access to alcohol, the “return” to experiencing a
birthday during weeks 1-4, 5-8 etc. would be the same for mothers turning 20 and mothers
turning 21. If the returns to maternal age are increasing at a decreasing rate, then our estimates
will be biased upward. We have conducted a number of “specification checks” where we saturate
the model with parametric controls for maternal age but the general pattern of results is
27
We have run our regressions separately by race and find no notable differences between whites and blacks with
respect to the patterns of the estimates. For brevity, we do not report those estimates here but they are available
from the authors upon request.
16
consistent.30 As a further robustness check, we have also re-estimated equation (2) replacing our
control group with infants born to women whose last menses was at age 21 (and who thus turned
22 during or just after pregnancy). An advantage of this control group is that the marginal impact
of mother’s age is likely to be smaller than among mothers who conceive at age 19.31
Nevertheless, the pattern of results is similar: in cases where we see statistically significant
impacts during weeks 1-40, the estimates on weeks 41-44 are of similar magnitude, which calls
into question the causal interpretation of the other estimates (See Appendix Table A.2).32
As in the MLDA analyses, we also explore the possibility that differential selection into
motherhood drives our lack of evidence in support of the hypothesis that legal access to alcohol
has spillover effects onto infant health. Figure 5 summarizes the results of six regression
analyses that replace the dependent variables in equation (2) with indicator variables
summarizing the mother’s race or ethnicity, her level of education, and whether information
about the father is missing from the child’s birth certificate. The figures show some evidence of
selection, mostly with regards to maternal education. Specifically, compared to mothers
experiencing a birthday postpartum, mothers who turn 21 during the first eight to twelve weeks
of pregnancy are relatively less likely to have completed some college compared to mothers who
turn 20 during the same point in the pregnancy. The direction of selection, however, likely biases
our estimates towards finding an effect of alcohol access on infant health. This suggests that the
difference-in-difference estimates are likely overestimates of the true impact on health outcomes.
On the other hand, one reason that we may have failed to detect strong evidence that
MLDA laws affect infant health is that the regressor of interest is measured with error. For
example, some mothers may be unable to recall the date of her last menses at time of delivery.
Where recall at delivery is not an issue, the initial determination of the date of last menses still
may be an issue (Kramer et. al, 1989; Rossavik and Fishburne, 1989). In terms of our research
design, this will cause us to incorrectly ascertain the week of gestation during which the mother’s
birthday occurred, and bias our estimates towards zero. Figure 6 lends weight to this concern.
There is substantive heaping in the number of births associated with a day-of-month of last
menses is a multiple of 5, when the true dates are presumably evenly distributed throughout the
30
31
Results available from the authors upon request.
A disadvantage of this control group is that the mothers were able to drink legally throughout their
pregnancies.
32
Results available from the authors upon request.
17
month. In order to address this concern, we have rerun our regression analyses dropping all
births where the day of last menses is at a heap, or where gestational lengths are implausibly
large (i.e. greater than 44 weeks). The results of this exercise are presented in Appendix Figure
A.3 and are similar to our main estimates.
V. Conclusion
The results from previous research imply that raising the MLDA would lead to better
birth outcomes among young mothers. The analyses presented in this paper suggest that such a
conclusion may be premature. Using two different types of estimation strategies, we find little
evidence to support the hypothesis that MLDA laws affect fetal health. We show that after
controlling for age specific time trends and cross state variation in the maternal age gradient,
there is little correlation between higher MLDAs and birth outcomes. Alternative identification
strategies that harness variation across maternal birthdates rather than state/year policy variation
also yield little evidence of MLDA effects, although statistical imprecision precludes ruling out
meaningful effect sizes.
It is important to note that our results do not necessarily imply that prenatal exposure to
maternal alcohol consumption has no effect on birth outcomes. There are at least two reasons
that this should be stressed. First, our design focuses on the effects of MLDA policies, not
alcohol consumption. It could be that MLDA laws have little effect on the drinking behavior of
pregnant women. Previous studies have shown that barriers to alcohol do substantively affect
drinking among young women (e.g. Carpenter and Dobkin, 2009) but to our knowledge no study
has looked at how they affect the drinking behavior of pregnant women in a quasi-experimental
framework.
It is also important to keep in mind that although we do not find evidence that MLDA
laws affect birth outcomes, some of our point estimates are actually quite large. For example, a
back of the envelope calculation using the estimated effect of turning 21 during weeks 5-8 on the
probability of being a low birthweight baby, together with Carpenter and Dobkin’s (2009)
estimate of the impact of turning 21 on women’s drinking behavior, implies that the additional
exposure to alcohol experienced by the fetus as a result of the change in its mother’s drinking
behavior increases the probability of being below 2500 grams by 60 percentage points.37 The
37
Our calculation is based on the following: the coefficient on the probability of low birth weight is approximately
18
imprecision with which our coefficients are estimated is also consistent with effect sizes that are
equal to zero, however.
It might also be that MLDA laws affect mothers’ drinking behavior, but that for most
infants the effects of in-utero exposure to alcohol are more evident later in childhood. The infant
health information that is available on birth certificates is collected within hours (or, more
usually, minutes) of a child’s birth and represent only a tiny fraction of the child outcomes that
might be affected. Using a natural experiment in Sweden, Nilsson (2008) finds large long-term
impacts as a result of changes in alcohol policies. Further research is needed to assess the full
impact of these laws on children’s development.
The main conclusion that we draw from this series of empirical analyses is that, contrary
to the results that are emphasized in the existing literature, the impact of MLDA laws on infant
health is still unknown. Our findings resonate with Armstrong (2003) who argues that, given the
quality of existing research, Americans may have jumped too quickly to the conclusion that
consumption of alcohol during pregnancy in any amount has devastating consequences.
Although the relationship between alcohol access and infant outcomes is intuitive, our research
indicates that the causal link is far from established.
0.006, or a 0.6 percentage point increase. Using a regression discontinuity approach, Carpenter and Dobkin (2009)
estimate that women turning 21 increase the proportion of days that they drink by 3.1 percentage points, and the
proportion of days that they binge drink by 1.2 percentage points. If we assume that when pregnant women turn 21
that the probability of drinking increases by 1 percentage point then the additional exposure to alcohol experienced
by the fetus increases his/her probability of being low birthweight by (0.006/0.01) =60 percentage points. The
statistical imprecision and conjecture regarding drinking behavior of pregnant women in our sample preclude
making strong predictions, however.
19
References
Albertsen, K., A.N. Anderson, J. Olsen, and M. Gronbaek (2004). “Alcohol Consumption
during Pregnancy and the Risk of Preterm Delivery.” American Journal of Epidemiology,
159(2): 155-161.
Almond, Douglas, Kenneth Chay and David Lee “The Costs of Low Birth Weight. The
Quarterly Journal of Economics, 120 (August 2005), 1031-1084.
Almond, Douglas and Janet Currie (2010). “Human Capital Development Before Age Five,”
Forthcoming Handbook of Labor Economics, Volume 4: Orley Ashenfelter and David Card,
editors.
Almond, Douglas, Lena Edlund and Marten Palme (2007). “Chernobyl’s Subclinical Legacy:
Prenatal Exposure to Radioactive Fallout and School Outcomes in Sweden.” NBER
Working Paper 13347.
Almond, Douglas, Hilary W. Hoynes and Diane Whitmore Schanzenbach, “Inside the War on
Poverty: The Impact of Food Stamps on Birth Outcomes,” The Review of Economics and
Statistics (forthcoming).
Armstrong, Elizabeth M. (2003). Conceiving Risk, Bearing Responsibility: Fetal Alcohol
Syndrome and the Diagnosis of Moral Disorder, Johns Hopkins University Press, Baltimore,
MD.
Baughman, Reagan, Stacy Dicker-Conlin, Michael Conlin and John Pepper (2001). “Slippery
When Wet: the Effects of Local Alcohol Access Laws on Highway Safety,” Journal of
Health Economics, 20(6): 1089-1096.
Berkowitz, G.S., Holford, T.R., and R.L. Berkowitz, (1982). “Effects of Cigarette Smoking,
Alcohol, Coffee and Tea Consumption on Pre-term Delivery,” Early Human Development,
7: 239-50.
Cagnacci, A., A. Renzi, S. Arangino, C. Alessandrini and A. Volpe (2004). “Influences of
Maternal Weight on the Secondary Sex Ratio of Human Offspring.” Human Reproduction,
19(2): 442.
Cameron, D.L. and Jenny Wlliams (2001). “Cannabis, Alcohol and Cigarettes: Substitutes or
Complements?” Economic Record,77: 19-34.
Carpenter, Christopher (2005) “Youth Alcohol Use and Risky Sexual Behavior: Evidence from
Underage Drunk Driving Laws,” Journal of Health Economics, 24(3): 613-28.
Carpenter, Christopher and Carolos Dobkin (2009). “The Effect of Alcohol Consumption on
Mortality: Regression Discontinuity Evidence from the Minimum Drinking Age,” American
Economic Journal - Applied Economics 1(1): 164-182.
20
Centers for Disease Control and Prevention (1995). “Update: Trends in Fetal Alcohol
Syndrome-United States, 1979-1993,” Morbidity and Mortality Weekly Report. 44:249-251.
Coate D., and M. Grossman (1988). “The Effects of Alcoholic Beverage Prices and Legal
Drinking Ages on Youth Alcohol Use.” Journal of Law and Economics, 22: 1053-1072.
Coles, Claire D. (1991). “Reading Test Scores Lower in Children Whose Mothers Drank Alcohol
During Last Trimester of Pregnancy.” Neurotoxicology and Teratology, 13: 357-367.
Cook, Philip J. (2007). Paying the Tab: The Costs and Benefits of Alcohol Control, Princeton
University Press, Princeton, N.J.
Cook, Philip and Michael Moore (2001). “Environment and Persistence in Youthful Drinking
Patterns,” in Risky Behavior Among Youths: An Economic Analysis. J. Gruber, Ed.
Chicago: University of Chicago Press.
Cook, Philip and Michael Moore (2002). “The Economics of Alcohol Abuse and AlcoholControl Policies” Health Affairs. 21(2): 120-133.
Cooper, M.L. (2002). “Alcohol Use and Risky Sexual Behavior Among College Students and
Youth: Evaluating the Evidence.” Journal of Studies on Alcohol 14(14), 101-117.
Day, N.L., N. Robles, G. Richardson, D. Geva, P. Taylor, M. Scher et al. (1991). “The effects of
prenatal alcohol use in the growth of children at three years of age.” Alcoholism: Clinical
and Experimental Research. 15: 67-71.
Decker, Sandra and Amy Ellen Schwartz (2000). “Cigarettes and Alcohol: Substitutes or
Complements?” NBER Working Paper #7535.
Dee, Thomas (2001). “The Effects of Minimum Legal Drinking Ages on Teen Chidlbearing,”
Journal of Human Resources, 36, 823-838.
DiNardo, John and Thomas Lemieux (2001). “Alcohol, Marijuana and American Youth: The
Unintended Consquences of Government Regulation,” Journal of Health Economics, 20(6):
991-1010.
Distilled Spirit Council of the United States (1996). “Minimum Purchase Age by State and
Beverage, 1933-Present,” Washington, DC: Distilled Spirits Council of the United States.
Donovan, C. and McEwan, R. (1995). “A Review of the Literature Examining the Relationship
Between Alcohol Use and HIV-Related Sexual Risk-Taking in Young People,” Addiction,
90:3, 319-328.
21
Fried, P. and C. O’Connell, (1987). “A Comparison of the Effects of Prenatal Exposure to
Tobacco, Alcohol, Cannabis and Caffeine on Birth Size and Subsequent Growth,”
Neurotoxicology and Teratology, 9: 79-85.
Graves, K.L., and B.L. Leigh (1995). “The Relationship of Substance Abuse to Sexual Activity
among Young Adults in the United States,” Family Planning Perspectives 27, 18-33.
Grossman, Michael, Robert Kaestner and Sara Markowitz (2004). “An Investigation of the
Effects of Alcohol Policies on Youth STDs,” NBER Working Paper #10949.
Grossman, Michael and Sarah Markowitz (2005). “I Did What Last Night? Adolescent Risky
Sexual Behaviors and Substance Use,” Eastern Economic Journal, 31(3): 383-405.
Gusella, J.L. and P.A. Fried (1984). “Language Skills Damage Easily from Light Social
Drinking,” Neurobehavioral Toxicology and Teratology, 6: 13-17.
Jaddoe V., R. Bakker, A. Hofman, J. Machenback, H. Moll, E. Steegers, J. Witteman (2007).
“Moderate Alcohol Consumption During Pregnancy and the Risk of Low Birthweight and
Preterm Birth: The Generation R Study,” Annals of Epidemiology, 17(10): 834-40.
Jones, N.C. Pieper, and L. Robertson (1992). “Effect of Legal Drinking Age on Fatal Injuries of
Adolescents and Young Adults,” American Journal of Public Health, 82: 112-115.
Kaestner, Robert (2000). “A Note on the Effect of Minimum Drinking Age Laws on Youth
Alcohol Consumption,” Contemporary Economic Policy, 18(3): 315-325.
Kaestner, Robert and T. Joyce (2001). “Alcohol and Drug Use: Risk Factors for Unintended
Pregnancy,” in M. Grossman and C. Hsieh (eds.) The Economic Analysis of Substance Use
and Abuse: The Experience of Developed Countries and Lessons for Developing Countries,
Edward Elgar Limited, United Kingdom.
Kesmodel, Ulrik (2001). “Binge Drinking in Pregnancy—Frequency and Methodology.”
American Journal of Epidemiology, 154(8): 777-782.
Kesmodel, U., Olsen, S.F., Secher, N.J. (2000). “Does Alcohol Increase the Risk of Preterm
Delivery?” Epidemiology, 11: 512-18.
Kramer, M.S., McLean, F.H., Boyd, M.E., Usher, R.H (1989). “The Validity of Gestational Age
Estimation by Menstrual Dating in Term, Preterm and Postterm Gestations.” Journal of the
American Medical Association, 261(16): 2329-30.
Marcussen, B .L ., Goodlett, C.R ., Mahoney, J .C ., and West, J .R . (1994). “Alcohol- induced
Purkinje cell loss during differentiation but not during neurogenesis.” Alcohol 11: 147-156 .
Markowitz, Sara (2006). “The Effectiveness of Cigarette Regulations in Reducing Cases of
Sudden Infant Death Syndrome,” NBER # 12527.
22
Markowitz, Sara, Kaestner, Robert, and Michael Grossman (2005). “An Investigation of the
Effects of Alcohol Consumption and Alcohol Policies on Youth Risky Sexual Behaviors.”
American Economic Review, 95(2): 263-266.
McDonald, A.D., B.G. Armstrong, M. Sloan, (1992). “Cigarette, Alcohol, and Coffee
Consumption and Prematurity,” American Journal of Public Health, 82: 87-90.
Mensch, B., and D.B. Kandel (1992). “Drug Use as a Risk Factor for Premarital Teen Pregnancy
and Abortion in a National Sample of Young White Women.” Demography, 29(3): 409-29.
Mills, J.L., B.I. Graubard, E.E. Harley, G.G. Rhoads, H.W. Berendes (1984). “Maternal alcohol
Consumption During Pregnancy: How Much Drinking During Pregnancy is Safe?” Journal
of the American Medical Association, 252(14): 1875-1879.
Niebyl, Jennifer R., and Joe Leigh Simpson (2007). “Drugs and Environmental agents in
pregnancy and lactation: embryology, teratology, epidemiology.” In: Steven Gabbe, ed.
Obstetrics, 5th ed. Philadelphia: Churchill Livingstone, Elsevier Science, 184-214.
Nilsson, J Peter (2008). "Does a pint a day affect your child's pay? The effect of prenatal alcohol
exposure on adult outcomes." cemmap working paper CWP22/08.
Painter, Rebecca C., Tessa J. Roseboom and Otto P. Blecker, “Prenatal Exposure to the Dutch
Famine and Disease in later Life: An Overview,” Reproductive Toxicology (2005), pp. 345352.
Rashad, I., Kaestner, R. (2004). “Teenage Sex, Drugs, and Alcohol Use: Problems Identifying
the Cause of Risky Behaviors.” Journal of Health Economics, 23, 493-503.
Rees, D.I., Laura Argys and Susan Averett (2001). “New Evidence on the Relationship between
Substance Use and Adolescent Sexual Behavior.” Journal of Health Economics, 20:5, 835845.
Rossavik I.K., Fishburne J.I. (1989). “Conceptional age, menstrual age, and ultrasound age: a
second-trimester comparison of pregnancies of known conception date with pregnancies
dated from the last menstrual period.” Obstetrics and Gynecology. 73(2):243-9.
Sampson, Paul D., Fred L. Boostein, Helen Barr, Ann Streissguth (1994). “Pre-natal alcohol
exposure, Birthweight and Measures of Child Size from Birth to Age 14 Years,” American
Journal of Public Health, 84(9) 1421-28.
Sanders, Nicholas J. and Charles F. Stoecker (2011) “Where Have All the Young Men Gone?
Using Gender Ratios to Measure Fetal Death Rates.” NBER Working Paper 17434.
Sen, B (2002). “Does Alcohol-Use Increase the Risk of Sexual Intercourse Among Adolescents?
Evidence from the NLSY97.” Journal of Health Economics, 21, 1085-1093.
23
Shaywitz, S.E., and D.J. Cohen (1990). “Hyperactivity – A.D.D. and Behavior Disorders Linked
with Alcohol Exposure,” Journal of Pediatrics, 96: 978.
Shrier, L.A., S. J. Emans, E.R. Woods, R.H. DuRant (1996). “The Association of Sexual Risk
Behaviors and Problem Drug Behaviors in High School Students,” Journal of Adolescent
Health 20: 377-83.
Shu, X. O, M.C. Hatch, J. Mills, J. Clemsn, M. Susser, (1995). “Maternal Smoking, Alcohol
Drinking, Caffeine Consumption, and Fetal Growth: Results from a Prospective Study,”
Epidemiology, 6(2): 115-120.
Streissguth, A.P., H.M. Barr, P.D. Sampson (1986). “Attention Deficit and Distractibility
Increase when Mothers consumed Alcohol During Pregnancy,” Neurobehavioral Toxicology
and Teratology, 8: 717-725.
Tough, Suzanne, Karen Tofflemire, Margaret Clarke, and Christine Newburn-Cook (2006). “Do
Women Change Their Drinking Behaviors While Trying to Conceive? An Opportunity for
Preconception Counseling.” Clinical Medicine & Research 4(2): 97-105.
Trivers, Robert L. and Dan E. Willard (1973). “Natural Selection of Parental Ability to Vary the
Sex Ratio of Offspring.” Science 179 (4068): 90.
Windham, G.C., L. Fenster, B. Hopkins and S.H. Swan (1995). “The Association of Moderate
Maternal and Paternal Alcohol Consumption with Birthweight and Gestational Age,”
Epidemiology, 6(6): 591-597.
Whitehead, N. and L. Lipscomb (2003). “Patterns of Alcohol Use before and During
Pregnancy, and the Risk of Small-for-Gestational-Age Birth.” American Journal of
Epidemiology, 158(7): 654-652.
24
Table 1: Summary of means, by mother’s race
National Natality Data, 1978-1989
Women aged 14 and 24 at time of conception
Birthweight
Birthweight < 2500g
Gestation
Gestation < 37 weeks
Apgar score (5 min)
Apgar <= 5
Congenital anomaly
Female
HS only
Some college
Dad’s age missing
Observations (1,000s)
All
3,273
.08
39
.12
8.9
.014
.08
.49
.4
.15
.3
19,780
Race of mother
White Black
3,323
3,084
.068
.13
40
39
.097
.18
9
8.8
.012
.021
.08
.079
.49
.49
.4
.41
.16
.16
.22
.55
15,295 3,913
Other
3,253
.073
39
.12
9
.012
.081
.49
.35
.17
.28
572
Notes: Age at conception is not reported in the National Natality data. We estimate age at conception by
subtracting one from mother’s age at delivery for all gestational lengths greater than 26 weeks; we assume
that age of delivery is the same as age at conception for gestational lengths less than 26 weeks. Our sample
is restricted to births that were conceived between 1978 and 1988. Date of conception is determined by
subtracting gestational length. When gestational length is missing, we assume a 40 week gestational length.
Also, day (of month) of delivery is not available in the 1989 file, so we assume the delivery took place on the
15th of the month.
Table 2: Summary of means, by mother’s race
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Birthweight (grams)
Birthweight < 2500 g
Gestation (weeks)
Gestation < 37 weeks
Any abnormal conditions
Infant death
Female
HS only
More than HS
Dad’s info missing
White
Black
Hispanic
Observations
All
3,316
.067
40
.11
.06
.0076
.49
.53
.22
.1
.38
.14
.45
271,956
Race of mother:
White
Black Hispanic
3,383
3,132
3,320
.057
.11
.061
40
39
40
.091
.15
.11
.068
.064
.051
.0076
.012
.0062
.49
.49
.49
.55
.55
.5
.24
.26
.19
.099
.15
.09
1
0
0
0
1
0
0
0
1
103,031 38,235 121,808
Other
3,281
.075
39
.12
.07
.0084
.49
.48
.31
.099
0
0
0
8,882
Notes: We restrict our sample to women who were born in California to mitigate possible migration effects
of turning 21. In addition, our sample is restricted to women who had a birthday within 49 weeks after the
last menses.
25
Table 3: MLDA analysis
National Natality Data, 1978-1989
Women aged 14-24 at conception
Outcome:
Column:
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
Low birthweight (x100)
(1)
(2)
(3)
Gestation < 37 weeks (x100)
(4)
(5)
(6)
-0.180
(0.050)***
0.492
(0.157)***
0.239
(0.085)***
-0.091
(0.055)*
0.170
(0.183)
0.133
(0.091)
Panel A: All races
-0.045
-0.258
(0.033)
(0.090)***
-0.063
0.799
(0.080)
(0.262)***
0.110
0.162
(0.051)**
(0.113)
-0.209
(0.096)**
0.470
(0.285)
0.178
(0.124)
-0.063
(0.068)
-0.063
(0.111)
0.003
(0.078)
-0.100
(0.035)***
0.251
(0.145)*
0.129
(0.061)**
-0.056
(0.034)
0.015
(0.146)
0.099
(0.058)*
Panel B: Whites only
-0.032
-0.137
(0.030)
(0.063)**
-0.186
0.450
(0.099)*
(0.192)**
0.114
0.024
(0.050)**
(0.081)
-0.099
(0.063)
0.106
(0.203)
0.058
(0.098)
-0.058
(0.066)
-0.111
(0.096)
0.025
(0.080)
-0.522
(0.139)***
1.119
(0.222)***
0.676
(0.119)***
-0.208
(0.156)
0.600
(0.336)*
0.140
(0.164)
Panel C: Blacks only
0.065
-0.655
(0.155)
(0.185)***
-0.107
1.291
(0.150)
(0.252)***
-0.123
0.610
(0.133)
(0.138)***
-0.326
(0.229)
0.609
(0.346)*
0.148
(0.169)
0.082
(0.137)
-0.344
(0.184)*
-0.337
(0.149)**
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Notes: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered on the state of residence. All regressions are
weighted by the number of births, where the outcome variable in question is non-missing, in each state-age-year-month
cell. The sample is restricted to women who conceived between 1978 and 1988. The number of observations and
summary means can be found in Table 1.
26
Table 4: Additional outcomes
National Natality Data, 1978-1989
Women aged 14-24 at conception
Outcome:
Column:
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
Birthweight Gestation
(1)
(2)
Five
Minute
Apgar
Score
(x100)
(3)
Apgar
<= 5
(x100)
(4)
Congenital Percent
anomaly
female
(x100)
(x100)
(5)
(6)
-0.561
(1.085)
4.454
(2.143)**
-1.325
(1.458)
Panel A: All races
0.011
0.221
0.000
(0.007)
(0.444)
(0.022)
0.015
0.737
-0.066
(0.012)
(0.489)
(0.029)**
-0.012
0.323
-0.037
(0.007)*
(0.256)
(0.029)
-0.907
(3.403)
0.577
(0.437)
0.357
(0.248)
0.020
(0.084)
0.021
(0.107)
0.152
(0.072)**
-1.161
(1.060)
6.126
(2.711)**
-0.516
(1.881)
Panel
0.006
(0.006)
0.020
(0.010)*
-0.011
(0.009)
B: Whites only
-0.027
0.004
(0.472)
(0.023)
0.658
-0.036
(0.428)
(0.034)
0.183
-0.018
(0.230)
(0.025)
-1.306
(3.052)
0.473
(0.414)
0.331
(0.238)
0.035
(0.092)
-0.069
(0.124)
0.041
(0.096)
-2.238
(3.366)
9.925
(3.175)***
2.470
(3.143)
Panel
0.016
(0.010)
0.026
(0.017)
0.005
(0.014)
C: Blacks only
0.929
0.021
(0.591)
(0.052)
0.089
-0.139
(0.727)
(0.061)**
0.405
-0.112
(0.438)
(0.063)*
0.869
(4.763)
0.356
(0.475)
0.237
(0.284)
-0.006
(0.164)
0.162
(0.181)
0.448
(0.192)**
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Notes: See notes to Table 3.
27
Yes
Yes
Yes
Yes
Yes
Yes
Table 5: MLDA analysis
Compositional effects
National Natality Data, 1978-1988
Outcome:
Mom is
black
(x100)
Less
than HS
(x100)
HS only
(x100)
Some
college
(x100)
Column:
(1)
(2)
(3)
(4)
Dad’s
info
missing
(x100)
(5)
-0.211
(0.211)
0.791
(0.459)*
0.243
(0.272)
0.251
(0.230)
-0.212
(0.400)
-0.213
(0.246)
0.030
(0.240)
0.181
(0.448)
0.024
(0.275)
-0.280
(0.188)
0.031
(0.335)
0.190
(0.219)
-0.520
(0.327)
1.079
(0.859)
0.492
(0.464)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MLDA is 18
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
Notes: See notes to Table 3.
28
0
10
Number of states
20
30
40
50
Figure 1: MLDA Law Changes
1977
1979
1981
1983
MLDA = 18
MLDA = 20
1985
1987
1989
MLDA = 19
MLDA = 21
Notes: Although the law mandating a MLDA of 21 was passed in 1984, the law was not enforced until late
in 1986. The law withheld a portion of a state’s Federal highway funds if the state did not enact a MLDA
of 21 by October 1, 1986. Source: Distilled Spirit Council of the United States (1996).
29
.04
1978
1980
1982
1984
Year of conception
1986
Panel A.1: Birthweight < 2500 g
1988
Placebo states
1982
1984
Year of conception
Treated states
1986
Panel B.1: Birthweight < 2500 g
1980
Placebo states
1982
1984
Year of conception
1988
1978
1980
Placebo states
1982
1984
Year of conception
Treated states
1986
Panel B.2: Gestation < 37 weeks
Treated states
1986
1988
1988
Notes: The placebo states include Arkansas, California, Indiana, Kentucky, Missouri, Nevada, New Mexico, North Dakota, Oregon, Pennsylvania, Utah,
and Washington.
1978
Treated states
1980
Panel B: Differences between 18-20 year olds and 21-24 year olds over time
Placebo states
1978
Panel A.2: Gestation < 37 weeks
Panel A: Differences between 14-17 year olds and 21-24 year olds over time
Figure 2: Differences in birth outcomes across age groups over time, by treated and placebo states
Birthweight < 2500g
.025
.03
.035
.008
.01
Birthweight < 2500g
.012
.014
.016
.018
.02
.07
Gestation < 37 weeks
.05
.06
.04
.025
Gestation < 37 weeks
.02
.015
30
.12
Birthweight < 2500g
.08
.1
.06
.11
Birthweight < 2500g
.08
.09
.1
.07
14
14
Placebo states
22
16
Placebo states
22
Treated states
18
20
Estimated age at conception
24
14
16
24
14
Treated states
22
16
Placebo states
22
Treated states
18
20
Estimated age at conception
Panel B.2: Gestation < 37 weeks
Placebo states
18
20
Estimated age at conception
Panel A.2: Gestation < 37 weeks
Panel B: Conception year is 1988
Treated states
18
20
Estimated age at conception
Panel B.1: Birthweight < 2500 g
16
Panel A.1: Birthweight < 2500 g
Panel A: Conception year is 1978
Figure 3: Differences in birth outcomes across age groups over time, by treated and placebo states
Notes: See notes to Figure 2.
.06
.2
Gestation < 37 weeks
.1
.15
.05
.2
Gestation < 37 weeks
.15
.1
31
24
24
Figure 4: Parameter estimates from D-in-D
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
1
Parameter estimate
0
−1
−2
−2
−1
Parameter estimate
0
1
2
Gestation < 37 weeks (x 100)
2
Birthweight < 2500 g (x 100)
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
Infant death (x 100)
−1
−40
−.5
−20
Parameter estimate
0
.5
Parameter estimate
0
20
1
40
Birthweight
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
Parameter estimate
0
−4
−1
−.5
−2
Parameter estimate
0
.5
1
2
1.5
4
Female (x 100)
2
Any abnormal conditions (x 100)
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
Week of pregnancy
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
Notes: The point estimates are represented by the diamonds. The bands represent two standard errors. The
estimates are clustered on the county of residence. The omitted category is to women who had birthday 45
to 48 weeks after the last menses. The full set of estimates can be found in the Appendix.
32
Figure 5: Parameter estimates from D-in-D
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Parameter estimate
0
−4
−3
−2
−2
Parameter estimate
−1
0
2
1
4
Mother is hispanic (x 100)
2
Mother is black (x 100)
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
−2
−2
Parameter estimate
0
2
Parameter estimate
0
2
4
HS degree (x 100)
4
Less than HS degree (x 100)
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
Parameter estimate
−1
0
−2
−4
Parameter estimate
−2
0
1
Dad’s information is missing (x 100)
2
More than HS degree (x 100)
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
Week of pregnancy
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
Notes: See notes to Figure 4.
33
5000
Number of births
10000
15000
20000
25000
Figure 6: Gestational length by day of month of last menses
Women 19 and 20 at time of last menses
California Natality Data, 1989-2004
1
5
10
15
20
Day of month
Notes: The square boxes are for days 1, 5, 10, 15, 20, 25, and 30.
34
25
30
Table A1: Difference-in-difference analysis
California Natality Data, 1989-2004
Outcome:
Column:
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
(1)
(2)
(3)
(4)
Any abnormal
conditions
(x100)
(5)
0.338
(0.402)
0.615
(0.397)
0.191
(0.451)
0.975
(0.450)**
0.469
(0.366)
0.049
(0.509)
0.006
(0.450)
0.402
(0.538)
0.092
(0.331)
0.169
(0.429)
0.305
(0.452)
-0.526
(0.297)*
-0.218
(0.232)
-0.508
(0.288)*
-0.186
(0.316)
-0.531
(0.253)**
-0.169
(0.261)
0.273
(0.388)
0.238
(0.316)
-0.122
(0.279)
0.217
(0.262)
0.005
(0.273)
0.164
(0.318)
Yes
Yes
294,971
-11.054
(7.169)
-16.721
(10.749)
-11.868
(9.425)
-19.618
(9.608)**
-10.530
(10.201)
-5.088
(9.894)
-1.646
(9.690)
-16.172
(11.511)
0.756
(9.405)
-1.794
(9.164)
-7.919
(9.781)
24.020
(6.522)***
14.779
(4.978)***
20.456
(5.692)***
8.403
(5.219)
15.547
(7.133)**
7.550
(5.466)
-5.744
(6.957)
1.211
(5.402)
7.477
(6.438)
-8.325
(6.356)
-4.515
(5.953)
-4.594
(6.979)
Yes
Yes
294,971
-0.251
(0.623)
0.211
(0.648)
-0.386
(0.526)
-0.194
(0.517)
-0.276
(0.562)
0.110
(0.602)
-0.521
(0.618)
-0.121
(0.770)
-0.027
(0.726)
-0.046
(0.599)
0.229
(0.580)
-0.071
(0.441)
0.710
(0.313)**
0.520
(0.306)*
1.036
(0.412)**
0.755
(0.371)**
0.924
(0.394)**
0.982
(0.385)**
0.721
(0.359)**
1.133
(0.325)***
1.045
(0.452)**
0.722
(0.390)*
1.011
(0.296)***
Yes
Yes
294,931
0.223
(0.257)
0.357
(0.148)**
0.322
(0.185)*
0.361
(0.153)**
0.420
(0.151)***
0.357
(0.117)***
0.361
(0.195)*
0.335
(0.196)*
0.242
(0.164)
0.299
(0.189)
0.319
(0.180)*
-0.358
(0.134)***
-0.190
(0.179)
-0.204
(0.143)
-0.232
(0.176)
-0.169
(0.111)
-0.158
(0.154)
-0.151
(0.108)
-0.209
(0.140)
-0.107
(0.158)
-0.210
(0.132)
-0.150
(0.164)
-0.231
(0.143)
Yes
Yes
277,209
0.485
(0.376)
0.686
(0.400)*
0.375
(0.504)
0.405
(0.307)
0.339
(0.346)
0.391
(0.347)
0.743
(0.412)*
0.437
(0.404)
0.592
(0.362)
0.333
(0.386)
0.689
(0.406)*
-0.625
(0.254)**
-0.623
(0.285)**
-0.601
(0.265)**
-0.457
(0.339)
-0.376
(0.278)
-0.511
(0.245)**
-0.074
(0.250)
-0.580
(0.319)*
-0.397
(0.309)
-0.445
(0.327)
-0.501
(0.280)*
-0.519
(0.237)**
Yes
Yes
294,762
Gestation
Birthweight
Birthweight < 37
< 2500 g
(grams)
weeks
(x100)
(x100)
Infant
death
(x100)
Female
(x100)
(6)
0.368
(0.734)
0.502
(1.019)
-0.601
(1.118)
-0.266
(0.799)
0.278
(0.914)
-0.777
(0.978)
0.140
(0.605)
0.581
(0.946)
-0.810
(0.912)
-0.795
(0.737)
-0.103
(0.654)
0.360
(0.581)
-0.622
(0.499)
-0.834
(0.565)
-0.073
(0.575)
-0.213
(0.464)
-0.924
(0.472)*
-0.236
(0.484)
-0.483
(0.471)
-1.074
(0.466)**
0.270
(0.457)
-0.167
(0.463)
-0.174
(0.429)
Yes
Yes
295,003
Notes: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered on the county of residence. The model was
estimated on the individual level data so weights were not required.
35
Table A2: Difference-in-difference analysis
Women aged 20 and 21 at last menses
California Natality Data, 1989-2004
Outcome:
Column:
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
(1)
(2)
(3)
(4)
Any abnormal
conditions
(x100)
(5)
0.763
(0.536)
0.853
(0.451)*
0.780
(0.468)
0.938
(0.507)*
0.768
(0.447)*
0.855
(0.425)**
1.054
(0.419)**
0.363
(0.391)
0.721
(0.354)**
0.034
(0.321)
0.885
(0.452)*
-0.262
(0.306)
-0.645
(0.300)**
-0.729
(0.297)**
-0.772
(0.273)***
-0.486
(0.344)
-0.449
(0.310)
-0.522
(0.241)**
-0.788
(0.237)***
-0.078
(0.262)
-0.406
(0.346)
0.164
(0.234)
-0.397
(0.310)
Yes
Yes
295,546
-11.289
(12.150)
-6.229
(11.220)
-7.194
(10.422)
-8.030
(8.609)
-2.119
(9.508)
-19.619
(12.758)
-12.439
(12.318)
-8.393
(9.639)
-13.003
(9.580)
-1.941
(8.732)
-7.437
(9.757)
-13.925
(7.096)*
15.008
(9.107)
9.449
(8.135)
3.664
(6.461)
3.675
(9.961)
-1.205
(6.163)
8.456
(9.162)
11.467
(7.283)
-0.654
(9.085)
5.244
(8.982)
-4.626
(5.943)
-5.596
(7.652)
Yes
Yes
295,546
0.627
(0.598)
0.597
(0.628)
0.852
(0.546)
0.261
(0.599)
0.327
(0.484)
0.871
(0.778)
0.381
(0.561)
0.417
(0.819)
0.495
(0.672)
-0.263
(0.860)
1.282
(0.645)*
-0.073
(0.398)
-0.179
(0.325)
0.136
(0.295)
-0.230
(0.378)
0.299
(0.432)
0.313
(0.396)
0.223
(0.342)
-0.186
(0.357)
0.592
(0.423)
0.512
(0.358)
0.932
(0.457)**
-0.021
(0.354)
Yes
Yes
295,508
0.138
(0.119)
0.247
(0.131)*
0.147
(0.148)
0.080
(0.083)
0.360
(0.118)***
0.365
(0.144)**
0.209
(0.143)
0.291
(0.134)**
0.062
(0.135)
0.102
(0.122)
0.181
(0.136)
-0.140
(0.087)
-0.111
(0.080)
-0.096
(0.071)
-0.060
(0.097)
0.109
(0.075)
-0.103
(0.060)*
-0.158
(0.098)
-0.058
(0.120)
-0.067
(0.077)
-0.036
(0.077)
0.041
(0.074)
-0.096
(0.100)
Yes
Yes
278,140
0.270
(0.472)
-0.058
(0.342)
-0.058
(0.481)
0.007
(0.417)
-0.252
(0.406)
0.204
(0.441)
0.194
(0.424)
0.061
(0.322)
-0.187
(0.394)
-0.212
(0.326)
0.130
(0.438)
0.175
(0.294)
-0.406
(0.382)
0.151
(0.251)
-0.016
(0.297)
0.031
(0.286)
0.097
(0.275)
0.127
(0.335)
-0.029
(0.296)
-0.012
(0.223)
0.334
(0.322)
0.048
(0.210)
0.056
(0.340)
Yes
Yes
295,329
Gestation
Birthweight
Birthweight < 37
< 2500 g
(grams)
weeks
(x100)
(x100)
Notes: See notes to Appendix Table A1
36
Infant
death
(x100)
Female
(x100)
(6)
-0.397
(0.684)
-0.780
(0.771)
-2.139
(0.707)***
-1.239
(0.703)*
-1.494
(0.850)*
-1.972
(0.663)***
-0.880
(0.564)
-1.623
(0.797)**
-1.466
(1.021)
-0.843
(0.719)
-0.025
(0.787)
1.363
(0.533)**
0.168
(0.497)
0.468
(0.713)
1.503
(0.761)*
0.779
(0.633)
0.861
(0.866)
0.970
(0.755)
0.566
(0.478)
1.127
(0.748)
0.961
(0.632)
-0.103
(0.576)
-0.224
(0.541)
Yes
Yes
295,573
Table A3: Difference-in-difference analysis
Compositional effects
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Outcome:
Black
(x100)
Hispanic
(x100)
Less
than HS
(x100)
HS only
(x100)
More
than HS
(x100)
Column:
(1)
(2)
(3)
(4)
(5)
Dad’s
info
missing
(x100)
(6)
0.277
(0.494)
-0.520
(0.642)
-0.355
(0.326)
0.353
(0.565)
-0.431
(0.458)
-0.640
(0.718)
-0.912
(0.960)
0.374
(0.507)
0.129
(0.373)
-0.887
(0.567)
0.054
(0.445)
-0.051
(0.451)
0.157
(0.481)
0.189
(0.412)
0.180
(0.300)
-0.722
(0.662)
-0.242
(0.313)
0.241
(0.485)
0.339
(0.694)
-0.386
(0.368)
0.185
(0.365)
1.015
(0.494)**
0.036
(0.409)
Yes
Yes
295,003
-1.041
(0.846)
-0.477
(0.860)
-1.387
(0.604)**
-0.932
(0.594)
-0.960
(0.840)
-0.186
(0.860)
0.527
(1.061)
-1.293
(0.740)*
-0.844
(0.684)
-0.202
(0.779)
-0.377
(0.894)
-1.201
(0.565)**
-0.562
(0.668)
-0.602
(0.560)
-0.202
(0.500)
-0.260
(0.474)
-0.035
(0.560)
-0.455
(0.566)
-0.613
(0.637)
0.961
(0.395)**
-0.158
(0.432)
0.258
(0.538)
0.440
(0.495)
Yes
Yes
295,003
-0.168
(0.657)
-0.205
(0.681)
1.254
(0.744)*
0.516
(0.654)
0.932
(0.661)
1.369
(0.686)*
-0.528
(0.697)
0.266
(0.672)
0.306
(0.683)
-0.873
(0.475)*
-0.822
(0.826)
-3.625
(0.507)***
-3.004
(0.406)***
-2.378
(0.435)***
-3.503
(0.449)***
-3.028
(0.468)***
-2.631
(0.493)***
-2.495
(0.472)***
-1.949
(0.506)***
-1.579
(0.653)**
-1.917
(0.326)***
-0.049
(0.435)
-0.372
(0.567)
Yes
Yes
292,146
1.678
(0.942)*
1.832
(0.713)**
0.602
(0.763)
0.281
(0.567)
-0.094
(0.644)
-0.714
(0.837)
0.868
(0.683)
-0.273
(0.654)
0.703
(0.864)
1.312
(0.754)*
0.468
(0.627)
-2.181
(0.562)***
-1.952
(0.555)***
-1.490
(0.473)***
-0.919
(0.468)*
-0.513
(0.593)
-0.050
(0.465)
-0.226
(0.550)
0.257
(0.544)
-0.171
(0.641)
-0.003
(0.640)
-1.099
(0.661)
0.561
(0.428)
Yes
Yes
292,146
-1.509
(0.653)**
-1.627
(0.681)**
-1.856
(0.996)*
-0.797
(0.592)
-0.837
(0.595)
-0.655
(0.601)
-0.340
(0.511)
0.007
(0.674)
-1.009
(0.645)
-0.439
(0.799)
0.355
(0.624)
5.806
(0.492)***
4.956
(0.391)***
3.869
(0.497)***
4.423
(0.634)***
3.541
(0.577)***
2.681
(0.314)***
2.722
(0.342)***
1.691
(0.364)***
1.750
(0.372)***
1.921
(0.595)***
1.148
(0.579)*
-0.189
(0.379)
Yes
Yes
292,146
-0.558
(0.418)
-0.443
(0.510)
-0.430
(0.419)
-0.164
(0.493)
-0.343
(0.451)
-0.320
(0.437)
-0.541
(0.500)
-0.393
(0.507)
-0.480
(0.485)
-0.167
(0.479)
-1.134
(0.385)***
-0.836
(0.374)**
-1.054
(0.307)***
-0.943
(0.299)***
-0.949
(0.292)***
-1.102
(0.373)***
-0.982
(0.337)***
-0.673
(0.374)*
-0.484
(0.249)*
-0.281
(0.359)
-0.356
(0.389)
0.063
(0.323)
0.515
(0.320)
Yes
Yes
295,003
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
Notes: See notes to Appendix Table A1
37
Table A4: Difference-in-difference analysis
White mothers only
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Outcome:
Column:
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
(1)
(2)
(3)
(4)
Any abnormal
conditions
(x100)
(5)
0.525
(0.635)
0.586
(0.718)
-0.016
(0.674)
0.512
(0.781)
0.747
(0.612)
0.084
(0.610)
-1.095
(0.625)*
0.226
(0.790)
-0.276
(0.711)
-0.653
(0.661)
0.332
(0.589)
-0.493
(0.430)
-0.465
(0.460)
-0.403
(0.608)
0.068
(0.578)
-0.306
(0.443)
0.124
(0.464)
0.295
(0.515)
0.968
(0.542)*
-0.279
(0.573)
0.513
(0.551)
0.480
(0.504)
0.146
(0.450)
Yes
Yes
111,415
-6.295
(16.678)
-10.383
(20.522)
-11.421
(17.340)
-2.793
(18.286)
-16.676
(18.728)
-2.223
(17.379)
10.162
(13.984)
-5.119
(18.698)
-1.712
(17.364)
6.857
(15.230)
-0.312
(11.712)
18.953
(13.054)
17.912
(10.947)
11.527
(13.519)
2.228
(11.298)
9.941
(12.454)
1.037
(12.216)
-3.426
(10.919)
-8.719
(9.482)
1.184
(12.139)
-3.125
(11.970)
-15.615
(10.060)
-5.295
(9.180)
Yes
Yes
111,415
-1.606
(1.085)
-0.312
(0.864)
-1.580
(1.046)
-0.966
(1.251)
-0.300
(1.092)
0.324
(0.760)
-2.041
(0.954)**
-0.172
(1.345)
0.499
(1.005)
-1.150
(1.163)
-0.186
(0.810)
0.314
(0.709)
1.667
(0.733)**
0.440
(0.708)
1.342
(0.795)*
0.503
(0.830)
0.974
(0.613)
0.751
(0.600)
1.182
(0.645)*
0.874
(0.779)
0.719
(0.809)
1.238
(0.822)
1.007
(0.697)
Yes
Yes
111,395
0.319
(0.324)
0.099
(0.294)
-0.124
(0.300)
0.170
(0.236)
0.202
(0.300)
0.264
(0.235)
0.136
(0.289)
0.231
(0.277)
0.039
(0.301)
-0.043
(0.321)
0.071
(0.267)
-0.223
(0.222)
-0.289
(0.232)
-0.094
(0.252)
-0.009
(0.274)
-0.250
(0.188)
-0.029
(0.219)
-0.151
(0.211)
0.001
(0.197)
-0.139
(0.220)
-0.139
(0.225)
0.009
(0.227)
-0.184
(0.209)
Yes
Yes
105,453
1.262
(0.696)*
1.119
(0.800)
0.545
(0.704)
0.027
(0.724)
0.301
(0.675)
0.819
(0.748)
1.224
(0.818)
0.942
(0.904)
0.727
(0.768)
0.705
(0.842)
1.538
(0.775)*
-1.354
(0.524)**
-1.157
(0.570)**
-1.231
(0.517)**
-0.470
(0.554)
-0.303
(0.748)
-0.803
(0.537)
-1.015
(0.586)*
-0.938
(0.627)
-0.900
(0.586)
-0.726
(0.772)
-0.895
(0.619)
-0.938
(0.450)**
Yes
Yes
111,298
Gestation
Birthweight
Birthweight < 37
< 2500 g
(grams)
weeks
(x100)
(x100)
Notes: See notes to Appendix Table A1
38
Infant
death
(x100)
Female
(x100)
(6)
0.407
(1.620)
0.702
(1.188)
-1.527
(1.316)
-0.129
(1.063)
-0.226
(1.203)
-1.738
(1.313)
0.427
(1.109)
-0.364
(1.578)
-0.102
(1.345)
-1.194
(1.680)
-0.613
(1.545)
0.623
(0.868)
-0.640
(0.829)
-2.130
(0.935)**
0.075
(0.824)
-1.353
(0.781)*
-0.547
(0.805)
-0.914
(0.897)
-1.302
(0.795)
-0.553
(0.953)
-1.614
(0.972)
-0.225
(1.016)
-0.106
(0.886)
Yes
Yes
111,430
Table A5: Difference-in-difference analysis
Black mothers only
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Outcome:
Column:
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
Gestation
Birthweight
Birthweight < 37
< 2500 g
(grams)
weeks
(x100)
(x100)
(1)
(2)
(3)
2.563
(1.583)
1.884
(0.844)**
2.093
(1.950)
3.195
(1.172)***
2.687
(1.395)*
0.047
(1.654)
3.205
(0.954)***
2.608
(1.729)
3.210
(1.291)**
3.328
(1.999)
3.478
(1.500)**
-2.183
(1.132)*
-1.039
(1.083)
-2.239
(0.664)***
-0.710
(1.458)
-1.508
(0.967)
-1.761
(0.841)**
-0.613
(0.857)
-1.894
(0.562)***
-1.333
(1.225)
-1.012
(0.870)
-1.841
(0.844)**
-1.642
(0.795)**
Yes
Yes
41,536
-44.826
(21.875)**
9.567
(25.795)
-8.776
(31.630)
-32.407
(27.861)
-26.251
(27.384)
10.559
(31.385)
-51.169
(24.215)**
-49.862
(31.930)
-38.554
(29.870)
-43.651
(30.924)
-57.572
(28.526)**
29.608
(21.730)
35.988
(15.794)**
15.989
(14.913)
5.844
(22.146)
28.315
(21.511)
28.601
(19.084)
-8.659
(17.099)
35.005
(16.589)**
32.743
(22.705)
-2.320
(17.701)
27.164
(17.772)
19.816
(18.493)
Yes
Yes
41,536
3.631
(1.385)**
0.514
(0.922)
0.326
(1.363)
3.137
(1.462)**
0.781
(2.039)
-1.329
(1.493)
2.084
(1.605)
0.328
(1.527)
1.178
(1.756)
1.376
(1.587)
2.949
(1.544)*
-0.948
(1.179)
-1.375
(0.723)*
0.228
(0.910)
0.641
(0.958)
-0.830
(0.931)
1.016
(0.989)
1.637
(0.739)**
-0.418
(0.836)
0.983
(0.745)
1.341
(0.969)
-0.047
(0.968)
-0.481
(0.816)
Yes
Yes
41,528
Notes: See notes to Appendix Table A1
39
Infant
death
(x100)
(4)
Any abnormal
conditions
(x100)
(5)
0.809
-0.277
(0.529)
(0.772)
0.699
0.482
(0.350)*
(1.049)
0.930
0.804
(0.721)
(0.923)
1.486
1.655
(0.476)*** (1.199)
0.945
0.649
(0.408)**
(1.253)
0.625
-1.442
(0.712)
(0.986)
1.645
0.536
(0.526)*** (1.124)
0.250
0.829
(0.600)
(0.881)
1.761
2.191
(0.533)*** (1.354)
0.693
-0.299
(0.431)
(0.918)
1.721
0.710
(0.498)*** (1.106)
-1.016
-0.361
(0.461)**
(0.750)
-0.480
0.005
(0.292)
(0.577)
-0.545
-0.496
(0.226)**
(0.730)
-0.347
-0.587
(0.405)
(0.525)
-0.860
-1.549
(0.358)** (0.670)**
-0.483
-0.319
(0.489)
(0.837)
-0.060
1.058
(0.510)
(0.559)*
-0.873
-0.692
(0.358)**
(0.799)
0.019
-0.968
(0.437)
(0.375)**
-0.960
-1.221
(0.408)**
(0.887)
-0.328
-0.413
(0.318)
(0.586)
-0.846
-0.533
(0.355)**
(0.706)
Yes
Yes
Yes
Yes
39,187
41,503
Female
(x100)
(6)
-1.198
(2.392)
-2.118
(2.269)
1.253
(2.773)
-4.687
(1.832)**
0.922
(2.162)
-2.814
(2.642)
-2.211
(2.165)
2.518
(2.783)
-2.297
(2.396)
-1.380
(2.146)
1.006
(1.738)
0.528
(1.578)
-0.826
(1.359)
-0.168
(1.037)
-0.969
(1.063)
0.996
(1.097)
-1.825
(1.015)*
2.774
(1.748)
1.217
(1.745)
-2.043
(1.439)
2.484
(1.727)
0.194
(1.117)
-0.218
(0.951)
Yes
Yes
41,539
Table A6: Difference-in-difference analysis
Hispanic mothers only
Women aged 19 and 20 at last menses
California Natality Data, 1989-2004
Outcome:
Column:
Weeks 1-4 x 21st
Weeks 5-8 x 21st
Weeks 9-12 x 21st
Weeks 13-16 x 21st
Weeks 17-20 x 21st
Weeks 21-24 x 21st
Weeks 25-28 x 21st
Weeks 29-32 x 21st
Weeks 33-36 x 21st
Weeks 37-40 x 21st
Weeks 41-44 x 21st
21st birthday
Weeks 1-4
Weeks 5-8
Weeks 9-12
Weeks 13-16
Weeks 17-20
Weeks 21-24
Weeks 25-28
Weeks 29-32
Weeks 33-36
Weeks 37-40
Weeks 41-44
County f.e.
Year-month f.e.
Observations
(1)
(2)
(3)
(4)
Any abnormal
conditions
(x100)
(5)
-0.386
(0.541)
0.153
(0.540)
-0.198
(0.743)
0.570
(0.565)
-0.074
(0.586)
0.256
(0.924)
-0.000
(0.724)
0.092
(0.636)
-0.328
(0.507)
0.028
(0.529)
-0.523
(0.944)
-0.063
(0.492)
0.107
(0.284)
-0.063
(0.284)
-0.374
(0.461)
-0.352
(0.445)
-0.070
(0.356)
0.406
(0.600)
0.244
(0.469)
0.287
(0.372)
0.214
(0.419)
0.113
(0.329)
0.563
(0.510)
Yes
Yes
132,399
-5.636
(12.749)
-30.921
(11.961)**
-13.534
(14.706)
-26.431
(16.943)
-5.892
(17.997)
-20.945
(18.321)
1.437
(15.829)
-13.970
(17.202)
13.007
(16.536)
-1.711
(14.110)
-2.650
(16.083)
25.013
(11.952)**
7.241
(7.452)
27.920
(7.709)***
13.948
(9.274)
10.614
(9.418)
4.700
(8.866)
-4.151
(10.294)
-1.277
(8.691)
1.975
(10.640)
-12.755
(10.237)
-3.089
(9.882)
-6.887
(10.510)
Yes
Yes
132,399
-0.344
(0.779)
0.541
(0.891)
0.526
(0.754)
-0.566
(0.542)
-0.230
(0.544)
0.595
(0.816)
-0.260
(0.760)
0.097
(0.702)
-0.751
(0.881)
0.484
(0.653)
-0.214
(0.809)
-0.097
(0.519)
0.355
(0.340)
0.626
(0.449)
0.727
(0.495)
1.429
(0.383)***
0.615
(0.504)
0.820
(0.585)
0.647
(0.353)*
1.090
(0.417)**
1.064
(0.464)**
0.417
(0.396)
1.340
(0.367)***
Yes
Yes
132,387
-0.062
(0.268)
0.404
(0.207)*
0.359
(0.157)**
0.009
(0.195)
0.473
(0.164)***
0.323
(0.199)
0.154
(0.325)
0.414
(0.223)*
-0.095
(0.165)
0.382
(0.223)*
0.062
(0.248)
-0.203
(0.126)
0.000
(0.179)
-0.099
(0.158)
-0.271
(0.144)*
0.203
(0.144)
-0.127
(0.122)
-0.124
(0.134)
-0.138
(0.214)
-0.091
(0.172)
-0.053
(0.147)
-0.155
(0.175)
-0.062
(0.141)
Yes
Yes
123,570
0.047
(0.459)
0.516
(0.587)
0.263
(0.615)
0.425
(0.489)
0.406
(0.578)
0.779
(0.512)
0.569
(0.450)
0.035
(0.436)
0.103
(0.468)
0.353
(0.499)
0.050
(0.426)
-0.232
(0.290)
-0.429
(0.422)
-0.223
(0.420)
-0.521
(0.522)
-0.026
(0.309)
-0.295
(0.421)
0.093
(0.422)
-0.284
(0.348)
0.188
(0.448)
0.005
(0.363)
-0.226
(0.384)
-0.226
(0.307)
Yes
Yes
132,345
Gestation
Birthweight
Birthweight < 37
< 2500 g
(grams)
weeks
(x100)
(x100)
Notes: See notes to Appendix Table A1
40
Infant
death
(x100)
Female
(x100)
(6)
0.458
(1.061)
1.267
(1.472)
-0.345
(1.595)
1.130
(1.159)
0.545
(1.079)
0.798
(1.339)
0.623
(1.097)
1.002
(0.825)
-0.765
(1.495)
-0.283
(0.996)
0.244
(0.993)
0.012
(0.689)
-0.150
(1.129)
0.206
(0.901)
-0.015
(0.730)
0.521
(0.746)
-0.764
(0.591)
-0.458
(0.729)
-0.183
(0.863)
-1.046
(0.694)
1.160
(0.811)
-0.060
(0.657)
-0.345
(0.754)
Yes
Yes
132,411
0
.05
.1
Density
.15
.2
.25
Figure A1: Gestational length (weeks)
20
24
28
32
36
Gestation (weeks)
41
40
44
48
Figure A2: Differences in birth outcomes across timing of birthday
Birthweight
.05
3280
3300
Birthweight (grams)
3320
3340
Birthweight < 2500 g
.06
.07
3360
.08
3380
Birthweight < 2500 g
19
19.5
20
20.5
21
Age at last menses
21.5
22
19
19.5
20.5
21
Age at last menses
21.5
22
21.5
22
21.5
22
Infant death
.09
.002
.004
Infant death
.006
.008
Gestation < 37 weeks
.1
.11
.01
.12
.012
Gestation < 37 weeks
20
19
19.5
20
20.5
21
Age at last menses
21.5
22
19
19.5
20
Female
.46
.05
.48
Female
.5
Any abnormal conditions
.06
.07
.52
.08
Panel E: Any abnormal conditions
20.5
21
Age at last menses
19
19.5
20
20.5
21
Age at last menses
21.5
22
19
19.5
20
20.5
21
Age at last menses
Notes: Each scatter point represents the average outcome for those women who had a birthday in a given
week of pregnancy. The fitted line was estimated using a least-squares smoother and a bandwidth of 0.5.
42
Figure A3: Parameter estimates from D-in-D
Dropping gestational lengths greater than 44 weeks
Dropping births where day of menses is at heap
California Natality Data, 1989-2004
Birthweight
−2
−40
−1
Parameter estimate
−20
0
20
Parameter estimate
0
1
40
2
Birthweight < 2500 g
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
−1
−2
−.5
Parameter estimate
−1
0
Parameter estimate
0
.5
1
1
Infant death
2
Gestation < 37 weeks
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
5−8
9−12
Week of pregnancy
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
−4
−1
−2
−.5
Parameter estimate
0
Parameter estimate
0
.5
1
2
1.5
4
Female
2
Any abnormal conditions
1−4
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
1−4
Week of pregnancy
5−8
9−12
13−16 17−20 21−24 25−28 29−32 33−36 37−40 41−44 45−48
Week of pregnancy
43