Tulane Economics Working Paper Series
A Pint for a Pound? Reevaluating the Relationship Between Minimum Drinking
Age Laws and Birth Outcomes
Alan I. Barreca
Department of Economics
Tulane University
New Orleans, Louisiana
[email protected]
Marianne E. Page
Department of Economics
University of California, Davis
Davis, California
[email protected]
Working Paper 1220
October 2012
Abstract
Previous research documents a substantive, positive, correlation between the minimum legal drinking age
(MLDA) and birth outcomes. Using an improved empirical framework, we reach a different conclusion:
there is little or no relationship between the minimum legal drinking age and the health of infants born
to young mothers. We do, however, find that MLDA policies are associated with the sex ratio at birth.
Our estimates suggest that raising the MLDA may reduce fetal losses.
Keywords: alcohol, minimum drinking age, infant health, birthweight, fetal death
JEL: I18, J13
A Pint for a Pound? Reevaluating the Relationship Between Minimum Drinking Age Laws
and Birth Outcomes
Alan I. 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]
October 2012
Abstract
Previous research documents a substantive, positive, correlation between the minimum legal
drinking age (MLDA) and birth outcomes. Using an improved empirical framework, we reach a
different conclusion: there is little or no relationship between the minimum legal drinking age
and the health of infants born to young mothers. We do, however, find that MLDA policies are
associated with the sex ratio at birth. Our estimates suggest that raising the MLDA may reduce
fetal losses.
Keywords: alcohol; minimum drinking age; infant health; birthweight; fetal death.
We thank seminar participants at University of California-Merced, University of Colorado- Denver, and
University of California-Irvine 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 legal drinking ages (MLDA) affect young women’s alcohol consumption
and the 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, and
through which mechanisms, the MLDA affects birth outcomes. The goal of our research is to
further explore this question using cross-state changes in MLDA laws that occurred during the
1970s and 1980s.
One previous study has used the same policy variation and documented a substantive,
positive, correlation between MLDAs and birth outcomes. Fertig and Watson (2009) (hereafter
FW) correlate changes in the MLDA with measures of health among infants born to women ages
14-20. To further control for policy endogeneity, FW use births to women ages 21-24 as an
additional control group. FW find that, among young mothers, a lower MLDA is associated with
a higher incidence of low birthweight and premature birth and they note that this result is most
likely due to selection. In particular, they find that a lower drinking age is associated with lower
levels of education among white mothers and the absence of paternal information on black
infants’ birth certificates. This selection effect is further supported 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.
Using a similar identification strategy, we reach a different conclusion. Specifically, we
find little or no correlation between an MLDA of 18 and birth outcomes once age specific time
trends and state-by-age fixed effects, are included. FW acknowledge that their estimates are
sensitive to the inclusion of age-specific trends; however, throughout their manuscript they
continue to reject the null hypothesis that increases in the MLDA have no impact on birth
outcomes.3 We present evidence that our specification better addresses possible omitted variables
1
See, for example, Carpenter (2005); Cook and Moore (2002); Cooper (2002); Grossman and Markowitz (2005);
Kaestner (2001); 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
Neither FW’s abstract, introduction, nor conclusion sections discuss this important robustness check, nor are the
results of this check presented in the tables of the published manuscript.
2
and document the importance of our preferred control variables by examining their economic
and statistical significance in 12 “placebo” states, which consistently maintained a MLDA of 21
during our sample period.4 The control variables turn out to have strong predictive power in both
treatment and placebo states, which suggests that previous evidence on the relationship between
MLDA laws and birth outcomes is likely driven by omitted variables.
At the same time, we present novel evidence that a low MLDA increases the probability
of fetal loss. Assuming that the surviving fetuses are the healthiest, our finding implies that
selection may be biasing the estimated relationship between access to alcohol and infant health
towards a positive relationship. In other words, the implications of our study are considerably
different from FW. Our results suggest future research should explore the link between alcohol
access and fetal loss, while FW’s results highlight the role of maternal selection into pregnancy.
Further research is needed to establish the mechanisms through which alcohol policies affect
birth outcomes.
II. Background on Minimum Drinking Age Laws
Empirical analyses of the relationship between restrictive alcohol policies and birth
outcomes have largely used variation in minimum drinking age laws across states and over time
as their source of identifying variation. 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
were compelled to adopt an MLDA of 21 by 1988.5 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.
We will exploit this variation to identify the effect of the laws on infant health.
4
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.
5
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)
3
III. Data
Data on MLDA laws come from the Distilled Spirits Council of the United States.6 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, the month the child was conceived, and the mother’s state of residence at delivery.
As we discuss below, treatment is assigned based on the mother's age at the time of conception,
and whether the mother was residing in a “low MLDA” state (i.e. a state with a MLDA of 18).
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.7 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 et al., 2007; Oreopoulos et al., 2008).8 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). Note that FW do
not present results for Apgar score or fraction female.9
To discern the mother’s age during her pregnancy, we rely on information in the NCHS
data on the mother’s age at delivery, the mother's state of residence at delivery, 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
6
We are grateful to Thomas Dee for providing us with these data.
The vast majority of states reported 100-percent samples. The remaining states report 50-percent samples.
8
Apgar scores are on a 10-point scale based on five categories of infant health.
9
FW state that “Apgar scores are not available in the Vital Statistics data during the relevant time period” (footnote
17 p. 743). However, there were actually 38 reporting states as of 1978. The number of states reporting Apgar score
increased gradually to 47 states by 1989.
7
4
was the same age during the majority of her pregnancy. To be consistent with FW, we assume
the mother's age at conception is one year less than her age at the child's birth.10
Our NCHS analyses are restricted to mothers who are between 14 and 24 years of age at
the time of conception. Women over the age of 21 are legally able to drink throughout the entire
period under study, so changes in the MLDA should not causally affect 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. Summary statistics for
our sample of births are presented in Table 1.
IV. Estimation Approach
Our identification strategy, which builds on FW’s approach, exploits variation in the
MLDA laws across states and over time. Because the MLDA laws vary across states, years and
cohorts we conduct our 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;11 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,
respectively12; "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
10
Following FW, 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. For gestational lengths less than 26 weeks, we assume the mother’s age at
delivery and age at conception are equal.
11
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.
12
We focus on whether the state had an MLDA of 18 in order to be consistent with FW. 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 generally produce qualitatively similar estimated relationships.
5
that vary by state and year that might affect infant health. States that initially had a low MLDA
and were required to raise the MLDA to 21 when the Uniform Drinking Act was passed in 1984,
are arguably different from states that maintained an MLDA of 21 throughout the 1970s and
1980s. The speed with which states complied with changes in the federal MLDA during the late
1980s may also be nonrandom. We address the possibility that the error term & is correlated
within states by clustering our standard error estimates at the state level.
Equation (1) is qualitatively similar to the model that FW use and produces very similar
estimates.13 The two control groups in this model include: (i) young mothers in states where the
MLDA was consistently above 18, and (ii) mothers who were too old to be affected by the
MLDA changes. The MLDA was never higher than 21 during the sample period, therefore,
infants born to mothers between the ages of 21 and 24 can effectively serve as the second control
group.
After estimating equation (1) we systematically add two important sets of control
variables. 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-by-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. For example,
increases in the MLDA may have coincided with other initiatives designed to improve birth
outcomes among young mothers. Alternatively, the MLDA could be a response to recent
declines in birth outcomes to young mothers. We show that including these statistically
significant controls substantively affects the estimated impact of alcohol access on birth
outcomes.
FW acknowledge that adding age trends reduces the estimated impact of the MLDA
laws.14 They offer two hypotheses: First, there were secular trends in birth outcomes that
impacted mothers differentially by age. Second, controlling for age-specific time trends absorbs
“useful variation”, making identification problematic. Although we cannot rule out the second
hypothesis, we present empirical evidence (below) to support the first and more worrisome
13
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 For example, we find that the
probability of a female birth increases with an MLDA of 18.
14
FW show how their estimates change with the inclusion of the age-specific trends in the NBER working paper
version of their manuscript, but this robustness check is not presented in the published manuscript.
6
hypothesis.15 FW do not thoroughly test the robustness of their results to the inclusion of stateby-age fixed effects.16
To help demonstrate the economic importance of our preferred control variables, we
present two sets of figures. First, Figure 2 plots differences in the fraction of low birthweight and
premature births to 14-17 old mothers and to 21-24 year old mothers. The differences are plotted
separately for states that did (“treatment states”), and did not (“placebo states”), experience
changes in their MLDA laws between 1978 and 1988. Panel A.1 illustrates that, relative to
infants born to 21-24 year old mothers, there was a notable decrease in the fraction of infants
born to 14-17 year old mothers who were classified as low birthweight. Panel B.1 illustrates that
18-20 year old mothers also experienced improved outcomes over time, relative to 21-24 year
olds. Differences in pre-term delivery rates (Panels A.2 and B.2.) show similar patterns.
Importantly, trends are similar in both the “treatment” and “placebo” states, which strongly
suggests that the patterns are driven by omitted factors and secular trends that are unrelated to the
changes in the MLDA. Thus, comparing birth outcomes to younger vs. older women, and failing
to control for age-specific trends, likely results in coefficient estimates that overstate the benefits
of MLDA increases. These relationships are similar for white mothers (Figure A1) and black
mothers (Figure A2).
Second, Figure 3 illustrates the age-outcome profiles for mothers in treatment and
placebo states. State-by-age fixed effects help control for the possibility that maternal ageoutcome profiles differ between treated and untreated states. One can see immediately that such
differences exist: Panels A and B show that, compared to young mothers in placebo states,
young mothers in treated states give birth to less healthy infants. In 1978, for example, the
probability that a 14-year-old mother gave birth to a low birthweight baby was approximately
0.12 in treated states, and approximately 0.11 in placebo states. Treatment and placebo
differences are less stark among the older mothers in our sample. In 1978, for example, the
15
Testing the second hypothesis is difficult. However, we note that including age-specific time trends does not
absorb useful “statistical variation”. In general, we find that the estimated standard errors increase with the addition
of the age-specific time trends, although only slightly. For example, when the dependent variable is low birthweight,
the standard error on the interaction term MLDA is 18 x mother is 14-17 increases from 0.157 to 0.183 (columns 1
and 2 of Table 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 statistical variation.
16
FW mention testing the robustness of their results to the inclusion age-by-year interactions, state-by-age
interactions and state–by–year interactions together. However, they note that they “do not have the power to identify
any significant effects using this less restrictive model.” (p. 744)
7
probability that a 24-year-old mother gave birth to a low birthweight baby was close to 0.06
across both treatment and placebo states. Thus, the maternal age-outcome profile is both higher
and steeper in treatment states, albeit slightly.
The fact that treatment states had a steeper age-outcome profile in 1978 is consistent with
a negative relationship between early access to alcohol and young mothers’ birth outcomes. If
MLDA laws were truly behind the observed pattern, however, then we would expect differences
between treatment and placebo states to narrow over the period of study. As can be seen in
Panel B, the differences across states persist in 1988. Given that babies born to young mothers
living in treated states were less healthy than those born to young mothers in placebo states
throughout the sample period, failing to control for state-by-age fixed effects may lead to an
overestimate of the effects of an MLDA of 18 on birth outcomes. The age-outcome profiles for
white women and black women are presented in Figure A3 and Figure A4, respectively; the
relationships follow a similar, although noisier, pattern across races.
Figure 2 and Figure 3 provide evidence that age-specific trends and state-by-age fixed
effects are likely to be economically important controls. In the regressions below, we also test
the statistical significance of these controls. Our statistical analyses reject the null hypothesis that
the controls do not belong in the model.
V. Results
Table 2 presents our estimates of the relationship between MLDAs and probability of
being classified as a low birthweight or premature birth. We present estimates produced by four
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 qualitatively similar to that used by FW and produces estimates that are nearly
identical.17 The second specification adds an age-specific linear trend to the set of control
variables. Our third specification is similar to the first, but includes state-by-age fixed effects.
Our fourth specification includes all of the controls.
When age-specific trends and state-by-age fixed effects are omitted from the regression
(columns 1 and 5), we find that an MLDA of 18 is associated with worse birth outcomes for both
17
For example, the three key coefficient estimates in Panel A column (1) are -0.18, 0.49, and 0.24. FW’s analogous
coefficient estimates are -0.17, 0.50, and 0.26, respectively.
8
the affected group (infants born to 18-20 year olds), and infants born to women who are younger
than the MLDA. The coefficient estimates suggest that relative to 21-24 year old mothers, and
MLDA of 18 increases 18-20 year old mothers’ probability of giving birth to a low birthweight
baby by 0.24 percentage points. The probability of a premature birth increases by 0.16
percentage points. To put these estimates into perspective, about 8 percent of infants born to 1820 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.
The estimated impact of the MLDA on the health of infants born to older mothers 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 age,18 an MLDA of 18 is
associated with a 0.18 percentage point lower probability of having a low birthweight baby, and
a 0.26 percentage point lower probability of having a premature birth. This tells us that births to
older mothers are helping to control for omitted factors that are associated with the MLDA
changes. We do not know what these omitted factors are, but the fact that the estimated
coefficient on MLDA is 18 is large (nearly half the magnitude) and of the opposite sign, is a
concern. One potential explanation is that the estimated effects are biased by differential trends
across age groups and/or differential age-outcome profiles in treated states.
We investigate this possibility by adding age-specific trends to the regression in columns
(2) and (6). Including age trends reduces the estimated MLDA coefficients dramatically. The
estimated coefficient on the variable MLDA is 18 x Mother is 14-17 falls from 0.49 (column 1) to
0.17 (column 2) and is no longer statistically significant. For premature births, the MLDA is 18 x
Mother is 14-17 estimate drops from 0.80 (column 5) to 0.48 (column 6) for 14-17 year olds.
Similar to Figure 2, the diminished coefficient is consistent with the increase in MLDAs being
correlated with relative improvements in the health of young mothers’ infants. In addition to
their economic significance, an F-test of joint significance indicates that the control variables are
statistically significant at the one percent level.
The inclusion of age-by-state fixed effects without the age-specific trends (columns 3 and
7) has a modest impact on the estimates for premature births, but little impact on the estimates
for low birthweight. For premature birth, the coefficient on MLDA is 18 x Mother is 14-17 drops
18
It is possible that 21-24 year olds may have been affected by the MLDA law change earlier in life.
9
from 0.80 (column 5) to 0.51 (column 7) once state-by-age fixed effects are included. For low
birthweight, the coefficient falls only slightly from 0.49 (column 1) to 0.44 (column 3). As with
the age-specific trends, the state-by-age fixed effects are jointly statistically significant at the one
percent level, consistent with the notion that the age-outcome profiles vary substantively across
states.19
Once the age-specific trends and state-by-age fixed effects are included (column 4 and 8),
nearly all the coefficients are statistically insignificant. For example, among mothers ages 14-17,
there is now a negative but statistically insignificant relationship between an MLDA of 18 and
the probability that an infant is low birthweight. However, there is a positive and statistically
significant relationship between an MLDA of 18 and low birthweight among mothers’ ages 1820, although the coefficient is less than half the magnitude (0.11 versus 0.24). The estimates on
preterm birth are both statistically and economically insignificant. Importantly, the F-statistics
on the added controls are statistically significant. This evidence, coupled with Figures 2 and 3,
strongly suggests that both age-specific trends and state-by-age fixed effects belong in the
regression model.
Table 3 and Table 4 are the analog of Table 2, except the samples are restricted to native
white mothers and native black mothers, respectively. There are a few estimates to highlight:
first, the age-specific trends affect the estimates much more than the state-by-age fixed effects.
Second, among white mothers 14-17, there is a statistically significant negative relationship
between an MLDA of 18 and low birthweight once the preferred controls are included (Table 3
column 4). As in Table 2, the coefficient on MLDA is 18 x Mother is 18-20 is positive and
statistically significant. Third, when we include our preferred controls and focus on pre-term
births to black women, there is a statistically significant negative coefficient on MLDA is 18 x
Mother is 18-20 (Table 4 column 8). This implies that an MLDA of 18 is correlated with better
birth outcomes.
Table 5 shows the relationship between alcohol policies and several other birth outcomes,
by race of mother, using our preferred set of control variables. Nearly all of the coefficient
estimates on the MLDA interactions are small and statistically insignificant. The patterns are
similar for both white and black mothers. There are a few notable exceptions: First, an MLDA of
19
For this test of joint significance on state-by-age terms, we assume iid errors. With standard errors clustered at the
state of residence, there are insufficient degrees of freedom to test the joint significance of the approximately 500
stage-by-age terms. Thus, this F-test should be interpreted as an upper bound of statistical significance.
10
18 is associated with a statistically significant increase in birthweight for both white and black
mothers who are between the ages of 14-17. Second, among 14-17 year old black mothers, an
MLDA of 18 is associated with a statistically significant reduction in the probability that the
Apgar score is less than or equal to 5. Third, there is a statistically significant decrease in the
probability of low Apgar scores (i.e. less than or equal to 5), especially among black women.
Fourth, among 18-20 year old mothers, an MLDA of 18 is associated with a higher fraction of
births that are female, especially among the sample of black mothers (Panel C). The magnitude
of the relationship between an MLDA of 18 and percent female is meaningful: for black women
18-20, an MLDA of 18 increases the probability of giving birth to a female by 0.5 percentage
points.20 Taken together with the fact that we see a statistically significant decrease in the
probability of a low Apgar score, these results may be indicative of higher rates of fetal deaths
(Trivers and Willard, 1973), and positive selection.21
Previous studies have found evidence that MLDA laws affect a different type of selection
other than fetal loss. In particular, Dee (2001) finds that MLDA laws affect selection into
motherhood. Table 6 explores this possibility in the presence of age specific trends and state-byage fixed effects. The coefficient estimates on the interaction term MLDA is 18 x Mother is 1417 suggest that an MLDA of 18 decreases the fraction of women without a high-school degree
by 0.2 percentage points, and increases the fraction of births where the father’s information is
missing by 1.1 percentage points, but most of these estimates are not statistically different from
zero. The estimated relationships among mothers 18-20 are qualitatively similar, but smaller in
magnitude. On the whole these estimates provide inconclusive evidence that an MLDA of 18
increases the fraction of births to more economically disadvantaged groups.
In sum, when the full spectra of outcomes are considered in light of the full set of
controls, we cannot reject the hypothesis that an MLDA of 18 has no impact on birth outcomes.
The estimated relationship between MLDAs and the sex ratio at birth hints at potentially
important selection effects, however. Such effects likely bias the estimated impact of infant
health towards zero. Thus, while we believe that previous studies have overstated the magnitude
20
Assuming all the fetal deaths were males (i.e. a lower bound), our estimates imply an MLDA of 18 is associated
with approximately 1,090 additional fetal deaths per 100,000 births for black women 18-20.
21
We cannot rule out the possibility that the MLDA selects mothers who are more likely to give birth to a female
into the sample.
11
of the correlation between MLDAs and birth outcomes, our improved regression model points to
an alternative link through which MLDA laws may exert important effects.
VI. Conclusion
Existing research suggests that raising the MLDA might lead to better birth outcomes via
changes in the selection of women who give birth. The analyses presented in this paper suggest
that such a conclusion may be premature. 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. Our results do not necessarily imply that prenatal exposure
to maternal alcohol consumption has no effect on birth outcomes because our design focuses on
the effects of MLDA policies, which may have little effect on the drinking behavior of pregnant
women. Previous studies have shown that the MLDA does substantively affect drinking among
young women (e.g. Carpenter and Dobkin, 2009) but it is less clear how such barriers affect the
drinking behavior of pregnant women.
Sample selection also complicates the interpretation of the estimates. Dee (2001) notes
that higher MLDAs are associated with higher birth rates among black mothers. We also see an
increase, although statistically insignificant, in the fraction of births for which paternal
information is missing from the birth certificate. This selection may bias the estimates towards
finding a negative relationship between a low MLDA and birth outcomes. Conversely, we find
evidence that a low MLDA is associated with increases in fetal loss, and the associated positive
selection of surviving fetuses may make it appear as though a low MLDA improves birth
outcomes. It is unclear whether the maternal composition effects are larger than the fetal loss
effects, so the net direction of the bias is difficult to determine. If we wish to obtain a clear
understanding of how these policies affect infant health then engaging in further research that
disentangles these countervailing forces is imperative.
Another explanation for our inability to reject the null hypothesis is that MLDA laws
affect mothers’ drinking behavior, but the effects of in utero exposure to alcohol surface later in
childhood and/or adulthood. The infant health information that is available on birth certificates is
collected within hours (or, more usually, minutes) of a child’s birth and represents only a tiny
fraction of the child outcomes that might be affected. Using a natural experiment in Sweden,
12
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 MLDA laws on children’s long-term development.
The main conclusion that we draw from our analyses is that, contrary to previous
findings, the impact of MLDA policies on birth outcomes is still largely unknown. Our results
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.
13
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16
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
Birthweight missing
Gestation missing
Apgar score missing
Congenital anomaly missing
Mom’s education missing
Observations (1,000s)
All
3,274
.08
39
.12
8.9
.014
.08
.49
.4
.15
.3
.002
.097
.12
.11
.079
19,780
Race of mother
White Black
3,326
3,080
.068
.13
40
39
.096
.18
9
8.8
.012
.021
.08
.079
.49
.49
.41
.41
.16
.16
.22
.56
.0015
.002
.09
.12
.12
.13
.11
.11
.076
.089
13,859 3,743
Other
3,274
.073
39
.12
8.9
.015
.082
.49
.35
.16
.27
.0064
.12
.12
.11
.12
2,178
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 inclusive. Date of conception is determined
by subtracting gestational length. When gestational length is missing, we assume a 40 week gestational
length. Also, delivery day-of-month is not available in the 1989 file, so we assume the delivery took place on
the 15th of the month. White and black categorization of race includes natives only. The white and black
categorization of race includes hispanic and non-hispanic women.
17
18
73,973
19749259
8.24
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.096
(0.056)*
0.173
(0.183)
0.133
(0.091)
73,973
19749259
3.42
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.178
(0.032)***
0.437
(0.079)***
0.246
(0.046)***
Low birthweight (x100)
(2)
(3)
73,973
19749259
13.50
0.00
3.37
0.00
Yes
Yes
Yes
Yes
Yes
Yes
-0.050
(0.032)
-0.060
(0.080)
0.111
(0.051)**
(4)
73,771
18071451
Yes
Yes
Yes
Yes
No
No
73,771
18071451
17.41
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.221
(0.096)**
0.478
(0.285)*
0.183
(0.124)
73,771
18071451
5.30
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.165
(0.066)**
0.506
(0.130)***
-0.005
(0.075)
Gestation < 37 weeks (x100)
(6)
(7)
-0.269
(0.092)***
0.800
(0.264)***
0.163
(0.113)
(5)
73,771
18071451
27.70
0.00
5.33
0.00
Yes
Yes
Yes
Yes
Yes
Yes
-0.076
(0.063)
-0.052
(0.111)
0.008
(0.079)
(8)
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 inclusive. More information on the data can be found in Table 1.
Number of cells
Observations
73,973
19749259
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
Age-specific trends F-stat
P-value
State-by-age f.e. F-stat
P-value
Yes
Yes
Yes
Yes
No
No
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
(1)
-0.184
(0.052)***
0.494
(0.158)***
0.240
(0.085)***
MLDA is 18
Outcome:
Column:
Table 2: MLDA analysis
National Natality Data, 1978-1989
Women aged 14-24 at conception
All races
19
Notes: See notes to Table 2.
Number of cells
Observations
Age-specific trends F-stat
P-value
State-by-age f.e. F-stat
P-value
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
Outcome:
Column:
73,195
13842703
Yes
Yes
Yes
Yes
No
No
-0.096
(0.036)**
0.243
(0.122)*
0.116
(0.057)**
(1)
73,195
13842703
5.31
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.055
(0.035)
0.006
(0.122)
0.093
(0.057)
73,195
13842703
1.84
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.091
(0.030)***
0.203
(0.083)**
0.120
(0.040)***
Low birthweight (x100)
(2)
(3)
73,195
13842703
5.92
0.00
1.82
0.00
Yes
Yes
Yes
Yes
Yes
Yes
-0.036
(0.036)
-0.167
(0.093)*
0.115
(0.054)**
(4)
(5)
72,894
12720724
Yes
Yes
Yes
Yes
No
No
72,894
12720724
19.72
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.113
(0.069)
0.101
(0.195)
0.060
(0.098)
72,894
12720724
2.36
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.114
(0.071)
0.389
(0.075)***
-0.070
(0.077)
26.15
0.00
2.33
0.00
Yes
Yes
Yes
Yes
Yes
Yes
-0.064
(0.070)
-0.132
(0.081)
0.008
(0.086)
(8)
72,894
12720724
Gestation < 37 weeks (x100)
(6)
(7)
-0.148
(0.068)**
0.439
(0.180)**
0.016
(0.080)
Table 3: MLDA analysis
National Natality Data, 1978-1989
Women aged 14-24 at conception
Whites only
20
Notes: See notes to Table 2.
Number of cells
Observations
Age-specific trends F-stat
P-value
State-by-age f.e. F-stat
P-value
State f.e.
Year-by-month f.e.
Age f.e.
State-specific trends
Age-specific trends
State-by-age f.e.
MLDA is 18 x mother
is 14-17
MLDA is 18 x mother
is 18-20
MLDA is 18
Outcome:
Column:
62,374
3,736,912
Yes
Yes
Yes
Yes
No
No
-0.581
(0.135)***
1.162
(0.241)***
0.717
(0.124)***
(1)
62,374
3,736,912
7.83
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.249
(0.159)
0.620
(0.374)
0.162
(0.188)
62,374
3,736,912
2.04
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.569
(0.143)***
0.980
(0.118)***
0.816
(0.114)***
Low birthweight (x100)
(2)
(3)
62,374
3,736,912
14.78
0.00
2.07
0.00
Yes
Yes
Yes
Yes
Yes
Yes
0.005
(0.155)
-0.046
(0.141)
-0.059
(0.149)
(4)
(5)
60,968
3,382,284
Yes
Yes
Yes
Yes
No
No
60,968
3,382,284
9.14
0.00
Yes
Yes
Yes
Yes
Yes
No
-0.387
(0.228)*
0.643
(0.370)*
0.158
(0.186)
60,968
3,382,284
1.86
0.00
Yes
Yes
Yes
Yes
No
Yes
-0.627
(0.133)***
1.060
(0.211)***
0.531
(0.163)***
Gestation < 37 weeks (x100)
(6)
(7)
-0.736
(0.179)***
1.345
(0.260)***
0.644
(0.137)***
Table 4: MLDA analysis
National Natality Data, 1978-1989
Women aged 14-24 at conception
Blacks only
60,968
3,382,284
20.25
0.00
1.92
0.00
Yes
Yes
Yes
Yes
Yes
Yes
0.023
(0.124)
-0.284
(0.198)
-0.335
(0.164)**
(8)
Table 5: 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)
-0.452
(1.075)
4.378
(2.162)**
-1.258
(1.460)
Panel A: All races
0.013
0.201
0.001
(0.007)*
(0.441)
(0.022)
0.014
0.696
-0.064
(0.013)
(0.497)
(0.030)**
-0.012
0.309
-0.038
(0.007)*
(0.257)
(0.029)
-0.912
(3.403)
0.580
(0.436)
0.358
(0.248)
0.020
(0.084)
0.023
(0.108)
0.157
(0.073)**
-1.246
(1.240)
6.153
(2.648)**
-0.442
(1.979)
Panel
0.007
(0.006)
0.021
(0.010)**
-0.009
(0.009)
-0.841
(2.886)
0.466
(0.381)
0.323
(0.234)
0.093
(0.090)
-0.090
(0.133)
0.027
(0.097)
-0.568
(3.761)
7.836
(3.343)**
0.250
(3.563)
Panel C: Blacks only
0.022
1.127
0.005
(0.011)*
(0.538)**
(0.053)
0.020
-0.104
-0.122
(0.019)
(0.629)
(0.058)**
0.003
0.360
-0.120
(0.015)
(0.373)
(0.066)*
1.756
(4.573)
-0.170
(0.211)
-0.104
(0.121)
-0.042
(0.162)
0.229
(0.183)
0.542
(0.204)**
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
B: Whites only
-0.083
0.007
(0.487)
(0.021)
0.523
-0.034
(0.413)
(0.036)
0.242
-0.028
(0.225)
(0.025)
Yes
Yes
Yes
Yes
Yes
Yes
Notes: See notes to Table 2.
21
Yes
Yes
Yes
Yes
Yes
Yes
Table 6: MLDA analysis
Compositional effects
National Natality Data, 1978-1988
Outcome:
Less
than HS
(x100)
HS only
(x100)
Some
college
(x100)
Column:
(1)
(2)
(3)
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.
Dad’s
info
missing
(x100)
(4)
0.230
(0.217)
-0.237
(0.393)
-0.213
(0.247)
Panel A: All races
0.041
-0.271
(0.238)
(0.183)
0.171
0.066
(0.448)
(0.332)
-0.006
0.218
(0.273)
(0.217)
-0.510
(0.326)
1.064
(0.860)
0.483
(0.464)
0.396
(0.230)*
0.038
(0.331)
-0.096
(0.274)
Panel B: Whites only
-0.161
-0.235
(0.204)
(0.180)
-0.088
0.051
(0.399)
(0.361)
0.007
0.089
(0.286)
(0.200)
-0.348
(0.313)
-0.136
(0.939)
0.351
(0.449)
0.057
(0.446)
-0.043
(0.632)
-0.523
(0.419)
Panel C: Blacks only
0.410
-0.466
(0.547)
(0.299)
-0.463
0.506
(0.616)
(0.435)
0.109
0.414
(0.447)
(0.326)
-0.243
(1.032)
1.323
(1.219)
0.227
(0.651)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Notes: See notes to Table 2.
22
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
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).
23
.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. Y axes scales vary across panels.
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
24
Notes: See notes to Figure 2.
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: Mean birth outcomes by mothers’ estimated age at conception, by treated and placebo states
.12
Birthweight < 2500g
.08
.1
.06
.12
Birthweight < 2500g
.08
.1
.06
.2
Gestation < 37 weeks
.1
.15
.05
.2
Gestation < 37 weeks
.1
.15
.05
25
24
24
.03
Birthweight < 2500g
.022
.024
.026
.028
.02
.012
Birthweight < 2500g
.009
.01
.011
.008
1978
1978
Treated states
1986
1980
1988
1978
1980
Placebo states
1982
1984
Year of conception
Placebo states
1982
1984
Year of conception
Treated states
1986
1988
1978
1980
Placebo states
1982
1984
Year of conception
Treated states
1986
Panel B.2: Gestation < 37 weeks
Treated states
1986
Panel A.2: Gestation < 37 weeks
Panel B: Differences between 18-20 year olds and 21-24 year olds over time
Placebo states
1982
1984
Year of conception
Panel B.1: Birthweight < 2500 g
1980
Panel A.1: Birthweight < 2500 g
Panel A: Differences between 14-17 year olds and 21-24 year olds over time
Figure A1: Differences in birth outcomes across age groups over time, by treated and placebo states
White mothers
Notes: See notes to Figure 2.
.007
.05
Gestation < 37 weeks
.035
.04
.045
.03
.018
Gestation < 37 weeks
.014
.016
.012
26
1988
1988
.02
Birthweight < 2500g
.005
.01
.015
0
−.005
.015
Birthweight < 2500g
0
.005
.01
−.005
1978
1978
Treated states
1986
1980
1988
1978
1980
Placebo states
1982
1984
Year of conception
Placebo states
1982
1984
Year of conception
Treated states
1986
1988
1978
1980
Placebo states
1982
1984
Year of conception
Treated states
1986
Panel B.2: Gestation < 37 weeks
Treated states
1986
Panel A.2: Gestation < 37 weeks
Panel B: Differences between 18-20 year olds and 21-24 year olds over time
Placebo states
1982
1984
Year of conception
Panel B.1: Birthweight < 2500 g
1980
Panel A.1: Birthweight < 2500 g
Panel A: Differences between 14-17 year olds and 21-24 year olds over time
Figure A2: Differences in birth outcomes across age groups over time, by treated and placebo states
Black mothers
Notes: See notes to Figure 2.
−.01
.06
Gestation < 37 weeks
.03
.04
.05
.02
.025
Gestation < 37 weeks
.005
.01
.015
.02
0
27
1988
1988
Notes: See notes to Figure 2.
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 A3: Mean birth outcomes by mothers’ estimated age at conception, by treated and placebo states
White mothers
.1
Birthweight < 2500g
.07
.08
.09
.06
.05
.1
Birthweight < 2500g
.07
.08
.09
.06
.05
.16
Gestation < 37 weeks
.1
.12
.14
.08
.06
.16
Gestation < 37 weeks
.1
.12
.14
.08
.06
28
24
24
14
16
18
20
Estimated age at conception
22
Panel A.1: Birthweight < 2500 g
24
14
16
18
20
Estimated age at conception
Notes: See notes to Figure 2.
Placebo states
16
22
24
Treated states
14
Placebo states
22
Treated states
18
20
Estimated age at conception
16
Placebo states
22
Treated states
18
20
Estimated age at conception
Panel B.2: Gestation < 37 weeks
Panel B: Conception year is 1988
Treated states
Panel B.1: Birthweight < 2500 g
Placebo states
14
Panel A.2: Gestation < 37 weeks
Panel A: Conception year is 1978
Figure A4: Mean birth outcomes by mothers’ estimated age at conception, by treated and placebo states
Black mothers
.15
Birthweight < 2500g
.12
.13
.14
Birthweight < 2500g
.12
.13
.14
.11
.15
.11
.25
Gestation < 37 weeks
.15
.2
.1
.25
Gestation < 37 weeks
.15
.2
.1
29
24
24