Alcohol consumption and risky sexual behavior among young
adults: Evidence from minimum legal drinking age laws∗
Ceren Ertan Yörük†
Barış K. Yörük‡
February 12, 2013
Abstract
This paper exploits the discrete jump in alcohol consumption at the minimum legal drinking
age (MLDA) in the United States and uses a regression discontinuity design to investigate the
relationship between drinking and risky sexual behavior among young adults. Using confidential
data from the National Longitudinal Survey of Youth (1997 Cohort), we document that young
adults tend to drink up to 2.1 days more once they are granted legal access to alcohol at age 21.
Under certain model specifications, we find that the discrete jump in alcohol consumption at the
MLDA is associated with an increase in the probability of having sex by up to 8.3 percentage
points. However, we also find that young adults, who gain legal access to alcohol at age 21, do not
have a tendency to engage in risky sexual behaviors. Furthermore, we document that the effect of
the MLDA on the probability of using several different birth control methods is not significant for
those who had sex in the past four weeks. Our results are robust to alternative sample and model
selections and imply that although the MLDA law is quite effective in reducing alcohol consumption
among young adults, spillover effects of this law on risky sexual behaviors are relatively limited.
Keywords: alcohol consumption, minimum legal drinking age, risky sexual behavior
JEL classification: I10, I18
∗
This paper uses confidential data provided by Bureau of Labor Statistics (BLS). The views expressed in this paper
are those of the authors and do not necessarily reflect those of the BLS.
†
Corresponding author. School of Management, The Sage Colleges, 140 New Scotland Ave., Albany, NY 12208.
E-mail: [email protected].
‡
Department of Economics, University at Albany, SUNY, 1400 Washington Ave., Albany, NY 12222. E-mail: [email protected].
1
1
Introduction
One of the most direct forms of regulation on alcohol availability and consumption in the United
States is imposing a minimum legal drinking age (hereafter, MLDA) of 21.1 Understanding the direct
and indirect effects of the MLDA is particularly important not only because alcohol consumption is
related to several undesirable health and economic outcomes, but also lowering the MLDA from 21 is
a current policy debate in many states. Opponents of the MLDA of 21 suggest that imposing a MLDA
encourages a majority of young adults under age 21 to consume alcohol in an irresponsible manner
and that lowering the drinking age would help young adults to learn how to drink gradually and in
moderation. On the other hand, proponents of the MLDA of 21 argue that states that previously
lowered the drinking age to 18, such as Massachusetts and Maine, experienced an increase in alcoholrelated traffic accidents among the 18 to 20 age group and that allowing teens to drink at an early
age can make them more vulnerable to drug and substance abuse, depression or other mental health
problems, and violence.
Several studies investigate the effect of the MLDA laws on alcohol consumption and alcohol
consumption related outcomes.2 The consensus in the existing literature is that once young adults
gain legal access to alcohol at age 21, their alcohol consumption increases significantly.3 Most of the
studies that investigate the direct and indirect effects of the MLDA laws use the state level variation
in the MLDA laws before 1988. However, states that originally imposed a lower MLDA might be
different in unobserved ways than those states that enforced the MLDA of 21. This is plausible since
states that observe relatively lower alcohol consumption rates among young adults may choose to
lower the MLDA, which makes the policy choice endogenous. If this is the case, then one cannot
directly estimate a consistent effect of the MLDA on alcohol consumption or alcohol consumption
related outcomes using the simple variation of the MLDA law at the state level. In order to address
1
By 1988, all states had set 21 as the MLDA. Since then, it has been illegal for youths under age 21 to purchase
or consume alcohol in the United States. In some states, alcohol consumption under 21 may be allowed under certain
circumstances. These include religious or educational purposes and the presence of a parent. We do not address these
exceptions directly because their impact is likely to be minor in the context of our study.
2
Wagenaar and Toomey (2002) and Carpenter and Dobkin (2011) provide an extensive review of the literature on
the effects the MLDA laws. The main focus of most of the existing studies is the impact of the MLDA laws on alcohol
related traffic accidents. See, for example, Dee (1999), Lovenheim and Slemrod (2010), Carpenter and Dobkin (2009),
and Kreft and Epling (2007).
3
The exception is Miron and Tetelbaum (2009) who use state level panel data to show that any nationwide impact
of the MLDA is driven by states that increased their MLDA prior to any inducement from the federal government.
2
this shortcoming, several recent papers exploit the discontinuity in drinking habits at age 21 and
use a regression discontinuity (hereafter, RD) design to estimate the causal effect of the MLDA on
alcohol consumption and alcohol consumption related outcomes. These studies include Carpenter
and Dobkin (2009), Carrell, Hoekstra, and West (2011), Ertan Yörük and Yörük (2012) and Yörük
and Ertan Yörük (2011).4 Carpenter and Dobkin (2009) investigate the effect of the MLDA on
alcohol consumption and alcohol consumption related mortalities. They argue that the MLDA of 21
is associated with discrete increases in several measures of alcohol consumption, including a 21 percent
increase in the number of days on which people drink. This increase in alcohol consumption leads to a
discrete 9 percent increase in the mortality rate at age 21. Carrell, Hoekstra, and West (2011) exploit
the discontinuity in drinking at age 21 at a college in which the minimum legal drinking age is strictly
enforced. They find that alcohol consumption is associated with significant reductions in academic
performance, particularly for the highest-performing students. Using data from National Longitudinal
Survey of Youth, 1997 Cohort (NLSY97), Yörük and Ertan Yörük (2011) show that MLDA of 21
is associated with significant increases in several measures of alcohol consumption. However, they
also argue that the spillover effects of the MLDA law on smoking and marijuana use among young
adults are often insignificant. Using data from NLSY97, Ertan Yörük and Yörük (2012) find that the
MLDA is not a significant determinant of psychological well-being among young adults.
In this paper, we use a RD design to investigate the spillover effects of the MLDA law on sexual
activity and risky sexual behavior among young adults. Although several studies have investigated
the relationship between alcohol consumption and risky sexual behavior before, they mostly rely on
instrumental variables to control for potential endogeneity of alcohol consumption. Recently, several
researchers have raised concerns regarding the validity of the instruments used in these studies. For
instance, Rashad and Kaestner (2004) argue that instruments that rely on state level variation in
alcohol policies such as state beer taxes and police expenditures are statistically weak. It is plausible
that states’ decision to adopt a particular alcohol control policy is based on direct and indirect effects
of alcohol consumption. For instance, a state may want to increase the tax on alcohol not only to
4
The main identifying assumption in these studies is that the observable and unobservable determinants of alcohol
consumption and alcohol consumption related outcomes are likely to be distributed smoothly across the age-21 cutoff.
Hence, the changes in alcohol consumption and alcohol consumption related outcomes at age 21 can solely be attributed
to the MLDA law itself. This assumption is partially testable. We discuss this identification problem in section four in
detail.
3
control alcohol consumption but also to curb the undesirable spillover effects of heavy drinking such
as violence or risky sexual behavior. If this is the case, then one cannot estimate a consistent effect of
alcohol consumption on risky sexual behavior using the simple variation of the alcohol control policies
at the state level. The RD approach used in this paper alleviates this shortcoming by removing the
bias from unobserved policy preferences. We use the same data that Yörük and Ertan Yörük (2011)
have used in their study but extend their analysis by investigating the impact of increased alcohol
consumption at the MLDA on sexual activity and risky sexual behavior among young adults. To our
best knowledge, this is the first paper which uses a RD design to investigate the relationship between
alcohol consumption and risky sexual behavior among young adults.
We use a confidential version of the NLSY97, which contains information on the exact birth dates
of the respondents, to investigate the impact of the MLDA law on alcohol consumption and risky
sexual behavior among young adults. In the context of a RD design, the information on exact birth
dates is particularly important since one can clearly identify the treatment and control groups and
compare the alcohol consumption related outcomes of young adults who are just about to turn 21 with
those who recently turned 21.5 Using several parametric models, we find that granting legal access to
alcohol at age 21 leads to an increase in several measures of alcohol consumption. In particular, our
estimates indicate that the MLDA of 21 is associated with up to a 2.1 day increase in the number
of days that young adults consume alcohol and up to a 1 day increase in the number of days that
they engage in binge drinking per month. Under certain model specifications, we also show that the
increased alcohol consumption at the MLDA is associated with up to a 8.3 percentage point increase
in the probability of having sex in the past four weeks. However, we find that the impact of the MLDA
law on risky sexual behaviors, that can result in unintended outcomes such as a sexually transmitted
disease (STD) or pregnancy, is not significant. Moreover, we document that for those who had sex at
least once in the past four weeks, the effect of the MLDA law on using several different birth control
methods is statistically insignificant. Hence, we find no evidence of the effect of the MLDA on various
5
As discussed in Yörük and Ertan Yörük (2011), without information on exact birth dates, it is not possible to
identify the treament and control groups around the MLDA cutoff. Suppose that one has information only on the month
and year of the birth date of each respondent and her interview date. A respondent who was born on January 30, 1980
and interviewed on January 1, 2001 will be mistakenly coded as a 21 year old and placed in a treatment group (those
who are 21 and older). But, this respondent is actually in the control group since she is 29 days younger than 21 at the
time of the interview. Furthermore, by definition, the RD approach estimates the local treatment effect, which calls for
a very detailed information around the age-21 cutoff.
4
indicators of risky sexual behaviors among young adults. This finding is also robust to selection of
alternative samples and models.
The rest of this paper is organized as follows. The next section summarizes the findings from
previous studies that have investigated the relationship between alcohol consumption and risky sexual
activity. Section three presents the data and summary statistics. Section four presents a discussion
of RD models. Section five presents the results and discusses the sensitivity of results to selection of
alternative specifications. Section six provides a discussion of policy implications and concludes.
2
Alcohol consumption and risky sexual behavior
There are several reasons why alcohol consumption might be associated with risky sexual behavior
among young adults. Alcohol consumption may increase sexual aggression, lower inhibitions, or
significantly affect the ability to assess potential risks. Heavy drinking could also serve as an excuse
to engage in behavior that otherwise would be considered socially unacceptable such as not using
a condom in a sexual intercourse (Rees, Argys, and Averett, 2001). A large body of earlier studies
in the existing literature find a strong and positive link between alcohol consumption and sexual
activity.6 However, identifying the causal effect of alcohol consumption on sexual activity is difficult
since individuals are likely to select into drinking based on their unobserved characteristics such as
individual’s propensity for risk-taking that may also be correlated with their sexual activity.
In order to address this problem, Rees, Argys, and Averett (2001) use whether the state of residence required schools to offer alcohol and drug prevention education, per capita local and state
expenditures on police protection, the number of arrests per violent crime in the county of residence,
and the number of total arrests per crime in the county of residence as instrumental variables that
influence substance use but not sexual activity. Compared with the findings of the previous studies
which treated alcohol consumption an exogenous variable, their results indicate that treating substance use as an endogenous variable produce much smaller and often insignificant estimates of the
effect substance use on sexual behavior. Grossman and Markowitz (2005) use data from the Youth
Risk Behavior Survey to investigate the causal relationship between substance use and risky sexual
behavior. Their instruments for substance use include taxes on beer, the monetary price of mari6
These studies include Butcher et al. (1991), Cooper et al. (1994), Bentler and Newcomb (1986), Mott and Haurin
(1988), and Staton et al. (1999).
5
juana, the number of outlets licensed to sell alcohol, and statutory fines and jail terms for possession
of small amounts of marijuana. They find that alcohol consumption does not increase the likelihood
of having sex or of having multiple partners but it lowers the probability of using birth control and
condoms among sexually active teens. Sen (2002) uses data from NLSY97 and instruments alcohol
consumption with state level policies such as per gallon beer tax in state of residence, the year in
which the state of residence increased the legal drinking age to 21, arrest rates for juvenile drunk
driving in country of residence, and per capita alcohol consumption by adults in state of residence.
She finds that moderate alcohol consumption increases the probability of sexual intercourse, even
after accounting for the potential endogeneity. However, consistent with Rees, Argys, and Averett
(2001), she concludes that there is less evidence that heavy drinking has a significant effect on sexual intercourse. Carpenter (2005) finds evidence that the adoption of age-targeted "Zero Tolerance"
drunk driving laws were associated with lower gonorrhea rates among white males in the relevant
age group. His reduced form estimates, which include controls for other alcohol control policies,
macroeconomic variables, demographic characteristics, unrestricted state and year fixed effects, and
state specific time trends, indicate that age-targeted drunk driving laws cause a 14% reduction in the
gonorrhea rate among 15—19-year-old white males. He also find that the "Zero Tolerance" laws do
not have a significant effect on the gonorrhea rate among 20—24-year-old white males. On the other
hand, Rashad and Kaestner (2004) call into question the instruments that are used in the literature.
They argue that, in most cases, instruments are not strongly correlated with substance use and tests
of exclusion restrictions indicate that in many cases they are not valid.
There are also a handful of papers in the literature that have investigated the spillover effects of
the MLDA laws on risky sexual behavior among young adults. Dee (2001) estimates reduced-form
childbearing models that are based on state-level panel data. He finds that alcohol availability and
use have large, independent, and statistically significant effects on childbearing among black teens but
not necessarily among white teens. Similarly, using state level data, Chesson, Harrison, and Kassler
(2000) show that higher alcohol taxes and MLDAs lead to lower sexually transmitted disease rates
in the United States. In contrast to these studies, we use individual level survey data that contain
detailed information on sexual activity and risky sexual behavior among young adults. Our study
also contributes to the existing literature by estimating the spillover effects of the MLDA law on
risky sexual behavior among young adults using a RD design. This methodology improves over the
6
previous studies since it does not require any instrumental variables that are correlated with alcohol
consumption for identification.
3
Data
We have obtained access to a confidential version of the NLSY97 with information on respondents’
exact date of birth and interview date for each survey year. For each respondent, we use this information to calculate the exact age in days at the time of the interview. We restrict our sample to those
respondents who were surveyed over the period 2000-2006 and were between ages 19 to 22, inclusive.7
The respondents of the NLSY97 were asked whether they have consumed alcohol since the date
of their last interview (DLI). Those who reported to have consumed alcohol since the DLI were asked
about their alcohol consumption habits over the past month.8 We focus on the answers from these
two questions to generate alcohol consumption outcomes. The relatively short reference period of one
month is desirable since our empirical strategy compares those who are slightly older than 21 with
those who are slightly younger than this cutoff age. In particular, we consider two different alcohol
consumption outcomes. These are the number of days that the respondent consumed alcohol over the
past month and the number of days that she had five or more drinks on the same occasion during the
past month as a measure of binge drinking. The sample statistics for these variables are presented in
Table 1.9 On average, young adults consume alcohol 4.6 days per month and engage in binge drinking
2 days per month.
The estimates from the existing literature that document the relationship between drinking and
sexual activity and risky sexual behavior are mixed and often suffer from potential omitted variable
7
This corresponds to a bandwidth of 732 days on either side of the age cutoff of 21. We follow Carpenter and Dobkin
(2009) and Yörük and Ertan Yörük (2011) in order to choose the age bandwidth.
8
Since the empirical results are based on self-reported survey data, young adults under 21 may also be more likely to
underreport their alcohol consumption since alcohol consumption is illegal for those who are under this cutoff age. This
could generate a discrete jump in reported level of alcohol consumption at age 21 even if there is no true change in actual
behavior. However, Yörük and Ertan Yörük (2011) argue that in the NLSY97, the alcohol consumption patterns of
21-year-olds are quite similar compared with 20 and 22-year-olds, which suggests that the empirical results documented
in this paper are not subject to a underreporting bias.
9
Yörük and Ertan Yörük (2011) use the same alcohol consumption outcomes in their analysis. Our results are similar
but slightly different than their results because their estimation sample includes only those who have consumed alcohol
at least once since the DLI. In our analysis, we also include those who did not consume alcohol since the DLI. Naturally,
for these respondents number of days that they consumed alcohol or engaged in binge drinking over the past month is
zero.
7
bias. Hence, understanding the impact of increased alcohol consumption at the MLDA on risky sexual
behavior is quite important. The respondents of the NLSY97 were asked whether they ever had sex
and whether they had sex since the DLI. Those who had sex since the DLI were also asked the number
of times they had sex in the past four weeks.10 We use the answers from these questions to generate
two outcome variables. These are the number times that the respondent had sex over the past four
weeks and whether the respondent had sex at least once over the past four weeks. Table 1 shows
that on average, 52.5% of the respondents had sex at least once over a four week period. The average
number of sexual intercourse among young adults during the same period is approximately 5.
For 2000-2005 survey years, the respondents of the NLSY97 who had sex at least once since
the DLI were also asked whether they used a birth control method in their most recent sexual
intercourse. Using information from these questions, we consider risky sexual behaviors that can
result in pregnancy or an STD. We assume that a young adult engaged in a risky sexual behavior
that can result in pregnancy if he or she had sex at least once over the past four weeks and did not
use any form of birth control method in his or her last sexual intercourse. Similarly, we assume that
a respondent was at risk of contracting an STD if he or she had sex at least once over the past four
weeks and did not use condom in his or her most recent sexual intercourse. The sample statistics
reported in Table 1 shows that 19.7% of the respondents engaged in a risky sexual behavior that can
lead to an STD and 9.5% of the respondents engaged in a risky sexual behavior that may result in
pregnancy.
Since RD analysis requires a short reference period, we also consider the outcomes for those
respondents who had sex at least once in the past four weeks. For this restricted sample, in order
to investigate the relationship between MLDA and risky sexual behavior, we consider four different
birth control variables. These are whether the respondent or her partner used any birth control
method in the most recent sexual intercourse, whether the respondent or her partner used condom in
the most recent sexual intercourse, whether the respondent or her partner used birth control pill in
the most recent sexual intercourse, and whether the respondent or her partner used any other birth
control method excluding condom or birth control pill in the most recent sexual intercourse. The
raw numbers reported in Table 1 show that on average 74% of the respondents who has sex at least
10
The wording of the question is as follows: "About how many times have you had sexual intercourse in the past four
weeks?". We exclude those who reported to have had sex more than 50 times in the past month (505 observations).
8
once over the past four weeks used a birth control method, 46% used condom, 36% used birth control
pill, and 30% used some other birth control method excluding condom or birth control pill.11 These
raw numbers are similar compared with other surveys that contain information on sexual activity.
For instance, in National Survey of Sexual Health and Behavior (NSSHB), 45% of males and 39% of
females in the 18-24 age group reported to have used condom in their past ten sexual intercourse.
As mentioned before, alcohol consumption, sexual activity, and risky sexual behavior outcomes
in the NLSY97 refer to a relatively short time period of either one month or four weeks. Although
these short reference periods are desirable in the context of RD design, there is also a potential
problem associated with the outcome variables. Suppose that a respondent who is 10 days older than
21 reported to have consumed alcohol only a single day over the past month. One cannot possibly
identify the exact day of alcohol consumption. Therefore, it is impossible to know whether this
respondent consumed alcohol before or after turning 21. However, this potential problem applies only
those who were interviewed during the first month or first four weeks after turning 21. In order to
address this problem, following Carpenter and Dobkin (2009) and Yörük and Ertan Yörük (2011),
we include a dummy variable, which is equal to 1 if the respondent was interviewed during the first
month after turning 21, to our alcohol consumption models. Similarly, we include a dummy variable,
which is equal to 1 if the respondent was interviewed during the first four weeks after turning 21, to
our sexual activity and risky sexual behavior models.12
4
Methodology
We use a RD design to estimate effect of the MLDA laws on alcohol consumption and risky sexual
behavior among young adults.13 MLDA law in the United States guarantees that alcohol consumption
11
In the 2000 survey year, there was a single question about the birth control method used in the most recent sexual
intercourse. Hence, respondents were allowed to choose only one of the birth control methods listed. However, in
the 2001 to 2005 survey years, respondents were first asked whether they used condom in their most recent sexual
intercourse. If the answer is a yes, then they were asked whether they used any other birth control method in their
most recent sexual intercourse. Therefore, in the 2001 to 2005 survey years, respondents were allowed to choose two
birth control methods instead of one. Hence, the percent of respondents who reported to have used condom, pill, or any
other birth control method exceeds 100% for the full sample.
12
Young adults may also tend to drink more in their birthdays. Therefore, it is hard to distinguish the birthday
celebration effect in the 21st birthday from the effect of the MLDA law. Carpenter and Dobkin (2009) argue that a
dummy variable, which is equal to 1 if the respondent was interviewed during the first month after turning 21, also
controls for the potential impact of the birthday celebration at the MLDA.
13
Imbens and Lemieux (2008) and Lee and Lemieux (2009) provide a detailed discussion of the RD design.
9
is legally allowed according to a simple age cutoff of 21. Therefore, one can compare outcomes across
young adults with similar income, educational attainment, and other observable characteristics, but
very different levels of alcohol consumption around this cutoff age. The main RD regression model
used through our empirical analysis is as follows:
Yi = β ′ Xi + ηTi + f(agei ) + εi
(1)
where Yi represents a particular outcome such as different measures of alcohol consumption, sexual
activity or risky sexual behavior for individual i. The set of observable characteristics for individual
i are denoted by Xi . Following Yörük and Ertan Yörük (2011), control variables include household
income, educational attainment, marital status, gender, and race of the respondent, binary controls
for student and employment status, and a binary variable which equals to 1 if the respondent was
interviewed during the first month (or the first four weeks) after turning 21.14 As documented in Yörük
and Ertan Yörük (2011), these control variables vary smoothly over the MLDA and therefore, have
very little impact on our results.15 A flexible function of age profile, f (agei ), is the forcing variable in
the context of RD design.16 Since the confidential version of the NLSY97 contains information on the
exact birth and interview date for each respondent, it is possible to calculate the difference between
the interview date and the respondent’s 21st birthday in days. Therefore, for each respondent, the
variable agei denotes the number of days before or after the 21st birthday. The treatment variable is
represented by Ti and is equal to 1 if the respondent is at least 21 years old at the interview date and
0 otherwise. The main coefficient of interest, η, denotes the effect of the MLDA law on the relevant
outcome variable.
Correctly modelling the age profile is one the main obstacles in implementing a RD design. We
estimate several parametric specifications and consider linear, quadratic, cubic, quartic models, al14
Following Yörük and Ertan Yörük (2011), we first calculate household income in 2006 prices. Next, we create
dummy variables for different income ranges (less than 20000, 20000 to 40000, 40000 to 60000, 60000 to 80000, 80000
to 100000, more than 100000, and a dummy for missing observations) and include these dummies to our regressions as
controls for income.
15
Our selection control variables follows Yörük and Ertan Yörük (2011), who use NLSY97 to investigate the spillover
effects of the MLDA law on smoking and marijuana use among young adults. Yörük and Ertan Yörük (2011) also test
the possibility that these control variables exhibit a discrete change at the MLDA. They show that there is no evidence
of a significant change in any of the control variables at the MLDA. Since we employ the same data set and consider
the same age bandwidth, this finding also applies to our analysis. This result also reduces the concerns about omitted
variable bias and suggests that parametric models estimated with or without controls should yield similar results.
16
In order to implement the RD design, the respondents should not have any control over the forcing variable. Since
the forcing variable is age, this condition is naturally satisfied.
10
lowing the slope of these functions to vary on each side of the age cutoff (i.e. linear, quadratic, cubic,
and quartic splines). Hence, our parametric models with different degrees of polynomials that are
fully interacted with the treatment variable contain the following age profile:
p
p
δ j ageji
f (agei ) =
j=1
λj (Ti × ageji ) for p = {1, 2, 3, 4}.
+
(2)
j=1
It is also possible to estimate the age profile non-parametrically. Carpenter and Dobkin (2009)
and Yörük and Ertan Yörük (2011) show that the effect of the MLDA on alcohol consumption is quite
similar under parametric or non-parametric RD models. Furthermore, non-parametric approach has
two main shortcomings. First, in contrast to parametric models, it is relatively hard to control for
observable characteristics in non-parametric models due to the well-known dimensionality problem.
Second, one has to balance the goals of staying as local to the cutoff point at the MLDA as possible
while ensuring to have enough data to yield informative estimates. This requires the selection of
optimal bandwidths for each outcome variable. However, there is currently no widely agreed-upon
method for selection of optimal bandwidths in the nonparametric RD context (Carpenter and Dobkin,
2009). Therefore, throughout the paper, we report results from parametric models only.17
5
Results
5.1
Alcohol consumption
In the first four columns of Table 2, we report the estimates from parametric regressions of the effect
of the MLDA on the number of days that young adults consumed alcohol or engaged in binge drinking
per month. These regressions contain linear, quadratic, cubic, and quartic polynomials of age that
are fully interacted with a dummy variable indicating an age over 21 and are estimated using sample
weights. As expected, models estimated with or without any control variables yield similar results.
We find that once they gain legal access to alcohol at the MLDA, young adults tend to increase
the number of days that they consume alcohol by 1.6 to 2.1 days per month. This effect is highly
17
Although not reported here, we also estimate non-parametric models following Hahn, Todd, and van der Klaauw
(2001). In these models, following Malamud and Pop-Eleches (2011), we use triangular kernel which has been shown
to be boundary optimal by putting more weight on observations closer to the cutoff point (Cheng, Fan, Marron, 1997)
and employ the bandwidth selection procedure suggested by Imbens and Kalyanamaran (2009). In general, similar to
parametric models, non-parametric models also suggest that the effect of the MLDA on risky sexual behaviors among
young adults is statistically insignificant.
11
significant. The MLDA is also associated with an increase in the number of days that young adults
engage in binge drinking per month. The third and fourth columns in Table 2 show that the increase
in the number of binge drinking days at the MLDA is around 0.5 to 1 days per month. This effect is
highly significant under all model specifications.
We also estimate the same models for those who had sex in the past four weeks. For this sample,
the effect of the MLDA on alcohol consumption is more pronounced and remains highly significant.
In particular, we find that those who had sex at least once in the past four weeks tend to increase
the number of days that they consume alcohol by up to 2.8 days per month and the number of days
that they engage in binge drinking by up to 1.6 days per month. In Figure 1, we superimpose the
quadratic fitted lines from the parametric model estimated without any controls over the mean value
of the outcome variables calculated for each 30-day age block. This figure confirms the estimation
results and shows that, consistent with the findings from the earlier studies that have used a RD
design to investigate the impact of the MLDA on alcohol consumption, the MLDA is associated with
a considerable increase in alcohol consumption and binge drinking among young adults.
5.2
Sexual activity
In Table 3, we report the estimates from parametric regressions of the effect of the MLDA on sexual
activity among young adults. The results presented in the first two columns suggest that the MLDA
is associated with up to a 8.3 percentage point increase in the probability of having sex at least once
in the past four weeks. This effect is significant at conventional significance levels in models that
contain cubic and quartic polynomial of age and in a model that contains a quadratic polynomial
of age and is estimated without any control variables. The last two columns of Table 3 show the
estimated effect of gaining legal access to alcohol at the MLDA on the number of times that the
respondent had sex over a four week period. Under most specifications, the estimated effect is
positive which suggests that increased alcohol consumption at the MLDA may also be associated
with increased sexual activity among young adults. However, under all models specifications, the
estimated coefficient of the treatment variable is statistically insignificant, which implies that the
MLDA does not have a significant effect on the number of times that the respondent had sex in the
past four weeks.
In Figure 2, we plot the results from models that are estimated using a quadratic polynomial of
12
age and without any control variables. Consistent with our findings, the probability of having sex
among young adults exhibits a discrete jump at the cutoff age of 21. On the other hand, the second
panel of Figure 2 shows a small increase in the number of times that the respondent had sex over the
past four weeks. However, this effect is insignificant.
Following Carpenter and Dobkin (2009), one can also estimate the direct relationship between
alcohol consumption and sexual activity. This is the ratio of the change in sexual activity to the
change in alcohol consumption at the MLDA. For instance, the results from the models estimated
using a cubic polynomial of age and without any control variables suggest that young adults tend to
increase their alcohol consumption by 1.71 days per month (1.6 days per four weeks). They are also
6.6 percentage points more likely to have sex during a four week period once they gain legal access
to alcohol at the MLDA. Therefore, a one day increase in alcohol consumption is associated with
0.066/1.6 = 0.041 (4.1 percentage points) increase in the probability of having sex at least once over
the past four weeks.
5.3
Risky sexual behavior
In Table 4, we investigate whether the increased alcohol consumption at the MLDA has a significant
impact on risky sexual behaviors among young adults. Results from the parametric models estimated
using cubic or quartic polynomial of age and from the model estimated with quadratic polynomial of
age and without any control variables show that the effect of the MLDA on a risky sexual behavior that
may lead to an STD is insignificant. Under remaining model specifications, the MLDA is associated
with up to a 3.1 percentage point decrease in the probability of engaging in a risky behavior that may
lead to an STD. On the other hand, the last two columns of Table 4 show that under all parametric
model specifications, the estimated impact of the MLDA on a risky sexual behavior that can result
in pregnancy is very small and statistically insignificant. In Figure 3, we plot the results from models
that are estimated using a quadratic polynomial of age and without any control variables. Consistent
with our findings, we observe small and insignificant changes in both variables at the MLDA.
In the first two columns of Table 5, we investigate whether the increased alcohol consumption at
the MLDA has a significant impact on the probability of using a birth control method in the most
recent sexual intercourse for those who had sex at least once over the past four weeks. Under all
parametric model specifications, the estimated impact of the MLDA on this outcome is very small
13
and statistically insignificant, which suggests that the probability of using some form of birth control
among those who had sex at least once over the past four weeks does not exhibit a significant change at
the MLDA. The third and fourth columns of Table 5 report the effect of the MLDA on the probability
of using condom in the most recent sexual intercourse for those who had sex at least once over the
past four weeks. Results from the most flexible models, i.e., parametric models estimated using the
fourth order polynomial of age that is fully interacted with the treatment variable, imply that the
increased alcohol consumption at the MLDA does not have a significant impact on condom use among
young adults who had sex in the past four weeks. However, under remaining model specifications, we
find that the MLDA is associated with up to a 11 percentage point increase in the probability of using
condom in the most recent sexual intercourse. The results reported in the fifth and sixth columns of
Table 5 show that the increased alcohol consumption at the MLDA decreases the probability of using
birth control pill in the most recent sexual intercourse among those who had sex at least once over
the past four weeks. However, under all specifications, this effect is not significant at conventional
significance levels. In the last two columns of Table 5, we investigate whether the increased alcohol
consumption at the MLDA has a significant impact on the probability of using any other birth control
method (excluding condom or birth control pill) in the most recent sexual intercourse for those who
had sex at least once over the past four weeks.18 Results from the parametric models estimated using
cubic or quartic polynomial of age suggest that the MLDA is not significantly associated with this
outcome. However, under the remaining model specifications, we find that the MLDA is associated
with up to a 7.4 percent increase in the probability of using any other birth control method in the
most recent sexual intercourse. As in other outcomes, in Figure 4, we plot the results from risky
sexual behavior models that are estimated using a quadratic polynomial of age and without any
control variables. Consistent with our findings, the probability of using condom in the most recent
sexual intercourse exhibits a sudden increase at the MLDA. On the other hand, Figure 4 shows that
the probabilities of using some form of birth control method, birth control pill, or any other birth
control method excluding condom and birth control pill are distributed smoothly around the age-21
cutoff.
18
Other birth control methods include foam, jelly, creme, sponge, or suppositories, withdrawal, diaphragm, safe time,
intrauterine device, norplant, depo-provera or injectables, and any other method.
14
5.4
Robustness checks
In the 2000 wave of the NLSY97, the respondents were asked a single question about the birth control
method that they or their partner used in their most recent sexual intercourse. Among several answer
choices including condom and birth control pill, they were allowed to choose only one. However, in
the 2001-2005 waves of the survey, the respondents were first asked whether they or their partner
used condom in their most recent sexual intercourse. If the answer was a yes, then the respondents
were asked whether they or their partner used any other birth control method in the most recent
sexual intercourse. Therefore, in the 2001-2005 survey years, the respondents were allowed to report
up to two birth control methods that they used in their most recent sexual activity. In order to test
whether the change in the survey methodology starting from the 2001 survey year has any impact
on our results, we exclude the 2000 survey year from our sample and reestimate the sexual activity
and risky sexual behavior models that contain quadratic or cubic polynomial of age.19 The results
reported in Table 6 show that the estimated impact of the MLDA on sexual activity among young
adults is robust to the exclusion of the 2000 survey year from the sample. This is expected since the
change in the survey methodology was not related to the questions about the sexual activity. We find
that under the parametric model estimated using a cubic polynomial of age, the MLDA is associated
with a 6.4 percentage point increase in the probability of having sex at least once in the past four
weeks. Furthermore, similar to the main results reported in Table 3, the effect of the MLDA on the
number of times that the respondent had sex in the past four weeks remains insignificant.
However, in contrast with our main findings, the results reported in Table 7 suggest that the effect
of the MLDA on a risky sexual behavior that may lead to an STD becomes insignificant once the
2000 survey year is excluded from the sample. On the other hand, the effect of the MLDA on a risky
sexual behavior that may result in pregnancy is robust to the exclusion of the 2000 survey year and
remains insignificant under alternative parametric models. The results reported in Table 8 imply that
for those who had sex at least once in the past four weeks, the exclusion of the 2000 survey year from
the sample has a considerable impact on the estimated effect of the MLDA on risky sexual behaviors.
In particular, once the 2000 survey year is excluded from the sample, the effect of the MLDA on
19
We use the full set of control variables in all regressions. Although not reported in this paper, the results from
models that contain linear or quartic polynomial of age yield similar results and are available from the authors upon
request.
15
all risky sexual behavior outcomes becomes insignificant under all model specifications. This finding
contradicts with the results reported in Table 5, which suggest that the MLDA is associated with an
increased probability of using condom or any other birth control method (excluding condom or birth
control pill) in the most recent sexual intercourse.
In our sample, more than 9 percent of the respondents are married. It is plausible to think that
the sexual activity of the married young adults are less likely to be affected by the increased alcohol
consumption at the MLDA. If this is the case, our results may underestimate the true effect of the
MLDA law on sexual activity among single young adults. In order to test this possibility, we exclude
the married respondents from the sample and reestimate the sexual activity and risky sexual behavior
models. Results reported in Tables 6, 7, and 8 show that our main results are robust to the exclusion of
married respondents from the sample. In particular, excluding the married respondents increases the
effect of the MLDA on the probability of having sex by only 0.2 percentage points, the probability
of using condom in the most recent sexual activity by only 0.3 to 0.5 percentage points, and the
probability of using a birth control method excluding condom or birth control pill by 1.3 percentage
points. The effect of the MLDA on other sexual activity and risky sexual behavior outcomes remains
either the same or statistically insignificant.
We also run separate regressions for males and females to test whether the effect of the MLDA on
sexual activity and risky sexual behavior among young adults differs by gender. Table 6 shows that
the increased alcohol consumption at the MLDA is associated with a 12 percentage point increase
in the probability of having sex for males. However, the relationship between the MLDA and sexual
activity is not significant for females. Results reported in Table 7 suggest that the MLDA is not
a significant determinant of risky sexual behaviors for male or female young adults. On the other
hand, results presented in Table 8 document that under certain model specifications, the MLDA is
positively associated with the condom use and propensity to use a some form of birth control method
other than condom or birth control pill for males. For females, results from the parametric model
estimated using a cubic polynomial of age suggest that the increased alcohol consumption at the
MLDA decreases the probability of using birth control pill by 12.5 percentage points. The effect of
the MLDA on other risky sexual behavior outcomes remains insignificant for both males and females.
We have documented that for the full sample, the relationship between the MLDA and risky
sexual behavior outcomes are not robust to the exclusion of the 2000 survey year from the sample.
16
In the last three specifications of Table 7 and 8, we investigate whether the results for single, male,
and female respondents are robust to the exclusion of this survey year from the sample. As for the
full sample, results for subsample of singles, males, and females are very sensitive to the exclusion of
the 2000 survey year from the empirical analysis. In particular, Table 7 shows that for singles, males,
or females, when this particular survey year is excluded from the sample, the effect of the MLDA on
risky sexual behavior outcomes becomes insignificant. Similarly, Table 8 shows that for those who
had sex at least once in the past four weeks, the effect of the MLDA on the probability of using
some form of birth control, condom, birth control pill, or any other birth control method excluding
condom or birth control pill becomes insignificant under all specifications once the 2000 survey year
is excluded. Therefore, we conclude that although there is some evidence that the increased alcohol
consumption at the MLDA is associated with an increased probability of having sex especially for
males, the MLDA is not significantly associated with risky sexual behaviors among young adults.
6
Conclusion
In this paper, we investigate the effect of the MLDA on alcohol consumption and various measures
of sexual activity and risky sexual behavior among young adults using a confidential version of the
NLSY97 which contains information on the exact birth date of the respondents. While there has
been a considerable amount of research on the spillover effects of the MLDA laws on several alcohol
consumption related outcomes, existing studies have two major limitations. First, although the
decision to adopt a lower MLDA might be endogenous at the state level, most of the existing studies
have made use of the state level variation in the MLDA laws that existed in the 1970s and 1980s.
Second, to our best knowledge, none of the existing studies explore the spillover effects of the MLDA
laws on risky sexual behavior among young adults using a RD design.
We exploit the discrete jump in alcohol consumption at the MLDA and estimate the causal
effect of alcohol consumption on several measures of sexual activity and risky sexual behavior among
young adults. In line with the findings from earlier literature, we document that the MLDA of 21 is
associated with an increase in the number of days that young adults consume alcohol or engage in
binge drinking per month. In particular, we show that once they gain legal access to alcohol at the
MLDA, young adults tend to increase the number of days that they consume alcohol by 1.6 to 2.1
17
days and the number of days that they engage in binge drinking days by around 0.5 to 1 days per
month. For those who had sex in the past four weeks, the effect of the MLDA on alcohol consumption
is larger. Those who had sex at least once in the past four weeks tend to increase the number of days
that they consume alcohol by up to 2.8 days per month and the number of days that they engage
in binge drinking by up to 1.6 days per month at the MLDA. These results are robust under several
parametric model specifications.
Although under certain model specifications, we document that the discrete jump in alcohol
consumption at the MLDA is associated with an increase in the probability of having sex by up
to 8.3 percentage points among young adults, the increase in alcohol consumption at the MLDA is
not a significant determinant of the number of times that young adults had sex in the past four
weeks. Furthermore, we find no evidence that the increased alcohol consumption at the MLDA is
significantly associated with risky sexual behaviors among young adults. In particular, we document
that the MLDA does not have a significant impact on risky sexual behaviors that may result in
pregnancy or an STD. Moreover, for those who had sex in the past four weeks, the probabilities
of using any form of birth control, condom, birth control pill, or any other birth control method
excluding condom or birth control pill in their most recent sexual intercourse are not significantly
associated with increased alcohol consumption at the MLDA.
Our findings imply that the MLDA law is quite effective in reducing alcohol consumption among
young adults, but also show that the spillover effects of the MLDA law on risky sexual behaviors
are relatively limited. Although our results have important policy implications, they should also be
interpreted with caution. By definition, the RD approach used in this paper has a very good internal
but limited external validity. Hence, our results hold for those who are around the MLDA (age-21)
cutoff, but cannot be generalized to whole population of young adults. Therefore, further research is
needed to investigate the effects of alcohol consumption on risky sexual behavior among youths. This
calls for detailed survey data on alcohol consumption and alcohol consumption related outcomes.
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21
Table 1. Definition of outcome variables and summary statistics
Outcome variable
Definition
No. of Obs.
Mean
S.D.
Alcohol
Number of days that the respondent consumed alcohol in the past month.
29111
4.590
6.339
Binge
Number of days that the respondent engaged in binge drinking in the past
month.
28971
2.038
4.206
Sex
=1 if the respondent had sex at least once in the past four weeks.
27989
0.525
0.499
No. of times had sex
Number of times that the respondent had sex in the past four weeks.
Those who reported more than 50 are excluded from the sample.
27989
4.999
8.131
STD
=1 if the respondent had sex in the past four weeks and did not use
condom in the most recent sexual intercourse.
20209
0.197
0.397
Pregnancy
=1 if the respondent had sex in the past four weeks and did not use any
form of birth control method in the most recent sexual intercourse.
20217
0.095
0.293
Birth control
=1 if the respondent or his/her partner used a birth control method in the
most recent sexual intercourse. The sample is restricted to those who had
sex in the past 4 weeks.
7402
0.739
0.439
Condom
=1 if the respondent or his/her partner used condom in the most recent
sexual intercourse. The sample is restricted to those who had sex in the
past 4 weeks.
7394
0.459
0.498
Pill
=1 if the respondent or his/her partner used birth control pill in the most
recent sexual intercourse. The sample is restricted to those who had sex in
the past 4 weeks.
7402
0.356
0.479
Other method
=1 if the respondent or his/her partner used some other birth control
method (excluding condom and birth control pill) in the most recent
sexual intercourse. The sample is restricted to those who had sex in the
past 4 weeks.
7402
0.299
0.458
Notes: Sample weighted means and standard deviations are reported for outcome variables. NLSY97 does not contain information on birth control
methods for the 2006 wave. Data for the first four outcomes come from 2000-2006 waves of the NLSY97. Data for the last six outcomes come from
2000-2005 waves of the NLSY97.
22
Table 2. The effect of the MLDA on alcohol consumption among young adults
Full sample
Had sex in the past 4 weeks
Alcohol
Binge
Alcohol
Binge
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Linear
1.622
(0.139)***
1.700
(0.143)***
0.535
(0.095)***
0.563
(0.097)***
2.110
(0.212)***
2.201
(0.215)***
0.699
(0.147)***
0.734
(0.150)***
Quadratic
1.659
(0.221)***
1.740
(0.241)***
0.603
(0.150)***
0.644
(0.163)***
2.077
(0.337)***
2.128
(0.364)***
0.722
(0.233)***
0.753
(0.252)***
Cubic
1.710
(0.326)***
1.936
(0.401)***
0.558
(0.227)**
0.578
(0.277)**
2.319
(0.477)***
2.696
(0.567)***
0.869
(0.334)***
1.008
(0.400)**
Quartic
1.703
(0.405)***
2.139
(0.579)***
0.812
(0.280)***
1.049
(0.388)***
2.353
(0.598)***
2.815
(0.825)***
1.183
(0.423)***
1.570
(0.578)***
Controls
No
Yes
No
Yes
No
Yes
No
Yes
29111
28838
28971
28699
14497
14356
14440
14300
No. of Obs.
Notes: All regressions are estimated using sample weights. Standard errors are clustered at the individual level and reported in parenthesis. The
signs *** and ** represent statistical significance at 1 and 5 percent, respectively.
23
Table 3. The effect of the MLDA on sexual activity among young adults
Sex
No. of times had sex
(1)
(2)
(3)
(4)
Linear
0.015
(0.011)
0.012
(0.012)
-0.039
(0.191)
-0.105
(0.194)
Quadratic
0.029
(0.017)*
0.024
(0.018)
0.195
(0.280)
0.052
(0.295)
Cubic
0.066
(0.026)**
0.064
(0.031)**
0.526
(0.414)
0.346
(0.481)
Quartic
0.068
(0.032)**
0.083
(0.043)*
0.472
(0.516)
0.070
(0.670)
Controls
No
Yes
No
Yes
27989
27725
27989
27725
No. of Obs.
Notes: All regressions are estimated using sample weights. Standard errors are clustered at the
individual level and reported in parenthesis. The signs ** and * represent statistical significance
at 5 and 10 percent, respectively.
24
Table 4. The effect of the MLDA on risky sexual behaviors among young adults
STD
Pregnancy
(1)
(2)
(3)
(4)
-0.022
(0.011)**
-0.022
(0.012)*
-0.006
(0.008)
-0.006
(0.009)
Quadratic
-0.026
(0.017)
-0.031
(0.019)*
-0.003
(0.012)
-0.006
(0.013)
Cubic
-0.018
(0.024)
-0.032
(0.029)
0.012
(0.017)
0.010
(0.020)
Quartic
-0.001
(0.029)
-0.004
(0.039)
0.013
(0.021)
0.006
(0.028)
Controls
No
Yes
No
Yes
20209
20021
20217
20029
Linear
No. of Obs.
Notes: All regressions are estimated using sample weights. Standard errors are clustered at the
individual level and reported in parenthesis. The signs ** and * represent statistical significance
at 5 and 10 percent, respectively.
25
Table 5. The effect of the MLDA on the probability of using a birth control method
Birth conrol
Condom
Pill
Other method
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Linear
0.017
(0.020)
0.019
(0.021)
0.060
(0.024)**
0.066
(0.025)***
0.019
(0.023)
0.016
(0.024)
0.064
(0.022)***
0.074
(0.023)***
Quadratic
0.007
(0.030)
0.009
(0.033)
0.066
(0.035)*
0.085
(0.039)**
-0.008
(0.035)
-0.025
(0.037)
0.048
(0.032)
0.066
(0.035)*
Cubic
-0.025
(0.041)
-0.029
(0.048)
0.066
(0.048)
0.108
(0.057)*
-0.009
(0.047)
-0.038
(0.056)
0.009
(0.043)
0.025
(0.052)
Quartic
-0.022
(0.051)
0.004
(0.066)
0.029
(0.059)
0.075
(0.079)
-0.046
(0.059)
-0.077
(0.078)
0.016
(0.052)
0.055
(0.071)
Controls
No
Yes
No
Yes
No
Yes
No
Yes
7402
7332
7394
7324
7402
7332
7402
7332
No. of Obs.
Notes: All regressions are estimated using sample weights. Standard errors are clustered at the individual level and reported in parenthesis. The
signs ***, **, and * represent statistical significance at 1, 5 and 10 percent, respectively.
26
Table 6. The effect of the MLDA on sexual activity among young adults:
Robustness checks
Quadratic
Cubic
No. of Obs.
Sex
No. of times
had sex
Sex
No. of times
had sex
Exclude 2000
24699
0.027
(0.019)
0.224
(0.308)
0.064
(0.032)**
0.538
(0.505)
Exclude married
25342
0.022
(0.020)
-0.031
(0.294)
0.066
(0.033)**
0.355
(0.479)
Female
13747
0.002
(0.026)
-0.087
(0.400)
-0.001
(0.044)
-0.572
(0.649)
Male
13978
0.041
(0.026)
0.162
(0.429)
0.117
(0.042)***
1.127
(0.706)
Notes: Results from models that contain a quadratic or cubic polynomial of age that is fully
interacted with a dummy variable indicating being 21 or older are reported. All regressions
include a set of control variables as discussed in the text and are estimated using sample weights.
Standard errors are clustered at the individual level and reported in parenthesis. The signs ***
and ** represent statistical significance at 1 and 5 percent, respectively.
27
Table 7. The effect of the MLDA on risky sexual behaviors among young adults:
Robustness checks
Quadratic
Cubic
STD
Pregnancy
STD
Pregnancy
Exclude 2000
-0.003
(0.019)
[16997]
-0.002
(0.014)
[17003]
-0.016
(0.030)
[16997]
0.012
(0.021)
[17003]
Exclude married
-0.032
(0.019)*
[19212]
-0.007
(0.013)
[19220]
-0.029
(0.029)
[19212]
0.007
(0.020)
[19220]
Female
-0.042
(0.029)
[9419]
0.005
(0.021)
[9420]
-0.051
(0.046)
[9419]
0.036
(0.031)
[9420]
Male
-0.023
(0.024)
[10602]
-0.015
(0.018)
[10609]
-0.022
(0.036)
[10602]
-0.014
(0.027)
[10609]
-0.008
(0.020)
[16359]
-0.002
(0.014)
[16365]
-0.014
(0.030)
[16359]
0.011
(0.021)
[16365]
Female / Exclude 2000
0.006
(0.030)
[7924]
0.009
(0.022)
[7925]
-0.015
(0.047)
[7924]
0.037
(0.033)
[7925]
Male / Exclude 2000
-0.013
(0.025)
[9073]
-0.013
(0.019)
[9078]
-0.022
(0.038)
[9073]
-0.010
(0.028)
[9078]
Exclude married /
Exclude 2000
Notes: Results from models that contain a quadratic or cubic polynomial of age that is fully
interacted with a dummy variable indicating being 21 or older are reported. All regressions
include a set of control variables as discussed in the text and are estimated using sample weights.
Standard errors are clustered at the individual level and reported in parenthesis. The number of
observations is reported in brackets. The sign * represents statistical significance at 10 percent.
28
Table 8. The effect of the MLDA on the probability of using a birth control
method: Robustness checks
Quadratic
Birth
control
Condom
Exclude 2000
0.023
(0.036)
[5914]
Exclude married
Cubic
Pill
Other
method
Birth
control
Condom
Pill
Other
method
0.054
(0.042)
[5908]
-0.041
(0.041)
[5914]
0.046
(0.039)
[5914]
-0.024
(0.052)
[5914]
0.085
(0.062)
[5908]
-0.056
(0.060)
[5914]
0.022
(0.057)
[5914]
0.011
(0.033)
[6938]
0.088
(0.040)**
[6930]
-0.034
(0.039)
[6938]
0.079
(0.037)**
[6938]
-0.017
(0.049)
[6938]
0.113
(0.060)*
[6930]
-0.043
(0.057)
[6938]
0.037
(0.055)
[6938]
Female
-0.036
(0.048)
[3588]
0.080
(0.055)
[3587]
-0.067
(0.054)
[3588]
0.051
(0.051)
[3588]
-0.125
(0.070)*
[3588]
0.068
(0.083)
[3587]
-0.033
(0.081)
[3588]
-0.083
(0.074)
[3588]
Male
0.051
(0.046)
[3744]
0.088
(0.054)*
[3737]
0.013
(0.053)
[3744]
0.085
(0.049)*
[3744]
0.060
(0.067)
[3744]
0.143
(0.079)
[3737]
-0.042
(0.077)
[3744]
0.124
(0.072)*
[3744]
0.015
(0.037)
[5643]
0.055
(0.043)
[5637]
-0.064
(0.042)
[5643]
0.060
(0.040)
[5643]
-0.021
(0.053)
[5643]
0.086
(0.064)
[5637]
-0.072
(0.061)
[5643]
0.039
(0.059)
[5643]
Female / Exclude 2000
-0.017
(0.052)
[2850]
0.029
(0.060)
[2849]
-0.073
(0.058)
[2850]
0.041
(0.056)
[2850]
-0.111
(0.075)
[2850]
0.037
(0.090)
[2849]
-0.054
(0.085)
[2850]
-0.086
(0.081)
[2850]
Male / Exclude 2000
0.061
(0.051)
[3064]
0.080
(0.059)
[3059]
-0.014
(0.058)
[3064]
0.054
(0.054)
[3064]
0.053
(0.073)
[3064]
0.130
(0.085)
[3059]
-0.056
(0.083)
[3064]
0.115
(0.078)
[3064]
Exclude married /
Exclude 2000
Notes: Results from models that contain a quadratic or cubic polynomial of age that is fully
interacted with a dummy variable indicating being 21 or older are reported. All regressions
include a set of control variables as discussed in the text and are estimated using sample weights.
Standard errors are clustered at the individual level and reported in parenthesis. The number of
observations is reported in brackets. The signs ** and * represent statistical significance at 5 and
10 percent, respectively.
29
Figure 1. The effect of the MLDA on alcohol consumption
A. Full Sample
2. Binge
No. of days that the respondent consumed alcohol
2
3
4
5
6
No. of days that the respondent engaged in binge drinking
1
1.5
2
2.5
1. Alcohol
-732
-366
0
366
Number of days before or after the MLDA
732
-732
-366
0
366
Number of days before or after the MLDA
732
B. Had sex in the past 4 weeks
4. Binge
No. of days that the respondent consumed alcohol
3
4
5
6
7
No. of days that the respondent engaged in binge drinking
1
1.5
2
2.5
3
3. Alcohol
-732
-366
0
366
Number of days before or after the MLDA
732
-732
-366
0
366
Number of days before or after the MLDA
Notes: Means of the outcome variables for each 30 day interval are plotted. The solid lines are a
quadratic polynomial fitted on individual observations on either side of the age 21 cutoff without
any control variables as reported in Table 2.
30
732
Figure 2. The effect of the MLDA on sexual activity
2. No. of times had sex
3
.4
.45
Prob. of having sex
.5
.55
No. of times that the respondent had sex
4
5
6
.6
1. Sex
-732
-366
0
366
Number of days before or after the MLDA
732
-732
-366
0
366
Number of days before or after the MLDA
Notes: Means of the outcome variables for each 30 day interval are plotted. The solid lines are a
quadratic polynomial fitted on individual observations on either side of the age 21 cutoff without
any control variables as reported in Table 3.
31
732
Figure 3. The effect of the MLDA on risky sexual behaviors
2. Pregnancy
.1
.06
Prob. of STD risk
.15
.2
Prob. of pregnancy risk
.08
.1
.12
.14
.25
1. STD
-732
-366
0
366
Number of days before or after the MLDA
732
-732
-366
0
366
Number of days before or after the MLDA
Notes: Means of the outcome variables for each 30 day interval are plotted. The solid lines are a
quadratic polynomial fitted on individual observations on either side of the age 21 cutoff without
any control variables as reported in Table 4.
32
732
Figure 4. The effect of the MLDA on the probability of using birth control
2. Condom
.6
.4
Prob. of using birth control
.65
.7
.75
Prob. of using condom
.45
.5
.55
.6
.8
1. Birth control
-732
-366
0
366
Number of days before or after the MLDA
732
-732
732
4. Other method
.2
.2
Prob. of using birth control pill
.25
.3
.35
Prob. of using other birth control method
.25
.3
.35
.4
.45
.4
3. Pill
-366
0
366
Number of days before or after the MLDA
-732
-366
0
366
Number of days before or after the MLDA
732
-732
-366
0
366
Number of days before or after the MLDA
Notes: Means of the outcome variables for each 30 day interval are plotted. The solid lines are a
quadratic polynomial fitted on individual observations on either side of the age 21 cutoff without
any control variables as reported in Table 5.
33
732