1) The document examines how zero tolerance drunk driving laws for those under 21 have impacted alcohol consumption behaviors using survey and sales data from 1984-2001. 2) By building an econometric model and controlling for demographics, state, year and trends, the author finds zero tolerance laws reduced heavy episodic drinking by 15-17% for males under 21 but results were mixed for females. 3) Additional evidence showed the laws reduced beer sales by 1.6% and arrests for public drunkenness in males aged 18-20 by 17%, indicating decreases in drinking among the targeted age group.

1. Rob Hollon
How do Zero Tolerance Drunk Driving Laws Work?
Background
An econometric model was built in this paper to examine the effect of zero tolerance laws
and how it is related to alcohol related behaviors. The paper opens by stating that policy analysts
have proposed many ways to reduce underage drinking and driving due to the high number of
vehicle crashes that pertain to it. Zero Tolerance (ZT) Laws began in 1983 and through
legislation all states implemented these laws by July of 1998. The ZT Laws make it illegal for
drivers under 21 to have any measurable BAC and results in immediate suspension of license
and fines. Research shows that these laws have reduced alcohol related crashes for drivers under
21. The author then states that the ZT Laws have made the impact that was expected however he
argues that it is less clear how behavior towards alcohol has changed for this age group. For
instance young adults might respond by abstaining from drinking, drinking more moderately,
plan for a designated driver, increasing consumption while at home, or drinking and using public
transportation.
Data
Alcohol consumption and drunk driving behavior is reported for young adults, 18 through
20, for the years 1984-2001 in the Behavioral Risk Factor Surveillance System (BRFSS). The
comparison group contains individuals between 22 and 24 who are not affected by the laws but
are similar to the treatment group. Sample period is 1984-2001 with information from every state
since 1993 and has a total of 49,076 observations.
By building a difference-in-difference-in-difference framework, the author can control
for many other unobservable variables. A dummy variable for having any alcohol during the past
month is created which is named Drinker and is represented by a 1 if yes and 0 otherwise. The
next dummy variable is named Heavy Episodic Drinking and is coded as a 1 if the individual has
had five or more drinks in one sitting at least once in the past month. The third dummy variable
is Drunk Driving and is coded as a 1 if the individual reports that they have driven when they
might have had too much to drink. By multiplying the dummy variables together, meaning
anyone that indicates that they have 1’s for all the above dummy variables, a new group of
interest in created. Everyone that has a 0 for any of the above dummy variables will be netted out
so the effect of Zero Tolerance Laws can be looked at directly for those that have driven drunk.
The author then goes on and adds fixed effects to every outcome variable. This would
control for unobserved year and season effects, state effects, and specific components related to
alcohol use. Also included is a state trend which he states controls for a variety of other variables
the might determine alcohol behavior such as religious sentiment.
Model
The first regression includes both males and females in the 18 to 20 age group and is
modeled by:
𝑌𝑖𝑠𝑡 = 𝛼 + 𝛽1 𝑋𝑖𝑠𝑡 + 𝛽2 𝑍𝑒𝑟𝑜 𝑇𝑜𝑙𝑒𝑟𝑎𝑛𝑐𝑒 𝑠𝑡 + 𝑢 𝑚 + 𝑤𝑠 + 𝑣𝑡 + ( 𝑡𝑟𝑒𝑛𝑑 ∗ 𝑤𝑠) + 𝜖
𝑌𝑖𝑠𝑡 is the variety of drinking behaviors
𝑋𝑖𝑠𝑡 is a variety of demographic information
𝑤𝑠 is the state fixed effect

2. Rob Hollon
𝑣𝑡 is the year fixed effect
𝛽2 𝑍𝑒𝑟𝑜 𝑇𝑜𝑙𝑒𝑟𝑎𝑛𝑐𝑒 𝑠𝑡 is the variable of interest
The model changes slightly when modeling the slightly older age group, most
importantly the variable for zero tolerance becomes:
𝛽4(𝑍𝑒𝑟𝑜 𝑇𝑜𝑙𝑒𝑟𝑎𝑛𝑐𝑒 ∗ 𝑈𝑛𝑑𝑒𝑟 21)𝑖𝑠𝑡
The coefficient is the marginal effect of the ZT Laws on the treatment group relative to
the control group. The treatment group is not impacted by the policy change and is close in age
which makes it a good comparative model.
Results
The first model that includes both males and females aged 18-20 finds that ZT Laws
reduce unconditional heavy episodic drinking by about 17%, significant at the 1% level. In
addition heavy episodic drinking is decreased by about 15%, significant at the 1% level.
Model 2 considers men and women separately and controls for the same variables as the
previous model. The results confirm reductions in heavy episodic drinking for both sexes.
Notably different though is that ZT laws reduce the number of drinks consumed in the previous
month for men (significant at 5%), yet the women have a positive coefficient (not significant).
The author attributes this on the perception of “too much to drink” when the ZT laws took effect.
In the third model the DDD framework is used to control for unobservable shocks in a
given state during a given year. The interaction term is negative and significant when comparing
different age groups [20 vs. 22], [19, 20 vs. 22, 23] indicating that the policy change decreased
heavy drinking in males under the age of 21. The main finding is that the Zero Tolerance laws
did reduce heavy episodic drinking and the number of drinks by males under 21. Results for
females are mixed but are positive and small in magnitude.
Comparisons
A previous paper documented that the largest reductions in fatalities associated with ZT
laws take place four to eight years after the adoption of the policy. In response the author uses
shipments of alcohol by state and year from 1980-2000. After transforming the data into a log of
beer sales per capita, the ZT laws reduced beer sales by about 1.6% (significant at 10% level).
The author also looked at the number of incidences of public drunkenness for males aged 18-20
from 1980-1997. He argues that reduction in heavy episodic drinking will reduce the number of
men arrested and finds that ZT laws reduced public drunkenness by about 17% (significant at
1%).
Discussion
His evidence shows that drinking has decreased among males in the targeted age group.
A few things that strike me, the first being that the greatest reductions in fatalities come many
years after the ZT laws were put into effect. If this were the case then making lagged variables
would seem to pinpoint where specifically this happened in each state. Also reductions in the
number of drinks may have been attributable to conditions not inside the model. It seems odd
that men reduce drinking but women stay about the same. The author’s explanation is non-
satisfying and he should have thought of other reasons. It might be that women drink less than

3. Rob Hollon
men anyways, so after the ZT laws were put into effect the women that did already drink chose
not to change their behavior. Also relying on survey data is always an issue although collecting
observational data would not be feasible. Lastly, there might have been a cultural shift during
this time in which drunk driving became widely opposed. By looking at macro level data, the
author might gain more insight into state and countrywide trends that he is accounting for.
Overall his data is quite thorough and includes a lot of methods that can be applied to time trend
data.