THE DETERMINANTS OF DRUNK-DRIVING RESTRICTIONS
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Egle McCaffrey
FALL
2013
© 2013
Egle McCaffrey
ALL RIGHTS RESERVED
ii
THE DETERMINANTS OF DRUNK-DRIVING RESTRICTIONS
A Thesis
by
Egle McCaffrey
Approved by:
__________________________________, Committee Chair
Craig Gallet, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
iii
Student: Egle McCaffrey
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Kristin Kiesel, Ph.D.
Department of Economics
iv
___________________
Date
Abstract
of
THE DETERMINANTS OF DRUNK-DRIVING RESTRICTIONS
by
Egle McCaffrey
Although many previous studies have addressed the impact of drinking and driving laws
on motor vehicle related deaths, less attention has been paid to what determines such
laws. Accordingly, this study examines factors affecting the probability that a state adopts
6 different restrictions on drinking and driving. Specifically, using a panel of state-level
annual data corresponding to the 50 U.S. states for the 1985-2002 period, we estimate
linear probability and random effects Probit models, paying particular attention to the
endogeneity of the motor vehicle fatality rate. Results reveal strong nation-to-state and
state-to-state policy diffusion across different drunk-driving laws.
_______________________, Committee Chair
Craig Gallet, Ph.D
_______________________
Date
v
ACKNOWLEDGEMENTS
I would like to thank Professor Craig A. Gallet for his strong support and assistance in
writing this thesis, and Professor Suzanne O’Keefe for helping me with the empirical
analysis. I would also like to thank Kristin Kiesel for serving as my graduate coordinator.
Lastly, I thank my husband Geoffrey McCaffrey for his patience, understanding, and
doing most of the household chores on his own, so I can complete this research project.
vi
TABLE OF CONTENTS
Page
Acknowledgements ............................................................................................................ vi
List of tables ....................................................................................................................... ix
List of figures ...................................................................................................................... x
Chapter
1. INTRODUCTION ........................................................................................................ 1
2. LITERATURE REVIEW ............................................................................................. 3
2.1
Alcohol-Control Policies ....................................................................................... 3
2.2
Tobacco-Control Policies..................................................................................... 11
2.3
A Limitation of the Existing Literature ............................................................... 16
3. DATA AND EMPIRICAL MODEL .......................................................................... 17
3.1
Data and Empirical Model ................................................................................... 17
3.2 Econometic Issues ................................................................................................ 25
4. EMPIRICAL RESULTS ............................................................................................. 29
4.1
Open Container Law Results (Exogenous) .......................................................... 29
4.2
Consumption of Alcohol in a Vehicle Law Results (Exogenous) ....................... 32
4.3
Dram-Shop Law Results (Exogenous)................................................................. 34
4.4
Preliminary Breath Test Law Results (Exogenous) ............................................. 36
4.5
BAC08 Law Results (Exogenous) ....................................................................... 38
4.6
Zero Tolerance of Underage Drinking Law Results (Exogenous) ...................... 40
vii
4.7
Motor Vehicle Fatality Rate Results .................................................................... 42
4.8
Open Container Law Results (Endogenous) ....................................................... 45
4.9
Consumption of Alcohol in a Vehicle Law Results (Endogenous) .................... 46
4.10 Dram-Shop Law Results (Endogenous).............................................................. 48
4.11 Preliminary Breath Test Law Results (Endogenous) .......................................... 49
4.12 BAC08 Law Results (Endogenous) .................................................................... 51
4.13 Zero Tolerance of Underage Drinking Law Results (Endogenous) ................... 53
5. CONCLUSION ........................................................................................................... 55
Appendix ........................................................................................................................... 56
References ......................................................................................................................... 57
viii
LIST OF TABLES
Tables
Page
Table 1: Variable Definitions and Descriptive Statistics ................................................. 17
Table 2: Open Results ....................................................................................................... 30
Table 3: AntiCon Results .................................................................................................. 33
Table 4: Dram Results ...................................................................................................... 35
Table 5: Prelim Results ..................................................................................................... 37
Table 6: BAC08 Results ................................................................................................... 39
Table 7: ZeroTol Results .................................................................................................. 41
Table 8: First-Stage Results for Endogeneity Correction ................................................. 43
Table 9: Open Results with Endogeneity Correction ....................................................... 46
Table 10: AntiCon Results with Endogeneity Correction ................................................ 47
Table 11: Dram Results with Endogeneity Correction ..................................................... 49
Table 12: Prelim Results with Endogeneity Correction ................................................... 50
Table 13: BAC08 Results with Endogeneity Correction .................................................. 52
Table 14: ZeroTol Results with Endogeneity Correction ................................................. 54
Table 15: Data Sources ..................................................................................................... 56
ix
LIST OF FIGURES
Figures
Page
Figure 1: Drunk-driving Laws .......................................................................................... 20
Figure 2: Average Real Beer Tax ..................................................................................... 22
Figure 3: Average Real Gas Tax ....................................................................................... 23
Figure 4: Motor Vehicle Death Rates (1985-2002) .......................................................... 24
x
1
Chapter 1
INTRODUCTION
Drunk-driving has become a big problem in the United States. According to the
Centers for Disease Control and Prevention (CDC), one in three vehicle crashes involves
an intoxicated driver and there are 30 fatalities a day due to drunk-driving. Alcoholrelated vehicle accidents amount to more than $51 billion annually (CDC, 2013). Due to
high economic costs associated with driving under influence, since the 1970’s the U.S.
Government has sought to decrease fatality rates from drunk-driving by reducing alcohol
abuse. For instance, in 1983 Congress enacted the Alcohol Traffic Safety Act that
provided financial incentives for states to enforce more stringent drunk-driving
restrictions.1
As discussed in the next chapter, economists have examined the influence of
various control policies that are intended to reduce the ill-effects associated with
consumption. However, with the exception of a few studies of tobacco-control laws,
which have analyzed the determinants of smoking restrictions, there is no research on
what determines the adoption of drunk-driving laws. Therefore, seeking to fill this gap
and assist politicians in their policy making, this study examines the determinants of U.S.
state-level laws on drinking and driving. More specifically, using state-level annual data
that corresponds to the 50 U.S. states for the 1985-2002 period, we estimate a series of
1
By the end of 1980s, a few states prohibited open containers of alcohol in the passenger sections of motor
vehicles, required minimum jail sentences or community service for driving under the influence, authorized
lawsuits against alcohol servers (referred to as dram shop laws), mandated license sanctions for drivers
refusing to submit to alcohol testing (referred to as implied consent laws), endorsed administrative per se
laws that require license suspension if a driver's blood alcohol content exceeds a specified level, and
sanctioned police to administer roadside breath tests for alcohol without the assistance of medical staff.
2
linear probability and Probit regressions, which tie the probability that a state adopts a
particular drunk-driving law to a number of factors. For instance, amongst other factors,
this study includes as determinants of drunk-driving restrictions per capita income, an
index of conservatism, gas and beer tax rates, the motor vehicle fatality rate, whether or
not the governor of each state is a Democrat, and Federal legislation. Briefly, we find
there is a strong nation-to-state and state-to-state diffusion of driving under influence
restrictions.
The remaining chapters in the thesis are organized as follows. Chapter 2 reviews
the literature relevant to this topic. Chapter 3 presents the data and empirical model,
while Chapter 4 presents the empirical results. The thesis concludes with a summary in
Chapter 5.
3
Chapter 2
LITERATURE REVIEW
Many economists have examined policies which seek to reduce the problems
associated with the consumption of alcohol and tobacco. While many studies have
examined the impact of tobacco- and alcohol-related laws on the demand for tobacco
and/or alcohol, as well as related harms, few studies have addressed what determines the
adoption of such laws. Indeed, relevant to this thesis, the literature is silent on what
factors influence the adoption of laws directed towards reducing drunk-driving. This
chapter reviews the varied literature and discusses its data, methodology and findings.
2.1
Alcohol-Control Policies
There are many studies that have analyzed the effectiveness of alcohol-control
policies to reduce alcohol abuse and alcohol-related harm. Cook and Moore (2010)
surveyed the economics literature on the effects of alcohol consumption laws on drinking
in the United States and other countries. Interestingly, the authors pointed out that the
trend of teenage drinking over time closely mimics that of adult drinking. Surveying the
literature, which utilizes a variety of data sets, Cook and Moore found the most common
policy tools that have been used to reduce alcohol use are price increases (via raising
excise taxes) and a variety of restrictions on the availability of alcohol. More specifically,
they discovered that higher alcohol prices have the greatest impact on heavy drinking,
alcohol-related motor vehicle fatalities, and family violence. In addition, such alcoholcontrol policies as minimum purchase age requirements, dram shop laws, and restrictions
4
on alcohol advertising are most effective at reducing alcohol abuse.2 Cook and Moore
concluded that even though alcohol-control policies are effective at reducing alcohol
abuse, the net benefits of government interventions need attention. Specifically, they
pointed out that current alcohol taxes across the states are too low, and that policies that
directly reduce alcohol-related harm also reduce the ideal tax. Therefore, more research is
needed to understand the alcohol-control measures.
Similar to Cook and Moore, Chaloupka et al. (2002) also reviewed studies that
examined the effects of price increases on alcohol demand and its adverse consequences.
In their summary of studies relying on the use of survey data, they found that alcohol
prices and taxes influence alcohol consumption, drinking and driving, motor vehicle
crashes, injuries, and crime. As they discussed, most economists rely on estimating the
price elasticity of demand for alcohol (used to assess the sensitivity of alcohol
consumption to changes in price) to infer the impact of price on various consequences of
excessive alcohol consumption.3 Interestingly, the authors pointed out that real alcohol
prices have declined in the past 50 years as a result of relatively small increases in excise
taxes over this period. Thus, given the decline in real alcohol prices over time, this could
contribute to increases in alcohol abuse over the past 50 years, ceteris paribus.
Nonetheless, given that studies often use the tax on beer as a proxy for the price of
alcohol, further analysis is needed to determine if results are sensitive to how price is
measured.
2
Dram shop laws refer to laws which hold the sellers of alcohol products (i.e., bars, liquor stores, etc.)
liable for selling alcohol to visibly intoxicated individuals who then injure others as a result of their
consumption of alcohol (e.g., deaths and injuries caused by drunk drivers).
3
Gallet (2007) reports the average price elasticity of demand for alcohol in the literature is -0.54, which
suggests price increases have a modest influence on alcohol consumption.
5
In addition to examining the influence of price on alcohol consumption, a number
of studies have analyzed the influence on alcohol abuse of placing limits on alcohol outlet
density. For instance, Campbell et al. (2009) surveyed existing research on the regulation
of the density of retail stores selling alcohol and its effects on excessive alcohol
consumption and related harm. Most of the studies included in their review found that
alcohol outlet density is positively related to alcohol consumption, injuries, crimes and
violence, as greater outlet density increases aggressive behavior tied to excessive alcohol
consumption. Furthermore, some of the reviewed studies found that privatization of
alcohol sales (as compared to beverage control states which effectively monopolize the
sale of alcohol), alcohol bans, and changes in license agreements have impacts on outlet
density. Although Campbell et al. (2009) concluded that laws to limit outlet density could
reduce alcohol consumption, more research is needed on this topic. Specifically, they
pointed out that it is important to evaluate what determines whether or not such laws are
adopted, which in turn could give insight into why policies differ across communities.
Although Cook and Moore (2010) suggest overall alcohol consumption among
adults and teens follow similar trends, there are many economists who have focused
attention on alcohol issues more prevalent among youths. For instance, Chaloupka and
Wechsler (1996) examined the impacts of beer prices, alcohol availability, and drinkingdriving laws on the incidence of heavy or binge drinking among youths and young adults.
Their study was based on data from a nationally representative survey, which included a
sample of 17,592 students at 140 different universities across the United States. Using
ordered and dichotomous Probit methods, the authors estimated a total of 12 regressions
6
for different samples, such as females, males, underage and 21 and older. They found that
an increase in beer price affects underage and binge drinking among female college
students, while male students’ drinking is not affected by price. In addition, Chaloupka
and Wechsler examined results from a hypothetical policy of increasing the tax on beer to
a comparable levy placed on distilled spirits, and found that such an increase would have
resulted in a 15% reduction in underage female drinking and a 20% reduction in female
binge drinking. However, the authors did point out that their results may be somewhat
questionable in that there could be biases in their chosen price measures. More
specifically, their use of the local retail alcohol price may not be a good proxy for the
price actually paid by students.4
Chaloupka and Wechsler (1996) also examined the influence of drunk-driving
laws on youth drinking behavior. They found that drunk-driving laws significantly reduce
overall male student drinking, as well as binge drinking. These laws also reduce overall
female student drinking, but do not affect female binge drinking. In addition, they found
that campus lifestyle has an important influence on drinking and binge drinking among
students. As Chaloupka and Wechsler (1996) acknowledge, though, they did not examine
whether campus lifestyle choices are endogenously determined with alcohol behavior
choices, which could affect the findings.
Another study of youth drinking by Grossman et al. (1994) summarized the
existing research on how alcohol prices and taxes affect alcohol consumption, the motor
vehicle accident death rate, and college completion rates. Using various data sets that
4
For instance, many students obtain alcohol in places where they do not have to pay for it, like social
gatherings where alcohol is free or sold at discounted prices to attract students.
7
span the period from 1974 through 1989, these studies found that increases in the price of
alcohol reduce alcohol consumption and motor vehicle accident mortality. In addition, an
increase in the price of alcohol increases college completion rates. Lastly, the authors
concluded there is much evidence that raising excise taxes on alcohol, as opposed to
raising the legal drinking age across states, will have the greatest influence on drunkdriving and other harms associated with alcohol abuse.
Various studies have examined the influence of different drunk-driving laws on
motor-vehicle fatalities. For example, using panel data from 48 states for the period of
1982 through 1988, Chaloupka et al. (1993) estimated a logistic specification of the
effects of major drunk-driving laws, minimum drinking age laws, and alcohol taxes on
drunk-driving related harm. In addition to other variables, the authors included as
regressors real per-capita personal income, the unemployment rate, miles driven per
vehicle, percent of drivers who are youths, seat-belt use laws, and religious affiliation. To
measure drunk-driving related harms, they constructed alcohol-involved-driver fatality
rates using information on the blood-alcohol concentration (BAC) found in drivers that
were killed in crashes. To avoid collinearity and omitted-variable bias issues, Chaloupka
et al. (1993) estimated several specifications with different sets of drunk-driving laws.
For instance, they used minimum legal drinking age, preliminary breath test, open
container, no plea bargaining, mandatory minimum penalties for driving under the
influence, and dram shop laws as indicators of the various policies related to drunkdriving. The different estimated specifications revealed that the beer tax, severe one-year
administrative license actions, minimum legal drinking age, preliminary breath test, and
8
dram shop laws were most effective in deterring drunk driving. For example, an increase
in the beer tax to its real value in 1951 would reduce motor vehicle fatalities by 11%.
However, similar to Chaloupka and Wechsler (1996), Chaloupka et al. (1993) did not
consider the possibility of endogeneity concerns. More specifically, if the alcoholinvolved-driver fatality rate and drunk-driving laws are endogenously determined, such
that the alcohol-involved-driver fatality rate affects the adoption of drunk-driving laws,
then failing to address this could lead to biased results.
In another study, Ruhm (1996) examined how beer taxes and different alcoholcontrol policies affect motor vehicle fatality rates. Using data for 48 states over the 1982
through 1988 time period, he estimated a fixed-effect logistic specification that included
preliminary breath test laws, dram shop laws, administrative per se laws, and minimum
drinking age laws as dichotomous independent variables. In addition, the econometric
model included the percent of the population that resides in dry counties, drivers between
the ages 15 and 24 years old, miles driven per vehicle, per capita income, and the
unemployment rate as factors that may influence traffic mortality. In contrast to the
Chaloupka et al. (1993) findings, Ruhm discovered that most of the regulations,
excluding dram shop and administrative per se laws, had little or no impact on traffic
mortality rates. He explained that such different findings from the previous literature
could be due to his detailed attention to omitted variable bias. For instance, when Ruhm
included per capita income, the unemployment rate, and all four drunk-driving laws in
the same regression, the previously predicted reduction in fatality rates from raising the
9
minimum drinking age from 18 to 21 becomes statistically insignificant.5 Yet he also
found, similar to the existing literature, that beer taxes impact fatality rates. Nonetheless,
relevant to this thesis, Ruhm did not consider the possibility that drunk-driving laws are
endogenously determined, for it could be that motor vehicle fatalities affect the passage
of drunk-driving laws. Such an oversight could affect the reliability of his results.
Kenkel (1993) also explored how deterrence and alcohol-control policies affect
drunk-driving. His study was based on self-reported data obtained from the 1985 national
health survey, which included a sample of 12,000 males and 16,000 females across the
United States. Using a Tobit model, Kenkel found that a 10% increase in the price of
alcohol would reduce drinking and driving by 8% in women and 7% in men. Among
people of ages 21 and younger, the price effect was greater, as a 10% increase in price
would reduce drunk-driving by 13%. In addition, his results showed that drunk-driving
can be reduced by 20% when states adopt mandatory jail terms for first-time offenders,
administrative license suspensions, preliminary breath tests, sobriety checkpoints, and
prohibitions on plea bargaining in drunk-driving cases (Kenkel 1993). However, Kenkel
argued that alcohol-control laws are more costly to implement than deterrence policies,
and thus it is not only imperative to understand which policies are most effective at
reducing traffic fatalities but also which policies yield the highest net benefit to society.
Understanding the net benefit of such policies can provide insights into what influences
their adoption.
5
However, the author did not explain his rationale for choosing to estimate fixed-effects models. It could
be, for instance, that estimating fixed-effects models with state and/or time dummies, as well as
dichotomous drunk-driving laws, could result in severe collinearity, thus masking the laws’ true effects.
10
In another study, Mast et al. (1999) used fixed effects models to also examine
factors that contribute to reductions in motor vehicle fatality rates for the years 1984 to
1992. More specifically, the authors’ goal was to examine the extent to which the beer
tax reduces traffic fatalities. Contrary to the literature, their results showed that the beer
tax has no effect on the overall motor vehicle fatality rate. Yet, it does have a negative
effect on the fatality rate for drunk drivers involved in nighttime or single-vehicle
crashes.6 Interestingly, though, the effects of the beer tax do depend on (i) whether or not
state fixed effects are included in the regression model and (ii) whether religious
affiliation is controlled for in the regressions.
Estimating a series of Negative Binomial models, Dee and Evans (2001)
examined the impact of key state policies on traffic fatalities among teens and young
adults. Their models were estimated with annual state-level data for 48 states for the
years 1977 through 1992. More specifically, they reviewed the existing literature, and
then estimated the impact on motor vehicle fatality rates of seat-belt laws, limits on youth
access to alcohol, laws that deter drunk-driving, and highway speed limits. The authors
noted that the literature has tended to find that motor vehicle fatality rates are most
sensitive to seat belt laws, illegal per se laws, blood alcohol concentration laws, zero
tolerance laws, administrative license revocation laws, and dram shop laws. In their
statistical analysis, which in addition to various drinking and driving laws included
several socio-economic factors (e.g., the unemployment rate and real per capita income)
6
In support of their findings, Mast et al. pointed out that several studies of beer demand find beer taxes
have small impacts on beer consumption. Interestingly, when Mast et al. controlled for policies directed
towards the consumption of beer, beer taxes became a more prominent determinant of traffic fatalities.
This led them to conclude that it is important for studies to measure the impact of the full price (i.e.,
monetary price plus restrictions) on motor vehicle fatality rates.
11
as determinants of motor vehicle fatality rates, they found that among the population in
the 16-17 age range (18-19 age range) seat belt laws reduced fatality rates by 8 % (10 %).
In addition, Dee and Evans found that minimum legal drinking age laws, illegal per se
laws, blood alcohol concentration laws, and administrative license revocation laws had
significant impacts on motor vehicle fatality rates. However, 65-MPH speed limit laws,
zero-tolerance laws, dram shop laws, and mandatory jail time for drunk-driving did not
have statistically significant effects on motor vehicle fatalities.
2.2
Tobacco-Control Policies
Since alcohol-control policies are often viewed in a similar light as tobacco-
control policies (i.e., the intent being to reduce the ill-effects associated with
consumption), it is important to review some of the literature related to tobacco-control.
In an early study, Chaloupka and Saffer (1992) studied how clean indoor air laws (which
restrict cigarette smoking in public and private places) affect the demand for cigarettes.
Using annual data from 1975 through 1985 for 50 states, they estimated a series of
demand equations. More specifically, they tested the impact of a number of factors,
including amongst others, public and private place clean indoor air laws, price, income,
religious affiliation, and cigarette smuggling on the demand for cigarettes. Interestingly,
the authors used a Wu-Hausman test to check for possible endogeneity of clean indoor air
laws, which they found could not in fact be treated as exogenous. Thus, they
simultaneously estimated a Probit regression to examine what determines whether or not
a state adopts a clean indoor air law, for which they found amongst other factors that
tobacco production, long distance smuggling, and population voting behavior are key
12
determinants of the decision of a state to adopt a clean indoor air law. Controlling for the
endogeneity issue, the authors further found that the adoption of public place clean indoor
air laws significantly reduces cigarette demand, while clean indoor air laws in private
places have no significant effect on cigarette demand. Thus, the authors concluded that
when examining the influence of tobacco-control policies on the demand for tobacco
products it is important to address potential endogeneity of such policies.
Although no study to date has examined why some states are more aggressive
than others when it comes to alcohol-control efforts, there are several studies that have
examined the determinants of tobacco-control laws. Since these studies play an important
role in this thesis, it is worthwhile to review this area of research. Consider, for instance,
the study by Gallet et al. (2006). This study examined what factors affect the probability
that a state chooses to adopt clean indoor air laws in a number of places (i.e., public
places, government buildings, private work places, schools, health care facilities, and
restaurants), utilizing data from a panel of U.S. states over the 1980-2000 period. They
tied the decision of a state to adopt a particular smoking ban on many factors, such as per
capita cigarette consumption, state-level cigarette tax rates, the prevalence of
conservative lawmakers, the percentage of the population that resides in a metropolitan
area, per capita income, and the value of tobacco production in each state.
As the authors explain in their paper, there are a few options when it comes to
estimating a dichotomous choice model within a panel data framework, namely fixed
effects Logit and random effects Probit. However, the fixed effects Logit model suffers
from (i) not being able to include time-invariant regressors and (ii) results being sensitive
13
to the inclusion of variables with little variation. And thus, Gallet et al. (2006) utilized
random effects Probit in their paper.
Next, Gallet et al. accounted for a possible endogeneity problem. Namely, since
studies find smoking bans impact cigarette demand, including cigarette consumption as a
determinant of the adoption of smoking bans leads to concerns over its endogeneity. To
control for this, the authors used a two-stage approach, where in the first stage cigarette
consumption is regressed on the exogenous variables in the model, as well as a set of
instrumental variables. Following Rivers and Vuong (1988), the residual from the firststage regression is then included as an additional variable in the random effects Probit
regression. Not only does this control for endogeneity concerns, but it has the added
benefit of allowing us to test if endogeneity exists by simply testing the significance of
the coefficient of this residual term (i.e., if the coefficient is significantly different from
zero, then cigarette consumption is endogenous; if the coefficient is insignificantly
different from zero, then cigarette consumption is exogenous).7 Briefly, the authors not
only found evidence that cigarette consumption is endogenous, but amongst other results
they found a lower probability of adopting smoking bans in states with lower per capita
income and higher tobacco production. Also, the role of the cigarette tax rate, be it a
policy complement or a substitute, depended on whether or not endogeneity issues were
addressed.
In a similar study, Gallet et al. (2009) analyzed the determinants of 9 different
state-level smoking restrictions among youths, utilizing data from 50 states for the period
7
Gallet et al. (2006) explored whether cigarette taxes are endogenously determined as well.
14
of 1991 through 2000. Specifically, using the random effects Probit Model, they
hypothesized the probability that a state will adopt a law which restricts youth access to
tobacco products is dependent upon a few factors. These factors included cigarette
consumption, the cigarette tax, how conservative are lawmakers, the percentage of the
population living in a metropolitan area, per capita income, the percentage of the
population that is young, and the cancer mortality rate. Utilizing a similar two-stage
procedure as Gallet et al. (2006), they found for a majority of the 9 youth smoking
restriction laws that cigarette consumption was endogenous, while cigarette taxes were
largely exogenous. When addressing endogeneity concerns, their results showed that the
adoption of youth access restrictions was most sensitive to per capita income, the
percentage of the population that is young, tobacco production, the percentage of the
population living in a metropolitan area, how conservative are lawmakers, and cancer
mortality. In contrast to their prior study, the cigarette tax rate played a much smaller role
in the determination of youth access restrictions.
In another study, Shipan and Volden (2006) examined whether or not there are
spillover effects concerning the adoption of antismoking policies. Specifically, they
investigated the extent to which there is vertical diffusion of policies (i.e., from the citylevel to the state-level and from the national-level to the state-level), as well as horizontal
diffusion of policies (i.e., from one state to the next). Utilizing state-level panel data, they
examined this issue in the context of 3 antismoking policies: smoking restrictions in
government buildings and restaurants, as well as restrictions on out-of-packet sales of
cigarettes. Estimating a series of Probit and Logit regressions, while not controlling for
15
panel data nor endogeneity issues, they found the adoption of such policies to be sensitive
to a number of factors. In particular, they found that local-to-state, state-to-state and
national-to-state diffusion indeed happens, and state politics are very important to this
relationship.8
Taking a very different approach, Boyes and Marlow (1996) obtained data from a
survey of 764 randomly selected individuals from San Luis Obispo in 1992, examining
their preferences towards smoking restrictions. Using a Logit model, they treated the
individual demand for smoking bans as a function of how often an individual visited a
restaurant or bar, whether an individual is a smoker or smoked in the past, whether an
individual is a local resident, as well as the gender, age, and education of the individual.
They found that the frequency of visits to restaurants and bar, local residency, age, and
education do not influence the demand for smoking restrictions. Yet an individual who is
an ex-smoker significantly supports the adoption of smoking bans in bars. In addition,
males have lower support for smoking bans than do females.
As Boyes and Marlow discuss, however, a limitation of these results is that they
do not address whether or not surveyed individuals believe that the private market could
deal effectively with the smoking issue. Accordingly, they re-estimated their model by
including a control variable to measure whether or not surveyed individuals believed that
(prior to the imposition of smoking bans) designating smoking or non-smoking sections
8
For instance, local-to-state diffusion depends on the degree of legislative professionalism and the strength
of health care lobbyists in the state. In addition, the proportion of neighbor-states with similar restrictions is
an important determinant of all 3 policies, implying that states are influenced by the actions of their
neighbors. Also, as evidence of national-to-state diffusion, the Synar Amendment, which threatens to
withhold block grants to states that do not seek to limit youth access to tobacco, does induce states to adopt
anti-smoking policies.
16
in restaurants and bars was sufficient to allocate air space. For this regression, they found
that individuals believed this was indeed sufficient to address smoking issues, and thus
private owners were able to internalize the smoking externality by allocating air space. 9
2.3
A Limitation of the Existing Literature
As Section 2.1 discusses, Chaloupka et al. (1993) and Ruhm (1996) did not
address a potential endogeneity issue. More specifically, it is plausible that motor vehicle
deaths is an endogenous variable, meaning that adoption of drunk-driving laws could
affect the motor vehicle death rate and vice versa. If the two variables are endogenous,
they are correlated with the error term, and the regression will provide biased estimates.
Consequently, this research addresses this limitation and will test for the existence of
endogeneity between the two variables. In addition, many studies have examined the
influence of various alcohol-control and tobacco-control policies on the demand for
alcohol and tobacco products, respectively, as well as some of the consequences of their
consumption (such as the impact of alcohol-control laws on motor vehicle fatalities).
However, with the exception of a few studies of tobacco-control laws (e.g., see Boyes
and Marlow (1992), Gallet et al. (2006, 2009), and Shipan and Volden (2006)), which
have examined preferences towards the adoption of smoking restrictions, no study to date
has examined preferences towards alcohol-control laws. Accordingly, as discussed in
Chapter 3, in this thesis a model will be estimated to assess factors which influence the
probability that states adopt laws designed to limit drinking and driving.
9
For respondents who thought that utilizing smoking and non-smoking sections was not an effective means
to address smoking concerns, they supported the imposition of a smoking ban.
17
Chapter 3
DATA AND EMPIRICAL MODEL
This chapter describes the data, empirical model, and econometric issues relevant
to the analysis. Section 3.1 provides sample statistics of the variables used in the
regression model, as well as the sources of the data, variable definitions, and expectations
regarding the impact of each explanatory variable on the probability that alcohol-control
laws are enacted. Section 3.2 discusses a few econometric issues.
3.1
Data and Empirical Model
A panel of state-level annual data corresponding to the 50 U.S. states for the
1985-2002 period is used in the analysis. Definitions of the variables used in the model,
as well as the means and standard deviations of each variable are provided in Table 1
below. Data sources are provided in the Appendix.
Table 1: Variable Definitions and Descriptive Statistics
MEAN
STANDARD
DEVIATION
Open Law ( dichotomous variable equals 1 if a state prohibits open alcohol container in a
vehicle, 0 if not)
0.52
0.50
AntiCon Law ( dichotomous variable equals1 if state prohibits consumption of alcohol in a
vehicle, 0 if not)
0.76
0.43
Dram Law ( dichotomous variable equals1 if owner of drinking establishment is liable for
damages resulting from serving alcohol to an intoxicated patron who then injures another
person in a vehicle accident, 0 if not)
0.65
0.48
Prelim Law ( dichotomous variable equals1 if a state authorizes preliminary breath test of
person suspected of driving intoxicated, 0 if not)
0.53
0.50
BAC08 Law ( dichotomous variable equals 1 if a state makes it illegal per se to have blood
alcohol content at or above 0.08 percent, 0 if not)
0.19
0.39
ZeroTol Law ( dichotomous variable equals1 if a state has zero tolerance law for underage
drinking (i.e., blood alcohol content for youth above 0.02 percent is illegal), 0 if not)
0.40
0.49
VARIABLE DESCRIPTION
Drunk-driving Laws
18
Table 1: Variable Definitions and Descriptive Statistics (Continued)
MEAN
STANDARD
DEVIATION
Realincome ( per capita income in dollars divided by the consumer price index)
12997
1982
Acu ( conservatism index from the American Conservative Union )
49.83
22.76
Realbeertax ( tax on beer in cents per gallon divided by the consumer price index )
16.33
12.97
Realgastax ( tax on gasoline in cents per gallon divided by the consumer price index)
12.17
3.19
Mvrate ( number of motor vehicle deaths per 100,000 people)
18.05
5.39
Govdem ( dichotomous variable equals 1 if a state has the governor who is a Democrat, 0 if
not)
0.49
0.50
Dummy00 ( dichotomous variable equals 1 after year 2000 when a federal law that require
each state to pass BAC08 law has been adopted, 0 if not)
0.44
0.50
Dummy95 (dichotomous variable equals 1 after 1995 when a federal law that require each
state to pass zero tolerance law has been adopted, 0 if not)
0.17
0.37
Neighbor Open ( percentage of neighboring states that have adopted the Open Law)
0.51
0.50
Neighbor AntiCon (percentage of neighboring states that have adopted the AntiCon Law)
0.82
0.39
Neighbor Dram (percentage of neighboring states that have adopted the Dram Law)
0.78
0.41
Neighbor Prelim (percentage of neighboring states that have adopted the Prelim Law)
0.51
0.50
Neighbor BAC08 (percentage of neighboring states that have adopted the BAC08 Law)
0.11
0.32
Neighbor ZeroTol (percentage of neighboring states that have adopted the ZeroTol Law)
0.40
0.49
West ( dichotomous variable equals 1 if a state is in West, 0 if not)
0.26
0.44
Northeast ( dichotomous variable equals 1 if a state is in Northeast, 0 if not)
0.18
0.38
Midwest ( dichotomous variable equals 1 if a state is in Midwest, 0 if not)
0.24
0.43
South ( dichotomous variable equals 1 if a state is in South, 0 if not)
0.32
0.47
5.48
1.74
170.90
235.78
VARIABLE DESCRIPTION
Regressors
Instrumental variables
Urate ( unemployment rate)
Popdensity (population per sq. mile of land area)
The goal is to examine the determinants of the probability that a state will adopt
laws directed at driving under the influence of alcohol. More specifically, this thesis
addresses 6 different state-level restrictions on drinking and driving, for which the data
were obtained from National Highway and Traffic Safety Administration. The 6 laws are
19
coded as dichotomous variables (i.e. equals 1 if the state adopts a particular law, 0 if not),
which serve as dependent variables in a series of regressions. As defined in Table 1, these
6 laws pertain to dram-shops, open containers of alcohol in a vehicle, consumption of
alcohol in a vehicle, blood alcohol content (BAC), zero tolerance of underage drinking,
and the use of preliminary breath tests. More specifically, dram-shop laws allow people
who are hurt by an intoxicated person to file a law suit against the establishment that sold
the alcohol. Open container laws make it illegal to possess any open container of alcohol
in a vehicle. Anti-consumption laws prohibit the consumption of alcoholic beverages in a
motor vehicle. BAC laws prohibit the driving of a vehicle with a BAC at or above 0.08
percent.10 Zero tolerance laws prohibit a person under 21 years old from operating a
motor vehicle if they have an alcohol concentration of 0.02 percent or above. Preliminary
breath test laws allow police to administer a breath test without medical staff assistance.
As an indication of the trend in adoption rates over time, Figure 1 depicts the
percentage of states that adopted one of these laws during 1985-2002 years. As the figure
illustrates, the adoption of drunk-driving laws has increased over time. For instance, zero
tolerance laws were rapidly adopted by states throughout the 1990s. As for setting the
legal BAC at 0.08%, only 5% of states adopted such laws during the 1985-88 period, but
by the end of 2002 nearly 60% adopted such laws. In addition, in 1985 nearly 35% of
states had adopted dram-shop laws, whereas the adoption of preliminary breath test, open
container and consumption of alcohol in the vehicle laws increased noticeably after 2000.
10
Historically, states have set the legal BAC at different levels. For instance, in earlier years many states
set the legal BAC at 10%. As we explain later, we chose to focus on the adoption of a legal BAC of 0.08%
because of Federal laws during our period of analysis which sought to induce states to set the legal BAC at
0.08%.
20
Figure 1: Drunk-driving Laws (1985-2002)
As mentioned, the probability that a state adopts a particular drunk-driving law is
hypothesized to depend upon several factors. We now discuss these determinants in
greater detail.
First, since Gallet et al. (2006, 2009) found per capita income influences the statelevel adoption of anti-smoking laws, this study also includes per capita income (deflated
by the consumer price index) as a determinant of drunk-driving laws. Given there is a
long-established positive correlation between income and health (e.g., see Hitiris and
Posnett, 1992), we expect states with higher income will face greater pressure to adopt
drunk-driving laws to enhance public health.
Second, this thesis includes an index of conservatism (created by the American
Conservative Union) as a factor that might influence the adoption of drinking and driving
laws. This variable measures (assigning a score between 0 and 100) the voting record of
each state’s representatives in Congress, with a higher score indicating a more
21
conservative voting record. Thus, it serves as a proxy for the degree of conservatism
among the voters in each state. This could affect the adoption of drunk-driving laws in
two possible ways. On the one hand, it may be that more conservative states promote
safety on the roads by “being tough on crime”, and thus are more likely to adopt laws
criminalizing drinking and driving. On the other hand, though, more conservative states
likely favor less government involvement, and thus may be less likely to adopt such laws.
Thus, depending on the story, it is possible that this index could have a positive or
negative influence on the probability that drinking and driving laws are adopted.
Third, this study includes the beer tax (also deflated by the consumer price index)
as another determinant of alcohol-control. It could be, for instance, that the beer tax is a
policy complement to drunk-driving laws, such that it is used jointly with drunk-driving
laws to reduce alcohol use. Accordingly, its coefficient would be positive. Alternatively,
the beer tax could be a policy substitute to drunk-driving laws. In this case, if states are
particularly concerned about raising revenue from the imposition of alcohol taxes, then
policies which seek to reduce alcohol use (e.g., drunk-driving laws) may be less likely to
be adopted in high beer tax states, leading to a negative coefficient on the beer tax.
Interestingly, Chaloupka et al. (2002) point out that alcohol prices have declined in the
past 50 years as result of alcohol tax policies. Indeed, as depicted in Figure 2 below,
during the 1985-2002 time period average real beer taxes across states did steadily
decline. Coupled with the rising adoption rates of drunk-driving laws (see Figure 1), it
may very well be there is a negative relationship between beer taxes and the adoption of
such laws.
22
Figure 2: Average Real Beer Tax (1985-2002)
Fourth, the gasoline tax (deflated by the consumer price index) is included as one
of the determinants of drunk-driving laws, with a similar argument as the beer tax in
regards to its influence on the adoption of drunk-driving laws. That is, the gas tax could
be a policy complement or a substitute to drunk-driving laws. If the gas tax and drunkdriving laws are complements, then policymakers use them hand-in-hand to reduce
drinking and driving. Alternatively, if they are substitutes to each other, then in high gas
tax states there may be less pressure to adopt laws which restrict driving, for this would
reduce the revenue earned from the gas tax. As illustrated in Figure 3 below, there has
been a modest reduction in real gas tax rates across states since the early 1990s.
23
Figure 3: Average Real Gas Tax (1985-2002)
Fifth, this thesis includes the motor vehicle fatality rate (i.e., the number of motor
vehicle deaths per 100,000 residents of each state) as a factor determining the adoption of
drunk-driving laws. It is expected that states with higher motor vehicle deaths will be
more likely to adopt drunk-driving laws as a means of protecting lives. As an illustration
of the trend of motor vehicle fatalities, Figure 4 depicts the national average motor
vehicle fatality rate (across all 50 states) for each year in the 1985-2002 period, as well as
the annual motor vehicle death rates for California, Massachusetts, Nebraska, and
Mississippi, states with fatality rates well below, near, and well above the national
average, respectively. Interestingly, while the national average, California and
Massachusetts’ motor vehicle death rate slightly dropped during this period, Nebraska
and Mississippi’s rates have risen.11
11
As we explain below, since several studies (e.g., Chaloupka et al. (1993)) have found evidence in favor
of drunk-driving laws reducing traffic fatalities, particularly those involving alcohol impairment, it is quite
possible that the motor vehicle fatality rate is endogenous within our regressions. Accordingly, we will
consider an instrumental variables procedure to address this issue.
24
Figure 4: Motor Vehicle Death Rates (1985-2002)
Sixth, this research also includes a dummy variable to control for the effects of
political affiliation on the adoption of driving under influence laws. More specifically, the
variable measures whether the Governor of the state was a Democrat during each of the
1985-2002 years. Since Shipan and Volden (2006) found that more liberal states are
likely to adopt anti-smoking restrictions, it is expected that Governors who are
Democrats will have a positive impact on the adoption of drunk-driving laws.
Seventh, to capture Federal government influence on the adoption of state-level
drinking and driving policies (i.e., national-to-state policy diffusion), this thesis creates
two dummy variables to control for two key laws passed at the Federal level in 1995 and
2000, respectively. Specifically, the 1995 law threatened to withhold Federal highway
funds from states that did not adopt zero tolerance laws against teenage drinking and
driving by 1998, whereas the 2000 law threatened to withhold Federal highway funds
25
from states that did not adopt a 0.08% BAC threshold. Accordingly, we expect these laws
to provide further impetus for states to adopt drinking and driving laws, particular those
involving zero tolerance and legal blood alcohol content.
Eighth, similar to Shipan and Volden (2006), this thesis also controls for state-tostate policy diffusion by including as determinants of drinking and driving laws the
percentage of neighboring states which have such laws. Accordingly, as defined in Table
1, six neighbor-state policy variables were created, one for each of the policies addressed
in this thesis. If there are positive spillovers across states, then we expect there will be a
positive impact of the percentage of neighbor states with a particular law on the
probability that the respective state adopts the law.
Lastly, to further address policy diffusion, regional dummy variables (see Table
1) are included to control for similar pressures on the adoption of drinking and driving
laws within U.S. regions.
3.2
Econometric Issues
There are several econometric issues concerning the regression of indicators of
drunk-driving laws on the variables mentioned in the previous section. To begin, as
mentioned, the 6 drinking and driving laws are coded as dummy variables, and thus our
model seeks to predict the probability that each law is adopted. In doing so, we adopt two
functional forms for our regressions. In the first case, we estimate a Linear Probability
Model (LPM) by simply regressing each drunk-driving law dummy variable on the set of
determinants, as indicated below:
26
Yit = 0 + 1Realincomeit + 2Acuit + 3Realbeertaxit + 4Realgastaxit + 5Mvrateit +
6Govdemi + 7Dummy95it + 8Dummy00it + 9Neighborlawit + 10Westit +
11Northeastit + 12Midwestit + it,
(1)
where i indexes each state (i=1,2,…50) and t indexes each year in our sample (t=1985,
1986,…2002). Yit corresponds to a particular drunk-driving law in state i in year t; 0 is a
constant term; 1 through 10 represent the coefficients of the regressors; while it is an
error term. Concerning equation (1), a Hausman test is used to determine whether a fixed
effects or random effects specification is preferable for these LPM regressions.
Given concerns with the LPM specification (e.g., predicted probabilities may lie
outside the 0-1 range), as well as the panel nature of our data, in the second case we
follow Gallet et al. (2006, 2009) and estimate a series of random effects Probit (RE
Probit) regressions of equation (1).12 Accordingly, we will be able to assess how sensitive
are the results to the chosen specification.
A further issue with the estimation of equation (1) concerns the potential
endogeneity of the motor vehicle fatality rate. More specifically, it is plausible that
drinking and driving laws are endogenous to motor vehicle deaths, meaning that the
adoption of drunk-driving laws could affect the motor vehicle death rate. If the two
variables are endogenous, then they are both correlated with error term, and thus the
regression will provide biased estimates. To overcome this problem, similar to Gallet et
al. (2006, 2009), this research uses a two-stage estimation procedure (see Rivers and
12
As mentioned in the previous chapter, a fixed effects Logit model is sometimes used to estimate
dichotomous choice panel data models. However, given that several of our variables are relatively stable
over time (e.g., see Figures 2-4), as Gallet et al. (2006, 2008) discuss, random effects Probit is preferable.
27
Vuong (1988)) by using the unemployment rate and population density as instrumental
variables. That is, in the first-stage regression, the motor vehicle fatality rate is regressed
on the set of exogenous and instrumental variables, according to the following:13
Mvrateit = α0 + α1Urateit + α2Popdensityit + α3Realincome + α4Acuit + α5Realbeertaxit +
α6Realgastaxit + α7Govdemit + α8Dummy95it + α9Dummy00it + α10Westit + α11Northeastit
+ α12Midwestit + α13Neighborlawit + εit
(2)
The residuals (uit) are obtained from the estimation of this first-stage regression and then
included as an additional regressor in equation (1), such that the second-stage regression
becomes:
Yit = 0 + 1Realincomeit + 2Acuit + 3Realbeertaxit + 4Realgastaxit + 5Mvrateit +
6Govdemi + 7Dummy95it + 8Dummy00it + 9Neighborlawit + 10Westit +
11Northeastit + 12Midwestit + ρuit + it,
(3)
Next, to test for the existence of endogeneity, the significance of the coefficient ρ needs
to be examined. In particular, if ρ is found to be statistically significant, then this
indicates the motor vehicle fatality rate in equation (1) is endogenous, and thus the results
in equation (3) are more appropriate. If ρ is found to not be statistically significant, then
this indicates the motor vehicle fatality rate in equation (1) is exogenous, and thus the
results in equation (1) are more appropriate.
In summary, we estimate a series of drunk-driving law regressions which control
for (i) different functional forms, (ii) different treatments of panel effects, and (iii)
13
Interestingly, Ruhm (1995, 1996) finds motor vehicle fatalities decline during recessionary periods (i.e.,
periods of higher unemployment), as individuals drive less during such periods. We also expect population
density to influence motor vehicle fatality rates, and thus also include it as an instrumental variable.
28
possible endogeneity of motor vehicle fatality rates. The results are discussed in the next
chapter.
29
Chapter 4
EMPIRICAL RESULTS
This chapter presents the results from estimating the Linear Probability Model
(LPM) and the Probit Model as discussed in the previous chapter. More specifically,
Sections 4.1 through 4.6 provide the results without endogeneity correction, while the
results with endogeneity correction are provided in Sections 4.7 through 4.13. In all the
tables (except for Table 8), Columns 1– 2 report results without the treatment of panel
effects, while Columns 3– 4 report results with the treatment of panel effects (using
random effects (RE) for the Probit regressions, and either fixed (FE) or random effects
for the LPM regressions, depending on Hausman test results). Heteroskedastic- and
autocorrelation-consistent standard errors appear in parentheses below the estimated
coefficients. In addition, at the bottom of each table is reported the value of R-squared for
the regressions, as well as the Hausman test statistic for the LPM panel regressions.
4.1
Open Container Law Results (Exogenous)
To begin, Table 2 provides the regression results for the 4 models estimated to
examine the probability that a state adopts a law restricting open containers of alcohol in
vehicles. As seen at the bottom of the table, the Hausman test favors using FE over RE in
the estimation of the linear model. In addition, based on the higher R-squared in Columns
3 and 4 in comparison to Columns 1 and 2, using individual state FE in the LPM and RE
in the Probit Model regressions is preferable.
Turning to the estimated coefficients, the results are somewhat sensitive to the
choice of specification. For instance, although often insignificantly different from zero,
30
the coefficients of real income, the conservatism index, the real beer tax and the real gas
tax switch signs in the models that treat for panel effects. However, rather than discuss
the results for each regression separately, we will focus on general patterns of the results.
Accordingly, starting at the top of Table 2, while only statistically significant in the RE
Table 2: Open Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
-8.37e-06
(1.06e-05)
-0.000288
(0.000700)
0.0114***
(0.00112)
0.00299
(0.00438)
-0.0310***
(0.00395)
-0.104***
(0.0290)
0.0628*
(0.0335)
0.180***
(0.0443)
0.0978***
(0.0326)
0.305***
(0.0422)
-0.386***
(0.0547)
0.308***
(0.0497)
-5.20e-05
(4.79e-05)
-0.00706**
(0.00298)
0.0639***
(0.00643)
0.00568
(0.0169)
-0.148***
(0.0188)
-0.336***
(0.110)
0.461***
(0.127)
0.862***
(0.187)
0.0621
(0.120)
1.601***
(0.206)
-1.688***
(0.225)
1.246***
(0.181)
1.96e-05
(2.43e-05)
0.000420
(0.00128)
-0.00276
(0.00630)
-0.0177**
(0.00817)
-0.0291***
(0.0100)
-0.0355
(0.0392)
0.0116
(0.0306)
0.119**
(0.0541)
-0.00515
(0.0530)
0.00132**
(0.000649)
0.0241
(0.0655)
-0.120
(0.113)
-0.171*
(0.0879)
-0.454**
(0.192)
-1.419
(1.078)
0.765
(1.547)
2.615***
(0.142)
1.742
(1.194)
Panel Effects
Observations
R-squared
No
900
0.41
No
900
0.38
FE
900
0.80
RE
900
0.75
Hausman
(p-value)
None
None
26.76
(0.0008)
None
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighboropen
west
northeast
midwest
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
31
Probit regression, the real income coefficient is positive in regressions that adjust for
panel effects and negative in regressions that do not. Similar to real income, the
coefficient of the conservatism index is significantly different from zero in only one of
the four regressions. Thus, across all regressions, it appears that per capita income and
the degree of conservatism in each state have little influence on the probability that open
container laws are adopted.
As for the beer tax and the gas tax, the coefficient of the real beer tax (real gas
tax) is positive (negative) and statistically significant in the regressions that do not (do)
control for panel effects. Therefore, the beer tax is a policy complement to drunk-driving
laws in the regressions that do not control for panel effects, while the gas tax is a policy
substitute to drunk-driving laws in the regressions that control for panel effects.
Next, the coefficient of the motor vehicle fatality rate is negative and significant
at least at the 5% level across all four regressions, which is unexpected, since we would
expect higher motor vehicle deaths to increase the likelihood of adoption of drunkdriving laws. Also surprising, in states with governors who are Democrats there is a
lower probability of adopting open container laws (although the respective coefficient is
insignificantly different from zero in the panel regressions). This suggests that more
liberal states are less likely to adopt the law, which is the opposite of what is expected.
However, there is strong Federal influence on state decisions to adopt open container
laws in that the coefficients of the 1995 and 2000 dummy variables controlling for the
two Federal laws are positive and largely significantly different from zero (although the
32
coefficient associated with the 1995 law is insignificantly different from zero in the two
panel regressions).
As for state-to-state policy diffusion, although its coefficient is most often
statistically insignificant, the coefficient of the percentage of neighboring states with
open container laws is positive. Also, there are strong state-to-state spillovers within
regions, as all three regional dummy variables are statistically significant at 1%
significance level. Thus, compared to Southern states, Western and Midwestern states
are more likely to adopt open container laws, while Northeastern states are less likely.
4.2
Consumption of Alcohol in a Vehicle Law Results (Exogenous)
Table 3 below provides the results for the regressions of laws restricting the
consumption of alcohol in motor vehicles. For the LPM regressions, unlike the open
container law regressions, the Hausman test favors using RE over FE. Yet similar to the
open container law regressions the higher R-squared value in Column 4, compared to
Column 2, suggests RE for the Probit Model regressions.
Surprisingly, the real income coefficient is negative across all four regressions
(yet insignificantly different from zero in the panel regressions). The coefficient of the
conservatism index is significant in three of the four regressions, while its sign is
sensitive to the specification, as it is negative in the regressions that do not control for
panel effects and positive in the ones that do. Unlike the previous section, the real beer
tax coefficient is statistically insignificant across all regressions, and the real gas tax
coefficient is positive and significant in the regressions without panel effects.
33
Similar to the open container law results, the motor vehicle fatality rate is
significant and negative across all four regressions. Yet unlike the previous section, the
political affiliation of the governor of each state has an insignificant influence on the
adoption of anti-consumption laws.
Table 3: AntiCon Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
-8.73e-05***
(1.20e-05)
-0.000893
(0.000696)
-0.000840
(0.00128)
0.00949*
(0.00512)
-0.0458***
(0.00424)
0.0333
(0.0273)
0.103***
(0.0305)
0.146***
(0.0407)
0.190***
(0.0435)
0.143***
(0.0320)
-0.137***
(0.0511)
-0.0689*
(0.0413)
-0.000357***
(5.22e-05)
-0.00674**
(0.00302)
0.000447
(0.00481)
0.0422**
(0.0174)
-0.187***
(0.0189)
0.134
(0.107)
0.512***
(0.133)
0.591***
(0.194)
0.656***
(0.141)
0.853***
(0.152)
-0.673***
(0.197)
-0.334**
(0.158)
-1.85e-05
(2.87e-05)
0.00289**
(0.00126)
0.000266
(0.00457)
0.00515
(0.00811)
-0.0185**
(0.00804)
-0.0141
(0.0547)
0.0424
(0.0477)
0.0564
(0.0505)
0.0175
(0.0848)
-6.82e-05
(0.000227)
0.0228*
(0.0121)
0.00547
(0.0599)
0.0436
(0.0867)
-0.142***
(0.0350)
-0.426
(0.901)
0.654
(0.860)
0.465*
(0.270)
1.045
(1.439)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighboranticon
west
northeast
midwest
Panel Effects
No
No
RE
RE
Observations
900
900
900
900
R-squared
0.24
0.24
0.09
0.53
Hausman
None
None
8.72
None
(p-value)
(0.3664)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
34
For the majority of the regressions, there appears to be nation-to-state policy
diffusion, as the coefficients of the two Federal law dummy variables are positive and
mostly statistically significant.
Furthermore, the positive coefficient (and statistically significant in the first two
Columns of Table 3) of the variable controlling for the percentage of neighboring states
having anti-consumption laws favors state-to-state diffusion of this law, as also do the
significant coefficients of the three region dummy variables. Indeed, in comparison to
states in the South, states in the West (Northeast and Midwest) have a higher (lower)
probability of adopting anti-consumption laws.
4.3
Dram-Shop Law Results (Exogenous)
Table 4 provides the results for the four different regressions involving dram-shop
laws. To begin, similar to Table 3, the Hausman test favors RE over FE when estimating
the linear model. Furthermore, similar to the open container and anti-consumption laws,
R-squared is highest in Column 4, favoring the RE Probit regressions.
Turning to the estimated coefficients, while the real income coefficient is now
statistically significant across all four regressions, it is positive in the regressions that
control for panel effects but negative in the regressions that do not control for panel
effects. As for the conservatism index, it is positive in all but the RE Probit regression,
but only significantly different from zero in the regressions that do not control for panel
effects. Regarding taxes, the real beer tax coefficient is insignificant in three of the four
regressions, whereas the real gas tax coefficient is significant in three of the four
35
regressions. Being negative in three of the four regressions, it appears the real gas tax is a
policy substitute to the adoption of dram-shop laws.
Table 4: Dram Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
-3.23e-05***
(1.11e-05)
0.00302***
(0.000721)
-0.00192
(0.00127)
-0.0229***
(0.00458)
-0.00401
(0.00483)
-0.0719**
(0.0305)
0.00721
(0.0360)
0.0241
(0.0424)
0.324***
(0.0402)
0.236***
(0.0380)
0.400***
(0.0519)
0.144***
(0.0497)
-0.000126***
(3.70e-05)
0.0112***
(0.00267)
-0.00574
(0.00410)
-0.0770***
(0.0166)
-0.0153
(0.0157)
-0.256***
(0.0983)
0.0267
(0.121)
0.104
(0.147)
0.950***
(0.124)
0.771***
(0.125)
1.401***
(0.194)
0.416***
(0.150)
4.85e-05**
(2.15e-05)
0.000154
(0.000584)
-0.00477
(0.00334)
-0.00221
(0.00750)
-0.00342
(0.00518)
-0.0297
(0.0288)
-0.0251
(0.0223)
-0.0477
(0.0383)
0.169***
(0.0477)
0.00173*
(0.00104)
-0.0109
(0.0100)
-0.220***
(0.0526)
0.238***
(0.0916)
-0.151*
(0.0880)
-0.788
(0.780)
0.666*
(0.392)
0.599
(1.462)
1.750*
(0.919)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighbordram
west
northeast
midwest
Panel Effects
No
No
RE
RE
Observations
900
900
900
900
R-squared
0.22
0.19
0.16
0.78
Hausman
None
None
10.97
None
(p-value)
(0.2033)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
While the coefficient of the motor vehicle fatality rate remains negative across all
four regressions, it is only significant in the RE Probit regression. Similar to the open
36
container law results, dram-shop laws are less likely in states with governors who are
Democrats.
Concerning policy diffusion, given the coefficients of the two Federal law dummy
variables are largely insignificant, there is less Federal pressure on states to adopt dramshop laws. Yet the percentage of neighboring states that have dram-shop laws has a
positive and significant effect on state adoption of dram-shop laws, which suggests there
is strong state-to-state diffusion of such laws. Furthermore, all the regional dummy
variables are positive and statistically significant at the 1% level, which also favors stateto-state diffusion of dram-shop laws.
4.4
Preliminary Breath Test Law Results (Exogenous)
The results for the estimation of the four different models addressing the adoption
of preliminary breath test laws are provided in Table 5. The Hausman test once again
favors RE over FE in the estimation of the linear model. Also, similar to Tables 1–4, Rsquared is highest in the RE Probit, as opposed to all other regressions.
Next, the results are again somewhat sensitive to the choice of specification. For
example, the real income coefficient is positive in all regressions except in Column 3, and
only statistically significant in the RE Probit regression. Unlike the previously discussed
laws, the coefficient of the conservatism index is not statistically significant in any of the
regressions. As for taxes, the coefficient of the real beer tax is negative and statistically
significant (at 1% significance level) in the regressions that do not control for panel
effects, thus suggesting beer taxes are a policy substitute. Yet its coefficient is
statistically insignificant in the regressions that control for panel effects. Further, the real
37
gas tax coefficient is positive (suggesting it is a policy complement) but only statistically
significant in the regressions that do not control for panel effects.
Table 5: Prelim Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
2.21e-05
(1.47e-05)
0.000494
(0.000823)
-0.00454***
(0.00112)
0.0162***
(0.00589)
-0.00746
(0.00513)
0.0747**
(0.0335)
0.0346
(0.0403)
0.0280
(0.0504)
-0.126***
(0.0364)
-0.251***
(0.0436)
-0.0566
(0.0611)
0.262***
(0.0483)
6.17e-05
(4.01e-05)
0.00136
(0.00238)
-0.0132***
(0.00361)
0.0455***
(0.0159)
-0.0200
(0.0148)
0.209**
(0.0964)
0.104
(0.115)
0.0897
(0.146)
-0.377***
(0.108)
-0.702***
(0.123)
-0.156
(0.169)
0.755***
(0.149)
-2.04e-05
(1.83e-05)
0.000485
(0.00115)
0.00106
(0.00314)
0.00754
(0.00692)
-0.00143
(0.00778)
0.00607
(0.0462)
0.0884**
(0.0403)
0.0771**
(0.0367)
-0.00873
(0.0899)
0.000669**
(0.000338)
0.0130
(0.0316)
0.0294
(0.105)
0.552
(0.413)
-0.0909
(0.200)
-1.383**
(0.572)
1.683***
(0.316)
1.867***
(0.391)
-1.814
(2.395)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborprelim
west
northeast
midwest
Panel Effects
No
No
RE
RE
Observations
900
900
900
900
R-squared
0.16
0.13
0.07
0.82
Hausman
None
None
5.56
None
(p-value)
(0.6960)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
Similar to the findings in the previous sections, the coefficient of the motor
vehicle fatality rate is negative, but now it is insignificant across all four regressions.
Also, there is some influence of the political affiliation of the governor in each state,
38
although the sign and significance of the respective coefficient is sensitive to model
specification. Regarding policy diffusion, the coefficients corresponding to the two
Federal law dummy variables are positive and significant in the regressions that control
for panel effects. However, unlike the findings in the previous sections, the coefficient of
the percentage of neighboring states that have a similar law is negative. Moreover, the
coefficient is significant at the 1% level only in regressions that do not adjust for panel
effects. Lastly, there are less regional differences in regards to preliminary breath test
laws, as the coefficient of the dummy variable corresponding to Northeast states is
insignificantly different from zero. Nonetheless, we do find evidence favoring states in
the West being less inclined to adopt preliminary breath test laws, while states in the
Midwest being more inclined to adopt such laws.
4.5
BAC08 Law Results (Exogenous)
Table 6 provides the results for the adoption of 0.08 % BAC law. At the bottom of
the table, we see the Hausman test continues to favor RE over FE estimation. The Rsquared in the linear model is slightly higher when controlling for state effects as opposed
to region effects, and the higher R-squared value in the Column 4, compared to the
Column 2, once again suggests RE in the Probit Model regressions.
As for the individual coefficients, contrary to expectations, the coefficient of real
income continues to be negative in several regressions, with the exception of the RE
Probit regression. The coefficients of the conservatism index, as well as the coefficients
of the real gas tax and the Democratic governor dummy variable, are insignificantly
39
different from zero in all four regressions. The coefficient of the real beer tax is positive,
but only significant in the regressions that do not control for panel effects.
Table 6: BAC08 Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
-5.11e-05***
(1.01e-05)
-0.000420
(0.000648)
0.00513***
(0.00107)
-0.00343
(0.00356)
-0.0217***
(0.00366)
-0.00695
(0.0248)
0.215***
(0.0308)
0.223***
(0.0477)
0.0612
(0.0517)
0.190***
(0.0355)
0.0544
(0.0474)
-0.0768**
(0.0348)
-0.000211***
(4.66e-05)
0.000194
(0.00268)
0.0213***
(0.00486)
-0.0108
(0.0182)
-0.0871***
(0.0184)
-0.0291
(0.120)
1.052***
(0.142)
0.804***
(0.161)
0.112
(0.160)
0.801***
(0.151)
0.322
(0.220)
-0.378*
(0.212)
-9.94e-06
(2.27e-05)
-0.00157
(0.00118)
0.00114
(0.00475)
-0.00568
(0.00727)
-0.0161**
(0.00688)
0.0442
(0.0424)
0.186***
(0.0562)
0.158***
(0.0449)
0.0928
(0.0891)
0.00123**
(0.000527)
-0.00800
(0.0197)
0.0769
(0.0484)
-0.00822
(0.139)
-0.174*
(0.104)
0.630
(0.537)
3.390***
(0.795)
1.650***
(0.209)
3.767***
(0.725)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborbac08
west
northeast
midwest
Panel Effects
No
No
RE
RE
Observations
900
900
900
900
R-squared
0.25
0.26
0.27
0.61
Hausman
None
None
10.74
None
(p-value)
(0.2168)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
As in previously discussed laws, the coefficient of the motor vehicle fatality rate
is significant and negative across all four regressions. Policy diffusion is very strong in
regards to nation-to-state diffusion, as the coefficients of the two Federal law dummy
40
variables are consistently positive and significant. This makes sense, especially in regards
to the 2000 law, which had the intent of inducing states to adopt a 0.08% BAC statute.
Yet state-to-state diffusion is less so as the coefficient of the neighbor state variable is
largely insignificant, as is the coefficient of the Northeast dummy variable as well.
4.6
Zero Tolerance of Underage Drinking Law Results (Exogenous)
Table 7 provides the results for the regressions involving zero tolerance laws.
Now the Hausman favors using FE over RE in the linear model. Also, slightly higher Rsquared values in Columns 3 and 4 in comparison to Columns 1 and 2 favors controlling
for state effects over regional effects.
Next, the estimated coefficients are mostly insensitive to the different
specifications. For instance, the real income coefficient is positive across all regressions,
although statistically insignificant in the first column. Regarding the conservatism index,
as well as the real beer and gas taxes, their respective coefficients being insignificantly
different from zero in all regressions suggests they have little influence on the adoption of
zero tolerance laws. Once again, the motor vehicle fatality rate coefficient is negative and
statistically significant across all four regressions, while the coefficient of the Democratic
governor dummy variable is negative (and significant in three of the four regressions).
Results are interesting when it comes to the diffusion of zero tolerance laws.
Both Federal laws positively (and significant at the 1% level for most regressions)
influence the adoption of zero tolerance laws. Similar to the BAC results, this makes
sense, since in the case of the 1995 law its intent was to induce states to adopt zero
tolerance laws.
41
Table 7: ZeroTol Results
VARIABLES
LPM
Probit
LPM
Probit
realincome
1.11e-05
(7.23e-06)
0.000334
(0.000494)
-0.000557
(0.000736)
0.00345
(0.00311)
-0.00773***
(0.00288)
-0.0512***
(0.0197)
0.549***
(0.0548)
0.218***
(0.0276)
0.139**
(0.0542)
-0.00216
(0.0273)
-0.0989***
(0.0358)
-0.0758**
(0.0302)
0.000178***
(5.09e-05)
0.00161
(0.00294)
-0.00368
(0.00542)
0.00887
(0.0219)
-0.0392**
(0.0195)
-0.381***
(0.134)
1.904***
(0.183)
5.481***
(0.934)
0.713***
(0.174)
-0.0556
(0.185)
-0.830***
(0.249)
-0.517***
(0.196)
0.000114***
(2.46e-05)
-0.000136
(0.00114)
0.00140
(0.00298)
-0.00853
(0.00541)
-0.0188***
(0.00564)
-0.0273
(0.0219)
0.457***
(0.0606)
0.0791**
(0.0393)
0.0802
(0.0748)
0.00301***
(0.000551)
0.00824
(0.0140)
-0.0188
(0.115)
-0.164*
(0.0855)
-0.265**
(0.109)
-0.962***
(0.283)
4.062***
(0.524)
6.784***
(1.286)
4.519**
(1.943)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborzerotol
west
northeast
midwest
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.70
0.52
0.75
0.60
Hausman
None
None
101.3
None
(p-value)
(0.000)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
There also is evidence favoring state-to-state diffusion of zero tolerance laws, as
for three of the four regressions the coefficient of the neighbor-state variable is positive
and statistically significant. Finally, the coefficients of the three regional dummy
variables are all negative, but statistically significant only for the Northeast and Midwest.
42
4.7
Motor Vehicle Fatality Rate Results
As discussed in Chapter 3, given the literature often finds motor vehicle fatality
rates are influenced by drunk-driving laws, the results reported in Tables 1 – 7 should be
interpreted with caution, as it may be that the motor vehicle fatality rate is endogenous.
To address this, as mentioned previously, a two-stage regression procedure is used,
whereby in the first-stage regression the motor vehicle fatality rate is regressed on the set
of exogenous and instrumental variables (i.e., population density and the unemployment
rate). These results are provided in Table 8 below.
Specifically, there are 12 regressions estimated to control for different panel
effects and neighbor-law variables. The Hausman test results provided at the bottom of
Table 8 consistently favor using FE over RE in this first-stage regression. Also, tests of
the joint significance of the two instruments consistently favors them being correlated
with the motor vehicle fatality rate.14
Next, the residuals are obtained from these regressions and then used in the
second-stage regressions. The results of these regressions are discussed in Sections 4.8
through 4.13.
14
Similar to Chaloupka et al. (1993), we find the coefficients of real income and the unemployment rate are
statistically significant and negative, thus implying these variables are negatively related to the motor
vehicle fatality rate.
43
Table 8: First-Stage Results for Endogeneity Correction
Open
AntiCon
Dram
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
urate
-0.0704
(0.0718)
-0.00160**
(0.000622)
-0.00153***
(0.000101)
0.0419***
(0.00498)
-0.0200**
(0.00995)
-0.170***
(0.0355)
-0.669***
(0.216)
-0.984***
(0.281)
1.256***
(0.341)
-1.934***
(0.364)
-4.306***
(0.321)
-4.121***
(0.319)
0.264
(0.250)
-0.238**
(0.114)
-0.0423***
(0.0139)
-0.000428
(0.000272)
0.0172
(0.0107)
0.0465
(0.0591)
-0.158***
(0.0569)
-0.240
(0.260)
-1.514***
(0.300)
0.657**
(0.289)
-0.0767
(0.0720)
-0.00172***
(0.000620)
-0.00153***
(0.000101)
0.0415***
(0.00502)
-0.0198*
(0.0108)
-0.170***
(0.0361)
-0.669***
(0.221)
-0.956***
(0.277)
1.321***
(0.337)
-1.866***
(0.350)
-4.335***
(0.320)
-4.032***
(0.309)
-0.250**
(0.116)
-0.0477***
(0.0153)
-0.000484*
(0.000269)
0.0204*
(0.0111)
0.0512
(0.0599)
-0.146**
(0.0582)
-0.282
(0.264)
-1.545***
(0.318)
0.565*
(0.289)
-0.0625
(0.0712)
-0.00174***
(0.000616)
-0.00151***
(0.000102)
0.0422***
(0.00503)
-0.0170*
(0.0101)
-0.173***
(0.0353)
-0.656***
(0.214)
-1.028***
(0.285)
1.293***
(0.335)
-1.901***
(0.349)
-4.424***
(0.326)
-4.014***
(0.304)
-0.295***
(0.106)
-0.0449***
(0.0142)
-0.000341
(0.000269)
0.0175*
(0.0103)
0.0427
(0.0604)
-0.120**
(0.0550)
-0.226
(0.259)
-1.638***
(0.323)
0.403
(0.299)
0.0718
(0.298)
-0.440
(0.391)
0.492*
(0.269)
-1.108***
(0.393)
popdensity
realincome
acu
realbeertax
realgastax
govdem
dummy95
dummy00
west
northeast
midwest
neighboropen
neighboranticon
neighbordram
-0.930**
(0.387)
Panel Effects
No
FE
No
FE
No
Observations
900
900
900
900
900
R-squared
0.69
0.89
0.69
0.89
0.68
Hausman
105.32
79.98
(p-value)
(0.000)
(0.000)
urate and popdensity F-test
4.33
10.74
4.94
11.35
4.87
(p-value)
(0.0135)
(0.0001)
(0.0074)
(0.0001)
(0.0079)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
FE
900
0.89
111.43
(0.000)
14.75
(0.0000)
44
Table 8: First-Stage Results for Endogeneity Correction (Continued)
Prelim
BAC08
ZeroTol
VARIABLES
(7)
(8)
(9)
(10)
(11)
(12)
urate
-0.0674
(0.0720)
-0.00171***
(0.000584)
-0.00155***
(9.89e-05)
0.0463***
(0.00522)
-0.0167*
(0.00965)
-0.180***
(0.0348)
-0.623***
(0.214)
-0.990***
(0.280)
1.304***
(0.334)
-1.623***
(0.353)
-4.316***
(0.323)
-4.302***
(0.324)
0.854***
(0.252)
-0.238**
(0.114)
-0.0475***
(0.0150)
-0.000457*
(0.000259)
0.0184*
(0.0105)
0.0623
(0.0596)
-0.144**
(0.0560)
-0.251
(0.269)
-1.516***
(0.309)
0.577*
(0.290)
-0.0714
(0.0727)
-0.00189***
(0.000609)
-0.00152***
(0.000100)
0.0416***
(0.00502)
-0.0203**
(0.0103)
-0.162***
(0.0359)
-0.676***
(0.216)
-0.912***
(0.281)
1.419***
(0.343)
-1.817***
(0.358)
-4.258***
(0.321)
-4.080***
(0.306)
-0.211*
(0.117)
-0.0472***
(0.0152)
-0.000426
(0.000266)
0.0172
(0.0103)
0.0471
(0.0585)
-0.149**
(0.0575)
-0.267
(0.263)
-1.504***
(0.310)
0.679**
(0.275)
-0.0745
(0.0728)
-0.00166***
(0.000624)
-0.00154***
(0.000102)
0.0415***
(0.00501)
-0.0193*
(0.0102)
-0.171***
(0.0357)
-0.663***
(0.216)
-1.194***
(0.375)
1.267***
(0.342)
-1.842***
(0.349)
-4.322***
(0.322)
-3.991***
(0.308)
-0.241**
(0.115)
-0.0467***
(0.0150)
-0.000383
(0.000263)
0.0199*
(0.0109)
0.0600
(0.0585)
-0.149**
(0.0576)
-0.281
(0.268)
-1.249***
(0.304)
0.544*
(0.295)
-0.514
(0.343)
-0.920*
(0.464)
0.340
(0.376)
-0.568*
(0.308)
popdensity
realincome
acu
realbeertax
realgastax
govdem
dummy95
dummy00
west
northeast
midwest
neighborprelim
neighborbac08
neighborzerotol
-0.589
(1.037)
Panel Effects
No
FE
No
FE
No
Observations
900
900
900
900
900
R-squared
0.69
0.89
0.69
0.89
0.69
Hausman
136.93
101.3
(p-value)
(0.000)
(0.000)
urate and popdensity F-test
5.32
11.12
5.97
10.46
4.55
(p-value)
(0.0050)
(0.0001)
(0.0027)
(0.0002)
(0.0108)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
FE
900
0.89
119.52
(0.000)
11.51
(0.0001)
45
4.8
Open Container Law Results (Endogenous)
Table 9 provides the results for the endogenous-controlled regressions concerning
open container laws. As mentioned in Chapter 3 and discussed in Gallet et al. (2006,
2009), if the coefficient of the residual from the first-stage regression (labeled resid1 and
resid2 in Tables 9 – 14) is significantly different from zero, then this favors the motor
vehicle fatality rate being treated as endogenous (and thus the results in Tables 9 – 14 are
preferred to those in Tables 2 – 7). Alternatively, if the residual from the first-stage
regression is insignificantly different from zero, then this favors treating the motor
vehicle fatality rate as exogenous (and thus the results in Tables 2 – 7 are preferred to
those in Tables 9 – 14). In the discussion that follows, we focus principally on the
endogeneity test results.
As shown in Table 9, the coefficient of the residual term is not statistically
significant in any of the regressions, implying that the motor vehicle fatality rate is
exogenous. Thus, while the results in Table 9 are often similar to those reported in Table
2 (with one noticeable exception being that the coefficients of the region dummy
variables are insignificantly different from zero in Table 9), we favor the results
presented in Table 2.
46
Table 9: Open Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
-7.33e-05
(7.48e-05)
0.00153
(0.00202)
0.0108***
(0.00130)
-0.00375
(0.00882)
-0.0307***
(0.00401)
-0.129***
(0.0374)
0.0296
(0.0490)
0.234***
(0.0761)
0.113***
(0.0370)
0.235***
(0.0857)
-0.564***
(0.206)
0.152
(0.176)
-0.0406
(0.0457)
-0.000352
(0.000314)
0.00153
(0.00854)
0.0611***
(0.00712)
-0.0260
(0.0372)
-0.148***
(0.0189)
-0.459***
(0.159)
0.305
(0.202)
1.105***
(0.318)
0.135
(0.144)
1.279***
(0.381)
-2.521***
(0.891)
0.519
(0.751)
-0.188
(0.192)
7.86e-07
(2.78e-05)
0.00111
(0.00135)
-0.00190
(0.00637)
-0.0245**
(0.0103)
-0.0261**
(0.0111)
-0.0498
(0.0394)
-0.0429
(0.0601)
0.133**
(0.0548)
-0.0524
(0.0549)
0.00103**
(0.000512)
0.00646
(0.0348)
-0.0593
(0.141)
-0.141
(0.0942)
-0.482***
(0.133)
-1.063*
(0.604)
1.196
(1.025)
2.408***
(0.565)
1.531*
(0.927)
-0.0398
(0.0336)
0.214
(0.170)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighboropen
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.40
0.38
0.80
0.75
Hausman
None
None
30.16
None
(p-value)
(0.0004)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
4.9
Consumption of Alcohol in a Vehicle Law Results (Endogenous)
Table 10 below provides the endogenous-correction results for restrictions on the
consumption of alcohol in a motor vehicle. Although the coefficient of the residual term
47
is significantly different from zero in the first column results, it is insignificant in the
other regressions.
Table 10: AntiCon Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
6.42e-05
(7.61e-05)
-0.00511**
(0.00207)
0.000446
(0.00149)
0.0252***
(0.00921)
-0.0467***
(0.00422)
0.0904**
(0.0406)
0.174***
(0.0466)
0.0124
(0.0813)
0.178***
(0.0425)
0.292***
(0.0812)
0.283
(0.218)
0.276
(0.179)
0.0945**
(0.0467)
0.000170
(0.000335)
-0.0219**
(0.00927)
0.00483
(0.00578)
0.0949***
(0.0349)
-0.189***
(0.0184)
0.338**
(0.170)
0.762***
(0.196)
0.137
(0.366)
0.615***
(0.139)
1.374***
(0.346)
0.770
(0.935)
0.860
(0.776)
0.327
(0.202)
1.72e-05
(3.73e-05)
0.00243
(0.00158)
0.000550
(0.00580)
0.0103
(0.0107)
-0.0205**
(0.00941)
0.00690
(0.0597)
0.114
(0.0747)
0.0349
(0.0548)
0.00340
(0.0812)
7.78e-05
(0.000221)
0.0188***
(0.00439)
-0.00358
(0.0513)
0.0714
(0.0805)
-0.167**
(0.0791)
-0.429
(0.753)
0.745**
(0.322)
0.381
(0.567)
1.134
(0.941)
0.0523
(0.0382)
0.133
(0.0921)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighboranticon
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.24
0.24
0.72
0.53
Hausman
None
None
37.84
None
(p-value)
(0.0000)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
48
Turning to the individual coefficients, the coefficients of real income, the real
beer tax, the Federal law passed in 2000, as well as the Northeast and Midwest dummy
variables, are all insignificantly different from zero in all the regressions. Thus, these
factors fail to influence the passage of anti-consumption laws in a meaningful way.
Similar to Table 3, the conservatism index has a positive or negative influence on the
adoption of anti-consumption laws, depending on the chosen specification.
Next, the coefficients of the real gas tax, the Democratic governor variable, the
percentage of neighbor-states with a similar law, and the West region variable are all
positive (although to varying degrees of significance). In comparison to the LPM results
from Table 3 that did not adjust for possible endogeneity of the motor vehicle fatality
rate, the coefficients are sensitive to endogeneity correction. For instance, fewer
coefficients are statistically significant in Table 10 as compared to Table 3.15
4.10
Dram-Shop Law Results (Endogenous)
Table 11 below provides the endogenous-correction results for the dram-shop law
regressions. Similar to the prior discussed laws, by and large the coefficient of the
residual term is insignificantly different from zero (with the exception in Column 4).
Furthermore, the results are similar in many respects to those reported in Table 4. Thus,
we favor those results presented in Table 4.
15
Interestingly, the coefficient of the motor vehicle fatality rate continues to be negative after adjusting for
its endogeneity. This suggests further analysis of this issue is warranted. For instance, it might be more
appropriate to include as a regressor in the model the alcohol-related motor vehicle fatality rate, since this
likely has a more intuitive impact on the passage of drunk-driving laws. Unfortunately, though, annual
state-level data for this variable was not available for the period of analysis. Also, it would be worthwhile
to consider alternative instruments to examine their appropriateness.
49
Table 11: Dram Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
-4.06e-05
(6.93e-05)
0.00325
(0.00200)
-0.00198
(0.00132)
-0.0237***
(0.00887)
-0.00397
(0.00487)
-0.0750**
(0.0376)
0.00281
(0.0497)
0.0314
(0.0726)
0.327***
(0.0457)
0.227***
(0.0771)
0.376**
(0.183)
0.125
(0.158)
-0.00520
(0.0423)
-4.86e-05
(0.000284)
0.00902
(0.00800)
-0.00509
(0.00480)
-0.0687*
(0.0367)
-0.0157
(0.0158)
-0.226
(0.144)
0.0690
(0.190)
0.0362
(0.279)
0.926***
(0.153)
0.851***
(0.311)
1.633**
(0.814)
0.599
(0.669)
0.0494
(0.177)
5.61e-05**
(2.21e-05)
-8.13e-05
(0.000608)
-0.00518
(0.00344)
-0.000971
(0.00762)
-0.00473
(0.00506)
-0.0264
(0.0280)
-0.0168
(0.0261)
-0.0533
(0.0388)
0.173***
(0.0483)
0.00167**
(0.000767)
-0.0110
(0.0172)
-0.187***
(0.0553)
0.206**
(0.0835)
-0.150
(0.162)
-0.681
(1.127)
0.934**
(0.380)
0.427
(1.766)
2.185***
(0.511)
0.00905
(0.00719)
0.232**
(0.105)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighbordram
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
RE
RE
Observations
900
900
900
900
R-squared
0.22
0.19
0.16
0.79
Hausman
None
None
9.84
None
(p-value)
(0.3632)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
4.11
Preliminary Breath Test Law Results (Endogenous)
Table 12 provides the results for the correction of endogeneity with respect to the
preliminary breath test laws. As the table shows, the coefficient of the residual term is
50
Table 12: Prelim Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
0.000626***
(9.00e-05)
-0.0179***
(0.00279)
-0.000356
(0.00128)
0.0815***
(0.0104)
-0.0106**
(0.00505)
0.284***
(0.0429)
0.332***
(0.0567)
-0.491***
(0.0926)
-0.447***
(0.0559)
0.245***
(0.0867)
1.589***
(0.241)
1.727***
(0.210)
0.372***
(0.0537)
0.00186***
(0.000272)
-0.0534***
(0.00851)
-0.00131
(0.00419)
0.243***
(0.0326)
-0.0314**
(0.0154)
0.842***
(0.133)
0.997***
(0.174)
-1.449***
(0.275)
-1.360***
(0.184)
0.753***
(0.250)
4.754***
(0.729)
5.174***
(0.651)
1.109***
(0.163)
-1.87e-05
(2.16e-05)
0.000350
(0.00116)
0.00187
(0.00334)
0.00853
(0.00779)
-0.00145
(0.00782)
0.0104
(0.0484)
0.109**
(0.0501)
0.0805**
(0.0381)
-0.0176
(0.0969)
0.000892*
(0.000460)
0.00887
(0.0321)
0.0136
(0.111)
0.552***
(0.169)
-0.117
(0.225)
-1.337***
(0.483)
1.950***
(0.481)
1.574***
(0.589)
-1.887
(1.919)
0.00862
(0.0119)
0.221**
(0.112)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborprelim
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.21
0.16
0.88
0.87
Hausman
None
None
9640.65
None
(p-value)
(0.0000)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
significant in three of the four regressions. Accordingly, endogeneity-correction is
warranted in this case, and thus we prefer the results in Table 12 to the results in Table 5.
Similar to Table 5, R-squared is higher in the regressions which control for state-level
panel effects.
51
Turning to the individual coefficients in Table 12, while some of the estimated
coefficients are sensitive to different specifications, there are several patterns in the sign
and significance of the coefficients. First, the coefficient of real income, the real gas tax,
the dummy variable for governors who are Democrats, the two Federal law dummy
variables, and the three region dummy variables are positive and significant for a
majority of the regressions. Second, the real beer tax fails to significantly influence the
passage of preliminary breath test laws in any of the regressions. Third, the results are
sensitive to model specification. For instance, the signs of the coefficients of several
variables (e.g., govdem and dummy00) change across the four regressions.
4.12
BAC08 Law Results (Endogenous)
Table 13 below provides the results for the endogenous-corrected regressions
involving the 0.08% BAC law. The coefficients of the residuals in the regressions that do
not adjust for panel effects are statistically significant, suggesting the motor vehicle
fatality rate is endogenous. However, the residual coefficients are not significant when
adjusting for panel effects. Accordingly, similar to other laws, the endogeneity test results
are sensitive to the model specification.
As for the estimated coefficients, the coefficients for most the variables (i.e., real
income, the real beer and gas taxes, the dummy variable for governors who are
Democrats, the two Federal law dummy variables, the percent of neighbor-states with a
similar law, and the three region dummy variables) are positive and significantly different
from zero for at least 50% of the regressions. Accordingly, compared to Table 6, there
52
appears to be slightly more policy diffusion, be it national-to-state or state-to-state,
exhibited in the results in Table 13.
Table 13: BAC08 Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
0.000251***
(5.51e-05)
-0.00890***
(0.00170)
0.00788***
(0.00117)
0.0265***
(0.00631)
-0.0236***
(0.00366)
0.109***
(0.0327)
0.350***
(0.0396)
-0.0584
(0.0706)
0.132***
(0.0504)
0.478***
(0.0635)
0.885***
(0.168)
0.617***
(0.130)
0.189***
(0.0346)
0.00143***
(0.000299)
-0.0454***
(0.00871)
0.0376***
(0.00586)
0.160***
(0.0383)
-0.0976***
(0.0192)
0.572***
(0.169)
1.831***
(0.209)
-0.694**
(0.317)
0.537***
(0.173)
2.362***
(0.323)
4.666***
(0.831)
3.364***
(0.708)
1.022***
(0.186)
-2.32e-05
(3.53e-05)
-0.000955
(0.00145)
-0.00144
(0.00891)
-0.0142
(0.00999)
-0.0134
(0.00923)
0.0216
(0.0412)
0.0778
(0.0778)
0.158***
(0.0510)
0.0108
(0.110)
0.00150*
(0.000874)
-0.0164
(0.0243)
0.0613
(0.0381)
0.0624
(0.113)
-0.211
(0.179)
0.622
(0.746)
3.577***
(0.936)
1.364*
(0.764)
3.247*
(1.859)
-0.0569
(0.0426)
0.208
(0.146)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborbac08
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.27
0.30
0.63
0.55
Hausman
None
None
17.1
None
(p-value)
(0.0472)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
53
Finally, other variables (e.g., conservatism index and motor vehicle fatality rate)
have coefficients that are often negative and significant, thus indicating a negative
influence on the probability of adopting a stricter BAC limit.
4.13
Zero Tolerance of Underage Drinking Law Results (Endogenous)
Table 14 below provides the endogenous-correction results for the zero tolerance
regressions. Briefly, as shown in the table, the coefficients of the residuals are not
statistically significant in any of the regressions. Accordingly, we favor the results
reported in Table 7.
The next chapter summarizes the results obtained in this thesis. We also offer
recommendations for future research.
54
Table 14: ZeroTol Results with Endogeneity Correction
VARIABLES
LPM
Probit
LPM
Probit
realincome
-1.69e-05
(5.27e-05)
0.00111
(0.00156)
-0.000791
(0.000855)
0.000541
(0.00666)
-0.00759***
(0.00292)
-0.0617**
(0.0284)
0.530***
(0.0675)
0.241***
(0.0516)
0.147***
(0.0565)
-0.0292
(0.0596)
-0.176
(0.153)
-0.139
(0.126)
-0.0174
(0.0334)
4.53e-05
(0.000434)
0.00439
(0.0123)
-0.00604
(0.00678)
0.0232
(0.0528)
-0.0630***
(0.0219)
-0.466**
(0.207)
1.788***
(0.354)
5.32***
(0.889)
0.512**
(0.223)
-0.0966
(0.465)
-0.978
(1.220)
-0.724
(1.000)
-0.0278
(0.271)
0.000121***
(2.46e-05)
-0.000426
(0.00121)
0.000884
(0.00303)
-0.00629
(0.00575)
-0.0201***
(0.00557)
-0.0213
(0.0236)
0.473***
(0.0657)
0.0765*
(0.0389)
0.0879
(0.0744)
0.00301***
(0.000892)
0.00132
(0.0138)
-0.0316
(0.0395)
-0.0885
(0.187)
-0.281***
(0.0687)
-0.872*
(0.520)
3.884***
(0.792)
6.689***
(1.897)
3.594
(2.785)
0.0142
(0.0127)
0.246
(0.173)
acu
realbeertax
realgastax
mvrate
govdem
dummy95
dummy00
neighborzerotol
west
northeast
midwest
resid1
resid2
Panel Effects
No
No
FE
RE
Observations
900
900
900
900
R-squared
0.70
0.52
0.73
0.60
Hausman
None
None
11967
None
(p-value)
(0.0000)
Note: Robust standard errors in parentheses. FE=Fixed Effects and RE=Random Effects. Probit reports marginal effects.
*** p<0.01, ** p<0.05, * p<0.1
55
Chapter 5
CONCLUSION
Since drunk-driving fatalities are associated with high economic costs,
policymakers have become more involved in reducing drunk-driving fatality rates by
discouraging alcohol abuse. Thus, to help lawmakers with their political decisionmaking, this thesis examined the impact of economic, demographic and fiscal
characteristics on state-level drunk-driving policies. We found that the probability of a
state adopting a particular drunk-driving restriction is most often sensitive to Federal and
state-to-state influences. In addition, while Gallet et al (2006) found income to have a
positive impact on the adoption of antismoking restrictions, we found income to have a
mixed relationship with drunk-driving laws. Next, the coefficient of the motor vehicle
fatality rate continued to be negative even after we adjusted for its endogeneity. Given
this unexpected result, further analysis is warranted to explore this finding in greater
detail. For instance, it might be more appropriate to include the alcohol-related motor
vehicle fatality rate or alcohol consumption as an explanatory variable, since this likely
has a more intuitive impact on the passage of drunk-driving laws. Also, the endogeneitycorrection results from the stage one regressions vary across the different laws. This
could happen due to our instruments being correlated with motor-vehicle mortality rate
and the probability laws are adopted. Therefore, future research should consider obtaining
data on factors that are correlated with the motor-vehicle mortality rate only and not with
the probability that laws are adopted.
56
APPENDIX
Table 15: Data Sources
VARIABLE
Drinking and driving laws
SOURCE
Digest of State Alcohol-Highway Safety Laws (National Highway
and Traffic Safety Administration)
Conservatism index
American Conservative Union (www.conservative.org)
Per capita income
Economagic.com (www.economagic.com)
Tax on beer
Brewers Almanac (Beer Institute)
Tax on gasoline
Statistical Abstract of the United States (U.S. Census Bureau)
Motor vehicle deaths rate
CDC Wonder (http://wonder.cdc.gov/)
Democratic Governor
Department of political science at Indiana State University
(www.indstate.edu/polisci/klarnerpolitics.htm)
Regions
U.S. Census Bureau (www.census.gov/)
Unemployment rate
Economagic.com (www.economagic.com)
Population Density
U.S. Census Bureau (www.census.gov/)
57
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