THE EFFECT OF BANNING DRIVER LICENSES TO UNDOCUMENTED
WORKERS ON TRAFFIC FATALITIES
Francisco G. González
B. A., California State University, Sacramento, 1993
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2010
THE EFFECT OF BANNING DRIVER LICENSES TO UNDOCUMENTED
WORKERS ON TRAFFIC FATALITIES
A Thesis
by
Francisco G. González
Approved by:
__________________________________, Committee Chair
Jonathan D. Kaplan, Ph.D.
__________________________________, Second Reader
Craig A. Gallet, Ph.D.
______________________________
Date
ii
Student: Francisco G. González
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
Jonathan D. Kaplan, Ph.D.
Department of Economics
iii
___________________
Date
Abstract
of
THE EFFECT OF BANNING DRIVER LICENSES TO UNDOCUMENTED
WORKERS ON TRAFFIC FATALITIES
by
Francisco G. González
In 1995 California implemented a law banning the issuance of driver’s licenses to
undocumented workers. Other states soon followed. This thesis presents an analysis of
the effects of these bans on traffic fatalities. Data used in the study cover the 50 states and
the District of Columbia from 1982-2006, yielding 1275 observations for each variable.
Panel data techniques are used to control for state and time fixed effects. In doing so, we
see that the results indicate that the laws prohibiting the issuance of driver’s licenses to
undocumented workers is negative and statistically significant at the one percent level,
suggesting that banning driving privileges to undocumented workers may have led to
fewer fatalities.
_______________________________, Committee Chair
Jonathan D. Kaplan, Ph.D.
________________________
Date
iv
ACKNOWLEDGEMENTS
I would like to thank the following people for their continued assistance and patience
during the research and completion of this study:
- Prof. Jonathan D. Kaplan, for his guidance, support, time, and patience in the
completion of this thesis.
- Prof. Craig A. Gallet, for his advice and assistance in the successful completion of this
project.
- The entire Economics faculty at California State University, Sacramento, for their
interest and dedication in the success of students.
- My wife Lucila Gonzalez, for her patience, understanding, and the time she sacrificed
during the endless days and nights necessary for the completion of this research project.
- To my children Luis, Brenda, and Adam for the sacrifices they endured by not having
me full time in their lives during the long process it took to complete this thesis.
- Lastly, I am especially grateful to my mother Petra Gomez Castro, to whom I am
grateful for all the love, moral support, and direction she provided me while growing up
and becoming the person I am today. Thanks Mama!
v
TABLE OF CONTENTS
Page
Acknowledgements ............................................................................................................ v
List of Tables ................................................................................................................. viii
List of Figures .................................................................................................................. ix
Chapter
1. INTRODUCTION ........................................................................................................ 1
2. LITERATURE REVIEW ............................................................................................. 4
2.1.
Alcohol Consumption ........................................................................................... 5
2.2.
Minimum Drinking Age ....................................................................................... 5
2.3.
Vehicle Speed ....................................................................................................... 6
2.4.
Young Drivers ....................................................................................................... 7
2.5.
Driving Learning Systems .................................................................................... 7
2.6.
Minimum Driving Age ......................................................................................... 8
2.7.
Elderly Drivers ...................................................................................................... 9
2.8.
Newer Vehicles ................................................................................................... 10
2.9.
Vehicle Mix ........................................................................................................ 10
2.10. Vehicle Inspections ............................................................................................. 11
2.11. Volume of Driving .............................................................................................. 11
2.12. Seatbelts and the Off-Setting Hypothesis ........................................................... 12
2.13. Summary ............................................................................................................. 15
vi
3. DATA AND EMPIRICAL MODEL .......................................................................... 17
3.1. Time Trends .......................................................................................................... 17
3.2. Data ...................................................................................................................... 22
3.3. Data Statistics........................................................................................................ 24
3.4. Empirical Model ................................................................................................... 31
4. EMPIRICAL RESULTS ............................................................................................. 34
4.1. Correlations Across Explanatory Variables .......................................................... 35
4.2. Regression Results ............................................................................................... 36
5. CONCLUSION ........................................................................................................... 42
5.1. Summary of Findings ............................................................................................ 42
5.2. Suggestions for Future Research ......................................................................... 44
References ..........................................................................................................................45
vi
LIST OF TABLES
Page
1.
Table 3.1. Years when States Put Law into Effect.….…………….………....….20
2.
Table 3.2 Variable Source and Definitions….……….…………….………....….22
3.
Table 3.3 Descriptive Statistics (1982-1994)….………………….…………...…25
4.
Table 3.4 Descriptive Statistics (1995-2006)…….……………….…………...…27
5.
Table 3.5 Descriptive Statistics (2002-2003)….………………….…………...…28
6.
Table 4.1 Correlation Coefficients for the Explanatory Variables......…….…….35
7.
Table 4.2 Regression Models……………….……………………...…………….37
viii
LIST OF FIGURES
Page
1.
Figure 3.1 Fatality Rate (1982-1994)…..……………………...…..…….………18
2.
Figure 3.2 Fatality Rate (1995-2006)…..……...………………….….……….…19
3.
Figure 3.3 Fatality Rate (2000-2003)..……....………...…………………..……21
ix
1
Chapter 1
INTRODUCTION
The latest wave of immigration both legal and undocumented to the U.S. came
from Latin America and Mexico. Mexico and the Central American countries contributed
the highest number of U.S. immigrants. During the early 1990s the U.S. experienced a
growing anti-immigration sentiment. This sentiment was mostly directed towards
immigrants from Mexico. This anti-immigration sentiment began at the border states
with Mexico. Voters of these bordering states began passing laws geared towards limiting
the right of undocumented workers. The array of laws voters passed ranged from limiting
the right to education to the children of undocumented workers, limiting access to
healthcare services, and most recently prohibiting the issuance of a driver’s license to
undocumented workers.
When it comes to banning the right to drive to undocumented workers there is no
lack of advocates on either sides of the heated debate. On one side proponents of the ban
argue that driving is a privilege granted to those that deserve to drive. Further, proponents
of the law argue that it sends the wrong message granting a privilege to someone that
broke the laws by coming to the U.S. undocumented. On the other side opponents of the
law argue that negating the right to drive to undocumented workers puts us all at risk of
death and that undocumented workers have the need to drive and will drive whether or
not they have a valid driver’s license. Opponents of the law banning undocumented
workers from driving argue that the ban eliminates the current mechanism to test and
2
ensure that undocumented workers know the rules of the road, thus, increasing traffic
fatalities.
There are a number of factors that contribute to traffic fatalities. In this thesis, the
analysis focuses on determining the effects on traffic fatalities resulting from prohibiting
the issuance of a driver’s license to undocumented workers. This thesis uses a group data
logit regression model to estimate the fatalities ratio. The calculation of the ratio uses
unlicensed drivers traffic fatalities divided by the total traffic fatalities regardless of the
driving license status. Further, it uses explanatory variables that represent other factors
that contribute to traffic fatalities. These are factors that were explored by other authors
in the past, such as alcohol consumption, income, seatbelt laws (including the year the
seatbelt law became effective and the year when primary enforcement of the law became
effective), driver’s age, quantity of driving, and population density. In addition, this paper
uses a variable that has not been explored in the literature before, that is, a binary variable
representing the year when the law prohibiting the issuance of driver’s license to
undocumented workers became effective in each state.
The statistical analysis uses yearly data for the 50 states and the District of
Columbia, from 1982 through 2006, with a total of 1,275 observations. The data for this
analysis came from different sources including the Fatality Analysis Reporting System,
where traffic fatality data including the number of fatalities related to unlicensed drivers
was obtained. Data on alcohol consumption came from the National Institute on Alcohol
Abuse and Alcoholism. The Bureau of Economic Analysis was the source for disposable
income data, The Statistical Abstract of the United States yielded data related to the
3
driver’s age as well as data for the quantity of driving and population density. The
United States Justice Foundation website provided data of when the different states
implemented laws prohibiting the issuance of a driver’s license to undocumented
workers. Dates when different seatbelt laws became effective was made available by the
Insurance Institute for Highway Safety. Last, the National Highway Safety
Administration was the source for dates when airbag laws became effective in the
different states.
The following chapters in this thesis are organized as follows: Chapter 2 presents
a literature review that highlights the different factors that have been used to explain
traffic fatalities. Chapter 3 presents a data analysis of three different time trend periods, a
description of the data used in the analysis, a detail explanation of the dependent and
independent variable, a presentation of the summary statistics of the data used in this
thesis, and a description of the empirical model used in the analysis. Chapter 4 presents
an analysis of the empirical results as well as comparison with the results found in the
literature. Last, Chapter 5 concludes by summarizing the empirical results and provides
suggestions for future research.
4
Chapter 2
LITERATURE REVIEW
There are a number of factors that determine traffic fatalities. It is a subject of
interest to a number of authors that have written on this subject to better understand the
determinants of traffic fatalities. Similarly, in an effort to reduce traffic fatalities
government entities have developed and implemented different laws and policies geared
towards its reduction. Further, traffic fatality factors identified in the literature can be
summarized as behavioral and technological developments.
The behavioral factors include the decision making exercised by drivers which
directly affects traffic fatalities such as risky actions taken by young drivers under 20
years of age and elderly drivers over 64 who may have reduced motor skills to react in an
emergency event. Other behavioral factors include the consumption of alcohol, the
volume or quantity of driving, which can have two offsetting effects: on one side it
increases driving skills and on the other it increases driving time which increases
exposure to traffic fatalities. Lastly, the moral hazard of the no-fault insurance system is a
behavioral factor, increasing careless driving since the driver no longer has to prove that
he or she was not at fault to settle insurance disputes.
The technological developments include increased vehicle safety changes, such as
the incorporation of anti-lock brakes, air bags, and better impact absorption designs for
newer vehicle models. Technological developments also include the implementation of
laws and policies designed to reduce automobile accidents and fatalities from those
accidents. One of these policies is the increase of the speed limit from 55 to 65 miles per
5
hour and changes in speed limit enforcement. Implementation of other laws and policies
includes driving restrictions for young drivers, such as changes to the minimum age one
can be to apply for a driver’s license and the incorporation of driving learning systems as
a driver licensing requirement. Finally, policies geared towards the incorporation of
heftier fines and penalties for driving under the influence and for driving without wearing
a seatbelt have also been examined. The following sections present an analysis of
conclusions reached in the related literature on these different factors.
2.1
Alcohol Consumption
There is a common conclusion reached among the authors reviewed regarding the
effects of alcohol consumption in the determination of traffic fatalities. Peltzman (1975)
estimated the accident or death rate per vehicle-mile as a function of income, alcoholic
intoxication, driving speed, driver age, and a secular trend for the time period of 1966 to
1972. In his study Peltzman used alcohol consumption as a proxy for alcohol
intoxication. His estimates suggest that alcohol intoxication is a significant determinant
of traffic fatalities. Similarly Zlatoper (1989, 1991) concluded that alcohol consumption
has a positive effect on traffic fatalities.
2.2
Minimum Drinking Age
The effect of the minimum drinking age on traffic fatalities is related to the
underlying factor of alcohol consumption which has a positive effect on traffic fatalities.
Conclusions on increasing the minimum legal drinking age reached by Koshal (1976),
Cook and Tauchen (1984), and Sommers (1985) suggest that increasing the minimum
6
legal drinking age has a significant effect in reducing traffic fatalities. This contrasts
with results obtained by Asch and Levy (1987) and Loeb (1987), who conclude in their
respective papers that increasing the minimum drinking age does not reduce such
fatalities but rather postpones them. Further, results obtained by Asch and Levy, and
Loeb indirectly support the conclusion that alcohol consumption has a positive effect on
traffic fatalities rather than the driver’s age.
2.3
Vehicle Speed
The literature shows that the vehicle speed factor is a variable that has mixed
results on the determination of traffic fatalities. Peltzman (1975) and Zlatoper (1989,
1991) infer that higher speed limits are directly related to increases in traffic fatalities.
Furthermore, Houston et al. (1995) examined state laws from 1967 to 1991 for the 50
states using data from the Federal Accident Reporting System (FARS), and Fowles and
Loeb (1995) analyzed the before and after effects of the speed limit law changes from 55
to 65 miles per hour that took place in 1987. The authors suggest that increasing the
speed limit increased traffic fatalities.
A contrasting view regarding vehicle speed limit law changes was suggested by
Lave and Elias (2003), who evaluated the resource allocation following the increase in
1987 of the federal speed limit to 65 mph. Results suggest that the 1987 U.S.
government increase of the driving speed to 65 miles per hour reduced statewide fatality
rates by 3.4% to 5.1% due to an improved resource allocation of law enforcement
personnel. In addition, Cohen and Dehejia (2004) showed that the decreases in traffic
7
fatalities associated with the 65 miles per hour speed limit may be due to vehicle safety
improvements that had occurred over the years.
2.4
Young Drivers
Driver’s age is a significant factor in the determination of traffic fatalities,
especially for young drivers. Responding to the significance of this factor, states have
implemented a number of laws and policies that restrict young peoples driving. These
restrictions range from curfew hours, the number of passengers, and prohibiting young
drivers from driving with underage passengers. Further, Williams (1997) examined the
causes of teenage driver’s fatal accidents and the uses of driving systems in the U.S.,
New Zealand, Australia, and Canada.1 He found that the crash rate among 16 years-olds
is eight times higher than for drivers 20 years-old and older, and three times higher than
that of drivers 25 to 64 years old. Further, Preusser et al. (2000), using a survey to
analyze crash rates among teenagers based on their daily activities, concluded that crash
rates among teenagers’ ages 16 to 19 years old are four times higher than crash rates
among ages 25 to 64.
2.5
Driving Learning Systems
Williams (1997) indicated that driving learning systems, also known as
graduating systems, have received endorsement from most safety organization as a
deterrent of teenage driving fatalities. Williams concluded that beginner drivers are
inexperienced drivers who are in great need of road experience. In addition, Preusser et
Driving systems are referred as the driver’s education requirement that a number states
implemented for young drivers when applying for a driver’s license
1
8
al. (2000) showed that driving learning systems addresses both immaturity and driving
experience. Further, Preusser et al. concluded that young students, in addition to having
completed a driving learning system, also have a number of other restrictions imposed by
their licensing entities such as curfew hours which limit their driving privileges. Finally,
Preusser et al. suggested that driving restrictions on young drivers reduce teenager crash
rates since it reduces their time behind the wheel and thus the chance they can cause an
accident.
2.6
Minimum Driving Age
The minimum driving age is a factor that has generated a number of law and
policy changes, and it has been explored by different authors. For instance, Houston et al.
(1995) analyzed state regulation efforts to improve traffic safety. Their study examined
state laws from 1967 to 1991 for the 50 states using data from FARS. Their results show
that an increase in minimum mandated driving age reduces fatalities.
Elsewhere, Groeger and Brown (1989) concluded that age itself is not a factor that
determines traffic fatalities. They argue that it is driving experience behind the wheel
rather than the age of the driver that determines traffic fatalities. In their study they
performed an assessment of one’s own and other’s driving ability and the influences of
gender, age, and experience of road users. They used two questionnaires that were
answered by a sample of 56 people divided in three groups, each group made up of nine
males and nine females. The groups were also categorized as younger, middle, and older
aged. The questionnaires included questions about rating their individual skill level, and
rating the members of the group on which they considered themselves and were asked
9
how safe drivers they were. The second questionnaire had questions regarding their
individual perception of themselves compared to the rest of the members of their own
group using a Variable Analogue Scale (VAS). The VAS technique requires subjects to
place a mark on a line of a given length (100 mm) in a way that reflects the perception of
themselves or others along a particular dimension. Groeger and Brown suggested that
there is a wide tendency for people to overestimate their driving ability and that age
differences are not significant when controlling for driving experience.
Similarly, Preusser et al. (2000), using a survey analyzed crash rates among
teenagers based on their daily activities, evaluated data from candidates selected from
four states that included urban and suburban communities, income, population density,
and number of private vehicles per household. They concluded that increases in the
minimum driving age decrease traffic fatalities.
2.7
Elderly Drivers
Literature shows that similar to young drivers, elderly drivers may be a factor that
determines traffic fatalities. In this thesis, the elderly driver factor is treated as a
technological development since it is explored from the perspective of the different laws
and policies in place by the different states to mitigate the effect of this factor. For
instance, Nelson et al. (1992) and Levy et al. (1995) in their respective studies found that
mandated vision tests reduces traffic fatalities among the elderly. Similarly, Grabowski
and Morrisey (2001) conclude that in-person driver’s license renewals reduce traffic
fatalities for the elderly.
10
Later Morrisey and Grabowski (2005) examined state motor vehicle laws and
older driver fatalities. The authors used data from FARS from 1985 through 2000 to
determine the effects of state laws dealing with license renewal, seatbelt use, and speed
limits. The authors concluded similarly as in their 2001 study, that in-person driver’s
license renewals reduce fatalities among the elderly. However, vision and road test and
the length of the license renewal cycle did not have an effect on the elderly fatality rate.
2.8
Newer Vehicles
The number of newer vehicles is a technological factor that contributes to traffic
fatalities, especially since newer vehicles come equipped with new technologies designed
to save lives in case of an accident. One of the first papers that explored this topic had a
different conclusion; Peltzman (1975) suggested that improvements in vehicle safety do
not affect traffic fatalities.
Later, Zlatoper (1989) and Fowles and Loeb (1995) refuted Peltzman’s finding.
Zlatoper, and Fowles and Loeb in their respective papers showed that increasing the
percentage of newer vehicles on the road also reduces traffic fatalities, especially since
newer vehicles are equipped with gadgets such as air bags, anti-lock brakes, and new
designs to absorb crash impacts more effectively. These two papers also concluded that
seatbelt use reduces traffic fatalities, which are present in newer vehicle models.
2.9
Vehicle Mix
There are a number of different sizes and types of vehicles that share the road
today. Crandall and Graham (1984), Graham (1984), and Zlatoper (1984) showed that
11
vehicle size has a positive relationship with traffic fatalities. Tay (2003) found that
changes to vehicle mix towards more homogenous vehicle sizes and types reduce
fatalities.
In addition, Tay suggests that increasing the number of buses reduces fatalities.
Tay’s rationale for the increase in the number of buses is based on the pretense that buses
travel at lower speeds and bus driver’s training is more rigorous when compared to the
training needed to obtain a driver’s license to drive a small vehicle.
2.10
Vehicle Inspections
The implementation of required vehicle inspections is another factor that
determines traffic fatalities. Loeb and Gilad (1984) employed for the first time the use of
time series analysis in determining the efficacy of vehicle inspection in the reduction of
traffic fatalities. Loeb and Gilad used data from the state of New Jersey and evaluated
various socio-economic factors such as income, gasoline consumption, technology, and
other driving related variables. Furthermore, Loeb (1985 and 1987) showed that states
that practice vehicle inspections experience a significant lower fatality rate. These results
are similar to the prior results from the state of New Jersey.
2.11
Volume of Driving
The volume or quantity of driving represents another factor that determines traffic
fatalities. The volume of driving in turn is related to other factors such as gasoline taxes,
weather, and increases in fuel efficiency in newer vehicles. Zlatoper (1991) found that
increases in the volume of driving contribute to an increase in traffic fatalities.
12
Leigh and Wilkinson (1991) analyzed the effect of gasoline taxes on traffic
fatalities. They found that a gasoline tax inversely affects traffic fatalities through
gasoline consumption. Results from their regression analysis suggest that a ten percent
increase in gasoline taxes reduces between 80 to 1,785 automobile fatalities for every
50,000 automobile fatalities annually.
Zlatoper (1991) showed that comfortable temperature also contributes to traffic
fatalities due to the increase in the volume of driving.2 In his paper, Zlatoper concluded
that western states also have a higher number of traffic fatalities due to better weather,
which is conducive to a higher volume of driving.
Further, Cohen and Dehejia (2004) suggested that owning a fuel efficient vehicle
increases the volume of driving which in turn also contributes to increases in traffic
fatalities. A contrasting view was presented by Noland (2004) showing that fuel efficient
vehicles do not contribute to traffic fatalities when controlling for the mandatory seatbelt
laws and the issuance of heftier penalties for drunk driving.
2.12
Seatbelts and the Off-Setting Hypothesis
At different points in time states have implemented laws requiring drivers to use
seatbelts. In the early stages of the implementation of these laws not all states had
primary enforcement of the seatbelt laws. A state without a primary enforcement did not
have law enforcement personnel stop a vehicle for the sole reason that the driver or the
For the temperature variable this paper uses the “average normal daily mean
temperature for a selected city within each state as reported in the U.S. Bureau of the
Census 1989”
2
13
passenger was not using a seatbelt. Later, most states incorporated primary enforcement
of the seatbelt laws.
Fowles and Loeb (1995) suggested there is a reduction in traffic fatalities given
that newer vehicles are equipped with seatbelts and other safety devices such as air bags,
anti-lock brakes, and new designs to better absorb the crash impact more effectively.
Similarly Cohen and Einav (2003) showed that the use of the seatbelt decreases the
overall traffic fatalities.
After the seatbelt laws were in effect in most states the argument centered on the
existence of the off-setting-hypothesis. The off-setting hypothesis suggests that vehicle
safety regulation may result in an increased sense of security for drivers, which induces
them to engage in riskier driving and therefore diminishes safety gains by increasing
traffic fatality rates. There are mixed results in the evaluation of the off-setting
hypothesis.
Calkins et al. (2001) assessed the effectiveness of mandated seatbelt usage. Their
analysis used two years (1988 and 1997) of state-level data to test for the presence of the
off-setting hypothesis by estimating models explaining total and non-occupant motor
vehicle deaths. The study uses three models as follows: model one calculates the death
rate using vehicle miles; model two estimates death rates in a double-log form to allow
for potential interaction among independent variables and interpretation of the estimated
coefficients as elasticities; and model three assesses the relationship between the
explanatory variables and the absolute number of fatalities. The variables used include
total fatalities, driver characteristics, disposable income, alcoholic beverage consumption,
14
percentage of drivers 16 to 24 years old, education, vehicle travel miles, temperature, and
population density. They found consistency with the presence of the off-setting
hypothesis, which presumes that use of the seatbelt gives the driver the perception of
having additional safety, which justifies in the driver’s mind additional risk taking.
Sen (2001) provided an empirical test of the off-setting hypothesis using a data set
of Canadian drivers from 1975 to 1992. The study included the following explanatory
variables; the presence of mandatory seatbelt legislation for drivers; penalties that capture
highway speed limits, and the severity of punishment such as license suspensions, fines
and imprisonment, alcohol consumption control laws, automobile insurance laws, and a
control exogenous variables that measure traffic density, road conditions, demographic
trends, medical care, economic conditions, and alcohol consumption and availability;
state and time fixed effects. Sen’s results suggested the existence of partial off-setting
behavior by drivers in response to the enactment of seatbelt legislation. Proponents of the
seatbelt law legislation had anticipated a 29 percent decrease in driver fatalities.
However, Sen’s econometric estimates indicate that the introduction of seatbelt
legislation showed only a 21 percent decline in driver fatalities.
Cohen and Einav (2003), using US panel data from FARS, evaluated the presence
of the off-setting hypothesis resulting from seatbelt use as well as the reduction of
fatalities resulting from seatbelt use. Their data includes observations for all 50 states
plus the District of Columbia for the period 1983 to 1997. They analyze traffic fatalities
related to traffic laws that influence seatbelt usage. Cohen and Einav introduced state
fixed and time effects in their analysis to account for unobservable state characteristic
15
that are fixed over time, like population characteristics, traffic and weather conditions,
and technological changes that introduce safer vehicles overtime, as well as advertising
campaigns targeting driving behavior. The results from their study showed that there is
no evidence of the seatbelt driver’s off-setting hypothesis, and that seatbelt use reduces
fatalities. Further, suggesting that if the national seatbelt usage would increase from 68 to
77 percent it will save from 500 to 1200 lives annually. In addition, the authors compared
their results to those from the National Highway Traffic Safety Administration; their
result on the reduction of fatalities is smaller than the reduction rate estimated by the
National Highway Traffic Safety Administration.
2.13
Summary
In summary, the literature reviewed above suggests that increasing alcohol
consumption increases traffic fatalities, while increasing the minimum drinking age
decreases fatalities, except for Asch and Levy (1987) and Loeb (1987), who suggest that
increasing the minimum drinking age does not reduces fatalities (i.e. it just postpones
them).
Literature related to the effect of young drivers, minimum age to drive and the
requirement of driving learning systems suggests that these factors are significant in the
determination of traffic fatalities. The elderly driver’s effects centers around the notion of
the decline of physical abilities and deterioration of vision among elderly drivers and the
state’s ability capture these issues at the time of the driver’s license renewals.
Factors related to vehicle safety developments also reduce traffic fatalities.
Increases in the number of newer vehicles in the road equipped with seatbelts, airbags,
16
and antilock brakes reduce traffic fatalities. Similarly, vehicle inspections and a more
homogeneous vehicle mix on the road reduce fatalities. On the other side, the volume of
driving, which is related to gasoline taxes, weather, and fuel efficiency vehicles
contribute to increases in traffic fatalities.
Most authors agree that seatbelt laws reduce fatalities. However, literature
suggests that there are mixed results on the presence of the seatbelt off-setting hypothesis.
Calkins, Zlatoper, and Thomas (2001) suggest consistency with the presence of the offsetting hypothesis. Sen (2001) showed the existence of a partial existence of the offsetting hypothesis. Cohen and Einav (2003) concluded that there is no evidence of the
seatbelt driver’s off-setting hypothesis
The following chapter presents a description and the rationale for the
incorporation of the data used in this thesis. Further, it presents a description of the data
used in this thesis and the empirical model used estimated with these data.
17
Chapter 3
DATA AND EMPIRICAL MODEL
This chapter begins with an exploration of the data on used in the analysis. The
data are divided in three time periods: 1982-1994, the period before the laws prohibiting
undocumented workers from obtaining a driver’s license were implemented, 1995-2006
the period after the implementation of the first such law, and 2000-2003 when the
majority of the states implemented a similar law. This chapter also includes a data section
where the sample statistics parameters, sources, and definitions are explained. The data
statistics section presents an analysis of the descriptive statistics. Last, the empirical
model section describes the logit model used in this thesis and the dependent and
explanatory variables included in the model.
3.1
Time Trends
Most papers reviewed in the prior chapter use either state population or number of
vehicle miles traveled per state to normalize the effect of large and small states. The
authors that use the number of vehicle miles traveled per state used it with the premise
that states with larger number of vehicle travel miles should have larger number of
vehicle fatalities due to the increased driving exposure; while authors that used state
population used it with the premise that states with larger population should have large
number of vehicle fatalities, since more people will drive in larger populated states.
In this thesis, we estimate fatality rate which is obtained by dividing the number
of unlicensed drivers involved in traffic fatalities over the total traffic fatalities for each
state in each year. This allows the analysis to consider changes in the share of the
18
fatalities involving an unlicensed driver relative to all fatalities. The following three
figures present trend lines of the fatality rate for three different time periods.
Figure 3.1 shows the fatality rate variable for the period 1982-1994, the period
before any laws banning the issuance of driver’s licenses to undocumented workers were
implemented. In this figure the fatality rate decreased from a predicted maximum of
0.065 in the year 1982 to a predicted minimum of 0.045 in 1994. This represents a
reduction of approximately 44 percent over the 13 year period. The decrease on the
fatality rate suggests there was a degree of effectiveness on the mix of measures
implemented to mitigate traffic fatalities. Intuition tells us that the overall traffic
fatalities from the period before the laws were implemented were decreasing as time
increased.
Figure 3.1 Fatality Rate (1982-1994)
0.39
0.36
Fatilities Rate
0.33
0.30
0.27
0.24
0.21
0.18
0.15
0.12
0.09
0.06
0.03
0.00
1982
1983
1984
1985
1986
1987
1988
Time
1989
1990
1991
1992
1993
1994
19
Figure 3.2 shows the fatality rate for the period 1995-2006. This represents the
period after the law became effective in the first state. The figure shows the fatality rate
increasing from a predicted minimum of 0.051 to a predicted maximum of 0.054, an
increase of 5.6 percent. From figure 3.1 above we inferred that the overall traffic fatalities
decreased as a result of the array of safety laws and policies as well as safer vehicle
design gains accomplished for the most part in the earlier years of their introduction and
subsequently these safety enhancements were later accepted by most drivers as the norm.
Intuition tells us that an increase in the fatality rate implies an increase of unlicensed
driver fatalities relative to all traffic fatalities.
Figure 3.2 Fatality Rate (1995-2006)
Fatalities Rate
0.21
0.20
0.18
0.17
0.15
0.14
0.12
0.11
0.09
0.08
0.06
0.05
0.03
0.02
0.00
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Time
The movement of banning undocumented workers from driving began with
California in 1995 being the first state to ban undocumented workers from being able to
20
get a drivers license. Thereafter an array of other states implemented similar laws after
the terrorist attacks of September 11, 2001 and the Patriot Act signed into law in October
26, 2001. Table 3.1 presents a list of the states and the corresponding years when the
laws requiring proof of identity and legal status of the applicant applying for a driver’s
license became effective.
Table 3.1 Years when States Put Law into Effect
Year
1995
2002
2003
2004
2005
2006
2007
2008
States
California
Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho,
Illinois, Indiana, Kentucky, Louisiana, Massachusetts, Minnesota,
Nevada, New Hampshire, New Jersey, New York, North Dakota, Ohio,
Rhode Island, South Carolina, Texas, and Vermont
Kansas, Oklahoma, and Virginia
Mississippi
Alabama, Arkansas, Iowa, Missouri, South Dakota, Tennessee, and
Wyoming
North Carolina, Pennsylvania, West Virginia, Wisconsin, and D.C.
Alaska, Michigan, and Nebraska
Hawaii, Maine, Maryland, New Mexico, Oregon, Utah, and
Washington
As mentioned previously, California was the first state to implement the law in
1995. Almost one half of states implemented similar laws by the year 2002. By 2005
eleven more states adopted similar laws.
A closer observation of table 3.1 suggests that most states implemented the laws
from the years 2000 to 2003. During this period of time half of the states implemented
the laws which banned the issuance of driver’s licenses to undocumented workers. There
were 23 states that implemented similar laws in 2002 and three states in 2003. There were
no states implementing the law in years 2000 and 2001.
21
Accordingly, figure 3.3 shows the trend line of the fatality rate for the time period
2000-2003, when most states implemented similar laws. The figure shows the predicted
fatality rate increasing from 0.050 to 0.053, an increase of 5.7 percent. The fatality rate
increase is similar in size to the increase from the period 1995-2006 as seen in figure 3.2.
This increase in the ratio could be the result of the increase observed in the unlicensed
driver deaths, as suggested by Figure 3.2.
Fatality Rate
Figure 3.3 Fatality Rate (2000-2003)
0.17
0.16
0.15
0.14
0.13
0.12
0.11
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
2000
2001
2002
2003
Time
In summary, the time trends analyzed in this section show that the period before
the law was implemented, the fatality rate was decreasing due to reduction in the number
of traffic fatalities. The period after the law was implemented and the period when most
states implemented similar laws had an increasing fatality rate driven by an increase in
unlicensed driver fatalities. The descriptive statistics section of this chapter will explore
22
further the period before the laws were implemented and the period when most
implemented similar law. The following section includes a description of the data used
in this thesis and its sources
3.2
Data
As previously mentioned, the data used in this thesis cover the 50 states and the
District of Columbia over the period of 1982 through 2006, yielding a total of 1,275
observations. Table 3.2 shows a list that includes the dependent and explanatory
variables used in this thesis, the sources of the data, and short definition of the variables.
Table 3.2 Variable Source and Definitions
Variable Label
Dependent:
Fat
Explanatory:
Ac
Y
Yd
Ed
Law
Sb1
Data Source
Definition
Fatality Analysis
The fatality rate is the number of unlicensed fatalities
Reporting System
divided by the total number of fatalities.
(http://www.nhtsa.dot.
gov/)
National Institute on
Alcohol Abuse and
Alcoholism
(www.niaaa.nih.gov/)
Per capita volume beverage and ethanol consumption for
States, census regions, and the United States, 1970–2006.
Bureau of Economic
Analysis
(www.bea.gov/)
Statistical abstract of
the United States
(www.census.gov/)
Statistical Abstract of
the United States
(www.census.gov/)
The United States
Justice Foundation
(www.usjf.net
/modules.php)
The Insurance
Institute for Highway
Safety
(www.iihs.org/laws/s
afetybeltuse.aspx)
Real per capita disposable income of 82-84 dollars in
thousands.
Percent of state's population that is between 0 and 19 years
of age.
Percent of state's population that is over 64 years old.
Year when the state law required driver's license applicants
to show proof of legal status in the united States (dummy
variable 1= year when law became in existence 0= law was
not present)
Year when the state law required the use of seatbelts
(dummy variable 1= year when law became in existence 0=
law was not present)
Table 3.2 Variable Source and Definitions (Continue)
23
Variable
Explanatory:
Sb2
Qd
Pd
Data Source
Definition
The Insurance
Institute for Highway
Safety
(www.iihs.org/laws/s
afetybeltuse.aspx)
Statistical abstract of
the United States
(www.census.gov/)
Statistical abstract of
the United States
(www.census.gov/)
Year when the state seatbelt law became a primary
enforcement (dummy variable 1= year when law became
in existence 0= law was not present)
Per capita number of vehicle mile traveled per state for the
year (in millions).
Number of people per square mile in hundreds.
The explanatory variables used in this thesis are similar to those used in prior
studies except for the law variable, which captures the effect of implementation of the
law that prohibits the issuance of a driver’s license to people unable to show proof of
identity and proof of legal admittance into the country. It is not clear whether this will
have a positive or a negative effect on Fat given that some unlicensed drivers may drive
without learning the rules of the road thus, increasing fatalities, and those who limit their
driving and are extra careful when they must drive to prevent being detected, thus,
decreasing traffic fatalities. In addition, this thesis includes an evaluation of those
explanatory variables that for the most part are used in the majority of papers included in
the literature review section of this thesis.
A variable not considered is the implementation of the mandatory airbags. The
airbag law became effective in 1976 for all states at the same time. This information is
excluded given time fixed effects are estimated in the analysis, which capture changes in
policies that affect all states the same.
24
The following section in this chapter presents the descriptive statistics of the data
used in thesis. The descriptive statistics are presented at three different intervals similar to
the prior time trends section.
3.3
Data Statistics
In this section we explore the descriptive statistics for the three periods explored
in the times trends section. The periods are as follow: period one (1982-1994), the period
before the law was implemented in any state, period two (1995-2006) is the period from
after the law was implemented in the first state through the end of sample period, and
period three (2002-2003), a period when the majority of the states implemented the laws
of banning the issuance of driver licenses to undocumented workers.
Table 3.3 below shows the descriptive statistics, which includes the mean,
standard deviation, and maximum and minimum values for each of the variables
considered in the regression for the period 1982-1994.
The dependent variable Fat has a mean of 0.054, which suggests that for every
1,000 traffic fatalities 54 are unlicensed drivers. The data shows that the state of
Alabama in 1988 had the maximum with a ratio of 0.25 suggesting that out of every 1000
fatalities 250 were unlicensed driver fatalities.
25
Table 3.3 Descriptive Statistics (1982-1994)
Variable
Mean
Std. Dev
Min
Max
Fat
0.054
0.031
0
0.250
Ac
2.01
0.55
0.91
4.52
Y
12.04
1.90
7.77
18.24
Yd
29.78
2.74
22.31
40.77
Ed
12.10
2.17
2.92
18.41
Law
0
0
0
0
Sb1
0.50
0.50
0
1
Sb2
0.11
0.32
0
1
Qd
8,373.89
1,417.97
4,575.49
14,404.25
Pd
358.90
1,396.42
0.79
10,395.26
The mean of the alcohol consumption variable is 2.01 gallons of alcohol per
capita per year. The District of Columbia in 1982 had the maximum of alcohol
consumption at 4.5 gallons per person. Utah in 1994 had the minimum alcohol
consumption at 0.91 gallons per person.
The income variable has a mean of $12,400. In 1982 Mississippi had the
minimum per capita income of $7,770. The District of Columbia had the maximum
income per capita 18,240 in 1993.
In this period 29.78 percent of the population can be classified as young people
from zero to 19 years of age. The District of Columbia in 1992 had the minimum percent
of young people at 22.31 percent and Utah in 1983 had the maximum at 40.77 percent.
The percent of elderly population in a given state had a mean of 12.10. Alaska in
1983 had the minimum percent of the elderly population at 2.92 and Florida in 1992 had
the maximum at 18.41.
26
The law variable is represented by a dummy variable and it was not in effect
during the prior period. The seatbelt laws are represented by a dummy variable also.
During this period half of the states implemented laws requiring seatbelt use.
Furthermore, primary enforcement of the seatbelt was present in 11 percent of the states.
The quantity of driving, Qd, had a mean of 8,373 miles driven in a given year.
The state of New York in 1992 had the minimum number of per capita miles driven at
4,575 and the state of Wyoming had the maximum number of per capita miles driven at
14,404.
On average there were 358 people living in a square mile. Alaska in 1982 had the
minimum population density at 0.79 people per square mile and the District of Columbia
had the maximum in 1986 at 10,395 people per square mile.
Table 3.4 below shows the descriptive statistics for the period 1995-2006. During
this timeframe the fatality rate decreased from 54 unlicensed driver fatalities to 51 for
every 1000 fatalities. The District of Columbia in 1996 had the maximum of 200
unlicensed driver fatalities for every 1000 deaths, a reduction of 50 deaths from the prior
period.
The alcohol consumption had a mean of 1.85 gallons per capita, a decrease of
almost eight percent from the prior period. New Hampshire had the maximum
consumption of alcohol in 2006 of 3.48 gallons per person. Similarly as in the prior
period Utah had the minimum consumption in 1995 of 0.89 gallons per person.
27
Table 3.4 Descriptive Statistics (1995-2006)
Variable
Fat
Ac
Y
Yd
Ed
Law
Sb1
Sb2
Qd
Pd
Mean
0.051
1.85
14.31
28.19
12.58
Std. Dev
0.029
0.41
2.21
2.19
1.84
Min
0
0.89
10.05
20.84
4.81
Max
0.200
3.48
24.64
37.81
18.35
0.26
0.98
0.33
10,202.40
379.66
0.44
0.14
0.47
1,868.47
2,251.18
0
0
0
5,802.75
1.72
1
1
1
18,461.00
16,354.56
The income variable had a mean of $14,310, and similarly as in the prior period
above the District of Columbia and Mississippi both had the maximum and minimum
income per capita respectively.
During this period 28.19 percent of the population were young people in 2003 the
District of Columbia had the lowest percentage of young people at 20.84 percent. On the
other hand Utah in 1995 had 37.81 percent of the young population.
The average percent age of elderly population was 12.58 percent. Florida in 1995
had the largest elderly population at 18.35 percent, and Alaska had the smallest elderly
population at 4.81 percent.
By the year 2006, 98 percent of the states had implemented a seat belt law and 33
percent of the states had implemented a primary enforcement of the seatbelt law.
28
In the average 10,202 miles per capita were driven during this period. Wyoming
had the maximum number of miles drive at 18.461 and the District of Columbia had the
minimum quantity of miles driven at 5,802 miles per capita.
The population density during this period was 379 people per square mile in the
average. The District of Columbia in1996 had the maximum with 16,354 people per
square mile and Alaska in 2006 had 1.72 people per square mile.
Furthermore, during the years 2002 and 2003 a total of 26 states implemented the
law banning the issuance of driver’s licenses to undocumented workers. The following
table shows the descriptive statistics of this period.
Table 3.5 Descriptive Statistics (2002-2003)
Variable
Mean
Std. Dev
Min
Fat
Ac
Y
Yd
Ed
Law
Sb1
Sb2
Qd
Pd
Max
0.053
0.027
0
0.122
1.88
14.86
27.74
12.51
0.50
0.98
0.38
10,449.32
0.40
2.13
2.16
1.78
0.50
0.14
0.49
1,975.01
0.99
11.35
20.84
6.08
0
0
0
6,115.52
3.32
23.08
35.42
17.16
1
1
1
18,422.00
367.15
1,310.53
1.12
9,428.45
During this period 26 states had implemented such a law and the fatality rate had
a mean of 0.053 or for every 1000 fatalities 53 were unlicensed drivers. This is a
reduction in the unlicensed driver fatalities of three per year from the period 1982-1994,
before the laws were implemented. The District of Columbia in 2003 had the highest
fatality rate at 0.12. This represents a reduction of more than half from the period before
29
the laws were implemented. Further, Vermont had the minimum fatality rate of zero. In
the prior period Vermont also had one of the minimum rates of zero for the years 1990,
1991, and 1994.
The mean of the alcohol consumption variable is 1.88 gallons of alcohol, a
reduction 0f 7.1 percent from the prior period before the laws were implemented
Vermont in 2003 had the maximum ratio of alcohol consumption of 3.32 gallons and
Utah in 2003 had the minimum alcohol consumption ratio of 0.99 gallons. On these three
periods Utah has consistently been the state with the lowest alcohol consumption per
capita.
In this period the income variable had a mean of $14,860. This represents an
increase of 6.7 percent from the period before the laws were implemented. As in the
period prior to the laws being implemented Mississippi and the District of Columbia had
the minimum and maximum incomes respectively. Further, during these three periods the
District of Columbia and Mississippi had the maximum and minimums income per capita
respectively.
During this period the average percentage of young people in a given state was
27.74. This represents a decrease of 6.9 percent in the percent of young people from one
period before the laws were implemented. As before, the District of Columbia in 2003
had the minimum percent of young people at 20.84 percent and Utah in 2002 had the
maximum at 35.42 percent.
The percentage of elderly population variable had a mean of 12.51 percent. This
represents a slight increase of 2.5 percent of the elderly population compared with the
30
period before the laws were implemented. As noted in earlier periods Alaska still has the
minimum percent of the elderly population at 6.08 percent and Florida in 2002 had the
maximum at 17.16 percent.
By 2003, 98 percent of the states had a seatbelt law. Primary enforcement of the
seatbelt law was present in 38 percent of the states compared to 11 percent from the
period before the ban. It is important to note that in 2003 there were more states with
primary enforcement of the seatbelt compared to 2006. This indicates that some states
dropped the primary enforcement of the seatbelt law.
The quantity of driving had a mean of 10,449 miles driven per capita. This
represents an increase of approximately 20 percent compared to the 1982-1994 period.
The District of Columbia in 2002 had the minimum number of miles driven at 6,115.
Similarly as in the earlier time period the state of Wyoming in 2003 had the maximum
number of miles driven at 18,422.
During this period there were 367 people per square mile, a small increase of 2.5
percent in the average from the period 1982-1994. Similar to before, the state of Alaska
in 2002 had the minimum population density at 1.12 people per square mile and the
District of Columbia had the maximum in 2002 at 9,428 people per square mile.
In summary, for the three periods discussed above, Utah had the lowest
consumption of alcohol per capita, and had the largest percentage of young people. The
District of Columbia has the largest income, the lowest percentage of young people, the
largest population density, and the lowest quantity of miles driven per capita. Alaska had
the lowest percentage of elderly population and the lowest population density.
31
Mississippi was the state with the lowest income. Florida had the largest percentage of
elderly population and Wyoming has the maximum number of miles driven.
The following section in this chapter presents an analysis of the empirical model
considered in this thesis, and a discussion of expectation of the explanatory variable
coefficients.
3.4
Empirical Model
This thesis considers a grouped data logit model to predict how changes in the
independent factors, including the law variable, affect the dependent variable fatality rate
(Fat). This model is used since the fatality rate is bound between zero and one and the
logit model provides a transformation that allows the model to remain within these upper
and lower limits. Giannakas, K. and Kaplan, J. D. (2005) used a logit model to estimate
empirical probabilities of dependent variables expressed as ratios bound between zero
and one. In their paper, the authors estimated the audit probability, which is represented
by the ratio of the number of audits divided by the number of program participants. In
addition, using a logit model the authors estimated the noncompliance share of program
participants which is expressed as the ratio bound between zero and one. Accordingly
the following model is estimated:
log(Fat/1-Fat) = cit + Yit + Ydit + Edit + Lawit
+Sb1it + Sb2it +  Qdit Pdit i + t + it
where i=1, 2....51; t=1, 2....25.3
3
The District of Columbia is included along with the 50 US states.
Eq. 3.1
32
In equation 3.1, the dependent variable is expressed as the probability of the
fatalities ratio which as previously mentioned it incorporates the unlicensed drivers with
relation to the all traffic fatalities for a state in a given year. through represent the
coefficients of the regressors and Ac, Y, Yd, Ed, Law, Sb1, Sb2, Qd, and Pd represent the
regressors as described in the previous chapter.
These explanatory variables are similar to those previously explored in the
literature, with the exception of the law variable. It is expected that Yd, Ed, Law, Pd, and
Qd all have a positive relation with the dependent variable. These expectations are in line
with results obtained by the authors reviewed in the literature review section of this
thesis, except for the Ed which has ambiguous results and Law which is a newly
introduced variable. On the other hand, Ac, Sb1, and Sb2 may have a negative relation to
the dependent variable. Similarly, the expectation of Ac is in line with results previously
obtained by all authors reviewed. Results obtained by the authors reviewed revealed that
the use of seatbelts has ambiguous results especially when consideration is given to the
off-setting hypothesis. Last, the sign on the coefficient for income (Y) is ambiguous.
The
represents the unobserved variable that accounts for state fixed effects
which varies from one state to the next but does not change overtime. One example of
state fixed effects is the stiffer drunk driving laws present in one state, while other states
may have more relaxed laws in this area. The represents time fixed effects which
controls for other unobserved variables that change over time yet change the same for all
states. An example of this time fixed effects is the technological improvements to make
33
vehicles safer, it evolves overtime but is constant across states. The represents the error
term, this includes any remaining omitted variables.
The following chapter presents an analysis of the empirical results of this thesis
and a comparison with the results obtained by the different authors explored in the
literature review section of this thesis.
34
Chapter 4
EMPIRICAL RESULTS
This chapter presents the results from the empirical analysis described in the
previous chapter. The analysis included an examination of the correlation coefficients
across explanatory variables and the estimation of four regression models to determine if
the law affected fatalities among unlicensed drivers. The correlation coefficients are used
to determine if multicollinearity is of concern, given inclusion of factors used in prior
research. If these factors are omitted we could face omitted variable bias; but if they are
included we could face a problem associated with multicollinearity.
The regression analysis allows us to more directly see if omitted variable bias
persists in estimating the effect of the law on the fatality rate. The first model is a
baseline model that incorporates factors examined in past studies. The next three models
are a state fixed effects model, a time fixed effects model, and a state and time fixed
effects model, respectively. Recall the state fixed effects are used to control for factors
that vary across states but do not change over time. The time fixed effects are used to
control for factors that change over time yet affect all states the same. The results from
the state and time fixed effects model indicate that the law has a statistically significant
and negative relationship with the fatality rate. More specifically, after the passage of the
law in a state the percentage of traffic fatalities from unlicensed drivers fell in relation to
all other drivers included in the data.
35
4.1
Correlations Across Explanatory Variables
The explanatory variables used in this model are those variables previously used
in the reviewed literature. The use of these factors should mitigate the effect of omitted
variable bias, as their omission could lead to biased estimates. If one or more of these
factors were omitted, and they were correlated with an included factor, as well as being a
determinant of the fatality rate, then the resulting coefficients would be biased. Table 4.1
shows the correlation coefficients for the explanatory variables. As is evident, there is
some linear correspondence among the variables, and if they determine the fatality rate
and were omitted our results would be biased. Furthermore, the lack of any correlation
coefficient close to one suggests that multicollinearity is not a major concern.
Table 4.1 Correlation Coefficients for the Explanatory Variables
Regressors
Ac
Y
Law
Sb1
Sb2
Qd
Yd
Ed
Pd
Ac
Y
Law Sb1 Sb2
Qd
Yd
Ed
Pd
1.00
0.29 1.00
0.00 0.37 1.00
-0.26 0.41 0.20 1.00
-0.15 0.28 0.20 0.32 1.00
-0.29 -0.02 0.24 0.37 0.08 1.00
-0.27 -0.56 -0.20 -0.28 -0.12 -0.03 1.00
-0.15 -0.02 0.03 0.11 -0.03 0.08 -0.59 1.00
0.43 0.38 -0.02 0.05 0.00 -0.31 -0.39 0.03 1.00
The correlation coefficient for percentage of the population that is young and
income is -0.56, suggesting that the percent of young population has a moderate negative
correlation with income. There is an even stronger linear relationship between the
percentages of young and elderly population as their correlation coefficient is -0.59.
Correlation results of the other variables show weaker correlations. Hence, we conclude
36
that multicollinearity is not a significant issue in the baseline model, especially with
respect to the Law variable.
4.2
Regression Results
Table 4.2 shows the regression results for the four models estimated to test if the
law affected the percentage of traffic fatalities associated from unlicensed drivers relative
to all drivers. Column 2 shows the results for Model 1 - the baseline model. Models 2, 3,
and 4 follow accordingly. The issues of heteroskedasticity and autocorrelation are
predominant in panel data. As such heroskedastic and autocorrelation consistent standard
errors appear in parentheses below the estimated coefficient in Table 4.2.
Model 1 shows that most of the explanatory variables are significant at the one
percent level except for the quantity of driving and the percent of elderly population.
These results suggest that when the law was passed the share of fatalities from unlicensed
drivers increase relative to all other drivers included in the data. However, omitted
variable bias is likely since state and time fixed effects are not included in the baseline
model. Further, in Model 1 we did not considered random effects estimation which is
typically used in the analysis of panel data when one assumes the non existence of fixed
effects. Therefore, this is a scope limitation of this thesis with respect to Model 1.
Next, the results from the three fixed effects models are presented to see if these
baseline results are robust to omitted variable bias arising from time and state fixed
effects.
37
Regressors
Ac
Y
Law
Sb1
Sb2
Qd
Yd
Ed
Pd
Table 4.2 Regression Models
Model 1
Model 2
Model 3
5E-04 *
4E-04 **
5E-04 *
(1.3E-04)
(2E-04)
(1.6E-04)
0.02 *
0.04 **
0.01
(4E-03)
(0.02)
(0.05)
0.10 *
0.03
0.04
(0.01)
(0.03)
(0.07)
-0.13 *
-0.15***
-0.03
(0.02)
(0.08)
(0.12)
0.41 *
0.18 *
0.39 *
(0.01)
(0.04)
(0.09)
-4.9E-06
-5E-05 **
-1E-05
(3.4E-06)
(2E-05)
(4E-05)
0.10 *
5E-03
0.01 *
(3E-03)
(0.03)
(0.03)
-2E-04
0.05***
3E-04
(4E-03)
(0.03)
(0.03)
-5E-05 **
9.8E-04 *
-4E-05
(2E-05)
(3E-04)
(4E-04)
Model 4
4.3E-04 *
(1E-04)
-0.02
(0.02)
-0.07 *
(0.03)
-0.05
(0.05)
0.12 *
(0.04)
3E-05
(2E-05)
0.05***
(0.02)
-7E-03
(0.03)
5.7E-04**
(2E-04)
State Effects
NO
YES
NO
YES
Time
Effects
NO
NO
YES
YES
0.013
0.026
0.015
0.028
Psuedo R2
* 1%, ** 5%, *** 10%, Statistical Significance
Robust Standard Errors In Parentheses.
Model 2 shows the results of the baseline model with state fixed effects.
Regression results show that population density and enforcement of the seatbelt law are
significant determinants of the fatality rate at the one percent significance level; alcohol
consumption, income, and the quantity of driving are significant at the five percent level;
and the seatbelt law and the percent of elderly drivers are significant at the ten percent
level. The law variable and the percent of young population are no longer significant.
38
The pseudo R2 doubles from 0.013 to 0.026, suggesting the addition of state fixed effects
increases the explanatory power of the model by 100 percent. An F-test to determine if at
least one state fixed effect was different from zero returned an F-statistic of 1821. This
indicates that we can reject the null hypothesis that all the state fixed effects are zero.
These results also indicate that the baseline model may be biased due to omitted state
fixed effects.
Model 3 incorporates time fixed effects into the baseline model. Regression
results suggest that alcohol consumption, enforcement of the seatbelt law, and the percent
of young population are significant at the one percent significance level. All other
explanatory variables are not significant. Last, the pseudo R2 is 0.015, a slight increase
over the 0.013 seen in the baseline model. An F-test on all the time fixed effects shows
the base model has some level of omitted variables that change overtime but remain
constant across states, as the F-test returned an F-statistic of 488. This suggests we can
reject the null hypothesis that all the time fixed effects are zero. Further, it appears as if
the time fixed effects control for some omitted variable bias given the noticeable change
in the estimated coefficient of the Law variable found in Model 1. This coefficient in
Model 3 is now statistically insignificant and has a value of 0.04 whereas in Model 1 it
was statistically significant with a value of 0.10.
Model 4 includes both state and time fixed effects. Adding in both state and time
fixed effects helps control for omitted variables that are constant over time and vary
across states and omitted variables that are constant across states but vary over time,
respectively. The details of these results are explored in greater detail given this model is
39
preferred over the others. The state and time fixed effects F-test returned an F-statistic of
1554, thus suggesting we can reject the null hypothesis that all of these fixed effects are
equal to zero. Further, we see that the inclusion of both state and time fixed effects
control for omitted variable bias as seen by noticeable changes in the estimated
coefficients.
Results of this model suggest that alcohol consumption is statistically significant
at the one percent significance level. This outcome is in line with results obtained by
Peltzman (1975) and Zlatoper (1989), as both concluded that alcohol consumption is a
significant determinant of traffic fatalities. This result appears to be robust across
specifications given the lack of any meaningful change in the estimated coefficient.
Further, the income explanatory variable has an insignificant negative relationship
with the fatality rate. Peltzman (1975) suggests that income has an ambiguous effect in
the determination of traffic fatalities with two types of knowledge; one being the demand
for safety which reduces fatalities and the other being the demand for thrills which
increases fatalities. The results obtained in this thesis suggest that the demand for thrills
may offset the demand for safety.
A key result is the law coefficient is statistically significant and positive in the
baseline model, but then switches to statistically significant and negative in Model 4. The
change in the direction of the law coefficient suggests the presence of omitted variable
bias subsequently addressed in Model 4. Results of the law coefficient in Model 4
suggests that banning driver licenses to undocumented drivers may have decreased the
percentage of traffic fatalities involving unlicensed drivers relative to traffic fatalities
40
involving all other drivers. This result is in agreement with proponents of the law who
argue that the risk of deportation must outweigh other choices and thus eliminating
licensing that ensure drivers know the rules of the road does not necessarily increase the
percentage of traffic fatalities attributed to these drivers.
Regression results also show that the seatbelt law is not statistically significant.
This may be due to the lack of primary enforcement of the seatbelt laws across the states,
which meant that a driver could not be stopped by a law enforcement officer under the
suspicion that the driver was not using the seatbelt. However, the enforcement of the
seatbelt law variable is statistically significant at the one percent significance level.
Simply having laws in the books is not necessarily a deterrent to traffic fatalities.
However, having the law with a primary enforcement feature reduces traffic fatalities.
Cohen and Einav (2003) suggest that the use of the seatbelt decreases the overall traffic
fatalities.
The quantity of driving is not statistically significant and has a positive
relationship with the fatality rate. This result, although not statistically significant, is
consistent with results suggested by a number of authors as follows: Cohen and Dehejia
(2004) which indicate that fuel efficiency vehicles increase the quantity of driving, which
in turn increases traffic fatalities. Leigh and Wilkinson (1991) concluded that gasoline
taxes have an inverse effect on fatalities since it reduces the volume of driving. Zlatoper
(1991) concluded that temperature also contributes to fatalities due to the increase in the
volume of driving associated with comfortable higher temperature and that western states
41
also have a higher number of fatalities due to the better weather which is conducive to a
higher volume of driving.
The percent of young people is statistically significant at the ten percent
significance level and the estimated coefficient is positive. This result is in accordance
with all the authors reviewed, for instance, Asch and Levy (1987), Saffer and Grossman
(1987), Garbacz and Kelly (1987), Fowles and Loeb (1995), and Williams (1997) all
concluded that young drivers greatly contribute to traffic fatalities. Further, Preusser et al.
(2000) concluded that increasing the driving age among the young decreases fatalities.
Groeger and Brown (1989) suggest that it is a driver’s experience, not their age, which
determines traffic fatalities.
The percentage of the elderly population is not statistically significant in the
determination of the fatality rate. However, Grabowski and Morrisey (2001), Nelson, et
al (1992) and Levy et al (1995) concluded that elderly drivers are significant in the
reduction of traffic fatalities due to the effectiveness of the in-person license renewals
and vision tests that weed out elderly drivers that no longer have the physical aptitude to
drive.
Last, population density is statistically significant at the five percent significance
level. This result agrees with the intuition which suggests that larger populated areas have
more vehicles and a higher probability that a fatal accident may occur.
The next chapter presents the conclusion reached in this thesis. It elaborates on
the objective of this thesis, summarizes the results of Model 4 that incorporates state and
time fixed effects, and provides suggestions for future research.
42
Chapter 5
CONCLUSION
The objective of this thesis was to present an analysis of the effects of banning
driver’s licenses to undocumented workers on traffic fatalities. The data used in this
thesis includes yearly data for the 50 states and the District of Columbia from 1982
through 2006, with a total of 1,275 observations. This thesis estimates the fatality rate,
calculated as the ratio of unlicensed driver fatalities divided by all traffic fatalities using a
grouped data logit model. The explanatory variables used in this thesis are similar to
those variables used in prior studies, except for the Law variable that captures the year
when the law banning the issuance of driver’s licenses to undocumented workers was
first introduced in a state.
This thesis presents the estimation results of four models. Model 1 is the baseline
model, Model 2 includes state fixed effects, Model 3 includes time fixed effects, Model 4
includes both; time and states fixed effects. Model 4 is the preferred model since adding
state and time effects helps control for omitted variables that are constant over time and
vary across states and omitted variables that are constant across the states but vary
overtime. Further we can see that the inclusion of both state and fixed affects control for
omitted variable bias as seen by noticeable changes in the estimated coefficients. The
following section summarizes the estimation results.
5.1
Summary of Findings
Estimation results from Model 4 show that the Law variable is significant at the
one percent level. The coefficient estimate of the Law variable is negative, suggesting
43
that banning driver licenses to undocumented workers may have decrease the percentage
of traffic fatalities involving unlicensed drivers relative to traffic fatalities involving all
other drivers. This suggest that the risk of deportation may outweigh other choices, thus
eliminating licensing that ensure drivers know the rules of the road does not necessarily
increase the percentage of traffic fatalities attributed to these drivers.
Explanatory variables that are statistically significant at the one percent
significance level includes; alcohol consumption and enforcement of the seatbelt.
Population density is statistically significant at the five percent level. The alcohol
consumption coefficient estimate is in line with results obtained by prior authors
suggesting that alcohol consumption increases traffic fatalities. The enforcement of the
seatbelt variable is also in line with the results obtained by prior authors that suggest that
using the using a seatbelt diminishes traffic fatalities. On the contrary, having a seatbelt
law without primary enforcement features is not a statistically significant deterrent of
traffic fatalities. The population density coefficient result agrees with the intuition that
suggests that larger populated areas have more vehicles and a higher probability that a
fatal accident may occur.
The percentage of young people variable is statistically significant at the ten
percent significance level. This result is also in accordance with the results from all the
authors reviewed suggesting that young drivers have a positive relationship with traffic
fatalities.
Other explanatory variables explored in this thesis that are not significant include
income, seatbelt law, quantity of driving, and the percent elderly population. The income
44
variable has a negative relationship with the fatality rate. This suggests that the demand
for safety may offset the demand for thrills, as previously explored by Peltzman (1975).
The quantity of driving variable has a positive relationship with the fatality rate.
Although the quantity of driving variable is insignificant its positive relationship is
consistent with a number of authors that suggest that measures geared towards increasing
the quantity driving such as the introduction of high mileage vehicles and decreases in the
gasoline tax tend to increase traffic fatalities. Last, the percent of elderly population
variable has an inverse relationship with the fatality rate, which is in line with results
obtained by authors reviewed; suggesting increases in the percent of elderly population
diminish traffic fatalities.
5.2
Suggestions for Future Research
The estimated result of the Law variable suggests that laws banning
undocumented workers from obtaining driver licenses may reduce the share of fatalities
from unlicensed drivers relative to all traffic fatalities. However, it is unclear if such a
decrease in the fatality rate is due to a reduction in the quantity of driving, the use of
driving substitutes, or a precautious driving behavior of undocumented workers to remain
anonymous members of society. Definitely more work is needed to determine the causes
behind the reduction in the fatality rate attributable to the Law variable.
Further, any cost benefit analysis that evaluates the presence of undocumented
workers in the country should include the effects associated with the causes of the
reduction on the fatality rate. Similarly, such analysis should include calculations on the
monetary savings associated with saving lives.
45
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