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American Economic Association
The Effectiveness of Seat-Belt Legislation in Reducing Injury Rates in Texas
Author(s): Peter D. Loeb
Source: The American Economic Review, Vol. 85, No. 2, Papers and Proceedings of the
Hundredth and Seventh Annual Meeting of the American Economic Association Washington,
DC, January 6-8, 1995 (May, 1995), pp. 81-84
Published by: American Economic Association
Stable URL: http://www.jstor.org/stable/2117896 .
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The Effectiveness of Seat-Belt Legislation in Reducing
InjuryRates in Texas
By
PETER
The effects of seat-belt regulations on
automobile-related fatality and injury rates
have been of great interest to economists
and policy-makers over the past few years.’
The effects of the laws have been evaluated
by various statistical techniques using timeseries data for particular states and pooled
time-series data for national models.2 The
results of these studies provide some evidence that seat-belt laws (SBL) reduce injury and fatality rates. However, the effects
of seat-belt laws vary across states and time
periods as well as across the levels of
injuries.
This study assesses the effects of the Texas
seat-belt law on injury rates using policereported accident data. The data are from
the U.S. Department of Transportation
State Traffic Accident Files and are compiled monthly for the period 1982-1987 for
driver-involved accidents. Furthermore, the
data comprise single- and multiple-vehicle
accidents. Only accidents involving towed
vehicles are used in the analysis so as to
normalize for changes in accident-reporting
thresholds over time.3 The analysis was conducted for several sets of injury classifica-
D.
LOEB*
tions using the KABCO scale, which indicates the numbers of fatalities (K), severe
injuries (A), moderate injuries (B), complaints of injuries (C), and no injuries (0).
I. The Model
The Texas seat-belt law went into effect
on September 1, 1985, and fines associated
with violating the law were imposed three
months later on December 1, 1985. Texas is
a primary enforcement state which has one
of the highest seat-belt usage rates in the
country (see B. J. Campbell et al., 1987). A
set of econometric models was developed to
examine the effect of the Texas seat-belt
law on driver-involved injury rates. These
models were of the form
(1)
ln Y =
(81 + 132 ln(Trend)
13
+ E PjX>-2+814D+8
j=3
where ln Y is the natural log of the driverinvolved injury rate defined as the number
of drivers injured divided by the number of
driver-involved accidents, ln(Trend) is the
natural log of a linear time trend, Xi> 2
(j = 3,4, …, 13) are dummy variables to account for seasonality, D is a dummy variable to account for the existence of seat-belt
regulations, and E is a random error term.
The model can be expanded to account for
the potential dynamics of the seat-belt law,
that is, to distinguish between the effect of
the law during the first three months it is in
place (the first-quarter effect) from its effect
in later months (the subsequent-quarter effect). In addition, the model can be expanded to account for other socioeconomic
and driving-related factors such as the unemployment rate, the imposition of a fine
* Department of Economics, Rutgers University,
Newark, NJ 07102. This research was sponsored under
the auspices of the U.S. Department of Transportation, NHTSA: Order No. DTNH22-89-P-07216; Requisition/ Reference No. NRD-01-9-07216. Additional
support was provided by the Rutgers University Research Council and the Graduate School Research
Award Program. I am indebted to Mark Edwards,
William Boehly, David Skinner, and Richard Woods
for comments.
ISusan C. Partyka (1988) has estimated that seat
belts are between 40-percent and 50-percent effective
in preventing fatalities when worn.
2See Loeb (1993) for details.
3Towed vehicles were defined to be those of a
particular level of vehicle damage as measured by the
National Safety Council’s Vehicle Damage Scale. Vehicle damage levels 5-7 were used to identify towed
vehicles. See National Safety Council (1984).
81
82
MAY 1995
AEA PAPERS AND PROCEEDINGS
for violating the seat-belt law, vehicle miles
driven (estimated), and a companion-variable effect. The companion variable attempts
to account for possible driver-involved injuries that are peculiar to Texas and not
adjusted for by the trend variable.4 Hence
one can examine the sensitivity of the estimated effects of the seat-belt law on driverinvolved injury rates due to the inclusion or
exclusion of these other factors.
II. Regression Results
Table 1 provides definitions of variables
and Table 2 provides a sample of regressions for (A + K) driver-involved injury
rates for single- and multiple-vehicle accidents.5 The regressions were estimated by
ordinary least squares. (To conserve space,
the monthly dummy variables to account for
seasonality are not reported.) The coefficients associated with the SBL variables in
the (A + K) regressions are stable across
specifications. They are consistently negative and generally significant (at the 0.05
level or better).6 The subsequent-quarter
effect, as measured by MULDSQ, is not
much different than the first-quarter effect
as seen in equation (ii). A priori expectations would have suggested a smaller coefficient associated with MULDSQ (see Hoxie
and Skinner, 1987). The results may be explained by the imposition of a fine for violating the law, starting in the second quarter
after the imposition of the law. The remaining regressions support this, with the coefficients associated with FINE being consis-
4The companion series looks at the influence of
non-SBL-covered fatalities on driver-involved injury
rates. Many different companion series were generated, and the companion variable that resulted in the
highest adjusted R2 was selected for presentation. See
Paul Hoxie and David Skinner (1987) for a further
discussion.
5Models for single-vehicle accidents and multiplevehicle accidents separately were also developed and
are available from the author upon request.
6These results remain intact when the companion
variable is excluded and when other variations of the
model are estimated. These results are available from
the author upon request.
TABLE 1-DEFINITIONS
OF INDEPENDENT VARIABLES
Variable
Definition
MULDS
A dummy variable indicating when the
seat-belt law was in effect. MULDS
takes on the value of 1 when the seatbelt law was in effect and 0 otherwise.
A dummy variable taking on the value of 1
for all months the seat-belt law was in
effect other than the first three months
and 0 otherwise. (MULDSQ = 1 corresponds to the period a fine was in effect.)
A dummy variable taking on the value of 1
for the first three months the seat-belt
law was in effect and 0 otherwise.
A dummy variable taking on the value of 1
for the fourth through the sixth month
the seat-belt law was in effect and 0
otherwise. (This variable accounts for
the first three months a fine was in
effect in Texas.)
A dummy variable taking on the value of 1
starting with the seventh month the
seat-belt law was in effect (three months
after a fine was initiated in Texas).
FINE2 = 0 otherwise.
Natural log of a linear trend.
Natural log of the unemployment rate.
Natural log of the estimated vehicle miles
driven in Texas.
A companion variable defined as the natural log of the ratio of the sum of motorcycle driver fatalities and other noncovered fatalities in Texas to the sum of
all KABCO injuries from single- and
multiple-vehicle accidents associated
with vehicle-damage levels 5-7.
A constant term.
MULDSQ
FSTQTR
FINE
FINE2
TRENDL
UNEMPL
MILESL
COMPAL
c
tently negative and generally significant (at
the one-tail 0.05 level) and the coefficients
for FINE2 being consistently negative and
statistically significant. In addition, it appears that the impact of the fine (lagged
three months) as measured by the coefficients associated with FINE2 is slightly
larger than the initial impact of the seat-belt
law.
The trend effect was included to account
for factors not directly incorporated in the
model such as possible changes in alcohol
consumption over time.7 The coefficients
are never significant. The unemployment
7Sam Peltzman (1975) suggests a time trend as a
proxy for permanent income.
ECONOMIC ANALYSIS OF SAFETYAND HEALTH REGULATION
VOL. 85 NO. 2
(A + K)
TABLE 2-TEXAS
REGRESSIONS,
1982-1987
TABLE 3-TEXAS
(A + B + K)
Regression
Variable
MULDS
(i)
– 0.081
(-2.599)
-0.085
(-2.949)
MULDSQ
FINE2
TRENDL
UNEMPL
MILESL
COMPAL
c
RBARSQW
DW
(iii)
– 0.086
(-2.852)
(iv)
0.722
1.939
0.717
1.925
– 0.087
(-2.806)
Variable
(i)
MULDS
-0.095
(-6.422)
FSTQTR
MULDSQ
-0.054
(-1.759)
-0.102
(-4.955)
– 0.002
0.003
0.002
(0.200)
(0.142) (-0.247)
-0.131
-0.129
-0.110
(-2.935) (-2.683) (-2.488)
– 0.064
-0.078
(-0.322) (-0.235)
0.104
0.105
0.085
(2.26)
(2.197)
(1.759)
-0.202
-1.029
-0.333
(-0.091) (-0.132) (-5.536)
0.728
2.093
REGRESSIONS,
1982-1987
Regression
-0.083
(-3.478)
FSTQTR
FINE
(ii)
83
-0.057
(-1.654)
-0.107
(-3.329)
– 0.005
(-0.287)
-0.160
(-2.112)
0.054
(0.191)
0.084
(1.714)
-1.531
(-0.582)
0.723
2.065
FINE
FINE2
TRENDL
UNEMPL
MILESL
COMPAL
c
RBARSQa
DW
(ii)
(iii)
(iv)
-0.051
(-3.023)
-0.13
(-8.415)
– 0.056
(-3.626)
– 0.057
(-3.553)
-0.102
(-6.494)
-0.149
(-14.09)
0.026
0.011
0.005
(2.633)
(1.225)
(1.244)
-0.101
-0.063
-0.042
(-3.642)
(-2.415)
(-1.860)
-0.363
– 0.088
(-2.430)
(-0.598)
0.079
0.063
0.044
(2.739)
(2.452)
(1.76)
3.136
0.494
-0.418
(2.28)
(0.363) (-4.38)
0.876
1.804
0.905
1.652
0.917
1.929
-0.103
(-5.874)
-0.151
(-9.184)
0.004
(0.451)
-0.040
(-1.565)
0.025
(0.175)
0.043
(1.716)
-0.653
(-0.484)
0.915
1.912
Notes: The table reports logarithmic regressions of
A + K driver-involved injury rates associated with single- and multiple-vehicle accidents and vehicle-damage
scale 5-7. All regressions include monthly dummy variables (not reported) to account for seasonality. Numbers shown within parentheses below the coefficients
are t statistics.
aAdjusted R2.
Notes: The table reports logarithmic regressions of A + B
+ K driver-involved injury rates associated with singleand multiple-vehicle accidents and vehicle-damage scale
5-7. All regressions include monthly dummy variables (not
reported) to account for seasonality. Numbers shown within
parentheses below the coefficients are t statistics.
aAdjusted R2.
rate is included to account for economic
conditions. The coefficients associated with
this variable are consistently negative and
statistically significant across models, as one
would expect a priori (see Partyka, 1984).
The coefficients associated with the variable
accounting for estimated miles traveled are
never statistically significant. The omission
of this variable [as seen for example in
equation (iii)] does not affect the estimates
associated with the seat-belt variables. Finally, the coefficients associated with the
companion variable (COMPAL) are consistently positive and appear to be significant
(at the one-tail 0.05 level or better). These
results conform with Hoxie and Skinner
(1987). When the companion variable is excluded from the model, the coefficients associated with the seat-belt-law variables remain similar to those reported.
Table 3 examines regressions similar to
those in the prior table for (A + B + K)
driver-involved injury rates. As such, the
regressions evaluate the effect of the seatbelt law (and other factors) on (A + B + K)
injury rates associated with single- and
multiple-vehicle accidents combined when
the vehicles are towed away (damage scale
5-7).8 The results are similar to those reported above. Most important, the estimated coefficients associated with the seatbelt-law variables are always negative and
statistically significant. Once again the co-
8It is common to combine injury levels in the manner shown, i.e., (A + K) and (A + B + K). See, for
example, Campbell et al., (1987).
84
AEA PAPERS AND PROCEEDINGS
efficient associated with MULDSQ is larger
than that associated with FSTQTR, which
may be attributed to the effect of the fine
which was imposed three months after the
SBL went into effect. The coefficients associated with the fine-related variables support this (i.e., the coefficients associated with
FINE and FINE2). The coefficients associated with the time trend are significant in
only one regression. The coefficients associated with the unemployment variable are
again consistently negative and generally
significant, while the coefficients associated
with miles traveled vary in terms of significance from model to model. Finally, the
coefficients associated with the companion
variable are consistently positive and significant (at the one-tail 0.05 level or
better).
III. Conclusion
This study has made use of econometric
models to evaluate the effect of the Texas
seat-belt law on various driver-involved injury rates using a large data set from the
U.S. Department of Transportation’s State
Traffic Accident Files. The data are normalized for vehicle damage levels so as to attempt to account for accidents resulting in
towed-away vehicles. The models account
for the general impact of the seat-belt law
in Texas as well as its dynamic effects (i.e.,
first-quarter versus subsequent-quarter effects), along with the effect of the fine imposed for violating the law. The models also
accounts for seasonal factors, a trend, unemployment rates, miles traveled, and companion effects.
The models indicate that the Texas seatbelt law resulted in a reduction in the various driver-involved injury rates examined.
The SBL coefficients are consistently negative and are generally statistically significant. It appears as if the estimated subse-
MAY 1995
quent-quarter effect is larger in absolute
value than the first-quarte-r effect, which
may be attributed to the introduction of a
fine in the second quarter.9
REFERENCES
Campbell,B. J.; Stewart,J. Richardand Campbell, FrancesA. 1985-1986 experiencewith
belt laws in the United States. Chapel Hill,
NC: University of North Carolina Highway Safety Research Center, September
1987.
Hoxie, Paul and Skinner, David. Effects of
mandatory seatbelt use laws on highway
fatalities in 1985. Cambridge, MA: U.S.
Department of Transportation, Research
and Special Programs Administration,
Transportation Systems Center, April
1987.
Loeb, Peter D. “The Effectiveness of Seat
Belt Legislation in Reducing Various
Driver-Involved Injury Rates in California.” Accident Analysis and Prevention,
April 1993, 25(2), pp. 189-97.
National Safety Council. Vehicle damage scale
for traffic accident investigators. Chicago:
National Safety Council, 1984.
Partyka,Susan C. “Simple Models of Fatality
Trends Using Employment and Population Data.” Accident Analysis and Prevention, June 1984, 16(3), pp. 211-22.
. Lives saved by seat-belts from 1983
through 1987. Washington DC: U.S. Department of Transportation, 1988.
Peltzman, Sam. “The Effects of Automobile
Safety Regulation.” Journal of Political
Economy, August 1975, 83(4), pp. 677725.
91t is interesting to note that the first-quarter effect
is smaller for the (A + B + K) regressions than for the
(A + K) regressions. The reverse seems to be true for
the subsequent-quarter effect.
Copyright of Southern Economic Journal is the property of Southern Economic Association and its content may
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Applied Economics, 2009, 41, 2905–2914
The impact of cell phones on motor
vehicle fatalities
Peter D. Loeba,*, William A. Clarkeb and Richard Andersonc
a
Department of Economics, Rutgers University, Newark, NJ 07102, USA
Department of Economics, Bentley College, Waltham, MA 02154, USA
c
Department of Economics, New Jersey City University, Jersey City,
NJ 07305, USA
b
This article develops a set of models for the determinants of automobile
fatalities with particular attention devoted to the effects of increased
cell phone usage. Cell phones have been associated with both life taking
and life-saving properties. However, prior statistical evaluations
of the effects of cell phones have led to fragile results. We develop
in this article econometric models using time-series data, allowing for
polynomial structures of the regressors. The models are evaluated with
a set of specification error tests providing reliable estimates of the effects
of the various policy and driving-related variables evaluated. The statistical
results indicate the effect of cell phones is nonmonotonic depending
on the volume of phones in use, first having a net life-taking effect, then
a net life-saving effect, followed finally with a net life-taking effect as the
volume of phone use increases.
I. Introduction
The determinants of motor vehicle accidents have
been the topic of interest among economists,
public policy makers and health professionals
for many years. Studies have been conducted on
the determinants of motor vehicle accidents in
aggregate, as well as by components, i.e. automobiles,
trucks, motorcycles, etc. The interest in transportation accidents also led to studies involving railroads,
ships and aircraft as well as accidents due
to the interaction of two or more modes of
transportation. In addition to interest in accidents
themselves, there has been an interest in the
determinants of the outcomes of these accidents, i.e.
injuries, fatalities and property damage.1 Centering
our discussion on motor vehicle accidents, numerous
studies have investigated the effect on motor vehicle
accidents due to: speed, speed variance, alcohol,
speed limits, vehicle miles travelled, measures
of income, unemployment rates, technology
advances, the age of the fleet, population characteristics, police enforcement, seat belt legislation and the
effects of the deregulatory climate which came
about in the 1980s, among others. More generally,
these potential determinants of accidents and factors
reducing accidents may be placed into three categories: those associated with the vehicles themselves,
e.g. technology improvements; those due to the
roadways, e.g. speed limits; and those relating to
drivers, e.g. alcohol consumption, income, seat belt
usage, etc. More recently, the question has arisen as
to the effect of cell phones on motor vehicle accidents.
While it may generally be argued that the probability
of a motor vehicle accident increases with the use
of cell phones by drivers, it is not necessarily
as obvious when considering motor vehicle fatalities.
Some analysts claim that fatalities, like ac …
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