Calkins and Zlatoper (2001)

Prévia do material em texto

The Effects of Mandatory Seat Belt Laws on
 
Motor Vehicle Fatalities in the
 
United States
 
*
Lindsay Noble Calkins, John Carroll University
Thomas J. Zlatoper, John Carroll University
Objective. This article assesses the effectiveness of mandated seat belt usage. The
theory of offsetting behavior asserts that when drivers feel safer, they compensate by
driving less cautiously. As a consequence, any lifesaving effects from mandated
safety devices such as seat belts could be significantly diminished or entirely offset.
Method. This article uses regression analysis and two years (1988 and 1997) of
state-level data to test for the presence of offsetting behavior by estimating models
explaining total and nonoccupant motor vehicle deaths. In addition to accounting
for several factors generally acknowledged as being determinants of highway deaths,
the models control for the impact of primary and secondary seat belt laws. Results.
The findings suggest the existence of offsetting behavior by drivers of motor vehi-
cles. Conclusions. We need to recognize the probability of such compensatory be-
havior and direct our efforts at ways of ameliorating the adverse effects.
“Seat belts save lives” is a popular adage so widely believed that, by year-
end 1997, 49 states plus Washington, DC, and Puerto Rico had adopted
mandated usage laws (National Highway Traffic Safety Administration,
1998:186). Although it is generally agreed that motorists wearing seat belts
will be better protected than those not using seat belts, new evidence has
emerged on the overall effectiveness of mandated usage. Beginning with the
path-breaking work of Lave and Weber (1971) and Peltzman (1975), theo-
retical and empirical work has suggested the possibility that higher rates of
usage of safety devices or regulations, which reduce the probability of death
from any given accident, may result in more risky driving. In particular, the
existence of such “offsetting” behavior would tend to counteract any life-
saving effects of increased seat belt usage. Other things being equal, occu-
pants should be safer in vehicles equipped with protective features, such as
*Direct all correspondence to Lindsay Noble Calkins, Department of Economics and
Finance, John Carroll University, 20700 N. Park Blvd., University Heights, OH 44118 <cal-
kins@jcu.edu>. We are grateful to Scott Boros for his research assistance and to the journal’s
editor, Robert L. Lineberry, and anonymous referees for helpful comments. The data are
available from the first-named author, including all coding materials, for purposes of replica-
tion. Any remaining errors are our responsibility.
SOCIAL SCIENCE QUARTERLY, Volume 82, Number 4, December 2001
©2001 by the Southwestern Social Science Association
Effects of Mandatory Seat Belt Laws 717
padded dashboards, seat belts, and air bags. However, the theory of offset-
ting behavior asserts that when drivers feel safer they compensate by driving
more recklessly. This “compensating” behavior increases the risk faced not
only by vehicle occupants, but also by nonoccupants. Furthermore, the
group most at risk from offsetting behavior may be nonoccupants: the pe-
destrians, motorcyclists, and bicyclists not protected by use of a seat belt.
Whether, on balance, a particular group is more or less safe because of vehi-
cle safety devices depends on the magnitude of the effect of offsetting be-
havior on that group.
Review of the Literature
Since the earliest articles on mandated safety devices, a spate of papers has
appeared that have examined not only the effects of mandated motor vehi-
cle legislation, but also the determinants of motor vehicle fatalities in gen-
eral.1 Much of this earlier research was conducted at the state level and
utilized cross-sectional data.2 More recent analyses have assessed the impact
of various motor vehicle safety regulations using time series data for a par-
ticular state3 or have attempted a pooled cross-sectional time series exami-
nation.4
The result is that we have a much clearer picture of the determinants of
motor vehicle fatalities in general. The effectiveness of mandated safety
regulations, however, is more ambiguous, both theoretically and empirically.
In particular, the evidence regarding the efficacy of seat belt laws is decid-
edly mixed, and there is a growing body of evidence in support of the off-
setting behavior hypothesis. For example, Asch et al. (1991), in their study
of the effectiveness of the New Jersey seat belt law, find that although injury
severity declined, accident frequency increased significantly, a result consis-
tent with offsetting behavior. Chirinko and Harper (1993) extend this line
of inquiry and estimate both a vulnerability rate (defined as the probability
of a fatal injury given an accident) and an accident rate (defined as the
probability of an accident). Their simulation results indicate that accidents
were higher than would have been the case in the absence of improved
safety regulations.
One of the key inferences of the offsetting behavior hypothesis is that
nonoccupants (pedestrians, bicyclists, and motorcyclists) could find them-
selves at substantially greater risk. Peltzman’s (1975) time series estimation
1 For example, see Zlatoper (1989), Hakim et al. (1991), and Loeb, Talley, and Zlatoper
(1994).
2 Representative examples include Peltzman (1975), Loeb (1985), Crandall et al. (1986),
Asch and Levy (1987), Fowles and Loeb (1989), Garbacz (1990), and Zlatoper (1991),
among others.
3 See, for instance, Legge (1990), Asch et al. (1991), and Loeb (1995).
4 Examples are Garber and Graham (1990), Evans and Graham (1991), Legge and Park
(1994), Houston, Richardson, and Neeley (1995), and Merrell, Poitras, and Sutter (1999).
718 Social Science Quarterly
suggests that increased occupant safety is completely offset by the increased
danger to nonoccupants. Garbacz (1990, 1992a) finds that seat belt laws
and seat belt usage rates increase nonoccupant fatalities. Evans and Graham
(1991) also find evidence of offsetting behavior for nonoccupants by con-
trolling for both primary and secondary seat belt laws but argue that the
lifesaving effects of such regulation swamp any risk to nonoccupants. Fi-
nally, Garbacz (1992b) tests whether front-seat laws (seat belt legislation
that requires seat belt usage only for front-seat occupants) put the unbelted
rear-seat passenger at greater risk. His results suggest that rear-seat passenger
fatalities are indeed higher in those states with front-seat laws.
In addition to the papers indicating direct evidence of offsetting behavior,
numerous papers provide what might be considered indirect or weak evi-
dence of offsetting behavior. Among these are Merrell, Poitras, and Sutter
(1999), Maguire, Faulkner, and Mathers (1996), Keeler (1994), Legge and
Park (1994), and Garber and Graham (1990). In these papers, a variable
reflecting either the presence of a state seat belt law or a safety regulation
variable is found to be a statistically insignificant determinant of motor ve-
hicle fatalities. Yet this is consistent with offsetting behavior: if offsetting
behavior is substantial, the increased protection afforded by seat belts might
be completely offset by the increase in fatalities caused by more reckless
driving. The empirical result would be a coefficient deemed not signifi-
cantly different from zero. Finally, it ought to be noted that a statistically
significant negative relationship between mandated seat belt usage and mo-
tor vehicle fatalities does not, per se, refute the offsetting behavior hypothe-
sis. A negative coefficient could reflect the combined effects of greater
protection from seat belts and offsetting behavior.5
This article uses state-level data for the United States to estimate a model
of motor vehicle fatalities and includes many of the factors generally ac-
knowledged to influence such fatalities. To better test for the presence of
offsetting behavior, this article adopts the followingmethodology. First, we
use two categories of fatalities: total motor vehicle fatalities and nonoccu-
pant fatalities. Both test for the presence of offsetting behavior, but the ef-
fect may be more definitive for nonoccupants. The net effect of mandatory
seat belt laws on total fatalities will be a combination of the lifesaving effect
of seat belts in the event of an accident and the effects of less cautious driv-
ing. In the case of the nonoccupants, who are not protected by seat belts,
however, there are not the same conflicting effects. If drivers are less cau-
tious in states with mandatory seat belt laws, we might predict greater non-
occupant fatalities.
5 Studies concluding that seat belts have a lifesaving effect are Crandall and Graham
(1984), Michener and Tighe (1992), Loeb (1995), and Houston, Richardson, and Neeley
(1995).
Effects of Mandatory Seat Belt Laws 719
Second, we control for the impact of both primary and secondary seat
belt laws. Conventional wisdom suggests that primary laws, which allow the
police to stop a motorist for not wearing a seat belt, may be more effective
at increasing seat belt usage than secondary laws, which permit officers to
cite a motorist for noncompliance only after stopping the motorist for an-
other traffic offense. Third, we use two years of data, 1988 and 1997, in
order to observe any differences over time. Fourth, we estimate three differ-
ent functional forms of the basic fatality regression. This is particularly im-
portant insofar as the statistical results may be sensitive to functional form.
Our more comprehensive approach permits an assessment of the robustness
of the results: results that are consistent across specifications are deemed to
be stronger and more reliable. Although a few earlier papers have controlled
for both primary and secondary seat belt laws or have examined differences
over time, fewer still have controlled for the different classes of fatalities or
have estimated alternative functional forms. Our specific contribution to
the literature on the offsetting behavior hypothesis is to combine all of these
different methodologies.6 In addition, separate functional forms also allow
us to evaluate the appropriateness of estimating death rates (here defined as
deaths per vehicle mile). Death rates, though commonly used in estima-
tions, presume a linear relationship between vehicle miles and motor vehicle
fatalities across states that may not exist (Hakim et al., 1991).
Model
The general form for the model of motor vehicle fatalities is the follow-
ing:
Totdeaths (or, alternatively, Nonoccupants) = f (Income, Speed Limit,
Pop Density, Alcohol, %Young, Urban, Capital Exp, Maintenance, Po-
lice, Temperature, West, Primary, Secondary, Vehicle Miles, Mlda88,
Education).
In total, we estimated 12 separate regression equations: for each class of fa-
talities (total and nonoccupant), we estimated three different functional
forms for each of two years (1988 and 1997). Model 1 examines the rela-
tionship between the set of explanatory variables and deaths per billion ve-
hicle miles (the death rate). Model 2 also focuses on death rates but is
estimated in double-log form to allow for the potential interaction among
6 Previous research that has controlled for the separate influences of primary and secondary
seat belt laws include Zlatoper (1991), Evans and Graham (1991), Garbacz (1992b), Mich-
ener and Tighe (1992), Houston, Richardson, and Neeley (1995), and Merrell, Poitras, and
Sutter (1999). Papers that have estimated separate categories of fatalities, particularly that of
nonoccupants, include Crandall and Graham (1984), Garbacz (1990, 1992a, 1992b), and
Evans and Graham (1991). Keeler (1994) examined two panels of cross-sectional data, and
Chirinko and Harper (1993) tested for the appropriateness of different functional forms.
720 Social Science Quarterly
the independent variables and the interpretation of the estimated coeffi-
cients as elasticities. Model 3 assesses the relationship between the explana-
tory variables and the absolute number of fatalities (albeit in double-log
form).
Nonoccupants represents nonoccupant fatalities and is the sum of re-
ported statistics on bicyclist, pedestrian, and motorcyclist deaths. Totdeaths
is the sum of driver fatalities, passenger fatalities, and Nonoccupants. As in
other studies of motor vehicle fatalities, we include economic factors and
driver characteristics. Income is per capita disposable income in constant
dollars. If the demand for safety dominates the income effect, as has been
found elsewhere, then the relationship between real income and fatalities
will be negative. Alcohol is apparent per capita ethanol consumption in
gallons for beer, wine, and distilled spirits. In accord with conventional wis-
dom and as confirmed by previous studies, the level of alcohol consumption
is expected to have a direct effect on fatalities.
%Young is the proportion of drivers aged 16 to 24. Again in accord with
both theory and evidence, we predict that a larger share of young drivers,
who are less experienced and more reckless, may be associated with higher
fatalities. Education is the percentage of the adult working population with
at least four years of college. Earlier studies (Leigh, 1990; Keeler, 1994) in-
dicate that better-educated drivers are more cautious and more likely to use
seat belts. Vehicle Miles, one of our size control variables, is defined as mil-
lions of total vehicle miles. It is expected that the greater the amount of
driving, the greater the probability of a fatal accident. Vehicle Miles is an
explanatory variable only in Model 3, in which the dependent variable is the
number of fatalities, rather than a fatality rate as in Models 1 and 2.
Environmental and geographic factors have also been shown to affect the
number and rate of motor vehicle fatalities. Pop Density is population per
square mile. Although larger states have greater numbers of traffic fatalities,
population density could have countervailing effects: a more dense popula-
tion could indicate greater congestion, more pedestrian traffic, and hence a
greater frequency of accidents. Yet greater congestion usually implies a lower
speed of travel, more sidewalks, and better public transportation, all of
which would serve to reduce fatalities.
Temperature is the normal daily mean temperature in degrees Fahrenheit.
Numerous studies, including Evans and Graham (1991), Houston,
Richardson, and Neeley (1995), Koshal (1976), and Zlatoper (1991), have
found this factor to be directly related to motor vehicle deaths, presumably
because colder climate conditions encourage less driving and more cautious
driving. West is a dummy variable representing the states of the Pacific and
Mountain Census regions. West equals 1 for states in these regions, 0 oth-
erwise. Consistent with Fowles and Loeb (1989) and Zlatoper (1991), West
should be directly related to motor vehicle fatalities.
Highway factors in the models include Urban, which is defined as the
ratio of urban to total vehicle miles. Theoretically, the direction of the effect
Effects of Mandatory Seat Belt Laws 721
of a greater share of urban driving is unclear: urban travel is usually associ-
ated with greater levels of congestion that imply lower speed but greater risk
of an accident. Previous studies have found evidence of an inverse relation-
ship between Urban and occupant deaths and a direct relationship for non-
occupants (Loeb, Talley, and Zlatoper, 1994). Police represents police and
safety expenditures per capita. We would expect an increase in such expen-
ditures to deter reckless driving and thus to lower fatalities. Capital Exp is a
three-year moving average of capital expenditures per capita on roads and
highways. Maintenance reflects the annual expenditures by all levels of gov-
ernment on the maintenance of roads and highways and is calculated on a
per capita basis. Although the general belief is that new, wider, or better-
maintained highwayswill reduce congestion and be safer for drivers (sug-
gesting fewer fatalities), there may be other incentives at work. As with seat
belts, offsetting behavior may be present if drivers are less cautious because
of the improved condition of the roads. Thus, a priori, the predicted effect
of highway capital and maintenance expenditures is unclear.
In addition to the factors above, we include government regulatory vari-
ables that are believed to influence motor vehicle fatalities. Mlda88 is a
dummy variable for states that had raised their minimum legal drinking age
(Mlda) to 21.7 By 1988, all states except Wyoming had a minimum drink-
ing age of 21, though it, too, finally acceded and raised its Mlda to 21 late
in 1988. Mlda88 is equal to 1 in the case of Wyoming and 0 otherwise.
Since all states had raised the drinking age to 21 by 1989, the Mlda variable
does not appear in the 1997 equations. Speed Limit for 1988 and 1997 is
defined as follows: in 1988 the value is 1 for those states where the rural
interstate speed limit had been increased to 65 miles per hour and 0 other-
wise. In 1997, the variable equals 1 for those states with rural interstate
speed limits 65 miles per hour and over and 0 for those states with interstate
speed limits of less than 65 miles per hour.
Primary is a dummy variable used to reflect the states with primary seat
belt laws. Primary equals 1 for states with primary laws and 0 otherwise. In
1988, only seven states had adopted primary seat belt laws, and this number
had increased to 15 by 1997. Secondary is a dummy variable representing
states with secondary laws. Twenty-five states had adopted secondary laws in
1988, but by 1997, 34 states had secondary laws. Secondary equals 1 for
states with secondary laws and 0 otherwise. By 1997, only one state, New
Hampshire, did not have a mandated usage law in effect. Assuming that
both types of law contribute to higher seat belt usage rates (though the
strength of each effect may not be equivalent), a negative coefficient should
7 The Federal Uniform Drinking Age Act of 1984 allowed the federal government to
withhold highway construction funds from any state with a minimum drinking age below 21
as of October 1, 1986.
722 Social Science Quarterly
indicate a net lifesaving effect, whereas a positive coefficient would be asso-
ciated with strong offsetting behavior.
Data
The collected data are of the 48 contiguous states. Income, Temperature,
and Pop Density data were collected from selected volumes of the Statistical
Abstract of the United States. The Education data were provided by the Cen-
ter for Regional Economic Issues. The information for Vehicle Miles,
Capital Exp, Maintenance, Police, and Urban are reported in Highway Sta-
tistics and the Statistical Abstract, various issues. Per capita alcohol consump-
tion is reported by Williams et al. (1991 and 1998). All fatality data are
taken from the Fatal Accident Reporting System (FARS).8 Information on
state seat belt laws was provided by the Insurance Institute for Highway
Safety and Accident Facts (1995 and 1998). The data to construct the
%Young variable were provided by the Federal Highway Administration
(Highway Statistics). Finally, the Speed Limit and Mlda88 variables were
constructed with data obtained from Accident Facts (1995 and 1998).
Estimation Results
Simple statistics for variables used in the analysis are reported in Table 1;
estimation results are reported in Tables 2 and 3. Note that the standard
errors of the estimated regression coefficients were adjusted for possible het-
eroscedasticity. Condition indices exceeded 30 in all cases, indicating severe
multicollinearity,9 which is discussed later in the text.
In general, the estimation results for 1988 (Table 2) are consistent with
our expectations and with the results of earlier analyses. More importantly,
our results are strongly suggestive of offsetting behavior. In the case of total
fatalities, the primary seat belt law variable was statistically significant10 and
positive in all three models. The secondary seat belt law variable was statisti-
cally significant and positive in Models 2 and 3 and not significant in
Model 1. Yet the statistical insignificance of the secondary seat belt law vari-
able is consistent with offsetting behavior strong enough to negate the life-
saving effects afforded by seat belts.
8 The 1997 data were taken from the National Highway Traffic Safety Administration’s
publication Traffic Safety Facts, which reports the FARS and GES (General Estimates System)
data. The 1988 data were taken from the NHTSA Fatal Accident Reporting System.
9 White’s (1980) heteroscedasticity-corrected standard errors were used to compute the
reported t-statistics in Tables 2 and 3 of this article. According to Gujarati (1995:338): “if the
CI [condition index] is between 10 and 30, there is moderate to strong multicollinearity and
if it exceeds 30 there is severe multicollinearity.”
10 We define statistical significance here to be at the .10 level or less.
Effects of Mandatory Seat Belt Laws 723
Of particular consequence are the 1988 estimation results for nonoccu-
pants (Table 2). If offsetting behavior is present, we argue that the observed
effect may be clearer in the case of the pedestrians, bicyclists, and motorcy-
clists who share the road with motor vehicles and who are not protected by
seat belts. For 1988, the primary seat belt law variable was statistically sig-
nificant and positive at the .01 level in all specifications, suggesting that
these laws are associated especially with greater nonoccupant fatalities.
A statistical interpretation of the primary- and secondary-law coefficients
is also instructive. In Model 1 of the total fatalities regressions (Table 2), the
coefficient on the primary-law variable of 1.685 indicates that there were
about 1.7 more fatalities per billion vehicle miles in primary-law states rela-
tive to the states with no mandated usage laws, ceteris paribus. Given that
the 1988 average number of vehicle miles per state was approximately 42
billion, our results suggest that there were 71 more deaths annually in the
states with primary seat belt laws, an increase of about 7.28% over fatalities
in nonlaw states.
The interpretation of the seat belt coefficients in the other two total fa-
talities models is consistent with the results of Model 1. Using the Halvor-
TABLE 1
Simple Statistics, 1988 and 1997
1988 1997
Variable Mean
Standard
Deviation Mean
Standard
Deviation
Total Fatalities 974.75 981.02 868.73 791.17
Nonoccupants 236.04 310.55 169.69 210.64
Income 13275.54 2198.81 21589.17 3395.22
Alcohol 2.47 0.61 2.23 0.50
%Young 0.17 0.02 0.14 0.02
Speed Limit 0.76 0.43 0.93 0.25
Primary 0.13 0.33 0.27 0.45
Secondary 0.50 0.51 0.71 0.46
Pop Density 149.32 205.36 157.97 209.68
Temperature 54.35 7.49 54.06 7.71
Capital Exp 150.53 61.53 192.95 65.72
Maintenance 88.99 30.03 106.85 40.67
Police 24.91 10.01 33.50 16.66
Mlda88 0.02 0.14 — —
West 0.23 0.42 0.23 0.42
Urban 0.50 0.17 0.52 0.17
Vehicle Miles 41894.19 43041.42 53014.83 52099.01
Education 23.69 4.02 23.06 4.30
NOTE: N = 48.
724 Social Science Quarterly
TABLE 2
Regression Results, 1988
Total Fatalities Nonoccupants
Independent
Variables
Expected
Sign
Model 1
Parameter
Estimates
Model 2
Parameter
Estimates
Model 3
Parameter
Estimates
Model 1
Parameter
Estimates
Model 2
Parameter
Estimates
Model 3
Parameter
Estimates
Intercept 40.252 6.436 –1.249 3.308 4.758 –1.881
(3.899) (2.791) (–0.573) (0.994) (1.069) (–0.434)
Income – –0.001** –0.525** –0.439** –0.001** –0.315 –0.344
(–2.144) (–2.075) (–1.806) (–1.893) (–0.626) (–0.709)
Alcohol + 2.990*** 0.329*** 0.275*** 1.215*** 0.456** 0.474**
(3.82) (3.398) (2.881) (4.135) (2.273) (2.279)
%Young + –28.981 –0.216 –.258 –2.390 0.015 0.030
(–1.012) (–1.115) (–1.276) (–0.231) (0.044) (0.082)
Speed Limit
88 + –2.900 –0.112 –0.073 0.045 –0.121 –0.134
(–2.053) (–1.778) (–1.101) (0.089) (–1.149) (–1.185)
Primary ? 1.685* 0.088** 0.117***1.548*** 0.276*** 0.266***
(1.722) (2.344) (2.882) (4.011) (4.712) (4.436)
Secondary ? 1.037 0.065* 0.086** 0.239 0.034 0.027
(1.173) (1.721) (2.159) (0.801) (0.580) (0.412)
Pop Density ? 31.446* 0.040 0.049 17.964*** 0.087 0.084
(2.017) (0.994) (1.318) (3.441) (1.250) (1.158)
Temperature + 0.254*** 0.629*** 0.733*** 0.036** 0.286 0.250
(4.344) (4.311) (4.435) (1.813) (1.137) (0.981)
Capital Exp ? –0.004 –0.020 –0.042 0.006* 0.100 0.108
(–0.448) (–0.361) (–0.685) (1.854) (1.071) (1.086)
Maintenance ? –0.001* –0.043*** –0.041*** –0.001*** –0.112*** –0.112***
(–1.616) (–2.928) (–2.993) (–3.309) (–4.200) (–4.231)
Police – –0.112*** –0.105** –0.090** –0.006 0.003 –0.003
(–2.767) (–2.282) (–1.848) (–0.414) (0.310) (–0.029)
Mlda88 + 4.900** 0.168** 0.183*** –2.826 –0.364 –0.369
(2.066) (2.452) (2.859) (–3.102) (–2.602) (–2.666)
Education – –0.428*** –0.317*** –0.316*** –0.107*** –0.446** –0.447**
(–2.937) (–2.671) (–2.949) (–2.549) (–1.926) (–1.925)
West + 3.856*** 0.150*** 0.143*** 1.446*** 0.304*** 0.306***
(3.869) (3.432) (3.291) (3.727) (3.915) (3.979)
Urban ? –14.189*** –0.288*** –0.267*** 2.046 0.160 0.153
(–3.461) (–3.739) (–3.676) (1.376) (1.106) (1.083)
Vehicle
Miles + 0.958*** 1.015***
(29.595) (22.754)
Adjusted R2 .658 .665 .986 .657 .579 .969
NOTE: In Model 1 the dependent variable is total fatalities per billion vehicle miles and is linear in the independent vari-
ables. Model 2 is Model 1 in double-log form. Model 3 is in double-log form, and the dependent variable is the log of
fatalities. T-statistics are in parentheses and were calculated with White’s (1980) corrected standard errors. The asterisks
reflect statistical significance (for the appropriate one- or two-tailed test): *** indicates significance at the .01 level, **
indicates significance at the .05 level, and * indicates significance at the .10 level.
Effects of Mandatory Seat Belt Laws 725
sen/Palmquist11 interpretation of dummy variables when the dependent
variable is in logarithmic form suggests the following: in Model 2, the total
death rate is 9% higher in primary-law states and the total death rate is
6.7% higher in secondary-law states than in nonlaw states. Model 3 results
tell a similar story: total fatalities are 12.4% higher in primary-law states
and almost 9% higher in secondary-law states than in nonlaw states.
Moreover, the results for the 1988 nonoccupant equations (Table 2) are
even more startling. Model 2 results suggest that the nonoccupant death
rate is about 32% greater in the states with primary seat belt laws relative to
states with no laws. Model 3 results indicate that total nonoccupant deaths
are 30% greater in the primary-law states.
The Alcohol variable was significant and positive, as expected, for both
classes of fatalities in 1988 and indicates that greater alcohol consumption is
associated with greater motor vehicle fatalities. Also as expected, Tempera-
ture and West were generally significant, indicating that these variables are
associated with greater motor vehicle fatalities. Pop Density, one of our size
control variables, was statistically significant only in Model 1 (the linear
version of the death rate equation) for both the total and nonoccupant
equations. The positive coefficient may reflect that a larger population pre-
sumably generates more traffic and, hence, a greater number of fatal acci-
dents. The other size control variable, Vehicle Miles, was similarly positive
and significant in both the total fatalities and nonoccupant equations of
Model 3. As anticipated, the greater the volume of driving, the greater the
probability of a fatal accident.
The minimum legal drinking age variable (Mlda88) was positive and sig-
nificant in all three specifications of the total fatality regressions. As stated
earlier, every state except Wyoming had raised its drinking age to 21 by
1988. Our results therefore indicate that Wyoming had greater motor vehi-
cle fatalities than states that had increased the drinking age to 21. Contrary
to our expectations, however, the Mlda variable appeared with a negative
sign in the nonoccupant equations but was not significant.12 Although the-
ory suggests that raising the minimum legal drinking age should reduce to-
11 See Wooldridge (1999:219): “Generally, if β1 is the coefficient on a dummy variable, say
x1, when log(y) is the dependent variable, the exact percentage difference in the predicted y
when x1 = 1 versus when x1 = 0 is
100 * [exp(β1 ) – 1] (7.10)
The estimate β1 can be positive or negative, and it is important to preserve its sign in com-
puting (7.10).”
12 Though the t-statistic of the Mlda88 variable is large, the coefficient is deemed to be
statistically insignificant because our prediction is that the coefficient of Mlda88 is greater
than zero. Thus, the null hypothesis is that βi = 0, and the alternative is that βi > 0. There-
fore, a negative coefficient would be less than the critical value necessary for statistical signifi-
cance. Similarly, other variables discussed later in the text have t-statistics that are large in
absolute value, but the estimated coefficients are not significant because they have unex-
pected signs.
726 Social Science Quarterly
tal highway fatalities by reducing the number of drivers under the influence
of alcohol, previous empirical research on the effects of the minimum legal
drinking age requirements have produced mixed results. Loeb, Talley, and
Zlatoper (1994) summarize much of the earlier literature and argue that
inexperience with alcohol may be a particularly important factor influencing
driving fatalities. Thus, increasing the drinking age to 21 may simply shift
the increase in highway fatalities from the younger group to the 21–24 age
group. Furthermore, because only Wyoming had resisted federal pressure to
increase its Mlda by 1988, this variable acts as a proxy for Wyoming and all
of its unique characteristics. Hence, the negative coefficient may reflect
some other characteristic(s) that may contribute to lower nonoccupant fa-
talities in that state.
In addition, our results imply that states with a greater proportion of ur-
ban driving experienced fewer traffic fatalities. This corroborates the results
of previous research and indicates that, although urban driving could po-
tentially serve to increase fatalities because of greater congestion, such con-
gestion may reduce travel speed and, thus, fatalities. Urban was not,
however, statistically significant in any of the nonoccupant equations. Simi-
larly, the %Young variable was not significant in any of the specifications for
either total or nonoccupant fatalities. Though other studies have deter-
mined that there is a positive relationship between young drivers or urban
driving and nonoccupant fatalities,13 given the magnitude of the condition
indices of each equation, at least part of the statistical insignificance noted
above may be attributable to the presence of multicollinearity.
Of particular interest is that Education appears to be associated with
fewer fatalities (both total and nonoccupant). Though the mechanism(s) by
which the level of education affects fatalities remains speculative, this find-
ing presents some intriguing policy implications. Finally, among the four
highway factors (Police, Capital Exp, Maintenance, and Speed Limit), we
found mixed results. Maintenance expenditures were significant and nega-
tive in all specifications and for each class of fatality. Police expenditures
were significant and negative, as expected, for total fatalities, but not signifi-
cant for any of the nonoccupant specifications. Capital Exp, the highway
capital expenditures variable, was generally not significant in the equations
for either total fatalities or for nonoccupants. This could be indicative of
offsetting behavior: on the one hand, newer, wider highways may be safer to
drive on, but on the other hand, they may also encourage more risky driving
behavior such as speeding. The Speed Limit variable was not significantin
any equation for either class of fatalities, possibly because this variable cap-
13 Loeb, Talley, and Zlatoper (1994:24, 32).
Effects of Mandatory Seat Belt Laws 727
tures speed limits only on rural interstates, which account for a very small
proportion of the total miles of roads and highways in each state.14
The results for 1997 are not as pronounced as those for 1988 but never-
theless reveal some interesting relationships. As with the 1988 regressions,
we examine two classes of fatalities: total fatalities and nonoccupants (Table
3). In 1997, the primary seat belt law variable was generally insignificant
except in Model 1 of the nonoccupant equations, where the positive and
significant coefficient suggests 1.848 more deaths per billion vehicle miles
(93 more deaths annually) in primary-law states than in the only nonlaw
state (New Hampshire). Similarly, the secondary seat belt law variable is
significant and positive for both total and nonoccupant fatalities only in
Model 1 and insignificant, though with a positive coefficient, elsewhere.
Our results indicate that, in 1997, there were 3.822 more total deaths and
1.742 more nonoccupant deaths per billion vehicle miles in the secondary-
law states than in New Hampshire. The statistical insignificance of the Pri-
mary and Secondary estimated coefficients, however, may be attributable to
the severe multicollinearity present in the models and to the fact that our
control group, states without mandated usage, is limited to one. Although
we recognize that there are various remedial measures for the problem of
multicollinearity, one of the accepted solutions is to do nothing. We have
chosen this latter remedy in light of the numerous statistically significant
results reported here and our desire to avoid an omitted variable problem.
Although these results for 1997 are not unambiguous support for the off-
setting behavior hypothesis, in most cases they are consistent with its pre-
dictions. Again, the insignificant coefficients in the total fatalities equations
could be indicative of offsetting behavior strong enough to completely
counteract the lifesaving effects of seat belts.
Temperature was positive and significant, as expected, in both the total
and nonoccupant equations, indicating that warmer states experience a
higher number and frequency of fatal accidents. Vehicle Miles remained a
significant and positive determinant of the total number of highway fatali-
ties. Urban, Maintenance, and to a lesser extent, Capital Exp were generally
negative and significant for both classes of fatalities. These results confirm
prior evidence that urban driving and highway expenditures are associated
with relatively fewer fatalities.
Unlike our results for 1988, where average alcohol consumption per cap-
ita appeared to be a significant determinant of highway fatalities, in 1997
Alcohol was significant and positive in only one specification of the nonoc-
cupants regressions. Although we are tempted to infer that the numerous
public and private campaigns to deter drinking and driving appear to have
14 Based on figures reported in Highway Statistics (1997), only 9.4% of total U.S. vehicle
miles were on rural interstate highways in 1997.
728 Social Science Quarterly
TABLE 3
Regression Results, 1997
Total Fatalities Nonoccupants
Independent
Variables
Expected
Sign
Model 1
Parameter
Estimates
Model 2
Parameter
Estimates
Model 3
Parameter
Estimates
Model 1
Parameter
Estimates
Model 2
Parameter
Estimates
Model 3
Parameter
Estimates
Intercept 17.824 2.924 –3.726 –5.006 –12.725 –19.488
(2.379) (1.336) (–1.803) (–2.252) (–2.389) (–3.955)
Income – –0.0004** –0.267** –0.323* 0.0001 0.990 0.957
(–2.213) (–1.280) (–1.583) (1.710) (2.053) (2.152)
Alcohol + 1.148 –0.085 –0.065 0.991*** 0.271 0.283
(1.295) (–0.845) (–0.658) (4.381) (1.153) (1.174)
%Young + –8.662 –0.315 –0.315 –1.966 –0.556 –0.556
(–0.481) (–1.977) (–1.972) (–0.244) (–1.522) (–1.548)
Speed Limit
97 + –4.978 –0.206 –0.209 0.120 0.064 0.063
(–3.572) (–3.765) (–4.231) (0.339) (0.691) (0.701)
Primary ? 2.481 –0.004 –0.037 1.848*** 0.150 0.131
(1.238) (–0.053) (–0.457) (2.753) (0.702) (0.564)
Secondary ? 3.822* 0.062 0.036 1.742*** 0.118 0.102
(1.910) (0.784) (0.436) (2.816) (0.607) (0.478)
Pop Density ? –0.005** –0.156*** –0.153*** –0.0001 –0.068 –0.066
(–2.194) (–5.499) (–5.129) (–0.105) (–1.195) (–1.198)
Temperature + 0.279*** 1.043*** 1.000*** 0.070*** 1.338*** 1.313***
(5.615) (6.805) (6.366) (3.338) (3.451) (3.678)
Capital Exp ? 0.004 –0.119* –0.089 –0.002 –0.346*** –0.329*
(0.723) (–1.907) (–1.316) (–0.898) (–2.832) (–2.025)
Maintenance ? –0.018* –0.097* –0.102* –0.005 –0.314*** –0.317***
(–2.022) (–2.041) (–2.141) (–1.560) (–2.990) (–3.069)
Police – –0.022 –0.045 –0.055* –0.006 –0.115* –0.120
(–1.275) (–1.136) (–1.373) (–0.909) (–1.368) (–1.304)
Education – 0.021 –0.058 –0.035 –0.016 0.202 0.215
(0.196) (–0.462) (–0.276) (–0.440) (0.799) (0.859)
West + 0.709 –0.059 –0.053 0.699* 0.235*** 0.238**
(0.864) (–1.255) (–1.050) (2.687) (2.461) (2.477)
Urban ? –9.443*** –0.127* –0.157** –1.522 –0.425** –0.442**
(–2.773) (–1.901) (–2.136) (–1.084) (–2.682) (–2.586)
Vehicle
Miles + 1.026*** 1.015***
(43.200) (16.736)
Adjusted R2 .793 .831 .988 .519 .545 .955
NOTE: In Model 1 the dependent variable is total fatalities per billion vehicle miles and is linear in the independent vari-
ables. Model 2 is Model 1 in double-log form. Model 3 is in double-log form, and the dependent variable is the log of
fatalities. T-statistics are in parentheses and were calculated with White’s (1980) corrected standard errors. The asterisks
reflect statistical significance (for the appropriate one- or two-tailed test): *** indicates significance at the .01 level, **
indicates significance at the .05 level, and * indicates significance at the .10 level.
been successful, further research may be able to shed greater light on this
important policy issue.
Effects of Mandatory Seat Belt Laws 729
Other differences from our 1988 results include West, the variable repre-
senting the states of the Pacific and Mountain Census regions. Our 1988
results were similar to other cross-sectional analyses and found West to be a
significant, positive determinant of motor vehicle fatalities. In 1997, how-
ever, West appears to affect only nonoccupants. Pop Density was not signifi-
cant across specifications for the nonoccupant class of fatalities but was
significant and negative for total fatalities. If true, the latter result suggests
that the congestion associated with the more populous urban areas may now
diminish the number and rate of fatalities by, presumably, lowering travel
speed. Finally, Education is not significant in any specification and, thus,
may no longer be an important determinant of either class of fatalities.
Again, however, we moderate our conclusions in light of the multicollinear-
ity problem.
Lastly, the use of different functional forms allowed us to test the appro-
priateness of estimating a death rate rather than the absolute number of fa-
talities. At a significance level of .10 or lower, the hypothesis that deaths are
directly proportional to vehicle miles is not rejected in all equations of
Model 3. Proportionality across states indicates that mileage death rates as
well as the number of fatalities are suitable forms of the dependent variable
in this cross-sectional analysis.
Conclusion
Using state-level data for the years 1988 and 1997, this article estimates
equations explaining two separate categories of motor vehicle fatalities: total
(driver, passenger, and nonoccupants) and nonoccupants. To assess the ro-
bustness of the results, we utilize three different model specifications that
reflect methodologies often used to estimate the determinants of motor ve-
hicle fatalities. With respect to many of the variables believed to affect fa-
talities, our results are, for the most part, consistent with that of earlier
research. We find that the number of vehicle miles, state averagedaily tem-
perature, highway maintenance expenditure, and the share of urban travel
are significant determinants of the number of deaths. We also find that lev-
els of education and police and safety expenditures are inversely related to
fatalities in 1988 but apparently are not significant determinants of fatalities
by 1997. Similarly, we find that state per capita alcohol consumption is as-
sociated with greater fatalities in 1988 but not in 1997. And perhaps of
greater interest are the results of Primary and Secondary, the seat belt law
dummy variables. Our results are suggestive of and consistent with the off-
setting behavior hypothesis. The 1988 results indicate that primary, and to a
lesser extent secondary, seat belt laws are associated with greater total and
nonoccupant deaths. This result holds across model specifications. The
1997 results, though not as pronounced, are for the most part consistent
with offsetting behavior.
730 Social Science Quarterly
We believe that our results add to the empirical research in support of the
offsetting behavior hypothesis. This is not to say that drivers should not
wear their seat belts or that we should not encourage the use of air bags or
other safety devices. Rather, the policy conclusion is that we need to recog-
nize the probability of such compensatory behavior and direct our efforts at
ways of ameliorating the adverse effects. For example, if drivers do, in fact,
take greater risks because of the improved crashworthiness of cars and the
additional safety afforded by seat belts and air bags, lowered speed limits
and increased police monitoring might mitigate the effects.15
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