1. SUVs and Pick Ups – Are they really safer?
George Ly
December 15th, 2016
ECON 410
Professor: Scott Farrow

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Introduction:
This paper looks at the risks involved with driving passenger cars, SUVs, and pickups in
the United States. There is a focus on examining the fatalities in each of the vehicle types over
the past 40 years. That will be used to determine whether a vehicle is safer than another one.
According to popular belief, driving a large vehicle is safer (Montoya, 2013). However, is this a
fact or a myth? Over the past 40 years there have been advancements in vehicular and roadway
safety. Have we reached the point that all vehicles are equally safe, or is there a greater risk in
driving a specific type of vehicle?
This is an important subject as many American rely on personal vehicles for personal
transportation to work and also for leisure. Traveling by personal vehicle is the most popular
mode of transportation in the U.S., so it appear that risk adverse individuals would choose the
safest vehicle to reduce the chances of a fatal accident (Fuller, 2008).
All of the data for vehicle miles is from the Federal Highway Administration (FHWA)
publications “Highway Statistics 2014”, table VMT-422C, and “Highway Statistics, Summary to
1995”, table VM-201A. The fatalities information is from the IIHS General Statistics Webpage,
table “Passenger vehicle occupant deaths by vehicle type, 1975-2014.”
Ambiguities
An ambiguity in the dataset is of the vehicle miles travelled (VMT), as the (VMT) was
not solely passenger vehicles. The Department of Transportation changed their method of
measuring VMT in the middle of the dataset. In order to combat this problem, total VMT was
used instead of passenger VMT. Thus, the amount of miles travelled are slightly higher than the
true mileage for passenger vehicles. Additionally, for the regression analysis for the different

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type of vehicles, it would have been more beneficial to use the vehicle miles for each type of
vehicle. However, that was not available. Thus, the total VMT were used instead of individual
vehicle type miles. It would have given a more accurate look into how VMT affect the fatalities
per vehicle type.
Model:
Dependent Variable: Total Passenger Vehicle Fatalities
5% Level of Significance
Variable Coefficient
Intercept 2455845.93*
(184273.1)
Vehicle Miles Travelled (Millions) 0.02*
(0.0012)
Year -1239.95*
(94.52)
R-Squared 0.873
N 40
Figure 1. Regression Table – Total Passenger Fatalities
The VMT variable in this regression of total fatalities has a coefficient of 0.02, which
increases fatalities. It takes approximately 50 million VMT for 1 fatality to occur. With a p-value
smaller than 0.05, we can conclude that the coefficient is significant at the 5% significance level.
The coefficient makes sense, as more miles driven leads to a higher risk of a fatal accident.
Driving more miles puts the individual at an increased risk of fatal accidents due to reasons like
distracted driving, impaired driving, and driver error, regardless of fault. Additionally, the year
coefficient is -1239.95 with a p-value smaller than 0.05, making it significant. The year
contributes greatly to lowering fatalities. This is due to the changing safety regulations and
technologies making vehicles safer throughout the years. Additionally, as people buy newer
vehicles they have access to new technologies that their previous vehicle did not offer, reducing

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the risk of a fatality. The R2 value showed that 88% of passenger fatalities is explained by the
year and VMT variables. This shows a relatively accurate fit for the data.
Dependent Variable: Car Occupant Deaths
5% Level of Significance
Variable Coefficient
Intercept 1975173.60*
(135781.29)
Vehicle Miles Travelled (Millions) 0.0131*
(0.0014)
Year -994.82*
(69.65)
R-Squared 0.946
N 40
Figure 2. Regression Table - Car Occupant Deaths
Dependent Variable: SUV Occupant Deaths
5% Level of Significance
Variable Coefficient
Intercept -32508.91
(62273.91)
Vehicle Miles Travelled (Millions) 0.0022*
(0.00063)
Year 15.00
(31.9)
R-Squared 0.903
N 40
Figure 3. Regression Table - SUV Occupant Deaths
Dependent Variable: Pickups Occupants Deaths
5% Level of Significance
Variable Coefficient
Intercept 423459.99*
(56083.92)
Vehicle Miles Travelled (Millions) 0.0044*
(0.00057)
Year -214.79*
(28.77)
R-Squared 0.621
N 40
Figure 4. Regression Table – Pickups Occupant Deaths

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Three regressions were done, each regression had the dependent variable of the fatality
per vehicle type. With the dependent variable for car fatalities, the year decreases the fatalities by
a large amount and the VMT increases the fatalities slightly. For the case of SUVS, the year
actually increases the fatalities slightly and similarly the VMT increases the fatalities marginally.
In the case of pickups, the year decreases the fatalities and similarly in all three cases, VMT
increases fatalities slightly. The VMT and year variable were significant at the 5% level of
significance for all three regressions, except for the SUV year variable. It was not significant
when comparing the p-values. The VMT coefficients will be evaluated for significance in order
to verify if it is possible to compare the coefficients. If confidence intervals intersect, then the
coefficients cannot be accurately compared. The confidence interval for cars, SUVs, and pickups
do not intersect. At the confidence interval of 95%, cars were between -1136 to -853, SUVs was
-50 to 80, and pickups were between -273 to -157. However, the confidence interval for vehicle
miles travelled intersected, thus it is not possible to compare those coefficients. Comparing the
year coefficients, it can be seen that cars have been getting safer over the years, then comes
pickups, and SUVs have been getting more dangerous, as the fatalities have increased.
Another thing to consider is the R2 value for the three regressions. For cars and SUVs, the
R2 values were approximately 0.9 or higher and pickups was at 0.6. The variables in the
regression for cars and SUVs explained more of the fatalities compared to pickups. Thus, there
must be other factors that affect the fatalities in pickups, but this is for all of the vehicle types.
However, years and VMT were still a significant factor, as it was over 60%. All three types of
vehicles had more variables that affected the fatalities, as the R2 value was not equal to 1.

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Figure 5. Fatalities per Mile (Millions) Graph
Generally, the fatalities per mile have decreased over time. Cars have seen the largest
decline, but SUVs and pickups have remained relatively constant over the years. It may be due to
SUVs increasing popularity (Kierstein, 2016). If there are more on the road, then there is an
increased likelihood they will get involved in an accident. However, cars are more popular due to
cost. Cars are still cheaper than a SUV/pickup, thus they will have a larger fatality per mile
because there are simply more cars on the road.
The risk of driving a larger vehicle compared to a smaller vehicle is the increased
probability of rollovers. Smaller vehicles have lower center of gravities, which decreases the
likelihood of the vehicle rolling over (Car rollover 101, 2014). However, they typically have a
smaller crumple zone compared to a SUV/pickup because of their size. Fatalities are more likely
to occur in smaller vehicles when they get into an accident with a larger vehicle. Additionally, in
multivehicle collisions, the largest or larger vehicles typically have the greatest probability of
surviving. This is due to their weight and size, which provides more protection to the occupants.
Rollover accidents are also very dangerous accidents, as in most SUV/Pickup fatal accidents the
vehicle rollover or was on its side at least once (General Statistics, 2016). Rollovers typically
-
0.00500
0.01000
0.01500
0.02000
0.02500
1975 1980 1985 1990 1995 2000 2005 2010 2015
FatalitiesPerMile(Millions)
Year
Fatalities Per Mile (Millions) over the Years
Fatalities Per Mile (All) Fatalies Per Mile (Car) Fatalities Per Mile (Pick Up) Fatalities Per Mile (SUV)

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occurs after the vehicle leaves the roadway, which also increases the chances of the passengers
being ejected from the vehicle. Ejection from the vehicle certainly increases the likelihood of a
fatality compared to remaining in the vehicle.
There are other risks involved, as driving is a risk that an individual takes when they get
into a vehicle. It doesn’t matter if they are the operator or not, there is always a risk that they can
be involved in a fatal accident. As shown in the first regression, every 50 million VMT a single
fatality is recorded. No one knows who is going to die in that set of 50 million miles. It can be a
risk adverse individual who chooses to drive the safest car possible and obeys all traffic laws, or
a risk loving individual who drives a SUV and erratically.
Some individuals are taking the risk of driving a certain vehicle because they do not have
a choice. It was the only vehicle in their price range, and within each vehicle class there are
vehicles that are safer than others. However, even then there are price ranges within a specific
vehicle that can restrict the consumer from making the most risk averse choice. For instance, a
sedan in their price range might not be the safest vehicle in that class, but it is the only one they
can afford. Another instance is that they cannot afford the higher trim level that offers the
premium safety features. Likewise some individuals take public transportation because they
don’t have a choice, which may introduce new risk factors while removing others. Such as, it
removes the road risk, but introduces the risks of subway travel. However, there is a common
factor that they all vehicles share, the road. One way to reduce the risks of driving in general
would be to have safer roads. Choosing roundabouts over intersections because they are safer
should be a consideration when constructing new roads (Shaw, 2016). Other safety features
could be additional rumble strips, reflective medians, guardrails, and incentives to follow traffic
laws like speed/red light cameras with stricter fines.

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Additionally, there are decisions related to purchasing a new vehicle with certain safety
features within a price range. The question that may come in to play, is how much is a certain
safety feature a value to us? Some features are standard like airbags, three-point seatbelts, and
automatic traction control. However, there are some optional premium features like lane watch,
blind-spot monitoring, and automatic emergency braking. These premium features typically have
a premium price, thus the value of our safety comes into the decision making process. What are
the expected values of these premium safety features? Are they willing to pay more to reduce the
risk of a fatal accident by getting these safety features? This brings in certainty equivalence. Risk
adverse individuals are willing to pay more to take away the risk. Thus, they will purchase the
safest car or pickup possible in their price range.
Another factor that affects the purchasing decision is the communication and perception
of risk. Risk communication of which vehicle is the safer choice will affect everyone on the road.
The way risk is communicated will affect how consumers decide. In some articles, there appears
to be a common preconception that the larger vehicle is safer than the smaller one. It is true, but
it tends to avoid the fact that larger vehicles are prone to rolling over and rollovers can be fatal.
However, it also occurs the other way. Smaller vehicles mention rollover resistance, but omit
that they are less safe in an accident with a larger vehicle. Not presenting the entire situation to
consumers may lead them to making the wrong decision. This can lead to the publisher, writer,
or car manufacture to lose future credibility by purposely or ignorantly omitting some
information. Then there is the perception of risk, which can be due to ignorance due to risk
communication. Poor risk communication can ultimately lead to individuals making poor
decisions because they did not fully understand the risks behind their decision. Culture and
society may also be a factor in the sense that certain vehicles are part of culture. For example, in

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specific areas of the U.S. SUVs/pickups are more popular compared to sedans. In the Midwest
and Great Lakes, the Ford F Series pickup is the most popular vehicle sold. Meanwhile in the
Mid-Atlantic and Northeast, sedans and small SUVs are more popular (Hyde, 2015).
Conclusion
Ultimately, it can be concluded from the regressions that over time cars have been the
safest vehicles. After that are pickups and SUVs have become more dangerous over the years.
There are multiple factors that could have contributed to these results such as, changes in
preferences between larger and smaller vehicles, increases in VMT, and developments in vehicle
and highway technology. In terms of vehicles types, the larger vehicles are safer in multivehicle
accidents, but are more prone to rolling over. Smaller vehicles have a lower probability of rolling
over, but do not fare as well against larger vehicles. The analysis has shown the cars are the safer
options. However, there is a trade off in benefits and risks for both vehicles, so the final choice is
up to the consumer. Some can choose the safest vehicle in the vehicle class, a car as the data
shows, and still cannot remove all of the risk that surrounds them. Some unavoidable risks are of
the actions of others, income limitations, and type of vehicles involved in an accident. Not all of
the risks are in the hands of the consumer, but when it is, a risk adverse individual should chose a
car or pickup over the SUV.

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References
Car Rollover 101 - Consumer Reports. (2014, April). Retrieved December 06, 2016, from
http://www.consumerreports.org/cro/2012/02/rollover-101/index.htm
Fuller, J. (2008, July 07). Why did cars become the dominant form of transportation in the
United States? Retrieved November 21, 2016, from http://auto.howstuffworks.com/cars-
dominant-form-transportation.htm
General statistics. (2016, February). Retrieved November 13, 2016, from
http://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/passenger-vehicles/2013
Heitmann, J., & Depcik, C. (2016). Automobile. In World Book Advanced. Retrieved from
http://worldbookonline.com/advanced/article?id=ar039020
Hyde, J. (2015, February 20). The most popular new vehicle in each state? Not what you might
expect. Retrieved December 04, 2016, from https://www.yahoo.com/news/bp/the-most-
popular-new-vehicle-in-each-state--not-what-you-might-expect-181118005.html
Kierstein, A. (2016, November 04). EPA report shows fuel economy increases despite truck and
SUV boom. Retrieved November 21, 2016, from
http://www.autoblog.com/2016/11/04/epa-fuel-economy-trends-report/
Montoya, R. (2013, March 21). Are Small Cars Safe? Edmunds.com. Retrieved November 21,
2016, from http://www.edmunds.com/car-safety/are-smaller-cars-as-safe-as-large-
cars.html
Shaw, J. (2016, May 31). Intersection Safety - Safety | Federal Highway Administration.
Retrieved December 06, 2016, from
http://safety.fhwa.dot.gov/intersection/innovative/roundabouts/
U.S. Federal Highway Administration, Highway Statistics, Summary to 1995 (1997), Table VM-
201A.
U.S. Federal Highway Administration, Highway Statistics 2014 (2016), Table VMT-422C