# Reflection Essay on Car Accidents

## Introduction

Crash, boom, bang! In an instant, a car accident can change a person’s life forever. Each year, many unsuspecting drivers, passengers, and pedestrians are killed on the roads of the United States. The main question we ask ourselves is why? Are people killed because of high speed crashes? Did the airbags not deploy at the proper time? Were the roads in acceptable conditions? Unfortunately, we can not always determine the causes of all accidents, simply because we were not on the scene of the accident. There are many different reasons why fatal car accidents occur.

Some accidents involve distractions, alcohol consumption, road hazards, or inclement weather. In this econometric paper, the goal is to determine why fatal car accidents occur and what we can do to prevent a possible fatal accident from occurring. II. Empirical Model Specification The following empirical equation is used to determine fatal car accidents (per 100,000 registered vehicles) using ten independent variables. Cross sectional data is collected from 2003, from all fifty states. Eq (1): FCA = f(FUN, SAF, MIL, GAS, SPD, SBT, ROD, DRIY, DRIS, SUV + error term)

Where FCA measures the total number of fatal car accidents per 100,000 registered vehicles. Table 1 lists the independent variables, their definitions, and their expected effect on fatal car accidents. Specifically, this variable is measured by the amount of dollars spent in 2003 (in thousands) for funding highways, divided by the total road length miles (in thousands) in 2003 for each of the fifty states. According to Peters (2004), SAFETEA or The Safe, Accountable, Flexible, and Efficient Transportation Equity Act of 2003 is greatly increasing highway funding and making roads safer.

When more money is spent per mile on highways, we would expect that fewer fatal car accidents will occur because roads are likely to be safer, due to newly constructed roads, more rumble strips, sturdier guard rails, and medians. Therefore, the expected sign of the coefficient of this independent variable is negative. The independent variable ROD measures the total amount of very good and good roads, divided by the total number of roads in each of the fifty states. The better the road conditions, the less likelihood of a fatal accident. When road conditions are very good or good, we consider them to be safe roads. For example, Persaud, Retting, and Lyon (2004) indicate that roads with rumble strips reduce fatalities by up to 25 percent; many good, safe roads have rumple strips.

Thus, safe roads often lead to fewer accidents because they will not be as dangerous at higher speeds to drivers as roads that are considered fair, mediocre, or poor. As a result, the expected sign of the coefficient of this independent variable is negative. The independent variable SAF is the amount of funding for highway safety programs per registered motor vehicle in 2003, measured by the total amount of allocated federal funds for safety programs in each of the fifty states (in thousands of dollars), divided by the total motor vehicle registrations in each of the fifty states (in thousands of registered drivers). According to Dorn and Barker (2004), drivers that follow highway safety professional driver training are safer drivers than those who do not follow a highway safety program.

When more money is spent per registered motor vehicle for highway safety programs, we would expect that fewer fatal car accidents will occur because drivers will be provided with education and safety programs. As a result, there will be a reduction in fatal car accidents. Thus, the expected sign of the coefficient of this independent variable is negative. The independent variable MIL is the average amount of total vehicle miles traveled per registered motor vehicle in each of the fifty states (in thousands) in 2003. The more miles a driver puts on a vehicle, the more likely they are to be involved in a fatal car accident because high mileage drivers spend a significant amount of time on the roads. As a result, the expected sign of the coefficient of this independent variable is positive.

The independent variable GAS is the average 2003 unleaded fuel price in each of the fifty states (in dollars). When gas prices increase, economic theory tells us that the quantity demanded for gasoline will decrease. The expected decrease in demand for gasoline will result in fewer miles driven. As a result, fatal accidents will decrease because fewer people will drive when gas prices are high; they will find alternative modes of transportation. Thus, the expected sign of the coefficient of this independent variable is negative. The independent variable SPD is the urban interstate speed limits in each of the fifty states, measured (in miles per hour) in 2003. High speeds often result in an increase chance in fatal car accidents.

Navon (2001) states that high driving speeds increase crash rates, injury rates, and the probability of a driver losing control of the automobile. Thus, the higher the speed limit, the more likelihood of a fatal car accident. As a result, the expected sign of the coefficient of this independent variable is negative. The independent variable SBT is the seat belt fines amount for each of the fifty states (in dollars) in 2003. If drivers are fined for not wearing seatbelts, they will likely take precaution in the future. The higher the seatbelt fine, the more likely a driver will start wearing a seatbelt on a regular basis because they will want to avoid receiving a hefty fine in the future.

Seatbelts have been proven to save lives. Robertson (1976) states that death occurs 50-80% less often in an accident when a person is restrained, rather than unrestrained. Wearing a seatbelt will lower the likelihood of a fatal accident. Therefore, the expected sign of the coefficient of this independent variable is negative. The independent variable DRIY is the percentage of motor vehicle drivers under the age of twenty-five in each of the fifty states. Younger drivers are inexperienced and are sometimes not familiar with hazardous road conditions. Many young drivers also tend to think of speed limits as insignificant, and often driver faster than the state speed limit.

According to Bingham and Shope, motor vehicle crashes are the leading cause of death in individuals under the age of 35. Moreover, young drivers are more likely to be involved with drug and alcohol misuse. Based on the above arguments, the expected sign of this independent variable is positive. The independent variable DRIS is the percentage of motor vehicle drivers over the age of sixty-five in each of the fifty states. Senior citizens often have health problems that can impair their driving, such as glaucoma or hearing loss. According to West, Gildengorin, et al (2003), poor vision is the most common impairment of senior drivers. The reflex skills and some motor skills of senior citizens are not at the same level as those much younger.

As a result, the expected sign of this independent variable is positive. The independent variable SUV is the ratio of sport utility vehicle registrations to the total vehicle registrations in each of the fifty states. Sport Utility Vehicles are popular today, but have an increased chance of rollovers. According to Rivara, Cummings, and Mock (2003), 60% of all rollover accidents occur in sport utility vehicles. Many Sport Utility Vehicles have safety features that are sub par to that of minivans, trucks, and small cars. Thus, the expected sign of the coefficient of this independent variable is positive, because increasing the chance of a rollover increases the chance of a fatality.

The independent variable DPM is the number of licensed drivers per squared mile in 2003, measured by the total number of licensed drivers per state, divided by the number of square miles per state. States located in the northeast tend to be heavily populated per square mile. As a result, there are many drivers in small areas. With a large number of drivers in a small area, we can expect that the occurrence of accidents is high, due to the amount of traffic and number of vehicles on the road. Therefore, the expected sign of the coefficient of this independent variable is positive, because large numbers of cars per square mile increases the chance of a fatal accident.

One might find it surprising to see West Virginia as having the highest amount of federal highway safety program funding. Typically, one would think that larger states, such as Texas or California would have the highest amount of funding, because of the size of the states. In calculating these values, consideration is placed on the dollar amount per registered driver. Nearly one quarter of West Virginia’s registered vehicles have drivers over the age of 65. This is surprising because when one thinks of a state with many senior citizens, Florida comes to mind. A reason for Florida not having the highest percentage may be that married senior citizens may only have one car. The percentages were calculated by the number of registered vehicles.

Thus, a vehicle may be registered to one person, but two people may drive the vehicle. III. Test of Multicollinearity Multicollinearity occurs when two or more independent variables have a linear relationship, or correlation, with one another. There are two important consequences associated with multicollinearity. First, standard errors of the coefficients contain higher than normal standard errors. The result of this is an increased probability type two error increases (failing to reject a false null hypothesis). Secondly, the most important consequence of multicollinearity is that the Ordinary Least Squares method of estimation will not run.

As a result, an accurate regression can not be done. A correlation coefficient matrix is used to show correlation (multicollinearity) between independent variables. With absolute values greater than.

After running the t-test, I discovered that the coefficients of the variables FUN, MIL, SPD, SBT, ROD, DRIS, SUV, and DPM were not significant at the 5% level. As a result, I cannot support the statement that any of these variables have a significant impact on the dependent variable FCA. The first coefficient of the variable that failed the t-test was FUN. At the 5% level of significance, the state funding per mile of highways in 2003, measured by the amount of dollars spent (in thousands) for funding highways, divided by the total road length miles (in thousands) for each of the fifty states does not have a significant impact on the dependent variable FCA. The second coefficient of the variable that failed the t-test was MIL.

At the 5% level of significance, the total average vehicle miles traveled per registered motor vehicle in each of the fifty states (in thousands) in 2003 does not have a significant impact on the dependent variable FCA. The third coefficient of the variable that failed the t-test was SPD. At the 5% level of significance, the urban interstate speed limit in each of the fifty states, (in miles per hour) in 2003 does not have a significant impact on the dependent variable FCA. The fourth coefficient of the variable that failed the t-test was SBT. At the 5% level of significance, the seat belt fine amount for each of the fifty states (in dollars) in 2003 does not have a significant impact on the dependent variable FCA. The fifth coefficient of the variable that failed the t-test was ROD.

At the 5% level of significance, the percentage of roads in very good and good conditions, measured by the total amount of very good and good roads, divided by the total number of roads in each of the fifty states in 2003 does not have a significant impact on the dependent variable FCA. The sixth coefficient of the variable that failed the t-test was DRIS. At the 5% level of significance, the 2003 percentage of drivers over age 65 in each of the fifty states does not have a significant impact on the dependent variable FCA. The seventh coefficient of the variable that failed the t-test was SUV. At the 5% level of significance, the 2003 percentage of sport utility vehicle ownership in each of the fifty states does not have a significant impact on the dependent variable FCA. The eighth coefficient of the variable that failed the t-test was DPM.

At the 5% level of significance, the number of licensed drivers per square mile does not have a significant impact on the dependent variable FCA. Of the ten independent variables and their coefficients, only three passed the t-test. The first coefficient of the variable that passed the t-test was SAF. At the 5% level of significance, the Federal Highway Safety Program funding programs per registered motor vehicle in 2003, measured by the total amount of allocated federal funds for safety programs in each of the fifty states (in thousands of dollars), divided by the total motor vehicle registrations in each of the fifty states (in thousands of registered drivers) is significant.

According to the regression analysis, when Federal Highway Safety Program funds are considered significant and increased by one thousand dollars, fatal car accidents, per 100,000 registered vehicles are decreased by 14. 67 deaths. The second coefficient of the variable that passed the t-test was GAS. At the 5% level of significance, the average gas price of unleaded fuel price in each of the fifty states (in dollars) in 2003 is significant. According to the regression analysis, when gas prices are increased by one dollar and considered significant, fatal car accidents, per 100,000 registered vehicles are decreased by 3800. 26 deaths. The third coefficient of the variable that passed the t-test was DRIY.

At the 5% level of significance, the percentage of drivers, under age 25 in each of the fifty states is significant. According to the regression analysis, for each one percent increase of drivers under the age of 25, fatal car accidents, per 100,000 registered vehicles are decreased by 136. 85 deaths. The reason for the opposite expected sign of the coefficient DRIY is that the eq (1) contains omitted variable bias. Omitted variable bias occurs when an important variable to the model is omitted. As a result, a low adjusted R2 is present. The low correlation results, presented in the multicollinearity section of this paper, are also most likely due to omitted variable bias.

After finding these results, I added the variable DPM to eq(1) to help adjust for the low adjusted R2. The newly added variable helped improve the regression results slightly. VI. Conclusions The results of my data are surprising. First, my analysis shows that my model had no heteroskedasticity or multicollinearity. It is rare that a model does not contain heteroskedasticity or multicollinearity. When estimating the model, I believed that some of the coefficients of the independent variables would be highly correlated with one another. All of the coefficients of the independent variables had a correlation of under |. 70| with one another, with the exception of FUN and DPM.

Perhaps what is even more surprising is that only three of the coefficients of my ten independent variables were significant in explaining the determinants of fatal car accidents. The coefficients of the independent variables SAF, GAS, and DRIY are significant in my model. I did not expect that the actual sign of DRIY would be a negative, based on what the media tells us of hazardous young drivers. My model suggests that when more young drivers are on the road, fewer fatalities occur. Empirical literature can probably back this claim, as well as disagree. The coefficient of the independent variables SAF and GAS are also considered to be significant in this model.

Unlike DRIY, I expected that increasing both funding for highway safety programs and increasing gas prices would result in a reduction in fatal car accidents; my hypothesis was correct. It is important to keep in mind that my model only captured part of the data that determines fatal car accidents. Much data could not be processed, due to its nature and difficulty to find. For instance, I could not capture the percentage of day time versus night time driving for drivers in each state. Factors like this help to further explain the determinants of fatal car accidents. My goal in examining this topic is to find out what determines fatal car accidents.

While I may not have captured all of the variables and their coefficients, I leave knowing more about fatal car accidents and why they occur than before. By applying econometrics and literature, I now know why some fatal accidents occur and how to help avoid them.

## Data Sources

- United States Department of Transportation, Federal Highway Administration. Highway Statistics 2003.
- November 2004. http://www. fhwa. gov/policy/ohpi/hss/index. htm. United States Department of Transportation, National Highway Traffic Safety Administration.
- Traffic Safety Facts 2003 Early Edition. October 2004. http://www-nrd. nhtsa. dot. gov/pdf/nrd-30/NCSA/TSF2003EarlyEdition. pdf.

## Works Cited

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- Accident Analysis and Prevention 36. 6 (2004): 1073-1079. Peters, Mary. “New Federal Transportation Safety Initiative: Implications for the States. ” Spectrum: Journal of State Government 77. 1 (2004): 25-26.
- Rivara, Fredrick, et al. “Injuries and Death of Children in Rollover Motor Vehicle Crashes in the United States. ” Injury Prevention 9. 1 (2003): 76-82. Robertson, Leon. Estimates of Motor Vehicle Seat Belt Effectiveness and Use: Implications for Occupant Crash Protection. ”
- American Journal of Public Health 66. 9 (1976): 859-864. Studenmund, A. H. Using Econometrics: A Practical Guide. Boston: Addison, Wesley, and Longman, 2001.
- West, Catherine, et al. “Vision and Driving Self-Restriction in Older Adults. ” Journal of the American Geriatrics Society 51. 10 (2003): 1348-1354. Wikipedia.
- “List of U. S. States by Area. ” 15 April 2005. http://www. mywiseowl. com/articles/List_of_U. S. _states_by_area. Crash, Boom, Bang: The Determinants of Fatal Car Accidents An Econometric Study by John White Economics 421 Submitted to Dr. Jacqueline Khorassani April 18, 2005