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Speed Thrills But Kills_Group 2

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Speed Thrills But Kills_Group 2

  1. 1. 1 | P a g e Econometrics “Speed Thrills but Kills!” Econ 330 Group 2 Ahsan Ahmad Muhammad Ovais Siddiqui Sarmad Alam Shan e Ahmad
  2. 2. 2 | P a g e Table of Contents Abstract 3 Introduction 4 Literature review 5 Econometric Analysis 7 Conclusion (Limitations) 14 Works Cited 15
  3. 3. 3 | P a g e Does an increase in the speed limit increase the total number of accidents on rural roads? Abstract
  4. 4. 4 | P a g e Introduction A sound policy question which we would like to investigate from the data of amount of accidents from 1981-1989 presented to us is: Will the increase in the speed limit from 55 mph to 65 mph affects the total number of accidents on the rural roads positively or negatively ? The governmental intervention and regulation of speed limit laws in the U.S have experienced variation with time. At times of fuel shortages stringent speed limits are enforced aimed at reducing the maximum speed limit. This is the reason why in 1974 due to the severe oil crisis a minimum speed limit of 55 mph was applied that remained in force till 1987. The recovery of U.S economy from recession in 1987 allowed the government at first to amend the National Maximum Speed Law and raise the maximum limit to 65 mph (as shown in the data set) and later to repeal it in 1996 by transferring the regulatory authority to the states. Since then many states like Kentucky (2007), Utah (2009), Ohio (2011) and Texas (2012) have decided to increase their posted maximum speed, with similar proposals underway in Illinois and South Carolina1 . However, there is a need of drawing a distinction between two sets of Highways: statewide rural interstate highways and urban non-interstate highways. Speed limit was raised in rural interstate highways in 1987 speed laws amendment because these highways had less traffic congestion as compared to urban non-interstate highways. Our concern in this study will only be regarding the highways in the rural states. 1 Do We Need Speed Limits on Freeways? Arthur van Benthem
  5. 5. 5 | P a g e Literature Review The effect of speed limits on accidents has been a subject of particular interest to economists since the very beginning. The study of these two variables is an important economical and social concept and a great deal of research has been done in this regard. A few researchers have tried to answer this question directly, whereas other have tried to relate the effects of speed limits to accidents through different measures like traffic, or the characteristics of rural or urban areas. Arthur Van Benthem of The Wharton School, University of Pennsylvania, in his research paper on the effects of Speed Limit changes on the number of accidents, found out that a 10 mph speed limit increase on highways leads to a 3-4 mph increase in travel speed which leads to 9-15 % increase in the road accidents and 34-60% more fatal accidents. He found out that the social costs of raising the speed limit from 55 to 65 mph are three to ten times larger than the social benefits.2 This is in coherence with our model which shows that increasing the speed limit to 65 mph leads to 16.3% increase in the total accidents on the rural roads. 2 Do We Need Speed Limits on Freeways? Arthur van Benthem
  6. 6. 6 | P a g e The government of U.S seems to be pursuing the policy of saving time to cater the fast pace of its capitalist economy. In the time versus fatality cost-benefit analysis U.S government has preferred to weigh the notion of saving travel time more than fatality caused by accidents. In their research, Orley Ashenfelter and Michael Greenstone found out that the 65 mph limit increased speeds by approximately 3.5% (i.e., 2 mph), and increased fatality rates by roughly 35%.3 Thus, there are several researches showing the same kind of relationship between the changes in speeds and resulting increase in the number of accidents. One of the exogenous variables in our regression analysis is seat belt law. Interestingly seat belt law shows a positive impact on the number of accidents in the U.S between 1981-1989 i.e. instead of the conventional perception that seat belts reduce accidents we find that seat belt law actually cause an increment in the accident rate. Our model verifies the views of Peltzman (1975), who argued that drivers wearing the seatbelts feel more secure, they drive less carefully, leading to more traffic accidents. Thus, although the use of seat belts decrease fatalities among drivers wearing them, fatalities among other individuals go up, offsetting the beneficial effects of seat belts. Findings similar to our findings have been reported by McCarthy (1999) who found that a mandatory seat belt law increases the number of fatal accidents, whereas Derrig et al. (2002) found no statistically significant effect of the implementation of seat belt law on road accidents. Moreover, the findings of Alexander C. Wagenaar in his paper, Effects of Macroeconomic Conditions on the Incidence of Motor Vehicle, show that there has been an inverse relation between unemployment and road accidents. An increase in unemployment in 1974 and 1975 was associated with a decrease road crashes and a decrease in unemployment in 1976 to 1978 was associated with an increase in road crashes. This inverse relation between these two variables is explained by the fact that higher unemployment would result in lesser road travel which leads to lesser road crashes. Moreover, high unemployment, which is associated with a fall in income, would reduce the discretionary driving and unemployed people would prefer to save their cost of traveling which, again, leads to lesser number of road accidents. 3 Orley Ashenfelter and Michael Greenstone, September 2002. Using Mandated Speed Limits to Measure the Value of a Statistical Life
  7. 7. 7 | P a g e Econometric Analysis Data Description: The data provided about road accidents of US was recorded over a period of 108 months from 1981-1989. The variables used in our model are listed below. The data was sorted in a time series framework where t is the time variable. Name of variable Description rtotacc Total accidents on rural 65 mph roads spdlaw =1 after 65 mph in effect unem State unemployment rate beltlaw =1 after seat belt law feb =1 if month is Feb. mar =1 if month is Mar. apr =1 if month is Apr. may =1 if month is May. jun =1 if month is Jun. jul =1 if month is Jul. aug =1 if month is Aug. sep =1 if month is Sep. oct =1 if month is Oct. nov =1 if month is Nov. dec =1 if month is Dec. lrtotacc Log(rtotacc)
  8. 8. 8 | P a g e Model Specification and Modification: The data we are taking is about the speed limit which, as explained earlier, was increased from 55 mph to 65 mph in May 1987. Our hypothesis is to see the general association of this increase in speed limit on the number of accidents on these rural roads. For that we run the regression of our basic model. After the regression, the Beta coefficient of spdlaw turns out to be positive. This shows that the modification in the law i.e. the increase of speed limit from 55 mph to 65 mph is associated with an increase in the total number of accidents on the rural roads by 133.5 accidents on average. This increase in total accidents (rural) can be attributed to the increase in speed limit. After allowing the people to drive at a higher speed, they are more likely to have accidents. However, this simple equation needs to incorporate the omitted variables and needs to be checked for several other requirements that should be fulfilled. 200300400500600 tot.acc.onrural65mphroads 0 20 40 60 80 100 time trend
  9. 9. 9 | P a g e Firstly, since time-series data generally follows a seasonal trend, it needs to be de- seasonalised. To do so we added the seasonal months’ dummy variables to the equation keeping January as the base group. In our particular example the effect of seasonality on the number of cars can be such that in different seasons the road conditions may affect the probability of the occurrence of an accident. This means that from our starting equation we have accounted for the effect of seasonality among different months that was formerly showing in the coefficient of spdlaw, thus the coefficient of spdlaw decreases as expected. The high coefficients for the months of June, July, August and September might be because people travel more often during this season which automatically increases the chance of an accident occurring. Further we performed the omitted variable test so as to determine whether the model suffers from any omitted variable bias. Since we get the p-value 0.3616 we conclude that there is no omitted variable bias. However we still add unemployment (unem) variable to the equation because it is a significant variable (according to prior research in the field) and adding it to the equation increases our R2 (the percentage of the total accidents explained by the several independent factors). Adding unem also brings down the coefficient of spdlaw by a significant amount.
  10. 10. 10 | P a g e Unem is highly correlated with total accidents and the relationship is negative. The negative relationship shows that an increase in unemployment rate is associated with a decrease in the total number of accidents. This may be because, as we discussed earlier, as the rate of unemployment increases more people are likely to stay at home and there will lesser road traveling thus less travelling would take place, thereby reducing the chances of accidents. To check for functional form misspecification we carry out the Regression Specific Error Test (RESET). The p-value for this equation’s RESET Test is 0.0432 which shows that functional for misspecification exists. To remove this functional form misspecification we take the log of rtotacc and repeat the RESET. Following this change the RESET shows that there is no more functional form misspecification.
  11. 11. 11 | P a g e Now, our interpretation also changes; the implementation of the policy is now associated with a 23.6% increase in total accidents on rural roads when speed law is implemented comparatively. Our data includes information on another legislation; the belt-law. The use of seat-belt, according to our data, was made mandatory in January 1986. We would like to include this variable as well into the equation so as to gauge the association of the use of belt law and the number of accidents. After the belt-law was implemented, the number of accidents increased on average by about 17.9% (keeping other independent variables fixed). A related explanation was 5 5.5 6 6.5 log(rtotacc) 0 20 40 60 80 100 time trend
  12. 12. 12 | P a g e recently offered by the British Psychological Society. This proposes that during narrow escapes while driving, the actual physical restraint experienced by seatbelt wearers leads to a reduced sense of threat to life. A reduced sense of threat may then lead to the adoption of a more dangerous driving style which increases the accidents. 4 Since we have added variables to our original equation we need to see if the standard errors are high due to multi-collinearity amongst the independent variables. For this we make use of Variance Inflation Factors (VIF). Our mean VIF is 2.07 and the maximum VIF among the independent variables is 2.93. Since these VIFs are well below the benchmark of 4 then this is good news for our model. Durbin-watson statistic (measure of serial correlation in a time-series model) comes out to be 0.996. To cure serial correlation we add lag variables for unemployment rate. This results in a decreased dwstat contributing to serial correlation. 4 The Puzzle of Seat Belts Explained, Press Release of the Annual Conference of the British Psychological Society, April 1999
  13. 13. 13 | P a g e To check for homoskedasticity we use archlm command since the data is time-series. The p-value comes out to be 0.7066 thus we fail to reject our null hypothesis that is no Autoregressive Conditional Heteroskedasticity. Hence our model is homoskedastic. To check if the error term follows a normal distribution or not we use sktest command in Stata. The null hypothesis, that there is no difference between the normal distribution and the distribution of the error term. Since we fail to reject our null hypothesis then we conclude that our error term follows the normal distribution.
  14. 14. 14 | P a g e Conclusion (limitations) Through our econometrics analysis we came to the conclusion that the increase in speed limit in 1987 was quite substantially related with the number of accidents on the rural roads. Our final model estimates that after the imposition of the new speed limit, the total rural accidents went up by 16.3%. The report by Arthur Van Benthem also claims that a 9-15% increase in the total number of accidents due to the increase in the speed limit. We also incorporated ‘The seat belt law’ (beltlaw) and ‘Unemployment’ (unem) variables in our regression model to purify the effect of speed law (spdlaw) on our dependent variable (lrtotacc). There were some limitations with respect to the data provided, for instance, if other driving laws statistics, eyesight laws of driving, other information regarding the condition of roads like frequency of street lights, sign boards, traffic signals and information about drivers for example, age of drivers, alcohol consumption level, experience and education of driving etc. The unemployment information used in our regression model is statewide whereas, the data we have taken into account for total accidents is for the rural roads only. Our model suffers from serial correlation which is the major drawback of our model. Even after incorporating the lag unemployment variables, the dwstat remained close to 0.98. This problem could be attributed to the lack of further available variables as mentioned.
  15. 15. 15 | P a g e Works cited Wagenaar, Alenadaer C. EFFECTS OF MACROECONOMIC CONDITIONS ON. Rep. U.S.A: Pergamon, 1984. Web. 11 Dec. 2012. <http://deepblue.lib.umich.edu/bitstream/2027.42/24788/1/0000214.pdf>. Cohen, Alma, and Liran Einav. THE EFFECTS OF MANDATORY SEAT BELT LAWS ON DRIVING. Rep. N.p.: n.p., n.d. Web. 11 Dec. 2012. <http://econweb.umd.edu/~vegh/courses/Econ396-397/Econ396/Computer-lab- material/Cohen.pdf>. Ashenfelter, Orley, and Michael Greenstone. "Using Mandated Speed Limits to Measure the Value of a Statistical Life." N.p., Nov. 2002. Web. 11 Dec. 2012. <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=331463>. Balkin, Sandy, and Keith Ord. "The Impact of Speed Limit Increases on Fatal Interstate Crashes." N.p., n.d. Web. 11 Dec. 2012. <http://www.consumersunion.org/other/speedlimits/speed031500a4.htm> Wagenaar, Alexander C. UNEMPLOYMENT ANDMOTOR VEHICLE ACCIDENTS IN MICHIGAN. Rep. U.S.A: n.p., 1983. Web. 11 Dec. 2012. <http://deepblue.lib.umich.edu/bitstream/2027.42/208/2/48596.0001.001.pdf>.

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