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Motor carrier deregulation and highway safety: an empirical analysis.

I. Introduction

The passage of the Motor Carrier Reform and Modernization Act of 1980 (MCA 1980) effectively marked the end of rate and entry regulation in the interstate trucking industry, and most economists agree that this legislation has created many important economic benefits during the 1980s. For example, shipping rates fell in real terms, service expanded and improved in quality, and generally resources were used more efficiently.(1) There is increasing concern, however, that deregulation has led firms to reduce safety expenditures in order to remain competitive, and that trucking accidents and fatalities have increased as a consequence [4; 9]. Indeed, Brock Adams (former U.S. Secretary of Transportation) claims that trucking accidents have increased since 1980 for this very reason.(2)

The limited empirical evidence reported in the literature suggests otherwise. Moore [17], for example, shows that accident, fatality, and injury rates have fallen since 1980 despite the rapid increase in the number of truck-miles traveled [13]. His analysis, however, is based on a comparison of rates across time, and does not reveal any of the potential economic or institutional forces that may be affecting the evolution of these data. in a more systematic analysis, Traynor [22] finds that deregulation has reduced accident rates in California, although it may be difficult to extend his results to the national experience as a whole.

This paper has two major objectives. The first is to estimate an empirical model using a pooled, cross section of state data to determine whether the evidence Traynor [22] reports for California holds, in general, for all other states. The second objective is to determine empirically those factors that may be driving the results in Moore [17]. Pooling these data allow us to test for the impact of deregulation on accident, fatality, and injury rates while holding state-specific factors constant; an empirical approach that is not possible at a higher level of aggregation.(3)

The paper is organized in the following manner. In the second section I discuss three possible ways deregulation may have affected accident rates in interstate trucking.(4) The third section presents the empirical model and regression results, and the final section summarizes the major conclusions drawn from this investigation.

II. Motor Carrier Deregulation and Highway Safety

The current theoretical literature provides a framework for discussing the potential link between changes in deregulation and highway safety.(5) I will use this framework to focus on three aspects of deregulation that are likely to affect highway safety in the trucking industry.

Before deregulation, rate bureaus acted as cartels and set shipping rates at supracompetitive levels.(6) Since the Interstate Commerce Commission (ICC) restricted the entry of new trucking firms, incumbent firms were able to earn economic rents that were shared with organized labor.(7) After deregulation, however, the ICC essentially permitted free entry and naturally there was an influx of new firms. Winston et al. [31], for example, report that the number of truckers with ICC operating authority increased from 18,045 in 1980 to 36,948 in 1986.[8] One implication for highway safety is that trucking accidents may have increased because of the additional traffic congestion. Moreover, if the new entrants were inexperienced in handling trucks on congested highways, it is likely accidents would have increased until the new drivers gained the necessary driving experience.(9)

The implicit assumption underlying this argument is that a driver's behavior towards safety remains unchanged in response to a change in the regulatory environment, which Peltzman [18] has questioned on theoretical grounds.(10) Although deregulation in the trucking industry did not involve a specific change in safety regulation, Peltzman's model provides a useful framework to examine the effect of deregulation on the behavior of owner-operators.(11)

The basic model is that drivers face a choice between driving intensity (e.g., faster speeds, longer hours) and the probability of an accident, which is affected by several economic factors: the price of an accident, income, and a regulatory parameter which we will associate with the driver's promotion of safety. In a cross-section model, it is difficult to argue that interstate drivers respond to differential "accident prices" across each state. Moreover, it is equally difficult to argue that secular changes in income that Peltzman discusses are at work to the same extent in this analysis since we are using data for a shorter time span. Therefore, I will ignore the first and second factors and focus on the third.

We can think of deregulation shifting the driver's demand for driving intensity in several ways. The widely-held view is that deregulation has led owner-operators to reduce safety expenditures to remain competitive, which raises the probability of an accident for a given level of driving intensity. It is not quite clear, however, why owners would reduce safety expenditures when their discounted future profits depend on providing timely and safe deliveries today.

On the other hand, deregulation may have induced firms, which employ drivers for hire, to increase their safety expenditures." Suppose a trucking firm uses two complementary inputs, labor (L) and safety (S), per truck to deliver a given quantity of goods. We can think of S broadly as the resources the firm uses to maintain some level of safe operation, which may include any investment in safety training for drivers or the purchase of truck-related safety equipment. In competitive markets, the level of safety the firm provides determines the wages the firm must pay to compensate drivers for any relative risks in trucking.(15) If, for example, the firm offers better training or installs more (or better) safety equipment, then wages would be commensurately lower. As wages fell after deregulation, this may have created an incentive for firms to hire more drivers and to provide additional safety to compensate drivers for any apparent risks in trucking. In addition, it is quite possible that firms provided the drivers with greater safety resources (i.e., equipment or training) because the new drivers were relatively inexperienced. The implication is that if firms increased safety expenditures because wages fell, then accidents would have probably fallen as well. Moreover, if the additional expenditures were used to purchase truck-related safety equipment, then fatalities and injuries would have fallen too.

And finally, deregulation may have affected driving intensity directly if drivers were induced to make more deliveries by driving longer hours and at faster speeds. Therefore, it would be important to control for differences in speed limit enforcement and vehicle inspections when attempting to explain differences in accident rates across states.

The above discussion indicates there are reasons to expect that accidents may have increased or decreased as the result of deregulation. The next section discusses the empirical model which attempts to determine the net impact of deregulation, while holding various other factors constant.

III. The Empirical Model and Regression Results

The Empirical Model

The sample consists of a pooled, cross-section of state data for 1977, 1982, and 1987. Since the MCA was passed in 1980, the sample can be partitioned into two periods: 1977 represents a regulation year, whereas 1982 and 1987 represent deregulation years. The variable descriptions and sources are discussed in the appendix.

The dependent variable is the number of accidents per truck-miles traveled per state. The numerator represents accidents that occurred in a particular state, which were reported by truck drivers engaged in interstate transportation. The denominator is an estimate of the truck-miles traveled per state, and includes all trucks that traveled more than 200 miles from their base of operation. To the extent that the denominator includes some intrastate travel, the denominator is likely to overstate the miles logged by interstate truckers. Therefore, the dependent variable is likely to understate the actual accident rate per truck-miles traveled for interstate carriers, and any effect of deregulation that we uncover would be a conservative estimate of the actual impact.

The set of independent variables include factors which have some theoretical or institutional basis for explaining the variation in accidents per truck-miles traveled across states. The first is the number of highway police officers per highway mileage (POLICE).(16) This measure is intended to control for differences in enforcement resources used to detect speed and weight violations. More officers per highway mile should lead to more careful driving and, consequently, less accidents. Thus, we expect a negative sign for this variable.

The second, third, and fourth variables relate to traffic conditions on interstate highways which could arguably affect accident rates across states. These conditions are: the average speed of interstate traffic per state (AVESPEED); the variance of speed on interstate highways; and a density variable (CONGESTION) to proxy the level of road congestion. Lave [14] argues that it is the variance of highway speed that causes accidents and not the mean speed. The intuition is that if all vehicles were traveling at the same speed (i.e., variance is 0), then chances of an accident occurring are almost zero at any mean speed. Recently, Levy and Asch [15] challenge this view and report some evidence which shows that both the mean and variance of highway speed affect accident rates. Since this issue appears to be unresolved, I have included both measures in the model.

AVESPEED is the estimated statewide average highway speed for all vehicles. The variance is calculated as the difference between "the speed at or below which 85 percent of the vehicles are traveling" (85TH) and the statewide average. In the regression model, one could write the expression as

[beta]AVESPEED + (85TH - AVESPEED).

Lave, however, suggests that the expression be rewritten as

([beta] - ) AVESPEED + 85TH.

The rationale is that the coefficient for AVESPEED in the second expression reflects the relative effect of both the average and variance of speed (i.e., [beta] - ). Thus, if the variance has a larger impact than the average (i.e., [beta] < ), then the coefficient on AVESPEED would be negative while the coefficient on 85TH would be positive. This explains why a negative sign for AVESPEED is counter intuitive, since the coefficient measures the relative size of each effect.

CONGESTION is simply the total number of automobile registrations norinalized by the estimated highway mileage for each state. This factor is intended to control for differences in road congestion across states, since greater congestion is likely to increase the probability of a collision between two vehicles. Thus, we expect a positive sign for this variable.

I included two additional variables to control for differences in weather conditions across states. The first is the average number of rain days per state (RAINDAYS). I anticipate a positive coefficient for this variable since rain is likely to impair vision and road conditions. The second is the average snowfall (SNOW) per state, and the expected sign for this variable is uncertain. On the one hand, one may argue that more snow leads to more accidents because of slippery roads. On the other hand, more snow might reduce travel if drivers wait until the roads are cleared, which makes a negative sign plausible.

Finally, I included a dummy variable (MCA80) in the model to control for differences attributable to deregulation; 1977 is a regulation year while 1982 and 1987 are deregulation years. Thus, MCA80 equals 1 for 1982 and 1987 and 0 otherwise. It is possible, however, that any empirical difference between the time periods is unrelated to the shift from regulation to deregulation, and I acknowledge this potential interpretation. Nonetheless, any difference that is uncovered in the analysis will be discussed in the context of the existing empirical literature [3; 17].

The Empirical Evidence: Rates per Truck-Miles Traveled

Table I reports the estimates from four regression equations, and several interesting findings emerge from these results. First, the MCA80 variable has a negative sign in each of the four equations, but is only significant (using a two-tailed test) at conventional levels in the fatality, injury, and property-damage equations. These results suggest that drivers experience the same accident rate that they did before deregulation, but that the accidents involved fewer fatalities and injuries.

The insignificance of MCA80 in the accident equation presents a puzzle; that is, how and why would deregulation affect the fatality and injury rates, but not the accident rate? One explanation may be that any increase in the accident rate because relatively inexperienced drivers were employed was offset to some extent by an overall reduction in driving intensity. Winston et al. [31, 62], for example, report that new entrants had higher accident rates when compared to more experienced drivers. The evidence presented in Table I, in addition, suggests that driving intensity has declined as well, since MCA80 has a negative effect on accidents when measured in terms of real property damage per truck-miles traveled. Thus, it is possible that the two effects offset each other, which would explain the insignificance of MCA80 in the accident regression.

[TABULAR DATA OMITTED]

The explanation for the reduction in fatality and injury rates may be that firms, in response to the decline in wages paid, provided their new drivers with additional safety training or equipped their trucks with additional safety features, which would have reduced the probability of an injury or fatality given that an accident did occur." Unfortunately, the data necessary to test this hypothesis are unavailable.

The other variables in the regression equations generally performed as expected. For example, the POLICE variable has a negative sign and is significant in all four regressions.", Similarly, AVESPEED and 85TH are significant (at conventional levels) with negative and positive signs, respectively, which means that variance has a larger impact than average speed in terms of explaining variations in accident, fatality, injury, and property-damage rates across states. And finally, CONGESTION has a positive sign and is significant in all regressions, which I interpret to mean that road congestion is an important factor affecting highway safety. This seems plausible to the extent that the large influx of new truck drivers since deregulation (see above) has not been matched by similar increases in the highway mileage per state. For example, the mean highway mileage for 1977 is 6,119 miles compared to 6,143 miles for 1982 and 1987 combined. Thus, the general message drawn from these results is that deregulation appears to have had a favorable impact on highway safety in the trucking industry, even after controlling for state-specific factors that are likely to affect accident rates across states.

The Empirical Evidence: Rates per Highway Mileage

In this part of the analysis, I estimate the model discussed above with one important modification; the dependent variables are normalized by an estimate of the highway mileage per state. The rationale is that the results reported in Table I may be sensitive to the variable chosen to normalize the relevant independent and dependent variables. Thus, this estimate of roadway mileage that interstate truckers are likely to travel over provides a reasonable alternative to the truck-miles traveled measure that was used in Table I. In addition, I have included truck-miles traveled (MILES) as an independent variable to control for differences in truck traffic across states.

Table II presents the regression equations, and two important results are revealed in this analysis. First, the deregulation dummy (MCA80) has a negative sign and is significant (at conventional levels) in the fatality, injury, and real-property damage equations, which is the same pattern found in Table I. This suggests that the empirical effect of MCA80 in these regressions does not vary with the choice of normalization.

[TABULAR DATA OMITTED]

Second, POLICE has a negative sign in three of four regressions, but is only significant in the accident equation. Interestingly, this is the regression model where MCA80 is insignificant. One interpretation is that differences in the number of highway patrol officers (per highway mileage) may be an important factor affecting accidents regardless of the regulatory environment, but that it may not be an important factor once the accident occurs. That is, there may be other factors which explain why the mean level of fatalities, injuries, and real-property damage (per highway mileage) have fallen since deregulation. For example, the discussion in section II above suggests that firms may have had an incentive to equip their trucks with additional safety equipment as wages paid to drivers declined. Moreover, it is quite possible that POLICE and "deregulation" are correlated to the extent that more patrol officers were hired in anticipation of the additional truck traffic following the passage of MCA 1980. The data are certainly consistent with this argument. For example, the mean and variance of the number of highway patrol officers in 1977 is approximately 918 and 967, whereas, in the combined 1982 and 1987 data, the mean and variance is approximately 988 and 1029.

Third, AVESPEED and 85TH have the same pattern of signs and significance as reported in Table I, which indicates the variance of highway speed has a larger impact than the average on variations in the dependent variables. However, with the exception of the fatality equation, the major difference is that the coefficients for AVESPEED are larger in absolute value than the coefficients for 85TH in Table II.(19) This indicates that the average highway speed has a negative impact on accidents per highway mileage while the variance has a positive impact. The result is puzzling since one would expect that higher average speeds would lead to more accidents, even when the dependent variables are normalized by highway mileage as compared to truck-miles traveled.

Fourth, the results indicate that differences in congestion (CONGESTION) are important while holding truck-miles traveled (MILES) constant. CONGESTION has a positive and significant coefficient in all four regression equations, which suggests that the slow growth in the nation's highway system is the likely factor affecting highway safety rather than a fundamental change in driving intensity following deregulation. In other words, the growth in highway construction has probably not kept pace with the increase in truck traffic since 1980. For example, the mean number of accidents in the 1970s was 30,222 compared to 31,953 in the 1980s; a modest 6 percent increase. In contrast, the mean number of fatalities increased from 2,243 in the 1970s to 2,518 in the 1980s; a 12 percent increase. At the same time, truck-miles traveled increased by 58 percent, while average highway mileage per state increased by 4 percent. Since the highway-mileage data are crude, it is safe to assume that highway mileage has changed very little, which lends some support to the aforementioned argument.(20)

Fifth, the weather variables (RAINDAYS and SNOW) are generally significant (at conventional levels) with signs that are plausible. For example, variations in rainfall across states have a positive effect on the accident and injury rates (per highway mileage), which is consistent with the hypothesis that wet weather makes road conditions more dangerous. This result is interesting because RAINDAYS is insignificant in the fatality equation. One interpretation may be that drivers decreased their driving speed, which reduced the chances of a fatality given that they were involved in an accident. Similarly, the negative effect of SNOW in all four equations is consistent with the hypothesis that drivers wait until the roads are cleared and then proceed with caution. Moreover, these findings are interesting to the extent that RAINDAYS and SNOW are insignificant in all regression equations in Table I, and this difference between Tables I and II may simply reflect the normalization of the dependent variables.

Overall, the empirical evidence presented in this paper corroborate the results in Traynor [22] and suggests that California was not fundamentally different from the rest of the United States in terms of its deregulation experience. More important, the evidence also reveals that other factors may be driving the data discussed in the Moore [17] analysis, in addition to any effect that deregulation may have had on highway safety in the trucking industry.

IV. Summary and Conclusions

The passage of the Motor Carrier Regulatory Reform and Modernization Act of 1980 created many economic benefits that economists and policymakers had anticipated; lower prices, increased output, and improved resource allocation. The empirical evidence presented in this paper, however, indicates that deregulation has yielded several benefits that policymakers had probably not anticipated; fatality and injury rates are lower in 1982 and 1987 as compared to 1977, notwithstanding the increase in truck traffic. In addition, it appears that driving intensity has declined as well, which is a finding at odds with the general perception of the trucking industry. On the other hand, the accident rate has remained unaffected since deregulation, which may change as the new truckers gain additional driving experience.

In any research, there are questions or problems that remain unresolved. In particular, this analysis has ignored the effect of deregulation on truck/automobile accidents and fatalities, which would be interesting in light of the recent research of Crandall and Graham [8]. Moreover, the effect of regulatory changes on safety expenditures needs to be examined systematically at a less aggregated level. The obvious impediment is the lack of good data that are reported at the firm level.

Appendix

This section describes the variables used in the empirical analysis. These data are reported for each of the 49 states (excluding Hawaii) for the years 1977, 1982, and 1987.

The accident, fatality, injury, and property-damage data were reported by commercial drivers engaged in the interstate transportation of property and were taken from Accidents of Motor Carriers of Property [29]. The property-damage data were converted to real terms using the CPI-All Items for 1977, 1982, and 1987. The CPI data were taken from the Economic Report of the President [10]. In Table I, I normalized the dependent variables using estimates of truck-miles traveled, which were found in Truck Inventory and Use Survey, "Long-Range of Operation" [24]. In Table II, I normalized the dependent variables using estimates of highway mileage in each state. For 1987, the total federal-aid primary including interstate mileage estimates were used. These data were taken from Highway Statistics 1987, "National Network For Trucks" [30]. The 1982 data were taken from Highway Statistics 1985, "National Network For Trucks" [30]. For 1977, the total federal-aid primary highway estimates were used. These data were taken from Highway Statistics 1977, "Total Road and Street Mileage" [30].

The POLICE variable represents the number of full-time highway patrol officers in each state. The data for Montana were unavailable for 1982; therefore, I used an average of the 1987 and 1977 data. These data were reported in Uniform Crime Reports for the United States, "Full-time State Law Enforcement Employees" [28]. I normalized these data by the estimates of highway mileage discussed above.

The INCOME variable measures the total personal income for each state, which I deflated using the CPI-All Items for 1977, 1982, and 1987. These data were found in the Survey of Current Business, "Total Personal Income for States and Regions" [27]. The population data for 1987 and 1982 were taken from the Statistical Abstract of the United States, "Resident Population-United States and Puerto Rico: 1960 to 1988" [26]. For 1977, the data were taken from the Current Population Reports, "Estimates of the Resident Population of States," Series P-25, No. 998 [25].

For 1982 and 1987, AVESPEED is an estimate of the statewide average highway speed for all vehicles, and 85TH is an estimate of "the speed at or below which 85 percent of the vehicles are traveling" (85th percentile speed). These data were taken from Highway Statistics, "Speed Data" [30]. For 1977, I used the average and 85th percentile speed for interstate rural highways, and these data were taken from Highway Statistics, "Annual Average of Quarterly Speed Monitoring Data" [30]. The data for Alaska and Delaware were unavailable for 1977; therefore, I used estimates for multi-lane divided highway instead.

RAINDAYS is the average number of days with rain for selected cities in each state, and SNOW is the annual average total snow and ice pellets for selected cities also. These data were found in the Statistical Abstract of the United States, "Average Number of Days With Precipitation of .01 Inch or More - Selected Cities," and "Average Total Snow and Ice Pellets - Selected Cities" [26].

CONGESTION is the total number of car registrations for each state. These data were taken from Highway Statistics, "State Motor-Vehicle Registrations" [30]. I normalized these data by the total highway mileage discussed above.

(1.) See Glaskowsky [11], Pustay [19], and Winston et al. [31] for a discussion of the benefits attributable to deregulation. Boyer [6], on the other hand, reports some evidence that shipping rates have risen slightly since deregulation because of the shift from truck-load (TL) to less-than-truck load (LTL) shipments. Ying and Keeler [32], however, report simulations which indicate that rates have fallen significantly since 1980. (2.) See Adams [1, 25] where he discusses the rise in the number of accidents reported by the Federal Highway Administration's Office of Motor Carriers, and the increase in accident rates reported by the Office of Technology Assessment. (3.) In Alexander [3], I use a sample of national data for the period 1960 to 1987 to examine the effect of deregulation on highway safety at a higher level of aggregation. (4.) I will use accident rates in the discussion recognizing that the arguments apply to fatality, injury, and property-damage rates as well. (5.) See Golbe [12], Moore [16], Peltzman [18], Traynor [22], and Traynor and McCarthy [23]. Moore's analysis examines the effects of changes in the regulatory constraint on industry output, and does not consider directly the effect on highway safety. (6.) The manner in which rates were determined is similar to the regulatory procedure used to set rates for electric utilities; except in trucking the bias was in the direction of hiring more labor. See Glaskowsky [11] and Moore [16] for more details. (7.) See Rose [20; 21] for more details. (8.) An anonymous referee has pointed out that the number has increased to 45,791 in 1990. (9.) Glaskowsky [11] notes that many of the new drivers were relatively inexperienced. (10.) See Alexander [2] for test of the Peltzman hypothesis involving lawn-mower safety standards, (11.) The trucking industry is comprised of owner-operators who are self-employed and firms that employ drivers. (12.) The exception is if the firm is on the verge of bankruptcy [12]. Chow [7], for example, finds that firms that are failing tend to spend less on safety, use older equipment, and hire more owner-operators compared to profitable finns. It is not clear, however, if these are reasons for failure, or simply the consequences. (13.) Traynor and McCarthy [23] provide a similar theoretical argument along these same lines. (14.) The American Trucking Associations publish a "Safety Catalog," which includes training programs designed to improve safer vehicle operation. (15.) Rose [20; 211 argues that organized labor captured a significant share of the economic rents created from regulation in the form of higher wages. However, it is possible that the union bargained for and received higher wages because of safety concems, which drivers were unable to obtain without the union. (16.) See Becker [5] for a discussion of the deterrent effect of law enforcement on criminal behavior. (17.) In Alexander [3], I report some evidence that, controlling for the effect of MCA 1980, injury and fatality rates have declined secularly since 1960. One may interpret this to mean that technological changes have had some impact on highway safety independent of any effect from deregulation. (18.) Initially, I included per capita income (INCOME) in the regressions to control for differences in overall economic activity across states. I discovered, however, that POLICE and INCOME were correlated, which seems reasonable, since wealthy states are likely to devote more resources to law-enforcement activities. Therefore, as an alternative, I regressed POLICE on INCOME and the, used the predicted value in the regression equations. (19.) In Table I, the coefficients for AVESPEED are all smaller in absolute value than the coefficients for 85TH. (20.) These estimates are calculated from the data used in this paper and are available upon request.

References

[1.] Adams, Brock. "Deregulation's Negative Effect on Safety," in Transportation Safety in an Age of Deregulation, edited by Leon N. Moses and Ian Savage. New York: Oxford University Press, 1989. [2.] Alexander, Donald L., "An Empirical Investigation of Lawn Mower Safety Regulation." Applied Economics, June 1990, 795-804. [3.] -----. "An Empirical Investigation of the Motor Carrier Act of 1980." Working Paper, Western Michigan University, September 1991. [4.] Baker, Forrest. "Safety Implications of Structural Changes Occurring in the U.S. Motor Carrier Industry." Discussion paper prepared for AAA Foundation for Traffic Safety, March 1985. [5.] Becker, Gary S. "Crime and Punishment: An Economic Approach," in The Economic Approach to Human Behavior, by Gary S. Becker. Chicago: The University of Chicago Press, 1976. [6.] Boyer, Kenneth D. "The Safety Effects of Mode Shifting Following Deregulation," in Transportation Safety in an Age of Deregulation, edited by Leon N. Moses and Ian Savage. New York: Oxford University Press, 1989. [7.] Chow, Garland. "Deregulation, Financial Distress, and Safety in the General Freight Trucking Industry," in Transportation Safety in an Age of Deregulation, edited by Leon N. Moses and Ian Savage. New York: Oxford University Press, 1989. [8.] Crandall, Robert W. and John D. Graham, "The Effect of Fuel Economy Standards on Automobile Safety." Journal of Law and Economics, April 1989, 97-118. [9.] Dempsey, Paul S., "The Deregulation of Intrastate Transportation: The Texas Debate." Baylor Law Review, Winter 1987, 1-28. [10.] Economic Report of the President. Washington, D.C.: U.S. Government Printing Office, February 1991. [11.] Glaskowsky, Nicholas A. Effects of Deregulation on Motor Carriers. Westport, Conn.: Eno Foundation for Transportation, Inc., 1986. [12.] Golbe, Devra L., "Safety and Profits in the Airline Industry." Journal of Industrial Economics, March 1986, 305-18. [13.] Jovanis, Paul P. "A System Perspective on the Effects of Economic Deregulation on Motor Carrier Safety," in Transportation Safety in an Age of Deregulation, edited by Leon N. Moses and Ian Savage. New York: Oxford University Press, 1989. [14.] Lave, Charles A., "Speeding, Coordination, and the 55 MPH Limit." American Economic Review, December 1985, 1159-64. [15.] Levy, David T. and Peter Asch, "Speeding, Coordination, and the 55-MPH Limit: Comment." American Economic Review, September 1989, 913-15. [16.] Moore, Thomas G., "The Beneficiaries of Trucking Regulation." Journal of Law and Economics, October 1978, 327-43. [17.] -----. "The Myth of Deregulation's Negative Effect on Safety," in Transportation Safety in an Age of Deregulation, edited by Leon N. Moses and Ian Savage. New York: Oxford University Press, 1989. [18.] Peltzman, Sam, "The Effects of Automobile Safety Regulation." Journal of Political Economy, August 1975, 677-721. [19.] Pustay, Michael W. "Evaluation of the Impact of the Motor Carrier Act of 1980 and Recent Administrative Regulatory Changes on the Performance of the Motor Carrier Industry." Final report submitted to U.S. Department of Transportation, April 1984. [20.] Rose, Nancy L., "The Incidence of Regulatory Rents in the Motor Carrier Industry." Rand Journal of Economics, Autumn 1985, 299-318. [21.] -----, "Labor Rent Sharing and Regulation: Evidence from the Trucking Industry." Journal of Political Economy, December 1987, 1146-78. [22.] Traynor, Thomas L. "The Effect of the Motor Carrier Reform Act on Highway Safety." Ph.D. dissertation, Purdue University, 1988. [23.] ----- and Patrick S. McCarthy. "The Effcct of the Motor Carrier Act on Highway Safety." Working Paper, Wright State University, Decembcr 1990. [24.] U.S. Bureau of the Census. Truck Inventory and Use Survey. Washington, D.C.: U.S. Government Printing Office, 1977, 1982, and 1987. [25.] U.S. Department of Commerce. Current Population Reports: Series P-25, No. 998. Washington, D.C.: U.S. Government Printing Office, 1986. [26.] -----. Statistical Abstract of the United States. Washington, D.C.: U.S. Government Printing Office, 1981, 1985, 1989, and 1990. [27.] -----. Survey of Current Business. Washington, D.C.: U.S. Government Printing Office, April 1988 and April 1991. [28.] U.S. Department of Justice. Uniform Crime Reports for the United States. Washington, D.C.: U.S. Government Printing Office, 1977, 1982, and 1987. [29.] U.S. Department of Transportation. Accidents of Motor Carriers of Property. Washington, D.C.: U.S. Government Printing Office, 1977, 1982, and 1987. [30.] -----. Highway Statistics. Washington, D.C.: U.S. Government Printing Office, 1977, 1982, 1985, and 1987. [31.] Winston, Clifford, Thomas M. Corsi, Curtis M. Grimm, and Carol A. Evans. The Economic Effects of Surface Freight Deregulation. Washington, D.C.: The Brookings Institution, 1990. [32.] Ying, John S. and Theodore E. Keeler, "Pricing in a Deregulated Environment: The Motor Carrier Experience." Rand Journal of Economics, Summer 1991, 264-73.
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Author:Alexander, Donald L.
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Date:Jul 1, 1992
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