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38 of occupational safety for drivers in the study. However, all 0.3 0.252 cells of the table contain x's, indicative of the authors' view 0.25 0.239 that all of these factors and behavioral indicators were inter- 0.2 0.178 related. 0.148 0.15 Driver OOS Rate Safety climate is commonly cited as a predictor of injury 0.1 0.084 0.075 0.069 Vehicle OOS Rate occurrence. In a survey of 2,680 employees of 18 large com- 0.048 0.05 panies in multiple industrial sectors, Huang et al. (2006) iden- tified the following indicators of a positive safety climate: 0 1 2-19 20-99 100+ Management strongly committed to safety Carrier Size Category (Number of Drivers) Fair return-to-work policies FIGURE 5 Roadside inspection out-of-service (OOS) rates by Reasonable post-injury administration policies carrier size category. Combined 20062009 data based on Effective safety training 9/24/2010 MCMIS snapshot. Number of carriers represented: Worker sense of safety control (i.e., workers feel knowl- 9,982 (1), 22,408 (219), 3,687 (2099), 719 (100+). Data edgeable about safety and are able to exercise control retrievals conducted and provided by FMCSA. over their own safety). Huang et al. found these characteristics to be inversely on an FMCSA retrieval of MCMIS data. Both driver and vehi- associated with worker injuries. These company characteris- cle OOS rates for carriers in the 219 vehicle carrier size cate- tics were also positively associated with each other. Worker gory are more than 50% higher than those for carriers in the sense of safety control appeared to be a critical bridge between 100+ vehicle category. However, there is a very important company practices and outcomes. Safety outcomes were best caveat attached to these and almost all MCMIS roadside when workers were knowledgeable about risks and correct inspection statistics, which is that roadside inspections are not practices, and were empowered to act on that knowledge. random samples of passing trucks. Rather, the Inspection Selection System has been designed to primarily target carri- Findings from DeJoy et al. (2004) reinforce these conclu- ers with poor safety performance based on SafeStat and now sions. They found that companies with the best safety cli- CSA. The size and consistency of the OOS rate differences by mate: (1) were generally well-run and had effective general carrier size suggest true underlying differences in compliance, management policies and procedures in place, apart from although the magnitudes of such differences may be affected safety; (2) had clear safety policies and programs in place; by the nonrandomness of inspection selection. and (3) reduced specific hazardous conditions associated with the work. They concluded that "A positive safety cli- As part of the I-95 Corridor Coalition Field Operational mate is more likely to exist in an environment that generally Test 10, Stock (2001) looked at 13 different measures of reg- supports and values its employees and where there is open ulatory compliance based on roadside inspections. Statistics, and effective exchange of information." broken down by seven carrier size categories, were based on U.S.DOT MCMIS statistics for an unspecified period. The 13 compliance measures were total OOS rate, vehicle OOS SMALL CARRIER VIOLATION AND CRASH RATES rate, driver OOS rate, average total violations per inspection, average number of vehicle violations, average brake viola- This section reviews available data on the roadside violation, tions, average steering component violations, average wheel moving violation, and crash rates of motor carriers of differ- violations, average total driver violations, average driver ent sizes. It provides recent federal data and reviews pub- qualifications violations, average medical certification viola- lished studies. Overall, statistics suggest that smaller carriers tions, average HOS violations, and average log violations. tend to have higher roadside inspection violation rates, and Without exception, each of the 13 measures showed clear that they may also have higher moving violation and crash relations to carrier size, with smaller carriers performing rates. However, there are important caveats attached to almost more poorly. Figure 6 shows total, vehicle, and driver OOS all of the statistics and studies cited in this section. Concerns rates by carrier size. Overall, OOS rates for the smallest fleets about methodology are cited in the context of each study. By were approximately 25% higher than those for the largest and large, the findings cited in this section are not definitive fleets. Figure 7 shows the average total number of violations, owing to methodological concerns. average number of vehicle violations, and average number of driver violations per inspection. The average total number of Roadside Inspection Data violations was approximately 50% higher for the smallest fleets than for the largest fleets. Average out-of-service (OOS) rates in roadside inspections vary inversely with carrier size. This is true both for driver and As with the Figure 5 statistics, these roadside inspection sta- vehicle OOS rates. Figure 5 shows this for 20062009 based tistics are not based on random samples. Instead, inspections

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39 0.35 3 0.3 2.5 0.25 2 0.2 0.15 Total OOS % 1.5 Total # 0.1 Vehicle OOS % 1 Vehicle # 0.05 Driver OOS % Driver # 0 0.5 0 1 2-5 6-10 11-25 26-50 51-100 >100 Carrier Number of Power Units Carrier Number of Power Units FIGURE 6 Total, vehicle, and driver OOS percentages FIGURE 7 Average total, vehicle, and driver violations by carrier size from Stock (2001). per inspection by carrier size from Stock (2001). for carriers of all sizes were targeted toward those with previ- lower rates than those with medium-sized or small compa- ous violations and other risk indicators. Moses and Savage nies. Unrelated to firm size, the study found that drivers under (1996) reported that a much larger percentage of smaller firms pressure to drive more hours and longer distances were also than larger firms are assigned negative safety ratings, which more likely to have logbook violations. There are several in turn means that a larger percentage of them are stopped caveats regarding this study. Its data are based on interviews, at roadside. Although this smalllarge firm difference probably which are inherently subject to error both from inaccurate reflects true differences in risk, it could also contribute to con- memory and possible lack of candor. The data are also more founding of inspection violation comparisons. The reliability of than a decade old and not controlled for driver mileage expo- carrier safety measurements is inherently related to carrier size sure. In addition, they are time-based rather than mileage- and the number of carrier safety observations (e.g., inspec- based. Driver percentage involvement in crashes and tions). Even with true safety performance held constant, fewer violations over a time period varies directly with their safety observations would mean greater dispersion resulting mileage exposure for that time period. If drivers from from chance and thus more extreme values. In a recent report, smaller companies tended to drive more miles, it would the GAO (2011) noted that a large majority of small carriers give them higher time-based likelihoods even if their have insufficient compliance data to be reliably ranked under mileage-based involvement rates were the same or similar the CSA SMS. to drivers from larger companies. Published Survey Statistics In the survey portion of the I-95 Corridor Coalition Field Operational Test 10, Stock (2001) assessed carrier attitudes In a large 1997 survey of company drivers (excluding owner and knowledge about regulatory compliance and enforce- operators), Monaco and Williams (2000) found a relation- ment. Figure 8 shows comparative responses for two truck ship between firm size and safety indicators. Drivers were company categories: those with 1124 trucks and those with interviewed and asked whether they had been involved in a more than 100 trucks. Carrier attitudes about the compliance crash, had a moving violation, and had a logbook violation in system were generally positive; favorable views were more the previous 12 months. Driver companies were classified by common than negative views for all carrier sizes. However, size and operational characteristics. Driver demographics smaller carrier respondents were more likely to have nega- and education were also examined. As shown in Table 14, tive views and were also generally less familiar with the the effects of firm size were most apparent at the high end. enforcement system. This study is significantly out of date; That is, drivers for very large companies had significantly federal enforcement systems and practices have dramatically TABLE 14 FIRM SIZE AND DRIVER CRASH AND VIOLATION INVOLVEMENT IN THE PAST YEAR % with Moving % with Logbook Firm Size % with Crashes Violations Violations 25 or fewer employees 18.8 40.4 54.5 25 to 99 employees 20.8 34.1 55.7 100 to 249 employees 16.2 24.7 61.7 250 to 499 employees 15.1 31.4 59.5 500 to 999 employees 8.0 12.9 68.2 1,000 to 4,999 employees 5.5 21.8 37.6 5,000 or more employees 11.1 12.3 27.6 From Monaco and Williams (2000).

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40 Selections for roadside inspection equitable? Are carriers treated fair during inspections? Inspections effective in improving road safety? 10-24 Compliance reviews effective in improving road safety? >100 Aware inspectors target carriers with poor safety records? Know how to check DOT inspection info on your fleet? 0% 20% 40% 60% 80% 100% FIGURE 8 Carrier respondent compliance-related attitudes and knowledge from Stock (2001). Respondents compared are from carriers with 1024 and >100 trucks, respectively. changed over the past decade. Nevertheless, the findings are They also had a higher average number of crashes per power probably directionally accurate in relation to the situation unit. The study did not control for mileage exposure or gen- today. erate crash rates for vehicle-miles traveled. Although these findings for CR fleets cast small carriers in a negative light, they should not be taken to represent the full population of Compliance Review Comparisons motor carriers. Because all carriers in the study had received In a review of both alternative and traditional motor carrier CRs, the sample was by definition skewed toward carriers with regulatory compliance schemes, Murray et al. (2011) com- poorer safety records. pared the effects of government Compliance Reviews (CRs) on safety outcomes for carriers of different sizes. Within the The study also compared 12-month pre- and post-CR traditional enforcement framework (especially before CSA crash likelihoods (per power unit) for carriers of different implementation), CRs were on-site safety audits, which sizes. Table 15 reproduces their results for 2004. The results assessed HOS compliance, driver qualifications and licens- for the next four years (20052008) were similar. One sees ing, drug and alcohol testing, and vehicle inspection and main- that smaller carriers generally had higher crash likelihoods tenance procedures. CR statistics for a five-year period from both pre- and post-CR. In part, this could reflect the concept June 30, 2003, through June 30, 2008, were compared for four that safety performance variability is inherently greater in carrier size categories: 149, 50249, 250999, and 1,000+ small carriers owing to the greater role played by chance in power units. The percent distribution of carrier ratings fol- their safety outcomes. The most dramatic carrier size differ- lowing a CR was related to carrier size; that is, smaller carri- ence, however, was in the effect of the CR. Small carrier ers were more likely to receive an "Unsatisfactory" rating crash likelihoods decreased by nearly one-half in the year and less likely to receive a "Satisfactory" rating. Moreover, following a CR, whereas large carrier crash likelihoods within each of the three carrier rating categories (Satisfac- decreased by only about 5%. These results could be inter- tory, Conditional, Unsatisfactory), small carriers consistently preted as suggesting that small carriers are more affected by had higher driver and vehicle OOS rates than larger carriers. CRs. For example, they may feel more threatened by CRs, or TABLE 15 2004 PRE- AND POST-CR AVERAGE CRASH LIKELIHOODS Pre-CR Crash Post-CR Crash % Change in Number of Pre-CR Number of CR Likelihood Likelihood Crash Power Units (PUs) Carriers (per 100 PUs) (per 100 PUs) Likelihood 15 3,213 11.3 5.7 -49.6 620 2,182 7.8 5.3 -31.5 21100 1,150 6.8 5.8 -14.8 101250 177 4.8 4.4 -9.1 2511,000 79 4.3 4.1 -5.4 1,001+ 16 4.4 4.2 -4.9 Source: Murray et al. (2011).