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Work-stress factors associated with truck crashes: An exploratory analysis

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Researchers have studied truck crashes extensively using methods appropriate for behavior, technology, and regulatory enforcement. Few safety studies associate crashes with economic pressure, a pervasive latent influence. This study uses data from the US Large Truck Crash Causation Study to predict truck crashes based on work pressure factors that have their origins in market pressures on motor carriers and truck drivers. Logistic regression shows that factors associated with the work process, including an index of work-pressure attributes, predict the likelihood that crash analysts consider the truck driver to be the person whose last action could have prevented the crash. While not proving causation, the data suggest that economic factors affecting drivers contribute significantly to truck crashes.
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Work-Stress Factors Associated with Truck Crashes: An Exploratory Analysis
Working Paper
Published in:
The Economic and Labour Relations Review, 9(3), 289-307 (August, 2018).
https://journals.sagepub.com/doi/full/10.1177/1035304618781654
Michael H Belzer
Wayne State University, USA
Corresponding author: Michael H Belzer, Department of Economics, 2127 Faculty
Administration Building, Wayne State University, 656 W. Kirby, Detroit, Michigan 48202,
United States. Email: Michael.H.Belzer@Wayne.edu
Abstract
Researchers have studied truck crashes extensively using methods appropriate for
behavior, technology, and regulatory enforcement. Few safety studies associate crashes
with economic pressure, a pervasive latent influence. This study uses data from the US
Large Truck Crash Causation Study to predict truck crashes based on work pressure factors
that have their origins in market pressures on motor carriers and truck drivers. Logistic
regression shows that factors associated with the work process, including an index of
work-pressure attributes, predict the likelihood that crash analysts consider the truck
driver to be the person whose last action could have prevented the crash. While not
proving causation, the data suggest that economic factors affecting drivers contribute
significantly to truck crashes.
JEL codes: J28, J33, L91
Keywords:
Commercial motor vehicles, compensation, crashes, economic pressure, heavy goods
vehicles , safety, stress, truck drivers, trucking
Introduction
In 2015, 4,311 large trucks and buses were involved in fatal crashes in the United States
(0.124 per million vehicle miles travelled, or VMT), continuing a seven-year upward trend
in gross fatal crash numbers, beginning with an all-time low of 3,193 (0.108 per million
VMT) in 2009the low point in the Great Recession and the nadir of U.S. commercial
truck and bus traffic. This recent trend reversed the broad downward trend that had
prevailed since 1975, when the Department of Transportation began to collect the data
(FMCSA [Federal Motor Carrier Safety Administration], 2015). This trend may signal a re-
emerging problem. While job characteristics vary, making comparison difficult, the truck
driver's occupation is one of the most dangerous in the U.S., even controlling for
exposure. The 2003–2008 Census of Fatal Occupational Injuries shows that 5,568
Driver/Sales Workers and Truck Drivers died violently on the job ¾ 17% of all US
occupational fatalities (Chen et al., 2014). It also is an unhealthy occupation, creating a
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significant public policy concern (Chen et al., 2015; Robinson and Burnett, 2005).
Very little research has addressed the upstream causes of commercial motor
vehicle (CMV) crashes; research tends to rely on proximal triggers, which are easier to
measure and trace. This study, using a multi-relational data set documenting large truck
crashes with the intent to identify their causes, provides a unique opportunity to measure
the pressures leading to truck crashes. Specifically, does work-related pressure create
preconditions for crashes? Trucks and buses operate within high-pressure markets
because transportation is a commodity; customers assume that regulations have assured
their safety and the safety of the goods they ship so they choose transport based on price
and service. The public, too, assumes that modern institutions and technology have
removed much of the uncertainty in transport safety. This research will test the
hypothesis that work-related pressure on commercial truck and bus drivers leads to
highway crashes.
Literature
Since large trucks engage in commercial activities, a study of commercial motor vehicle
crashes should analyze economic forces in the commercial delivery of freight. Previous
research has demonstrated a strong relationship between truck driver pay and safety. A
survival analysis of individual drivers at a single firm showed that at the mean, every 10%
in driver pay rate is associated with a 34% lower probably of crash, and every 10% in driver
pay raise is associated with an additional 6% lower crash probability (Rodriguez, 2006)
Belzer and Sedo (2018) used a standard labor supply model along with an
extension of that theory to model the particular choice long-haul truck drivers make
between lower overall earnings in short-haul compared with higher earnings in long-haul,
which requires especially long hours. They constructed a backward-bending labor supply
curve, using data from a survey of truck drivers conducted by the University of Michigan
Trucking Industry Program (Belman et al., 2005), to demonstrate that they work long
hours to achieve target earnings. This analysis showed that at the margin, truck drivers
will work more hours if a higher pay rate is offered for the work, up to the current average
mileage pay rate in the labor market.
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As the drivers’ pay rate rises to and exceeds the
mean, however, they will work fewer hours, trading more leisure for labor as anticipated
in conventional economic theory. At the time the data were collected, in 1997 and 1998,
road drivers worked on average approximately 64.5 hours per seven-day week (almost
10% more than the legal limit), but as driver pay increased above the mean, drivers
reduced their working time to the legal limit and below (Belzer and Sedo, 2018).
A 2010 survey by the National Institute for Occupational Safety and Health
(NIOSH) has shown that over-the-road truck drivers continue to work similar unusually
long hours, with long-haul truck drivers working an average of 60 hours per week and
regularly exceed maximum working hours prescribed by Federal Motor Carrier Safety
Administration (FMCSA) hours-of-service regulations (Chen et al., 2015). While
economists know that workers make tradeoffs between working and non-working time,
this direct relationship between truck driver payfor both driving and non-driving time
and the economics of working time seems to have received less emphasis in motor
carrier safety research than one might expect, especially given the unusually long hours
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of work.
This and other research suggests that there may be a significant difference in crash
rates when drivers are paid by the hour compared with when they are paid piecework.
For many years, safety advocates have argued that truck drivers should be paid by the
hour to align their incentives with public safety. Some have also suggested that the
problem is less due to mileage pay than to the piecework pay structure for non-driving
labor; the 2010 NIOSH survey showed that 56% of all employee drivers are not paid for
any non-driving labor. Viscelli (2016) found that minimal payment for non-driving labor
time led drivers to work between 80 and 100 hours weekly, much of it off the record.
Substantial research has shown both theoretically and empirically that truck driver
compensation should predict safety. Efficiency wage theory, along with empirical
research testing it, supports the hypothesis that carriers will reap superior performance
and greater workforce stability if they pay drivers better total compensation than
commercial motor vehicle (CMV) drivers would otherwise expect if they worked for a
carrier in a lower-rate segment of the trucking market, and better total compensation
than they would otherwise receive in a comparable non-trucking labor market (Yellen,
1984; Bulow and Summers, 1986; Holzer, 1990; Lazear, 1995; Summers, 1988; Weiss,
1990). Reciprocity, or fair wage theory, further suggests that drivers who earn better
compensation will reciprocate because they believe their employer (or freight broker or
motor carrier to which they are contracted) is treating them fairly, and this reciprocity
may include both greater productivity and greater safety (Burks, 1999; Fehr and Tyran,
1996; Fehr and Gächter, 1998; Fehr and Schmidt, 2000; Milgrom and Roberts, 2002).
Drivers who anticipate deferred compensation in the form of pension or other retirement
benefits also will protect those benefits by driving in a responsible manner (Lazear, 1990).
However, drivers also are motivated by target earnings; that is, they work until they
reach an earnings level sufficient to pay their bills, and when they reach target earnings
they will tend to trade leisure for labor and work fewer hours (Belzer and Sedo, 2018).
Researchers have paid insufficient attention to the influence of market pressures
on occupational health and safety in trucking (notable exceptions include Mayhew and
Quinlan, 1997; Mayhew and Quinlan, 2000; Mayhew and Quinlan, 2006; Quinlan, 2001;
Quinlan et al., 2006; National Transport Commission, 2008; Williamson et al., 1996). Much
of the research on trucking safety seems to focus on the effectiveness of various
engineering interventions, such as information technology, mechanical design, and
materials technology, on trucking safety. Additional research focuses on behavioral
interventions; among those are regulations designed to limit the effects of drivers'
economic preference to work more hours in order to earn more money, such as hours-
of-service regulatory limitations. While such efforts are important, they do not address
the organizational and market problems directly. As long as economic competition in
trucking provides incentives for drivers, motor carriers, and cargo owners in the supply
chain to seek economic advantage by undercutting these standards, pressure will remain
strong and truckers will comply with standards only insofar as regulators and enforcers
can maintain enforcement pressure. In other words, markets and regulations will
continue to have opposite internal logics that remain in tension with each other and do
not necessarily result in safe operations or achieve safety efficiently.
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Data
This study uses the Congressionally mandated Large Truck Crash Causation Study (LTCCS),
a one-time data-collection effort managed by FMCSA in partnership with the National
Highway Traffic Safety Administration (NHTSA) of the US Department of Transportation
(DOT) and their internal contractor, the National Automotive Sampling System (NASS),
and their subcontractor, Veridian, using state crash investigators (FMCSA, 2006c, 2006d)
The full LTCCS database includes 49 data sets, 34 of which have been concatenated for
this analysis. FMCSA’s contractors collected data on approximately 1,000 variables (see
Appendices A and B) in 967 crashes, including 1,127 large trucks, 959 non-truck vehicles,
251 fatalities, and 1,408 injuries (ibid.). Variable names reflect the relation or dataset
from which they were obtained and are the original LTCCS names. The database includes
all crashes of trucks larger than 10,000 pounds (4,536 kg), including both local and long-
distance trucks; this includes all trucks larger than personal vehicles.
The LTCCS database lacks measures of exposure or case controls. Data collectors
elected to proceed into the field without the input of the Committee for Review of the
Federal Motor Carrier Safety Administration’s Large Truck Crash Causation Study.
Committee experts insisted that scientific validity would be compromised by the lack of
exposure data (in this case, VMT), making it difficult to calculate even an odds ratio
(Council, 2003; Hedlund, 2003) To remedy this problem, the Committee engaged James
Hedlund and Daniel Blower to develop a method with which researchers could simulate
exposure.
Hedlund and Blower (2006) argue that because FMCSA collected no exposure
data, the only way to conduct statistical analysis of the LTCCS is by using ‘induced
exposure. This method requires analysts to separate out cases in which the truck is
assigned the critical reason for the critical eventfrom cases in which another vehicle is
assigned the critical reason. If indications of the critical event (in particular, the critical
reason for the critical event) without which the crash would not have occurred were not
attributed to the CMV or the CMV driver, then the exposure was considered to be induced
by the actions of others. This does not mean that the CMV or the CMV driver was at fault
but more narrowly whether the vehicle or driver is associated with being the prime-mover
of the event (Hedlund and Blower, 2006).This method is far from perfect, but at least it
allows researchers to separate the crashes in which contractors considered the truck
driver to have been the last vehicle operator capable of avoiding the crash from those in
which the contractor believed another person or vehicle operator made the final action
that made the crash inevitable (Hedlund and Blower, 2006; Blower and Campbell, 2005).
This study uses this induced exposure approach and attempts to determine statistical
relationships between economic factors and the critical reason for the critical event.
Although local drivers frequently are paid by the hour (some have percentage of
revenue and flat by the loadpay regimes), almost all long-distance truck drivers earn
pieceworkpay (Belzer, 2000). That is, they are paid by the theoretical distance travelled
(a fixed shortest practical distance as calculated by a computer program) or a percentage
of revenue (which itself is based on distance, type and value of freight, and other
contingencies such as weight, volume, and handling characteristics that contribute to the
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market-driven freight rate), or by the load (which is the same as the foregoing, only more
opaque). Some drivers are paid by the hour for some or all non-driving labor time (such
as loading, unloading, or waiting for freight or repairs) or at least paid a flat rate per stop
or per activity; many long-haul drivers, however, are not paid at all for non-driving labor
time. Piecework payment therefore tends to put productivity pressure on the driver.
Specifically, Thompson et al. show the inherent logic of work pressure using a simulation
of an agent-based model in which economic pressures create incentives for risky behavior
(Artz and Heywood, 2015; Braver et al., 1992; Prendergast, 1999; Thompson et al., 2015).
This analysis narrows the definition used by LTTCS to drivers of large commercial
trucks only. For this study, analysis is further limited to those drivers holding a Commercial
Driver License (CDL) and operating trucks requiring a CDL (weighing more than 24,000
pounds [10,866 kg]).
Dependent Variable
The LTCCS ‘uses a method developed by [Kenneth] Perchonok, a late associate of the
Veridian staff, that identifies for each crash a critical event and critical reasons for that
event’ (Perchonok, 1972; Treat et al., 1977; Council, 2003: 59). In almost exactly 50% of
all crashes, the truck or truck driver was assigned the critical reason for the critical event
(ACRCriticalEvent) and the codebook specifies 64 different reasons ranging from falling
asleep to having a heart attack to unknown. This does not mean that 50% of all large truck
crashes are caused by truck drivers; this is an unexplained circumstance of the study and
perhaps a consequence of hindsight bias (Donaldson, 2005). It means, however, that this
variable, used as a dependent variable in the logistic regression, is evenly distributed in
attribution between truck and non-truck drivers.
Independent Variables
The variable GVE CDL Truck indicates those vehicles that fall as close to the definition of
CMV as is possible within the LTCCS data set. The data set defines the category as greater
than 12,000 kg (26,455 pounds), which is the approximate threshold that requires a driver
to hold a CDL. For ACRCriticalEvent, controls for GVE Truck and GVE CDL Truck did not
change the outcome materially; the difference between them is only the size of the truck.
In fact, there is little difference in most variables when controlling for these two
categories. When analyzed using logistic regression, different truck designations are not
statistically significant in any model.
Data collectors did not collect data symmetrically on automobiles and their drivers
as well as on trucks and truck drivers. In other words, because the FMCSA carried out the
LTCCS to understand the causes of large truck crashes only, investigators did not believe
they could compel cooperation by automobile drivers, leaving data on crashes involving
both cars and trucks incomplete. In addition, 107 cases had to be thrown out because
they came from the pilot phase of the LTCCS.
ii
Fatigue’, another variable in the LTCCS, was attributed stably as a factor in about
15% of all crashes and almost only on the part of the truck driver, for the reason discussed
above. While fatigue is extremely important (Panel on Research Methodologies and
Statistical Approaches to Understanding Driver Fatigue Factors in Motor Carrier Safety
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and Driver Health, 2016), data collectors did not systematically collect fatigue data from
automobile drivers so there is no real way to compare the groups; analysis can reveal only
the extent to which data collectors judged fatigue to be a factor for the truck driver.
Finally, the complex derived concept ‘Fatigueonly crudely approximates the true state
of alertness of the driver, so readers should interpret any analysis using this variable with
caution. Researchers often call this kind of variable a dummy because it is a dumb
placeholder for a concept, and when the concept actually is complex, using such an
indicator variable (1/0) may be misleading. However, this Fatigue variable is included in
the analysis on the assumption that it will account for some of the variation in the
regression analysis and while imperfect, it is the best proxy for fatigue available in these
data.
AggressionCount (an ordinal variable) is computed from the following variables in
the DriverDecisionAggression data set of the LTCCS: SpeedingBehavior,
TailgatingBehavior, Weaving, LightViolation, RapidAcceleration, Honking, Flashing,
ObsceneGestures, BlockingOthers, and OtherAggression. It is a construct created by the
LTCCS data development team. Prior research suggests that these attributes may
contribute to crashes. AggressionCount is included in this analysis because it is reasonable
to believe that aggressiveness is related to work-pressure, at least in part.
WorkPressureCount, a variable that exists in the LTCCS data, provides an
incomplete measure of the pressure factor, a count of the number of work pressure
variables’. WorkPressureCount includes the attributes NewPosition, ShippingDeadline,
EXPWorkPressure, Quotas, ExtraLoads, Demoted, SelfInducedIllegal and
SelfInducedOther (both variables have the same definition), and OtherPressure. An
examination of the variable reveals that at most only two attributes are coded to each
case, making interpretation very difficult; only fourteen cases had two attributes and data
collectors coded only 118 cases for any work pressure attributes.
To expand the analysis using this concept, I developed a summative index based
on factors known in the industrial relations field to create work pressure, which expands
the number of attributes in the concept. WorkPressureTotal incorporates NewPosition,
ShippingDeadline, EXPWorkSchedule, Quotas, ExtraLoads, Demoted, SelfInducedIllegal,
SelfInducedOther, OtherPressure (the variables in the original work pressure variable), as
well as LoadPressureIndicator, ShortNoticeTrips, FillInTrips, UnpaidLoading,
OtherRelations, and Hurrying. The use of a derived variable constructed from a much
larger set of variables for the WorkPressureTotal index allows us to treat it as an ordinal
continuous variable, since drivers have from zero to seven of these attributes and the
index only counts the presence or lack of presence of a code for each.
iii
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Table 1: WorkPressureTotal
NewPosition
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ShippingDeadline*
1 if the driver experienced work pressure as a result of
being under time-related pressures associated with
production/shipping deadlines
EXPWorkSchedule*
1 if the driver was experiencing any pressure on the job as it
relates to his/her work schedule
Quotas*
1 if the driver experienced any work pressure with regard to
additional production or sales requirements
ExtraLoads*
1 if the driver was under pressure from his/her employer to
accept loads with little or no advance notice
Demoted*
1 if the driver had recently been forced to accept a
demotion and/or pay decrease
SelfInducedIllegal and
SelfInducedOther*
1 if the driver experienced self-induced work pressure, as
opposed to employer-induced pressure"; both variables
have same definition
OtherPressure*
1 if the driver experienced any work-related pressure that
was not captured under other work-pressure variables
LoadPressureIndicator
1 if the driver was under pressure to accept scheduled and
unscheduled loads, loads proffered on short notice or when
over legal driving hours
RotatingShift
1 if the driver experienced work pressure due to his/her
carrier scheduling trips in a manner that requires the driver
to work rotating shift schedules with an associated rotating
sleep pattern
ShortNoticeTrips
1 if the driver was required by his/her carrier to accept
short notice trips
FillInTrips
1 if the driver was under pressure by his/her carrier to fill in
for other drivers (i.e. perform extra work) when other
drivers are absent
UnpaidLoading
1 if the driver was required by his/her carrier to complete
uncompensated loading/unloading activities
OtherRelations
1 if there were other carrier relation factors not captured in
other carrier relation variables that may have had a bearing
on crash occurrence").
Hurrying
1 if driver was in a hurry prior to crash occurrence.
ScheduledExtensions
1 if the driver experienced work pressure due to his/her
carrier scheduling trips in a manner that requires extended
work shifts to complete
UnscheduledExtensions
1 if the driver experienced work pressure due to his/her
carrier pressing the driver to accept unscheduled
loads/trips that require the driver to operate while fatigued
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* Variables incorporated into WorkPressureCount in the original LTCCS
DriverAssessment Data Set. (Federal Motor Carrier Safety Administration; U.S.
Department of Transportation, 2006a)
Investigators conducting the data collection neither collected nor reported data
on compensation level; data collectors erroneously reported that all truck drivers earn
the same salary(in the US, the Fair Labor Standards Act prohibits paying production
workers a flat salary). Compensation method, however, using the non-public interview
data in the IntvwDrDriver Data Set, indicates that 211 out of 854 CDL drivers reported
being paid by the mile. MileagePayThisTrip(Driver), an indicator variable created by
coding by the mileconservatively as 1 and all other responses (percent of gross trip
revenue, by the hour, other, by the load, not applicable, and unknown
iv
) as 0 provides a
measure that is slightly more robust for unknown reasons than the one created from the
non-public IntvwCarrier Data Set on the same attribute. According to the data, 196 CDL
drivers were paid percentage of revenue, 276 were paid hourly, and 41 were paid by the
load; pay method for 48 drivers is undocumented. Irregularities in data collection and an
unrepresentative NASS sampling frame probably explain at least part of the discrepancy,
but the extent and direction of bias are unknown. In addition, the same question asked
of more than one individualin this case, the driver and the carriershows different
answers. My decision to code compensation as mileage-based is conservative in this case
because pay by the mileis explicit and specific. Payment by the mile is a clear piecework
compensation system.
Control variables that might be expected to contribute to ACR, but do not, are not
reported here; they are statistically insignificant. They include White(an indicator
variable created from EthnicOrigin); Owner-Operator; all variables related to whether
drivers loaded or unloaded freight; and all variables related to whether the company pays
drivers to load and/or unload. Because the responses to questions regarding driverspay
for loading and unloading are contingent on whether they were paid for loading and
unloading this trip, and because missing responses on this contingency reduces the n,
these also are insignificant and cannot be analyzed. Neither detention time nor
compensation for detention time were recorded.
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Model and Results
The following model seeks to determine the effects of these work factors on truck
crashes.
AssignedCriticalReason = a +b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + e
where b1 = WorkPressureTotal, b2 = AggressionCount, b3 = Fatigue, b4 = ClassYears, b5 =
ClassYearsSq, b6 = SafetyBonus, b7 = HoursDriving, b8 = MileagePayThisTrip(Driver), and e
= error.
Descriptive statistics for variables used in the model indicate that the
DriverAssessment datasets showed a total of 183 WorkPressure attributes for CDL Truck
drivers, distributed across 1,014 CDL truck drivers. LTCCS data coded most truck drivers
with only one attribute, but some drivers had more and two drivers had six attributes
(Table 2). AggressionCount (from the DriverDecisionAggression Data Set) showed that
most drivers were coded with one aggressive act, but four were coded with two. Among
CDL drivers, 121 were coded for fatigue (from the DriverAssessment dataset), 154 CDL
drivers reported their carrier pays a safety bonus to eligible drivers (from the
IntvwDrDriver dataset), and 211 reported that they were paid by the mile (from the
IntvwDrDriver dataset). Eleven drivers were coded for work-related Hurrying (from the
DriverAssessment dataset). Finally, on average each CDL driver was experienced, with
12.7 years of experience (from the IntvwDrDriver dataset).
Table 2: Frequency Breakdown of WorkPressureTotal
Cases selected according to GVE CDL Truck
2284 total cases of which 1276 are missing (including non-trucks)
Total Cases: 1,008
Group
Count
%
Cumulative %
0
883
87.599
87.599
1
81
8.036
95.635
2
25
2.480
98.115
3
15
1.488
99.603
4
2
0.198
99.802
5
0
0
99.802
6
2
0.198
100.000
10
Table 3: Descriptive Statistics for CDL Truck Drivers
2,284 cases for which 1,014 are trucks large enough to require a CDL and have been
coded AssignedCriticalEvent. Data collectors assigned the critical event or the critical
reason for the critical event, or both, to trucks in approximately 50% of all crashes.
Attribute
Frequencies
Cases
WorkPressureTotal
NewPosition
17
1,014
ShippingDeadline
3
1,014
EXPWorkSchedule
17
1,014
Quotas
1
1,014
ExtraLoads
7
1,014
Demoted
1
1,014
SelfInducedIllegal
10
1,014
SelfInducedOther
25
1,014
OtherPressure
17
1,014
LoadPressureIndicator
35
1,014
ShortNoticeTrips
16
1,014
FillInTrips
6
1,014
UnpaidLoading
9
1,014
OtherRelations
19
1,014
WorkPressureTotal
183
AggressionCount
55 have one attribute;
4 have two attributes
978
Fatigue
121
851
SafetyBonus
154
902
MileagePayThisTrip
211
854
Hurrying
11
1,008
Attribute
Summary Statistics
ClassYears
Mean: 12.7
Median: 10
Standard Deviation: 11.2
857
HoursDriving
Mean: 4.1
Median: 3.7
Standard Deviation: 3.3
785
WorkPressureTotal was tested using a Principal Components Analysis (PCA)
instead of Cronbach’s Alpha because PCA is more robust to missing data. One of the
challenges using the LTCCS is that data collection on non-truck drivers is almost entirely
absent, especially on the variables of interest for this analysis. For example, if work
pressure is a significant predictor of whether truck drivers are assigned the critical reason
for the critical event, and if non-truck vehicles involved in crashes were driven either by
people who were working at the time of the crash (e.g., a salesman) or people who were
11
commuting to or from work, or had work problems on their minds, the data do not
capture this fact. This asymmetry means that it is impossible to analyse the overall effect
of work pressure on crash causation, hence the large number of missing variables (non-
trucks). Rather, it only captures the effect of truck drivers’ work pressure. In contrast, PCA
shows the first-ranked factor has eigenvalues with the same sign, consistent with the
existence of a systematic relationship among the variables.
Table 3: Descriptive Statistics for CDL Truck Drivers
2,284 cases for which 1,014 are trucks large enough to require a CDL and have been coded
AssignedCriticalEvent. Data collectors assigned the critical event or the critical reason for
the critical event, or both, to trucks in approximately 50% of all crashes.
Attribute
Frequencies
Cases
WorkPressureTotal
NewPosition
17
1,014
ShippingDeadline
3
1,014
EXPWorkSchedule
17
1,014
Quotas
1
1,014
ExtraLoads
7
1,014
Demoted
1
1,014
SelfInducedIllegal
10
1,014
SelfInducedOther
25
1,014
OtherPressure
17
1,014
LoadPressureIndicator
35
1,014
ShortNoticeTrips
16
1,014
FillInTrips
6
1,014
UnpaidLoading
9
1,014
OtherRelations
19
1,014
WorkPressureTotal
183
AggressionCount
55 have one attribute;
4 have two attributes
978
Fatigue
121
851
SafetyBonus
154
902
MileagePayThisTrip
211
854
Hurrying
11
1,008
12
Attribute
Summary Statistics
ClassYears
Mean: 12.7
Median: 10
Standard Deviation: 11.2
857
HoursDriving
Mean: 4.1
Median: 3.7
Standard Deviation: 3.3
785
Table 4: Principal Component Analysis
cases selected according to GVE CDL Truck
2284 total cases of which 1270 are missing
EigenValues
Variance
Values
Proportion
e1
2.507
27.9
e2
1.897
21.1
e3
1.161
12.9
e4
0.876
9.7
e5
0.770
8.6
e6
0.626
7.0
e7
0.459
5.1
e8
0.393
4.4
e9
0.311
3.5
13
EigenVectors
V1
V2
V3
V4
V5
V6
V7
V8
V9
ShippingDeadline_m
-0.310
0.510
0.020
0.189
-0.078
-0.045
-0.307
-0.575
-0.419
EXPWorkSchedule_m
-0.278
-0.003
0.485
-0.671
0.057
-0.422
-0.225
0.072
0.024
Quotas_m
-0.185
0.540
-0.203
0.154
-0.372
-0.215
-0.165
0.493
0.392
ExtraLoads_m
-0.384
-0.316
-0.402
0.108
0.031
-0.411
0.109
0.316
-0.546
LoadPressureIndicator_m
-0.427
-0.134
0.330
0.140
-0.007
0.646
-0.284
0.385
-0.157
UnscheduledExtensions_m
-0.328
-0.273
-0.203
-0.291
-0.700
0.198
0.206
-0.311
0.153
ScheduledExtensions_m
-0.337
-0.119
0.495
0.524
0.009
-0.279
0.432
-0.136
0.263
ShortNoticeTrips_m
-0.413
-0.174
-0.394
0.002
0.508
0.027
-0.289
-0.226
0.501
FillInTrips_m
-0.267
0.460
-0.103
-0.316
0.322
0.264
0.650
0.082
-0.068
Unrotated Factor Matrix
F1
F2
F3
F4
F5
F6
F7
F8
F9
ShippingDeadline_m
-0.491
0.703
0.022
0.177
-0.068
-0.036
-0.208
-0.360
-0.233
EXPWorkSchedule_m
-0.439
-0.004
0.523
-0.628
0.050
-0.334
-0.152
0.045
0.013
Quotas_m
-0.293
0.744
-0.219
0.144
-0.326
-0.170
-0.112
0.309
0.218
ExtraLoads_m
-0.608
-0.435
-0.434
0.101
0.027
-0.325
0.074
0.198
-0.304
LoadPressureIndicator_m
-0.677
-0.185
0.356
0.131
-0.006
0.511
-0.192
0.242
-0.088
UnscheduledExtensions_m
-0.520
-0.376
-0.219
-0.272
-0.614
0.157
0.140
-0.195
0.085
ScheduledExtensions_m
-0.534
-0.163
0.533
0.491
0.008
-0.221
0.293
-0.085
0.147
ShortNoticeTrips_m
-0.653
-0.240
-0.425
0.002
0.445
0.022
-0.196
-0.142
0.279
FillInTrips_m
-0.422
0.633
-0.111
-0.296
0.282
0.209
0.440
0.052
-0.038
14
A GLM (general linear model) analysis for AssignedCriticalReason uses logistic
regression to estimate the model and the coefficients. The model predicts whether the
truck driver would be assigned the critical reason for the critical eventthat led to the
crash. OLS ANOVA demonstrates that all the variables in the model have significant
predictive value, including both continuous variables (AggressionCount, ClassYears,
HoursDriving, and WorkPressure) as well as discrete variables (Fatigue, IDRSafetyBonus,
and MileagePayThisTrip[Driver]). The LogLikelihood of this model is -430.81855, which is
significant, and it converges in five iterations. All variables are significant individually with
high F-ratios, again for both continuous and discrete variables. Coefficients produced by
this model are all significant, as expected, and the Wald test is appropriate for small
coefficients. Scheffe post-hoc tests yield the same results and are significant. Calculating
the predicted values from the logistic regression and then calculating the correlation
between the predicted values and the dependent variable allows the derivation of the
model fit. The Pearson Product-Moment Correlation, 0.383, is squared to obtain the R2 of
14.75% (Table 5).
Table 5: General Linear Model for Assigned Critical Reason
Type of analysis: Logistic; ANOVA
Cases selected according to GVE CDL Truck
2284 total cases of which 1574 cases are missing (including non-trucks)
R2: 14.75%
Iteration
LogLikelihood
Convergence
1
-433.26054
—————————
2
-430.86683
0.11826015
3
-430.81859
0.01815587
4
-430.81855
0.00055343
5
-430.81855
0.00000058
Source
df
Sums of
Squares
Mean
Square
F-ratio
P-value
Intercept
1
18.1651
18.1651
18.198
≤ 0.0001
AggressionCount*
1
15.1209
15.1209
15.148
0.0001
Fatigue**
1
30.4849
30.4849
30.539
≤ 0.0001
ClassYears*
1
9.04029
9.04029
9.0565
0.0027
IDRSafetyBonus**
1
8.74275
8.74275
8.7584
0.0032
HoursDriving*
1
12.0612
12.0612
12.083
0.0005
WorkPressureTotalD*
1
8.99809
8.99809
9.0142
0.0028
MileagePayThisTrip(Driver)**
1
5.37788
5.37788
5.3875
0.0206
Error
702
700.746
0.998213
Total
709
786.266
* Continuous ** Discrete
15
Coefficients of AssignedCriticalReason for Continuous Variables
Covariate
Coefficient
std. err.
Wald
p-value
Level of Intercept
0.8318
0.2420
11.82
0.0006
AggressionCount
1.484
0.3817
15.12
0.0001
ClassYears
-0.0231
7.693e-3
9.040
0.0026
HoursDriving
-0.0974
0.0281
12.06
0.0005
WorkPressureTotal
0.5822
0.1941
8.998
0.0027
Coefficients of AssignedCriticalReason for Discrete Variables
Covariate
Coefficient
std. err.
Wald
p-value
Fatigue
0.9145
0.1656
30.48
≤ 0.0001
IDRSafetyBonus
-0.3187
0.1078
8.743
0.0031
MileagePayThisTrip(Driver)
-0.2245
0.0968
5.378
0.0204
Scheffe Post Hoc Tests
Covariate
Difference
std. err.
P-value
Fatigue
1.82900
0.3310
0.000000
IDRSafetyBonus
-0.637436
0.2154
0.003186
MileagePayThisTrip(Driver)
-0.448953
0.1934
0.020567
Coefficients for the independent variables replace the betas in the equation
above:
AssignedCriticalReason for the critical event = 0.8318+ (0.5822)WorkPressureTotal +
(1.484)AggressionCount + (0.9145)Fatigue + (-0.0231)ClassYears +
(-0.3187)IDRSafetyBonus + (-0.0974) HoursDriving +
(-0.2245)MileagePayThisTrip(Driver) + e
Interpretation of the foregoing results indicates the extent to which the presence
of ‘1’ in the dependent variable (that is, the data collectors’ assignment of the critical
reason for the critical event to the truck) is associated with the independent variables that
predict this assignment. That is, every assignment of the critical reason to the truck is
associated positively and significantly with WorkPressureTotal, AggressionCount, and
Fatigue, according to the coefficients reported above. Every assignment of the critical
reason to the truck is associated negatively and significantly with ClassYears, SafetyBonus,
HoursDriving (this trip, since an eight-hour break), and MileagePayThisTrip(Driver),
according to the coefficients reported above.
In other words, more work pressure, more driver aggressiveness, and more
fatigue are associated with a finding that the truck driver's action led to the crash. On the
other hand, greater years of experience in that class of truck, payment of a safety bonus,
the number of hours of driving during the trip in which the crash occurred, and pay by the
mile (piecework’) offset the preceding effects. The three most salient work-pressure
16
attributes work pressure, driver aggression, and fatigue have the strongest effects
on outcomes. While arguably driver aggressiveness and fatigue could be due to non-work
factors, the context of these attributes, associated with being in a hurry and having a
strenuous and demanding job, suggests that work-related pressures contribute
significantly to crashes.
The negative work-related attributes with paradoxical results, HoursDriving and
MileagePayThisTrip(Driver), may be noisy because HoursDriving is merely the elapsed
time between when the driver began the crash trip and when the crash occurred, and
MileagePayThisTrip(Driver) reflects the driver interview only. In other words, the ‘hours
driving’ variable is attenuated by the fact that the reported number of hours truncates at
the time of the crash and does not represent driving hours generally performed by the
CMV driver. Prior research, as well as intuition, would suggest that ‘mileage pay this trip’
should predict a greater crash likelihood. Both may have negative signs because they
reflect only reported circumstances (hours driving and payment structure) associated
with the most recent trip. Because almost all over-the-road drivers are paid by the mile
or on another contingent basis (as discussed above), and because measurement of the
pay concept is so weak in the LTCCS, the negative sign on the variable may just proxy the
distinction between long-distance work and local work, for which the LTCCS has no
measure. Most local truck and bus driving is paid by the hour.
Conclusion
Previous research has shown that human capital and labor market factors are very
important predictors of truck crashes, which reflects on wage levels (Rodriguez, 2003;
Rodriguez, 2006). This exploratory analysis supports previous findings that work
organization, economic pressure, and compensation directly affect safety. In the logistic
regression used for this analysis, factors embedded within the regression suggests that
these contributing factors are significant, particularly when considered as a system, as is
the employment relationship in organized business activity within a service market. In
other words, crash causation analysis makes sense only within an economic framework
in which CMV drivers perform their activity in a mobile workplace. These work-pressure
factors, which were created from variables that are marginally significant individually, are
highly significant when combined into a single factor, supporting their use in an index in
this model. Most research on truck crashes fail to fully engage the economic pressures
underlying the activity that led to the crash.
Economic theory predicts that driver quality including individual driver
characteristics associated with safety is directly related to the human capital of the
worker, and compensation is the ideal proxy for human capital within a market (Abowd
et al., 2005; Becker, 1975) In addition, pressure exerted on workers by their employers,
their customers, or by the collective pressure of the market itself (which includes pressure
on individuals), clearly influences the driver’s safety. Though this analysis does not
determine the causes of fatigue and driver aggressiveness here, these findings suggest
they are associated with work-related pressures.
These results further suggest that efforts on the part of regulators to create and
reinforce an environment conducive to reducing the stresses that cause fatigue and
17
aggressiveness will help to reduce crashes. Such efforts might include strict regulations
limiting hours of work or, more effectively, creating an enforceable minimum wage in
trucking that sets such a wage at a rate conducive to safety, including pay for all non-
driving labor. Belzer and Sedo (2018) suggest that a safe mileage rate, in the United
States, would be around 60 cents per mile, which is approximately 50% greater than the
current average rate. Evidence in this study and elsewhere, which suggests that truck
drivers systematically log unpaid non-driving labor off duty, implies that policy-makers
could achieve greater compliance with hours-of-service regulation and greater safety by
mandating that motor carriers and ultimately cargo owners pay truckers for their non-
driving work time. Indeed, a study of detention time by the Office of the Inspector General
of the U.S. Department of Transportation shows that truck drivers detained beyond two
hoursloading or unloading have a significant crash risk. Even after ignoring the first two
hours of such labor time (for which most drivers earn little or no compensation), an
additional fifteen minutes of delay is associated with a 6.2% increase in the average
expected crash rate (Office of the Inspector General - U.S. Department of Transportation,
2018). Since approximately 25% of all driver labor time is unpaid non-driving labor, this is
a major influence on safety. This exploratory analysis using the Large Truck Crash
Causation Study data therefore confirms that these factors are strongly related to truck
safety, but further data collection designed to remedy the flaws in the LTCCS is needed to
examine these factors in more depth.
Specifically, this study shows that pressure on truck drivers exerted by work
organization and competitive market intensity predict 15% of truck crash probability,
separate from the level of compensation, which LTCCS did not collect. The lack of reliable
information on compensation level in the LTCCS data does not allow researchers to
incorporate both pay rates and work pressure into the same model, so further research
is needed to establish this link definitively. Research has shown, however, the connection
between wage levels and safety (Rodriguez, 2003; Rodriguez, 2006). In addition, because
pay rates predict the number of hours truck drivers will work (Belzer, 2018), and because
drivers are more likely to have crashes as their hours of work increase (Jovanis, 2005), this
research contributes to a body of scholarly work showing the connection between
economic pressures and highway safety. Public policy that encourages truck drivers to
work excessive hours, with intense performance pressure, will continue to contribute to
highway safety risk. A conservative conclusion based on the LTCCS shows that at least
15% of truck crash probability can be predicted from economic factors associated with
the work process alone, independent of compensation; a reduction in these risk factors
will reduce crashes. A carefully designed survey in which key economic factors that might
predict crashes are identified and collected would provide a sound basis on which to
determine the extent to which such factors contribute to truck crashes.
18
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Funding
This study was carried out with funding from the Alfred P. Sloan Foundation and the US
Department of Transportation Federal Motor Carrier Safety Administration DTRS57-04-
D30043, TRACX.
Author biography
Michael H Belzer, an associate professor in the economics department at Wayne State
University, is an internationally recognised expert on the trucking industry, especially
the institutional and economic impact of deregulation. He is the author of Sweatshops
on Wheels: Winners and Losers in Trucking Deregulation (Oxford University Press, 2000).
He has written many peer-reviewed articles on trucking industry economics, labour,
occupational safety and health, infrastructure, and operational issues, and initiated a
strategic economic development plan to transform Southeast Michigan into a global
freight transportation hub.
i
Truck drivers and their employers make a joint decision to work more hours and research has not
disentangled the actors in this joint choice.
ii
See LTCCS Codebook’ (FMCSA, 2006b: 1) and ‘LTCCS Analytical Users Manual’ (FMCSA, 2006a:
632) for supporting documentation.
iii
Attributes are not weighted because weights would be speculative.
iv
The codebook says drivers paid both by the mile and the hour are coded ‘44’ but the data set does not
include any such coded drivers.
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... In fact, research has also shown that wages in the transport sector motivate drivers to work longer hours [10,11], which can encourage drivers to drive overtime, shorten rest periods (daily and weekly), and manipulate tachographs and driver cards [12]. However, this type of behavior may be the result by pressures from employers to make as many journeys as possible [6][7][8][9][10][11][12][13] and the conscious, adapted behavior of truck drivers to maximize earnings [8,9]. Thus, our first hypothesis is as follows: ...
... This lack of research is surprising, given the size and importance of the transport sector in the area. The results build on the findings of previous studies that address the importance of financial incentives [4][5][6]13,19,44,46] and non-financial incentives [16]. A contribution is made to the following areas of compensation in the transport sector: ...
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... For example, drivers with fewer dispatches have lower likelihoods of crash involvement (Rodriguez, Targa, & Belzer, 2006). Further, pace of work constitutes a form of work pressure (Brodie, Lyndal, & Elias, 2009), and work pressures have been associated with crashes (Belzer, 2018). An increased pace of work can induce risky driver behaviors that correspond with increased crash risks, such as driving while fatigued and speeding (Belzer, 2018;Cantor, Corsi, Grimm, & Özpolat, 2010;Farrell et al., 2016;Federal Motor Carrier Safety Administration, 2018a). ...
... Further, pace of work constitutes a form of work pressure (Brodie, Lyndal, & Elias, 2009), and work pressures have been associated with crashes (Belzer, 2018). An increased pace of work can induce risky driver behaviors that correspond with increased crash risks, such as driving while fatigued and speeding (Belzer, 2018;Cantor, Corsi, Grimm, & Özpolat, 2010;Farrell et al., 2016;Federal Motor Carrier Safety Administration, 2018a). As the other significant predictors of HOS violations in the current study, greater worknight sleep duration, fewer daily work hours, and fewer miles per week predicted less frequent HOS violations. ...
... As the other significant predictors of HOS violations in the current study, greater worknight sleep duration, fewer daily work hours, and fewer miles per week predicted less frequent HOS violations. Other studies have similarly shown that decreased sleep duration (Hanowski, Hickman, Fumero, Olson, & Dingus, 2007;Hanowski, Wierwille, & Dingus, 2003;Pack et al., 2006) and increased work hours (Belzer, 2018;Blanco et al., 2011;C. Chen & Xie, 2014b;Gander, Marshall, James, & Le Quesne, 2006) and miles per week (Beilock, 2003;Rocha, 2003;Rodriguez et al., 2006) to be associated with an increased risk of crashes. ...
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Introduction U.S. long-haul truck drivers (LHTD) experience the most work-related fatalities of any occupation. Hours-of-service (HOS) regulations constitute key public policies aimed at improving safety outcomes; however, little is known about the factors that are associated with HOS compliance, and questions remain about the efficacy of HOS laws in improving safety. This study seeks to identify factors associated with HOS compliance and to determine the significance of HOS compliance in sleep-related safety risk. Materials and methods Using cross-sectional survey data from 260 U.S. LHTD that measured demographic, work organization, sleep health, hours-of-service compliance, and sleep-related safety performance characteristics, we: 1) compiled descriptive statistics to summarize the variables included in this study; 2) performed bivariate correlation analyses between an HOS composite variable called “Hours-of-Service Violations” and the demographic, work organization, and sleep health variables; 3) conducted an ordinal logistic regression analysis, using the HOS composite variable as the outcome variable; and 4) conducted a multinomial logistic regression analysis, using a sleep-related safety performance composite variable called “Sleep-Related Safety Risk” as the outcome variable. Results Higher scores on the HOS composite variable were significantly associated with more miles driven per week, longer daily work hours, a higher frequency of a fast pace of work, shorter sleep duration, and poorer sleep quality. Statistically significant predictor variables in the Hours-of-Service Violations composite variable model were driving less than 2,500 miles per week (OR = 0.53), working less than 11 h daily (OR = 0.19) or between 11 and 13 h daily (OR = 0.43); a lower frequency of fast pace of work (OR = 0.42); and worknight sleep duration (OR = 0.80). Fewer than 11 h of work daily (OR = 0.37), a higher perception of supervisor support (OR = 0.17), and ever having told supervisor about being too tired to drive (OR = 0.42) were significant predictors in the Sleep-Related Safety Risk composite variable model, while the hours-of-service compliance variables were not. Conclusions Reducing daily work hours and pace of work, strengthening driver-supervisor relationships and improving supervisor leadership and risk management techniques, making driver compensation fairer, and revisiting HOS policies may represent high-leverage targets for improving regulatory compliance and safety outcomes.
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... Industrial Health 2020, 58, 399-402 this pressure leads to crashes and chronic illness. Stress and fatigue lead to CMV crashes, including injuries and deaths to public road users of all kinds 6,7) , as well as the cost associated with these crashes. When safety and health costs are passed along to the public, this damages the market, the tax system, and drivers and passengers who fail to carry sufficient insurance coverage to protect themselves from the acts of others. ...
... In addition to weekly work hours, LTL drivers have less delivery schedule tightness, which can intensify job stress and fatigue. Indeed, Belzer (2018) suggests that work pressure increases the probability of crashes. In the NISOH survey, 34.86 percent of LTL drivers answer that they have never been given an unrealistically tight delivery schedule. ...
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Long-haul trucks have been described as sweatshops on wheels. The typical long-haul trucker works the equivalent of two full-time jobs, often for little more than minimum wage. But it wasn't always this way. Trucking used to be one of the best working-class jobs in the United States. The Big Rig explains how this massive degradation in the quality of work has occurred, and how companies achieve a compliant and dedicated workforce despite it. Drawing on more than 100 in-depth interviews and years of extensive observation, including six months training and working as a long-haul trucker, Viscelli explains in detail how labor is recruited, trained, and used in the industry. He then shows how inexperienced workers are convinced to lease a truck and to work as independent contractors. He explains how deregulation and collective action by employers transformed trucking's labor markets--once dominated by the largest and most powerful union in US history - into an important example of the costs of contemporary labor markets for workers and the general public.
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