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Paying for Safety:
An Economic Analysis of the Effect of
Compensation on Truck Driver Safety
Michael H. Belzer*
Project Director and Principal Investigator
Daniel Rodriguez**
Investigator
Stanley A. Sedo***
Investigator
September 10, 2002
Direct Sponsor
Science Applications International Corporation
Prime Sponsor
Federal Motor Carrier Safety Administration
Prime Contract Number DTFH 61-98-C-0061
2
* Associate Professor
College of Urban, Labor, and Metropolitan Affairs
Wayne State University
and
Research Scientist
Institute of Labor and Industrial Relations
University of Michigan
** Assistant Professor
Department of City and Regional Planning
University of North Carolina, Chapel Hill
***Assistant Research Scientist
Institute of Labor and Industrial Relations
and Lecturer
Economics Department
University of Michigan
Address all correspondence to:
Michael H. Belzer
Associate Professor
College of Urban, Labor, and Metropolitan Affairs
Wayne State University
3198 Faculty/Administration Building
656 W. Kirby
Detroit, MI 48202
Phone: 313-577-1328
Fax: 313-577-8800
E-mail: Michael.H.Belzer@wayne.edu
3
TABLE OF CONTENTS
Executive Summary.......................................................................................................................6
I. Introduction..............................................................................................................................15
II. Literature Review....................................................................................................................17
Introduction............................................................................................................................... 17
Motivation.................................................................................................................................. 17
The Role of Employee Compensation......................................................................................... 18
Compensation Level................................................................................................................... 19
Direct Compensation...........................................................................................................................................................19
Efficiency wages ..................................................................................................................................................................20
Wage-deferral or wage-tilting............................................................................................................................................20
Transaction cost theory.......................................................................................................................................................21
Incentive theory ....................................................................................................................................................................21
Equalizing differences theory ............................................................................................................................................22
Fair wage theory...................................................................................................................................................................22
Compensation Method............................................................................................................... 22
Direct Compensation...........................................................................................................................................................23
Deferred Compensation......................................................................................................................................................24
III: Driver Compensation and Driver Safety: Evidence from Trucking Research...................27
Safety Studies of the Trucking Industry: Firm-Level Characteristics ........................................ 27
Firm profitability..................................................................................................................................................................28
Specific Firm Safety Practices ...........................................................................................................................................29
Fleet Ownership ...................................................................................................................................................................29
Demographics of firm driver force....................................................................................................................................30
Firm age .................................................................................................................................................................................30
Union presence.....................................................................................................................................................................30
Firm size................................................................................................................................................................................31
Industry segment ..................................................................................................................................................................31
Summary................................................................................................................................................................................31
Empirical Evidence for the Effect of Methods and Level of Compensation in the Trucking
Industry: Driver-Level Research................................................................................................ 32
Other Issues in the Relationship Between Driver Compensation and Safety.............................. 34
Indirect Links Between Driver Compensation and Driver Safety............................................... 35
Indirect Effects, Compensation Level and Method..................................................................... 36
Indirect Effects, Driver Safety.................................................................................................... 37
Age..........................................................................................................................................................................................37
Work experience...................................................................................................................................................................37
Fatigue....................................................................................................................................................................................38
Turnover................................................................................................................................................................................39
Safety Climate......................................................................................................................................................................39
Driver Safety and Driver Crashes.............................................................................................. 39
IV. DATA......................................................................................................................................41
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UMTIP Drivers Survey.............................................................................................................. 42
National Survey of Driver Wages............................................................................................... 43
SAFER Web Site........................................................................................................................ 44
MCMIS Crash File..................................................................................................................... 44
MCMIS Carrier Profiles............................................................................................................ 45
Financial and Operating Statistics Form M Data....................................................................... 45
Firm-Specific Case Studies......................................................................................................... 46
V. RESEARCH STRATEGIES...................................................................................................47
Theoretical Background............................................................................................................. 47
The Standard Model............................................................................................................................................................47
Extensions of the Standard Model....................................................................................................................................49
Theoretical Arguments: The Tradeoff between Pay Rate and Hours of Work........................... 52
Labor Supply Curve Estimation........................................................................................................................................54
Firm Level Data......................................................................................................................... 60
Individual Level Survey Data..................................................................................................... 62
Quantitative Firm Case Study at the Individual Driver Level.................................................... 62
Estimation Techniques............................................................................................................... 63
VI. RESULTS...............................................................................................................................64
Pay Level and Method, Cross Sectional Analysis ....................................................................... 64
Data.........................................................................................................................................................................................64
Results....................................................................................................................................................................................64
Pay Level and Safety: The Case of a Large Pay Raise................................................................ 71
Hazard Rate...........................................................................................................................................................................74
Incorporation of Unobserved Heterogeneity...................................................................................................................75
Data.........................................................................................................................................................................................75
Crash modeling.....................................................................................................................................................................78
TL Case Study: Turnover Analysis............................................................................................ 88
Event: Leave the firm = 1........................................................................................................... 89
The University of Michigan Trucking Industry Program Driver Survey................................... 92
VII. CONCLUSIONS...................................................................................................................99
Labor Supply Curve .................................................................................................................. 99
Signpost..................................................................................................................................... 99
J.B. Hunt.................................................................................................................................... 99
Driver Survey............................................................................................................................100
VIII. BIBLIOGRAPHY.............................................................................................................101
Appendix A.................................................................................................................................112
Appendix B.................................................................................................................................115
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Figures
Figure 1. Direct and Indirect Effects — Compensation Method and Level.................35
Figure 2: The Standard Model.............................................................................48
Figure 3: Extension of the Standard Model...........................................................50
Figure 4: Method of Pay......................................................................................51
Figure 5: Labor Supply Curve for Over-the-Road Truck Drivers..............................53
Figure 6: Predicted Crashes................................................................................70
Figure 7: Kaplan-Meier Empirical Crash Hazard....................................................78
Figure 8: Elasticity of Crash Probabilities by Pay Rate and Tenure..........................82
Figure 9: Crash Risk and Age..............................................................................84
Figure 10: Crash Risk and Tenure .......................................................................84
Figure 11: Negative Duration Dependence and the Role of Experience.....................85
Figure 12: Effect of total experience on crash risk.................................................86
Figure 13: Raw Turnover Risk.............................................................................89
Figure 14: Turnover Probability by Driver Age.......................................................90
Figure 15: Turnover Probability by Driver Tenure..................................................91
Figure 16: Estimated Semi-Parametric Baseline Turnover Hazard...........................92
Tables
Table 1: Summary Statistics................................................................................58
Table 2: Mileage Rate Equation............................................................................59
Table 3: Weekly Hours Equation..........................................................................60
Table 4: Summary Statistics................................................................................65
Table 5: Negative Binomial Regression Results......................................................69
Table 6. Descriptive statistics summarized at the individual level...........................76
Table 7: Descriptive statistics summarized at the individual level before and after pay
raise ...........................................................................................................77
Table 8 Driver Discrete Time Proportional Crash Hazards Model with Gaussian-
Distributed Unobserved Heterogeneity............................................................80
Table 9 Driver Discrete Time Proportional Crash Hazards Model with Gaussian-
Distributed Unobserved Heterogeneity –Months of Experience Subset...............86
Table 10: Driver Discrete Time Proportional Crash Hazards Model with Gaussian-
Distributed Unobserved Heterogeneity –Months of Experience AND Moving
Violations Subsets........................................................................................88
Table 11: Driver Discrete Time Proportional Turnover Hazards Model with Gaussian-
Distributed Unobserved Heterogeneity............................................................89
Table 12: Summary Statistics: Drivers’ Survey......................................................94
Table 13: Probit Results: Drivers’ Survey..............................................................97
6
Executive Summary
Paying for Safety:
An Economic Analysis of the Effect of Pay on Truck Driver Safety
Michael H. Belzer, Project Director and Principal Investigator
Daniel Rodriguez, Investigator
Stanley A. Sedo, Investigator
Address all correspondence to:
Michael H. Belzer
Associate Professor
College of Urban, Labor, and Metropolitan Affairs
Wayne State University
3198 Faculty/Administration Building
656 W. Kirby
Detroit, MI 48202
Phone: 313-577-1328
Fax: 313-577-8800
E-mail: Michael.H.Belzer@wayne.edu
May 1, 2002
Direct Sponsor
Science Applications International Corporation
Prime Sponsor
Federal Highway Administration / Federal Motor Carrier Safety Administration
Prime Contract Number DTFH 61-98-C-0061
This report examines the link between truck driver pay and driver safety. It establishes a
relationship that is important for policy purposes because it suggests that low driver pay, which
we expect is linked to low but unmeasured human capital, may be an important predictor of truck
driver safety. The study uses three different data sets at three different levels of analysis to
demonstrate this link. The study also includes an estimation of the truck driver labor supply
curve, an important contribution to understanding drivers’ (and carriers’) preferences for
balancing income and work time. One model includes the entire population of drivers at a very
large truckload motor carrier and uses survival analysis (also known as duration modeling) to
measure individual crash probabilities over time while controlling for individual and work
characteristics. Another model uses a cross section of more than 100 truckload carriers to link
driver pay with safety performance across firms. The third model uses a representative sample
of individual drivers across all firms engaged in over-the-road operations to demonstrate the
effect of driver pay in predicting crashes.
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Previous Research
Research has shown that:
• Over-the-road drivers ordinarily are paid on a piecework basis;
• Real pay levels for trucking industry personnel have declined over the past two decades; real
pay levels have declined relative to employees in other industries;
• Benefits availability and level of benefits have declined, and deferred compensation in the
form of pensions has declined;
• Unionization has declined, further reducing compensation;
• The trucking industry has been increasingly competitive and firms and drivers are under
great pressure to deliver loads just-in-time and quickly.
Theory
We expect driver compensation to predict safety outcomes because:
• Employee earnings levels affect the quality of drivers attracted to the job;
• Employee expected earning levels also determine the quality of the drivers attracted to the
job; both earnings levels and expected earnings affect employee behavior;
• Employee pay methods affect employee behavior;
• Turnover, a likely independent predictor of safety, is related to compensation.
Economic theory would lead us to predict that low pay levels would be associated with
low human capital and lower human capital would be associated with inferior performance
outcomes. We hypothesize that low human capital is associated with unsafe driving, since
higher quality workers can be expected to perform better in their jobs and since safe driving is an
important attribute of high performing truck drivers.
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Data
Study 1
Data for the cross-sectional analysis of the truckload sector come from the following.
Executive Summary Table 1
Data set Variable Year
National Survey of Driver Wages Mileage pay 1998
Raise
Safety bonus
Production bonus
Health insurance
Life Insurance
Paid time off
Length of run
Governor Speed
National Motor Carrier Directory Power Units
MCMIS DOT reported crashes 1998
UMTIP firm-level survey Unpaid non-driving time 2000
Power units
Miles
The National Survey of Driver Wages, for which the shorthand term “Signpost” is used
throughout this report, is a privately collected but purchasable dataset which covered 198
truckload (TL) firms (mostly general freight but including some specialized carriers) in 1998,
175 of which we judged to be independent firms (some were subsidiaries, divisions, or otherwise
subordinate parts of parent firms). While this dataset is not representative of the population of
TL firms, as only those carriers willing to provide data to Signpost are included, it does cover a
large part of the TL sector and is cited widely as an authoritative source of driver wage
information. We conducted our own survey of Signpost firms to develop a measure of unpaid
non-driving time, since Signpost declined to collect this information systematically on the
presumption that drivers simply are not paid for this time (which we found not to be true). The
UMTIP firm-level survey collected information on firm pay method and level for non-driving
time and supplements Signpost. The Motor Carrier Management Information Systems data set is
a data file maintained by the U. S. Department of Transportation.
Study 2
Data for the individual firm driver-level study come from truckload carrier J.B. Hunt over
two periods of 13 months each. The dataset included observations on 11,540 individuals for one
to 26 months; a total of 92,528 person-months were observed. Drivers were observed at Hunt
before and after a major wage increase. Hunt raised wages in an effort to reduce crashes and
turnover, so the wage increase was accompanied by other efforts designed to achieve these goals,
such as a promise to send drivers home within two weeks of a request. These data are
proprietary and not available to the public.
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Elements of the dataset include:
• Age
• Gender
• Race (white and non-white)
• Marital status
• Base pay (cents/mile)
• Pay increase from period 1 to period 2
• Miles driven per month
• Dispatches per month
• Driving season (Winter)
• Hiring date
• Tenure with firm
• Prior moving violations (only for a subset of the data)
• Driving experience prior to hire (only for a subset of the data)
• Crash occurrence
• Date of termination, if employee is terminated during observed periods
Study 3
Data for the individual driver study come from the University of Michigan Trucking
Industry Program. Drivers were selected using a stratified random sample of truck stops
(stratifying on size of truck stop proxied by number of parking spaces) in Michigan, Ohio,
Indiana, Illinois, and Wisconsin and randomly selecting drivers at each truck according to a
carefully developed sampling design. Data were collected in two “waves,” one during the
summer of 1997 and another during four seasonal periods beginning in the spring of 1998 and
continuing to the winter of 1998/1999. Data are proprietary and not available to the public. All
of the information is self-reported.
• Crash during the past year
• Yearly Miles
• Mileage Rate
• Unpaid Time
• Paid Days
• Health Insurance
• Late Penalty
• Safety Bonus
• On Time Bonus
• Tenure
• Experience
• High School Grad
• Weekly Hours
• % Non-Driving time
• % Night Driving
• Union Membership
• Firm Size
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• Type of trailer used
Findings
Truck Driver Labor Supply Curve
An important component of this study involves modeling the labor supply curve for truck
drivers. Using the UMTIP driver survey data we demonstrate a classic backward-bending labor
supply curve, which is predicted but rarely found in other data because of institutional and other
limitations on actual work practice. In the case of over-the-road truck drivers, whose hours are
not constrained by the Fair Labor Standards Act and whose maximum hours enforcement
agencies find very difficult to regulate, we see a full backward-bending curve within the range of
valid observations.
While such a curve theoretically represents drivers’ preferences in trading labor and
leisure, our institutional knowledge leads us to think that driver and firm preferences are not
independent. That is, this curve represents the joint choice by drivers to work more or less hours
depending on their rate of pay as well as the firms’ choice (at various levels of pay) to ask or
require drivers to work more hours or, alternatively, to limit their hours. We would expect firms
paying lower wages to require drivers to work more hours (take more runs) and that drivers
working for lower wages would tend to want to take more runs (work more hours) to reach their
target earnings (they would collaborate with firms to work more hours). Firms which pay higher
wages tend to be unionized, and union wages and bargaining power give workers a higher rate of
pay (and less need to work longer hours to reach target earnings) and greater leverage with the
firm to refuse extra work.
• At 20 cents per mile, drivers have a positive economic incentive to work 48.9 hours.
• At 25 cents per mile, drivers have a positive incentive to work 60.1 hours.
• At 31.4 cents per mile, drivers - on average - choose to work 65.1 hours per week.
Above this pay level drivers’ preference for more work hours declines.
• At 37.8 cents per mile, the drivers’ preference for work declines to 59.9 hours per week
• At 42.1 cents per mile, the drivers’ preferred work level drops to 50.6 hours.
This finding demonstrates conclusively that increasing driver pay decreases the
likelihood that drivers will work more hours. This finding is entirely consistent with prevailing
economic theory with respect to the labor - leisure tradeoff.
Safety Study 1: Firm-level cross sectional analysis
• Average pay is $0.286 per mile for drivers with three years experience.
• The average driver works 0.004 hours of unpaid time per mile driven, or 3.6 hours of unpaid
time per trip with an average reported trip length of 906 miles.
• The average expected annual raise in driver pay is $.0007 per mile.
• 49% of firms pay a safety bonus.
• 28.4% of firms pay a production bonus.
• The average driver pays $166.84 monthly for health insurance.
• The average amortized value of a driver’s available life insurance policy is $15,505.
• The average driver receives $773.56 per year in paid time off.
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• The average run is 905.85 miles.
• 20.6% of all firms primarily use flat bed trailers.
• 51.0% of all firms primarily use van trailers.
• The average carrier has 683 power units.
• 76.5% of carriers use governors to limit truck speeds.
We ran a negative binomial regression to predict the number of crashes in each firm as a
function of various pay variables and other carrier characteristics. The results are highly
significant, with most compensation variables except “pay raise” significant at the 0.01% level
(pay raise is significant at the 10% level; paid time off is not significant). Incentive variables
produce uneven results: “safety bonus” is significant, while “production bonus” is not.
We converted the estimates to elasticities to explain most clearly the effect of each of the
independent variables. If we sum up all of the compensation effects tested in this model, we find
that compensation and crashes are inversely related on nearly a 1:1 level. To be specific, for
every 10% more in average driver compensation (mileage rate, unpaid time, anticipated annual
raise, safety bonus, health insurance, and life insurance), the carrier will experience 9.2% fewer
crashes.
Safety Study 2: Individual driver level study at one firm
A pay raise by a major TL carrier gave researchers an ideal scenario for a quasi-
experimental research design. How much does driver pay predict safety? What is the effect of a
major pay increase on safety?
Table 2 shows the raw effects of the pay raise on demographic and occupational factors.
Executive Summary Table 2: Before and After Descriptive Data
Before the raise After the raise
Age 38.0 41.6
Female 3.6% 2.0%
White 72.9% 78.2%
Non-married 53.6% 43.7%
Base pay (dollars/mile) $0.262 $0.336
Percent pay raised 10%
Miles per month 9,155 9,190
Dispatches per month 15.6 16.2
The variable “percent pay raised” substantially understates the increase because all
drivers did not receive a pay raise. Drivers who were hired at a low rate during the first period
and who were retained under the new regime received pay raises, while drivers who were hired
during the first period and who did not remain after the pay raise are not in the dataset. In
addition, drivers who were hired at the higher rate in the second period also did not receive a
raise. Among drivers receiving a pay raise, the average increase is 38%.
Both driver pay rate and the pay increase have statistically significant effects on the
probability of observing a crash each month. All predictors are significant at the 0.01%
12
significance level except marital status, season, and the interactive effect of age and time
observed, which are significant at the 0.05% confidence level. Other covariates that were not
statistically significant include gender, number of dispatches and time of hire (before or after pay
raise).
The key findings are, controlling for the other effects in the model:
• Driver crash risk decreases with age until the driver reaches 41 years of age, when the
effect changes direction. A driver who is 20 years old has a crash risk similar to the crash
risk of a driver 62 years of age, all other characteristics held equal.
• Non-married drivers are safer.
• Higher pay rates are associated with greater driver safety.
• Pay increases are associated with greater driver safety.
• The more miles drivers drive the safer they are (probably reflecting miles on Interstate
highways).
• Longer driver tenure contributes to safety.
• Drivers are safer in winter.
• The interaction of driver age and driver base pay over time also significantly contribute to
higher safety outcomes.
• There are unmeasured attributes, perhaps at the driver or at the operations level, that
suggest that crash risk decreases over time, even after controlling for the variables
described above.
What does this mean in terms of elasticities? The results show that at the mean, for every
penny in a driver’s base pay rate, the risk of crash is 11% lower; in percentage terms, at the
mean a 10% higher driver base pay rate (hiring rate or the rate at which the drivers were paid
before the pay increase, which generally is the hiring rate) leads to a 34% lower probability of
crash. The effect is not linear, so this elasticity will change above and below the mean. In
addition, for every 10% raise in driver pay that occurred while we observed the drivers, there is
a 6% reduction in crash risk. However, this effect cannot be attributed solely to the pay raise,
since individuals getting the pay raise tend to be, on average, more safe than other individuals.
These crash effects are independent of the demographic changes that resulted from the pay
increase.
We ran a further analysis on the drivers who worked for Hunt during the second period
(after the pay raise). For these drivers we have prior driving experience measures, and thus
together with tenure at the firm, we can construct a measure of total driving experience. As with
age, we found that the relationship between total driving experience and crash risk is quadratic:
as experience increases, crash risk is lowered, but at a decreasing rate. Evaluated at the mean
experience for the sample (5.2 years), this suggests an elasticity of -4.94; at the mean, 10% more
experience leads to a 49.4 percent lower probability of crash. However, this decreasing effect of
experience rarely is observed in the data since experience is associated with lower crashes for the
first 18 years of a driver’s experience.
Overall, we conclude from these analyses that higher driver pay is associated with a
lower probability of crash. Conventional economic theory supports the assertion that pay is a
proxy for human capital. Most of this human capital is unmeasured: we simply have no good
measures in conventional data sets nor in our own data sets to calculate this effect, so it remains
13
captured by proxies such as pay, race, and other factors. In addition to serving as a proxy for
human capital, and consistent with our other studies, we also find that a pay increase appears to
have an “incentive” effect that results in safer driver behavior. The causal underpinnings of such
behavioral outcomes are a matter for further research. For the purpose of public policy,
however, it may not make any difference for safety outcomes whether higher pay results from
the sorting effect (which is another term for selection effect) or from the incentive effect: the
consequence is still greater highway safety.
Safety Study 3: Individual driver level; random sample of all drivers
Our final analysis is based on our driver survey. While this survey included 1,000
drivers, we narrowed our analysis to “employee” drivers who are paid a mileage rate. This
excluded hourly drivers and owner-operators, as well as company drivers and owner-operators
whose earnings are based on a percentage of revenue. We did this to reduce the noise in the data
and develop a consistent measure.
The drivers in the sample look similar to drivers in other studies, including the two other
studies included in this report. The average driver earns $0.295 per mile, drives 121,380 miles
per year, receives 14.7 paid days off, and works 62.1 hours per week. We found that 85% have
health insurance, 26.7% receive an on-time bonus, 57.9% get a safety bonus, and 62.8% will
suffer a penalty if they pick up or deliver a load late. Surprisingly, the average driver has worked
for his current employer nearly 4 years and has more than 14 years of experience. Drivers put in
a great deal of non-driving time (18.3% of their time) and work more than 20% of their hours
during the night. Only 9.3% of over-the-road drivers we surveyed are union members (we
probably undersampled this group because they are somewhat less likely to stop at truck stops
and do not fuel on the road).
A probit regression was used to estimate the likelihood that a driver reported having a
crash during the past year. While some individual statistics are significant, our overall model is
not significant because the data set has so much “noise.” While this may be disappointing, the
fact that we achieved very strong results on the two pay variables (pay rate and paid time off,
both significant at the 0.05 level) supports our hypothesis that driver pay strongly predicts truck
driver safety. Measured at the mean value of all characteristics, a 10% increase in the mileage
rate from $0.295 to $0.324 is estimated to reduce the probability of a crash from 13.8% to
10.86%, which is a 21% decrease in this probability. Similarly, increasing the number of paid
days off also reduces the estimated crash risk. A 10% increase in the number of paid days off
decreases the crash risk from 13.8% to 12.79%, which is a 7% decrease.
Conclusion
This study demonstrates that driver pay has a strong effect on safety outcomes. These
results are consistent with economic theory because we expect that carriers pay drivers according
to their market value, and that value is determined by their personal employment history, driving
record, training and education experience, driving skills, temperament, and other unmeasured
factors. Since very few of the drivers studied in our datasets are union members, we expect that
the differences in safety outcomes are likely due to different individual characteristics for which
they are paid differentially. Firm size probably is associated also with greater driver safety, as
two of the three data sets suggest (though the results are ambiguous because the trend does not
14
appear to be linear), and firm size has shown to be an independent predictor of employee pay
rates.
It is difficult to come up with a single summary estimate of the effect of driver pay, as
elasticities vary across datasets and model specifications, but conservatively we can say that the
relationship between safety and pay probably is better than 2:1. Higher pay produces superior
safety performance for firms and for drivers. The precise driver-level study of Hunt suggests this
relationship may be as high as 1:4 while the cross-sectional study of Signpost carriers shows that
even with an imprecise pay variable, the relationship between safety and pay rate is 1:0.5 and the
relationship between safety and compensation is 1:0.92 – a ratio of nearly 1:1. Clearly truck
driver pay is an extremely strong predictor of driver safety.
15
Paying for Safety:
An Economic Analysis of the Effect of Pay on Truck Driver Safety
I. Introduction
Trucking safety has become an increasingly important transportation issue in recent
years. Between 1992 and 1997 there was a 20% increase in the number of persons who died in
crashes involving large trucks. Although such an increase might be expected given the
simultaneous 25% increase in the annual number of miles traveled by large trucks, a GAO study
points out that fatality levels continue to exceed national goals for reducing fatalities. While
trucks experience fewer crashes per mile than passenger cars, the majority of all fatally injured
persons involved in truck-related crashes were occupants of passenger cars (Scheinberg 1999).
Furthermore, 38% of all crashes and 30% of fatal crashes involving trucks were not reflected in
federal statistics in 1997 (Scheinberg 1999).
Clearly, there is both a solid rationale for public concern and a strong impetus for
improved data collection and research on the causes of trucking crashes. Larger trucks, increased
congestion and deregulation have all been considered as possible explanations for the number of
crashes and deaths related to trucking. In addition, research has been focused on such issues as
the incidence of nighttime driving, driver fatigue, and increases in the average length of trips.
However, little effort has been focused on the effects of monetary compensation on trucking
crashes. The purpose of this study is to determine how the level and method of pay influence
safety related outcomes in the trucking industry.
Monetary compensation can influence worker behavior in a number of ways. Yellen
(1984) hypothesizes that an employer paying higher than average “efficiency” wages will
discourage workers from shirking, since losing their job imposes a cost on the worker. If the cost
of monitoring workers is higher than that of the increased wages, Yellen (1984) argues that this
can be an efficient way for the employer to elicit effort from workers. In addition to the level of
compensation, the type of payment can also influence worker behavior. Although this practice is
no longer as common outside of transportation, the practice of paying “piecework” rates has a
long history of providing an incentive for workers – and especially contract workers – to increase
their effort (Belzer 2000). While the efficiency wage argument appeals to the long run interest of
the worker to maintain employment, the piecework system is designed to create an immediate
incentive to increase production by paying higher wages to those workers who are more
productive.
One of the few places where piecework pay is still the norm is the trucking industry. The
vast majority of both truckload (TL) and less-than-truckload (LTL) road drivers are paid by the
mile or in some manner by the load, rather than an hourly wage. This method of pay is so
pervasive that in the industry, mileage often is the sole determinant of compensation, regardless
of what other tasks the driver might undertake. The treatment of loading and unloading time is a
good example. Drivers frequently wait long periods of time for their loads, and in many cases
must load or unload their own freight. However, it is generally the case that these drivers are
underpaid, relative to their driving time, or not paid at all, for these efforts. We hypothesize that
while these compensation practices may be useful in eliciting more work effort from drivers,
16
they also create incentives that encourage behaviors that have a negative influence on safety-
related outcomes.
Both the method and level of compensation in the trucking industry create short run
economic incentives that may lead to unsafe driving practices. These behaviors may include
neglecting safety inspections and repairs as well as driving too fast for conditions (and faster
than legally allowed). In addition, these compensation practices can lead drivers to work more
than the number of hours allowed by the hours of service rules. This last area of driver behavior
is of particular interest to this study. In many instances it may be the case that a driver requires a
minimum or ‘target’ level of income that is necessary in order to meet basic living expenses. If
the mileage rate is sufficiently low so that this target cannot be reached, the driver may feel
compelled to work hours that are in excess of the legal maximum, and economic theory supports
this expectation. These incentives can be compounded under conditions where the drivers are
either underpaid relative to their driving time, or not paid at all for loading and unloading. In
these instances, there is an incentive to underreport the amount of time spent on the lower paid
loading time in order to conserve more available hours for the relatively higher paid driving time.
This underreporting of loading and unloading time combined with additional driving time means
that drivers might often work hours in excess of those allowed by law. While this may provide
short run economic benefit to the drivers, in the long run it would result in an excessive supply of
labor to the marketplace for a fixed number of workers, driving wage rates down and
encouraging further excessive hours of work. Given a fixed labor market, each individual driver
will tend to work more hours than allowable and this “sweating” of labor will encourage each
individual driver to work even harder and longer, in a sense expanding the labor market
(measured as the number of hours provided to the market) artificially and increasing all drivers’
crash risk accordingly. These longer hours create safety concerns that affect not only the
industry, but the broader population as well. If the cost of this additional safety hazard is
insufficiently captured by the market for individual driver services, it would represent a market
imperfection that might have significant policy consequences.
The remainder of this report is organized as follows. Parts II and III provide a review of
the literature on the subject of trucking safety. Part IV describes the data that have been used in
the study. Part V describes the research strategies used in the study, including the theoretical
basis for the hypotheses and the methodologies that have been employed in testing them. Part VI
provides results. Part VII gives some conclusions drawn from these studies and Part VIII is a
bibliography of sources cited.
17
II. Literature Review
Introduction
Employee earnings levels and the method of compensation are believed to have an
influence on employee behavior. This research hypothesizes that the level and method of
compensating truck drivers affects their driving and non-driving behavior, which ultimately
influences their involvement in crashes. From the perspectives of the driver, the firm, and
society, driver safety is a serious concern. It is therefore important to determine how different
methods and levels of compensation influence the behavior of drivers, and how these behaviors
result in desirable or undesirable outcomes.
Truck driver attitudes and behaviors have been studied in various contexts. In most cases,
the motivation for these studies is to understand the immediate mechanisms that influence certain
driver behaviors. These studies, however, often focus on particular behaviors (e.g., speeding,
working – and especially driving – excessively long hours, and not getting enough sleep) rather
than confronting the factors that motivate such behaviors at different organizational levels. Such
factors can include economic pressures, personal characteristics, pay rate, and the compensation
method itself, among others.
By reviewing the literature on employee compensation (method and level) and its
influence on workers’ safety outcomes, this study seeks to account for the fundamental
motivations of certain driver behaviors. In particular, we are concerned with uncovering and
understanding the existing body of research that links individual compensation with safety both
directly and indirectly. For this purpose, journals in several disciplines, including human
resource policy, economics, and psychology, have been surveyed.
The review is organized as follows. First, we present the motivation for studying
compensation and safety. Second, we provide a brief discussion of the role and relevance of
employee compensation. Third, we address worker compensation level and its implications for
the employee and the firm. Fourth, we review the literature on methods of compensating
employees. Fifth, we provide a summary of the evidence suggesting a link between driver
compensation and safety in the trucking industry. Due to the paucity of research directly linking
compensation and safety, we then summarize the research literature covering any indirect effects
that might link the two. Finally, we present conclusions and identify research designs used here
to address the direct and indirect links between driver compensation and driver safety.
Motivation
In 1990, the National Transportation Safety Board called for a review of trucking
industry structure, operations, and conditions that may create incentives for drivers to violate
hours of service regulations and to use drugs (National Transportation Safety Board 1990). In a
1995 report, a NTSB study raised “questions about the influence of pay policies on truck driver
fatigue,” and about “a link between method of compensation and fatigue-related accidents”
(National Transportation Safety Board 1995). Although the study comes from analyses of a
convenience sample, and hence is not representative of the population of truck drivers, its
summary statistics regarding compensation method and the prevalence of fatigue are consistent
18
with other studies (National Transportation Safety Board 1990; Beilock 1994; National
Transportation Safety Board 1995).
From the driver’s perspective some consideration has been given to the compensation
issue and its influence on safety. Pay level has been studied more consistently than pay method.
Low levels of pay have been considered by many as a motivator of long driving hours, illegal
substance use, the onset of fatigue, and other practices and phenomena (Hensher et al. 1991;
General Accounting Office 1991; Saccomanno et al. 1997). Other studies, however, have
suggested that truck driver compensation level has a less important role than the one regularly
attributed to it (McElroy et al. 1993).
Groups of drivers participating in different focus groups have characterized the prevailing
piece rate (per mile) compensation method as limiting income and encouraging cheating (Mason
et al. 1991; Cadotte et al. 1997). Drivers readily identified the compensation system in place as a
motivation for unsafe driver behavior. Piece rate systems coupled with hours of service
regulations limit the income opportunities of drivers (Chatterjee et al. 1994). Forty-five percent
of respondents to a New York State driver survey thought it would be useful to pay by the hour
in order to reduce driver drowsiness (McCartt et al. 1997).
Management also has recognized the importance of better understanding driver
compensation. A 1995 mail survey of 1,464 drivers at 57 for-hire truckload dry van, flatbed,
refrigerated, and tank carriers showed that an overall driver compensation factor emerged as the
important dimension for human resources improvement (Stephenson and Fox 1996). Similarly,
in a survey of 148 trucking company personnel managers, other researchers found that managers
believed that pay level was the most important factor in drivers’ choice of motor carriers for
employment (Southern et al. 1989).
The Role of Employee Compensation
An initial discussion of the roles that have been attributed to employee compensation will
serve as a guide for the discussion in subsequent sections. The first role traditionally assigned to
employee compensation is to allocate prices. As a method of allocating resources, employee
earnings are a pricing mechanism used to direct labor to its most productive use. This function,
very much in line with traditional microeconomics, explains variations in the distribution of
earnings as emerging from the interactions of supply and demand where certain observable
characteristics are taken into account.
A second role of compensation is to serve as a tool for social stratification and cohesion.
In this role, employee earnings are seen as a prime determinant of standard of living. Earnings
play the role of providing social legitimacy within organizations and society. Compensation
policies play a role in determining what is a “fair” wage level (Akerlof et al. 1988; Akerlof and
Yellen 1988; Akerlof and Yellen 1990).
A third, and increasingly prominent, role of employee compensation is as a management
tool that can be used to elicit higher employee effort and align employees’ core skills with the
organization's interests. Earnings become a key element in the management-employment
relationship. There are multiple theories about the role of pay in this relationship. They range
from that of the transaction cost perspective (Williamson 1975), where opportunistic behavior is
to be minimized, to that of efficiency wage theorists (Yellen 1984; Holzer 1990; Weiss 1990;
19
Lazear 1995), where above-market wages result in desired behavioral outcomes for a group of
employees. These outcomes, discussed below, can range from reduced shirking and enhanced
effort (Yellen 1984) to outcomes more directly relevant to the present study, such as adherence
to hours of service regulations, behaviors oriented towards reducing risk of fatigue and dozing
while driving, and generally safe-driving behaviors. However, no previous study has utilized
efficiency wage theory to study truck driver behavior.
We took these three roles as starting points for the literature review. As highlighted by
some authors, recent changes in wage structures, such as the impact of economic deregulation,
have created increased interest in the roles that compensation plays in society (Rubery 1997).
Belzer (1993) traced the post-regulation transition from regulation-related truck industry
segmentation to market segmentation, and the resulting impact on industrial relations, including
compensation practices. Wage levels were modeled as a function of a variety of firm-level
factors including industry segment, average haul, unionization, market share, profitability, and
locational variables such as urbanism and regionalization. Unionization and industry sector
(LTL) was most strongly associated with higher wages. He also found that market share affected
wages positively (consistent with previous findings) as did location (Southern carriers had
significantly lower wages) (Belzer 1993; see also Belzer 1995). These findings have yet to be
extended to the impact of variation in compensation levels on safety outcomes. For instance, do
firms in the Southern region or with small market shares also evidence sub-standard safety
records? Do nonunion firms have worse safety records? Do lower-paying firms have more safety
problems? We found a very limited number of cross-industry studies linking compensation
policy and safety outcomes. As a result, in the next two sections we present what we consider to
be a predominant emphasis in the literature regarding the rationalization of the wage structure.
Subsequent sections cover compensation policy and safety in the trucking industry more
explicitly.
Compensation Level
The need to consider employee compensation as an integral package was perceived early
in the study. The term “compensation level” is often discussed in the context of a hierarchical
conception of pay (Milkovich and Newman 1993), where the compensation system is
disaggregated into its fundamental components, such as method, level, changes in earnings over
increasing job tenure and similar factors. Employee compensation is understood as the overall
employee earnings for a period of time, including direct compensation (e.g., wages) and deferred
compensation (e.g., pension plans).
Direct Compensation
Organizations can have varying pay levels, depending on the flow of work and the
organization, yet pay differences between similar jobs in similar organizations often are observed
(Seiler 1984; Leonard 1987; Chen 1992). It is important to understand the circumstances under
which this occurs, the consequences for employees, and why these consequences might be
important.
The field of economics has contributed considerably to the discussion about
compensation levels. Weiss provides a useful summary of issues associated with direct
compensation (Weiss 1990). The literature consistently shows that increases in relative wages
20
(after controlling for occupation and human capital) are associated with increases in productivity.
There seems to be less agreement about the magnitude of the effects and whether the increase in
productivity is large enough to pay for the wage increase (Levine 1992). It also is difficult to
disentangle cause and effect. Rather than focusing on the literature covering the stylized effects
of wages on employee behavior, we focus on prevalent theories about the mechanisms by which
compensation levels affect workers and firms. Next, we introduce the concepts of efficiency
wages, wage deferral (also known as “wage-tilting”), transaction costs, incentives, equalizing
differences, and fair-wage theories.
Efficiency wages
Theorists of efficiency wages argue that some employers are not price-takers with respect
to wage levels (they do not pay market-clearing wages). Instead, they offer above market-
clearing wages that allow them to induce employees to be more efficient. This efficiency
increase can occur in several ways.
Reduction in shirking. Since employees have a higher compensation level with efficiency
wages than they would have otherwise, the cost of being fired due to shirking behavior is higher.
This leads to a reduction in worker shirking. Some research suggests that greater wage premia
are in fact associated with lower levels of shirking as measured by disciplinary dismissals
(Yellen 1984; Cappelli and Chauvin 1991). However, shirking and discipline also are dependent
on conditions in the labor market where, for example, the costs associated with shirking are
correlated with the difficulty in finding alternative employment (Groshen and Krueger 1990).
Quality of workers. It is reasonable to expect, and empirical research has shown, that
high compensation levels attract more qualified workers than do lower compensation levels
(Groshen and Krueger 1990; Chen 1992). This is the “creaming effect.” Acting as a mechanism
for selection, the compensation level attracts more productive employees. For example, positive
consequences often associated with having a more qualified pool of workers include the reduced
need to supervise employees and a reduction of employee shirking. For example, Groshen and
Krueger found that hospitals that paid high wages to staff nurses employed fewer supervisors
(Groshen and Krueger 1990). It is unclear, however, if this is due to greater work effort from the
average existing nurse workforce or due to higher wages attracting better nurses who needed less
supervision (the creaming effect). Another study, however, concludes that the negative
correlation between supervisory intensity and worker's wages hypothesized was not apparent
from the statistical results (Leonard 1987).
Turnover costs. Increases in tenure also can be explained by higher wage levels; higher
wages may tend to reduce turnover. Turnover costs include advertising, search, and training costs
(Becker 1975; Salop and Salop 1976; Arnold and Feldman 1982; Cotton and Tuttle 1986; Chen
1992). One study of high school graduates correlated higher wages with longer job tenure
(Holzer 1990). In many instances the turnover effects are hard to determine because few
companies evaluate their recruiting programs well enough to show that higher wages did in fact
allow them to choose superior applicants.
Wage-deferral or wage-tilting
The wage-deferral or wage-tilting model argues that, in order to invest in human capital,
firms need to obtain long-term commitments from their workers. Under the turnover threat, firms
21
under-invest in employee training. Requiring workers to share in the firm-specific investment in
human capital is a way of receiving this commitment. Such a sharing arrangement is achieved,
for example, by having workers earn below-market wages during the early years of employment
in the firm; during later years they earn above market wage, reflecting a return on this
investment. This is similar in nature to the use of deferred compensation to encourage lower
turnover, as shown later. Proponents argue that the wage tilt profile can be used to favor older
workers (Ippolito 1991), dissuade workers from shirking (Lazear 1979), or attract a higher
quality of workers (Salop and Salop 1976). Contrary to the popular (but little tested) hypothesis
that wage-tilt is important in inducing workers to make long-term commitments to the firm,
some researchers have shown that the wage-tilt had no significant effect on tenure, except
indirectly through its effect on pension quit costs (Ippolito 1991).
Transaction cost theory.
Transaction cost theory, also known as the New Institutional Economics (NIE), is based
on the premise that people act in their own self interest; this assumption is similar to the one on
which neoclassical economics is based. Transactions cost analysis also begins with the notion
that exchange is costly; the explicit assumption of costly exchange distinguishes it from
neoclassical economics. There is no third party to enforce the bargain costlessly (no referee) —
thus it pays to minimize transactions cost; and transactors are self-seeking with guile — they will
hold their cards closely. While neoclassical economics assumes perfect information, transactions
cost theory assumes information is imperfect and asymmetric. In addition, where neoclassical
economics assumes rationality, the NIE does not: people do not necessarily make economically
rational choices. Finally, while neoclassical economics assumes a free-flow of information, the
NIE assumes information impactedness: outcomes are bundled and it is hard or impossible to be
sure you are getting what you pay for. These assumptions lead theory in a very different
direction, one fundamentally more cautious about market transactions and more supportive of
institutions and contracts (Williamson 1975; Milgrom and Roberts 1992).
Incentive theory
Incentive theory is related closely to efficiency-wage-based theories for motivating
higher employee effort. There are several incentive-based theories among which content and
process theories are very relevant. Content theories focus on what motivates employees. The two
most popular content incentive theories, Maslow’s hierarchy of needs (Maslow 1954) and
Herzberg's hygiene theory (Herzberg 1966), include pay as an important factor in employee
motivation (Milkovich and Newman 1993). In the former, pay supplies a series of basic needs:
e.g., the need to acquire food and shelter. Beyond attending basic needs, pay also can be
associated with other higher needs, such as recognition and satisfaction at the workplace.
In contrast to content theories, process theories focus on how people are motivated (rather
that what motivates them) while recognizing the importance of content. There are several lines of
research within process theories. The operant conditioning literature, for example, focuses on
how types of reinforcement schedules best motivate high performance. Similarly, the utility of
expectancy theory models work motivation as a three step process involving an evaluation of the
effort needed for task completion, valuing the completed task, and linking effort and task
completion outcomes and the individual's value system (Deci 1985). Again, pay is a fundamental
22
component of reward systems and hence is also important within process theories (Milkovich
and Newman 1993).
Equalizing differences theory
This theory is based on the thought that low employee monitoring goes hand in hand with
low wages. The theory assumes that employees dislike being monitored, and therefore closely
supervised workers will exhibit higher wages because they need to be compensated for the lack
of privacy. Groshen and Krueger’s research on nurse turnover concluded that the wages of staff
nurses tended to fall with the extent of supervision, suggesting that workers do not receive a
compensating wage premium for close supervision, thereby claiming to disprove this theory
1
(Groshen and Krueger 1990). In the context of the trucking industry, the equalizing differences
theory seems linked to the argument behind Ouellet’s Pedal to the Metal (Ouellet 1994). In his
book, Ouellet argues that truck drivers are a unique group with specific tastes that are
significantly different from the tastes of the average workforce. A notion of drivers as “highway
cowboys” who enjoy a high degree of independence certainly is aligned with the assumption of
the equalizing differences theory.
Fair wage theory
This is yet another conception of efficiency wages based on the idea that “fairness”
provides explanations for (a) wage compression, (b) the positive correlation between industry
profits and industry wages, and (c) the inverse correlation between unemployment and skill. The
fundamental hypothesis is that in industries where it is advantageous to pay some employees
highly, it is considered fair also to pay other employees well and hence the “fair wage/effort
hypothesis” (Akerlof et al. 1988; Akerlof and Yellen 1990; Rice et al. 1990; Milkovich and
Newman 1993). In other words, in some industries and firms, high wages paid to one group must
also be paid to another or tensions may arise due to the perceived inequity. Other theories
incorporating the notion of fairness and similar social norms include the rent-sharing (Levine
1992) and reciprocal-gift models (Milgrom and Roberts 1992; Burks 1997).
Compensation Method
We now move from compensation level to the way workers are compensated.
Compensation methods that deviate from the traditional time rates and salaries have become
more popular. Most of these new compensation methods attempt to align the employee’s
interests with those of the firm. While performance-based methods have a long history in some
areas of manufacturing, the have become increasingly common in other industries and
particularly in the service sector. Piecework, where pay is related directly to specific units of
output, is a common performance-based pay measure, as is incentive pay, which provides
bonuses for meeting or exceeding a target output. In the next section we focus on piece rates and
time rates and their implications for individual and firm productivity. We focus on these two
methods of direct compensation because of their prevalence in the trucking industry.
1
Again, it is unclear if nurses that were monitored less were qualitatively similar to nurses that had more monitoring
but similar or lower wages. Any unobserved heterogeneity can bias the interpretation of the results by Groshen. The
direction of causation, moreover, may be reversed.
23
Direct Compensation
Applied at the individual level, piece rates give individual financial recognition to more
productive or harder-working employees who are thus encouraged to work more intensively.
Because they are tied so closely to output, piece rates provide incentives for employees to exert
themselves to produce more output and generate firm revenues.
Research on compensation methods and piece rates vis-à-vis time rates has developed
over more than 30 years (Keselman et al. 1974). In most of the work reviewed, individuals
receiving pay contingent on performance were more productive than individuals on a time-pay
basis (Fernie and Metcalf 1996; LaMere et al. 1996) . For example, in a recent study of tree
planters in British Columbia, workers compensated under piece rates produced more, on average,
than those on time rates. Interestingly, however, the productivity of piece-rate planters fell with
the number of consecutive days worked (Paarsch and Shearer 1996). This result becomes
especially important in understanding the effects of long daily and weekly working hours on the
trucking industry, in terms of both driver productivity and safety.
If piece rates produce higher output, one would think this should be reflected in higher
worker earnings. In a study of over 100,000 employees in 500 firms within two industries, Seiler
(1984) examined the effect of piece rates on employee earnings and the impact of incentives on
earning. Two incentive effects are observed. First, incentive workers’ earnings are more
dispersed (i.e., the distribution is wider) than identical hourly workers’ earnings. Second, on
average the incentive workers earn 14% more money, controlling for other factors. This
premium is partly a compensation for the greater variation in their income and partly a result of
an incentive-effort effect (Seiler 1984).
Two interesting questions emerge from these results. First, does contingent pay, or more
broadly, do productivity-based incentives, actually increase productivity (the motivation effect)
or do they simply attract the most productive workers (the sorting or selection effect) (Blinder
1990; Lazear 1995)? This is similar to the issue raised by trying to understand the way
compensation level affects workers’ productivity and behavior. Second, in contingent pay, part
of the earnings risk is passed on to workers. Therefore, risk averse workers may prefer time-
rates, which further strengths the sorting mechanism described above.
Advocates of the sorting effect argue that piece rates differentially attract workers who
are harder working, or who are more productive, than are those attracted by hourly rates, ceteris
paribus. By eliciting higher effort levels, the effect of piece rates on earnings produces an
“earnings effect.” Piece rates also affect other non-earnings situations, the “non-earnings effect.”
For example, a break or a visit to the restroom has a high opportunity cost for the employee
working in a piece rate compensation system. Therefore, given the choice, people who are more
apt to increase effort intensity and effort duration may choose piece rate methods, while
individuals who value the negative non-earning consequences more than the positive earnings
consequences of piece-rates may tend to select time-based pay schedules. In a study of
agricultural workers, Rubin and Perloff found that the non-earnings effect captures the change
with age in a worker’s relative taste for piece rate work. For the very young and very old, the
non-earnings effect of age dominates the earnings effect (Rubin and Perloff 1993).
Piece rate compensation is attractive to business because it seemingly solves the
problems associated with adverse selection and moral hazard. In addition, by paying piece rates,
24
the firm allows workers to receive the full value of their own marginal product, thereby
eliminating some of the firm’s a priori need for information on productivity, thus reducing
monitoring costs (or transferring that cost to the worker, who reaps the savings). Arguably these
incentives may also reduce the need for employee monitoring and observation to determine
individual merit or performance pay necessary when using other compensation systems.
Piece rate compensation, however, can bring some disadvantages. As indicated above, it
introduces a source of randomness into workers’ earnings. Also, piece rates alone encourage
employees to ignore other valuable activities. As a result, piece rate workers are tempted to
reduce quality to increase measured quantity and engage in other non-productive activities
(Burawoy 1979). Another commonly cited disadvantage of piece rate compensation is the
difficulty of observing actual productivity (information and observation problems), which may
lead to shirking behavior in the short term (Gibbons 1987).
Bloom et al (1995) suggest that adverse selection and moral hazard, as described above,
only tells part of the story of the effects of piece rates. The problem is one of “principals” and
“agents”, where the firm is the principal and the employee or subcontractor is the agent. That is,
firms might act to align the workers’ interest with their own through the use of payment
incentives, but its effect on agent behavior may be more complex than typically assumed by
agency-based research. The incentives and earnings risk-sharing tradeoff, for example, might
lead to imposing “greater uncertainty in the employment relationships” or other adverse
outcomes (Bloom and Milkovich 1995). Surely there are other responses to incentive payments
that affect the individual and organizational climate. These are reviewed in subsequent sections.
A 1991 study commissioned by the U.S. Office of Personnel Management to assess the
contemporary research literature on employee job performance and performance-based pay
concluded that individual incentives (including piece rates) can have positive effects on
performance, though the context of implementation remains important (Milkovich et al. 1991).
The report cites some negative consequences of incentive pay, including neglecting aspects of
the job not covered in the incentives, encouraging gaming or reporting of invalid data, and a
potential clash with group norms. Scholars conclude that individual incentive plans are
inappropriate when there is high complexity of tasks (Brown 1990; Brown 1992) and a required
focus on quality rather than quantity.
There is limited literature associating compensation methods and safety outcomes.
Hopkins, as cited in Hofmann, argued that incentive pay was not the root of unsafe behaviors in
several coal mines studied (Hofmann et al. 1995). Instead, unsafe behaviors were fueled by the
organizational climate and the workers’ perceptions of the nature of the job (e.g., being unmanly
to be careful and safe) (Hofmann et al. 1995).
Deferred Compensation
The lower labor turnover found in large firms relative to smaller firms has been cited by
some as evidence that large firms pay workers a wage above their opportunity cost (Even and
Macpherson 1996). Large firms, it is argued, can afford efficiency wages. Several studies have
disputed this claim by investigating an alternative possible explanation: size-related differences
in the availability, portability, or generosity of pension plans (Even and Macpherson 1996).
Pensions, as wage-tilts discussed in the previous section, can be a mechanism for encouraging
25
long-term employment relationships beneficial to firms. Other mechanisms, such as up-front fees
and bonds are rarely actually observed, but steep age-earnings profiles and deferred
compensation plans are equivalent to bonding in their effects on behavior. Several scholars argue
that deferred compensation (e.g., pension plans, profit sharing, contribution thrift, ESOPs)
directly substitutes for employee wages (Lazear 1979; Lazear 1995; Salop and Salop 1976).
Arvin argues persuasively, however, that in imperfect capital markets where individuals cannot
borrow freely, deferred compensation and wages are not perfect substitutes (Arvin 1991).
Lower turnover in jobs covered by pensions than in other jobs seems to be a well-
documented finding in the worker mobility literature. The hypothesis that pensions (which act as
deferred compensation) discourage turnover is supported by the finding that turnover is only
about half as great for workers covered by pension plans as for workers without pensions. This
relationship remains consistently strong even after controlling for other factors such as pay level,
union membership, and tenure (Gustman and Steinmeier 1994). Ippolito found that pensions
increased tenure in the firm, on average, by more than 20 percent (Ippolito 1991). Lazear argues
persuasively that the pension plan’s vesting provisions affect turnover the most and constitute the
real incentive effect (Lazear 1990). Other research shows that capital loss is the main factor
responsible for lower turnover in jobs covered by pensions, but self-selection and compensation
levels also play an important role. Allen provides direct evidence that bonding is important for
understanding long-term employment relationships (Allen et al. 1993). Somewhat contrary to
these results, Arvin found that pension portability was not an important factor in determining
turnover and that further research was needed (Arvin 1991).
A self-selection concern similar to the effect of efficiency wages also occurs with
pensions. Employees prone to have lower mobility would tend to prefer deferred compensation.
A study found virtually no association between firm size and labor turnover for workers not
covered by a pension (Even and Macpherson 1996). In contrast, a smaller study of the trucking
industry found a significant positive correlation between size of firm and turnover (LeMay et al.
1993), not controlling for the presence of a pension plan. From this the authors warn growing
firms about fast growth and the effects it may have on turnover.
Two alternative interpretations are plausible. First, larger firms may tend to select a
method of compensation (Soguel 1995) that actually increases turnover and crash rates (Brown
1990; Brown 1992). Second, pensions were not included in the study, so the correlation may be a
result of the mere existence of a pension plan or its vesting characteristics (Lazear 1990; Lazear
1995).
Several unresolved questions about deferred compensation remain. First, the pension loss
involved in quitting could be offset by a salary increase. This means that deferred compensation
is relevant in the context of the entire level of compensation. It is argued, for example, that firms
offering deferred compensation tend to have higher compensation levels overall. Hence it is not
the existence of deferred compensation (which is merely a compensation method), but its
existence in the context of other compensation and the overall level reached (Gustman and
Steinmeier 1993). Second, low turnover rates have been observed for employees under both
defined contribution and defined benefits plans, which suggests that pension portability is not an
issue and suggests the existence of an unobserved sorting mechanism which is causing the
turnover reduction (Arvin 1991). This may be an issue in trucking, however, since turnover
26
generally is high in the non-union TL sector and therefore drivers may be unable to vest and to
take advantage of defined contribution pensions (Belzer 2000).
Finally, it has been assumed throughout the discussion that compensation levels and
methods are independent of one another. Chen tested inter-industry wage differentials across
different methods of pay. He argued that his evidence showed that efficiency wage
considerations are less important for piece-rate wages than for time-rate wages under three
efficiency-wage-related models: adverse selection or worker-quality, turnover, and shirking
models. In the main, he concludes that industry wage differentials observed are less prominent in
piece-rate compensation (Chen 1992). The importance of this finding will be apparent in
subsequent sections.
Other studies reviewed assume that compensation method is an exogenous variable. A
limited number of studies viewed compensation method as a firm policy variable (Brown 1990;
Brown 1992; Gustman and Steinmeier 1994). Along these lines, Brown found lower inter-
industry wage differentials among workers under piece rates than under time rates. Gustman and
Steinmeier argue that wages and pensions (or other forms of deferred compensation) are
determined simultaneously by the firm and therefore single equation models tend to bias this
relationship.
27
III: Driver Compensation and Driver Safety: Evidence from Trucking Research
The paucity of empirical evidence linking compensation level and method to worker
safety also involves the trucking industry. First, we review studies which focus on the effect of
various firm characteristics on trucking safety, but which do not directly address the role of
compensation level and method. Next we review the studies and papers that have included either
compensation level or method in the study of trucking crashes. We also extend the review to
include those studies that have correlated compensation with behaviors traditionally associated
with high crash rates, such as speeding and violation of hours-of-service regulations.
Safety Studies of the Trucking Industry: Firm-Level Characteristics
The complex possible causal paths of large truck crashes were charted in a
comprehensive manner as early as 1988 (Office of Technology Assessment 1988). The
originating factors in the overall causal mechanism seen as influencing truck crashes were traced
to macro-social factors such as societal values and market forces and their impact on macro-
structural features such as government policy and legislation, motor carrier industry segment
goals, and shipping and distribution interests. These large-scale social forces and structures were
seen as influencing two major sets of micro-structural sources of organizational action. On the
one hand, there were federal and state agency actions such as regulations, roadway design,
inspection and enforcement. On the other hand, there were firm actions related to road
operations, driver selection and training, and vehicle maintenance and specifications. Finally, at
the level closest to the actual set of crashes, there were factors such as roadway conditions,
traffic conditions, other highway users, driver performance, vehicle performance, load
characteristics, weather and unpredictable situations. Another causal model also identified
management operating practices as a key element in the crash causation chain ( National
Highway Traffic Safety Administration 1987).
In both models, driver error, haphazard road conditions or equipment failure were the
immediate determinant of a crash. But Loeb et al. pointed out that the direct causes of crashes
“may have been influenced by a prior occurrence (for example, insufficient driver training) that
may have been affected by an earlier policy action (for example, regulation on driver
qualifications). Furthermore, societal values or economic considerations may have prompted
adoption of a particular policy” (Loeb et al. 1994). There has been increased attention recently to
the importance of the economic conditions facing the trucking industry, and how they can be
manifest in after-inflation declines in freight rates, tightening of schedules to meet shipper
demands, and increased interfirm competition (Hensher et al. 1989; Belzer 2000).
Despite awareness of the complexity of the policy environment and the stochastic nature
of the crash environment, the predominant sets of variables found in large truck safety research
have been driver characteristics and behavior, load characteristics, vehicle characteristics, and
roadway conditions. Relatively little research attention has addressed motor carrier operations
(such as compensation level and method) and driver selection and training. Yet both were
identified as important in the Gearing Up for Safety report (Office of Technology Assessment
1988).
28
A new literature thus is emerging which seeks to take firm characteristics such as these
into account in modeling trucking safety. A number of firm-level characteristics, other than the
compensation-related variables reviewed in the next section, have been identified. Where data
are available, these may serve as control variables in the present study. These include firm
profitability, specific firm safety practices, fleet ownership, demographics of the firms’ driver
force, firm age, union presence, firm size and industry segment.
Firm profitability
Firm profitability is one firm characteristic posited to be related to safety of
transportation operations. Corsi, Fanara and Roberts (1984) found that a profitability measure
(net operating income) could not be established as a statistically significant predictor of crash
rates, although there was an inverse relationship (Corsi et al. 1984). Chow et al. found a
suggestive association between a carrier’s financial condition and its safety performance. They
suggested that carriers close to bankruptcy skimp on maintenance, use older equipment, and use
owner-operators (Chow et al. 1987). Blevins and Chow further studied the profitability-safety
relationship during the post-deregulation era. Using bivariate analyses, they compared results for
bankrupt and non-bankrupt firms, and found that bankrupt firms did in fact spend less on
insurance and safety, maintenance, and equipment replacement, and also were more likely to
have unsatisfactory compliance ratings, but the results were not statistically significant (Blevins
and Chow 1988). Corsi, Fanara, and Jarrell found operating ratio (operating expenses divided by
operating revenue) as having a statistically significant and positive relationship with crash rates
for Class I and II carriers in 1977 and 1984 (Corsi et al. 1988).
Seeking to improve on these earlier, rather inconclusive studies, Bruning (1989) found
that higher return on investment was associated with lower crash rates. He used a 1984 database
based upon Bureau of Motor Carrier Safety records of crashes causing at least $2000 in property
damage and federal Financial and Operating Statistics from the Form MCS-50T report of 468
Class I and II general freight and specialized carriers. Bruning made two linked assumptions: (1)
that managers substitute