**Paying for Safety:**

**An Economic Analysis of the Effect of**

**Compensation on Truck Driver Safety**

**Executive Summary**

**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 Highway Administration / Federal Motor Carrier Safety
Administration

Prime Contract Number DTFH 61-98-C-0061

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

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.

# 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.

# Data

## Study 1 

Data for the cross-sectional analysis of the truckload sector come from
the following.

### Executive Summary Table 1

+------------------------------------+-------------------------+-------+
| **Data set**                       | **Variable**            | **Y   |
|                                    |                         | ear** |
+------------------------------------+-------------------------+-------+
| **National Survey of Driver        | ### Mileage pay         | 1998  |
| Wages**                            |                         |       |
+------------------------------------+-------------------------+-------+
|                                    | Raise                   |       |
+------------------------------------+-------------------------+-------+
|                                    | Safety bonus            |       |
+------------------------------------+-------------------------+-------+
|                                    | Production bonus        |       |
+------------------------------------+-------------------------+-------+
|                                    | Health insurance        |       |
+------------------------------------+-------------------------+-------+
|                                    | Life Insurance          |       |
+------------------------------------+-------------------------+-------+
|                                    | Paid time off           |       |
+------------------------------------+-------------------------+-------+
|                                    | Length of run           |       |
+------------------------------------+-------------------------+-------+
|                                    | Governor Speed          |       |
+------------------------------------+-------------------------+-------+
| **National Motor Carrier           | Power units             |       |
| Directory**                        |                         |       |
+------------------------------------+-------------------------+-------+
| **MCMIS**                          | #                       | 1998  |
|                                    | ## DOT reported crashes |       |
+------------------------------------+-------------------------+-------+
| **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. 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.

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

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-   Tenure with firm

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-   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
truckstop 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

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-   Weekly Hours

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-   \% Non-Driving time

-   \% Night Driving

-   Union Membership

-   Firm Size

-   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.

-   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]{.underline}

  ---------------------------- --------------------- --------------------
                               **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% 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 that 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 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 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 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.