Introduction
Trucking safety has become a critical issue in transportation, with a notable 20% increase in fatalities from large truck crashes between 1992 and 1997, despite a 25% rise in miles traveled by these vehicles A GAO study indicates that fatality rates still surpass national reduction goals, highlighting the vulnerability of passenger car occupants, who represent the majority of fatalities in truck-related accidents Moreover, in 1997, it was reported that 38% of all truck crashes and 30% of fatal crashes were not captured in federal statistics, emphasizing the need for improved safety measures and accurate reporting in the trucking industry.
Public concern regarding trucking crashes is justified, prompting a need for enhanced data collection and research into their causes Factors such as larger trucks, increased congestion, and deregulation have been identified as potential contributors to the rising number of crashes and fatalities Additionally, issues like nighttime driving, driver fatigue, and longer trip lengths have garnered research attention However, the impact of monetary compensation on trucking accidents remains underexplored This study aims to investigate how different levels and methods of pay affect safety outcomes within the trucking industry.
Monetary compensation can influence worker behavior in a number of ways Yellen
Employers offering above-average "efficiency" wages can effectively discourage worker shirking by imposing a cost on job loss, as noted by Yellen (1984) When monitoring costs exceed the benefits of higher wages, this strategy can efficiently motivate employee effort Additionally, the method of compensation, such as the historical practice of "piecework" rates, has been shown to incentivize workers, particularly contract employees, to enhance their productivity (Belzer 2000) While efficiency wages focus on long-term job security for workers, piecework systems provide immediate incentives by rewarding higher productivity with increased pay.
In the trucking industry, piecework pay remains prevalent, with most truckload (TL) and less-than-truckload (LTL) drivers compensated by the mile or per load instead of receiving hourly wages This compensation structure often leads to mileage being the primary factor in determining pay, overshadowing additional tasks such as loading and unloading Consequently, drivers frequently endure long wait times and are often required to handle their own freight without adequate compensation for these efforts While this pay model may incentivize increased work effort, it also fosters behaviors that could negatively impact safety outcomes.
The compensation structure in the trucking industry incentivizes unsafe driving practices, such as neglecting safety inspections and exceeding legal driving hours Drivers often face pressure to meet a minimum income level, leading them to work beyond the legally permitted hours when mileage rates are low This situation is exacerbated by underpayment for loading and unloading times, prompting drivers to underreport these hours to maximize their driving time Consequently, many drivers exceed legal working hours, creating a temporary economic advantage but ultimately flooding the labor market, which depresses wages and encourages longer working hours This cycle increases crash risks not only for drivers but also for the general public, highlighting a market imperfection that necessitates policy intervention.
This report is structured into several key sections: Parts II and III review existing literature on trucking safety, while Part IV outlines the data utilized in the study Part V details the research strategies, including the theoretical foundations of the hypotheses and the methodologies applied for testing The results are presented in Part VI, followed by conclusions in Part VII, and a bibliography of cited sources in Part VIII.
Literature Review
Employee compensation significantly impacts behavior, particularly among truck drivers This study posits that both the level and method of compensation can affect drivers' on-road and off-road behaviors, potentially leading to an increase or decrease in crash involvement Given the importance of driver safety for individuals, companies, and society as a whole, it is crucial to analyze how varying compensation strategies influence driver behavior and the subsequent outcomes, whether positive or negative.
Research on truck driver attitudes and behaviors has primarily aimed to uncover the immediate factors influencing specific actions, such as speeding and excessive driving hours However, many studies tend to overlook the underlying motivations at various organizational levels, including economic pressures, individual traits, pay rates, and compensation methods Understanding these broader factors is essential for addressing the root causes of unsafe driving behaviors.
This study examines the relationship between employee compensation methods and levels and their impact on worker safety outcomes, aiming to uncover the motivations behind specific driver behaviors By analyzing existing research that connects individual compensation to safety—both directly and indirectly—this review encompasses literature from various fields, including human resource policy, economics, and psychology.
This review explores the critical relationship between employee compensation and safety, beginning with the motivation for this study It highlights the significance of employee compensation and examines the implications of compensation levels for both employees and firms The article reviews various methods of compensating employees and summarizes evidence linking driver compensation to safety within the trucking industry Given the limited research directly connecting compensation to safety, it also discusses indirect effects that may exist between these two factors Finally, the review concludes by outlining the research designs employed to investigate both direct and indirect relationships between driver compensation and safety.
In 1990, the National Transportation Safety Board emphasized the need to evaluate the trucking industry's structure and operations, highlighting factors that may encourage drivers to breach hours of service regulations and engage in drug use.
A 1995 NTSB report highlighted concerns regarding how pay policies may influence truck driver fatigue and suggested a potential connection between compensation methods and fatigue-related accidents While the study utilized a convenience sample and may not represent the broader truck driver population, its findings on compensation methods and fatigue prevalence align with results from other research conducted in the field.
Research indicates that driver compensation significantly impacts safety, with a focus on pay levels rather than pay methods Low pay is often linked to extended driving hours, substance abuse, fatigue, and other risky behaviors (Hensher et al 1991; General Accounting Office 1991; Saccomanno et al 1997) However, some studies argue that the role of compensation in influencing driver behavior may be overstated (McElroy et al 1993).
Focus groups of drivers have indicated that the current piece rate compensation method, which pays per mile, restricts their income potential and promotes dishonest practices (Mason et al 1991; Cadotte et al 1997) Many drivers believe this compensation structure contributes to unsafe driving behaviors Additionally, the combination of piece rate systems and hours of service regulations further limits drivers' earning opportunities (Chatterjee et al 1994) A significant 45% of New York State drivers surveyed expressed that hourly pay could be beneficial in mitigating driver drowsiness (McCartt et al 1997).
Management has acknowledged the significance of understanding driver compensation, as evidenced by a 1995 survey of 1,464 drivers across various trucking sectors, which highlighted driver compensation as a key area for human resources enhancement (Stephenson and Fox 1996) Additionally, a survey of 148 trucking company personnel managers revealed that these managers considered pay level to be the most crucial factor influencing drivers' decisions when selecting a motor carrier for employment (Southern et al 1989).
The Role of Employee Compensation
Employee compensation plays a crucial role in resource allocation, functioning as a pricing mechanism that directs labor toward its most productive uses This traditional economic perspective highlights how variations in earnings distribution arise from the interplay of supply and demand, taking into account specific observable characteristics.
A second role of compensation is to serve as a tool for social stratification and cohesion.
Employee earnings significantly influence the standard of living, serving as a key factor in establishing social legitimacy within both organizations and society Compensation policies are instrumental in defining what constitutes a "fair" wage level, as highlighted by Akerlof and Yellen's research from 1988 to 1990.
Employee compensation increasingly serves as a management tool to enhance employee effort and align their skills with organizational goals Earnings play a crucial role in the management-employment relationship, with various theories exploring this dynamic These theories range from the transaction cost perspective, which aims to minimize opportunistic behavior, to the efficiency wage theory, which suggests that higher wages can lead to increased productivity and reduced turnover.
According to Lazear (1995), offering above-market wages can lead to positive behavioral outcomes among employees, including reduced shirking and increased effort (Yellen, 1984) Specifically, in the context of truck drivers, these outcomes may include better adherence to hours of service regulations, behaviors aimed at minimizing fatigue and the risk of dozing off while driving, and overall safer driving practices Notably, no prior research has applied efficiency wage theory to examine the behavior of truck drivers.
Recent changes in wage structures, particularly due to economic deregulation, have heightened interest in the role of compensation in society (Rubery 1997) Belzer (1993) examined the shift from regulation-related segmentation to market segmentation in the trucking industry, revealing that wage levels are influenced by various firm-level factors such as industry segment, unionization, and market share His findings indicate that unionized firms and those in specific industry sectors, like LTL, tend to offer higher wages, while market share and location also play significant roles, with Southern carriers reporting lower wages (Belzer 1993; Belzer 1995) However, the relationship between compensation levels and safety outcomes remains underexplored, raising questions about safety records in firms with lower wages or nonunion status This article aims to address the limited research linking compensation policies to safety outcomes, with subsequent sections focusing on compensation policy and safety in the trucking industry.
Recognizing employee compensation as a comprehensive package is essential, as highlighted in early studies The concept of "compensation level" is frequently examined within a hierarchical framework, where the compensation system is broken down into its key components, including methods, levels, and changes in earnings over time Overall, employee compensation encompasses total earnings over a specified period, incorporating both direct compensation, such as wages, and deferred compensation, like pension plans.
Driver Compensation and Driver Safety: Evidence from Trucking Research
The lack of empirical evidence connecting compensation levels and methods to worker safety is evident in the trucking industry This article first examines studies that analyze the impact of different firm characteristics on trucking safety without directly considering compensation It then reviews research that incorporates either compensation levels or methods in relation to trucking accidents Additionally, the review encompasses studies that have linked compensation to behaviors commonly associated with higher crash rates, including speeding and violations of hours-of-service regulations.
Safety Studies of the Trucking Industry: Firm-Level Characteristics
In 1988, a comprehensive analysis identified complex causal pathways leading to large truck crashes, linking macro-social factors like societal values and market forces to government policies and motor carrier industry goals These broad influences shaped two main areas of organizational action: federal and state agency regulations, including roadway design and enforcement, and firm-level practices such as driver training, vehicle maintenance, and road operations At the crash level, contributing factors included roadway and traffic conditions, driver and vehicle performance, load characteristics, weather, and unpredictable events Additionally, management operating practices were recognized as a crucial element in the chain of crash causation.
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
The trucking industry has recently garnered significant attention regarding the impact of economic conditions, which can lead to after-inflation declines in freight rates, stricter schedules to satisfy shipper demands, and heightened competition among firms These economic factors may be influenced by prior occurrences, such as inadequate driver training, which in turn could stem from earlier policy actions like regulations on driver qualifications Additionally, societal values and economic considerations play a crucial role in shaping the adoption of specific policies within the industry.
While the complexity of the policy environment and the unpredictable nature of crash scenarios are recognized, large truck safety research primarily focuses on driver characteristics, load and vehicle attributes, and roadway conditions There has been limited exploration of motor carrier operations, including compensation methods and driver selection and training, despite their significance highlighted in the Gearing Up for Safety report by the Office of Technology Assessment in 1988.
A new body of literature is developing that incorporates firm characteristics into models of trucking safety Key firm-level factors, beyond compensation-related variables, have been identified for consideration When data is accessible, these factors can be utilized as control variables in the current study Notable characteristics include firm profitability, specific safety practices, fleet ownership, driver demographics, firm age, union presence, size, and industry segment.
Firm profitability is linked to the safety of transportation operations, although studies have shown mixed results Corsi, Fanara, and Roberts (1984) found that net operating income was not a statistically significant predictor of crash rates, despite an inverse relationship Chow et al (1987) suggested that carriers nearing bankruptcy tend to cut maintenance costs, utilize older equipment, and rely on owner-operators, indicating a potential association between financial health and safety performance Further research by Blevins and Chow (1988) revealed that bankrupt firms spent less on insurance, safety, maintenance, and equipment replacement, leading to unsatisfactory compliance ratings, though these findings were not statistically significant Additionally, Corsi, Fanara, and Jarrell (1988) established a statistically significant positive relationship between the operating ratio and crash rates for Class I and II carriers in 1977 and 1984.
Bruning (1989) aimed to enhance earlier inconclusive studies by analyzing a 1984 database from the Bureau of Motor Carrier Safety, which recorded crashes with at least $2000 in property damage, alongside federal Financial and Operating Statistics from the Form MCS-50T report of 468 Class I and II carriers He posited that managers optimize production-related expenses to maximize profits and that increased revenue leads to reduced spending on maintenance and training His findings revealed an inverse relationship between carrier profitability and crash rates for most general freight and specialized carriers, with statistically significant results primarily observed in larger firms, while smaller firms did not exhibit this trend.
He also found that profitability in preceding periods (measured in 1980 and 1982) explained safety performance in 1984 (Bruning 1989).
Moses and Savage analyzed a substantial dataset of 75,577 federal safety audits and crash records from 1986 to 1991, finding no statistically significant effects on carrier profitability (Moses and Savage 1994) However, their earlier study revealed that carriers flagged as unprofitable in safety audits experienced significantly more crashes (Moses and Savage 1992) The differing outcomes can be attributed to variations in statistical methods and the industry segments assessed, highlighting the necessity of controlling for firm size and industry segment in such analyses.
Hunter and Mangum assessed carrier financial stability through three key metrics: revenue per mile, the net debt to equity ratio, and the operating ratio, which is calculated by dividing total annual operating expenses by annual gross revenue They considered the operating ratio to be a significant indicator of a company's long-term profitability (Hunter and Mangum 1995).
The difficulty of establishing such a relationship in any industry was shown by Golbe
Golbe's 1986 cross-sectional study of the airline industry revealed no statistically significant correlation between profitability and the square root of total crashes, likely due to the smaller number of firms and crashes compared to trucking The higher federal oversight of maintenance in airlines may also contribute to reduced variability in crash rates among firms Golbe emphasized the necessity of understanding firm risk preferences and specific industry cost and demand conditions to accurately assess the relationship between profitability and safety Additionally, Chow (1989) highlighted that short-term profitability is only one aspect of a firm's financial health and may not adequately represent its long-term strengths or weaknesses.
Obtaining direct measures of firm profitability can be challenging for companies that do not report financial and operational data to the federal government However, a useful proxy for assessing a firm's financial health is the ratio of sales volume to power units or the ratio of sales volume to the number of employees This data is accessible over several years for firms that do submit federal statistics, as well as for businesses of all sizes through Dun and Bradstreet’s TRINC file.
Research has consistently shown a link between specific safety practices and actual safety outcomes in firms Key practices include driver oversight and equipment monitoring, as highlighted by the National Transportation Safety Board (1988) Moses and Savage (1994) identified additional significant practices such as timely accident report filing, disciplinary actions against drivers in preventable crashes, and the ability of carriers to clarify hours of service regulations Interestingly, some studies, including those by Corsi and Fanara, found a paradoxical relationship between carrier maintenance spending and crash rates, attributing this to the age of the fleet—older vehicles incur higher repair costs Moreover, their findings indicated that high compliance with hours of service regulations and stringent driver qualifications correlated with increased crash rates, suggesting that poor crash rates may prompt costly safety management improvements that are not immediately reflected in cross-sectional data.
A key data point for firm-level analyses is the distribution of a company's fleet, which includes the percentage of vehicles owned by the company and driven by employees, leased vehicles operated by employees, and those driven by owner-operators.
Research by Corsi, Fanara, and Roberts (1984) and Corsi, Fanara, and Jarrell (1988) indicates that a greater reliance on owner-operators in Class I and II firms correlates with increased crash levels Similarly, Chow (1989) suggests that a higher percentage of owner-operators may adversely impact crash rates In contrast, Bruning (1989) found no significant relationship between the natural logarithm of rented power units with drivers and the total number of power units.
Demographics of firm driver force
Driver age and experience can be analyzed individually or as firm characteristics, particularly regarding their impact on crash rates Studies utilizing length of service data from the MCMIS crash file reveal a significant inverse relationship between driver tenure and crash occurrences, with over 50% of crashes involving drivers with less than a year of experience at their firm While these findings cannot be seen as indicators of firm turnover or minimum hiring experience requirements, they suggest that greater driver tenure may correlate with reduced crash rates This study aims to further explore driver tenure as a predictor of safety outcomes.
DATA
In preparation for the research design in Section V of this report, a comprehensive review of data sources related to compensation and safety in the trucking industry was conducted This process was informed by two prior reviews, including the 1988 study "Gearing Up for Safety," commissioned by the U.S Congress This report emphasized the necessity of creating a thorough and accurate database of key accident and exposure statistics, highlighting the significant gaps in data concerning heavy truck accidents The establishment of such a database is crucial for identifying the causal factors that contribute to the frequency and severity of accidents.
In 1990, the Committee for the Truck Safety Data Needs Study prepared a special report, Data
The 1990 Transportation Research Board report on truck safety monitoring highlighted the inadequacy of existing data sources related to truck accidents and travel It emphasized the need for improved data to support regulatory, enforcement, and planning efforts The report included several recommendations for enhancement, many of which have since been implemented.
A decade of data analysis reveals significant progress in the availability of individual truck crash data, vehicle and driver violations, and trucking firm safety compliance Despite advancements in crash scene characteristics and aggregate data on fleet and workforce violations, there remains a scarcity of comprehensive firm-level data that could elucidate the factors influencing a company's overall safety performance This underscores the necessity of exploring the integration of various existing databases to identify and link essential causal variables and control factors, thereby facilitating a deeper investigation into how compensation levels and methods impact trucking safety.
Previous studies have explored the link between firm characteristics and safety outcomes, often relying on single data sources like MCMIS safety audits and crash files, which can lead to questionable measurements, such as using driver tenure to assess turnover rates Notably, research by Bruning (1989) examined the relationship between profitability and safety performance in the trucking industry by integrating federal crash data and financial statistics Similarly, Corsi et al (1988) found that merging BMCS accident data with financial statistics revealed a correlation between high vehicle maintenance costs and lower accident rates These studies highlight the value of combining multiple data sources to better understand the influence of firm characteristics on safety outcomes Additionally, Belzer's research on firm compensation practices further underscores the potential for future studies to leverage various trucking industry and federal/state data sources for enhanced insights.
The following data sources were reviewed, although all of them were not used in this study: The University of Michigan Trucking Industry Program (UMTIP) driver survey (Wave 1
The article discusses various resources and databases related to motor carrier safety and trucking industry statistics, including the SAFESTAT Motor Carrier Safety Status Measurement System, the NHTSA state data system program, and directories from the American Trucking Association and National Motor Freight Traffic Association It highlights key surveys such as the National Survey of Driver Wages and the ATA 1997 Compensation Survey, along with critical data sources like the Fatality Analysis Reporting System (FARS) and MCMIS files, which provide detailed profiles and crash data for individual trucking firms Additionally, it references the VIUS/TIUS Census Data, emphasizing the importance of these resources in understanding the trucking industry's safety and operational metrics.
The analysis utilizes various data sources, including the Transportation Annual Survey from the Bureau of the Census, the TRINC database from Dun and Bradstreet, and the Bureau of Transportation Statistics Form M Data, which provides financial and operational statistics for Class I and Class II firms The results section clearly outlines the specific datasets employed in the study.
The remainder of this section describes these data sources.
The University of Michigan Trucking Industry Program (UMTIP) conducted its first wave of driver surveys in 1997, interviewing approximately 900 truck drivers at 19 Midwest truck stops A second wave took place in 1998 and 1999, expanding the sample size to 1,019 valid interviews by incorporating seasonal data The survey collected comprehensive information on compensation systems, including pay methods, non-driving time treatment, and benefits like health insurance and retirement plans Additionally, safety-related metrics were gathered, such as self-reported drowsiness while driving and accident involvement Findings can be segmented by industry type and driver affiliation, although the proprietary data from UMTIP cannot be publicly released at this time.
The survey employed a two-stage randomized design to ensure representative coverage of truck stops, stratifying locations by parking space size and state The selection of truck stops was based on the proportion of total parking spaces in each group In the second stage, drivers were recruited at randomly determined times and by randomly selected interviewers, screening every nth eligible driver based on truck stop size The response rate was approximately two-thirds, which is impressive given drivers' tight schedules and the inability to convert initial refusals Recognizing that truck drivers often spend extended periods away from home, the study effectively sampled drivers who utilize truck stops for essential needs, making the sampling frame valid This research provides valuable individual-level data on various compensation methods and their correlation with drivers' reported safety experiences.
National Survey of Driver Wages
The National Survey of Driver Wages, conducted quarterly by Signpost, Inc., provides compensation data for truckload firms Our analysis is based on the 1998 sample, which includes a diverse group of 198 truckload firms across different sizes and industry subsectors.
We judged 175 of which to be independent firms (some were subsidiaries, divisions, or otherwise subordinate parts of parent firms) Our final sample used in the study was 101 firms.
We excluded tanker firms from our analysis due to their distinct pay practices compared to general freight carriers Additionally, we removed firms with incomplete data in critical areas, such as mileage rates and miles driven, as well as those lacking essential information from our follow-up phone survey on non-driving time.
The article presents data from major truckload carriers, including medium-sized and smaller operators, focusing on compensation for both company drivers and owner-operators Published quarterly in both spreadsheet and hard copy formats, the data primarily highlights compensation figures, while also providing ordinal information on the number of drivers categorized into ranges: under 100, 100-250, 251-500, 501-1000, and over 1000 drivers.
Signpost offers compensation data for various trailer types, resulting in multiple entries for each firm within the dataset The number of drivers associated with each trailer type serves as a rough indicator of the predominant trailer type utilized by each company.
The selection of truckload firms was based on the Commercial Carrier Journal (CCJ) list and other top 100 rankings Signpost claims that nearly all firms from the A.T.A.'s top 100 are included, although many non-subscribing firms are also part of the sample, particularly larger carriers, while smaller ones may be overlooked While Signpost could not evaluate the overall representativeness of their sample, it predominantly features national and some regional carriers, which likely reflects the labor market effectively Additionally, the validity of the Signpost data is supported by its use in a compensation study by the American Trucking Association’s Research Foundation.
The Signpost data on compensation necessitates clarification, as it typically includes standard metrics such as cents per mile and dollars per hour However, our analysis has led us to develop a comprehensive index that consolidates various payment methods into a singular, manageable measure The original data on pay for loading and unloading is presented in multiple formats, including flat rates and hourly amounts, among others.
The terms ".030 cse," "100% rev," "112 cwt," and "Cust" refer to various payment structures in the transportation industry, with ".030 cse" indicating pay per case, "100% rev" representing customer payments for loading/unloading, "112 cwt" denoting cents per hundred weight, and "Cust" typically signifying no payment from the customer This classification allows for understanding whether drivers are definitely paid, sometimes paid, or not paid at all, but it lacks the granularity needed for a detailed pay scale for loading and unloading tasks To address this gap, a survey of Signpost firms was conducted in the summer of 2000 to investigate both the methods and levels of pay for non-driving time, with findings detailed below.
RESULTS
Pay Level and Method, Cross Sectional Analysis
The National Survey of Driver Wages by Signpost, Inc serves as the primary data source for this firm-level study, encompassing a quarterly analysis of 198 truckload carriers of varying sizes, including major carriers and a selection of medium and smaller firms These companies were identified through the Commercial Carrier Journal (CCJ) rankings and other top carrier lists While Signpost includes its subscribers in the dataset, smaller carriers may not be represented, and the randomness of the sample has not been assessed Nonetheless, the data is deemed reliable, having been utilized in a compensation study by the American Trucking Association’s Research Foundation, and provides a reasonable estimate of driver pay within the industry The data analyzed pertains to the fourth quarter of 1998, focusing on DOT recordable crashes from that year.
A notable limitation of the signpost data is its insufficient detail on non-driving time To address this gap, researchers from the University of Michigan Trucking Industry Program conducted a phone survey of Signpost firms in the summer of 2000 The survey included comprehensive questions about the duration of time spent on non-driving activities and the compensation associated with this time The findings from this survey were utilized to calculate the variable known as "unpaid time."
The study analyzes the number of DOT reported crashes in 1998, focusing on a sample of 102 firms from the UMTIP survey, which had a two-thirds response rate from the 178 firms that paid their employee drivers by mileage Each firm reported an average of 64 crashes, with drivers earning an average starting pay of 28.6 cents per mile, ranging from 23 to 38 cents The variable “unpaid time” indicates the hours of unpaid work per mile driven, averaging 0.004 hours, which translates to approximately 3.624 hours of unpaid time per trip of 906 miles The hypothesis suggests that a higher percentage of unpaid time may incentivize drivers to work longer hours, potentially leading to an increase in crash occurrences.
Crashes Number of DOT Reportable Crashes
Unpaid Time: Number of hours of unpaid time per mile driven in a typical run
Raise: Average yearly increase in mileage pay
Safety Bonus: 1 if firm offers a safety bonus, zero otherwise
Production Bonus 1 if firm offers a production bonus, zero otherwise
Health Insurance Contribution of DRIVER to health plan, per month
Life Insurance Amortized value of company paid life insurance policy
Governor Speed 1 if firm uses a governor, zero otherwise
Miles per Run Number of miles driven in a typical run
Miles Per Year Total number of miles driven by all drivers in the firm
Flat Beds 1 if primary trailer type is a flat bed, zero otherwise
Vans 1 if primary trailer type is a van, zero otherwise
Power Units Number of power units owned and leased by the firm
The RAISE variable indicates the annual mileage rate increase for drivers, ranging from zero to four cents per mile Approximately 50% of firms provide a safety bonus, while 28% offer production bonuses The HEALTH INS variable reflects the driver's monthly contribution for company health insurance, averaging $167, with a range from zero to $368 Similarly, the LIFE INS variable captures the value of life insurance policies, varying from no coverage to amounts exceeding $50,000 Additionally, the PAID TIME OFF variable represents the total value of vacation, holiday, and sick pay, with an average annual offering of $774, ranging from $250 to $2,000.
The GOVERNOR SPEED variable indicates that approximately 75% of firms limit their truck speeds The average run length is 906 miles, with firms collectively driving around 128 million miles, suggesting a potential bias towards longer runs that may understate unpaid work hours per mile About 21% of firms primarily transport flatbeds, while 51% use vans, and the remainder focuses on refrigerated loads Tanker firms were excluded from the sample due to behavioral differences On average, firms operate 693 power units, with numbers ranging from 24 to over 7000.
Conventional economic theory suggests that higher compensation levels are linked to fewer crashes, as firms offering better remuneration can attract more qualified drivers This includes not only direct payments but also benefits such as higher mileage pay, substantial raises, generous life insurance, and ample paid time off Conversely, unpaid time and health insurance contributions by drivers are expected to correlate negatively with crash rates The study did not include 401K plans due to insufficient data on their existence and levels among firms, and the lengthy vesting period typically required makes them less relevant to driver behavior.
The coefficients for safety bonuses and governor speed are anticipated to be negative, indicating a nominal interest in safety, while production bonuses are expected to be positive, as they may encourage drivers to work longer hours or drive faster, potentially compromising safety for productivity The relationship between run length and safety is ambiguous; however, longer runs may be perceived as safer compared to shorter runs, which often involve urban driving and higher crash rates Additionally, the number of power units and yearly mileage are expected to positively correlate with crash rates, reflecting the firm's size and exposure.
To estimate the parameters of interest in crash data analysis, a negative binomial model was employed due to its ability to handle non-negative integer values Unlike Poisson regression, which assumes that the variance equals the mean, the negative binomial model effectively addresses the issue of overdispersion, where this assumption is violated The significance of the overdispersion parameter in the negative binomial model further confirms its suitability for the data.
Table 5 presents the results from the firm-level model, highlighting the impact of compensation-related variables on crash frequency Notably, an increase in mileage pay significantly reduces the number of crashes, with an estimated elasticity of -0.52, suggesting that a 10% rise in mileage pay could lead to a 5.2% decrease in crashes Additionally, a 10% increase in unpaid time correlates with a 1% reduction in crashes While higher raises also contribute to fewer crashes, their significance is only observed at the 10% level.
The Poisson model can be viewed as a simplified version of the negative binomial model, particularly when the number of trials (N) approaches infinity and the probability of success (P) approaches zero, maintaining a constant mean (λ) By applying l'Hôpital's rule, it can be demonstrated that the logarithm of the probabilities for both models aligns While the Poisson model provides consistent point estimates despite overdispersion, its estimated standard errors are biased, rendering t-statistics invalid In contrast, the negative binomial model effectively addresses this issue, as indicated by a significantly different overdispersion parameter, making it the preferred choice over the Poisson model in such scenarios.
The inclusion of fourth-order terms for "power units" in firm-level data is essential for accurately modeling crash rates, as power units serve as a proxy for employee numbers and firm size While a second-order term may seem sufficient, it leads to significant overprediction of crashes for small firms and underprediction for large firms Although there is no direct theoretical justification for higher-order terms, they are crucial for defining the curve's shape, which indicates that crash rates consistently increase with the number of power units within the relevant data range Ultimately, while the fourth-order curve mirrors the second-order curve's pattern of increasing at a decreasing rate, the higher-order terms are necessary to fully capture the data's underlying trends.
Firm size and power units measure different aspects, making it essential to avoid generalizations regarding firm size based on revenue or employee count without further analysis The Signpost data indicates that the dependent variable is the number of crashes over a year, which tends to rise with increasing firm size However, the relationship is not necessarily proportional; the question remains whether doubling the size of a firm results in a less than doubling, a doubling, or more than doubling of crashes Figure 6 illustrates that crash rates increase rapidly for both very small and very large firms, while moderately sized firms experience a slower increase This suggests that as small firms grow, they can invest in better safety measures, but this benefit diminishes once firms reach a certain size Ultimately, while crashes do increase with firm size, the rate of increase is slower, indicating that doubling a firm's size leads to less than a doubling of crash incidents.
Research indicates that individual drivers employed by larger companies experience lower crash probabilities compared to those at smaller firms This suggests that increasing the number of drivers within a company results in a less than proportional increase in crash rates, leading to safer driving conditions for employees at larger organizations.
Figure 6 illustrates the curve of fourth order power units, highlighting a maximum point of 6,580 power units, where the curve begins to decline In this sample, only two firms approach this level, with one firm reporting 5,878 power units.
Table 5: Negative Binomial Regression Results
The analysis of health insurance firms indicates a generally upward sloping relationship between firm size and safety management, with the curve exhibiting a rapid rise, a plateau, and another increase This suggests that once firms reach a certain size, they effectively manage safety, but may struggle with control as they grow larger While industry segment could influence these results, preliminary findings indicate that larger carriers experience a significant increase in crash rates, warranting further investigation.
CONCLUSIONS
Research indicates a strong correlation between higher driver compensation and improved safety outcomes in operations Increased pay, particularly for non-driving hours, significantly reduces the likelihood of accidents This effect is largely attributed to labor market dynamics, where carriers offering higher wages can attract and retain more qualified drivers, enhancing overall safety through the selection of individuals with superior, albeit less visible, skills and attributes.
Using the UMTIP Driver Survey Data, we developed a labor supply curve that illustrates the trade-off between pay rates and hours worked, reflecting a collaborative decision between employers and employees Our findings confirm the “sweatshop” hypothesis, indicating that drivers, particularly those earning below the average, aim for target earnings of approximately $750 per week by increasing their working hours.
Our research indicates that drivers and firms exhibit a preference for increased working hours as pay rises, with an average rate of 30.5 cents per mile and a maximum of 65 hours However, once the pay reaches around 37.5 cents per mile, drivers tend to favor working fewer hours This trend suggests that higher pay rates correlate with a preference for reduced working hours among drivers.
The Signpost study yields robust findings, indicating a nearly 1:1 relationship between driver pay and crash rates, despite the inherent noise in the statistical model due to imprecise measurements of firm-level driver pay While the study utilizes the pay rate of drivers with three years of experience, it lacks data on the average experience level across the firms, as carriers are unable to provide this information Consequently, the variability in pay levels contributes to the noise in the analysis.
Unpaid time (amount of unpaid time per loaded mile)
Health insurance (safety declines insofar as drivers pay out-of-pocket for family coverage
B Hunt
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The following curves demonstrate these effects dynamically.
Predicted crash probability for different ages and pay rates
P re d ic te d P ro b a b ili ty o f C ra sh H a za rd
17 cents/mile 20 cents/mile 25 cents/mile 35 cents/mile
Predicted crash probability by age and perecent pay increase
P re di ct ed P ro ba bi lit y of C ra sh H az ar d
Predicted crash probability by pay rate and tenure
P re di ct ed P ro ba bi lit y of C ra sh H az ar d
Predicted crash probability by age and tenure
Age (years) P re di ct ed P ro ba bi lit y of C ra sh H az ar d
The following survey was administered to the carriers reporting in the Signpost dataset.
Hello, my name is - and I'm reaching out from the University of Michigan Trucking Industry Program I am collaborating with Mike Belzer on a survey focused on trucking companies within the truckload sector of the industry.
We aim to explore how companies compensate their drivers, particularly regarding non-driving time To gain insights into this topic, we would like to ask you a few questions.
Your responses will remain confidential, accessible only to me, principal investigator Dr Michael Belzer, and project supervisor Dr Stanley Sedo Any publicly shared results will not include company names or any information that could identify a specific firm.
Is there any way that you could spend a little time helping us to understand this issue by answering a few questions?
To begin, I will inquire about the starting point and endpoint of your runs Next, I will pose similar questions regarding any intermediate or additional stops Lastly, I will ask a few concise questions about your company to provide context for your answers If you find any question unclear, please don't hesitate to ask for clarification.
Section One: Origin and Destination
In this article, we explore key questions regarding the origin and destination of delivery runs The origin refers to the dock where the driver begins their first pick-up, while the destination signifies the dock where the driver completes their final delivery Understanding these points is crucial for optimizing logistics and improving efficiency in transportation.
On average, how much time does a driver spend at the origin of a run?
Of this time, how much time is spent by the driver loading freight or monitoring the loading when it is done by someone other than the driver?
On average, how much time does a driver spend at the destination of a run?
Of this time, how much time is spent by the driver unloading freight or monitoring the unloading when it is done by someone other than the driver?
Now we’d like to ask about pay for drivers for loading and unloading at origin and destination
Do you pay drivers when they are required to load or help load the truck themselves at the point of origin?
When determining driver compensation, consider whether you pay by the hour, a flat fee per load, by cents per hundredweight, by cents per case, or utilize another payment method If you employ multiple compensation strategies, you have the flexibility to select more than one approach.
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight which must be handled?
Do you pay drivers when they are required to unload or help unload the truck themselves at the point of destination?
When determining driver compensation, options include hourly wages, flat fees per load, payment based on cents per hundredweight, cents per case, or alternative methods If multiple compensation strategies are employed, you have the flexibility to choose more than one approach.
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight which must be handled?
Drivers may be compensated for non-driving time at the origin or destination under certain circumstances This includes situations such as dropping or hooking trailers, waiting for loading or unloading, and monitoring the loading or unloading process, even if the driver is not directly involved in the physical tasks.
At the point of origin, do you pay drivers when they don’t do any loading themselves but are required to monitor or check the process of loading?
How do you pay your drivers for this?
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight which must be monitored or checked?
At the point of destination, do you pay drivers when they don’t do any unloading themselves but are required to monitor or check the process of unloading?
How do you pay your drivers for this?
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight which must be monitored or checked?
Now I’d like to ask a few questions about dropping and hooking at origin or destination
Do you pay drivers for the work of dropping or hooking a trailer?
What method or methods do you use to pay your drivers for dropping? If there is more than one method, you can choose more than one.
No pay (Button should put zero amounts in b, c and d below)
By the hour How much?
What method or methods do you use to pay your drivers for hooking? If there is more than one method, you can choose more than one.
No pay (Button should put zero amounts in b, c and d below)
Same as for dropping (Button should make c, d and e below equal to Question #19 b, c, d)
By the hour How much?
Next we’d like to ask about pay for various kinds of waiting time at origin or destination
Do you pay your drivers while they wait for loading or unloading to begin?
What method or methods do you use to pay your drivers for this? If there is more than one method, you can choose more than one.
By the hour How much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of waiting time?
What percentage of your pickups involve only dropping or hooking at the point of origin?
What percentage of your deliveries involve only dropping or hooking at the point of destination?
What percentage of your runs involve the loading of freight by your drivers at the point of origin?
What percentage of your runs involve the unloading of freight by your drivers at the point of destination? Section Two: Intermediate Stops
This section addresses various types of pay associated with intermediate stops, defined as stops occurring between the origin and destination of a run We will explore four distinct categories of compensation: first, a flat fee known as stop pay for making an intermediate stop; second, payment for loading tasks performed by the driver at these stops; third, compensation for overseeing the loading or unloading process; and fourth, remuneration for any waiting time incurred during these stops.
First we’d like to ask about whether you pay drivers for making an intermediate stop
Do you pay drivers a flat rate for making an intermediate stop?
How much is this flat rate?
Are there any requirements that must be met before receiving this pay such as a minimum amount of time spent at an intermediate stop?
When compensating drivers for intermediate stops, do you provide equal payment for subsequent stops as you do for the first one? If not, please clarify your payment structure for these additional stops In our case, drivers receive the same payment for all intermediate stops as they do for the initial stop.
Next, we’d like to ask about pay when drivers load or unload the truck themselves at intermediate stops
Do you pay drivers when they are required to load or unload freight at intermediate stops?
How do you pay your drivers for this?
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight which must be handled?
The question at hand is whether the payment for loading or unloading is an additional fee on top of a flat rate for making an intermediate stop, or if it replaces that flat rate Essentially, does this payment serve as A 'an addition to any flat rate' or B 'a substitute for this flat rate'?
In addition to any flat rate
Instead of this flat rate
What percentage of intermediate stops require the driver to load or unload freight?
Do you pay drivers at intermediate stops when they don’t do any loading or unloading themselves but are required to monitor or check the process of loading or unloading?
How do you pay your drivers for this?
By the hour How much?
Flat amount per load How much?
Cents per hundredweight How much?
Cents per case How much?
Other method What method and how much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of freight involved?
Next we’d like to ask about pay for waiting time at intermediate stops
Do you pay your drivers while they wait for loading or unloading to begin?
What method or methods do you use to pay your drivers for this? If there is more than one method, you can choose more than one.
By the hour How much?
Are there any requirements that must be met before receiving this pay such as a minimum amount of waiting time?
What percentage of runs involve at least one intermediate stop?
(If answer to Question #43 is 0%, skip to question #47).
For those runs which have at least one intermediate stop, how many intermediate stops are there, on average?
How much elapsed time does a driver spend at the average intermediate stop?
Drivers often spend significant time loading or unloading freight, or overseeing these tasks when performed by others Understanding this time allocation is crucial for optimizing logistics and enhancing operational efficiency.