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Many states have legalized medical and recreational marijuana use in the past decade, which has potential consequences on roadway freight safety. Using a state-level panel of heavy truck crash statistics from 2005 to 2019 and a difference-indifference estimation strategy, we test whether legalization has affected the crash rates of heavy trucks. Our results show that legalization does not increase average crash rates. Crash responses are heterogeneous across states, with Vermont and Washington showing large crash reductions, while Nevada shows a large increase in crashes. These results suggest that heavy truck accidents are not closely related to marijuana legalization.
Marijuana Legalization and Truck Safety: Does the Pineapple Express
Damage More Pineapples?
May 2022
Andrew Balthrop
Sam M. Walton College of Business
University of Arkansas
Ron Gordon
Sam M. Walton College of Business
University of Arkansas
Jonathan Phares
Debbie and Jerry Ivy College of Business
Iowa State University
Alex Scott
Haslam College of Business
University of Tennessee
Many states have legalized medical and recreational marijuana use in the past decade, which
has potential consequences on roadway freight safety. Using a state-level panel of heavy truck
crash statistics from 2005 to 2019 and a difference-in-difference estimation strategy, we test
whether legalization has affected the crash rates of heavy trucks. Our results show that
legalization does not increase average crash rates. Crash responses are heterogeneous across
states, with Vermont and Washington showing large crash reductions, while Nevada shows a
large increase in crashes. These results suggest that heavy truck accidents are not closely
related to marijuana legalization.
Marijuana Legalization and Truck Safety: Does the Pineapple Express
Damage More Pineapples?
Many states have legalized medical and recreational marijuana use in the past decade, which
has potential consequences on roadway freight safety. Using a state-level panel of heavy truck
crash statistics from 2005 to 2019 and a difference-in-difference estimation strategy, we test
whether legalization has affected the crash rates of heavy trucks. Our results show that
legalization does not increase average crash rates. Crash responses are heterogeneous across
states, with Vermont and Washington showing large crash reductions, while Nevada shows a
large increase in crashes. These results suggest that heavy truck accidents are not closely
related to marijuana legalization.
Trucking is a critical component of supply chains that, unlike most other supply chain functions,
shares infrastructure with the broader public. This can be costly and deadly; for example, heavy
trucks were involved in more than 5,000 fatal crashes in 2019 (FMCSA 2021). Owing to these
safety concerns, the trucking industry is subject to extensive regulatory and managerial oversight
(Corsi 2018). Accidents caused by drug and alcohol use are often viewed as preventable (Scott
and Balthrop 2021). As such, accidents (and public policy designed to prevent them) garner
particular regulatory attention (e.g., Cantor, Corsi, and Grimm 2006; Miller, Golicic, and Fugate
2018). One set of policies leverages drug testing procedures focused on reducing drug usage
amongst truck drivers (Voss and Cangelosi 2020) whereby drivers are required to pass drug tests
as a precondition of employment and submit to random drug testing thereafter. Drivers who fail
drug tests are listed in the Federal Motor Carrier Safety Administration’s (FMCSA) Drug and
Alcohol Clearinghouse database and precluded from future employment in the occupation until
the successful completion of an authorized regimen of substance abuse counseling and a clean
drug test. This drug testing requirement and its associated infringement on employee privacy
makes the occupation less attractive to potential workers and hiring more difficult (West 2020).
While the trucking industry has been tightening substance abuse regulation
, the trend
elsewhereparticularly with marijuanahas been liberalization. As of December 2019, most
US states have passed some form of medical marijuana legalization, while nine states have
passed statutes allowing recreational use. This legalization trend has put the trucking industry in
a difficult position. Drivers are prohibited from engaging in activities which the state may have
legalizedand this prohibition holds even when they are off work. Marijuana violations already
account for 56 percent of positive tests among truck drivers
, while studies have shown that
marijuana legalization tends to reduce prices and make marijuana more readily available
(Anderson et al. 2013; Anderson and Rees 2021). These patterns lead to safety concerns, which
are the subject of the central question in this paper: does medical and recreational marijuana
legalization lead to an increase in heavy truck crashes?
To answer this question, we use data on heavy truck crashes collected by the FMCSA to
compare crash rate trends before and after legalization in states that legalized marijuana use to a
control group of states which have not legalized. We find no evidence to suggest that medical or
recreational legalization results in more heavy-truck crashes. Indeed, the evidence suggests that
crashes are actually reduced in states with legalization.
Our research makes several contributions. While studies have found medical and
recreational legalization lead to reduced fatalities among the broader population (Anderson et al.
2013; Aydelotte et al. 2017; Cook et al. 2020; Hansen et al. 2020), no studies have focused on
As evidenced by the passage of the Omnibus Transportation Employee Testing Act of 1991 and the 2020 rollout of
the Drug and Alcohol Clearinghouse by the FMCSA
the trucking industry, or on crashes in general (a broader category than crash-related fatalities).
Secondly, there remain unaddressed empirical challenges in the literature. Legalization dates are
staggered because states have passed medical and recreational legalization statutes at different
times (or not at all). Difference-in-difference estimates in this context have a tendency to assign
negative weights to some observations, resulting in potential sign reversals for estimated
treatment effects (De Chaisemartin and D’Haultfœuille 2020). Another problem is that
legalization states have varying degrees of permissiveness regarding licensing, regulatory
oversight, and sales restrictions (NCSL 2022). In this context, the average treatment effect
estimate may hide important heterogeneous effects. Finally, trucking is an interstate activity, so
legalization in one state may cause spillover effects in neighboring states. We demonstrate the
robustness of our results to these concerns by using De Chaisemartin and D’Haultfœuille’s
(2020) estimator to control for staggered adoption and heterogeneity. We also examine
heterogeneity by estimating a series of synthetic control models, one for each recreationally
legalized state. Finally, we estimate generalized spatial two-stage least squares (GS2SLS)
models to control for potential spatial spillovers (Kelejian and Piras 2017).
We find that medical marijuana legalization does not have a statistically significant effect
on crashes. Additionally, measurement of medical legalization is confounded by staggered
adoption so that empirical analysis requires use of the De Chaisemartin and D’Haultfœuille
estimator. Recreational legalization results in a slight but significant reduction in crashes. These
findings on heavy truck crashes are similar in magnitude to previous findings on recreational
legalization and fatalities population-wide (Anderson and Rees 2021). Still, there is substantial
heterogeneity in the effects of legalization, with treatment effects ranging from a 21.5 percent
reduction (Vermont) to a 25.6 percent increase in heavy truck crashes (Nevada). We do not find
significant evidence that legalization in contiguous states spills over into increased accidents for
the home state, at least not when controlling for covariates.
The mechanisms behind these heavy-truck crash reductions warrant further study.
Legalization may increase illicit drug use among truck drivers, increasing the rate of impaired
driving, which would tend to increase crashes. The crash reductions we observe may be
attributable to other drivers (i.e., non-truckers). Previous studies have attributed crash reductions
from legalization to less risky behaviors by young men 15 to 24 years of age (Anderson et al.
2013; Cook et al. 2020). A conservative policy recommendation from these results is that
increasing legalization at the state level does not warrant increased drug enforcement effort or
government-mandated testing for truckers beyond what is already being undertaken.
Nonetheless, legalization does highlight challenges to the industry, particularly in-terms of hiring
and accident liability, which policymakers must address.
Our article proceeds as follows. In the next section, we review the legal and industry
context as well as research on trucking and roadway safety in relation to marijuana use. Then, we
synthesize previous findings and logistics management theories to formulate our hypotheses and
study design. Next, we summarize our data collection approach, analysis, and results. Finally, we
discuss and interpret our results, managerial and policy recommendations, as well as important
remaining questions for future research.
Marijuana and Road Traffic Safety
Several studies have examined marijuana’s effect on passenger vehicle drivers. Researchers have
found that impaired drivers perform slightly or moderately worse than their sober counterparts
(Robbe 1998; Ramaekers et al. 2000), with factors such as dosage and frequency of use
determining the degree of impairment. Driving simulator and closed course experiments have
shown that tetrahydrocannabinol (THC) the main psychoactive compound in marijuana
diminishes drivers’ ability to perform key tasks such as maintaining the correct road position
(Robbe, 1998; Ménétrey et al. 2005; Hartman and Huestis 2013; Bondallez et al. 2016), quickly
reacting to unexpected events (Casswell, 1977; Smiley et al. 1981; Lenné et al. 2010), and
focusing on the task at hand (Ramaekers et al. 2004). For perspective, commonly prescribed
drugs such as benzodiazepines, antidepressants, and opioids have also been shown to negatively
impact driver performance, even at therapeutic doses (Dubois et al. 2008; Sansone and Sansone
2009; Chihuri and Li 2019).
Though marijuana can adversely affect driver performance, legalizing it may not make
roads less safe. This is largely because legalization appears to lead to lower alcohol use
(Anderson et al. 2013; Kelly and Rasul 2014; Sabia et al. 2017; Dragone et al. 2019; Baggio et
al. 2020; Alley et al. 2020). Marijuana legalization gives would-be drinkers easy access to an
intoxicant that is generally consumed at home rather than publicly in bars or restaurants
(Anderson et al. 2013). Moreover, comparative studies suggest that marijuana may be less
harmful to driver performance than alcohol (Robbe and O’Hanlon 1993; Sewell et al. 2009).
While alcohol-impaired drivers often engage in risky behaviors like speeding (Ward & Dye
1999; Fillmore et al. 2008; Ronen et al. 2008; Shyhalla 2014), marijuana-impaired drivers tend
to compensate for their impairment by slowing down and driving more cautiously (Ronen et al.
2008; Sewell et al. 2009; Anderson et al. 2010; Hartman and Huestis 2013; Bondallez et al.
2016). Notably, that compensatory effect generally goes away when drivers mix marijuana and
alcohol (Robbe, 1998; Ramaekers et al. 2000; Sewell et al. 2009). Sutton (1983) finds that
experienced marijuana users show virtually no functional impairment while driving under the
influence of THC alone, but significant impairment when mixing marijuana and alcohol.
While our study is the first to examine marijuana legalization’s impact on heavy truck
crashes, it is preceded by several inquiries into the link between marijuana legalization and
overall fatal crash rates (i.e., crashes involving only passenger vehicles in addition to heavy
trucks). Those studies have yielded mixed results. In a study that covers 14 states and the District
of Columbia between 1990 and 2010, Anderson et al. (2013) find that the legalization of medical
marijuana was associated with an 8-11 percent decrease in traffic fatalities in the first full year
after the laws took effect. This was largely due to fewer deadly crashes involving alcohol, fewer
nighttime crashes, and fewer weekend crashes. Cook et al. (2020) report a similar 9 percent
reduction in traffic fatalities in medical marijuana states between 2010 and 2017. However, Cook
et al. (2020) also find that city-level marijuana decriminalization (reducing legal penalties for
possessing small amounts of marijuana) was associated with a 13 percent increase in fatal
crashes involving male drivers aged 15 to 24 years old.
Aydelotte et al. (2017) and Hansen et al. (2020) find no evidence that the 2014
legalization of recreational marijuana in Colorado and Washington State affected fatal crash
rates. Santaella-Teneorio et al. (2020) report similar results for Washington State, but not
Colorado, where they find an increase of approximately 75 excess traffic deaths per year. They
suggest that differences in how the two states implemented their laws, out-of-state marijuana
tourism, and local factors may explain the contrasting results.
Aydelotte et al. (2019) find that fatal crashes increased in both Colorado and Washington
state during the five years following recreational legalization. Kamer et al. (2020) report that
traffic fatality rates increased in Colorado, Washington, Oregon, and Alaska after recreational
legalization. They estimate that nationwide recreational legalization would lead to 6,800 excess
roadway deaths each year.
Trucking Safety
Trucking safety has been the subject of extensive research that examines the outcomes related to
poor safety and the antecedents of safety at the driver and carrier levels (for a recent review, see
Douglas (2021)). Poor safety performance can increase insurance premiums (Corsi and Fanara
1988), increase tort costs (Cantor et al. 2006), and reduce revenue due to customers taking their
business to safer carriers (ATRI 2011). Antecedents of trucking safety at the driver level include
driver demographics (Monaco and Williams 2000), job switching behaviors (Staplin and Gish
2005), fatigue (Hanowski et al. 2003; Morrow and Crum 2004), prior crashes and failed driver
inspections (Cantor et al. 2010), and whether drivers are owner-operators (Cantor et al. 2013).
Antecedents of trucking safety at the carrier level can be broken into three groups. First,
how carriers interact with their drivers can impact safety performance. These antecedents include
driver management practices (Mejza et al. 2003), delivery scheduling (Beilock 1995; 2003;
Crum and Morrow 2002), use of pay incentives (Rodriguez et al. 2006), unionization (Corsi et
al. 2012), and turnover (Miller et al. 2017). Second, safety performance can be impacted by firm
characteristics. These antecedents include carrier financial performance (Bruning 1989; Hunter
and Mangum 1995; Miller and Saldanha 2016), size (Cantor et al. 2016), growth and contraction
(Miller et al. 2018), and prior safety performance (Miller et al. 2017; Miller, 2017). Third, safety
performance can be impacted by strategic decisions. These antecedents include the use of
onboard technologies (Cantor et al. 2009; Scott, Balthrop, and Miller 2021; Yan et al. 2015),
type of product hauled (Horrace and Keane 2004), extent of owner-operator use (Monaco and
Redmon 2012; Miller et al. 2018), and response to government implementation of information
disclosure systems (Miller 2017).
This manuscript extends the trucking safety literature by examining the impact of
legislation that is not directed at the trucking industry but nonetheless has the potential to
influence driver behaviors that can impact safety performance. Further, research investigating the
impact of drugs on trucking safety is limited to examining the impact of drug testing laws on
fatalities in trucking crashes (Jacobson, 2003). This manuscript also contributes to the trucking
safety literature by providing a first look at the impact of drug legalization and driver drug use.
Finally, we expand this research exploring the impact of drug use on trucking safety by including
all serious truck crashes rather than solely focusing on fatalities, which is narrowly focused and
subject to randomness.
Marijuana and Trucking
Federal regulators have been extensively involved with drug testing and enforcement for
decades. Marijuana served as the catalyst for drug testing in trucking. The revelation that
members of a train’s crew had used marijuana shortly before a fatal 1987 collision led to the
passage of the Omnibus Transportation Employee Testing Act of 1991.
While the specifics of the testing program have evolved over the years, its essence
remains unchanged. Today, trucking firms must screen all potential hires and randomly test 50
percent of their drivers annually using a five-panel urine test that detects cocaine, opioids,
amphetamines, PCP, and marijuana. Drivers who fail a test must complete a lengthy return-to-
duty process before they are eligible to drive again, though there is no guarantee that they will be
able to find employment. In January 2020, the FMCSA rolled out a Drug and Alcohol
Clearinghouse database to give carriers access to potential hires drug testing history.
Testing is intended to improve roadway safety by discouraging drug use by truck drivers,
a group with risk correlates for substance abuse. A vast majority of truckers are men (Scott and
Davis-Sramek 2021) and men are significantly more likely than women to use illicit drugs
(McHugh et al. 2018). Long-haul drivers’ work exposes them to many stressors that may fuel
substance abuse (Gay Anderson and Riley 2008; Girotto et al. 2014; Dini et al. 2019), including
long hours (Belzer and Sedo 2018), tight delivery schedules (Chen et al. 2021), inadequate rest
(Häkkänen and Summala 2000), health issues (Sieber et al. 2014), and extended periods of
separation from family and friends (Williams et al. 2017).
The drug testing program is structured in a way that should deter drivers from using
marijuana. While FMCSA-approved medical examiners have leeway to approve drivers’ use of
prescription amphetamines or opioids, no such exemptions are made for medical marijuana,
which remains illegal federally. Moreover, urinalysis is particularly effective at detecting
marijuana use. Cocaine, opioids, and amphetamines are typically detectable in urine for a few
days after their last use (Wolff et al. 1999; Preston et al. 2002; Cody et al. 2004; Jufer et al.
2006), but THC can be detected weeks after a frequent user stops (Cone 1997; Smith-Kielland et
al. 1999; Palamar et al. 2019).
Despite policymakers’ efforts, decades of drug testing have not eradicated marijuana use
from trucking. In 1998, 1999, and 2007, the Oregon State Police conducted a series of roadside
inspections where they collected voluntary and anonymous urine samples to determine the extent
of undetected drug use among drivers (Oregon Department of State Police 2007). 3.04 percent,
3.13 percent, and 3.70 percent of the screened drivers tested positive for THC. Marijuana was the
most commonly detected drug in the 1998 and 2007 inspections and the second most frequently
detected drug in the 1999 inspections, behind amphetamines.
The FMCSA’s monthly Drug and Alcohol Clearinghouse reports provide further
evidence that a significant number of drivers use marijuana. As of November 2021, 58,904
positive THC test results had been submitted to the Clearinghouse (FMCSA 2021). Combined,
all other substances identified in failed drug tests during that time add up to 46,644 positive
Given the intense regulatory focus on drug testing in trucking, the shortcomings of
testing processes, and the continued use of drugs by truck driversparticularly marijuana
research is needed to better understand how marijuana use affects trucking safety.
Legalization lowers the price of marijuana and increases its availability (Anderson et al. 2013),
and has been shown to increase marijuana use in adults (Han and Palamar 2018). Marijuana and
other illicit drugs have been demonstrated to impair driving abilities in a variety of contexts
(Robbe, 1998; Ramaekers et al. 2000). A meta-analysis of case-control, culpability and collision
studies indicates that driving under the influence of marijuana increases the odds of a crash by
1.92 times that of unimpaired drivers (Asbridge, Hayden and Cartwright 2012). Increased use of
marijuana, other things equal, should result in more impaired drivers, and therefore increase
crashes in states that legalize. Indeed Aydelotte et al. (2019) and Kamer et al. (2020) report that
traffic fatalities from crashes have increased in states that have legalized recreational marijuana.
Thus, we hypothesize:
H1a: Marijuana legalization increases heavy truck crashes.
However, other things are not held equal during legalization. First, previous research has
found evidence that legalization influences other risky behaviors, particularly among the most
dangerous cohort of drivers, men aged 16 to 24 (NHTSA 2019). Young adults are less likely to
binge drink after legalization (Alley et al. 2020) and less likely to be involved in fatal crashes
involving alcohol (Anderson et al. 2013). While young adults do not make up a substantial share
of truck drivers (Phares and Balthrop 2021), they do share the roads with heavy trucks, and may
thus contribute to a reduction in crashes involving heavy trucks. Second, medical and
recreational legalization does not apply to truck drivers. Federal regulations prohibit marijuana
use by truckers, while the legal liabilities from crashes make impaired driving a serious concern
for trucking companies and managers (Voss and Cangelosi 2020). Frequent drug testing and the
potential for employment, financial, and criminal penalties for truck drivers who violate federal
drug rules may provide sufficient incentive to discourage increased use among drivers even after
marijuana becomes more available and cheaper. Thus, we expect that truck drivers are less likely
to use marijuana and users are less likely to be on the road once marijuana is legalized. Stated
H1b: Marijuana legalization reduces or has no effect on heavy truck crashes.
State-level fiscal and legal policies can have spillover effects on neighboring states
(Oates 1972). A study by Hansen et al. (2020) shows that this may be a particularly salient issue
for marijuana, where legalization has been shown to affect cross-border sales. Compounding this
concern is that trucks are frequently involved in interstate travel. Interstate over-the-road
truckers, in particular, may traverse several states. It is therefore possible for marijuana
legalization in one state to affect drug usage and crashes in neighboring states if truck drivers
transport marijuana across state lines or cross state lines soon after use. This argument also holds
for non-truckers. Hao and Cowen (2020) find that recreational legalization in Colorado and
Washington led to increased marijuana possession in neighboring states; though, some of this
effect maybe the endogenous result of stepped-up drug enforcement in neighboring states. Given
the cross-border travel of interstate truckers and the potential for increased drug enforcement in
neighboring states, we expect trucking safety to be impacted in the states neighboring states that
legalize marijuana. Stated formally,
H2: Marijuana legalization results in spillover effects for contiguous states, with the sign
of these effects determined by H1.
To test these hypotheses, we take a difference-in-difference (DID) approach, an empirical
strategy that is well-established within empirical logistics research (Cui et al. 2021; Kistler et al.
2020; Scott et al. 2021; Wiedmer et al. 2021). We examine the difference in crashes before and
after legalization in states where legalization occurs,  
 
, where represents
crashes in legalized states after and before legalization, respectively. The change in crashes
around legalization, , will not solely be attributable to legalization if it is confounded by other
factors that change concurrently. To control for these other factors, we establish a control group
of states that do not legalize, allowing us to understand patterns in crashes in the absence of a
policy change:  
  
 . By subtracting the treatment group difference from the
control group difference, we are able to eliminate confounding variation, and are left with an
estimate for the effect of legalization:   . It is straightforward to show that can also
be estimated through the following regression:
        
Where, s indexes states and t indexes time, and are respectively state and time fixed-effects,
 is an indicator variable for whether legalization has occurred in state s at time t, and
represents the effect of legalization on crashes. This regression framework also allows the
potential for controlling for additional covariates and spatial regression specifications, which we
discuss in the results section.
To examine the relationship between marijuana legalization and the roadway safety of heavy
vehicles, we use data on heavy truck crashes provided by the FMCSA as our key dependent
These data covers all crashes of DOT-registered vehicles involving a fatality or injury,
or requiring a vehicle to be towed away. As such, the crash variable provides a broader measure
of crashes involving heavy trucks, whereas prior studies have focused overwhelmingly on
roadway fatalities involving any type of vehicle (Anderson et al. 2003; Aydelotte et al. 2017;
Aydelotte et al. 2019; Cook et al. 2020; Hansen et al. 2020; Hansen et al. 2020; Kamer et al.
2020; Santaella-Tenorio et al. 2020). We aggregate the number of crashes by state and month
and divide by the monthly non-institutionalized state population from the Bureau of Labor
Statistics (BLS). The data cover the period 2005 to 2019, inclusive. Six states are excluded
because of incomplete crash reporting.
We also exclude Hawaii because it lacks contiguity with
any other state.
Our key explanatory variable is a dummy variable indicating whether a state has
legalized medical or recreational marijuana. We obtained data on medical legalization (mml)
from the Marijuana Policy Project. Recreational legalization dates (rml) are taken from Anderson
et al. (2021). There is significant heterogeneity in legalization, (e.g., whether identification cards
are required for purchase, whether dispensaries must be licensed, etc.) which we examine more
extensively in Table 4. A map of states that legalized marijuana use by December of 2019 is
These states are Alaska, Arizona, Georgia, Michigan, Montana and the District of Columbia.
provided in Figure 1. Figure 2 presents a graph of legalization trends for medical and recreational
Figure 1: Marijuana legalization
Figure 2: Marijuana legalization trends
Other explanatory variables include the mean age of the population for each state and the
percent of the population that is male aged 16 to 25 provided by the US Census Bureau and
Notes: Alaska, Arizona, District of Columbia, Georgia, Montana and Michigan excluded from analysis because of
missing crash data. Hawaii is also excluded.
reported yearly. Information on the number of licensed drivers, the number of registered motor
vehicles, and the number of vehicle miles traveled in each state is provided at a yearly frequency
by the Federal Highway Administration (FHA). We also control for whether marijuana
possession has been decriminalized (i.e., misdemeanor and not felony). Economic variables
include state monthly unemployment rate (BLS), yearly highway safety expenditures per capita
by state (FHA), and a series of GDP variables to control for roadway freight markets reported
quarterly by the Bureau of Economic Analysis (BEA). Summary statistics and a description of
each variable can be found in Table 1.
Table 1: Summary statistics, variable descriptions and sources
To examine the effects on heavy truck accidents of medical and recreational marijuana
legalization, we initially estimate population-weighted difference-in-difference specifications
following Anderson et al. (2013). In particular, we estimate
          
Where s indexes states and t indexes month-years. X contains control variables listed in Table 1,
including mean age, percent of population men aged 16 to 25, vehicle miles traveled per person,
variable name description Obs. Mean Std.Dev. Min. Max.
log(crash rate) Natural log of heavy truck accidents per person (FMCSA) 7,920 1.54 0.52 -3.31 4.01
mml Medical marijuana legalization (MPP) 7,920 0.34 0.47 0 1
rml Recreational marijuana legalization (NBER) 7,920 0.05 0.22 0 1
age Mean age (Census) 7,920 38.00 1.66 31.3 42.9
young men % Percent men 16-25 by state (Census) 7,920 7.07 0.48 5.83 9.28
vmt Vehicle miles traveled per person (BTS) 7,920 13563 2517 7814 23949
drivers Licensed drivers per person (BTS) 7,920 0.91 0.07 0.67 1.32
vehicles Registered vehicles per person (BTS) 7,920 1.13 0.21 0.37 1.91
decrim Decriminalization 7,920 0.29 0.45 0 1
urate Unemployment rate % (BLS) 7,920 5.6 2.2 1.5 14.1
safe_exp Highway safety expenditures per person (FHA) 7,920 83 53 2389
gdp_ag GDP per person in agriculture (chained real $, BEA) 7,920 1314 1650 610815
gdp_const GDP per person in construction (chained real $, BEA) 7,920 2639 858 1217 8470
gdp_man GDP per person in manufacturing (chained real $, BEA) 7,920 8055 3350 1984 29113
gdp_ret GDP per person in retail trade (chained real $, BEA) 7,920 4020 635 2860 8369
gdp_tnsp GDP per person in transportation (chained real $, BEA) 7,920 2066 1007 863 7341
licensed drivers per person, registered vehicles per person, a dummy variable indicating whether
marijuana possession has been decriminalized, monthly unemployment rates, and a series of
quarterly GDP per person variables in different industrial sectors to control for supply and
demand conditions. The variable of interest is the dummy variable for marijuana legalization, ml,
which alternatively controls for medical (Table 2) or recreational legalization (Table 3).
Following Bertrand et al. (2004), standard errors are clustered by state. Crash trends for the
treatment and control group before and after medical legalization can be found in Figure 3, and
in Figure 4 for recreational legalization.
Figure 3: Accident trends around medical legalization
Figure 4: Accident trends around recreational legalization
Regression results for medical legalization can be found in Table 2. The first column
gives the pure difference-in-difference specification without controlling for covariates. Medical
marijuana legalization results in 10.7 percent increases in crashes (    ), although
this is statistically insignificant. Controlling for covariates results in tightened standard errors
and a significant coefficient estimate in specification (2), but this result is not robust to the
inclusion of state-specific linear crash trends in column (3). Specifications (4)-(6) examine
evidence of lagged and anticipatory effects of medical legalization (legalization has been re-
coded in these specifications so that the time categories are mutually exclusive, i.e., coefficient
estimates are instantaneous to time category and not additive). Again, there is no evidence of
significant changes in crash rates when state-specific linear accident trends are included.
Table 2: DID estimates for medical marijuana legalization
Table 3 presents results for recreational legalization. Column 1 presents the pure DID
results, which is statistically insignificant. Inclusion of control variables in specification 2 results
in large and significant crash reductions; however, this attenuates to in column 3 with the
inclusion of state-specific crash trends. In our preferred specification (column 3) recreational
legalization results in an 11.0 percent reduction in heavy truck crashes. Dynamic specifications
in columns (4)-(6) indicate that this effect persists for up to 3 years afterwards, with no
significant evidence of anticipatory effects to legalization.
Table 3: DID estimates for recreational marijuana legalization
DID analysis in Tables 2 and 3 are based on staggered adoption across states of the
treatment (recreational and/or medical marijuana legalization, see Figure 2). Here the coefficient
estimates are weighted averages of the state-specific treatment effects. De Chaisemartin and
D’Haultfœuille (2020) show that staggered adoption designs can result in negative weights,
leading to situations where regression coefficients can be negative even when all treatment
effects are positive. In Table 4 we conduct the De Chaisemartin-D’Haultfœuille test to test for
negative weighting and provide alternative estimates to those in Table 2 and 3.
Table 4: De Chaisemartin-D’Haultfœuille Analysis
Table 4 indicates that a significant number of treatment effects in the medical marijuana
legalization analysis receive negative weights (643/2663). Coefficient estimates in Table 2 are
thus compatible with an average treatment effect estimate of 0 given plausible heterogeneity in
treatment effects across states (st. dev. 0.0463). Using the control group and the treatment group
at the instant of switching results in a negative and insignificant reduction in crashes of 3.0
percent (in contrast to table 2). On the other hand, there are no negative weights for recreational
marijuana legalization. Thus, the analysis in Table 3 is valid. Given the inconclusive results
related to medical marijuana legalization and the reduction in crashes related to recreational
marijuana legalization, we do not find support for H1a and find partial support for H1b.
Another consideration is that the population is free to move across states, truckers in
particular. This can result in contamination of the control group if people in bordering areas are
exposed to changes in treatment (either medical or recreational legalization). To examine this,
we estimate a DID model of the effect of legalization where legalization can have spillover
effects to neighboring states. Following Kapoor et al. (2007) we estimate variations along the
following model
          
Note the added spatial term is , where is a 1 x 45 vector of spatial weights and
represents the spatial lag of legalization at time t. The coefficient measures the intensity of
spatial spillovers, i.e., whether legalization in nearby or neighboring states has any effect on
crash rates within state s. We estimate the regression alternately with two types of spatial
weights. 
specifies contiguity weights taking a value of 1 when state s is contiguous to state
k, and 0 otherwise. 
 specifies inverse distance weights between state centroids. This latter
specification allows for more “global” effects: states closer to s receive greater weight, but all
weights are non-zero.
Results estimated via maximum likelihood are presented in Table 5.
Table 5: Difference-in-difference estimates with spatial lag of legalization
Column 1 presents results for the DID estimation with contiguity weights, indicating
crashes increase significantly once medical marijuana is legalized, a result that is consistent with
the inclusion of covariates (column 2) and when spatial weights are specified with inverse
State s receives a weighting of 0 with itself in both weighting schemes.
distance weighting (columns 3 and 4). These results are consistent with those in Table 2, but we
should note that they also suffer the same drawback of bias from negatively weighted treatment
effects. There is some evidence of spillover effects: legalization in nearby states tends to reduce
the accidents in home state, but this effect attenuates when covariates are included for both
contiguity and inverse-distance weights. Significance suggests that the effect on accidents from
legalization is attenuated when nearby states have already legalized. While coefficients for the
contiguity estimates represent marginal effects, the coefficient estimates for the inverse distance
weight matrix must be interacted with inverse distance. Taking the estimate in column 3 as an
example, the marginal effect of legalization in a state with centroid 100 miles away from the
home state results in a 
   percent reduction in crashes in the home state.
Columns 5-8 in Table 5 iterate on the previous results, but with the focus on recreational
legalization. Here, similar to results in table 3, we produce no evidence that recreational
legalization results in an increase in truck crashes; indeed, our estimates suggest that recreational
legalization results in a reduction in crashes. With contiguity weighting, spillover effects are not
robust to the inclusion of covariates. Inverse distance weighting indicates that legalization in a
close-by state tends to reduce crashes in the home state. Thus, given the negative weighting issue
associated with medical marijuana legalization and the slight reduction in crashes related to
recreational marijuana legalization using the global spillover analysis, we also find partial
support for H2.
In summary, there is no evidence that marijuana legalization results in a significant
increase in heavy truck crashes. Indeed, there is evidence that recreational legalization leads to a
small reduction in crashes. There is evidence to suggest that legalization in nearby or
neighboring states tends to reduce crashes.
Heterogeneity and Robustness
To test the robustness and heterogeneity of our results, we use a synthetic control
approach to compare observed and expected crash rates for states legalizing recreational
marijuana (Abadie, Diamond, & Hainmueller 2010; 2015). Given difficulties associated with
selecting appropriate control states, the synthetic control approach produces counterfactuals by
generating weighted combinations of control states. This is accomplished through systematic
identification of multiple combination of states with similar crash rates to those observed in
treated states as well as similarities across a vector of other covariates, the same vector of
covariates used in the main analyses. The best fitting synthetic control is the combination of
control states that produces the lowest mean squared prediction error (MSPE) during the pre-
treatment period. During the post-treatment period, a higher MSPE indicates a divergence
between the treated state and the synthetic control (Santaella-Tenorio, et al. 2020). This
divergence is assessed via a ratio of the post-treatment MSPE to the pre-treatment MSPE. This
indicates that the selected synthetic control provides a strong estimate of the crash rates observed
in the treated states. Finally, the average treatment effect (ATE) is the mean of differences
between the observed values and predicted estimates for all periods in the post-treatment period
(Angrist & Pischke 2009). For all treated states except Vermont, we used a 36-month pre-
treatment period and a 36-month post-treatment period. For Vermont, we used a 36-month pre-
treatment period and a 17-month post-treatment period because our data time frame extended
only 17 months beyond when recreational marijuana was legalized in Vermont.
Table 6: Difference in Crashes Between RML States and Synthetic Controls
Using the synth2 command in Stata MP 17.0, we identified synthetic controls for each of
the treated states. Table 6 summarizes the results of our synthetic control analysis. The low pre-
treatment MSPE values indicate the synthetic controls we identified were good fits for each state.
However, we find higher MSPEs during the post-treatment period only for Colorado (MSPE
ratio = 3.7549), Oregon (MSPE ratio = 1.9107), Vermont (MSPE ratio = 1.3372), and
Washington (MSPE ratio = 3.9773), indicating these states diverged dramatically from their
synthetic controls once recreational marijuana was legalized. In the rest of the states, differences
between estimated and observed values were consistent during both the pre-treatment and post-
treatment periods. This indicates little divergence between the treated states and their synthetic
Findings from this analysis provides additional support for the results from the main
analysis indicating that recreational marijuana legalization contributes to the reduction of heavy
truck crashes. This analysis also provides additional perspective regarding the heterogeneity of
the effects of recreational marijuana legalization between states. The main analysis results
provide the average treatment effect of marijuana legalization for all states, which provides
valuable insights in the aggregate, but may obscure our understanding of the variation in the
trucking safety response to marijuana legalization. Results from the synthetic control analysis
MSPE Ratio
Avg Treatment
indicate that while many states saw a meaningful reduction in crashes following recreational
marijuana legalization, not all states saw a reduction or any meaningful change.
Our findings provide support for H1bthat marijuana legalization has little effect or even
decreases crashes for heavy trucks. These findings are in-line with other studies that have found
legalization corresponds to no change or small reductions in fatal crashes (Anderson et al. 2013;
Aydelotte et al. 2017; Cook et al. 2020; Hansen et al. 2020). Our robustness analysis finds that
medical legalization has significant problems with negative weighting that are large enough to
result in sign reversal. There is evidence of considerable heterogeneity in treatment effects
estimates for both medical and recreational legalization, which must also be considered. Thus,
the effects of legalization in one state do not directly translate into likely effects for other states.
Special care must be taken in making comparisons, establishing control groups, and applying
treatment effects. For example, while recreational legalization tends to reduce heavy truck
crashes on average, it has done so significantly more in Colorado, while it has apparently
increased crashes in Nevada.
Importantly, our treatment effect results are also robust to the inclusion of controls for
spatial spillovers. We find evidence for spillover effects in legalization, consistent with H2.
While legalization in neighboring contiguous states has little effect on heavy truck crashes in the
“home” state, we do find significant evidence for long-range spillovers that are mediated by
inverse distance spatial weights. Recreational legalization anywhere tends to slightly reduce
accidents everywhere, with larger effects the closer to the legalized state.
Theoretical implications
Our findings contribute to the literature in several ways. First, our findings extend the literature
pertaining to the effects of marijuana and its legalization on general road traffic safety. While
marijuana use is associated with reduced driver performance (Robbe 1998; Ramaekers et al.
2000), this study provides additional support for the body of research that finds that marijuana
legalization does not lead to reduced road traffic safety, specifically when considering heavy
truck safety. This is particularly important given the limited research exploring these
relationships in trucking. Second, our findings extend the trucking safety literature the impact of
driver drug use and drug policies on trucking crashes. Prior research has linked driver
characteristics and behaviors to trucking safety (Monaco and Williams 2000; Hanowski et al.
2003; Morrow and Crum 2004; Cantor et al. 2010) but have yet to link driver drug use to
trucking safety. This is particularly relevant to the societal impact of and policymaking related to
trucker drug use given the role that logistics and supply chain management research can play in
influencing the policymaking process (Tokar & Swink 2019).
Finally, this research highlights the need for caution in employing difference-in-
difference research designs when considering multiple treatments and staggered adoption. We
show treatment effects estimates for medical marijuana legalization can be negatively weighted,
leading to erroneous conclusions about legalization and safety. This necessitates revisiting
previous studies in this area that use difference-in-difference estimation (Anderson et al. 2021).
Difference-in-difference estimation has fast become a popular estimation technique in
operations, logistics, and supply chain management research (e.g., Jung, Cho, & Shin 2021;
Kistler et al. 2021; Zhou & Wan 2022). Thus, our research may serve as catalyst to heighten
awareness of potential pitfalls in implementation.
Policy implications
As of December 2019, 33 states have legalized marijuana for medical or recreational use. This
trend is likely to continue. The Pew Research Center finds that the number of Americans who
favor marijuana legalization more than doubled between 2000 and 2019. Further, in 2021 that 91
percent of U.S. adults favor either recreational legalization or medical legalization, while only 8
percent believe it should not be legalized (Van Green 2021).
Our findings suggest that this legalization trend has not negatively affected trucking
safety, and that current government and managerial policies are adequate in preventing increased
impaired driving after legalization. Indeed, there may even be some space for relaxing existing
federal policies. Though it would be unwise to remove restrictions on marijuana use entirely
our results have no information on the effect of such non-marginal policy shifts treating
marijuana use more like alcohol use could alleviate the industry’s hiring woes.
While researchers are still hashing out marijuana’s impact on driver performance and
road traffic safety, there is no such debate surrounding alcohol, which has long been known to
negatively impact both (Martin et al. 2013). Nonetheless, FMCSA guidelines allow truckers to
drive with blood alcohol concentrations under .04 percent, approximately half the legal limit for
passenger vehicle drivers. Policymakers should support research to determine and enforce a
similar threshold for marijuana. Marijuana breathalyzers are available, but their reliability is
disputed (McCartney et al. 2021). As states await a reliable means of conveniently performing
roadside THC tests, some use blood testing with a legal limit of 5 nanograms of THC per
milliliter. However, that threshold may not accurately gauge driver impairment (Logan et al.
2016). Policymakers can help resolve these matters by facilitating further research. National
Academies of Science, Engineering, and Medicine (2017) states that “the lack of evidence-based
information on the health effects of cannabis and cannabinoids poses a public health risk” and
encourages policymakers to remove regulatory barriers that stifle marijuana research.
It is also important to consider marijuana rules in the context of the long-standing driver
shortage. In October 2021, the American Trucking Association estimated a need for 80,000 more
drivers (Dean 2021). The FMCSA’s November 2021 Drug and Alcohol Clearinghouse report
shows that it received 58, 904 positive marijuana test results in the 23 months after the database
went public (FMCSA 2021). While Phares and Balthrop (2021) find that the “shortage” largely
stems from trucking’s failure to keep pace with wage increases in alternate occupations, trucking
nonetheless commands a wage premium. Loosening restrictions on marijuana use could erode
this premium, making it easier to hire new drivers.
Managerial implications
While regulators may eventually allow drivers to use marijuana during their downtime, the
FMCSA currently has a zero-tolerance policy. Carriers should not view our findings as a signal
to turn a blind eye toward marijuana use by drivers. If a driver is involved in a crash and post-
crash testing detects THC in the driver’s system, the legal and financial ramifications could be
Amidst increasingly common court-ordered “nuclear verdicts,” many carriers supplement
FMCSA-mandated urine drug testing with optional hair testing (Voss & Cangelosi 2020), which
can detect substances ingested months earlier (Hadland & Levy 2016). Carriers who perform hair
testing report that it detects far more drug use than urinalysis (Voss & Cangelosi 2020), allowing
them to make personnel decisions that reduce the risk of being held liable for a drug-induced
crash. However, firms should note that some research suggests that hair testing may be biased
against dark-haired people (Henderson et al. 1998; Hubbard et al. 2000; Borges et al. 2003).
While researchers disagree over whether hair testing is discriminatory (Kelly et al. 2000; Voss &
Cangelosi 2020), carriers must weigh its benefits against potential legal consequences. Courts
have upheld hair testing in the past, but that may be changing. In 2019, the Massachusetts
Supreme Judicial Court found that a white man’s application to the Boston Police Department
should not have been rejected based on hair testing results alone (Ellement 2019). In 2020, the
Massachusetts Appeals Court ruled that the city of Boston owed millions in back pay to six
police officers five black men and one white woman who were fired because of hair
testing results (Gartsbeyn 2020).
Soon carriers will likely have to make another testing-based decision. In 2020, the
Substance Abuse and Mental Health Services Administration gave federal agencies the
greenlight to collect either urine testing results or oral fluid testing results (Substance Abuse and
Mental Health Services Administration 2019). If the FMCSA allows carriers to submit oral fluid
testing results in lieu of urinalysis results, many will likely choose oral fluid testing because it is
more convenient and less prone to tampering. However, carriers should be mindful that oral fluid
testing is relatively ineffective at detecting marijuana use. While urine testing can detect THC
that was ingested weeks earlier, oral fluid testing’s marijuana detection window is typically
measured in hours (Hadland & Levy 2016). So, carriers who choose oral fluid testing over
urinalysis may face greater risk of a driver testing positive for THC after a crash.
Limitations and directions for future research
As evident in the policy and managerial sections, marijuana legislation, rules and enforcement
touch a number of important issues in trucking, including roadway safety, trucking employment,
driver health, and legal liabilities. We provide insight into the effects of marijuana use on
roadway safety, yet more research is needed into the mechanisms underlying this safety effect.
We show that medical and recreational legalization have not resulted in an increase in crashes
involving heavy trucks. Nonetheless, from conversations with hiring managers, legalization has
made hiring drivers more difficult, particularly when the labor market for drivers is already tight.
Any relaxation of rules may increase the number of drivers available, but this change in the
driver pool will have its own safety consequences. Careful monitoring and assessment of rule
changes and their safety consequences will be needed going forward.
The conclusion that marijuana legalization has not, on average, increased heavy truck
crashes warrants further study. Given the potential safety ramifications, replication studies using
other data sets and methodologies are necessary. Investigation of the mechanisms behind crash
reductions is also important. Our study is not able to reliably observe marijuana use; yet this data
is available in the Drug and Alcohol Clearinghouse, or via the drug-testing records and safety
histories of drivers maintained by carriers. Further microeconometric studies using this data and
controlling for driver-level covariates and risk factors is needed to strengthen managerial and
policy recommendations in this area.
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While heavy truck drivers are prohibited from using marijuana and other drugs, monitoring is
infrequent and monitoring effort can be frustrated by driver avoidance behaviors. Research has
frequently applied agency theory in trucking contexts (Miller 2017, Scott et al. 2021). When
marijuana becomes less costly and more readily attainable, truck drivers will use more
frequently. Controlling for monitoring intensity, this should result in increased drug violations
among truckers.
H3 Medical and recreational marijuana legalization increase marijuana violations among heavy
truck drivers.
We use the number of drug violations of heavy truck driver for each state-month (drug viol) to
assess the degree to which legalization affects roadside drug violations. These data are obtained
from the FMCSA citations file.
Given the evidence of reduced crashes, at least among states with recreational
legalization, the question arises as to what mechanism causes the reduction. Crash reductions can
be from reduced unsafe driving among truckers, or from reduced unsafe driving among drivers
of passenger vehicles sharing the road. Previous studies have highlighted the substitutability
between alcohol and marijuana, especially among younger male drivers (Anderson et al. 2013).
Here we look to see whether there is evidence of increased drug usage by truckers in states
which have legalized recreational marijuana. As the dependent variable we use the number of
drug violations cited by FMCSA inspectors in each state during each month.
We estimate a
Poisson regression using age, percent young men, population, urate, decrim, nbr_insp, and the
The violations are for any evidence of illegal drug usage, not just marijuana.
vector of GDP variables and highway safety expenditures in levels (i.e., not per capita).
Regression results can be found in Table 6.
Table A1: Poisson regressions of count of drug violations among truckers
Table 6 displays incidence rate ratios. We do not find compelling evidence that recreational
legalization has led to an increase in the number of drug violations among truckers in
recreational states (columns (1) and (2)). Inclusion of state-specific linear trends results in a
slightly reduced incidence of drug violations, but this is insignificant (column (3)), and generally
remains so in dynamic specifications (columns (4)-(6)).
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To date, 16 states have passed medical marijuana laws, yet very little is known about their effects. Using state-level data, we examine the relationship between medical marijuana laws and a variety of outcomes. Legalization of medical marijuana is associated with increased use of marijuana among adults, but not among minors. In addition, legalization is associated with a nearly 9 percent decrease in traffic fatalities, most likely to due to its impact on alcohol consumption. Our estimates provide strong evidence that marijuana and alcohol are substitutes.
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A perceived driver shortage causing difficulty in hiring and retaining qualified drivers threatens carrier productivity and profitability. Logistics literature concerning driver turnover has focused on three primary groups of factors contributing to the driver turnover: driver demographics, manager quality, and work environment. A key consideration, however, is that drivers have an outside option to work in other industry‐occupations. We leverage the Roy model of occupational choice to develop hypotheses regarding the effects of wages and tenure on workers’ occupational choice. To test our theory, we conduct a series of econometric studies on a panel data set we assemble from publicly available data sets. Our results show that relative wages strongly influence the choice of whether to drive a truck for a living. A particularly important finding is that the effect of relative wages varies significantly across industry‐occupations. In fact, contravening industry wisdom that wages are insufficient to attract qualified workers, wage elasticities are greater in trucking than in competing industry‐occupations, such as construction or retail. These results persist after accounting for a wage–tenure interaction. This indicates that the persistent “driver shortage” faced by the industry is the result of wages failing to keep pace with wage increases in competing industry‐occupations.
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In this paper, we study the disruptive power of the mobile digital sharing economy to the road freight logistics industry. New information technologies, such as the mobile internet, mobile payment methods, and GPS, have enabled platforms to match freight shippers’ demand with carriers’ supply by utilizing the convenience and mobility of smartphones. We empirically study the influence of the emergence of mobile digital freight matching platforms in the U.S. on the profitability and stock performance of two types of incumbent road freight logistics companies: freight arrangement companies (freight forwarders and brokers) and trucking companies (freight carriers). Since the mobile digital freight matching platforms mainly match small and mid‐sized trucking companies with shipping demand, we expect the platforms to introduce interference (direct) competition to traditional freight arrangement companies and exploitation (indirect) competition to large trucking companies, which potentially raises operational challenges for both types of incumbents. We use Difference‐in‐Differences (DID) analyses to study the incumbents’ profitability and the event study method to evaluate their stock performance. We find that after the advent of the mobile digital freight matching platforms in the U.S., the profitability of the traditional freight arrangement companies counter‐intuitively remained unchanged. In contrast, the profitability of the large trucking companies increased. Further, we examine the changes in sales and costs behind these profitability changes. Our dynamic analyses suggest a time lag of the influence of the mobile digital freight matching platforms. Besides, we find that the stock value of the traditional freight arrangement companies received a significant negative shock, whereas the stock value of the large trucking companies showed no significant reaction. This study contributes to understanding the disruptive power of an emerging technology innovation of sharing economy to firm performances in an increasingly competitive and innovative business environment.
Virtually everything we own was transported by truck at some point. Around 3.5 million truck drivers haul almost 71% of U.S. freight. To ensure the safety of our roadways, the U.S. government requires all drivers to pass urinalysis drug screens. However, urinalysis drug screens are easily thwarted and some trucking companies use hair drug screens, a more stringent test. This research examines trucking industry data and finds about 300,000 truck drivers would be removed from their positions if forced to pass a hair drug test. Hair testing opponents argue that the test is biased against ethnic minority groups. Comparing urine and hair pass/fail rates for various ethnic groups, our results indicate ethnic groups are significantly different irrespective of testing procedure. Factors other than testing method seem to underlie ethnic group pass/fail rate differences.
Blood and oral fluid Δ⁹-tetrahydrocannabinol (THC) concentrations are often used to identify cannabis-impaired drivers. We used meta-analytic techniques to characterise the relationships between biomarkers of cannabis use, subjective intoxication, and impairment of driving and driving-related cognitive skills. Twenty-eight publications and 822 driving-related outcomes were reviewed. Each outcome was measured in concert with one or more biomarkers of cannabis/THC use and/or subjective intoxication. Higher blood THC and 11-OH-THC concentrations, oral fluid THC concentrations and subjective ratings of intoxication were associated with greater impairment in ‘other’ (mostly occasional) cannabis users (p’s<0.05); blood 11-COOH-THC concentration was associated with impairment post-inhaling, but not orally ingesting, cannabis/THC. These ‘biomarker–performance’ relationships (R) were very weak (blood THCpost-ingestion: -0.08; blood THCpost-inhalation: -0.10; blood 11-OH-THCpost-ingestion: -0.13), weak (blood 11-OH-THCpost-inhalation: -0.24; oral fluid THCpost-inhalation: -0.36; subjective intoxication: -0.29) and moderate (blood 11-COOH-THCpost-inhalation: -0.43) in strength. No significant relationships were observed in ‘regular’ (weekly or more often) cannabis users (p’s>0.10), although the analyses were less robust. Blood and oral fluid THC concentrations are relatively poor indicators of cannabis/THC-induced impairment.
Relying on firms to self‐report information is an information‐gathering mechanism that often results in biased measures due to the incentives of the reporting firms. What is less commonly understood is that using self‐reported information for decision‐making results in endogenous selection bias, which creates spurious associations between the measure being reported and factors that influence reporting. Thus, conditioning on self‐reported information can lead to inaccurate evaluations of firms and bias predictions of future performance, even when the self‐reported measure is not intentionally misrepresented. We examine endogenous selection bias in self‐reporting regimes using directed acyclic graphs (DAGs). We illustrate the problem using data from a policy change by the U.S. Department of Transportation that allowed firms to report not‐at‐fault for accidents. We find that large for‐hire firms are much more likely to report not‐at‐fault for accidents—over 40 times more likely than independent drivers—even after controlling for time, location, and weather. When comparing independent drivers with large firms, the reporting disparities make large firms appear 25% safer when using at‐fault accidents versus all accidents while providing no improvement in predicting future accidents. This study highlights the consequences of poorly designed information‐gathering mechanisms and the usefulness of DAGs for understanding causality in supply chain research.
Abstract This article presents results of a systematic review of the US motor-carrier safety literature in transportation, logistics, and safety journals. The discipline has seen growth in research over the decades, and growth of the field rapidly increased in the last decade. We organize the literature into a systems framework and summarize the research across industry system levels to include government, regulators, carriers, and drivers. We then apply a goal-framing approach to reveal some of the dynamic interactions between system levels and the environment, as entities work to minimize risk to life and property during freight operations while striking a balance between the industry's welfare and societal welfare. This article provides recommendations for future research to fill gaps in the current body of knowledge and to aid government officials, regulators and law enforcement officials, carrier managers, and drivers in addressing industry challenges and maintaining safe roads in 2020 and beyond.
Our study aims to deepen the understanding of personalized digital nudges by evaluating their effects on energy‐saving behavior. We conducted a field experiment with a leading smart metering company in South Korea to investigate whether customers save more energy when a personalized goal and feedback are provided, and how the impacts of nudges vary according to the types of misperception. Specifically, we focused on the behavior of customers who underestimate or overestimate their past electricity usage compared to their actual consumption. We merged daily energy consumption with a pre‐experiment survey for the customers. We found that goal‐setting and feedback mechanisms have a markedly different impact on each type of misperception. Underestimating customers reduced energy consumption only under the “goal setting with feedback treatment”. Conversely, overestimating customers reduced energy consumption even under the “goal setting without feedback” condition. The underlying mechanism is suggested as updating biased beliefs towards goal achievement. Overall, the results demonstrate that personalized nudges lead to heterogeneous behavioral responses and that service providers and policymakers can use these signals to enrich their planning of behavioral nudges.
Objectives The study objectives were to examine U.S. long-haul truck drivers (LHTDs)’ opinions on their safety needs and to assess the associations of driver reported unrealistically tight delivery schedules with: (1) their opinions on their compensation, maximum speed limits, and Hours-of-Service (HOS) regulations, and (2) their behaviors of noncompliance with these safety laws and regulations. Methods National Institute for Occupational Safety and Health analyzed data from its 2010 national survey of LHTD health and injury. A total of 1,265 drivers completed the survey. Logistic regression was used to examine the associations between driver reported unrealistically tight delivery schedule and their opinion on safety and unsafe driving behaviors. Results Drivers who reported often receiving an unrealistically tight delivery schedule (an estimated 15.5% of LHTDs) were significantly more likely than drivers who reported never receiving an unrealistically tight delivery schedule to report that: (1) increasing the current maximum speed limit on interstate highways by 10 miles per hour (mph) would improve safety (odds ratio (OR) = 2.1); (2) strictly enforcing HOS rules would not improve safety (OR = 1.8); (3) they often drove 10 mph or more over the speed limit (OR = 7.5); (4) HOS regulations were often violated (OR = 10.9); (5) they often continued to drive despite fatigue, bad weather, or heavy traffic because their must delivery or pick up a load at a given time (OR = 7.5); and (6) their work was never adequately rewarded (OR = 4.5). When presented with 11 potential safety strategies, the largest percentage of LHTDs (95.4%) selected that building more truck stops/parking areas would improve truck driver safety. Conclusions Driver reported unrealistically tight delivery schedules are associated with drivers’ beliefs in safety laws/regulations and risk-taking behaviors. LHTDs see building more truck stops/rest areas as the most wanted safety need among the 11 potential safety strategies that were asked about in the survey.