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American Journal of Public Health logoLink to American Journal of Public Health
. 2014 Aug;104(8):1370–1377. doi: 10.2105/AJPH.2014.301894

Impact of Texting Laws on Motor Vehicular Fatalities in the United States

Alva O Ferdinand 1, Nir Menachemi 1,, Bisakha Sen 1,, Justin L Blackburn 1, Michael Morrisey 1, Leonard Nelson 1
PMCID: PMC4103220  PMID: 24922151

Abstract

Using a panel study design, we examined the effects of different types of texting bans on motor vehicular fatalities.

We used the Fatality Analysis Reporting System and a difference-in-differences approach to examine the incidence of fatal crashes in 2000 through 2010 in 48 US states with and without texting bans. Age cohorts were constructed to examine the impact of these bans on age-specific traffic fatalities.

Primarily enforced laws banning all drivers from texting were significantly associated with a 3% reduction in traffic fatalities in all age groups, and those banning only young drivers from texting had the greatest impact on reducing deaths among those aged 15 to 21 years. Secondarily enforced restrictions were not associated with traffic fatality reductions in any of our analyses.


Motor vehicle safety has been described as one of the 10 great public health achievements in the United States in the past decade,1 with car manufacturers and highway engineers making significant improvements to car and roadway safety features.2 Despite these improvements, traffic fatalities remain one of the leading causes of death in the United States,3 with an estimated 32 788 such deaths in 2010.4 Thus, road traffic fatalities continue to be a significant public health concern,5,6 garnering much attention from state lawmakers.

In an effort to reduce motor vehicle fatalities, states have enacted restrictions on drunk driving, implemented graduated driver’s license programs, and mandated seatbelt use and special licensing procedures for older adults. Most recently, states have focused on restricting texting while driving.7–9 Generally, states define texting as reading, manual composition, or sending of electronic communications—text messages, instant messages, or e-mails—via a portable electronic device. Portable electronic devices include mobile (i.e., cellular) phones, personal digital assistants, and laptop computers. Texting while driving is a serious threat to road safety,10–13 given that research has shown that mobile phone use is associated with impaired following distance,14 improper lane position,11,15 longer reaction times,11,14,16 and crashes,11,17 which can all lead to significant adverse public health outcomes, including death.18 Unlike talking on a mobile phone while driving, texting poses a unique threat in that it requires drivers to take their eyes off the road for several seconds at a time.14

Our current understanding of the impact of texting laws on driving outcomes is limited. To our knowledge, 2 studies have empirically examined the impact of texting laws on adverse motor vehicle outcomes. The first was published by the Highway Loss Data Institute.19 It examined the relationship of collision claim frequency and texting bans in just 4 states (CA, LA, MN, and WA). The authors found that texting bans were associated with increased collision claims. They speculated that this increase might be due to drivers hiding their phones from view to avoid fines and, in so doing, taking their eyes off the road more than they did before the bans. More recently, Abouk and Adams20 published the first national-level study of texting bans’ impacts on traffic fatalities. They examined the impact of texting-while-driving bans on the occurrence of only single-vehicle, single-occupant accidents between 2007 and 2010. Their findings indicated that stronger bans that are applied to all drivers were associated with decreases in single-vehicle, single-occupant accidents.

The purpose of this study is to add to the knowledge base concerning the effectiveness of texting laws, particularly by considering the varying stringency levels of these laws. Texting bans can be secondarily enforced (i.e., an officer must have another reason to stop a vehicle before citing a driver for texting while driving) or primarily enforced (i.e., an officer does not have to have another reason for stopping a vehicle). Furthermore, some states ban texting among learner’s permit holders, and some ban texting among all those aged 18 years, 21 years, or younger, and still other states ban all drivers from texting. Some states have no texting laws at all. We consider the impact of each of these policy nuances on traffic fatalities in 48 states over an 11-year period. Moreover, given that younger individuals are more likely to text while driving,21 we examine the impact of texting laws on age-specific traffic fatalities. Overall, this study will be of interest to policymakers, law enforcement personnel, and other stakeholders interested in improving roadway safety and, by extension, public health.

METHODS

Our study is a longitudinal panel analysis (2000–2010) examining within-state changes in motor vehicle fatalities after the enactment of state texting bans. Fatality data were captured from the Fatality Analysis Reporting System as collected annually by the National Highway Traffic Safety Administration.22 Inclusion criteria for this data set are that a death of a vehicle occupant (driver or passenger) must have resulted within 30 days from a crash that occurred on a roadway typically accessible to the public. Information on the total number of motor vehicle fatalities among young drivers (aged 15–21 years), adult drivers (aged 22–64 years), and older drivers (aged 65 years or older) was compiled from the Fatality Analysis Reporting System data set by state, year, and month. We differentiate these age cohorts because the various regulations are expected to have differing effects by age group. Because of missing data, and to be consistent with studies examining motor vehicle fatalities,23–28 we excluded data from Alaska, Distrrict of Columbia, and Hawaii from this study. The final data set contained 132 months (11 years) of data from 48 states (n = 6336 state–month observations). We note that previous research has shown that longer time periods are suitable for examinations of roadway outcomes to counteract periods with zero fatality or crash counts as a result of nonconstant probabilities of these outcomes.29

Texting Laws

We first accessed a list of all state laws (including statute numbers) that ban mobile devices as compiled by the Public Health Law Research Program.30 This list includes information on activities regulated, the targeted populations, the associated enforcement level, and levels of potential fines levied for infractions. Given that this list does not distinguish between bans on use of handheld mobile phones and explicit bans on texting, we accessed each state law by statute number via the LexisNexis legal database to identify states with specific legislative language banning texting while driving.

We characterized texting prohibitions by using binary indicators for whether, in a given month and year, a state had a texting regulation in effect. We also made distinctions on the basis of which drivers are prohibited from texting (e.g., only young drivers) and whether the laws are primarily or secondarily enforced.

Recognizing that other factors play a role in crash risk exposure and, by extension, roadway safety, our models additionally controlled for variables that have previously been used in motor vehicle fatality research. We describe these factors as generally falling under 3 primary headings—economic, legal, and population specific—and provide justification for their use in our models.

Economic factors include gasoline prices (obtained from the Energy Information Administration and inflation adjusted to 2010 cents), state unemployment rates, and state per capita income (obtained from the US Bureau of Labor Statistics and the US Census Bureau, respectively) because these factors influence the number of miles driven, and consequently, crash risk.25,31–33 For example, the state unemployment rate is routinely adjusted for in motor vehicle fatality studies.20,23–25 Controlling for the state of the economy has been demonstrated to be important because the unemployment rate may reduce roadway fatalities if fewer drivers are on the road as a result of decreased economic activity.31

Legal factors include the presence of seatbelt laws, handheld bans, blood alcohol concentration (BAC) limits and speed limits, and graduated driver licensing (GDL) programs. Drunk driving laws have been shown to be significant predictors of traffic fatalities.34 Several states decreased their BAC limits from 0.1 to 0.08 during the study period. Thus, we included a binary variable that indicated whether it was illegal to drive with a BAC of 0.08 or more in a given month and year. Additionally, we included a binary variable that indicated whether a state’s licensing authority could suspend driving privileges before court action related to drunk driving (per se administrative license revocation) in a given month and year.

Moreover, seatbelt laws and lower interstate speed limits reduce motor vehicle fatality rates.25,35,36 Thus, we included a binary variable that indicated whether, in a given state and month, a primary seatbelt law was in place or whether the state had interstate speed limits of 70 miles per hour or more. Additionally, given some states’ passing of handheld bans, that is, laws making it illegal to use handheld cell phones while driving, and given that some states banned all drivers from handheld use and some banned only young drivers, we included 2 binary indicators for whether, in a given state and month, a handheld ban was in effect regulating all drivers or only young drivers. Because handheld bans were generally in effect before texting bans, we treated these binary variables in the same way as we treated other previously enacted laws in our models.

States have used GDL programs to improve roadway safety by limiting young drivers from driving at night and by limiting the number of passengers transported by them.37 Supervision of these drivers is also a key component of GDL programs.38 Given the effectiveness of these programs in reducing traffic fatalities,23,25 we included a binary control variable indicating whether a GDL program was in effect in a given month for each state. We obtained information on implementation dates of per se administrative license revocation, seatbelt laws, interstate speed limits, handheld bans, and GDL programs from the Insurance Institute for Highway Safety. We obtained information on BAC effective dates from the Alcohol Policy Information System.

Finally, consistent with previous traffic fatality research,23,25 we accounted for each state’s exposure to crash risk in a given year by including state population estimates (obtained from the US Census Bureau) by year in our models.

Models

We used a difference-in-differences methodology with state, month, and year dummy variables to assess the relationship between the presence of texting laws and motor vehicular fatalities across the 48 contiguous states. Because some states in our study passed texting bans (treatment states) and some did not (control states), our empirical strategy compared the changes in fatality counts within treatment states with the contemporaneous changes in fatality counts in the control states. We estimated all equations as count data models in which our dependent variable was a fatality count in a given state, month, and year. Justification for this approach lies in the fact that many state–month–year cells contained very small numbers of fatalities. For example, nearly 10% of our state–month–year observations had 10 or fewer fatalities and more than 26% had 25 or fewer fatalities. Thus, because fatality incidents were not normally distributed in our data, always took integer values, and the state–month–year conditional variances were larger than the conditional means, we used conditional negative binomial regressions. The state-level dummy variables controlled for all state-specific factors that were potentially correlated with motor vehicle crash–related fatalities and were largely time invariant, such as a state’s weather patterns and degree of law enforcement. The month-level dummy variables controlled for factors that vary from month to month that may be correlated with fatality counts, such as periods of widespread travel. The year dummy variables controlled for unobserved factors that vary from year to year in all states that could have some bearing on fatality counts, such as improved lifesaving medical protocols, automotive technologies, and car safety standards.

Our model specifications took the following basic functional form:

graphic file with name AJPH.2014.301894equ1.jpg

where Yimt is the vehicle fatality count for state i at month m and year t and Textimt is the presence of a texting ban for state i at month m and year t. Limt is a vector of legal factors affecting crash risk exposure (handheld bans, seatbelt laws, BAC laws, GDL programs, speed limit), Zimt is a vector of other time-varying covariates (gasoline prices, state unemployment rate, per capita income, state population estimates), and Si is a vector of state dummy variables. Mm is a vector of month dummy variables, and Tt is a vector of year dummy variables. Recognizing that observations within states are correlated in some way, we handled autocorrelation by clustering on the 48 states in our study.

In addition to examining the effects of texting laws on the overall population, we conducted a series of sensitivity analyses by constructing age cohorts to determine whether various texting laws affect different age groups differently. We used older individuals (aged 65 years or older) as counterfactuals with the idea that, regardless of the presence of a law, older individuals are generally less inclined to text39 and arguably less likely to be affected by texting laws. Moreover, given that some motor vehicle fatalities result from single-vehicle accidents, we examined the extent to which the laws directly affect fatality counts of drivers of different ages. We subsequently collapsed these deaths by young drivers (aged 15–21 years), adult drivers (aged 22–64 years), and older adult drivers (aged 65 years or older). We conducted all analyses in STATA version 12 (StataCorp, College Station, TX), and statistical significance is reported at the .01 and .05 levels.

RESULTS

Of the 31 states that had passed a texting-while-driving law during our study period, 24 banned all drivers, and 7 banned only young drivers (i.e., drivers younger than 21 years, intermediate license and permit holders, or both). Delaware was the first state to enact any such law, with its law taking effect on April 14, 2005. The last state to enact such a law during the study period was Wisconsin (effective date December 1, 2010).

Table 1 presents the descriptive statistics for our panel data representing 6336 state–month–years. An average 69.2 traffic fatalities occurred in a given state–month. The average gasoline price was approximately $0.208 (in 2010 cents), and the average state per capita income was $38 043.60 (in 2010 dollars). Alcohol-related laws used in the model were in effect for the longest proportion of time (80%). Texting-while-driving laws were in effect for 9% of the study period duration.

TABLE 1—

Descriptive Statistics for State Panel Data: United States, 2000–2010

Variable States with Texting Ban, Mean (SD) States without Texting Ban, Mean (SD)
Texting while driving law 0.09 (0.28) . . .
Primary enforcement or bans
 All drivers 0.05 (0.21) . . .
 Young drivers only 0.02 (0.16) . . .
Secondary enforcement or bans
 All drivers 0.01 (0.09) . . .
 Young drivers only 0.01 (0.09) . . .
Traffic fatalities
 Per state–month–year 50.36 (49.65) 71.05 (69.89)
 Total young deaths, 15–21 y 11.02 (11.22) 17.57 (17.76)
 Total deaths, 22–64 y 33.05 (32.68) 44.40 (44.45)
 Total deaths, ≥ 65 y 7.97 (8.68) 10.88 (10.92)
 Total young driver deaths, 15–21 y 6.55 (6.39) 10.76 (10.64)
 Total deaths, 22–64 y 22.61 (20.86) 30.87 (29.54)
 Total deaths, ≥ 65 y 4.81 (4.63) 6.66 (6.28)
Gasoline prices, 2010 cents 256.09 (41.59) 203.33 (62.19)
State per capita income, 2010 dollars 40 145.66 (5110.49) 37 841.40 (5668.88)
State unemployment rate, % 7.84 (2.44) 5.26 (1.83)
Handheld bans
 All drivers 0.13 (0.34) 0.03 (0.16)
 Young drivers only 0.05 (0.23) . . .
Speed limit ≥ 70 mph 0.19 (0.39) 0.26 (0.44)
Seat belt law, primary enforcement 0.78 (0.41) 0.39 (0.49)
Administrative license revocation 0.92 (0.27) 0.80 (0.40)
Illegal per se at 0.08 BAC 0.99 (0.10) 0.79 (0.41)
Graduated driver licensing law 0.98 (0.13) 0.74 (0.44)

Note. BAC = blood alcohol concentration. The sample size was n = 6336 state–month–years. The mean values for legal factors are interpreted as the proportion of the 6336 state–month–years in which the law was in effect.

The evaluation results from the difference-in-differences models for all traffic fatalities over the 11-year study period are presented in Table 2. The model 1 column presents the most parsimonious empirical specification, which includes traffic fatality counts as the dependent variable, a single dummy variable representing the presence of a texting law, and state, month, and year fixed effects. This model showed that on average, the presence of a texting law was associated with a coefficient of −.023, thus suggesting a 2.3% reduction in traffic fatalities among all drivers (incidence rate ratio [IRR] = 0.98; 95% confidence interval [CI] = 0.96, 0.99). This implies that the average state that passed a law explicitly banning texting while driving experienced 1.6 fewer deaths per month.

TABLE 2—

Conditional Negative Binomial Regression Results for the Effects of Texting Laws on Traffic Fatalities: United States, 2000–2010

Variable Model 1, IRR (95% CI) Model 2, IRR (95% CI) Model 3, IRR (95% CI) Model 4, IRR (95% CI)
Texting law 0.98* (0.96, 0.99) 0.98 (0.96, 1.01) 0.98 (0.96, 1.01)
Texting law, primary
 Bans all drivers 0.97 (0.95, 1.00)
 Bans young drivers only 0.95 (0.91, 1.00)
Texting law, secondary
 Bans all drivers 1.01 (0.95, 1.07)
 Bans young drivers only 1.05 (0.98, 1.12)
Handheld ban
 All drivers 0.96** (0.93, 0.99) 0.98 (0.95, 1.01) 0.98 (0.95, 1.01)
 Young drivers only 1.06 (0.99, 1.14) 1.00 (0.93, 1.07) 0.98 (0.90, 1.07)
Speed limit ≥ 70 mph 1.51** (1.25, 1.82) 1.58** (1.30, 1.92) 1.57** (1.29, 1.92)
Administrative license revocation 0.66** (0.52, 0.82) 0.63** (0.50, 0.80) 0.63** (0.50, 0.80)
Seatbelt law, primary enforcement 0.99 (0.97, 1.00) 1.00 (0.98, 1.02) 1.00 (0.98, 1.02)
Illegal per se at 0.08 BAC 1.01 (0.99, 1.03) 1.01 (1.00, 1.03) 1.01 (0.99, 1.03)
Graduated driver licensing law 0.97** (0.96, 0.99) 0.96** (0.95, 0.98) 0.97** (0.95, 0.98)
Gasoline prices, 2010 cents 0.99** (0.99, 0.99) 0.99** (0.99, 0.99)
Per capita income, 2010 dollars 1.00** (1.00, 1.00) 1.00** (1.00, 1.00)
State unemployment rate 0.99** (0.98, 0.99) 0.99** (0.98, 0.99)

Note. BAC = blood alcohol concentration; CI = confidence interval; IRR = incidence rate ratios. Each model includes state, month, and year dummy variables as controls and accounts for state population estimates. The sample size was n = 6336 state–month–years.

*P < .05; **P < .01.

Model 2 in Table 2 introduces control variables for earlier laws aimed at reducing traffic fatalities. This model showed no statistical association between the presence of a texting law and crash-related fatalities (IRR = 0.98; 95% CI = 0.96, 1.01). However, handheld bans on all drivers (IRR = 0.96; 95% CI = 0.93, 0.99), per se administrative license revocation (IRR = 0.66; 95% CI = 0.52, 0.82), and presence of a graduated driver licensing law (IRR = 0.97; 95% CI = 0.96, 0.99) were associated with reductions in traffic fatality counts.

In model 3, we introduced the economic control variables. As with model 2, this model showed no statistical association between the presence of a texting law and reductions in crash-related fatalities (IRR = 0.98; 95% CI = 0.96, 1.01). Administrative license revocation (IRR = 0.63; 95% CI = 0.50, 0.80) and the presence of graduated driver licensing laws (IRR = 0.96; 95% CI = 0.95, 0.98) remained significantly associated with traffic fatality reductions in this model. Moreover, gasoline prices (IRR = 0.99; 95% CI = 0.99, 0.99) and state unemployment rates (IRR = 0.98; 95% CI = 0.98, 0.99) were also associated with fatality reductions. Handheld bans on all drivers were not significantly associated with traffic fatality reductions in this third model (IRR = 0.98; 95% CI = 0.95, 1.01).

The final column in Table 2 presents estimation results from the model that examined the effects of the extent to which texting laws are enforced and applicable to different groups of individuals. In model 4, we replaced the single texting law dummy with a set of 4 dummy variables representing whether the texting law banned all drivers with primary enforcement, banned only young drivers with primary enforcement, banned all drivers with secondary enforcement, or banned only young drivers with secondary enforcement. Texting laws that ban all drivers and are primarily enforced showed a 3% reduction in total traffic fatalities among all age groups, but this association did not reach statistical significance (IRR = 0.97; 95% CI = 0.95, 1.00). Texting bans on only young drivers that were primarily enforced also showed a marginal reduction in total traffic fatalities but did not reach statistical significance (IRR = 0.95; 95% CI = 0.91, 1.00). Secondarily enforced laws were not associated with decreases in traffic fatality counts.

Table 3 presents sensitivity analyses on total traffic fatality counts by different age cohorts. Primary laws banning only young drivers from texting had the biggest impact on fatality reductions among those aged 15 to 21 years (IRR = 0.89; 95% CI = 0.81, 0.98). Primary laws banning all drivers were also associated with traffic fatality reductions for this age group (IRR = 0.95; 95% CI = 0.91, 0.99). Neither primary laws banning all drivers from texting (IRR = 0.99; 95% CI = 0.98, 1.02) nor primary laws banning only young drivers from texting (IRR = 0.98; 95% CI = 0.92, 1.04) were associated with fatality reductions among those aged 22 to 64 years (IRR = 0.97; 95% CI = 0.95, 0.99). However, primarily enforced handheld bans on all drivers (IRR = 0.96; 95% CI = 0.93, 0.99) were significantly associated with traffic fatality reductions among this age group. Among individuals aged 65 years or older, primarily enforced texting laws showed a marginal reduction in traffic fatalities (IRR = 0.96; 95% CI = 0.90, 1.01), but this association did not reach statistical significance. Secondarily enforced laws were not associated with decreases in fatality counts among any age group.

TABLE 3—

Regression Results for Total Traffic Fatalities for Different Age Cohorts: United States, 2000–2010

Variable Aged 15–21 Years, IRR (95% CI) Aged 22–64 Years, IRR (95% CI) Aged ≥ 65 Years, IRR (95% CI)
Texting law, primary
 Bans all drivers 0.95* (0.91, 0.99) 0.99 (0.98, 1.02) 0.96 (0.90, 1.01)
 Bans young drivers only 0.89* (0.81, 0.98) 0.98 (0.92, 1.04) 0.97 (0.87, 1.07)
Texting law, secondary
 Bans all drivers 0.95 (0.85, 1.06) 1.02 (0.95, 1.09) 1.06 (0.93, 1.22)
 Bans young drivers only 1.10 (0.98, 1.23) 1.06 (0.99, 1.14) 0.94 (0.81, 1.09)
Handheld ban
 All drivers 1.01 (0.96, 1.07) 0.96* (0.93, 0.99) 1.03 (0.97, 1.09)
 Young drivers only 1.08 (0.93, 1.24) 0.96 (0.87, 1.05) 0.84 (0.69, 1.03)
Speed limit ≥ 70 mph 1.77** (1.23, 2.53) 1.53** (1.18, 1.99) 1.40 (0.89, 2.22)
Administrative license revocation 0.66* (0.43, 0.99) 0.62** (0.45, 0.85) 0.54 (0.29, 1.02)
Seatbelt law, primary enforcement 1.01 (0.97, 1.04) 0.99 (0.97, 1.02) 1.00 (0.96, 1.04)
Illegal per se at 0.08 BAC 0.99 (0.96, 1.01) 1.01 (0.99, 1.03) 1.06** (1.02, 1.09)
Graduated driver licensing law 0.94** (0.91, 0.98) 0.98* (0.95, 0.99) 0.98 (0.95, 1.02)
Gasoline prices, 2010 cents 0.99** (0.99, 0.99) 0.99 (0.99, 1.00) 0.99 (0.99, 1.00)
Per capita income, 2010 dollars 1.00 (0.99, 1.00) 1.00** (1.00, 1.00) 1.00 (0.99, 1.00)
State unemployment rate 0.98** (0.97, 0.99) 0.98** (0.98, 0.99) 1.00 (0.99, 1.01)

Note. BAC = blood alcohol concentration; CI = confidence interval; IRR = incidence rate ratio. Each model includes state, month, and year dummy variables as controls and accounts for state population estimates.

*P < .05; **P < .01.

Table 4 presents results for our examination of driver deaths only. These estimates provide insight into the extent to which the laws have a direct impact on drivers. Texting restrictions that banned only young drivers from texting and entailed primary enforcement had the greatest impact among drivers aged 15 to 21 years (IRR = 0.88; 95% CI = 0.79, 0.98). This level of enforcement was not associated with reductions in traffic fatalities among the other 2 age cohorts. However, texting bans on all drivers with primary enforcement were associated with traffic fatalities among drivers aged 65 years or older at the 95% confidence level (IRR = 0.93; 95% CI = 0.87, 0.99). Although texting bans were not associated with traffic fatality reductions among those aged 22 to 64 years, handheld bans on all drivers with primary enforcement were (IRR = 0.95; 95% CI = 0.91, 0.99). Furthermore, secondarily enforced laws, whether banning all drivers or only young drivers, had no effect on driver fatality reductions.

TABLE 4—

Regression Results for Driver Fatalities for Different Driver Age Cohorts: United States, 2000–2010

Variable Aged 15–21 Years, IRR (95% CI) Aged 22–64 Years, IRR (95% CI) Aged ≥ 65 Years, IRR (95% CI)
Texting law, primary
 Bans all drivers 0.95 (0.90, 1.01) 0.97 (0.94, 1.01) 0.93* (0.87, 0.99)
 Bans young drivers only 0.88* (0.79, 0.98) 0.99 (0.93, 1.05) 0.98 (0.87, 1.10)
Texting law, secondary
 Bans all drivers 0.91 (0.79, 1.05) 1.02 (0.94, 1.10) 1.10 (0.94, 1.28)
 Bans young drivers only 1.07 (0.94, 1.23) 1.05 (0.97, 1.15) 0.97 (0.81, 1.15)
Handheld ban
 All drivers 1.03 (0.96, 1.10) 0.95** (0.91, 0.99) 1.05 (0.97, 1.14)
 Young drivers only 1.04 (0.87, 1.24) 0.92 (0.83, 1.02) 0.92 (0.74, 1.15)
Speed limit ≥ 70 mph 1.34 (0.65, 2.79) 1.61** (1.11, 2.35) 2.70 (0.66, 11.15)
Administrative license revocation 0.48 (0.15, 1.59) 0.58* (0.36, 0.92) 0.50 (0.12, 2.01)
Seatbelt law, primary enforcement 1.00 (0.96, 1.04) 0.99 (0.97, 1.02) 1.01 (0.96, 1.06)
Illegal per se at 0.08 BAC 0.98 (0.95, 1.01) 1.01 (0.99, 1.03) 1.06** (1.01, 1.10)
Graduated driver licensing law 0.96* (0.92, 0.99) 0.97* (0.95, 0.99) 0.98 (0.94, 1.03)
Gasoline prices, 2010 cents 0.99** (0.99, 0.99) 0.99 (0.99, 1.00) 0.99 (0.99, 1.00)
Per capita income, 2010 dollars 1.00* (1.00, 1.00) 1.00* (1.00, 1.00) 1.00 (0.99, 1.00)
State unemployment rate 0.97** (0.96, 0.98) 0.98** (0.97, 0.99) 0.99 (0.98, 1.01)

Note. BAC = blood alcohol concentration; CI = confidence interval; IRR = incidence rate ratio. Each model includes state, month, and year dummy variables as controls and accounts for state population estimates.

*P < .05; **P < .01.

DISCUSSION

Three main findings emerged from our analyses. First, our results suggest that there are substantive differences in the effectiveness of laws that are primarily enforced versus secondarily enforced. Consistent with Abouk and Adams’s20 findings, we note that secondary laws, whether banning all drivers or only young drivers, do not appear to be effective in reducing traffic fatalities. In fact, though not statistically significant, states with secondarily enforced laws saw increases in total fatality counts. It may be that drivers in states with secondary laws perceive that their chances of being cited for texting are slim and consequently have not substantially curtailed their texting-while-driving behaviors. Moreover, although states with a texting law saw significant decreases in total traffic fatalities compared with states without such a law, our results suggest that the states with primarily enforced laws are seeing the most improvements in terms of mortality reduction. Thus, our findings suggest that states with secondarily enforced texting laws should consider adjusting their bans to entail primary enforcement. Furthermore, states that have not enacted any legislation on texting while driving should consider doing so at the primary enforcement level.

We constructed age cohorts to investigate whether texting laws, which are arguably aimed at younger individuals given their greater proclivity to text,39 even while driving,39,40 yield significant reductions in traffic fatalities among younger people. Our analyses indicate that primarily enforced texting laws are associated with fatality reductions among younger individuals, both drivers and nondrivers. Thus, our second main finding is that our results provide strong evidence that the primarily enforced texting laws seem to be reaching the intended subpopulations who are most at risk for texting while driving.40

Although not the primary focus of this study, our analyses also indicated that states with handheld bans for all drivers saw reductions in traffic fatalities during the study period, particularly among those aged 22 to 64 years. This was true for both drivers and nondrivers in this age group. Thus, the third main finding was that, although texting laws are most effective for reducing traffic-related fatalities among young individuals, handheld bans appear to be most effective for adults. Consequently, to the extent that states are also interested in reducing traffic fatalities among adults, they should consider enacting legislation that bans handheld use of mobile devices.

We mention that some of the other state laws included in our models were not statistically significant even though previous research has found alcohol and seatbelt policies to be significantly associated with reductions in traffic fatalities.41,42 Given that our study period is shorter and more recent than those of previous studies, we primarily captured within-state variations in texting policies and not within-state variations in the other, less recently established policies. We therefore had weak power for measuring the effect of these other laws on traffic fatalities.

We note some limitations. Given the relative novelty of many texting laws, we were unable to examine the long-term impact on fatalities—rather, just what happened in the study period. Moreover, our study did not determine whether states who were first to ban texting had differential impacts on fatalities than states who adopted these bans later. Future research should examine this issue. Furthermore, our study did not distinguish between fatalities resulting from single-occupant versus multiple-occupant collisions. Given some evidence that drivers engage in safer driving behavior in the presence of passengers,43–45 future research should examine whether the prevalence of fatalities resulting from multiple-occupant collisions is affected differently by texting bans than those resulting from single-occupant collisions. Additionally, this study ultimately provided evidence for the impact of texting laws on just 1 traffic outcome—death. Texting laws may have an influence on less serious traffic outcomes including hospitalization, emergency visits, and acute injuries. Moreover, our study did not examine other nonclinical outcomes such as property damage. Future research should investigate these relationships to further expand our understanding of the impact of texting laws. Furthermore, because of the lack of national data on traffic fatalities that were a result of a driver texting, we are unable to explicitly say that reductions in traffic fatalities reflect reductions in texting-related traffic fatalities. Last, we note that our study examined the presence of texting laws in states, not the extent to which the laws are actually enforced. This is an important distinction given the noted difficulties faced by law enforcement personnel in enforcing these bans.46,47 Despite these limitations, we believe that this study adds to the limited knowledge concerning the impact of texting-while-driving laws and could potentially inform efforts to enhance enforcement.

Human Participant Protection

This study was approved by the University of Alabama at Birmingham’s institutional review board.

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