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Risk Management and Insurance Review

C© Risk Management and Insurance Review, 2018, Vol. 21, No. 3, 413-433 DOI: 10.1111/rmir.12110

FEATURE ARTICLE

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE: AN INITIAL ASSESSMENT Martin F. Grace Juliann Ping

ABSTRACT

This article explores the impacts of new auto technologies and their financial effects on insurance markets, a set of complementary services, and state rev- enues. We use data from the National Association of Insurance Commissioners, the National Highway Traffic Safety Administration’s Fatality Analysis Report- ing System, the Bureau of Justice Statistics, and the Census Bureau to create a data set that links industry and state finance variables to a set of variables related to driving. Our purpose in this initial assessment is to estimate the sen- sitivity of these financial variables to different indices of driving including the number of drivers, the number of cars licensed per year, and the number of vehicle miles driven. The resulting estimates are used to create elasticities to show how sensitive each is to changes brought about by the new technologies.

INTRODUCTION One of the most salient social risks, the risk of automobile crashes, is predicted to change with the introduction of new driverless or autonomous technologies. Also, other benefits associated with of driverless technologies may also reduce other costs associated with driving such as its associated pollution, the demand for oil, and the widespread productivity losses due to both traffic congestion and crashes. This article attempts to document the effect of driverless technologies on insurance markets specifically as well as state revenues and services related to automobile insurance. As a first endeavor, we try to analyze the macro effects of a reduction in driving activity and its corresponding impact on losses and other types of accident-related expenditures.

The United States experiences a significant cost due to auto crashes. A National High- way Traffic Safety Administration (NHTSA) report (2015) estimates the cost of driving crashes to be about $836 billion in 2010 (in 2018 dollars, $960 billion), which—in addition to the deaths, injuries, and property damages—also includes costs due to pollution, con- gestion, and reductions in quality of life. One of the reasons autonomous vehicles are so

Martin F. Grace is the Harry Cochran Professor of Risk Management at Fox School of Business, Temple University, Philadelphia, Pennsylvania; e-mail: martin.grace@temple.edu. Juliann Ping is a research assistant in the Department of Risk, Insurance and Healthcare Management at Fox School of Business, Temple University, Philadelphia, Pennsylvania.

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interesting is because of their potential for significantly reducing these costs. Evidence that even the lowest level of automation, so-called Level 1 automation, which implies one automatic activity (like automatic braking systems [ABS], blind spot monitoring, lane departure warning, or forward collision warning) has reduced crashes.1

Manufacturers claim that self-driving cars will be significantly safer than human-driven cars as driverless technology will allow for more precise driving and quicker deci- sion making. This increase in safety potential reduces the propensity for auto crashes (Litman, 2014). However, self-driving cars in combination with human-driven cars on today’s public roads may temporarily hinder the ideal prospects of a driverless society. Conjecture exists that most self-driving cars will produce lower noxious emissions as the cars will be designed as lightweight, two-passenger vehicles (Burns, 2013). Further, these cars could be 10 times more energy efficient than today’s typical car (Burns, 2013). Additionally, since one need not "drive" a self-driving car, the opportunity cost of transit will be diminished (Frisoni et al., 2016). Driverless technology thus becomes an attractive opportunity for automakers and consumers alike.

By utilizing the Society of Automotive Engineers (2016) international levels and defi- nitions of driving automation, we can approach the projections of autonomous driving with more uniformity and clarity. The levels are as follows:

1. Level 1: driver assistance,

2. Level 2: partial automation,

3. Level 3: conditional automation,

4. Level 4: high automation,

5. Level 5: full automation.

Different projections have been announced by various vehicle and auto parts manufac- turers on their products and plans. Table 1 illustrates the level of automation that each manufacturer expects to release in the form of a fleet of cars for either taxis or commercial sale.

As seen in Table 1, the majority of manufacturers estimate their releases of Level 4 vehicle technology to be by 2020. Waymo, the division of Alphabet, has already released a fleet of autonomous cars without safety drivers for testing in the Phoenix, Arizona metro area (Ohnsman, 2017). Levels 1 and 2 are being used in vehicles today. These technologies range from ABS to lane monitoring to unassisted parking. Level 3 represents a car that the driver can shift certain functions to the vehicle to carry out but is still able to take over if needed.

Levels 4 and 5 have significant automation capabilities, and the difference between them lies in the fact that Level 5 automation requires self-driving cars to be reliable in all driving conditions (i.e., bad weather or a rural environment). Before cars advance

1 ABS, for example, while not effective in reducing fatal crashes, reduce nonfatal crashes by 6-8 percent (NHTSA, 2009). See also Harper et al. (2016) who conclude that these Level 1 technologies could reduce fatal crashes by over 10,000 per year.

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 415

TABLE 1 Automation Level Projections According to Manufacturers

Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Audi 2 3 3 3.5 3.5 3.5 3.5 3.5 3.5 3.5 4 4 4 4

Daimler/Uber 4 4 4 4 4 5 5 5 5 5 5

Delphi/MobilEye 4 4 4 4 4 4 4 4 4 4 4 4

Ford/Lyft 4 4 4 4 4 4 4 4 4 4 4

General Motors 4

Hondaa 2 2 2 3 3 3 3 3 3 3 3 3 3 3

Hyundai 2 4 4 4 4 4 4 4 4 4 4 4.5

Kia 2 2 2 3 3 3 3 3 3 3 3 3 3 4

Mercedes-Benz 3

Nissan 3 3 3 4 4 4 4 4 5 5 5 5 5 5

NuTonomy (Delphi) 4 4 4 4

Nvidia 5 5 5 5 5 5 5 5 5

Otto (Uber) 5 5 5 5

Tesla 3 4

Toyota 3 3 3 3 3 4 4 4 4 4 4

Volvo/Uber 4 4 4 4 4.5

Source: Jaynes (2016), Kessler (2017), Khalid (2017), Kubota (2015), McFarland (2016), Payne (2017), Ron (2017), Ross (2017), Valdes-Dapena (2017), Walker (2017), Yu, Kim, and Ananthraraman (2017), Ziegler (2016), and Zimmer (2016). aHonda estimated that Honda vehicles would experience no crashes by 2040.

to the Level 5 technology standard, we can at least expect that Level 4 technology will be increasingly utilized in densely populated cities and preprogrammed routes through large fleets and limited navigation routes.

Widespread implementation of self-driving vehicles into the market will likely be limited due to initial high costs, slow fleet turnover (cars currently on the road), and design of safety requirements (Litman, 2014) and the actual implementation of these requirements (NCOIL, 2017). Further, any fatal accidents caused by experimentation like that of the experimental Uber car in the spring of 2018 may cause temporary halts to technological experimentation until immediate safety concerns are met. Together, this creates a poten- tially significant cost increase and a steep learning curve to the large-scale adoption of autonomous vehicles by everyday consumers.

While the costs of implementation are significant, some markets are directly connected to the growth of the use of self-driving vehicles. Arguably, self-driving cars will be safer and less expensive to insure. Google claimed that its self-driving Waymo will cut U.S. auto crashes and deaths by 90 percent (Poczter and Jankovic, 2014).

Auto insurers will see a decrease in claim payouts, and there is a suggestion that we can expect premiums to drop significantly to as low as 90 percent of today’s typical

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car insurance premium (Poczter and Jankovic, 2014). Because insurance is typically a “cost + markup” business, the reduction in costs will reduce the total profitability of auto-related insurance.

Other industries will likely be affected. The healthcare industry, for example, could lose patients and revenue because of the decrease in crashes promised by the increase in safety of the new vehicles. However, there could be more frequent patient travel as travel becomes affordable for patients who currently may miss appointments due to cost, inability to drive, or a failure to secure a reliable driver for appointments. This will save an estimated $19 billion in missed appointment costs (Bits and Atoms, 2017). Also, at the same time, there will be significant gains in social welfare if fatalities and injuries are avoided. This translates into higher income for society.

State governments will experience a decrease in traffic tickets and fines. Thereby, there will be less demand for highway patrol officers. If advanced traffic management is im- plemented (through cloud computing), there will be no need for traffic lights, parking meters, or other utilities. Regarding public transit, paratransit may be reconstructed to diminish the current large operating deficits as autonomous vehicles better serve pas- senger needs (Bits and Atoms, 2017). Further, ride sharing and autonomous technologies may substitute for regional transportation systems.

Urban property values could also decline as a result of the fall in demand of city parking lots, which will lead to more city space and less commercial revenue attributed to parking (Poczter and Jankovic, 2014).

The transition from today’s vehicles to self-driving vehicles could reduce our reliance on oil. To the extent that self-driving cars will be more energy efficient, induce more car sharing, or use electric technologies, gas tax revenues could drop. While the demand for roads will not likely change, policymakers may have to find another source for highway funds. For example, the State of Georgia adopted an annual license fee for electric cars that was supposed to replace the lost revenue from the gas tax.2

Autonomous vehicles will also likely prove a detriment to employment in the taxi and commercial trucking industry. Self-driving taxis will replace the need for taxi drivers and, self-driving truck fleets will replace the need for truck drivers. The trucking industry has had a historical labor shortage for good drivers and this technology could solve one of its most vexing problems and increase efficiencies. Trucks are expected to be at the forefront of widespread fleet conversion to driverless technology, which is then to be followed by taxis (McKinsey, 2016). Additionally, McKinsey (2016) predicts that one-third of new trucks sold worldwide will be automated by 2025: a huge threat to the employability of truck drivers. Also, the use of autonomous farm implements in agriculture will change (even more) the economics of farming.

Because of the gradual introduction of new technology, there will be displacements in the insurance industry and other industries. This article primarily focuses on how the substitution of current technologies might affect the insurance industry as well as some complements to the provision of auto insurance.

2 The tax is $200 per year for private passenger autos and $300 per year for commercial vehicles. See, for example, https://www.afdc.energy.gov/laws/11602.

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 417

FIGURE 1 Alcohol Fatalities and All Fatalities Over Time [Color figure can be viewed at wileyonlinelibrary.com]

Source: NHTSA, FARS.

The article proceeds as follows. First, we examine the current trends in fatalities, injuries, safety-related expenditures, health and hospital expenditures, and insurance losses. We then estimate a series of reduced-form models attempting to obtain elasticity estimates between measures of accident cost and driving activity. We then undertake a rough wel- fare analysis based on our estimates. Finally, we conclude and present some additional thoughts about future work.

BACKGROUND ON MARKETS AND SERVICES LIKELY AFFECTED BY DRIVERLESS TECHNOLOGY NHTSA collects and disseminates a large amount of data regarding traffic crashes, which we use in our analysis. However, it is illustrative to look at some trends to put our analysis in context. We first present summary information to provide background on the estimation of how insurance markets may change with the introduction of driverless technology.

Figure 1 shows the times series of fatal crashes over the last 15 years. At around 2004, we see a drop in fatal crashes, but in 2014, there is an unexpected upturn in total fatalities. This trend is conjectured to be due to distracted driving, and it has been cited by insurers as a rationale for increasing insurance prices (Scism and Friedman, 2017). Notably, alcohol-related fatalities are decreasing, and this is likely due to the tremendous enforcement efforts, new legislation, and public awareness. If driverless cars are going to be impactful, it will likely reduce fatalities related to alcohol-related car accident, distracted driving, and accidental causes.

Figure 2 shows the state-level loss ratio over time for the personal lines (auto damage and liability) as measured by the direct losses incurred divided by the direct premiums earned for both private passenger and commercial. The loss ratio appears to vary but has a flat trend for the personal auto damage line of business. Also, if we look at the within- year variation as measured by the vertical dotted line, there is considerable variance

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FIGURE 2 Loss Ratio Over Time [Color figure can be viewed at wileyonlinelibrary.com]

Source: NAIC Annual Statements.

among the states each year. The vertical dotted lines at each year represent the range of the standard deviation of the loss ratio (in effect the mean loss ratio +/– 1 standard deviation) among the states. If we think of the loss ratio as an imperfect measure of profitability (lower loss ratio implies higher premiums per dollar of loss), we see that while it goes up and down, the trend over the last two decades (solid blue line) is about flat.

In contrast, in the auto liability lines of business, we see that there is some variability over time, but that the time trend in the loss ratio is downward sloping. The loss ratio is declining (implying higher profits per unit of loss). So, if one makes a simple conjecture, the auto physical damage line of business is about the same level of profitability as in the past, but the auto liability business is increasing in profitability. However, this is somewhat simplistic as the liability line is a relatively long tail line and the eventual profitability will not be known until sometime in the future. This implies that if driverless cars do a better job of providing safer transportation, the insurers will lose revenues and their associated costs from both lines, but the loss of profits from liability lines may be greater if we make the strong assumptions of holding interest rates and inflation constant.

We now focus on losses in another dimension. Figure 3 shows the real per capita losses for auto physical damage and auto liability over time.3 For both lines (damage and liability), we see declining real per capital losses. This is consistent with a

3 To calculate the real per capita levels shown in the figures and used in the empirical anal- ysis below, we employ the CPI ALL Items series from the Bureau of Labor Statistics where 1982–1984 = 100.

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 419

FIGURE 3 Real Per Capita Losses Incurred [Color figure can be viewed at wileyonlinelibrary.com]

Source: NAIC and U.S. Census Bureau.

general decrease in deaths shown in Figure 1. We also see that in the most recent years, both lines of business experienced increases in real losses which is consistent with the slight increase in fatalities and injuries, and property damage likely due to distracted driving.

Figure 4 shows the real per capita hospital (Panel A) and health (Panel B) expendi- tures by state. In both panels, we see an upward trend over the period from 1996 through 2013. Hospital and health expenditures are increasing for many reasons, per- haps, in part, because of auto crashes. Reducing the likelihood of crashes could re- duce the cost of health care, which would also affect the price of health insurance. Figure 5 shows the trend in the real per capita cost of judicial administration (Panel A) and police services (Panel B). Both show an upward sloping trend. These costs could be rising for several reasons, including the cost of administering and policing auto crashes.

Finally, we come to another possible effect to consider. This would be a disruption of state insurance tax collections. Most states have premium taxes, which are a gross receipts tax on the premiums written within the state. In 2016, the property–casualty industry paid approximately $11 billion in premium taxes to the states (S&P Global Market Intelligence, Combined Industry, Expense Exhibit, 2016). This represents about 1.25 percent of the average state’s revenue. Further, as we can see in Figure 6, the average amount of premium taxes to registered vehicles is ranges from about $11 to $16 over the recent past. This series is likely also driven by general economic conditions, but we can see that as the premium for auto insurance declines (as the risk of crashes is reduced) there will be some reduction on state revenues.

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FIGURE 4 Real Per Capita Health Care Costs [Color figure can be viewed at wileyonlinelibrary.com]

Source: Area Resource File.

FIGURE 5 Real Per Capita Expenditures on Judicial Administration and Police [Color figure can be viewed at wileyonlinelibrary.com]

Source: Bureau of Justice Statistics.

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 421

FIGURE 6 Ratio of Total Premium Taxes Collected Per Capita [Color figure can be viewed at wileyonlinelibrary.com]

1995 2000 2005 2010 2015

Taxes Per Capita

A vg

I ns

ur an

ce T

ax es

P er

C ap

ita ( S

td E

rr or

is S

ha de

Source: NAIC and Watanabe (2017).

INITIAL EMPIRICAL ESTIMATES OF THE EFFECT OF NEW TECHNOLOGY As the current driverless car technology is not sufficient to get a true indicator of the changes in costs, we resort to a thought experiment. If we look at the effect of measures of driving on various levels of costs or losses, we can see how sensitive these expenditures are to changes in driving-related activities. We have three sources of evidence for driving activity: the number of licensed drivers in a state in a given year, the number of vehicle miles driven in a state in a given year, and the number of registered vehicles in the state in a given year (data retrieved from NHTSA). These three variables are highly correlated, so we use them independently in our analysis below. Because the driving indices change over time and differ among states, we can assess how each of them affects our variables of interest and use these differences to predict the effect of a change in driving on expenditures or losses.

The typical model we employ to obtain our estimates use the following reduced form:

log(yst ) = α + β log(Ist ) + ωZst + ηt + εs + μ.

This is a fixed-effect model where we have time fixed effects (t), state fixed effects (s), and a random error for each observation. The variable log(y) is a dependent variable of interest, say direct losses incurred in state (s) and year (t). Ist is an index for the variable we believe might be related to y. We consider, as our index of driving, the log of the number of drivers in a state, the log of the number of vehicle miles driven in a state, and

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the log of the number of cars in the state. The coefficient β is the effect of the driving index variable on our variable of interest. In this regression, the coefficient is interpreted as an elasticity so that a given percentage change in the driving index can be interpreted as a β percent change on y. For reference proposes, we refer to this style of a model as a log–log model. This estimated β is what we will report and discuss in the empirical section below. In turn, Z is a vector of explanatory variables that we obtain that varies over time and among the states. We estimate numerous versions of these models using state real per capita income, the percent of miles each year driven on rural highways, and a variable describing the auto insurance regulatory stringency in the state.4 We also estimate this model using weighted least squares (WLS) to minimize the effect of the strong heteroskedasticity within the data. We use, as a weight, the average of the state’s total vehicles miles driven over the period to mitigate the heteroskedastic effect and then we use robust standard errors to account for any other unknown heteroskedasticity. We only show the variables of interest in the tables below, in part because the indices of driving variables are our primary interest.5

We also employ generalized linear models (GLMs) to obtain our elasticity estimates. Certain models such as the Poisson or the negative binomial model account for the fact that the some of the data we employ are count data and are not distributed normally. We use the negative binomial regression (Cameron and Trivedi, 2010) to estimate a conditional two-way fixed effects model of the form:

E [yst ] = f (exp(β log(Ist ) + ωZst + ηt + εs + μ)).

In these negative binomial estimations, we calculate the elasticity by examining the marginal effect of the regression estimates for the variable of interest.

In the tables below, there are two tests of significance mentioned. The first is the tradi- tional test that the coefficient of interest is different from zero (β = 0). This will provide us with information regarding the size of the elasticity estimate and its difference from zero. However, if one thinks about some of these relationships, one could conjecture that the change in the driving index should be proportional to the reduction in fatalities or injuries. In this case, the test would be that the coefficient is different from one (β = 1). Both test results are shown in the tables.6

4 We employ Harrington’s (2002) description of regulatory stringency updated to 2015. We use this just as a control variable for the insurance related regressions to control for the regulatory environment within the state. We also use the real per capita income from the Bureau of Economic Affairs (https://apps.bea.gov/iTable/index_regional.cfm) as another state control variable with the population estimates from Watanabe’s (2018) curated Census state population estimate series (https://scholar.harvard.edu/awatanabe/data), and the percent of total miles driven in rural areas of the states from the Federal Highway Administration’s Highway Statistics Series, table VM-2 (https://www.fhwa.dot.gov/policyinformation/quickfinddata/qftravel.cfm). In most of our regressions, we employ state fixed effects to control for other differences between the states.

5 Full regression results are available in Stata log files in pdf format for each table. These are available as online materials.

6 For the case of whether the coefficient is different from one only those that are different at the 10 percent level or better are indicated by a (†) symbol. This is merely to increase the readability

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 423

TABLE 2 Regression Elasticity Estimates for Types of Loss Events by Driving Index Variables (Ist)

Ist

Dependent Variable (y) Estimation Method Drivers Cars Miles

Panel A

Log total fatalities 2-way FE 0.299 ns 0.054 * 0.662 **

Log total fatalities WLS 2-way FE 0.342 ** 0.117 0.662 0.825 *** a

Total fatalities Conditional NB 2-way FE 0.068 ** 0.029 *** 0.004 ns

Panel B

Log total fatalities/pop 2-way FE 0.057 0.023 0.349

Panel C

Log total fatal crashes 2-way FE 0.496 ** 0.081 ** 0.941 *** a

Log total fatal crashes WLS 2-way FE 1.322 *** 0.098 * 1.233 *** a

Total fatal crashes Conditional NB 2-way FE 0.167 *** 0.002 ns 0.002 ns

Panel D

Log total alcohol-related

fatalities

2-way FE 0.216 ns 0.079 * 0.916 *** a

Log total alcohol-related

fatalities

WLS 2 × FE 0.280 ns 0.104 1.419 *** a

Total alcohol fatalities Conditional NB 2-way FE 0.549 *** 0.000 ns 0.005 *

Panel E

Log total injuries 2-way FE –0.064 ns 0.051 ns 0.767 *** a

Log total injuries WLS 2 × FE 0.008 ns 0.038 ns 1.073 *** a Total injuries Conditional NB 2-way FE –0.006 ns 0.003 ns 0.043 ***

Note: ns: no significance at reasonable levels. All standard errors are robust. ***Significant at .01 level. **Significant at .05 level. *Significant at .10 level. aTest of hypothesis that β = 1, is not rejected.

Table 2 shows the results of our estimates that focus on fatalities and injuries. The main question of interest is: does an index measure of driving activity relate to these fatalities

of the table. All estimate results including tests of the coefficient being different from zero are provided in the online materials.

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or injuries? We find that the number of drivers is related (in a statistical sense) to total fatalities, total fatal crashes, and single and multiple car fatalities.

In Panel A of Table 2, we examine two types of models. The WLS log–log model shows the estimated elasticity for drivers and total fatalities is 0.342. Thus, a 10 percent reduction in drivers in the average state yields a 3.42 percent reduction in fatalities. The unweighted log–log panel estimate is close to the WLS estimated but is just outside standard levels of statistical significance. Agreement in size suggests some robustness in the estimate. In contrast, the negative binomial model provides an elasticity estimate slightly lower than the WLS or log–log model. All three methods provide a relatively consistent point estimate even though not all are significant. Looking at other indicators of driving, we see that the number of cars in a state is not associated with fatalities, but that miles driven are statistically related to fatalities. One might suggest that the miles driven index is likely to be the most relevant for our analysis as it is the index most related to driving intensity. The number of licensed cars and drivers is potentially related to fatalities, but miles driven may be more directly related. We find that the elasticity of miles driven is generally higher for all models estimated in Table 2, suggesting that injuries and fatalities are more sensitive to changes in miles driven. For miles driven, the log–log models (both weighted an unweighted) are higher, suggesting a 10 percent decrease in miles driven will result in a reduction in fatalities in the range of 6.62–8.25 percent using the point estimates of the elasticities.7 Using our thought experiment, suppose there is a 10 percent reduction in miles driven by human-driving vehicle (and replaced with a corresponding “safe” miles driven by a self-driving vehicle). This would imply a reduction in fatalities of between 2,478 and 3,090 per year.

Panel B of Table 2 provides evidence on a per capita basis using a log–log model. We see that these estimates are lower but of the same order of magnitude as those in Panel A but none are significant. Panel C shows the analysis using fatal crashes as a dependent variable. For miles driven, we see something like a unit elastic result—a 10 percent reduction in miles driven is related to a reduction in crashes by 9.41 percent to 12.33 percent. One cannot reject the hypothesis that these estimates are different from one.

Panel D of Table 2 shows the models for fatal alcohol crashes. Again, we see a near unit elasticity measure for alcohol crashes using fixed effects and WLS log–log models. A 10 percent reduction in miles driven is related to a 9.1–14.2 percent reduction in alcohol- related fatalities, which translates to a range of deaths avoided of 1,075–1,675. Finally, in Panel E, we also see a near unit reduction injuries from reducing miles driven. A 10 percent reduction in human-miles driven yields a reduction in injury causing accidents of 6,940–9,100 accidents per year.

Table 3 shows a similar table describing the relationship between the various indices of driving activity and some state expenditures on safety and health. We see that our index variables for the number of miles driven are positively associated with judicial expenditures, police expenditures, and health expenditures. Of the three expenditures,

7 One final note to discuss is the WLS log–log elasticity estimate is not significantly different from one. For the weighted regression, we can reject the hypothesis that the coefficient is equal to one at standard levels of significance (p = 0.186).

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 425

TABLE 3 Regression Elasticity Estimates of Expenditures Related to Indices of Driving

Ist

Dependent Variable Log(y) Estimation Method Drivers Cars Miles

Health expenditures 2 × FE 0.738 nsa –0.111 ns 0.501 ns WLS 2 × FE 0.978 ** a –0.027 ns 0.518 **

Judicial expenditures 2 × FE 2.760 *** 0.132 ns 0.267 *** WLS 2 × FE 0.401 ns 0.075 ns 0.455 *

Police expenditures 2 × FE 0.493 nsa 0.089 ns 1.143 ** a WLS 2 × FE 0.400 *** 0.104 ** 0.725 ***

TABLE 4 Regression Elasticity Estimates of Various Public Safety Measures

Ist

Dependent Variable Log(y) Method Drivers Cars Miles

EMT employment 2 × FE –0.077 ns –0.118 ns –0.005 ns WLS 2 × FE 0.100 ns –0.283 *** 0.347 ns

HW patrol expenditures 2 × FE –0.917 ns –0.055 ns 0.021 ns WLS 2 × FE –0.917 ** –0.080 ns 0.027 ns

Fire fighter employment 2 × FE 0.315 ns 0.077 ns –0.477 ns WLS 2 × FE 0.691 ns 0.088 ns 0.634 ns

Note: ns: no significance at reasonable levels. All standard errors are robust. ***Significant at .01 level. **Significant at .05 level. *Significant at .10 level.

expenditures on police seem most sensitive to changes in miles driven with a 10 percent reduction in miles driven being associated with a 7.25–11.43 percent reduction in po- lice expenditures. Health and judicial expenditures are statistically associated with a reduction in miles driven but are less sensitive.

Other safety-related expenditures are also not related to our driving indices in the hy- pothesized way. We examine state-level aggregated employment of emergency medical technicians (EMT), firefighters, and the real state expenditures on the highway patrol.

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TABLE 5 Elasticity Estimates for Auto Insurance Direct Losses

Ist

Dependent Variable Log(y) Method Drivers Cars Miles

Real personal auto damages 2 × FE 0.706 *** a 0.070 ns 0.805 *** WLS 2 × FE 0.851 *** a 0.135 *** 0.820 *** a

Real auto personal liability 2 × FE 0.254 ns 0.037 ns 0.705 *** WLS 2 × FE 0.293 *** 0.734 *** 0.043 ns

Real auto commercial damages 2 × FE 1.294 *** 0.052 ns 1.377 *** a WLS 2 × FE 1.680 *** 0.240 *** 1.456 ***

Real auto commercial liability 2 × FE 0.750 *** 0.019 ns 1.300 *** a WLS 2 × FE 0.856 *** a 0.027 ns 1.348 ***

As one would expect, these expenditures may relate to many variables other than our indices of driving activity. We were not able to discern relationships in the hypothesized direction. This is likely because dealing with automobile crashes, while an important part of these public safety officials’ jobs, is not important enough to have a statisti- cal effect on employment or expenditures. Also, we have the smallest amount of data (4 years) for these series, and there may not be enough variation within the period to be able to identify relationships.

Table 5 focuses directly on the insurance industry. We examine the effect on real di- rect losses incurred for personal auto and commercial auto damage and corresponding liability coverage. Unlike the tables above, the direct losses incurred are more likely to be directly related to driving activity whether it is the number of drivers, the number of cars, or the miles driven in a state. Again, if we focus on the measure of intensity- miles driven, we see near unit elastic relationships. A 10 percent reduction in miles driven is associated with a 0.80–0.82 percent reduction in real personal auto dam- ages. The most elastic seems to be real auto commercial damages—a 10 percent re- duction in miles driven would be associated with a reduction in direct loses between 13.77 and 14.56 percent reduction incurred losses. All the lines of business are rela- tively sensitive to reductions in the number of drivers compared to miles driven or the number of cars registered. It is also important to note that the numbers of drivers are also more likely to be significantly related to the losses incurred for all lines of business.

Table 6 looks at the insurance premium tax, the gas tax, and tax or fee revenues from motor vehicle licenses. These results suggest that a 10 percent reduction in miles driven will yield a 6.33 percent reduction in the premium tax collected for auto liability for

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 427

TABLE 6 Elasticity Estimates of Effect on State Revenues

Ist

Dependent Variable Log(y) Method Drivers Cars Miles

Taxes from auto liability personal 2 × FE 0.314 ns 0.067 ns 0.633 ** a WLS 2 × FE 0.413 *** 0.602 ns 0.636 ***

Taxes from auto damages personal 2 × FE 0.828 *** a 0.166 * 0.921 *** a WLS 2 × FE 0.823 *** a 0.150 *** 0.812 ***

Taxes from auto liability commercial 2 × FE 0.515 ns –0.021 ns 0.867 *** a WLS 2 × FE 0.119 ns –0.002 ns 0.370 ***

Taxes from auto damages commercial 2 × FE 0.824 ** a 0.066 1.277 *** a WLS 2 × FE 0.863 *** a 0.286 *** 0.872 *** a

Tax revenue from MV licensesb 2 × FE 2.466 *** 0.117 ns 0.327 *** WLS 2 × FE 2.267 *** 0.127 ns 0.286 ***

Tax revenue from fuelb 2 × FE 0.912 *** 0.009 ns 0.162 *** WLS 2 × FE 0.967 *** a 0.032 ns 0.156 ***

personal passenger lines. Consistent with our results in direct losses incurred, commer- cial policies are slightly more sensitive than the personal auto policies regarding drivers. For small changes in the number of drivers with driverless car technology, there will likely be real changes to auto losses as well as premium taxes collected.

The last two taxes shown in Table 6 are revenues from car tags and revenues from the gas tax. These models were estimated slightly differently from the others as they were estimated without state fixed effects. There are about 40 states with these taxes, and we had data for 4 years, so estimating without the state fixed effects as there was not much variation over time. Tag revenue is very elastic (over 2) for drivers—thus a 10 percent reduction in licensed drivers is associated with a 24.66 percent reduction in tag revenues. It is interesting that we do not see this same relationship with registered cars. We do see a relationship in the 3 percent range for miles driven. For fuel, we seen unit relationships for drivers and inelastic relationship with miles driven where a 10 percent reduction in miles driven is associated with a 15.6–16.2 percent reduction in tax revenues from gas taxes.

In addition to state revenues, insured losses, and public expenditures other industries may be affected by a reduction in crashes. We obtained data from the Bureau of Labor

428 RISK MANAGEMENT AND INSURANCE REVIEW

TABLE 7 Elasticities From Related Industries

Ist

Dependent Variable Log(y) Method Drivers Cars Miles

Employment in law offices 2 × FE 0.191 ns 0.064 *** 0.512 *** WLS 2 × FE 0.046 ns 0.033 *** 0.427 ***

Employment in auto repairs 2 × FE 0.195 ns 0.024 ns 0.320 *** WLS 2 × FE 0.194 *** 0.026 ns 0.320

Employment in auto parts 2 × FE 0.102 ns 0.012 ns 0.264 ** WLS 2 × FE 0.102 ** 0.013 ns 0.265 ***

Employment in places serving alcohol 2 × FE 0.023 ns –0.014 ns 0.458 * WLS 2 × FE 0.039 ns –0.079 0.395 **

Note: ns: no significance at reasonable levels. All standard errors are robust. ***Significant at .01 level. **Significant at .05 level. *Significant at .10 level.

Statistics Quarterly Census of Employment and Wages program. These data are the underlying data for the unemployment compensation system. It collects for each location, the wages and employment of workers at the firm and are aggregated to the NAICS level by state. We obtained employment for the NAICS codes for law offices (5,411), auto body shops (8,111), auto parts stores (4,231), and places serving alcohol (7,224). We find that law office employment is sensitive all three driving indices, auto body repair is sensitive to the number of drivers and the number of miles, and auto parts employment is not significantly associated with any driving index. These employment figures, for the case of law offices, include lawyers and their staff. A 10 percent reduction in drivers yields a 5.12 percent reduction in law office employment. That translates to approximately 1,100 fewer employees in law offices for the average state. For auto repairs, we see relatively inelastic results for drives and miles. The same is true for auto parts. Finally, for employment in places that serve alcohol, the thought experiment is a bit different. If consumers could go to a bar or restaurant without worrying about driv- ing home with the risk of a DUI, they might be encouraged to do so. Thus, a 10 percent increase in miles driven is related to a 3.95–4.58 percent increase in employment in bars and restaurants serving alcohol. This is still inelastic, but significantly different from one too.

Finally, one can do a relatively simple welfare analysis from the data that are summa- rized in Table 8. We assume that self-driving vehicles of Level 4 and 5 technologies are introduced into the market, and a suitable proportion (say 10 percent) of miles driven are completed in autonomous vehicles. Thus, if 10 percent fewer miles are driven, based on our regression estimates from Table 2, total fatalities in the United States will decrease by 6.62 percent. Using 2016 data on fatalities, this translates to 2,480 lives saved in a given year. The Department of Transportation uses a value of statistical life estimate of

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 429

TABLE 8 Welfare Analysis of a Reduction of Miles Driven by 10 Percent

Cost Breakdown Related to Motor Vehicle Crashes in 2016

Assuming an Elasticity of 0.662 Between Fatalities and Miles

Driven

Total Fatalities,

2016

Saved Lives

Assuming a

10 Percent

Reduction in

Drivers

37,461 2,480

Breakdown of Costs Total Costs 2016

Estimated Total

Savings 2016

Value of a statistical life from USDOT $9,600,000 $359,625,600,000 $23,807,214,720

Total costs from NHTSA 100% $359,625,600,000 $23,807,214,720

% Medical costs due to injuries 10% $35,962,560,000 $2,380,721,472

% Congestion costs 12% $43,155,072,000 $2,856,865,766

% Property damages 31% $111,483,936,000 $7,380,236,563

% Property-damage-only 30% $107,887,680,000 $7,142,164,416

% Crashes not reported to police 17% $61,136,352,000 $4,047,226,502

Sources of Payment for Motor Vehicle Crash Costs in 2016

Motor vehicle crash costs paid by Total Costs Total Savings

% Federal revenues 4% $14,385,024,000 $952,288,589

% State and localities 3% $10,788,768,000 $714,216,442

% Programs subsidized by public

revenues (Medicare/Medicaid)

1% $3,596,256,000 $238,072,147

% Private insurers 54% $194,197,824,000 $12,855,895,949

% Individual crash victims 23% $82,713,888,000 $5,475,659,386

% Third parties (i.e., motorists delayed,

charities, healthcare providers)

16% $57,540,096,000 $3,809,154,355

Source: NHTSA, The Economic and Societal Impact of Motor Vehicle Crashes, May 2015; Depart- ment of Transportation, 2016.

$9.6 million (2016) for regulatory and policy decisions, and this yields a welfare savings of nearly $23.8 billion per year regarding lives saved. We do not have good data (e.g., a sufficiently long series) on crashes resulting in nonfatal injuries, but if these injuries were accounted for, the welfare cost of reduced personal injuries and property damages is even higher. Our assumption of the relationship between fatalities and miles driven understates the effect on all crashes as we do not include the costs of nonfatal crashes, so our estimate is conservative.

430 RISK MANAGEMENT AND INSURANCE REVIEW

We can also look at the incidence of accident costs. NHTSA undertook a very detailed eco- nomic analysis of the cost of traffic crashes and was able to apportion the costs to various actors who bear the burden of the losses. Table 8 also shows the distribution of the costs of crashes among the various actors who bear the costs of crashes. Insurers (auto, health, life) are the direct payers for most of the costs. However, that is exactly the role insurers serve in the market. The industry’s total saving from a 10 percent reduction in miles driven is $12.85 billion per year in the reduction of losses and loss adjustment expenses.

DISCUSSION AND CONCLUSIONS In this article, we obtain rough estimates of the relationships between expenditures and losses that are related to automobile crashes. This allowed us to estimate a conservative welfare savings of $23.8 billion per year assuming a 10 percent reduction in miles driven on the road. Some of the reduced-form estimates for the relationships between fatalities and injuries, insurance losses, and tax revenues are likely helpful for policymakers to understand the initial ramifications of changes in how a state’s population drives and the kind of autonomous technology that is necessary to reduce the cost of risk of crashes. This technology will affect many industries, but the insurance and health industries are likely to bear the largest brunt.

As this a first pass at this exercise, there are likely many improvements to the estimation approach and the construction of variables of interest. First, we make an implicit assumption that all safety technologies are perfectly safe. We know that that is not true. The way we approach our thought experiment is that a 10 percent reduction in miles driven is associated with an x percent reduction in one of our variables of interest. We could just add in some friction to adjust for the imperfect safety costs. However, we have no idea what those cost really are. In this article, we are making an order of magnitude assessment of how losses or expenditures react to changes in miles driven.

Second, it would be beneficial to separate commercial drivers from total drivers, com- mercial vehicles from private vehicles, and other more micro variables that may be related to emergency services such as ER and ER staffing. A third refinement is one that we break down healthcare expenditures into accident-related costs, insurance-covered costs, and taxpayer-covered costs.

Fourth, there is the possibility of disruption in other industries. For example, will we need high opportunity cost commercial real estate in central cities devoted to parking? What will be the effect on commercial trucking? Drivers will no longer need time to rest, and trucks will operate 24 × 7. Also, this new technology may affect employment in industries with high levels of defined benefit plans penetration. For example, Laughlin (2017) reports that the Philadelphia, Pennsylvania area transportation utility (SEPTA) is concerned about the substitute for bus rides for Uber or Lyft rides. Uber wait times have declined dramatically, and this has caused bus riders to shift to the ride-sharing services. This has implications for publicly financed transportation networks and their pension plans. O’Toole (2017) reports that the unfunded liabilities for just the health component of SEPTA employees’ retirement package is nearly as large as the organization’s current operating budget. SEPTA is not the only large transportation authority with these issues, and they are compounded by the backload of repair and maintenance necessary to bring these systems to an acceptable operating level. O’Toole asserts that three-fourths of the

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON INSURERS AND THE STATE 431

loss of riders in 2016 was due to people switching to ride-share services. These public transport services are also highly subsidized, and the ride-sharing services seem to com- pete. Autonomous vehicles and ride-sharing could replace portions of these networks, stranding investment and putting pensions at risk. Thus, while property–casualty in- surers are likely at the greatest risk of transformation due to driverless technology, other industries and governments are also likely to see changes in operations and services that they provide.

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