Beer taxation and alcohol-related traffic fatalities.

• Author: Mast, Brent D.

1. Introduction

   Traffic accidents cause thousands of deaths in the U.S. every year, and a substantial proportion of those deaths are estimated to have been alcohol related. For instance, 17,126 trafficaccident deaths in the U.S. during 1996 were alcohol related (40.9% of total traffic fatalities) as defined by the National Highway Traffic Safety Administration (NHTSA) (accidents in which one or more of the drivers involved had been drinking, i.e., had a blood-alcohol content [BAC] of 0.01 or above). Economists have explored the relative effectiveness of policies intended to reduce this carnage on the roads, generally by estimating the deterrence effects on traffic fatalities of various laws aimed at drunk drivers, age restrictions on alcohol consumption, and alcohol taxes. Forced to select among many policy variables in order to keep reduced-form regressions manageable, however, researchers have virtually always controlled for beer taxes and drinkingage laws in reduced-form models, while the selection of other variables has been much less consistent. Therefore, even though most studies find that some laws significantly curb driving under the influence of alcohol (DUI), the literature has not provided consistent evidence of any particular source of direct deterrenee due to the probability and/or severity of punishment. Increasing the legal drinking age from 18 to 21 is generally shown to significantly reduce auto deaths, a conclusion that offers little guidance for public policy since 21 is now standard. However, raising alcohol taxes, and particularly beer taxes, also appears to be an important policy tool for reducing DUI.

   Several studies support findings first reported in Cook (1981) that higher excise taxes on beer significantly reduce traffic mortality rates. Indeed, Phelps (1988, p. 19) concludes that "[t]he effectiveness of a higher alcohol tax on vehicle fatalities does not seem open to serious question (e.g., Cook 1981; Saffer and Grossman 1987a, b). The primary issue is 'how much' rather than 'whether.'" Similarly, the U.S. Department of Health and Human Services (1988, p. 18) reports that "research evidence shows that an increase in the excise tax could have the largest long-term effect on alcohol-impaired driving of all policy and program options available." Subsequent studies reinforce this interpretation (e.g., Evans, Neville, and Graham 1991; Chaloupka, Saffer, and Grossman 1993; Mullahay and Sindelar 1994; Ruhm 1995, 1996), so similar conclusions to those quoted above are drawn in more recent reviews such as Chaloupka (1993) and Grossman et al. (1993). The strong and consistent findings regarding the effectiveness of alcohol taxes are somewhat surprising, however, because beer market studies suggest that beer taxes only have a small impact on consumption, and studies of drinking behavior using survey data indicate that heavy drinkers are the least responsive to prices.

   Section 2 outlines a model of the relationship between alcohol taxes and DUI fatalities and discusses related literature on alcohol markets and drinking behavior in order to suggest why alcohol taxes should not be as important as they appear to be. Then, given this contention that a strong relationship between alcohol taxes and traffic fatalities seems unlikely, the question becomes, why do beer taxes appear to be such an important deterrent to traffic fatalities in many studies that use state-level data? Possible answers are proposed in section 2, and the primary focus of this study is an empirical exploration of two of those potential answers. Section 3 discusses the data employed for many of the previous empirical tests and for those presented in sections 4 and 5. Section 4 explores and supports the first potential answer: that the results derived from the data periods used in many studies may not be replicated for other time periods. Section 5 considers another potential answer: possible missing variable biases. Three potential missing variable issues are explored. One, a lack of control for law enforcement effort, does not appear to bias the tax results. The other two, a lack of consideration for determinants of alcohol price and consumption other than taxes and drinking age and frequent failure to control for factors that may simultaneously determine drinking behavior and support for alcohol taxes, may bias the tax coefficient, however. Therefore, even though data limitations prevent definitive conclusions with regard to some issues raised here, the results suggest that the focus on taxes as a primary policy tool to control drunk driving deserves additional consideration. Concluding remarks in section 6 elaborate on this point.

2. Modeling the Relationship Between Alcohol Taxes and DUI Fatalities: A Reevaluation of the Impact of Taxes on DUI

   No direct measure of DUI offenses exists. Therefore, studies of DUI use various measures of traffic fatalities to explore the impacts of potential DUI deterrents. Theoretically, alcoholrelated traffic deaths in a state, R, are a function of unmeasurable drunk driving offenses (D) and a vector T containing measures of traffic, vehicle safety, and driver safety and is expressed as

   R = f(= D, T). (1)

   Drunk driving and the amount of traffic should have positive effects on R, while vehicle safety and driver safety should reduce R. Drunk driving is in turn a function of alcohol consumption ([Q.sub.a]), the expected punishment from drunk driving, determined by the probability of being arrested and convicted (P) and the expected severity of punishment (S), and a vector N of variables measuring the likelihood of driving (drunk or not) for people who drink, that is,

   D = f(= [Q.sub.a], P, S, N). (2)

   [Q.sub.a] and N should have positive effects on D, but P and S should be negatively related to D. Since no data exist to measure the drunk driving offense rate, the driver-involvement equation must be estimated in reduced form by substituting Equation 2 into Equation 1 as

   R = f(= [Q.sub.a], P, S, T, N). (3)

   [Q.sub.a] should be positively related to R. It is also hypothesized that deterrence works; that is, as P and S rise, the driver-involvement rate should fall. An empirical model of Equation 3 appears in Benson, Mast, and Rasmussen (1999b). Most studies have not considered [Q.sub.a] directly, however, but instead have included various determinants of [Q.sub.a], primarily taxes and drinking age, in the reduced-form model.

   Alcohol consumption, [Q.sub.a], is determined by the interaction of supply and demand. The quantity demanded, [Q.sub.d], depends on the price ([P.sub.a]), a vector of laws that affect alcohol availability (L), including the legal drinking age, and a vector of nonprice determinants of demand (M) such as income and population characteristics, which include attitudes toward alcohol consumption, such that

   [Q.sub.d] = f(= [P.sub.a], L, M). (4)

   Quantity supplied, [Q.sub.s], also depends on price ([P.sub.a]), a vector of variables influencing the level of competition (C) such as laws regarding entry and market practices, and costs of supplying alcohol. Assuming that production costs are roughly equal for a particular alcohol type (e.g., beer), differences in the unit costs across states should reflect transportation costs (Tr) and taxes (Ta) so that

   [Q.sub.s] = f(= [P.sub.a], C, Tr, Ta). (5)

   In equilibrium, [Q.sub.s] = [Q.sub.d], and because the price is endogenous, the equilibrium quantity, [Q.sub.a], can be estimated in reduced form, including only exogenous variables from Equations 4 and 5 (Sass and Saurman 1993) as

   [Q.sub.a] = f(= L, M, C, Ta, Tr). (6)

   Laws limiting availability, policies reducing the intensity of competition, and taxes should be negatively related to [Q.sub.a]. The effects on [Q.sub.a] of various nonprice determinants of demand are discussed below when the empirical model is specified. The standard model of DUI estimates the driver-involvement equation in reduced form, as noted above, by implicitly substituting Equation 6 into Equation 3 as

   R = f(= L, M, C, Ta, Tr, P, S, T, N). (7)

   A large and significant impact of alcohol taxes on highway fatalities is surprising in such a model for several reasons.

   Why Are Strong Tax Effects Surprising?

   Drunk driving obviously requires alcohol consumption, which is in turn at least partially determined by alcohol price, and one determinant of price is alcohol excise taxes. Recent studies of alcohol markets indicate that excise taxes have only a relatively small impact on the money price of alcohol, however, and that money price in turn has only a relatively small impact on consumption decisions (e.g., Sass and Saurman 1993). The law of demand certainly holds, but the price elasticity is relatively low (particularly for heavy drinkers, as noted below) as other factors such as transactions costs due to market structure characteristics and regulations, religious beliefs, and age distribution are also important determinants of demand. Most DUI studies imply price elasticities of alcohol demand that are much higher than direct studies of alcohol consumption suggest exist, perhaps because many determinants of demand are not included in most of the reduced-form DUI models. Indeed, as Dee (1999) notes, the magnitude of the beertax elasticities of traffic fatalities reported in previous DUI studies is inconsistent with the evidence from studies of drinking.

   Recent empirical evidence from studies using survey data also indicates that the price elasticity of demand for alcohol may be lowest among heavy drinkers (Sloan, Reilly, and Schenzler 1994b; Chaloupka and Wechsler 1996; Kenkel 1996). Chaloupka and Wechsler (1996) find that male college students apparently do not respond significantly to money price changes, for instance, although female students do. In this same vein, Sloan, Reilly, and Schenzler (1994b) do not find a significant impact of alcohol price on motor vehicle fatality rates among 21-24year-old drivers (or...