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The deterioration of deterrence effects of driving legislation: Have we been giving wrong signals to policymakers?
Journal of Criminal Justice. Vol. 12. pp. 115-13(1 (1984) Pergamon Press. Printed in U.S.A.
0047-2352/84 $3.11t) + .t~) Copyright (' 1984 Pcrgamcm Press Ltd.
THE D E T E R I O R A T I O N OF D E T E R R E N C E EFFECTS OF D R I V I N G LEGISLATION: H A V E WE BEEN GIVING WRONG SIGNALS TO POLICYMAKERS?
HAROLD L .
V O T E Y , JR.
Department of Economics University of California Santa Barbara, California 93106
ABSTRACT This paper suggests that the apparently observed initial success of legislation to control drunken driving accidents by law enforcement and sanctions, followed by a return of accident levels to initial trends may be an artifact of failure to properly model the accident process. The point is illustrated by simulating a model of accidents in which drunken driving is controllable with a change in laws. It shows that this control effect can easily be swamped by other plausible accident inducing forces. Finally, it is argued that the costs of failing to maintain efforts to control drunken driving may be greater than the social costs of maintaining high enforcement levels and stiff penalties.
INTRODUCTION The control of accidents attributable to motoring offenses--in particular drunken driving, but also speeding and reckless driving--has been a frustrating problem for policymakers and researchers virtually from the onset of legislation and enforcement efforts to deal with the problem. No small part of the difficulty stems from the longlived debate on whether various measures imposed with the objective of controlling dangerous driving behavior have the expected and/or desired effect. While some policymakers charge ahead with the conviction that stiffer penalties and more intensive enforcement will reduce casualties, others delay, hoping to find a concensus develop-
ing in the research literature to provide guidance for policymaking. To be sure, all measures that would reduce accidents are costly. It seems only prudent to ask whether greater effort can produce social benefits commensurate with those increased costs. If not, there would seem to be no justification for more intensive policing, longer jail terms, more rigid controls on drinking, or other policies whose express purpose is to control carnage on the highways. ~ The control problem can be depicted very simply. For purposes of illustration, let us suppose that drunken driving levels and accidents are linearly related and that the average cost per accident is a constant independent of the frequency of accidents. If those presumptions hold, the relationship
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F i g u r e 1. A g r a p h i c a l r e p r e s e n t a t i o n o f cost r e l a t i o n s h i p s a n d the s o c i a b l y o p t i o n a l level of c o n t r o l o f d r u n k e n driving.
b e t w e e n v i c t i m costs a n d d r u n k e n d r i v i n g can be d e p i c t e d as with c u r v e O V in F i g u r e 1. L e t us also s u p p o s e that d r u n k e n d r i v i n g is c o n t r o l l a b l e by e n f o r c e m e n t of d r i v i n g l e g i s l a t i o n a n d the i m p o s i t i o n o f s a n c t i o n s . T h e n . as m o r e is s p e n t on e n f o r c e m e n t a n d s a n c t i o n s , d r u n k e n d r i v i n g levels will d e cline. T h e cost f u n c t i o n for c o n t r o l can then be d e p i c t e d as with c u r v e C C in F i g u r e 1. T h e total social costs a s s o c i a t e d with d r u n k e n d r i v i n g will be the sum o f victim costs a n d the costs s o c i e t y i m p o s e s u p o n itself in e f f o r t s at c o n t r o l , which will s i m p l y be the v e r t i c a l s u m m a t i o n o f the two classes of cost, y i e l d i n g c u r v e SS in F i g u r e 1. In this case, the social o p t i m u m is u n a m b i g u o u s a n d will lie at DD,,. T h e c h a l l e n g e to statisticians will be to a r r i v e at the c o r r e c t level o f c o n t r o l cost C,, to a c h i e v e that optimum. A m o r e t r a c t a b l e p r o b l e m is that o f d e t e r m i n i n g w h e t h e r s o c i e t y is at a level o f d r u n k e n d r i v i n g in the vicinity of DD~ o r
D D : , a n d , thus, w h e t h e r c o n t r o l efforts s h o u l d be i n c r e a s e d as w o u l d be the case at DD~ or s h o u l d be d e c r e a s e d as w o u l d be the case at D D : . B e f o r e such a c o n c e r n b e c o m e s r e l e v a n t , h o w e v e r , we w o u l d w a n t to be a s s u r e d that the c o n t r o l - c o s t r e l a t i o n s h i p really d o e s l o o k like curve C C , s l o p i n g d o w n w a r d to the right, a n d not like C'C', e s s e n t i a l l y flat, the case in which c o n t r o l efforts have no effect on d r u n k e n d r i v i n g o r a c c i d e n t c o n t r o l . In the l a t t e r case, of c o u r s e , the social o p t i m u m w o u l d be at a z e r o level of e x p e n d i t u r e for c o n t r o l , so that the total social cost w o u l d be r e p r e s e n t e d by victim cost curve O V . U n f o r t u n a t e l y , the r e s e a r c h l i t e r a t u r e , t a k e n in t o t a l , has not b e e n helpful in e s t a b l i s h i n g a c o n s e n s u s on the r e l a t i o n b e t w e e n c o n t r o l efforts a n d d r u n k e n d r i v i n g a n d / o r a c c i d e n t levels. T h e r e have b e e n c w f l u a t i o n s in which r e s e a r c h e r s a p p e a r to a g r e e that i n t e r v e n t i o n s , in t e r m s of s t r o n g e r legislation o r intensification o f en-
The Deterioration of Deterrence Effects of Driving Legislation forcement and sanctioning within the existing legal structure, have caused a reduction in accidents. Even in those cases, however, it frequently appears that the decline in deaths and injuries is a short-lived phenomenon.-" This apparent decay in control effectiveness is certain to have been a discouragement to all but the most dedicated advocates of intensive enforcement policies. The purpose of this paper is to help clarify thinking on the general problem of control, to consider the appropriateness of basing policy decisions on what previous research seems to have demonstrated, and to suggest directions in which further research might prove profitable. The methodological approach will be to consider the elements of control within a multi-equation deterrence model that permits distinctions to be made among alternative impacts on driving behavior. The use of such simultaneous systems analysis to consider deterrence questions is not new, and, in fact, has created shock waves in the community of criminologists stemming from its application to the question of the effectiveness of the death penalty. One consequence of the response to initial empirical research using simultaneous systems analysis has been the well-known investigation into the appropriateness of the current deterrence research conducted by Blumstein, Cohen, and Nagin (1978). To avoid the potential pitfalls of much of current research as e n u m e r a t e d by those authors, the approach adopted here is analogous to proof by contradiction. It begins by developing a model in which deterrence is 15ostulated to work and seeks to determine whether, or under what conditions, an apparent decay in control effectiveness with respect to accident control is potentially cnsistent with a world in which deterrence is very much alive. 3 There is hardly a need to conduct a review of the deterrence evidence for such an undertaking. Surveys of the deterrence literature have been done extensively and well by others (Blumstein, Cohen, and Nagin, 1978; Palmer, 1977: Taylor, 1978). That uncer-
l 17
tainty exists about effects is sufficient reason to pursue this approach. The results of doing so suggest that the policy implications provided by much of the existing research may be misguided due to a failure to thoroughly evaluate the evidence. The first step in establishing this conclusion is to develop the model for analysis.
THE DETERRENCE MODEL The central elements of a deterrence model are hardly new, dating back to Becearia, Bentham, and the literature of the Utilitarians. There have been extensive refinements to the logical analysis that have surfaced over time. These provide persuasive arguments for why deterrence may or may not work and the technical basis for more sophisticated empirical studies of existing data to test whether it does. 4 The points to be made here do not require a complex model of behavior. We need merely to postulate that the level of sanctions and the threat of their imposition will deter antisocial behavior. That is, in the shorthand of functional notation, the sum of such behavior can be expressed as: MO = m (PCON~ S, SE),
(1)
where MO is motoring offenses, PCON is the probability of apprehension, conviction, S, is the magnitude of sanctions, and SE is a vector of social, economic, and environmental factors that may have an impact on the sum of individual behavior. Consistent with classical criminological theory and modern decision theory, it is assumed that offense rates are negatively influenced by the expected cost of sanctions. The impact of the social and environmental variables will be discussed further as we proceed. To illustrate the accident problem, it is assumed there is only one class of motoring offenses, drunken driving (DD), and that the only causal factor that contributes to its incidence is alcohol consumption (ALC). To further simplify the problem, assume that sanctions are uniform and invariant so that the relation can be written
118
HAROLD L. VOTEY, JR.
DD = d (PCON, ALC).
(2)
O t h e r things equal, an increase in alcohol c o n s u m p t i o n can be e x p e c t e d to increase d r u n k e n driving. A n d , if d e t e r r e n c e and control w o r k , an increase in the probability of a p p r e h e n s i o n and conviction can be e x p e c t e d to negatively affect d r u n k e n driving levels. T h e probability of conviction is an o u t p u t of the system of criminal justice. P r o d u c t i o n t h e o r y indicates that it should rise with the addition of police and o t h e r system resources and be negatively influenced by the load o f rising offense levels. Thus, we can write P C O N = b ( D D , L),
(3)
where L is an index o f system resources used to c o m b a t the p r o b l e m . A g a i n , for simplicity, let us s u p p o s e that system resources are e x p e n d e d b a s e d on a b u d g e t established in a previous p e r i o d so that, in any given period, L can be t a k e n as given. T h e interaction of the b e h a v i o r a l relation, e q u a t i o n (2), with the system r e s p o n s e , e q u a t i o n (3), will lead to an equilibrium level of system effectiveness ( P C O N ) and level of offenses ( D D ) . A c c i d e n t s , it can be p r e s u m e d , are a function of the level of m o t o r i n g offenses in a recursive fashion and o t h e r factors that affect driving risk such as the nature of the driving e n v i r o n m e n t , the t r a n s p o r t t e c h n o l o g y , and the load on the t r a n s p o r t a t i o n system. This can be c a p t u r e d by the relation: AC = a (DD, MD, VM...
)
(4)
(i.e., accidents, A C , are also a function of mileage driven ( M D ) , and vehicle mix ( V M ) , as well as m o t o r i n g offenses), s O t h e r factors r e p r e s e n t i n g road or vehicle activity, are also likely to influence accident levels. We will assume the latter are invariant for this exercise and, thus, can be safely ignored. E q u a t i o n s (2), (3), and (4) thus comprise a system within which we can m a n i p u l a t e control efforts (L), drinking levels ( A L C ) , and o t h e r factors affecting risk ( M D and VM). Such a m o d e l is useful because it will let us e x a m i n e intervention effects in a
world where responses, normally not observable, are known. This can be d o n e with a simulated annual time series.
SIMULATION OF THE MODEL Consider first a world in which all of the variables are, in fact. stationary. Aside from the possibility of r a n d o m elements entering, the time series will be flat if the e x o g e n o u s f o r c e s - - m i l e a g e driven, alcohol c o n s u m p t i o n , vehicle mix, and resources d e v o t e d to law e n f o r c e m e n t - - a r e invariant over time. Suppose. for example, that the m o d e l and its p a r a m e t e r s are defined as follows: D D = C PCON',' A L C a,
(5)
P C O N = B D D "-I L r~,
(6)
and A C = A D D ~ M D K V M ~.
(7)
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ct [3 y 6 k
= 0.2 = 0.2 = -0.8 = 0.8 = 0.5 K = 0.5 0 = 4.0.
Suppose initial values for the e x o g e n o u s forces are: ALC = 3.62 M D = 1841 VM = 0.126. Law e n f o r c e m e n t sumed e x o g e n o u s simulation with a jointly d e t e r m i n e d have the values: PCON
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0.010
40.07 100.
Let us suppose a time period of 36 years, say 1946-1981. A plot of accidents o v e r
The Deterioration of Deterrence Effects of Driving Legislation the thirty-six .years will show a stationary series for the accident index at the level of 100 (see line A B , Figure 2). Assume some intervention at the midpoint of the series. For example, the adoption of per se laws might m a k e it m o r e easy to obtain convictions for the same level of resource inputs and a given accident level. We could show this in the model by a larger constant term B in equation (6) or by a larger coefficient [5 on resources, indicating a higher marginal productivity and hence output elasticity. In the example, the latter choice is made for illustrative purposes with the new value for fS, after the intervention, of 0.21. Such a change will yield new equilibrium levels of the probability of con,;,iction (0.018), drunken driving (36.18), and hence the level of accidents (95.03). The change in the accident level is indicated in Figure 2 by the interrupted time series line AC. Such a change is readily identified by observation. In the real world we wouldn't observe such a change so readily because the world can be expected to be stochastic rather than deterministic. The effect of such r a n d o m effects on the observed series is illustrated in Figure 3 in which a r a n d o m term has been added to the fiat series for accidents, yielding curve AB. The r a n d o m term has been constrained to have m e a n zero over the series, and, for the two such periods before and after the intervention, the means of the stochastic terms are also zero. The variances are approximately equal for the two subperiods as well. The series used is presented in the Appendix. If the same intervention is imposed on this series, it is not so clear by observation that accidents have been reduced by the intervention, and it is certainly not clear at what point the intervention took place (see curve AC. Figure 3). A calculation of means based on the known intervention date does reveal, however, a statistically significant shift in the accident index, and statistical tests could not reject mid-1963 as the point in time. In such a world, the intervention can be seen to have a permanent effect. D e p e n d i n g upon the interven-
119
tion costs and the social savings inputed to the accidents prevented, it should be a straightforward matter to determine whether the intervention has been socially cost effective.
A NON-PERMANENT INTERVENTION Any time an intervention takes place in an area of concern for the criminal justice system, society is hopeful that the intervention leads to an i m p r o v e m e n t that is p e r m a nent. Perusal of the evidence with respect to many efforts to control drunken driving, however, has led some researchers to the conclusion that, in general, deterrence does not seem to lead to p e r m a n e n t effects. A noted example is the Road Safety Act (1967) in Britain that showed immediate and dramatic results in reducing accidents, but that by Christmas, 14 months later, "it was evident that the benefits were wearing off. ''7 This and other similar evidence has led H. Laurence Ross to state that even in cases "where the increased threat (of punishment) has taken the form of a p e r m a n e n t change in the law, subsequent events have revealed a gradual return of the drinkingdriving problem to the level of a pre-existing trend" (Ross, 1981:1). The effect that appears to be observed is illustrated by the curve drawn in Figure 4. If we observed such a pattern in a case in which the values for the exogenous variables to the system remained invariant as originally postulated, this would imply t h a t ' o u r new "constant" term was not, in fact, invariant over time or that the marginal productivity of criminal justice resources was perhaps declining to the old (or some other) level after the intervention for any n u m b e r of possible reasons. In any event, it would seem to be a clear signal that the intervention was w e a r ing o u t - - l o s i n g its effect. The fact that time series plots reveal such a pattern is hardly evidence that such is the case, however. To be able to m a k e such a point presumes that it can be established that other exogenous forces a r e invariant over this time series, and they rarely are.
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Figure 4. T i m e series of accident index with intervention that decays. A SEEMINGLY NON-PERMANENT INTERVENTION To m a k e this point, it is instructive to consider a m o r e realistic time series. Consider the case in which, over the entire period alcohol consumption per capita for the drinking age population is growing at the rate of 3.2 percent per year. Suppose further that driving distance per capita has been increasing at the rate of 3.7 percent per year. Suppose also that the ratio of twowheel to four-wheel vehicles has been declining at the rate of 1.1 percent per year. All of these changes are consistent with a rising per capita income and, in fact, are not so different from secular growth rates that have been o b s e r v e d for a n u m b e r of countries. ~ Suppose, at the same time, that resources devoted to the p r o b l e m continue to be held fixed. The time series that will be generated by these effects, still incorporat-
ing the same intervention effect originally postulated, will appear as curve A C in Figure 5. If we could isolate and eliminate the stochastic term, the series would a p p e a r as curve D E in Figure 6, and the p e r m a n e n t effect of the intervention, beginning in mid-1963, is clearly revealed. If we c o m p a r e the observed accident rate (AC) with the long-term trend, however, we would be misled. The vagaries of the error structure a p p e a r to indicate that the effect of the intervention may have taken place a year earlier in mid-1962. F u r t h e r m o r e , by 1971 the entire effect of the intervention appears to have decayed, and the long-term pattern falls largely above the linear trend line. Even if we examine the series with the stochastic element removed (as shown in Figure 6), we see that by 1979, sixteen years after the intervention, the accident series has risen above the long-term linear trend.
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The Deterioration of Deterrence Effects of Driving Legislation The explanation, in this case, is that, while drinking-driving is affected, the pattern of driving, of alcohol consumption, and of change in the vehicle mix all continue unabated over time. Since all of these have independent influences on accidents, accident rates continue to change as well, even after taking into account the partial effect of the reduction in drunken driving achieved through more effective policy. In this example, the change from the low of 1964 can be partitioned into several partial effects: a 26 percent rise in accidents as alcohol consumption rises, a 13 percent rise attributable to an increase in driving intensity, and an offsetting 32.2 percent decline in accidents due to a beneficial change in the vehicle mix, yielding a net increase of 6.8 percent in accident levels by 1971 that overshadows the p e r m a n e n t effect of the intervention. The failure in this case is n o t in the effectiveness of the measures adopted, but it is a policy failure in the sense that resources devoted to control have not been augmented as the magnitude of the problem has increased. If this is the path that policy takes, then an apparent decay in the effectiveness of intervention should be the expected result and no surprise at all. If such a series, incorporating the postulated intervention, is regarded as an "interrupted" time series and examined simply by visual inspection, a researcher is virtually certain to make a Type II error, rejecting the hypothesis of a deterrent effect when in fact it is true. In this case, statistically more sophisticated tests would reveal a shift in the intercept. That is, since all of the exogenous variables that are varying do so at exponential rates, the elimination of the exponential trend and separate analysis of the pre- and post-intervention series would reveal such a shift. The problem would be more difficult if the exogenous variables bear the same relationship to the jointly determined variables but move in a less systematic fashion. Multiple regression analysis would identify the parameters of the system if sufficient information were available. In the case of drunken driving, however, we are unlikely to have a satisfactory means to measure the
125
level o f drunken driving or the probability of conviction. It is not even easy to measure the level of criminal justice inputs. With a sufficient index of criminal justice inputs (L) and the other exogenous variables (MD, VM, A L C ) , a reduced form relation could be estimated that would reveal the impact of criminal justice resources on accidents, and similarly, the extent to which increasing alcohol consumption and driving levels contribute to the apparent decay of the deterrence effect• The difficulties of such analysis cannot be treated lightly. It will be obvious to any statistician that, with the data series postulated, it would be impossible to relate the impact of the intervention to criminal justice inputs simply because there is no variance in that series. We are faced here with the situation that a powerful and socially important consequence of policy cannot be measured directly. Of course, we could isolate the reality of the intervention effect with a dummy variable reflecting the intervention point, and it would turn out to be highly significant. Even this will be difficult to isolate, however, in cases in which, for whatever reason, enforcement resources do not continue to be applied consistently after the intervention, and the variation in their application cannot be measured.
IMPLICATIONS FOR POLICY The implications of these illustrations should not be ignored if we wish to avoid policy error. A common stance for policymakers is that, if we can't prove that a particular policy measure is effective, we cannot justify the expenditure of public funds. In an era in which many of us are concerned with the misuse of public funds, that seems a laudable position to take. It presumes, however, that the possibilities for errors all fall on one side, that is, we can err by spending, but not by not spending, so that there is no cost to making a Type II error. In the case of attempting to reduce highway fatalities, this is simply not true. There is a gigantic cost to failing to enforce
126
HAROLD L. VOTEY, JR.
laws that work. Failing to enforce them because we can't prove they work is no more defensible logically than enforcing them at public expense because we have faith in the theory and logic of deterrence. The implications of such an error can be seen by returning to Figure 1. Suppose society is at a point such as DD1 in terms of incidence of drunken driving. Should policymakers conclude that control is ineffective and reduce control efforts from a cost level of C~ to C3, when in fact control works, victim's costs would rise from V~ to V> The difference between V3 and VI less the reduction in e n f o r c e m e n t costs (Ct minus C3) would be the cost of a T y p e II error. In contrast, the cost of a T y p e I error, accepting the hypothesis that control works when it doesn't, would be the cost of increased e n f o r c e m e n t (C0 minus C~) in attempting to reach DD0, when in fact DD~ would continue to prevail. If we had no prior estimates of the probability that either hypothesis were true and assumed either equally likely, the expected benefits would obviously be greater from attempting to diminish drunken driving by increased control expenditures. While this a p p e a r s to be the direction society wants to m o v e , it tends to be in spite of the message coming from much of the research literature. Past decisions in regard to e n f o r c e m e n t or non-enforcement, of drinking and driving laws have been m a d e largely on the beliefs of policymakers based on theory and logic but in the absence of prooffl Because of the nature of statistical techniques c o m m o n l y being utilized to evaluate driving legislation and e x p e r i m e n t a l e n f o r c e m e n t actions, it is highly likely that the influences on policymakers from the research c o m m u n i t y have been to push them away from this position in the direction of potential T y p e II errors. The reasons are clear. Most studies do not go as far as the data will permit in taking into account influences that may be obscuring the existence of deterrence effects. For example, none of the studies cited by Ross (1981) take into account the m a n y exogenous factors influencing accident levels or even standardize for variations in enforce-
ment intensity. In spite of this, one finds the statement repeated frequently in print that interventions and p e r m a n e n t policy changes invariably 10se their effectiveness. One is not justified in drawing such conclusions, however, unless one can clearly d e m o n strate that other exogenous forces that contribute to drunken driving or accidents in general cannot have simply overshadowed highly effective policy. The threat of punishment may be deterring drunken driving, but if the population of drinkers is increasing as more persons drink, or if the average drinker consumes more, the threat may only m o d e r a t e the rise in drinking-driving. Likewise, if all people are driving more, or more people have access to vehicles, per capita accident rates are likely to rise in spite of more severe enforcement. Simply showing that the levels of blood alcohol a m o n g killed and injured in motoring accidents have not fallen does not establish the lack of deterrence in the face of the increase in the potential population of drunken drivers, m The concern over such matters is not an idle one, since it can easily be established that rising per capita alcohol consumption has been the norm across the broad array of countries in recent years. For example, in Norway from 1954 to 1978, the annual growth of per capita consumption has been 3.27 percent. For Sweden from 1960-1976, it has been 2.75 percent. For a list of 28 mostly advanced industrial nations between the years of 1973 and 1977, twenty-two have shown increases, and those that have declined have done so only moderately relative to the rates of increase. See Table 1 for a partial list of these countries" experience. For the United States in that period, the average annual rise was 5.73 percent, for Canada 2.9 percent, and for England 1.25 percent. As is made clear by the model, such rises in alcohol consumption levels would easily a p p e a r to swamp intervention effects. Driving levels have risen uniformly in these same countries prior to the oil crisis in the early 197(1s. To be able to effectively impute the impact of driving levels on
127
The Deterioration of Deterrence Effects of Driving Legislation
TABLE 1 ANNUAL CONSUMPTION PER CAPITA OF 1 0 0 % ALCOHOL FOR
SELECTED COUNTRIES, 1973 AND 1977 (liters)
Country
1973
1977
Average Annual Rate of Change
(%) France Portugal West G e r m a n y Australia New Z e a l a n d Denmark Canada England Holland United States Finland Sweden Norway
16.9 12.8 12.3 8.7 8.4 8.3 8.0 7.8 7.5 6.6 5.7 5.5 4.0
16.4 14.0 12.4 9.7 9.5 8.9 9.0 8.2 8.8 8.3 6.4 5.7 4.4
-0.75 2.25 0.24 2.72 3.08 1.74 2.95 1.25 4.00 5.73 2.50 0.89 2.38
SOURCE: Centralforbundat for alkohol-och narkotihaupplyshning, Rapport, Stockholm: 1973, 1979.
accidents since that time, one would need to know the extent that they have changed for the high risk population. It is easily possible that changes in driving levels, vehicle mix, and congestion on highways have had a measurable impact on changes in accidents that may further help to conceal the effects of interventions in virtually any country that has a t t e m p t e d them. Investigations into the effects of c o u n t e r m e a s u r e s over time that fail to account for such factors can m a k e no claim to isolating the effects that are of importance for policy decisions. It is interesting to note that the "Blennerhassett Report'" had investigated all of these factors in a c o m p r e h e n s i v e analysis of the "'wearing o f f " of the British R o a d Safety ~/ct's effectiveness ( H M S O , 1976:12). There has not been to date, however, any effort to measure the extent of the effects of the factors involved in order to determine whether or not any residual of the deterrence effect still exists, although the "'Cheshire blitz" was an experiment that lends support to the arguments presented here.l~
The danger of suggestions that deterrence measures don't last is that they are likely to be, to a very real extent, self-fulfilling. If e n f o r c e m e n t officers come to believe a measure has no effect, they will cease to invoke it. The prosecutors will cease to prosecute, and the courts will cease to sanction. For all of them to participate in such Type II errors could be a socially costly mistake. Researchers have an obligation to be sure they don't inadvertently contribute to such a process by failing to point out that an intervention that is " p e r m a n e n t " (e.g., a change in the law) can only have a p e r m a nent effect if the resources to enforce it k e e p up, over time, with the causal forces that are generating drinking-driving behavior. 12Failing this, the most that one should hope for in any modification of law e n f o r c e m e n t or sanction policy is that the result will be a lower level of accidents than would have occurred in the absence of the change and that the reduction costs no more to achieve than the value of life, health, and p r o p e r t y that is preserved. Determining whether one
128
H A R O L D L, VOTEY, JR.
o r t h e o t h e r is t r u e c a n o n l y b e p o s s i b l e w i t h research designs adequate to take into a c c o u n t all t h e c r u c i a l c h a n g e e f f e c t s . B a s ing policy recommendations on anything less will s u r e l y r e n d e r a disservice to society.
REFERENCES Becker, G.S. (1968). Crime and punishment: An economic approach. J. o f Pol. Ec. 2:76, 169-217. Codling, P.J. (1972). Weather and road accidents. In Proceedings: Symposium on climatic resources and economic activio,, ed. University College of Wales. Wales: Aberystuy. Blumstein. A.; Cohen. J . and Nagin, D. (1978). Deterrence and incapacitation: Establishing the efti'cts o f criminal sanctions on crime rates. Washington, DC: The National Academy of Sciences.
NOTES t This point is made in greater detail in Votey (1977). -+Ross (1981) provides us with a comprehensive enumeration of the evidence.
Her Majesty's Stationary Office (HMSO) (1976). Drinking and driving: Report of the departmental committee. London: Department of the Environment.
Such a model, used as a basis for empirical analysis, is presented in Votey (1982).
Johnson. H.D. (1970). Road accidents and casualty rates in 1968: Report LR 348. Crowthorne, England: Transport and Road Research Laboratory.
The revitalization of these ideas owes much to Gary Becket (1968) and to a host of economists who have made use of his refinements in the original theory.
Palmer, J. (1977). Economic analysis of the deterrent effect of punishment: A review. J. of Res. in Crime and Delinq. 1:14, 4-21.
The effects of environmental and technological factors have been studied extensively in many places. Of particular note is the voluminous list of papers from the (British) Transport and Road Research Laboratory. See for example Codling (1972) and Johnson (1968).
Phillips, L., and Votey, H.L., Jr. (1981). The economics o f crime control. Beverly Hills, CA: Sage Publications, Inc. Ross, H.L. (1975). The Scandinavian myth: The effectiveness of drinking and driving legislation in Sweden and Norway. The J. of Legal Studies 4:285-310,
Parameter values used for illustrative purposes are similar to those from a study of Norway, Votey (1978). 7 Her Majesty's Stationary Office (HMSO), Drinking and Driving: Report o f the Departmental Committee (The Blennerhassett Report) London: Department of the Environment (1976). s These are, in fact, the growth rates observed in Norway 1954-1975. ~ This is the point of Ross (1975) with respect to the Scandinavian experience. tu HMSO, Drinking and Driving, p. 12, notes that by 1971 the proportion of drivers killed with excess blood alcohol had reached the pre-Road Safety Act levels (25 percent) and continued to rise to a new high (34 percent). H Ross, Deterrence o f the Drinking Driver, pp. 38ff summarizes the Cheshire experiment of September 1975.
- (1981). Deterrence o f the drinking driver: An international survey. Washington, DC: National Technical Information Service. Taylor, J.B. (1978). Econometric models of criminal behavior: A review. In Economic models o f criminal behavior, ed. J.M. Heineke, pp. 35-81. Amsterdam: North Holland. Votey, H.L., Jr. (1977). A rational policy for the control of accidents caused by motoring offenses. Oslo: Institute of Transport Economics. --(1978). The deterrence of drunken driving in Norway and Sweden: An econometric analysis of existing policies. In Drinking and Driving in Scandinavia, ed. R. Hauge. Scandinavian Studies in Criminol+ 6: 79-99. -
t,, This point has been made in regard to major felony crime in Phillips and Votey (1981), Ch. 10.
(1982). Scandinavian drinking driving control: Myth or intuition. The J. of Legal Studies 11:93-116. -
129
The Deterioration of Deterrence Effects of Driving Legislation
APPENDIX S T A T I O N A R Y SERIES
No Intervention
With Intervention
Year
Accidents
Stochastic Accident Series
Random Error Term
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 197/I 1971 1972 1973 1974 1975 1976 1977 1978 1979 198(} 1981
100.016 1110.016 100.016 100.016 1110.016 100.016 100.016 100.016 100.016 100.016 100,016 100.016 100.016 1011.016 10(L016 1011.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100,016 100.016 100,016 100,016 100.016 100.016 100.016 100.016 100.016 100.016 1110.016
102,331 95.5649 103.254 100.265 100.570 98.6159 98.9859 101.212 98.5289 104,419 96.4649 101.841 102.740 102.187 98.1189 97.0149 101.889 96.2849 103.749 101.190 97.7329 96.7549 101. 802 96.6879 96.7379 102. 352 102.820 98.8899 101.251 100. 582 96. 3409 101.803 103. 470 102.278 96. 0839 99.7599
2.3151111 -4.45100 3.230011 0.24900(I 0.5541100 - 1.400011 - 1.03000 1.1961111 - 1.48700 4.40300 -3.55100 1.82500 2.72400 2.17100 - 1.89700 -3.00100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1.78600 - 3.32800 - 3. 27800 2. 33600 2.80400 - 1,12600 1,23500 0,566000 - 3,67500 1.78700 3. 45400 2. 26200 - 3. 93000 -0.256000
Mean Std. Dev.
100.0 0.0
100.0 2.59
0.0 2.59
Period I Mean Std. Dev.
100.0 0.0
100.0 2.58
0.0 2.58
Period 11 Mean Std. Dev.
100.0 0.0
100.0 2.60
0.0 2.60
Accidents
Stochastic Accident Series
Random Error Term
1011.016 100.016 100.016 100.016 1110.016 100.016 100.016 100,016 100,016 1110.016 100.016 100.016 100.016 1/10.016 100.016 100,016 100.016 100.016 95.0270 95.0270 95.0270 95.0270 95.0270 95. 0270 95.0270 95. 0270 95.0270 95.0270 95.0270 95. 0270 95. 0270 95.0270 95. 0270 95. 0270 95. 0270 95.0270
1112.331 95.5649 1113.254 1(10.265 100.5711 98.6159 98.9859 101.212 98.5289 104.419 96.4649 101.841 102.7411 102.187 98.1189 97.0149 101.889 96.2849 98.7600 96.2010 92.7440 91.7660 96.8130 91. 6990 91. 7490 97. 3630 97.8310 93.9010 96.2620 95. 5930 9 I. 3520 96.8140 98.4810 97.2890 91.0970 94.7710
2.315.1111 -4.45100 3.2311t 10 0.2490(10 0.55400(I - 1.401100 - 1.(}30(}0 1.19600 - 1.48711(I 4.4(}30(} -3.55100 1.825(10 2.7240() 2,171(10 - 1,89700 -3,0(1100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1. 78600 - 3. 32800 - 3.27800 2. 33600 2.80400 - 1.12600 1.23500 0. 566000 - 3. 67500 1.78700 3. 45400 2. 26200 - 3. 93000 -0.256000
97.5 2.99
97.5 3.60
0.0 2.59
100.1 0,0
100.1 2.58
0.0 2.58
95.0 0,0
95.0 2.60
0.0 2.60
130
H A R O L D L. VOTt~Y. JR.
TRENDED
SERIES
With Intervention
No Intervention
Year
Accidents
Stochastic Accident Series
Random Error Term 2.31500 -4.45100 3.23800 0.249000 0.554000 - 1.40000 - 1.03000 1.19600 - 1,48700 4.40300 -3.55100 1.82500 2.72400 2.17100 - 1.89700 -3.00100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1.78600 - 3.32800 - 3.27000 2.33600 2.8(1400 - 1.12600 1.23500 0.566(100 - 3.67500 1.78700 3.4540(I 2.262(10 - 3.93(100 -0.256000
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 197(I [971 1972 1973 1974 [975 1976 1977 1978 1979 198(I 1981
81.7439 82.6311 83.6778 84.7027 85.7000 86.6597 87.6130 88.7677 89.9101 90.7670 91.7843 93.1093 94.1519 95.3647 96.2967 97.7216 98.9064 100,016 100.902 1(11.793 102.957 103,722 104.791 105.837 106,827 107.784
84.0589 78.1301 86.9158 84.9517 86.2540 85.2597 86.5930 89.9637 88.4231 95.1700 88.2333 94.9343 96.8759 97.5357 94.3997 94.7206 100.779 96.2849 104.635 102.967 10(/. 674 100.461 106.577 102.509 103.549
109.072
111.876 108.805 112.378 112,481 109.363 115.895 118,590 113.812 113,550 118,512
Mean Std. Dev.
L(}0.0 10.99
Period 1 Mean Std. Dev. Period 11 Mcan Std. Dev.
109,931 111.143 111.915 113,038 114,108 115,136 116.550 117.479 lt8.838
110.120
Accidents 81.7439 82.6311 83.6778 84.7027 85.7(700 86.6597 87,6130 88.7677 89.9101 90.7670 91,7843 93.1093 94.1519 95.3647 96.2967 97.7216 98.9064 100.016 95.8690 96.7155 97.8211 98,5482 99.5636 100.557 101,498 102.408 103.631 104.447
105.599 106.332 107.399 108.416 1(19.393 110.736 111.629 112,911
Stochastic Accident Series
Random Error Term
84.0589 78.1301 86.9158 84.9517 86.2540 85.2597 86,5830 89.9637 88.4231 95. 1700 88.2333 94,9343 96.8759 97.5357 94,3997 94, 7206 10(I.779 96.2849 99.6020 O7.8325 95.5301 95.2872 I01.35(7 97.2292 90,2205 104.744 l(16,435 103,321 l(16,834 106.898 103,724 110.203 112.847 112.998 107,699 112.655
2.31500 -4,45100 3.23800 0.249( )0O 0.554000 - 1.40000 - 1.0300(} I. 196(I(} - 1.48700 4.40300 -3.55100 1.82500 2. 72400 2.17100 - 1.89700 -3.00100 1.8730(1 -3.73100 3.73300 I. 174(10 -2.28300 -3.26100 1.78600 - 3.32(100
- 3.27800 2.33600 2.8( )4( )0 - I. 12600 1.23500 0.566000 - 3.67500 1.78700 3.45400 2.26200 - 3.93000 -0.2560( )0
0.0 2.59
97.3 8,65
97.3 8.92
O.0
I 1.20
90.5 5.58
90.5 5.83
0.0 2,58
90.5 3.58
90,5 5.83
0.0
109.5 5.43
I(19.5 6.01
0.(I 2.60
104.1 5.16
104. l 5.77
0.0 2.60
100.0
_.~9
..)8
0047-2352/84 $3.11t) + .t~) Copyright (' 1984 Pcrgamcm Press Ltd.
THE D E T E R I O R A T I O N OF D E T E R R E N C E EFFECTS OF D R I V I N G LEGISLATION: H A V E WE BEEN GIVING WRONG SIGNALS TO POLICYMAKERS?
HAROLD L .
V O T E Y , JR.
Department of Economics University of California Santa Barbara, California 93106
ABSTRACT This paper suggests that the apparently observed initial success of legislation to control drunken driving accidents by law enforcement and sanctions, followed by a return of accident levels to initial trends may be an artifact of failure to properly model the accident process. The point is illustrated by simulating a model of accidents in which drunken driving is controllable with a change in laws. It shows that this control effect can easily be swamped by other plausible accident inducing forces. Finally, it is argued that the costs of failing to maintain efforts to control drunken driving may be greater than the social costs of maintaining high enforcement levels and stiff penalties.
INTRODUCTION The control of accidents attributable to motoring offenses--in particular drunken driving, but also speeding and reckless driving--has been a frustrating problem for policymakers and researchers virtually from the onset of legislation and enforcement efforts to deal with the problem. No small part of the difficulty stems from the longlived debate on whether various measures imposed with the objective of controlling dangerous driving behavior have the expected and/or desired effect. While some policymakers charge ahead with the conviction that stiffer penalties and more intensive enforcement will reduce casualties, others delay, hoping to find a concensus develop-
ing in the research literature to provide guidance for policymaking. To be sure, all measures that would reduce accidents are costly. It seems only prudent to ask whether greater effort can produce social benefits commensurate with those increased costs. If not, there would seem to be no justification for more intensive policing, longer jail terms, more rigid controls on drinking, or other policies whose express purpose is to control carnage on the highways. ~ The control problem can be depicted very simply. For purposes of illustration, let us suppose that drunken driving levels and accidents are linearly related and that the average cost per accident is a constant independent of the frequency of accidents. If those presumptions hold, the relationship
115
116
HAROLD L. VOTEY. JR.
S COSTS
I~\
SOCIAL COSTS
V3 So
. . . . . .
........... Co
.
CT
--
C3
0
.
.
.
.
.
VICTIM COSTS
I. . . . .
,
i
! !
I
I
i--.
.
.
.
.
"
-
- -,,~""
-
CONTROL COSTS
-'--I t I I I
I I I I I
DD2
DDo
C' C DD1
DD 3 DRUNKEN DRIVING
F i g u r e 1. A g r a p h i c a l r e p r e s e n t a t i o n o f cost r e l a t i o n s h i p s a n d the s o c i a b l y o p t i o n a l level of c o n t r o l o f d r u n k e n driving.
b e t w e e n v i c t i m costs a n d d r u n k e n d r i v i n g can be d e p i c t e d as with c u r v e O V in F i g u r e 1. L e t us also s u p p o s e that d r u n k e n d r i v i n g is c o n t r o l l a b l e by e n f o r c e m e n t of d r i v i n g l e g i s l a t i o n a n d the i m p o s i t i o n o f s a n c t i o n s . T h e n . as m o r e is s p e n t on e n f o r c e m e n t a n d s a n c t i o n s , d r u n k e n d r i v i n g levels will d e cline. T h e cost f u n c t i o n for c o n t r o l can then be d e p i c t e d as with c u r v e C C in F i g u r e 1. T h e total social costs a s s o c i a t e d with d r u n k e n d r i v i n g will be the sum o f victim costs a n d the costs s o c i e t y i m p o s e s u p o n itself in e f f o r t s at c o n t r o l , which will s i m p l y be the v e r t i c a l s u m m a t i o n o f the two classes of cost, y i e l d i n g c u r v e SS in F i g u r e 1. In this case, the social o p t i m u m is u n a m b i g u o u s a n d will lie at DD,,. T h e c h a l l e n g e to statisticians will be to a r r i v e at the c o r r e c t level o f c o n t r o l cost C,, to a c h i e v e that optimum. A m o r e t r a c t a b l e p r o b l e m is that o f d e t e r m i n i n g w h e t h e r s o c i e t y is at a level o f d r u n k e n d r i v i n g in the vicinity of DD~ o r
D D : , a n d , thus, w h e t h e r c o n t r o l efforts s h o u l d be i n c r e a s e d as w o u l d be the case at DD~ or s h o u l d be d e c r e a s e d as w o u l d be the case at D D : . B e f o r e such a c o n c e r n b e c o m e s r e l e v a n t , h o w e v e r , we w o u l d w a n t to be a s s u r e d that the c o n t r o l - c o s t r e l a t i o n s h i p really d o e s l o o k like curve C C , s l o p i n g d o w n w a r d to the right, a n d not like C'C', e s s e n t i a l l y flat, the case in which c o n t r o l efforts have no effect on d r u n k e n d r i v i n g o r a c c i d e n t c o n t r o l . In the l a t t e r case, of c o u r s e , the social o p t i m u m w o u l d be at a z e r o level of e x p e n d i t u r e for c o n t r o l , so that the total social cost w o u l d be r e p r e s e n t e d by victim cost curve O V . U n f o r t u n a t e l y , the r e s e a r c h l i t e r a t u r e , t a k e n in t o t a l , has not b e e n helpful in e s t a b l i s h i n g a c o n s e n s u s on the r e l a t i o n b e t w e e n c o n t r o l efforts a n d d r u n k e n d r i v i n g a n d / o r a c c i d e n t levels. T h e r e have b e e n c w f l u a t i o n s in which r e s e a r c h e r s a p p e a r to a g r e e that i n t e r v e n t i o n s , in t e r m s of s t r o n g e r legislation o r intensification o f en-
The Deterioration of Deterrence Effects of Driving Legislation forcement and sanctioning within the existing legal structure, have caused a reduction in accidents. Even in those cases, however, it frequently appears that the decline in deaths and injuries is a short-lived phenomenon.-" This apparent decay in control effectiveness is certain to have been a discouragement to all but the most dedicated advocates of intensive enforcement policies. The purpose of this paper is to help clarify thinking on the general problem of control, to consider the appropriateness of basing policy decisions on what previous research seems to have demonstrated, and to suggest directions in which further research might prove profitable. The methodological approach will be to consider the elements of control within a multi-equation deterrence model that permits distinctions to be made among alternative impacts on driving behavior. The use of such simultaneous systems analysis to consider deterrence questions is not new, and, in fact, has created shock waves in the community of criminologists stemming from its application to the question of the effectiveness of the death penalty. One consequence of the response to initial empirical research using simultaneous systems analysis has been the well-known investigation into the appropriateness of the current deterrence research conducted by Blumstein, Cohen, and Nagin (1978). To avoid the potential pitfalls of much of current research as e n u m e r a t e d by those authors, the approach adopted here is analogous to proof by contradiction. It begins by developing a model in which deterrence is 15ostulated to work and seeks to determine whether, or under what conditions, an apparent decay in control effectiveness with respect to accident control is potentially cnsistent with a world in which deterrence is very much alive. 3 There is hardly a need to conduct a review of the deterrence evidence for such an undertaking. Surveys of the deterrence literature have been done extensively and well by others (Blumstein, Cohen, and Nagin, 1978; Palmer, 1977: Taylor, 1978). That uncer-
l 17
tainty exists about effects is sufficient reason to pursue this approach. The results of doing so suggest that the policy implications provided by much of the existing research may be misguided due to a failure to thoroughly evaluate the evidence. The first step in establishing this conclusion is to develop the model for analysis.
THE DETERRENCE MODEL The central elements of a deterrence model are hardly new, dating back to Becearia, Bentham, and the literature of the Utilitarians. There have been extensive refinements to the logical analysis that have surfaced over time. These provide persuasive arguments for why deterrence may or may not work and the technical basis for more sophisticated empirical studies of existing data to test whether it does. 4 The points to be made here do not require a complex model of behavior. We need merely to postulate that the level of sanctions and the threat of their imposition will deter antisocial behavior. That is, in the shorthand of functional notation, the sum of such behavior can be expressed as: MO = m (PCON~ S, SE),
(1)
where MO is motoring offenses, PCON is the probability of apprehension, conviction, S, is the magnitude of sanctions, and SE is a vector of social, economic, and environmental factors that may have an impact on the sum of individual behavior. Consistent with classical criminological theory and modern decision theory, it is assumed that offense rates are negatively influenced by the expected cost of sanctions. The impact of the social and environmental variables will be discussed further as we proceed. To illustrate the accident problem, it is assumed there is only one class of motoring offenses, drunken driving (DD), and that the only causal factor that contributes to its incidence is alcohol consumption (ALC). To further simplify the problem, assume that sanctions are uniform and invariant so that the relation can be written
118
HAROLD L. VOTEY, JR.
DD = d (PCON, ALC).
(2)
O t h e r things equal, an increase in alcohol c o n s u m p t i o n can be e x p e c t e d to increase d r u n k e n driving. A n d , if d e t e r r e n c e and control w o r k , an increase in the probability of a p p r e h e n s i o n and conviction can be e x p e c t e d to negatively affect d r u n k e n driving levels. T h e probability of conviction is an o u t p u t of the system of criminal justice. P r o d u c t i o n t h e o r y indicates that it should rise with the addition of police and o t h e r system resources and be negatively influenced by the load o f rising offense levels. Thus, we can write P C O N = b ( D D , L),
(3)
where L is an index o f system resources used to c o m b a t the p r o b l e m . A g a i n , for simplicity, let us s u p p o s e that system resources are e x p e n d e d b a s e d on a b u d g e t established in a previous p e r i o d so that, in any given period, L can be t a k e n as given. T h e interaction of the b e h a v i o r a l relation, e q u a t i o n (2), with the system r e s p o n s e , e q u a t i o n (3), will lead to an equilibrium level of system effectiveness ( P C O N ) and level of offenses ( D D ) . A c c i d e n t s , it can be p r e s u m e d , are a function of the level of m o t o r i n g offenses in a recursive fashion and o t h e r factors that affect driving risk such as the nature of the driving e n v i r o n m e n t , the t r a n s p o r t t e c h n o l o g y , and the load on the t r a n s p o r t a t i o n system. This can be c a p t u r e d by the relation: AC = a (DD, MD, VM...
)
(4)
(i.e., accidents, A C , are also a function of mileage driven ( M D ) , and vehicle mix ( V M ) , as well as m o t o r i n g offenses), s O t h e r factors r e p r e s e n t i n g road or vehicle activity, are also likely to influence accident levels. We will assume the latter are invariant for this exercise and, thus, can be safely ignored. E q u a t i o n s (2), (3), and (4) thus comprise a system within which we can m a n i p u l a t e control efforts (L), drinking levels ( A L C ) , and o t h e r factors affecting risk ( M D and VM). Such a m o d e l is useful because it will let us e x a m i n e intervention effects in a
world where responses, normally not observable, are known. This can be d o n e with a simulated annual time series.
SIMULATION OF THE MODEL Consider first a world in which all of the variables are, in fact. stationary. Aside from the possibility of r a n d o m elements entering, the time series will be flat if the e x o g e n o u s f o r c e s - - m i l e a g e driven, alcohol c o n s u m p t i o n , vehicle mix, and resources d e v o t e d to law e n f o r c e m e n t - - a r e invariant over time. Suppose. for example, that the m o d e l and its p a r a m e t e r s are defined as follows: D D = C PCON',' A L C a,
(5)
P C O N = B D D "-I L r~,
(6)
and A C = A D D ~ M D K V M ~.
(7)
Let us assume further a set of p a r a m e t e r values as follows: ~ A = 1460.9 B = 0.0793 C = 0.3711
ct [3 y 6 k
= 0.2 = 0.2 = -0.8 = 0.8 = 0.5 K = 0.5 0 = 4.0.
Suppose initial values for the e x o g e n o u s forces are: ALC = 3.62 M D = 1841 VM = 0.126. Law e n f o r c e m e n t sumed e x o g e n o u s simulation with a jointly d e t e r m i n e d have the values: PCON
=
DD AC
= =
m a n p o w e r (L), is asand invariant for the value of 100. For the variables we will then
0.010
40.07 100.
Let us suppose a time period of 36 years, say 1946-1981. A plot of accidents o v e r
The Deterioration of Deterrence Effects of Driving Legislation the thirty-six .years will show a stationary series for the accident index at the level of 100 (see line A B , Figure 2). Assume some intervention at the midpoint of the series. For example, the adoption of per se laws might m a k e it m o r e easy to obtain convictions for the same level of resource inputs and a given accident level. We could show this in the model by a larger constant term B in equation (6) or by a larger coefficient [5 on resources, indicating a higher marginal productivity and hence output elasticity. In the example, the latter choice is made for illustrative purposes with the new value for fS, after the intervention, of 0.21. Such a change will yield new equilibrium levels of the probability of con,;,iction (0.018), drunken driving (36.18), and hence the level of accidents (95.03). The change in the accident level is indicated in Figure 2 by the interrupted time series line AC. Such a change is readily identified by observation. In the real world we wouldn't observe such a change so readily because the world can be expected to be stochastic rather than deterministic. The effect of such r a n d o m effects on the observed series is illustrated in Figure 3 in which a r a n d o m term has been added to the fiat series for accidents, yielding curve AB. The r a n d o m term has been constrained to have m e a n zero over the series, and, for the two such periods before and after the intervention, the means of the stochastic terms are also zero. The variances are approximately equal for the two subperiods as well. The series used is presented in the Appendix. If the same intervention is imposed on this series, it is not so clear by observation that accidents have been reduced by the intervention, and it is certainly not clear at what point the intervention took place (see curve AC. Figure 3). A calculation of means based on the known intervention date does reveal, however, a statistically significant shift in the accident index, and statistical tests could not reject mid-1963 as the point in time. In such a world, the intervention can be seen to have a permanent effect. D e p e n d i n g upon the interven-
119
tion costs and the social savings inputed to the accidents prevented, it should be a straightforward matter to determine whether the intervention has been socially cost effective.
A NON-PERMANENT INTERVENTION Any time an intervention takes place in an area of concern for the criminal justice system, society is hopeful that the intervention leads to an i m p r o v e m e n t that is p e r m a nent. Perusal of the evidence with respect to many efforts to control drunken driving, however, has led some researchers to the conclusion that, in general, deterrence does not seem to lead to p e r m a n e n t effects. A noted example is the Road Safety Act (1967) in Britain that showed immediate and dramatic results in reducing accidents, but that by Christmas, 14 months later, "it was evident that the benefits were wearing off. ''7 This and other similar evidence has led H. Laurence Ross to state that even in cases "where the increased threat (of punishment) has taken the form of a p e r m a n e n t change in the law, subsequent events have revealed a gradual return of the drinkingdriving problem to the level of a pre-existing trend" (Ross, 1981:1). The effect that appears to be observed is illustrated by the curve drawn in Figure 4. If we observed such a pattern in a case in which the values for the exogenous variables to the system remained invariant as originally postulated, this would imply t h a t ' o u r new "constant" term was not, in fact, invariant over time or that the marginal productivity of criminal justice resources was perhaps declining to the old (or some other) level after the intervention for any n u m b e r of possible reasons. In any event, it would seem to be a clear signal that the intervention was w e a r ing o u t - - l o s i n g its effect. The fact that time series plots reveal such a pattern is hardly evidence that such is the case, however. To be able to m a k e such a point presumes that it can be established that other exogenous forces a r e invariant over this time series, and they rarely are.
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Figure 4. T i m e series of accident index with intervention that decays. A SEEMINGLY NON-PERMANENT INTERVENTION To m a k e this point, it is instructive to consider a m o r e realistic time series. Consider the case in which, over the entire period alcohol consumption per capita for the drinking age population is growing at the rate of 3.2 percent per year. Suppose further that driving distance per capita has been increasing at the rate of 3.7 percent per year. Suppose also that the ratio of twowheel to four-wheel vehicles has been declining at the rate of 1.1 percent per year. All of these changes are consistent with a rising per capita income and, in fact, are not so different from secular growth rates that have been o b s e r v e d for a n u m b e r of countries. ~ Suppose, at the same time, that resources devoted to the p r o b l e m continue to be held fixed. The time series that will be generated by these effects, still incorporat-
ing the same intervention effect originally postulated, will appear as curve A C in Figure 5. If we could isolate and eliminate the stochastic term, the series would a p p e a r as curve D E in Figure 6, and the p e r m a n e n t effect of the intervention, beginning in mid-1963, is clearly revealed. If we c o m p a r e the observed accident rate (AC) with the long-term trend, however, we would be misled. The vagaries of the error structure a p p e a r to indicate that the effect of the intervention may have taken place a year earlier in mid-1962. F u r t h e r m o r e , by 1971 the entire effect of the intervention appears to have decayed, and the long-term pattern falls largely above the linear trend line. Even if we examine the series with the stochastic element removed (as shown in Figure 6), we see that by 1979, sixteen years after the intervention, the accident series has risen above the long-term linear trend.
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The Deterioration of Deterrence Effects of Driving Legislation
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The Deterioration of Deterrence Effects of Driving Legislation The explanation, in this case, is that, while drinking-driving is affected, the pattern of driving, of alcohol consumption, and of change in the vehicle mix all continue unabated over time. Since all of these have independent influences on accidents, accident rates continue to change as well, even after taking into account the partial effect of the reduction in drunken driving achieved through more effective policy. In this example, the change from the low of 1964 can be partitioned into several partial effects: a 26 percent rise in accidents as alcohol consumption rises, a 13 percent rise attributable to an increase in driving intensity, and an offsetting 32.2 percent decline in accidents due to a beneficial change in the vehicle mix, yielding a net increase of 6.8 percent in accident levels by 1971 that overshadows the p e r m a n e n t effect of the intervention. The failure in this case is n o t in the effectiveness of the measures adopted, but it is a policy failure in the sense that resources devoted to control have not been augmented as the magnitude of the problem has increased. If this is the path that policy takes, then an apparent decay in the effectiveness of intervention should be the expected result and no surprise at all. If such a series, incorporating the postulated intervention, is regarded as an "interrupted" time series and examined simply by visual inspection, a researcher is virtually certain to make a Type II error, rejecting the hypothesis of a deterrent effect when in fact it is true. In this case, statistically more sophisticated tests would reveal a shift in the intercept. That is, since all of the exogenous variables that are varying do so at exponential rates, the elimination of the exponential trend and separate analysis of the pre- and post-intervention series would reveal such a shift. The problem would be more difficult if the exogenous variables bear the same relationship to the jointly determined variables but move in a less systematic fashion. Multiple regression analysis would identify the parameters of the system if sufficient information were available. In the case of drunken driving, however, we are unlikely to have a satisfactory means to measure the
125
level o f drunken driving or the probability of conviction. It is not even easy to measure the level of criminal justice inputs. With a sufficient index of criminal justice inputs (L) and the other exogenous variables (MD, VM, A L C ) , a reduced form relation could be estimated that would reveal the impact of criminal justice resources on accidents, and similarly, the extent to which increasing alcohol consumption and driving levels contribute to the apparent decay of the deterrence effect• The difficulties of such analysis cannot be treated lightly. It will be obvious to any statistician that, with the data series postulated, it would be impossible to relate the impact of the intervention to criminal justice inputs simply because there is no variance in that series. We are faced here with the situation that a powerful and socially important consequence of policy cannot be measured directly. Of course, we could isolate the reality of the intervention effect with a dummy variable reflecting the intervention point, and it would turn out to be highly significant. Even this will be difficult to isolate, however, in cases in which, for whatever reason, enforcement resources do not continue to be applied consistently after the intervention, and the variation in their application cannot be measured.
IMPLICATIONS FOR POLICY The implications of these illustrations should not be ignored if we wish to avoid policy error. A common stance for policymakers is that, if we can't prove that a particular policy measure is effective, we cannot justify the expenditure of public funds. In an era in which many of us are concerned with the misuse of public funds, that seems a laudable position to take. It presumes, however, that the possibilities for errors all fall on one side, that is, we can err by spending, but not by not spending, so that there is no cost to making a Type II error. In the case of attempting to reduce highway fatalities, this is simply not true. There is a gigantic cost to failing to enforce
126
HAROLD L. VOTEY, JR.
laws that work. Failing to enforce them because we can't prove they work is no more defensible logically than enforcing them at public expense because we have faith in the theory and logic of deterrence. The implications of such an error can be seen by returning to Figure 1. Suppose society is at a point such as DD1 in terms of incidence of drunken driving. Should policymakers conclude that control is ineffective and reduce control efforts from a cost level of C~ to C3, when in fact control works, victim's costs would rise from V~ to V> The difference between V3 and VI less the reduction in e n f o r c e m e n t costs (Ct minus C3) would be the cost of a T y p e II error. In contrast, the cost of a T y p e I error, accepting the hypothesis that control works when it doesn't, would be the cost of increased e n f o r c e m e n t (C0 minus C~) in attempting to reach DD0, when in fact DD~ would continue to prevail. If we had no prior estimates of the probability that either hypothesis were true and assumed either equally likely, the expected benefits would obviously be greater from attempting to diminish drunken driving by increased control expenditures. While this a p p e a r s to be the direction society wants to m o v e , it tends to be in spite of the message coming from much of the research literature. Past decisions in regard to e n f o r c e m e n t or non-enforcement, of drinking and driving laws have been m a d e largely on the beliefs of policymakers based on theory and logic but in the absence of prooffl Because of the nature of statistical techniques c o m m o n l y being utilized to evaluate driving legislation and e x p e r i m e n t a l e n f o r c e m e n t actions, it is highly likely that the influences on policymakers from the research c o m m u n i t y have been to push them away from this position in the direction of potential T y p e II errors. The reasons are clear. Most studies do not go as far as the data will permit in taking into account influences that may be obscuring the existence of deterrence effects. For example, none of the studies cited by Ross (1981) take into account the m a n y exogenous factors influencing accident levels or even standardize for variations in enforce-
ment intensity. In spite of this, one finds the statement repeated frequently in print that interventions and p e r m a n e n t policy changes invariably 10se their effectiveness. One is not justified in drawing such conclusions, however, unless one can clearly d e m o n strate that other exogenous forces that contribute to drunken driving or accidents in general cannot have simply overshadowed highly effective policy. The threat of punishment may be deterring drunken driving, but if the population of drinkers is increasing as more persons drink, or if the average drinker consumes more, the threat may only m o d e r a t e the rise in drinking-driving. Likewise, if all people are driving more, or more people have access to vehicles, per capita accident rates are likely to rise in spite of more severe enforcement. Simply showing that the levels of blood alcohol a m o n g killed and injured in motoring accidents have not fallen does not establish the lack of deterrence in the face of the increase in the potential population of drunken drivers, m The concern over such matters is not an idle one, since it can easily be established that rising per capita alcohol consumption has been the norm across the broad array of countries in recent years. For example, in Norway from 1954 to 1978, the annual growth of per capita consumption has been 3.27 percent. For Sweden from 1960-1976, it has been 2.75 percent. For a list of 28 mostly advanced industrial nations between the years of 1973 and 1977, twenty-two have shown increases, and those that have declined have done so only moderately relative to the rates of increase. See Table 1 for a partial list of these countries" experience. For the United States in that period, the average annual rise was 5.73 percent, for Canada 2.9 percent, and for England 1.25 percent. As is made clear by the model, such rises in alcohol consumption levels would easily a p p e a r to swamp intervention effects. Driving levels have risen uniformly in these same countries prior to the oil crisis in the early 197(1s. To be able to effectively impute the impact of driving levels on
127
The Deterioration of Deterrence Effects of Driving Legislation
TABLE 1 ANNUAL CONSUMPTION PER CAPITA OF 1 0 0 % ALCOHOL FOR
SELECTED COUNTRIES, 1973 AND 1977 (liters)
Country
1973
1977
Average Annual Rate of Change
(%) France Portugal West G e r m a n y Australia New Z e a l a n d Denmark Canada England Holland United States Finland Sweden Norway
16.9 12.8 12.3 8.7 8.4 8.3 8.0 7.8 7.5 6.6 5.7 5.5 4.0
16.4 14.0 12.4 9.7 9.5 8.9 9.0 8.2 8.8 8.3 6.4 5.7 4.4
-0.75 2.25 0.24 2.72 3.08 1.74 2.95 1.25 4.00 5.73 2.50 0.89 2.38
SOURCE: Centralforbundat for alkohol-och narkotihaupplyshning, Rapport, Stockholm: 1973, 1979.
accidents since that time, one would need to know the extent that they have changed for the high risk population. It is easily possible that changes in driving levels, vehicle mix, and congestion on highways have had a measurable impact on changes in accidents that may further help to conceal the effects of interventions in virtually any country that has a t t e m p t e d them. Investigations into the effects of c o u n t e r m e a s u r e s over time that fail to account for such factors can m a k e no claim to isolating the effects that are of importance for policy decisions. It is interesting to note that the "Blennerhassett Report'" had investigated all of these factors in a c o m p r e h e n s i v e analysis of the "'wearing o f f " of the British R o a d Safety ~/ct's effectiveness ( H M S O , 1976:12). There has not been to date, however, any effort to measure the extent of the effects of the factors involved in order to determine whether or not any residual of the deterrence effect still exists, although the "'Cheshire blitz" was an experiment that lends support to the arguments presented here.l~
The danger of suggestions that deterrence measures don't last is that they are likely to be, to a very real extent, self-fulfilling. If e n f o r c e m e n t officers come to believe a measure has no effect, they will cease to invoke it. The prosecutors will cease to prosecute, and the courts will cease to sanction. For all of them to participate in such Type II errors could be a socially costly mistake. Researchers have an obligation to be sure they don't inadvertently contribute to such a process by failing to point out that an intervention that is " p e r m a n e n t " (e.g., a change in the law) can only have a p e r m a nent effect if the resources to enforce it k e e p up, over time, with the causal forces that are generating drinking-driving behavior. 12Failing this, the most that one should hope for in any modification of law e n f o r c e m e n t or sanction policy is that the result will be a lower level of accidents than would have occurred in the absence of the change and that the reduction costs no more to achieve than the value of life, health, and p r o p e r t y that is preserved. Determining whether one
128
H A R O L D L, VOTEY, JR.
o r t h e o t h e r is t r u e c a n o n l y b e p o s s i b l e w i t h research designs adequate to take into a c c o u n t all t h e c r u c i a l c h a n g e e f f e c t s . B a s ing policy recommendations on anything less will s u r e l y r e n d e r a disservice to society.
REFERENCES Becker, G.S. (1968). Crime and punishment: An economic approach. J. o f Pol. Ec. 2:76, 169-217. Codling, P.J. (1972). Weather and road accidents. In Proceedings: Symposium on climatic resources and economic activio,, ed. University College of Wales. Wales: Aberystuy. Blumstein. A.; Cohen. J . and Nagin, D. (1978). Deterrence and incapacitation: Establishing the efti'cts o f criminal sanctions on crime rates. Washington, DC: The National Academy of Sciences.
NOTES t This point is made in greater detail in Votey (1977). -+Ross (1981) provides us with a comprehensive enumeration of the evidence.
Her Majesty's Stationary Office (HMSO) (1976). Drinking and driving: Report of the departmental committee. London: Department of the Environment.
Such a model, used as a basis for empirical analysis, is presented in Votey (1982).
Johnson. H.D. (1970). Road accidents and casualty rates in 1968: Report LR 348. Crowthorne, England: Transport and Road Research Laboratory.
The revitalization of these ideas owes much to Gary Becket (1968) and to a host of economists who have made use of his refinements in the original theory.
Palmer, J. (1977). Economic analysis of the deterrent effect of punishment: A review. J. of Res. in Crime and Delinq. 1:14, 4-21.
The effects of environmental and technological factors have been studied extensively in many places. Of particular note is the voluminous list of papers from the (British) Transport and Road Research Laboratory. See for example Codling (1972) and Johnson (1968).
Phillips, L., and Votey, H.L., Jr. (1981). The economics o f crime control. Beverly Hills, CA: Sage Publications, Inc. Ross, H.L. (1975). The Scandinavian myth: The effectiveness of drinking and driving legislation in Sweden and Norway. The J. of Legal Studies 4:285-310,
Parameter values used for illustrative purposes are similar to those from a study of Norway, Votey (1978). 7 Her Majesty's Stationary Office (HMSO), Drinking and Driving: Report o f the Departmental Committee (The Blennerhassett Report) London: Department of the Environment (1976). s These are, in fact, the growth rates observed in Norway 1954-1975. ~ This is the point of Ross (1975) with respect to the Scandinavian experience. tu HMSO, Drinking and Driving, p. 12, notes that by 1971 the proportion of drivers killed with excess blood alcohol had reached the pre-Road Safety Act levels (25 percent) and continued to rise to a new high (34 percent). H Ross, Deterrence o f the Drinking Driver, pp. 38ff summarizes the Cheshire experiment of September 1975.
- (1981). Deterrence o f the drinking driver: An international survey. Washington, DC: National Technical Information Service. Taylor, J.B. (1978). Econometric models of criminal behavior: A review. In Economic models o f criminal behavior, ed. J.M. Heineke, pp. 35-81. Amsterdam: North Holland. Votey, H.L., Jr. (1977). A rational policy for the control of accidents caused by motoring offenses. Oslo: Institute of Transport Economics. --(1978). The deterrence of drunken driving in Norway and Sweden: An econometric analysis of existing policies. In Drinking and Driving in Scandinavia, ed. R. Hauge. Scandinavian Studies in Criminol+ 6: 79-99. -
t,, This point has been made in regard to major felony crime in Phillips and Votey (1981), Ch. 10.
(1982). Scandinavian drinking driving control: Myth or intuition. The J. of Legal Studies 11:93-116. -
129
The Deterioration of Deterrence Effects of Driving Legislation
APPENDIX S T A T I O N A R Y SERIES
No Intervention
With Intervention
Year
Accidents
Stochastic Accident Series
Random Error Term
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 197/I 1971 1972 1973 1974 1975 1976 1977 1978 1979 198(} 1981
100.016 1110.016 100.016 100.016 1110.016 100.016 100.016 100.016 100.016 100.016 100,016 100.016 100.016 1011.016 10(L016 1011.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100.016 100,016 100.016 100,016 100,016 100.016 100.016 100.016 100.016 100.016 100.016 1110.016
102,331 95.5649 103.254 100.265 100.570 98.6159 98.9859 101.212 98.5289 104,419 96.4649 101.841 102.740 102.187 98.1189 97.0149 101.889 96.2849 103.749 101.190 97.7329 96.7549 101. 802 96.6879 96.7379 102. 352 102.820 98.8899 101.251 100. 582 96. 3409 101.803 103. 470 102.278 96. 0839 99.7599
2.3151111 -4.45100 3.230011 0.24900(I 0.5541100 - 1.400011 - 1.03000 1.1961111 - 1.48700 4.40300 -3.55100 1.82500 2.72400 2.17100 - 1.89700 -3.00100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1.78600 - 3.32800 - 3. 27800 2. 33600 2.80400 - 1,12600 1,23500 0,566000 - 3,67500 1.78700 3. 45400 2. 26200 - 3. 93000 -0.256000
Mean Std. Dev.
100.0 0.0
100.0 2.59
0.0 2.59
Period I Mean Std. Dev.
100.0 0.0
100.0 2.58
0.0 2.58
Period 11 Mean Std. Dev.
100.0 0.0
100.0 2.60
0.0 2.60
Accidents
Stochastic Accident Series
Random Error Term
1011.016 100.016 100.016 100.016 1110.016 100.016 100.016 100,016 100,016 1110.016 100.016 100.016 100.016 1/10.016 100.016 100,016 100.016 100.016 95.0270 95.0270 95.0270 95.0270 95.0270 95. 0270 95.0270 95. 0270 95.0270 95.0270 95.0270 95. 0270 95. 0270 95.0270 95. 0270 95. 0270 95. 0270 95.0270
1112.331 95.5649 1113.254 1(10.265 100.5711 98.6159 98.9859 101.212 98.5289 104.419 96.4649 101.841 102.7411 102.187 98.1189 97.0149 101.889 96.2849 98.7600 96.2010 92.7440 91.7660 96.8130 91. 6990 91. 7490 97. 3630 97.8310 93.9010 96.2620 95. 5930 9 I. 3520 96.8140 98.4810 97.2890 91.0970 94.7710
2.315.1111 -4.45100 3.2311t 10 0.2490(10 0.55400(I - 1.401100 - 1.(}30(}0 1.19600 - 1.48711(I 4.4(}30(} -3.55100 1.825(10 2.7240() 2,171(10 - 1,89700 -3,0(1100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1. 78600 - 3. 32800 - 3.27800 2. 33600 2.80400 - 1.12600 1.23500 0. 566000 - 3. 67500 1.78700 3. 45400 2. 26200 - 3. 93000 -0.256000
97.5 2.99
97.5 3.60
0.0 2.59
100.1 0,0
100.1 2.58
0.0 2.58
95.0 0,0
95.0 2.60
0.0 2.60
130
H A R O L D L. VOTt~Y. JR.
TRENDED
SERIES
With Intervention
No Intervention
Year
Accidents
Stochastic Accident Series
Random Error Term 2.31500 -4.45100 3.23800 0.249000 0.554000 - 1.40000 - 1.03000 1.19600 - 1,48700 4.40300 -3.55100 1.82500 2.72400 2.17100 - 1.89700 -3.00100 1.87300 -3.73100 3.73300 1.17400 -2.28300 -3.26100 1.78600 - 3.32800 - 3.27000 2.33600 2.8(1400 - 1.12600 1.23500 0.566(100 - 3.67500 1.78700 3.4540(I 2.262(10 - 3.93(100 -0.256000
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 197(I [971 1972 1973 1974 [975 1976 1977 1978 1979 198(I 1981
81.7439 82.6311 83.6778 84.7027 85.7000 86.6597 87.6130 88.7677 89.9101 90.7670 91.7843 93.1093 94.1519 95.3647 96.2967 97.7216 98.9064 100,016 100.902 1(11.793 102.957 103,722 104.791 105.837 106,827 107.784
84.0589 78.1301 86.9158 84.9517 86.2540 85.2597 86.5930 89.9637 88.4231 95.1700 88.2333 94.9343 96.8759 97.5357 94.3997 94.7206 100.779 96.2849 104.635 102.967 10(/. 674 100.461 106.577 102.509 103.549
109.072
111.876 108.805 112.378 112,481 109.363 115.895 118,590 113.812 113,550 118,512
Mean Std. Dev.
L(}0.0 10.99
Period 1 Mean Std. Dev. Period 11 Mcan Std. Dev.
109,931 111.143 111.915 113,038 114,108 115,136 116.550 117.479 lt8.838
110.120
Accidents 81.7439 82.6311 83.6778 84.7027 85.7(700 86.6597 87,6130 88.7677 89.9101 90.7670 91,7843 93.1093 94.1519 95.3647 96.2967 97.7216 98.9064 100.016 95.8690 96.7155 97.8211 98,5482 99.5636 100.557 101,498 102.408 103.631 104.447
105.599 106.332 107.399 108.416 1(19.393 110.736 111.629 112,911
Stochastic Accident Series
Random Error Term
84.0589 78.1301 86.9158 84.9517 86.2540 85.2597 86,5830 89.9637 88.4231 95. 1700 88.2333 94,9343 96.8759 97.5357 94,3997 94, 7206 10(I.779 96.2849 99.6020 O7.8325 95.5301 95.2872 I01.35(7 97.2292 90,2205 104.744 l(16,435 103,321 l(16,834 106.898 103,724 110.203 112.847 112.998 107,699 112.655
2.31500 -4,45100 3.23800 0.249( )0O 0.554000 - 1.40000 - 1.0300(} I. 196(I(} - 1.48700 4.40300 -3.55100 1.82500 2. 72400 2.17100 - 1.89700 -3.00100 1.8730(1 -3.73100 3.73300 I. 174(10 -2.28300 -3.26100 1.78600 - 3.32(100
- 3.27800 2.33600 2.8( )4( )0 - I. 12600 1.23500 0.566000 - 3.67500 1.78700 3.45400 2.26200 - 3.93000 -0.2560( )0
0.0 2.59
97.3 8,65
97.3 8.92
O.0
I 1.20
90.5 5.58
90.5 5.83
0.0 2,58
90.5 3.58
90,5 5.83
0.0
109.5 5.43
I(19.5 6.01
0.(I 2.60
104.1 5.16
104. l 5.77
0.0 2.60
100.0
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..)8