An Evaluation of the Swedish Drunken Driving Legislation Implemented on February 1, 1994 B. Borschos

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1 An Evaluation of the Swedish Drunken Driving Legislation Implemented on February 1, 1994 B. Borschos Centre for Social Research on Alcohol and Drugs, Stockholm University S Stockholm, Sweden Abstract Today there are two levels of drunken driving in Sweden (drunken driving and aggravated drunken driving). In the year 1990 the lower BAC limit was reduced from 0.05 to 0.02 percent. Some years later, in 1994, the upper BAC limit was also reduced from 0.15 to 0.10 percent. In addition, the maximum penalty for aggravated drunken driving was increased and the police got more far-reaching possibilities of control. This study examines the effects of the most recent drunken driving legislation on fatal road-traffic accidents and road-traffic accidents with severe personal injury. The assessment of the effect is carried out by ARIMA analysis on monthly data for 1986 to The models include alcohol sales (measure of alcohol consumption) and delivery of petrol to retailers (measure of traffic density) as well as the two legislative reforms implemented in 1990 and The analysis showed a significant reduction of fatal road-traffic accidents depending on the 1990 and 1994 legislation. The results concerning road-traffic accidents with severe personal injury are more unstable, but the results points in the same direction. Keywords Driving under the influence, Evaluation, Legislation, Traffic accidents, Time series research Introduction A Swedish law on drunken driving was first introduced in Today s legislation was implemented in 1951 and consists of two levels of drunken driving drunken driving and aggravated drunken driving. Since the beginning of the 1990s there have been two major reforms of the drunken driving legislation in Sweden. The first was implemented on July 1 st 1990 and the second on February 1 st The 1990 legislation involved two amendments. The lower BAC-limit was reduced from 0.05 to 0.02 percent and, from the judicial system s point of view, there were shifts in judgement of the severity of drunken driving and the attitude to sanctions in relation to drunken driving (1). Partly as a reaction to the 1990 legislation, the 1994 legislation, which focused upon aggravated drunken driving, entered into force. The new legislation involved: (A) a reduction of the upper BAC-limit from 0.15 to 0.10 percent; (B) an increase from one to two years imprisonment as the maximum punishment for aggravated drunken driving and; (C) imprisonment should once again be the usual punishment for aggravated drunken driving. Several other legislative changes with close connection to the drunken driving legislation was also implemented at the same time (1). The main purpose of these changes was to increase road safety (2,3). The correlation between drunken driving and the risk of traffic accidents has been established on the individual as well as the aggregate level. On the individual level there is an increase in accident risk already when the BAC level is below 0.05 percent (4,5). Furthermore, the individual risk of traffic accidents increases exponentially with the BAC (4,6). For example,

2 the risk of a fatal single-vehicle accident almost doubles with each 0.02 percent increase in the BAC (7). In terms of relationship between intoxication and the accident s degree of severity, the more severe the type of accident the larger the percentage of the drivers who will be intoxicated (8). This conclusion is supported by Swedish data. More than half of those killed in single-vehicle road-traffic accidents were driving under the influence of alcohol (9). Also, the percentage of alcohol-induced traffic accidents increases with the severity of the accident (10). In the light of this individual-level correlation, there should exist a similar connection between drunken driving and the number of traffic accidents on the aggregate level. The correlation between aggregate alcohol consumption and the number of reported drunken driving offences has been shown in different studies (11,12). Several studies have furthermore established a correlation between aggregate alcohol consumption and the number of reported traffic accidents (10,13,14). The aim of this study is to investigate to what extent the 1994 legislation has contributed to improve road safety. A prerequisite for such legislation to be successful in increasing road safety is a preventive effect on drunken driving. Such behavioural changes can only follow from this kind of legislation if the changes are in line with the legal traditions of the country (15). The conditions are favourable for a successful legislative change in the field of drunken driving in Sweden. The drunken driving laws are well known to Swedish inhabitants. Different opinion polls have furthermore shown that the Swedish population is in favour of restrictive drunken driving legislation (16). Other studies show that drunken driving is regarded to be a serious offence by much of the general public (17,18). Drunken driving also tends to be viewed as an irresponsible behaviour (19). Method The impact assessment of the 1994 legislation will be carried out using interrupted timeseries analysis. The basic idea behind this quasi-experimental method is to compare values in a time-series before and after an intervention. The intervention effect is then estimated with statistical analysis. A common problem when analysing time-series data is that the trend in the series can produce a spurious correlation between the input and the output series. By using the ARIMA method (20), it is possible, by means of differencing, to eliminate the trends in the series. Differencing yields stationary series, i.e. series without any trend, by creating a new series consisting of the change between observations according to equation (A). When analysing monthly data, it can be necessary to apply a different kind of differencing. Subtracting from the value, for example in January, the value in the previous January seasonally differences the series (see equation (B)). (A) ÑY t =Y t -Y t-1 (B)ÑY t =Y t -Y t-12 Another important advantage of the ARIMA method is that the error-term, which includes all other causal factors, is allowed to be systematic. The systematic variation in the error- or noise-term can then be included in the model by using appropriate parameters, so-called autoregressive (AR) and moving average (MA) parameters. Thus, it is possible to statistically control for causal factors not included in the model.

3 The impact of the 1994 legislation will be estimated from changes in the number of fatal road-traffic accidents and accidents resulting in severe personal injury. All statistics used in this analysis are monthly data from January 1986 to December The data on number of traffic accidents are from official statistics and do not have any direct connection to impaired driving. This poses a problem, since there is no possibility to select the alcohol-related accidents. However, the studies of individual risk tell us that a reduction of number of intoxicated drivers should manifest itself as a reduction of traffic accidents at the group level. Changes in the number of traffic accidents can be explained by several different factors besides an alteration of the laws on drunken driving. The two most relevant factors are traffic density and alcohol consumption. Increased per capita consumption reflects an increase in the number of drinking occasions. Hence, other things being equal, an increase in the number of driving occasions and the number of drinking occasion are likely to be followed by an increase in the number of traffic accidents. Consequently, if not controlled for, changes in traffic density and per capita consumption may produce effects which might be confounded with the effect of the legislative intervention. Deliveries of petrol to petrol stations will be used as an indicator of traffic density. The data refers to the official number of 100 million litres delivered. Retail sales of alcohol (centilitres of pure alcohol) per inhabitant are used as an indicator of the total alcohol consumption. The validity of this consumption indicator is not perfect, since it does not include the consumption of illegal/unregistered alcohol, and neither is the sale of medium strength beer or alcohol sold at the restaurants included in this amount. However, since the analysis is based on the monthly change in alcohol consumption rather than the absolute levels this should reduce the magnitude of the problem. Furthermore, the analysis must take into account the effects of the legislation implemented in This is possible if a dummy variable is included in the model, taking the value zero before the intervention (until June 1990) and the value one after. The intervention effect evaluated in this article is measured by using a similar dummy variable, which takes the value zero until January 1994 and then one. Figures 1a-1d show the number of fatal traffic accidents, accidents with severe injury, deliveries of petrol and sales of alcohol in Sweden, from January 1986 to December There are marked seasonal variations and downward trends in the accidents and alcohol series. Deliveries of petrol also show a seasonal pattern, but have been constant during the period.

4 Fatal Severe injury Petrol Alcohol Figure 1a-1d: Number of fatal traffic accidents, accidents with severe injury, deliveries of petrol and sales of alcohol in Sweden, from January 1986 to December The intervention effect is estimated with semi-logarithmic models. There are two main reasons for specifying the models as semi-logarithmic. Firstly, the variance becomes more stable, which is an important feature of a stationary series. Secondly, the estimates in a semilogarithmic model are easily interpreted. If multiplied by one hundred the estimated parameters approximately represent the percentage change in the output series by a unit change in the input series. When seasonally differenced, the auto-correlation function (ACF) from the output series shows signs of being over differenced (r>-0.5 on lag 1). Since over differenced series yields less stable estimates, an alternative solution has been examined. Differencing on lag 1 yields stationary series and reduces the variance, which indicates that this is a better solution then differencing on lag 12. One hazard when differencing is that when the systematic variance is reduced the possibility of rejecting a true hypothesis of correlation between two series increases (10). Consequently, it can be of relevance, as a complement, to estimate the models on undifferenced series. In the present analysis the intervention effect will be estimated directly in the model. Another possibility is to estimate the intervention effect on the residual series from a model including all control variables. This can however result in a biased estimate, since the AR and MA parameters included in the first model are likely to explain a part of the intervention effect. Thus, the following models will be estimated: (1a) ÑlnFat 1 =bñalc 1 + bñpetrol 1 + bñd bñd lnn (1b) lnfat 1 =balc 1 + bpetrol 1 + bd bd lnn (2a) ÑlnAcc 1 =bñalc 1 + bñpetrol 1 + bñd bñd lnn (2b) lnacc 1 =balc 1 + bpetrol 1 + bd bd lnn In these equations, LnFat denotes the natural logarithm of fatal traffic accidents, LnAcc the natural logarithm of traffic accidents with severe personal injury, Alc the alcohol sales and

5 Petrol the deliveries of motor petrol to retailers. Since deliveries of motor petrol is used an indicator of miles driven by car, there is reason to expect a certain lag in the effects on accidents. Therefore the series has been constructed as the average delivery of petrol during the three previous months. This is the same lag-scheme that was empirically constructed and used in an analysis performed by Norström and Andersson (10). D9007 denotes the intervention effect of the 1990 legislation and the D9402 the intervention effect to be assessed in this article. The operator Ñ indicates that the series is differenced at lag 1 and b is the parameter to be estimated. N represents the noise term, which includes other causal factors. Results The result from the previously specified models is summarised in Tables 1 and 2. When the model is adequately specified the noise term consists only of random variation. This is often referred to as white noise and indicates a well-specified model. Three different diagnostics indicate that the noise terms in our analysis are white noise. Firstly, there are no autocorrelated residuals. Secondly, The average value of the noise term is zero. Thirdly, the noise term is normally distributed. The models show a statistically significant effect between number of fatal accidents and alcohol consumption and traffic density respectively. When alcohol sales and deliveries of petrol increase the number of accidents tends to increase. Table 1: Estimated effects of alcohol consumption, traffic density and legislative changes on different types of traffic accidents. Semi-logarithmic models estimated on differenced monthly data from January 1986 to December Model 1a Fatal accidents Model 2a Severe injury Estimate SE Estimate SE Control variables Alcohol 0.015** * Petrol 0.320** ** Reform * (*) Intervention effect Reform ** Noise MA(1) 0.980** ** MA(2) ** SAR(1) 0.258** ** Diagnostics 1 Q=17.6 p=0.616 (lag 20) Q=23.1 p=0.284 (lag 20) 1 Q: Box-Ljungs test for auto-correlated residuals. ** p<0.01 * p<0.05 (*) p<0.1 Table 2: Estimated effects of alcohol consumption, traffic density and legislative changes on different types of traffic accidents. Semi-logarithmic models estimated on raw monthly data from January 1986 to December 1997.

6 Model 1b Fatal accidents Model 2b Severe injury Estimate SE Estimate SE Control variables Alcohol 0.015** * Petrol 0.327** ** Reform * ** Intervention effect Reform ** * Noise Constant 1.945** ** MA(1) ** SAR(1) 0.252** ** Diagnostics 1 Q=16.3 p=0.695 (lag 20) Q=23.6 p=0.261 (lag 20) 1 Q: Box-Ljungs test for auto-correlated residuals. ** p<0.01 * p<0.05 Since the intervention parameters are dichotomous variables (0 and 1), the estimates (multiplied by one hundred) represent the average change in percent between the preintervention and the post-intervention periods. Approximately, there was a ten-percent reduction in fatal accidents caused by the 1990 legislation and a 13 percent reduction after the 1994 legislation. The results concerning accidents with severe personal injury are not as certain as the results concerning fatal accidents. When assessing the intervention effect from the 1994 legislation on differenced series the model does not yield a statistically significant parameter. At the same time the parameter referring to the 1990 legislation is a borderline case with regard to statistical significance. Since there is an increased risk of rejecting a true hypothesis of correlation between two time-series when differencing the series we turn to the models estimated on raw data. The models estimated on raw data shows significant effects from all control variables as well as the intervention effect on the number of traffic accidents. According to this analysis a 12 percent reduction of accidents with severe personal injury followed the 1990 legislation and a 9.5 percent reduction after the 1994 legislation. The fact that these parameters are similar to the parameters from the model estimated on differenced series (ten and six percent reduction respectively) speaks in favour of these being stable results. To sum up, the 1994 legislation is followed by a statistically significant reduction in number of fatal accidents. The intervention effect on the number of accidents with severe personal injury is less certain. With a slightly bigger risk of accepting a false hypothesis of a negative correlation between the intervention and number of accidents, model 2b indicates that the intervention also affected the number of accidents with severe personal injury. On the basis of the estimated intervention effects in models 1a and 2a it is possible to calculate how much of the reduction in traffic accidents that can be attributed to the intervention. The average reduction in number of fatal accidents and accidents with severe personal injury during the post-intervention period are 27.6 and 23.5 percent respectively. This implies that the intervention has contributed to approximately 50 percent (13/27.6=0.47)

7 of the reduction in fatal accidents and 25 percent (6/23.5=0.255) of the reduction in accidents with severe personal injury. Discussion There has been a decline in number of fatal traffic accidents and accidents resulting in severe personal injury during the nineties. This analysis has shown that a substantial part of this reduction can be attributed to the reformations of the drunken driving legislation, which were implemented in 1990 and The effect of the legislation implemented in 1990, has previously been evaluated (10). This study used monthly data during the period from July 1985 to June 1993 and found a statistically significant reduction of reported traffic crashes following the new legislation. According to Norström and Andersson (10) there was an eight-percent reduction in fatal accidents as a consequence of the 1990 legislation (controlling for changes in traffic density and alcohol consumption). Their result corresponds to the estimated effect in this evaluation. Furthermore, it is shown that approximately 50 percent of the reduction in fatal accidents and 25 percent of the reduction in accidents with severe personal injury during the postintervention period can be attributed to the legislation implemented in As previously mentioned the interpretation of the results from this study can only be true if there is an actual reduction in drunken driving. Other data support a reduction in drunken driving. According to a series of surveys conducted yearly since 1981 there has been a reduction in number of persons reported driving after consumption of alcohol (1). According to other official accident statistics there has been a more pronounced decline in number of intoxicated drivers involved in traffic accidents than persons involved in traffic accidents in general. Since the analysis is carried out on the aggregate level, using interrupted time-series analysis, there is no possibility to isolate the effects of the intervention from other legislative changes implemented at the same time, i.e. on February 1 st Hence, it is not possible to ascribe the intervention effect specifically to reduced BAC levels, increased maximum punishment for aggravated drunken driving or even both these changes. Instead the limitations of the analysis compel us to conclude, that these changes, together with enlarged police control measures, changes in the law concerning the offence of causing death, bodily harm or illness and an increase in scope for using a pre-sentence inquiry, affected road safety. Intuitively, the effects of the alteration of the law on drunken driving in association with the increase in police control measures exceeds the effects of the other changes. The effectiveness of the 1994 legislation on road safety depends on previous countermeasures against drunken driving. The rather extensive legislation on drunken driving and the farreaching control possibilities, with reference to drunken driving, allotted to the Swedish police, most certainly interact with the reform evaluated in this article. Furthermore, the provision that all drunken drivers have their licence suspended and that, since 1991, every driver found guilty of aggravated drunken driving has to turn in a certificate of sobriety in order to get their licence back, probably interact with the intervention. In several international studies suspension of driving licence has been shown to have positive effects on the level of drunken driving (21). Also other factors with no direct connection to drunken driving legislation, such as mass media coverage of drunken driving accidents and campaigns against

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