Are traffic violators criminals? searching for answers in experiences of European countries

1 ARE TRAFFIC VIOLATORS CRIMINALS? SEARCHING FOR ANSWERS IN EXPERIENCES OF EUROPEAN COUNTRIES 1. INTRODUCTION. Road traffic accidents (RTAs) are a major cause of mortality and serious morbidity with high costs along the entire European Union (EU) geography (Castillo-Manzano et al., 2014; Orsi et al., 2012). Worldwide, among the issues that contribute to the occurrence of RTAs, most of studies highlight behavioral factors as a principal explanation; particularly those that reduce capabilities (drowsiness, fatigue, alcohol or drug consumption) or increase risk while driving (traffic offences, driver stress, violence and aggressiveness in traffic, motor vehicle crimes) (Petridou & Moustaki, 2000 analyze this classification in depth). After initial studies such as Portefield (1960), in the last two decades special attention has been paid in the literature to the connection between road traffic safety and criminal behavior. The roles of both traffic law enforcement (TLE) and legal systems have been highlighted as deterrence and punitive measures to improve compliance with traffic rules (see the comprehensive international overviews provided by Brace et al., 2009 and Zaal, 1994, respectively). Much research has provided evidence worldwide that people with antisocial attitudes who commit aggressive and other offences due to a lack of assimilation of social norms, are more frequently involved in risky situations in general (see e.g. Fischer and Poland, 1998 on how social exclusion may even serve as a control mechanism when traditional means of social control such as punishments or deterrence are ineffective or insufficient) and are specifically more prone to RTAs and traffic violations (Broughton, 2007; Săucan et al., 2012; Sümer, 2003; West et al., 1993). 2 In fact, the relationship between road safety and criminality has been addressed by applying different psychological, sociological and criminological research approaches (Brace et al., 2009; Junger et al., 1995a, for a broad analysis). For instance: the tendency to commit crime according to personality traits (with the theory labeled as “Mild Social Deviance” based on involvement in accidents associated with expression of hostile attitudes, aggression and antisocial forms of behavior, Meadows et al., 1998; West et al., 1993); diverse situational hypotheses that suggest that the way one drives is closely linked to other behavioral traits in one’s personal and social life, because driving behavior is actually a part of a complex system determined by attitudinal and motivational factors that affect individuals in their relations with society (the so-called “Self-control Theory” and “Social Control Theory” and the “Hierarchical Approach” applied to traffic research by Hatakka et al., 2002; Junger et al., 1995b; Junger & Tremblay, 1999, among others). However, the connection between criminal or unsafe behavior and road safety seems to be a relatively under-researched topic both nationally and above all internationally. Different causal links are explored in the literature, predominantly: between general crimes and traffic offences with an special emphasis in recidivism (Broughton, 2007; Palk et al., 2005; Rose, 2000); between antisocial or negative behavior (homicide, robbery, consumption of alcohol and illicit drugs) and dangerous driving (Everett et al., 1999; Marshall et al., 1996); and to a minor degree, the relationship between general criminal behavior and RTAs (in terms of the number or accidents, fatalities or injury rates) (Giacopassi & Forde, 2000; Porterfield, 1960). Although there is a general consensus on the main theoretical hypotheses that a strong correlation holds, certain authors state that this might be spurious and that the statistical association between crime and RTAs is actually the result of a common causal process related 3 to socioeconomic context (age and sex, risk exposure to accident, income level, unemployment, etc.), (Junger et al., 1995a, 1995b; Junger & Tremblay, 1999; Sivak, 1983). Our contribution to the literature falls into this field. The main aim of the current study is to examine econometrically the association between criminal behavior and traffic fatalities per capita through a panel for the 28 current member states of the European Union (EU28) for the 1999-2010 period. We aim to test the hypothesis that crime rates (and specifically, motor vehicle-related crimes) may be considered as predictors of fatal RTAs. Following Brace et al. (2009) and Giacopassi & Forde (2000), there has been very little research undertaken to explore the effects of the relationship between general criminal behavior and crash involvement specifically. We consider that our research has several advantages over previous studies that guarantee its originality. Firstly, this issue has received some attention from researchers in certain countries that have analyzed the issue at the national level; although, to our knowledge, no previous study considers a broad panel of countries, or focuses specifically on the influence of criminal behavior (not only general crime but also specific motor vehicle crime) on death from traffic accidents. Secondly, our study also aims to explore the role of the legal system on the relationship between criminality rates and RTAs from a geographical point of view, for the EU28 countries. Logically, the EU28 countries show a heterogeneous spectrum of cultural attitudes and social norms (Vereeck & Vrolix, 2007), and also a wide range of traffic law frameworks (e.g., several countries with better road safety records, such as the Netherlands, processes traffic offenses under administrative laws, while other leaders such as the United Kingdom apply a criminal traffic law system; ETSC, 1999). In fact, some studies state that compliance 4 with the traffic rules (and with general law) in each country, is dependent not only on the social attitudes of road users or certain behavior, but also on the optimization of the law and its enforcement (TLE for traffic) that should form part of a global strategy to improve road safety: legislation, education, enforcement and judicial procedures (see e.g. Wegman, 1992). The effects of changes in the level of sanctions (tickets and fines, jail sentences, demerit/penalty points systems) or enforcement (number of police officers, number of breath tests for alcohol/drugs, speed cameras) on the level of road safety (accident rates) has been analyzed by much economic research interested in an optimal allocation of scarce resources, but with no conclusive findings. Nevertheless, there seems to be general agreement that enforcement of road rules should be aimed primarily at deterrence, being accompanied by publicity that is unpredictable and difficult to avoid, with a mixture of highly visible and less visible activities and continued over a long period of time because the effects are short term (Stanojević et al., 2012; Zaal, 1994). Special attention has been paid by authors such as Deshapriya & Iwase (1996) and Tay (2005, 2010) to the severity of sanctions as a deterrence mechanism to increase the individual’s perceived risk of apprehension and punishment. In the road safety arena, stricter sanctions seem to achieve better and faster results if they are mainly determined by the legislature and administrative/judiciary system (i.e., the introduction of tougher jail sentences and punishments may reduce traffic violation rates, although for a limited period; see for example cases as varied as Castillo-Manzano et al., 2011 for Spain; Li et al., 2014 for Hong Kong; Liberatti et al., 2001 for Brazil; Montag, 2014 for the Czech Republic or Sen, 2001 for Canada). Therefore, in this paper we examine the nature of the most severe legal systems as determining factors of the level of EU28 road safety. In short, we attempt to determine if there may be a clear correlation between road fatality rates and the mainstream types of crime at an international level, which would serve to justify, at least prima facie, the increasingly widespread tendency to consider traffic offences as 5 crimes and toughen the penalties imposed in the penal codes. Following this argument, we simultaneously analyze whether the degree of severity of the legal system applied to traffic offences could be a good tool in the constant struggle of Western societies to diminish the drama of RTAs. After this theoretical introduction, the following sections present the methodological framework of our research, a discussion of our findings and finally a summary of our main conclusions. 2. VARIABLES AND METHODOLOGY. In our empirical analysis, we develop a fixed-effects model that takes the following form for country i during period t: Yit = α + βkXit + γkZit + λkWit + μi + νt + εit (1) where Yit is the log of the total per capita fatality rate (within 30 days of the accident, according to the Vienna Convention definition) 1 , Xit refers to variables that identify the criminal attitudes of the country’s inhabitants, Zit contains the vector of the country’s economic and demographic attributes, and Wit are both the severity of legal system-related variables as proxies of law enforcement, and specific variables related to road safety policies. μi are country fixed effects that control for omitted time-invariant country-specific variables, νt are year dummies that control for the common trend in all the countries in the dataset and εit is a mean-zero random error. 1 Albalate (2008), Albalate & Bel (2011), Dee (2001) and Eisenberg (2003) consider that this is the most appropriate dependent variable for assessing road traffic fatalities as the interpretation of policy variables is clearer. 6 We use data for the EU28 countries for the time period from 1999 to 2010. This means that this is the broadest database in terms of both time and geography used in an analysis with these objectives. Table 1 provides the descriptions, information sources and descriptive statistics (mean and standard deviation) of all the variables used in the analysis. [INSERT TABLE 1] As we state above, the main aim of our empirical analysis is to examine whether the criminal attitudes of the inhabitants of a country have an effect on traffic fatality rates. According to earlier studies (Brace et al., 2009; Junger et al., 1995a; Junger et al., 2001b; Porterfield, 1960; Sivak, 1983; Sümer, 2003), we may expect a positive correlation between criminal activities and the attitudes of drivers on the road, in the sense of a higher likelihood that they are involved in a fatal RTA. The hypothesis we test here is that levels of social aggression are a predictor of traffic accidents: higher crime rates are associated with more deaths on the roads, bearing in mind the possible influence of the spuriousness thesis supported by scholars such as Junger et al. (1995a) and Sivak (1983), as we mentioned in the Introduction above. In equation (1) we include four explanatory variables that identify the criminality rates of a country’s population, and we also consider some control variables that are described in the following paragraphs. We collected complete data for two variables related with general criminality rates, also used by previous empirical research, although it only considered specific countries (Giacopassi & Forde, 2000; Junger et al., 1995b; Junger et al., 2001a; Sivak, 1983): the number of homicides and the number of robberies. Moreover, as we mention above in the Introduction, almost all 7 the earlier research focused on general crime variables or on typical driving violations-related variables, such as alcohol consumption (Deshapriya & Iwase, 1996; Petrie et al., 2010; West et al., 1993) or others, such as so-called “joy-riding” crime (Marshall et al., 1996). By other hand, authors such as Bromley and Nelson, 2002, find that alcohol is noted as a contributory factor in recorded crimes. Furthermore, we include two variables of crimes specifically linked to road vehicle traffic: the number of drug trafficking crimes and the number of motor vehicle thefts. All these variables are measured in relative terms according to the country’s population. While we may expect that the coefficient associated with the crime variables is positive, such an effect should be particularly relevant for those variables, such as drugs, that are more linked to road vehicle traffic, in agreement with Gjerde et al. (2011) and Smink et al. (2005) for certain countries. Along with variables related with different types of crime in the country’s population, we also analyze whether the severity of the criminal justice system influences road mortality ratios. As a proxy for the severity of the penal/legal system in a country, we include an index variable that takes the value zero if no life imprisonment sentence may be applied, the value one if such a sentence is revisable and the value 2 if the indefinite sentence is not revisable. Our expectation is that the coefficient associated with this variable will be negative, following prior results of e.g., Castillo-Manzano et al. (2011), Liberatti et al. (2001) Montag, 2014 or Sen (2001) regarding the beneficial effects of stricter punishment applied to traffic violators, specifically on road safety outcomes. Indeed, the traffic fatality rates may be lower in countries with more severe penal/legal/administrative systems as the penal systems may have a strong influence on the social behavior of citizens and drivers. 8 We also consider additional variables for road safety policies. Following previous studies on road safety (Albalate, 2008; Castillo-Manzano & Castro-Nuño, 2012; Castillo-Manzano et al., 2013, 2014; Deshapriya & Iwase, 1996; Eisenberg, 2003; McCarthy, 2005), we consider the influence of preventive policies such as legal blood alcohol limits, and include a dummy variable that takes a value of one for countries and periods where the maximum blood alcohol concentration allowed is lower than 0.5 g/L. Most EU28 countries have set the limit at 0.5 or lower, so we are able to test whether blood alcohol concentration rates lower than 0.5 are effective in reducing road traffic fatalities. Additionally, we include a variable that captures the application of points-based driving licenses. Indeed, one of the explanatory variables in equation (1) is an index variable that takes the value one if a penalty driving license system is applied, the value two if a demerit driving license system is applied and the value zero if any points system is applied. We expect to find a negative sign in the coefficient associated with this variable. The introduction and application of any points system to driving licenses may lead to lower traffic fatality rates (i.e. the strong deterrence effect analyzed by De Paola et al., 2013 for Italy), although the longterm effect of this policy has been questioned (see Castillo-Manzano & Castro-Nuño, 2012, for an international meta-analysis of the effects of such policies). Another road traffic policy variable that we take into account is for the maximum speed limit allowed on motorways. According to previous studies (Afukaar, 2003; Elvik, 2012), we may expect a positive sign in the coefficient associated with this variable, as a higher speed limit (and its subsequent enforcement) may have as a consequence higher road traffic fatalities. 9 With respect to control factors, we include variables that are typically used in studies of road traffic safety and that are related to the country’s economic conditions. In this vein, we include the per capita gross domestic product (GDP) as an indicator of the country’s economic development (Castillo-Manzano et al., 2013, 2014; García-Ferrer et al., 2007; Grimm & Treibich, 2012; Orsi et al., 2012). The relationship between a country’s economic development and road traffic fatalities is complex and we may expect that it takes a non-linear form (Anbarci et al., 2006; Bishai et al., 2006; Kopits & Cropper, 2005). Indeed, fatality rates may increase with economic development in very poor countries due to increased exposure to RTAs. However, the relationship between economic development and traffic fatality rates may become flat or even invert after reaching a certain wealth threshold. We test the hypothesis concerning nonlinearity between GDP and traffic fatalities by including the GDP and the square of the GDP as explanatory variables. If the non-linear relationship is confirmed, then the coefficient associated with the GDP variable will be positive and the coefficient associated to the square of the GDP variable will be negative. Meanwhile, studies show that overall crime rates and property crimes are higher when economic conditions are poor due to unemployment or recession (Krüger, 2011) with a positive relation evidenced for the EU by Altindag (2012). Other studies even suggest how income inequality may play an important role in the determination of the crime rate (Sachsida et al., 2010), following the rational crime theory, because the opportunity cost of committing a crime seems to decrease and the net individual’s benefit might be higher (Krüger, 2013). GDP is therefore quite a useful control variable when the intention is to study any possible correlation with crime rates. 16 study is of a broad sample: EU28 and a full twelve years of research, and shows that this is not a generic relationship but is limited to specific criminal behavior being linked to reckless driving. These are specifically the offense categories of motor vehicle theft and drug trafficking, which closely correlate with traffic accidents. Another relevant result of our research is that there is also a clear correlation between a reduction in the road fatality rate and the life imprisonment variable, which can be considered to be a clear proxy of the general stringency of each country’s criminal code. Thus, the severity of the sanction may be used as a deterrence mechanism to increase the individual’s perceived risk of apprehension and punishment (a signal about the visibility of stricter TLE and heavier sanctions); and social control might be achieved through deterrence by stricter laws. All these comments support the idea of a clear correlation between criminal behavior and RTAs, inasmuch as specific strategies used to contend with the former also help to reduce RTAs. The road fatality rates seem to be higher in European countries where the inhabitants show more socially aggressive behavior, while they are lower in countries with more severe penal systems. 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Variables Description Source Mean Standard Deviation Per capita fatalities Fatality rates per million inhabitants CARE (EU road accidents database) 108.44 45.04 Life imprisonment Index variable that takes the value 0 if no life imprisonment sentence may applied; 1 if imprisonment sentence is revisable (pardon, amnesty or other release may be applied); 2 if indefinite sentence is not revisable INTERNATIONAL JUSTICE RESOURCE CENTER 1.57 0.67 Homicides Number of homicides per 100,000 inhabitants (Intentional killing of a person, including murder, manslaughter, euthanasia and infanticide) EUROSTAT 2.24 2.31 Robbery Robbery crimes per 100,000 inhabitants (Stealing from a person with force or threat of force, including muggingsbag-snatching and theft with violence) EUROSTAT 88.83 70.35 25 Drugs drug trafficking crimes per 100,000 inhabitants (Illegal possession, cultivation, production, supplying, transportation, importing, exporting, financing etc. of drug operations which are not solely in connection with personal use) EUROSTAT 66.34 65.82 Motor vehicle theft Motor vehicle theft per 100 000 inhabitants (Theft of all land vehicles with an engine that run on the road which are used to carry people, including cars, motorcycles, buses, lorries) EUROSTAT 219.55 184.68 Per capita GDP Per capita gross domestic product in International Comparable Prices (US$ at 2005 prices and PPP) UNECE Statistical Division Database, compiled from official national and international (CIS, EUROSTAT, IMF, OECD) sources 24960 11653 Motorization Number of registered vehicles per 1000 inhabitants UNECE transport division, Eurostat, World Bank and national databases 424.74 111.99 Vehicles-km driven Number of passenger-cars-km expressed in 1000 million km and weighted by national population European Commission (Directorate General for mobility and transport) 39.97 317.52 Motorway density Number kms of motorways divided by square km of the country EUROSTAT and UNECE 1.69 1.73 Old % population over 60 years old EUROSTAT 20.67 2.52 Young % population aged 20-39 years EUROSTAT 28.75 1.92