Gender Issues

Summary

Objective and methodology

The ESRA project (E-Survey of Road users' Attitudes) is a joint initiative of road safety institutes, research organisations, public services and private sponsors, aiming at collecting comparable (inter)national data on road users' opinions, attitudes and behaviour with respect to road traffic risks.

The project is funded by the partners' own resources and covers countries all over the world.

The basis is a jointly developed questionnaire which is translated into national language versions. The themes covered include: self-declared behaviour, attitudes and opinions on unsafe traffic behaviour, enforcement experiences and support for policy measures. The survey addresses different road safety topics (e.g. driving under the influence of alcohol, drugs and medicines, speeding, distraction) and targets different types of road users.

The first edition of the ESRA survey (ESRA1) was carried out in three waves between 2015-2017. Data was gathered from almost 40,000 road users in 38 countries across 5 regions. The present report is based on the second edition of this global survey, which was conducted in 2018 (ESRA2_2018). In this first wave, data from more than 35,000 road users were collected across 32 countries and the sample was expanded in a second wave in 2019.

Hence, the ESRA database is a comprehensive dataset, which enables a wide range of analyses which are useful for understanding road safety risks and the effectiveness of measures. An overview of the project and the results are available on: www.esranet.eu.

The purpose of this thematic report on gender issues is to explore the cultural effect on gender differences in reported risky behaviours while driving. This ESRA thematic report analyses gender differences in self-reported data on driving behaviour, attitudes and beliefs, comparing countries and regions. The four regions based on a geographical criterion, were used to distinguish potential cultural differences on a meso-level, while gender differences were also investigated on a microlevel, by analysing the differences by country. For the sake of brevity and clarity, data from the same hypothetical psychological construct available in the ESRA questionnaire were grouped together into aggregate scores. The scores of men and women were compared at the level of each country and region. The focus was on the items concerning psychological constructs on which we can expect gender differences, according to literature: self-declared and acceptability of unsafe behaviours, self-efficacy, perceived safety, road safety policy support, risk perception, number of crashes, social desirability and compliance intention, law perception, descriptive norms, enforcement, and perception of automated vehicles.

Key results

Acceptability of risky driving behaviours

For perceived social acceptability, gender differences were seen for 18 out of the 32 countries although all the effect sizes were small. For all the countries where gender difference was significant, males perceived higher social acceptability of risky behaviours than females. Perceived social acceptability was highest for both genders in Africa.

For personal acceptability, gender differences were seen for 27 out of the 32 countries. Again, all the effect sizes were small. For all the countries where gender difference was significant, males perceived higher personal acceptability of risky behaviours than females. The gap between males and females was lowest in Africa.

Linear regression models also confirmed that scores of perceived social acceptability and personal acceptability were higher among males and younger drivers. The level of income seems to have a positive effect on road safety, by decreasing the level of social acceptability of risky behaviours, but it also had negative effect by increasing the personal acceptability of violations. Gender equality seems to have positive effects, by lowering social and personal acceptability of violating behaviours. Gender Issues Declared risky driving behaviours Significant gender differences were seen for 30 out of the 32 countries. All the effect sizes were small, except for Greece, Italy and Portugal where the effect sizes were medium. For all the countries where gender difference was significant, males declared higher frequency of risky driving behaviours than females, except in India where females declared riskier behaviours than males.

The number of transgressions reported by males was higher in all regions, compared to females. The gender gap was largest in Europe, while Africa and North America showed the highest score for male transgressions.

Linear regression models also confirmed risky declared behaviours were higher among males and younger drivers. The level of income seems to have positive effect on road safety, by decreasing the level of risky driving behaviours. Gender equality has negative effects, by increasing risky behaviours declared. In contrast to the whole sample, for the Europe region, the country level of income seems to have a negative effect on road safety, with higher incomes increasing the risky behaviours declared.

Among the gender equality indices, national levels of education and economic participation of females are related to higher levels of reported risk behaviours. At the European country level, the national level of female economic participation reinforces the risk behaviours reported by participants. It is also observed that, while the behaviours reported by the male group are only affected by female economic participation (the more females have economic participation, the higher the level of risk behaviours reported by males), the behaviours reported by females are related to all indices of gender equality: female level of education and economic participation at the national level reinforces the risk behaviours reported by females, while national levels of female health and political involvement are related to lower levels of self-reported risk behaviours among females.

It was also found that for countries with higher gender equality in terms of educational attainment, the increase in risky driving behaviours are higher for females than for males indicating that their reported risky driving behaviours are increasing to be more similar to males in countries with high gender equality for educational attainment.

Self-efficacy in risky driving behaviours Gender differences were seen for 29 out of the 32 countries. All the effect sizes were small, except for Czech Republic, Denmark, Finland, Italy, The Netherlands, and Portugal where the effect sizes were medium. For all the countries where gender difference was significant, males declared higher level of self-efficacy than females. This effect was significant in all regions, but the effect size was larger in Europe and in North America.

Linear regression models confirmed that scores of self-efficacy were higher among males and younger drivers. The level of income seems to have positive effect on road safety, by decreasing the feeling of self-efficacy in risky situations. Gender equality seems to have negative effects, by increasing selfefficacy feeling in risky situations.

In contrast to the whole sample, for the Europe region, the country level of income seems to have a negative effect on road safety, with higher incomes increasing the feeling of self-efficacy in risky situations.

Perceived safety in driving Gender differences were seen for 21 out of the 32 countries. All the effect sizes were small. For all the countries where gender difference was significant, males declared higher perceived safety in driving than females. This was especially true in Europe, where the effect size was the largest.

Linear regression models confirmed that scores of perceived safety were higher among males and younger drivers. The level of income seems to have positive effect on road safety, by decreasing the perceived safety of driving. Gender equality seems to have negative effects by increasing perceived safety of driving. Gender Issues

In contrast to the whole sample, for the Europe region, the country level of income seems to have a negative effect on road safety, with higher incomes increasing the perceived safety of driving.

Road safety policy support Gender differences were seen for 30 out of the 32 countries. All the effect sizes were small, except for Finland, Greece and Sweden were the effect sizes were medium. For 29 out of 30 countries where gender difference was significant, females declared higher road safety policy support than males, except in Morocco, where males declared higher road safety support. Females were more likely to support road safety policies than males were, in all regions, except in Africa, where the support was similar for both genders. However, in Europe and North America, the gender gap was bigger, with a lower general support, especially in North America.

Linear regression models confirmed that scores of road safety policy support were higher among females and older drivers. The level of income seems to have a negative effect by weakening the support for road safety policies. Gender equality seems to have positive effects by increasing support for road safety policies.

Perception of risky behaviours

Gender differences were seen for 24 out of the 32 countries. All the effect sizes were small. For 23 out of 24 countries where gender difference was significant, females declared higher risk perception than males. The opposite was true in Morocco, where males had a higher risk perception. In each region, except in Africa, females perceived a higher risk of driving in a risky manner compared to males. Regional differences are also evident, as Asia-Oceania perceived lower risk compared to the three other regions.

Linear regression models confirmed that scores of risk perception were higher among females and older drivers. The level of income seems to have a negative effect by lowering the perception of risk. Gender equality seems to have positive effects by increasing risk perception.

Number of crashes

Significant gender differences were seen for 9 out of the 32 countries. Gender differences were significant for Belgium, Canada, Egypt, France, Germany, Greece, Morocco, the Netherlands, and Slovenia. All the effect sizes were small. For 8 countries out of the 9 countries where gender difference was significant, males declared higher number of crashes than females, except for Morocco where females declared higher number of crashes than males. The number of crashes was particularly high in Africa and Asia-Oceania, where no gender differences were found. However, a significant gender gap was observed in Europe and North America.

Linear regression models confirmed that the number of crashes were higher among males and younger drivers. The level of income seems to have positive effect on road safety, by decreasing the number of crashes. Gender equality seems to have positive effects, by lowering the number of crashes. In contrast to the whole sample, for the Europe region, the country level of income seems to have a negative effect on road safety, with higher incomes increasing the number of crashes.

Social desirability and compliance intention

Significant gender differences were seen for 12 out of the 32 countries. All the effect sizes were small. For 11 countries out of the 12 countries where gender difference was significant, females declared higher compliance intention than males, whereas for Kenya males declared higher compliance intention than females. Females had a higher social desirability score than males in Asia-Oceania and in Europe.

Although not statistically significant, the results were reversed in Africa, with a higher social desirability for male.

Linear regression models confirmed that scores of social desirability were higher among females and older drivers. Gender Issues Compliant law perception Significant gender differences were seen for 6 out of the 11 countries who used this bonus question. Gender differences were significant for Canada, Egypt, Japan, Slovenia, South Africa and United Kingdom. All the effect sizes were small. For 5 countries out of the 6 countries where gender difference was significant, females declared a higher importance for respecting the law than males, whereas for Egypt males declared a higher importance than females. The effect size of the gender gap in Asia-Oceania was particularly important.

Risky social norms

Significant gender differences were seen for 9 out of the 32 countries. All the effect sizes were small. For all the countries where gender difference was significant, males declared risker descriptive norms than females. In all regions, with the exception of Africa, males perceived their friends to act more in a risky manner than females perceived, except in Africa where the gender gap wasn't significant. The maximum gender gap appeared in North America.

Linear regression models confirmed that scores of descriptive norms were higher among males and younger drivers. Scores of social desirability, road safety policy support and risk perception were higher among females and older drivers. The level of income seems to have positive effect on road safety, by decreasing the level of descriptive norms.

Perceived probability of enforcement

Gender differences were seen for 21 out of the 32 countries. All the effect sizes were small. For all the countries where gender difference was significant, males perceived higher probability of enforcement than females. In all regions, males perceived their likelihood to encounter enforcement related to different transgressions more likely than females did. The perceived likelihood was lower in North America.

Linear regression models confirmed that scores of perceived enforcement were higher among males and younger drivers. The level of income seems to have a negative effect by lowering the perceived enforcement likelihood. Gender equality seems to have negative effects by weakening perceived enforcement likelihood.

Positive perception of automated vehicles

Gender differences were seen for 22 out of the 32 countries. All the effect sizes were small. For all the countries where gender difference was significant, males declared higher positive perception of automated and semi-automated vehicles than females. Globally, males judged the likelihood of benefits of fully and semi-automated cars more likely than females did. The gender gap was higher in Europe and North America, where benefits were seen as more unlikely in these regions.

Linear regression models confirmed that scores of positive perception of automated vehicles were higher among males and younger drivers.

Advanced analysis

Linear regression models suggested that all attitudinal variables have a significant effect on reported risky driving behaviours, with the exception of the social acceptability of risk behaviours. The significant effects of gender and age on declared risky driving behaviours were likely to be mediated by the attitudinal variables. Furthermore, reported risky driving behaviours were higher in countries with a high level of Gender Equality, while all things being equal, these behaviours decreased as income level increased. More precisely, the more females have economic participation in the country, the higher the level of risk behaviours reported by males. However, the reported risk behaviours of females were higher when the female level of education and economic participation was high but also when the female health and political involvement was low. Structural Equation Model further indicated that Gender Issues females' risky behaviours increased to be more similar to males in countries with high gender equality for educational attainment.

Conclusion

Globally, an effect of gender on psychological constructs was found, and a significant effect of region. More occasionally, an interaction between gender and region was revealed, leading us to conclude that gender differences are also culturally constructed. This was the case for personal acceptability, declared behaviours, self-efficacy, perceived safety, road safety policy support, social desirability, law perception and perception of automated vehicle. However, the fact that some constructs are only explained by gender without interaction with cultural context can be interpreted in two manners. Firstly, we can understand that the biologic aspect of gender cannot be denied when trying to explain gender differences in attitudes and other related constructs in road safety. However, we can also argue the fact that maybe the way people are socialized as men and women all around the world is not different enough to find cultural differences on gender's effect on some constructs.

Key recommendations

Policy recommendations at national and regional level

• Continue to study the impact of gender on crash risk and aim programs at those most at risk. In this study males were found to be more at risk than females. Even in Europe and North America, whereas crashes are less common, men are still more at risk than women.

• A reduction in the gap of equality measures between men and women is associated with a reduction of the gender differences in risky road behaviours, with women becoming more prevalent in typical riskier "masculine" behaviours. Explanations for this behavioural adaptation should be identified and strategies for mitigating this outcome implemented.

• To reduce risky driving behaviours in risk prone subsets of the population (especially men but also young drivers), target psychological constructs that have the greatest impact on risky driving, such as personal acceptability and self-efficacy.

Specific recommendations to particular stakeholders

• [To Non-Governmental Organizations (NGOs)] Contribute to education and awareness campaigns and events aimed at reducing risky behaviours, especially among males. The strong influence of personal acceptability on risky behaviours calls for action on the perception of risks and rules. In particular, "male" values that influence individual behaviour should be targeted, including among women in countries where gender equality is high.

• [To vehicle manufacturers, other companies and research organisations] Develop research aimed at understanding the psychological mechanisms by which gender influences risky behaviours and those aimed at influencing this relationship.

The ESRA project has demonstrated the feasibility and the added value of joint data collection on road safety attitudes and performance by partner organizations in a large number of countries across the world. The intention is to repeat this initiative on a triennial basis, retaining a core set of questions in every wave, allowing the development of time series of road safety performance indicators. This will become a solid foundation for a joint international monitoring system on road safety attitudes and behaviour. Gender Issues

Introduction

In 8th place among the causes of death in the world (WHO, 2009), road crashes remain a significant public health issue, where the role of gender is undeniable. Even when exposure is controlled for, males are more often involved in road crashes than females (GHE, 2016), including those involving a car, motorcycle, bicycle or on foot [START_REF] Prati | Gender differences in cyclists' crashes: an analysis of routinely recorded crash data[END_REF][START_REF] Pulido | The role of exposure on differences in driver death rates by gender and age: results of a quasi-induced method on crash data in Spain[END_REF][START_REF] Stimpson | Fatalities of pedestrians, bicycle riders, and motorists due to distracted driving motor vehicle crashes in the US, 2005-2010[END_REF][START_REF] Zhu | Why more male pedestrians die in vehiclepedestrian collisions than female pedestrians: a decompositional analysis[END_REF], especially when they are young [START_REF] Twisk | Changing mobility patterns and road mortality among pre-license teens in a late licensing country: an epidemiological study[END_REF]. For example, although females represent 51% of the world's population, their share of road deaths is only 24% [START_REF] Rodrigues | The perception of road safety communication campaigns: the gender influence[END_REF]Traffic Safety Basic Facts, 2016). Among those killed, females are also less often the drivers (31% compared to 70% of males) (Traffic Safety Basic Facts, 2016) and less often judged to be responsible for the crash (ONISR, 2018). On the contrary, the mere presence of a male in the passenger compartment is sufficient to increase young driver fatality rates [START_REF] Williams | Passenger effects on teenage driving and opportunities for reducing the risks of such travel[END_REF].

Although males exhibit higher risk factors, such as driving on less dense networks than females (motorways, rural areas), slightly more kilometres travelled and more use of two-wheelers, this does not seem to be sufficient to explain such a gender differential in road crashes and injuries [START_REF] Waylen | Cradle Attitudes-Grave Consequences-The Development of Gender Differences in Risky Attitudes and Behaviour in Road Use[END_REF]. Ninety per cent of road crashes involve human factors as a cause (Alonso et al. 2002, cited in Gonzalez-Iglesias, Gomez-Fraguela, Luengo-Martin, 2012). Therefore, to further understand the impacts of gender on road crash risk, the relationship of gender to attitudes towards driving skills and road safety measures have been the subject of much analysis.

The observed gender differences in road crashes may be partly explained by males' greater involvement in risky and illegal behaviour [START_REF] Barr | Gender differences in perceptions and self-reported driving behaviors among teenagers[END_REF][START_REF] Butters | Gender differences and demographic influences in perceived concern for driver safety and support for impaired driving countermeasures[END_REF][START_REF] Scott-Parker | I drove after drinking alcohol" and other risky driving behaviours reported by young novice drivers[END_REF], their greater sensation-seeking [START_REF] Cestac | Young drivers' sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention: How risk-taking motivations evolve with gender and driving experience[END_REF] and the lower use of safety measures that could protect them (e.g. seat belts, helmets) [START_REF] Fernandes | A systematic investigation of the differential predictors for speeding, drink-driving, driving while fatigued, and not wearing a seat belt, among young drivers[END_REF]Jiménez-Mejías et al., 2014). Males represent 75% of the young risk-taking driver population, who consider crashes to be related to external causes and therefore difficult to control [START_REF] Lucidi | Young novice driver subtypes: relationship to driving violations, errors and lapses[END_REF]. They tend to perceive themselves as immune to risks that threaten others, and overestimate their driving skills [START_REF] Glendon | Age and gender differences in perceived accident likelihood and driver competences[END_REF], particularly in risky situations [START_REF] Farrow | Risk for DWI: A new look at gender differences in drinking and driving influences, experiences, and attitudes among new adolescent drivers[END_REF]. Conversely, some studies propose that females perceive a greater overall risk on the road and feel less able to cope with the difficulties they might encounter, compared to males [START_REF] Farrand | Risk perception in novice drivers: the relationship between questionnaire measures and response latency[END_REF][START_REF] Glendon | Evaluating a novice driver and pre-driver road safety intervention[END_REF]. For example, they perceive greater risks in speeding [START_REF] Obst | Age and gender comparisons of driving while sleepy: Behaviours and risk perceptions[END_REF][START_REF] Holocher | Speeding. ESRA2 Thematic report nr[END_REF] and in using their phone while driving [START_REF] Struckman-Johnson | Gender differences in psychosocial predictors of texting while driving[END_REF].

However, these observed gender differences may not be universal. According to [START_REF] Lund | Cross-cultural comparisons of traffic safety, risk perception, attitudes and behaviour[END_REF], the fact that females are more risk-sensitive and perceive a higher risk on the road than males is only valid in high-income countries. Indeed, in Ghana, the perception of risk is similar between males and females, because as inhabitants of developing countries, they are more accustomed to risk, which could affect their perception [START_REF] Flynn | Gender, race, and perception of environmental health risks[END_REF]. Other researchers found no difference between genders in the perception of the frequency and probability of a crash [START_REF] Cordellieri | Gender effects in young road users on road safety attitudes, behaviors and risk perception[END_REF]. Nevertheless, males consider risky behaviour as less serious [START_REF] Dejoy | An examination of gender differences in traffic accident risk perception[END_REF]Gonzalez-Iglesias et al., 2012), feel less concerned about the risk of it happening to them [START_REF] Cordellieri | Gender effects in young road users on road safety attitudes, behaviors and risk perception[END_REF]Gonzalez-Iglesias et al., 2012) or of injuring someone [START_REF] Glendon | Evaluating a novice driver and pre-driver road safety intervention[END_REF]. They may consider driving to be dangerous, but not for them [START_REF] Dejoy | An examination of gender differences in traffic accident risk perception[END_REF]. Thus, risk-taking among males is not explained by a lower perception of risk but by a more detached attitude towards it, since risk-taking can be rewarding for this population [START_REF] Guého | Approche psychosociale des effets de l'identité sexuée sur les comportements à risque déclarés au volant et dans différents domaines chez les jeunes conducteurs[END_REF]. Conversely, females feel concerned by all road safety problems [START_REF] Butters | Gender differences and demographic influences in perceived concern for driver safety and support for impaired driving countermeasures[END_REF].

The way in which males and females assess their skills also varies. With respect to driving, two forms of competence can be distinguished: competence in terms of driving skills and competence in terms of safety [START_REF] Sibley | A gender role socialization model of explicit and implicit biases in driving self-enhancement[END_REF]. Most individuals express a bias regarding their skills, perceiving them as superior to those of the average driver [START_REF] Sibley | A gender role socialization model of explicit and implicit biases in driving self-enhancement[END_REF]. However, males value their skills more in terms of ability, while females value their caution, both overtly and implicitly, automatically [START_REF] Sibley | A gender role socialization model of explicit and implicit biases in driving self-enhancement[END_REF]. However, it is interesting to note that males still perceive themselves as safe driverseven more so than females -even though they report more risky behaviour [START_REF] Barr | Gender differences in perceptions and self-reported driving behaviors among teenagers[END_REF]Rodrigues Gender Issues et al., 2015). As a result, they express more negative attitudes towards road enforcement actions (e.g., [START_REF] Awialie Akaateba | DRIVER ATTITUDE TOWARDS TRAFFIC SAFETY VIOLATIONS AND RISK TAKING BEHAVIOUR IN KUMASI: THE GENDER AND AGE DIMENSION[END_REF][START_REF] Bener | Gender and age differences in risk taking behaviour in road traffic crashes[END_REF][START_REF] Butters | Gender differences and demographic influences in perceived concern for driver safety and support for impaired driving countermeasures[END_REF][START_REF] Corbett | Gender differences in responses to speed cameras: typology findings and implications for road safety[END_REF] as well as traffic law [START_REF] Møller | Peer influence on speeding behaviour among male drivers aged 18 and 28[END_REF] and report more intentions to break traffic rules in the future [START_REF] Scott-Parker | I drove after drinking alcohol" and other risky driving behaviours reported by young novice drivers[END_REF]. These attitudes may be partly explained by the fact that males typically feel excitement in traffic situations and vehicles that they find difficult to control [START_REF] Redshaw | Dangerous gender performances: 'Hydraulic masculinity' as a norm for young male drivers[END_REF]. In general, males experience more positive emotions and interest in driving, which predicts risky driving [START_REF] Harré | Gender differences and areas of common concern in the driving behaviors and attitudes of adolescents[END_REF]Rhodes & Pivik, 2010). Conversely, females have more positive attitudes to driving safely and more satisfaction in complying with traffic regulations than males [START_REF] Rodrigues | The perception of road safety communication campaigns: the gender influence[END_REF].

Many traffic offences increase the risk of road crashes, sometimes occurring simultaneously, and males tend to commit more traffic offences than females [START_REF] Barr | Gender differences in perceptions and self-reported driving behaviors among teenagers[END_REF][START_REF] Butters | Gender differences and demographic influences in perceived concern for driver safety and support for impaired driving countermeasures[END_REF][START_REF] Scott-Parker | I drove after drinking alcohol" and other risky driving behaviours reported by young novice drivers[END_REF]. Even with regard to driving while fatigued, males represent a population at greater risk (Gonçalves et al., 2015;[START_REF] Obst | Age and gender comparisons of driving while sleepy: Behaviours and risk perceptions[END_REF]. They more frequently report driving while fatigued and do not perceive this to be as risky as females do [START_REF] Obst | Age and gender comparisons of driving while sleepy: Behaviours and risk perceptions[END_REF]. As a result, they are more likely to have narrowly escaped a collision when fatigue is a factor [START_REF] Obst | Age and gender comparisons of driving while sleepy: Behaviours and risk perceptions[END_REF]. Furthermore, males use seat belts to a lesser extent and also require their passengers to use them less [START_REF] Barr | Gender differences in perceptions and self-reported driving behaviors among teenagers[END_REF], as shown in the study by [START_REF] Granié | Les différences de sexe en sécurité routière: accidentalité, comportements à risque, accès au permis de conduire[END_REF], in North America, Europe and Africa, but not in Asia-Oceania, where the proportion is the same. However, [START_REF] Obeng | Gender differences in injury severity risks in crashes at signalized intersections[END_REF] found that among those who had been in a car crash, females were less likely to wear seatbelts than males.

Gender differences are even greater in crashes involving substances use (alcohol, illegal drugs or medication), where males are over-represented in general [START_REF] Amarasingha | Gender differences of young drivers on injury severity outcome of highway crashes[END_REF][START_REF] Romano | Traffic environment and demographic factors affecting impaired driving and crashes[END_REF]. They are at greater risk of driving while drunk [START_REF] Mouloua | Gender differences and aggressive driving behavior: A factor analytic study[END_REF] and of being arrested because they have used a substance [START_REF] Vaca | Female drivers increasingly involved in impaired driving crashes: actions to ameliorate the risk[END_REF]. Thus, some authors have come to consider that gender differences in road crashes may be explained by differences in alcohol consumption [START_REF] Kelley-Baker | Female involvement in US nonfatal crashes under a three-level hierarchical crash model[END_REF]. However, it would appear that this gender gap has narrowed in recent decades, with a much larger increase in the rate of females arrested for driving under the influence compared to males [START_REF] Vaca | Female drivers increasingly involved in impaired driving crashes: actions to ameliorate the risk[END_REF]. This is partly explained by a change in the behaviour of females [START_REF] Vaca | Female drivers increasingly involved in impaired driving crashes: actions to ameliorate the risk[END_REF].

Distracted driving, another risky behaviour, has increased in recent decades with the widespread use of mobile phones. Among young adults, the frequency of sending messages or making calls with a mobile phone while driving is 94% [START_REF] Nemme | Texting while driving: Psychosocial influences on young people's texting intentions and behaviour[END_REF]. However, this frequency is higher among males in studies conducted in the United States [START_REF] Barr | Gender differences in perceptions and self-reported driving behaviors among teenagers[END_REF] and Qatar [START_REF] Bener | Gender and age differences in risk taking behaviour in road traffic crashes[END_REF] but not in those conducted in Australia [START_REF] Nemme | Texting while driving: Psychosocial influences on young people's texting intentions and behaviour[END_REF][START_REF] Struckman-Johnson | Gender differences in psychosocial predictors of texting while driving[END_REF], demonstrating a cultural effect in this gender difference. Similarly, in the study by Pires, Areal and Trigoso (2019), mobile phone use is higher among males than females in Europe and Africa but not in Asia-Oceania and the South and North America. However, in Australian studies, more females consider the behaviour to be dangerous and that it should be prohibited [START_REF] Struckman-Johnson | Gender differences in psychosocial predictors of texting while driving[END_REF].

Finally, speed is considered a major cause of road crashes: for example, it is the leading cause of fatal collisions in France, responsible for 18% of deaths (OMS, 2018). Males and females behave differently towards speed control radars. For example, females are more compliant with speed regulations, whereas males show manipulative behaviour, avoiding speed cameras or slowing down just as they pass [START_REF] Corbett | Gender differences in responses to speed cameras: typology findings and implications for road safety[END_REF]. As a result, the rate of males getting caught twice or more by a speed camera is double than that of females [START_REF] Corbett | Gender differences in responses to speed cameras: typology findings and implications for road safety[END_REF]. This male tendency to drive fast is observed in many countries around the world, such as in Qatar [START_REF] Bener | Gender and age differences in risk taking behaviour in road traffic crashes[END_REF], England [START_REF] Corbett | Gender differences in responses to speed cameras: typology findings and implications for road safety[END_REF], Australia [START_REF] Horvath | The beliefs which motivate young male and female drivers to speed: A comparison of low and high intenders[END_REF] and Ukraine [START_REF] Sullman | Gender roles and the expression of driving anger among Ukrainian drivers[END_REF], which was confirmed in a first analysis of ESRA2 data. Across all regions, males consistently report more speeding than females, with varying regional differences [START_REF] Granié | Les différences de sexe en sécurité routière: accidentalité, comportements à risque, accès au permis de conduire[END_REF]. This is also reflected in their intentions to drive excessively fast. Among individuals intending to exceed the speed limit in a specific situation, males are more willing to do so than females [START_REF] Horvath | The beliefs which motivate young male and female drivers to speed: A comparison of low and high intenders[END_REF].

Classically, the tendency for males to take risks has been explained by a combination of biological and evolutionary theories [START_REF] Granié | Gender stereotypes associated with vehicle driving among French preadolescents and adolescents[END_REF]. Males are thought to have a higher rate of sensation seeking and take more risks than females because they produce more androgens [START_REF] Zuckerman | Psychobiology of personality[END_REF]. According to evolutionary theory, this tendency would be the natural consequence of the function of Gender Issues males to protect the community and reproduce their genetic heritage by competing to attract females [START_REF] Daly | Evolutionary psychology and family violence[END_REF]. Social relations between males would therefore be more competitive [START_REF] Yagil | Gender and age-related differences in attitudes toward traffic laws and traffic violations[END_REF], leading them to perceive social pressure to drive fast, which is less the case for females [START_REF] Cestac | Young drivers' sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention: How risk-taking motivations evolve with gender and driving experience[END_REF]. However, these hypotheses do not seem to be sufficient to explain such differences and the results, sometimes contradictory, discussed above. Some studies highlight the fact that gender differences on the road could be explained, not in terms of gender differences, but in terms of gender roles and would result from socialisation [START_REF] Granié | Les différences de sexe en sécurité routière: accidentalité, comportements à risque, accès au permis de conduire[END_REF][START_REF] Oppenheim | Can traffic violations be traced to gender-role, sensation seeking, demographics and driving exposure?[END_REF][START_REF] Sibley | A gender role socialization model of explicit and implicit biases in driving self-enhancement[END_REF]. Gender roles and gender stereotypes refer to a set of social beliefs about what a male and a female should be in a given society [START_REF] Ashmore | Gender stereotypes[END_REF]. As these stereotypes vary across cultures, the behaviours expected of males and females may also vary. For [START_REF] Simon | Road traffic offending, stress, age, and accident history among male and female drivers[END_REF], gender differences are simply a reflection of gender role differences, presenting the female role as passive, non-competitive and cautious while the male role is risk-taking, competitive and non-compliant. Norms of masculinity even prescribe a minimization of danger, coupled with reckless behaviour [START_REF] Struckman-Johnson | Gender differences in psychosocial predictors of texting while driving[END_REF]. Males then engage in risky behaviours in order to demonstrate their masculinity, adopting typical behaviours and thus departing from feminine behaviours [START_REF] Courtenay | Constructions of masculinity and their influence on men's well-being: a theory of gender and health[END_REF].

At the crossroads of biological and social explanations, some authors suggest that an individual's level of risk taking depends on two factors: on the one hand, endogenous trends and, on the other, constraints and restrictions on risk-taking in the individual's culture (laws, norms, educational practices, etc.) [START_REF] Arnett | Reckless behavior in adolescence : A developmental perspective[END_REF]. The most recent research thus attempts to show both the biological and social origins -innate and acquired -of gender differences in risk-taking. [START_REF] Brown | Sex Differences in First-Time DWI Offenders : Role of Alcohol and Neurobiological Factors[END_REF] thus identifies a double risk factor for the male population. The male gender leads to a high level of androgens, a lower effect of alcohol on psychomotor performance and slower neurocognitive development, which reduces risk perception and increases impulsivity and sensation seeking in adolescence. The masculine psychosocial gender role brings a cultural, social and individual value to risk-taking, aggressiveness, competition and alcohol consumption, and greater exposure in terms of driving frequency. On the contrary, females have a double protective factor against the risk of crashes: the female gender but also the feminine gender role constitute obstacles to the biological and social factors that explain risk-taking. This last explanation suggests that gender differences could vary across cultures and gender roles socially expected for females and males. Although some studies have explored cultural differences between road users from both developed and developing countries (Üzümcüoğlu et al., 2018), and some others have looked at gender differences across cultures [START_REF] Schmitt | Why can't a man be more like a woman ? Sex differences in Big Five personality traits across 55 cultures[END_REF], most of the research on gender differences has so far been based on developed countries. To our knowledge, no study has yet attempted to compare gender differences in driver behaviours according to the geographical and cultural context. Such an approach might show that gender differences in car driving behaviours vary across countries and geographical and cultural contexts and thus support psychosocial explanations of gender differences in driving behaviour. Some studies have shown, for example, that there are already variations in gender differences within countries between high-and low-income regions. For example, in low-income regions, gender differences are more pronounced, with females having even fewer car crashes than in wealthier regions [START_REF] Al-Balbissi | Role of gender in road accidents[END_REF].

The first analysis of gender differences in the ESRA2 data by [START_REF] Granié | Les différences de sexe en sécurité routière: accidentalité, comportements à risque, accès au permis de conduire[END_REF] revealed cultural variations for four transgressions: drunk driving, driving while using a telephone, speeding, and seat belt use. However, regions were constructed on the basis of geographical proximity of countries and could therefore include culturally different countries. The aim of this study is to build on the results previously found by analysing gender differences across the entire sample, by region, but also by comparing countries among themselves. In particular, we will investigate the cultural differences between these countries, based on their gender equality indices and income levels. This thematic ESRA report aims at describing the differences between males and females in the self-declared behaviours and attitudes related to driving in a sample from 32 countries worldwide. Factors that influence these self-declared behaviours are examined within each of the four regions: Europe20, NorthAmerica2, AsiaOceania5 and Africa5. Gender Issues

Methodology

The ESRA project (E-Survey of Road users' Attitudes) is a joint initiative of road safety institutes, research organisations, public services and private sponsors, across 46 countries aiming at collecting comparable (inter)national data on road users' opinions, attitudes and behaviour with respect to road traffic risks. The initiative is funded by the partners' own resources.

ESRA is an extensive online panel survey, using a representative sample (at least N=1,000) of the national adult populations in each participating country. A jointly developed questionnaire is translated into national language versions. The themes covered include: self-declared behaviour, attitudes and opinions on unsafe traffic behaviour, enforcement experiences and support for policy measures. • North America: Canada, USA;

• Asia and Oceania: Australia, India, Israel, Japan, Republic of Korea;

• Africa: Egypt, Kenya, Morocco, Nigeria, South Afrika.

The project has been initiated and is coordinated by the Vias institute (BE). Twelve institutes: BASt (DE), bfu (CH), CTL (IT), IATSS (JP), IFSTTAR (FR), ITS (PL), KFV (AT), NTUA (EL), PRP (PT), SWOV (NL), TIRF (CA) and Vias institute (BE) -combined their expertise and resources to analyse the common data and to disseminate the results. The results of the ESRA2_2018 survey will be published in a Main Report and fifteen thematic reports (Table 1:). There are also country fact sheets in which the main results per country are compared with a regional average. An overview of the project and the results are available on www.esranet.eu.

An overview of the data collection method and the sample per country can be found in (LINK Methodological report). A weighting of the data was applied to the descriptive analyses. This weighting considered small corrections with respect to national representativeness of the sample based on gender and six age groups: 18-24y, 25-34y, 35-44y, 45-54y, 55-64y, 65y+; based on population statistics from United Nations data (United Nations Statistics Division, 2019). More information about the weighting is available in Appendix 2: ESRA2 weights. Note that in the African countries a lower percentage of people has access to and use the internet (in Kenya and Nigeria less than 30%). Within the African countries the numbers of 65+ respondents who answered the ESRA2 survey were quite low (with the exception Gender Issues of South Africa), so that the answers of this particular age group in African countries cannot be considered to be representative.

The present report summarizes the ESRA2_2018 results with respect to gender issues on psychological variables that may affect the declared behaviours. In investigating the gender differences across regions and countries, the focus is only on participants who reported driving, at least a few days a month during the past 12 months. The resulting sample consists of 25,459 individuals: 13,540 males (53.18% of the sample) and 11.919 females (46.82% of the sample) equally distributed in the four regions studied (Table 2). The analyses are focused on this population, as the sample of each gender is not equally distributed or not large enough on the other transport modes and/or the number of survey items devoted to these modes were not sufficient to the analyses. The purpose of this thematic report is to explore the cultural effect on gender differences in reported risk behaviours while driving. The four regions differentiated above, based on a geographical criterion, will be used to distinguish potential cultural differences on a meso-level, while gender differences will be also on a microlevel, by analysing the differences in aggregated scores by country.

For the purposes of analysis and for the sake of clarity and conciseness, we have chosen to focus on the variables taken as a whole rather than going into detail about each of the behaviours included in the ESRA questionnaire. For details by behaviour, the reader is invited to consult the other thematic reports, in which gender differences are analysed for each item.

We focus on the items concerning psychological constructs on which we can expect gender differences, according to literature. To consider the general psychological constructs, we performed a Principal Component Analysis on each psychological construct studied, in order to verify that all the items were of the same dimension (saturation >.40 on the 1st axis), then we calculated aggregate scores on each construct. These 13 factors are hypothetical and have not, however, been validated upstream by an exploratory factor analysis. The variables considered and the corresponding question number from the survey found in Appendix 1 are:

-Social acceptability of risky behaviours (Q13.1)

-Personal acceptability of risky behaviours (Q14.1)

-Declared risky behaviours (Q12.1b and Q12.1a). Note that behaviours concerning children's use of seat-belt were excluded As the effect of age on driver perceptions, attitudes and behaviour is already well demonstrated in the literature [START_REF] Borowsky | Age, skill, and hazard perception in driving[END_REF], age was also included in the analysis, in addition to gender and regional effects, to control for its effect.

First, two-way analysis of variance was carried out on each variable to assess the effect of gender and age on the whole sample. Three-way analysis of variance (ANOVA) was carried out to assess the effect of gender, region and age on each aggregated score explained above. The between subject factors comprised two gender groups (males and females), four regional groups (Europe20, North America2, Asia-Oceania5 and Africa5) and six age groups (18-24, 25-34, 35-44, 45-54, 55-64, 65+).

Two-way analyses (ANOVA) were carried out to assess the effect of gender and age by country. The between subject factors comprised two gender groups (males and females), and six age groups (18-24, 25-34, 35-44, 45-54, 55-64, 65+).

Given the focus of this paper on the effect of gender and region/country and their interactions, we report the main effect of age but not the interaction between age and the two other factors. For each significant F test, we give eta squared (²) value as a measure of effect size. The scale of magnitude given by [START_REF] Cohen | Statistical power analysis for the behavioral sciences[END_REF] is as follows: small when ²=.01, medium when ²=.06, large when ²=.14.

For the ANOVAs, the mean and standard deviation are presented in a table for each variable for each country and region. Correlations between the 13 psychological constructs are presented in Appendix 3. Stars identify and differentiate levels of statistically significant differences that are synthetized in the result section.

Linear regression analyses were also carried out to analyse first the effect of gender and countries on each variable, and second the effect of gender, country and attitudinal variables measured on the declared behaviours. To further explain the differences by country, we use the gender equality indices and income level of each country as explanatory variables of the effect of country on the analysis of gender differences in the whole sample. For controlling the effect of the income level, we use the income categories proposed by the World Bank, dividing word's economies in four groups: high, uppermiddle, lower-middle, and low.

Gender equality indices used here are those indicated by the World Economic Forum (WEF, 2018). They benchmark progress toward parity in four dimensions: Health and Survival (gender ratio at birth and gender gap in healthy life expectancy); Political Empowerment (gender ratio in ministerial and parliamentary positions and in years in national executive office) and Educational Attainment (gender ratio in primary, secondary and tertiary level of education); and Economic Participation and Opportunity (employment remuneration and advancement gaps between males and females). Gender Issues

Results & discussion

General results

Descriptive results by gender per item are presented in the various thematic reports. In this report on gender aspects, we present the means and tests of the significance of the difference between the two gender groups in each country for each aggregate variable we have constructed.

Social acceptability of risky driving behaviours

The aggregated score of social acceptability of risky driving behaviours has been calculated after verifying by PCA that all the items correctly loaded on the first factor. The calculated score included the following items:

Where you live, how acceptable would most other people say it is for a CAR DRIVER to….?

• drive when he/she may be over the legal limit for drinking and driving

• drive 1 hour after using drugs (other than medication)

• drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• not wear a seatbelt while driving

• talk on a hand-held mobile phone while driving

• read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving

As shown in Table 3, the tests of analysis of variance showed significant gender differences for 18 out of the 32 countries and all the regions of the ESRA sample. Gender differences are not significant for Austria, Egypt, Finland, Greece, India, Ireland, Kenya, Morocco, Poland, Portugal, Serbia, South Africa, Sweden and the United States. All the effect sizes were small. For all the countries and regions where gender difference was significant, males perceived higher social acceptability of risky driving behaviours than females. The aggregated score of personal acceptability of risky driving behaviours has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

How acceptable do you, personally, feel it is for a car driver to…?

• drive when he/she may be over the legal limit for drinking and driving

• drive 1 hour after using drugs (other than medication)

• drive after taking a medication that may influence the ability to drive

• drive faster than the speed limit inside built-up areas

• drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• drive faster than the speed limit on motorways/freeways

• not wear a seatbelt while driving

• transport children in the car without securing them (child's car seat, seatbelt, etc.)

• talk on a hand-held mobile phone while driving

• talk on a hand-free mobile phone while driving

• read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving

• drive when they're so sleepy that they have trouble keeping their eyes open

As shown in Table 4, the tests of analysis of variance showed significant gender differences for 27 out of the 32 countries and all the regions of the ESRA sample. Gender differences are not significant for Egypt, India, Kenya, Morocco and Nigeria. All the effect sizes were small. For all the countries and all the regions where gender difference was significant, males perceived higher personal acceptability of risky driving behaviours than females. The aggregated score of declared risky driving behaviours has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The items related to the transport of children have been excluded due to the high number of missing values. The calculated score included the following items:

Over the last 12 months, how often did you as a car driver …?

• drive after drinking alcohol

• drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• read a text message or email while driving

Over the last 30 days, how often did you as a car driver …?

• drive when you may have been over the legal limit for drinking and driving

• drive after drinking alcohol

• drive 1 hour after using drugs (other than medication)

• drive after taking medication that carries a warning that it may influence your driving ability

• drive faster than the speed limit inside built-up areas

• drive faster than the speed limit outside built-up areas (but not on motorways/freeways) Gender Issues

• drive faster than the speed limit on motorways/freeways

• drive without wearing your seatbelt

• talk on a hand-held mobile phone while driving

• talk on a hands-free mobile phone while driving

• read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving

• drive when you were so sleepy that you had trouble keeping your eyes open.

As shown in Table 5, the tests of analysis of variance showed significant gender differences for 30 out of the 32 countries and all regions of the ESRA sample. Gender differences are not significant for Kenya and Morocco. All the effect sizes were small, except for Greece, Italy and Portugal where the effect sizes are medium. For all the countries and all the regions where gender difference was significant, males declared higher frequency of declared risky driving behaviours than females, except in India where females declared riskier behaviours than males. The aggregated score of self-efficacy in risky behaviours has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

To what extent do you agree with each of the following statements?

• I trust myself to drive after having a glass of alcohol

• I have the ability to drive when I am a little drunk after a party

• I am able to drive after drinking a large amount of alcohol (e.g. half a liter of wine)

• I trust myself when I drive significantly faster than the speed limit

• I am able to drive fast through a sharp curve

• I trust myself when I check my messages on the mobile phone while driving

• I have the ability to write a message on the mobile phone while driving

• I am able to talk on a hand-held mobile phone while driving

As shown in Table 6, the tests of analysis of variance showed significant gender differences for 29 out of the 32 countries and all the regions of the ESRA sample. Gender differences are not significant for India, Morocco and Nigeria. All the effect sizes were small, except for Czech Republic, Denmark, Finland, Italy, The Netherlands, and Portugal where the effect sizes are medium. For all the countries and regions where gender difference was significant, males declared a higher level of self-efficacy in risky behaviours than females. The score of perceived safety is based on the score on the item "How safe or unsafe do you feel when using the following transport modes in [country]?" for the following transport modes:

• drive a car (non-electric or non-hybrid)

• drive a hybrid or electric car

As shown in Table 7, the tests of analysis of variance showed significant gender differences for 21 out of the 32 countries and all the regions of the ESRA sample. Gender differences are not significant for Austria, Czech Republic, Egypt, India, Italy, Japan, Kenya, Morocco, Nigeria, Republic of Korea and Spain. All the effect sizes were small. For all the countries and regions where gender difference was significant, males declared higher perceived safety in driving than females. The aggregated score of road safety policy support has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items: Do you oppose or support a legal obligation to …?

• install an alcohol "interlock" for drivers who have been caught drunk driving on more than one occasion (technology that won't let the car start if the driver's alcohol level is over the legal limit)

• have zero tolerance for alcohol (0,0 ‰) for novice drivers (licence obtained less than 2 years)

• have zero tolerance for alcohol (0,0 ‰) for all drivers

• install Intelligent Speed Assistance (ISA) in new cars (which automatically limits the maximum speed of the vehicle and can be turned off manually)

• install Dynamic Speed Warning signs (traffic control devices that are programmed to provide a message to drivers exceeding a certain speed threshold)

• have a seatbelt reminder system for the front and back seats in new cars

• require all cyclists to wear a helmet

• require cyclists under the age of 12 to wear a helmet

• require all moped drivers and motorcyclists to wear a helmet

• require pedestrians to wear reflective material when walking in the streets in the dark

• require cyclists to wear reflective material when cycling in the dark

• require moped drivers and motorcyclists to wear reflective material when driving in the dark

• have zero tolerance for using any type of mobile phone while driving (hand-held or hands-free) for all drivers

• not using headphones (or earbuds) while walking in the streets

• not using headphones (or earbuds) while riding a bicycle

As shown in Table 8, the tests of analysis of variance showed significant gender differences for 30 out of the 32 countries and all regions of the ESRA sample. Gender differences are not significant for India and the Republic of Korea. All the effect sizes were small, except for Finland, Greece and Sweden were the effect sizes are medium. For 29 out of 30 countries and all the regions where gender difference was significant, females declared higher road safety policy support than males, except in Morocco, where males declared higher road safety support. The aggregated score of risk perception of risky behaviours has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

How often do you think each of the following factors is the cause of a road crash involving a car?

• driving after drinking alcohol

• driving after taking drugs (other than medication)

• driving faster than the speed limit

• using a hand-held mobile phone while driving

• using a hands-free mobile phone while driving

• inattentiveness or day-dreaming while driving

• driving while tired

As shown in Table 9, the tests of analysis of variance showed significant gender differences for 24 out of the 32 countries and all regions of the ESRA sample. Gender differences are not significant for Czech Republic, Egypt, Germany, Greece, Italy, Japan, Kenya and Republic of Korea. All the effect sizes were small. For 23 out of 24 countries and all the regions where gender difference was significant, females declared higher risk perception of risky behaviours than males, except in Morocco, where males had a higher risk perception. Gender Issues The aggregated score of the number of crashes has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

In the past 12 months, how many times have you personally been involved in road crashes…

• in which you or somebody else had to be taken to the hospital?

• with only minor injuries (no need for hospitalisation) for you or other people?

• with only material damage?

As shown in Table 10, the tests of analysis of variance showed significant gender differences for 9 out of the 32 countries and 3 of the 4 regions of the ESRA sample. Gender differences are significant for Belgium, Canada, Egypt, France, Germany, Greece, Morocco, the Netherlands, and Slovenia and for the regions North America, Asia Oceania and Europa. All the effect sizes were small. For 8 countries out of the 9 countries and the 3 regions where gender difference was significant, males declared higher Gender Issues number of crashes than females, except for Morocco where females declared higher number of crashes than males. To what extent are the following statements true?

• I always respect the highway code, even if the risk of getting caught is very low

• I would still respect speed limits at all times, even if there were no police checks

• I have never driven through a traffic light that had just turned red As shown in Table 11, the tests of analysis of variance showed significant gender differences for 12 out of the 32 countries and 2 of the 4 regions of the ESRA sample. Gender differences are significant for Austria, Belgium, Czech Republic, Greece, Hungary, Italy, Kenya, Nigeria, Poland, Spain, Sweden and Switzerland, and for the regions Asia Oceania and Europa. All the effect sizes were small. For 11 countries out of the 12 countries and the 2 regions where gender difference was significant, females declared higher social desirability and compliance intention than males, whereas for Kenya males declared higher compliance intention than females. • all laws should be strictly obeyed

• obeying the law ultimately benefits everyone in the community

• there are times when it is ok to ignore the law (reversed score)

As shown in Table 12, the tests of analysis of variance showed significant gender differences for 6 out of the 11 countries of the ESRA sample who use this bonus question and 3 of the 4 regions. Gender differences are significant for Canada, Egypt, Japan, Slovenia, South Africa and United Kingdom and for the regions North America, Asia Oceania and Europa. All the effect sizes were small. For 5 countries out of the 6 countries and all the regions where gender difference was significant, females declared a higher compliant law perception than males, whereas for Egypt males declared a higher compliant law perception than females. To what extent do you agree with each of the following statements?

• Most of my friends would drive after having drunk alcohol

• Most of my friends would drive 20 km/h over the speed limit in a residential area

As shown in Table 13, the tests of analysis of variance showed significant gender differences for 9 out of the 32 countries and 3 of the 4 regions of the ESRA sample. Gender differences are significant for Belgium, Canada, Denmark, Israel, Italy, the Netherlands, Poland, Republic of Korea and the United States, in North America, Asia Oceania and Europa. All the effect sizes were small. For all the countries and regions where gender difference was significant, males declared riskier social norms than females. Gender Issues The aggregated score of perceived probability of enforcement has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

On a typical journey, how likely is it that you (as a car driver) will be checked by the police for…

• … alcohol, in other words, being subjected to a Breathalyser test

• … the use of illegal drugs

• … respecting the speed limits (including checks by a police car with a camera, fixed cameras, mobile cameras, and section control systems)

• … wearing your seatbelt

• … the use of hand-held mobile phone to talk or text while driving Gender Issues

As shown in Table 14, the tests of analysis of variance showed significant gender differences for 21 out of the 32 countries and all regions of the ESRA sample. Gender differences are not significant for Czech Republic, Finland, Greece, India, Japan, Morocco, the Netherlands, Nigeria, Poland, Republic of Korea and Sweden. All the effect sizes were small. For all the countries where gender difference was significant, males perceived higher probability of enforcement than females. The aggregated score for the positive perception of automated vehicle has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

How interested would you be in using the following types of automated passenger car?

• semi-automated passenger car

• fully-automated passenger car Gender Issues How likely do you think it is that the following benefits will occur if everyone would use a semiautomated / automated passenger car?

• fewer crashes

• reduced severity of crash

• less traffic congestion

• shorter travel time

• lower vehicle emissions

• better fuel economy

• time for functional activities, not related to driving (e.g. working)

• time for recreative activities, not related to driving (e.g. reading, sleeping, eating)

As shown in Table 15, the tests of analysis of variance showed significant gender differences for 22 out of the 32 countries and all regions of the ESRA sample. Gender differences are not significant for Australia, Czech Republic, Egypt, Greece, Hungary, India, Japan, Kenya, Morocco and Poland. All the effect sizes were small. For all the countries and all regions where gender difference was significant, males declared higher positive perception of automated and semi-automated vehicles than females.

Advanced analyses: gender differences by regions

Section 4.1 explores gender differences across variables and countries. In this section, we analyse the interaction between gender and region for each variable constructed. Three-way analysis of variance (ANOVA) was carried out to assess the effect of gender, region and age on each aggregated score. The between subject factors comprised two gender groups (males and females), four regional groups (Europe20, North America2, Asia-Oceania5 and Africa5) and six age groups (18-24, 25-34, 35-44, 45-54, 55-64, 65+). Age was added to the models to control its effect but will not be discussed in detail here.

Social acceptability of risky driving behaviours

Participants were asked how acceptable most other people would perceive some risky behaviours (e.g., using a hand-held mobile phone while driving, driving after drinking alcohol), on a Likert scale from 1 (unacceptable) to 5 (acceptable). As As Figure 1 shows, social acceptability of risky driving behaviour is higher in the Africa region. Regardless of the region, males always considered that most other people perceived a higher acceptability of transgressions compared to females. Gender Issues Figure 1: Mean values for 'social acceptability of risky driving behaviours' by gender and region

Personal acceptability of risky driving behaviour

Another factor grouped all the personal acceptability score of risky driving behaviours for the same risky behaviours.

Participants had to answer how they personally found certain behaviours as acceptable on a Likert scale from 1 (unacceptable) to 5 (acceptable). As Table 17 shows, ANOVA revealed a significant main effect of gender, F(1,25426) = 204.96, p < .001, η² = .01, age, F(5,25426) = 107.79, p < .001, η² = .02, and region, F(3, 25426) = 12.06, p < .001, η² < .01. A significant interaction between gender and region was found, F(3, 25426) = 7.91, p <.001, η² < .01 (see Figure 2), as an interaction between gender and age, F(5,25426) = 3.35, p = .005, η² < .01, and between age and region, F(15, 25426) = 14.91, p <.001, η² = .01. As Figure 2 shows, personal acceptability of risky driving behaviours is higher in Europe and North America and, in all regions, males accepted significantly more risky behaviours at a personal level, than females did, especially in Europe and North America. The gap between males and females was lowest in Africa. Participants were asked how often, during the past 30 days, they behaved in a risky manner. As Table 18 shows, significant main effects of gender, F(1, 25426) = 292.21, p < .001, η² = .01, age, F(5, 25426) = 90.70, p < .001, η² = .02 and region, F(3, 25426) = 43.17, p < .001, η² < .01, were found. The analysis also revealed significant interactions between gender and region, F(3, 25426) = 8.36, p < .001, η² < .01 (see Figure 3) and between age and region, F(15, 25426) = 8.681, p < .001, η² < .01. The declared driving behaviour is safer in Asia-Oceania. The number of transgressions reported by males was higher in all regions, compared to females. The gender gap was largest in Europe, while Africa and North America showed the highest score for male transgressions. Participants rated to what extent they felt capable of engaging in some risky behaviours, on a Likert scale from 1 (disagree) to 5 (agree). As Table 19 shows, the analysis detected a significant main effect As Figure 4 shows, self-efficacy in risky driving behaviours is lower in Asia-Oceania and males perceived themselves as more capable of engaging in risky behaviours compared to what females perceived. This effect for gender was significant in all regions, but the effect size was larger in Europe and in North America.

Figure 4: Mean values for 'self-efficacy in risky driving behaviours' by gender and region

Perceived safety in driving

Participants were asked how safe, they felt when they drove a car, on a Likert scale from 0 (very unsafe)

to 10 (very safe). Results showed a significant main effect of gender, F( As shown in Figure 5, perceived safety in driving is lower in Asia-Oceania and males always felt more secure than females, especially in Europe, where the effect size was the largest. Road safety policy support is highest in Africa. Females were more likely to support road safety policies than males were, in all regions, except in Africa, where the support was similar for both genders. However, in Europe and North America, the gender gap was bigger, with a lower general support, especially in North America (Figure 6). To evaluate their perception of risky driving behaviours, participants had to answer how often they thought some factors were responsible for road crashes involving a car, on a Likert scale from 1 (never) to 6 (almost) always). As Table 22 shows, significant main effects of gender, F(1, 25426) = 141.55, p < .001, η² = .01, age, F(5, 25426) = 6.64, p < .001, η² < .01 and region, F(3, 25426) = 165.67, p < .001, η² = .02, were found. Likewise, the analysis revealed a significant interaction between age and region, F(15, 25426) = 12.81, p < .001, η² = .01, but no other interactions. As Figure 7 shows, in each region, except in Africa, females perceived a higher risk of risky behaviours compared to males. Region differences are also blatant, as Asia-Oceanian perceived lower risk of risky behaviours compared to the three other regions. In accordance with [START_REF] Lund | Cross-cultural comparisons of traffic safety, risk perception, attitudes and behaviour[END_REF], gender differences in risk perception in African countries were not statistically significant while they were significant in the three other regions. The number of crashes was particularly high in Africa and Asia-Oceania, where no gender differences were found. However, a significant gender gap was observed in Europe and North America, where males have more crashes than females (Figure 8). Participants were asked to indicate to what extent they agreed with some statements, which are considered socially desirable, on a Likert scale from 1 (very untrue) to 5 (very true). These statements could also be interpreted as rules internalisation. As shown in Table 24, ANOVA revealed a significant main effect of gender, F(1, 25426) = 7.12, p = .008, η² < .01, age, F(5, 25426) = 14.36, p < .001, η² < .01 and region, F(3, 25426) = 57.70, p < .001, η² = .01. We also found significant interactions between gender and region, F(3, 25426) = 5.64, p = .001, η² < .01 (see Figure 10), gender and age, F(5, 25426) = 5.82, p < .001, η² < .01, and between age and region, F(15, 25426) = 12.94, p < .001, η² = .01. Figure 9 shows social desirability and intention to comply is higher in Africa and females had a higher social desirability score than males in Asia-Oceania and in Europe. Although not statistically significant, the results were reversed in Africa, with a higher social desirability for males.

Figure 9: Mean values for 'social desirability and intention to comply' by gender and region

Compliant law perception

A limited sample of countries had questions posed about compliant law perception (N = 10). For those, participants had to indicate to what extent they agreed with statements concerning their opinions with respect to traffic laws, on a Likert scale from 1 (strongly disagree) to 5 (strongly agree). As Table 25 shows, significant main effects of gender, F(1,7809) = 32.50, p Figure 10 shows females perceived traffic laws as more important to follow than males did, except in Africa, where the law support was high and independent of gender. The effect size of the gender gap in Asia-Oceania was particularly important. Participants were asked to indicate how likely their friends would be to engage in some risky behaviours, on a Likert scale from 1 (disagree) to 5 (agree). As As Figure 11 shows, the social norms are riskier in Africa and, regardless of region, males perceived their friends to act more in a risky manner than females perceived, except in Africa where the gender gap was not significant. The maximum gender gap appeared in North America.

Figure 11: Mean values for 'risky social norms' by gender and region 4.2.12. Perceived probability of enforcement These items were meant to evaluate the perceived probability of enforcement from the public authorities for different transgressions, on a Likert scale from 1 (very unlikely) to 7 (very likely). ANOVA revealed a significant main effect of gender, F(1, 25426) = 137.85, p < .001, η² = 0.01, age, F(5, 25426) = 41.50, p < .001, η² = 0.01, and region, F(3, 25426) = 123.84, p < .001, η² = 0.01. The interaction between region and age, F(15, 25426) = 1.80, p = .029, η² < 0.01, was the only one significant (Table 27). Figure 12 shows, in all regions, males perceived their probability to encounter enforcement related to different transgressions higher than females did. The perceived probability was lowest in North America and highest in Africa.

Figure 12: Mean values for 'perceived probability of enforcement' by gender and region 4.2.13. Positive perception of automated vehicles Participants indicated how likely some benefits could occur if every driver drove a semi-or a fullyautomated car, on a Likert scale from 1 (very unlikely) to 7 (very likely) and how interested they were in semi-and fully-automated vehicles, on a Likert scale from 1 (not at all interested) to 7 (very interested). The aggregated score of "positive perception of automated vehicles" includes all the items concerning the potential benefits and interest in these two types of vehicles. The results indicated a significant main effect of gender, F(1, 25426) = 115.06, p < .001, η² < .01, age, F(5, 25426) = 72.57, p < .001, η² = .01, and region, F(3, 25426) = 159.41, p < .001, η² = .02. Significant interactions between gender and region, F(3, 25426) = 5.16, p = .001, η² < .01, gender and age, F(5, 25426) = 2.48, p = .03, η² < .01 and age and region, F(15, 25426) = 3.99, p <.001, η² < .01, were found as well (Table 28). Globally, automated vehicles are less positively perceived in North America and Europe and males have more positive judgments than females. The gender gap was higher in Europe and North America (Figure 13).

Analysis of the role of gender and culture on attitudinal and behavioural variables

In this section, we analyse the effects of gender, age and culture on constructed attitudinal and behavioural variables. To do so, we conducted linear regression analyses of each constructed variable as a function of gender (0=male, 1=female), age (continuous) and culture. The effect of culture is examined via proxy variables for each country: the country's income category and the gender equality indexes constructed by the World Bank (WEF, 2018). These indices assess progress towards gender parity in four dimensions: Health and Survival (gender ratio at birth and gender gap in healthy life expectancy), Political Empowerment (gender ratio in ministerial and parliamentary positions and in years of national executive power), Educational Attainment (gender ratio in primary, secondary and tertiary education), and Economic Participation and Opportunities (wage levels and gender gaps in promotion).

For theses analyses, we used the Global Gender Equality Index (GGEI) aggregating these 4 indices. The effect of these variables is examined for the total sample and for countries in the European region. The analysis could not be carried out on the other 3 regions. The strong correlations between the Gender Equality indices of the component countries of the non-European regions did not allow linear regression analyses to be carried out.

For the whole sample

For the whole ESRA32 sample, all the models are statistically significant, but the R² values were low indicating that the variation in the data is not well explained by the models (Table 29).

The linear regression analyses did confirm the relation between gender and age and the aggregated variables constructed. Scores of social acceptability, personal acceptability, descriptive norms, selfefficacy, perceived safety, perceived enforcement, positive perception of automated vehicles, number of crashes and risky declared behaviours were higher among males and younger drivers. Scores of social desirability, road safety policy support and risk perception were higher among females and older drivers.

The linear regressions also showed attitudinal and behavioural variables are linked to GGEI and country income level and their effects are not identical. Thus, the level of income seems to have a positive effect on road safety, by decreasing the level of social acceptability of risky behaviours, the feeling of selfefficacy in risky situations, the perceived safety of driving, the number of crashes and the risky behaviours declared, but it also had negative effect by increasing the personal acceptability of violations, lowering the perception of risk, weakening the support for road safety policies and lowering the perceived enforcement likelihood. Countries level of GGEI also shows positive and negative effects on attitudinal and behavioural variables. GGEI seems to have positive effects, by lowering social and personal acceptability of violating behaviours and crash number, and by increasing support for road safety policies and risk perception. But it also has negative effects, by increasing self-efficacy feeling in risky situations and perceived safety of driving, by weakening perceived enforcement likelihood and increasing risky behaviours declared.

For Europe20

For the Europe region sample, all the models were again statistically significant, but R² were low for all the models indicating that the variation in the data is not well explained by the models (Table 30). Gender Issues

The linear regression analyses confirmed the relation between gender and age and the aggregated variables constructed for the Europe region. Scores of social acceptability, personal acceptability, descriptive norms, self-efficacy, perceived safety, perceived enforcement, positive perception of automated vehicles, number of crashes and risky declared behaviours were higher among males and younger drivers. Scores of social desirability, road safety policy support and risk perception were higher among females and older drivers.

The linear regressions also showed attitudinal and behavioural variables are linked to GGEI and country income level. However, in contrast to the whole sample, for the Europe20 region, their effects are quite similar. The country level of gender equality and income seem to have positive effects on road safety, by decreasing the risky descriptive norms, but also the social acceptability of risky behaviours concerning the income level and lowering the number of crashes for the GGEI.

The level of income as the country level of gender equality seems to have a negative effect on road safety, with higher incomes increasing the personal acceptability of risky behaviours, the feeling of selfefficacy in risky situations, the perceived safety of driving, and by decreasing the intention to comply to Highway code, the support of road safety policies, the perceived enforcement, but also by lowering the perception of risk and increasing the risky behaviours declared for the GGEI. Linear regression models were estimated for each country (Table 31 andTable 32), to understand the attitudinal and demographic variables explaining the risky behaviours declared by drivers. The linear regression models are statistically significant for all the countries in the ESRA2 sample. The R² are high for all models, ranging from .387 for Nigeria to .732 for Canada indicating that they explain a significant amount of the variation in the data. The models show gender, after controlling for the effects of attitudinal variables, has no further effect in a majority of samples. However, the effect of gender is still significant for 13 out of the 32 countries of the ESRA sample: Austria, Belgium, Germany, Greece, India, Italy, Japan, Nigeria, Portugal, Serbia, Slovenia, South Africa and Spain. In all cases, risky behaviours are still more frequently declared by male drivers than female drivers after controlling for other Gender Issues demographic variables and attitudinal variables. In the same way, age, after controlling for the effect of the other variables included in the models, still has an effect on declared behaviours in 6 out of 32 countries: Belgium, Canada, Japan, Morocco, Republic of Korea and Sweden. In two countries (Belgium and Morocco), younger drivers report higher rates of risky driving behaviours. In four countries however (Canada, Japan, Republic of Korea and Sweden), after controlling for the effect of gender and attitudinal variables, older drivers report higher rates of risky driving behaviours.

Concerning the attitudinal variable effects on declared behaviours, the regression analyses show some variables systematically affect the risky behaviours declared. Higher personal acceptability of risky behaviours and higher driving self-efficacy in risky situations are linked to higher risk behaviours declared in all the countries of the sample. Some other variables affect risky behaviours in the same way but are not significant in all the countries. Riskier descriptive norms are linked to riskier behaviours in 28 countries but not for Czech Republic, the Netherlands, Nigeria and United States. In the same way, the greater the intention to comply with traffic laws, the lower is the rate of reported risky driving behaviours, in all countries except for Egypt. Driver's number of crashes is linked to risky behaviours for 22 countries, but not for Australia, Canada, Czech Republic, France, Israel, Italy, Japan, Republic of Korea, Serbia and Spain. Risky behaviours increase with the perceived enforcement for half of the countries and had no significant effect for the other half of the countries. Crash risk perception has some links with risky behaviours for less than the half the countries. Perceived safety of driving has a significant effect only in few countries, with higher rates of risky driving behaviours when driving is perceived as safe. Gender Issues As shown in Table 34, these analyses allow us to specify differentiated effects of the different dimensions of Gender Equality. They show in the overall sample that, among the gender equality indices, national levels of education and economic participation of females are related to higher levels of reported risk behaviours. At the European country level, the national level of female economic participation reinforces the risk behaviours reported by participants.

It is also observed that, while the behaviours reported by the male group are only affected by female economic participation (the more females have economic participation, the higher the level of risk behaviours reported by males), the behaviours reported by females are related to all indices of gender equality: female level of education and economic participation at the national level reinforces the risk behaviours reported by females, while national levels of female health and political involvement are related to lower levels of self-reported risk behaviours among females.

4.4.4. Structural Equation Models (SEM) studying the effect of Gender Equality Sub-Indexes on declared behaviours

The SEM analysis tested a hypothetical model, shown in Figure 14, about the relationship between the respondent's global score of declared risky behaviours and the aggregated scores of number of crashes, perception of risky behaviours, perceived safety in driving, risky social norms, social and personal acceptability of risky driving behaviours, self-efficacy in risky behaviours, road safety policy support, and social desirability and intention to comply. Variables are also included for GGEI for economic participation and opportunity and for educational attainment for the country of origin which vary by Gender Issues

gender. An unobserved latent variable of risk acceptance was also defined. Age (measured in years as a continuous variable), gender (0: male, 1: female), and income group category (1: low, 2: medium, 3: high) of the country of origin of respondents, although not represented in the figure, were included in both models as exogenous variables.

The model results, shown in Table 35, confirmed that the hypothesized indicators were significant to describe the relationships between the global declared behaviour variable and other variables included (p<0.000). The goodness-of-fit test indicated that the model has a satisfactory fit with an SRMR of 0.056 [START_REF] Li-Tze | Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives[END_REF]. The following statistically significant effects are observed for declared risky driving behaviours:

• Females and older drivers are less likely to report risky driving behaviours.

• Drivers from higher income countries are less likely to report risky driving behaviours.

• Drivers from countries with higher gender equality in terms of economic participation and opportunity are more likely to report risky driving behaviour. The effects for males and females are very close with no statistically significant differences.

• Drivers from countries with higher gender equality in terms of educational attainment are more likely to report risky driving behaviour. In these countries, the effects are higher for females indicating that their reported risky driving behaviours are increasing to be more similar to males in countries with high gender equality for educational attainment.

• Drivers who perceive driving as a safe activity are more likely to report risky driving behaviours.

• Drivers who have experienced road crashes are more likely to report risky driving behaviours.

• Drivers who have a high social desirability score and those who perceive risky driving behaviours as a factor in road crashes are less likely to report risky driving behaviours.

Limitations of the data

In general, self-reported data are vulnerable to a number of biases [START_REF] Choi | A catalog of biases in questionnaires[END_REF][START_REF] Krosnick | Questionnaire design[END_REF]: bias through misunderstanding of questions (e.g. questions with difficult words, long questions); or recall error -unintentional faulty answers due to memory errors; desirability bias -the tendency of respondents to provide answers which present a favourable image of themselves, e.g. individuals may over-report good behaviour or under-report bad, or undesirable behaviour. Women tend to have greater social desirability. However, in the specific area of driving behaviour, men may perceive social desirability in reporting risky behaviour that they may not have had, in order to display "typically male" behaviour.

One limitation of the results presented above is the fact that, even if gender differences are overall significant, they are also generally small in effect size (with some exceptions), which had to be considered. This is problematic as we have a large sample, which increases the probability to find significant differences.

Furthermore, the 13 psychological constructs were only subject to separate factor analyses on each hypothetical construct identified in the questionnaire. An exploratory factorial analysis on the whole set of variables would be necessary in order to observe whether this set could be reduced to fewer constructs that are better differentiated from each other.

Despite the advantages of online surveys, the representativeness of the surveyed populations may be a problem, mainly for countries with low rates of internet use. That is the case of some of the countries of ESRA2 survey where the percentage of population using the internet is low (lower than 30% in Kenya and Nigeria, and low than 50% in India and Egypt).

The number of African respondents aged 65 or older was quite low, so that the answers of this particular age group in African countries cannot be considered to be representative. This seems particularly important to consider as some results of African countries (especially Morocco and Kenya) were opposite to the others. For example, in Morocco, there were only three women aged 65 or older but they had a high rate of recent crashes (M = 12.67, SD = 6.80) which can explain that we find a higher probability of women to have crashes than men do.

Conclusions

This thematic ESRA report on gender issues analyses gender differences in self-report data concerning driving behaviours, attitudes and beliefs by region, and by comparing countries among themselves. The purpose of this thematic report is to explore the cultural effect on gender differences in reported risky behaviours while driving. The four regions based on a geographical criterion, were used to distinguish potential cultural differences on a meso-level, while gender differences were also investigated on a microlevel, by analysing the differences by country.

Rather than going into detail about each of the behaviours included in the ESRA questionnaire, a Principal Component Analysis was undertaken on each psychological construct studied, to calculate aggregate scores on each construct. The focus was on the items concerning psychological constructs on which we can expect gender differences, according to literature. The variables considered and the corresponding question number from the survey found in Appendix 1 are: Gender Issues For the more sophisticated analyses, age was also included in addition to gender and regional effects. Gender equality indices used were those indicated by the World Economic Forum (2018). They benchmark progress toward parity in four dimensions: Health and Survival (gender ratio at birth and gender gap in healthy life expectancy); Political Empowerment (gender ratio in ministerial and parliamentary positions and in years in national executive office) and Educational Attainment (gender ratio in primary, secondary and tertiary level of education); and Economic Participation and Opportunity (employment remuneration and advancement gaps between males and females).

For the descriptive comparison at the country by country level (see 4.1), the results show that gender differences are evident in most countries, dependent on the question being asked. However, while these observed differences are statistically significant, the magnitude of these differences is typically small. For the countries where gender differences are statistically significant, males typically report higher rates of risky driving behaviours, perceived social acceptability, personal acceptability, self-efficacy, perceived safety of driving, reported crash involvement, riskier social norms, perceived probability of encountering enforcement and a favourable perception of automated vehicles. Conversely, males typically have lower support for road safety enforcement, perception of risky behaviours, declared intention to conform to rules, and a declared importance for respecting traffic rules.

To further investigate the role of gender, the same variable constructs were again analysed, controlling for age and comparing results across regions (see 4.2). Results confirmed that regardless of region, males reported higher rates of perceived social acceptability, which was highest for both genders in Africa. For personal acceptability, males accepted significantly more risky behaviours at a personal level, especially in Europe and North America. The gap between males and females was lowest in Africa. The number of transgressions reported by males was higher in all regions, compared to females. The gender gap was largest in Europe, while Africa and North America showed the highest score for male transgressions. In all regions, with the exception of Africa, males perceived their friends to act more in a risky manner than females perceived, except in Africa where the gender gap wasn't significant. The maximum gender gap appeared in North America. Males consistently perceived themselves as more capable of engaging in risky behaviours compared to what females perceived. This effect was significant in all regions, but the effect size was larger in Europe and in North America. Males always felt more secure than females, especially in Europe, where the effect size was the largest. The level of perceived safety was smaller in Asia-Oceania for both genders. Females were more likely to support road safety policies than males were, in all regions, except in Africa, where the support was similar for both genders. However, in Europe and North America, the gender gap was bigger, with a lower general support, especially in North America. In each region, except in Africa, females perceived a higher risk of driving in a risky manner compared to males. Regional differences are also evident, as Asia-Oceania perceived Gender Issues lower risk compared to the three other regions. The number of crashes was particularly high in Africa and Asia-Oceania, where no gender differences were found. However, a significant gender gap was observed in Europe and North America. Females had a higher social desirability score than males in Asia-Oceania and in Europe. Although not statistically significant, the results were reversed in Africa, with a higher social desirability for male. Females perceived traffic laws as more important to follow than males did, except in Africa, where the law support was high and independent of gender. The effect size of the gender gap in Asia-Oceania was particularly important. In all regions, males perceived their likelihood to encounter enforcement related to different transgressions more likely than females did. The perceived likelihood was lower in North America. Globally, males judged the likelihood of benefits of semi-and fully-automated cars more likely than females did. The gender gap was higher in Europe and North America, where benefits were seen as more unlikely in these regions.

Linear regression models for the constructed attitudinal variables (see 4.3.1.) also confirmed that scores of social acceptability, personal acceptability, social risky norms, self-efficacy, perceived safety, perceived probability of enforcement, positive perception of automated vehicles, number of crashes and risky declared behaviours were higher among males and younger drivers. Scores of social desirability, road safety policy support and perception of risky behaviours were higher among females and older drivers. The linear regressions also showed that attitudinal variables are linked to Gender Equality Indices and country income level and their effects are not identical. The level of income seems to have positive effect on road safety, by decreasing the level of social acceptability of risky behaviours, the feeling of self-efficacy in risky situations, social risky norms, the perceived safety of driving, the number of crashes and the risky behaviours declared, but it also had negative effect by increasing the personal acceptability of violations, lowering the perception of risk, weakening the support for road safety policies and lowering the perceived enforcement likelihood. Countries level of gender equality also shows positive and negative effects on attitudinal and behavioural variables. Gender equality seems to have positive effects, by lowering social and personal acceptability of violating behaviours and crash number, and by increasing support for road safety policies and risk perception. But it also has negative effects, by increasing self-efficacy feeling in risky situations and perceived safety of driving, by weakening perceived enforcement likelihood and increasing risky behaviours declared.

A similar analysis restricted to European countries found similar results (see 4.3.2.). However, in contrast to the whole sample, for the Europe region, the country level of income seems to have a negative effect on road safety, with higher incomes increasing the feeling of self-efficacy in risky situations, the perceived safety of driving, and increasing the risky behaviours declared. Linear regression models for the constructed behavioural variable confirmed that risky declared behaviours were higher among males and younger drivers. The analysis also showed that the level of income seems to have positive effect on road safety, by decreasing the level of risky behaviours declared, although the effect is opposite when looking at Europe alone. Gender equality seems to have negative effects, by increasing risky behaviours declared.

Linear regression models were estimated for each country (see 4.4.1.), to understand the attitudinal and demographic variables explaining the risky behaviours declared by drivers. The models show gender, after controlling for the effects of attitudinal variables, has no further effect in a majority of samples, except for Austria, Belgium, Germany, Greece, India, Italy, Japan, Nigeria, Portugal, Serbia, Slovenia, South Africa and Spain, where risky behaviours are still more frequently declared by male drivers than female drivers after controlling for other demographic variables and attitudinal variables.

We then analysed the effect of demographic and attitudinal variables as well as the effect of culture on reported risky driving behaviours (see 4.4.2.), with culture operationalized through the World Bank's Gender Equality Index and the country's income level. Results showed that the significant effect of gender and age decreased once the effect of the attitudinal variables is controlled for, suggesting that the effect of these two variables is mediated by the latter. With the exception of the social acceptability of risk behaviours, all attitudinal variables have a significant effect on reported risky driving behaviours. Furthermore, reported risky driving behaviours was higher in countries with a high level of Gender Equality, while all things being equal, risky driving behaviours decrease as income level increases. When separate linear regression models are developed by gender, analyses show that the same attitudinal variables play an important role and that the same effects of culture are observed. However, the results for the female group show that, for this group, age and social acceptability of risk behaviours have a Gender Issues significant inhibitory effect on reported behaviours, which is not the case for the male group. Considering these results, we can assume that women are more sensitive to social norms than men.

Additional linear regression models using the sub-indexes of Gender Equality instead of the global index (see 4.4.3.) show that, among the gender equality indices, national levels of education and economic participation of females are related to higher levels of reported risk behaviours. At the European country level, the national level of female economic participation reinforces the risk behaviours reported by participants. It is also observed that, while the behaviours reported by the male group are only affected by female economic participation (the more females have economic participation, the higher the level of risk behaviours reported by males), the behaviours reported by females are related to all indices of gender equality: female level of education and economic participation at the national level reinforces the risk behaviours reported by females, while national levels of female health and political involvement are related to lower levels of self-reported risk behaviours among females.

A Structural Equation Model (see 4.4.4.) was developed for the relationship between the respondent's global score of declared behaviours and the aggregated scores of attitudinal variables, age, gender, country gender equality indices and country income. These results confirmed those found in the linear regression models but further indicated that for countries with higher gender equality in terms of educational attainment the increase in risky driving behaviours are higher for females than for males indicating that their reported risky driving behaviours are increasing to be more similar to males in countries with high gender equality for educational attainment.

Globally, an effect of gender on psychological constructs was found, as a significant effect of region. More occasionally, an interaction between gender and region was revealed, leading us to conclude that gender is indeed culturally constructed. This was the case for personal acceptability, declared behaviours, self-efficacy, perceived safety, road safety policy support, social desirability, law perception and positive perception of automated vehicle. The fact that some constructs are only explained by gender can be interpreted in two manners. Firstly, we can understand that the biologic aspect of gender cannot be denied when trying to explain gender differences in attitudes and other related constructs in safety road theme. However, we can also argue the fact that the way people are socialized as men and women all around the world is not different enough to find cultural differences on gender's effect. This impact of culture needs to be approached in further analyses, for example by using Schwarz values [START_REF] Schwartz | Refining the Theory of Basic Individual Values[END_REF] as a proxy for culture.

One of the main take-aways from this research is that equality between the sexes in this context can also have negative consequences if it means that women also adopt risky driving behaviours that are typically more often displayed by men. It is therefore essential to tailor countermeasures to better address risks and needs of men versus women, especially as a society becomes more equal. Moreover, effect sizes were small in all cases and the results showed that effects of gender, age and culture on risky behaviours seem also to be mediated by attitudinal variables and that those 3 variables interacted in explaining attitudes and behaviours. Furthermore, the 13 psychological constructs used in this report still are hypothetical since we did not conduct an exploratory factorial analysis on all the items aimed at identifying the distinct dimensions that make up the questionnaire for the population surveyed. This should be examined in depth in subsequent studies using these data.

Recommendations

Policy recommendations at national and regional level

• Continue to study the impact of gender on crash risk and aim programs at those most at risk. In this study males were found to be more at risk than females. Even in Europe and North America, whereas crashes are less common, men are still more at risk than women. Gender Issues

•

A reduction in the gap of equality measures between men and women is associated with a reduction of the gender differences in road risky behaviours, with women becoming more prevalent in typical riskier "masculine" behaviours. Explanations for this behavioural adaptation should be identified and strategies for mitigating this outcome implemented.

•

To reduce risky driving behaviours in risk prone subsets of the population (especially men but also young drivers), target psychological constructs that have the greatest impact on risky driving, such as personal acceptability and self-efficacy.

Specific recommendations to particular stakeholders

• [To Non-Governmental Organizations (NGOs)] Contribute to education and awareness campaigns and events aimed at reducing risk behaviours, especially among males. The strong influence of personal acceptability on risk behaviours calls for action on the perception of risks and rules. In particular, "male" values that influence individual behaviour should be targeted, including among women in countries where gender equality is high.

• [To vehicle manufacturers, other companies and research organisations] Develop research aimed at understanding the psychological mechanisms by which gender influences risk behaviours and those aimed at influencing this relationship.

The initial aim of ESRA was to develop a system for gathering reliable and comparable information about people's attitudes towards road safety in a number of European countries. This objective has been achieved and the initial expectations have even been exceeded. The ESRA has become a global initiative which already conducted surveys in 46 countries across 6 regions. The outputs of the ESRA project have become building blocks of a global road safety monitoring system that goes beyond monitoring road traffic casualties and also includes indicators for possible underlying causal factors.

The ESRA project has also demonstrated the feasibility and the added value of joint data collection on road safety attitudes and performance by partner organizations in a large number of countries. The intention is to repeat this initiative on a triennial basis, retaining a core set of questions in every wave allowing the development of time series of road safety performance indicators. This will become a solid foundation for a joint global monitoring system on road safety attitudes and behaviour. Gender Issues • drive after taking a medication that may influence the ability to drive • drive faster than the speed limit inside built-up areas • drive faster than the speed limit outside built-up areas (but not on motorways/freeways) • drive faster than the speed limit on motorways/freeways

List of figures

• not wear a seatbelt while driving • transport children in the car without securing them (child's car seat, seatbelt, etc.)

• talk on a hand-held mobile phone while driving • talk on a hand-free mobile phone while driving • read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving • drive when they're so sleepy that they have trouble keeping their eyes open

Attitudes towards safe and unsafe behaviour in traffic

Q15) To what extent do you agree with each of the following statements? You can indicate your answer on a scale from 1 to 5, where 1 is "disagree" and 5 is "agree". The numbers in between can be used to refine your response. Binary variable: agree (4-5) -disagree/neutral (1-3) Items (random):

Normative believes & subjective norms (including injunctive norms from Q13)

• Most of my friends would drive after having drunk alcohol.

• Most of my friends would drive 20 km/h over the speed limit in a residential area.

Behaviour believe & attitudes

• For short trips, one can risk driving under the influence of alcohol.

• I have to drive fast; otherwise, I have the impression of losing time.

• Respecting speed limits is boring or dull.

• For short trips, it is not really necessary to use the appropriate child restraint.

• I use a mobile phone while driving, because I always want to be available.

• To save time, I often use a mobile phone while driving. Perceived behaviour control (here: self-efficacy)

• I trust myself to drive after having a glass of alcohol.

• I have the ability to drive when I am a little drunk after a party • I am able to drive after drinking a large amount of alcohol (e.g. half a liter of wine).

• I trust myself when I drive significantly faster than the speed limit.

• I am able to drive fast through a sharp curve.

• I trust myself when I check my messages on the mobile phone while driving.

• I have the ability to write a message on the mobile phone while driving.

• I am able to talk on a hand-held mobile phone while driving. Habits

• I often drive after drinking alcohol.

• Even when I am a little drunk after a party, I drive.

• It sometimes happens that I drive after consuming a large amount of alcohol (e.g. a liter of beer or half a liter of wine). • I often drive faster than the speed limit.

• I like to drive in a sporty fast manner through a sharp curve.

• It happens sometimes that I write a message on the mobile phone while driving.

• I often talk on a hand-held mobile phone while driving.

• I often check my messages on the mobile phone while driving.

Intentions

• I will do my best not to drive after drinking alcohol in the next 30 days.

• I will do my best to respect speed limits in the next 30 days.

• I will do my best not to use my mobile phone while driving in the next 30 days.

Quality control items

• Indicate number 1 on the answering scale.

• Indicate number 4 on the answering scale.

Subjective safety & risk perception

•

  Vias institute, Belgium: Uta Meesmann, Katrien Torfs, Huong Nguyen, WouterVan den Berghe ESRA2 core group partners • BASt -Federal Highway Research Institute, Germany: Susanne Holocher, Hardy Holte • bfu -Swiss Council for Accident Prevention, Switzerland: Yvonne Achermann, Philip Derrer • CTL -Centre for Transport and Logistics, Italy: Veronica Sgarra, Davide Usami • IATSS -International Association of Traffic and Safety Sciences, Japan (: Toru Kakinuma, Hideki Nakamura • Université Gustave Eiffel -IFSTTAR -The French Institute of Science and Technology for transports, developmalet and networks, France: Marie-Axelle Granié • ITS -Motor Transport Institute, Poland: Ilona Buttler • KFV -Austrian Road Safety Board, Austria: Gerald Furian, Susanne Kaiser, Christian Brandstätter • NTUA -National Technical University of Athens, Greece: George Yannis, Alexandra Laiou • PRP -Portuguese Road Safety Association, Portugal: Alain Areal, José Trigoso, Carlos Pires • SWOV -Institute for Road Safety Research, Netherlands: Charles Goldenbeld • TIRF -Traffic Injury Research Foundation, Canada: Ward Vanlaar, Steve Brown, Heather Woods-Fry, Craig Lyon ESRA2 supporting partners • AAAFTS -AAA Foundation for Traffic Safety, USA: Woon Kim, Lindsay Arnold, Tara Kelley-Bake • Australian Governmalet -Departmalet of Infrastructure and Regional Developmalet, Australia: Cynthia Wallace, Adam Sutherland, Olivia Sherwood, Nikolina Rajchinoska • AVP -Slovenian Traffic Safety Agency, Slovenia: Vesna Marinko, Tina Bizjak • CDV -Transport Research Centre, Czech Republic: Pavlina Skladana • Departmalet for Transport, United Kingdom: Catherine Mottram • DGT -Traffic General Directorate, Ministry of Interior, Spain: Fermina Sánchez, Juan Carlos González Luque • Group Renault, France: Bruno Hernandez, Thierry Hermitte • IIT Kharagpur -Indian Institute of Technology Kharagpur; Civil Engineering Departmalet, India: Sudeshna Mitra • KOTI -The Korea Transport Institute, Republic of Korea: Sangjin Han, Hyejin Lee Gender Issues

--

  Self-efficacy in risky behaviours (Q15.i.j.k.l.m.n.o.p) Gender Issues -Perceived safety in driving (Q16). Only items concerning feelings of safety in cars were considered -Road safety policy support (Q18) -Perception of risky driving behaviours (factors causing a crash) (Q17) -Number of crashes (Q23.1a.2a.3a) -Social desirability and intention to comply (Q28) -Compliant law perception (bonus question used by 20 countries in the ESRA2019 Risky social norms (15.a.b) -Perceived probability of enforcement (enforcement perception) (Q20) -Positive perception of automated vehicles (Q24, Q25.1, Q25.2).

Figure 2 :

 2 Figure 2: Mean values for 'personal acceptability of risky driving behaviours' by gender and region

Figure 3 :

 3 Figure 3: Mean values for 'declared risky driving behaviours' by gender and region 4.2.4. Self-efficacy in risky driving behaviours

Figure 5 :

 5 Figure 5: Mean values for 'perceived safety in driving' by gender and region 4.2.6. Road safety policy support Support for road safety policies was also investigated by asking participants to what extent they would support policies aimed at reducing road crashes, on a Likert scale from 1 (oppose) to 5 (support). As shown in Table21, ANOVA revealed a significant main effect of gender, F(1, 25426) = 281.46, p < .001, η² = .01, age, F(5, 25426) = 82.38, p < .001, η² = .01 and region, F(3,25426) = 113.03, p < .001, η² = .01. It also revealed a significant interaction between gender and region, F(3, 25426) = 16.76, p < .001, η² < .01 (see Figure7), gender and age, F(5, 25426) = 5.54, p < .001, η² < .01 and between age and region, F(15, 25426) = 32.70, p < .001, η² = .02.

Figure 6 :

 6 Figure 6: Mean values for 'road safety policy support' by gender and region 4.2.7. Perception of risky driving behaviours

Figure 8 :

 8 Figure 8: Mean values for 'number of crashes' by gender and region 4.2.9. Social Desirability and intention to comply

Figure 10 :

 10 Figure 10: Mean values for 'compliant law perception' by gender and region 4.2.11. Risky social norms

Figure 13 :

 13 Figure 13: Mean values for 'positive perception of automated vehicles' by gender and region

  of gender, age and attitudinal variables on declared behaviours in each country

Figure 14 :

 14 Figure 14: SEM model structure for global score of declared risky driving behaviours

-

  Social acceptability of risky behaviours (Q13.1) -Personal acceptability of risky behaviours (Q14.1) -Declared risky behaviours (Q12.1b and Q12.1a). Note that behaviours concerning children's use of seat-belt were excluded -Self-efficacy in risky behaviours (Q15.i.j.k.l.m.n.o.p) -Perceived safety in driving (Q16). We used only items concerning safety feeling in cars -Road safety policy support (Q18) -Perception of risky driving behaviours (factors causing a crash) (Q17) -Number of crashes (Q23.1a.2a.3a) -Social desirability and intention to comply (Q28) -Compliant law perception (bonus question used by 20 countries of the ESRA2 sample) -Risky social norms (15.a.b) -Perceived probability of enforcement (Q20) -Positive perception of automated vehicles (Q24, Q25.1, Q25.2)

Figure 1 :Figure 2 :

 12 Figure 1: Mean values for 'social acceptability of risky driving behaviours' by gender and region .......37 Figure 2: Mean values for 'personal acceptability of risky driving behaviours' by gender and region ...38 Figure 3: Mean values for 'declared risky driving behaviours' by gender and region ..........................39 Figure 4: Mean values for 'self-efficacy in risky driving behaviours' by gender and region .................40 Figure 5: Mean values for 'perceived safety in driving' by gender and region ....................................41 Figure 6: Mean values for 'road safety policy support' by gender and region ....................................42 Figure 7: Mean values for 'perception of risky driving behaviours' by gender and region ...................43 Figure 8: Mean values for 'number of crashes' by gender and region ...............................................44 Figure 9: Mean values for 'social desirability and intention to comply' by gender and region..............45 Figure 10: Mean values for 'compliant law perception' by gender and region ....................................46 Figure 11: Mean values for 'risky social norms' by gender and region ..............................................47 Figure 12: Mean values for 'perceived probability of enforcement' by gender and region ..................48 Figure 13: Mean values for 'positive perception of automated vehicles' by gender and region ...........49 Figure 14: SEM model structure for global score of declared risky driving behaviours .......................57

•

  KTI -KTI Institute for Transport Sciences Non-Profit Ltd., Hungary: Péter Holló, Miklós Gábor, Pauer GaborPéter Holló, Miklós Gábor Belgium, Switzerland, Czech Republic, Germany, Denmark, Greece, Spain, Finland, France, Hungary, Ireland, Italy, Netherlands, Poland, Portugal, Serbia, Sweden, Slovenia and United Kingdom NorthAmerica2 Region that includes Canada and United States AsiaOceania5 Region that includes Australia, Israel, India, Japan and Republic of Korea Africa5 Region that includes Egypt, Kenya, Morocco, Nigeria and South Africa

		Gender Issues Gender Issues
	List of Abbreviations
	•	Liikenneturva -Finnish Road Safety Council, Finland: Juha Valtonen, Leena Pöysti
	• Country codes RSA -Israel National Road Safety Authority, Israel: Elisheva Hecht, Yiftach Gordoni
	• • AT Austria RSA -Road Safety Authority, Ireland: Sharon Heffernan, Velma Burns RTSA -Road Traffic Safety Agency, Serbia: Lidija Stanojević, Andrijana Pešić, Jelena Milošević BE Belgium
	• CA Canada Sikkertraffic -The Danish Road Safety Council, Denmark: Pernille Ehlers, Lise Heiner Schmidt
	• CH Switzerland VTI -Swedish National Road and Transport Research Institute, Sweden: Anna Vadeby, Astrid Linder, Gunilla
	Sörensen DE Germany
	EL Greece
	FR France
	IT Italy
	JP Japan
	NL Netherlands
	PL Poland
	PT Portugal
	Region codes
	Europe20 Region that includes Austria, Other abbreviations
	ESRA	E-Survey of Road Users' Attitudes
	EU	European Union
	GGEI	Global Gender Equality Index
	RSO	Road Safety Observatory
	SEM	Structural Equation Model

Table 1 :

 1 ESRA2 thematic reports

	Driving under influence	Child restraint systems	Cyclists
	Speeding	Unsafety feeling & risk perception	Moped drivers & motorcyclists
	Distraction (mobile phone use)	Enforcement	Young road users
	Fatigue	Vehicle automation	Elderly road users
	Seat belt	Pedestrians	Gender aspects

Table 2 :

 2 Distribution of the sample over the four regions studied according to gender

	Continent
	Sample

Table 3 :

 3 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for the score of social acceptability of risky driving behaviours by country and region *p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	9.98***	4.76	8.89	3.26	.02
	Austria	13.45	5.27	12.97	5.72	<.01
	Belgium	11.65***	4.78	10.85	4.18	.01
	Canada	11.35***	5.58	10.11	4.04	.02
	Czech Republic	11.31***	4.10	9.86	3.19	.04
	Denmark	10.41**	3.84	9.49	2.87	.01
	Egypt	14.74	5.95	14.35	6.86	<.01
	Finland	12.68	3.74	12.20	3.96	<.01
	France	11.60*	4.48	10.86	4.31	.01
	Germany	12.49***	5.32	11.41	4.80	.01
	Greece	14.05	6.90	13.43	6.30	<.01
	Hungary	9.95**	3.63	9.09	2.95	.01
	India	12.48	5.80	12.84	7.11	<.01
	Ireland	9.86	4.24	9.31	3.73	<.01
	Israel	12.32**	5.25	11.20	5.00	.01
	Italy	11.04*	4.69	10.43	4.48	.01
	Japan	11.57***	4.58	10.31	3.93	.02
	Kenya	13.40	6.23	13.80	6.52	<.01
	Morocco	13.13	5.92	13.02	6.69	<.01
	The Netherlands	10.21**	3.69	9.41	3.28	.01

4.1.2. Personal Acceptability of risky driving behaviours

Table 4 :

 4 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for the score of personal acceptability of risky driving behaviours by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	17.61***	7.70	15.40	4.74	.03
	Austria	22.42***	7.88	20.41	7.54	.01
	Belgium	21.03***	7.29	19.12	6.16	.02

Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean 4.1.3. Declared risky driving behaviour

Table 5 :

 5 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for declared risky driving behaviour score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	22.39 **	8.59	20.84	5.97	.01
	Austria	28.20***	8.65	25.04	7.40	.03
	Belgium	26.14***	8.47	23.34	7.22	.03
	Canada	26.78***	10.38	23.91	8.20	.03
	Czech Republic	24.85***	7.34	22.21	6.37	.04
	Denmark	26.19***	8.15	23.09	5.77	.04
	Egypt	26.72*	8.65	25.30	9.16	.01
	Finland	26.22***	7.28	24.02	6.50	.02
	France	25.65***	8.95	23.46	7.64	.01
	Germany	25.53***	8.04	23.15	7.48	.02
	Greece	25.85***	7.14	21.98	6.40	.06
	Hungary	22.87***	6.25	20.27	5.30	.03
	India	23.63	8.57	24.91**	12.51	.01
	Ireland	23.91***	8.06	21.24	6.36	.02
	Israel	25.47***	6.72	23.51	6.35	.02
	Italy	24.66***	8.07	21.08	6.07	.05
	Japan	23.64***	6.81	21.18	7.24	.02
	Kenya	26.75	8.98	24.48	9.82	.01
	Morocco	24.17	7.94	23.18	8.08	<.01
	The Netherlands	24.63***	7.76	21.87	6.43	.03
	Nigeria	25.16***	7.81	22.43	7.11	.03
	Poland	25.65***	7.89	22.59	6.44	.04
	Portugal	27.64***	7.77	23.49	7.11	.06
	Republic of Korea	24.98***	7.52	21.95	6.35	.03
	Serbia	24.48***	7.34	21.58	5.72	.04
	Slovenia	25.88***	6.94	22.83	5.78	.05
	South Africa	27.34***	9.49	23.83	7.33	.02
	Spain	24.79***	7.75	21.17	6.94	.04
	Sweden	26.19***	7.78	23.57	6.71	.02
	Switzerland	25.59***	7.84	22.77	5.82	.04
	United Kingdom	23.60**	8.99	21.21	7.64	.01
	USA	26.38*	9.28	24.72	8.57	.01
	Africa	26.06***	8.65	23.79	8.28	.02
	AsiaOceania	24.08***	7.76	22.49	8.02	.01
	Europa	25.51***	7.96	22.62	6.78	.04
	NorthAmerica	26.59***	9.85	24.32	8.41	.02

Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean Gender Issues 4.1.4. Self-efficacy in risky behaviours

Table 6 :

 6 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for the score of self-efficacy in risky behaviours by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	12.95***	6.03	10.89	3.57	.04
	Austria	16.72***	5.90	14.07	5.49	.05
	Belgium	15.20***	6.12	12.67	5.27	.04
	Canada	14.39***	6.49	12.08	5.01	.04
	Czech Republic	13.68***	5.91	11.05	4.58	.06
	Denmark	14.62***	5.56	11.74	4.06	.06
	Egypt	13.28*	5.08	12.60	4.65	.01
	Finland	15.10***	5.89	11.90	4.40	.08
	France	14.05***	5.52	11.73	4.88	.04
	Germany	15.13***	5.75	12.97	4.83	.04
	Greece	13.91***	5.87	12.02	4.24	.03
	Hungary	13.24***	5.79	10.73	3.81	.04
	India	14.26	5.74	14.56	6.97	<.01
	Ireland	13.52***	5.98	11.40	4.27	.03
	Israel	13.48***	5.46	11.17	4.20	.04
	Italy	14.44***	5.86	11.72	4.51	.05
	Japan	11.49**	4.01	10.45	4.41	.01
	Kenya	14.75**	6.14	12.91	5.43	.01
	Morocco	12.34	4.96	11.90	5.08	<.01
	The Netherlands	14.89***	5.44	12.13	4.33	.07
	Nigeria	14.29	5.79	13.21	6.44	<.01
	Poland	13.50***	5.19	11.76	4.52	.03
	Portugal	14.88***	5.96	12.04	4.48	.05
	Republic of Korea	12.44***	4.84	10.60	3.78	.03
	Serbia	13.22***	5.82	11.10	3.75	.04
	Slovenia	14.84***	6.36	12.32	4.88	.04
	South Africa	16.95***	7.12	13.84	5.64	.03
	Spain	13.23***	5.37	10.81	4.35	.05

Table 7 :

 7 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for perceived safety in driving score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	8.25**	2.88	7.62	2.54	.01
	Austria	9.12	3.81	8.79	3.10	<.01
	Belgium	8.20***	3.36	7.69	3.00	.01
	Canada	8.94**	3.36	8.42	3.05	.01
	Czech Republic	7.77	3.31	7.19	3.27	.01
	Denmark	9.71*	3.68	9.00	3.26	.01
	Egypt	8.99	4.62	9.01	4.90	<.01
	Finland	9.50***	3.60	8.49	3.23	.02
	France	8.42**	3.59	7.58	2.87	.01
	Germany	8.85**	3.15	8.43	2.64	<.01
	Greece	8.12**	4.22	6.90	3.22	.01
	Hungary	8.42**	3.95	7.60	3.40	.01
	India	9.26	4.43	9.59	4.83	<.01
	Ireland	8.51**	3.62	7.86	3.06	.01
	Israel	8.81*	4.51	8.02	3.71	.01
	Italy	8.13	3.61	7.71	3.20	<.01
	Japan	7.95	3.54	7.30	3.25	<.01
	Kenya	9.43	4.85	8.37	4.15	<.01
	Morocco	8.62	3.97	8.53	5.14	<.01
	The Netherlands	9.02***	3.51	8.15	3.33	.02
	Nigeria	8.80	4.36	8.24	4.54	<.01
	Poland	8.27*	4.15	7.62	3.96	.01
	Portugal	9.16**	3.53	8.15	3.58	.01
	Republic of Korea	7.20	3.30	6.92	3.54	<.01
	Serbia	8.10***	3.41	7.22	3.22	.02
	Slovenia	9.12***	3.86	7.64	3.26	.04
	South Africa	7.73*	3.82	7.10	3.38	.01
	Spain	8.55	3.26	8.26	3.72	<.01
	Sweden	10.10***	4.57	8.87	4.00	.02
	Switzerland	9.99***	3.88	8.74	3.20	.03

Table 8 :

 8 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for road safety policy support score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	58.66	10.26	62.06***	8.59	.03
	Austria	54.54	11.81	59.20***	10.72	.03
	Belgium	57.41	10.57	61.11***	9.08	.03
	Canada	59.43	10.28	62.24***	9.84	.02

Table 9 :

 9 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for risk perception of risky behaviour score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	28.94	7.99	31.35***	7.53	.02
	Austria	27.98	8.02	29.72**	8.44	.01
	Belgium	27.86	7.06	29.55***	7.40	.01
	Canada	27.94	8.97	30.13**	7.91	.01
	Czech Republic	30.32	6.59	31.51	7.69	.01
	Denmark	27.87	7.45	30.10***	6.45	.02
	Egypt	26.44	11.44	26.66	12.88	<.01
	Finland	28.69	5.87	31.37***	5.91	.04
	France	28.00	8.94	30.21***	8.36	.01
	Germany	29.21	7.69	30.00	7.79	<.01
	Greece	28.67	9.71	29.43	11.15	<.01
	Hungary	29.43	7.92	31.17**	8.85	.01
	India	23.86	12.26	26.37**	12.47	.01
	Ireland	25.55	10.08	28.32**	9.55	.01
	Israel	29.76	7.53	32.55***	7.66	.03
	Italy	28.33	9.75	29.72	10.29	<.01
	Japan	20.55	10.18	19.26	10.95	<.01
	Kenya	32.39	8.88	33.81	7.77	<.01
	Morocco	27.54*	10.97	26.03	13.67	.01
	The Netherlands	27.25	7.32	29.56***	6.73	.03
	Nigeria	31.03	9.13	32.63*	9.82	.01
	Poland	28.60	7.80	30.90***	8.48	.02
	Portugal	29.92	7.89	32.18***	8.61	.02
	Republic of Korea	18.30	10.00	19.29	11.95	<.01
	Serbia	30.29	8.18	32.25***	8.39	.02
	Slovenia	28.03	7.60	30.87***	7.30	.03
	South Africa	26.54	10.09	30.05*	9.27	.02
	Spain	30.01	7.80	32.79***	9.31	.02
	Sweden	28.14	7.71	29.54*	7.08	.01
	Switzerland	27.64	7.66	29.56**	7.89	.01
	United Kingdom	28.89	7.76	30.52*	7.85	.01
	USA	26.54	9.95	29.28***	9.39	.02
	Africa	28.76	10.39	29.86**	11.05	<.01
	AsiaOceania	24.41	10.72	26.45***	11.56	<.01
	Europa	28.52	8.04	30.67***	8.31	.01
	NorthAmerica	27.24	9.48	29.68***	8.74	.02
	Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean
	4.1.8. Number of crashes

Table 10 :

 10 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for number of crashes by country and region *p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean 4.1.9. Social desirability and compliance intentionThe aggregated score of social desirability and compliance intention has been calculated after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items:

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	0.27	1.38	0.13	0.96	<.01
	Austria	0.50	1.95	0.46	1.78	<.01
	Belgium	0.35**	1.18	0.22	0.77	<.01
	Canada	0.75***	3.61	0.29	1.39	.02
	Czech Republic	0.38	1.07	0.30	0.68	<.01
	Denmark	0.31	1.24	0.20	0.79	<.01
	Egypt	2.37***	3.91	1.84	3.86	.02
	Finland	0.18	0.96	0.12	0.39	<.01
	France	0.20*	0.82	0.15	0.67	<.01
	Germany	0.29*	1.11	0.21	0.71	<.01
	Greece	0.42**	1.08	0.21	0.71	.01
	Hungary	0.28	0.93	0.37	1.06	<.01
	India	2.20	3.32	2.14	3.35	<.01
	Ireland	0.27	0.86	0.23	1.18	<.01
	Israel	0.43	1.09	0.42	0.98	<.01
	Italy	0.32	1.36	0.26	0.84	<.01
	Japan	0.27	0.97	0.18	0.51	.01
	Kenya	1.57	2.51	1.21	1.90	<.01
	Morocco	1.51	2.94	2.46***	4.81	.04
	The Netherlands	0.48***	1.91	0.15	0.61	.02
	Nigeria	1.62	2.67	1.59	2.97	<.01
	Poland	0.40	1.19	0.42	0.94	<.01
	Portugal	0.32	1.07	0.27	0.82	<.01
	Republic of Korea	1.53	2.28	1.35	2.15	<.01
	Serbia	0.21	0.62	0.23	0.60	<.01
	Slovenia	0.21*	0.54	0.14	0.46	.01
	South Africa	0.53	1.43	0.49	1.48	<.01
	Spain	0.33	1.18	0.38	1.70	<.01
	Sweden	0.29	1.14	0.22	0.74	<.01
	Switzerland	0.22	0.92	0.16	0.51	<.01
	United Kingdom	0.43	2.10	0.20	1.06	<.01
	USA	0.23	0.84	0.19	0.80	<.01
	Africa	1.47	2.83	1.44	3.12	<.01
	AsiaOceania	0.95*	2.18	0.81	1.99	<.01
	Europa	0.32***	1.22	0.24	0.92	<.01
	NorthAmerica	0.50*	2.66	0.24	1.11	<.01
	Note: **

  • I do not care what other drivers think about me Gender Issues • I always remain calm and rational in traffic • I am always confident of how to react in traffic situations

Table 11 :

 11 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for social desirability and compliance intention score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	22.94	4.35	23.13	4.11	<.01
	Austria	20.30	4.54	21.08*	4.44	.01
	Belgium	21.51	4.27	22.09*	4.38	<.01
	Canada	22.18	4.37	22.32	4.27	<.01
	Czech Republic	22.37	4.13	23.16*	4.21	.01
	Denmark	21.56	4.44	21.57	4.13	<.01
	Egypt	23.13	5.69	22.81	6.10	<.01
	Finland	21.03	4.35	21.52	4.47	<.01
	France	21.60	4.26	22.30	4.18	<.01
	Germany	20.73	4.36	20.88	4.72	<.01
	Greece	21.89	4.60	23.56***	4.13	.02
	Hungary	20.97	4.62	22.22**	4.88	.01
	India	24.79	4.66	24.95	4.27	<.01
	Ireland	22.26	4.34	23.09	4.36	<.01
	Israel	22.74	4.37	22.92	4.60	<.01
	Italy	22.55	4.43	23.33*	4.25	.01
	Japan	20.24	4.21	20.78	4.23	<.01
	Kenya	23.67**	4.13	22.98	4.97	.01
	Morocco	23.55	5.18	23.06	6.26	<.01
	The Netherlands	21.19	4.24	21.72	4.19	<.01
	Nigeria	23.51	4.28	23.85*	5.20	.01
	Poland	22.51	4.31	23.17*	4.23	.01
	Portugal	21.21	4.31	21.97	4.91	<.01
	Republic of Korea	21.25	4.44	21.67	4.12	<.01
	Serbia	23.44	4.41	24.12	4.13	<.01
	Slovenia	22.88	4.26	23.27	4.51	<.01
	South Africa	22.38	4.42	22.38	4.24	<.01
	Spain	22.12	4.12	22.95*	4.55	.01
	Sweden	20.47	4.53	21.69**	4.30	.01
	Switzerland	21.75	4.15	22.66*	4.36	.01
	United Kingdom	22.19	3.92	21.79	4.00	<.01
	USA	22.07	4.59	22.06	4.76	<.01
	Africa	23.22	4.78	22.98	5.28	<.01
	AsiaOceania	22.43	4.66	22.76*	4.49	<.01
	Europa	21.67	4.41	22.31***	4.48	<.01
	NorthAmerica	22.12	4.48	22.18	4.54	<.01
	Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean

4.1.10. Compliant law perception

The aggregated score of compliant law perception has been calculated for the 11 countries that used this bonus question without modification and after verifying by PCA that all the items correctly loaded of the first factor. The calculated score included the following items: Gender Issues To what extent do you agree with each of the following statements?

• people should do what the law says

Table 12 :

 12 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for compliant law perception score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Canada	15.47	3.31	16.16**	3.27	.01
	Egypt	16.85*	3.44	16.83	3.85	.01
	France	14.21	3.65	14.71	3.52	.01
	Japan	14.30	3.21	15.30***	3.26	.02
	Kenya	17.81	2.87	17.49	3.04	.01
	Morocco	16.68	3.49	16.63	4.31	<.01
	Nigeria	17.86	2.88	17.85	3.04	<.01
	Serbia	16.74	3.29	17.08	2.86	<.01
	Slovenia	14.91	3.59	15.87***	3.53	.02
	South Africa	16.78	3.29	17.37*	3.15	.01
	United Kingdom	15.23	3.27	16.03**	3.23	.01
	Africa	17.19	3.24	17.25	3.47	<.01
	AsiaOceania	12.56	3.16	13.17***	3.06	.01
	Europa	14.14	3.66	14.74***	3.45	<.01
	NorthAmerica	15.46	3.31	16.16**	3.27	.01

Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean. The regions only included the countries where these items where asked

4.1.11. Risky social norms

The score of risky social norms is based on the score on the 2 items of perceived descriptive norm:

Table 13 :

 13 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for the risky social norms score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	2.98	1.51	2.87	1.26	<.01
	Austria	4.04	1.71	4.01	1.77	<.01
	Belgium	4.37*	1.82	4.17	1.85	<.01
	Canada	3.77***	1.86	3.41	1.57	.01
	Czech Republic	3.87	1.72	3.75	1.74	<.01
	Denmark	3.26***	1.57	2.88	1.24	.02
	Egypt	3.60	1.59	3.54	1.67	<.01
	Finland	3.17	1.40	3.02	1.35	<.01
	France	3.77	1.77	3.61	1.69	<.01
	Germany	3.90	1.72	3.77	1.69	<.01
	Greece	5.23	2.17	4.75	2.24	<.01
	Hungary	3.80	1.61	3.61	1.65	<.01
	India	4.57	1.80	4.49	2.15	<.01
	Ireland	3.69	1.96	3.42	1.67	<.01
	Israel	3.83**	1.80	3.39	1.61	.01
	Italy	4.62***	1.90	3.90	1.89	.03
	Japan	3.51	1.44	3.69	1.63	<.01
	Kenya	5.15	2.37	4.99	2.37	<.01
	Morocco	3.73	1.72	3.40	1.70	<.01
	The Netherlands	3.71**	1.82	3.41	1.53	.01
	Nigeria	4.25	2.05	4.25	2.05	<.01
	Poland	4.42**	1.92	3.98	1.78	.01
	Portugal	4.99	1.89	4.69	2.00	<.01
	Republic of Korea	3.51**	1.54	3.11	1.34	.01
	Serbia	4.71	2.13	4.92	2.20	<.01
	Slovenia	4.20	1.83	4.08	1.84	<.01
	South Africa	5.16	2.23	4.98	2.27	<.01
	Spain	4.15	1.70	4.26	2.06	<.01
	Sweden	3.02	1.29	2.79	1.26	<.01
	Switzerland	3.60	1.62	3.64	1.71	<.01
	United Kingdom	3.86	1.87	3.65	1.70	<.01
	USA	4.37*	2.01	4.06	1.91	<.01
	Africa	4.41	2.12	4.28	2.25	<.01
	AsiaOceania	3.68***	1.71	3.48	1.70	<.01
	Europa	4.05***	1.87	3.85	1.85	<.01
	NorthAmerica	4.07**	1.96	3.76	1.79	<.01
	Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean
	4.1.12. Perceived probability of enforcement

Table 14 :

 14 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for perceived probability of enforcement score by country and region

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	16.60*	7.73	15.22	7.72	<.01
	Austria	15.62***	7.59	13.72	7.72	.02
	Belgium	15.66*	6.77	14.89	7.03	<.01
	Canada	13.00**	7.50	11.47	7.22	.01
	Czech Republic	17.91	8.02	16.89	8.11	<.01
	Denmark	13.90***	7.56	11.03	5.68	.05
	Egypt	18.88***	8.23	16.35	8.15	.02
	Finland	14.28	5.61	14.22	5.98	<.01
	France	13.81***	7.68	11.37	6.78	.02
	Germany	14.28***	7.23	12.73	6.95	.01
	Greece	15.03	7.55	14.97	7.56	<.01
	Hungary	16.92*	8.12	15.18	8.87	.01
	India	16.19	9.76	14.79	9.73	<.01
	Ireland	15.13**	8.28	13.18	7.36	.01
	Israel	14.19***	7.17	11.61	7.42	.03
	Italy	16.02***	7.83	13.82	7.99	.02
	Japan	14.35	8.17	13.08	8.47	<.01
	Kenya	19.43***	7.76	18.55	8.73	.02
	Morocco	17.57	8.69	15.54	9.72	<.01
	The Netherlands	16.20	6.37	15.69	6.83	<.01
	Nigeria	16.81	8.03	15.66	9.14	<.01
	Poland	19.36	7.46	20.38	7.68	.01
	Portugal	16.34***	8.50	13.68	8.92	.02
	Republic of Korea	11.93	7.27	11.33	7.84	.02
	Serbia	20.34***	8.06	17.14	8.65	.03
	Slovenia	17.62***	7.96	15.00	8.55	.02
	South Africa	16.35***	7.67	13.62	7.47	.02
	Spain	17.85**	7.66	15.54	8.83	.01
	Sweden	13.32	6.94	12.70	6.77	<.01
	Switzerland	15.58***	7.14	12.54	6.68	.04
	United Kingdom	12.37**	7.60	10.54	6.49	.01
	USA	12.54**	7.67	10.94	7.13	.01
	Africa	17.73***	8.15	15.69	8.68	.01
	AsiaOceania	14.60***	8.21	13.21	8.35	<.01
	Europa	15.84***	7.72	14.15	7.77	.01
	NorthAmerica	12.77***	7.58	11.19	7.17	.01
	Note: ***p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean
	4.1.13. Positive perception of automated vehicles

Table 15 :

 15 Mean, standard deviation and partial eta square for the analysis of variance of gender difference for the positive perception of automated vehicle score by country and region *p<.001, **p<.01, *p<.05. The symbol of significance is presented next to the significantly highest mean

			Gender
	Country	Males		Females		²
		Mean	SD	Mean	SD
	Australia	71.88	25.78	70.17	25.47	<.01
	Austria	75.30***	27.25	63.80	24.98	.04
	Belgium	73.37***	25.34	65.52	24.45	.02
	Canada	77.17**	25.52	71.11	27.34	.01
	Czech Republic	73.07	24.59	73.02	21.61	<.01
	Denmark	78.17**	26.19	71.44	24.82	.01
	Egypt	87.42	23.19	86.38	26.40	<.01
	Finland	66.52**	27.24	61.97	24.44	.01
	France	74.56***	24.14	68.90	24.70	.02
	Germany	79.00***	25.59	69.62	25.02	.03
	Greece	78.21	24.63	78.28	22.04	<.01
	Hungary	80.30	24.87	79.04	25.85	<.01
	India	94.32	20.46	93.11	21.22	<.01
	Ireland	76.04***	28.32	65.09	24.56	.03
	Israel	85.08***	24.28	76.66	23.45	.03
	Italy	79.16*	23.34	73.64	25.29	.01
	Japan	82.96	21.59	78.64	20.64	<.01
	Kenya	89.76	19.87	88.85	23.74	<.01
	Morocco	83.20	23.54	83.06	28.11	<.01
	The Netherlands	75.09**	24.46	68.82	23.67	.01
	Nigeria	90.40**	18.55	86.60	22.36	.01
	Poland	74.45	23.01	73.12	22.71	<.01
	Portugal	85.63**	22.39	79.41	26.89	.01
	Republic of Korea	83.51*	18.41	79.41	17.87	.01
	Serbia	82.83*	25.84	78.25	25.24	.01
	Slovenia	83.01***	24.79	75.53	25.06	.02
	South Africa	85.73**	23.87	80.32	24.44	.01
	Spain	82.26*	22.30	76.91	25.54	.01
	Sweden	74.90***	25.75	65.91	21.57	.03
	Switzerland	75.38***	24.79	65.72	23.93	.03
	United Kingdom	72.40*	25.81	67.69	22.72	.01
	USA	74.38**	27.78	67.14	29.13	.01
	Africa	87.29**	22.02	84.59	25.07	<.01

Table 16 :

 16 Means and standard deviation of the social acceptability of risky driving behaviour according to region and gender

				Social Acceptability
	Region	Gender
			Mean	SD	η²
	Africa5	Male	13.04	5.86	<.01
		Female	12.61	6.22
		Total	12.84	6.03
	AsiaOceania5	Male	11.59	4.94	.01
		Female	10.71	4.88
		Total	11.18	4.93
	Europe20	Male	11.53	4.58	<.01
		Female	10.81	4.41
		Total	11.19	4.51
	NorthAmerica2	Male	11.33	5.24	.01
		Female	10.44	4.18
		Total	10.88	4.75
	TOTAL	Male Female	11.75 11.02	4.90 4.76	<.01
		Total	11.40	4.85

Table

16

shows, ANOVA revealed a significant main effect of gender, F(1, 25426) = 67.69, p < .001, η² < .01, age, F(5, 25426) = 85.48, p < .001, η² = .02 and region, F(3, 25426) = 74.43, p <.001, η² = .01. The analysis also pointed out a significant interaction between age and region, F(15, 25426) = 7.07, p < .001, η² < .01, but no interaction between gender and region (see figure

1

) and age and gender, p > .05.

Table 17 :

 17 Means and standard deviation of the personal acceptability of risky driving behaviours according to gender and region

				Personal Acceptability
	Region	Gender	Mean	SD	η²
	Africa5	Male	19.25	7.45	<.01
		Female	18.32	7.13
		Total	18.82	7.33
	AsiaOceania5	Male	19.79	7.32	.01
		Female	18.12	7.32
		Total	19.01	7.37
	Europe20	Male	20.18	6.65	.02
		Female	18.06	5.52
		Total	19.17	6.22
	NorthAmerica2	Male	20.31	8.45	.02
		Female	18.26	6.48
		Total	19.28	7.57
	TOTAL	Male	19.99	6.99	.01
		Female	18.12	6.09
		Total	19.10	6.64

Table 18 :

 18 Means and standard deviation of declared risky driving behaviour according to gender and region

				Declared Behaviour
	Region	Gender	Mean	SD	η²
	Africa5	Male	26.07	8.61	.01
		Female	23.79	8.25
		Total	25.02	8.52
	AsiaOceania5	Male	24.06	7.74	.01
		Female	22.47	7.98
		Total	23.32	7.90
	Europe20	Male	25.40	7.43	.03
		Female	22.48	6.26
		Total	24.00	7.03
	NorthAmerica2	Male	26.59	9.88	.01
		Female	24.34	8.41
		Total	25.45	9.22
	TOTAL	Male	25.36	7.83	.01
		Female	22.79	6.98

Table 20 :

 20 Means and standard deviation of perceived safety in driving according to gender and region

	1,25426) = 77.96, p < .001, η²

Table 21 ,

 21 ANOVA revealed a significant main effect of gender, F(1, 25426) = 281.46, p < .001, η² = .01, age, F(5, 25426) = 82.38, p < .001, η² = .01 and region, F(3,25426) = 113.03, p < .001, η² = .01. It also revealed a significant interaction between gender and region, F(3, 25426) = 16.76, p < .001, η² < .01 (see Figure7), gender and age, F(5, 25426) = 5.54, p < .001, η² < .01 and between age and region, F(15, 25426) = 32.70, p < .001, η² = .02.

Table 21 :

 21 Means and standard deviation of road safety policy support according to gender and region

				Road Safety Policy Support
	Region	Gender
			Mean	SD	η²
	Africa5	Male	64.01	10.51	<.01
		Female	65.00	10.99
		Total	64.46	10.73
	AsiaOceania5	Male	59.79	10.27	.02
		Female	62.75	9.28
		Total	61.18	9.94
	Europe20	Male	59.35	9.94	.03
		Female	63.01	8.66
		Total	61.10	9.51
	NorthAmerica2	Male	57.71	11.60	.03
		Female	61.69	10.07

Table 22 :

 22 Means and standard deviation of risk perception of risky driving behaviours according to gender and region

	66,00
	64,00	***		***	***
	62,00
	60,00
	58,00
	56,00
	54,00
	Africa5	AsiaOceania5	Europe20	NorthAmerica2
		Man	Woman
				Risk Perception
	Region	Gender
			Mean	SD	η²
	Africa5	Male	28.78	10.33	<.01
		Female	29.89	10.98
		Total	29.29	10.63
	AsiaOceania5	Male	24.50	10.67	.01
		Female	26.54	11.53
		Total	25.46	11.12

Table 23 :

 23 Participants were asked if and how many times they had been involved personally in a crash in the past 12 months. ANOVA revealed a significant main effect of gender, F(1, 25426) = 23.29, p < .001, η² < .01, age, F(5, 25426) = 76.95, p < .001, η² = .01 and region, F(3, 25426) = 470.25, p < .001, η² = .05. It also pointed out significant interactions between age and gender, F(5, 25426) = 7.79, p < .001, η² < .01 and between age and region, F(15, 25426) = 31.69, p < .001, η² = .02, but no interaction between gender and region, p >.05 (Table23). Means and standard deviation of number of crashes according to gender and region

				Number of crashes
	Region	Gender
			Mean	SD	η²
	Africa5	Male	1.48	2.82	<.01
		Female	1.43	3.10
		Total	1.46	2.94
	AsiaOceania5	Male	0.93	2.15	<.01

Figure 7: Mean values for 'perception of risky driving behaviours' by gender and region 4.2.8. Number of crashes

Table 24 :

 24 Means and standard deviations of social desirability and intention to comply according to gender and region

	1,40
	1,20
	1,00
	0,80				***	***
	0,60
	0,40
	0,20
		Africa5	AsiaOceania5	Europe20	NorthAmerica2
			Man	Woman
				Social desirability
	Region	Gender
			Mean		SD	η²
	Africa5	Male	23.22	4.75	<.01
		Female	22.98	5.25
		Total	23.11	4.97

Table 25 :

 25 < .001, η² < .01, age, F(5, 7809) = 9.95, p < .001, η² = .01 and region, F(3, 7809) = 142.96, p < .001, η² = .05, were revealed. Significant interactions between region and gender, F(3, 7809) = 6.05, p < .001, η² < .01, between age and gender, F(5, 7809) = 2.26, p = .046, η² < .01, and age and region, F(15, 7809) = 7.13, p < .001, η² Means and standard deviation of compliant law perception according to gender and region

	23,30
	23,10		*
	22,70 22,90			***
	22,50
	22,30
	22,10
	21,90
	21,70
	21,50
		Africa5	AsiaOceania5	Europe20	NorthAmerica2
			Man	Woman
	= .01, were found as well.
				Law perception
	Region	Gender
			Mean	SD	η²
	Africa5	Male	17.20	3.22	<.01
		Female	17.26	3.45
		Total	17.23	3.32

Table 26 :

 26 Table26shows, ANOVA revealed a significant main effect of gender, F(1,25426) = 39.01, p < .001, η² < .01, age, F(5, 25426) = 29.87, p < .001, η² = .01 and region, F(3,25426) = 81.95, p < .001, η² = .01 and only a significant interaction between age and region, F(15, 25426) = 2.48, p = .001, η² < .01. Means and standard deviation of the risky social norms according to gender and region

	17,40
	15,90 16,40 16,90		***	***	**
	15,40
	14,90
	14,40
	13,90
	13,40
	12,90
	12,40
		Africa5	AsiaOceania5	Europe20	NorthAmerica2
			Man	Woman
				Descriptive Norms
	Region	Gender
			Mean	SD	η²
	Africa5	Male	4.42	2.11	<.01
		Female	4.28	2.24
		Total	4.36	2.17
	AsiaOceania5	Male	3.67	1.71	<.01
		Female	3.47	1.70

Table 27 :

 27 Means and standard deviations of perceived probability of enforcement according to gender and region

	4,20 4,40			***	***
	4,00		***
	3,80
	3,60
	3,40
	3,20
		Africa5	AsiaOceania5	Europe20	NorthAmerica2
			Man	Woman
				Enforcement
	Region	Gender
			Mean	SD	η²
	Africa5	Male	17.74	8.11	.01
		Female	15.70	8.63
		Total	16.79	8.40

Table 28 :

 28 Means and standard deviation of the positive perception of automated vehicles according to gender and region

	18,00	***
	17,00			***
	16,00		***
	15,00
	14,00				***
	13,00
	12,00
	11,00
	10,00
		Africa5	AsiaOceania5	Europe20	NorthAmerica2
			Man	Woman
				Automated vehicle perception
	Region	Gender
			Mean	SD	η²

Table 29 :

 29 Standardized betas and R² for the linear regression analyses for each attitudinal and behavioural constructed variable by gender, age, global gender equality index and income for entire ESRA32 sample

	Gender
	(0=man; 1=woman) Age	GGEI	Income	R²

Table 30 :

 30 Standardized betas and R² for linear regression analyses for each attitudinal and behavioural constructed variable by gender, age, global gender equality index and income for Europe

Table 31 :

 31 Standardized betas and R² for the linear regression analyses of drivers declared risky behaviours by demographic, attitudinal and cultural variables by country (from Australia to Italy)

		Australia	Austria	Belgium	Canada	Czech Republic	Denmark	Egypt	Finland	France	Germany	Greece	Hungary	India	Ireland	Israel	Italy
	Gender (0=man; 1=woman)	.021	-.057*	-.037*	.010	.001	-.022	-.021	.009	.040	-.049**	-.089***	-.032	.050*	-.030	.003	-.051*
	Age	-.007	-.046	-.042*	.044*	-.038	-.026	-.013	.045	-.028	-.023	.034	-.002	-.005	.033	-.014	-.047
	Social acceptability of risky	-.008	-.055*	-.007	-.020	.192***	.008	.003	-.035	-.051	-.033	-.080**	-.071*	.048	-.008	-.042	-.033
	driving
	Personal acceptability of risky	.301***	.387***	.392***	.462***	.255***	.324***	.228***	.450***	.430***	.370***	.149***	.369***	.271***	.377***	.312***	.286***
	driving
	Self-efficacy in risky driving	.391***	.310***	.368***	.387***	.372***	.358***	.433***	.239***	.391***	.280***	.390***	.310***	.439***	.285***	.379***	.455***
	behaviours
	Perceived safety in driving	.039	-.007	-.003	.020	.033	.051*	.016	.039	.007	.040*	.080**	.058*	.019	.020	.042	-.007
	Road safety policy support	.095***	-.003	.060**	.056*	.041	.084**	-.094**	.037	.037	.045*	.005	-.011	-.102***	.012	.021	.039
	Perception of risky driving	.056*	.004	.045*	.006	-.003	.029	.072*	.055*	.015	.042*	.028	-.039	.085***	.043	-.020	-.005
	behaviours
	Number of crashes	.031	.099***	.095***	.036	.041	.062*	.073*	.066*	.017	.140***	.060*	.072**	.132***	.169***	.034	.008
	Social desirability and
	intention to comply

Table 32 :

 32 Standardized betas and R² for the linear regression analyses of drivers declared risky behaviours by demographic, attitudinal and cultural variables by country (from Japan to United-States)

		Japan	Kenya	Morocco	The Netherlands	Nigeria	Poland	Portugal	Republic of Korea	Serbia	Slovenia	South Africa	Spain	Sweden Switzerland	United Kingdom	United States of America
	Gender (0=man; 1=woman)	-.080**	-.042	-.047	-.012	-.122***	-.063*	-.092***	-.037	-.071**	-.080**	-.066**	-.071**	-.002	-.015	-.005	.010
	Age	.135***	-.020	-.065*	-.013	-.035	-.049	-.018	.071**	-.008	-.040	.001	-.035	.084**	.037	.029	-.027
	Social acceptability of risky	-.040	-.026	.009	.061	.087*	-.077*	-.058*	.129***	-.019	-.073**	.026	.017	.000	-.057	.061	-.044
	driving
	Personal acceptability of risky	.367***	.212***	.210***	.380***	.227**	.357***	.280***	.430***	.221***	.348***	.209***	.464***	.425***	.459***	.388***	.371***
	driving
	Self-efficacy in risky driving	.263***	.465***	.433***	.357***	.395***	.379***	.340***	.298***	.456***	.346***	.468***	.251***	.338***	.349***	.292***	.443***
	behaviours
	Perceived safety in driving	.068*	-.006	.041	-.009	.022	.048	.054*	.034	-.019	.012	.041	.021	.014	.030	.023	.022
	Road safety policy support	-.020	.049	-.077*	.036	.015	.083	.004	-.008	.037	.028	.060*	.023	.063*	.068**	-.017	.000
	Perception of risky driving	.063*	.057	.081**	-.006	.064*	.049**	-.055*	-.012	.006	.028	.067**	.003	-.015	.002	.053*	.068**
	behaviours
	Number of crashes	-.001	.108***	.096**	.055*	.086**	.095***	.048*	.017	.034	.065**	.125***	.012	.058*	.080**	.100***	.053*
	Social desirability and intention	-.173*** -.181*** -.149***	-.170***	-.193*** -.202*** -.196*** -.119*** -.205*** -.232*** -.138*** -.179*** -.207***	-.093***	-.101*** -.165***
	to comply
	Risky social norms	.147***	.093**	.103**	.035	-.002	.108***	.096***	.062**	.083**	.084***	.113***	.065**	.084**	.106***	.092**	.035
	Perceived probability of	.060*	.039	.000	.012	.086**	.004	.058*	.001	.058*	.062**	.054*	.081***	.044	.041	.103***	.037
	enforcement
	Adjusted R²	.450***	.456***	.519***	.638***	.387***	.557***	.525***	.697***	.544*	.587***	.542***	.641***	.648***	.575***	.693***	.668***
	***p<.001, **p<.01, *p<.05

Table 35 :

 35 SEM model estimates for global score of declared behaviours

				[95% Conf.
	Parameter	Coef.	P-value	Interval]
	Score Declared Behaviours
	Gender	-8.599	0.001 -13.759	-3.440
	Age	-0.073	<0.001	-0.081	-0.066
	Income Group Category	-0.497	<0.001	-0.743	-0.250
	Risk Acceptance	1.890	<0.001	1.798	1.981
	GEI Economic Participation and Opportunity If Male	7.624	0.010	6.282	8.966
	GEI Economic Participation and Opportunity If Female	7.201	<0.001	5.749	8.654
	GEI Educational Attainment If Male	5.508	<0.001	1.320	9.696
	GEI Educational Attainment If Female	12.335	<0.001	7.341	17.329
	Number of crashes	0.856	<0.001	0.737	0.975
	Score Social desirability and intention to comply	-0.262	<0.001	-0.283	-0.241
	Score Perceived safety in driving	0.194	<0.001	0.162	0.225
	Score Perception of risky driving behaviours	-0.024	<0.001	-0.033	-0.016
	Risk Acceptance
	Gender	-1.095	<0.001	-1.185	-1.004
	Age	-0.052	<0.001	-0.055	-0.048
	Income Group Category	0.353	<0.001	0.272	0.434
	Number of crashes	0.331	<0.001	0.284	0.377
	Score Perceived safety in driving	0.067	<0.001	0.054	0.081
	Score Perception of risky driving behaviours	-0.013	<0.001	-0.018	-0.008
	Score Social acceptability of risky driving behaviours
	Risk Acceptance	1 (constrained)
	Gender	-1.095	<0.001	-1.185	-1.004
	Age	-0.052	<0.001	-0.055	-0.048
	Income Group Category	0.353	<0.001	0.272	0.434
	Number of crashes	0.331	<0.001	0.284	0.377
	Score Perceived safety in driving	0.067	<0.001	0.054	0.081
	Score Perception of risky driving behaviours	-0.013	<0.001	-0.018	-0.008
	Score Personal acceptability of risky driving behaviours
	Risk Acceptance	1.799	<0.001	1.761	1.836
	Gender	-1.969	<0.001	-2.129	-1.808
	Age	-0.093	<0.001	-0.099	-0.087
	Income Group Category	0.635	<0.001	0.486	0.783
	Number of crashes	0.595	<0.001	0.511	0.679
	Score Perceived safety in driving	0.121	<0.001	0.097	0.145
	Score Perception of risky driving behaviours	-0.023	<0.001	-0.032	-0.014
	Score Risky social norms
	Risk Acceptance	0.238	<0.001	0.225	0.251
	Gender	-0.261	<0.001	-0.287	-0.235
	Age	-0.012	<0.001	-0.013	-0.011
	Income Group Category	0.084	<0.001	0.065	0.104
	Number of crashes	0.079	<0.001	0.067	0.090
	Score Perceived safety in driving	0.016	<0.001	0.013	0.019
	Score Perception of risky driving behaviours	-0.003	<0.001	-0.004	-0.002
	Score Self-efficacy in risky driving behaviours
	Risk Acceptance	1.306	<0.001	1.249	1.363
	Gender	-1.429	<0.001	-1.563	-1.295
	Age	-0.068	<0.001	-0.072	-0.063
	Income Group Category	0.461	<0.001	0.353	0.568
	Number of crashes	0.432	<0.001	0.371	0.492
	Score Perceived safety in driving	0.088	<0.001	0.070	0.105

Table 25 :

 25 Means and standard deviation of compliant law perception according to gender and region ...................................................................................................................................................45Table 26: Means and standard deviation of the risky social norms according to gender and region ....46 Table 27: Means and standard deviations of perceived probability of enforcement according to gender and region ...................................................................................................................................47 Table 28: Means and standard deviation of the positive perception of automated vehicles according to gender and region ........................................................................................................................48 Table 29: Standardized betas and R² for the linear regression analyses for each attitudinal and behavioural constructed variable by gender, age, global gender equality index and income for entire ESRA32 sample ............................................................................................................................50 Table 30: Standardized betas and R² for linear regression analyses for each attitudinal and behavioural constructed variable by gender, age, global gender equality index and income for Europe ...................................................................................................................................................51 Table 31: Standardized betas and R² for the linear regression analyses of drivers declared risky behaviours by demographic, attitudinal and cultural variables by country (from Australia to Italy) .....53 Table 32: Standardized betas and R² for the linear regression analyses of drivers declared risky behaviours by demographic,attitudinal and cultural variables by country (from Japan to United-States).........................................................................................................................................54 Table 33: Standardized betas and R² for the linear regression analyses of drivers declared risky driving behaviours by demographic variables, perception and attitudes toward risky behaviours, gender equality indices and income ..........................................................................................................55 Table 34: Standardized betas and R² for the linear regression analyses on declared behaviours with gender gap sub-indexes for the whole sample and for Europe20 .....................................................56 Table 35: SEM model estimates for global score of declared behaviours ..........................................58

Gender Issues Appendix 1: ESRA2_2018 Questionnaire

Gender Issues

Other variables have different and contradictory effects on behaviours according to countries. Thus, social acceptability reinforces risky behaviours in Czech Republic and Republic of Korea but lowers them in Austria, Greece, Hungary, Poland or Slovenia. Road safety policy support lowers risky behaviours in Egypt, India or Morocco, but increases them in Australia, Belgium, Denmark, Germany, South Africa and Switzerland. 4.4.2. Effect of gender, age, attitudinal variables and culture on declared behaviours for the whole ESRA32 sample and for each gender group

We then analysed the effect of demographic and attitudinal variables as well as the effect of culture on reported risky driving behaviours. As in previous analyses, culture of each country is operationalized by two proxies: the World Bank's Gender Equality Index and the country's income level. First, this analysis was done on the overall sample by a series of hierarchical multiple linear regression analyses, using the "input" method. In a first model, only the two demographic variables (gender and age) were included. In a second model, attitudinal variables were added and in a third model, cultural variables were added. Table 33 presents the results for these three models. All three models are significant. The integration of the attitudinal variables greatly increases the share of variance explained by the model, compared to the demographic and cultural variables.

As shown in Table 33, gender and age are significant from Model 1, confirming higher reported risk behaviours among males and younger age groups. Their effects remain significant in the following models, but the betas decrease sharply once the effect of the attitudinal variables is controlled for, suggesting that the effect of these two variables is mediated by the latter.

With the exception of the social acceptability of risk behaviours, all attitudinal variables have a significant effect on reported risky driving behaviours. High personal acceptability of risky driving behaviours and high driving self-efficacy are strongly associated with an increase in reported risk behaviours, while high intent to comply strongly decreases these behaviours. Other variables (risky social norms, perceived safety of driving, road safety policy support, perception of risky driving behaviours, number of crashes and perceived probability of enforcement) play a less important role.

The third model also shows an effect of cultural variables. The level of reported risky driving behaviours is higher in countries with a high level of Gender Equality, while all things being equal, risky driving behaviours decrease as income level increases.

Multiple hierarchical linear regression analyses show similar effects for both gender groups. Table 33 shows that the same attitudinal variables play an important role (personal acceptability and self-efficacy Gender Issues reinforce, while intention to comply inhibits), and that the same effects of culture are observed. However, the results for the female group show that, for this group, age and social acceptability of risk behaviours have a significant inhibitory effect on reported behaviours, which is not the case for the male group. 4.4.3. Effect of Gender Equality Sub-Indexes on declared behaviours

We wanted to deepen our understanding of the influence of the gender equality policies of the countries involved in ESRA2 on reported risk behaviours. To this end, we relaunched the regression analyses, using for each country the sub-indexes of Gender Equality instead of the global index. We recalculated the hierarchical regression analyses for the overall sample, for each gender group and for the European region, using gender, age, the psychological constructs for each individual and GGEI sub-indexes and income level to characterized each country. It was not possible to conduct the analyses separately for the other regions due to the small number of countries within these regions. non-electric -drive a motorcycle (> 50 cc and > 4 kW non-electric) -drive an electric moped (≤ 4 kW) -drive an electric motorcycle (> 4 kW) -drive a powered personal transport device such as an electric step, hoverboard, solowheel,… -drive a car (non-electric or non-hybrid) -drive a taxi -drive a bus as a driver -drive a truck/lorry -drive a hybrid or electric car -take a taxi or use a ride-hail service (e.g. Uber, Lyft) -take the train -take the bus -take the tram/streetcar -take the subwaytake the aeroplane -take a ship/boat or ferry -be a passenger in a car -use another transport mode Q11) Over the last 30 days, have you transported a child (<18 years of age) in a car? yesno Items: below 150cm -above 150cm

Self-declared safe and unsafe behaviour in traffic

Q12_1a) Over the last 12 months, how often did you as a CAR DRIVER …?

You can indicate your answer on a scale from 1 to 5, where 1 is "never" and 5 is "(almost) always".

The numbers in between can be used to refine your response.

Binary variable for all items: at least once (2-5) -never (1) Items (random):

• drive after drinking alcohol • drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• read a text message or email while driving Q12_1b) Over the last 30 days, how often did you as a CAR DRIVER …?

You can indicate your answer on a scale from 1 to 5, where 1 is "never" and 5 is "(almost) always".

The numbers in between can be used to refine your response.

Binary variable for all items: at least once (2-5) -never (1) Items (random):

• drive when you may have been over the legal limit for drinking and driving • drive after drinking alcohol • drive 1 hour after using drugs (other than medication)

• drive after taking medication that carries a warning that it may influence your driving ability • drive faster than the speed limit inside built-up areas • drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• drive faster than the speed limit on motorways/freeways • drive without wearing your seatbelt • transport children under 150cm without using child restraint systems (e.g. child safety seat, cushion) • transport children over 150cm without wearing their seatbelts • talk on a hand-held mobile phone while driving • talk on a hands-free mobile phone while driving • read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving • drive when you were so sleepy that you had trouble keeping your eyes open Q12_2) Over the last 30 days, how often did you as a CAR PASSENGER …? You can indicate your answer on a scale from 1 to 5, where 1 is "never" and 5 is "(almost) always". The numbers in between can be used to refine your response. Binary variable for all items: at least once (2-5) -never (1) Item:

• travel without wearing your seatbelt in the back seat Gender Issues

Q12_3) Over the last 30 days, how often did you as a MOPED DRIVER OR MOTORCYCLIST …?

You can indicate your answer on a scale from 1 to 5, where 1 is "never" and 5 is "(almost) always". The numbers in between can be used to refine your response.

Binary variable for all items: at least once (2-5) -never (1) Items (random):

• ride when you may have been over the legal limit for drinking and driving • ride faster than the speed limit outside built-up areas (but not on motorways/freeways) answer on a scale from 1 to 5, where 1 is "never" and 5 is "(almost) always". The numbers in between can be used to refine your response.

Binary variable for all items: at least once (2-5) -never (1) Items (random):

• listen to music through headphones as a pedestrian while walking in the streets • read a text message/email or check social media (e.g. Facebook, twitter, etc.) while walking in the streets • cross the road when a pedestrian light is red • cross the road at places other than at a nearby (distance less than 30m) pedestrian crossing Acceptability of safe and unsafe traffic behaviour Q13_1) Where you live, how acceptable would most other people say it is for a CAR DRIVER to….? You can indicate your answer on a scale from 1 to 5, where 1 is "unacceptable" and 5 is "acceptable". The numbers in between can be used to refine your response. Binary variable: acceptable (4-5) -unacceptable/neutral (1-3) Items (random):

• drive when he/she may be over the legal limit for drinking and driving

• drive 1 hour after using drugs (other than medication)

• drive faster than the speed limit outside built-up areas (but not on motorways/freeways)

• not wear a seatbelt while driving • transport children in the car without securing them (child's car seat, seatbelt, etc.)

• talk on a hand-held mobile phone while driving • read a text message/email or check social media (e.g. Facebook, twitter, etc.) while driving Q14_1) How acceptable do you, personally, feel it is for a CAR DRIVER to…? You can indicate your answer on a scale from 1 to 5, where 1 is "unacceptable" and 5 is "acceptable". The numbers in between can be used to refine your response. Binary variable: acceptable (4-5) -unacceptable/neutral (1-3) Items (random)

• drive when he/she may be over the legal limit for drinking and driving

• drive 1 hour after using drugs (other than medication) Gender Issues

Q16) How safe or unsafe do you feel when using the following transport modes in [country]?

You can indicate your answer on a scale from 0 to 10, where 0 is "very unsafe" and 10 is "very safe". The numbers in between can be used to refine your response. Items (random) = Items indicated by the respondent in Q10 are displayed.

Q17) How often do you think each of the following factors is the cause of a road crash involving a car? You can indicate your answer on a scale from 1 to 6, where 1 is "never" and 6 is "(almost) always". The numbers in between can be used to refine your response.

Binary variable: often/frequently (4-6) -not that often/not frequently (1-3) Items (random)

• driving after drinking alcohol • driving after taking drugs (other than medication)

• driving faster than the speed limit • using a hand-held mobile phone while driving • using a hands-free mobile phone while driving • inattentiveness or day-dreaming while driving • driving while tired

Support for policy measures

Q18) Do you oppose or support a legal obligation to …?

You can indicate your answer on a scale from 1 to 5, where 1 is "oppose" and 5 is "support". The numbers in between can be used to refine your response. Binary variable: support (4-5) -oppose/neutral (1-3) Items (random)

• install an alcohol "interlock" for drivers who have been caught drunk driving on more than one occasion (technology that won't let the car start if the driver's alcohol level is over the legal limit) • have zero tolerance for alcohol (0,0 ‰) for novice drivers (licence obtained less than 2 years) • have zero tolerance for alcohol (0,0 ‰) for all drivers • install Intelligent Speed Assistance (ISA) in new cars (which automatically limits the maximum speed of the vehicle and can be turned off manually) • install Dynamic Speed Warning signs (traffic control devices that are programmed to provide a message to drivers exceeding a certain speed threshold) • have a seatbelt reminder system for the front and back seats in new cars • require all cyclists to wear a helmet • require cyclists under the age of 12 to wear a helmet • require all moped drivers and motorcyclists to wear a helmet • require pedestrians to wear reflective material when walking in the streets in the dark • require cyclists to wear reflective material when cycling in the dark • require moped drivers and motorcyclists to wear reflective material when driving in the dark • have zero tolerance for using any type of mobile phone while driving (hand-held or handsfree) for all drivers • not using headphones (or earbuds) while walking in the streets • not using headphones (or earbuds) while riding a bicycle Q19_1) What do you think about the current traffic rules and penalties in your country for driving or riding under the influence of alcohol? agree -disagree Items:

• The traffic rules should be stricter.

• The traffic rules are not being checked sufficiently.

• The penalties are too severe.

Q19_2

) What do you think about the current traffic rules and penalties in your country for driving or riding faster than the speed limit? agree -disagree Items: Q19_1 Gender Issues Q19_3) What do you think about the current traffic rules and penalties in your country for using a mobile phone while driving or riding? agree -disagree Items: Q19_1 Enforcement Q20_1) On a typical journey, how likely is it that you (as a CAR DRIVER) will be checked by the police for… You can indicate your answer on a scale from 1 to 7, where 1 is "very unlikely" and 7 is "very likely". The numbers in between can be used to refine your response.

Binary variable: likely (5-7) -unlikely/neutral (1-4) Items (random)

• … alcohol, in other words, being subjected to a Breathalyser test • … the use of illegal drugs • … respecting the speed limits (including checks by a police car with a camera, fixed cameras, mobile cameras, and section control systems) • … wearing your seatbelt • … the use of hand-held mobile phone talk or text while driving Q21_1) In the past 12 months, how many times have you been checked by the police for using alcohol while DRIVING A CAR (i.e., being subjected to a Breathalyser test)? never -1 time -at least 2 times -I prefer not to respond to this question Binary variable: at least once -never (removing "I prefer not to respond to this Q) Q22_1) In the past 12 months, how many times have you been checked by the police for the use of drugs (other than medication) while DRIVING A CAR? never -1 time -at least 2 times -I prefer not to respond to this question Binary variable: at least once -never (removing "I prefer not to respond to this Q)

Involvement in road crashes

Introduction: The following questions focus on road crashes. With road crashes, we mean any collision involving at least one road vehicle (e.g., car, motorcycle, or bicycle) in motion on a public or private road to which the public has right of access. Furthermore, these crashes result in material damage, injury, or death. Collisions include those between road vehicles, road vehicles and pedestrians, road vehicles and animals or fixed obstacles, road and rail vehicles, and one road vehicle alone.

Q23_1a) In the past 12 months, how many times have you personally been involved in road crashes in which you or somebody else had to be taken to the hospital? ___ times (number; max. 10) if 0 → Q23_2a; if >0 → Q23_1b → Q23_2a Binary variable: at least once -never Q23_1b) Please indicate the transport modes you were using at the time of these crashes.

Items indicated by the respondent in Q10 are displayed; Threshold = 'at least a few days a year'. Number to be indicated after each transport mode; note the sum should be equal to the number indicated in Q23_1a Q23_2a) In the past 12 months, how many times have you personally been involved in road crashes with only minor injuries (no need for hospitalisation) for you or other people? ___ times (number; max. 10) if 0 → Q23_3a; if >0 → Q23_2b → Q23_3a Binary variable: at least once -never Q23_2b) = Q23_1b Q23_3a) In the past 12 months, how many times have you personally been involved in road crashes with only material damage? Gender Issues ___ times (number; max. number 10) if 0 → skip Q23_3b; if >0 → Q23_3b → next Q Binary variable: at least once -never

Vehicle automation I2) Introduction: The following questions focus on your opinion about automated passenger cars. We talk about two different levels of vehicle automation: Semi-automated passenger cars: Drivers can choose to have the vehicle control all critical driving functions, including monitoring the road, steering, and accelerating or braking in certain traffic and environmental conditions. These vehicles will monitor roadways and prompt drivers when they need to resume control of the vehicle. Fully-automated passenger cars: The vehicle controls all critical driving functions and monitoring all traffic situations. Drivers do not take control of the vehicle at any time.

Q24) How interested would you be in using the following types of automated passenger car? You can indicate your answer on a scale from 1 to 7, where 1 is "not at all interested" and 7 is "very interested". The numbers in between can be used to refine your response. Binary variable: interested (5-7) -not interested/neutral (1-4) Items:

• semi-automated passenger car • fully-automated passenger car Q25_1) How likely do you think it is that the following benefits will occur if everyone would use a semi-automated passenger car? You can indicate your answer on a scale from 1 to 7, where 1 is "very unlikely" and 7 is "very likely". The numbers in between can be used to refine your response.

Binary variable: likely (5-7) -unlikely/neutral (1-4) Items (random):

• fewer crashes

Social desirability scale

Introduction: The survey is almost finished. The following questions have nothing to do with road safety, but they are important background information. There are no good or bad answers.

Q28)

To what extent are the following statements true? You can indicate your answer on a scale from 1 to 5, where 1 is "very untrue" and 5 is "very true". The numbers in between can be used to refine your response. Items (random):

• I always respect the highway code, even if the risk of getting caught is very low.

• I would still respect speed limits at all times, even if there were no police checks.

• I have never driven through a traffic light that had just turned red.

• I do not care what other drivers think about me.

• I always remain calm and rational in traffic. (if needed pop-up: rational = non-emotional)

• I am always confident of how to react in traffic situations.

Appendix 2: ESRA2 weights

The following weights are used to calculate representative means on national and regional level. They are based on UN population statistics (United Nations Statistics Division, 2019). The weighting took into account small corrections with respect to national representativeness of the sample based on gender and six age groups (18-24y, 25-34y, 35-44y, 45-54y, 55-64y, 65y+). For the regions, the weighting also took into account the population size of each country in the total set of countries from this region.

Individual country weight