Accident Analysis and Prevention 71 (2014) 273–285
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
The relationship between social capital and traffic law violations: Israeli Arabs as a case study Samira Obeid a,b,c,∗ , Victoria Gitelman d , Orna Baron-Epel a a
School of Public Health, Faculty of Social Welfare and Health Studies, University of Haifa, Mount Carmel 31905, Israel North District Health Office, Ministry of Health, Israel Nursing Faculty, The Max Stern Yezreel Valley College, Yezreel Valley, 19300 Israel d Ran Naor Road Safety Research Center, Technion - Israel Institute of Technology, Technion City, Haifa 32000, Israel b c
a r t i c l e
i n f o
Article history: Received 12 November 2013 Received in revised form 2 May 2014 Accepted 31 May 2014 Keywords: Traffic law violations Social capital Questionnaire survey Israeli Arab drivers
a b s t r a c t Social aspects of a community may be correlated with driver’s involvement in road traffic accidents. This study focused on examining this association in the context of the social capital theory. A survey of 600 Arab drivers living in 19 towns and villages was conducted using a face-to-face interview. Structural equation modeling was applied to explore paths of associations between the model components. Most of the proposed relationships in the path model were found to be significant, where the model explained 37% of the variation. The results indicate that only volunteering and reciprocity have direct correlations with traffic law violations. While the other correlations (except political involvement), were mediated by attitudes toward traffic laws violation. Hence, it can be concluded that it is not always possible to generalize the positive mechanisms of the social capital theory, and in certain populations such as the Arab minority it can give undesirable results. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background of the study Road traffic accidents pose one of the main challenges for public health and injury prevention worldwide. More than a million people are killed on the roads each year (WHO, 2013). Road accident contributing factors are subdivided into human factors, road environment factors, vehicle factors and their combinations (PIARC, 2003), where over 90% of motor vehicle accidents involve some degree of driver behavior (Petridou and Moustaki, 2000; PIARC, 2003). Road safety research recognized the impact of social factors such as social norms, social environment and socio-economic status on the involvement in road accidents mainly via driving behaviors (Huguenin, 2005; Factor et al., 2007; Nabi et al., 2005; Evans et al., 1987; Perry, 1986).
∗ Corresponding author at: School of Public Health Faculty of Social Welfare and Health Studies University of Haifa, Mount Carmel 31905, Israel. Tel.: +972 4 8288009; fax: +972 4 8288017. E-mail addresses: [email protected], [email protected] (S. Obeid). http://dx.doi.org/10.1016/j.aap.2014.05.027 0001-4575/© 2014 Elsevier Ltd. All rights reserved.
Several studies pointed to the role of families and peers in moderating or encouraging risky behavior (e.g., Ferguson et al., 2001 and Horvath and Zuckerman, 1993). Empirical findings indicate that lower social control, lower parent-friends compatibility and more frequent exposure to models of problem-behavior are related to higher proneness to problem-behavior (Jessor and Jessor, 1977). Parker et al. (1992) found that drivers who feel that their referents would disapprove of them committing any of the driving violations, also report weaker intentions to commit the violations. Moreover, Ferguson et al. (2001) found that youngsters driving records, in the first two years of licensure, are correlated to their parents driving records (accident involvement as well as traffic violations). In addition, scholars showed that significant ethnic group differences exist in a range of road-traffic areas such as: accident involvement (Norris et al., 2000), attitudes toward road safety (Yagil, 1998), seatbelt use (Shin et al., 1999), crossing against a red light (Retting and Williams, 1996), and speeding (Gabany et al., 1997). It is obvious from these studies that individual and social factors may all have an effect on involvement in road accidents. As social capital theory encompasses a large range of social characteristics, this study focused on the correlation between social capital and road accidents. Social capital theory suggests that societies can be characterized via three main categories: (1) social networks and the social
274
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
structure in which individuals operate; (2) social relationships and (3) the quality of associational activity. These dimensions are cited as the main components of the social capital concept (Burt, 2000; Granovetter, 2005; Lin, 2001; Putnam, 2000; Woolcock and Narayan, 2000). Social capital was also defined as “features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit” (Putnam, 1995, p. 67). Putnam (1995) suggested five indicators missioning social capital: (1) social involvement, (2) political involvement (participating and attitudes), (3) volunteering and reciprocity, (4) trust and (5) social support. Other authors applied various indicators of social capital in different contexts. Examples include: trust (Cox and Caldwell, 2000; Falk and Guenther, 1999; Glaeser et al., 2000; Guenther and Falk, 1999; Kolankiewicz, 1996); membership in formal and informal networks (Baum and Ziersch, 2003; O’connell, 2003; Price, 2002; Warde et al., 2003; Wollebaek and Selle, 2003); membership, trust and norms of reciprocity (Isham et al., 2002; Skrabski et al., 2003; Staveren, 2003); and network resources (Zhao, 2002). Social capital was widely considered to have an influence on health (De Silva et al., 2004; Hawe and Shiell, 2000; Kawachi et al., 1997). In health research, social capital has been measured by indicators such as levels of interpersonal trust, the presence of reciprocal exchanges between citizens, and membership in civic organizations (Veenstra et al., 2005). Research has also shown that social integration can enhance population well-being (Haughton McNeill et al., 2006; Kim et al., 2006). It is suggested that communities with high levels of social capital are more effective at exercising social control over different health behaviors and therefore improving health of the community (Berkman and Glass, 2000; Subramanian et al., 2002). 1.2. Social capital and road accidents: conceptual links Further understanding of how the social capital dimensions may influence driving behaviors can contribute to the understanding of how to help societies to be more effective at exercising social control over safe road user behaviors and to form health enhancing social norms, such as driving. One aspect of social capital is social networks; these can spread unsafe driving norms and knowledge, by exposing the members to unsafe behaviors of others, such as speeding and breaking traffic laws, which consequently propagate negative social norms regarding safe behaviors. Scholars found that social norms have a greater impact on individual behavior than biological, personal, familial, religious, or cultural factors (Berkowitz and Perkins, 1986; Borsari and Carey, 2001; Kandel, 1985, and Perkins, 2002). Unsafe norms and behaviors were also found to be related to road accidents (Zaidel, 1992; De Pelsmacker and Janssens, 2007; Elvik et al., 2009; Forward, 2009). Another aspect of social capital is social support. Perceiving support has the potential to eliminate stress, anger and anxiety which leads to safe driving. Scholars have found a relationship between stress, aggression and frustration, and road accident involvement. For example, Arnett et al. (1997) showed that anger was related to excessive speeding. Deffenbacher et al. (2003) demonstrated that during simulated driving, angrier drivers maintained a higher average speed and higher speed deviation than less angry drivers. Additionally, Carbonell Vaya et al. (1997) showed that anxiety was related to more self-reported near-accidents. An additional aspect of social capital is political involvement. Participation in democratic processes can ensure government accountability and proper delivery of public services, in the present context—urban planning and management, construction and maintenance of roads, and effective enforcement of traffic rules. Political awareness and activism may provide better access to resources
and material goods, facilitate coping mechanisms of individual and community and buffer negative unsafe behaviors. Volunteering and reciprocity is another aspect of social capital and is based on the individual involvement in networks. Membership in volunteer networks with high amounts of reciprocity can foster trust between citizens and help develop norms of solidarity and reciprocity which are essential for stable communities. Stable and cohesive communities have more social control over unhealthy and unsafe behaviors and are more trustworthy. This affects the capacity of people to come together to collectively resolve problems such as road accidents and achieve outcomes of mutual benefit. To the authors’ knowledge, the relationship between social capital and road accident involvement was reported only by Incla’n et al. (2005), in Mexico. The study revealed that there is an association between road accidents and casualties, on the one hand, and propensity for cooperation in local communities in Mexico, on the other. The paper argued that, due to a lack of reciprocity and capacity for collective action, residents fail to perceive high traffic mortality as a common problem and, hence, they are unable to jointly take measures to resolve it. No other studies were found on this issue and for other communities and, therefore, there is a need for further research of the relationship between social capital and road accidents.
1.3. The Israeli Arabs population Israel has two main ethnic groups: a majority of Jews (79.4%) and an Arab minority constituting about 20.6% of the Israeli population, or 1.65 million people (CBS, 2012a,b). The indigenous Arab population in Israel is ethnically Palestinian. About 90% of them reside in Arab only villages and towns; the other 10% live in mixed cities (ACAP, 2012). The Arab community is largely an underprivileged minority with a history of disadvantage in income, education and employment. To some extent, the Arabs in Israel suffer from prejudice and discrimination, and it has been suggested that discrimination plays a part in the income disparities between Arabs and Jews (Bushman and Bonacci, 2004; Haberfeld and Cohen, 2007; Okun and Friedlander, 2005 and Wolkinson, 1999). The impact of social capital on health was examined by BaronEpel et al. (2008), who measured personal trust within the Arab society and found it to be low in comparison to the Jewish population of Israel. The share of the Israeli Arabs among road accident casualties is higher than their relative share in the general population: although Arabs comprise approximately 20% of the general population in Israel, 27% of all road accident casualties and 34% of all fatalities are Arabs (CBS, 2010). The probability that an Arab driver will be involved in a fatal or serious accident is 1.6 times greater than that for a Jewish driver (Factor et al., 2008). Surveys had already evidenced that Arab drivers have higher rates of traffic violations, such as speeding and less use of seat restraints (Gitelman et al., 2003; NRSA, 2010). The Arab society in Israel was selected as a case-study for exploring the association between social capital and road accident involvement as their road accident rates is higher and their social capital is lower than the Jewish majority. Therefore, this study aimed to examine the relationship between the aforementioned five components of the social capital and traffic law violations, which lead to road accident involvement, among Israeli Arab drivers. This relationship is explored by means of a detailed questionnaire reflecting driving behavior, norms and attitudes of the population studied as well as social capital components and driver’s background characteristics.
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
2. Methodology 2.1. Building a sample A random cross sectional face-to-face survey of 600 Israeli Arab drivers aged 18 and over, was conducted during the year 2009. The selection of the sample was performed in three stages: First all the Arabs towns and villages (of the north and Tri angle regions) were divided into five layers according to the number of residents who live in every town and village (for example, layer No.1 represented villages and towns with a population count of more than 20,000 residents, layer No. 2 represented those with 10,000–20,000 residents and etc.). Nineteen towns and villages were selected randomly according to their size and layer. Second, the number of all the residents of the towns and villages of each layer were calculated, along with the percentage they represent out of the total number of the Arab residents living in the north and the triangle regions. Third, the sample size from each locality was calculated according to the percentage of the residents. For example, in layer No. 1 there were 195,100 residents, which represent 25.7% of the Arab citizens living in the north and the triangle regions. This rate is equal to 154 interviewees (25.7 × 600(600 is the sample size of the study)/100 = 154), and so on for the other layers. The interviewees were randomly chosen from the various neighborhoods of each locality, in the north, south, west and east. In each neighborhood the first house from the north–west was selected and in it the first driver who agreed to participate was interviewed. The distance between the selected houses was set according to the size of the village or town; in the large towns and villages, a distance of 20 houses between selected houses was chosen. In the smaller places a distance of 15, 10, 5 and 3 houses were chosen. The interviewers returned later if no one was home and if there was no suitable interviewee, the next house was chosen. The Israeli CBS ranks local authorities and municipalities according to a ten-point socio-economic scale where cluster 10 represents the wealthiest localities, and cluster 1 the poorest ones (CBS, 2012a,b). Localities of all socio-economic levels were randomly selected in each layer. The selection methods applied, enabled representation of both small and large Arab towns and villages, with lower and higher socio-economic levels. The interviewers were Arab speaking, from the Arab community. The response rate of the survey was 77.3%. 2.2. The questionnaire The questionnaire included questions about self-reported traffic law violations, descriptive driving norms, attitudes toward driving laws, social capital and background characteristics. Generic questions were asked to estimate the distance driven by participants each month, on a scale of 4, i.e. fewer than 1000 km, 1000–2500 km, 2501–3500 km and over 3500 km. The reported distance traveled included all types of vehicles and included distance traveled during work. In addition, demographic information was collected such as: gender, income and years of education. 2.2.1. Traffic law violation Traffic law violation is one of the variables examined in this study and it was measured using the Driver Behavior Questionnaire (DBQ). This questionnaire was originally developed by Reason et al. (1990). The questionnaire measures the frequency of selfreported traffic violations on a scale from 1 to 5, where 5 indicate full agreement with the item and 1 indicates total non-agreement. Higher scores indicated better driving. DBQ was originally divided into three causes of accidents: intentional violations, mistakes and mishaps. Previous researchers found that intentional violations
275
is the most common cause leading to the involvement in traffic accidents (Sullman and Meadows, 2000; Lawton et al., 1997). Therefore, only this cause was included in the current study’s questionnaire. The reliability test of the final seven items—Cronbach’s alpha was 0.82. Answers from the questionnaire were divided into two groups, i.e. answers 1–3 indicated safe self-reported driving and coded as “1”, while answers 4–5 indicated unsafe self-reported driving and coded “0”. In addition, a combined mean variable was calculated for each respondent.
2.2.2. Descriptive driving behavior norms A list of 8 questions was developed describing various unsafe behaviors that were common in the Arab towns and villages. This list was developed by distributing a form via email to a convenience sample of 20 people from different Arab localities. The respondents were asked to describe their perceived unsafe driving behaviors that were common among Arab drivers inside and outside the villages and towns. A total of 80 such behaviors were suggested by the participants. Eight unsafe behaviors were chosen as they were suggested by all 20 participants, including: the use of car restraints by adults and children; parking in places that disturb other drivers or pedestrians; driving without a driving license; driving while speaking on a hand-held cell-phone device; behaviors of young drivers who drive around with their car radio at full volume, do not adhere to road signs and drive a car with a higher than permitted number of passengers. A questionnaire including 16 questions was then designed, two for each behavior, asking the respondents to estimate the percent of drivers performing this behavior within their town or village and outside them (descriptive norms). The respondents were asked to rate their answers on a scale of 1–5, where 1 indicated a very low percentage of drivers acting according to the described behavior and 5 indicated a very high percentage behaving in such way. A “do not know” option was available but not included in the analysis. The questionnaire was pre-tested via random telephone interviews of 50 adults, which were selected randomly from the telephone book. The internal reliability test (Cronbach’s alpha) for the final sample was 0.76 for both the 8 questions describing behaviors within the towns and villages and the 8 questions describing behaviors outside the towns. Using this questionnaire, combined mean variables representing descriptive norms of driving behaviors within the towns or villages and outside them were calculated. The higher the score the more frequent are the estimated non-safe behaviors, meaning that the descriptive norms are worse. Answers from the questionnaire were divided into two groups: answers from 1 to 3 indicated safe driving norms and were coded 1, and answers from 4 to 5 indicated unsafe driving norms and were coded 0.
2.2.3. Attitudes towards traffic safety laws Statements describing attitudes toward traffic safety laws were based on Iverson’s questionnaire (Iverson, 2004). The original questionnaire includes mainly attitudes about violation of laws. The questionnaire included 7 items of the original questionnaire focusing on the issues of attitudes toward maintaining the driving law. The respondents were asked to rate their answers on a scale from 1 to 5, with 1 indicating no agreement, and 5 indicating full agreement. A “do not know” option was available. Internal reliability Cronbach’s alpha of the 7 items was 0.85. The answers from the questionnaire were divided into two groups: answers from 1 to 3 indicated the positive attitudes toward driving laws–safe driving and coded 1; answers from 4 to 5 indicated unsafe driving attitudes toward driving laws and coded 0. A combined variable was created calculating the mean answer. The higher the score the more negative are the attitudes towards safe driving.
276
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
2.2.4. Social capital questionnaire The questionnaire was based on the European social survey (ESS) questionnaire. The ESS survey was conducted simultaneously in over 30 European countries including Israel (ESS, 2010). The questionnaire was originally translated into three languagesArabic, Hebrew and Russian. The validation of the ESS questionnaire was examined by eleven studies. All these studies found the tools able to distinguish between the different theoretical constructs of social capital and therefore to have acceptable for health studies (De Silva et al., 2006). The five social capital components were measured as follows:
(1) Participating in social activities indicator measures the involvement in activities they undertake with a group, for example visiting relatives, friends and colleagues or participating in religious ceremonies. The groups can be formal organizations, or informal groups. The answers were on a scale of 1–7 with a higher value indicating a higher degree of socializing. Internal reliability Cronbach’s alpha was 0.74. (2) The intensity of participating in political activities was measured through 10 indicators, each of which referred to a different way of being involved in political activity, including attitudes toward being a good citizen. Two variables represent the political activities: (1) active participation such as voting and membership of a political party, and (2) attitude towards active participation and good citizenship. The attitude is given by scale 1–10 (where 1—do not agree, and 10—fully agree) representing the extent of being a good citizen (including voting, being a member of a political party, etc.). Internal reliability Cronbach’s alpha was 0.64. (3) Volunteering and reciprocity was also measured. Generalized reciprocity is a phenomenon which describes people helping each other not for a financial reward, but maybe because they assume they would be helped in a similar way. The answers were on a scale of 1–5, with a higher value indicating a higher degree of reciprocity. Volunteering is based on involvement in networks such as membership in networks of volunteers, working for volunteering organizations or donating money. For example, questions included; “How many clubs do you go to?” or “How often do you donate money?”. Answers were “Yes/No” for each question. Internal reliability Cronbach’s alpha of the reciprocity items was 0.78. (4) Norms of Trust includes personal, generalized, civic and institutional trust. The answers were on a scale of 1–10, with a higher value indicating a higher degree of trust. Internal reliability Cronbach’s alpha was 0.79. (5) Support measured the perceived helpfulness factor of social capital. It shows to what extent individuals can rely on the support of others, including personal, psychological and financial support. The answers were in the form of a scale from 1 to 5, with a higher value indicating a higher degree of support. Internal reliability Cronbach’s alpha was very low 0.45. One of the indicators of the support component is binary (Do you have anyone with whom you can discuss intimate and personal matters? 1. Yes 2. No), this is different from other indicators and can explain the low reliability. While removing it increases the reliability to 0.61. Reanalyzing the data without this indicator didn’t change the results. Another explanation may be due the fact that Cronbach’s alpha is related to the number of indicators, this component includes only four indicators which can affect the reliability. For these reasons and since it was decided to adapt the whole original ESS social capital questionnaire this study included also the support component as one variable.
For all the social capital measures described above the variables were arithmetically averaged using the corresponding surveyweighted individual-level measures (Kim and Kawachi, 2006). It is argued that the most appropriate method for such aggregation is principal component analysis (Whiteley, 2000; Hjøllund and Svendsen, 2000; Van Oorschot and Arts, 2005). Since the values of the social capital questionnaire answers are of unequal scale, for example the answers of the question regarding trust norms were on a scale of 1–10, and the answers of active political participation such as voting and membership of a political party were yes or no answers. Therefore, all the answers were normalized on a percentage scale. These aggregated area-level variables, gauged on a continuous scale from 1 to 100; reflect the contextual effects of social capital. There is a growing consensus that social capital cannot be measured by one single variable on one hand and overly-aggregated, heterogeneous indexes or latent constructs, on the other hand (Parts, 2009). The ESS (2010) methodological team suggested capturing all the information of individual social capital indicators into one variable. Parts (2008) tested the empirical validity of the multidimensionality of social capital, exploratory factor analysis was used. The results of this research showed that the indicators of social capital clearly divided into groups describing pre-defined dimensions of social capital; social and political involvement, trust, support, volunteering and reciprocity. The data of the current study was analyzed using the confirmatory factor analysis. The results indicated better fit (Chi-Square DF 120, P < .0001, RMSEA0.0617, AGFI-0.9184, AIC-474, 9007) for the aggregated variable model with 6 components (political involvement–attitudes and participating’ social networks, trust, support, volunteering and reciprocity), than single combined model (Chi-Square DF 135, P < .0001, RMSEA-0.0953, AGFI-0.8121, AIC-886, 4324). Traffic law violations, attitudes toward driving laws and the driving norm questionnaire answers have the same scale of 1–5. For the bivariate analysis a dichotomy variable was calculated for each of these variables. A combined mean variable was calculated for the structural model equitation analysis.
2.3. Statistical analysis First, bivariate analysis was applied to determine the association between traffic law violations, perceived norms of driving safety behaviors, attitudes toward driving laws, social capital components, road exposure variables as well as demographic variables. Pearson’s correlation coefficients were estimated (Rodgers and Nicewander, 1988), to identify dependence between the variables and its statistical significance. Then, Structure Equation Modeling (SEM) was applied using AMOS 20.0 software (Vinokur, 2005). To overcome violation of multivariate normality assumption, confidence intervals and significance of direct and indirect effects were obtained by bootstraping procedure with 5000 bootstrap samples. Traffic law violation was entered as an endogenous variable, attitudes toward driving laws was entered into model number 1 as an exogenous variable and into model number 2 as an endogenous variable, and driving norms were entered as an exogenous variable. Driving rate, Km driven and gender were entered as control variables and the social capital six components (Political involvement component has two components as described previously—attitudes and participating) as exogenous variables. It should be noted that the sample data in this study can be considered as a multistage sample as it includes individual level and community level (19 towns/villages). Therefore, a multilevel analysis was considered but found not suitable (Hox, 2002) as the interclass index was less than 0.1.
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285 Table 1 Demographic characteristics of the study population percent and number (N).
277
The drivers’ behaviors and attitudes were measured using three variables (see Section 2.2) and the results were as follows:
Study population % (N)
Total
Gender (drivers)
Male Female
67.7 (404) 32.2 (193)
100 (596)
Age
18–38 39–58 59–80
57.9 (345) 37.8 (225) 4.4 (26)
100 (596)
Education–years
0–12 13–15 16+
52.6 (316) 26.5 (159) 20.8 (125)
100 (600)
Religion
Muslims Christians Druze
65 (387) 22 (132) 12 (75)
100 (600)
Employment
Yes No
73 (435) 26 (155)
100 (600)
Income per family, NIS
Up to 4600 4601–9200 9201 and above
16 (88) 50 (275) 35 (192)
100 (600)
3. Results 3.1. Description of the sample population A third of the participants were female and two thirds were male. That is consistent with the distribution of driver’s gender in the Arab population (CBS, 2013). Half of the drivers were under the age of 38 (mean age 36.9, SD—12.3 years). About 23% reported between 12 and 15 years of schooling and 12% of the participants reported 16 years of schooling or more. Most survey participants were Muslims, the rest were Christians and Druze, representing relevant sectors within the Arab community (CBS, 2012a,b). About a quarter of the participants were unemployed. Sixteen percent of participants earned up to 4600 New Israeli Shekels (NIS) a month, much less than the average income in Israel which is 8500 NIS (CBS, 2010). Table 1 shows the sample distribution and their percentages. 3.2. Description of driving characteristics
1. Self-reporting traffic law violations: most of the drivers reported that the behaviors described in the statements of the traffic law violations questionnaire did not characterize their driving. Thirty six percent reported that they performed at least one of the traffic violations last year and the average score for these questions was 2.3. 2. Respondents were asked to relate to the descriptive driving norms within their villages or towns and outside them. The results suggest that most drivers (73%) reported unsafe driving norms (scores 4 and 5) within their villages/towns and outside of them, with an average score of 3.06 (as mentioned above, 5 indicates a high percentage of drivers driving unsafely and 1 indicates low percentage). More than 88% of respondents reported that, in their opinion, a high or very high percentage of drivers do not restrain themselves in the car when driving within the town or village, and 56% reported that a high or very high percentage of drivers do not restrain their children in the car within the town or village. 3. Results of the drivers’ attitudes towards traffic safety laws indicate very low agreement with statements describing negative attitudes toward driving laws. Two percent of the drivers reported full agreement with the statements and the average score was 1.98 (scale 1–5, where 1 indicates full agreement, and 5 indicates no agreement).
The analysis revealed that traffic law violations were significantly correlated (p < 0.05) to some of the socio-demographic variables including:
• gender (rs 0.10), where men reported more unsafe driving behaviors than women; • employment (rs 0.21), where employed drivers reported more unsafe driving behaviors than unemployed; • mean driving distance (rs 0.08), where drivers, who drive more km per month, reported more unsafe behaviors.
The majority of the drivers (88%) in the survey drove every day. Most of them drove up to 2500 km. Results are presented in Table 2. Table 2 Road expositor characteristics.
KM driving
Less than 1000 km/month Between 1000 and 2500 km/month 2500–3500 km/month More than 3500 km
% (N)
Total
42 (252)
100(600)
46 (276) 7 (42) 5 (30)
The strength of the correlations for most variables was not high. Moreover, statistically significant positive correlations were found between traffic law violations and attitudes towards traffic safety laws (rs 0.59), where drivers reporting unsafe behaviors expressed negative attitudes towards traffic laws. In addition, negative correlation was observed between traffic law violations and descriptive driving norms (rs −.11), where drivers reported safe descriptive norms, also reported less frequent law violations, as seen in Table 3. No correlation was found between traffic law violations and age of the driver.
Table 3 Correlation between: traffic law violations, descriptive norms and attitudes toward driving law. Mean (N) STD and r (Spearman correlation). Traffic law violations
Descriptive driving norms
Attitudes toward driving laws
Traffic law violations (1–5)
Mean (N) STD r
2.3 (596) ± 0.87 1
–
–
Descriptive driving norms (1–5)
Mean (N) STD r
– −0.11*
3.06 (595) ± 0.49 1
– –
Attitude s toward driving laws (1–6)
Mean (N) STD r
– 0.05
1.9 (595) ± 0.8 1
* **
p < 0.05. p < 0.001.
– 0.59**
278
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
Table 4 Associations between the social capital components and traffic law violations, norms and attitudes toward driving law-Mean (N) STD-(Spearman correlation). Social capital components
Political involvementattitudes
Mean(N)STD
73.9 (593) ± 16.6
Mean(N)STD
35.5 (597) ± 22.6
Mean(N)STD
52.7 (596) ± 15
rs (P. Value) Volunteering/ reciprocity
Attitudes toward driving laws
High safety behaviors
Low safety behaviors
High safety norms
Low safety norms
Positive attitudes
Negative attitudes
45.4(99) ± 25.8
42.1(458) ± 27.7
45.9(208) ± 28.8
40.9(311) ± 26.3
44.7(64 ± 27.3
42.4(494) ± 27.4
69.0(119) ± 16.5
30.6(98) ± 8.4 0.06 (0.127) 38.7(110) ± 22.2
Mean(N)STD
74.7(471) ± 16.6
75.0(223) ± 14.9
73.4(326) ± 17.8
31.7(413) ± 9.2 −0.06 (0.135) 31.8(459) ± 21.9
30.0(195) ± 9.9
69.5(80) ± 15.1
74.6(511) ± 16.8
0.08 (0.039)*
0.01 (0.79)
−0.21 (0.000)**
rs (P. Value)
−0.14 (0.001)**
0.06 (0.134)
0.14 (
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
The relationship between social capital and traffic law violations: Israeli Arabs as a case study Samira Obeid a,b,c,∗ , Victoria Gitelman d , Orna Baron-Epel a a
School of Public Health, Faculty of Social Welfare and Health Studies, University of Haifa, Mount Carmel 31905, Israel North District Health Office, Ministry of Health, Israel Nursing Faculty, The Max Stern Yezreel Valley College, Yezreel Valley, 19300 Israel d Ran Naor Road Safety Research Center, Technion - Israel Institute of Technology, Technion City, Haifa 32000, Israel b c
a r t i c l e
i n f o
Article history: Received 12 November 2013 Received in revised form 2 May 2014 Accepted 31 May 2014 Keywords: Traffic law violations Social capital Questionnaire survey Israeli Arab drivers
a b s t r a c t Social aspects of a community may be correlated with driver’s involvement in road traffic accidents. This study focused on examining this association in the context of the social capital theory. A survey of 600 Arab drivers living in 19 towns and villages was conducted using a face-to-face interview. Structural equation modeling was applied to explore paths of associations between the model components. Most of the proposed relationships in the path model were found to be significant, where the model explained 37% of the variation. The results indicate that only volunteering and reciprocity have direct correlations with traffic law violations. While the other correlations (except political involvement), were mediated by attitudes toward traffic laws violation. Hence, it can be concluded that it is not always possible to generalize the positive mechanisms of the social capital theory, and in certain populations such as the Arab minority it can give undesirable results. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background of the study Road traffic accidents pose one of the main challenges for public health and injury prevention worldwide. More than a million people are killed on the roads each year (WHO, 2013). Road accident contributing factors are subdivided into human factors, road environment factors, vehicle factors and their combinations (PIARC, 2003), where over 90% of motor vehicle accidents involve some degree of driver behavior (Petridou and Moustaki, 2000; PIARC, 2003). Road safety research recognized the impact of social factors such as social norms, social environment and socio-economic status on the involvement in road accidents mainly via driving behaviors (Huguenin, 2005; Factor et al., 2007; Nabi et al., 2005; Evans et al., 1987; Perry, 1986).
∗ Corresponding author at: School of Public Health Faculty of Social Welfare and Health Studies University of Haifa, Mount Carmel 31905, Israel. Tel.: +972 4 8288009; fax: +972 4 8288017. E-mail addresses: [email protected], [email protected] (S. Obeid). http://dx.doi.org/10.1016/j.aap.2014.05.027 0001-4575/© 2014 Elsevier Ltd. All rights reserved.
Several studies pointed to the role of families and peers in moderating or encouraging risky behavior (e.g., Ferguson et al., 2001 and Horvath and Zuckerman, 1993). Empirical findings indicate that lower social control, lower parent-friends compatibility and more frequent exposure to models of problem-behavior are related to higher proneness to problem-behavior (Jessor and Jessor, 1977). Parker et al. (1992) found that drivers who feel that their referents would disapprove of them committing any of the driving violations, also report weaker intentions to commit the violations. Moreover, Ferguson et al. (2001) found that youngsters driving records, in the first two years of licensure, are correlated to their parents driving records (accident involvement as well as traffic violations). In addition, scholars showed that significant ethnic group differences exist in a range of road-traffic areas such as: accident involvement (Norris et al., 2000), attitudes toward road safety (Yagil, 1998), seatbelt use (Shin et al., 1999), crossing against a red light (Retting and Williams, 1996), and speeding (Gabany et al., 1997). It is obvious from these studies that individual and social factors may all have an effect on involvement in road accidents. As social capital theory encompasses a large range of social characteristics, this study focused on the correlation between social capital and road accidents. Social capital theory suggests that societies can be characterized via three main categories: (1) social networks and the social
274
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
structure in which individuals operate; (2) social relationships and (3) the quality of associational activity. These dimensions are cited as the main components of the social capital concept (Burt, 2000; Granovetter, 2005; Lin, 2001; Putnam, 2000; Woolcock and Narayan, 2000). Social capital was also defined as “features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit” (Putnam, 1995, p. 67). Putnam (1995) suggested five indicators missioning social capital: (1) social involvement, (2) political involvement (participating and attitudes), (3) volunteering and reciprocity, (4) trust and (5) social support. Other authors applied various indicators of social capital in different contexts. Examples include: trust (Cox and Caldwell, 2000; Falk and Guenther, 1999; Glaeser et al., 2000; Guenther and Falk, 1999; Kolankiewicz, 1996); membership in formal and informal networks (Baum and Ziersch, 2003; O’connell, 2003; Price, 2002; Warde et al., 2003; Wollebaek and Selle, 2003); membership, trust and norms of reciprocity (Isham et al., 2002; Skrabski et al., 2003; Staveren, 2003); and network resources (Zhao, 2002). Social capital was widely considered to have an influence on health (De Silva et al., 2004; Hawe and Shiell, 2000; Kawachi et al., 1997). In health research, social capital has been measured by indicators such as levels of interpersonal trust, the presence of reciprocal exchanges between citizens, and membership in civic organizations (Veenstra et al., 2005). Research has also shown that social integration can enhance population well-being (Haughton McNeill et al., 2006; Kim et al., 2006). It is suggested that communities with high levels of social capital are more effective at exercising social control over different health behaviors and therefore improving health of the community (Berkman and Glass, 2000; Subramanian et al., 2002). 1.2. Social capital and road accidents: conceptual links Further understanding of how the social capital dimensions may influence driving behaviors can contribute to the understanding of how to help societies to be more effective at exercising social control over safe road user behaviors and to form health enhancing social norms, such as driving. One aspect of social capital is social networks; these can spread unsafe driving norms and knowledge, by exposing the members to unsafe behaviors of others, such as speeding and breaking traffic laws, which consequently propagate negative social norms regarding safe behaviors. Scholars found that social norms have a greater impact on individual behavior than biological, personal, familial, religious, or cultural factors (Berkowitz and Perkins, 1986; Borsari and Carey, 2001; Kandel, 1985, and Perkins, 2002). Unsafe norms and behaviors were also found to be related to road accidents (Zaidel, 1992; De Pelsmacker and Janssens, 2007; Elvik et al., 2009; Forward, 2009). Another aspect of social capital is social support. Perceiving support has the potential to eliminate stress, anger and anxiety which leads to safe driving. Scholars have found a relationship between stress, aggression and frustration, and road accident involvement. For example, Arnett et al. (1997) showed that anger was related to excessive speeding. Deffenbacher et al. (2003) demonstrated that during simulated driving, angrier drivers maintained a higher average speed and higher speed deviation than less angry drivers. Additionally, Carbonell Vaya et al. (1997) showed that anxiety was related to more self-reported near-accidents. An additional aspect of social capital is political involvement. Participation in democratic processes can ensure government accountability and proper delivery of public services, in the present context—urban planning and management, construction and maintenance of roads, and effective enforcement of traffic rules. Political awareness and activism may provide better access to resources
and material goods, facilitate coping mechanisms of individual and community and buffer negative unsafe behaviors. Volunteering and reciprocity is another aspect of social capital and is based on the individual involvement in networks. Membership in volunteer networks with high amounts of reciprocity can foster trust between citizens and help develop norms of solidarity and reciprocity which are essential for stable communities. Stable and cohesive communities have more social control over unhealthy and unsafe behaviors and are more trustworthy. This affects the capacity of people to come together to collectively resolve problems such as road accidents and achieve outcomes of mutual benefit. To the authors’ knowledge, the relationship between social capital and road accident involvement was reported only by Incla’n et al. (2005), in Mexico. The study revealed that there is an association between road accidents and casualties, on the one hand, and propensity for cooperation in local communities in Mexico, on the other. The paper argued that, due to a lack of reciprocity and capacity for collective action, residents fail to perceive high traffic mortality as a common problem and, hence, they are unable to jointly take measures to resolve it. No other studies were found on this issue and for other communities and, therefore, there is a need for further research of the relationship between social capital and road accidents.
1.3. The Israeli Arabs population Israel has two main ethnic groups: a majority of Jews (79.4%) and an Arab minority constituting about 20.6% of the Israeli population, or 1.65 million people (CBS, 2012a,b). The indigenous Arab population in Israel is ethnically Palestinian. About 90% of them reside in Arab only villages and towns; the other 10% live in mixed cities (ACAP, 2012). The Arab community is largely an underprivileged minority with a history of disadvantage in income, education and employment. To some extent, the Arabs in Israel suffer from prejudice and discrimination, and it has been suggested that discrimination plays a part in the income disparities between Arabs and Jews (Bushman and Bonacci, 2004; Haberfeld and Cohen, 2007; Okun and Friedlander, 2005 and Wolkinson, 1999). The impact of social capital on health was examined by BaronEpel et al. (2008), who measured personal trust within the Arab society and found it to be low in comparison to the Jewish population of Israel. The share of the Israeli Arabs among road accident casualties is higher than their relative share in the general population: although Arabs comprise approximately 20% of the general population in Israel, 27% of all road accident casualties and 34% of all fatalities are Arabs (CBS, 2010). The probability that an Arab driver will be involved in a fatal or serious accident is 1.6 times greater than that for a Jewish driver (Factor et al., 2008). Surveys had already evidenced that Arab drivers have higher rates of traffic violations, such as speeding and less use of seat restraints (Gitelman et al., 2003; NRSA, 2010). The Arab society in Israel was selected as a case-study for exploring the association between social capital and road accident involvement as their road accident rates is higher and their social capital is lower than the Jewish majority. Therefore, this study aimed to examine the relationship between the aforementioned five components of the social capital and traffic law violations, which lead to road accident involvement, among Israeli Arab drivers. This relationship is explored by means of a detailed questionnaire reflecting driving behavior, norms and attitudes of the population studied as well as social capital components and driver’s background characteristics.
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
2. Methodology 2.1. Building a sample A random cross sectional face-to-face survey of 600 Israeli Arab drivers aged 18 and over, was conducted during the year 2009. The selection of the sample was performed in three stages: First all the Arabs towns and villages (of the north and Tri angle regions) were divided into five layers according to the number of residents who live in every town and village (for example, layer No.1 represented villages and towns with a population count of more than 20,000 residents, layer No. 2 represented those with 10,000–20,000 residents and etc.). Nineteen towns and villages were selected randomly according to their size and layer. Second, the number of all the residents of the towns and villages of each layer were calculated, along with the percentage they represent out of the total number of the Arab residents living in the north and the triangle regions. Third, the sample size from each locality was calculated according to the percentage of the residents. For example, in layer No. 1 there were 195,100 residents, which represent 25.7% of the Arab citizens living in the north and the triangle regions. This rate is equal to 154 interviewees (25.7 × 600(600 is the sample size of the study)/100 = 154), and so on for the other layers. The interviewees were randomly chosen from the various neighborhoods of each locality, in the north, south, west and east. In each neighborhood the first house from the north–west was selected and in it the first driver who agreed to participate was interviewed. The distance between the selected houses was set according to the size of the village or town; in the large towns and villages, a distance of 20 houses between selected houses was chosen. In the smaller places a distance of 15, 10, 5 and 3 houses were chosen. The interviewers returned later if no one was home and if there was no suitable interviewee, the next house was chosen. The Israeli CBS ranks local authorities and municipalities according to a ten-point socio-economic scale where cluster 10 represents the wealthiest localities, and cluster 1 the poorest ones (CBS, 2012a,b). Localities of all socio-economic levels were randomly selected in each layer. The selection methods applied, enabled representation of both small and large Arab towns and villages, with lower and higher socio-economic levels. The interviewers were Arab speaking, from the Arab community. The response rate of the survey was 77.3%. 2.2. The questionnaire The questionnaire included questions about self-reported traffic law violations, descriptive driving norms, attitudes toward driving laws, social capital and background characteristics. Generic questions were asked to estimate the distance driven by participants each month, on a scale of 4, i.e. fewer than 1000 km, 1000–2500 km, 2501–3500 km and over 3500 km. The reported distance traveled included all types of vehicles and included distance traveled during work. In addition, demographic information was collected such as: gender, income and years of education. 2.2.1. Traffic law violation Traffic law violation is one of the variables examined in this study and it was measured using the Driver Behavior Questionnaire (DBQ). This questionnaire was originally developed by Reason et al. (1990). The questionnaire measures the frequency of selfreported traffic violations on a scale from 1 to 5, where 5 indicate full agreement with the item and 1 indicates total non-agreement. Higher scores indicated better driving. DBQ was originally divided into three causes of accidents: intentional violations, mistakes and mishaps. Previous researchers found that intentional violations
275
is the most common cause leading to the involvement in traffic accidents (Sullman and Meadows, 2000; Lawton et al., 1997). Therefore, only this cause was included in the current study’s questionnaire. The reliability test of the final seven items—Cronbach’s alpha was 0.82. Answers from the questionnaire were divided into two groups, i.e. answers 1–3 indicated safe self-reported driving and coded as “1”, while answers 4–5 indicated unsafe self-reported driving and coded “0”. In addition, a combined mean variable was calculated for each respondent.
2.2.2. Descriptive driving behavior norms A list of 8 questions was developed describing various unsafe behaviors that were common in the Arab towns and villages. This list was developed by distributing a form via email to a convenience sample of 20 people from different Arab localities. The respondents were asked to describe their perceived unsafe driving behaviors that were common among Arab drivers inside and outside the villages and towns. A total of 80 such behaviors were suggested by the participants. Eight unsafe behaviors were chosen as they were suggested by all 20 participants, including: the use of car restraints by adults and children; parking in places that disturb other drivers or pedestrians; driving without a driving license; driving while speaking on a hand-held cell-phone device; behaviors of young drivers who drive around with their car radio at full volume, do not adhere to road signs and drive a car with a higher than permitted number of passengers. A questionnaire including 16 questions was then designed, two for each behavior, asking the respondents to estimate the percent of drivers performing this behavior within their town or village and outside them (descriptive norms). The respondents were asked to rate their answers on a scale of 1–5, where 1 indicated a very low percentage of drivers acting according to the described behavior and 5 indicated a very high percentage behaving in such way. A “do not know” option was available but not included in the analysis. The questionnaire was pre-tested via random telephone interviews of 50 adults, which were selected randomly from the telephone book. The internal reliability test (Cronbach’s alpha) for the final sample was 0.76 for both the 8 questions describing behaviors within the towns and villages and the 8 questions describing behaviors outside the towns. Using this questionnaire, combined mean variables representing descriptive norms of driving behaviors within the towns or villages and outside them were calculated. The higher the score the more frequent are the estimated non-safe behaviors, meaning that the descriptive norms are worse. Answers from the questionnaire were divided into two groups: answers from 1 to 3 indicated safe driving norms and were coded 1, and answers from 4 to 5 indicated unsafe driving norms and were coded 0.
2.2.3. Attitudes towards traffic safety laws Statements describing attitudes toward traffic safety laws were based on Iverson’s questionnaire (Iverson, 2004). The original questionnaire includes mainly attitudes about violation of laws. The questionnaire included 7 items of the original questionnaire focusing on the issues of attitudes toward maintaining the driving law. The respondents were asked to rate their answers on a scale from 1 to 5, with 1 indicating no agreement, and 5 indicating full agreement. A “do not know” option was available. Internal reliability Cronbach’s alpha of the 7 items was 0.85. The answers from the questionnaire were divided into two groups: answers from 1 to 3 indicated the positive attitudes toward driving laws–safe driving and coded 1; answers from 4 to 5 indicated unsafe driving attitudes toward driving laws and coded 0. A combined variable was created calculating the mean answer. The higher the score the more negative are the attitudes towards safe driving.
276
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
2.2.4. Social capital questionnaire The questionnaire was based on the European social survey (ESS) questionnaire. The ESS survey was conducted simultaneously in over 30 European countries including Israel (ESS, 2010). The questionnaire was originally translated into three languagesArabic, Hebrew and Russian. The validation of the ESS questionnaire was examined by eleven studies. All these studies found the tools able to distinguish between the different theoretical constructs of social capital and therefore to have acceptable for health studies (De Silva et al., 2006). The five social capital components were measured as follows:
(1) Participating in social activities indicator measures the involvement in activities they undertake with a group, for example visiting relatives, friends and colleagues or participating in religious ceremonies. The groups can be formal organizations, or informal groups. The answers were on a scale of 1–7 with a higher value indicating a higher degree of socializing. Internal reliability Cronbach’s alpha was 0.74. (2) The intensity of participating in political activities was measured through 10 indicators, each of which referred to a different way of being involved in political activity, including attitudes toward being a good citizen. Two variables represent the political activities: (1) active participation such as voting and membership of a political party, and (2) attitude towards active participation and good citizenship. The attitude is given by scale 1–10 (where 1—do not agree, and 10—fully agree) representing the extent of being a good citizen (including voting, being a member of a political party, etc.). Internal reliability Cronbach’s alpha was 0.64. (3) Volunteering and reciprocity was also measured. Generalized reciprocity is a phenomenon which describes people helping each other not for a financial reward, but maybe because they assume they would be helped in a similar way. The answers were on a scale of 1–5, with a higher value indicating a higher degree of reciprocity. Volunteering is based on involvement in networks such as membership in networks of volunteers, working for volunteering organizations or donating money. For example, questions included; “How many clubs do you go to?” or “How often do you donate money?”. Answers were “Yes/No” for each question. Internal reliability Cronbach’s alpha of the reciprocity items was 0.78. (4) Norms of Trust includes personal, generalized, civic and institutional trust. The answers were on a scale of 1–10, with a higher value indicating a higher degree of trust. Internal reliability Cronbach’s alpha was 0.79. (5) Support measured the perceived helpfulness factor of social capital. It shows to what extent individuals can rely on the support of others, including personal, psychological and financial support. The answers were in the form of a scale from 1 to 5, with a higher value indicating a higher degree of support. Internal reliability Cronbach’s alpha was very low 0.45. One of the indicators of the support component is binary (Do you have anyone with whom you can discuss intimate and personal matters? 1. Yes 2. No), this is different from other indicators and can explain the low reliability. While removing it increases the reliability to 0.61. Reanalyzing the data without this indicator didn’t change the results. Another explanation may be due the fact that Cronbach’s alpha is related to the number of indicators, this component includes only four indicators which can affect the reliability. For these reasons and since it was decided to adapt the whole original ESS social capital questionnaire this study included also the support component as one variable.
For all the social capital measures described above the variables were arithmetically averaged using the corresponding surveyweighted individual-level measures (Kim and Kawachi, 2006). It is argued that the most appropriate method for such aggregation is principal component analysis (Whiteley, 2000; Hjøllund and Svendsen, 2000; Van Oorschot and Arts, 2005). Since the values of the social capital questionnaire answers are of unequal scale, for example the answers of the question regarding trust norms were on a scale of 1–10, and the answers of active political participation such as voting and membership of a political party were yes or no answers. Therefore, all the answers were normalized on a percentage scale. These aggregated area-level variables, gauged on a continuous scale from 1 to 100; reflect the contextual effects of social capital. There is a growing consensus that social capital cannot be measured by one single variable on one hand and overly-aggregated, heterogeneous indexes or latent constructs, on the other hand (Parts, 2009). The ESS (2010) methodological team suggested capturing all the information of individual social capital indicators into one variable. Parts (2008) tested the empirical validity of the multidimensionality of social capital, exploratory factor analysis was used. The results of this research showed that the indicators of social capital clearly divided into groups describing pre-defined dimensions of social capital; social and political involvement, trust, support, volunteering and reciprocity. The data of the current study was analyzed using the confirmatory factor analysis. The results indicated better fit (Chi-Square DF 120, P < .0001, RMSEA0.0617, AGFI-0.9184, AIC-474, 9007) for the aggregated variable model with 6 components (political involvement–attitudes and participating’ social networks, trust, support, volunteering and reciprocity), than single combined model (Chi-Square DF 135, P < .0001, RMSEA-0.0953, AGFI-0.8121, AIC-886, 4324). Traffic law violations, attitudes toward driving laws and the driving norm questionnaire answers have the same scale of 1–5. For the bivariate analysis a dichotomy variable was calculated for each of these variables. A combined mean variable was calculated for the structural model equitation analysis.
2.3. Statistical analysis First, bivariate analysis was applied to determine the association between traffic law violations, perceived norms of driving safety behaviors, attitudes toward driving laws, social capital components, road exposure variables as well as demographic variables. Pearson’s correlation coefficients were estimated (Rodgers and Nicewander, 1988), to identify dependence between the variables and its statistical significance. Then, Structure Equation Modeling (SEM) was applied using AMOS 20.0 software (Vinokur, 2005). To overcome violation of multivariate normality assumption, confidence intervals and significance of direct and indirect effects were obtained by bootstraping procedure with 5000 bootstrap samples. Traffic law violation was entered as an endogenous variable, attitudes toward driving laws was entered into model number 1 as an exogenous variable and into model number 2 as an endogenous variable, and driving norms were entered as an exogenous variable. Driving rate, Km driven and gender were entered as control variables and the social capital six components (Political involvement component has two components as described previously—attitudes and participating) as exogenous variables. It should be noted that the sample data in this study can be considered as a multistage sample as it includes individual level and community level (19 towns/villages). Therefore, a multilevel analysis was considered but found not suitable (Hox, 2002) as the interclass index was less than 0.1.
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285 Table 1 Demographic characteristics of the study population percent and number (N).
277
The drivers’ behaviors and attitudes were measured using three variables (see Section 2.2) and the results were as follows:
Study population % (N)
Total
Gender (drivers)
Male Female
67.7 (404) 32.2 (193)
100 (596)
Age
18–38 39–58 59–80
57.9 (345) 37.8 (225) 4.4 (26)
100 (596)
Education–years
0–12 13–15 16+
52.6 (316) 26.5 (159) 20.8 (125)
100 (600)
Religion
Muslims Christians Druze
65 (387) 22 (132) 12 (75)
100 (600)
Employment
Yes No
73 (435) 26 (155)
100 (600)
Income per family, NIS
Up to 4600 4601–9200 9201 and above
16 (88) 50 (275) 35 (192)
100 (600)
3. Results 3.1. Description of the sample population A third of the participants were female and two thirds were male. That is consistent with the distribution of driver’s gender in the Arab population (CBS, 2013). Half of the drivers were under the age of 38 (mean age 36.9, SD—12.3 years). About 23% reported between 12 and 15 years of schooling and 12% of the participants reported 16 years of schooling or more. Most survey participants were Muslims, the rest were Christians and Druze, representing relevant sectors within the Arab community (CBS, 2012a,b). About a quarter of the participants were unemployed. Sixteen percent of participants earned up to 4600 New Israeli Shekels (NIS) a month, much less than the average income in Israel which is 8500 NIS (CBS, 2010). Table 1 shows the sample distribution and their percentages. 3.2. Description of driving characteristics
1. Self-reporting traffic law violations: most of the drivers reported that the behaviors described in the statements of the traffic law violations questionnaire did not characterize their driving. Thirty six percent reported that they performed at least one of the traffic violations last year and the average score for these questions was 2.3. 2. Respondents were asked to relate to the descriptive driving norms within their villages or towns and outside them. The results suggest that most drivers (73%) reported unsafe driving norms (scores 4 and 5) within their villages/towns and outside of them, with an average score of 3.06 (as mentioned above, 5 indicates a high percentage of drivers driving unsafely and 1 indicates low percentage). More than 88% of respondents reported that, in their opinion, a high or very high percentage of drivers do not restrain themselves in the car when driving within the town or village, and 56% reported that a high or very high percentage of drivers do not restrain their children in the car within the town or village. 3. Results of the drivers’ attitudes towards traffic safety laws indicate very low agreement with statements describing negative attitudes toward driving laws. Two percent of the drivers reported full agreement with the statements and the average score was 1.98 (scale 1–5, where 1 indicates full agreement, and 5 indicates no agreement).
The analysis revealed that traffic law violations were significantly correlated (p < 0.05) to some of the socio-demographic variables including:
• gender (rs 0.10), where men reported more unsafe driving behaviors than women; • employment (rs 0.21), where employed drivers reported more unsafe driving behaviors than unemployed; • mean driving distance (rs 0.08), where drivers, who drive more km per month, reported more unsafe behaviors.
The majority of the drivers (88%) in the survey drove every day. Most of them drove up to 2500 km. Results are presented in Table 2. Table 2 Road expositor characteristics.
KM driving
Less than 1000 km/month Between 1000 and 2500 km/month 2500–3500 km/month More than 3500 km
% (N)
Total
42 (252)
100(600)
46 (276) 7 (42) 5 (30)
The strength of the correlations for most variables was not high. Moreover, statistically significant positive correlations were found between traffic law violations and attitudes towards traffic safety laws (rs 0.59), where drivers reporting unsafe behaviors expressed negative attitudes towards traffic laws. In addition, negative correlation was observed between traffic law violations and descriptive driving norms (rs −.11), where drivers reported safe descriptive norms, also reported less frequent law violations, as seen in Table 3. No correlation was found between traffic law violations and age of the driver.
Table 3 Correlation between: traffic law violations, descriptive norms and attitudes toward driving law. Mean (N) STD and r (Spearman correlation). Traffic law violations
Descriptive driving norms
Attitudes toward driving laws
Traffic law violations (1–5)
Mean (N) STD r
2.3 (596) ± 0.87 1
–
–
Descriptive driving norms (1–5)
Mean (N) STD r
– −0.11*
3.06 (595) ± 0.49 1
– –
Attitude s toward driving laws (1–6)
Mean (N) STD r
– 0.05
1.9 (595) ± 0.8 1
* **
p < 0.05. p < 0.001.
– 0.59**
278
S. Obeid et al. / Accident Analysis and Prevention 71 (2014) 273–285
Table 4 Associations between the social capital components and traffic law violations, norms and attitudes toward driving law-Mean (N) STD-(Spearman correlation). Social capital components
Political involvementattitudes
Mean(N)STD
73.9 (593) ± 16.6
Mean(N)STD
35.5 (597) ± 22.6
Mean(N)STD
52.7 (596) ± 15
rs (P. Value) Volunteering/ reciprocity
Attitudes toward driving laws
High safety behaviors
Low safety behaviors
High safety norms
Low safety norms
Positive attitudes
Negative attitudes
45.4(99) ± 25.8
42.1(458) ± 27.7
45.9(208) ± 28.8
40.9(311) ± 26.3
44.7(64 ± 27.3
42.4(494) ± 27.4
69.0(119) ± 16.5
30.6(98) ± 8.4 0.06 (0.127) 38.7(110) ± 22.2
Mean(N)STD
74.7(471) ± 16.6
75.0(223) ± 14.9
73.4(326) ± 17.8
31.7(413) ± 9.2 −0.06 (0.135) 31.8(459) ± 21.9
30.0(195) ± 9.9
69.5(80) ± 15.1
74.6(511) ± 16.8
0.08 (0.039)*
0.01 (0.79)
−0.21 (0.000)**
rs (P. Value)
−0.14 (0.001)**
0.06 (0.134)
0.14 (
