Inter-group differences in road-traffic crash involvement Accident Analysis and Prevention 40 (2008) 2000–2007

Objective Analyzing the data from Israeli census database with logistics regression, the study aimed to investigate the severe and fatal car accidents between different groups based on social-cultural model.

Literature review Factors affecting road-traffic –road-traffic accidents based on SC (social- cultural) model (Factor et al., 2007) Ethnic and socioeconomic –crash involvement (Norris et al., 2000) –attitudes toward road safety (Yagil, 1998) –seat-belt use (Shin et al., 1999) –crossing against a red light (Retting and Williams, 1996) –speeding (Gabany et al., 1997)

Literature review effects of social-economic feature on car accidents –Gender –Age –Education level –Ethnic –Asset

Literature review Gender –Compare to female, car accidents involvement of male is double. –Men were inclined to shorten the distances between cars.

Literature review Age –Resulting from car accidents, death mostly happened to aged. (Sivak, 2002) –Young drivers have exaggerated sense of their driving ability, and underestimated the danger. (DeJoy, 1992; Gabany et al., 1997)

Literature review Education level –Mortality rates was highly related to people with lower education, even after controlling for reported seat-belt use. (Braver, 2003) –Increasing education, increasing compliance with speed-limit regulation. (Hemenway and Solnick, 1993)

Literature review Ethnic –Correlation between ethnics and driving violations, perception of risk, and road-accident involvement. (Outcalt et al., 2003; Stiles and Grieshop, 1999; Vredenburgh and Cohen, 1995)

Literature review Asset –Lower income, more injuries. –Lower socioeconomic status, more road accidents. (Braver, 2003;Zambonand Hasselberg, 2006) –Pupils from low social status schools were less buckled up. –Lower-income drivers observed speed limits more. (Shinar et al., 2001)

Literature review Studies based on SC model was difficult to conduct. –Aggregative calculation of casualty or injury rates –It was hard to distinguish the effects of demographic and socioeconomic characteristics. –Ethnic, socioeconomic status, gender, and education.

Methodology Data resource: Israel’s Central Bureau of Statistics (1999~2004) Method: logistic regression Independent variables –1.age, 2.gender, 3.ethnic, 4.religion, 5.living continent, 6.family outcome, 7.years of schooling, 8.daily traveling distance, 9.license type, 10.marriage, 11.social class, 12.ever experienced car accidents.

Methodology Sample distribution (n=409,051) With accidentsWithout accidents People98.9%1.1% Age GenderMaleFemale ReligionJew Continent of livingAfricaEurope Daily traveling distance50.95 km43.27 km Years for schooling11.8 years12.5 years License typeMore than one licenseCar only Monthly family income$820$1072 Social classNon-skilled workerWhite collar

Methodology Socioeconomic measures –Class schema (Erikson et al., 1979) –Occupation and personal status at work White collar Blue collar Non-skilled workers (Erikson and Goldthorpe, 1992)

Methodology Exposure estimation –The database didn’t include daily driving distance. –Used a proxy measure. –Israeli Travel Habits Survey 1996/1997 –52 groups: gender, age, and areas.

Methodology Transformed the regression coefficients into probabilities with equation: Its objective was to compare the group differences, not getting an “accurate value”, so this equation was adequate in certain level.

Results

Conclusion Higher rates –Africa and Asia –Lower income –Lower education level –16-29 aged –Non-Jewish

Conclusion Social and cultural characteristics were associated the car accidents, and the frequency can be decreased via driving behavior changes. As for practical way, changing the group’s perception was a great idea, instead of focusing on individual level. The results offered school about road safety controlling by teaching students how to prevent injuries, especially focusing on teaching “traits” of different social groups.

Thank You!