- Email: [email protected]
The determinants of bicycle helmet use: Evidence from Germany
Accident Analysis and Prevention 43 (2011) 95–100
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
The determinants of bicycle helmet use: Evidence from Germany Nolan Ritter a,∗ , Colin Vance a,b,1 a b
Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Hohenzollernstraße 1-3, 45128 Essen, Germany Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
a r t i c l e
i n f o
Article history: Received 12 April 2010 Received in revised form 14 July 2010 Accepted 27 July 2010 Keywords: Bicycle helmet Household data Heteroskedastic probit
a b s t r a c t Previous research has shown that the risks of serious injury or death from bicycling can be mitigated by the decision to wear a helmet. Drawing on a nationwide household survey conducted in 2008 in Germany, this analysis investigates the determinants of voluntary helmet use through a combination of descriptive analyses and econometric methods, the latter relying on variants of the probit- and heteroskedastic probit model. Confirming results uncovered elsewhere in the literature, we find that household demographics, residential location, and riding patterns are significant correlates of helmet use. Contrasting with other studies, however, we also find that women are significantly less likely to use a helmet than men, a discrepancy that holds over most of the adult life-cycle. The paper concludes by highlighting the scope for designing strategic information campaigns to promote helmet use. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Despite advances in road safety and emergency medical treatment, bicycling incurs non-negligible risks of serious injury or death. In Germany, where roughly 11% of the population uses the bicycle at least once each day (German Federal Statistical Office, 2006), cyclists suffer a per-kilometer fatality rate that is more than three times that of automobile motorists, with over 400 deadly accidents occurring annually (German Federal Statistical Office, 2009; Verkehr in Zahlen, 2010). Although the fatality frequency per year has fallen by about 25% since 2000, the injury frequency per year has steadily increased. In 2000, there were about 72,000 recorded bicyclist injuries, a figure that rose to over 79,000 by 2008 (Fig. 1). While the German Statistical Office does not disclose information on the types of injuries sustained, Mills (1989) and Simpson (1996) find that roughly 50% of bicyclist casualties sustain injuries to the head or face. To the extent that government promotion of bicycle use contributes to environmental stewardship, physical fitness, and other public policy objectives (BMVBS, 2009), there is an increased need for understanding the determinants of bicycle safety. To date, much of the work in this vein has focused on the severity of bicycle injuries, and specifically on the role of helmets in mitigating trauma (e.g. Cummings et al., 2006; Dorsch et al., 1987; Jacobson et al., 1998; Kim et al., 2007; Linn et al., 1998; McDermott et al., 1993;
∗ Corresponding author. Tel.: +49 201 8149 240; fax: +49 201 8149 200. E-mail addresses: [email protected] (N. Ritter), [email protected], [email protected] (C. Vance). 1 Tel.: +49 201 8149 237; fax: +49 201 8149 200. 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.07.016
Rivara et al., 1997; Scuffham et al., 2000; Shafi et al., 1998; Spaite et al., 1991; Thomas et al., 1994; Thompson et al., 1989, 1990, 1996; Wassermann et al., 1988). While most evidence compiled in these studies demonstrates that helmets reduce the risk of injury and death, voluntary helmet use rates in most countries remain low, and little is known about the underlying behavioral motivations. Better understanding of these factors can help policy makers gauge the efficacy of alternative measures for increasing helmet use, be these based on voluntary approaches such as targeted information campaigns or on mandatory approaches such as helmet laws. The present paper aims to address this issue with an econometric analysis of the determinants of helmet use in Germany. The paper builds on a small body of studies beginning with the work of Rodgers (1995), who analyzes bicycle helmet use patterns in the United States using probit regression on individual level data. Among his key findings are that household demographics, traffic volumes, and riding patterns are significant correlates of helmet use. Subsequent research from the U.S. by Bolen et al. (1998) and Harlos et al. (1999) confirm the importance of geography and demographic traits, with both papers finding a higher rate of helmet use among females than males. More recently, Gkritza (2009) addresses the determinants of motorcycle helmet use with observational data from Iowa, finding weather and road conditions to be particularly important factors. To our knowledge, this is the first study of this topic using data from Europe, employing nearly 20,000 observations taken from the German survey “Mobility in Germany 2008”. Following the probabilistic approaches employed by the above authors, we use variants of the probit model to estimate the influence of individual, household, and geographic characteristics in determining helmet
96
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Table 1 Incidence of bicycle helmet use. Regular helmet use frequencies
Entire sample City residents East Germans Daily bikers Weekly bikers Monthly bikers High-school diploma College diploma Children under 14 present
Non-helmet use frequencies
Total sample
Total
Women
Men
Total
Women
Men
2,440 2,215 412 659 893 581 910 665 810
960 876 159 246 310 233 362 232 382
1,480 1,339 253 413 583 348 548 433 428
17,206 14,868 4,655 5,241 5,528 3,772 4,951 3,279 3,797
9,096 7,824 2,471 3,011 2,875 1,731 2,500 1,552 2,224
8,110 7,044 2,184 2,230 2,653 2,041 2,451 1,727 1,573
Fig. 1. Bicycle casualties and fatalities (German Federal Statistical Office, 2009).
19,646 17,083 5,067 5,900 6,421 4,353 5,861 3,944 4,607
acteristics, including the area of residence, gender, age, income, frequency of bike use, and educational attainment. An additional variable records helmet use, distinguishing between those who report regular (12.4%), occasional (9.4%), and non-usage (78.2%). Upon grouping together the latter two categories, we convert this variable into a binary variable that equals 1 if the individual reports regularly using a helmet when bicycling and zero otherwise.2 Table 1 presents frequencies that provide some insights into the breakdown of this variable by gender. Contrasting with the findings of Bolen et al. (1998) and Harlos et al. (1999), women use helmets less than men, and comprise 39.3% of the 12.4% of the sample who report regularly using a helmet. Moreover, this pattern holds after partitioning the sample according to other socioeconomic attributes. The largest share of helmet users, at 17.6%, is among those living in households with children. Interestingly, this is also the group for which the share of women in the total, 47.2%, approaches that of men. We performed Pearson’s chi-square tests and found differences in helmet use among women and men that are significant at the 1% level for all categories in Table 1. 3. Empirical method
use among Germany’s adult population. In addition to a standard probit model, we also employ a heteroskedastic probit model that allows us to control for unobserved heterogeneity that could otherwise bias the parameter estimates. To facilitate interpretation, our assessment of the model results moves beyond the standard focus on the significance and magnitude of the coefficients to consider their implications for the predicted probabilities. For this purpose, Monte Carlo simulation is employed to explore the predictions of the model and the associated degree of uncertainty. Although our analysis uncovers many of the same determinants identified by the U.S. studies, including age and rural versus urban residence, one notable anomaly is a significantly lower rate of helmet use among women. The simulated probabilities reveal this discrepancy to hold over most of the age range, but to be particularly high in the 45–55 bracket. We also find a significant impact of income, with the likelihood of helmet use increasing for individuals in wealthier households. The paper concludes with a discussion of policy implications of these findings and suggestions for future research. 2. Data The data source used in this research is drawn from the survey “Mobility in Germany” (MID), a nationwide travel study conducted in 2008 that was financed by the German Federal Ministry of Transport, Building and Urban Affairs (MID, 2010). We limit the sample to individuals over 17 years of age and exclude those who report never using the bicycle (3.4%). The resulting data set comprises 19,646 observations. The survey respondents participate in a phone interview eliciting general household information and person-related char-
3.1. The estimator To explore the pattern presented in Table 1 more rigorously, the empirical methodology proceeds by specifying a structural model describing the probability of bicycle helmet use: yi∗ = xi ˇ + εi
(1)
where x is a 1 × k vector of explanatory variables, ε is an error term,  is a k × 1 vector of estimated coefficients, and the subscript i denotes the observation. The latent variable yi∗ measures the utility associated with wearing a helmet, and is therefore unobservable. We do, however, observe the associated outcome, which can be denoted by the dichotomous variable yi : yi = 1 if yi∗ > 0 and 0 otherwise
(2)
In the present analysis, yi equals unity for individuals who regularly wear a helmet and zero otherwise. If the error term is assumed to have a normal distribution, then the parameters  can be estimated using the maximum likelihood method on the basis of the classical probit model: P(Yi = 1) = ˚(xi ˇ)
(3)
where ˚ is the standard normal distribution function. One of the key assumptions underlying the probit model is homoskedasticity of the disturbances, the violation of which results
2 In the empirical analysis, we explored the application of ordered probit models to accommodate the original three categories, but found the qualitative results to remain unchanged.
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
97
Table 2 Variable definitions and descriptive statistics (19,646 total observations). Variable
Definition
Mean
S.D.
Frequency
Helmet Female Age City Children College High-School Income Daily rider Weekly rider Monthly rider East
1 if respondent always wears a bicycle helmet 1 if female Age of respondent in years 1 if respondent lives in a city 1 if respondent has children under the age of 14 1 if respondent’s highest degree is a college diploma 1 if respondent’s highest degree is a high-school diplomaa Real monthly income in 1000 Euro of 2008 1 if respondent rides bike daily 1 if respondent rides bike 1–3 times a week 1 if respondent rides bike 1–3 times a month 1 if respondent lives in the eastern part of Germany
0.124 0.512 49.877 0.870 0.235 0.201 0.298 2.927 0.300 0.327 0.222 0.258
0.330 0.500 15.006 0.337 0.424 0.401 0.458 1.467 0.458 0.469 0.415 0.437
2,440 10,056 – 17,083 4,607 3,944 5,861 – 5,900 6,421 4,353 5,067
a
This refers to diplomas awarded by a Gymnasium, Germany’s highest tier of secondary school, which qualifies students to attend university.
in spurious inferences regarding statistical significance and – contrasting with linear regression – inconsistent parameter estimates (Yatchew and Griliches, 1984). To address this issue, the present analysis draws on a more general class of models collectively referred to as heterogeneous choice models. These methods can be employed in the context of binary or ordinal models to model the variance of individual level choices and thereby correct for biases induced by heterogeneity. The probit variant of the heterogeneous discrete choice model is:
P(Yi = 1) = ˚
(xi ˇ) exp(zi )
(4)
where the numerator is referred to as the choice equation while the denominator is the variance equation. This latter equation contains a 1 × m vector of variables, z, that are posited to determine the error variances, and an associated m × 1 vector of estimated coefficients, . For example, women may exhibit greater unobserved variance in their choices than men, which would dictate the inclusion of a gender dummy among the z. In this regard, one of the main challenges in employing such a model is in correctly identifying the cause of the heteroskedasticity, as theoretical considerations generally provide little guidance. Following Williams (2009a), a stepwise procedure, elaborated below, is consequently employed to specify the variables in the variance equation. 3.2. Interpretation In interpreting the estimates from the probit model, interest generally focuses on the effects of changes in one of the explanatory variables on the probability of, in this example, wearing at helmet. For the standard probit, this marginal effect is given by: ∂P(Yi = 1) = (xi ˇ)ˇk ∂xik
(5)
where is the standard normal density function. The corresponding formula for the heteroskedastic probit accommodates the possibility that the explanatory variable of interest, w, appears in the vector x, z, or both:
∂P(Yi = 1) xi ˇ = exp(zi ) ∂wik
ˇk − (xi ˇ)k exp(zi )
(6)
where the second term drops out if w is only included in the choice equation, while the first term drops out if it is only included in the variance equation.3
3
As the probit and heteroskedastic probit are both nonlinear, the marginal effect varies over each observation in the data. When interpreting the results, we follow the convention of evaluating the marginal effects at the means of the explanatory variables.
To further facilitate interpretation, the predicted outcomes and associated 99% confidence intervals are plotted using a statistical simulation technique suggested by King et al. (2000). Recognizing that the parameter estimates from a maximum likelihood model are asymptotically normal, the method employs a sampling procedure akin to Monte Carlo simulation in which a large number of values – say 1000 – of each estimated parameter is drawn from a multivariate normal distribution. Taking the vector of coefficient estimates from the model as the mean of the distribution and the variance–covariance matrix as the variance, each of the 1000 simulated parameter estimates can then be multiplied by corresponding predetermined values of the explanatory variables to generate 1000 expected values. The range of these expected values conveys the associated degree of uncertainty. For example, we may be interested in the predicted probability of helmet use for a person of age 50, setting the remaining explanatory variables to their mean values. We would multiply the 1000 simulated parameter estimates by these predetermined values of the explanatory variables to generate 1000 expected probabilities. By ordering these probabilities from lowest to highest and then referencing the 5th and 995th positions in the array, we obtain an estimate of the 99% confidence interval. As illustrated below, the generation of confidence intervals, in particular, reveals insights that would otherwise be neglected were the analyst to focus exclusively on the parameter estimates and their standard errors.4 3.3. Explanatory variables The suite of variables selected for inclusion measures the individual and household-level attributes that are hypothesized to influence the choice of wearing a helmet in maximizing utility. Variable definitions and descriptive statistics are presented in Table 2. As many of these variables could either positively or negatively affect the probability of wearing a helmet, it is in most cases impossible to state a priori which effects are expected to prevail. Two exceptions are the income level and the presence of children in the household, both of which are ascribed positive signs as they increase the opportunity cost of becoming incapacitated from a head injury. Children in the household may also promote role model effects. Moreover, these effects may operate in both directions: via parents teaching their children safe bicycling practices, and via children who typically learn such practices through school programs that encourage helmet use.
4 Tomz et al. (2001) have written a program called Clarify for implementing this technique, downloadable from http://gking.harvard.edu/. As this program does not cover the heteroskedastic probit model, separate code was written in Stata for generating the simulations presented here, an annotated version of which is available from the authors upon request.
98
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Table 3 Empirical results (n = 19,646). Choice equation
Female Age Age2
Classical binary probit
Heteroskedastic probit
Heteroskedastic probit with interactions
Coefficient (p-values)
Marginal effect (p-values)
Coefficient (p-values)
Marginal effect (p-values)
Coefficient (p-values)
Marginal effect (p-values)
−0.308 (<0.0005) 0.024 (<0.0005) −0.00026 (<0.0005)
−0.060 (<0.0005) 0.005 (<0.0005) −0.00005 (<0.0005)
−0.699 (0.001) 0.026 (<0.0005) −0.00028 (<0.0005)
−0.058 (<0.0005) 0.004 (<0.0005) −0.00005 (<0.0005)
0.077 (0.062) 0.099 (0.033) 0.066 (0.088) 0.172 (<0.0005) 0.109 (0.006) −0.260 (<0.0005) 0.040 (<0.0005) 0.223 (<0.0005) 0.174 (<0.0005) −1.926 (<0.0005)
0.015 (0.067) 0.020 (0.040) 0.013 (0.093) 0.035 (<0.0005) 0.022 (0.009) −0.047 (<0.0005) 0.008 (<0.0005) 0.046 (<0.0005) 0.031 (<0.0005)
−0.085 (0.449) 0.124 (0.034) 0.425 (<0.0005) 0.236 (<0.0005) 0.145 (0.003) −0.268 (<0.0005) 0.045 (<0.0005) 0.232 (<0.0005) 0.177 (<0.0005) −2.059 (<0.0005)
0.018 (0.032) 0.022 (0.032) 0.016 (0.057) 0.042 (<0.0005) 0.026 (0.003) −0.042 (<0.0005) 0.008 (<0.0005) 0.042 (<0.0005) 0.028 (<0.0005)
−0.547 (0.009) 0.029 (<0.0005) −0.0003 (<0.0005) −0.006 (0.014) −0.088 (0.415) 0.130 (0.029) 0.415 (<0.0005) 0.254 (<0.0005) 0.155 (0.002) −0.276 (<0.0005) 0.047 (<0.0005) 0.230 (<0.0005) 0.181 (<0.0005) −2.182 (<0.0005)
−0.008 (0.684) 0.005 (<0.0005) −0.00005 (<0.0005) −0.0009 (0.818) 0.016 (0.044) 0.022 (0.029) 0.017 (0.042) 0.042 (<0.0005) 0.026 (0.002) −0.041 (<0.0005) 0.008 (<0.0005) 0.039 (<0.0005) 0.027 (<0.0005)
Female × age High-school College Daily rider Weekly rider Monthly rider East Income Children City Constant Variance function Daily rider Female High-school Fit log-likelihood
−7074.7
−0.265 (<0.0005) 0.278 (0.020) 0.137 (0.070)
−0.235 (<0.0005) 0.352 (0.005) 0.131 (0.062)
−7067.6
−7063.9
4. Results Table 3 catalogues coefficient estimates and corresponding marginal effects from the probit models. The models are developed sequentially, starting with a standard probit, then adding controls for unobserved heterogeneity, and finally by including interaction terms to test for differential effects of the explanatory variables by gender. Turning first to the standard probit in the left panel of the table, most of the coefficients on the individual attributes are statistically significant at the 5% level, and there are no unexpected results with respect to the sign. Confirming findings from the descriptive statistics in Table 1, the female dummy has a negative coefficient, suggesting that women have a 6% lower probability of wearing a helmet than men, holding other factors fixed. The variable age, which is specified as a quadratic, has a nonlinear effect. Increases in age initially increase but subsequently decrease the probability of wearing a helmet, with the reversal point occurring at roughly an age of 46 years. Individuals with a college diploma have a higher probability of helmet use, as do individuals who ride on a weekly or monthly basis. Given that the excluded category for the cycling frequency dummies is less than once a month, this pattern also reveals a non-linearity: those who ride very frequently or very infrequently have a lower probability of helmet use than those who ride occasionally. Turning to the household-level variables, a particularly large effect is seen for the dummy indicating individuals in house-
holds located in eastern Germany, which decreases the probability by 4.7% relative to individuals located elsewhere in Germany. As expected, higher income and the presence of children both increase the likelihood of wearing a helmet. The dummy indicating residence in an urban area also has a positive effect. To explore the robustness of these results, the middle panel of Table 3 presents estimates from the heteroskedastic probit model, which controls for differences in the unobserved variance. Drawing from the full set of predictors in the choice equation, those in the variance equation were selected using a stepwise procedure that excluded variables whose significance levels were greater than 10% (Williams, 2009a,b). We find three variables – the dummies indicating daily riders, females and those with a high-school degree – to be statistically significant in accounting for unobserved heterogeneity. Of these, only the former has a negative coefficient, suggesting that individuals who ride daily have less variability in the propensity to wear a helmet. On the other hand, the positive coefficients for women and people with high-school degrees are indicative of higher residual variability. While a likelihood-ratio test indicates that the addition of the heteroskedasticity parameters significantly improves the fit of the model (chi2 (3) = 14.34, P < 0.0025), the conclusions emerging with respect to the explanatory variables remain largely unchanged. A comparison of the marginal effects calculated from the two models suggests negligible differences. One exception is the coefficient for the dummy that controls for people with high-school diplomas. Although the magnitude of the marginal effect is only a little higher,
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
99
those in lower income brackets. In addition, although individuals in households with children have the highest rates of use, this may be a segment where there is scope for additional gains from school programs. Such programs not only have direct effects in informing children about the benefits of helmet use, but also may additionally confer spillover effects from children taking this information home and serving as role-models for their parents. Currently in Germany, some 95% of children are covered by bicycle safety programs (Verkehrswacht, 2010), though the emphasis on bicycle helmet use has not been documented. While the present study has clearly demonstrated a positive effect of children on adult helmet use, data constraints precluded us from isolating the exact mechanism. A worthwhile area for future research would thus involve examining the diffusion of information from school-based bicycle safety programs across other members of the household. Acknowledgements Fig. 2. Simulated probabilities of helmet use by age and gender.
it is now significant at the 5%-level compared to the standard binary probit model. Given the significant differences in helmet use between women and men indicated by the descriptive analysis, the final step in developing the model involved the inclusion of interaction terms to test for whether the impact of the explanatory variables varied by gender. Several specifications were explored using alternative sets of interaction terms modeled either individually or jointly, none of which had a substantial bearing on the remaining coefficient estimates. Ultimately, as presented in the final two columns of Table 3, only the coefficient estimate of age was found to have significantly differential impacts by gender, with a lower magnitude among females. Further insight into this difference can be gleaned from Fig. 2, which shows the simulated probabilities of helmet use and confidence intervals using Monte Carlo simulation (King et al., 2000). Based on the estimates from the heteroskedastic probit with the interaction, the simulations are generated over a range of values for the variable age while holding the other variables in the model fixed at their mean values. To generate the curve for females, the female dummy variable was set equal to one when calculating the expected values, while it was set equal to zero to generate the curve for males. Men have higher probabilities of helmet use for the whole range of data. Moreover, as indicated by the non-overlap of the confidence intervals, statistically significant differences by gender are discernible between the ages of 25 and 70. 5. Conclusions Drawing on a household survey conducted in 2008 in Germany, this analysis has investigated the determinants of helmet use through a combination of descriptive analyses and econometric methods, the latter of which relied on variants of the probit- and heteroskedastic probit model to control for the effects of unobserved heterogeneity. The descriptive statistics presented at the outset of the analysis suggest that men are more likely to regularly use a helmet than women, a finding that is confirmed by econometric modeling that holds fixed the influence of other confounding factors related to the individual and their socioeconomic environment. We also find that urban residency, household income, and the presence of children in the household positively affect the probability of helmet use. Taken together, these results can be used for strategic targeting of information campaigns to promote the use of bicycle helmets. The key target groups would include those living in the east, where helmet use is lowest, as well as women, non-urban residents, and
The authors thank Manuel Frondel for valuable comments and suggestions. References BMVBS, 2009. Bundesministerium für Verkehr, Bau und Stadtentwicklung, Fahrradverkehr, Berlin, Germany. http://www.bmvbs.de/Verkehr/Strasse/Fahrrad,1435.22685/Fahrradverkehr.htm. Bolen, J.R., Kesnew, M.-J., Sacks, J.J., 1998. Reported bicycle helmet use among adults in the United States. Archives of Family Medicine 7, 72–77. Cummings, P., Rivara, F.P., Olson, C.M., Smith, K.M., 2006. Cahnges in traffic crash mortality rates attributed to use of alcohol, or lack of a seat belt, air bag, motorcycle helmet, or bicycle helmet, United States, 1982–2001. Injury Prevention 12, 148–154. Dorsch, M., Woodward, A., Somers, R., 1987. Do bicycle safety helmets reduce the severity of head injuries in real crashes? Accident Analysis and Prevention 19 (3), 183–190. German Federal Statistical Office, 2006. Verkehr in Deutschland 2006, Artikelnummer 1021216069004. Statistisches Bundesamt, Wiesbaden, Germany. German Federal Statistical Office, 2009. Verkehrsunfälle, Zweiradunfälle im Straßenverkehr, Artikelnummer 5462408087004. Statistisches Bundesamt, Wiesbaden, Germany. Gkritza, K., 2009. Modeling motorcycle helmet use in Iowa: evidence from six roadside observational surveys. Accident Analysis and Prevention 41 (3), 479–484. Harlos, S., Warda, L., Buchan, N., et al., 1999. Urban and rural patterns of bicycle helmet use: factors predicting usage. Injury Prevention 5, 183–188. Jacobson, G., Blizzard, L., Dwyer, T., 1998. Bicycle injuries: road trauma is not the only concern. Australian and New Zealand Journal of Public Health 22 (4), 451–455. Kim, J.-K., Kim, S., Ulfarsson, G.F., Porrello, L.A., 2007. Bicyclist injury severities in bicycle-motor vehicle accidents. Accident Analysis and Prevention 39, 238–251. King, G., Tomz, M., Wittenberg, J., 2000. Making the most of statistical analyses: improving Interpretation and presentation. American Journal of Political Science 44, 347–361. Linn, S., Smith, D., Sheps, S., 1998. Epidemiology of bicycle injury, head injury, and helmet use from British Columbia: a 5 year descriptive study. Injury Prevention 4, 122–125. McDermott, F., Love, J., Brazenor, G., Debney, E., 1993. The effectiveness of bicyclist helmets: a study of 1710 casualties. The Journal of Trauma 34, 834–844. MID, 2010. Mobility in Germany. Berlin, Germany. http://www.mobilitaet-indeutschland.de/engl%202008/index.htm. Mills, P., 1989. Pedal cycle accidents – a hospital based study. Research Report 220. Transport & Road Research Laboratory. Rivara, F., Thompson, D., Thompson, R., 1997. Epidemiology of bicycle injuries and risk factors for serious injury. Injury Prevention 3, 110–114. Rodgers, G.B., 1995. Bicycle helmet use patterns in the United States, a description and analysis of National Survey Data. Accident Analysis and Prevention 27 (1), 43–56. Scuffham, P., Alsop, J., Cryer, C., Langley, J.D., 2000. Head injuries to bicyclists and the New Zealand bicycle helmet law. Accident Analysis and Prevention 32 (4), 565–573. Shafi, S., Gilbert, J., Loghnamee, F., Allen, J., et al., 1998. Impact of bicycle helmet safety legislation on children admitted to a regional pediatric trauma center. Journal of Pediatric Surgery 33, 317–321. Simpson, H., 1996. Comparison of hospital and police casualty data: a National Study. TRL Report 173. Transport Research Laboratory, Crowthorne, Berks. Spaite, D., Murphy, M., Criss, E., Valenzuela, T., Meislin, H., 1991. A prospective analysis of injury severity among helmeted and nonhelmeted bicyclists involved in collisions with motor vehicles. Journal of Trauma 31 (4), 151–216. Thomas, S., Acton, C., Nixon, J., Battistutta, D., Pitt, W., Clark, R., 1994. Effectiveness of bicycle helmets in preventing head injury in children: case control study. British Medical Journal 308, 173–176.
100
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Thompson, R., Rivara, F., Thompson, D., 1989. A case–control study of the effectiveness of bicycle safety helmets. New England Journal of Medicine 320 (21), 136–167. Thompson, R., Rivara, F., Thompson, D., 1990. A case control study of the effectiveness of bicycle safety helmets in preventing facial injury. American Journal of Public Health 320 (21), 136–167. Thompson, D., Nunn, M., Thompson, R., Rivara, F., 1996. Effectiveness of bicycle safety helmets in preventing serious facial injury. Journal of the American Medical Association 276, 1968–1971. Tomz, R., Wittenberg, J., King, G., 2001. Clarify: software for interpreting and presenting statistical results. http://gking.harvard.edu/clarify/clarify.pdf. Verkehr in Zahlen, 2010. Verkehr in Zahlen 2009/2010, Bau und Stadtentwicklung Bundesministerium für Verkehr (Herausgeber). Deutscher Verkehrs-Verlag; Auflage 38, aktualisierte Auflage, Germany.
Verkehrswacht, 2010. Radfahrausbildung – Kernstück schulischer Verkehrserziehung, Verkehrswacht Medien & Service-Center, Bonn, Germany. http://www.verkehrswacht-medien-service.de/radfahrausbildung kern.html. Williams, R., 2009a. Using heterogeneous choice models to compare logit and probit coefficients across groups. Sociological Methods and Research 37, 531– 559. Williams, R., 2009b. OGLM: Stata module to estimate Ordinal Generalized Linear Models. http://econpapers.repec.org/software/bocbocode/s453402.htm. Accessed April 1, 2009. Wassermann, R., Waller, J., Monty, M., Emery, A., Robinson, D., 1988. Bicyclists, helmets and head injuries: a rider based study of helmet use and effectiveness. American Journal of Public Health 78 (9), 1220–1221. Yatchew, A., Griliches, Z., 1984. Specification error in probit models. Review of Economics and Statistics 66, 134–139.
Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
The determinants of bicycle helmet use: Evidence from Germany Nolan Ritter a,∗ , Colin Vance a,b,1 a b
Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Hohenzollernstraße 1-3, 45128 Essen, Germany Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
a r t i c l e
i n f o
Article history: Received 12 April 2010 Received in revised form 14 July 2010 Accepted 27 July 2010 Keywords: Bicycle helmet Household data Heteroskedastic probit
a b s t r a c t Previous research has shown that the risks of serious injury or death from bicycling can be mitigated by the decision to wear a helmet. Drawing on a nationwide household survey conducted in 2008 in Germany, this analysis investigates the determinants of voluntary helmet use through a combination of descriptive analyses and econometric methods, the latter relying on variants of the probit- and heteroskedastic probit model. Confirming results uncovered elsewhere in the literature, we find that household demographics, residential location, and riding patterns are significant correlates of helmet use. Contrasting with other studies, however, we also find that women are significantly less likely to use a helmet than men, a discrepancy that holds over most of the adult life-cycle. The paper concludes by highlighting the scope for designing strategic information campaigns to promote helmet use. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Despite advances in road safety and emergency medical treatment, bicycling incurs non-negligible risks of serious injury or death. In Germany, where roughly 11% of the population uses the bicycle at least once each day (German Federal Statistical Office, 2006), cyclists suffer a per-kilometer fatality rate that is more than three times that of automobile motorists, with over 400 deadly accidents occurring annually (German Federal Statistical Office, 2009; Verkehr in Zahlen, 2010). Although the fatality frequency per year has fallen by about 25% since 2000, the injury frequency per year has steadily increased. In 2000, there were about 72,000 recorded bicyclist injuries, a figure that rose to over 79,000 by 2008 (Fig. 1). While the German Statistical Office does not disclose information on the types of injuries sustained, Mills (1989) and Simpson (1996) find that roughly 50% of bicyclist casualties sustain injuries to the head or face. To the extent that government promotion of bicycle use contributes to environmental stewardship, physical fitness, and other public policy objectives (BMVBS, 2009), there is an increased need for understanding the determinants of bicycle safety. To date, much of the work in this vein has focused on the severity of bicycle injuries, and specifically on the role of helmets in mitigating trauma (e.g. Cummings et al., 2006; Dorsch et al., 1987; Jacobson et al., 1998; Kim et al., 2007; Linn et al., 1998; McDermott et al., 1993;
∗ Corresponding author. Tel.: +49 201 8149 240; fax: +49 201 8149 200. E-mail addresses: [email protected] (N. Ritter), [email protected], [email protected] (C. Vance). 1 Tel.: +49 201 8149 237; fax: +49 201 8149 200. 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.07.016
Rivara et al., 1997; Scuffham et al., 2000; Shafi et al., 1998; Spaite et al., 1991; Thomas et al., 1994; Thompson et al., 1989, 1990, 1996; Wassermann et al., 1988). While most evidence compiled in these studies demonstrates that helmets reduce the risk of injury and death, voluntary helmet use rates in most countries remain low, and little is known about the underlying behavioral motivations. Better understanding of these factors can help policy makers gauge the efficacy of alternative measures for increasing helmet use, be these based on voluntary approaches such as targeted information campaigns or on mandatory approaches such as helmet laws. The present paper aims to address this issue with an econometric analysis of the determinants of helmet use in Germany. The paper builds on a small body of studies beginning with the work of Rodgers (1995), who analyzes bicycle helmet use patterns in the United States using probit regression on individual level data. Among his key findings are that household demographics, traffic volumes, and riding patterns are significant correlates of helmet use. Subsequent research from the U.S. by Bolen et al. (1998) and Harlos et al. (1999) confirm the importance of geography and demographic traits, with both papers finding a higher rate of helmet use among females than males. More recently, Gkritza (2009) addresses the determinants of motorcycle helmet use with observational data from Iowa, finding weather and road conditions to be particularly important factors. To our knowledge, this is the first study of this topic using data from Europe, employing nearly 20,000 observations taken from the German survey “Mobility in Germany 2008”. Following the probabilistic approaches employed by the above authors, we use variants of the probit model to estimate the influence of individual, household, and geographic characteristics in determining helmet
96
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Table 1 Incidence of bicycle helmet use. Regular helmet use frequencies
Entire sample City residents East Germans Daily bikers Weekly bikers Monthly bikers High-school diploma College diploma Children under 14 present
Non-helmet use frequencies
Total sample
Total
Women
Men
Total
Women
Men
2,440 2,215 412 659 893 581 910 665 810
960 876 159 246 310 233 362 232 382
1,480 1,339 253 413 583 348 548 433 428
17,206 14,868 4,655 5,241 5,528 3,772 4,951 3,279 3,797
9,096 7,824 2,471 3,011 2,875 1,731 2,500 1,552 2,224
8,110 7,044 2,184 2,230 2,653 2,041 2,451 1,727 1,573
Fig. 1. Bicycle casualties and fatalities (German Federal Statistical Office, 2009).
19,646 17,083 5,067 5,900 6,421 4,353 5,861 3,944 4,607
acteristics, including the area of residence, gender, age, income, frequency of bike use, and educational attainment. An additional variable records helmet use, distinguishing between those who report regular (12.4%), occasional (9.4%), and non-usage (78.2%). Upon grouping together the latter two categories, we convert this variable into a binary variable that equals 1 if the individual reports regularly using a helmet when bicycling and zero otherwise.2 Table 1 presents frequencies that provide some insights into the breakdown of this variable by gender. Contrasting with the findings of Bolen et al. (1998) and Harlos et al. (1999), women use helmets less than men, and comprise 39.3% of the 12.4% of the sample who report regularly using a helmet. Moreover, this pattern holds after partitioning the sample according to other socioeconomic attributes. The largest share of helmet users, at 17.6%, is among those living in households with children. Interestingly, this is also the group for which the share of women in the total, 47.2%, approaches that of men. We performed Pearson’s chi-square tests and found differences in helmet use among women and men that are significant at the 1% level for all categories in Table 1. 3. Empirical method
use among Germany’s adult population. In addition to a standard probit model, we also employ a heteroskedastic probit model that allows us to control for unobserved heterogeneity that could otherwise bias the parameter estimates. To facilitate interpretation, our assessment of the model results moves beyond the standard focus on the significance and magnitude of the coefficients to consider their implications for the predicted probabilities. For this purpose, Monte Carlo simulation is employed to explore the predictions of the model and the associated degree of uncertainty. Although our analysis uncovers many of the same determinants identified by the U.S. studies, including age and rural versus urban residence, one notable anomaly is a significantly lower rate of helmet use among women. The simulated probabilities reveal this discrepancy to hold over most of the age range, but to be particularly high in the 45–55 bracket. We also find a significant impact of income, with the likelihood of helmet use increasing for individuals in wealthier households. The paper concludes with a discussion of policy implications of these findings and suggestions for future research. 2. Data The data source used in this research is drawn from the survey “Mobility in Germany” (MID), a nationwide travel study conducted in 2008 that was financed by the German Federal Ministry of Transport, Building and Urban Affairs (MID, 2010). We limit the sample to individuals over 17 years of age and exclude those who report never using the bicycle (3.4%). The resulting data set comprises 19,646 observations. The survey respondents participate in a phone interview eliciting general household information and person-related char-
3.1. The estimator To explore the pattern presented in Table 1 more rigorously, the empirical methodology proceeds by specifying a structural model describing the probability of bicycle helmet use: yi∗ = xi ˇ + εi
(1)
where x is a 1 × k vector of explanatory variables, ε is an error term,  is a k × 1 vector of estimated coefficients, and the subscript i denotes the observation. The latent variable yi∗ measures the utility associated with wearing a helmet, and is therefore unobservable. We do, however, observe the associated outcome, which can be denoted by the dichotomous variable yi : yi = 1 if yi∗ > 0 and 0 otherwise
(2)
In the present analysis, yi equals unity for individuals who regularly wear a helmet and zero otherwise. If the error term is assumed to have a normal distribution, then the parameters  can be estimated using the maximum likelihood method on the basis of the classical probit model: P(Yi = 1) = ˚(xi ˇ)
(3)
where ˚ is the standard normal distribution function. One of the key assumptions underlying the probit model is homoskedasticity of the disturbances, the violation of which results
2 In the empirical analysis, we explored the application of ordered probit models to accommodate the original three categories, but found the qualitative results to remain unchanged.
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
97
Table 2 Variable definitions and descriptive statistics (19,646 total observations). Variable
Definition
Mean
S.D.
Frequency
Helmet Female Age City Children College High-School Income Daily rider Weekly rider Monthly rider East
1 if respondent always wears a bicycle helmet 1 if female Age of respondent in years 1 if respondent lives in a city 1 if respondent has children under the age of 14 1 if respondent’s highest degree is a college diploma 1 if respondent’s highest degree is a high-school diplomaa Real monthly income in 1000 Euro of 2008 1 if respondent rides bike daily 1 if respondent rides bike 1–3 times a week 1 if respondent rides bike 1–3 times a month 1 if respondent lives in the eastern part of Germany
0.124 0.512 49.877 0.870 0.235 0.201 0.298 2.927 0.300 0.327 0.222 0.258
0.330 0.500 15.006 0.337 0.424 0.401 0.458 1.467 0.458 0.469 0.415 0.437
2,440 10,056 – 17,083 4,607 3,944 5,861 – 5,900 6,421 4,353 5,067
a
This refers to diplomas awarded by a Gymnasium, Germany’s highest tier of secondary school, which qualifies students to attend university.
in spurious inferences regarding statistical significance and – contrasting with linear regression – inconsistent parameter estimates (Yatchew and Griliches, 1984). To address this issue, the present analysis draws on a more general class of models collectively referred to as heterogeneous choice models. These methods can be employed in the context of binary or ordinal models to model the variance of individual level choices and thereby correct for biases induced by heterogeneity. The probit variant of the heterogeneous discrete choice model is:
P(Yi = 1) = ˚
(xi ˇ) exp(zi )
(4)
where the numerator is referred to as the choice equation while the denominator is the variance equation. This latter equation contains a 1 × m vector of variables, z, that are posited to determine the error variances, and an associated m × 1 vector of estimated coefficients, . For example, women may exhibit greater unobserved variance in their choices than men, which would dictate the inclusion of a gender dummy among the z. In this regard, one of the main challenges in employing such a model is in correctly identifying the cause of the heteroskedasticity, as theoretical considerations generally provide little guidance. Following Williams (2009a), a stepwise procedure, elaborated below, is consequently employed to specify the variables in the variance equation. 3.2. Interpretation In interpreting the estimates from the probit model, interest generally focuses on the effects of changes in one of the explanatory variables on the probability of, in this example, wearing at helmet. For the standard probit, this marginal effect is given by: ∂P(Yi = 1) = (xi ˇ)ˇk ∂xik
(5)
where is the standard normal density function. The corresponding formula for the heteroskedastic probit accommodates the possibility that the explanatory variable of interest, w, appears in the vector x, z, or both:
∂P(Yi = 1) xi ˇ = exp(zi ) ∂wik
ˇk − (xi ˇ)k exp(zi )
(6)
where the second term drops out if w is only included in the choice equation, while the first term drops out if it is only included in the variance equation.3
3
As the probit and heteroskedastic probit are both nonlinear, the marginal effect varies over each observation in the data. When interpreting the results, we follow the convention of evaluating the marginal effects at the means of the explanatory variables.
To further facilitate interpretation, the predicted outcomes and associated 99% confidence intervals are plotted using a statistical simulation technique suggested by King et al. (2000). Recognizing that the parameter estimates from a maximum likelihood model are asymptotically normal, the method employs a sampling procedure akin to Monte Carlo simulation in which a large number of values – say 1000 – of each estimated parameter is drawn from a multivariate normal distribution. Taking the vector of coefficient estimates from the model as the mean of the distribution and the variance–covariance matrix as the variance, each of the 1000 simulated parameter estimates can then be multiplied by corresponding predetermined values of the explanatory variables to generate 1000 expected values. The range of these expected values conveys the associated degree of uncertainty. For example, we may be interested in the predicted probability of helmet use for a person of age 50, setting the remaining explanatory variables to their mean values. We would multiply the 1000 simulated parameter estimates by these predetermined values of the explanatory variables to generate 1000 expected probabilities. By ordering these probabilities from lowest to highest and then referencing the 5th and 995th positions in the array, we obtain an estimate of the 99% confidence interval. As illustrated below, the generation of confidence intervals, in particular, reveals insights that would otherwise be neglected were the analyst to focus exclusively on the parameter estimates and their standard errors.4 3.3. Explanatory variables The suite of variables selected for inclusion measures the individual and household-level attributes that are hypothesized to influence the choice of wearing a helmet in maximizing utility. Variable definitions and descriptive statistics are presented in Table 2. As many of these variables could either positively or negatively affect the probability of wearing a helmet, it is in most cases impossible to state a priori which effects are expected to prevail. Two exceptions are the income level and the presence of children in the household, both of which are ascribed positive signs as they increase the opportunity cost of becoming incapacitated from a head injury. Children in the household may also promote role model effects. Moreover, these effects may operate in both directions: via parents teaching their children safe bicycling practices, and via children who typically learn such practices through school programs that encourage helmet use.
4 Tomz et al. (2001) have written a program called Clarify for implementing this technique, downloadable from http://gking.harvard.edu/. As this program does not cover the heteroskedastic probit model, separate code was written in Stata for generating the simulations presented here, an annotated version of which is available from the authors upon request.
98
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Table 3 Empirical results (n = 19,646). Choice equation
Female Age Age2
Classical binary probit
Heteroskedastic probit
Heteroskedastic probit with interactions
Coefficient (p-values)
Marginal effect (p-values)
Coefficient (p-values)
Marginal effect (p-values)
Coefficient (p-values)
Marginal effect (p-values)
−0.308 (<0.0005) 0.024 (<0.0005) −0.00026 (<0.0005)
−0.060 (<0.0005) 0.005 (<0.0005) −0.00005 (<0.0005)
−0.699 (0.001) 0.026 (<0.0005) −0.00028 (<0.0005)
−0.058 (<0.0005) 0.004 (<0.0005) −0.00005 (<0.0005)
0.077 (0.062) 0.099 (0.033) 0.066 (0.088) 0.172 (<0.0005) 0.109 (0.006) −0.260 (<0.0005) 0.040 (<0.0005) 0.223 (<0.0005) 0.174 (<0.0005) −1.926 (<0.0005)
0.015 (0.067) 0.020 (0.040) 0.013 (0.093) 0.035 (<0.0005) 0.022 (0.009) −0.047 (<0.0005) 0.008 (<0.0005) 0.046 (<0.0005) 0.031 (<0.0005)
−0.085 (0.449) 0.124 (0.034) 0.425 (<0.0005) 0.236 (<0.0005) 0.145 (0.003) −0.268 (<0.0005) 0.045 (<0.0005) 0.232 (<0.0005) 0.177 (<0.0005) −2.059 (<0.0005)
0.018 (0.032) 0.022 (0.032) 0.016 (0.057) 0.042 (<0.0005) 0.026 (0.003) −0.042 (<0.0005) 0.008 (<0.0005) 0.042 (<0.0005) 0.028 (<0.0005)
−0.547 (0.009) 0.029 (<0.0005) −0.0003 (<0.0005) −0.006 (0.014) −0.088 (0.415) 0.130 (0.029) 0.415 (<0.0005) 0.254 (<0.0005) 0.155 (0.002) −0.276 (<0.0005) 0.047 (<0.0005) 0.230 (<0.0005) 0.181 (<0.0005) −2.182 (<0.0005)
−0.008 (0.684) 0.005 (<0.0005) −0.00005 (<0.0005) −0.0009 (0.818) 0.016 (0.044) 0.022 (0.029) 0.017 (0.042) 0.042 (<0.0005) 0.026 (0.002) −0.041 (<0.0005) 0.008 (<0.0005) 0.039 (<0.0005) 0.027 (<0.0005)
Female × age High-school College Daily rider Weekly rider Monthly rider East Income Children City Constant Variance function Daily rider Female High-school Fit log-likelihood
−7074.7
−0.265 (<0.0005) 0.278 (0.020) 0.137 (0.070)
−0.235 (<0.0005) 0.352 (0.005) 0.131 (0.062)
−7067.6
−7063.9
4. Results Table 3 catalogues coefficient estimates and corresponding marginal effects from the probit models. The models are developed sequentially, starting with a standard probit, then adding controls for unobserved heterogeneity, and finally by including interaction terms to test for differential effects of the explanatory variables by gender. Turning first to the standard probit in the left panel of the table, most of the coefficients on the individual attributes are statistically significant at the 5% level, and there are no unexpected results with respect to the sign. Confirming findings from the descriptive statistics in Table 1, the female dummy has a negative coefficient, suggesting that women have a 6% lower probability of wearing a helmet than men, holding other factors fixed. The variable age, which is specified as a quadratic, has a nonlinear effect. Increases in age initially increase but subsequently decrease the probability of wearing a helmet, with the reversal point occurring at roughly an age of 46 years. Individuals with a college diploma have a higher probability of helmet use, as do individuals who ride on a weekly or monthly basis. Given that the excluded category for the cycling frequency dummies is less than once a month, this pattern also reveals a non-linearity: those who ride very frequently or very infrequently have a lower probability of helmet use than those who ride occasionally. Turning to the household-level variables, a particularly large effect is seen for the dummy indicating individuals in house-
holds located in eastern Germany, which decreases the probability by 4.7% relative to individuals located elsewhere in Germany. As expected, higher income and the presence of children both increase the likelihood of wearing a helmet. The dummy indicating residence in an urban area also has a positive effect. To explore the robustness of these results, the middle panel of Table 3 presents estimates from the heteroskedastic probit model, which controls for differences in the unobserved variance. Drawing from the full set of predictors in the choice equation, those in the variance equation were selected using a stepwise procedure that excluded variables whose significance levels were greater than 10% (Williams, 2009a,b). We find three variables – the dummies indicating daily riders, females and those with a high-school degree – to be statistically significant in accounting for unobserved heterogeneity. Of these, only the former has a negative coefficient, suggesting that individuals who ride daily have less variability in the propensity to wear a helmet. On the other hand, the positive coefficients for women and people with high-school degrees are indicative of higher residual variability. While a likelihood-ratio test indicates that the addition of the heteroskedasticity parameters significantly improves the fit of the model (chi2 (3) = 14.34, P < 0.0025), the conclusions emerging with respect to the explanatory variables remain largely unchanged. A comparison of the marginal effects calculated from the two models suggests negligible differences. One exception is the coefficient for the dummy that controls for people with high-school diplomas. Although the magnitude of the marginal effect is only a little higher,
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
99
those in lower income brackets. In addition, although individuals in households with children have the highest rates of use, this may be a segment where there is scope for additional gains from school programs. Such programs not only have direct effects in informing children about the benefits of helmet use, but also may additionally confer spillover effects from children taking this information home and serving as role-models for their parents. Currently in Germany, some 95% of children are covered by bicycle safety programs (Verkehrswacht, 2010), though the emphasis on bicycle helmet use has not been documented. While the present study has clearly demonstrated a positive effect of children on adult helmet use, data constraints precluded us from isolating the exact mechanism. A worthwhile area for future research would thus involve examining the diffusion of information from school-based bicycle safety programs across other members of the household. Acknowledgements Fig. 2. Simulated probabilities of helmet use by age and gender.
it is now significant at the 5%-level compared to the standard binary probit model. Given the significant differences in helmet use between women and men indicated by the descriptive analysis, the final step in developing the model involved the inclusion of interaction terms to test for whether the impact of the explanatory variables varied by gender. Several specifications were explored using alternative sets of interaction terms modeled either individually or jointly, none of which had a substantial bearing on the remaining coefficient estimates. Ultimately, as presented in the final two columns of Table 3, only the coefficient estimate of age was found to have significantly differential impacts by gender, with a lower magnitude among females. Further insight into this difference can be gleaned from Fig. 2, which shows the simulated probabilities of helmet use and confidence intervals using Monte Carlo simulation (King et al., 2000). Based on the estimates from the heteroskedastic probit with the interaction, the simulations are generated over a range of values for the variable age while holding the other variables in the model fixed at their mean values. To generate the curve for females, the female dummy variable was set equal to one when calculating the expected values, while it was set equal to zero to generate the curve for males. Men have higher probabilities of helmet use for the whole range of data. Moreover, as indicated by the non-overlap of the confidence intervals, statistically significant differences by gender are discernible between the ages of 25 and 70. 5. Conclusions Drawing on a household survey conducted in 2008 in Germany, this analysis has investigated the determinants of helmet use through a combination of descriptive analyses and econometric methods, the latter of which relied on variants of the probit- and heteroskedastic probit model to control for the effects of unobserved heterogeneity. The descriptive statistics presented at the outset of the analysis suggest that men are more likely to regularly use a helmet than women, a finding that is confirmed by econometric modeling that holds fixed the influence of other confounding factors related to the individual and their socioeconomic environment. We also find that urban residency, household income, and the presence of children in the household positively affect the probability of helmet use. Taken together, these results can be used for strategic targeting of information campaigns to promote the use of bicycle helmets. The key target groups would include those living in the east, where helmet use is lowest, as well as women, non-urban residents, and
The authors thank Manuel Frondel for valuable comments and suggestions. References BMVBS, 2009. Bundesministerium für Verkehr, Bau und Stadtentwicklung, Fahrradverkehr, Berlin, Germany. http://www.bmvbs.de/Verkehr/Strasse/Fahrrad,1435.22685/Fahrradverkehr.htm. Bolen, J.R., Kesnew, M.-J., Sacks, J.J., 1998. Reported bicycle helmet use among adults in the United States. Archives of Family Medicine 7, 72–77. Cummings, P., Rivara, F.P., Olson, C.M., Smith, K.M., 2006. Cahnges in traffic crash mortality rates attributed to use of alcohol, or lack of a seat belt, air bag, motorcycle helmet, or bicycle helmet, United States, 1982–2001. Injury Prevention 12, 148–154. Dorsch, M., Woodward, A., Somers, R., 1987. Do bicycle safety helmets reduce the severity of head injuries in real crashes? Accident Analysis and Prevention 19 (3), 183–190. German Federal Statistical Office, 2006. Verkehr in Deutschland 2006, Artikelnummer 1021216069004. Statistisches Bundesamt, Wiesbaden, Germany. German Federal Statistical Office, 2009. Verkehrsunfälle, Zweiradunfälle im Straßenverkehr, Artikelnummer 5462408087004. Statistisches Bundesamt, Wiesbaden, Germany. Gkritza, K., 2009. Modeling motorcycle helmet use in Iowa: evidence from six roadside observational surveys. Accident Analysis and Prevention 41 (3), 479–484. Harlos, S., Warda, L., Buchan, N., et al., 1999. Urban and rural patterns of bicycle helmet use: factors predicting usage. Injury Prevention 5, 183–188. Jacobson, G., Blizzard, L., Dwyer, T., 1998. Bicycle injuries: road trauma is not the only concern. Australian and New Zealand Journal of Public Health 22 (4), 451–455. Kim, J.-K., Kim, S., Ulfarsson, G.F., Porrello, L.A., 2007. Bicyclist injury severities in bicycle-motor vehicle accidents. Accident Analysis and Prevention 39, 238–251. King, G., Tomz, M., Wittenberg, J., 2000. Making the most of statistical analyses: improving Interpretation and presentation. American Journal of Political Science 44, 347–361. Linn, S., Smith, D., Sheps, S., 1998. Epidemiology of bicycle injury, head injury, and helmet use from British Columbia: a 5 year descriptive study. Injury Prevention 4, 122–125. McDermott, F., Love, J., Brazenor, G., Debney, E., 1993. The effectiveness of bicyclist helmets: a study of 1710 casualties. The Journal of Trauma 34, 834–844. MID, 2010. Mobility in Germany. Berlin, Germany. http://www.mobilitaet-indeutschland.de/engl%202008/index.htm. Mills, P., 1989. Pedal cycle accidents – a hospital based study. Research Report 220. Transport & Road Research Laboratory. Rivara, F., Thompson, D., Thompson, R., 1997. Epidemiology of bicycle injuries and risk factors for serious injury. Injury Prevention 3, 110–114. Rodgers, G.B., 1995. Bicycle helmet use patterns in the United States, a description and analysis of National Survey Data. Accident Analysis and Prevention 27 (1), 43–56. Scuffham, P., Alsop, J., Cryer, C., Langley, J.D., 2000. Head injuries to bicyclists and the New Zealand bicycle helmet law. Accident Analysis and Prevention 32 (4), 565–573. Shafi, S., Gilbert, J., Loghnamee, F., Allen, J., et al., 1998. Impact of bicycle helmet safety legislation on children admitted to a regional pediatric trauma center. Journal of Pediatric Surgery 33, 317–321. Simpson, H., 1996. Comparison of hospital and police casualty data: a National Study. TRL Report 173. Transport Research Laboratory, Crowthorne, Berks. Spaite, D., Murphy, M., Criss, E., Valenzuela, T., Meislin, H., 1991. A prospective analysis of injury severity among helmeted and nonhelmeted bicyclists involved in collisions with motor vehicles. Journal of Trauma 31 (4), 151–216. Thomas, S., Acton, C., Nixon, J., Battistutta, D., Pitt, W., Clark, R., 1994. Effectiveness of bicycle helmets in preventing head injury in children: case control study. British Medical Journal 308, 173–176.
100
N. Ritter, C. Vance / Accident Analysis and Prevention 43 (2011) 95–100
Thompson, R., Rivara, F., Thompson, D., 1989. A case–control study of the effectiveness of bicycle safety helmets. New England Journal of Medicine 320 (21), 136–167. Thompson, R., Rivara, F., Thompson, D., 1990. A case control study of the effectiveness of bicycle safety helmets in preventing facial injury. American Journal of Public Health 320 (21), 136–167. Thompson, D., Nunn, M., Thompson, R., Rivara, F., 1996. Effectiveness of bicycle safety helmets in preventing serious facial injury. Journal of the American Medical Association 276, 1968–1971. Tomz, R., Wittenberg, J., King, G., 2001. Clarify: software for interpreting and presenting statistical results. http://gking.harvard.edu/clarify/clarify.pdf. Verkehr in Zahlen, 2010. Verkehr in Zahlen 2009/2010, Bau und Stadtentwicklung Bundesministerium für Verkehr (Herausgeber). Deutscher Verkehrs-Verlag; Auflage 38, aktualisierte Auflage, Germany.
Verkehrswacht, 2010. Radfahrausbildung – Kernstück schulischer Verkehrserziehung, Verkehrswacht Medien & Service-Center, Bonn, Germany. http://www.verkehrswacht-medien-service.de/radfahrausbildung kern.html. Williams, R., 2009a. Using heterogeneous choice models to compare logit and probit coefficients across groups. Sociological Methods and Research 37, 531– 559. Williams, R., 2009b. OGLM: Stata module to estimate Ordinal Generalized Linear Models. http://econpapers.repec.org/software/bocbocode/s453402.htm. Accessed April 1, 2009. Wassermann, R., Waller, J., Monty, M., Emery, A., Robinson, D., 1988. Bicyclists, helmets and head injuries: a rider based study of helmet use and effectiveness. American Journal of Public Health 78 (9), 1220–1221. Yatchew, A., Griliches, Z., 1984. Specification error in probit models. Review of Economics and Statistics 66, 134–139.