612
European Journal of Public Health
23 Macroencuesta Violencia de Ge´nero (Macro survey on gender violence) [survey results]. Madrid; Observatorio violencia de ge´nero (gender violence observatory); 2011 Available at: http://www.observatorioviolencia.org/upload_images/File/ DOC1329745747_macroencuesta2011_principales_resultados-1.pdf. 24 Vives-Cases C, Gil-Gonzalez D, Ruiz-Perez I, et al. Identifying sociodemographic differences in Intimate Partner Violence among immigrant and native women in Spain: a cross-sectional study. Prev Med 2010;51:85–7.
29 Ruiz-Perez I, Plazaola-Castano J, Alvarez-Kindelan M, et al. Sociodemographic associations of physical, emotional, and sexual intimate partner violence in Spanish women. Ann Epidemiol 2006;16:357–63. 30 Zarza MJ, Ponsodab V, Carrilli R. Predictors of violence and lethality among Latina immigrants: implications for assessment and treatment. J Aggress Maltreat Trauma 2009;18:1–16. 31 Plazaola-Castano J, Ruiz-Perez I, Montero-Pinar MI. Apoyo social como factor protector frente a la violencia contra la mujer en la pareja. (The protective role of social support and intimate partner violence). Gac Sanit 2008;22: 527–33.
26 Ruiz-Perez I, Plazaola-Castano J, Vives-Cases C. Methodological issues in the study of violence against women. J Epidemiol Commun Health 2007;61 (Suppl 2): ii26–31.
32 Small R, Taft AJ, Brown SJ. The power of social connection and support in improving health: lessons from social support interventions with childbearing women. BMC Public Health 2011;11(Suppl 5):S4.
27 Whitaker DJ, Baker CK, Pratt C, et al. A network model for providing culturally competent services for intimate partner violence and sexual violence. Violence Against Women 2007;13:190–209.
33 Ellison CG, Trinitapoli JA, Anderson KL, Johnson BR. Race/ethnicity, religious involvement, and domestic violence. Violence Against Women 2007;13: 1094–112.
28 Asian Women, Domestic Violence and Mental Health: A Toolkit for Health Professionals. EACH (Enabling Change Rebuilding Lives) and Government Office for London, 2009. Available at: http://www.ndvf.org.uk/files/document/889/ original.pdf.
34 Todhunter RG, Deaton J. Relationship between religious and spiritual factors and the perpetration of intimate personal violence. Journal of Family Violence 2010; 25:9.
......................................................................................................... European Journal of Public Health, Vol. 24, No. 4, 612–614 ß The Author 2014. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/cku014 Advance Access published on 24 February 2014
.........................................................................................................
Short Report
.........................................................................................................
Association between economic fluctuations and road mortality in OECD countries Gang Chen Flinders Health Economics Group, School of Medicine, Flinders University, Adelaide, Australia Correspondence: Gang Chen, PhD, Research Fellow, Flinders Health Economics Group, School of Medicine, A Block Level 1, Repatriation General Hospital, Flinders University, Daws Road, Daw Park, Adelaide, SA 5041, Australia. Tel: +61 425811029, Fax: +61 882752854, e-mail: [email protected], [email protected]
Using longitudinal data from 32 Organization for Economic Co-operation and Development (OECD) countries (1970–2010), this article investigates association between annual variations in road mortality and the economic fluctuations. Two regression models (fixed-effects and random-coefficients) were adopted for estimation. The cross-country data analyses suggested that road mortality is pro-cyclical and that the cyclicality is symmetric. Based on data from 32 OECD countries, an increase of on average 1% in economic growth is associated with a 1.1% increase in road mortality, and vice versa.
.........................................................................................................
Introduction oad traffic injuries are recognized as a major global public health 1 According to the World Health Organization (WHO), road traffic accidents kill more than 1.3 million people around the world every year and it is predicted that without concerted action, road traffic injuries will be the fifth leading cause of death by the year 2030.2,3 Based on the cross-country longitudinal data from developed countries, over the long term, an inverse U-shape has been observed to characterize the relationship between economic development and road mortality, such that road mortality initially increases with per-capita gross domestic product (GDP) and then eventually decreases.4,5 To better understand the relationship between economic development and road mortality, apart from studying the long-run pattern, the short-term link between economic fluctuations (i.e.
Rissue.
ups and downs) and road mortality warrants greater attention from the public health sector. In contrast with maternal and infant mortality indicators, which are found to be counter-cyclical with economic booms, there is evidence that road mortality is procyclical—that is, it increases when the economy expands and decreases during times of recession.6–8 This short report aims to investigate the cyclical relationship of road mortality by analysing longitudinal data from 32 Organization for Economic Co-operation and Development (OECD) countries.
Methods To begin the analysis, longitudinal data on road deaths, population size and real per-capita GDP (i.e. GDP per head, 1000 US$, constant 2005 price) from 32 OECD countries (ranging from 1971 to 2010) were compiled from the OECD statistics (http://www.oecd.org).
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
25 Zorrilla B, Pires M, Lasheras L, et al. Intimate partner violence: last year prevalence and association with socio-economic factors among women in Madrid, Spain. Eur J Public Health 2010;20:169–75.
Economic fluctuations and road mortality in OECD countries
Wooldridge.9 All analyses were performed in Stata version 12.1 (StataCorp LP, College Station, Texas, USA).
Road mortality is defined as the number of road deaths per 100 000 population. The details of the countries and time periods included in the longitudinal data are presented in the Appendix. A regression framework was utilized, in which the change in the natural logarithm of road mortality (or road mortality growth) was regressed upon the change in the natural logarithm of real per-capita GDP (or economic growth). The estimated coefficient of economic growth indicates the cyclical nature of road mortality. The following equation was used: lnðMORit Þ ¼ i þ 1i lnðGDPit Þ þ "it
Results
ð1Þ
þ 3i ð1 DÞ lnðGDPit Þ þ "it
ð2Þ
where D equals 1 if economic growth is positive [i.e. ln(GDPit) > 0], and D equals 0 if economic growth is negative [i.e. ln(GDPit) < 0]. Under this model setting, 2 and 3 represent the index of road mortality cyclicality during economic ups and downs separately. If estimated 2 and 3 are statistically different, an asymmetric cyclical relationship is suggested. Two econometrics methods were adopted to estimate Equations (1) and (2). First, a fixed-effects model was utilized, which is essentially a common slope estimator (i.e. i = ).8 Second, a more general random-coefficients model which allows for countryspecific slope coefficients was adopted. A detailed explanation of the theoretical background to these two models can be found in
Table 1 Economic fluctuations and road mortality in OECD countries Fixed-effects
Random-coefficients
1.084 (0.851–1.318)**
1.058 (0.754–1.361)**
Panel B - Full sample (heterogeneous effects: positive and negative per-capita GDP variation) Positive changes in natural log GDP per capita 1.171 (0.809–1.533)** Negative changes in natural log GDP per capita 0.928 (0.374–1.481)**
1.133 (0.594–1.672)** 0.977 (0.088–1.866)*
Panel C - Robustness analysis based on balanced sample (Observation = 975, No. of countries = 25) Changes in natural log GDP per capita 0.957 (0.701–1.214)**
1.022 (0.726–1.318)**
Panel A - Full sample (Observation = 1124, No. of countries = 32) Changes in natural log GDP per capita
Panel D - Robustness analysis based on balanced sample (heterogeneous effects: positive and negative per-capita GDP variation) Positive changes in natural log GDP per capita 1.029 0.985 (0.644–1.415)** (0.595–1.375)** Negative changes in natural log GDP per capita 0.814 0.986 (0.187–1.442)* (0.063–1.909)*
Dependent variable: change in natural log road mortality. 95% confidence intervals in parentheses. **P < 0.01, *P < 0.05. Fixed-effects model enables the time-invariant unobserved country-specific heterogeneity be eliminated; however, all countries share a common slope parameter. Random-coefficients model allows each country to have its own vector of slopes randomly drawn from a distribution common to all counties.
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
The regression results using the full sample from 32 OECD countries are presented in Panels A and B in table 1. To see whether the results are sensitive to the time periods and/or countries selection, as a robustness analysis, 25 of the sample OECD countries that had no missing observations on key variables (i.e. balanced longitudinal data) for 1970–2009 were included, and as seen in Panels C and D of table 1, the results are comparable to those attained using the full sample. Panel A, table 1 presents the estimation of Equation (1) using the full sample. The coefficients of economic fluctuations obtained from the two methods are presented separately for the fixed-effects and random-coefficients models. Both two estimates were statistically significant (P < 0.01) with similar magnitudes (ranging from 1.058 to 1.084). Panel B shows the differential relationships between economic fluctuations and road mortality during economic ups and downturns. As can be seen, the two economic fluctuation coefficients were significant (P < 0.05), with similar magnitudes (ranging from 0.928 to 1.171). A Wald test was performed and the null hypothesis of equal coefficients (i.e. 2 = 3) cannot be rejected, suggesting that the relationship between economic fluctuations and road mortality was symmetrical. For the random-coefficients model, a parameter constancy test was performed to determine whether the country-specific i differed significantly from each other. The null hypothesis of equal coefficient across the 32 countries was rejected (P < 0.05). The fixed-effects model has the advantage of eliminating the unobservable country-specific timeinvariant characteristics (that is, i) while the random-coefficients model allows for more general heterogeneity on the slope coefficient . Both model estimates consistently suggest that a 1% increase in real per-capita GDP was associated with a 1.1% increase in road mortality. The robustness analysis results are reported in Panels C and D in table 1, and are comparable to those obtained for the full sample. One different model specification test result was found: the null hypothesis of equal coefficient across countries was not rejected in Panel C. Nevertheless, the results of both the full
where is the first difference operator, ln(MOR) and ln(GDP) represent the natural logarithms of road mortality and real GDP per capita, respectively, i is a country-specific term, " is an idiosyncratic error term, and the subscript i and t denote the country and time period, respectively. The chosen of natural logarithmic functional form is consistent with the literature5 and the first difference operator further assist the key aim of studying the short-run association between road mortality and economic fluctuations. A significant positive value of 1 implies pro-cyclical behaviour, a significant negative value implies counter-cyclical behaviour, and an insignificant value implies acyclical behaviour. To account for the potential existence of an asymmetric relationship such that the road death rate may respond differently to economic ups and economic downs, Equation (2) was adopted8: lnðMORit Þ ¼ i þ 2i D lnðGDPit Þ
613
614
European Journal of Public Health
Public health interventions on road safety during economic booms should be established.
sample and the robustness analysis from the three different methods reached the same conclusion: that road mortality is pro-cyclical and the relationship is symmetric during periods of economic fluctuations.
Discussion
Conflicts of interest: None declared.
Key points Road traffic injuries are recognized as a major global public health issue. Road mortality is pro-cyclical: on average, a 1% increase/ decrease in economic growth is associated with 1.1% increase/decrease in road mortality. The cyclicality of road mortality is symmetric.
1
Sharma BR. Road traffic injuries: a major global public health crisis. Public Health 2008;122:1399–406.
2
World Health Organization. Global Status Report on Road Safety: Time for Action. Geneva: World Health Organization; 2009.
3
Lancet. Reducing road dangers. Lancet 2011;377:1543.
4
van Beek EF, Borsboom GJJ, Mackenbach JP. Economic development and traffic accident mortality in the industrialized world, 1962-1990. Int J Epidemiol 2000;29: 503–9.
5
Kopits E, Cropper M. Traffic fatalities and economic growth. Accid Anal Prev 2005; 37:169–78.
6
Tanaboriboon Y, Satiennam T. Traffic accidents in Thailand. IATSS Res 2005;29: 88–100.
7
Stuckler D, Basu S, Suhrcke M, et al. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. Lancet 2009;374: 315–23.
8
Cruces G, Glu¨zmann P, Calva LFL. Economic crises, maternal and infant mortality, low birth weight and enrollment rates: evidence from Argentina’s downturns. World Development 2012;40:303–14.
9
Wooldridge JM. Introduction Econometrics: A Modern Approach, 4th edn. Cincinnati, Ohio: South-Western College Publishing, 2008.
10 World Health Organization. World Report on Road Traffic Injury Prevention. Geneva: World Health Organization, 2004.
Appendix The 32 OECD countries (time periods) included in the full sample cross-country longitudinal data were: Australia (1971–2010), Austria (1971–2010), Belgium (1971–2009), Canada (1971–2009), Czech Republic (1994–2010), Denmark (1971–2010), Estonia (1994–2010), Finland (1971–2010), France (1971–2009), Germany (1971–2009), Greece (1971–2009), Hungary (1992–2010), Iceland (1971–2010), Ireland (1971–2010), Italy (1971–2009), Japan (1971–2009), Korea (1971–2010), Luxembourg (1971–2009), Mexico (1971–2009), Netherlands (1971–2009), New Zealand (1971–2010), Norway (1971–2010), Poland (1992–2010), Portugal (1971–2009), Slovak Republic (1994–2010), Slovenia (1996–2010), Spain (1971–2010), Sweden (1971–2010), Switzerland (1981–2010), Turkey (1971–2010), United Kingdom (1971–2010), United States (1971–2010). Chile and Israel were excluded from this analysis since the road fatality data were unavailable.
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
The conclusion that road mortality is pro-cyclical is in line with previous research which incorporated single-country data6 and cross-country data from 26 European Union countries.7 In addition, the cross-country analysis results in this article suggest that the relationship between economic fluctuations and road mortality is symmetric—road mortality increases during economic ups and decreases during economic downs to a similar degree. The responses of traffic demand and alcohol consumption behaviours to economic fluctuations could be the two key reasons that behind the observation. Several interventions have been found to effectively reduce the number of road traffic injuries and deaths,10 among which the strategies that focus on reducing traffic volume and/or travelling distances may be the most valuable. In addition, fiscal policy such as alcohol tax could also be particularly effective in this context. So far no specific knowledge is available to inform understanding of which of those interventions would be more effective during periods of economic boom. It should be noted that in this study only the overall road mortality was used. A further analysis using age-specific road mortality data would offer more information regarding the cyclical behaviour of road mortality and would contribute towards the effective design and implementation of intervention strategies. In addition, the strength of different potential factors that contribute to the pro-cyclically road mortality was not studied in this study. Economic prosperity is associated with improved road safety from a long-term perspective; however, economic boom in the short term is also likely to be associated with detrimental impacts on road safety—a phenomenon that deserves greater academic and public attention.
References
European Journal of Public Health
23 Macroencuesta Violencia de Ge´nero (Macro survey on gender violence) [survey results]. Madrid; Observatorio violencia de ge´nero (gender violence observatory); 2011 Available at: http://www.observatorioviolencia.org/upload_images/File/ DOC1329745747_macroencuesta2011_principales_resultados-1.pdf. 24 Vives-Cases C, Gil-Gonzalez D, Ruiz-Perez I, et al. Identifying sociodemographic differences in Intimate Partner Violence among immigrant and native women in Spain: a cross-sectional study. Prev Med 2010;51:85–7.
29 Ruiz-Perez I, Plazaola-Castano J, Alvarez-Kindelan M, et al. Sociodemographic associations of physical, emotional, and sexual intimate partner violence in Spanish women. Ann Epidemiol 2006;16:357–63. 30 Zarza MJ, Ponsodab V, Carrilli R. Predictors of violence and lethality among Latina immigrants: implications for assessment and treatment. J Aggress Maltreat Trauma 2009;18:1–16. 31 Plazaola-Castano J, Ruiz-Perez I, Montero-Pinar MI. Apoyo social como factor protector frente a la violencia contra la mujer en la pareja. (The protective role of social support and intimate partner violence). Gac Sanit 2008;22: 527–33.
26 Ruiz-Perez I, Plazaola-Castano J, Vives-Cases C. Methodological issues in the study of violence against women. J Epidemiol Commun Health 2007;61 (Suppl 2): ii26–31.
32 Small R, Taft AJ, Brown SJ. The power of social connection and support in improving health: lessons from social support interventions with childbearing women. BMC Public Health 2011;11(Suppl 5):S4.
27 Whitaker DJ, Baker CK, Pratt C, et al. A network model for providing culturally competent services for intimate partner violence and sexual violence. Violence Against Women 2007;13:190–209.
33 Ellison CG, Trinitapoli JA, Anderson KL, Johnson BR. Race/ethnicity, religious involvement, and domestic violence. Violence Against Women 2007;13: 1094–112.
28 Asian Women, Domestic Violence and Mental Health: A Toolkit for Health Professionals. EACH (Enabling Change Rebuilding Lives) and Government Office for London, 2009. Available at: http://www.ndvf.org.uk/files/document/889/ original.pdf.
34 Todhunter RG, Deaton J. Relationship between religious and spiritual factors and the perpetration of intimate personal violence. Journal of Family Violence 2010; 25:9.
......................................................................................................... European Journal of Public Health, Vol. 24, No. 4, 612–614 ß The Author 2014. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/cku014 Advance Access published on 24 February 2014
.........................................................................................................
Short Report
.........................................................................................................
Association between economic fluctuations and road mortality in OECD countries Gang Chen Flinders Health Economics Group, School of Medicine, Flinders University, Adelaide, Australia Correspondence: Gang Chen, PhD, Research Fellow, Flinders Health Economics Group, School of Medicine, A Block Level 1, Repatriation General Hospital, Flinders University, Daws Road, Daw Park, Adelaide, SA 5041, Australia. Tel: +61 425811029, Fax: +61 882752854, e-mail: [email protected], [email protected]
Using longitudinal data from 32 Organization for Economic Co-operation and Development (OECD) countries (1970–2010), this article investigates association between annual variations in road mortality and the economic fluctuations. Two regression models (fixed-effects and random-coefficients) were adopted for estimation. The cross-country data analyses suggested that road mortality is pro-cyclical and that the cyclicality is symmetric. Based on data from 32 OECD countries, an increase of on average 1% in economic growth is associated with a 1.1% increase in road mortality, and vice versa.
.........................................................................................................
Introduction oad traffic injuries are recognized as a major global public health 1 According to the World Health Organization (WHO), road traffic accidents kill more than 1.3 million people around the world every year and it is predicted that without concerted action, road traffic injuries will be the fifth leading cause of death by the year 2030.2,3 Based on the cross-country longitudinal data from developed countries, over the long term, an inverse U-shape has been observed to characterize the relationship between economic development and road mortality, such that road mortality initially increases with per-capita gross domestic product (GDP) and then eventually decreases.4,5 To better understand the relationship between economic development and road mortality, apart from studying the long-run pattern, the short-term link between economic fluctuations (i.e.
Rissue.
ups and downs) and road mortality warrants greater attention from the public health sector. In contrast with maternal and infant mortality indicators, which are found to be counter-cyclical with economic booms, there is evidence that road mortality is procyclical—that is, it increases when the economy expands and decreases during times of recession.6–8 This short report aims to investigate the cyclical relationship of road mortality by analysing longitudinal data from 32 Organization for Economic Co-operation and Development (OECD) countries.
Methods To begin the analysis, longitudinal data on road deaths, population size and real per-capita GDP (i.e. GDP per head, 1000 US$, constant 2005 price) from 32 OECD countries (ranging from 1971 to 2010) were compiled from the OECD statistics (http://www.oecd.org).
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
25 Zorrilla B, Pires M, Lasheras L, et al. Intimate partner violence: last year prevalence and association with socio-economic factors among women in Madrid, Spain. Eur J Public Health 2010;20:169–75.
Economic fluctuations and road mortality in OECD countries
Wooldridge.9 All analyses were performed in Stata version 12.1 (StataCorp LP, College Station, Texas, USA).
Road mortality is defined as the number of road deaths per 100 000 population. The details of the countries and time periods included in the longitudinal data are presented in the Appendix. A regression framework was utilized, in which the change in the natural logarithm of road mortality (or road mortality growth) was regressed upon the change in the natural logarithm of real per-capita GDP (or economic growth). The estimated coefficient of economic growth indicates the cyclical nature of road mortality. The following equation was used: lnðMORit Þ ¼ i þ 1i lnðGDPit Þ þ "it
Results
ð1Þ
þ 3i ð1 DÞ lnðGDPit Þ þ "it
ð2Þ
where D equals 1 if economic growth is positive [i.e. ln(GDPit) > 0], and D equals 0 if economic growth is negative [i.e. ln(GDPit) < 0]. Under this model setting, 2 and 3 represent the index of road mortality cyclicality during economic ups and downs separately. If estimated 2 and 3 are statistically different, an asymmetric cyclical relationship is suggested. Two econometrics methods were adopted to estimate Equations (1) and (2). First, a fixed-effects model was utilized, which is essentially a common slope estimator (i.e. i = ).8 Second, a more general random-coefficients model which allows for countryspecific slope coefficients was adopted. A detailed explanation of the theoretical background to these two models can be found in
Table 1 Economic fluctuations and road mortality in OECD countries Fixed-effects
Random-coefficients
1.084 (0.851–1.318)**
1.058 (0.754–1.361)**
Panel B - Full sample (heterogeneous effects: positive and negative per-capita GDP variation) Positive changes in natural log GDP per capita 1.171 (0.809–1.533)** Negative changes in natural log GDP per capita 0.928 (0.374–1.481)**
1.133 (0.594–1.672)** 0.977 (0.088–1.866)*
Panel C - Robustness analysis based on balanced sample (Observation = 975, No. of countries = 25) Changes in natural log GDP per capita 0.957 (0.701–1.214)**
1.022 (0.726–1.318)**
Panel A - Full sample (Observation = 1124, No. of countries = 32) Changes in natural log GDP per capita
Panel D - Robustness analysis based on balanced sample (heterogeneous effects: positive and negative per-capita GDP variation) Positive changes in natural log GDP per capita 1.029 0.985 (0.644–1.415)** (0.595–1.375)** Negative changes in natural log GDP per capita 0.814 0.986 (0.187–1.442)* (0.063–1.909)*
Dependent variable: change in natural log road mortality. 95% confidence intervals in parentheses. **P < 0.01, *P < 0.05. Fixed-effects model enables the time-invariant unobserved country-specific heterogeneity be eliminated; however, all countries share a common slope parameter. Random-coefficients model allows each country to have its own vector of slopes randomly drawn from a distribution common to all counties.
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
The regression results using the full sample from 32 OECD countries are presented in Panels A and B in table 1. To see whether the results are sensitive to the time periods and/or countries selection, as a robustness analysis, 25 of the sample OECD countries that had no missing observations on key variables (i.e. balanced longitudinal data) for 1970–2009 were included, and as seen in Panels C and D of table 1, the results are comparable to those attained using the full sample. Panel A, table 1 presents the estimation of Equation (1) using the full sample. The coefficients of economic fluctuations obtained from the two methods are presented separately for the fixed-effects and random-coefficients models. Both two estimates were statistically significant (P < 0.01) with similar magnitudes (ranging from 1.058 to 1.084). Panel B shows the differential relationships between economic fluctuations and road mortality during economic ups and downturns. As can be seen, the two economic fluctuation coefficients were significant (P < 0.05), with similar magnitudes (ranging from 0.928 to 1.171). A Wald test was performed and the null hypothesis of equal coefficients (i.e. 2 = 3) cannot be rejected, suggesting that the relationship between economic fluctuations and road mortality was symmetrical. For the random-coefficients model, a parameter constancy test was performed to determine whether the country-specific i differed significantly from each other. The null hypothesis of equal coefficient across the 32 countries was rejected (P < 0.05). The fixed-effects model has the advantage of eliminating the unobservable country-specific timeinvariant characteristics (that is, i) while the random-coefficients model allows for more general heterogeneity on the slope coefficient . Both model estimates consistently suggest that a 1% increase in real per-capita GDP was associated with a 1.1% increase in road mortality. The robustness analysis results are reported in Panels C and D in table 1, and are comparable to those obtained for the full sample. One different model specification test result was found: the null hypothesis of equal coefficient across countries was not rejected in Panel C. Nevertheless, the results of both the full
where is the first difference operator, ln(MOR) and ln(GDP) represent the natural logarithms of road mortality and real GDP per capita, respectively, i is a country-specific term, " is an idiosyncratic error term, and the subscript i and t denote the country and time period, respectively. The chosen of natural logarithmic functional form is consistent with the literature5 and the first difference operator further assist the key aim of studying the short-run association between road mortality and economic fluctuations. A significant positive value of 1 implies pro-cyclical behaviour, a significant negative value implies counter-cyclical behaviour, and an insignificant value implies acyclical behaviour. To account for the potential existence of an asymmetric relationship such that the road death rate may respond differently to economic ups and economic downs, Equation (2) was adopted8: lnðMORit Þ ¼ i þ 2i D lnðGDPit Þ
613
614
European Journal of Public Health
Public health interventions on road safety during economic booms should be established.
sample and the robustness analysis from the three different methods reached the same conclusion: that road mortality is pro-cyclical and the relationship is symmetric during periods of economic fluctuations.
Discussion
Conflicts of interest: None declared.
Key points Road traffic injuries are recognized as a major global public health issue. Road mortality is pro-cyclical: on average, a 1% increase/ decrease in economic growth is associated with 1.1% increase/decrease in road mortality. The cyclicality of road mortality is symmetric.
1
Sharma BR. Road traffic injuries: a major global public health crisis. Public Health 2008;122:1399–406.
2
World Health Organization. Global Status Report on Road Safety: Time for Action. Geneva: World Health Organization; 2009.
3
Lancet. Reducing road dangers. Lancet 2011;377:1543.
4
van Beek EF, Borsboom GJJ, Mackenbach JP. Economic development and traffic accident mortality in the industrialized world, 1962-1990. Int J Epidemiol 2000;29: 503–9.
5
Kopits E, Cropper M. Traffic fatalities and economic growth. Accid Anal Prev 2005; 37:169–78.
6
Tanaboriboon Y, Satiennam T. Traffic accidents in Thailand. IATSS Res 2005;29: 88–100.
7
Stuckler D, Basu S, Suhrcke M, et al. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. Lancet 2009;374: 315–23.
8
Cruces G, Glu¨zmann P, Calva LFL. Economic crises, maternal and infant mortality, low birth weight and enrollment rates: evidence from Argentina’s downturns. World Development 2012;40:303–14.
9
Wooldridge JM. Introduction Econometrics: A Modern Approach, 4th edn. Cincinnati, Ohio: South-Western College Publishing, 2008.
10 World Health Organization. World Report on Road Traffic Injury Prevention. Geneva: World Health Organization, 2004.
Appendix The 32 OECD countries (time periods) included in the full sample cross-country longitudinal data were: Australia (1971–2010), Austria (1971–2010), Belgium (1971–2009), Canada (1971–2009), Czech Republic (1994–2010), Denmark (1971–2010), Estonia (1994–2010), Finland (1971–2010), France (1971–2009), Germany (1971–2009), Greece (1971–2009), Hungary (1992–2010), Iceland (1971–2010), Ireland (1971–2010), Italy (1971–2009), Japan (1971–2009), Korea (1971–2010), Luxembourg (1971–2009), Mexico (1971–2009), Netherlands (1971–2009), New Zealand (1971–2010), Norway (1971–2010), Poland (1992–2010), Portugal (1971–2009), Slovak Republic (1994–2010), Slovenia (1996–2010), Spain (1971–2010), Sweden (1971–2010), Switzerland (1981–2010), Turkey (1971–2010), United Kingdom (1971–2010), United States (1971–2010). Chile and Israel were excluded from this analysis since the road fatality data were unavailable.
Downloaded from http://eurpub.oxfordjournals.org/ by guest on November 16, 2015
The conclusion that road mortality is pro-cyclical is in line with previous research which incorporated single-country data6 and cross-country data from 26 European Union countries.7 In addition, the cross-country analysis results in this article suggest that the relationship between economic fluctuations and road mortality is symmetric—road mortality increases during economic ups and decreases during economic downs to a similar degree. The responses of traffic demand and alcohol consumption behaviours to economic fluctuations could be the two key reasons that behind the observation. Several interventions have been found to effectively reduce the number of road traffic injuries and deaths,10 among which the strategies that focus on reducing traffic volume and/or travelling distances may be the most valuable. In addition, fiscal policy such as alcohol tax could also be particularly effective in this context. So far no specific knowledge is available to inform understanding of which of those interventions would be more effective during periods of economic boom. It should be noted that in this study only the overall road mortality was used. A further analysis using age-specific road mortality data would offer more information regarding the cyclical behaviour of road mortality and would contribute towards the effective design and implementation of intervention strategies. In addition, the strength of different potential factors that contribute to the pro-cyclically road mortality was not studied in this study. Economic prosperity is associated with improved road safety from a long-term perspective; however, economic boom in the short term is also likely to be associated with detrimental impacts on road safety—a phenomenon that deserves greater academic and public attention.
References
