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Economic factors and traffic crashes in New Zealand

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  • P. A. Scuffham

Abstract

The aim of this study was to examine the changes in the trend and seasonal patterns of fatal crashes in New Zealand in relation to changes in economic conditions between 1970 and 1994. The Harvey (Harvey and Durbin, Journal of the Royal Statistical Society, 149 (3), 187-227, 1986) Structural Time Series Model (STSM), an 'unobserved components' class of model, was used to estimate the quarterly number of fatal traffic crashes. Independent variables included distance travelled, the unemployment rate (UER), real gross domestic product per capita (RGDP), the proportion of motorcycles, the proportion of young males in the population, alcohol consumption per capita, the open road speed limit, and dummy variables for the 1973 and 1979 oil crises and seatbelt wearing laws. Distance travelled, RGDP, UER, and alcohol consumption per capita were significant factors in explaining the short-run dynamics of fatal crashes with the effect of RGDP greater than UER. Increases in either RGDP or UER were related with decreases in fatal crashes. The STSM is a feasible approach to modelling the effect of economic factors on traffic crashes whilst accounting for unobserved components.

Suggested Citation

  • P. A. Scuffham, 2003. "Economic factors and traffic crashes in New Zealand," Applied Economics, Taylor & Francis Journals, vol. 35(2), pages 179-188.
    • Handle: RePEc:taf:applec:v:35:y:2003:i:2:p:179-188
    DOI: 10.1080/0003684022000017566
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    References listed on IDEAS

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    1. Victor R. Fuchs, 1974. "Some Economic Aspects of Mortality in Developed Countries," International Economic Association Series, in: Mark Perlman (ed.), The Economics of Health and Medical Care, chapter 11, pages 174-201, Palgrave Macmillan.
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    5. Harvey, A. C., 1986. "The effects of seat belt legislation on British road casualities: A case study in structural modelling : A.C. Harvey and J. Durbing, Journal of the Royal Statistical Society, Series A 149 (1986) (in p," International Journal of Forecasting, Elsevier, vol. 2(4), pages 496-497.
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    Cited by:

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    2. Murali Adhikari & Krishna Paudel & Laxmi Paudel & James Bukenya, 2007. "Modelling swine supply response using a structural time series approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(7), pages 467-472.
    3. Rohan Best & Paul J. Burke, 2019. "Fuel prices and road accident outcomes in New Zealand," New Zealand Economic Papers, Taylor & Francis Journals, vol. 53(2), pages 109-124, May.
    4. Elizabeth Kopits & Maureen Cropper, 2008. "Why Have Traffic Fatalities Declined in Industrialised Countries?: Implications for Pedestrians and Vehicle Occupants," Journal of Transport Economics and Policy, University of Bath, vol. 42(1), pages 129-154, January.
    5. Yoshitsugu Kitazawa, 2010. "Size of economic activity and occurrence of fatal traffic accidents: a count panel data analysis on Fukuoka prefecture in Japan," Discussion Papers 41, Kyushu Sangyo University, Faculty of Economics.
    6. Shin-Jong Lin, 2009. "Economic fluctuations and health outcome: a panel analysis of Asia-Pacific countries," Applied Economics, Taylor & Francis Journals, vol. 41(4), pages 519-530.
    7. Biondi, Beatrice & Mazzocchi, Mario, 2024. "An empirical analysis of the effect of economic activity and COVID-19 restrictions on road traffic accidents in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    8. Taruwere Yakubu, Ahmed & Aremu Muhammed, Ismail, 2021. "Economic Condition And Road Transport Crashes In Nigeria: Evidence From State Level Data," Ilorin Journal of Economic Policy, Department of Economics, University of Ilorin, vol. 8(2), pages 36-44, June.
    9. Murali Adhikari & Krishna Paudel & Jack Houstan & James Bukenya, 2007. "Dairy supply response under stochastic trend and seasonality," Applied Economics Letters, Taylor & Francis Journals, vol. 14(12), pages 887-891.

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