1. Introduction

A steep decline in road fatalities in Australia was found within past few decades. It is worthwhile investigating causes， implications as it provides important insight to reduce road fatalities. The implementation of alcohol limit laws was a statistically proven factor in decreasing road fatalities （Gaudry， 2014）. Unemployed people are incapable of buying cars and thus have less exposure to traffic accidents. Another factor is inflation related with actual costs of using motor vehicles. Transportation habits may change due to high petrol and servicing costs. This report examines the issue of vehicle fatalities from macro-views of alcohol laws， unemployment and inflation， which contributes to policy making under dynamic economy.

2. Data

Annual road deaths data from 1925 to 2016 was gathered from Bureau of Infrastructure， Transport and Regional Economics （2017）. The dependent variable FATALITIES refers to total number of annual road deaths per million people. Independent variables of ALCOHOL is the pure alcohol consumption per capita with data from Australian Bureau of Statistics （2017c）； UNEMPLOYMENT is annual Australian unemployment rate with data from Federal Reserve Bank of St. Louis （2017）； INFLATION is annual Australia inflation rate with data from Australian Bureau of Statistics（2017d）. Unit root test for stationarity will be conducted and returned p-values will suggest whether to reject the null hypothesis of nonstationarity. P-values all larger than 0.05 from test suggests non-stationarity. The same test on differenced variables indicates significant results of stationarity.

3. Methodology and Results

Due to the failure to find the co-integration between original variables， first differenced stationary variables are put into OLS regression. The model to estimate relationship between dependent and independent variables is as follow：

（Standard error） （1.284） （4.930） （1.518） （0.676）

（p-value） （0.004） （0.008） （0.004） （0.046）

1 increase alcohol consumption raises fatalities by 13.731 fatalities. 1% increase in INFLATION and UNEMPLOYMENT result in 4.6 and 1.4 decrease by FATALITIES. Findings confirm our hypotheses that alcohol consumption has a positive relationship but inflation and unemployment rate have inverse relationships with road fatalities. Also the adjusted R2 indicates that the model can explain 25.33% of the variations in . Therefore having found significant correlation between the explanatory variables and road deaths has been a great step forward in understanding the possible causes of road deaths. This can assist legislatures or other policy-makers when creating and deciding on effective strategies to lower the national fatality rate. Therefore this adds credibility that to our hypothesis， in that all variables investigated have proven to be significant in explaining Australias road fatalities.

4. Conclusions

The relationship between road fatalities and socio-economic factors is not straightforward due to nonstationary variables and noncointegration between variables. However， with stationarity of first differenced variables， the relationship between the change in road fatality amount and alcohol consumption， unemployment and inflation rate can be constructed through OLS regression. It was then found that the change in fatalities positively related to alcohol consumption， but negatively to unemployment and inflation rates. They provide supportive evidence to answer initial research questions. Whats more， the result not has constructive meanings on policymakers to reduce road fatalities. It also suggests policy makers in Australia to redirect their legislative focus on the factor with greater influence. In this model， change in alcohol is most significant to the change of vehicle fatality amounts. Therefore， it indicates that the priority should be to reduce alcohol consumption. Unemployment rate and inflation rate are involved in greater national macroeconomic regulatory field which presents its own inherent challenges in achieving change in such a dynamic system.

References：

[1]Australian Bureau of Statistics. （2017）. Motor Vehicle Census； Apparent Consumption of Alcohol.

[2]Bureau of Infrastructure， Transport and Regional Economics. （2017）. Australian Road Deaths Database.

[3]Department of Transport and Regional Services， Australian Transport Safety Bureau. （2003）.

[4]Federal Reserve Bank of St. Louis. （2017）. Harmonized Unemployment Rate： Total： All Persons for Australia.

[5]Gaudry， M. （2014）. National road fatality maxima： why the 1972 cluster？. Securitas Vialis， 6（1-3）， 47-55.

【作者簡(jiǎn)介】李榴心，北京外國(guó)語大學(xué)國(guó)際商學(xué)院。