Estimation of Socio-Economic Loss due to Road Traffic Accident in India

India witnessed 151 417 road deaths in the year 20181 and this alarming number is due to increased urbanization, motorization and per capita income. India is home to the 2nd largest road network in the world and accounts for the highest number of road deaths globally. Curbing the menace of road deaths requires tactical road safety policies and their effective implementation. The meagre availability of factual data regarding socio-economic loss due to road crashes is proving to be a hindrance to the ideation and implementation of the policies. The Planning Commission estimated the social costs of road accidents to be ₹ 55 000 crore in 1999-2000 which was roughly 3% of the country’s GDP and this value was revised to ₹ 100 000 crores in 2011. Absence of data regarding the loss in recent times due to road crashes have been a motivating factor to our study. Road deaths bring about a great deal of human suffering in terms of social, medical and economic costs. In our study, data from In-depth accident databases, police records, medical, and insurance and government reports has been utilized to bring about a realistic estimation of the magnitude of loss to the eyes of the policymakers and general public. To evaluate these costs, various methods were assessed in this study like the Human Capital (HC), Willingness-To-Pay (WTP) and iRAP’s Rule of thumb (RoT) methods. Irrespective of the method, the socio-economic loss of road accident death for the year 2018 is estimated to be 0.66-0.89 % of India’s GDP. The estimated average cost of a fatal road crash in India is about 0.13 million $. This reflects the paramount economic drag due to road deaths and highlights the importance of bringing in road safety policies to reduce road crashes, deaths and injuries. This study is one of the first to put the current accident figures in relation to the economic costs specifically for India and can be used as a basis for policy makers to further develop their studies to derive cost-benefit estimates for different measures.