The relationship between per capita GDP and road accidents in Sri Lanka: an ARDL bound test approach
Additional contact information
T. Bhavan: Department of Economics, Faculty of Commerce and Management, Eastern University, Sri Lanka
Asian Journal of Empirical Research, 2018, vol. 8, issue 7, 238-246
The objective of this study is to examine the relationship between road accidents and per capita GDP in Sri Lanka during the period of 1977 to 2016. The study examines the existence of Kuznets curve relationship between number of road accidents and per capita GDP in Sri Lanka, and the relationship of some other variables such as merchandise imports from various regions and different age group of population as well. The Auto Regressive Distributed Lags (ARDL) bound test approach is employed to analyse the time series data. The findings of this study confirm the existence of the Kuznets curve relationship between road accidents and per capita GDP. The results further reveal that there is a long-run relationship between road accidents and merchandise imports from high income and East Asian countries, urban population and age group of population 30-34 and 35-39. Coefficients of these variables are statistically significant at 1% level and positively associated with number of accidents in Sri Lanka.
Keywords: Sri Lanka; Road accidents; Kuznets curve; ARDL bound test; Per capita GDP; Long-run relationship (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:asi:ajoerj:2018:p:238-246
Access Statistics for this article
Asian Journal of Empirical Research is currently edited by Kashif Imran ( Managing Editor)
More articles in Asian Journal of Empirical Research from Asian Economic and Social Society 2637 E Atlantic Blvd #43110 Pompano Beach, FL 33062, USA.
Bibliographic data for series maintained by Chan Hoi Yan ().