Original Research

Causes of road accidents in Botswana: An econometric model

Thuso Mphela
Journal of Transport and Supply Chain Management | Vol 14 | a509 | DOI: https://doi.org/10.4102/jtscm.v14i0.509 | © 2020 Thuso Mphela | This work is licensed under CC Attribution 4.0
Submitted: 01 April 2020 | Published: 16 September 2020

About the author(s)

Thuso Mphela, Department of Management, University of Botswana, Gaborone, Botswana


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Abstract

Background: Road traffic accidents claim over 1.3 million lives annually around the globe and remain a key socio-economic challenge today. At 20.1 per 100 000, Botswana’s fatality rate is higher than the global average of 17.4. Previous studies on the causes of road crashes in Botswana have not explored statistical causality. This study is thus grounded on the theory of causality.

Objectives: This study sought to determine the causes of road traffic accidents and fatalities in Botswana. For this purpose, the article discusses the accident count model based on Botswana data.

Method: The study used road accident data from 2008 to 2017. Econometric modelling on Gretl was used to compute two ordinary least squares (OLS) regression models. Manual elimination of insignificant variables was performed through the iterations.

Results: Both models are statistically significant at p ≤ 0.001, but the accident count model, with an adjusted R2 value of 0.99 against 0.83, is more robust and has a better predictive power as opposed to the fatalities model. At the individual variable level, the analysis shows mixed results.

Conclusion: The study contends that increased exposure and night-time travel increase road crashes, whilst expansion of road infrastructure is inversely related to road accidents. An increase in both population density and exposure leads to increased fatalities. Regulating the importation of used vehicles and investment in rail transport is a potential policy panacea for developing economies. Future studies should investigate the causes of pedestrian fatalities and night accidents.


Keywords

road traffic accidents; accident modelling; econometric modelling; forecasting; exposure; night-time travel; road infrastructure.

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