Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Kennedy Ekeya Emoru^1, , Joel Cheruiyot Chelule¹, Herbert Orango Imboga¹ and Ayubu Okango Anapapa¹

¹Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya

Cite this paper:
Kennedy Ekeya Emoru, Joel Cheruiyot Chelule, Herbert Orango Imboga and Ayubu Okango Anapapa. Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp. American Journal of Applied Mathematics and Statistics. 2020; 8(3):96-102. doi: 10.12691/ajams-8-3-3

Abstract

Duration taken to release cargo is one of World Customs Organisation¡¯s most significant instruments of measuring trade facilitation. It is a way of reviewing clearance procedure by measuring the time taken to release cargo at border entry points. This research therefore aimed to model clearance time using the Extended Cox model, which is a modification of the proportional hazard Cox model in which proportional hazard assumptions are not met, and tested the practicability of the model and compared the performance of Extended Cox model with the other conventional methods using secondary data on time clearance that was obtained from the KRA, Malaba OSBP, for the period 1st January 2015 to 31st July 2019. The variables in the study were both time depend (time taken from registration to release) and time independent (customs regimes, origin, destination, customs value and description of goods). The variables used in the research project were tested for proportional hazard assumptions. Extended Cox model was the most suitable model for analysis duration taken to release cargo data has it was found to have a lower AIC and BIC as compared to conventional cox model. Therefore, customs administration should embrace the use of Extended Cox model to analysis duration taken to release cargo data as it is statistically significant and give efficient results that are based on proper statistical methods. It is recommended that further studies to be done on a large data set that encompasses both entry and exit cargo in all the Kenyan border points.

Keywords:
cox model duration cargo kenyan border entry points

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Bellera, C. A., MacGrogan, G., Debled, M., de Lara, C. T., Brouste, V., & MathoulinP¨¦lissier, S.. Variables with time-varying effects and the cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC medical research methodology, 2010, 10(1), 20.
[2]	Husain, H., Thamrin, S. A., Tahir, S., Mukhlisin, A., & Apriani, M. M.. The application of extended cox proportional hazard method for estimating survival time of breast cancer. In Journal of physics: Conference series 2018 (Vol. 979)
[3]	Klein, J. P., & Moeschberger, M. L.. Survival analysis: techniques for censored and truncated data. Springer Science & Business Media 2006.
[4]	Kleinbaum, D. G., & Klein, M. Extension of the cox proportional hazards model for time-dependent variables. In Survival analysis Springer 2012(pp. 241-288).
[5]	Mungasi, S., & Odhiambo, C. Comparison of survival analysis approaches to modelling credit risks. American Journal of Theoretical and Applied Statistics, 2019 8(2), 39-46.
[6]	Njuguna, A. Modelling survival in hiv/aids treatment: Cox proportional hazard versus accelerated failure time models. (Unpublished doctoral dissertation). University of Nairobi 2013.
[7]	Shingo Matsuda. The Time Release Study as a performance measurement tool for a supply chain and an international corridor. WCO Journal (2011).
[8]	Orwa, A. O., Orwa, G. O., & Odhiambo, R.. Deriving penalized splines for estimation of time varying effects in survival data American journal of Science 2014.