The major determinants of logistic performance in a global perspective: evidence from panel data analysis

ABSTRACT

The global logistics industry has grown significantly and logistics has become an important sector of the business economic system and a major global economic activity in recent years. Logistics activities accelerate economic and productivity growth. Efficient logistics is also important to a country’s competitiveness and source of employment. The purpose of this paper is to uncover and understand the major determinants of logistics performance (LP) to further lift the LP of countries. Using unbalanced panel data of 93 selected countries from 2007 to 2014, the present research attempts to critically investigate the major determinants of LP. In estimating the model, this study prefers to use static panel data approach owing to limited data. The findings of the present study reveal that (a) countries with low level of corruption and stable political environment are likely to yield a high level of LP; (b) improvement in resources supply such as infrastructure, technology, labour, and education also have a significant positive effect on LP. Therefore, institutional reforms and upgrading resources will effectively accelerate LP.

KEYWORDS:

• Logistic performance
• institutional quality
• corruption
• political stability

Acknowledgement

The authors would like to thank the two anonymous reviewers for their constructive comments to the earlier draft of this paper. We would also like to acknowledge the Ministry of Higher Education Malaysia.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Wai Peng Wong http://orcid.org/0000-0002-0875-9199

Chor Foon Tang http://orcid.org/0000-0003-2242-9222

Notes

1 The list of countries selected for investigation based on availability of data are given in Appendix 1. This study uses unbalanced panel data meaning that the time point in each panel is not the same.

2 It is best to note that the dynamic panel generalised method of moments (GMM) is not applicable in this study because time-series dimension cover only 2–4 observations. Therefore, it is insufficient to apply the dynamic panel GMM estimation which it requires lagged variables term as well as first differencing of the estimate variables.

3 Another important issue to be concerned is the presence of time-specific effect. We include time dummies into the models, but the dummies are jointly insignificant at the 5 per cent level where the calculated F-statistic is only 2.56. This is nothing to be surprised because the period of time-series data in this study is short. Therefore, our analysis is mainly based on the one-way fixed-effect model. In addition, Baltagi (2016) also documented that one-way model is commonly used model in panel data analysis.

Additional information

Funding

This work was supported by Ministry of Higher Education Malaysia (MOHE) under Fundamental Research Grant Scheme (FRGS) and Universiti Sains Malaysia Bridging Grant: [grant number 203/PMGT/6711513 and 304/PMGT/6316008].