Long term productivity gains in the privatised British passenger rail industry – A case study of Malmquist productivity index measurements

Cowie, Jonathan

Abstract

This paper assesses long term productivity in the privatised British passenger rail industry using a Malmquist productivity index (MPI) generated using three different efficiency assessment methods, namely data envelopment analysis (DEA), corrected ordinary least squares (COLS) and the stochastic production frontier (SPF). The
results indicate that over the period 1997 to 2015, the industry achieved annual average productivity gains of around 1% to 1 1/2%, with most of this improvement coming in the first half of the period, and the second half of the period displayed unitary productivity returns. Virtually all gains have come as a result of technical change, however the rate of increase was found to gradually decreasing throughout the whole time period, hence future
productivity gains may be more difficult to achieve. The results from the three approaches are considered, and the COLS is found to be most consistent with the idea of total factor productivity and technical change as long run concepts and efficiency change as a short run phenomenon.

Citation

Cowie, J. (2018). Long term productivity gains in the privatised British passenger rail industry – A case study of Malmquist productivity index measurements. Research in Transportation Business & Management, https://doi.org/10.1016/j.rtbm.2018.01.001

• Journal Article Type: Article
• Acceptance Date: Jan 19, 2018
• Online Publication Date: Feb 3, 2018
• Publication Date: 2018-02
• Deposit Date: Feb 1, 2018
• Publicly Available Date: Feb 4, 2019
• Journal: Research in Transportation Business & Management RTBM
• Print ISSN: 2210-5395
• Electronic ISSN: 2210-5409
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.rtbm.2018.01.001
• Keywords: Rail privatisation, malmquist productivity index, total factor productivity, efficiency change, technical change, data envelopment analysis, corrected ordinary least squares
• Public URL: http://researchrepository.napier.ac.uk/Output/1033612
• Contract Date: Feb 2, 2018