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Persistence and dynamics in the efficiency of toll motorways: The Spanish case
This study performs an empirical analysis of the productive efficiency of toll motorway concessionaire companies in Spain. We estimate a dynamic stochastic frontier model using an input-oriented distance function for 30 concessionaires during the 2003–2015 period. Considering a multi-output producti...
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Published in: | Economics of transportation (England) 2022-09, Vol.31, p.100270, Article 100270 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This study performs an empirical analysis of the productive efficiency of toll motorway concessionaire companies in Spain. We estimate a dynamic stochastic frontier model using an input-oriented distance function for 30 concessionaires during the 2003–2015 period. Considering a multi-output production technology with light and heavy vehicles, we estimate an autoregressive dynamic specification under a Bayesian framework that acknowledges persistence in firm efficiency due to adjustment costs. Our results reveal: (i) large persistence in productive inefficiency in the toll motorway sector, (ii) technical change from 2006 onwards, and (iii) increasing returns to scale. We derive both short- and long-run inefficiency estimates and document that long-run inefficiency increases with the number of stretches a firm manages; however, inefficiency is unrelated to the political authority that grants the concession. We also find that the marginal cost of light vehicle-kilometres is about half that for heavy vehicles.
•The efficiency of Spanish toll motorway concessionaire companies is examined.•We consider a multi-output technology using Bayesian stochastic frontier analysis.•We study persistence in firm inefficiency due to adjustment costs.•The average long-run technical efficiency is about 80%.•Our estimates indicate the marginal cost of light vehicles is half the one for heavy vehicles. |
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ISSN: | 2212-0122 2212-0130 |
DOI: | 10.1016/j.ecotra.2022.100270 |