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Regulatory and Environmental Effects on Public Transit Efficiency. A Mixed DEA-SFA Approach

The aim of this paper is to account for the impact of statistical noise and exogenous regulatory and environmental factors on the efficiency of public transit systems in a DEA-based framework. To this end, we implement a three-stage DEA-SFA mixed approach based on Fried et al. (2002) using a 1993-1999 panel of 42 Italian public transit companies. This allows us to decompose input-specific DEA inefficiency measures into three components: exogenous effects, pure managerial inefficiency, and statistical noise. First, the initial evaluation of producer performance is carried out using conventional variable returns to scale DEA (Banker et al., 1984). Second, a SFA approach (Battese and Coelli, 1992) is used to regress single input slacks on subsidies regulation (cost-plus versus fixed-price contracts) and a set of environmental variables including network speed and user density. Finally, third stage re-runs DEA on inputs purged of both exogenous effects and statistical noise. Results are such that adjusting for the type of regulatory scheme, environmental conditions, and statistical noise increases average efficiency in the industry and reduces dispersion among firms. Furthermore, the implementation of fixed-price subsidies is found to enhance efficiency in the usage of “drivers” and “materials and services” inputs. Such a result sheds some light on the determinants of input-specific efficiency differentials in the industry, improving the existing evidence on mean overall cost efficiency (e.g. Gagnepain e Ivaldi, 2002; Piacenza, 2006). As a policy implication, it is confirmed the relevance of regula tion aimed at replacing cost-plus subsidization mechanisms with high-powered incentive contracts as well as improving operating conditions of public transport networks.

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Paper provided by Institute for Economic Research on Firms and Growth - Moncalieri (TO) in its series CERIS Working Paper with number 200613.

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Length: 25 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:csc:cerisp:200613
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  1. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-32.
  2. Boame, Attah K., 2004. "The technical efficiency of Canadian urban transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(5), pages 401-416, September.
  3. Philippe Gagnepain & Marc Ivaldi, 2002. "Incentive Regulatory Policies: The Case of Public Transit Systems in France," RAND Journal of Economics, The RAND Corporation, vol. 33(4), pages 605-629, Winter.
  4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  5. DE BORGER, Bruno & KERSTENS, Kristiaan & COSTA, Álvaro, . "Public transit performance: What do we learn from frontier studies?," Working Papers 2000019, University of Antwerp, Faculty of Applied Economics.
  6. KERSTENS, Kristiaan, 1995. "Technical efficiency measurement and explanation of French urban transit companies," SESO Working Papers 1995020, University of Antwerp, Faculty of Applied Economics.
  7. C. Cambini & M. Filippini, 2003. "Competitive Tendering and Optimal Size in the Regional Bus Transportation Industry: An Example from Italy," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 74(1), pages 163-182, 03.
  8. Massimiliano Piacenza, 2006. "Regulatory Contracts and Cost Efficiency: Stochastic Frontier Evidence from the Italian Local Public Transport," Journal of Productivity Analysis, Springer, vol. 25(3), pages 257-277, 06.
  9. Dalen, Dag Morten & Gomez-Lobo, Andres, 1997. "Estimating cost functions in regulated industries characterized by asymmetric information," European Economic Review, Elsevier, vol. 41(3-5), pages 935-942, April.
  10. Philippe Gagnepain & Marc Ivaldi, 2002. "Stochastic Frontiers and Asymmetric Information Models," Post-Print hal-00622849, HAL.
  11. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  13. repec:cup:cbooks:9780521336017 is not listed on IDEAS
  14. Giovanni Fraquelli & Massimiliano Piacenza & Graziano Abrate, 2004. "Regulating Public Transit Networks: How do Urban-Intercity Diversification and Speed-up Measures Affect Firms' Cost Performance?," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 75(2), pages 193-225, 06.
  15. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
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