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Chair of Energy Economics and Public Sector Management
Outsourcing in Local Public Transport:
A Hidden Efficiency Determinant?
Matthias Walter
15 May 2009
Historically Organized Market
Characterized by the Substantial Need
for Transfers
Several Measures to
Increase Efficiency
• High fragmentation of the market: several
100 operators
• Unbalanced panel: 254
observations for 39 multioutput companies from
1997 until 2006
• Mergers and acquisitions, especially for
companies operating on a network with
connecting lines, e.g. Mannheim,
Heidelberg and Ludwigshafen
• Integrated operators of bus and tram or
light railway or metro in medium-sized and
larger cities
• Physical data from VDV
statistics, monetary data
from annual reports
• Monetary data in 2006
prices inflated by the
German producer-priceindex (Destatis, 2008)
• Tenders: so far only for bus services in
Hesse and for regional bus services
around Hamburg and Munich, maybe in
the future also for other services
• Very low level of cost coverage with
mean of 73.8% (Verband Deutscher
Verkehrsunternehmen, 2008)
• Capital price including
material costs, other
operating expenses,
depreciations, interests on
borrowed capital and the
opportunity cost of capital
(equity base x interest
rates for corporate bonds
(Deutsche Bundesbank
(2007) plus 2% risk
• Efficiency Analysis
• for incentive regulation
• Mostly municipal ownership with high
degree of political intervention (Public
service obligation, decision over new lines,
Descriptive Statistics
• for sunshine regulation (“naming and
Outsourcing (part of
material costs) moves
personnel costs to 3rd
Data Correlations
ID = (population in the
supplied area) / (bus line
and length rail-bound
track length) not
dependent on congestion
Translog Cost Function
Function Design
Heteroscedastic Stochastic Cost Frontier Models
• Local point of
approximation: Mean
• Use of random effects model to exploit the panel data structure and because of low within variation (at most
6% based on overall variation for costs, outputs and the remaining factor price)
• Time dummies with
neutral technical
change following Farsi
et al. (2005) and Farsi
and Filippini (2009)
• 1) Random Effects Model (ML estimation) as suggested by Pitt and Lee (1981):
• No reliable results
with linear time trend
and time trend varying
with input and output
levels (Saal et al.,
• Heteroscedastic inefficiency determinants (ID, UR), revealing management quality following Bhattacharyya
et al. (1995), Hadri et al. (2003) and Greene (2007):
• 2) True Random Effects Model (Simulated ML) based on Greene, 2004 and 2005:
• 3) Random Parameter Model allowing for heterogeneity in the outputs
• Heteroscedastic inefficiency term for the Random Parameter Models as for the Random Effects Model, except
for the time-variant inefficiency determinants:
Regression Results
Descriptive Efficiencies
results for
Efficiency Rank Correlations
Significant cost
decreases over
10 years
(up to 25%)
• Optimization of outsourcing as positive efficiency determinant should be in focus for the industry
• Vast differences in the vehicle utilization rate for railcars determines efficiency predictions
Tests on Variable Specification
• Wald Tests on Random Parameter Models
• LR-Tests on Random Effects Model
- Model w UR better than model w/o OUT & UR (p = 0.014)
- Model w OUT & UR better than model w OUT only (p = 0.000 each)
- Model w UR better than model w OUT & UR (q = 0.009)
- Model w OUT & UR better than model w UR only (p = 0.073, 0.067)
- Improvement options can be related to enhancing speed through infrastructure measures (separate rail
embankments, prioritization at traffic lights, tunnels in inner-city areas, new tracks, express trains, etc.)
- Furthermore maintenance times could be reduced and procurement optimized
• Profit and cost efficiency
- Relatively high mean efficiencies suggest that the problem is likely to be not only on the cost side, where
improvements through wage reductions have happened in the past
Kernel Density of Efficiency Predictions
- Cost saving potential for the dataset: 1.40 - 4.43 bn EUR based on 28.23 bn EUR total costs (in 2006 prices)
- The revenue side should bear further optimization potential and should be analyzed in the future
• Negative skewness of all curves
• Similar distributions for True
Random Effects and Random
Parameter Models
Kernel Density Estimate
• Bimodal distribution in the Random
Effects Model against the
expectation, Farsi and Filippinis’
(2009) explanation: “cost
differences that are not due to
inefficiencies but to other external
Random Effects Model
True Random Effects Model
Random Parameter Model
References (selected)
Bhattacharyya, A., Kumbhakar, S., Bhattacharyya, A., 1995. Ownership structure and cost efficiency: A study of publicly owned passenger-bus transportation companies in India. Journal of
Productivity Analysis 6 (1), 47–61.
Destatis, 2008. Preise und Preisindizes für gewerbliche Produkte (Erzeugerpreise) – Juni 2008. Statistisches Bundesamt Fachserie 17 Reihe 2. Wiesbaden.
Deutsche Bundesbank, 2007. Monatsbericht August 2007. On the Internet:, retrieved 27 April 2009.
Farsi, M., Filippini, M., Greene, W., 2005. Efficiency measurement in network industries: Application to the Swiss railway companies. Journal of Regulatory Economics 28 (1), 69–90.
Farsi, M., Filippini, M., 2009. An analysis of cost efficiency in Swiss multi-utilities. Energy Economics 31 (2), 306–315.
Greene, W., 2007. Limdep Version 9.0 Reference Guide. Econometric Software, Plainview.
Greene, W. H., 2005. Reconsidering heterogeneity in panel data estimators of the Stochastic Frontier model. Journal of Econometrics 126 (2), 269–303.
Greene, W., 2004. Distinguishing between heterogeneity and inefficiency: Stochastic Frontier Analysis of the World Health Organization’s panel data on national health care systems. Health
Economics 13 (10), 959–980.
Hadri, K., Guermat, C., Whittaker, J., 2003. Estimation of technical inefficiency effects using panel data and doubly heteroscedastic stochastic production frontiers. Empirical Economics 28 (1),
Pitt, M., Lee, L., 1981. The measurement and sources of technical inefficiency in Indonesian weaving industry. Journal of Development Economics 9 (1), 43–64.
Saal, D. S., Parker, D., Weyman Jones, T., 2007. Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh
water and sewerage industry: 1985-2000. Journal of Productivity Analysis 28 (1-2), 127–139.
Verband Deutscher Verkehrsunternehmen, 2008. VDV Statistik 2007. Köln.
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