Diploma_Thesis_Poster_FGiessing

1
Chair of Energy Economics and Public Sector
Management
Outsourcing in Local Public Transport A Hidden
Efficiency Determinant? Matthias Walter 15 May
2009
Motivation
Data
Facts
Descriptive Statistics

• Unbalanced panel 254 observations for 39
  multi-output companies from 1997 until 2006
• Physical data from VDV statistics, monetary data
  from annual reports
• Monetary data in 2006 prices inflated by the
  German producer-price-index (Destatis, 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 premium)

Historically Organized Market Characterized by
the Substantial Need for Transfers
Several Measures to Increase Efficiency

• High fragmentation of the market several 100
  operators
• Integrated operators of bus and tram or light
  railway or metro in medium-sized and larger
  cities
• Very low level of cost coverage with mean of
  73.8 (Verband Deutscher Verkehrsunternehmen,
  2008)
• Mostly municipal ownership with high degree of
  political intervention (Public service
  obligation, decision over new lines, )

• Mergers and acquisitions, especially for
  companies operating on a network with connecting
  lines, e.g. Mannheim, Heidelberg and Ludwigshafen
• 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
• Efficiency Analysis
• for incentive regulation
• for sunshine regulation (naming and shaming)

Outsourcing (part of material costs) moves
personnel costs to 3rd parties
Data Correlations
ID (population in the supplied area) / (bus
line and length rail-bound track length) not
dependent on congestion
Methodology
Heteroscedastic Stochastic Cost Frontier Models
Translog Cost Function
Function Design

• Local point of approximation Mean
• Time dummies with neutral technical change
  following Farsi et al. (2005) and Farsi and
  Filippini (2009)
• No reliable results with linear time trend and
  time trend varying with input and output levels
  (Saal et al., 2007)

• 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)
• 1) Random Effects Model (ML estimation) as
  suggested by Pitt and Lee (1981)
• 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

,

Results
Regression Results
Descriptive Efficiencies

• Significant modeling results for unobserved
  heterogeneity and heterogeneous output
  characteristics

Efficiency Rank Correlations

• Significant cost decreases over 10 years
  (up to 25)

Conclusions

• 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
• 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
• 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

Tests on Variable Specification

• LR-Tests on Random Effects Model
• Model w UR better than model w/o OUT UR (p
  0.014)
• Model w UR better than model w OUT UR (q
  0.009)

• Wald Tests on Random Parameter Models
• - Model w OUT UR better than model w OUT only
  (p 0.000 each)
• - Model w OUT UR better than model w UR only (p
  0.073, 0.067)

Kernel Density of Efficiency Predictions

• Negative skewness of all curves
• Similar distributions for True Random Effects and
  Random Parameter Models
• 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
  factors

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),
4761. 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
http//www.bundesbank.de/download/volkswirtschaft/
monatsberichte/2007/200708mb_bbk.pdf, 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), 6990. Farsi, M., Filippini, M., 2009. An
analysis of cost efficiency in Swiss
multi-utilities. Energy Economics 31 (2),
306315. 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), 269303. Greene, W., 2004.
Distinguishing between heterogeneity and
inefficiency Stochastic Frontier Analysis of the
World Health Organizations panel data on
national health care systems. Health Economics 13
(10), 959980. 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), 203222. Pitt, M.,
Lee, L., 1981. The measurement and sources of
technical inefficiency in Indonesian weaving
industry. Journal of Development Economics 9 (1),
4364. 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),
127139. Verband Deutscher Verkehrsunternehmen,
2008. VDV Statistik 2007. Köln.