Efficiency in Italian Airports Management: The Implications for Regulation

1. Efficiency in Italian Airports Management:The Implications for Regulation Paolo Malighetti+ Gianmaria Martini+ Renato Redondi§ and Stefano Paleari+ +University of Bergamo §University of Brescia Workshop HERMES 2007 Quale futuro per il settore del trasporto aereo?

2. Plan • The problem • Past studies • Performance measurement methods (DEA) • Variables used • Results • Price regulation • Conclusions

3. The problem • Air transportation industry has shown important developments during the last decades • Regulation has • opened the market to new comers; • increased the effective competition among incumbent firms; • adopted more efficient regulation schemes to compute airports' fares. • Players (airlines) • New business models have deeply renewed the competitive arena, especially with the appearance and progressive strengthening of the low cost carries.

4. The problem • In this new context both the airports management and pricing play a crucial role. • Pricing may start a phase of competition between airports. • Within a given area both passengers and freights show a sufficiently high propensity to move • An airport has the chance, by charging lower fares than its local competitors, to modify the carriers’ decisions and to increase the number of connections supplied

5. The problem • Airport’s management should have an incentive to improve their efficiency, and, through it, to increase both their profit margins and market shares. • Efficiency target may also be induced by an effective regulation • The latter needs a severe assessment about the efficiency in airport’s management. • These issues are particularly relevant for the Italian air transportation market, which represents the fourth one at an European level, and where a price cap regulation “has recently been introduced” • The goal of this paper: • to assess the current efficiency in management of Italian airports • to present a method to compute the efficiency targets for the regulation period, so that a severe assessment may be possible.

6. Past studies • The efficiency in the airports' management has been investigated by several contributions. • US • Gillen and Lall [1997], • Sarkis and Talluri [2004] • Oum and Yu [2004] • EU • Pels et al. [2003] • UK • Parker [1999] • Japan • Yoshida [2004] • Yoshida and Fujimoto [2004] • Australia • Hooper and Hensher [1997] • Abbott and Wu [2002] • Brazil • Pacheco and Fernandes [2003] • Spain • Martin--Cejas [2005] • To the best of our knowledge this paper is the first attempt to apply the DEA analysis to Italian airports, using inputs data.

7. Performance measurement methods • Price cap regulation involves • Five-year regulatory period (usually) • Set CPI - X price-cap • CPI = consumer price index • X factor based on regulator’s assessment of potential productivity growth • If firms “beat the cap” they keep the profits => incentives • Information on productivity potential may be derived from • Multi-input, multi-output empirical techniques (DEA) • They use data from a number of businesses • Measure annual total factor productivity (TFP) as the growth in the industry plus obtained firm – level relative efficiency measures • Results should form the basis for the discussion

8. Performance measurement methods • Data Envelopment Analysis (DEA) • Frontier estimation method • Linear programming technique • Fits a piece-wise linear surface over the input/output data of a sample of firms • Inefficiency = distance a firm is from the frontier • Multi-input and multi-output • Advantages - does not impose functional form & easy to calculate • Disadvantages – assumes no data noise

9. Performance measurement methods DEA an example VRS frontier (variable returns to scale) Compare firms of similar size output CRS frontier VRS frontier A further issue: Are all firms at optimal size? Can a firm increase productivity by becoming larger? This is the idea of SE To measure SE in DEA we estimate an extra frontier: the Constant Returns to Scale (CRS) frontier The CRS frontier allows small firms to be compared to big firms, and vice versa. airports TECRS Technical Efficiency (TE) Horizontal distance from the frontier TE Technical Efficiency CRS (TECRS) Horizontal distance from the CRS frontier SE input Scale Efficiency (SE) Horizontal distance between the VRS and CRS frontiers

10. Performance measurement methods • Technical efficiency (TE) • minimum inputs used to produce given outputs • Scale efficiency (SE) • potential productivity gain from achieving optimal size of firm • Formally, we adopt linear programming to solve this problem

11. Performance measurement methods • To avoid the problem of infinite solutions we solve • The dual is (with a lower number of constraints) the “envelope” problem The constraint is added to compute IRS (SE<1 and ) and DRS (SE<1 and )

12. Performance measurement methods • From DEA compute Malmquist input oriented total productivity indeces • Intuition • Overtime the frontier may change (technical progress effect) • Index TC (Technical Change) • Overtime the airport may catch up the frontier • Index EC (Efficiency Change) • Total factor productivity change (TFPC) • aggregate change in outputs net of inputs • It is the combination of TC and EC

13. Performance measurement methods y OFt+1 C B E OFt yt+1 Same airport at dates t (A) and t +1 (B) A F yt D TFPC = EC x TC x xf xt xd xc xt+1 xe

14. A model of airport management • We follow Pels et al. [2003] and consider two outputs • Air Transport Movements (ATM) • Air Passenger Movements (APM) • ATM is considered as input when the APM frontier is studied • We consider the following inputs • ATM model • Entire airport area (AREA) • Total lenght runways (RUNWAYS) • Total number of aircraft parking positions (PARKING) • APM model • ATM • Terminal surface (TERMINAL) • Number of aircraft parking positions (PARKING) • Number of check – in desks (CHECK) • Number of lines for baggage claims (CLAIM)

15. The data • Population is composed by 37 Italian airports (all members of Assaeroporti – they account for more than 90% of Italian traffic) • We had to run a field investigation to collect the data • The data set is composed by 27 Italian airports (73% of population) • Data for 2005 and 2006

16. The data • Both ATM (mean) and APM) increase from 2005 to 2006, but also its variability across airports (standard deviation). • RUNWAYS is the unique input unchanged between the two years. • All other inputs have increased, on average • In 2006 the typical Italian airport has • A terminal surface of 44.297 sm • About 28 aircraft parking positions • About 47 check--in desks • 5 lines of baggage claims • It is extended on an area of 319 hectares.

17. Results - ATM

18. Results - ATM • Average TE • 2006 = 0.83 • Airports on the VRS frontier • 2006 = 8 airports (30%) • Average SE • 2006 = 0.62 • Airports on the CRS frontier • 2006 = 3 airports (11%) • IRS • 2006 = 20 airports (74%) • DRS • 2006 = 5 airports (18%) • 3 out of 4 largest airports (Fiumicino, Malpensa and Venice) • CRS • 2006 = 2 airports DRS (IRS) means that to become efficient an airport has to vary its size by taking into account that the proportional increase in inputs will induce a less (more) than proportional increase in output DRS is a proxy of congestion

19. Results - ATM EC (mean) = +1.93% TFPC mean -0.04% EC up in 17 airports Productivity up in 13 airports (all largest) EC down in 3 airports TC (mean) = -1.89% Productivity down in 6 airports TC up in 5 airports TC down in 21 airports Large airports exploit the technical progress

20. Results - APM

21. Results - APM • Average TE • 2006 = 0.89 (higher than ATM) • Airports are more efficient in passenger movements rather than aircraft movements • Airports on the VRS frontier • 2006 = 13 airports (48%) • Average SE • 2006 = 0.82 (much higher than ATM) • Airports are closer to the optimal size under APM rather than ATM • Airports on the CRS frontier • 2006 = 7 airports (26%) • IRS • 2006 = 14 airports (52%), lower than ATM • DRS • 2006 = 8 airports (30%) higher than ATM • 3 out of 4 largest airports (Linate, Malpensa and Venice) • CRS • 2006 = 5 airports (18%) (Fiumicino between others) Again DRS might be a proxy of congestion Also for passengers large airports tend to experience it The number of airports with CRS is higher than under ATM It is easier to reach the optimal size under APM than under ATM

22. Results - APM TFPC mean 0.3% (negative for ATM) EC (mean) = +0.9% EC up in 6 airports Productivity up in 13 airports (all largest) EC down in 8 airports TC (mean) = -0.6% Productivity down in 9 airports TC up in 14 airports Change in productivity for the whole Italian economy -0.9% (OECD) TC down in 10 airports Not only large airports exploit the technical progress

23. Price regulation • In applying Price Cap regulation the computation of the productivity target is crucial (x factor) • The Malmquist Indeces allow to estimate it • The method is the following (an example): • Take measure of the sector TFP change (e.g. 1.1% per year) • Take measure of TE obtained for each firm by conducting a DEA analysis (Table 2 for ATM or Table 4 for APM) • Assume a five-year regulatory period • Ask firms to achieve 1.1% plus also catch-up 50% towards frontier • Suppose that airport BETA has scored (TE = 0.755) • Catch-up required = 50% of (1-0.755) over 5 years = 2.3% per year • => X-factor = 1.1%+2.3% = 3.4%

24. Price regulation • Estimate of “maximum” target x factor • ATM => 1.58% per year • APM => 1.27% per year • Maximum target because airport’s sources of inefficiences are (Pels et al.): • Input indivisibilities (timing in reaching optimal use of input is not immediate) • Governmental regulation, climatic conditions • Airlines inefficiencies • Management inefficiencies (x – inefficiency) • The above target does not distinguish between them and the management is directly responsible only for the last one.

25. Conclusions • Many airports can improve their efficiency • Observed efficiency is higher for passenger movements rather than for aircraft movements • Average sector productivity seems to be higher than whole Italian economy • Size effect: • DRS for large airports (signal of congestion?) • Higher exploitation of technical progress in ATM • ATM • TE => 0.83; SE => 0.62 • APM • TE => 0.89; SE => 0.82 • Maximum target for x factor in Price Cap regulation • ATM => 1.58% • APM => 1.27%