Technology and incentive regulation in the Italian motorways industry

Transcription

1 XI Riunione Scientifica SIET Napoli, 3-5 ottobre 2007 Technology and incentive regulation in the Italian motorways industry Luigi Benfratello Alberto Iozzi Paola Valbonesi Università di Torino Università di Roma Università di Padova and Ceris-CNR Tor Vergata

2 Introduction Rather limited analysis of the technology adopted by motorways concessionaires However, motorways concessions are very common in the EU Several differences between concession agreements across and also within EU countries Size Ownership Financing Tariff regulation Institutions Is there any best solution?? Italian motorways industry is a very good laboratory to study some of these issues 2

3 The Italian motorways industry More than km of motorways (around 20% of the network in EU15), most of it built before concessionaires, managing (and sometimes building) different portions of the motorways network Concessions awarded at different stages (mostly during the 60s) and expiring between 2012 and 2050 Huge differences between concessionaires: e.g. ASPI: 2.854,6 km (3.402,3 km with partecipates) VE-PD: 41,8 km In the late 90s, motorways in Italy experienced a change in regulatory regime ( from ROR to PC) ownership ( privatisation) 3

4 Our dataset We build a unique dataset with information over the period for 20 Italian motorways concessionaires. The database contains financial and traffic indicators (costs, revenues, inputs, kms travelled, etc), characteristics of the motorways (length, no. of stonework, % of network with 3 lanes, etc) concessionaires institutional characteristics (ownership, type of regulation, etc). Currently working on updating the dataset to more recent years 4

5 Our analysis Our main aim is to explore the technology adopted by motorways concessionaires, especially with respect to Economies of scale Economies of density Technical progress Useful policy indications, not only for Italy: Optimal size of a concession Toll levels and toll update mechanism (e.g. X factor) Need for further network expansion We also control for possible effects on the productivity of the concessionaires of the regulatory reform of the 90s. Effects of the changes in ownership Effects of the changes in regulatory regime 5

6 Regulatory reform (1) Regulatory reform during the 90s, main provision is Delibera Cipe of December All concession agreements renewed after the definition of this new regulatory framework Regulatory framework resulting from these provisions rather incomplete and somehow contradictory More recently, further provisions attemp to modify, complete and clarify the regulatory framework However, the analysis of these more recent provisions is beyond the scope of this paper, since their effects is not captured by our data. 6

7 Regulatory reform (2) Before ~1996: Most concessionaries publicly owned Tariffs set at the start of the concession period to ensure cover of all costs borne over the concession period Tariffs increased uniformly (across concessionaires and types of vehicles) by law each year Building costs covered partly with government funds and partly with highway revenues Also, Preferences for publicly owned firms in the award of the concessions Loans taken up (for building purposes) by publicly owned concessionaires covered by government backup Excess profits to be transferred to the government 7

8 Regulatory reform (3) After ~1996: Massive privatisation programme New regulatory framework: No discrimination according to ownership Prices set at the start of the concession period to ensure cover of all costs borne over the concession period Prices updated each year according to a price cap formula, with parameters (possibly) specific to each concessionaire Price cap formula parameters reviewed every 5 years Pricing flexibility Also, No back-up for loans taken up for building purposes Share of revenues transferred to the government 8

9 Regulatory reform (4) Regulatory regime switch Concessionaires under new regulatory regime Ownership change Private concessionaires

10 Price cap mechanism Average toll level set at the beginning of the concession to cover cost over the concession period Concessionaires free to vary tolls each year, provided they satisfy the Laspeyre-type price cap formula i i p p t i q t 1 i t 1 i q t 1 i 1 x 100 ΔRPI + β ΔQ ΔRPI change in the Retail Price Index X offset productivity factor ΔQ change in a composite quality index β scaling positive factor X normally redetermined every 5 years to reflect changes in the economic environment (traffic, productivity, costs, etc) X 10

11 Quality regulation (1) Quality correction term allows the concessionaires to sell higher quality for higher prices Quality index Q based on: accidents roughness of road surface Choice lead initially by data availability (but not amended later on) As for β 11

12 Quality regulation (2) ASPI Other concessionaires if ΔQ > 0 Other concessionaires if ΔQ < 0 12

13 Quality regulation (3) Main features Higher quality buys higher prices (and viceversa) For a given increase in the quality index, the effect on prices is larger the higher is the starting level of quality For a given reduction of the quality index, the effect on prices is larger the smaller is the starting level of quality Effects are magnified when the starting level of quality if above an acceptable level (Q=60) Rationale For quality increases, the quality correction term is cost- rather than welfare-related It ensures the firm recovers the cost of quality-related investments, very much in the spirit of cost-of-service regulation 13

14 Data (1) Balance sheet data of 20 concessionaires over the period (costs, revenues, employees) Other variables recovered from Aiscat publications (kms travelled, network characteristics) Ownership and regulation information from concessionaires official reports Unbalanced panel, 253 observations 14

15 Data (2) 15

16 Data (3) 16

17 Data (4) 17

18 Data (5) 18

19 Model and econometric results (1) Total cost function (translog, three inputs) 19

20 Model and econometric results Estimation method: Seemingly Unrelated Regressions (cost function and two cost shares) Some specifications include concessionaires effects in the cost function to capture individual heterogeneity Cobb-Douglas models as a robustness check 20

21 Technological measures (1) Three crucial measures: 1. Density economies Measures the inverse of the % increase in total cost due to a % increase in output, holding the network length fixed. ε d = ε 1 y A value above 1 shows increasing returns to density, so that an increase in output induces a less-thanproportional increase in total costs. 21

22 Technological measures (2) Three crucial measures: 2. Scale economies Inverse of the % increase in total cost due to a % increase in output and in the network length. ε s = ε y 1 + ε n A value above 1 indicates increasing returns to scale, so that an increase in output and network length induces a less-than-proportional increase in total costs. 22

23 Technological measures (3) Three crucial measures:. 3. Yearly rate of technical progress The % increase (or decrease) in total cost due to the passing of one more year ε t TC = ln t 23

24 Econometric results (1) FE FE FE 24

25 Econometric results (1) Coefficients for kms travelled quite similar across models FE Coefficients positive, almost identical across the two models and highly significant The coefficients for network length and kms travelled have the expected sign and are highly significant. Technological progress around % per year Coefficients for network differ quite sensibly: high variability of the estimates due the slowly time variant network variable low confidence in results without FE individual dummies are jointly significant, highlighting concessionaires' heterogeneity 25

26 Coefficients almost unchanged, only slightly lower technical progress Econometric results (1) Add institutional variables Regulation dummy not significant FE Ownership dummy different in sign and magnitude. Without FE, omitted variable bias due to unobserved heterogeneity correlated with regressors, e.g. positive correlation between characteristics of the network and ownership dummies 26

27 Econometric results (1) Number of stonework positevely affect costs network composition does not seem to affect costs Add network characteristics. Since almost time invariant, cannot estimate model with FE coefficient of the ownership dummy not significant and less pronounced, showing that network characteristics partially explain the apparent cost disadvantage of private firms found in model (4). 27

28 Econometric results (2) 28

29 Scale and density elasticities 29

30 Summary of the econometric results High density economies Significant technical progress Scale economies never exhausted in the sample Our controls Positive impact of privatisation on productivity No impact of regime change on productivity 30

31 Some additional issues: X factor and traffic risk In 2002, on the occasion of the price cap review for ASPI, harsh debate on how should bear traffic risk Different positions resulting in different ways of setting the X factor Other things being equal (and when the firms is already efficient), if the concessionaire bears the traffic risk X = technical progress if the concessionaire is insured against traffic risk X = technical progress + changes in cost due to changes in traffic Using our results technical progress: 0.5% (lowest estimate) change in costs due to density economies with the observed demand increase ( 3%): 3% - [exp(β y *ln1.03) - 1]*100 = 3% - 1.5% = 1.5% 31

32 Some additional issues: prices Use of internal prices for our estimates, with known drawbacks: Not real prices Capture of variability of other regressors However, external prices are not a good alternative for this sample No cross-sectional variability!! But, most importantly As recognised in the literature for other regulated firms (see Atkinson & Halvorsen, IER, 1984), concessionaires may base their decision on unobservable shadow prices reflecting the effect of regulation Indeed, the quality adjustment in the price cap formula reduces the actual cost of quality-related investment (e.g. resurfacing) 32

33 Final comments Study of technological aspects of the motorways industry Technical progress Existence of economies of scale and density Optimal network extension etc. Possible important effect on better regulatory policies Setting of the X factor Effects on revenues of chenges in traffic levels Reform of quality regulation etc. On more general issues: study of the effect of regime and ownership changes 33

34 Price cap mechanism (2) In principle, the correct setting of X requires (at least): actual cost determination demand forecast assessment of industry-wide (expected) technological progress assessment of firm-specific (expected) efficiency gains, possibly through benchmarking 34

35 Price cap mechanism (3) In practice, X factor set to the same value for a large set of firms For other firms, different values chosen according to specific circumstances: new investments systematic previous unbalance between cost and revenues Year #. obs X Mode 1,09 (5) 0,98 (6) 0,90 (8) 0,83 (8) Min -15,86-10,0-10,0-9,82 Max 1, , ,77 (7) -9,82 1,30 35

36 Quality regulation (1) Theory suggest that when there is a quality correction term in the PC: Composite quality index should reasonably reflect service quality (as perceived by consumers) Price changes should be based on impact of quality changes on consumers welfare 36

37 Quality regulation (2) Practical effects Quality index Q based on: accidents roughness of road surface Choice lead by data availability As for β Anno # obs Mean 0,38 0,01 0,31 βδq Min -0, ,93 Max 1,39 0,90 1, ,44-0,79 1, ,45-0,97 1,61 37