Regulatory contracts and cost efficiency: the French urban public transport case

Transcription

1 Regulatory contracts and cost efficiency: the French urban public transport case Joanna Katarzyna Piechucka 1 Working paper March 15, 2015 Abstract The goal of this paper is to study the impact of different regulatory contracts on the operating costs of the urban public transport industry in France throughout the years In particular, regulatory choices are considered to be endogenously determined choices explained by an unsophisticated regulator. The paper leans on a positive analysis to study the determinants of regulatory contract choices, which in turn impact the costs of operating urban public transport. The results show that given similar network characteristics, networks operated under a fixed-price contract exert lower costs than those regulated under cost-plus contracts. This finding is in line with the theoretical prediction of new regulatory economics, that fixed-price contracts provide more incentives for efficiency. In addition, several elements advocated by the private-interest theory appear to be relevant in the industry. In particular, the legal status of the operator or the identities of the group to which the operator belongs to give a better understanding of regulatory choices. Our findings provide useful policy implications by suggesting that significant reductions of costs can be obtained by switching to mechanisms with high-powered incentive schemes. Moreover, they highlight the importance of accounting for the endogeneity of regulatory contract choice. Ignoring this aspect could lead to undervaluing the importance of regulatory incentives for the transport operator. JEL classification: C21, L51, L92 1 Paris School of Economics-Université Paris 1. 1

2 1 Introduction The financing needs of urban transport in France currently amount to 8.9 billion euros 2. While the industry has seen a significant increase in the supply of transport, this is not accompanied by a sufficiently strong demand for the service. As a result, the financial situation of transport networks is deteriorating. This situation reinforces the need to improve the management of these services. In France, the local public authority is responsible for organizing urban public transport by defining, financing and organizing regular public passenger transport in the urban network. It is left the choice to organize and provide the service itself, or to delegate the relevant responsibilities to a transport operator. In the latter case, a public-private partnership is established and regulated through an agreement. In practice, two types of contracts are observed, i.e. cost-plus and fixed-price contracts. Understanding the rationale that drives regulatory choices in the industry which in turn impact the operating costs of the networks is of particular importance. There exist few empirical studies that analyze the impact of regulatory schemes on the cost efficiency in the transport network (Gagnepain (2002), Piacenza (2006), Gautier and Yvrande- Billon (2013)) and even fewer have addressed the potential endogeneity of the regulatory contract (Dalen and Gomez-Lobo (2002), Gagnepain and Ivaldi (2012)). The objective of this paper is to overcome the gaps in the literature. In particular, we study the impact of different regulatory contracts on the cost of operating urban public transport in France. In addition, egulatory choices are considered to be endogenously determined choices explained by an unsophisticated regulator. We consider a regulator, whose decisions could be seen be seen as a result of powerful interest groups exerting pressure on him to capture rents. We thus take a positive approach to study the determinants of regulatory contract choices, which is in line with the private-interest theory initiated by Stigler (1971) and further developed by Peltzman (1976) and Becker (1981). These elements seem to play an important role in the French urban public transport industry. The paper leans thus on a positive analysis to study the determinants of regulatory contract choices which in turn impact the costs of operating urban public transport. In addition, the richness of our database, covering the years , allows us to have a particularly interesting look at the industry. 2 GART (2013). 2

3 The paper is organized as follows. Section 2 describes the French urban public industry in France. In particular, the organizational background and the types of regulatory schemes observed in the industry are discussed. Section 3 presents the econometric approach taken to study the impact of regulatory schemes on the cost efficiency of transport operators. Section 4 presents the results of estimating the model introduced in section 3. Section 5 concludes. 2 The French urban public industry 2.1 The legal and organizational background The legal framework of urban public transport in France dates back to the Transport Law of , which provided a guideline for public passenger transport in the urban transport area and established the concept of economic and social efficiency by providing the right to lowcost public transport. The institutional organization of public transport was then clarified by separating the functions of the organizer and operator for the relevant service. The public authority is responsible for the organization of urban public transport by defining, financing and organizing regular public passenger transport in the urban transport area. It is left the choice to organize and provide the service itself, or to delegate the relevant responsibilities to a transport operator. In the latter case, a public-private partnership is established and regulated through an agreement. Until 1993, the automatic renewal of contracts was a common practice. The Sapin Law 4 made competitive bidding compulsory before awarding a contract for the provision of a public service. The aim of the law was to prevent collusion and corruption and enhance competition between the operators in the industry. It did not, however, forbid the use of negotiation in the procedure. As a result, operators can be selected in a two-step procedure, i.e. a preselection step with the use of competitive bidding and a negotiation phase, the latter allowing for subjective selection criteria. Yvrande Billon (2006) shows that compulsory bidding in the industry did not translate into better performance. She explains this phenomenon by the lack of expertise of local authorities and the existence of serious collusive practices undertaken by the main operators in the industry, i.e. Keolis, Veolia Transport and Transdev 5. The French model of regulation, 3 Loi n du 30 décembre 1982 d orientation des transports intérieurs. 4 Loi n du 29 janvier 1993 relative à la prévention de la corruption et à la transparence de la vie économique et des procédures publique. 5 On July 5, 2005, following a referral by the Minister of Economy, Finance and Industry, the French Compe- 3

4 combining competitive tendering with negotiation, gives place for subjective selection criteria, which will play an important role in regulatory relations between the local authority and the transport operator. We now turn a more detailed presentation of the regulatory contracts observed in practice in the industry. 2.2 Regulatory contracts As defined by the French legal framework, the local public authority is responsible for defining the transport policy for the relevant urban area and organizing the provision of the relevant public services. At the same time, it can decide either to organize the services itself, or to delegate this task to a fully private or public-private operator. Currently, 90% of the transport networks in France are operated through delegated management 6. In this case, the regulator chooses an operator to which it will entrust the operation of the service. A public-private partnership is then established and regulated through an agreement. The key feature of the French model is the attribution of the contract to only one operator who will carry the responsibility of providing the relevant service in the whole urban transport area. This agreement specifies the characteristics of the service, the operator s obligations to the passengers, the terms and conditions of operator financing, payment and fares, as well as the choice of regulation. The choice of regulation translates in turn into the division of risk-taking between the parties concerned. The basic trade-off concerns the choice between a fixed-price and cost-plus contract (see Laffont & Tirole (1993)). These two types of contracts are indeed the ones observed in practice in the urban transport industry in France. In networks regulated under a fixed-price contract, operators receive subsidies according to their expected operating deficits. Therefore, any cost changes do not affect affect their profit. On the other hand, in networks regulated by tition Authority decided to punish with a fine Kéolis, Connex (afterwards Veolia Transport) and Transdev for entering into a nationwide anticompetitive agreement between the years 1996 and The public passenger sector in the period concerned was defined as an oligopolistic market dominated by three groups, the directors of which formed a cartel in the intention of dividing the market for public passenger urban transport by refraining from competition when a contract held by them came up for renewal. In the case where they had an objective advantage of their own interests, they were able to exchange markets. This resulted in the companies imposing their prices on local and regional authorities, and consequently the administrations concerned had to face higher costs as compared to a case of open competition. Justified by the the serious anticompetitive practises carried out by the parties concerned, the French Competition Authority decided to the highest, in force at the time of the facts, fines, i.e.: Kéolis was fined 3.90 million euros, Connex million euros, and Transdev million euros. 6 GART (2013). 4

5 cost-plus contracts, the organizing authority collects commercial receipts and fully reimburses the operator s operating costs, increased by a pre-defined additional amount. Under this regulatory scheme, the regulator provides the operator with subsidies to cover its actual deficits. This regulatory schemes is risk-free for the operator, as any cost changes do not affect his profit. Following Gagnepain and Ivaldi (2012), we assume regulatory choices are endogenously determined decisions of a local authority. Different actors taking part in providing the service (the local authority itself, the operator and the transport groups the operators are affiliated to) may have a preference for a certain type of contract and therefore may be willing to impact the regulatory decision. The organizing authorities are politicians representing the municipal council elected for a six year period. In this context, local authorities responsible for organizing the transport service may be interested in reelection and thus will undertake actions to maximize political support in the favor of votes or campaign contribution in the next election. Those who leave the government may attempt to find high-level jobs in the industry they were responsible for regulating before. The characteristics of the industry seem to support this approach. As already mentioned, most of the operators in the industry belong to major transport groups. Operators belonging to these groups may want to maximize profits aggressively and, therefore, have a preference for fixed-price contracts. In addition, the French model of regulation, combining competitive tendering with negotiation, gives place for subjective selection criteria. Thereby, operators seem to have an important bargaining power when facing the regulator. Taking into account the characteristics of the actors involved in providing the service seems to be of particular importance when studying the French urban transport industry. We now turn to the econometric approach taken to study the impact of regulatory schemes on the cost efficiency of the transport operators. 3 The econometric approach There are two main approaches to cost frontier analysis, i.e. Data Envelopment Analysis (DEA) based on mathematical programming and the parametric approach based on econometric methods. The main advantage of DEA is that it does not require any explicit functional form of the cost function. However, it does not allow for statistical inference and therefore is unable 5

6 to distinguish between statistical noise and inefficiency. On the other hand, the econometric approach can handle statistical noise, but imposes a restricted functional form on technology (see Bauer (1990)). We take an econometric approach to estimating frontiers which uses a parametric representation of technology with a two-part composed error-term, pioneered by Aigner et al. (1977) and Meeusen and van den Broeck (1977). In particular, we use a fixed-effects methodology allowing the cost function to have a different intercept for different transport networks, exploiting the time-series properties of the data to determine the effect of contract type on costs. In addition, we do not restrict cost changes to follow a particular time pattern for all firms (e.g. Schmidt and Sickles (1984), Cornwell et al. (1990), Kumbhakar (1990)). Also, in contrast to other stochastic frontier models (e.g. Kumbhakar et al. (1991), Battesse and Coelli (1993)) we do not make specific distributional assumptions for the composite error terms. This approach is similar to the one taken by Ng and Seabright (2001) in the study of the costs of providing air transport services. The model to be estimated is given by the following set of equations: lnv C it = lnv C(Y it, w it, Z it, DT t ; β) + (α i + ξf P it ) + ε it (1) F P it = F P (A it, G it, N it, t; γ) (2) where V C(.) is the deterministic variable cost function, Y it is a vector of output, w it represents a vector of input prices, Z it encompasses network characteristics, DT t are time specific shifts, α i are firm specific shifts. Moreover, F P it is introduced to capture the contract type under which a network is regulated at a given period. Among variables affecting contract choice, A it represents the ownership characteristics of the transport company, G it represents the transport groups the operators are affiliated to, N it is a vector of network characteristics and, finally, t is a time trend. β, α, ξ, γ are the parameters to be estimated. The subscript i (i = 1,..., I) indexes the urban transport networks and t indexes time (t = 1,..., T ). Given this specification, the cost inefficiency is defined by: F rontier : ˆF = min i (α i + ξf P it ) (3) 6

7 Inefficiency : ˆ CI = (αi + ξf P it ) ˆF. (4) We assume that operating costs can be represented by a restricted transcendental logarithmic cost function, defined by Christensen and Greene (1976). For network i at time t, the cost function is the following: ( ) V Cit ln = (β Y lny it + β N lnn it + β L ln w L,it + β CS lncs it + β Y N lny it lnn it + w M,it w M,it + β Y CS lny it lncs it + β LY lny it ln w L,it w M,it + β LN ln w L,it w M,it lnn it + β LCS ln w L,it w M,it lncs it + + β NCS lnn it lncs it β LLln w L,it w M,it ln w L,it w M,it β Y Y lny it lny it β NN lnn it lnn it T 2 β 1 I 1 CSCSlnCS it lncs it + β t DT t ) + ( α i DF i + ζf P it ) + ε it, (5) where the normalization of variable costs V C it and the price of labor w L,it with respect to the price of materials w M,it imposes homogeneity of degree one in input prices. t=1 i=1 In addition, F P it is a binary endogenous variable that is assumed to stem from an an unobservable latent variable: F P it = γ 0 + γ priv P riv + γ K Keolis it + γ T T ransdev it + +γ v V eolia it + γ t t it + γ N lnn it + η it. (6) The value of F P it is taken accordingly to the rule: F P it = { 1 if F Pit > 0. (7) 0, otherwise The error terms (ε it,η it ) are assumed to be correlated bivariate normal with V ar(ε it ) = σ 2, V ar(η it ) = 1 and Cov(ε it, η it ) = ρσ 2. 4 Empirical results We first present the data and comment on the construction of variables that enter the model. In particular, we describe the necessary variables to estimate the operating costs of bus networks in France and the regulatory contract choices. We then present the estimation results of our model presented in (5)-(7). 7

8 4.1 Data and variables Our study uses a 16-year panel of 103 urban public transport networks in France for the years , with a total of 1,349 observations. The database has been created from an annual survey conducted by the Centre d Études sur les Réseaux, les Transports, l Urbanisme et les constructions publiques (CERTU) in collaboration with the Groupement des Autorités Responsables de Transport (GART) and the Union des Transports Publics et ferroviaires (UTP). For purposes of homogeneity across observations, only bus networks of more than 30,000 inhabitants have been selected for the analysis. For the same reason overseas networks have been excluded. Estimating the cost function requires information on operating costs, quantity of output and input prices. Variable costs V C are defined as the sum of labor and material costs. Output Y is measured by the number of seat-kilometers, i.e. the number of seats available in all buses multiplied by the number of kilometers traveled on all routes. Labor price w l is obtained by dividing labor costs by the annual number of employees. Material price w m is obtained by dividing material costs by the annual level of fuel consumption. The size of the network N put at the disposal of the operator is measured as the total length of the network measured in kilometers. In addition, to account for geographical and traffic conditions, the average commerical speed of buses CS was included in the analysis (see Gautier and Yvrande-Billon (2013), Piacenza (2006)). Studying the determinants of the contract choice requires data on the contract itself, the features of the network as well as the characteristics of the actors involved in the process of providing the service. The characteristics of the regulatory contracts are given in table 2. A first look at the data suggests that the affiliation to a major group may be an important factor explaining regulatory choices. Net-cost contracts are most frequently observed when the operator belongs to the Transdev group. Also, another important factor to take into consideration is the legal status of the operator, i.e. whether it is private or public-private. In the case of private operators fixed-price contracts are more frequently chosen. On the basis of the observed contractual choices, we build a binary variable representing the local authority s choice of a fixed-price contract. The actors involved in providing the service include, aside the local authority, the operators of the network. Most of the operators belong to the three major groups, i.e. Keolis, Veolia Transport and Transdev. For each of them, 8

9 we construct a dummy variable. We take the legal status of the operators into consideration by introducing a dummy variable for private companies. Moreover, each observation in the database is treated as a realization of a regulatory contract choice for a given transport system in an urban transport network during a time period. We present the results of estimating our model in the next section. 4.2 Results The results of estimating the restricted translog cost function presented in (5) are given in table 3. Model 1 provides the estimates of the cost function, assuming the contract type to be exogenous. In model II the contract type is considered to be an endogenously determined decision as defined in (6). In order to test the relevance of the contract type as an endogenous regressor, a Wald test of independent equations is performed. The results of the test suggest that the contract type is indeed an endogenous regressor. The obtained results highlight the importance of accounting for the endogeneity of contract choice. From now on, unless otherwise mentioned, we will refer to model II in our discussions. In addition, in order to verify whether the transcendental logarithmic cost function gives a good representation of the cost structure of the transport operators in France, likelihood-ratio tests on the technology restrictions implied by a Cobb-Douglas functional form and homotheicity are performed. The results of this exercice, presented in table 5, lead to the rejection of these restrictions, and therefore to retain the model presented in (5). As all variables were normalized to their sample mean value (except for time dummies, network-specific dummies and contract type), the first-order coefficients can be interpreted as cost elasticities for an average transport network in the industry. In particular, we find that a 10% increase in seat-kilometers results in an increase of costs by approximately 2%. Moreover, average commercial speed seems to have a negative and significant impact on operating costs. Moreover, a 10% increase in network size results in nearly a 1% increase in costs. Hence, for a given level of inputs and output, a greater network size increases costs. A 10% increase in the average commercial speed of buses results in a reduction of operating costs by nearly 2%. As managing infrastructure is the public authority s responsibility, this result underlines the importance of public policies concerning traffic regulation. In addition, as underlined by 9

10 Gagnepain (1998), improving commercial speed has a twofold effect. It both increases the demand for the service and reduces operating costs. We now turn to the impact of different regulatory schemes on costs of transport operators. Given similar network characteristics, networks operated under a fixed-price contract appear to exert nearly 21% lower costs than those regulated under cost-plus contracts. This result is in line with the theoretical prediction of new regulatory economics, that fixed-price contracts provide more incentives for efficiency. In addition, the results of modeling contract choice show that several elements advocated by the private-interest theory appear to be relevant in the industry. In particular, fixed-price contracts seem to be preferred by private operators. The identities of the group also give a better understanding of regulatory choices. Operators belonging to Transdev and Keolis seem to be more likely to choose a fixed-price. Further, the positive and significant sign of the impact of network size on the choice of a fixed-price contract suggests that there is a preference for fixed-price contracts in bigger networks. Also, a positive and significant sign of the parameter associated to the trend suggests a move towards high-powered incentive schemes. 5 Conclusion The results of this study show a significant and important impact of regulatory choices on the costs in the urban public transport industry in France. Given similar network characteristics, networks operated under a fixed-price contract exert nearly 21% lower costs than those regulated under cost-plus contracts. Our findings are in line with the theoretical prediction of new regulatory economics, that fixed-price contracts provide more incentives for efficiency. In this context, several elements advocated by the private-interest theory appear to be relevant in the industry. Moreover, our findings show the importance of local traffic regulation policies. In particular, improving commercial speed reduces operating costs. Concluding, our main findings provide useful policy implications by suggesting that significant reductions of costs can be obtained by switching to mechanisms with high-powered incentive schemes. Moreover, they highlight the importance of accounting for the endogeneity of contract choice. Ignoring this aspect could lead to undervaluing the importance of regulatory incentives for the urban transport operator. 10

11 References Aigner, D., Lovell, K. C. A. and Schmidt, P. G. (1977) Formulation and Estimation of Stochastic Frontier Production Function Models, Journal of Econometrics, vol. 6, pp Battese, G. and Coelli, T. (1993) A stochastic frontier production function incorporating a model for technical inefficiency effects, Working Papers in Econometrics and Applied Statistics, no. 69, Department of Econometrics, University of New England, October. Bauer, P. W. (1990) Recent developments in the econometric estimation of frontiers, Journal of Econometrics, , pp Becker, G. (1983) A Theory of Competition among Pressure Groups for Political Influence, The Quarterly Journal of Economics, vol. 98, pp Christensen, L. and Greene, Mr. (1976) Economics of scale in US electric power generation, Journal of Political Economy, vol. 84, no. 4, pp Cornwell, C., Schmidt, P. and Sickles, R. (1990) Production frontiers with cross-sectional and time selies variation in efficiency levels, Journal of Econometrics, vol. 46, pp Dalen, D.M. and Gomez-Lobo, A. (2002) Regulatory contracts and cost efficiency in the Norwegian Bus Industry: Do high - powered contracts really work?, Discussion Paper 6/2002. Gagnepain, P. (1998) Structures Productives de l Industrie du Transport Urbain et Effets des Schemas Reglementaires, Economie et Prevision, vol. 135, pp Gagnepain, P. and Ivaldi, M. (2002) Incentive Regulatory policies: The Case of Public Transit Systems in France, Rand Journal of Economics, vol. 33, pp Gagnepain, P. and Ivaldi, M. (2012) Choice of regulation, incentives, and political capture: The case of local transport services in France, CEPR working Paper. 11

12 GART (2011) L année 2010 des transports urbains, Paris. GART (2013) L année 2012 des transports urbains, Paris. Gautier, A. and Yvrande-Billon, A. (2013) Contract Renewal as an Incentive Device. An Application to the French Urban Public Transport Sector, Review of Economics and Institutions, Università di Perugia, vol. 4(1). Kumbhakar, S. (1990) Production frontiers and panel data, and time varying technical inefficiency, Journal of Econometrics, vol. 46, no. 1/2, pp Kumbhakar, S., Ghosh, S. and McGuckin, J.T. (1991) A generalised production frontier approach for estimating determinants of inefficiency in US dairy fanns, Journal of Business and Economic Statistics, vol. 9, pp Laffont, J.J., and Tirole, J. (1993) A Theory of Incentives in Procurement and Regulation, Cambridge: MIT Press. Meeusen, W. and J. Van den Broeck. (1977) Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error, International Economic Review, vol. 18, pp Ng, C. K. and Seabright, P. (2001) Competition, Privatisation and Productive Efficiency: Evidence from the Airline Industry, Economic Journal, Royal Economic Society, vol. 111(473), pp Peltzman, S. (1976) Toward a More General Theory of Regulation, Journal of Law & Economics, vol. 19, pp Piacenza, M. (2006) Regulatory Contracts and Cost Efficiency: Stochastic Frontier Evidence from the Italian Local Public Transport, Journal of Productivity Analysis, Springer, vol. 25(3), pp

13 Schmidt, P. and Sickles, R. (1984) Production frontiers and panel data, Journal of Business and Economic Statistics, vol. 4, pp Stigler, G.J. (1971) The Theory of Economic Regulation, Bell Journal of Economics, vol. 2, pp Yvrande-Billon, A., (2006) The Attribution Process Of Delegation Contracts In The French Urban Public Transport Sector: Why Competitive Tendering Is A Myth, Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 77(4), pp

14 Table 1: Summary statistics for the variables of the cost function Variable Mean Standard Deviation Minimum Maximum V C: Variable costs ( 000 ) w l : Labor price ( 000 /employee) w m: Material price ( 000 /fuel) , Y : Output (seat-km) N: Network size (km) CS: Commercial speed (km/h) Labor share Material share Table 2: Summary statistics for the variables of the contract choice function Frequency Percentage Networks regulated under FP 77.5% Private operator 979 FP if private operator 80.7% FP if public operator 69.2% Keolis 550 FP if Keolis 79.6% Veolia 316 FP if Veolia 73.1% Transdev 239 FP if Transdev 98.7% Others 244 FP if Others 57.8% Note: FP refers to fixed-price contracts. 14

15 Table 3: Maximum-likelihood estimates for the parameters of the cost and contract choice functions Model I Model II Parameters Exogenous contract type Endogenous contract type Estimates St. Error Estimates St. Error Cost function β Y *** *** β N *** *** β CS ** *** β L *** *** β LL *** β Y Y *** *** β NN ** *** β CSCS *** *** β LY *** *** β LN β LCS ** β Y N *** *** β Y CS *** *** β NCS *** ζ *** *** time dummies yes yes network dummies yes yes Contract choice function γ γ priv *** γ K *** γ V γ T *** γ t *** γ N *** Sample size Note: ***: Significant at 1%, **: Significant at 5%, *: Significant at 10%. Null hypothesis H 0 :ρ = 0 Table 4: Wald test of independent equations χ 2 statistic Decision Reject H 0 Table 5: Likelihood-ratio tests for the parameters of the translog cost function Log-likelihood χ 2 statistic Decision Null hypothesish 0 :Cobb-Douglas specification (β Y N = β Y CS = β LY = β LN = β LCS = β NCS = β LL = β Y Y = β NN = β CSCS = 0) Null hypothesish 0 :Homotheicity (β LY = β LN = β LCS = 0) Reject H 0 Reject H 0 15