Intermodal competition: studying the pricing behavior of the French Rail Monopoly

1. Intermodal competition: studying the pricing behavior of the French Rail Monopoly Patricia Perennes, RFF Centre d’économie de la Sorbonne

2. Introduction 1/2 In France, the railway national passengers’ transport is still characterized by a monopoly. However, is SNCF’s pricing behavior the one of a monopoly? • For certain service there is a strong intermodal competition of air and road transportation • Train tickets’ prices are not totally freely set by SNCF (price regulation) The fact this price cap based regulation exists is also an opportunity for an economist to analyze how a transportation company facing intermodal competition sets its prices. Usually, such an analysis is hard to conduct since transportation price are set following yield management principles (Antes et al. (2004), Bergantino(2012)) This article also gives some insight on the type of competition that would better suit passengers’ rail transportation • The European commission wants to liberalize this industry • Which kind of competition is better suited for this industry (open access vs. franchise)?

3. Introduction 2/2 This article analyses SNCF’s pricing strategy on most of the O&D it operates from/to Paris with HS trains, taking into account the limited leeway that the company enjoys to set its prices because of price regulation. Two analyses • Empirical analysis of SNCF’s pricing behavior on most of the origin/destination pairs (O&D) it operates from/to Paris with high-speed (HS) trains • Qualitative analysis based on prices data for 19 selected routes and for 2 time periods This article relies on two unique data sets entirely collected for the present study

4. Intermodal competition • Rail network: • HS trains covering most of mainland France (even if HS tracks network is much more smaller) • Pricing behavior: for HS trains yield management to a certain extent, price based on the number of kilometers for “regular” trains • Airlines: • Air France has quite a dense network, but the number of routes it offers has decreased since the 80s because of the competition of the HS trains • Pricing behavior: Yield management • Road network: • A well developed motorway network, with relatively expensive tolls (fixed prices monitored by the state) • A secondary network free of charge

5. Intermodal competition

6. Regulation of train tickets’ prices Regular trains: • “Kilometric reference” • BF_ICi= A*kmi + B • kmi is the number of kilometers for the O&D i • A and B are numeric constants set each year by SNCF and approved by the French Secretary of Transportation (SoT) • Reduction coefficient may be applied HS trains: • System is more complex: state monitoring and yield management • State monitoring: price cap • Yield management: price can be freely set above this price cap • The price caps are choosen by SNCF (and approved by SoT) for each O&D • This price cap cannot deviate too much from the “kilometric reference” (40% leeway) • (1-40%)( A*kmi + B) BF_TGVi (1+40%)( A*kmi + B)

7. Empirical analysis We use the ratio between actual basic fare and kilometric basic fare This ratio should be included between 0.6 and 1.4 (in reality between 0.9 and 1.39) If this ratio is below 1, that means the tickets is relatively cheap compared to other destinations • Strong competition ? If this ratio is above 1, that means the tickets is relatively expensive compared to other destinations • Low competition ? To conduct this empirical analysis, we use a data set entirely collected for the present study: • To calculate the endogenous variable R, we collect the 172 basic fares and the yearly constants A and B, the number of kilometers (tariff kilometers) • For the exogenous variables we collect driving times, flying times, train riding times, driving costs (gas and tolls), driving times to the closest airport, numbers of tracks kilometers, track access charges, numbers of psgers in the destination station, etc.

8. Model Panel data model: • Data are available for 6 years (2007-2012) (i: city pairs, t: years) • Explanatory variables (intramodal competiton): • Relative driving time (driving time/train riding time) • Existence of a plane alternative (base on the relative total length of the travel wen flying) • Driving costs (tool fees and gas expense) • Existence of a LCC service • Control variables: • Number of yearly passengers in the destination stations • Track access charges • “Region” (all destination cities were grouped in 9 “regions”)

9. Results 1/2 Explanatory variables’ coefficients are significant • The existence of an air alternative to train impacts train ticket price • Decrease of €6 to €8 for a €100 ticket if there is an airline alternative • The existence of a low cost carrier service decreases train ticket price • Tickets are less expensive if the relative driving time is small (i.e. close to 1) • For a €100 ticket for a service where driving or taking a train have similar duration, the price would have been €9 to €12 more expensive if the train was two time faster. • The most expensive the driving cost per kilometer, the higher the ratio • For a €100 ticket an increase of €0.05 of the driving cost per kilometer (the average driving cost per kilometer is €0.16) would lead to an increase of €3 to €5 euros. “Region” is not significant The coefficient associated to the annual numbers of passengers is negative. • Passengers getting off in important stations pay relatively less than passengers getting off in smaller one. Access charge, seems counter intuitive • Endogeneity?

10. Results 2/2 More than one third of the variation is explained with R2 around 0.35-0.48

11. Qualitative analysis 1/2 Given the nature of the data at disposal, the analysis conducted in this part is more qualitative than quantitative • The goal of this analysis is to corroborate the previous empirical analysis Based on another data set, also entirely collected for this study: • Prices were collected during three months on SNCF’s, LCC’s and Air France’s websites • Simple comparison of prices evolution for 19 O&D (Air France, LCC and SNCF) This qualitative analysis boils down to 3 “rules” • SNCF’s and Easy Jet’s prices seem correlated • Air France’s prices are usually much higher than Easy Jet’s/SNCF’s prices. However a strong increase in Air France’s prices is usually followed by a small increase in SNCF’s prices • A few days before the train/plane departure SNCF’s prices hit the maximum price set by regulation, therefore taking the train is a much cheaper option than flying

12. Qualitative analysis 2/2 • Main result: • The regulation effectively impact SNCF’s pricing beavior: • On the 151 TGVs in our data set, 133 TGVs hit the cap set by the regulation • A few days before departure, taking the train is therefore a much cheaper option than flying (for 100% of the destinations) • Regarding intramodal competition: • LCC: • Price difference between LLC and train tickets are low on average €20.40 (regular train ticket are between €79 and €122) • Air France: • Less conclusive. A graphical comparison of prices series also indicates that a strong increase in Air France’s prices is usually followed by a small increase in SNCF’s prices. However, this effect is hard to summarize in a synthetic index.

13. Conclusions Is SNCF’s pricing behavior the one of a monopoly ? • SNCF adapts its price depending on the potential intermodal competition it faces • Prices regulation effectively restricts SNCF’s ability to set its prices Food for thought in the context of the rail industry liberalization: • Intermodal competition dampers the monopolistic behavior of the railroad incumbent. Need for an intramodal competition for all the routes? Regarding French price regulation: • It definitively has an impact on SNCF’s price behavior • But what is exactly the goal of this legislation? Public service? Antitrust?