1. Marc IVALDI Workshop on Advances on Discrete Choice Models in the honor of Daniel McFadden Cergy-Pontoise – December 18, 2015 A Welfare Assessment of Revenue Management Systems

2. Joint with Nicolas Dupuis,Toulouse School of Economics Jerome Pouyet, Paris School of Economics 2

3. Going to Lyon from Paris by Train 3

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11. Going to Lyon from Paris Paris – Lyon ― 4 hours 21 minutes by car Different trains Different service conditions ― First and second classes Outcome ― Wide range of fares depending on the buying date ― Availability of seats is changing 11

12. Revenue Management A form of intertemporal price discrimination ― Transport industry: Airlines, railroads ― Other industries: Movie theaters, hotels Policy debate on the outcome ― For the same product, large range of prices among purchasers ― Fairness versus Efficiency Research ― Welfare analysis ― Impact on producer profit, consumer surplus, total welfare oMonopoly oCompetition 12

13. Contribution Limited number of articles and studies on RM Main results ― RM increases consumer surplus ― RM may have a redistribution effect between leisure and business Passengers ― Competition among operators decreases prices but increases its dispersion ― Competition does not necessarily yield an optimal capacity and operators tend to exploit at the maximum of the available capacity 13

14. Methodology No data, no econometric analysis ― Very complex and heavy ― Not useful for practitioners Simulation ― Allow to exhibit / to elicitate the business strategies of buyers 14

15. Basic ingredients of the basic model A product ― Train ticket ― Constrained availability oNumber of days before departure oNumber of seats left A railroad (monopoly) ― Set of possible prices for the different trains ― Closing option = put an infinite price A traveller ― Homogenous consumers ― Random arrival on the website or at the ticket window ― Myopic expectations ― Decision: buy the ticket or take the outside option 15

16. The revenue manager’s problem Objective ― Choose the sequence of prices in ordre to maximize expected revenue at each period Expected revenue 16

17. Computation 17 Supply ― Train capacity is fixed = 400 seats ― 2 prices (low = 40, high = 90) Demand ― Choice (buying) probability based on a multinomial logit specification ― Calibrate the parameters to yield realistic estimate of demand elasticities Market size ― No more than one consumer per period ― Function of the arrival rate from 0.2 to 0.9 (step 0.1) ― Time constraint = number of periods before departure ― Example oMedium size = 1000 (arrival rate = 0.4) oLarge size = 1400 (arrival rate = 0.7)

18. Standard profile 18

19. Profile under high demand 19

20. 20 Revenue Management versus Optimal Fixed Price Change in profit

21. 21 Revenue Management versus Optimal Fixed Price Change in consumer surplus

22. Heterogenous Consumers Two types of train: ― Off peak ― Rush hour Two types of passengers (i.e., different demand elasticity) ― Leisure ― Business Two pricing strategies ― Fixed Price ― Revenue Management 22

23. Change in Profit from Fixed Price to Revenue Management 23

24. Change in Surplus of Business Passengers from Fixed Price to Revenue Management 24

25. Change in Surplus of Leisure Passengers from Fixed Price to Revenue Management 25

26. Load rates of Leisure and Business Passengers from Fixed Price to Revenue Management 26

27. Competition between revenue managers Type of competition ― Indirect competition: Rail versus Road ― Direct competition: Rail versus Air Two horizontally differentiated products Two industrial structure ― A multiproduct monopoly ― A duopoly 27

28. Results Higher profit under monopoly than under duopoly Higher surplus under duopoly tahn under monopoly Price dispersion 28

29. Conclusion Revenue management ― A practice in general beneficial to both consumers and firms Key variables ― Market size and capacity constraints Further work ― More strategic behavior of consumers 29

30. 30 Thank you!