Escobari, Diego (2017): Airport, airline and departure time choice and substitution patterns: An empirical analysis. Forthcoming in: Transportation Research Part A: Policy and Practice
Preview |
PDF
MPRA_paper_79857.pdf Download (373kB) | Preview |
Abstract
This paper uses the random-coefficients logit methodology that controls for potential endogeneity of prices and allows for general substitution patterns to estimate various demand systems. The estimation takes advantage of an original ticket-level revealed preference data set on travels from the New York City area to Toronto that contains prices and characteristics of not only flight choices but also of all non-booked alternative flights. Consistent with having higher valuations, our results show that travelers buying closer to departure have a higher utility of flying. Moreover, consumers' heterogeneity decreases as the flight date nears. At the carrier level, we identify which carriers have more price-sensitive consumers and which carriers face greater competition. In addition, the results suggest that our multi-airport metropolitan area can be considered as a single market and that JFK and Newark are relatively closer substitutes. Overall, consumers are more willing to switch to alternative carriers than between airports or departure times.
Item Type: | MPRA Paper |
---|---|
Original Title: | Airport, airline and departure time choice and substitution patterns: An empirical analysis |
Language: | English |
Keywords: | Airline choice; Airport choice; Departure time choice; Substitution patterns; Airline demand; Elasticities |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C36 - Instrumental Variables (IV) Estimation D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis D - Microeconomics > D4 - Market Structure, Pricing, and Design > D40 - General L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L93 - Air Transportation R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise |
Item ID: | 79857 |
Depositing User: | Diego Escobari |
Date Deposited: | 24 Jun 2017 05:29 |
Last Modified: | 26 Sep 2019 22:18 |
References: | Anderson, T. and Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375):598-606. Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2):277-297. Bachis, E. and Piga, C. A. (2011). Low-cost airlines and online price dispersion. International Journal of Industrial Organization, 29(6):655-667. Basar, G. and Bhat, C. (2004). A parameterized consideration set model for airport choice: an application to the San Francisco Bay area. Transportation Research Part B: Methodological, 38(10):889-904. Barbot, C. (2009). Airport and airlines competition: Incentives for vertical collusion. Transportation Research Part B: Methodological, 43(10):952-965. Barbot, C., D'Alfonso, T., Malighetti, P., and Redondi, R. (2013). Vertical collusion between airports and airlines: An empirical test for the European case. Transportation Research Part E: Logistics and Transportation Review, 57(1):3-15. Belobaba, P. P. (1989). Application of a probabilistic decision model to airline seat inventory control. Operations Research, 37(2):183-197. Berry, S. (1994). Estimating discrete-choice models of product differentiation. Rand Journal of Economics, 25(2):242-262. Berry, S., Carnall, M., and Spiller, P. (2006). Airline hubs: Costs, markups and the implications of customer heterogeneity, ed. Darin Lee, volume 1 of Advances in Airline Economics: Competition Policy and Antitrust, pages 183{214. Amsterdam: Elsevier. Berry, S. and Jia, P. (2010). Tracing the woes: An empirical analysis of the airline industry. American Economic Journal: Microeconomics, 2(3):1-43. Berry, S., Levinsohn, J., and Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63(4):841-890. Berry, S., Linton, O. B., and Pakes, A. (2004). Limit theorems for estimating the parameters of differentiated product demand systems. Review of Economic Studies, 71(3):613-654. Bilotkach, V. (2007). Asymmetric regulation and airport dominance in international aviation: Evidence from the London-New York market. Southern Economic Journal, 74(2):505-523. Bilotkach, V., Gorodnichenko, Y., and Talavera, O. (2012). Sensitivity of prices to demand shocks: A natural experiment in the San Francisco Bay area. Transportation Research Part A: Policy and Practice, 46(7):1137-1151. Bishop, J. A., Rupp, N. G., and Zheng, B. (2011). Flight delays and passenger preferences: An axiomatic approach. Southern Economic Journal, 77(3):543-556. Borenstein, S. and Rose, N. L. (1994). Competition and price dispersion in the US airline industry. Journal of Political Economy, 102(4):653-683. Brons, M., Pels, E., Nijkamp, P., and Rietveld, P. (2002). Price elasticities of demand for passenger air travel: A meta-analysis. Journal of Air Transport Management, 8(3):165-175. Brueckner, J. K. (2002). Airport congestion when carriers have market power. American Economic Review, 92(5):1357-1375. Brueckner, J. K., Lee, D., and Singer, E. (2014). City-pairs versus airport-pairs: A market-definition methodology for the airline industry. Review of Industrial Organization, 44(1):1-25. Chamberlain, G. (1987). Asymptotic efficiency in estimation with conditional moment restrictions. Journal of Econometrics, 34(3):305-334. Dana, Jr., J. D. (1998). Advance-purchase discounts and price discrimination in competitive markets. Journal of Political Economy, 106(2):395-422. de Luca, S. (2012). Modelling airport choice behaviour for direct flights, connecting flights and different travel plans. Journal of Transport Geography, 22:148-163. Deaton, A. and Muellbauer, J. (1980). An almost ideal demand system. American Economic Review, 70(3):312-326. Deneckere, R. and Peck, J. (2012). Dynamic competition with random demand and costless search: A theory of price posting. Econometrica, 80(3):1185-1247. Drabas, T. and Wu, C.-L. (2013). Modelling air carrier choices with a segment specific cross nested logit model. Journal of Air Transport Management, 33(1):8-16. Escobari, D. (2009). Systematic peak-load pricing, congestion premia and demand diverting: Empirical evidence. Economics Letters, 103(1):59-61. Escobari, D. (2012). Dynamic pricing, advance sales, and aggregate demand learning in airlines. Journal of Industrial Economics, 60(4):697-724. Escobari, D. (2014). Estimating dynamic demand for airlines. Economics Letters, 124(1):26-29. Escobari, D. and Jindapon, P. (2014). Price discrimination through refund contracts in airlines. International Journal of Industrial Organization, 34(3):1-8. Escobari, D. and Mellado, C. (2014). The choice of airport, airline, and departure date and time: Estimating the demand for flights, volume 4 of Advances in Airline Economics: The Economics of International Airline Transport, pages 177-198. Emerald Group Publishing. Escobari, D., Rupp, N., and Meskey, J. (2017). Price discrimination and focal points for tacit collusion: Evidence from the airline industry. Working Paper. East Carolina University. Available at SSRN: https://ssrn.com/abstract=2815279. Fu, X., Homsombat, W., and Oum, T. H. (2011). Airport-airline vertical relationships, their effects and regulatory policy implications. Journal of Air Transport Management, 17(6):347-353. Gallego, G. and van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8):999-1020. Gerardi, K. and Shapiro, A. (2009). Does competition reduce price dispersion? new evidence from the airline industry. Journal of Political Economy, 117(1):1-37. Harvey, G. (1987). Airport choice in a multiple airport region. Transportation Research Part A: General, 21(6):439-449. Hensher, D. A., Stopher, P. R., and Louviere, J. J. (2001). An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: An airline choice application. Journal of Air Transport Management, 7(6):373-379. Hess, S. (2010). Evidence of passenger preferences for specific types of airports. Journal of Air Transport Management, 16(4):191-195. Hess, S., Adler, T., and Polak, J. W. (2007). Modelling airport and airline choice behavior with the use of stated preference survey data. Transportation Research Part E: Logistics and Transportation Review, 43(3):221-233. Hess, S. and Polak, J. W. (2005a). Accounting for random taste heterogeneity in airport choice modelling. Transportation Research Record, 1915:36-43. Hess, S. and Polak, J. W. (2005b). Mixed logit modelling of airport choice in multi-airport regions. Journal of Air Transport Management, 11(2):59-68. Hess, S. and Polak, J. W. (2006). Exploring the potential for cross-nesting structures in airport-choice analysis: A case-study of the Greater London area. Transportation Research Part E: Logistics and Transportation Review, 42(2):63-81. Ishii, J., Jun, S., and Van Dender, K. (2009). Air travel choices in multi-airport markets. Journal of Urban Economics, 65(2):216-227. Knittel, C. R. and Metaxoglou, K. (2014). Estimation of random-coefficient demand models: Two empiricists' perspective. Review of Economics and Statistics, 96(1):34-59. Koster, P., Kroes, E., and Verhoef, E. (2011). Travel time variability and airport accessibility. Transportation Research Part B: Methodological, 45(10):1545-1559. Loo, B. P. (2008). Passengers airport choice within multi-airport regions (MARs): Some insights from a stated preference survey at Hong Kong International Airport. Journal of Transport Geography, 16(2):117-125. Marcucci, E. and Gatta, V. (2011). Regional airport choice: Consumer behaviour and policy implications. Journal of Transport Geography, 19(1):70-84. Mayer, C. and Sinai, T. (2003). Network effects, congestion externalities, and air traffic delays: Or why not all delays are evil. American Economic Review, 93(4):1194-1215. McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior, ed. P. Zarembka, pages 105-142. Frontiers of Econometrics. New York: Academic Press. Morrison, S. A. and Winston, C. (2007). Another look at airport congestion pricing. American Economic Review, 97(5):1970-1977. Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. Rand Journal of Economics, 31(3):395-421. Nevo, A. (2000). A practitioner's guide to estimation of random-coefficients logit models of demand. Journal of Economics & Management Strategy, 9(4):513-548. Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69(2):307-342. Njegovan, N. (2006). Elasticities of demand for leisure air travel: A system modelling approach. Journal of Air Transport Management, 12(2):33-39. Ortuzar, J. de D. and Simonetti, C. (2008). Modelling the demand for medium distance air travel with the mixed data estimation method. Journal of Air Transport Management, 14(6):297-303. Pels, E., Nijkamp, P., and Rietveld, P. (2000). Airport and airline competition for passengers departing from a large metropolitan area. Journal of Urban Economics, 48(1):29-45. Pels, E., Nijkamp, P., and Rietveld, P. (2001). Airport and airline choice in a multiple airport region: An empirical analysis for the San Francisco Bay area. Regional Studies, 35(1):1-9. Pels, E., Nijkamp, P., and Rietveld, P. (2003). Access to and competition between airports: a case study for the San Francisco Bay area. Transportation Research Part A: Policy and Practice, 37(1):71-83. Pels, E., Njegovan, N., and Behrens, C. (2009). Low-cost airlines and airport competition. Transportation Research Part E: Logistics and Transportation Review, 45(2):335-344. Petrin, A. (2002). Quantifying the benefits of new products: The case of the minivan. Journal of Political Economy, 110(4):705-729. Proussalogloua, K. and Koppelman, F. S. (1999). The choice of air carrier, flight, and fare class. Journal of Air Transport Management, 5(4):193-201. Reynaert, M. and Verboven, F. (2014). Improving the performance of random coefficients demand models: The role of optimal instruments. Journal of Econometrics, 179(1):83-98. Rupp, N. G. (2009). Do carriers internalize congestion costs? Empirical evidence on the internalization question. Journal of Urban Economics, 65(1):24-37. Skinner, R. E. (1976). Airport choice: An empirical study. Journal of Transportation Engineering, 102(4):871-883. Stavins, J. (2001). Price discrimination in the airline market: The effect of market concentration. Review of Economics and Statistics, 83(1):200-202. Tan, K. M. and Samuel, A. (2016). The effect of de-hubbing on airfares. Journal of Air Transport Management, 50:45-52. Vincent, D. W. (2015). The Berry-Levinsohn-Pakes estimator of the random-coefficient logit demand model. Stata Journal, 15(3):854-880. Williams, K. R. (2014). Dynamic airline pricing and seat availability. Working Paper. Yale University. Zhang, A., Fu, X., and Yang, H. G. (2010). Revenue sharing with multiple airlines and airports. Transportation Research Part B: Methodological, 14(8-9):944-959. Zhang, Y. and Xie, Y. (2005). Small community airport choice behavior analysis: A case study of GTR. Journal of Air Transport Management, 11(6):442-447. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/79857 |