Conclusions and Future Research Directions

6.6. Conclusions and Future Research Directions

An instrumented variable approach (two-stage least squares regression) is used to control for price endogeneity, allowing consistent parameter estimation. A set of instruments are found to pass all validity tests, and are offered as instruments that can be used in disaggregate air travel models of demand. The instruments are based on Hausman-type price instruments, which use a firm’s own prices in other markets as instruments for a market of interest, as well as measures of the level of market power by multiproduct firms (as in Stern, 1996). We build the instruments by using prices and flight frequencies from data compiled from an OTA website.

The price coefficient of the 2SLS regression model, which corrected for price endogeneity, is found to be 2.9 times more negative than the price coefficient for an uncorrected model, demonstrating the importance of correcting for endogeneity. For the OLS regression model, the estimated price elasticity of demand (evaluated at the mean of price) is 0.64, which represents inelastic demand. After correcting for endogeneity using 2SLS, the estimated price elasticity of demand is 1.84, which represents elastic demand.

It would be interesting to aggregate our data and/or to mis-specify the model using average prices instead of disaggregate prices. A priori, it is expected that price elasticities from an aggregated model would be less elastic than the price elasticities of our model. This is because on a daily basis, airline customers can choose to purchase departure dates with lower prices, or they can choose a different airline with a lower price offering for the day, or they can wait to purchase when a lower price is offered. This dynamic would not be captured in aggregate data.

We find that the total number of bookings is decreased during ongoing promotional sales of JetBlue’s low cost competitor Virgin America. In future research, it would be interesting to model JetBlue and Virgin America demand together in the same model using a nested logit model to capture the degree of substitution between the airlines. JetBlue and Virgin America are likely to be close substitutes. Incorporating a major carrier into the models could add insight about the substitutability of LCCs versus major carriers.

Also, there is a future research need for incorporating competitor prices into revenue management forecasts. Within the airline industry, there has been growing interest in developing the next generation of revenue management (RM) systems that can more accurately represent how customers make decisions in today’s online environments. The development of these next-generation “choice-based” RM systems require information about the prices (or “choices”) viewed by customers at the time of booking – both on the carrier of interest and, potentially, across several different competitors.

6.7. References

Arrington, M. (September 2, 2010) Virgin America rides Loopt taco truck special to fifth largest revenue day ever. TechCrunch. (accessed 07.05.11).
Carrier, E. (2008) Modeling the choice of an airline itinerary and fare product using booking and seat availability data. Dissertation for Doctor of Philosophy, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology.
Civil Aeronautics Board (1970) Effect on Total Market Traffic of Changes in Quality of Service (QSI). Docket 21136 (box 1196). Exhibit BOR-R-300. In Exhibit Series Rebuttal Exhibits of the Bureau of Operating Rights by J.F. Adley and C.J. Caridi.
Coldren, G.M. (2005) Modeling the competitive dynamic among air-travel itineraries with generalized extreme value models. Dissertation for Doctor of Philosophy, Department of Civil and Environmental Engineering, Northwestern University.
Coldren, G.M., Koppelman, F.S., Kasturirangan, K. and Mukherjee, A. (2003) Modeling aggregate air-travel itinerary shares: Logit model development at a major U.S. airline. Journal of Air Transport Management, 9 (6), 361-369.
Gillen, D.W., Morrison, W.G. and Stewart, C. (2002) Air travel demand elasticities: Concepts, issues and measurement. Final Report, Department of Finance, Canada. (accessed 05.20.13).
Granados, N., Gupta, A. and Kauffman, R.J. (2012) Online and offline demand and price elasticities: Evidence from the air travel industry. Information Systems Research. INFORMS. 23 (1), 164-181.
Hausman, J.A. (1996) Valuation of new goods under perfect and imperfect competition. The Economics of New Goods eds Robert J. Gordon and Timothy F. Bresnahan, 207–248. University of Chicago Press, Chicago.
Hausman, J., Leonard, G. and Zona, J.D. (1994) Competitive analysis with differentiated products. Annals of Economics and Statistics, No. 34, 159–180.
Hsiao, C-.Y. (2008) Passenger demand for air transportation in a hub-and-spoke network. Dissertation for Doctor of Philosophy, Civil and Environmental Engineering, University of California, Berkeley. (accessed 05.18.11).
InterVISTAS (2007) Estimating air travel demand elasticities: Final report. (accessed 05.20.13).
Oum, T.H., Zhang, A. and Zhang, Y. (1993) Inter-firm rivalry and firm-specific price elasticities in deregulated airline markets. Journal of Transport Economics and Policy, (27) 2, 171-192.
Stern, S. (1996). Market definition and the returns to innovation: Substitution patterns in pharmaceutical markets. Working paper, Sloan School of Management, Massachusetts Institute of Technology.

CHAPTER 7: Conclusions and Future Research Directions

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