Airline Ticket Prices and Flight Characteristics: Lowest Price Roundtrip Flights from Los Angeles to New York

Transcription

1 Airline Ticket Prices and Flight Characteristics: Lowest Price Roundtrip Flights from Los Angeles to New York Abstract This paper examines variation in airline ticket prices and relates this to observable differences in flight time, how far in advance the ticket was purchased, the time in transit for roundtrip flight, airport used, and airline carrier. It relates these differences to the underlying theory of hedonic pricing and price discrimination and the special nature of air travel in a hub-and-spoke system. The paper notes how cost of time in transit may be influenced by uncertainty, especially when the possibility of missed connecting flights in introduced. An empirical examination of the lowest posted ticket prices for flights between three Los Angeles airports and New York s LaGuardia Airport estimates that while the average lowest price when flying from Ontario (ONT) and Orange County (SNA) airports are 5.9% and 21% higher than from Los Angeles International (LAX), when adjusted for other flight characteristics they are 8.2% and 22.8% higher, respectively. The data also suggest that ticket prices are lowest three weeks before the departure date, and average ticket price for USAir is 24% lower than for Southwest - possibility reflecting amenity differences not included in the analysis. This analysis also finds average lowest ticket price falls by twelve cents for each additional expected minute in transit, implying a traveler time value of $7.20 per hour. Bruce Brown, for presentation at: Professor and Dept. Chair, Economics 89 th WEAI Conference, June 30,2014 Cal State Polytechnic University Denver CO Pomona, CA Phone: FAX: bbrown@csupomona.edu web: Key Words: Hedonic Pricing Price Discrimination Airline Ticket Pricing Airline On-Time Performance Hub-and-Spoke System JEL Classification Codes: A General Economics (Education and Teaching) L-9 Industry Studies: Transportation and Utilities M Business Economics D Microeconomics

2 Variation in the price of traveling between two specific cities receives significant attention both from economists and non-economists. Non-economists often focus on perceived fairness aspects. In writing for the New York Times, Nate Silver (2011) refers to airlines labyrinthine pricing schemes which ensure that customers who aren t prepared to exploit the pricing oddities will instead become the suckers. Economists typically view variation in prices of items such as plane tickets as the result of price discrimination, cost differences, or more realistically some combination of the two. Besanko and Braeutigam (2008) describe how standard third degree price discrimination involves charging more for product characteristics which are highly desired by consumer groups with less elastic demand. Although this can be viewed as a monopoly strategy, it can also increase efficiency by expanding output and potentially increasing both profit and consumer surplus. In theory, price discrimination results from demand characteristics, not differences in production costs. However a seller may experience increased cost in order to differentiate its products and so charge higher prices. These cost differences may be viewed as endogenously resulting from sellers attempts to take advantage of demand elasticities, or resulting from producers attempts to provide what consumers want, benefiting both groups. In the same way that Alfred Marshall described equilibrium market prices as the result of both demand and supply conditions in his famous scissor analogy, price differentials for characteristics of the same good can also be explained by the interplay of both utility and cost. Variation in airline ticket prices may be usefully be considered as the result of price discrimination as in recent work by Mantin and Koo (2010), and Puller and Taylor (2012), but cost differences undeniably plan a role. While the extra legroom and champagne for first class passengers serve to separate fliers into different groups, they also increase costs. 1

3 The paper describes differences in airline ticket prices from a theoretical standpoint and presents an empirical analysis for support. Section I discusses the theory underlying price differences by considering the product attributes model of Kelvin Lancaster (1966) and hedonic pricing model of Sherwin Rosen (1974). It also considers peculiarities of frequent flier programs. Section II presents a straightforward empirical analysis of airline ticket prices between Los Angeles and New York. Section III discusses how a hub-and-spoke system of airline routes can complicate the nature of differentiation of plane flights, with the length of time and its uncertainty as dis-amenities which should reduce consumers willingness to pay. Section IV summarizes and concludes, with mention of possible directions for future research. I. Hedonic Model, Product Attributes, and Airline Ticket Prices Before his work on the winner take all society Sherwin Rosen (1974) clarified economic research regarding pricing of product characteristics by developing his hedonic model. Although most often used to describe real estate prices with the focus on locational amenities, it can apply to any differentiated product. His model is based on the hedonic hypothesis, that goods are valued for their utility-bearing attributes or characteristics. Rosen defines hedonic prices as the implicit prices of attributes revealed to economic agents from observed prices of differentiated products and the specific amounts of characteristics associated with them. His view validates the econometric estimation of these implicit shadow prices by regressing product price on characteristics. I employ this standard technique in the empirical section of this paper. The inability to observe some product characteristics represents a challenge for econometric estimation. In cases where characteristics are connected to location (e.g., real estate) researchers may use spatial fixed effects models. Recent work by Abbott and Klaiber 2

4 (2011) highlight difficulties with this methodology and with the problem of estimating shadow prices in general. This is also a concern with the analysis presented in this paper. In some sense the fixed effects approach is built upon, or at least related to, the earlier theoretical work of Kelvin Lancaster (1966) and his product attributes model. This model has three key analytical components: i) levels of the product attributes ii) the budget constraints (formally efficiency frontier in the model), and consumer indifference curves. Each product embodies a bundle of attributes and consumers have preferences for these attributes, not for the product, per se. Consumers select from bundles of product attributes inherent in goods and services. Given that different consumers place different value on particular characteristics, development of differentiated products both benefit consumers and enable firms to earn short run profits. Within this analytic framework, a flight from Los Angeles to New York is not merely generic transportation, but represents a bundle of attributes, of different value to different consumers. The following section highlights three such attributes: total travel time, how far in advance of departure the consumer must purchase, and Los Angeles airport of embarkation. All three relate to the notion of convenience. Consumers may value and be willing to pay for greater convenience associated with a trip of shorter time duration, from a closer airport, and a ticket purchase locked-in a later date closer to the date of departure. The following empirical analysis attempts to hold other attributes constant by considering only flights from three LA area airports to one airport in New York City, non-refundable seats in coach, and only the lowest ticket price available for a flight on a particular day. Variation in other amenities, including seat size, legroom, baggage policy, in-flight perks, seat location, safety and on-time performance were not 3

5 measured, but could be controlled to some extent by considering each airline individually or inclusion of a dummy for airline. One amenity deserves special mention as it differs from the others: frequent flier programs. While they may be considered as supporting a type of second-degree price discrimination by lowering the effective price for consumers who purchase larger quantities from one airline carrier; for those whose travel costs are paid by a third party such as their employer, the fact that the discount accrues to the traveler and not necessarily the payer deserves attention. The standard assumption in economics is that consumers make the choice as to what to purchase, enjoy the benefits, and spend their own money on these goods and services. If the consumer makes the choice of airline ticket, but does not pay the price himself, his decision may be affected. A consumer may be more inclined to purchase a ticket from an airline with which they participate in a frequent flier program even if the price is relatively high, if the ticket price will be paid by another party. In this view, frequent flier programs may reduce the elasticity of demand for the group of participating frequent fliers, and increase the price they (or their sponsors) pay for a ticket. II. Empirical Analysis for of Ticket Prices, Los Angeles to New York, Summer 2014 The primary objective of this analysis is to quantify the shadow price of select characteristics: i) time required for travel, ii) how far in advance the consumer must purchase, and iii) Los Angeles airport of departure. Toward this end, I perform three regressions of posted ticket price on the reported total time the trip; how far in advance the ticket will be purchased; and departure airport. Regression have a standard form: 4

6 P 0 1TravelTime 2ONT 3SNA 4USAir k k AdvancePurchase 5 k 13 P = lowest available ticket price for a flight leaving and arriving on specific days. TravelTime = total time in transit, as listed online; this includes time at connecting airport. ONT = dummy for Ontario International Airport SNA = dummy for Orange County (Santa Ana) International Airport USAir = dummy for USAir, with omitted dummy being Southwest, for first specification. Advance Purchase = seven dummies indicating time until the flight will depart. As mentioned, only the lowest ticket for the relevant day was considered. In some cases this was problematic as two flights were possible, one with a slightly lower price but significantly longer transit time. I followed the procedure and recorded the lowest priced option alone. Travel time was readily available and provided a clear, controllable variable. Although all Southwest trips involved one stopover, the USAir trips often required two stopovers, particularly when flying out of Ontario (ONT) and Orange County (SNA). This typically resulted in longer travel time, but not always. Some of the flights for USAir indicated a cross listed American Airline flight. Although USAir recently merged with American Airlines, their flights are not totally integrated. Departures from three Los Angeles area airports were considered. Dummies for Ontario (ONT) and Orange County (SNA) were included in all three regressions, with flights leaving from Los Angeles International (LAX) serving as the reference. A dummy for USAir was included in the first regression, with Southwest serving as the reference. 5

7 All data was collected on June 19, Two different date combinations were observed: Friday to Monday, and Friday to Friday for four weeks. The omitted dummy was for flights leaving on Friday June 20 and returning Monday June 23. Thus estimated coefficients on the other seven dummies indicate estimated price differentials compared to this flight choice without dummy included. 450 Figure 1 Average Plane Ticket Prices Over Time, Nominal, Three major LA Area Airports 400 Average Nomical Price Los Angeles, CA (Current $) Ontario/San Bernardino, CA (Current $) 50 Santa Ana (Orange County), CA (Current $) Year Source: Bureau of Transportation Statistics: Research and Innovative Technology Administration (RITA) < Figure 1 shows average aggregate ticket prices from these three airports. This type of aggregate average data, readily available from the Bureau of Transportation Statistics, is unfortunately not much use for attempting to obtain shadow prices of flight amenities. In any 6

8 case the average ticket price for LAX is generally higher than for the other two airlines, but this likely reflects the fact that longer flights generally leave from LAX than from ONT or SNA. In fact the apparent relative increase in ticket prices at ONT likely reflects an increase in the average distance of flights leaving from ONT over time. Table 1 reports all ticket prices for 120 one way trips, reflecting 60 round-trip ticket prices. Websites for both Southwest and USAir show prices of individual legs. A check of several options show that prices on the airline sites were the same or very close to prices available from Orbitz, Travelocity, and Expedia for USAir. Southwest fares do not appear on these aggregate travel sites. One can see minimum prices posted were higher for Southwest at all three LA area airports. The overall average price was $ for Southwest and $ for USAir. Note this differential is unadjusted for both observed differences such as length of time for the flight and unobserved differences such as baggage policies for flights provided by the two carriers. In fact, many of the flights offered by USAir involved stops in two hubs, while for Southwest all flights required only one connecting flight. This and the more generous baggage policy of Southwest could partially explain this price differential. Table 2 Shows the time required to complete each flight. Travel times for 120 flights represent 60 roundtrip flights and these values represent sixty values for this independent variable 30 for each of the two regressions on data subsets. Table 3 reports estimates from aggregate regression on all data. Generally estimated values are intuitive and interesting. Coefficients on ONT and SNA imply flights leaving from Ontario and Orange County cost $67.57 and $ more than those leaving from Los Angeles International; and on USAir imply its flights have a price $ less than Southwest after adjusting for other included explanatory variables. Estimates on travel date dummies show after 7

9 adjusting for the other explanatory variables, the price of tickets purchased three weeks in advance are the lowest, $ and $ lower for the four and eight day round trips respectively as compared to a four day round-trip starting on June 20, only one day before the flight. Perhaps the most interesting result reported in Table 3 is the coefficient estimate of on travel time. Although not statistically significant, this point estimate implies that each additional minute of total travel time (which includes time waiting at hub airports) reduces the lowest ticket price offered by twelve cents. This implies a plausible traveler implicit time value of $7.20 per hour in transit. Table 4 reports estimates on two disaggregated regressions for both airlines. Both airlines show similar patterns in the price differential based on advanced purchase time, with the impact of purchasing three weeks before the flight more negative (beneficial) on price. One should realize this result may be specific to air travel to New York during the summer. For example, if some important event were occurring in New York between July 18 th and 25 th this could increase the price of flights into New York during this period, and so cause the benefit of purchasing five weeks in advance (from June 19) to be diminished. One difference in the estimated coefficients deserves comment, the difference in coefficient estimates on travel time between the two airlines: -1.1 for USAir, but for Southwest. As mentioned, many of the least-cost flights for USAir required two stopovers. In so far as these multi-stop flights were also longer, if such multiple stops were viewed as a disamenity by travelers reducing price, then the coefficient on travel time may be biased downward. Our result that purchasing three weeks before the flight departs results in the lowest price is consistent with some previous findings. Specifically Besanko and Breutigam (2008) report a 8

10 study of United Airlines ticket prices in which purchasing twenty six days before a specific flight resulted the lowest price, lower even than if purchased even earlier. III. Hub-and-Spoke Flight Networks and Travel Time Although the estimated impacts of travel time on airline ticket price for all three regressions in the empirical analysis are not statistically significant, and in one case the estimate has the opposite sign from what one would expect, it nonetheless seems likely that travel time is a significant dis-amenity affecting consumers choice of flights. Increased travel time should reduce utility and so reduce ticket price. It may not be simply time, but uncertainty as well about how long a flight will take, which reduces the benefit of a trip for travelers. Specifically, not only longer trips, but trips on airlines with a lower percent of flights arriving on time that should be viewed less favorably by travelers. In their insightful statistics textbook, Moore, McCabe and Craig (2009) use airline ontime data to illustrate the phenomenon of Simpson s Paradox: a situation in which the direction of a relationship reverses as the level of aggregation changes. In their example AmericaWest (now part of USAir) had a more favorable overall on-time record than Alaska Airlines, but if one disaggregated the results Alaska Airlines had better on-time performance than AmericaWest at nearly all individual airports. These different results were possible because Alaska Airlines disproportionately flew into airports in cities prone to bad weather, while the opposite was true for AmericaWest, centered in Phoenix AZ. They concluded that one should not favor the airline with a better overall record, but one with a better record on the route on which the traveler would travel. Within a hub-and-spoke system this analysis is complicated as flights are not merely from one city to another. If a traveler will fly from LA to New York but stop in Chicago, then it 9

11 is most important for them to arrive on-time in Chicago so they will not miss their connecting flight. Prior to deregulation in the late 1970 s air fares in the United States were regulated and airlines competed primarily in non-price dimensions. Point-to-point routes provided the convenience of nonstop direct flights between major cities. Although variation in passenger load implied planes often flew far below capacity, the additional cost could be covered by high regulated fares. The price competition which followed deregulation made cost-cutting imperative. In order to increase the number of flights at or near capacity while expanding cities served, hub-and-spoke networks developed, requiring a change of plane for passengers on most long distance domestic flights. Consider flights between the West and East Coast. A point-to-point system involved direct flights between all major cities on both coasts. Development of hub and spoke systems reduced the number of direct flights. Airlines using hub-and-spoke system could better cope with variation in the number of passengers traveling between specific cities. Airlines needed only predict the number of West Coast passengers flying into a specific city on the East Coast, not the number flying between individual cities. Figure 2 illustrates the distinction: 10

12 Figure 2 Point-To-Point Hub-And-Spoke W1 E1 W1 E1 W2 E2 W2 E2 W3 E3 W3 E3 Note that all West Coast and East Coast cities are interconnected with the hub-and-spoke system but additional routes are required to connect W1 with E2 and E3; W2 with E1 and E3; and W3 with E1 and E2. It is particularly important for flights arriving at hub airports to be on time, or at least not so late that one misses their connecting flight. This possibility of missing a connecting flight can be a significant dis-amenity for fliers. Even if one does not miss their connecting flight, the worry caused by such a possibility can adversely impact travelers, and cause them to choose direct flights, flights with a longer stay over at the hub airport, or airlines with better on-time records. For passengers who pass through hub cities on their way to a final destination, the disaggregated on-time performance may be less relevant than an airline s overall on-time performance, if that overall superior on-time performance is due to disproportionate use of a hub with a superior on-time record for all airlines. 11

13 A simple numerical example illustrates this point. Figure 2 shows a hypothetical situation with two airlines (A & B) and two hubs (H A & H B ). A passenger flying from W 1 or W 2 to either of two destination cities E 1 or E 2 may pass through and transfer planes at either of the two hubs. Suppose the two hubs have a disproportionate number of flights from one airline (airline A into Hub H A or B into Hub H B ) but that some of the other airline s flights go through the competitor s hub. Figure 2 shows the number of flights from each airline through each hub, both total and on-time. Ignore the issue of delayed departures from the hub airports, and consider only the percent of flights that arrive on-time into the hubs. In order to arrive at their destination (E 1 or E 2 ) on-time, passengers must arrive at their hub airport on time. Figure 2 Two Airlines A & B; Two Hubs H A & H B ; and Two Destinations W1 E1 H A W2 H B E2 Total Flights Hypothetical Example Overall Hub H A Hub H B Percent Airline On-Time Total % On-Time On-Time Total % On-Time On-Time A % % 67.3% B % % 82.7% Although possible to fly on airline A through hub H B assume the relative scarcity of flights makes this difficult. Similarly assume it is difficult to fly through hub H A on airline B. In this situation, in order to increase the probability of on-time arrival, a passenger flying from 12

14 either W 1 or W 2 to either E 1 or E 2 should choose to fly on airline B. This is because a disproportionate number of airline B s flights use hub H B. In this hypothetical example correct reasoning brings the decision maker back to the choice favored by a less sophisticated customer. A naïve passenger may choose airline B because of their favorable overall on-time performance. A passenger familiar with Simpson s Paradox would favor airline A since its performance is superior at each airport considered individually. A passenger who fully takes into account the nature of the hub-and-spoke system of airline flights will return to the choice of airline B because the hub airport it primarily uses has a better on-time performance. IV. Summary and Conclusion Most products in modern markets are differentiated, possessing individual attributes which may complement or substitute for attributes of other goods. This aspect of realistic differentiated goods may be examined through the lens of Lancaster s product attributes model, or the hedonic pricing model popularized by Rosen. In any case a flight from Los Angeles to New York is not simply a trip from point A to point B; but has value connected to convenience, safety and certainty. In this paper I focus on four variables directly related to the convenience of flight characteristics: i) time required for travel, ii) how far in advance the consumer must purchase, iii) Los Angeles airport of departure, and iv) airline: USAir or Southwest. Estimates from the aggregate regression with all data imply: purchasing a ticket three weeks prior to departure will minimize ticket price; ticket prices of LaGuardia-bound flights are higher if departing from Ontario or Orange County airports rather than Los Angeles International; and that after adjusting 13

15 for other measured flight differences, the estimated ticket prices are $236 lower on USAir than on Southwest. It also estimates the price of a roundtrip flight falls by twelve cents for each additional minute of total travel time. This implies a value of travel time of seven dollars per hour. Although on-time arrival and departure data were not included in the empirical analysis, this paper suggests such data would help clarify and quantify the dis-amenity of longer travel times with greater uncertainty. Specifically, future research could consider the effect of flight on-time performance and ticket price. Future research could also attempt to quantify the extent to which variation in airline ticket prices reflect product characteristics or price discrimination. The effect of product characteristics can be more extensively examined within a hedonic pricing framework with better and more extensive observation of product amenities and characteristics. The role of price discrimination might be analyzed by statistical analysis in which the unit of observation is consumer and/or consumer groups, their characteristics and differences in the prices they pay. Examination of airline ticket prices can inform us about more than just this one market. It can help to highlight the extent to which such price variation for differentiated goods and services benefit consumers and producers by better matching production to consumers preferences, and/or the extent to which it reflects exercise of monopoly power, increasing firm profits possibly at the expense of consumer welfare. 14

16 References Abbott, Joshua K. and H. Allen Klaiber, An Embarrassment of Riches: Confronting Omitted Variable Bias and Multiscale Capitalization in Hedonic Price Models, The Review of Economics and Statistics, November 2011, 93(4): Bailey, Elizabeth E., and Dong Liu, Airline Consolidation and Consumer Welfare, Eastern Economic Journal, Vol. 21(4), Fall 1995: Besanko, David and Ronald Braeutigam, Microeconomics, 3 rd edition, Wiley, Brown, Bruce, Simpson s Paradox, Aggregation, and On-Time Airline Performance, International Business Association 2005 Conference Proceedings, pgs , April, Brueckner, Jan K., Nichola J. Dyer, and Pablo T. Spiller, Fare Determination in Airline Hub and-spoke Networks, Rand Journal of Economics, Vol. 23(3) 1992: Good, David H., Robin Sickles, and Jesse Weiher, A Hedonic Price Index for Airline Travel, Review of Income and Wealth Series 54, Number 3, September 2008: Gwin, Carol F. and Carl R. Gwin, The Product Attributes Model: A Tool for Evaluating Brand Positioning Dec. 1, 2003, Baylor University, < Lancaster, Kelvin J. A New Approach to Consumer Theory, The Journal of Political Economy, Vol. 74, No. 2 (Apr., 1966): Mantin, Benny and Bonowoo Koo, Weekend Effect in Airfare Pricing, Journal of Air Transport Management 16 (2010): Moore, David S., George P. McCabe and Bruce Craig, Introduction to the Practice of Statistics, 6th edition, W. H. Freeman, Puller, Steven and L. Taylor, Price discrimination by day-of-week of Purchase: Evidence from the U.S. Airline Industry, Journal of Economic Behavior & Organization 84 (2012): Rosen, Sherwin, Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition, The Journal of Political Economy, Vol. 82, No. 1. (Jan. - Feb., 1974): Thompson, Eric, Roger Butters and Benjamin Schmitz, The Property Value Premium of a Place of Worship, Contemporary Economic Policy, Vol. 30, No. 2, April 2012: U.S. Department of Transportation, Bureau of Transportation Statistics, Research and Innovative Technology Administration, < 15

17 Appendix Graphics of Website for Data Collection 16

18 Table 1 Lowest Listed Ticket Prices to LaGuardia Southwest Airlines Avg. All Travel Dates From LAX From Ontario (ONT) From Orange Co (SNA) Three Out In RT Total Out In RT Total Out In RT Total RT June $599 $433 $1,032 $600 $598 $1,198 $602 $597 $1, $599 $599 $1,198 $600 $374 $974 $602 $597 $1, $599 $247 $846 $596 $598 $1,194 $598 $597 $1, $599 $315 $914 $596 $596 $1,192 $598 $598 $1,196 July 4 7 $314 $398 $712 $340 $598 $938 $376 $598 $ $314 $391 $705 $340 $338 $678 $376 $598 $ $325 $395 $720 $367 $598 $965 $598 $597 $1, $325 $391 $716 $367 $338 $705 $598 $597 $1, $599 $398 $997 $596 $598 $1,194 $598 $597 $1, $599 $599 $1,198 $596 $338 $934 $598 $598 $1,196 Average $487 $417 $904 $500 $497 $997 $554 $597 $1,152 $1, St. Dev $144 $110 $196 $126 $130 $198 $94 $1 $94 USAir Travel Dates From LAX From Ontario (ONT) From Orange Co (SNA) Out In RT Total Out In RT Total Out In RT Total June $660 $454 $1,114 $535 $541 $1,076 $535 $541 $1, $660 $335 $995 $535 $394 $929 $535 $463 $ $335 $319 $654 $397 $363 $760 $455 $362 $ $335 $315 $650 $397 $333 $730 $455 $447 $902 July 4 7 $315 $324 $639 $285 $350 $635 $288 $350 $ $315 $335 $650 $285 $330 $615 $288 $354 $ $330 $364 $694 $320 $376 $696 $536 $382 $ $330 $335 $665 $320 $283 $603 $536 $354 $ $330 $330 $660 $353 $338 $691 $359 $344 $ $330 $330 $660 $353 $332 $685 $359 $394 $753 Average $394 $344 $738 $378 $364 $742 $435 $399 $834 $ St. Dev $140 $41 $170 $91 $69 $150 $103 $65 $149 Avg. Both Airlines $ $ $ $ $ $ $ $ $ $ Note: Data accessed from Southwest and USAir websites on June 19, 2014 Prices were for the lowest price option; coach, non-refundable. 17

19 Table 2 Travel Time for Lowest Price Flights to LaGuardia Southwest Airlines Travel Dates From LAX From Ontario (ONT) From Orange Co. (SNA) Out In RT Total Out In RT Total Out In RT Total June July Average St. Dev USAir Travel Dates From LAX From Ontario (ONT) From Orange Co. (SNA) Out In RT Total Out In RT Total Out In RT Total June July Average St. Dev Note: Data accessed from Southwest and USAir websites on June 19, 2014 All Southwest Flights required one stopover Most USAir flights required two stop-overs, and some were on codeshare airlines (USAir is integrating American Airlines Flights during this period) 18

20 Table 3 Regression Results Dependent Variable = Lowest Roundtrip Ticket Price Both Southwest and USAir Independent Estimated Variable Coef. t Stat P-value Intercept Travel Time Travel Dates June Reference Flight, no dummy July LAX Reference Airport, no Dummy ONT SNA SWA Reference Airline, no Dummy USAir Note: R 2 =.76 Adj. R 2 =.69 Std. Err. = Obs. = 60 Data accessed from Southwest and USAir websites on June 19, 2014 Prices were for the lowest price option; coach, non-refundable. 19

21 Table 4 Regression Results - Disaggregated Dependent Variable = Lowest Roundtrip Ticket Price Southwest Airlines USAir Independent Estimated Estimated Variable Coef. t Stat P-value Coef. t Stat P-value Intercept Travel Time Travel Dates June Reference Flight, no dummy July LAX Reference Airport, no Dummy ONT SNA Note: R 2 =.72 R 2 =.90 Adj. R 2 =.53 Adj. R 2 =.83 Std. Err. = Std. Err. = Obs. = 30 Obs. = 30 Data accessed from Southwest and USAir websites on June 19, 2014 Prices were for the lowest price option; coach, non-refundable. 20