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Airline daily fare differentiation in a medium-size travel market
ARTICLE IN PRESS Journal of Air Transport Management 14 (2008) 168– 174
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Airline daily fare differentiation in a medium-size travel market K. Obeng Department of Economics and Transportation/Logistics, School of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA
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a b s t r a c t
Keywords: Fare differentiation Fixed effects Medium-size city Competition Inelastic demand Fare discount Fare premium
This paper examines variation in airline fares for trips in a medium-size travel market. It develops a conceptual model of fares offered, and uses daily information about fare, plane and flight characteristics, and trip characteristics easily available on the internet. Based on this information it estimates a twoway fixed effects model of airline fares. The results show large differences in fares among the airlines, large variation in daily fares offered, and provide evidence of fare differentiation in the travel market analyzed. & 2008 Elsevier Ltd. All rights reserved.
1. Introduction The literature on airline pricing abounds with works explaining differences in airfares. These works include those by (Pels and Rietveld 2004; Giaume and Guillou, 2004; Hayes and Ross, 1998; Cohas et al., 1995). The general premise in them is that third degree price discrimination is a source of fare differentiation in the airline industry. This discrimination involves airlines dividing their markets into travel segments, then bundling their services with restrictions and selling them to each segment at different fares. By this premise, travelers on leisure trips who have elastic demand and time to purchase their tickets well in advance and are willing to stay weekend’s pay lower fares compared with business travelers with inelastic demand and inflexible schedules who sometimes must make their trips on demand. The effect of this price discrimination is that on any given flight there are many travelers paying different fares. Another source of fare differentiation is rationing where airlines use fares to allocate their supply of limited seats among travelers. Those for whom a trip yields a large marginal benefit and are willing to pay higher prices get seats as the number of available seats reduces or as the flight day approaches. This rationing is efficient because it equalizes the marginal cost of providing an additional seat and the marginal benefit to the consumer and transfers consumer surplus to airlines thereby increasing their profit. For heavily traveled markets this rationing is reasonable but not so where the level of demand is so low as to create excess capacity (Botimer, 1996). Even here, the ability of airlines to easily switch to smaller aircrafts and route passengers through con-
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gested hubs where seat demand is very high creates competition among travelers for seats and allows airlines to increase their fares. In medium-size markets closely located near major hubs, we could expect very little difference in airfares among competing airlines1 because they may have excess capacity. Alternatively, we could expect large increases in airfares from carriers routing their flights through hubs and incurring additional costs. If demand in such a travel market fare is inelastic especially in the absence of serious airline competition or in the presence of airline dominance and market power fares are likely to be high. Cost differences are also sources of airline fare differentiation. For example, because peak service is costly to provide airlines charge higher prices for them. Which of these sources of fare differentiation is found in any given market is an empirical question to which this note provides some answers. It studies air fares in a medium-size market close to hubs using on-line daily fare offerings information for trips with the objective of identifying the sources of fare differentiation and when it is most advantageous to travelers to purchase their tickets.
2. Conceptual model Consider an airline that segments its customers and charges each segment different fares. This airline practices price discrimination, and does so by offering various incentives and penalties for each fare. For example, it may offer reduced economy fares for weekend stay or advance ticket purchases. This type of airline fare differentiation is well-documented and researched under yield management. An aspect of yield management that is 1 In this paper a market is defined in terms of a corridor where airlines compete among themselves to provide service.
ARTICLE IN PRESS K. Obeng / Journal of Air Transport Management 14 (2008) 168–174
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Fare / Fare Premium
Mean Fare P
Fare Offered Curve Minimum fare
Pmin
Days before
0 Fare Adjustment Curve
T
Day of mean fare Fare Discount Fig. 1. Changes in airline fares: a conceptual model.
receiving attention lately regards daily adjustments to airfares by airlines to reflect demand, available seats, and plane capacity. A rationale for these adjustments is that travelers, particularly business travelers, value their trips very highly and are willing to pay higher fares for them as the trip day approaches. Another is that they are ways for firms facing competitive pressures and having high fixed costs to maintain their survival (Baumol and Swanson, 2003). Airlines also make these adjustments to account for the high costs of accommodating late bookings (e.g. finding good connections, searching many flights, possibility of using multiple carriers for customers). Fig. 1 is a conceptual diagram of the offered fares and their adjustments. It shows that the fare offered curve declines as the number of days before a trip increases. This curve is similar to that in Weatherford and Bodily (1992), and it is a reverse of what (Pels and Rietveld, 2004; Button and Vega, 2007) presented in their researches. There is also a minimum fare Pmin that the airlines charge and to which the fare offered curve is asymptotic. Assume a mean policy fare level of P for a round-trip ticket set by considering competition, plane capacity and costs whose level ensures normal profit.2 This is the fare after accounting for cost, demand, airline and period fixed effects and other exogenous influences. Subtracting the mean fare from the fare offered each day gives the fare adjustment curve whose intersection with the horizontal axis shows the days after (before) which airlines upwardly (downwardly) adjust their fares. This day is unknown to travelers but management and because of differences in management styles, network circuity and costs, when it occurs is likely to differ among airlines. As the fare adjustment curve shows the fare increase (decrease) above (below) the mean fare is not uniform; steep discounts (premiums) are possible the farther away (nearer) is the flight day. Several factors explain these fare adjustments. Lee and LuengoPrago (2005) found that the presence of a low-cost carrier and passenger density exert downward pressures on fares as is flight length. Cost differences among airlines, competition and willingness of consumers to switch to another airline have been found to be the main sources of fare differentiation (Borenstein and Rose, 1994) as have direct services, trip length, and airport market
2
The seats flown are the sum of those in business and economy classes.
share (Evans and Kessides, 1994). In Giaume and Guillou (2004) a high level of market concentration was negatively related to price level and price discrimination. A similar result was reported by Hayes and Ross (1998), who also found that the number of carriers serving an airport, flights ending or beginning in major cities, length of route and intermediate stopover were sources of fare differentiation. Besides these studies Pels and Rietveld (2004) estimated fare models for different airlines and found that the lowest fares were offered 21–28 days before departure. Although their results are interesting, it can be argued that estimating different fare models for each airline as Pels and Rietveld (2004) did is at the expense of sample size and that perhaps better results could be obtained using panel data and estimating a fixed-effect model. Such a model allows for controlling systemic differences in fares that could occur through airline and period fixed effects.3
3. Methodology To address some of the concerns in Pels and Rietveld (2004) and further explain fare differences, consider an airline round-trip to be taken on a future date from a non-hub airport in a mediumsize travel market. The trip begins on a weekday and returns the same week on a weekday. Different airlines A1, A2 ,y, Ai compete in this oligopoly market, where i is the number of airlines. Each airline reserves most of its seats for full-fare paying passengers, and a fixed number of discount seats for consolidators who in turn sell them. Some flights are direct and are provided by single carriers while others are single- or multiple-carrier flights some of which have connections in a third city where the airlines have hub operations. The airlines (i) operate different aircrafts (k) each with a capacity of xik seats of which zik are discount seats offered at a discount fare of pit, where t is the number of days before a trip is taken and zikpxik.4 Assume further that each airline’s fare is based 3 Interestingly, these authors rejected a fixed effects model in favor of a random effects model. 4 We do not use passengers in this paper because it is not possible to get that data on a daily basis. Because the type of plane flown and hence plane capacity depends upon forecasted demand we assume that plane capacity is a proxy for demand.
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upon in-flight time Fit, which is a proxy for flight distance and network circuity, layover or transfer time Lit, if the flight is direct (D), plane capacity (xik), peak service (H), and the number of discount seats (zik) available that day.5 With these assumptions Eq. (1) shows the hypothesized fare offered model estimated in this study pit ¼ a0 þ a1o xikto þ a1c xiktc þ a2o zikto þ a2c ziktc þ aF F it þ aL Lit þ aD Dit þ aFD Dit F it aH H þ aHseat Hðxiktc þ xikto Þ þ 0:5a44 L2it þ bi þ ft þ it
(1)
The subscripts (o, c) show originating and connecting flights, respectively, and each day is treated as a binary variable as is each airline and a direct flight. The unknown coefficients are the sets given by a, b, f. The coefficients denoted by a capture the marginal effects of the exogenous variables on airfares. However, the signs of a1o, a1c, a2o, a2c are indeterminate a0 priori because the number of seats on a plane affects cost and it is influenced by demand. To the extent that it costs more to fly large planes this cost would be recouped through higher fares and these coefficients would be positive. On the other hand since plane capacity, particularly the capacity of the originating flight, is a proxy for demand the signs of these coefficients could be negative. If they are positive they would show that the cost-influencing effects of seats on fares are larger than the demand-influencing effects of seats while if negative the reverse is true. Also, we expect the impact of each additional discount seat on fares to be larger than the impact of each additional seat on a plane for two reasons. First, web-based airline consolidators compete among themselves to sell their discount seats at various fares and may have a lot of flexibility in passing their discounts on to travelers in terms of lower fares. The more discount seats consolidators have the more they are willing to lower their fares by a large amount. Second, because the marginal cost of an additional airline seat (or an additional passenger) is very low the coefficient of plane capacity would be also low to show how much of this small cost is recovered from fares. In-flight time is expected to have a positive coefficient because longer trips use more fuel and labor and lead to more wear and tear on aircrafts.6 The costs associated with these trips would be recouped through higher fares. This expected positive sign is opposite what Lee and Luengo-Prago (2005) and Hayes and Ross (1998) found but consistent with what Giaume and Guillou (2004) found. If this coefficient is positive it would also show that the cost effect of this variable dominates its demand effect in this market. Additionally, if we argue that airline flights are for long trips the marginal effect of an hour saved on such a flight is not expected to have a significant impact on demand. Therefore, we expect the cost-influencing effect of in-flight time to dominate its demand-influencing effect. In comparison, we expect the coefficient of layover time to be negative. This is because layover is an inconvenience to the traveler, especially long waits to connect with other flights. Layover could show high demand or frustrations with the service that make airline passengers less willing to take a flight unless the fare is low or if they have no choice. It could also affect airline cost in terms of increased space to accommodate waiting passengers. 5 We consider only one travel market. Therefore, dominance and concentration are fixed and are not considered in this paper. 6 An exception is when smaller turboprop planes are operated. In this case although in-flight time is high plane operating cost is very low. To account for the fact that in-flight time and plane capacity may be related an interacting term involving these two variables was initially included in the model and it did not give good results as many of the variables had statistically insignificant coefficients.
Hence, it is possible for fares to increase with layover time. To capture both effects, Eq. (1) includes linear and quadratic terms in layover time. If airlines practice peak pricing their fares would be higher at the peak than off-peak. For morning trips the peak occurs quite early. To capture this peak we use a binary variable for flights leaving before 8 a.m. Because peak travel imposes costs on airlines in terms of increased resources airlines must increase their fares to recoup this cost. Therefore, the coefficient of this term is expected to be positive. Additionally, the constant term is expected to be positive because it shows the mean fare. This term aside, both f and b capture period and group fixed effects, respectively, reflecting price differentiation and heterogeneity of service.7 Respectively, the signs of these coefficients are expected to be positive (or negative) to show a premium (discount) as the flight day approaches or to show differences in fares charged by each airline due to unobserved heterogeneity effects. Holding everything else constant, the coefficients of airlines show their fare premiums and discounts. Similarly, the coefficient of each day gives the daily fare discount or premium. Both the period and airline fixed effects provide some evidence of fare differentiation. Because we do not expect the marginal seat on an originating flight to be valued differently from a marginal seat on a connecting flight we impose the following restriction a1o ¼ a1c. Similarly, a2o ¼ a2c because a marginal seat at an offered fare is assumed to be valued equally on the originating and connecting flights.
4. Data 4.1. Choice of study area To estimate the above equation requires data. The data were collected for a weekday trip from Greensboro, North Carolina to Boston, Massachusetts leaving before noon on Monday, April 9, 2007 and returning any time on Friday, April 13, 2007. The chosen air travel corridor is a medium-size market with services provided by American, Delta, US Airways (hereafter USAir), Continental, United, and Northwest. Only Delta provides direct service to Boston. The reason for studying this market regards a recent controversy between the Piedmont Triad International Airport Authority (PTI) and Delta Airlines. PTI claims it is losing on the average 2300 passengers a day to Charlotte’s Douglas International airport about 100 miles away, and to Raleigh–Durham airport 75 miles away because in the absence of a low-cost carrier Delta being the dominant carrier has inflated its fares making other airlines do the same.8 PTI cites the example of flights between Greensboro and Boston where Delta charged $798.00 in 2006 compared with $138.00 for a similar flight from Raleigh– Durham International airport (News and Record, 2006). Delta counters this claim that it charges what the traffic will bear and that its fares are competitive. After this ‘‘finger-pointing’’, negotiations ensued between PTI and the airlines serving the airport with PTI asking them to reduce their fares. USAir obliged 7 A functional form of Fig. 1 could also be estimated to provide a smooth curve. We opted for the fixed effect model because using specific forms of this figure did not give good results. Also, changes in airfares are discrete and not continuous especially since blocks of seats are released at a time. 8 The data were collected before PTI announced that a low-cost carrier, Skybus Airlines, would begin operation at this airport beginning May 29, 2007 with service to Columbus OH and some seats priced as low as $10 on each trip. Another low-cost carrier, Allegiant Air has been wooed to the airport and will begin flights on May 24, 2007 with large jets. None of these low-cost airlines would provide service to Boston MA.
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Table 1 Descriptive statistics Variables
American
Continental
Delta
Delta direct and united
Delta and USAir
Northwest
United
USAir
All carriers
Fare offered ($) Discount seat initially available on the first plane Capacity of first plane (number of seats) Discount seats initially available on the second plane Capacity of second plane (number of seats) In-flight time (hours) Layover time (hours) Direct service (originating flight) Proportion of flights departing before 9 a.m. Number of flights recorded
1758 6
666 22
792 17
1140 17
761 19
879 18
828 9
646 22
813 17
49
62
57
52
55
53
103
59
64
25
58
19
17
44
36
14
73
43
199
130
75
52
84
159
126
118
118
4.91 3.48 0.00
3.01 2.08 0.00
2.64 0.66 0.65
2.02 0.00 0.86
2.38 0.54 0.00
3.65 2.20 0.00
2.70 1.88 0.00
2.80 1.08 0.00
2.93 1.41 0.08
1.00
0.51
0.75
1.00
0.69
0.64
0.33
0.48
0.59
34
47
68
as did the other airlines but not Delta. Given this background it would be interesting to study airfares in this market.9 4.2. Data collection The data collection used the ORBITZ Internet search engine because it provided seat data which most of the other search engines did not provide. It was done between four and six o’clock in the evening each weekday of the 3 weeks (i.e., 3/19–4/6/2007) preceding the flight date (4/9/2007) for single- and multiplecarrier flights. For each flight and its connection the information collected included flight numbers, posted fare, in-flight time, layover (connection) time, seats available at posted price on the originating and the connecting flights, aircraft capacity in terms of seats on the originating and connecting flights, direct service, number of carriers involved, airline name, and time of departure. Return trip data were not collected except carrier name to help completely enumerate joint flights.10 The data treat multiple carrier flights as if they were offered by separate carriers. Such a flight is one in which more than one carrier is involved in the round-trip journey. We also assume symmetry so that Delta–USAir and USAir–Delta flights, for example, are the same. Further, flights were recorded by fare offered because the same originating flight may be offered at different fares and may connect with different flights on both the front-haul and the return. Finally, only flights with available seats on all legs at the offered fares were recorded. Some modifications in recording the data were made for multiple carrier flights that had very few observations. There was one Delta–Continental flight, one Northwest–Delta flight, one Northwest–United flight, and one USAir–Continental flight. These flights were, respectively, added to Delta, Northwest, Northwest, and USAir because they provided the originating flights. Similarly, there were three flights each for United–USAir and USAir–United that were added to United and USAir, respectively.11 Direct flights by Delta connecting with United on the return were treated separately because of their very high fares despite being few, and 9
This agreement was reached after the data were collected. Although fares are affected by the characteristics of the return trip we did not collect such data to see their impacts on fares. 11 We also considered if adding these flights to the other connecting airlines and to each other’s flight would affect the results and found no differences in the results. This is because these flights are few. 10
7
113
84
94
157
604
because they show the monopoly power and dominance of Delta airlines in providing direct services in this corridor.12 With these modifications the data form an unbalanced panel consisting of 604 flights sorted by carrier type in Table 1.
4.3. Descriptive statistics Table 1 shows that 8.28% of the flights are direct and that USAir offers most flights in this market followed by Delta, United and Northwest in that order not counting joint flights.13 On the average each flight takes 2.93 h excluding 1.41 h of connection time at another airport. The shortest in-flight times of less than 3 h are offered by Delta, United, USAir, Delta–USAir, and Delta– United flights, and the longest by American (4.91 h) followed by Northwest (3.65 h) and Continental (3.01 h). Delta airline’s joint flights with USAir have the shortest average connection time of 0.54 h, and about 58.44% of the flights depart before eight o’clock in the morning. The table also shows that the airlines use small aircrafts (seating 64 passengers on the average) and offer 17 discount seats on originating flights. Additionally, each connecting flight has a capacity of 118 seats on the average 43 of which are offered at a discount. Comparing the airlines American and Northwest use large planes seating on the average 199 and 159 passengers, respectively, on connecting flights. More discount seats (22) are provided by USAir and Continental than any other airline on both originating and connecting flights. Besides providing information about plane capacity and discount seats the table shows that American airline charges the highest average fare ($1758) followed by Delta–United ($1140.00).14 The average fare on Delta–USAir flights is $761.00, while Northwest, United, Delta and Continental charge average 12 The Delta flights considered connected in Atlanta, New York City La Guardia airport, and USAir flights ha connections in Charlotte NC, Philadelphia PA and New York City La Guardia airport. United Airline flights had connections in Washington DC Dulles airport, Northwest flights connected in Detroit MI Wayne airport, Continental in Newark NJ, and American in Miami FL. 13 The aircrafts used for the originating trips are Embraer RJ145, Embraer RJ135-145, Boeing 737, and Canadair 700 because the flight is between a mediumsize city and a large city. The connecting flights generally use large aircrafts such as MD 80, A319, Boeing 737, Boeing 757 and A320. 14 The fares on American airlines are as high as $2857 while the highest fare on United is $2260.
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Table 2 Estimated fixed effects model Variables
Estimate
In-flight time (hours) Layover time (hours) Discount seat on first plane Discount seat on second plane Capacity of plane 1 (seats flown) Capacity of plane 2 (seats flown) Departure time before 8 a.m. (yes ¼ 1, no ¼ 0) Departure hours after 6 a.m. sum of discount seat on all planes 0.5 layover layover Constant American Continental Delta Delta–United Northwest United USAir USAir–Delta 3 days before flight 4 days before flight 5 days before flight 6 days before flight 7 days before flight 10 days before flight 11 days before flight 12 days before flight 13 days before flight 14 days before flight 17 days before flight 18 days before flight 19 days before flight 20 days before flight 21 days before flight
138.0645 62.2665 1.4281 1.4281 0.4798 0.4798 59.8192 0.2351 12.0928 569.5806 702.4150 79.0970 83.3338 285.5727 43.7383 25.7711 45.0976 114.3850 347.0602 189.9011 260.1060 128.2508 99.7223 20.0225 156.2650 57.9225 116.3050 201.8020 153.2350 192.3440 202.1430 256.4770 366.4890
Tests (1) (2) (3) (4) (5)
of the classical model Constant term only Group effects only X variables only X and Group X, group, and period effects Likelihood ratio test
(2) (3) (4) (4) (4) (5)
vs. vs. vs. vs. vs. vs.
(1) (1) (1) (2) (3) (4)
Chi-squared 319.131 405.915 503.099 183.969 97.184 224.032
Std. err 23.7566 28.5858 0.4126 0.4126 0.2267 0.2267 33.0783 0.1124 10.2014 78.9974 71.4778 38.1574 34.2903 99.1397 27.6722 34.5306 47.3332 24.9977 38.5451 44.1055 50.1013 31.3982 34.5226 26.0115 34.0653 28.5456 33.8818 40.4693 50.5411 55.9408 76.3486 89.9080 96.1584
t-Value 5.8120 2.1780 2.9840 2.9840 2.1170 2.1170 1.8080 2.0920 1.1850 7.2100 9.8270 2.0729 2.4302 2.8805 1.5806 0.7463 0.9528 4.5758 9.0040 4.3056 5.1916 4.0847 2.8886 0.7698 4.5872 2.0291 3.4327 4.9865 3.0319 3.4384 2.6476 2.8527 3.8113
Log likelihood 4439.75 4280.18 4236.79 4188.20 4076.18
R-squared 0.0000 0.4104 0.4893 0.5652 0.7000
Denom. 596 594 589 589 589 575
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
F Tests D/F 7 9 14 7 7 14
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
F 59.272 63.244 47.697 23.223 14.639 18.443
Num. 7 9 14 7 7 14
Notes: vs. ¼ versus. p o0.05. 0.05 op o0.10.
fares of $879, $828, $792 and $666, respectively, on their singlecarrier flights. Overall, a weekday roundtrip in this travel market leaving on Monday before noon and returning anytime on Friday has an average fare of $813.
5. Estimation Eq. (1) with the restrictions imposed can be estimated as in Hayes and Ross (1998) as a two-way random effects model though attempts to do so in this paper failed. Another approach is to estimate it as a two-way fixed effects model.15 This second 15
To make the slopes for each airline different interaction terms between inflight time, layover, and airlines were initially included in the model. Their
approach gives estimates of the coefficients of all the airlines and days. Since the focus is on daily and firm-by-firm fare adjustments this latter approach is used in this paper. However, to be estimable the following additional restrictions are imposed Sibi ¼ Stfit ¼ 0. Furthermore, because Delta is the only airline that provides direct service between the two cities its effect is captured by its fixed effect. Therefore, the direct service term and the term involving interaction between layover time and direct service are omitted from Eq. (1). Table 2 shows the results of estimating the two-way fixed effects model by LIMDEP (Greene, 2002). The bottom part
(footnote continued) coefficients were not statistically significant so they were dropped from further analysis.
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compares this model to others and shows that the two-way fixed effects model is an improvement over a model with only airline fixed effects, and far better than a model with no fixed effects based upon the likelihood ratio tests and the percentage of variation explained. Also, most of the estimated coefficients are statistically significant and have the expected signs except the coefficients of the squared layover term and the fixed effects of Northwest, United, and USAir. From these results the exogenous variables with negative coefficients are the sources of fare discounts, and those with positive coefficients are the sources of fare premiums. The constant term being positive and statistically significant shows that after accounting for all the independent variables including period and group fixed effects the mean airfare is $569.58. Using the estimated coefficients the following are the sources of fare differentiation.
6. Sources of fare differentiation 6.1. Cost In-flight time is a major source of fare variation in this market according to the results. Its coefficient is positive and statistically significant and shows that an hour increase in it increases air fares by $138.06. This increase reflects attempts to recoup the high cost of routing flights through off-line hubs since the distance from Greensboro to Boston is fixed.16 Another source of fare variation is peak travel. The results show a fare premium of $59.82 on flights leaving before eight o’clock in the morning. Airlines in this market use this premium to recover their additional costs of providing peak services. Because of this premium early morning flights are more expensive than late morning flights. 6.2. Demand Demand factors are another source of fare differentiation and unlike cost they have negative relationships with fares. For example, the coefficient of layover time is 62.2665 and statistically significant and shows that an hour increase in it decreases air fares by $62.27. This shows that in this market flights that inconvenience travelers with long connections such as those routed through hubs, all things being equal, attract low fares. A possible explanation is that airlines internalize passenger inconvenience in setting fares and compensate for it by charging low fares for this type of flight. In Table 2 discount seats and plane capacity have negative and statistically significant coefficients of 1.4281 and 0.4798, respectively, showing that they too account for some of the variation in fares. These results show that the marginal effect of a discount seat is larger than the marginal effect of an additional seat on a plane and it is expected. Since we instrument passengers with plane capacity on the originating flight the inverse of its coefficient can be used to calculate fare elasticity of demand. Using this inverse, the mean values of fares and plane capacity on the originating flight, the average fare elasticity of demand is 0.1637 for trips in this market. By the same approach the following are the fare elasticities of demand for flights by American, Continental, Delta, Delta–United, Delta–USAir, Northwest, United, and USAir 0.058, 0.194, 0.150, 0.095, 0.151, 0.259 and 0.190, respectively. These elasticities show that travelers in this market are very insensitive to changes in fares, especially changes in the fares of American and Delta–United joint flights. This insensitivity somewhat explains the observed high 16
An off-line hub is defined as not on the direct path between the two cities.
173
passenger fares in this market and suggests that travelers here may be captive to this airport and lack reasonable choices for their trips. The inconvenience of accessing alternate airports very early in the morning makes travelers continue to use PTI despite the very high fares they must pay for flights there. 6.3. Price discrimination 6.3.1. Airline fixed effects In Table 2 there are clear differences in how the unobserved airline effects affect airfares in this market. American airline flights and joint Delta–United flights carry hefty premiums of $702.42 and $285.57 above the average, respectively, which are statistically significant and should be added to the constant term to get the mean fares of these airlines after controlling for exogenous influences. If this is done and everything else is held constant the estimated mean fares are $1272.00 and $855.15 for American and Delta–United flights, respectively. While the American airline premium is due to its long in-flight time such is not the case for Delta–United flights.17 This is because Delta–United flights are direct by Delta for the front-haul with the return on United through Washington, DC. Therefore, Delta airline is a monopoly in providing direct flights to Boston MA and the high premium on Delta–United flights is an attempt to earn monopoly profits. These high fares are more than the sum of the full singlecarrier fares of the two carriers. To be specific the estimated mean Delta–United fare is more than twice the estimated mean single carrier fare on flights by Delta or United.18 A source of these high fares could be the absence of economies of joint services, attempts to pass on all fixed costs of providing such flights onto passengers, or price discrimination because the two airlines have relatively very inelastic demand. It could also be that the flights studied are mostly used by business travelers who are willing to pay high fares to save time. The group fixed effects coefficients of Continental, Delta and USAir-Delta are negative and statistically significant showing that there are exogenous influences not accounted for in this paper that make the fares of these airlines lower than the mean fare of $569.58. Holding the period fixed effects and the other explanatory variables constant these influences make the fares of joint USAir-Delta flights, and the single-carrier flights by Continental and Delta $114.38, $79.10 and $83.33 cheaper than the mean fare, respectively, possibly reflecting service quality differences. The fixed effects coefficients of Northwest, United and USAir are not statistically significant; therefore their mean fare is $569.58. These group effects show large differences in fares among the airlines in this market further evidenced by the coefficient of variation of 44.4% showing a large dispersion of the fares around the mean. Since we account for cost and some demand factors the source of this dispersion could be market dominance. Comparing the airline fixed effects the joint Delta–United flights are the ones to blame for the rather high airfares in this market. This is because American airline is not the dominant carrier given its few daily flights and its astronomically high fares, which probably deter many potential customers from using it for this trip. 6.3.2. Period fixed effects Table 2 also shows the daily variation in fares. Holding everything else constant except the period fixed effects the fare 17 The data were collected before American announced that it would begin new direct flights to Chicago O’Hare airport. After this announcement a check of American flights to Boston found that most of them were being routed through Chicago instead of Dallas and Miami and have lower fares. 18 A proportional fare is some percentage of the sum of the full fares for the different legs of a trip.
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adjustments that prevail in this market based upon our results are also shown. Very clearly, except the 10th day before the flight, all other days have statistically significant coefficients. Some of these coefficients such as those of tickets bought 21 days before the flight down to 10 days before the flight are negative and show discounts. Also, the absolute sizes of these discounts decline as the flight day approaches and there are statistically significant differences in daily fare adjustments starting from 2 weeks before the flight but not earlier. For example, 14 days before the flight the discount is $201.80 and it drops to $116.31 about 13 days before the flight (t ¼ 2.01), where the t-test value is in parentheses. Comparatively, 21 and 20 days before the flight the discounts are, respectively, $366.49 and $256.48 (t ¼ 1.1). The largest discount of $366.49 is offered 21 days before the flight declining to $20.02 10 days before the flight. Additional results from the table show that the fixed effect coefficients are positive and larger for tickets bought 7 days before the flight down to 3 days before the flight showing daily premiums as the flight day approaches. For example, a week before the flight this premium is $99.72 increasing to $347.06 3 days before the flight is to be taken. Holding everything else constant and adding these premiums to the constant term the average fare is $916.64 3 days before the flight compared with $669.30 a week before the flight. Since the fare discount 10 days before the flight is not statistically significant the fare is not different from the mean fare of $569.58. Therefore, close to 10 days corresponds to when the fare adjustment curve in Fig. 1 crosses the abscissa and when fare premiums begin and discounts end in this market. Tickets bought earlier than 10 days before the flight attract discounts according to our results.
7. Conclusion This paper analyzes airline fares in a medium-size market using on-line daily fare data for trips. It estimates a group and period fixed effects model of airline fares that accounts for factors such as peak departure times, in-flight time, layover time, number of discount seats and plane capacity on the originating flight used as a proxy for demand. Of these factors, the marginal effect of inflight time is the largest being $138.06 increase in fares for each additional in-flight hour, followed by peak hour flight which increases fares by $59.82 for flights leaving before 8 a.m. It also finds consistent fare discounts of between $58 and $367 for tickets purchased at least 10 days before a flight, and premiums ranging from $99.72 7 days before a flight to almost $350 3 days before a flight. Large differences in fares charged by competing airlines in this market are also found. After accounting for
exogenous influences, period and airline fixed effects the highest fares are those on American and Delta–United flights. Furthermore, the period fixed effects are larger and much more volatile than the airline fixed effects. Thus, even though the evidence suggests fare differentiation among airlines in this market it is more evident as the flight day approaches than at any other time. This fare differentiation suggests that if PTI is interested in lowering fares it should attract carriers that are willing to offer competing direct services in the Greensboro–Boston market. Perhaps USAir should be encouraged to offer direct services to Boston since it offers the lowest fares. Since Delta has a monopoly in direct flights in this market pressures on it to reduce its fares could greatly influence other airlines to do the same.
Acknowledgment I wish to acknowledge the invaluable comments of R. Sakano and H. Snarr of the Department of Economics and Transportation/ Logistics, North Carolina A&T State University, Greensboro N.C. on an earlier version of this paper. References Baumol, W., Swanson, D., 2003. The new economy and ubiquitous price discrimination: identifying defensible criteria for market power. Antitrust Law Journal 70, 661–685. Borenstein, S., Rose, N.L., 1994. Competition and price dispersion in the US airline industry. Journal of Political Economy 102, 653–683. Botimer, T.C., 1996. Efficiency considerations in airline pricing and yield management. Transportation Research A 30, 307–317. Button, K., Vega, H., 2007. The uses of the ‘‘temporal-fares-offered curve’’ in air transportation. In: Paper presented at the 48th Annual Transportation Research Forum, Boston. Cohas, F.J., Belobaba, P.P., Simpson, R.W., 1995. Competitive fare frequency effects in airport market share modeling. Journal of Air Transport Management 2, 33–45. Evans, W.N., Kessides, I.N., 1994. Living by the ‘‘Golden Rule’’: multimarket contact in the US airline industry. The Quarterly Journal of Economics 109, 341–366. Giaume, S., Guillou, S., 2004. Price discrimination and concentration in European airline markets. Journal of Air Transport Management 10, 305–310. Greene, W., 2002. LIMDEP Version 8.0: Econometric Modeling Guide. Econometric Software Inc., Plainview. Hayes, K.J., Ross, L.B., 1998. Is airline price dispersion the result of credit planning or competitive forces? Review of Industrial Organization 13, 523–541. Lee, D., Luengo-Prago, M., 2005. The impact of passenger mix on reported ‘‘hub premiums’’ in the airline industry. Southern Economic Journal 72, 372–394. News and Record, Friday, October 13, 2006. Pels, E., Rietveld, P., 2004. Airline pricing behaviour in the London–Paris market. Journal of Air Transport Management 10, 279–283. Weatherford, L.R., Bodily, S.E., 1992. A taxonomy and research overview of perishable asset revenue management: yield management, overbooking and pricing. Operations Research 40, 831–844.
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Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Airline daily fare differentiation in a medium-size travel market K. Obeng Department of Economics and Transportation/Logistics, School of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA
article info
a b s t r a c t
Keywords: Fare differentiation Fixed effects Medium-size city Competition Inelastic demand Fare discount Fare premium
This paper examines variation in airline fares for trips in a medium-size travel market. It develops a conceptual model of fares offered, and uses daily information about fare, plane and flight characteristics, and trip characteristics easily available on the internet. Based on this information it estimates a twoway fixed effects model of airline fares. The results show large differences in fares among the airlines, large variation in daily fares offered, and provide evidence of fare differentiation in the travel market analyzed. & 2008 Elsevier Ltd. All rights reserved.
1. Introduction The literature on airline pricing abounds with works explaining differences in airfares. These works include those by (Pels and Rietveld 2004; Giaume and Guillou, 2004; Hayes and Ross, 1998; Cohas et al., 1995). The general premise in them is that third degree price discrimination is a source of fare differentiation in the airline industry. This discrimination involves airlines dividing their markets into travel segments, then bundling their services with restrictions and selling them to each segment at different fares. By this premise, travelers on leisure trips who have elastic demand and time to purchase their tickets well in advance and are willing to stay weekend’s pay lower fares compared with business travelers with inelastic demand and inflexible schedules who sometimes must make their trips on demand. The effect of this price discrimination is that on any given flight there are many travelers paying different fares. Another source of fare differentiation is rationing where airlines use fares to allocate their supply of limited seats among travelers. Those for whom a trip yields a large marginal benefit and are willing to pay higher prices get seats as the number of available seats reduces or as the flight day approaches. This rationing is efficient because it equalizes the marginal cost of providing an additional seat and the marginal benefit to the consumer and transfers consumer surplus to airlines thereby increasing their profit. For heavily traveled markets this rationing is reasonable but not so where the level of demand is so low as to create excess capacity (Botimer, 1996). Even here, the ability of airlines to easily switch to smaller aircrafts and route passengers through con-
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gested hubs where seat demand is very high creates competition among travelers for seats and allows airlines to increase their fares. In medium-size markets closely located near major hubs, we could expect very little difference in airfares among competing airlines1 because they may have excess capacity. Alternatively, we could expect large increases in airfares from carriers routing their flights through hubs and incurring additional costs. If demand in such a travel market fare is inelastic especially in the absence of serious airline competition or in the presence of airline dominance and market power fares are likely to be high. Cost differences are also sources of airline fare differentiation. For example, because peak service is costly to provide airlines charge higher prices for them. Which of these sources of fare differentiation is found in any given market is an empirical question to which this note provides some answers. It studies air fares in a medium-size market close to hubs using on-line daily fare offerings information for trips with the objective of identifying the sources of fare differentiation and when it is most advantageous to travelers to purchase their tickets.
2. Conceptual model Consider an airline that segments its customers and charges each segment different fares. This airline practices price discrimination, and does so by offering various incentives and penalties for each fare. For example, it may offer reduced economy fares for weekend stay or advance ticket purchases. This type of airline fare differentiation is well-documented and researched under yield management. An aspect of yield management that is 1 In this paper a market is defined in terms of a corridor where airlines compete among themselves to provide service.
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169
Fare / Fare Premium
Mean Fare P
Fare Offered Curve Minimum fare
Pmin
Days before
0 Fare Adjustment Curve
T
Day of mean fare Fare Discount Fig. 1. Changes in airline fares: a conceptual model.
receiving attention lately regards daily adjustments to airfares by airlines to reflect demand, available seats, and plane capacity. A rationale for these adjustments is that travelers, particularly business travelers, value their trips very highly and are willing to pay higher fares for them as the trip day approaches. Another is that they are ways for firms facing competitive pressures and having high fixed costs to maintain their survival (Baumol and Swanson, 2003). Airlines also make these adjustments to account for the high costs of accommodating late bookings (e.g. finding good connections, searching many flights, possibility of using multiple carriers for customers). Fig. 1 is a conceptual diagram of the offered fares and their adjustments. It shows that the fare offered curve declines as the number of days before a trip increases. This curve is similar to that in Weatherford and Bodily (1992), and it is a reverse of what (Pels and Rietveld, 2004; Button and Vega, 2007) presented in their researches. There is also a minimum fare Pmin that the airlines charge and to which the fare offered curve is asymptotic. Assume a mean policy fare level of P for a round-trip ticket set by considering competition, plane capacity and costs whose level ensures normal profit.2 This is the fare after accounting for cost, demand, airline and period fixed effects and other exogenous influences. Subtracting the mean fare from the fare offered each day gives the fare adjustment curve whose intersection with the horizontal axis shows the days after (before) which airlines upwardly (downwardly) adjust their fares. This day is unknown to travelers but management and because of differences in management styles, network circuity and costs, when it occurs is likely to differ among airlines. As the fare adjustment curve shows the fare increase (decrease) above (below) the mean fare is not uniform; steep discounts (premiums) are possible the farther away (nearer) is the flight day. Several factors explain these fare adjustments. Lee and LuengoPrago (2005) found that the presence of a low-cost carrier and passenger density exert downward pressures on fares as is flight length. Cost differences among airlines, competition and willingness of consumers to switch to another airline have been found to be the main sources of fare differentiation (Borenstein and Rose, 1994) as have direct services, trip length, and airport market
2
The seats flown are the sum of those in business and economy classes.
share (Evans and Kessides, 1994). In Giaume and Guillou (2004) a high level of market concentration was negatively related to price level and price discrimination. A similar result was reported by Hayes and Ross (1998), who also found that the number of carriers serving an airport, flights ending or beginning in major cities, length of route and intermediate stopover were sources of fare differentiation. Besides these studies Pels and Rietveld (2004) estimated fare models for different airlines and found that the lowest fares were offered 21–28 days before departure. Although their results are interesting, it can be argued that estimating different fare models for each airline as Pels and Rietveld (2004) did is at the expense of sample size and that perhaps better results could be obtained using panel data and estimating a fixed-effect model. Such a model allows for controlling systemic differences in fares that could occur through airline and period fixed effects.3
3. Methodology To address some of the concerns in Pels and Rietveld (2004) and further explain fare differences, consider an airline round-trip to be taken on a future date from a non-hub airport in a mediumsize travel market. The trip begins on a weekday and returns the same week on a weekday. Different airlines A1, A2 ,y, Ai compete in this oligopoly market, where i is the number of airlines. Each airline reserves most of its seats for full-fare paying passengers, and a fixed number of discount seats for consolidators who in turn sell them. Some flights are direct and are provided by single carriers while others are single- or multiple-carrier flights some of which have connections in a third city where the airlines have hub operations. The airlines (i) operate different aircrafts (k) each with a capacity of xik seats of which zik are discount seats offered at a discount fare of pit, where t is the number of days before a trip is taken and zikpxik.4 Assume further that each airline’s fare is based 3 Interestingly, these authors rejected a fixed effects model in favor of a random effects model. 4 We do not use passengers in this paper because it is not possible to get that data on a daily basis. Because the type of plane flown and hence plane capacity depends upon forecasted demand we assume that plane capacity is a proxy for demand.
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upon in-flight time Fit, which is a proxy for flight distance and network circuity, layover or transfer time Lit, if the flight is direct (D), plane capacity (xik), peak service (H), and the number of discount seats (zik) available that day.5 With these assumptions Eq. (1) shows the hypothesized fare offered model estimated in this study pit ¼ a0 þ a1o xikto þ a1c xiktc þ a2o zikto þ a2c ziktc þ aF F it þ aL Lit þ aD Dit þ aFD Dit F it aH H þ aHseat Hðxiktc þ xikto Þ þ 0:5a44 L2it þ bi þ ft þ it
(1)
The subscripts (o, c) show originating and connecting flights, respectively, and each day is treated as a binary variable as is each airline and a direct flight. The unknown coefficients are the sets given by a, b, f. The coefficients denoted by a capture the marginal effects of the exogenous variables on airfares. However, the signs of a1o, a1c, a2o, a2c are indeterminate a0 priori because the number of seats on a plane affects cost and it is influenced by demand. To the extent that it costs more to fly large planes this cost would be recouped through higher fares and these coefficients would be positive. On the other hand since plane capacity, particularly the capacity of the originating flight, is a proxy for demand the signs of these coefficients could be negative. If they are positive they would show that the cost-influencing effects of seats on fares are larger than the demand-influencing effects of seats while if negative the reverse is true. Also, we expect the impact of each additional discount seat on fares to be larger than the impact of each additional seat on a plane for two reasons. First, web-based airline consolidators compete among themselves to sell their discount seats at various fares and may have a lot of flexibility in passing their discounts on to travelers in terms of lower fares. The more discount seats consolidators have the more they are willing to lower their fares by a large amount. Second, because the marginal cost of an additional airline seat (or an additional passenger) is very low the coefficient of plane capacity would be also low to show how much of this small cost is recovered from fares. In-flight time is expected to have a positive coefficient because longer trips use more fuel and labor and lead to more wear and tear on aircrafts.6 The costs associated with these trips would be recouped through higher fares. This expected positive sign is opposite what Lee and Luengo-Prago (2005) and Hayes and Ross (1998) found but consistent with what Giaume and Guillou (2004) found. If this coefficient is positive it would also show that the cost effect of this variable dominates its demand effect in this market. Additionally, if we argue that airline flights are for long trips the marginal effect of an hour saved on such a flight is not expected to have a significant impact on demand. Therefore, we expect the cost-influencing effect of in-flight time to dominate its demand-influencing effect. In comparison, we expect the coefficient of layover time to be negative. This is because layover is an inconvenience to the traveler, especially long waits to connect with other flights. Layover could show high demand or frustrations with the service that make airline passengers less willing to take a flight unless the fare is low or if they have no choice. It could also affect airline cost in terms of increased space to accommodate waiting passengers. 5 We consider only one travel market. Therefore, dominance and concentration are fixed and are not considered in this paper. 6 An exception is when smaller turboprop planes are operated. In this case although in-flight time is high plane operating cost is very low. To account for the fact that in-flight time and plane capacity may be related an interacting term involving these two variables was initially included in the model and it did not give good results as many of the variables had statistically insignificant coefficients.
Hence, it is possible for fares to increase with layover time. To capture both effects, Eq. (1) includes linear and quadratic terms in layover time. If airlines practice peak pricing their fares would be higher at the peak than off-peak. For morning trips the peak occurs quite early. To capture this peak we use a binary variable for flights leaving before 8 a.m. Because peak travel imposes costs on airlines in terms of increased resources airlines must increase their fares to recoup this cost. Therefore, the coefficient of this term is expected to be positive. Additionally, the constant term is expected to be positive because it shows the mean fare. This term aside, both f and b capture period and group fixed effects, respectively, reflecting price differentiation and heterogeneity of service.7 Respectively, the signs of these coefficients are expected to be positive (or negative) to show a premium (discount) as the flight day approaches or to show differences in fares charged by each airline due to unobserved heterogeneity effects. Holding everything else constant, the coefficients of airlines show their fare premiums and discounts. Similarly, the coefficient of each day gives the daily fare discount or premium. Both the period and airline fixed effects provide some evidence of fare differentiation. Because we do not expect the marginal seat on an originating flight to be valued differently from a marginal seat on a connecting flight we impose the following restriction a1o ¼ a1c. Similarly, a2o ¼ a2c because a marginal seat at an offered fare is assumed to be valued equally on the originating and connecting flights.
4. Data 4.1. Choice of study area To estimate the above equation requires data. The data were collected for a weekday trip from Greensboro, North Carolina to Boston, Massachusetts leaving before noon on Monday, April 9, 2007 and returning any time on Friday, April 13, 2007. The chosen air travel corridor is a medium-size market with services provided by American, Delta, US Airways (hereafter USAir), Continental, United, and Northwest. Only Delta provides direct service to Boston. The reason for studying this market regards a recent controversy between the Piedmont Triad International Airport Authority (PTI) and Delta Airlines. PTI claims it is losing on the average 2300 passengers a day to Charlotte’s Douglas International airport about 100 miles away, and to Raleigh–Durham airport 75 miles away because in the absence of a low-cost carrier Delta being the dominant carrier has inflated its fares making other airlines do the same.8 PTI cites the example of flights between Greensboro and Boston where Delta charged $798.00 in 2006 compared with $138.00 for a similar flight from Raleigh– Durham International airport (News and Record, 2006). Delta counters this claim that it charges what the traffic will bear and that its fares are competitive. After this ‘‘finger-pointing’’, negotiations ensued between PTI and the airlines serving the airport with PTI asking them to reduce their fares. USAir obliged 7 A functional form of Fig. 1 could also be estimated to provide a smooth curve. We opted for the fixed effect model because using specific forms of this figure did not give good results. Also, changes in airfares are discrete and not continuous especially since blocks of seats are released at a time. 8 The data were collected before PTI announced that a low-cost carrier, Skybus Airlines, would begin operation at this airport beginning May 29, 2007 with service to Columbus OH and some seats priced as low as $10 on each trip. Another low-cost carrier, Allegiant Air has been wooed to the airport and will begin flights on May 24, 2007 with large jets. None of these low-cost airlines would provide service to Boston MA.
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171
Table 1 Descriptive statistics Variables
American
Continental
Delta
Delta direct and united
Delta and USAir
Northwest
United
USAir
All carriers
Fare offered ($) Discount seat initially available on the first plane Capacity of first plane (number of seats) Discount seats initially available on the second plane Capacity of second plane (number of seats) In-flight time (hours) Layover time (hours) Direct service (originating flight) Proportion of flights departing before 9 a.m. Number of flights recorded
1758 6
666 22
792 17
1140 17
761 19
879 18
828 9
646 22
813 17
49
62
57
52
55
53
103
59
64
25
58
19
17
44
36
14
73
43
199
130
75
52
84
159
126
118
118
4.91 3.48 0.00
3.01 2.08 0.00
2.64 0.66 0.65
2.02 0.00 0.86
2.38 0.54 0.00
3.65 2.20 0.00
2.70 1.88 0.00
2.80 1.08 0.00
2.93 1.41 0.08
1.00
0.51
0.75
1.00
0.69
0.64
0.33
0.48
0.59
34
47
68
as did the other airlines but not Delta. Given this background it would be interesting to study airfares in this market.9 4.2. Data collection The data collection used the ORBITZ Internet search engine because it provided seat data which most of the other search engines did not provide. It was done between four and six o’clock in the evening each weekday of the 3 weeks (i.e., 3/19–4/6/2007) preceding the flight date (4/9/2007) for single- and multiplecarrier flights. For each flight and its connection the information collected included flight numbers, posted fare, in-flight time, layover (connection) time, seats available at posted price on the originating and the connecting flights, aircraft capacity in terms of seats on the originating and connecting flights, direct service, number of carriers involved, airline name, and time of departure. Return trip data were not collected except carrier name to help completely enumerate joint flights.10 The data treat multiple carrier flights as if they were offered by separate carriers. Such a flight is one in which more than one carrier is involved in the round-trip journey. We also assume symmetry so that Delta–USAir and USAir–Delta flights, for example, are the same. Further, flights were recorded by fare offered because the same originating flight may be offered at different fares and may connect with different flights on both the front-haul and the return. Finally, only flights with available seats on all legs at the offered fares were recorded. Some modifications in recording the data were made for multiple carrier flights that had very few observations. There was one Delta–Continental flight, one Northwest–Delta flight, one Northwest–United flight, and one USAir–Continental flight. These flights were, respectively, added to Delta, Northwest, Northwest, and USAir because they provided the originating flights. Similarly, there were three flights each for United–USAir and USAir–United that were added to United and USAir, respectively.11 Direct flights by Delta connecting with United on the return were treated separately because of their very high fares despite being few, and 9
This agreement was reached after the data were collected. Although fares are affected by the characteristics of the return trip we did not collect such data to see their impacts on fares. 11 We also considered if adding these flights to the other connecting airlines and to each other’s flight would affect the results and found no differences in the results. This is because these flights are few. 10
7
113
84
94
157
604
because they show the monopoly power and dominance of Delta airlines in providing direct services in this corridor.12 With these modifications the data form an unbalanced panel consisting of 604 flights sorted by carrier type in Table 1.
4.3. Descriptive statistics Table 1 shows that 8.28% of the flights are direct and that USAir offers most flights in this market followed by Delta, United and Northwest in that order not counting joint flights.13 On the average each flight takes 2.93 h excluding 1.41 h of connection time at another airport. The shortest in-flight times of less than 3 h are offered by Delta, United, USAir, Delta–USAir, and Delta– United flights, and the longest by American (4.91 h) followed by Northwest (3.65 h) and Continental (3.01 h). Delta airline’s joint flights with USAir have the shortest average connection time of 0.54 h, and about 58.44% of the flights depart before eight o’clock in the morning. The table also shows that the airlines use small aircrafts (seating 64 passengers on the average) and offer 17 discount seats on originating flights. Additionally, each connecting flight has a capacity of 118 seats on the average 43 of which are offered at a discount. Comparing the airlines American and Northwest use large planes seating on the average 199 and 159 passengers, respectively, on connecting flights. More discount seats (22) are provided by USAir and Continental than any other airline on both originating and connecting flights. Besides providing information about plane capacity and discount seats the table shows that American airline charges the highest average fare ($1758) followed by Delta–United ($1140.00).14 The average fare on Delta–USAir flights is $761.00, while Northwest, United, Delta and Continental charge average 12 The Delta flights considered connected in Atlanta, New York City La Guardia airport, and USAir flights ha connections in Charlotte NC, Philadelphia PA and New York City La Guardia airport. United Airline flights had connections in Washington DC Dulles airport, Northwest flights connected in Detroit MI Wayne airport, Continental in Newark NJ, and American in Miami FL. 13 The aircrafts used for the originating trips are Embraer RJ145, Embraer RJ135-145, Boeing 737, and Canadair 700 because the flight is between a mediumsize city and a large city. The connecting flights generally use large aircrafts such as MD 80, A319, Boeing 737, Boeing 757 and A320. 14 The fares on American airlines are as high as $2857 while the highest fare on United is $2260.
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Table 2 Estimated fixed effects model Variables
Estimate
In-flight time (hours) Layover time (hours) Discount seat on first plane Discount seat on second plane Capacity of plane 1 (seats flown) Capacity of plane 2 (seats flown) Departure time before 8 a.m. (yes ¼ 1, no ¼ 0) Departure hours after 6 a.m. sum of discount seat on all planes 0.5 layover layover Constant American Continental Delta Delta–United Northwest United USAir USAir–Delta 3 days before flight 4 days before flight 5 days before flight 6 days before flight 7 days before flight 10 days before flight 11 days before flight 12 days before flight 13 days before flight 14 days before flight 17 days before flight 18 days before flight 19 days before flight 20 days before flight 21 days before flight
138.0645 62.2665 1.4281 1.4281 0.4798 0.4798 59.8192 0.2351 12.0928 569.5806 702.4150 79.0970 83.3338 285.5727 43.7383 25.7711 45.0976 114.3850 347.0602 189.9011 260.1060 128.2508 99.7223 20.0225 156.2650 57.9225 116.3050 201.8020 153.2350 192.3440 202.1430 256.4770 366.4890
Tests (1) (2) (3) (4) (5)
of the classical model Constant term only Group effects only X variables only X and Group X, group, and period effects Likelihood ratio test
(2) (3) (4) (4) (4) (5)
vs. vs. vs. vs. vs. vs.
(1) (1) (1) (2) (3) (4)
Chi-squared 319.131 405.915 503.099 183.969 97.184 224.032
Std. err 23.7566 28.5858 0.4126 0.4126 0.2267 0.2267 33.0783 0.1124 10.2014 78.9974 71.4778 38.1574 34.2903 99.1397 27.6722 34.5306 47.3332 24.9977 38.5451 44.1055 50.1013 31.3982 34.5226 26.0115 34.0653 28.5456 33.8818 40.4693 50.5411 55.9408 76.3486 89.9080 96.1584
t-Value 5.8120 2.1780 2.9840 2.9840 2.1170 2.1170 1.8080 2.0920 1.1850 7.2100 9.8270 2.0729 2.4302 2.8805 1.5806 0.7463 0.9528 4.5758 9.0040 4.3056 5.1916 4.0847 2.8886 0.7698 4.5872 2.0291 3.4327 4.9865 3.0319 3.4384 2.6476 2.8527 3.8113
Log likelihood 4439.75 4280.18 4236.79 4188.20 4076.18
R-squared 0.0000 0.4104 0.4893 0.5652 0.7000
Denom. 596 594 589 589 589 575
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
F Tests D/F 7 9 14 7 7 14
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
F 59.272 63.244 47.697 23.223 14.639 18.443
Num. 7 9 14 7 7 14
Notes: vs. ¼ versus. p o0.05. 0.05 op o0.10.
fares of $879, $828, $792 and $666, respectively, on their singlecarrier flights. Overall, a weekday roundtrip in this travel market leaving on Monday before noon and returning anytime on Friday has an average fare of $813.
5. Estimation Eq. (1) with the restrictions imposed can be estimated as in Hayes and Ross (1998) as a two-way random effects model though attempts to do so in this paper failed. Another approach is to estimate it as a two-way fixed effects model.15 This second 15
To make the slopes for each airline different interaction terms between inflight time, layover, and airlines were initially included in the model. Their
approach gives estimates of the coefficients of all the airlines and days. Since the focus is on daily and firm-by-firm fare adjustments this latter approach is used in this paper. However, to be estimable the following additional restrictions are imposed Sibi ¼ Stfit ¼ 0. Furthermore, because Delta is the only airline that provides direct service between the two cities its effect is captured by its fixed effect. Therefore, the direct service term and the term involving interaction between layover time and direct service are omitted from Eq. (1). Table 2 shows the results of estimating the two-way fixed effects model by LIMDEP (Greene, 2002). The bottom part
(footnote continued) coefficients were not statistically significant so they were dropped from further analysis.
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compares this model to others and shows that the two-way fixed effects model is an improvement over a model with only airline fixed effects, and far better than a model with no fixed effects based upon the likelihood ratio tests and the percentage of variation explained. Also, most of the estimated coefficients are statistically significant and have the expected signs except the coefficients of the squared layover term and the fixed effects of Northwest, United, and USAir. From these results the exogenous variables with negative coefficients are the sources of fare discounts, and those with positive coefficients are the sources of fare premiums. The constant term being positive and statistically significant shows that after accounting for all the independent variables including period and group fixed effects the mean airfare is $569.58. Using the estimated coefficients the following are the sources of fare differentiation.
6. Sources of fare differentiation 6.1. Cost In-flight time is a major source of fare variation in this market according to the results. Its coefficient is positive and statistically significant and shows that an hour increase in it increases air fares by $138.06. This increase reflects attempts to recoup the high cost of routing flights through off-line hubs since the distance from Greensboro to Boston is fixed.16 Another source of fare variation is peak travel. The results show a fare premium of $59.82 on flights leaving before eight o’clock in the morning. Airlines in this market use this premium to recover their additional costs of providing peak services. Because of this premium early morning flights are more expensive than late morning flights. 6.2. Demand Demand factors are another source of fare differentiation and unlike cost they have negative relationships with fares. For example, the coefficient of layover time is 62.2665 and statistically significant and shows that an hour increase in it decreases air fares by $62.27. This shows that in this market flights that inconvenience travelers with long connections such as those routed through hubs, all things being equal, attract low fares. A possible explanation is that airlines internalize passenger inconvenience in setting fares and compensate for it by charging low fares for this type of flight. In Table 2 discount seats and plane capacity have negative and statistically significant coefficients of 1.4281 and 0.4798, respectively, showing that they too account for some of the variation in fares. These results show that the marginal effect of a discount seat is larger than the marginal effect of an additional seat on a plane and it is expected. Since we instrument passengers with plane capacity on the originating flight the inverse of its coefficient can be used to calculate fare elasticity of demand. Using this inverse, the mean values of fares and plane capacity on the originating flight, the average fare elasticity of demand is 0.1637 for trips in this market. By the same approach the following are the fare elasticities of demand for flights by American, Continental, Delta, Delta–United, Delta–USAir, Northwest, United, and USAir 0.058, 0.194, 0.150, 0.095, 0.151, 0.259 and 0.190, respectively. These elasticities show that travelers in this market are very insensitive to changes in fares, especially changes in the fares of American and Delta–United joint flights. This insensitivity somewhat explains the observed high 16
An off-line hub is defined as not on the direct path between the two cities.
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passenger fares in this market and suggests that travelers here may be captive to this airport and lack reasonable choices for their trips. The inconvenience of accessing alternate airports very early in the morning makes travelers continue to use PTI despite the very high fares they must pay for flights there. 6.3. Price discrimination 6.3.1. Airline fixed effects In Table 2 there are clear differences in how the unobserved airline effects affect airfares in this market. American airline flights and joint Delta–United flights carry hefty premiums of $702.42 and $285.57 above the average, respectively, which are statistically significant and should be added to the constant term to get the mean fares of these airlines after controlling for exogenous influences. If this is done and everything else is held constant the estimated mean fares are $1272.00 and $855.15 for American and Delta–United flights, respectively. While the American airline premium is due to its long in-flight time such is not the case for Delta–United flights.17 This is because Delta–United flights are direct by Delta for the front-haul with the return on United through Washington, DC. Therefore, Delta airline is a monopoly in providing direct flights to Boston MA and the high premium on Delta–United flights is an attempt to earn monopoly profits. These high fares are more than the sum of the full singlecarrier fares of the two carriers. To be specific the estimated mean Delta–United fare is more than twice the estimated mean single carrier fare on flights by Delta or United.18 A source of these high fares could be the absence of economies of joint services, attempts to pass on all fixed costs of providing such flights onto passengers, or price discrimination because the two airlines have relatively very inelastic demand. It could also be that the flights studied are mostly used by business travelers who are willing to pay high fares to save time. The group fixed effects coefficients of Continental, Delta and USAir-Delta are negative and statistically significant showing that there are exogenous influences not accounted for in this paper that make the fares of these airlines lower than the mean fare of $569.58. Holding the period fixed effects and the other explanatory variables constant these influences make the fares of joint USAir-Delta flights, and the single-carrier flights by Continental and Delta $114.38, $79.10 and $83.33 cheaper than the mean fare, respectively, possibly reflecting service quality differences. The fixed effects coefficients of Northwest, United and USAir are not statistically significant; therefore their mean fare is $569.58. These group effects show large differences in fares among the airlines in this market further evidenced by the coefficient of variation of 44.4% showing a large dispersion of the fares around the mean. Since we account for cost and some demand factors the source of this dispersion could be market dominance. Comparing the airline fixed effects the joint Delta–United flights are the ones to blame for the rather high airfares in this market. This is because American airline is not the dominant carrier given its few daily flights and its astronomically high fares, which probably deter many potential customers from using it for this trip. 6.3.2. Period fixed effects Table 2 also shows the daily variation in fares. Holding everything else constant except the period fixed effects the fare 17 The data were collected before American announced that it would begin new direct flights to Chicago O’Hare airport. After this announcement a check of American flights to Boston found that most of them were being routed through Chicago instead of Dallas and Miami and have lower fares. 18 A proportional fare is some percentage of the sum of the full fares for the different legs of a trip.
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adjustments that prevail in this market based upon our results are also shown. Very clearly, except the 10th day before the flight, all other days have statistically significant coefficients. Some of these coefficients such as those of tickets bought 21 days before the flight down to 10 days before the flight are negative and show discounts. Also, the absolute sizes of these discounts decline as the flight day approaches and there are statistically significant differences in daily fare adjustments starting from 2 weeks before the flight but not earlier. For example, 14 days before the flight the discount is $201.80 and it drops to $116.31 about 13 days before the flight (t ¼ 2.01), where the t-test value is in parentheses. Comparatively, 21 and 20 days before the flight the discounts are, respectively, $366.49 and $256.48 (t ¼ 1.1). The largest discount of $366.49 is offered 21 days before the flight declining to $20.02 10 days before the flight. Additional results from the table show that the fixed effect coefficients are positive and larger for tickets bought 7 days before the flight down to 3 days before the flight showing daily premiums as the flight day approaches. For example, a week before the flight this premium is $99.72 increasing to $347.06 3 days before the flight is to be taken. Holding everything else constant and adding these premiums to the constant term the average fare is $916.64 3 days before the flight compared with $669.30 a week before the flight. Since the fare discount 10 days before the flight is not statistically significant the fare is not different from the mean fare of $569.58. Therefore, close to 10 days corresponds to when the fare adjustment curve in Fig. 1 crosses the abscissa and when fare premiums begin and discounts end in this market. Tickets bought earlier than 10 days before the flight attract discounts according to our results.
7. Conclusion This paper analyzes airline fares in a medium-size market using on-line daily fare data for trips. It estimates a group and period fixed effects model of airline fares that accounts for factors such as peak departure times, in-flight time, layover time, number of discount seats and plane capacity on the originating flight used as a proxy for demand. Of these factors, the marginal effect of inflight time is the largest being $138.06 increase in fares for each additional in-flight hour, followed by peak hour flight which increases fares by $59.82 for flights leaving before 8 a.m. It also finds consistent fare discounts of between $58 and $367 for tickets purchased at least 10 days before a flight, and premiums ranging from $99.72 7 days before a flight to almost $350 3 days before a flight. Large differences in fares charged by competing airlines in this market are also found. After accounting for
exogenous influences, period and airline fixed effects the highest fares are those on American and Delta–United flights. Furthermore, the period fixed effects are larger and much more volatile than the airline fixed effects. Thus, even though the evidence suggests fare differentiation among airlines in this market it is more evident as the flight day approaches than at any other time. This fare differentiation suggests that if PTI is interested in lowering fares it should attract carriers that are willing to offer competing direct services in the Greensboro–Boston market. Perhaps USAir should be encouraged to offer direct services to Boston since it offers the lowest fares. Since Delta has a monopoly in direct flights in this market pressures on it to reduce its fares could greatly influence other airlines to do the same.
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