Electronic copy available at: http://ssrn.com/abstract=1681337

Refundability and Price:

Empirical Analysis on the Airline Industry∗

Seongman Moon† Makoto Watanabe‡

Universidad Carlos III de Madrid Universidad Carlos III de Madrid

March 12, 2011

Abstract

This paper provides new evidence on price dispersion in the US airline indus-try. Using the observed fare differences between refundable and non-refundabletickets, we first document evidence on the prices passengers pay for an option ofreturning their tickets. We find that the factors related to the value of refundoption and customers’ individual demand uncertainties have a significant effect onthe relative refund fares. This finding turns out to be robust for various marketstructures. Further, taking into account the variations of the relative refund fares,we investigate the effects of market structure on price dispersion.

Keywords: Price discrimination, Refundability, Competition, Airline industry

JEL Classification Number: D43, L13, L93

∗Financial support from the Spanish government in the form of research grant, ECO2009-10531 and SEJ

2007-63098, and research fellowship, Ramon y Cajal, is gratefully acknowledged.†Department of Economics, Universidad Carlos III de Madrid, Calle Madrid 126, 28903 Getafe Madrid,

SPAIN. Email: smoon@eco.uc3m.es, Tel.: +34-91624-8668, Fax: +34-91624-9329.‡Department of Economics, Universidad Carlos III de Madrid, Calle Madrid 126, 28903 Getafe Madrid,

SPAIN. Email: makoto.wtnb@gmail.com, Tel.: +34-91624-9331, Fax: +34-91624-9329.

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Electronic copy available at: http://ssrn.com/abstract=1681337

I. Introduction

The airline industry is well characterized by highly dispersed prices. In their seminal work,

Borenstein and Rose (1994) (hereafter, BR) show that the expected absolute difference in prices

between two passengers on a route is 36% of the airline’s average price. They also find that this

substantial variation in prices decreases with market concentration: routes characterized by

higher levels of competition exhibit more price dispersion. BR attribute this finding to airline

pricing practices that are based on exploiting heterogeneity in customers brand preference,

rather than solely on heterogeneity in reservation prices for air travel.

The objective of our study is to provide new evidence on price dispersion for the US airline

industry. Our starting point is to realize that airline tickets can be purchased in advance, i.e.

long before their actual date of flight. During the time between purchase and flight, customers

learn their demand over time whether or not they are able to make a trip on the scheduled

date. To the extent that the uncertainty related to individual demand is important, carries

would be able to price discriminate among travelers who differ in the preference of flexibility

in their itinerary.

To further motivate our view, Figure 1 plots the average posted fares of refundable and

non-refundable tickets, starting from seven weeks prior to some given flight dates. The only dif-

ference between these two tickets is that the refundable tickets allow customers to change their

flight schedule, or to return their tickets in exchange of refund, whereas the non-refundable

tickets do not have such an option.1 First of all, the fare difference would be impossible if

customers know for sure that they do not need to change their flight schedule. The figure

in the right panel plots the prices of round trips and shows that the fare difference between

the refundable and non-refundable tickets steadily decreases over sales periods as the flight

date approaches. This suggests the presence of individual demand uncertainty: the later a

customer books a given flight, the more sure he is about his demand and thus the lower price

he is willing to pay for the refund option; on the flight date, the fare difference disappears.1In general, customers who purchase a refundable ticket have two options in case they wish to change their

schedule: (i) return their tickets for the full refund (if they decide not to travel or possibly go with other

airlines); or (ii) obtain a new ticket by paying the fare difference. On the other hand, customers who purchase

a non-refundable ticket have to: (i) give up their tickets; or (ii) pay a fixed amount of penalty (each time

they change) and the fare difference. The fixed fee is 100 USD (150 USD for the American Airline) for all the

carrier-routes during our sample period.

2

The figure in the left panel is for one-way trips where the fare difference appears to be zero

during the entire observational periods. Comparing these two figures, one can infer that the

refund option benefits passengers more for round trips than for one-way trips, since the former

require a longer time and are subject to a larger demand uncertainty than the latter. Given

the individual demand uncertainties, carriers might be able to set a larger fare difference when

customers purchase round trip tickets.

In this paper, we use cross-sectional data on transacted airfares (from the DB1B dataset)

and study the determinants of the relative price between refundable and non-refundable tickets.

We also examine the effect of market competition and check the robustness of our findings to

market structures. Further, after controlling for the relative prices, we investigate the effect of

market concentration on price dispersion. We join the rapidly growing literature that studies

the underlying mechanism of price discrimination in the airline industry and its effect on the

link between market structure and price dispersion, such as Borenstein (1989), BR, Escobari

and Jindapon (2009), Gaggero and Piga (2011), Gerardi and Shapiro (2009), Hernandez and

Wiggins (2009), Puller, Sengupta, and Wiggins (2008), Sengupta and Wiggins (2006) and

Stavins (2001).

The theoretical work on the refund contract is pioneered by Courty and Li (2000) who char-

acterize the optimal refund contract of a monopolistic seller. In essence, the refund contract

serves as a screening device to discriminate customers who face individual demand uncertainty.

We provide evidence which supports this theory, and identify the underlying factors of the op-

tion value of refundability and demand uncertainty that are empirically relevant for such a

practice of price discrimination. To abstract from the effect of competition, we select routes

which are operated by only one firm in the baseline analysis. Our empirical findings support

the implications of the theory of the refund contract and are summarized as follows.

The distance of travel turns out to be the most significant determinant of the relative price

between the refundable and non-refundable tickets. Our interpretation is that the availability

and usefulness of alternative transportation modes tend to be limited on long-distance routes

relative to short-distant routes, thereby the refund option to offer an alternative flight benefits

travelers by more on longer distance routes. A similar logic applies to our other findings. We

find a positive effect of the hub dummy and of flight density on the relative refund fare: an

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airline would increase its ability to offer alternative flights by operating in its hub airport;

refund-customers would be able to find another flight with a greater ease and convenience on

routes characterized by larger flight alternatives.

We extend our analysis to various market structures. The result turns out to be quite

robust. Further, we find a significant and negative effect of market concentration on the

relative refund fare. While there is no existing theory to link competition and the refund

contracts, our empirical findings suggest that the effect of competition is relatively weaker

on the refund price than on the non-refund price, presumably because there exists a non-

negligible proportion of customers who have a strong preference towards flexible itinerary over

lower prices.

Finally, we investigate the effect of market structure on price dispersion. First of all,

our sample can replicate the negative relationship between market concentration and price

dispersion as first documented in BR. However, if we control for the refundability of tickets,

we find that: (i) there is a very strong positive effect of the relative price on price dispersion;

(ii) competition decreases price dispersion; (iii) the effect of market concentration on price

dispersion changes the sign if the variation of the relative refund price is taken into account.

These results are related to a recent study by Gerardi and Shapiro (2009) (hereafter, GS)

who point out a potential bias in the BR regression, due to the correlation between the time-

invariant route-specific instrumental variable, i.e., distance, and the error term. In line with

their suggestion, our approach to include an additional variable, the estimated relative refund

fare, alleviates the potential bias. All in all, it is possible that the negative relationship between

market concentration and price dispersion observed in the literature can be largely explained

by the individual demand uncertainty of customers.

The sequel of the paper is organized as follows. We first review the existing literature.

Section II describes the data and the summary statistics of the key variables. Section III

presents the estimation results from our baseline model using a sample of monopoly routes.

Section IV provides an extension of the analysis for various market structures and examines the

effect of market competition. Section V investigates the relationship between market structure

and price dispersion. Section VI concludes.

4

Literature Review

BR first document evidence on price dispersion in the US airline markets. They find that

a significant amount of price dispersion exists and competition may increase price dispersion.

GS generalize BR to conduct the panel analysis. Controlling for time invariant route specific

effects using the DB1B data sets from 1993 to 2006, they find that competition in fact decreases

price dispersion. Further, GS explain the difference between the two studies is mainly due to

the estimation method: the cross-sectional analysis fails to control for the effects of route

specific characteristics. They show that, once this bias is controlled, the negative relationship

between market concentration and price dispersion becomes not significant or altered even in

the cross-sectional regression, just like we find in our specification.

The major difference between these two papers and ours is that their studies emphasize the

presence of brand royal customers for the practice of price discrimination and for the effect of

competition, whereas the central to our study is the refund contracts carriers offer to customers

who face individual demand uncertainties. Indeed, it is exactly with this mechanism that we

control for the potential bias pointed out by GS. Our view is that their approaches and ours

are complimentary to obtain the full picture of the pricing behaviors and competitive structure

in the airline industry.

The following three papers address some related issues, but with focussing on carriers’

markups over the cost. Borenstein (1989) uses three different percentiles from the fare distri-

bution, weighted by the number of passengers, as the dependent variables in his regressions.

Focusing on the effects of the airline’s airport and route dominance on its market power, he

finds that competition tends to lower the carrier’s low-end (20th percentile) prices but to

raise its high-end (80th percentile) prices. Stavins (2001) considers ticket restrictions, such as

advance-purchase and Saturday-night stay-over requirements, as a price discrimination tool.

She estimates the marginal effects of each ticket restriction on the price and finds that the price

dispersion attributed to the ticket restrictions increases as markets become more competitive.

Hernandez and Wiggins (2009) use a unique data set which enables them to separate tickets

into several categories by quality. They find a positive relationship between competitiveness

and price dispersion and a negative relationship between quality premium and market con-

centration. The latter result is consistent with ours. Broadly speaking, the approaches taken

5

by these papers differ depending on the objective of studies and hence on the treatment of

data (e.g., whether to include all individual tickets in the regressions or to use a representa-

tive carrier-route ticket). One major difference from our study is that the distance of itinerary

and/or the ticket type (i.e., one-way versus round trip) are assumed to have the same effects on

the prices across different categories. We allow for the effects of these variables to differ among

the fare categories, since they can capture the important link between price discrimination and

individual demand uncertainty – the central issue of our study.

A recent work by Escobari and Jindapon (2009) considers the individual demand uncer-

tainty of customers as the main ingredient for the practice of price discrimination. Using a

model of risk-averse customers’ choice between refundable and non-refundable tickets, they

show that the fare difference between refundable and non-refundable tickets decreases as the

departure date approaches and consumers learn their demand over time. While we share with

them the view that refund contracts are important for understanding the pricing behaviors in

the airline industry, there are two important differences. First, Escobari and Jindapon focus

only on the time pattern of a given flight’s fares using a posted price data, like the one we use in

Figure 1 (but only to motivate our view). In contrast, we follow the tradition of BR to pursue

the cross sectional study based on transacted price data. Second, they interpret the pricing

pattern, like the one illustrated in Figure 1, as a sort of option prices that the monopolist

charges to discriminate among customers who have state dependent utilities. Our focus is not

on explaining the time path of fares, and our cross sectional analysis on the monopoly routes

can be naturally interpreted based on the theory of refund contracts by Courty and Li (2000),

without resource to the additional, state dependence of customers’ utility (see also Akan, Ata

and Dana (2009)). Our contribution in this respect is to find evidence which supports the

theory of refund contracts, and to provide its empirical implementation in the context of the

BR framework.

Finally, although our cross sectional analysis is silent on the time patterns of air fares for

some given flights, our study is largely related to the recent theory literature on intertemporal

price discrimination with Advance Purchase Discounts (i.e. increasing price schedule over

time for a given flight). One approach emphasizes the role of individual demand uncertainty of

customers who learn their preference over time (see Courty (2003), DeGraba (1995), Moller and

6

Watanabe (2010), and Nocke, Peitz and Rosar (2010)), whereas the other approach emphasizes

the role of capacity costs under aggregate demand uncertainty (see Dana (1998, 1999, 2001)

and Gale and Holmes (1993)). Our motivation is akin to the former approach. To control

the variations of unobservable capacity costs, we follow the standard procedure employed

in BR and GS. In this respect, we should mention a recent work by Puller, Sengupta, and

Wiggins (2008). They find only modest support of the theories based on capacity costs and

conclude that theories in which ticket characteristics segment customers and facilitate price

discrimination may play a major role in airline pricing. Sengupta and Wiggins (2006) also

reveal that ticket characteristics explain much of the variation in fares.

II. Data

We focus our attention on US domestic, non-stop (direct), economy-class, refundable/non-

refundable tickets of seven major carriers.2 The observation period is the third quarter of

2006. The coverage of our data set is standard and most of our data are publicly available

from the Bureau of Transportation Statistics such as the DB1B, T-100 Domestic Segment,

and On-Time Performance databases. In a related study, BR used the same data set for the

second quarter of 1986 and GS for the period from 1993 to 2006. The only additional dataset

we use is related to the information on the refundability of tickets. Below, we outline how this

information enables us to construct the relative price between refundable and non-refundable

tickets. The details are relegated to Appendix A.

A. Identification of Refundability

We collect ‘fare-basis codes’ per carrier-route from the Government Filing System (GFS),

an automated filing system that provides all the relevant agents in the airline industry with

the fare related information.3 It is posted fare information. Using the posted codes, we divide2The carriers in our sample include American (AA), Alaskan (AS), Continental (CO), Delta (DL), Northwest

(NW), United (UA), and US Airways (US). Unlike GS, we do not treat TWA and America West (HP) as an

independent carrier because TWA was acquired by AA in 2001 and HP was merged with US in 2005.3The GFS is managed by the Airline Tariff Publishing Company (ATPCO) [www.atpco.net]. It is a ‘single’

source of the fare-related data for the major global distribution systems, computer reservation systems, airlines,

and governments. Many airlines (including all the carriers in our sample) have managed the fare products

through the GFS since 1996. Anyone who pays a subscription fee can access to the GFS. The data used in our

study are available upon request.

7

the (economy-class) transacted fares from the DB1B into two categories: the refund and the

non-refund fares. Our procedures follow. First, we discard the fares which are greater than the

highest refundable fare in the GFS because those are likely to be business-class or first-class

fares.4 Second, based on the fact that refundable fares are always greater than the highest

non-refundable fare at a given time for each carrier-route, we use the lowest refundable fare

from the GFS to divide the transacted fares: fares in the DB1B are considered as refundable

if they are greater than the lowest refundable fare; otherwise, they are considered as non-

refundable. Third, since the data frequency in the DB1B is quarterly but the fare-basis codes

are managed on a daily-basis, we estimate the ‘representative’ lowest and highest refund fares

per carrier-route based on the middle date of the quarter (see Appendix A for the details on

the fare codes and the estimation procedure).5

While we believe the above procedure can divide the fares fairly accurately, we should

mention the limitations of matching the two different data sources. The first issue is related to

the treatment of round trip tickets. The nonstop round trip flights, by definition, involve two

directions, thereby there are three possible groups of round trip tickets: one contains tickets

whose fares consist of a non-refund fare for one direction and a refund fare for the other; the

other two contain either non-refund or refund fares for both directions. Although our method

can identify the latter two groups accurately, an identification problem may arise with the first

group. The second issue is related to the discrepancy in sampling frequency between the two

databases. We are not able to capture the possible time variations of the lowest refund fares.

This may cause errors in the classification of refund and non-refund fares. However, these

measurement errors, if any, would not affect the essentials in our estimation results (given in

the following sections), since there is no convincing reason for their systematic relationship to

explanatory variables.4To be consistent with the previous studies, we also use the ‘fare class’ information given in the DB1B-coupon

to identify the business-class and first-class fares.5We also examined a different selection date to estimate the representative fares such as the first date of the

quarter. The results reported in the paper are not sensitive to this change.

8

B. Summary Statistics on Relative Refund Price

Table 2a provides summary statistics on the relation between the relative refund prices and

the distance of itinerary.6 In general, greater relative prices are observed in longer distance

routes. For example, for the round trip tickets, the median relative price per route is on

average 2.32 in the short distance group (less than 400 miles), while it is 3.16 in the long

distance group (greater than 400 miles).7 This pattern remains unchanged across ticket types

and market structures (see Table 2b). The following example illustrates this relation more

vividly: a non-stop flight between Los Angeles (LAX) and Las Vegas (LAS) has a distance

of 236 miles and the median relative price of AA is 2.03, whereas a non-stop flight between

LAX and Honolulu (HNL) has a distance of 2556 miles and its median relative price is 4.34. It

may take about four to five hours by car from LAS to LAX, whereas it is not even possible to

drive from HNL to LAX. In short, our view is that the value of a refund option guaranteeing

an alternative flight is greater for customers traveling on long distance routes than on short

distance routes.

Table 2a also reports the relation between the relative price and the ticket type (i.e.,

round/one-way trips). The relative price tends to be higher for the round trip tickets than

for the one-way trip. For instance, the median relative price per route is on average 2.97 for

the round trip tickets, while it is 2.51 for the one-way tickets. To the extent that a longer

travel time causes a larger amount of uncertainties related to the consumption value of the

ticket, customers may be willing to pay a larger price difference on the round trip than on the6It is worth comparing our statistics with the ones provided in a related study by Hernandez and Wiggins

(2009) who used a different database. While the comparison is not exact since the sample period and the coverage

of carrier-routes are different, we follow their computation method as much as possible to be comparable. We

report the corresponding statistics obtained in our sample in the third row of Table 2a. In general, our numbers

appear to lie within the corresponding range of fare category groups classified by Hernandez and Wiggins. For

example, the ratio between the averages of refundable and non-refundable prices weighted by distance is 2.48 as

given in the eighth column. On the other hand, the ratio between Group 1 (which includes refundable business,

full coach, and coach tickets) and Group 2 (which includes non-refundable tickets without travel and/or stay

restrictions) in Hernandez and Wiggins is 2.19 and the ratio between Group 1 and Group 3 (which include non-

refundable tickets with travel and/or stay restrictions) is 3.35 – see the upper panel of Figure 5 in Hernandez

and Wiggins (2009, p. 50).7The selection of the cutoff value is based on the travel time by an alternative transportation mode, a car,

between the origin and destination airports. We choose the cutoff value of 400 miles because it may approximate

the longest travel distance per day by car. Nevertheless, the pattern is not sensitive to the selection of the cutoff

value. We also divide the sample by several groups with respect to distance and find the similar pattern that

the relative price is positively related to distance.

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one-way trip. In the following section, we formally test if the round trip tickets have a greater

relative price than the one-way tickets, with controlling for the other relevant factors.

III. Empirical Analysis on Relative Refund Price

A. Framework

The objective of this section is to identify observable factors that determine the relative

price between refundable and non-refundable tickets. The theory of refund contract by Courty

and Li (2000) suggests that the option value of refund tickets determines critically the optimal

refund contracts of a monopolistic seller. The refund contracts serve as a device to screen

customers who face individual demand uncertainties. To be consistent with this theory, we

first focus on routes operated by only one firm (see Table 1 for the list of monopoly routes).

We examine the robustness of our results to market structure and the effect of competition in

the following section. Our cross-sectional regression follows8 :

ln PRij /PN

ij = β0 + β1 ln F TOT ij + β2 lnDISTi + β3HUBij + β4HUBij ∗ ln BUSIi (1)

+ β5 lnTOURi + β5H ln TOURi ∗ lnHIGHi + β5L ln TOURi ∗ ln LOWi

+ β6 lnLOADij + β7 ln WEEKENDij + β8SLOTi + β9SMALLi + δj + γi + ηij ,

where PRij (PN

ij ) is the median refundable (non-refundable) price of a carrier j on route i.

The error terms specify fixed carrier effects δj , random route effects γi, and the remaining

disturbance ηij . Since all these variables are commonly used in the literature of airline pricing,

their complete description is relegated to Appendix B. Here, we only discuss the relevant effect

of each variable on the relative refund price.

• FTOTij is the total number of flights by carrier j in route i: a greater flight frequency will

8Throughout the paper, we use a symbol x to refer to the variable x which is instrumented. Flight frequency,

capacity, and capacity utilization are variables that can vary with exogenous characteristics of a carrier-route

and/or a route. In particular, the Hausman specification test provides evidence of bias if ln FTOTij is treated

as exogenous in regression (1). We use the total number of enplaned passengers and population variables as

instruments for FTOTij . LOADij is assumed to be exogenous since the Hausman test does not reject the

hypothesis of exogeneity at the conventional significant levels. Nevertheless, when LOADij is treated as an

endogenous variable, most estimates and standard errors become larger, similar to Borenstein (1989). Since we

do not have an instrument for WEEKENDij , it is treated as exogenous. We also examined the mean relative

price, rather than the median relative price, per carrier-route, but the results (available upon request) turned

out to be quite similar to the ones we present below.

10

provide more alternatives to refund customers who wish to change their flight schedule,

and increase the option value of the refundable ticket (β1 > 0).

• DISTi is the nonstop mileage between two endpoint airports: the refund option to offer

an alternative flight is expected to benefit passengers more on longer distance routes

(β2 > 0).

• HUBij is a dummy variable equal to one if one of the two endpoint airports is a hub

airport, capturing a carrier j’s ability to allocate an alternative itinerary to passengers

who demand a schedule change on route i. Such a demand would be likely to be fulfilled

successfully when the carrier dominates the airport (β3 > 0).

• BUSIi is the employment-population ratio on route i. Business travelers presumably

place a high value on the flexibility of their schedule and thus are willing to pay more

for the refund option, which will magnify the hub dominance effect (β4 > 0).

• TOURi is the ratio of accommodation earnings to non-farm earnings on route i. An

increase in the number of tourists would lead to a smaller likelihood of having passengers

with relatively large demand uncertainty (β5 < 0).

• HIGHi is the ratio of the number of households whose income is greater than $150,000 to

the total number of households on route i. LOWi is the ratio of the number of households

whose income is between $35,000 and $50,000 to the total number of households. The

customers with higher income tend to have greater opportunity costs of time and thus

to face a higher time cost of changing their social schedule than low-income customers

(β5H > 0, β5L < 0).

• LOADij , the airline j’s quarterly average load factor on route i, is a control variable

for capacity utilization. Flights with higher load factors are likely to operate at peak

demand times and to have a higher opportunity cost. Hence, load factors may affect the

relative price in two ways. On the one hand, carriers may translate the relatively high

cost unevenly to fares, possibly by charging a relatively higher price to refund customers.

On the other hand, a larger demand would imply a higher price for all passengers. As

11

these two effects could work oppositely on the relative refund fares, we cannot determine

the combined effect a priori.

• WEEKENDij , the ratio of the number of flights during the weekends to the total

number of flights, is a control variable for the fleet utilization rate. The flights scheduled

for the weekends are likely to have more discount seats since businessmen are less likely

to travel. Thus, operating more weekend flights relative to weekday flights may result in

lowering non-refundable fares on average (β7 > 0).

• SLOTi, a dummy variable equal to one if the route contains an airport where the number

of takeoffs and landings is regulated, is a control variable for the airport congestion. The

slot-controlled airports tend to operate near its capacity limit, with a relatively high

capacity cost. This effect on the relative price is not obvious because a high shadow cost

of providing a seat may result in both higher refundable and non-refundable prices.

• SMALLi, a dummy variable equal to one if both origin and destination airports on the

route are not congested, is a control variable for the shadow cost of providing service at

the airport level. The airport utilization rate at relatively small-scaled airports is usually

much lower than the capacity limit, and thus the capacity cost would be negligible for

the pricing decisions at these airports. To the extent the price of a refund option is

related to this capacity cost, we expect β9 < 0.

We perform the instrumental variable regression analysis on equation (1), separately for

the round trip and one-way trip tickets. This allows us to compare the estimated relative

refund fares and to examine the effect of the individual demand uncertainty related to the

travel time. As the round trips require longer time than the one way trips (by definition), we

expect the estimated relative price to be higher for the former than the latter.

B. Results

Table 3a reports the regression results. Overall, the results support our hypothesis on the

price discrimination by means of the ticket restriction on refundability.

The most striking result is the effect of the distance of travel on the relative price. The

12

estimates of lnDISTi are positive and significant at the 1% level in all specifications explored,

implying that the longer the distance of a travel route, the higher the price of refund option.

The travelers usually face more limited access to the transportation modes alternative to the

airline travel on longer distance routes, hence the refund option would benefit the customers

of longer routes more than those of shorter routes.

The results on HUBij and HUBij ∗ lnBUSIi are broadly consistent with our predictions.

The estimates of these variables are all positive and statistically significant for round trips, but

not significant for one-way trips. The positive effect of HUBij on the relative price implies

that an airline operating its hub airport has a greater ability to offer alternative available

flights to refund travelers, which contributes to the higher option value of refundable tickets.9

Further, the positive effect of HUBij ∗ ln BUSIi implies that the hub effect leads to a higher

refund price on routes with relatively more business travelers. The estimate of lnFTOT ij is

not statistically significant, with a positive sign for the one-way trip tickets.

The comparison result on the estimated relative prices between the ticket types (i.e, one-

way trips versus round trips) turns out to be exactly what we expected. We conduct the

t-test on the hypothesis for the equality of the fitted values of the relative price between

round and one-way trips, with controlling variations of the relative price across carrier-routes.

The equality hypothesis is decisively rejected at the 1% level for all specifications considered,

against an alternative for a greater median relative price of round trip than one-way trip

tickets.

The results on consumer heterogeneity are mixed. For example, the estimate of lnTOURi

is positive, but not statistically significant for both ticket types. However, when we separate

these effects by income level, we find that the estimate of lnTOURi ∗ ln LOWi is negative

for both types of tickets and significant at the 5% level for one-way tickets, consistent with

our predictions on the relative price. The estimate of lnTOURi ∗ ln HIGHi is not significant.

The refundable tickets are likely to be offered with relatively lower prices to customers in the

regions with relatively low average income, say Bangor, than in richer regions, say San Jose,

even when the customers in both regions travel to the same destination, say Las Vegas.9This can be related to Borenstein’s (1989) result that the effects of the airline’s airport dominance on

high-end (80th percentile) prices are three times greater than those on low-end (20th percentile) prices.

13

Finally, the effects of capacity costs and fleet utilization rates are weak, but broadly consis-

tent with our predictions: The estimate of lnWEEKENDij is positive and significant at the

5% level for the one-way trip, implying that customers with relatively less tight schedules may

be induced to travel in off-peak periods when more discount tickets are offered; the estimate

of SLOTi is positive and significant at the 5% level for both ticket types; the estimate of

SMALLi is close to zero; the estimate of lnLOADij is not significant for all the specifications

considered.

C. Robustness

In this subsection, we provide robustness analysis of our estimation results. First, we

examine if the econometric model (1) is appropriate for our study. In the previous works, the

distance variable was used as a proxy for the operating costs to transport passengers especially

when the price markups over costs were estimated, (see, e.g., Borenstein (1989), Stavins (2001),

and Hernandez and Wiggins (2009)). As long as the distance proxies the physical operating

costs, it should not influence the relative price, because the cost is the same between refund

and non-refund passengers for a given flight. Therefore, it will be appropriate to use lnDISTi

as an exogenous variable for the relative price in regression (1). To validate this point, we

conduct the Hausman specification test. Table 3b reports the results.

We find that lnDISTi should be used as an exogenous variable, not as an instrumental

variable, in our relative refund price regression. Specifically, the J-statistics from the over-

identification test of the instrument set increase significantly in all the specifications explored,

if lnDISTi is used as an instrumental variable. For example, the J-statistics are 5.2 and 1.2

for the round and one-way trips, respectively, as reported in the second and third columns

of Table 3a (where the distance is used as an exogenous variable), whereas the corresponding

statistics are 20.5 and 7.3, respectively, as reported in the second and third columns of Table

3b (where the distance is used as an instrumental variable). Further, ln F TOT ij becomes

negative for all these specifications and statistically significant at the 1% level for round trips,

implying that inclusion of distance in the instruments may generate a downward bias.10

10To understand this point, suppose that ln DISTi is omitted from regression (1). Then, the error term will

contain the effect of the distance if (1) is the correct specification form. In that case, there will be a positive

correlation between the instrument and the error term. On the other hand, ln DISTi is negatively related

14

We now discuss the potential effects of omitted cost variables. Although we control for

the effects of some cost variables in regression (1) and the operating cost for a given flight is

identical for both the refund and non-refund customers, one may argue that there can be other

omitted cost variables which affect the regression estimates. For instance, airlines may consider

aggregate demand uncertainty as a bigger factor in routes with higher operating costs. If the

price of a refund option is influenced by this concern and this omitted variable has a strong

relation with distance, then the estimates of the distance on the relative price in (1) would

be biased. To control for this effect, we follow GS and add a new variable, ASEATCAPij ,

which is the ratio of the total available seats to the total number of flights by airline j on route

i. In general, larger operating costs accrue in longer distance trips and larger-scaled aircrafts

tend to be used in such routes. In this sense, the variable can capture the differential effects

of operating costs across routes. The regression results, with adding lnASEATCAPij as a

new explanatory variable, are essentially the same as before (see the columns labeled as (i) in

Table 3c).

Finally, we consider a possible nonlinear specification by dividing the entire routes into

several groups by distance. We introduce three dummy variables: SDi equals to one if the

distance between the two endpoint airports is less than or equal to 400 miles; MDi is equal

to one if the distance is between 400 and 1800 miles; and LD1i is equal to one if the distance

is greater than 1800 miles. We replace the original continuous distance variable with these

discrete variables in (1). The columns labeled as (ii) – (v) in Table 3c report the regression

results. As is consistent with the previous results, these dummy variables have strong positive

effects on the relative price (statistically significant at the 1% level), while leaving the effects

of the other variables unchanged. The result is not affected by the selection of the range of

two endpoint miles.

to ln FTOTij in the data. Consequently, the instrumental vaiable estimator, ln F TOT ij , would be biased

downward. This result can be related to a point raised by GS that potential biases exist in the cross-sectional

regressions of price dispersion (see equation (5) in GS) due to the presence of the time invariant route specific

factors in the error terms. We will provide more detailed discussions on this issue in Section V.

15

IV. Relative Price and Competition

We now extend our analysis to various market structures. The objective here is to investi-

gate if the results obtained from the monopoly sample can be extended to the entire sample,

and to investigate the effect of market competition on the relative refund prices. Following

Borenstein (1989) and BR, we measure the market structure by the Herfindahl index on a

given route i, HERFi, and a carrier j’s market share on route i, MKTSHAREij . We also

take into account the effects of the presence of Southwest Airlines, using a dummy variable

SWi which equals to one if its market share is greater than or equal to 5% on route i. As in

BR and GS, we perform the instrumental variable regression, taking into account the possible

endogeneity of FTOTij , HERFi, and MKTSHAREij .11

Table 4 reports the estimation results. As is consistent with the monopoly sample, the

results for the entire sample support our hypothesis on the practice of price discrimination via

the refund tickets. In general, most of the estimates are statistically significant at the 1% level

and the estimated signs are consistent with the predictions described in the previous section.

We also run the regression by replacing the two market competition measures with the log of

total number of firms in a route, but the results are qualitatively similar.

One major difference in the estimation results between the monopoly sample and the entire

sample is the effects of ln F TOT ij – it is positive and not significant in the monopoly sample,

but negative and significant in the entire sample. This implies that the flight density may

have two opposing effects on the relative price. On the one hand, a larger flight density

provides greater (ex post) options for refund customers who wish to change their itinerary.

This contributes to increasing the relative refund fares. On the other hand, with market

competition it also implies a larger number of (ex ante) options among which customers can

choose the best deal given their itinerary. The results suggest that the latter effect works

negatively on the relative refund fares, presumably because the increasing substitutability of

customers may introduce a relatively more intensified competition on the refundable tickets

than on the non-refundable tickets.

The effects of the variables related to market structure turn out to be non-uniform on11The list of our instruments is detailed in Appendix B. We do not consider the possible endogeneity of SWi

due to the lack of instruments for this variable.

16

the relative prices, but the results seem to be suggestive on the relationship between the

relative price and competition. First of all, holding the effects of an airline’s market share

constant, the result shows that the relative refund price is larger on less concentrated routes:

the estimates of ln HERF i are negative and statistically significant at the 1% level for all the

specifications. This positive effect of competition on the relative price suggests the existence

of a non-negligible proportion of customers who have stronger preferences towards flexible

itinerary over lower prices. On the other hand, the effects of the market share are all positive,

suggesting that carriers with a larger market power are able to charge higher prices for the

refund option. This interpretation is in line with our other results that the variables related to

airport dominance, HUBij and HUBij ∗ ln BUSIi, have a strong positive effect on the relative

price.

Finally, the presence of the Southwest on a route significantly reduces the relative refund

prices of the legacy carriers on that route: the estimate of SWi is negative and statistically

significant at the 1% level for the round trip tickets. This suggests that legacy carriers may

compete with Southwest by lowering the refund fares relatively more than the non-refund fares.

Our result is broadly consistent with GS who find in their panel regressions that the number

of low cost carriers has a negative effect on market prices, which is larger for the high-end

price level (the 90th percentile price level) than for the low-end price level (10th percentile

price level) (see also Ito and Lee (2004)).

V. Relative Price and Price Dispersion

So far, we have identified one form of price discrimination based on refundability of tickets.

We now investigate if this ticket restriction can help explain the behaviors of price dispersion.

For this purpose, we follow BR as close as possible and consider the following cross-sectional

regression,

ln Giniij = β0 + β1 ln HERF i + β2 ln MKTSHAREij + β3 ln F TOT ij + β4SWi

+ β5 lnTOURi + β6HUBij + β7HUBij ∗ ln BUSIi

+ β8 lnWEEKENDij + β9SLOTi + β10SMALLi

+ β11 ln PRij /PN

ij + δj + γi + ηij , (2)

17

where Giniij is the Gini coefficient for carrier j on route i, which is a conventional measure of

price dispersion in the literature.12 The main difference from BR is the inclusion of the relative

price, an endogenous explanatory variable, which captures the effects of the ticket restriction

induced by the refund contract. One econometric issue that we should take into account is the

choice of instrumental variables for lnPRij /PN

ij for the estimation of regression (2). In principle,

all exogenous variables in the relative price regressions can be potential candidates as long as

they do not affect the Gini coefficient. We choose lnDISTi as an instrument and use all the

others as exogenous variables in regression (2). In BR, lnDISTi is used for instrumenting the

total number of flights on a route. However, GS show that the distance of the route may be

positively correlated with the error term in the BR regression where route effects γi are treated

as random. As will be discussed in detail below, our specification (2) significantly alleviates

this potential bias since both the distance and the relative price capture a route specific effect.

Except for this, we use the same instruments as BR.

The columns 6 – 9 in Table 5 report the results from regression (2). For comparison, we

also attempt to reproduce the cross-sectional regression results of BR by excluding ln PRij /PN

ij .

The results are in the columns 2 – 5. Our main findings are13 : (i) there is a very strong

positive effect of the relative price on price dispersion; (ii) competition decreases price dis-

persion; (iii) the sign of the estimates of market concentration variable ln HERF i changes in

regression (2) with and without ln PRij /PN

ij . The last two results ae consistent with GS: they

point out a possible downward bias for the estimation of ln HERF i due to a route-specific,

omitted-variable problem in the BR cross-sectional regressions. GS propose that an addi-

tional explanatory variable such as plane size can capture the potential correlation between

the error term and the route-specific variable in the instrument, i.e. distance: carriers tend

to use larger-scaled planes on longer distance routes because of fuel considerations. In their

cross sectional regression, the sign change occurs once the proxy for plane size is included.

Our identification based on individual demand uncertainty is in line with their methodology:12We use ln FTOTij , the total number of flights of carrier j in route i, in the regression (2) to be consistent

with the previous two regressions of the relative price, while BR and GS use ln FTOTi, the total number of

flights in route i. The results are robust to this change and available upon request.13Our findings are not sensitive to the measures of competition: we consider an alternative specification whereln HERF i and ln MKTSHAREij are replaced with the log of the total number of firms per route, ln bNi, as in

GS. As reported in Table 6, however, the results are robust to this change.

18

in our case the distance is used as an exogenous variable to explain the relative price in the

relative price regressions.

VI. Conclusion

We have provided new evidence on price dispersion in the US airline industry. We have

found that the factors related to the customers’ demand uncertainty have a significant effect on

the relative price between refundable and non-refundable tickets. Our findings are consistent

with the theory of refund contract based on individual demand uncertainty. The variables

used in our study are standard and commonly used in the literature.

We have also found a negative effect of competition on the relative refund fares. This

result suggests that the effect of competition on the refundable and non-refundable price is

not uniform, due to the presence of travelers who have strong preference for flexible itinerary

over lower prices.

Finally, taking into account the variations of the relative refund fare, we have investigated

the effect of market structure on price dispersion. While price dispersion tends to be larger on

routes with higher relative prices, our results show that the consideration of the refundability

of tickets can alter the negative relationship between market concentration and price dispersion

observed in the previous studies.

19

References

[1] Akan, M., Ata, B., and J. Dana (2009) Revenue Management by Sequential Screening,mimeo.

[2] Borenstein, S. (1989) Hubs and High Fares: Dominance and Market Power in the U.S.Airline Industry, RAND Journal of Economics 20(3), 344-65

[3] Borenstein, S. (1991) The Dominant-Firm Advantage in Multiproduct Industries: Evi-dence from the U.S. Airlines, Quarterly Journal of Economics 106(4), 1237-66.

[4] Borenstein, S. and N. Rose. (1994) Competition and Price Dispersion in the U.S. AirlineIndustry, Journal of Political Economy 102 (4), 653-83.

[5] Courty, P., Li, H. (2000) Sequential Screening, Review of Economic Studies 67(4), 697-717.

[6] Courty, P. (2003) Ticket Pricing under Demand Uncertainty, Journal of Law and Eco-nomics 46, 627-652.

[7] Dana Jr., J. D. (1998) Advance Purchase Discounts and Price Discrimination in Compet-itive Markets, Journal of Political Economy, vol. 106, pp. 395-422.

[8] Dana Jr., J. D. (1999) Equilibrium Price Dispersion under Demand Uncertainty: TheRoles of Costly Capacity and Market Structure, RAND Journal of Economics, vol. 30(4),pp. 632-660.

[9] Dana Jr., J. D. (2001) Monopoly Price Dispersion under Demand Uncertainty, Interna-tional Economic Review, vol. 42, pp. 649-670.

[10] DeGraba, P. (1995) Buying Frencies and Seller Induced Excess Demand, RAND Journalof Economics, vol. 26(2), pp. 331-342.

[11] Escobari,D., Jindapon, P. (2009) Price Discrimination through Refund Contracts in Air-lines, mimeo.

[12] Gaggero, A.A., Piga, C.A. (2011) Airline Market Power and Intertemporal Price Disper-sion, forthcoming in Journal of Industrial Economics.

[13] Gale, I. L., Holmes, T. J. (1993) Advance Purchase Discounts and Monopoly Allocationof Capacity, American Economic Review 83(1), 135-146.

[14] Gerardi, K., Shapiro, A. H. (2009) Does Competition Reduce Price Discrimination? NewEvidence from the Airline Industry, Journal of Political Economy 117(1) 1-34.

[15] Hernandez, M. A., Wigginsy, S. N. (2009) Nonlinear Pricing Strategies and Market Con-centration in the Airline Industry, mimeo.

20

[16] Ito, H. and D. Lee. (2004) Incumbent Responses to Lower Cost Entry: Evidence from theU.S. Airline Industry, mimeo.

[17] Moller, M., Watanabe, M. (2010) Advance Purchase Discounts versus Clearance Sales,Economic Journal 547, pp. 1125-1148.

[18] Nocke, V., Peitz, M., Rosar, F. (2010) Advance-Purchase Discounts as a Price Discrimi-nation Device, forthcoming in Journal of Economic Theory.

[19] Puller, S.L., Sengupta, A., Wiggins, S. N. (2008) Testing Theories of Scarcity Pricing andPrice Dispersion in the Airline Industry, mimeo.

[20] Sengupta, A., Wiggins, S. N. (2006) Airline Pricing, Price Dispersion and Ticket Charac-teristics On and Off the Internet, mimeo.

[21] Stavins, J. (2001) Price Discrimination in the Airline Market: The Effect of MarketConcentration, Review of Economics and Statistics 83(1), 200-202.

Appendix A: Data Construction

The construction of our carrier-route observations mainly uses four databases: the Origin-Destination Survey (DB1B), the T-100 Domestic Market Segment, the On-Time Performance,and the Government Filing Systems (GFS). The first three databases are from the BTS [at itsonline webpage, Transtats] and the other is from Airline Tariff Pricing Company (ATPCO)[www.atpco.net]. Since we mainly follow GS for processing the former databases, we focus ondescribing the main differences between the two studies and refer the common details to GS.

DB1B, T-100 Domestic Market Segment, and On-Time Performance Data

The DB1B survey data set includes information on carriers, origin and destination airports,ticket type, miles flown, number of coupons, itinerary fares, passenger quantities, fare class, etc.The database consists of three sub-components: DB1B-coupon, DB1B-market, and DB1B-ticket. Since no single sub-component contains enough information to complete an itinerary,we combine variables mainly from the DB1B-market and DB1B-ticket to construct a workingsample of tickets.

We use only domestic nonstop (direct) economy-class itineraries. The selection of ticketsfollows: (i) Drop tickets if the credibility of their fares is questioned by the BTS; (ii) Dropfirst-class and business-class itineraries; (iii) Drop tickets with more than 2 coupons (a couponmeans a segment or a boarding pass) for the round trip and more than 1 coupon for the one-way trip; (iv) Drop interline tickets; (v) Drop all fares that are less than or equal to 20 for theround tickets (10 for the one-way tickets).

21

We define a ‘route’ by a directional ‘airport pair’ following BR and GS.14 The procedureof constructing routes follows. (i) Select the 175 largest airports in terms of the number ofpassengers in 2006 obtained from the US Department of Transportation. (ii) Drop 9 airportssince information for constructing some route specific factors such as a tourism index is notavailable. Finally, our sample contains routes constructed based on these 166 airports.

Our sample only keeps traditional legacy carriers (listed in the text) in order to maintainconsistency with the sample used by BR as well as due to the data availability.15 However, wegroup carriers together that belong to the same corporation in 2006: AA includes AmericanEagle (MQ) and TWA; US includes HP. The main reason for grouping carriers is that theairlines within a group are recognized as the same company in the GFS and produce the samefare products. Following GS, a carrier is considered as operating on a route where it has atleast 100 passengers.

The main difference between GS and our method is the identification of one-way triptickets. The DB1B-market section defines a market by a trip break. For example, an itineraryof CHI-LAX-CHI would have two markets CHI-LAX and LAX-CHI if the trip break occurredat LAX. GS chose either one of the two directions to avoid the problem of ”double counting”round trip tickets [see the detail in GS] in expense of dropping some one-way trip tickets. Thisis mainly because the DB1B-market section does not provide information on a ticket type.Instead, our method combines both DB1B-ticket and DB1B-market sections and keeps all theone-way and round trip tickets. Both sections have a variable called ‘ItinId’ which contains aunique id for each itinerary. By matching the variable ‘ItinId’, we are able to combine bothsections and construct a complete itinerary with information on the ticket type, while avoidingthe problem of double counting round trip tickets.

The On-Time Performance database contains ‘daily’ on-time arrival data for non-stopdomestic flights by major airlines, and provides information of origin and destination airports,flight numbers, scheduled and actual departure and arrival times, and non-stop distance. Weconstruct a carrier-route variable, WEEKEND, by summing the number of flights operatedper day over the third quarter of 2006 for each carrier-route.

We exactly follow GS for merging the DB1B data with the monthly T-100 Segment data aswell as the On-Time Performance data to construct route-specific and cost-related variables.We refer the detailed procedure to Appendix B in GS.

GFS Data

In the text, we already described a brief introduction of the GFS as well as the main featuresof our procedure for identifying refundable fares in the DB1B. Hence, in this appendix, wefocus on describing the detailed information of the data in the GFS as well as the detailed

14We also defined a route by a directional city pair. The results are robust to this change and available upon

request.15The GFS does not have fare basis codes from some low cost carriers such as SW in 2006.

22

matching procedure omitted in the text.A fare-basis code contains information of (i) when the code was introduced by an airline,

(ii) when it was taken out, (iii) the name of the airline, (iv) origin-destination airports, (iv)ticket type, (v) a fare, and (vi) fair rules including information of ticket restrictions such asrefundability, business-/economy-class, advance purchase discounts, Saturday-night stay-over,etc. Fares in the GFS include a value added tax but do not include airport-assessed PassengerFacility Charges (PFCs) of up to 18 US dollars and government-imposed September 11thSecurity Fee of up to 10 dollars. In general, itinerary fares charged by the airlines includethese fees of about 10 dollars for a nonstop one-way trip ticket and of about 20 for a nonstopround trip ticket. To be consistent with this, we subtract 10 dollars for a nonstop one-waytrip (20 for a nonstop round trip) from an itinerary fare in the DB1B.

We illustrate how airlines use these fare-basis codes to offer posted fares by picking up acarrier-route in the GFS. AA had produced and managed on average about 50 ‘daily’ fare-basis codes on CHI-LAX route in 2006. Of them, AA had used 4 fare codes to produceeither business- or first-class fares and 21 fare codes for offering both refund and non-refundeconomy-class fares: 14 codes for non-refundable fares and 7 codes for refundable fares.16 AAcombines two fare codes in order to produce a round trip fare since, in general, a fare basiscode is applied to a directional trip, say, either CHI-LAX or LAX-CHI. This means that AAcould have generated up to 441 (21 times 21) economy-class round trip fare products on thisroute at a given time but 21 one-way trip fare products. So, when a customer on this routeinquired an economy-class round trip fare, AA may have offered one of those 441 fares throughthe application of restrictions on ticket purchase and travel.

We now discuss how to estimate ’representative’ lowest and highest economy-class refundfares per carrier-route. This is necessary because the data frequency in the DB1B is quarterly.We use a simple method: for each carrier-route, we collect fare basis codes on August 15, 2006and then obtain the lowest and highest refund fares among them. To avoid the problem of anextreme event, the following criteria are applied: first choose refund fares which appear morethan 10 days in the GFS for offering posted fares and then select the lowest and highest fares.The mean and median durations of the lowest refund fare that appears in the GFS are 76 and61 days, respectively, suggesting that on average an airline could have offered the same lowestrefund fare for about two months.

Appendix B: Definitions of variables

We closely follow BR and GS for constructing variables. The definitions of variables follow:16The remaining fare codes are used to determine fares for government or military personnel, senior citizens,

children, or infant, which are not relevant to our study.

23

1. ln Giniij is the logarithm of the gini coefficient of carrier j’s price distribution on routei, calculated using data from the DB1B.

2. ln PRij /PN

ij is the logarithm of the median relative price between refundable and non-refundable fares of carrier j on route i, calculated combined data from the DB1B andthe GFS.

3. ln HERFi is the logarithm of the Herfindahl index on route i, calculated using passengershares obtained from the DB1B.

4. ln MKTSHAREij is the logarithm of the share of total passengers on route i operatedby carrier j, calculated from the DB1B.

5. ln Ni is the logarithm of the total number of carriers operating on route i, obtained fromthe DB1B.

6. ln FTOTij is the logarithm of the total number of departure performed by carrier j onroute i, calculated from the T-100 Segment data.

7. HUBij is a dummy variable equal to one if one of two endpoint airports is a hub airportotherwise zero.

8. ln BUSIi is the logarithm of the maximum of the ratio of the number of employeesexcluding Mining, Forestry, Accommodation, and Food Services to total population forthe origin and destination cities on route i, calculated using the data from the 2006County Business Patterns.

9. ln TOURi is the logarithm of the maximum of the ratio of accommodation earnings tototal non-farm earnings for the origin and destination cities on route i, obtained fromthe Bureau of Economic Analysis.

10. ln HIGHi is the logarithm of the maximum of the ratio of the number of householdswhose income is greater than 150,000 USD to the total number of households for theorigin and destination cities on route i, calculated using the data from the 2006 AmericanCommunity Survey.

11. ln LOWi is the logarithm of the maximum of the ratio of the number of householdswhose income is between 35,000 and 50,000 USD to the total number of households forthe origin and destination cities on route i, calculated using the data from the 2006American Community Survey.

12. SDi is a dummy variable equal to one if nonstop distance between endpoint airports isless than or equal to 400 miles.

13. MDi is a dummy variable equal to one if nonstop distance between endpoint airports isgreater than 400 miles and less than or equal to 1800 miles.

24

14. LD1i is a dummy variable equal to one if nonstop distance between endpoint airports isgreater than 1800 miles.

15. SWi is a dummy variable equal to one if the market share of Southwest on route i isgreater than or equal to 5%.

16. ln WEEKENDij is the logarithm of the ratio of the number of flights operated duringthe weekends (Saturday and Sunday) to the total number of flights, calculated usingdata from the On-Time Performance.

17. ln LOADij is the logarithm of the average load factor of airline j on route i.

18. SLOTi is a dummy variable equal to one if a route includes an airport where the num-ber of takeoffs and landings is regulated. Slot-controlled airports include Washington-National (DCA), New York-Kennedy (JFK), and New York-LaGuardia (LGA).

19. SMALLi is a dummy variable equal to one if a route does not include a big city whichis defined by the 25 largest metropolitan areas in terms of population.

20. ln ASEATCAPij is the logarithm of the average seat capacity calculated by the totalnumber of available seats divided by total number of departures on route i by carrier j,obtained from the T-100 Segment data.

21. ln DISTi is the logarithm of nonstop distance in miles between endpoint airports on routei, obtained from the DB1B. lnDISTi is used as an explanatory variable in the relativeprice regressions but used as an instrument variable in the price dispersion regression(2).

We follow BR for selecting instrumental variables which are defined:

1. ln AMEANPOPi is the logarithm of the arithmetic mean of the metropolitan populationof endpoint cities, calculated using data from the 2006 American Community Survey.

2. ln GMEANPOPi is the logarithm of the geometric mean of the metropolitan populationof endpoint cities, calculated using data from the 2006 American Community Survey.

3. ln PASSRTEi is the logarithm of the total enplaned passengers on route i, obtainedfrom the T-100 Segment data.

4. ln IRUTHEREi is the square of the fitted value for MKTSHAREi from its first-stageregression, plus the scaled sum of the squares of all other carrier’s shares. It is equal toln[ MKTSHARE

2

ij +HERFi−MKTSHARE2

ij

(1−MKTSHAREij)2(1− MKTSHAREij)2].

5. ln GENSP = ln√

ENPj1ENPj2Pk

√ENPk1ENPk2

, where k indexes all airlines, j is the observed air-line, and ENPk1 and ENPk2 are airline k’s average quarterly enplanements at the twoendpoint airports. Data on enplanements are obtained from the T-100 Segment data.

25

Table 1. List of Monopoly Routes

routes routes routes routes routes routes routes routes

ABQ ATL BOS IAD CLT TPA DCA ALB DTW JFK IAD SLC LAX RDU MSP BZN

ABQ CVG BOS IAH CLT MCO DCA SYR DTW MCI IAD FLL LGA MEM MSP FAR

ABQ DFW BOS MIA CLT BDL DCA PBI DTW MEM IAH IND LGA MSP MSY ORD

ABQ IAH BOS PHX CLT ROC DCA PVD DTW MKE IAH JAX LGA MSY MSY PHL

ABQ MSP BOS PIT CLT SYR DCA MHT DTW MSP IAH JFK LGA PIT MSY STL

ABQ ORD BOS SAN CLT RSW DEN HNL DTW MSY IAH LAS LGA STL OAK ORD

ATL AUS BOS SEA CLT PBI DEN IAD DTW ORF IAH LAX LGB SEA OMA ORD

ATL BUR BOS STL CLT FLL DEN ICT DTW PDX IAH LGA MCI MEM ONT SFO

ATL CLE BOS SLC CLT PVD DEN MSY DTW PHL IAH MCI MCI MSP ONT SLC

ATL CMH BOS RSW CLT RIC DEN OAK DTW PIT IAH MEM MEM MIA ORD RDU

ATL COS BOS PBI CMH DCA DEN OKC DTW RDU IAH MSY MEM MKE ORD SMF

ATL CVG BUF CLT CMH DEN DEN ONT DTW SAN IAH OAK MEM MSP ORD TUL

ATL ELP BUF DCA CMH DFW DEN TUS DTW SAT IAH OKC MEM MSY ORD TUS

ATL HNL BUF DTW CMH EWR DEN DSM DTW SDF IAH OMA MEM ORD ORD ROC

ATL JFK BUF EWR CMH IAH DEN BIL DTW SEA IAH ONT MEM PHL ORD ALB

ATL OAK BUF MSP CMH LAX DFW ELP DTW BDL IAH PDX MEM PHX ORD DAY

ATL OKC BUF ORD CMH MEM DFW FAT DTW ROC IAH PIT MEM SAT ORD GSO

ATL ORF BUF PHL CMH MSP DFW HNL DTW ALB IAH RDU MEM SEA ORD BOI

ATL PDX BUR DEN CMH ORD DFW IAD DTW SYR IAH SAN MEM SFO ORD MDT

ATL SAN BUR DFW COS DFW DFW ICT DTW MSN IAH SAT MEM STL ORD RNO

ATL SAT BUR SEA COS MSP DFW IND DTW PVD IAH SEA MEM TPA ORD PVD

ATL SJC BUR SFO CVG DCA DFW JAX DTW MHT IAH SJC MEM MCO ORD MHT

ATL SMF BWI IAH CVG DEN DFW JFK DTW SAV IAH SMF MEM BTR ORD RIC

ATL SNA BWI MEM CVG DFW DFW MCI DTW GRB IAH SNA MEM FLL ORD GEG

ATL STL BWI MIA CVG EWR DFW MIA ELP IAH IAH TPA MEM VPS ORD BTV

ATL TUS BWI MSP CVG HNL DFW MSY ELP ORD IAH TUL MIA MSY ORD XNA

ATL BDL BWI SFO CVG LAS DFW OAK EWR HNL IAH TUS MIA PHX PDX PHL

ATL SLC CLE EWR CVG LAX DFW OKC EWR IAH IAH MCO MIA PIT PDX SNA

ATL ALB CLE IAH CVG LGA DFW OMA EWR IND IAH BDL MIA RDU PHL SEA

ATL LIT CLE LAX CVG PDX DFW ONT EWR JAX IAH BHM MIA SEA PHL SLC

ATL SYR CLE LGA CVG PHX DFW ORF EWR MEM IAH MFE MIA SFO PHX BDL

ATL CHS CLE MIA CVG RDU DFW PDX EWR MSY IAH RSW MIA STL PHX BOI

ATL JAN CLE MSP CVG SAN DFW RDU EWR PDX IAH PBI MIA TPA PHX ANC

ATL DAB CLE ORD CVG SEA DFW SAN EWR RDU IAH FLL MIA MCO PIT SAN

ATL RNO CLE PHL CVG SFO DFW SAT EWR SAN IAH ANC MIA SLC PIT SEA

ATL PVD CLE SEA CVG SNA DFW SDF EWR SAT IND LGA MSP MSY PIT SFO

ATL VPS CLE SFO CVG TPA DFW SJC EWR SJC IND MEM MSP OMA PIT FLL

ATL MOB CLE TPA CVG MCO DFW SMF EWR SNA IND MIA MSP PDX RDU SLC

AUS DFW CLE PBI CVG BDL DFW SNA EWR TPA IND MSP MSP PHL SAN SFO

AUS EWR CLT DCA CVG SLC DFW STL EWR MCO IND PHL MSP PIT SAT STL

AUS IAH CLT EWR CVG FLL DFW TPA EWR PBI IND SEA MSP RDU SEA SNA

AUS MSP CLT IAD DCA DFW DFW TUL EWR SAV JAX MSP MSP SAT SEA STL

AUS ORD CLT IND DCA EWR DFW TUS EWR MYR JFK MIA MSP SJC SEA TUS

AUS SFO CLT JAX DCA IAH DFW BDL HNL IAH JFK PHX MSP SMF SFO SNA

Note for Table 1: Each route represents the two endpoint airport codes. This table lists part of routes from

our monopoly sample.

26

Table 2a. Relative price between refund and non-refund tickets

Round One-way Both

All SD LD All SD LD All SD LD

Average of P Rij /P N

ij 3.06 2.32 3.16 2.55 2.27 2.59 3.10 2.44 3.20

Average of QRij/(QN

ij + QRij) 0.09 0.23 0.07 0.28 0.38 0.26 0.12 0.24 0.10

P RD /P N

D 2.44 1.71 2.30 2.15 1.75 2.15 2.48 1.79 2.37

Note for Table 2a: P Rij (P N

ij ) is the median refund (non-refund) fares on route i by carrier j. P RD (P N

D ) is

the average of refund (non-refund) fares per mile. QRij (QN

ij ) is the number of the refund (non-refund) tickets

sold on route i by carrier j. ‘Both’ represents the entire sample which includes both round and one-way trip

tickets. ‘All’ includes all routes in the corresponding sample, ‘SD’ the short distance routes, and ‘LD’ the long

distance routes. The short distance group includes routes whose distance is less than or equal to 400 miles and

the long distance group includes routes whose distance is greater than 400 miles.

Table 2b. Relative price and price dispersion

by market structure for a given distance group

All SD LD

Mono Duo Comp Mono Duo Comp Mono Duo Comp

Average of P Rij /P N

ij 3.14 3.09 3.17 2.40 2.51 2.97 3.26 3.17 3.19

Average of Giniij 0.26 0.25 0.24 0.24 0.25 0.26 0.27 0.25 0.23

Note for Table 2b: ‘All’ includes all routes in the entire sample. ‘Mono’ includes the monopoly routes where

only one firm is operating. The classification of duopoly (Duo) and competitive (Comp) routes follows BR.

27

Table 3a. The relative price in monopoly markets: Regression (1)

[Third Quarter of 2006]

R O R O R O R O

ln FTOT -0.02 0.02 -0.01 0.03 -0.03 0.03 -0.02 0.04

s.e. 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

ln DIST 0.14∗∗∗ 0.08∗∗∗ 0.14∗∗∗ 0.10∗∗∗ 0.12∗∗∗ 0.11∗∗∗ 0.12∗∗∗ 0.12∗∗∗

s.e. 0.04 0.03 0.04 0.04 0.03 0.02 0.03 0.03

HUB 0.77∗∗∗ 0.05 0.82∗∗∗ 0.15 0.76∗∗∗ 0.06 0.81∗∗∗ 0.16

s.e. 0.18 0.17 0.18 0.18 0.18 0.17 0.18 0.18

HUB ∗ ln BUSI 0.78∗∗∗ 0.15 0.83∗∗∗ 0.26 0.78∗∗∗ 0.16 0.82∗∗∗ 0.27

s.e. 0.17 0.16 0.18 0.17 0.17 0.16 0.18 0.17

ln TOUR 0.06 0.02 0.06 0.01

s.e. 0.05 0.05 0.05 0.05

ln TOUR ∗ ln High -0.02 0.01 -0.02 0.01

s.e. 0.02 0.02 0.02 0.02

ln TOUR ∗ ln Low -0.04 −0.06∗∗ -0.04 −0.06∗∗

s.e. 0.03 0.03 0.03 0.03

ln LORD -0.19 0.18 -0.17 0.17

s.e. 0.23 0.19 0.23 0.19

ln WEEKEND 0.14 0.18∗ 0.14 0.15 0.11 0.21∗∗ 0.11 0.18∗∗

s.e. 0.10 0.10 0.09 0.09 0.08 0.09 0.08 0.08

SLOT 0.23∗∗∗ 0.10∗ 0.21∗∗∗ 0.14∗∗ 0.24∗∗∗ 0.09 0.22∗∗∗ 0.13∗

s.e. 0.06 0.06 0.06 0.07 0.05 0.06 0.06 0.07

SMALL 0.00 -0.01 0.00 -0.01 0.00 -0.01 0.00 -0.01

s.e. 0.05 0.04 0.04 0.04 0.05 0.04 0.04 0.04

Obs 420 455 420 455 420 455 420 455

Fitted Values 3.0 2.4 3.0 2.4 3.0 2.4 3.0 2.4

t-statistics 21.0 20.7 21.1 20.7

J-statistics 5.2 1.2 5.4 2.4 5.9 1.0 5.9 2.2

Table 3b. The relative price in monopoly markets: Specification Test

ln FTOT −0.07∗∗∗ -0.01 −0.07∗∗ 0.00 −0.07∗∗∗ 0.00 −0.07∗∗ 0.00

s.e. 0.03 0.02 0.03 0.03 0.03 0.02 0.03 0.03

J-statistics 20.5 7.3 15.7 9.6 22.1 17.5 16.8 17.0

28

Table 3c. Robust analysis on Regression (1)

[Third Quarter of 2006]

(i) (ii) (iii) (iv) (v)

ln FTOT -0.03 -0.03 -0.04 -0.03 −0.05∗ −0.05∗ −0.05∗∗ −0.05∗ −0.08∗∗∗−0.08∗∗∗

s.e. 0.03 0.03 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03

ln DIST 0.13∗∗∗ 0.12∗∗∗

s.e. 0.04 0.04

LD1 + MD 0.31∗∗∗ 0.30∗∗∗ 0.30∗∗∗ 0.30∗∗∗

s.e. 0.06 0.05 0.06 0.05

LD1 0.31∗∗∗ 0.30∗∗∗ 0.29∗∗∗ 0.29∗∗∗

s.e. 0.06 0.05 0.05 0.05

MD 0.26∗∗∗ 0.22∗∗∗ 0.19∗∗∗ 0.16∗∗

s.e. 0.07 0.08 0.07 0.08

HUB 0.77∗∗∗ 0.82∗∗∗ 0.68∗∗∗ 0.69∗∗∗ 0.69∗∗∗ 0.67∗∗∗ 0.68∗∗∗ 0.70∗∗∗ 0.69∗∗∗ 0.69∗∗∗

s.e. 0.18 0.18 0.18 0.18 0.18 0.18 0.17 0.18 0.17 0.18

HUB ∗ ln BUSI 0.78∗∗∗ 0.82∗∗∗ 0.70∗∗∗ 0.68∗∗∗ 0.70∗∗∗ 0.66∗∗∗ 0.69∗∗∗ 0.69∗∗∗ 0.69∗∗∗ 0.66∗∗∗

s.e. 0.17 0.17 0.17 0.17 0.17 0.18 0.17 0.17 0.17 0.17

ln TOUR 0.05 0.09∗∗ 0.10∗∗ 0.06 0.06

s.e. 0.05 0.04 0.05 0.05 0.05

ln TOUR ∗ ln High -0.02 −0.04∗∗ −0.05∗∗∗ −0.03∗ −0.04∗∗

s.e. 0.02 0.01 0.02 0.02 0.02

ln TOUR ∗ ln Low -0.04 -0.03 -0.02 -0.03 -0.01

s.e. 0.03 0.03 0.03 0.03 0.03

ln LORD -0.19 -0.16 -0.03 -0.05 0.03 0.04 -0.11 -0.11 0.00 0.01

s.e. 0.23 0.23 0.18 0.18 0.20 0.19 0.20 0.20 0.21 0.20

ln ASEATCAP 0.10 0.10 0.21∗∗ 0.18∗∗ 0.28∗∗∗ 0.26∗∗∗

s.e. 0.10 0.10 0.09 0.09 0.09 0.09

ln WEEKEND 0.14 0.13 0.05 0.06 0.03 0.04 0.04 0.05 0.00 0.00

s.e. 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

SLOT 0.23∗∗∗ 0.21∗∗∗ 0.23∗∗∗ 0.17∗∗∗ 0.23∗∗∗ 0.16∗∗∗ 0.21∗∗∗ 0.18∗∗∗ 0.21∗∗∗ 0.16∗∗∗

s.e. 0.06 0.06 0.06 0.06 0.05 0.06 0.06 0.06 0.06 0.06

SMALL 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00 0.00

s.e. 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Obs 420 420 420 420 420 420 420 420 420 420

Note for Table 3: Instruments in Table 3a include ln AMEANPOPi and ln PASSRTEi. ‘s.e.’ represents

heteroscedastic robust standard errors. ‘t-statistic’ tests the hypothesis for the equality of the relative prices

between the two ticket types. ‘J-statistic’ tests the over-identification of the instrument set. Table 3b reports

part of the results from the regression where the distance is included in the instrument set.

29

Table 4. The relative price and competition: Extension of Regression (1)

[Third Quarter of 2006]

R O R O R O R Odln N 0.07∗∗ 0.08∗∗ 0.07∗∗ 0.09∗∗

s.e. 0.04 0.03 0.04 0.03

ln HERF −0.40∗∗∗−0.36∗∗∗−0.41∗∗∗−0.36∗∗∗

s.e. 0.08 0.08 0.08 0.08

ln MKTSHARE 0.18∗∗∗ 0.15∗∗∗ 0.19∗∗∗ 0.14∗∗∗

s.e. 0.04 0.04 0.04 0.04

ln FTOT −0.08∗∗∗−0.06∗∗∗−0.08∗∗∗ −0.05∗∗ -0.03 -0.02 -0.03 -0.01

s.e. 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

SW −0.15∗∗∗ -0.03 −0.15∗∗∗ -0.03 −0.19∗∗∗ -0.06 −0.19∗∗∗ -0.06

s.e. 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

ln DIST 0.10∗∗∗ 0.04∗ 0.10∗∗∗ 0.05∗∗ 0.14∗∗∗ 0.07∗∗∗ 0.14∗∗∗ 0.08∗∗∗

s.e. 0.02 0.02 0.03 0.03 0.02 0.02 0.02 0.02

HUB 0.61∗∗∗ 0.12 0.63∗∗∗ 0.19 0.68∗∗∗ 0.17 0.72∗∗∗ 0.26∗

s.e. 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14

HUB ∗ ln BUSI 0.61∗∗∗ 0.16 0.61∗∗∗ 0.23∗ 0.69∗∗∗ 0.23∗ 0.72∗∗∗ 0.31∗∗

s.e. 0.14 0.13 0.14 0.13 0.13 0.13 0.13 0.13

ln TOUR 0.02 0.02 0.02 0.02

s.e. 0.02 0.02 0.02 0.02

ln TOUR ∗ ln High -0.02 0.00 0.00 0.01

s.e. 0.01 0.01 0.01 0.01

ln TOUR ∗ ln Low -0.01 −0.04∗∗ -0.02 −0.05∗∗∗

s.e. 0.02 0.02 0.02 0.02

ln LORD -0.11 0.14 -0.10 0.14 -0.03 0.22∗ -0.02 0.21∗

s.e. 0.14 0.12 0.14 0.12 0.13 0.12 0.13 0.12

ln WEEKEND -0.01 0.14∗ -0.01 0.12 -0.04 0.12 -0.05 0.09

s.e. 0.08 0.09 0.08 0.09 0.08 0.08 0.08 0.08

SLOT 0.22∗∗∗ 0.15∗∗∗ 0.21∗∗∗ 0.16∗∗∗ 0.22∗∗∗ 0.14∗∗∗ 0.22∗∗∗ 0.17∗∗∗

s.e. 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

SMALL -0.01 -0.01 -0.02 -0.02 -0.02 -0.02 -0.03 -0.03

s.e. 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.03

Obs 927 989 927 989 927 989 927 989

Fitted Values 2.9 2.4 2.9 2.4 2.9 2.4 2.9 2.4

t-statistics 19.8 19.7 21.6 21.4

Note for Table 4: This table reports the results from the extension of regression (1) which includes the

competition measures. Instruments used in the regressions include ln AMEANPOPi, ln GMEANPOPi,

ln PASSRTEi, ln GENSP , ln IRUTHEREi. These instrumental variables are the same as BR except for

ln DISTi. ‘s.e.’ represents heteroscedastic robust standard errors. ‘t-statistic’ tests the hypothesis for the

equality of the relative prices between the two ticket types.

30

Table 5. Price dispersion and the relative price: Regression (2)

[Third Quarter of 2006]

(i) (ii) (iii) (iv) (i) (ii) (iii) (iv)

ln P R/P N 0.33∗∗∗ 0.39∗∗∗ 0.41∗∗∗ 0.47∗∗∗

s.e. 0.13 0.12 0.13 0.14

ln HERF −0.10∗∗ −0.09∗ −0.08∗ -0.08 0.12 0.15∗ 0.16∗ 0.17∗∗

s.e. 0.05 0.05 0.05 0.05 0.09 0.08 0.08 0.09

ln MKTSHARE 0.08∗∗∗ 0.07∗∗∗ 0.06∗∗ 0.05∗∗ -0.03 -0.04 -0.05 -0.05

s.e. 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.04

ln FTOT -0.01 0.00 -0.01 -0.01 0.03∗ 0.04∗∗ 0.04∗∗ 0.05∗∗

s.e. 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02

SW −0.04∗∗ −0.04∗∗ 0.02 0.02

s.e. 0.02 0.02 0.03 0.03

HUB 0.07∗∗∗ 0.25∗∗∗ 0.24∗∗∗ 0.24∗∗∗ 0.07∗∗∗ 0.00 -0.02 -0.05

s.e. 0.02 0.08 0.08 0.08 0.02 0.11 0.12 0.13

HUB ∗ ln BUSI 0.18∗∗ 0.18∗∗ 0.18∗∗ -0.07 -0.09 -0.12

s.e. 0.08 0.08 0.08 0.11 0.11 0.12

ln TOUR −0.07∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗

s.e. 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02

ln LORD 0.01 -0.07

s.e. 0.07 0.07

ln WEEKEND −0.17∗∗∗−0.16∗∗∗−0.16∗∗∗−0.16∗∗∗−0.19∗∗∗−0.20∗∗∗−0.21∗∗∗−0.19∗∗∗

s.e. 0.04 0.04 0.04 0.05 0.04 0.05 0.05 0.05

SLOT 0.08∗∗∗ 0.09∗∗∗ 0.08∗∗∗ 0.08∗∗∗ 0.01 0.00 0.00 -0.02

s.e. 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.04

SMALL 0.00 -0.01 0.00 0.00 -0.01 -0.01 -0.01 -0.01

s.e. 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Obs 992 992 992 992 992 992 992 992

Note for Table 5: We use the same instruments as BR and GS. ’s.e.’ represents heteroscedastic robust standard

errors.

31

Table 6. Price dispersion and the relative price: An alternative to Regression (2)

[Third Quarter of 2006]

(i) (ii) (iii) (iv) (i) (ii) (iii) (iv)

ln P R/P N 0.24∗∗∗ 0.26∗∗∗ 0.28∗∗∗ 0.31∗∗∗

s.e. 0.06 0.06 0.07 0.08dln N -0.03 -0.03 -0.01 -0.01 −0.04∗∗ −0.05∗∗ −0.05∗∗ −0.05∗∗

s.e. 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

ln FTOT 0.01 0.01 0.01 0.00 0.02∗∗ 0.02∗∗ 0.02∗∗ 0.03∗∗

s.e. 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

SW −0.06∗∗ −0.05∗∗ 0.00 0.01

s.e. 0.02 0.02 0.03 0.03

HUB 0.07∗∗∗ 0.28∗∗∗ 0.27∗∗∗ 0.26∗∗∗ 0.07∗∗∗ 0.07 0.06 0.04

s.e. 0.02 0.08 0.08 0.08 0.02 0.09 0.09 0.10

HUB ∗ ln BUSI 0.22∗∗∗ 0.20∗∗ 0.19∗∗ 0.00 -0.01 -0.03

s.e. 0.08 0.08 0.08 0.09 0.09 0.10

ln TOUR −0.07∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗−0.06∗∗∗

s.e. 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

ln LORD 0.06 -0.06

s.e. 0.06 0.06

ln WEEKEND −0.16∗∗∗−0.14∗∗∗−0.15∗∗∗−0.17∗∗∗−0.19∗∗∗−0.19∗∗∗−0.19∗∗∗−0.18∗∗∗

s.e. 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.05

SLOT 0.08∗∗∗ 0.09∗∗∗ 0.08∗∗∗ 0.08∗∗∗ 0.03 0.03 0.03 0.02

s.e. 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03

SMALL -0.01 -0.02 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01

s.e. 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Obs 992 992 992 992 992 992 992 992

Note for Table 6: ln HERF and ln MKTSHARE in regression (2) are replaced with dln N . See also note in

Table 5.

32

Figure 1: Patterns of one-way and round trip posted fares

7w 6w 5w 4w 3w 2w 1w 1d260

280

300

320

340

360

380

weeks before departure

price

s

Convergence of oneway−trip fares: AA

PN

PR

7w 6w 5w 4w 3w 2w 1w 1d100

150

200

250

300

350

400

weeks before departure

price

s

Convergence of round−trip fares: AA

PN

PR

Note: The data are obtained from the ticket booking websites, Expedia (www.expedia.com) and American

Airlines (www.aa.com). Our sample includes the posted fares of route LAX-ORD between October 24 and

November 6, 2006. There are 56 departure date-hours, and for each of the flight date-hours we have the

fare information starting from seven weeks till one day before the departure date. For example, if the flight

departure date is October 24, then the fare information covers 8 different time periods posted from September 5

to October 23. PR denotes the average refund fares and PN denotes the average of non-refund fares, measured

in the US dollars.

33