This paper estimates a Frequent Flyer Programs (FFP) price premium -- higher fares associated with a larger proportion of travelers using FFP. The results show that FFP affect the entire price distribution, but the effect is larger on lower end fares. In addition, airport dominance increases the premium on less expensive fares but has no effect on the premium associated with the right tail of the price distribution.Frequent Flyer Programs; Pricing; Airlines; Panel Data

Escobari, Diego

English

Research Papers in Economics

Frequent Flyer Programs Premium and the Role of Airport
Dominance
Diego Escobari
November 30, 2010
Abstract
This paper estimates a Frequent Flyer Programs (FFP) price premium|higher
fares associated with a larger proportion of travelers using FFP. The results show that
FFP aect the entire price distribution, but the eect is larger on lower end fares. In
addition, airport dominance increases the premium on less expensive fares but has no
eect on the premium associated with the right tail of the price distribution.
Keywords: Frequent Flyer Programs; Pricing; Airlines; Panel Data.
JEL Classications: C23; L11; L93.
1 Introduction
Frequent Flyer Programs (FFP) have grown signicantly since the introduction of AAdvan-
tage in 1981, the deregulation of the industry and the introduction of computer reservation
systems. It is calculated that FFP have more that 80 million participants. The largest
FFP in the U.S. are AAdvantage from American Airlines, Mileage Plus from United, and
Sky Miles from Delta, each having more than 20 million members.1 On the other hand,
deregulation also replaced the traditional point-to-point service with the hub-and-spoke
system raising the importance of hubs and airport dominance on pricing.
Existing empirical evidence, e.g. Borenstein (1989), shows how airport dominance in
u-
ences the carriers ability to charge higher prices. In a related paper, Lederman (2008) uses
Department of Economics & Finance, The University of Texas - Pan American, Edinburg, TX 78539,
Phone: (956) 665-3366, Fax: (956) 665-5020, Email: escobarida@utpa.edu
1For more on the description of these programs see www.frequent
ier.com.
1the formation of partnerships to estimate that at least 25% of the `hub premium'|higher
fares charged by hub airlines for 
ights originating at its hub|can be accounted by FFP.
Other papers on FFP include Lederman (2007) who looks at the eect of international
partnerships on domestic demand and Bilotkach (2009) who looks at partnerships and fre-
quency. Despite the importance of FFP, research in this area is scarce mainly because
information on individual miles balances are not available to researchers. This paper uses
a novel way to measure the extent of FFP to overcome this obstacle.
The current paper extends existing literature in three aspects. First, it provides an esti-
mate of the FFP premium|higher fares associated with FFP. Second, it assesses the eect
of FFP on the distribution of fares, and third, it evaluates the role of airport dominance
on the eect of FFP on dierent percentiles of fares. The results show that a one percent
increase in the proportion of travelers that use frequent 
yer programs increases average
fares by 1.16%. The eect is larger on the lower end of the price distribution than on more
expensive tickets. Moreover, the eect of FFP on lower end fares was found to be greater
when the carrier has a dominant position in the departing airport.
The rest of the paper is structured as follows. Section 2 describes the construction of
the data, Section 3 presents the empirical model and Section 4 provides the results. Finally,
Section 5 concludes.
2 Data
The data set for this paper comes from the market and the ticket sub-sections of the
DB1B database and the segment sub-section of the T-100 database from the Bureau of
Transportation and Statistics (BTS). The DB1B is a 10% random sample quarterly data of
airline passenger ticket transactions with information on the ticket price, origin, destination
and any connecting airports, carrier, type of ticket and service class. The T-100 has
information on number of performed departures as well as number of seats and transported
passengers between an origin and destination airport pair.
The paper focuses on domestic, round-trip, coach class tickets between the rst quarter
of 2000 and the third quarter of 2009. We restrict the analysis to round-trip tickets because
these tickets allow us to identify the originating airport of the ticket. To restrict the analysis
2to economically signicant routes, the sample includes only routes that had at least one
carrier transporting an average of 40 passengers per week, by either direct or connecting
service. The construction of the data set is such that each observation in the sample
corresponds to a route|a pair of origin and destination airports|served by a carrier on
a quarter. The carriers considered are AirTran, Alaska, American, Continental, Delta,
Frontier, JetBlue, Northwest, Spirit, Trans World Airlines, United, and US Airways, each
with its corresponding FFP partners. Because frequent 
yer miles can also be obtained
by traveling with a carrier's FFP partner, we identied the partners of each of the main
carriers and considered those tickets as belonging to the main carrier. e.g. American Eagle
miles count towards American Airlines' frequent 
yer program.
3 Empirical Model
To investigate the relationship between pricing and the extent of frequent 
yer programs,
we estimate the following reduced-form pricing equation:
logMeanfareijt =   Ffpijt + X0
ijt + t + "ij + ijt (1)
where each observation refers to route i, carrier j during quarter t. The dependent variable
log Meanfare is the logarithm of the average of fares. To further analyze the eect of
loyalty programs on the tails of the price distribution we will also use the log 20th and the
log 80th percentiles of fares as dependent variables, log 20Pctfare and log 80Pctfare,
respectively. The main variable of interest is Ffp, a measure of FFP. It is obtained as the
ratio of frequent 
yer tickets to the total number of tickets, where the number of frequent

yer tickets is the number of tickets with a price equal to zero as recorded in the DB1B
database.2 The vector of controls X0
ijt includes time-variant carrier-route characteristics
not captured by the xed eects. It includes Propdepaijt, a measure of airport dominance
that is constructed as the proportion of departures out of the departing airport in route j
that belong to carrier i during time t. Constructed in the same fashion, Propdestijt is the
proportion of nonstop destinations of carrier i out of the departing airport in route j and
2The typical approach in empirical studies that use the DB1B database is to control for tickets sold
through frequent 
yer programs by eliminating tickets prices below $10 or $20. See, for example, Borenstein
and Rose (1994) or more recently Gerardi and Shapiro (2009).
3Propdirectijt is the proportion of direct 
ights. Also in X0
ijt, Numdestijt is the total
number of destination of carrier i out of the departing airport on route j and Loadfactijt
is the load factor or capacity utilization. Finally, t denotes the unobservable time specic
eect, "ij captures any unobservable carrier-route time-invariant specic eect and ijt is
the remaining disturbance. The xed eects control, for example, for changes over time in
industry-level prices and for time-invariant hub eects on pricing.
A positive  coecient will be evidence of a positive FFP premium. To analyze the
role of airport dominance on the FFP premium we present two additional specications.
The rst includes the interaction term Ffpijt  Propdepaijt in Equation 1 to see how the
premium changes as a carrier increases its presence at the departing airport. The second
approach separates the eect of Fftijt on fares in two, when the proportion of departures
is below its median and when it is above its median:
logMeanfareijt = 1  Ffpijt  1[Propdepa > med(Propdepa)] (2)
+ 2  Ffpijt  1[Propdepa  med(Propdepa)] + X0
ijt + t + "ij + ijt:
The indicator variable 1[Propdepa > med(Propdepa)] is equal to one when Propdepaijt is above
its median, zero otherwise. The estimation of Equations 1 and 2 will additionally consider
various log percentiles of fares as dependent variables.
4 Estimation Results
The summary statistics of the variables is presented in Table 1 and the results from the
estimation of Equation 1 are presented in Table 2. All specications include time and
carrier-route xed eects. The gures in parentheses are cluster-robust standard errors,
clustered by airline. The positive coecient on Ffp in Column 1, that has log Meanfare as
the dependent variable, indicates that average fares are higher when the fraction of frequent

yer travelers is larger|positive FFP premium. In particular, this highly statatistically
signicant eect indicates that a one percent increase in the proportion of travelers who
use FFP increases average fares by 1.16%. To assess the eect of the programs on the tails
of the pricing distribution, Columns 2 and 3 present the estimates when the logs of the
20th and the 80th percentiles of fares are used as dependent variables. The results show
4that a one percent increase in the proportion of travelers who use FFP increases the 80th
percentile of fares by 1.03%, but increases the 20th percentile by 3.06%. This is evidence
that the programs have a larger eect on the lower tail of the price distribution.
To provide additional insights on the eect of the programs on the distribution of
fares, Figure 1 provides the estimates of  in the estimation of Equation 1 with the log of
percentiles 5 through 95 of fares (with increments of 5) as the dependent variable. The
shaded area is the 95% condence interval, * denotes signicance at 1% and the numbers
in parentheses are clustered robust standard errors. While the eect is fairly constant for
the 30th percentile and more expensive fares, the eect on the lower tail of fares is larger.
We now turn to the analysis of the role of airport dominance on the eect of the
programs on fares. The positive and highly signicant coecient on the interaction term
in Column 4 indicates that the eect of the programs on average fares is larger when the
carrier has a larger proportion of departing 
ights from the originating airport. Columns
5 and 6 show that the eect is larger for the lower tail of fares and that airport dominance
plays no role in the eect of the programs on more expensive fares. Figure 2 takes an
additional step and shows the estimated ^ 1 and ^ 2 from Equation 2 at various log of
percentiles of fares as the dependent variable. While the left-hand-side axis shows the
values of the estimates, the right-hand-side axis show the p-values for the null of 1 = 2.
The results indicate that the eect of the programs on the lower tail of fares is larger when
the proportion of departures is larger than its median. There is no statistically signicant
dierence between 1 and 2 for higher end fares.
5 Conclusions
Our measure of frequent 
yer programs shows an intuitive positive and highly statistically
signicant FFP premium. While the positive premium aects the entire price distribution,
the eect was found to be larger for lower end fares. A one percent increase in the proportion
of travelers who use frequent 
yer miles increases average fares by 1.16% and increases the
20th percentile and the 80th percentile of fares by 3.06% and 1.03%, respectively. Airport
dominance showed to play an important role by increasing the FFP premium only on lower
end fares.
5Table 1: Summary Statistics
(1) (2) (3) (4)
VARIABLES mean sd min max
Ffp 0.024 0.042 0.000 0.699
Meanfare 173.4 57.52 27.37 774.9
20Pctfare 110.5 41.37 1.000 676.0
80Pctfare 225.4 92.45 33.00 1,150
Propdepa 0.136 0.146 0.000 1.000
Propdest 0.411 0.270 0.007 1.000
Propdirect 0.459 0.462 0.000 1.000
Numdest 29.60 31.02 1.000 135.0
Loadfact 0.745 0.101 0.000 1.000
Notes: Number of observations: 236,919.
Table 2: Regression Results
(1) (2) (3) (4) (5) (6)
log of log of log of log of log of log of
VARIABLES Meanfare 20Pctfare 80Pctfare Meanfare 20Pctfare 80Pctfare
Ffp 1.158* 3.057* 1.029* 0.981* 1.541* 0.929*
(0.0959) (0.596) (0.0965) (0.125) (0.436) (0.129)
Ffp  Propdepa 0.874* 7.494* 0.496
(0.243) (1.831) (0.374)
Propdepa 0.132 0.369y 0.137 0.0713 -0.148 0.103
(0.134) (0.132) (0.165) (0.129) (0.0904) (0.159)
Propdest 0.0321 0.110 0.0218 0.0303 0.0948 0.0208
(0.0481) (0.0959) (0.0572) (0.0479) (0.0991) (0.0571)
Propdirect -0.0159 0.0925z -0.0460* -0.0158 0.0933z -0.0459*
(0.0158) (0.0428) (0.0147) (0.0158) (0.0438) (0.0147)
Numdest/103 -0.0430 -1.430 0.179 0.0522 -0.613 0.233
(0.437) (1.820) (0.453) (0.403) (1.850) (0.423)
Loadfact -0.131y 0.356* -0.231y -0.134y 0.337* -0.232y
(0.0471) (0.0910) (0.0751) (0.0465) (0.0928) (0.0748)
Within R-squared 0.190 0.051 0.132 0.192 0.060 0.132
Notes: The dependent variable is the log of Meanfare in Columns 1 and 4, the log of 20Pctfare in
Columns 2 and 5 and the log of 80Pctfare in Columns 3 and 6. Number of observations: 236,919. Figures
in parentheses are cluster-robust standard errors, clustered by airline. z signicant at 10%; y signicant at
5%; * signicant at 1%.
66.371*
(1.017)
3.057*
(0.596)
4
5
6
7
8
9
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0.938*
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0.798*
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0.854*
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1.333*
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 0            10            20            30           40            50            60            70           80            90          100
E
f
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Fare Percentile 
Figure 1: Eects of Fft on the log of fare percentiles. The shaded area is the 95% condence interval. *
signicant at 1%. The numbers in parentheses are cluster-robust standard errors, clustered by airline.
7.472*
(1.096)
3.675*
(0.664) 4.210*
(0.802)
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0.788*
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0.836*
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(0.107)
1.307*
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-2.6
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-2.2
-2.0
-1.8
-1.6
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1
2
3
0 10 20 30 40 50 60 70 80 90 100
Fare Percentile 
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Figure 2: Eects of Fft on the log of fare percentiles. * signicant at 1%. The numbers in parentheses
are cluster-robust standard errors, clustered by airline.
7References
Bilotkach, V. 2009. Parallel Frequent Flier Program Partnerships: Impact on Frequency.
Working Paper. University of California, Irvine.
Borenstein, S. 1989. Hubs and High Fares: Dominance and Market Power in the U.S.
Airline Industry. Rand Journal of Economics 20, 344-365.
Borenstein, S. and N.L. Rose, 1994. Competition and price dispersion in the U.S. airline
industry. Journal of Political Economy 102, 653-683.
Gerardi, K.S. and A.H. Shapiro, 2009. Does Competition Reduce Price Dispersion? New
Evidence from the Airline Industry. Journal of Political Economy 117, 1-37.
Lederman, M., 2007. Do Enhancements to Loyalty Programs Aect Demand? The Impact
of International Frequent Flyer Partnerships on Domestic Airline Demand. Rand Journal
of Economics 38, 1134-1158.
Lederman, M., 2008. Are Frequent Flyer Programs a Cause of the `Hub Premium'. Journal
of Economics and Management Strategy 17, 35-66.
8

Frequent flyer programs premium and the role of airport dominance

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