HOW DO INCUMBENTS RESPOND TO THE THREAT
OF ENTRY? EVIDENCE FROM THE MAJOR AIRLINES∗
AUSTAN GOOLSBEE AND CHAD SYVERSON
We examine how incumbents respond to the threat of entry by competitors (as
distinct from how they respond to actual entry). We look specifically at passenger
airlines, using the evolution of Southwest Airlines’ route network to identify particular
routes where the probability of future entry rises abruptly. We find that
incumbents cut fares significantly when threatened by Southwest’s entry. Over
half of Southwest’s total impact on incumbent fares occurs before Southwest starts
flying. These cuts are only on threatened routes, not those out of non-Southwest
competing airports. The evidence on whether incumbents are seeking to deter or
accommodate entry is mixed.
I. INTRODUCTION
In this paper we examine how incumbents respond to the
threat of entry by a competitor. Though this topic has been the object
of considerable theoretical and policy debate, it has received
little empirical attention, mainly due to the problems of identifying
the threat of entry separately from actual entry.
We will examine this issue in the passenger airline industry.
We are able to identify discrete shifts in the threat of entry in
this circumstance by using the expansion patterns of the industry’s
most famous potential competitor—Southwest Airlines.1
In
particular, we look at situations where Southwest begins or even
announces it will begin operating in the second endpoint airport
of a route (having already been operating out of the first endpoint),
but before it starts flying the route itself. We investigate
how incumbents respond to such threats.
∗We thank Gary Becker, Severin Borenstein, Dennis Carlton, Edward Glaeser,
Robert Gordon, Justine Hastings, Elena Krasnokutskaya, Mara Lederman, Chris
Mayer, Nancy Rose, Fiona Scott Morton, Mike Whinston, anonymous referees, and
seminar participants at University of California San Diego, Chicago, Dartmouth
(Tuck), Illinois, Minneapolis Federal Reserve Bank, Minnesota, Massachusetts Institute
of Technology, North Dakota, Northwestern, Pittsburgh, Rochester, Wharton,
Wisconsin, Yale, the Society for Economic Dynamics Annual Meeting, the
Harvard Business School Strategy Conference, the NET Institute Conference, and
the joint Federal Reserve Bank-George Washington University seminar for helpful
comments. Brian Melzer and Luis Andres provided superior research assistance.
Financial support from the NET Institute (http://www.NETinst.org) is gratefully
acknowledged. Goolsbee also thanks the National Science Foundation, the Initiative
on Global Markets, and the American Bar Foundation for financial support.
1. Southwest’s network has been expanding rapidly for some time and the
impact of their actual entry on prices in a market is well documented (see, for
example, Morrison [2001]).
C 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of
Technology.
The Quarterly Journal of Economics, November 2008
1611
1612 QUARTERLY JOURNAL OF ECONOMICS
As an illustrative example, consider Southwest’s entry into
Washington Dulles International Airport. On October 5, 2006,
Southwest began operations at Dulles (IAD) with nonstop flights
to four other cities in its network, and one-stop service to several
others. One route Southwest did not offer service on immediately
upon entering Dulles was IAD-Cleveland (CLE). Cleveland
is a Southwest airport; the airline flew between CLE and other
airports, just not the CLE-IAD route. But once Southwest announced
and then began operating out of both endpoints on the
route—CLE first and now IAD—we might expect that competitors
would understand that the probability that Southwest would
soon start flying the route itself had risen dramatically. We will
document that operating in both endpoints is correlated with a
probability of entering the route around 70 times higher than for
other routes. With that increase in probability, we can then look
at, say, United and Continental Airlines’ (the incumbents) fares
on the CLE-IAD route when faced with the threat of Southwest’s
entry by looking at prices around the time Southwest starts operations
in the second endpoint airport, but when it has yet to
actually start flying.2
The paper builds on the extensive literature on airline competition,
especially the work relating to airport presence and the
sources of airline market power. Most of these papers have not
looked at preemptive actions or the threat of entry but rather
at market behavior after entry occurs.3
Our empirical strategy
is perhaps closest to Whinston and Collins’s (1992) study of the
impact on incumbents’ stock prices of announcements of People
Express’s impending (actual) entry into particular routes.
The paper provides an empirical setting for testing the
considerable body of theoretical work on strategic entry deterrence
and accommodation, particularly that offering rationale
for preemptive action. These include, for example, Dixit’s (1979)
capacity commitment story, the strategic learning-by-doing of
Spence (1981), cost-signaling as in Milgrom and Roberts (1982),
Aghion and Bolton’s (1987) long-term contracting environment,
and switching costs as in Klemperer (1987) and Farrell and
2. We use the IAD example for purposes of illustration. It occurred too recently
to be included in our actual sample.
3. Examples include Borenstein (1989, 1991, 1992); Hurdle et al. (1989); Reiss
and Spiller (1989); Berry (1990, 1992); Brueckner, Dyer, and Spiller (1992); Whinston
and Collins (1992); Evans and Kessides (1993); Borenstein and Rose (1994);
Peteraf and Reed (1994); Hendricks, Piccione, and Tan (1997); Mayer and Sinai
(2003); and Bamberger, Carlton, and Neumann (2004)
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1613
Klemperer (2004). These rationales were put forward to counter
the traditional argument that preemptive action is irrational,
either because it is not subgame perfect (in the spirit of Selten’s
[1978] chain-store paradox), or because costly competitive actions
should be delayed until entry actually occurs.
Our primary results indicate that incumbents in the airline
industry do respond to the threat of entry. Incumbents drop average
fares substantially when Southwest threatens a route (before
Southwest actually starts flying the route). This is true even when
we compare the fare changes on threatened routes to those on incumbents’
other routes out of the same airports, suggesting that
shifts in airport-specific operating costs are not driving the results.
The lower prices, in turn, appear to increase the number of
passengers flying on the incumbents prior to entry. We also find,
interestingly, that although incumbents cut fares on the directly
threatened route, they do not cut prices on routes to nearby airports
in the same market (e.g., no cuts on the Chicago-O’Hare
route when Southwest threatens a Chicago-Midway route).
Going beyond the fact of preemptive action, we also present
suggestive evidence on the explanation for preemptive fare cuts.
The evidence of an entry deterrence motive is mixed. We find little
support for strategic investment/excess capacity theories of preemptive
action: there is at best weak evidence that airlines add capacity
in response to entry threats. Consistent with deterrence, on
the other hand, is that on routes where Southwest’s entry is guaranteed,
and deterrence therefore not possible, incumbents do not
appear to cut prices sharply before actual entry occurs. However,
we do not have enough statistical power to reject the possibility
that they make the same price cuts as on threatened routes.
II. DATA
We build the core of our sample from the U.S. Department of
Transportation’s DB1A files from the first quarter of 1993 through
the final quarter of 2004. These data provide a 10% sample of all
domestic tickets in each quarter. From these, average logged ticket
prices and the logged total number of passengers within each
route-carrier-quarter combination are constructed (unfortunately
the data do not report specific travel dates within the quarter).4
4. We use Severin Borenstein’s cleaned files, which are already aggregated up
to the route-carrier-quarter level, because this is the level of our analysis, rather
1614 QUARTERLY JOURNAL OF ECONOMICS
FIGURE I
Identifying a Threatened Incumbent Route
We define a route by its two endpoint airports and we look at socalled
“direct flights” on a route. (Direct flights are predominantly
nonstop flights but technically also include itineraries where the
passenger stops but does not change planes.) We restrict our core
sample to routes between the 59 airports that Southwest ever flies
any flights to in our sample.
The threatened entry events that we study are identified from
the observed expansion patterns of Southwest Airlines. Southwest
grew tremendously over our sample period, with its revenue and
passenger volumes almost tripling from $2.3 to $6.5 billion and
from 18.8 to 53.4 billion passenger-miles. It also added service to
22 new airports.5
Every time Southwest begins service in a new airport, it
raises the threat that Southwest will enter routes connecting
that airport with other airports in its network. We illustrate this
in Figure I. Southwest enters Washington Dulles in the fourth
quarter of 2006 and immediately begins flights to Chicago Midway.
Southwest is already flying out of Cleveland to other points
in its network besides Dulles. Now, though, its entry into Dulles
makes Southwest much more likely to start flying the IAD-CLE
than the individual ticket. Note that because the DB1A data are a 10% random
sample of all tickets, our observed passenger counts will be one-tenth those of
actual traffic on average.
5. Southwest exited one airport during our sample, San Francisco International
(SFO), in 2001. It had operated there since before 1993.
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1615
TABLE I
PROBABILITY OF SOUTHWEST’S ENTRY INTO A ROUTE
Southwest operates in one endpoint airport in the previous quarter 0.0025
(single presence) (0.0002)
Southwest operates in both endpoint airports in the previous quarter 0.1851
(dual presence) (0.0203)
N 163,952
Notes. The table shows marginal effects estimates from a probit estimation for Southwest’s entry into
a route in a particular quarter, conditional on the number of the route’s endpoint airports served by Southwest
in the previous quarter. The excluded category includes observations where Southwest does not serve
either endpoint airport in the previous quarter. Quarter fixed effects are included. Standard errors are in
parentheses.
route in the near future.6
Actually, even the announcement of the
initial entry into Dulles ought to indicate to incumbents that the
probability of future entry has risen.
Airport presence is a well-known predictor of future route
entry.7
To verify the impact of having a presence in both route
endpoints in our own sample, we present in Table I the estimated
marginal effects from a simple probit regression of whether Southwest
starts flying a route in a given quarter, conditional on the
number of endpoints at which Southwest is already operating in
the previous quarter. (Quarter dummies are also included in the
regression.) This is only a descriptive exercise, of course, not an
explicit model of Southwest’s entry decision. The results show that
having a presence in one airport is correlated with a probability of
entry that is small but significantly higher than the baseline probability
(which is close to zero). Having a presence in both airports,
however, raises it by another factor of 70—to 18.5% per quarter.
At any point in time in our sample, we take the existing airport
service network as given and look at incumbents’ fares on a
6. We used the local business press and press releases (via Lexis-Nexis) to
confirm that Southwest did in fact start service at the airports where Southwest’s
entry is indicated in the DB1A data. We discovered through this search an important
airport error in the DB1A data. In several quarters, the DB1A source data
indicate that Southwest operated flights out of Dallas/Ft. Worth Airport (DFW)
in the late 1990s for a few quarters and then exited. Although the data show
them flying from DFW to many different airports, the airline code for DFW must
be mistaken. There is no record of Southwest operating these numerous flights
out of DFW in the local business press at the time or in other Department of
Transportation data such as the T-100 (see the capacity section below) and the
On-Time Performance Data. We therefore dropped these DFW observations from
the sample.
7. See Bailey (1981) for a narrative of a particular episode where this idea
was applied in antitrust policy toward the industry. Empirical work that has used
endpoint airport presence as a potential predictor of entry (albeit in the cross
section rather than within particular markets over time) includes Berry (1992)
and, in a paper themed similarly to ours, Peteraf and Reed (1994).
1616 QUARTERLY JOURNAL OF ECONOMICS
route once it becomes clear that Southwest is looming as a competitor.
We capture the price responses to threatened entry using
dummies in the quarters surrounding Southwest’s establishment
of operations in both endpoint airports (but without flying the
route) and control for actual entry with dummies in quarters
during and after Southwest starts flying the route. We restrict
our attention to the major carriers operating during our sample:
American, Continental, Delta, Northwest, TWA, United, and US
Airways.
We observe hundreds of routes threatened with entry over
the period. In most of these, Southwest eventually starts flying
the route later in our sample; in others, Southwest establishes a
presence in both airports but has not yet begun flying the route
by the end of our observation period (up to three years later).
We exclude routes from our sample where Southwest establishes
a second endpoint airport presence simultaneously with actually
flying the route. In such cases we do not have a clear period to
identify the heightened threat of entry separately from actual
entry. We will, however, look at such routes below when discussing
the issue of entry deterrence.
For each route in our sample, we look at the 25-quarter window
surrounding the quarter in which Southwest establishes a
presence in both endpoints (three years before to three years
after) and define Southwest’s actual entry as occurring when it
establishes direct service—i.e., flights without a change of plane—
between the two airports.8
This follows the findings from U.S. antitrust
authorities that nonstop service and connecting service can
be considered separate markets, or at least substantially differentiated
products. However, we did find similar results defining
entry as also including change-of-plane service.
In all, we observe Southwest threatening entry into 704
routes over the sample period, 533 of which Southwest had actually
entered with direct flights by the final quarter of 2004,
the end of our observation period. This yields over 19,000 routecarrier-quarter
observations of average logged fares and passenger
counts for major airlines’ direct flights on threatened routes.
The standard deviation of average logged fares across observations
is 0.45, and for logged passengers it is 2.02.
8. Several routes are not in the sample for the full 25-quarter period, either
due to truncation at the beginning of the sample—we exclude any routes that
are truncated by the end of the sample—or because the airline does not fly them
during the entire window.
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1617
III. HYPOTHESES AND EMPIRICAL SPECIFICATIONS
Our baseline model measures the impact of Southwest establishing
a presence in both endpoints of a route by looking at the
periods before, during, and after this event, while controlling for
other influences—like a standard event study. The basic specification,
with some slight abuse of summation notation as explained
below, is as follows:
yri,t = γri + µit +
3+
τ=−8
βτ (SW in both airports)r,t0+τ
+
3+
τ=0
βτ (SW flying route)r,te+τ + Xri,tα + εri,t,(1)
where yri,t is the outcome of interest (e.g., mean logged fares)
for incumbent carrier i flying route r in quarter t. SW in both
airportsr,t0+τ are time dummies surrounding the period when
Southwest establishes a presence in both endpoints of a route
but without flying the route. SW flying router,te+τ are time dummies
that commence in the period when Southwest actually starts
flying the route. The dummies are mutually exclusive, so the implied
effects on the dependent variable given by their coefficients
are not additive. γri and µit are carrier-route and carrier-quarter
fixed effects, respectively. Some specifications also include a set of
controls Xri,t.
In all regressions, we weight observations by the average
number of passengers flying the route-carrier over the sample.
This allows us to measure the “aggregate” responses to Southwest’s
entry (and is particularly important when we look at passenger
volume responses, because logged passenger numbers are
particularly volatile on low-traffic routes). We also cluster the
standard errors by route-carrier to account for intertemporal correlation
in the error terms.
The covariates of interest for determining the impact of
threatened entry on incumbents’ prices are the SW in both
airportsr,t0+τ coefficients. We include these dummies for eight
quarters prior to the quarter when Southwest establishes dual
endpoint presence on the threatened route, and for this establishment
quarter (which we denote as t0) itself. We also include
dummies for the two quarters after t0, and a single dummy for
the period three or more quarters after t0. These post-t0 dummies
1618 QUARTERLY JOURNAL OF ECONOMICS
take a value of one only if Southwest has not yet entered the
route. Essentially, because we include route-carrier fixed effects
in the regressions, reported coefficients show the relative sizes
of the dependent variable in the dummy period relative to its
average value in the excluded period between two and three
years (that is, the ninth through twelfth quarters) prior to t0.
As discussed above, the conventional, static-model view of
threatened entry is that incumbents should not respond until they
actually face competition. This notion, in the spirit of the classic
Chicago School critiques of limit pricing, is based on the seemingly
simple proposition that incumbents should not cut prices
before they have to. Doing so entails losing profits in the short
run and has no impact on future profits. This view implies that
the coefficients on the entry threat period dummies should be
zero.
For preemptive action to be rational, there needs to be some
mechanism tying Southwest’s actions before Southwest enters to
the market in later periods. Incumbents might (as discussed in the
literature cited above) be trying to deter entry or to accommodate
entry but weaken the new entrant once it enters. The difficulties
of testing between various theories of strategic behavior are well
known. Nevertheless, we will offer some suggestive evidence as
to incumbents’ intentions for preemptive action. We do not find
evidence of strategic investment in excess capacity. We also look
at routes that Southwest has precommitted to enter, finding suggestive,
but not definitive, evidence that incumbents do not cut
prices as aggressively until entry actually occurs.
IV. MAIN RESULTS: DOCUMENTING PREEMPTIVE ACTION
Column (1) of Table II presents the results from estimating
specification (1) using the average logged fares on incumbent carriers’
routes that are faced with the threat of entry by Southwest.
Incumbent fares drop significantly before Southwest begins flying
the route. By the time Southwest starts operating on both sides of
the route (period t0), prices are 17% lower (exp(−0.186) = 0.830)
than in the excluded period (recall that the coefficients show the
value of average logged fares relative to their average over the
ninth through twelfth quarters before t0). As time passes without
Southwest entering, prices fall further. On routes where Southwest
threatens but does not enter for at least three quarters, fares
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1619
TABLE II
INCUMBENT RESPONSES TO THE THREAT OF ENTRY
(1) (2) (3)
ln(P) ln(Q) Cost controls
Southwest in both airports (no flights) −0.030 −0.177∗∗ −0.025
t0 − 8 (0.024) (0.088) (0.024)
Southwest in both airports (no flights) −0.071∗∗ −0.155 −0.053∗
t0 − 7 (0.030) (0.110) (0.029)
Southwest in both airports (no flights) −0.065∗ 0.013 −0.059
t0 − 6 (0.035) (0.103) (0.037)
Southwest in both airports (no flights) −0.079∗ 0.083 −0.072
t0 − 5 (0.044) (0.119) (0.046)
Southwest in both airports (no flights) −0.100∗ 0.068 −0.093∗
t0 − 4 (0.049) (0.134) (0.051)
Southwest in both airports (no flights) −0.142∗∗ 0.097 −0.137∗∗
t0 − 3 (0.056) (0.146) (0.059)
Southwest in both airports (no flights) −0.132∗∗ 0.072 −0.123∗∗
t0 − 2 (0.056) (0.159) (0.061)
Southwest in both airports (no flights) −0.135∗∗ 0.165 −0.125∗
t0 − 1 (0.065) (0.193) (0.071)
Southwest in both airports (no flights) −0.186∗∗ 0.196 −0.162∗∗
t0 (0.073) (0.201) (0.079)
Southwest in both airports (no flights) −0.215∗∗ 0.240 −0.185∗∗
t0 + 1 (0.073) (0.217) (0.080)
Southwest in both airports (no flights) −0.228∗∗ 0.123 −0.201∗∗
t0 + 2 (0.075) (0.223) (0.082)
Southwest in both airports (no flights) −0.277∗∗ 0.167 −0.243∗∗
t0 + 3 to t0 + 12 (0.079) (0.224) (0.085)
Southwest flying route −0.237∗∗ 0.267 −0.211∗∗
te (0.082) (0.239) (0.091)
Southwest flying route −0.288∗∗ 0.224 −0.260∗∗
te + 1 to te + 2 (0.087) (0.232) (0.095)
Southwest flying route −0.344∗∗ 0.329 −0.316∗∗
te + 3 to te + 12 (0.113) (0.271) (0.117)
Operating cost control, 0.106
endpoint airport 1 (0.065)
Operating cost control, 0.158∗∗
endpoint airport 2 (0.048)
N 19,414 19,414 18,176
Notes. The dependent variable in columns (1) and (3) is the passenger-weighted average logged fares. In
column (2) it is logged total passengers. Standard errors are in parentheses and are clustered by route-carrier.
The sample includes all routes where Southwest threatens entry, as defined in the text. The “Southwest in
both airports” dummies denote Southwest having flights involving airports on both ends of a route previous to
actually flying the route. The “Southwest flying route” dummies denote Southwest actually operating flights
on the route. ∗Denotes significance at a 10% level. ∗∗Denotes significance at a 5% level.
1620 QUARTERLY JOURNAL OF ECONOMICS
are 24% below the excluded period, and are significantly lower
than the average level at t0.9
Prices are also lower in quarters before t0 than in the excluded
period. Imprecision in our estimates precludes pinning down an
exact price breakpoint, but the patterns suggest that prices begin
to fall around the third quarter before t0. A t0 − 3 breakpoint
is bolstered by the fact the coefficients for t0 − 3 and t0 − 2 are
significantly different at a 5% level from the average coefficients
for periods t0 − 8, t0 − 7, and t0 − 6, whereas the t0 − 4 coefficient
is not. Regardless of the specific period, it is not surprising that
prices begin to fall before t0. The relevant time period should be
when the incumbents first realize that Southwest’s chances of
entering a route have risen. Southwest announces entry months
before the entry actually occurs in order to advertise, sell tickets,
hire workers, and such. Southwest’s entry into Washington
Dulles, for example, was publicly announced six months before
the first day of operations. (This lead time was typical of what we
found in examining the business press for several such episodes in
our sample.) Further, industry insiders are likely to find out about
impending entries before the public announcement, as Southwest
must negotiate gate leases with the airport authority and so on.10
Once Southwest actually enters the route at time te (seen in
Table II in the Southwest flying route coefficients), prices fall 21%
below the baseline and then continue falling to over 29% by the
end of the period. The entry-threat and actual-entry coefficients
are not additive, so the price drop due to Southwest’s actual entry
is the difference between the preemptive 17% price drop and the
ultimate 29% price drop.11
9. This continued decline is not the subject of our analysis because it may
reflect selection issues rather than strategic behavior. If Southwest waits longer to
enter routes where incumbents cut fares the most, this could skew the coefficients
negatively and provide a natural alternative explanation to the strategic view that
Southwest’s entry hazard is increasing in the delay since entering the airport,
thereby causing larger price cuts by the incumbents.
10. This early response to impending discount carrier arrival at an airport
is also supported by anecdotal evidence; Johnsson (2006) documents that the announcement
that JetBlue would begin operating at Chicago O’Hare was greeted
with fare cuts by American and United (the two incumbents with hubs at O’Hare)
some months before JetBlue began operations.
11. Notice that the te results reflect a convex combination of threatened and
actual entry responses, because entry does not generally occur on the first day of
the quarter. Therefore some of the underlying microdata for the quarter reflect
patterns prior to actual entry. Also, although all of the post-entry coefficients correspond
to periods after the price drops reflected in the threat of entry coefficients,
the time difference between t0 and te varies across routes. Southwest actually enters
some threatened routes one quarter after t0, others several quarters after, and
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1621
The results suggest that preemptive price cuts are important.
More than half the total price effect that Southwest Airlines has
on incumbents’ prices takes place before Southwest ever actually
starts flying the route.
We deepen this initial analysis in several ways. The first looks
at the number of passengers flying on the incumbents’ threatened
routes during this period of preemptive price cutting. We expect
the number of passengers flying on the incumbent carriers to rise
in response to these lower prices. Column (2) of Table II presents
results where the log number of passengers is the dependent variable.
The estimates are imprecise, but the point estimates suggest
that passenger traffic rises on threatened routes in the period
before and around when Southwest enters the second endpoint
airport. Comparing the fare and quantity changes from their
baselines over the periods surrounding t0 suggests that the magnitude
of the quantity response is roughly twice that of the fare
changes, suggesting a demand “elasticity” of around −1.12
We will
see below that an independent data source on passenger volumes
documents similar quantity movements during the relevant time
frame.
Our second expansion in the benchmark results considers
the potential role of cost shocks as an alternative explanation. For
example, if Southwest chooses to enter airports where operating
costs are falling, this will lead to a spurious correlation between
our measure of Southwest’s threat of entry and the decline in
incumbents’ fares. To account for such cost shocks, we control
in the price regressions for the incumbents’ passenger-weighted
average fares (normalized per mile to facilitate aggregation across
routes) on other routes involving the same airports on one end but
non-Southwest airports on the other.
We illustrate the principle behind the routes in control groups
in Figure II. In the Washington Dulles-Cleveland example, one
control is the average logged fare on (say) United’s routes between
still others it does not enter at all (at least by the end of our three-year window).
The estimated impact of Southwest entry seen here is a bit smaller than that
estimated in some previous work such as Morrison (2001). In that case, though,
he estimates fare impacts using fare variation across routes rather than within a
route across time as we do here. Our sample is also a selected one, because our
results here exclude the routes that Southwest enters immediately. (We do look
at these immediate-entry routes below when examining why preemptive actions
occur.)
12. This is not exactly a carrier-route-specific elasticity, because we are not
holding the prices of competitors fixed.
1622 QUARTERLY JOURNAL OF ECONOMICS
FIGURE II
Comparison Routes for CLE-IAD
IAD and airports to which Southwest doesn’t fly (we restrict
alternative airports to those in the top 100 to be comparable),
and the second is similarly defined for routes between CLE and
non-Southwest airports. These two sets of routes (referred to as
the “operating cost controls” in the table) should embody any
airport-specific operating cost shocks at either of the route’s
endpoint airports. Once they are controlled for, the observed price
changes should then reflect the causal impact of Southwest’s
threatened and actual entry.
Column (3) of Table II shows the results of this specification.
The cost controls have significant and positive coefficients, as one
would expect. That is, when an incumbent’s fares rise on nonthreatened
routes from an airport, its fares also rise on the threatened
route out of that city. The rest of the coefficients in column
(3) are slightly smaller in magnitude than in column (1), but still
statistically significant and economically substantial—incumbent
fares are down 15% from the excluded period by t0, for example.
Most of the preemptive price cuts by the incumbents therefore
appear to be restricted to the threatened routes from the airport.
We also check whether our results are somehow being driven
by our choice of a three-year event window or by the choice of the
baseline comparison period. In Table III we estimate a specification
that expands the event window out to four years before t0 and
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1623
TABLE III
BASELINE ESTIMATES WITH A LONGER EVENT WINDOW
Dependent variable: ln(p)
Southwest in both airports (no flights)
t0 − 14 −0.021
(0.023)
t0 − 13 −0.045
(0.033)
t0 − 12 −0.033
(0.037)
t0 − 11 −0.009
(0.040)
t0 − 10 −0.012
(0.040)
t0 − 9 −0.025
(0.045)
t0 − 8 −0.042
(0.050)
t0 − 7 −0.082
(0.052)
t0 − 6 −0.078
(0.058)
t0 − 5 −0.080
(0.070)
Period incumbent learns of t0 − 4 −0.111
increase in Pr(SW Entry) (0.073)
t0 − 3 −0.150∗
(0.079)
t0 − 2 −0.140∗
(0.081)
t0 − 1 −0.146
(0.090)
t0 −0.189∗
(0.097)
t0 + 1 −0.220∗∗
(0.097)
t0 + 2 −0.234∗∗
(0.100)
t0 + 3 to t0 + 12 −0.277∗∗
(0.103)
Southwest flying route
te −0.240∗∗
(0.106)
te + 1 to te + 2 −0.288∗∗
(0.112)
te + 3 to te + 12 −0.340∗∗
(0.139)
N 20,979
Notes. This table shows estimates from passenger-weighted average logged fares for our baseline sample,
but with an expanded event window (see text for details). All regressions include route-carrier and carrierquarter
fixed effects. Standard errors are in parentheses and are clustered by route-carrier. See also Table II
notes for variable definitions. ∗Denotes significance at a 10% level. ∗∗Denotes significance at a 5% level.
1624 QUARTERLY JOURNAL OF ECONOMICS
breaks the timing dummies out to t0 − 14 (the excluded period is
therefore the fifteenth and sixteenth quarters before t0). The results
are consistent with the baseline findings. Prices begin to be
significantly lower than the baseline period three quarters before
t0, and fall more thereafter. The magnitudes of the price drops are
similar to the earlier results as well.13
Finally, we examine the passenger traffic when Southwest
threatens entry to a metropolitan area’s secondary airport (which
tends to exist only in the largest markets). In such cases, the incumbents’
price cuts at the directly threatened (i.e., secondary)
airport ought to draw passengers away from the main airport.
To examine this, we look specifically at routes flying out of
LaGuardia, JFK, and Newark airports (when Southwest threatens
entry into routes from Islip, Long Island), Miami (Southwest:
Ft. Lauderdale), Reagan National and Washington Dulles (Southwest:
BWI; recall that the sample ends before Southwest’s entry
into Dulles), Boston (Southwest: Providence and Manchester), and
Chicago O’Hare (Southwest: Midway). We must exclude the Los
Angeles, San Francisco, Houston, and Dallas markets from this
regression because, during our sample period, Southwest operated
in virtually all the airports in these metro areas, or else regulation
prevented competition.14
We date the entry threat from Southwest’s actions in the
other airport. So, for example, when Southwest started operations
in Orlando in 1994, they were operating on both endpoints of the
Orlando-Chicago Midway route without flying the route itself.
Our previous results examined incumbent responses on the
Orlando-Midway route; here we look at Orlando-O’Hare, even
though Southwest does not fly to O’Hare.
Columns (1) and (2) of Table IV look at the prices and
passenger counts, respectively, of incumbents on these nearby
but not threatened routes. There are no significant patterns in
either series. Coefficients on the time dummies tend to be small
13. An analogous specification using logged passenger traffic as the dependent
variable gave qualitatively similar results.
14. Southwest operates in the four largest Los Angeles airports: Burbank,
Orange County, Ontario, and LAX. Long Beach was the only neighboring airport it
did not fly into and has only a tiny amount of incumbent major airline traffic in our
sample. In the San Francisco Bay area, Southwest operated in the Oakland, San
Jose, and San Francisco airports for most of our sample (until exiting from SFO in
2001). Southwest operates in both Houston Hobby and Houston Intercontinental.
In Dallas, flights from Love Field to anywhere but Alabama, Arkansas, Kansas,
Louisiana, Mississippi, New Mexico, Oklahoma, and Texas were prohibited by law
during our sample, so competition with DFW is quite limited.
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1625
TABLE IV
RESULTS FOR “NEARBY” ROUTES
(1) (2)
ln(p) ln(q)
nearby airport nearby airport
Southwest in both airports (no flights) 0.016 0.097
t0 − 8 (0.013) (0.050)
Southwest in both airports (no flights) 0.038 −0.042
t0 − 7 (0.016) (0.055)
Southwest in both airports (no flights) 0.006 0.012
t0 − 6 (0.024) (0.080)
Southwest in both airports (no flights) 0.008 −0.018
t0 − 5 (0.028) (0.081)
Southwest in both airports (no flights) −0.007 0.042
t0 − 4 (0.027) (0.085)
Southwest in both airports (no flights) −0.002 0.025
t0 − 3 (0.029) (0.090)
Southwest in both airports (no flights) −0.015 0.037
t0 − 2 (0.037) (0.098)
Southwest in both airports (no flights) 0.008 −0.005
t0 − 1 (0.038) (0.112)
Southwest in both airports (no flights) −0.022 0.096
t0 (0.030) (0.122)
Southwest in both airports (no flights) −0.034 0.142
t0 + 1 (0.031) (0.133)
Southwest in both airports (no flights) −0.031 0.049
t0 + 2 (0.036) (0.157)
Southwest in both airports (no flights) −0.019 0.042
t0 + 3 to t0 + 12 (0.037) (0.174)
Southwest flying route 0.003 −0.047
te (0.035) (0.149)
Southwest flying route 0.000 0.026
te + 1 to te + 2 (0.039) (0.167)
Southwest flying route 0.010 −0.019
te + 3 to te + 12 (0.044) (0.197)
N 16,510 16,510
Notes. All regressions are weighted by passengers and include route-carrier and carrier-quarter fixed
effects. The table shows incumbents’ price and quantity responses on routes to neighboring airports to which
Southwest does not fly but that are in the same market as a Southwest airport. Standard errors are in
parentheses and are clustered by route-carrier. The dependent variables are route-carrier’s average logged
fares during the quarter in column (1) and its logged number of passengers in column (2). See also Table II
notes for variable definitions.
in magnitude and occasionally alternate signs. It is worth noting
that the major incumbents at the Southwest airport and at
the nearby airport may not be the same, so we are not strictly
comparing the actions of the same airlines across the two sets
of routes. In Chicago, for example, Continental and Northwest
1626 QUARTERLY JOURNAL OF ECONOMICS
are incumbents with substantial operations at Midway, whereas
United flies exclusively out of O’Hare.
Taken together, the results in this section suggest that incumbents
do engage in preemptive price cutting when they find
out that Southwest is likely to enter a route in the near future. In
the next section we examine some evidence regarding what they
are trying to accomplish by doing so.
V. REASONS FOR PREEMPTIVE ACTION: DETERRENCE,
CAPACITY, ACCOMMODATION
The evidence documenting the existence of preemptive price
cutting by incumbent airlines is much easier to establish than the
reasoning behind it—especially when we lack access to important
data such as the fare class of the tickets, the type of passengers,
and whether they are frequent flyers. However, we can at least
provide a few suggestive pieces of evidence that may indicate
support for some theories at the expense of others.
V.A. Strategic Investment in Excess Capacity
We first test whether incumbents’ actions reflect strategic
investment in excess capacity, as in Dixit (1979) and others. Unfortunately,
the DB1A files making up our core sample are of
passenger itineraries, not flights, so they cannot speak to capacity
issues such as seats or flights available. We can get this type
of information, however, from the T-100 data of the U.S. Department
of Transportation. The T-100 contains the number of passengers,
flights, and available seats by segment-carrier-month; we
aggregate by route-carrier-quarter to match our DB1A-based data
though there are many more missing observations in the T-100.
Two problems arise in comparing the T-100 data to our core
sample. The first is that the T-100 data are based on segments
(any airport-to-airport trip by an aircraft) rather than itineraries
(defined by origin and destination city regardless of any intermediate
stopovers) as in the DB1A. The T-100 will count one-stop
flights with no plane change as two separate segments, whereas
the DB1A would consider this a single, direct flight. Second, the
T-100 has poor coverage of segments with few passengers. Of the
19,414 direct flight route-carrier-quarters in our DB1A sample,
only 3,152 have matches in the T-100. Segment traffic appears to
start, stop, and start again in the T-100 even if continuous in the
DB1A. This coverage gap is clearly concentrated in the smallest
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1627
routes, however. Although the T-100 data contain only about onesixth
of the route-carrier-quarters in our DB1A sample, they cover
90% of the total passengers. Because our regressions are weighted
by passenger traffic, the impact of the coverage gap should be mit-
igated.
Column (1) of Table V shows logged passenger responses for
route-carrier-quarters. Although noisy, the point estimates from
these independent passenger-quantity data suggest the same
general pattern of rising traffic around the period when the threat
of entry rises (save for an anomalous coefficient in quarter t0 − 2).
Columns (2) and (3) show two measures of incumbent capacity
on threatened routes: the logged number of seats available (column
(2)) and the logged number of flights (column (3)). We cannot
definitively rule out a rise in capacity given the point estimates
and the coefficients’ precision, but there is no indication at conventional
significance levels that either available seats or the number
of flights rise. Further, we look in column (4) at what happens
to the log of the load factor (the share of available seats on the
flights that had passengers in them). Here there is statistically
significant evidence that, whatever is going on with the number of
seats or flights, there are significantly more passengers per unit of
capacity when the entry threat materializes. This is not consistent
with the use of capacity investment as a preemptive action.
V.B. Entry Deterrence or Accommodation?
The other basic issue we may have some ability to address in
our data is whether incumbents cut prices early to deter Southwest’s
entry on a route, or instead to try to soften competition once
entry occurs—that is, whether they are engaging in deterrence or
accommodation.
To test this, we compare preemptive behavior on the routes
in our basic sample—where Southwest’s future entry behavior
is unknown and could in principle be deterred—to the pricing
behavior of incumbents on a set of routes where Southwest’s entry
is preannounced and therefore all but guaranteed. In the spirit of
Dafny (2005) and Ellison and Ellison (2007), if entry deterrence is
the motivation, we should not see price cutting where deterrence
is impossible.
The routes where deterrence seems extremely unlikely
are those where Southwest begins direct service between two
endpoint airports in the same quarter it starts operating in the
second endpoint airport. Using the example of Southwest’s entry
1628 QUARTERLY JOURNAL OF ECONOMICS
TABLE V
INCUMBENT RESPONSES IN CAPACITY: PASSENGERS VERSUS SEATS, FLIGHTS, AND
LOAD FACTORS
Dependent variable
(1) (2) (3) (4)
ln(q) ln(seats) ln(flights) ln(load factor)
T100 T100 T100 T100
Southwest in both airports −0.050 −0.055 −0.044 0.005
(no flights) t0 − 8 (0.171) (0.155) (0.159) (0.041)
Southwest in both airports −0.005 −0.004 0.010 −0.001
(no flights) t0 − 7 (0.107) (0.096) (0.097) (0.039)
Southwest in both airports −0.046 −0.064 −0.053 0.017
(no flights) t0 − 6 (0.191) (0.186) (0.184) (0.049)
Southwest in both airports −0.078 −0.066 −0.044 −0.012
(no flights) t0 − 5 (0.206) (0.168) (0.165) (0.057)
Southwest in both airports 0.116 0.046 0.075 0.071∗
(no flights) t0 − 4 (0.146) (0.129) (0.127) (0.041)
Southwest in both airports 0.093 0.030 0.054 0.062
(no flights) t0 − 3 (0.149) (0.131) (0.128) (0.043)
Southwest in both airports −0.026 −0.016 0.016 −0.010
(no flights) t0 − 2 (0.155) (0.137) (0.131) (0.045)
Southwest in both airports 0.157 0.081 0.110 0.076∗
(no flights) t0 − 1 (0.167) (0.155) (0.150) (0.040)
Southwest in both airports 0.213 0.100 0.129 0.112∗∗
(no flights) t0 (0.187) (0.170) (0.164) (0.047)
Southwest in both airports 0.219 0.128 0.157 0.090∗
(no flights) t0 + 1 (0.188) (0.164) (0.159) (0.053)
Southwest in both airports 0.195 0.084 0.118 0.111∗
(no flights) t0 + 2 (0.190) (0.166) (0.163) (0.057)
Southwest in both airports 0.190 0.051 0.063 0.140∗∗
(no flights) t0 + 3 to t0 + 12 (0.187) (0.174) (0.167) (0.053)
Southwest flying route 0.319 0.193 0.201 0.126∗∗
te (0.198) (0.177) (0.174) (0.058)
Southwest flying route 0.232 0.089 0.104 0.143∗∗
te + 1 to te + 2 (0.219) (0.195) (0.191) (0.065)
Southwest flying route 0.288 0.116 0.131 0.172∗∗
te + 3 to te + 12 (0.277) (0.256) (0.251) (0.071)
N 3,152 3,152 3,152 3,152
Notes. All regressions are weighted by passengers and include route-carrier and carrier-quarter fixed
effects. Standard errors are in parentheses and are clustered by route carrier. The dependent variable in
column (1) is the log number of passengers. The dependent variable in (2) is the log of the total number of
seats available on the route. In (3) it is the log number of flights actually flown. In (4) it is the share of the
seats flown that are filled with passengers. The specifications use the T-100 data, as explained in the text.
See also Table II notes for variable definitions. ∗Denotes significance at a 10% level. ∗∗Denotes significance
at a 5% level.
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1629
into Washington Dulles, these would be routes that Southwest
immediately begins flying on October 5, 2006, such as Dulles to
Chicago Midway. In principle, Southwest might still be deterred
from entering these routes even though they are preannounced,
but we are unaware of any cases in our sample where Southwest
announced they would enter a route and did not. There are
223 such routes in our data; we observe them for roughly 6,400
route-carrier-quarter observations.
The results from this no-deterrence-motive sample are shown
in column (2) of Table VI. The coefficients show no significant fare
declines before Southwest’s actual entry, as compared to the substantial
drops in the benchmark specification (shown in column
(1) for comparison purposes; note that because entry is immediate
in the column (2) sample, t0 and te are synonymous, and the threatened
entry dummies for periods after t0 do not exist). Thus when
there is no such possibility of deterrence, as on the routes in this
sample, incumbents take, at best, modest action. This suggests
that their actions in threatened routes are driven by their hopes
of deterring Southwest from entry. We are reluctant to make too
much out of this finding, however. We estimated a fully interacted
model that allowed us to test for the significance of differences between
the two samples, and could not reject equality among any
of the pairs of time dummy coefficients at the 10% level, except at
quarter t0 − 7, well before Southwest enters. It appears that the
data are too thin here to speak definitively to incumbents’ motives.
The manner through which either deterrent or accommodative
price cuts might operate is an open question. A commonly
postulated mechanism through which preemptive behavior would
operate involves “dynamic demand”: customer loyalty or similar
mechanisms that make demand today a function not just of today’s
prices, but of previous prices as well. One plausible explanation
in this vein for the observed preemptive price cutting is that it reflects
efforts by incumbents to generate loyalty or lock-in among
existing valuable customers, making them less likely to switch
to Southwest should it enter. The consumer loyalty might be as
direct as something like frequent flyer programs, but it could certainly
be less tangible than that; all that is required is that it can
be built through price cuts.15
15. Discussions of the role of consumer loyalty and lock-in for the airline
industry can be found in work such as Cairns and Galbraith (1990), Borenstein
(1996), and Lederman (2007). As Becker, Grossman, and Murphy (1994) point out,
1630 QUARTERLY JOURNAL OF ECONOMICS
TABLE VI
DETERRENCE VERSUS ACCOMMODATION: PRICE RESPONSE ON ROUTES WHERE
SOUTHWEST’S ENTRY DATE IS PREANNOUNCED
Dependent variable: ln(p)
(1) (2)
Not certain Preannounced
Southwest in both airports (no flights) −0.030 −0.003
t0 − 8 (0.024) (0.033)
Southwest in both airports (no flights) −0.071∗∗ 0.006
t0 − 7 (0.030) (0.036)
Southwest in both airports (no flights) −0.065∗ 0.019
t0 − 6 (0.035) (0.042)
Southwest in both airports (no flights) −0.079∗ −0.013
t0 − 5 (0.044) (0.036)
Southwest in both airports (no flights) −0.100∗ −0.036
t0 − 4 (0.049) (0.038)
Southwest in both airports (no flights) −0.142∗∗ −0.060
t0 − 3 (0.056) (0.041)
Southwest in both airports (no flights) −0.132∗∗ −0.024
t0 − 2 (0.056) (0.045)
Southwest in both airports (no flights) −0.135∗∗ −0.057
t0 − 1 (0.065) (0.055)
Southwest in both airports (no flights) −0.186∗∗
t0 (0.073)
Southwest in both airports (no flights) −0.215∗∗
t0 + 1 (0.073)
Southwest in both airports (no flights) −0.228∗∗
t0 + 2 (0.075)
Southwest in both airports (no flights) −0.277∗∗
t0 + 3 to t0 + 12 (0.079)
Southwest flying route −0.237∗∗ −0.200∗∗
te (0.082) (0.071)
Southwest flying route −0.288∗∗ −0.337∗∗
te + 1 to te + 2 (0.087) (0.082)
Southwest flying route −0.344∗∗ −0.389∗∗
te + 3 to te + 12 (0.113) (0.082)
N 19,414 6,423
Notes. The dependent variable in each column is the passenger-weighted average logged fares. Standard
errors are in parentheses and are clustered by route-carrier. The sample in column (1) is the same as the
baseline sample from Table II. The sample in column (2) includes all routes where Southwest begins flying
the route simultaneously with entering the second airport. In such circumstances, of course, t0 and te are the
same and there are no periods after t0 where Southwest is not yet flying the route, so those coefficients are
left out of the specification. ∗Denotes significance at a 10% level. ∗∗Denotes significance at a 5% level.
given the complementarity between current and past consumption in dynamic
demand functions, lowering prices today to stimulate current and future demand
will be less effective (and even possibly entirely ineffective) if the firm cannot
commit to keeping prices low in the future: consumers rationally infer that building
a current consumption stock today might allow firms to extract more surplus
HOW INCUMBENTS RESPOND TO THE THREAT OF ENTRY 1631
VI. DISCUSSION AND CONCLUSIONS
This paper has looked at the response of incumbent major
airlines to the threat of entry by examining how the incumbents
respond when Southwest starts operating in the airports on both
ends of a route but before it actually starts flying that route.
The nature of Southwest’s network means that the likelihood of
their entering such a route rises dramatically when Southwest announces
that operations will begin in the second endpoint airport,
thus generating a discrete change in incumbents’ expectations
about the likelihood of new competition through entry.
The results indicate that incumbents do indeed react preemptively
to Southwest’s entry threat. Incumbents drop fares significantly
before entry occurs. This does not appear to be driven by
airport-specific cost shocks; incumbents’ fares drop on threatened
routes relative to their fares on other routes from the same airports.
The fare declines appear to be accompanied by an increase
in passenger traffic on the incumbents’ threatened routes. They
do not extend to routes into neighboring airports in the same MSA
where Southwest is not directly threatening entry.
Beyond the fact of preemptive action, there is some suggestive
evidence on why such action takes place. In particular, there
does not seem to be much significant evidence in favor of strategic
investment in excess capacity. A comparison of incumbents’
actions on threatened routes to their behavior on routes where
entry deterrence is impossible suggests that incumbents may be
using the preemptive actions to deter Southwest, though at the
same time we cannot reject the possibility of accommodation.
from them tomorrow. This price commitment issue does seem to apply to the
airline industry. However, it is still plausible—and the empirical evidence seems
to bear this out—that the direct demand-enhancing effect outweighs consumers’
wariness with respect to airlines’ future price commitments. Unfortunately, as
stated above, conclusively testing this or any other story of dynamic demand
driving preemptive fare cuts requires detailed information on passengers’ histories
with particular airlines, ticket search behaviors, or frequent flyer program status.
The sensitive nature of this sort of information means it is not readily available for
even an individual carrier’s passengers, much less at a comprehensive, industrywide
level. A more suggestive approach we used in an earlier draft was to see how
fare cuts varied between routes that are likely to have different concentrations
of business travelers, who are the heaviest users of loyalty programs. We found
that leisure routes, defined either via the importance of the hotel industry in the
states of their endpoint airports (as in Borenstein [1989]) or through temperature
differences across endpoint cities (Brueckner, Dyer, and Spiller 1992), saw smaller
price declines than routes likely to have higher relative levels of business traffic.
Although certainly not conclusive, these results are consistent with incumbents
making efforts to raise loyalty among their business-travel customers in order to
reduce the probability that they lose them if Southwest enters.
1632 QUARTERLY JOURNAL OF ECONOMICS
This paper’s findings suggest that the documented powerful
competitive effect Southwest Airlines has in the U.S. passenger
airline industry does not operate solely through Southwest’s
head-to-head competition with major carriers. Merely the threat
of competing with Southwest is enough to induce substantial fare
reductions from major carriers. We have focused on this industry
in particular because it offers a good setting to empirically identify
the causes and effects of interest, and therefore to add to the
still sparse empirical literature on the threat of entry. If the response
of incumbents here is anything like the responses in other
industries, the study of preemption and customer loyalty may be
a fruitful avenue for future empirical research.
UNIVERSITY OF CHICAGO GRADUATE SCHOOL OF BUSINESS, AMERICAN BAR
FOUNDATION, AND NATIONAL BUREAU OF ECONOMIC RESEARCH
UNIVERSITY OF CHICAGO GRADUATE SCHOOL OF BUSINESS AND NATIONAL BUREAU OF
ECONOMIC RESEARCH
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