The Use of Regional Airlines as a
Barrier to Entry to Low-Cost Carriers∗
Kerry M. Tan†
June 2011
Abstract
Legacy carriers outsource the operation of certain routes to regional airlines. It has been
speculated in previous works that this partnership could be partly motivated by legacy carriers attempting to inhibit entry by low-cost carriers. This paper examines whether the use of
regional airlines serves as an effective barrier to entry to low-cost carriers in the U.S. airline
industry. Using a two-way fixed effects logit estimation method, I test whether entry rates by
four low-cost carriers have been negatively affected by regional airlines. The results show
that the presence of a regional airline on a route has a negative, yet statistically insignificant effect on deterring entry by low-cost carriers. Furthermore, this effect is nonexistent
even when considering routes where the potential impact of regional airlines would be the
strongest. The upshot is that regional airlines are not an effective barrier to entry to low-cost
carriers. This paper adds to the literature on the strategic behavior between legacy carriers
and low-cost carriers and provides new insight into the competitive incentive for vertical
integration between legacy carriers and regional airlines.
∗
I would like to thank Matt Lewis for his guidance and helpful advice. I would also like to thank M. Saif Mehkari, Jim Peck, Michael Sinkey,
and Huanxing Yang for their suggestions and comments.
†
Department of Economics, The Ohio State University, [email protected]
1
1
Introduction
Regional airlines are contracted by legacy carriers to operate certain routes that connect the
legacy carrier’s spoke airports to its hub. The planes are owned by the regional airlines, yet
are painted to resemble the legacy carrier’s fleet. The pilots and flight attendants are employed
by the regional airlines, yet the legacy carriers are responsible for ticketing and operations at
the airport. Legacy carriers employ regional airlines due to their cost advantage.1 Forbes and
Lederman (2009) examine the factors that determine the vertical integration decision made by
legacy carriers. They find that although the operating costs are much lower for flights operated
by regional airlines, legacy carriers would not always want to outsource the operation of shorthaul or medium-haul routes to regional airlines. In fact, legacy carriers would want to operate
these routes themselves when the route’s airports are vulnerable to inclement weather or when
the route is heavily integrated into the legacy carrier’s overall network. They conclude that
legacy carriers operate routes themselves when control over schedule adjustments is especially
important.
Previous works have examined the effect of entry by low-cost carriers, particularly Southwest Airlines. Boguslaski, Ito, and Lee (2004) identify the factors that affect entry decisions by
Southwest Airlines. Goolsbee and Syverson (2009) find that incumbents decrease their price
when facing potential competition from Southwest Airlines, a low-cost carrier. However, the
existing literature has not formally studied the role of regional airlines in the entry decision by
low-cost carriers.
Forbes and Lederman (2007) posit that outsourcing the operation of a route to a regional airline could serve as a barrier to entry to low-cost carriers in the conclusion of their co-authored
work. They conjecture that a legacy carrier incumbent may decide to contract the operation of
a route to a regional airline if that route is determined to be particularly attractive to a potential low-cost carrier entrant. By establishing a lower-cost operator on the threatened route, the
legacy carrier would signal its intention that the route could become unprofitable for the potential low-cost carrier entrant, thereby deterring entry.2 However, they leave the formal analysis
up for future research. The purpose of this paper is to empirically analyze the notion offered in
Forbes and Lederman (2007) and test whether regional airlines do in fact serve as an effective
barrier to entry to low-cost carriers.
1
Hirsch (2007) found that senior pilots and flight attendants at United Airlines make 80 percent more and 32
percent more than their counterparts at regional airlines, respectively.
2
A similar story is mentioned in both Holloway (2003) and O’Connor (2001). Neither of these books cite any
corroborating academic work.
2
I use a two-way fixed effects logit model to estimate the effect that regional airlines have on
entry by low-cost carriers. The coefficient for the number of regional airlines operating on a
route is statistically insignificant, suggesting that regional airlines do not serve as an effective
barrier to entry to low-cost carriers. Moreover, the results hold not only for Southwest Airlines,
who has a reputation for not being deterred by incumbents,3 but also for other low-cost carriers.4
In order to ensure that the non-result is truly a zero-result, I isolate routes where the potential
effect of regional airlines would be most prominent. The impact of regional airlines is not statistically significant even on these short-haul routes in sparsely populated markets. Thus, the
notion suggested in the previous literature that legacy carriers use regional airlines as a barrier
to entry against low-cost carriers is found to be untenable.
The rest of the paper is structured in the following manner. Section 2 presents background
information on the airline industry, with a particular emphasis on the similarities and differences
between regional airlines and low-cost carriers. Section 3 describes the data used for this study,
while Section 4 details the estimation model and its results. Section 5 offers concluding remarks.
2
Industry Background
Before the airline industry became deregulated in 1978, regional airlines operated as commuter airlines, servicing thin and short-haul routes. At the time, the Civil Aeronautics Board
heavily regulated price and entry in the airline industry. Airlines were allowed to set high prices
on long-haul routes, which cross-subsidized profit losses made on low-margin short-haul routes.
Regional airlines, however, were exempt from regulation as long as their fleet contained planes
below a certain size.5 As such, they operated independently from the major airlines at the time.
Prior research has been conducted on the effects of the Airline Deregulation Act of 1978,
particularly on the evolution towards hub-and-spoke networks.6 Legacy carriers7 altered their
route structure to concentrate traffic through certain airports in the United States. Over time,
legacy carriers would align themselves with rival airlines, leading to codesharing agreements
between two legacy carriers8 and to contractual agreements with regional airlines regarding the
operation of certain routes. In fact, the use of regional airlines by legacy carriers has drastically
3
See Goolsbee and Syverson (2009) for details.
The other low-cost carriers studied in this paper are AirTran Airways, JetBlue Airways, and Spirit Airlines.
5
The size limit effectively limited regional airlines to planes with 20 to 30 seats.
6
See Borenstein (1989), Brueckner, Dyer, and Spiller (1992), and Brueckner and Spiller (1994) for more details.
7
Legacy carriers get their name from the fact that they have existed prior to deregulation. The major legacy
carriers still in existence include American Airlines, Delta Air Lines, United Airlines, and US Airways.
8
See Goetz and Shapiro (2010) for a detailed analysis on codeshairing.
4
3
increased over time. According to the Regional Airlines Association, an industry trade group,
the number of passengers enplaned by regional airlines increased from 14.69 million in 1980
to 159.45 million in 2009 thanks in large part to the fact that each of the major legacy carriers
now use regional airlines.9 Regional airlines and legacy carriers have developed a symbiotic
relationship as regional airlines depend on their contracts with legacy carriers for passengers,
whereas legacy carriers rely on regional airlines as an important feeder of passengers within
their route network.
The rise of low-cost carriers is another outcome of a deregulated airline industry. Their
name stems from the fact that these airlines have a lower cost of operation due to their usage
of a point-to-point network, non-unionized labor, and a fleet consisting of the same aircraft.10
From 1998:Q1 to 2009:Q4, there have been 940 instances of entry by low-cost carriers into routes
with a maximum distance of 1,500 miles, with AirTran Airways entering 424 routes, JetBlue Airways entering 136 routes, Southwest Airlines entering 322 routes, and Spirit Airlines entering 58
routes. Each route is defined as a one-way airport pair. For example, two routes were considered
to be entered when Southwest Airlines started flying from Detroit Metro Airport to Philadelphia
International Airport and vice versa in 2004:Q2. This paper examines whether the presence of
regional airlines affects the entry decision by four low-cost carriers (AirTran Airways, JetBlue
Airways, Southwest Airlines, and Spirit Airlines).
Regional airlines and low-cost carriers compete against each other on certain routes. They
both serve short-haul routes that connect airports in small- to medium-sized cities. For example,
both United Airlines and Southwest Airlines fly nonstop between Seattle-Tacoma International
Airport and Portland International Airport, yet United Airlines uses a regional airline, SkyWest
Airlines, to operate that route. However, these two types of airlines do not service all of the
same markets. There are routes that regional airlines service that low-cost carriers would consider too thin to be profitable. These routes typically link a small spoke airport with one of the
legacy carrier’s hub airports, such as when Delta Air Lines uses Comair to operate the route
between Hartsfield-Jackson Atlanta International Airport and Harrisburg International Airport.
No low-cost carriers service this route. On the other hand, low-cost carriers operate on some
routes where the distance between the two endpoints are too far for the aircraft used by regional airlines. United Airlines uses its own fleet and aircrew to operate the route between
Baltimore/Washington International Airport and Denver International Airport, where it also
competes with Southwest Airlines. Although regional airlines and low-cost carriers cannot be
9
10
RAA 2010 Annual Report: www.raa.org
Southwest Airlines operates only Boeing 737 planes, which decreases the cost of maintenance and inventory
4
considered as perfect substitutes, they both operate at some of the same airports.
3
Data
The main dataset used in this paper is the Airline Origin and Destination Survey (DB1B),
which is published quarterly by the Bureau of Transportation Statistics. It is a ten percent survey of domestic air travel and contains data on the origin, destination, non-stop distance between
endpoints, ticketing carrier, operating carrier, market fare, and number of passengers paying a
particular market fare. The Bureau of Transportation Statistics also publishes the Airline OnTime Performance Data, which contains monthly data on the number of delayed flights at the
route-level. Finally, I use yearly data from the Local Area Personal Income tables on population,
per capita personal income, and personal income by major source and earnings at the metropolitan statistical area-level, which are created and distributed by the Bureau of Economic Analysis.
I use the personal income by major source and earnings dataset to obtain information on both
accommodation and nonfarm earnings for metropolitan statistical areas (MSA). Data from 1998
to 2009 were collected from each of the three data sources.
The following steps were undertaken to clean the data. First, I eliminate all observations
where the distance was equal to zero or the market fare is less than $10. Observations with an
unidentified ticketing carrier were also dropped. Only observations related to nonstop flights
were kept. I then limit the sample to routes within the continental United States11 with a maximum distance of 1,500 miles since regional airlines would not be used on longer routes and
restrict the sample to the 2,500 routes with the highest number of passengers from 1998:Q112 to
2009:Q4. I drop routes that were never serviced by a legacy carrier in order to focus on routes
where there is the potential for strategic behavior between legacy carriers and low-cost carriers. In some cases, data on the number of delayed flights or accommodation earnings are not
reported. Routes with incomplete information on either of these two variables are eliminated.
Finally, observations from the different datasets are aggregated into year-route combinations.
The final dataset contains 12,790 observations on 1,161 routes from 1998 to 2009.
Entry is identified when an airline starts servicing a route and remains on that route for at
least two quarters. In some cases, airlines are seen in the DB1B to operate on a particular route
only to drop out for a quarter and reappear in the subsequent quarter. This surely is not an
example of actual entry but represents an issue with the DB1B being a ten percent sample of
11
This effectively removes observations on routes involving airports in Alaska, Hawaii, and Puerto Rico.
The Bureau of Transportation Statistics did not ask carriers to report whether the operating carrier differed
from the ticketing carrier until 1998.
12
5
airline tickets. Nevertheless, this problem was resolved by qualifying entry when the carrier did
not service the route in question for at least four quarters before the identified quarter of entry.
The carrier must have also flown at least 100 passengers on the entered route in the quarter of
entry.
4
Empirical Strategy
The previous literature conjectures that regional airlines can be an effective barrier to entry
to low-cost carriers. I use three approaches to formally test this hypothesis. First, I study whether
the number of regional airlines operating on a route affects the entry rate of low-cost carriers in
general. The four low-cost carriers used in this analysis are AirTran Airways, JetBlue Airways,
Southwest Airlines, and Spirit Airlines. Second, I examine whether the use of regional airlines
affects Southwest Airlines differently than other low-cost carriers. Goolsbee and Syverson (2008)
find that incumbents are unlikely to deter entry by Southwest Airlines, especially if Southwest
Airlines already operates at both endpoint airports. As such, I am interested in whether other
low-cost carriers are also immune to entry deterrence strategies. Finally, I consider routes in
which regional airlines would have the strongest effect as a barrier to entry. As mentioned in
Section 2, regional airlines and low-cost carriers are both commonly found on short-haul routes
in small markets. If regional airports would have any effect, it would most likely be on these
routes in particular. This third approach looks at whether the entry rates by low-cost carriers
are significantly affected by the presence of regional airlines on these routes. The rest of this
section describes the estimation strategy and reports the regression results used to estimate the
efficacy of regional airlines.
4.1
Estimation Strategy
Previous papers have estimated barriers to entry using a logit model. Cotterill and Haller
(1992) find that the number of large supermarket chains in a particular market serves as an
effective barrier to entry. Cetorelli and Strahan (2006) conclude that banks with market power
erect a significant financial barrier to entry. These papers generally use entry in the relevant
market as a dependent variable and test whether particular market conditions affect entry rates.
They determine that a particular variable is an effective barrier to entry if the corresponding
logit coefficient is negative and statistically significant. In order to test whether regional airlines
serve as a barrier to entry to low-cost carriers, I utilize a two-way fixed effects logit model that
incorporates a route fixed effect and a year fixed effect.
6
Three dependent variables are constructed in this paper. The first approach of the paper
is to test whether low-cost carriers are deterred by the number of regional airlines operating
on the route. For this approach, I use LCCentry, an indicator variable that turns on when a
low-cost carrier enters the route in the following year. This dependent variable is also used in
the third approach, which tests whether there is an effect in a best-case scenario, where the
regional airlines would have the highest potential influence. The second approach utilizes two
dependent variables, W N entry and otherLCCentry, which are indicator variables for when
Southwest Airlines or any other low-cost carrier enters a route, respectively. These two dependent variables are used when trying to differentiate entry effects between different low-cost
carriers. These three dependent variables test if regional airlines have an overall effect on lowcost carriers (LCCentry) and if there are potentially heterogeneous effects on entry (W N entry
and otherLCCentry).
There are three types of control variables: market variables, demographic variables, and
competition variables. Market variables include the natural log of the number of passengers
on a route (lndensity) and the percentage of flights on a route that were delayed at least 15
minutes (pdelay). Demographic variables include the natural log of the maximum of the ratio of
accommodation earnings to nonfarm earnings for each endpoint on a route (lntourism), as well
as the geometric mean of the population (pop) and per capita income (income) of the MSA where
the origin and destination airports are located. Finally, I include competition variables to control
for the maximum market share of a servicing airline on that route (maxshare) and the routelevel Herfindahl-Hirschman Index (HHIroute). I also control for the number of competing
airlines by including the number of legacy carriers (nLEG), low-cost carriers (nLCC), regional
airlines (nREG), and other airlines (nOT HER) operating on that route. Table 1 reports the
summary statistics for the dataset used in this paper.
The basic specification is as follows:
yi,t+1 = γi + µt + αXi,t + βnREGi,t + i,t ,
(1)
where γi is the route fixed effect,13 µt is the year fixed effect, nREGi,t is the number of regional
airlines on route i in year t, Xi,t are the other control variables explained above, and yi,t+1 is
either LCCentryi,t+1 , W N entryi,t+1 , or otherLCCentryi,t+1 for route i in year t + 1. Note that
the control variables are in terms of period t, whereas the dependent variable relates to period
13
The route fixed effect captures several variables that could potentially affect entry by low-cost carriers, including distance, the existence of multiple airports in a given market, or the usage of an airport slot policy. Running a
logit model that takes these variables into account yields qualitatively similar results.
7
t + 1. In other words, I am looking at the effect that the control variables in a particular year will
have on entry by low-cost carriers the following year.
Table 1: Summary Statistics
Variable
distancei
passengersit
pdelayit
popit
incomeit
tourismit
HHIrouteit
maxshareit
nLEGit
nLCCit
nREGit
nOT HERit
Routes
N
4.2
Definition
Mean
(Std. Dev.)
Distance (in miles) between the endpoints of route i
723.65
(339.84)
Number of passengers on route i in time period t
29859.29
Note: lndensityit = ln(passengersit )
(29346.83)
Percentage of flights delayed over 15 minutes on route i in time period t
0.202
(0.084)
Geometric mean of population (in millions) of origin and destination
3.402
airports’ MSA on route i in time period t
(2.204)
Geometric mean of per capita income (in tens of thousands) of origin
3.582
and destination airports’ MSA on route i in time period t
(0.547)
Maximum of the percentage of accommodation income in nonfarm income
1.745
of origin and destination airports’ MSA on route i in time period t
(20.415)
Herfindahl-Hirschman Index for route j in time period t
0.578
(0.206)
Maximum of the market share of carriers on route i in time period t
0.691
(0.184)
Number of legacy carriers operating route i in time period t
2.647
(1.456)
Number of low-cost carriers carriers operating route i in time period t
0.572
(0.593)
Number of regional airlines servicing route i in time period t
1.057
(1.269)
Number of other carriers (not legacy carrier, low-cost carrier, or regional
0.596
airline) servicing route i in time period t
(0.890)
Number of routes in the sample
1,161
Number of observations
12,790
Results
There are three potential outcomes for nREG, the variable of interest. First, a negative and
statistically significant coefficient would suggest that regional airlines deter entry by low-cost
carriers, which corroborates the hypothesis suggested in the previous literature. However, if
the estimated coefficient is positive and significant, then one can conclude that low-cost carriers
are more likely to enter routes where regional airlines operate. This would occur if low-cost
carriers and regional airlines are attracted to the same routes. Finally, a statistically insignificant
estimate suggests that regional airlines have no effect on entry decisions by low-cost carriers.
8
A significantly positive or statistically insignificant negative estimate for nREG would suggest
that regional airlines serve as an ineffective barrier to entry to low-cost carriers.
Table 2: Entry by Low-Cost Carriers (Main Results)
Dep. variable
variable
lndensity
pdelay
pop
income
tourism
HHIroute
maxshare
nLEG
nLCC
nREG
nOT HER
N
LCCentry
Logit
Standard
coefficient
error
-0.622*
(0.271)
-3.691†
(1.155)
-0.193
(0.724)
-0.802
(0.971)
-0.060
(0.210)
-0.112
(2.011)
-1.200
(2.184)
-0.081
(0.175)
-4.280†
(0.298)
-0.016
(0.091)
0.239
(0.123)
3,597
Note: Route and year fixed effects suppressed.
* indicates significance at 5% level.
† indicates significance at 1% level.
The first approach looks at whether regional airlines affect entry by low-cost carriers. Table 2
reports the results from the main regression specification, which uses the occurrence of low-cost
carrier entry as the dependent variable. The control variable in question is nREG, which is the
number of regional airlines operating on the route. The logit coefficient for nREG is negative,
yet statistically significant, suggesting that regional airlines have no effect on entry by low-cost
carriers. This discredits the conjecture made in the previous literature that legacy carriers use
regional airlines as a barrier to entry to low-cost carriers.
The regression results point to three significant factors to low-cost carrier entry. Both the
natural log of the number of passengers on the route (lndensity) and the percentage of delayed
flights (pdelay) have a negative and significant effect, implying that low-cost carriers generally
tend to avoid congested routes that are likely to cause disruptions to their route network. Interestingly, a higher number of low-cost carriers operating on a route (nLCC) significantly inhibits
rival low-cost carriers from entering a route. That is not to say that low-cost carrier presence
completely blocks entry. I do see entry by low-cost carriers despite a rival servicing the route, but
the results suggest that this type of competition discourages a possible low-cost carrier entrant
9
from actually entering the route.
Industrial organization economists have been interested in Southwest Airlines as a case study
on the effect of low-cost carriers in the airline industry. Particular attention has been given to
the entry pattern of Southwest Airlines.14 Boguslaski, Ito, and Lee (2004) identify which market
characteristics factor into Southwest Airlines’s entry strategy. The second approach to this paper
seeks to examine whether determinants of entry by Southwest Airlines also exude a similar
influence on other low-cost carriers.
Table 3: Entry by Southwest Airlines vs. Other Low-Cost Carriers
Dep. variable
variable
lndensity
pdelay
pop
income
tourism
HHIroute
maxshare
nLEG
nLCC
nREG
nOT HER
N
W N entry
otherLCCentry
Logit
Standard
Logit
Standard
coefficient
error
coefficient
error
0.430
(0.737)
-0.809*
(0.340)
-3.559
(3.044)
-3.916†
(1.267)
-4.569
(2.598)
-1.489
(0.845)
1.633
(2.739)
-2.285*
(1.089)
2.072*
(0.885)
-0.361
(0.276)
-7.875
(5.505)
3.253
(2.283)
3.150
(6.213)
-2.505
(2.423)
0.299
(0.459)
-0.242
(0.201)
-6.422†
(1.144)
-3.561†
(0.306)
-0.246
(0.268)
0.038
(0.102)
0.218
(0.377)
0.266
(0.148)
869
2,992
Note: Route and year fixed effects suppressed.
* indicates significance at 5% level.
† indicates significance at 1% level.
The results in Table 3 suggest that certain factors have heterogenous effects on entry by
Southwest Airlines and other low-cost carriers. Some factors that affect entry by Southwest
Airlines do not affect entry by other low-cost carriers, and vice-versa. Southwest Airlines are
notably apt to enter routes in touristy markets. On the other hand, other low-cost carriers are
not inclined to enter dense routes that are prone to delays. They also shy away from markets
with high per capita income, which reflects the trend that lower-income markets tend to offer
attractive operational conditions, including lower airport operating costs. The only determinant
that significantly affects both Southwest Airlines and other low-cost carriers is the number of
14
See Goolsbee and Syverson (2008), Morrison (2001), and Vowles (2001) for empirical papers on the so-called
Southwest Effect.
10
low-cost carriers operating on the route (nLCC), which suggests that the presence of low-cost
carriers significantly deters entry by low-cost carriers, in general.
The key insight from Table 3 is that regional airlines do not have an effect on either Southwest Airlines or other low-cost carriers. The logit coefficient for the number of regional airlines
operating on a route (nREG) are statistically insignificant in both regressions. These coefficients, coupled with the previously mentioned result from Table 2, strongly implies that regional
airlines serve as an ineffective barrier to entry.
Boguslaski, Ito, and Lee (2004) estimate the effect of certain market characteristics on entry by
Southwest Airlines. They run a simple probit model using cross-sectional data to conclude that
Southwest Airlines tend to avoid long-distance routes connecting hub airports in high-income
MSAs, while market size and density exhibit a positive effect on entry. It is important to note that
they do not include the number of competitors on a route in their estimation strategy. As previously mentioned, the presence of low-cost carriers on a route has a significant effect on entry.
Therefore, their analysis might suffer from a potential omitted variable bias. Furthermore, I am
able to exploit the panel structure of my data by incorporating both route and year fixed effects.
However, the caveat of this model is that it excludes routes with all positive or all negative outcomes. Since there exists routes that never experience entry by a low-cost carrier in the sample
time period, several observations are dropped in the two-way fixed effects logit regression. As
a result, the coefficients experience diminished efficiency since there are less observations taken
into account. In order to check for the sensitivity of my main results, I run a pooled logit model
with panel-robust standard errors. These results are qualitatively similar to the two-way fixed
effects logit model discussed in this paper.
The results from Tables 2 and 3 both suggest that regional airlines have a statistically insignificant effect on entry by low-cost carriers. One might caution that this result simply stems
from “noisy" estimates. In order to suggest that the non-result is truly a zero-result, I focus the
data sample on routes in which the potential effect of regional airlines as a barrier to entry would
be at its highest. Both low-cost carriers and regional airlines operate on short-haul routes that
connect small markets. Therefore, I truncate the data using two criteria:15 1) the route must have
a maximum distance of 650 miles and 2) both endpoints of the route must be located in an MSA
with a maximum population of 3.25 million. I then run the two-way fixed effects model using
entry by low-cost carriers (LCCentry) as the dependent variable. Table 4 presents the resulting
regression estimates.
15
As a robustness check, I used different distance and population thresholds that still identified short-haul routes
in small markets. The results were qualitatively similar.
11
Table 4: Entry by Low-Cost Carriers (Best-Case Scenario)
Dep. variable
variable
lndensity
pdelay
pop
income
tourism
HHIroute
maxshare
nLEG
nLCC
nREG
nOT HER
N
LCCentry
Logit
Standard
coefficient
error
0.607
(1.196)
-18.125
(13.080)
20.129
(17.907)
5.909
(7.932)
3.178
(2.350)
-15.658
(27.733)
6.001
(31.430)
-1.581
(1.182)
-0.203*
(3.736)
-0.791
(0.633)
-0.794
(1.183)
308
Note: Route and year fixed effects suppressed.
* indicates significance at 5% level.
† indicates significance at 1% level.
The third approach of this paper focuses on the best-case scenario for regional airlines as a
barrier to entry to low-cost carriers. If regional airlines were found to have no effect in these
markets, then I conclude that the presence of regional airlines does not inhibit low-cost carriers from entering a route. The results from Table 4 show that the number of regional airlines
operating on a route (nREG) has a negative, yet statistically insignificant effect on entry by
low-cost carriers, which is consistent with the results from the previous two approaches. In fact,
the only variable that affects low-cost carrier entry is determined to be the number of low-cost
carriers operating on the route (nLCC). Therefore, it is the presence of rival low-cost carriers,
not regional airlines, that affects entry by low-cost carriers.
Previous academic works have speculated that regional airlines could serve as a barrier to
entry to low-cost carriers. Although it seems plausible that a low-cost carrier might find a route
with regional airline operation to be unattractive, the regression results suggest that this is not
the case. Legacy carriers who outsource the operation of a route to a regional airline do not
inhibit low-cost carriers to enter that particular route. In fact, the regression results suggest that
the biggest factor deterring low-cost carrier entry is the existence of a rival low-cost carrier. The
upshot is that regional airlines are not an effective barrier to entry to low-cost carriers.
12
5
Conclusion
The previous literature on low-cost carriers focuses on the price response of incumbent
legacy carriers. Particular attention has been given to the so-called Southwest Effect, in which
entry by Southwest Airlines induces a sharp decrease in average airfares coupled with a boost to
the number of passengers. This paper broadens the perspective on the strategic interaction between legacy carriers and low-cost carriers by studying a potential strategy that legacy carriers
undertake in order to undermine entry by low-cost carriers.
This paper empirically analyzes whether the presence of a regional airline operating on a
route serves as an effective barrier to entry to low-cost carriers. This strategic interaction has
been speculated in a few academic works, but no one has thoroughly tested their speculation.
This paper seeks to fill that hole in the literature. Using a two-way fixed effects logit model, I
estimate three approaches on the effect that the presence of regional airlines have on low-cost
carrier entry. The key result is that the number of regional airlines operating on a route does not
appear to significantly deter entry by low-cost carriers. Second, regional airlines do not influence
entry strategies of either Southwest Airlines or other low-cost carriers. Finally, regional airlines
do not erect a barrier to entry even in routes where their potential effect is strongest. Therefore,
I conclude that regional airlines serve as an ineffective barrier to entry to low-cost carriers. As
such, this paper adds to the existing literature on barriers to entry.
There is a relative dearth in the literature on regional airlines. Perhaps the most influential
work on the topic thus far is the paper by Forbes and Lederman (2009), in which they study
the vertical integration decision made by legacy carriers. They find that legacy carriers choose
to operate routes themselves as opposed to outsourcing the operation of the route to a regional
airline when the route is susceptible to inclement weather or is strongly connected to their route
structure. In other words, the vertical integration decision is made for quality control purposes.
This paper adds to the literature on regional airlines by testing whether there is a competitive
motive to the use of regional airlines by legacy carriers. Since legacy carriers are not able to
erect a barrier to entry by outsourcing to regional airlines, I conclude that the potential for a
competitive motive in the vertical integration decision is nonexistent. Therefore, my results
further strengthen the quality control story on the relationship between legacy carriers and
regional airlines.
13
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