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 beenspeculated in previous works that this partnership could be partly motivated by legacy carri-ers attempting to inhibit entry by low-cost carriers. This paper examines whether the use ofregional airlines serves as an eUective barrier to entry to low-cost carriers in the U.S. airlineindustry. Using a two-way Vxed eUects logit estimation method, I test whether entry rates byfour low-cost carriers have been negatively aUected by regional airlines. The results showthat the presence of a regional airline on a route has a negative, yet statistically insigniV-cant eUect on deterring entry by low-cost carriers. Furthermore, this eUect is nonexistenteven when considering routes where the potential impact of regional airlines would be thestrongest. The upshot is that regional airlines are not an eUective barrier to entry to low-costcarriers. This paper adds to the literature on the strategic behavior between legacy carriersand low-cost carriers and provides new insight into the competitive incentive for verticalintegration 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, tan.178@osu.edu

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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 Weet. The pilots and Wight 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 Vnd that although the operating costs are much lower for Wights operated

by regional airlines, legacy carriers would not always want to outsource the operation of short-

haul 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 eUect of entry by low-cost carriers, particularly South-

west Airlines. Boguslaski, Ito, and Lee (2004) identify the factors that aUect entry decisions by

Southwest Airlines. Goolsbee and Syverson (2009) Vnd 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 air-

line 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 poten-

tial 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 unproVtable for the poten-

tial 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 oUered in

Forbes and Lederman (2007) and test whether regional airlines do in fact serve as an eUective

barrier to entry to low-cost carriers.

1Hirsch (2007) found that senior pilots and Wight attendants at United Airlines make 80 percent more and 32percent more than their counterparts at regional airlines, respectively.

2A similar story is mentioned in both Holloway (2003) and O’Connor (2001). Neither of these books cite anycorroborating academic work.

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I use a two-way Vxed eUects logit model to estimate the eUect that regional airlines have on

entry by low-cost carriers. The coeXcient for the number of regional airlines operating on a

route is statistically insigniVcant, suggesting that regional airlines do not serve as an eUective

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

eUect of regional airlines would be most prominent. The impact of regional airlines is not sta-

tistically signiVcant 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 diUerences

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 oUers concluding remarks.

2 Industry Background

Before the airline industry became deregulated in 1978, regional airlines operated as com-

muter 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 proVt losses made on low-margin short-haul routes.

Regional airlines, however, were exempt from regulation as long as their Weet 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 eUects 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 traXc 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

3See Goolsbee and Syverson (2009) for details.4The other low-cost carriers studied in this paper are AirTran Airways, JetBlue Airways, and Spirit Airlines.5The size limit eUectively limited regional airlines to planes with 20 to 30 seats.6See Borenstein (1989), Brueckner, Dyer, and Spiller (1992), and Brueckner and Spiller (1994) for more details.7Legacy 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.8See Goetz and Shapiro (2010) for a detailed analysis on codeshairing.

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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 Weet 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 Air-

ways entering 136 routes, Southwest Airlines entering 322 routes, and Spirit Airlines entering 58

routes. Each route is deVned as a one-way airport pair. For example, two routes were considered

to be entered when Southwest Airlines started Wying from Detroit Metro Airport to Philadelphia

International Airport and vice versa in 2004:Q2. This paper examines whether the presence of

regional airlines aUects 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 Wy 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 con-

sider too thin to be proVtable. 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 HartsVeld-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 re-

gional airlines. United Airlines uses its own Weet 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

9RAA 2010 Annual Report: www.raa.org10Southwest Airlines operates only Boeing 737 planes, which decreases the cost of maintenance and inventory

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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 sur-

vey 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 On-

Time Performance Data, which contains monthly data on the number of delayed Wights 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 metropoli-

tan 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

unidentiVed ticketing carrier were also dropped. Only observations related to nonstop Wights

were kept. I then limit the sample to routes within the continental United States11 with a max-

imum 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 carri-

ers. In some cases, data on the number of delayed Wights or accommodation earnings are not

reported. Routes with incomplete information on either of these two variables are eliminated.

Finally, observations from the diUerent datasets are aggregated into year-route combinations.

The Vnal dataset contains 12,790 observations on 1,161 routes from 1998 to 2009.

Entry is identiVed 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

11This eUectively removes observations on routes involving airports in Alaska, Hawaii, and Puerto Rico.12The Bureau of Transportation Statistics did not ask carriers to report whether the operating carrier diUered

from the ticketing carrier until 1998.

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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 identiVed quarter of entry.

The carrier must have also Wown 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 eUective 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 aUects 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

aUects Southwest Airlines diUerently than other low-cost carriers. Goolsbee and Syverson (2008)

Vnd 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 eUect 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 eUect, it would most likely be on these

routes in particular. This third approach looks at whether the entry rates by low-cost carriers

are signiVcantly aUected 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

eXcacy of regional airlines.

4.1 Estimation Strategy

Previous papers have estimated barriers to entry using a logit model. Cotterill and Haller

(1992) Vnd that the number of large supermarket chains in a particular market serves as an

eUective barrier to entry. Cetorelli and Strahan (2006) conclude that banks with market power

erect a signiVcant Vnancial barrier to entry. These papers generally use entry in the relevant

market as a dependent variable and test whether particular market conditions aUect entry rates.

They determine that a particular variable is an eUective barrier to entry if the corresponding

logit coeXcient is negative and statistically signiVcant. In order to test whether regional airlines

serve as a barrier to entry to low-cost carriers, I utilize a two-way Vxed eUects logit model that

incorporates a route Vxed eUect and a year Vxed eUect.

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Three dependent variables are constructed in this paper. The Vrst 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 eUect in a best-case scenario, where the

regional airlines would have the highest potential inWuence. The second approach utilizes two

dependent variables, WNentry and otherLCCentry, which are indicator variables for when

Southwest Airlines or any other low-cost carrier enters a route, respectively. These two de-

pendent variables are used when trying to diUerentiate entry eUects between diUerent low-cost

carriers. These three dependent variables test if regional airlines have an overall eUect on low-

cost carriers (LCCentry) and if there are potentially heterogeneous eUects on entry (WNentry

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 Wights 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 route-

level HerVndahl-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 (nOTHER) operating on that route. Table 1 reports the

summary statistics for the dataset used in this paper.

The basic speciVcation is as follows:

yi,t+1 = γi + µt + αXi,t + βnREGi,t + εi,t, (1)

where γi is the route Vxed eUect,13 µt is the year Vxed eUect, 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,WNentryi,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

13The route Vxed eUect captures several variables that could potentially aUect entry by low-cost carriers, includ-ing distance, the existence of multiple airports in a given market, or the usage of an airport slot policy. Running alogit model that takes these variables into account yields qualitatively similar results.

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t+1. In other words, I am looking at the eUect that the control variables in a particular year will

have on entry by low-cost carriers the following year.

Table 1: Summary Statistics

Variable DeVnition Mean(Std. Dev.)

distancei Distance (in miles) between the endpoints of route i 723.65(339.84)

passengersit Number of passengers on route i in time period t 29859.29Note: lndensityit = ln(passengersit) (29346.83)

pdelayit Percentage of Wights delayed over 15 minutes on route i in time period t 0.202(0.084)

popit Geometric mean of population (in millions) of origin and destination 3.402airports’ MSA on route i in time period t (2.204)

incomeit Geometric mean of per capita income (in tens of thousands) of origin 3.582and destination airports’ MSA on route i in time period t (0.547)

tourismit Maximum of the percentage of accommodation income in nonfarm income 1.745of origin and destination airports’ MSA on route i in time period t (20.415)

HHIrouteit HerVndahl-Hirschman Index for route j in time period t 0.578(0.206)

maxshareit Maximum of the market share of carriers on route i in time period t 0.691(0.184)

nLEGit Number of legacy carriers operating route i in time period t 2.647(1.456)

nLCCit Number of low-cost carriers carriers operating route i in time period t 0.572(0.593)

nREGit Number of regional airlines servicing route i in time period t 1.057(1.269)

nOTHERit Number of other carriers (not legacy carrier, low-cost carrier, or regional 0.596airline) servicing route i in time period t (0.890)

Routes Number of routes in the sample 1,161N Number of observations 12,790

4.2 Results

There are three potential outcomes for nREG, the variable of interest. First, a negative and

statistically signiVcant coeXcient would suggest that regional airlines deter entry by low-cost

carriers, which corroborates the hypothesis suggested in the previous literature. However, if

the estimated coeXcient is positive and signiVcant, 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 insigniVcant

estimate suggests that regional airlines have no eUect on entry decisions by low-cost carriers.

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A signiVcantly positive or statistically insigniVcant negative estimate for nREG would suggest

that regional airlines serve as an ineUective barrier to entry to low-cost carriers.

Table 2: Entry by Low-Cost Carriers (Main Results)

Dep. variable LCCentryLogit Standard

variable coeXcient errorlndensity -0.622* (0.271)pdelay -3.691† (1.155)pop -0.193 (0.724)income -0.802 (0.971)tourism -0.060 (0.210)HHIroute -0.112 (2.011)maxshare -1.200 (2.184)nLEG -0.081 (0.175)nLCC -4.280† (0.298)nREG -0.016 (0.091)nOTHER 0.239 (0.123)N 3,597

Note: Route and year Vxed eUects suppressed.* indicates signiVcance at 5% level.† indicates signiVcance at 1% level.

The Vrst approach looks at whether regional airlines aUect entry by low-cost carriers. Table 2

reports the results from the main regression speciVcation, 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 coeXcient for nREG is negative,

yet statistically signiVcant, suggesting that regional airlines have no eUect 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 signiVcant factors to low-cost carrier entry. Both the

natural log of the number of passengers on the route (lndensity) and the percentage of delayed

Wights (pdelay) have a negative and signiVcant eUect, implying that low-cost carriers generally

tend to avoid congested routes that are likely to cause disruptions to their route network. Inter-

estingly, a higher number of low-cost carriers operating on a route (nLCC) signiVcantly 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

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from actually entering the route.

Industrial organization economists have been interested in Southwest Airlines as a case study

on the eUect 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

inWuence on other low-cost carriers.

Table 3: Entry by Southwest Airlines vs. Other Low-Cost Carriers

Dep. variable WNentry otherLCCentryLogit Standard Logit Standard

variable coeXcient error coeXcient errorlndensity 0.430 (0.737) -0.809* (0.340)pdelay -3.559 (3.044) -3.916† (1.267)pop -4.569 (2.598) -1.489 (0.845)income 1.633 (2.739) -2.285* (1.089)tourism 2.072* (0.885) -0.361 (0.276)HHIroute -7.875 (5.505) 3.253 (2.283)maxshare 3.150 (6.213) -2.505 (2.423)nLEG 0.299 (0.459) -0.242 (0.201)nLCC -6.422† (1.144) -3.561† (0.306)nREG -0.246 (0.268) 0.038 (0.102)nOTHER 0.218 (0.377) 0.266 (0.148)N 869 2,992

Note: Route and year Vxed eUects suppressed.* indicates signiVcance at 5% level.† indicates signiVcance at 1% level.

The results in Table 3 suggest that certain factors have heterogenous eUects on entry by

Southwest Airlines and other low-cost carriers. Some factors that aUect entry by Southwest

Airlines do not aUect 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 reWects the trend that lower-income markets tend to oUer

attractive operational conditions, including lower airport operating costs. The only determinant

that signiVcantly aUects both Southwest Airlines and other low-cost carriers is the number of

14See Goolsbee and Syverson (2008), Morrison (2001), and Vowles (2001) for empirical papers on the so-calledSouthwest EUect.

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low-cost carriers operating on the route (nLCC), which suggests that the presence of low-cost

carriers signiVcantly deters entry by low-cost carriers, in general.

The key insight from Table 3 is that regional airlines do not have an eUect on either South-

west Airlines or other low-cost carriers. The logit coeXcient for the number of regional airlines

operating on a route (nREG) are statistically insigniVcant in both regressions. These coeX-

cients, coupled with the previously mentioned result from Table 2, strongly implies that regional

airlines serve as an ineUective barrier to entry.

Boguslaski, Ito, and Lee (2004) estimate the eUect 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 eUect on entry. It is important to note that

they do not include the number of competitors on a route in their estimation strategy. As pre-

viously mentioned, the presence of low-cost carriers on a route has a signiVcant eUect on entry.

Therefore, their analysis might suUer from a potential omitted variable bias. Furthermore, I am

able to exploit the panel structure of my data by incorporating both route and year Vxed eUects.

However, the caveat of this model is that it excludes routes with all positive or all negative out-

comes. 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 Vxed eUects logit regression. As

a result, the coeXcients experience diminished eXciency 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 Vxed

eUects logit model discussed in this paper.

The results from Tables 2 and 3 both suggest that regional airlines have a statistically in-

signiVcant eUect 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 eUect 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 Vxed eUects model using

entry by low-cost carriers (LCCentry) as the dependent variable. Table 4 presents the resulting

regression estimates.

15As a robustness check, I used diUerent distance and population thresholds that still identiVed short-haul routesin small markets. The results were qualitatively similar.

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Table 4: Entry by Low-Cost Carriers (Best-Case Scenario)

Dep. variable LCCentryLogit Standard

variable coeXcient errorlndensity 0.607 (1.196)pdelay -18.125 (13.080)pop 20.129 (17.907)income 5.909 (7.932)tourism 3.178 (2.350)HHIroute -15.658 (27.733)maxshare 6.001 (31.430)nLEG -1.581 (1.182)nLCC -0.203* (3.736)nREG -0.791 (0.633)nOTHER -0.794 (1.183)N 308

Note: Route and year Vxed eUects suppressed.* indicates signiVcance at 5% level.† indicates signiVcance 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 eUect in these

markets, then I conclude that the presence of regional airlines does not inhibit low-cost carri-

ers 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 insigniVcant eUect on entry by

low-cost carriers, which is consistent with the results from the previous two approaches. In fact,

the only variable that aUects 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 aUects 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 Vnd 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 eUective barrier to entry to low-cost carriers.

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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 EUect, 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 be-

tween 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 eUective 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 Vll that hole in the literature. Using a two-way Vxed eUects logit model, I

estimate three approaches on the eUect 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 signiVcantly deter entry by low-cost carriers. Second, regional airlines do not inWuence

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 eUect is strongest. Therefore,

I conclude that regional airlines serve as an ineUective 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 inWuential

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 Vnd 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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