Investigating the Dynamic Spillover Effects of Low ...

Netw Spat Econ DOI 10.1007/s11067-015-9300-z

Investigating the Dynamic Spillover Effects of Low-Cost Airlines on Airport Airfare Through Spatio-Temporal Regression Models Dapeng Zhang 1 & Xiaokun Wang 1

# Springer Science+Business Media New York 2015

Abstract Low airfare is associated with low passengers’ transportation expense, high regional accessibility, and active related industry market. Low-cost airlines have been found to be effective on airfare reduction. However, most previous studies examined their impacts from the perspectives of certain routes and companies. This paper extends the literature by examining the impact of lowcost airlines from the perspective of airports. A spatio-temporal regression model is used to analyze the relationship between airport airfare and low-cost airlines’ market shares. The spatial consideration, which is also underinvestigated in the air literature, creates a chance to analyze the spatial interaction of airfare among airports. Along with the temporal consideration, this analytic framework helps to quantitatively show the impact of low-cost airline over time and space by the dynamic spillover-effect function. A case study of market share improvement of AirTran Airway at Albany International Airport (ALB) is used to illustrate the dynamic spillover effect. Results show that the average airfare of ALB will drop $18.64 right after AirTran takes 10 % market share, continue to drop in the following years, and stabilize at around 10 years. Airfare at other airports will also drop, and the magnitudes decrease as time and distance to ALB increase. Keywords Dynamic spillover effect . Spatio-temporal regression . Low-cost airlines . Airport airfare

* Xiaokun Wang [email protected] 1

Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 4032 JEC Building, 110 8th Street, Troy, NY 12180-3590, USA

D. Zhang, X. C. Wang

1 Introduction Lower airfares can reduce passengers’ transportation costs, increase regional accessibility (Pels et al. 2000; Grubesic and Zook 2007) and promote economics (Redondi et al. 2011; Tchouamou Njoya 2013). Low-cost airlines are widely believed by the industry and academia to bring low airfares due to their innovative business strategies including high aircraft utilization, a unified class of seating, and usage of secondary airports, among others (Gillen and Morrison 2003; Francis et al. 2006). These characteristics of low-cost airlines have drawn intense attention in existing studies and most of them investigated low-cost airlines’ effects from the perspective of certain routes or airlines (Dresner et al. 1996; Morrison 2001; Alamdari and Fagan 2005). However, few studies have investigated the problem from the perspective of airports (Francis et al. 2003), which is also very important. Low airfares at airports can benefit the airport accessibility and the city’s economic development. For example, Australian travelers, who contribute a significant portion of New Zealand’s tourism revenue, are sensitive to airfare to New Zealand (Schiff and Becken 2011). Australian travelers have impromptu trips to New Zealand when the airfare is low. In another London’ airport study, business travelers are also found to be attracted by the low airfare. Business travelers turned to use secondary airports instead of Heathrow Airport, the major airport in London (Mason 2001). As a result, the increased number of passengers at the secondary airport brought the development of related industries in the secondary airport’s area. The airport itself is often one of the major businesses that offers employment positions, generates tax revenue, and fosters the tourism industry. Prosperity of an airport is thus critical for regional economic development. In addition, regional developers have also realized the importance of airports’ airfare. For example, New York State senator Charles Schumer urged low-cost carrier, JetBlue, to bring service to the Albany International Airport (ALB) in order to drive down the airfare (Anderson 2014). Accordingly, ALB also has prepared incentives to attract new carriers, such as breaks on landing fees, terminal rents, and marketing assistance. In light of the importance of airport airfare, this paper investigates the low-cost airlines’ effects on airfare at airports. Airport airfare is determined by many factors. Besides the impact of low-cost airlines, factors such as the equilibrium between supply and demand, service quality, taxes and fees, and pricing models, may all play significant roles. An effective way to assess the effect of low-cost airlines is to build connections between the airfare and all potentially influential factors using regression models. The impact of low-cost airlines can then be evaluated based on the estimated marginal effects. Another important fact that may improve the assessment is that airport airfare usually follows a certain temporal trend. The specification of time series can improve the regression model’s explanatory power and offer good forecast ability. Spatial trends may also exist. Airfare offered at one airport has to be competitive among nearby airports in order to attract travelers. If the air service at one airport is significantly pricier than its neighbors, travelers may drive to the airports offering lower airfares. This spatial interaction implies the spillover effects. That is, change of airfare at one airport caused by the market share improvement of low-cost airlines may impact the airfare at nearby airports. However, studies on such spatial effects are nonexistent in the airport airfare literature. This paper fills the void of existing literature by specifying a spatio-temporal regression model with which the dynamic spillover effect of low-cost airlines can be assessed.

Spatial Temporal Analysis of Airport Airfare Spillover Effects

The spatio-temporal model accommodates the possibility that (1) the market share improvement may affect the airfare at the targeted airport instantaneously; (2) the effect may spillover to nearby airports, and (3) the effects may also occur in later time periods. This spatio-temporal modeling technique enables the possibility to simulate the evolution of low-cost airlines’ effects over time and space by a dynamic spillover function. This is a crucial examination of low-cost airlines effects over time and space. This paper uses a case study of AirTran at ALB to illustrate the dynamic spillover effect of low-cost airlines. The next section of the paper provides a review of the existing literature. The data description and model specification are then discussed. Results analysis and the dynamic spillover effect illustration are then presented, followed by conclusions.

2 Literature Review Low-cost carriers have drawn extensive attention since the passage of the Airline Deregulation Act in 1978 (Bailey et al. 1985; Card 1986; Levine 1986; Moore 1986; Ohri 2012). This act intended to remove government controls over fares, routes, and new airlines’ market entry from commercial aviation. Low-cost airlines have been booming since the deregulation and have become part of the most important carriers in the U.S. Low-cost airlines have many unique features compared to conventional airlines, such as operational concepts, labor economics, and organizational behavior, etc. (Gillen and Morrison 2003) For example, Gillen and Lall (2004) found that many low-cost airlines used a point-to-point service network structure, which had the strategic advantage over the spoke-hub structure used by conventional airlines. Ito and Lee (2003) discovered that low-cost carriers in the U.S. had become the primary source for reshaping the U.S. airline industry. Among the studies of low-cost airlines, most focused on the ticket prices and claimed that the cheap ticket was the most important feature of low-cost airlines (Borenstein 1992; Graham and Vowles 2006). For example, the largest low-cost airline in the U.S., Southwest Airlines, was famous for the BSouthwest effects^, which explicitly described the airline’s contribution on airfare reduction. Studies have found that, in terms of expense savings and market competitiveness, Southwest Airlines offered lower airfare than other airlines and resulted in forcing other airlines to lower airfare (Richards 1996; Morrison 2001; Gittell 2005). However, the majority of current literature analyze this effect from the perspective of either a certain flight route (Windle and Dresner 1995; Boguslaski et al. 2004) or a certain airline (Bamberger et al. 2001; Goolsbee and Syverson 2008). Analyses from the perspective of an airport were limited in the literature (Dresner et al. 1996; Francis et al. 2003; Zhang et al. 2015). Furthermore, these studies mainly focused on the European markets where low-cost airlines used secondary airports that were of small sizes and long distances from major cities. The airfare at an airport is influenced by a series of factors, such as local travelers’ socioeconomic features, airline companies’ pricing strategies, and the airport’s characteristics. In addition to these factors, airfare is also expected to exhibit temporal and spatial patterns. Although the spatio-temporal studies on airfares are limited in literature, temporal trends of price have been found in oil (Perron 1989) and electricity (Nogales et al. 2002). The spatial correlations of price have been found in housing


(Anselin and Le Gallo 2006) and food (Kalnins 2003). As airfare is essentially the price for a service that is subjected to the market influence, the temporal and spatial effects should also be considered. The spatio-temporal regression techniques have been developing rapidly in the past decade to deal with such issues (Anselin 2010; Elhorst 2014). For example, Holly et al. (2010) used a spatio-temporal model to analyze the house price in the U.S. Chi and Voss (2011) forecasted the future population in small areas using spatio-temporal models. Depending on the specification of spatial effects, spatio-temporal regression models have different specifications and can be estimated with a range of approaches, such as bias corrected estimators (Korniotis 2010; Lee and Yu 2010), generalized methods of moments (Elhorst 2010), and Bayesian Markov Chain Monte Carlo inference (Parent and LeSage 2010; Elhorst 2012). This paper builds on a panel data model with spatial autocorrelation terms. This model is estimated using the maximum likelihood estimation (MLE) method with a fixed-effect consideration. The low-cost airlines’ effect can be examined by the change of their market shares at airports. The change can be treated as a shock in the system of airports. The spatial specification complicates the formulation of the shock’s effect. For a model without spatial consideration, the impact is simply related to the targeted airport, and there is no impact on other airports. However, for this dynamic spillover effect, the shock has effects not only at the targeted airport (i.e., direct effect) but also other airports (i.e., indirect effect). It even has long term effects. The magnitude of the shock’s effect can be derived by calculating the marginal effects. This derivation procedure has been provided by existing literature (Elhorst 2012) and applied in many empirical studies. Autant-Bernard and LeSage (2011) examined both direct and indirect effects to assess the spatial spillover of public and private research expenditures across industries. Vega and Elhorst (2013) used these partial derivatives to detect the propagation of shocks caused by labor demand in space and time. Fischer and LeSage (2014) also studied endowments on regional income levels by applying these partial derivatives. In this paper, the mathematical forms of direct and indirect effects will also be provided. By applying these partial derivatives, a simulation can be used to illustrate the impact of low-cost airlines over time and space.

3 Data Descriptions The effect of low-cost airlines is illustrated using an example of the market share improvement of AirTran at the Albany International Airport. The prerequisite of such an analysis is to obtain the marginal effects of the AirTran’s market share on airport airfare, which can be obtained from a regression model with airfare as the dependent variable and the market share as the independent variable. The dependent variable, airfare, is defined as the logarithm of average domestic airfares: For each of the 150 most heavily used U.S. airports airport, the average airfare of all trips originated from that airport (10 % sampling rate) is calculated for each year between 1998 and 2012 (Bureau of Transportation Statistics 2014a, b). Such an airportlevel airfare serves as an intuitive indicator for the service expenses at an airport. The time begins in 1998 because Southwest Airlines, the largest low-cost airline in the U.S., began reporting complete round-trip journeys in 1998. Airfare of each trip is calculated


as the price charged by airlines plus all additional taxes and fees levied at the time of purchase. The fare does not include other fees, such as baggage fees paid at the airport or on the aircraft. Airfares include round-trip fares and one-way fares for which no return is purchased. Frequent-flyer reward tickets and abnormally high reported fares are excluded. The independent variables of low-cost airlines’ market shares are calculated as the proportion of low-cost airlines’ passengers to the total passengers of that airport. Flights, both departing from and arriving at the airport, are considered in the market share calculation. These passenger volume data comes from Bureau of Transportation Statistics (2013). Market shares of the seven largest low-cost airlines in the U.S. are considered in the regression model, as listed in Table 1. Apart from the effect of low-cost airlines, the average airfare is also characterized by other factors, which can be categorized as market-related, operation-related, and eventrelated. The market-related factors describe the demand and supply of air travel at each airport. The population served by the airport is used as a proxy of demand, and the number of available seats offered by all aircrafts is used to measure supply. The population data is derived from the U.S. Census Bureau (2014) at the county level from 1998 to 2012. The geographic area served by an airport is defined as a buffer zone centering at the airport with a radius of 100 miles, which is generally considered as a reasonable driving distance. It is worth noting that an airport’s catchment areas may better configure the service area (Lieshout 2012). However, the socio-economic data needed for defining catchment areas is not available for some of the 150 airports. Therefore, this study uses buffer zones with 100 miles radius as approximation. The population in each buffer zone is then calculated as the weighted summation of county’s population where weights are determined by the proportion of the overlapped area to the county’s area. Data for the onboard seats during the 15 years is collected by the Research and Innovative Technology Administration (RITA) (2013) of the U.S. Department of Transportation (2014). Operation-related factors consist of aircraft load factors, airborne time, and gas prices as these factors directly contribute to operation costs and the airfare. Load factors reflect the operation concept of airlines. Some airlines prefer high load factors in order to be cost-efficient, while others, prioritizing market expansion, may prefer lower load factors. Airborne time is approximately proportional to fuel consumption, management expenses, and consequently ticket prices. Therefore, airborne time is used to assess the effect of service length on airfare. Gas price directly influences the service costs. The data for load factors and airborne time are obtained from the RITA T-100 Domestic Market dataset (2014). The gas price data for the study period is the company outlets price at the state level and applied to the airports according to the locations (U.S. Energy Information Administration 2014). The event-related factor in this study refers to the occurrence of the September 11 attacks, which was found to have impacted significantly the air transportation markets nationwide and dropped the airfares significantly according to previous studies (Flouris and Walker 2005; Ito and Lee 2005; Martín and Román 2006; Chao et al. 2009) spat. It should be noted that airfare may also be influenced by many other events, such as regulation, deregulation, and major accidents. By controlling for only one major event, the effects of other events will all be attributed to the uncertainty term. The summary statistics of the variables used are listed in Table 1.

D. Zhang, X. C. Wang Table 1 Summary statistics of variables used in the spatio-temporal regression model Name

Definition

Mean

Standard Deviation

Min

Max

6.01

0.23

4.89

6.67 98

Dependent variables ln_fare

Logarithm of inflation-adjusted annual average airfare

Independent variables SW

The market share of Southwest (%)

14.49

22.26

0

AT

The market share of AirTran (%)

3.10

8.85

0

100

JB

The market share of JetBlue (%)

1.77

7.15

0

80

VA

The market share of Virgin America (%)

0.07

0.55

0

11

FT

The market share of Frontier (%)

0.68

2.82

0

56

SP

The market share of Spirit (%)

1.06

7.63

0

100

SC

The market share of Sun Country (%)

0.08

0.51

0

7

AG

The market share of Allegiant (%)

1.66

6.57

0

95

U3

The market share of USA 3000 (%)

0.14

2.55

0

84

Pop

Logarithm of total population in the service area (in thousands)

16.26

1.39

12.73

18.93

Seat

Logarithm of total available seats of the airport (in thousands)

14.54

1.44

10.64

17.77

LF

Average passenger load factors at airports: Ratio of passenger miles to available seat miles

0.72

0.08

0.43

0.91

AirTime

Logarithm of total airborne time at airport level (in minutes)

14.51

1.47

11.08

17.74

Eff911

Binary variables: 1 if the year is after 2001; 0 otherwise

0.73

0.44

0

1

GasPrc

Gasoline price through company outlets by all sellers at the state level (in dollar)

1.60

0.72

1.03

2.96

4 Model Specification The annual average domestic airfare yit at the airport i(i=1…N) in year t(t=1…T) is specified as ! F G X X X yit ¼ ϕ f yi;t− f þ ρg wi j y j;t−g þ X t β þ ci þ εit ð1Þ g¼0

f ¼1

j≠i

The first summation on the right hand side captures the temporal effects, which is specified as time series autocorrelation (AR) terms with a lag length of f. The AR term represents how airfare of f years ago affects current year’s airfare. The second summation is a spatial autocorrelation term including both concurrent and time-lagged spatial effects. When g=0, the term ρ0 ∑ wi j y j;t captures the concurrent spatial effect (i.e., how j≠i

the neighbors’ airfares at the same period affect the airfare at the airport of interest). When g>0, the term assesses the influence of neighbors’ previous airfares. The wij is the element of the matrix W, which is a spatial weight matrix whose elements are the


inverse distance between airports. Such a definition of W is based on the assumption that closer airports tend to have stronger influences on the airfare. The spatial weight matrix W is standardized by dividing by the largest characteristic root (Elhorst 2014). φf and ρg are estimable parameters, representing the magnitude of temporal and spatial effects. The lag lengths, indicated by f and g, can be determined based on the information theory (Hamilton 1994). This study uses a combination of the Akaike Information Criterion (AIC) (Akaike 1974) and the Bayesian Information Criterion (BIC) (Schwarz 1978) to make the selection. The matrix Xt represents the explanatory variables with associated parameters β. The ci is an airport-specific term, which captures the heterogeneity of airports. This term can be treated as either a fixed panel effect or a random panel effect in estimation. This paper treats ci as a fixed effect term, because this airport specific term is very likely correlated with the explanatory variables Xt, such as the market share of low-cost airlines. In particular, the market share is determined by the boarding gate availability and aircraft parking fees of airports, which are the possible components of ci. Treating ci as a random effect term would violate the exogeneity condition of regression models, which would result in biased estimators. Wooldridge (2010) and Hayashi (2000) have done comprehensive discussion on the comparison between fixed effects and random effects. In addition, it is worth mentioning that some dynamic panel models also include a time-specific term, which aims at capturing time-specific effect. However, this paper does not control for this term because estimation shows a negligible variance of the time-fixed-effect term. The term εit is the disturbance that follows a normal distribution with a mean of 0 and a variance of σ2. It is worth noting that the dynamic spatio-temporal regression model in this study can also include a term ∑ wi j x j (Elhorst 2014) to capture the effects of j≠i

neighbors’ characteristics on airport airfare. This paper neglects this term to emphasize the effect of the neighboring airfare, at the cost of model flexibility. This dynamic spatio-temporal model is estimated using the maximum likelihood estimation (MLE). The process can be viewed as an extension of the time series model estimation where the conditional likelihood is optimized, given the first few periods’ data. Ergo, given the first l(l=max(f,g)) year’s data, the likelihood function of later years’ data can be written as T 1 1 X 0 2 εt εt L ¼ − N ðT −l Þln 2πσ þ ðT −l ÞlnjI−ρ0 W j− 2 2 2σ t¼lþ1 F

G

f ¼1 T

g¼0

ð2Þ

g f where εt ¼ yet − ∑ ϕ f yg t− f − ∑ ρg W yt−g −X t β (Hamilton 1994). The demeaned prog ft . A problem of the fixed effect model is yt and X cess yet ¼ yt − T1 ∑ yt also applies to W t¼1

the incidental parameter problem. This problem arises when the period is short (15 years) and the number of individuals is large (150 airports). As a result, the airport-specific term cannot be estimated consistently. Lee and Yu (2010) proposed a bias-corrected maximum likelihood estimator to address this problem. However, the


estimation of β is not impacted by the incidental parameter problem. As this study mainly focuses on the estimation of low-cost airlines effect (β), the incidental parameter problem does not matter for this particular empirical analysis (Elhorst 2003). The estimation process is coded in MATLAB.

5 Results Analysis The estimation results of the spatio-temporal regression model are summarized in Table 2. The final model has a 1-year temporal lag and a 1-year spatio-temporal lag. The lag length is determined by the lowest the AIC and BIC values, and is reported in Table 3. As this model involves the temporal (and spatio-temporal) terms, the marginal effects of explanatory variables need to be analyzed from two aspects, non-dynamic and dynamic. 5.1 Non-Dynamic Analysis Without the consideration of dynamic effects, the estimated coefficients are able to explain how explanatory variables concurrently contribute to the airport airfare. Nevertheless, this explanation process is still not the same as that of a standard regression due to the significant coefficient ρ0 of the instantaneous spatial lag, which raises the endogeneity issue. Specifically, the change of an explanatory variable at an airport i would influence not only the airfare at the airport i, but also other airports j(j=1,…,150, j ≠i). Such influences on airports j reverberate to the airport i. This back and forth spatial spillover effect must be captured in the marginal effects, which can be obtained by calculating the partial derivatives of airfares with respect to the explanatory variable xk at airport i. The derivatives can be written as (omit t) 2 6 6 6 6 6 6 6 6 6 4

∂y1 ∂xik ⋮ ∂yi ∂xik ⋮ ∂yN ∂xik

3 7 2 7 B1i βk 7 7 6 ⋮ 7 6 7 ¼ 6 Bii βk 7 4 ⋮ 7 7 BNi βk 5

3 7 7 −1 7; whereB ¼ ðI N −ρ0 W Þ 5

ð3Þ

The i th row in Eq. 3 indicates the marginal effect of xik on airport i, which is the direct effect. Other rows in the equation indicate the effect of xik on other airports j, which is the indirect effect. All explanatory variables have both direct and indirect effects, and the last column of Table 2 only reports the direct effects. These effects are calculated by taking the average of direct effects of at the 150 investigated airports. These direct effects provide interesting and important insights into the airport airfare problem. Among the examined low-cost airlines, most of them are found to be significantly influential to airfare reduction (except the Sun Country Airlines). Virgin America has

Spatial Temporal Analysis of Airport Airfare Spillover Effects Table 2 Model estimation results and direct effects Variables

Definitions

Coef.

t-stat

Dependent variable: ln_fare

Direct Effect to airfare fare

Low-cost airlines market share variables SW

Southwest

−0.0004

−1.45

−0.05 %

AT

AirTran

−0.0041

−13.44

−0.44 %

JB

JetBlue

−0.0012

−3.86

−0.13 %

VA

Virgin America

−0.0048

−2.16

−0.52 %

FT

Frontier

−0.0010

−2.25

−0.10 % −0.38 %

SP

Spirit

−0.0035

−4.31

SC

Sun Country

0.0021

0.68

0.23 %

AG

Allegiant

−0.0025

−10.41

−0.27 %

U3

USA 3000

−0.0022

−4.63

−0.23 %

Pop

Logarithm of total population in the service area (in thousands)

0.0256

0.72

2.91 %

Seat

Logarithm of total available seats of the airport (in thousands)

−0.0759

−7.45

−8.17 %

LF

Average passenger load factors at airports: Ratio of passenger miles to available seat miles

−0.1327

−4.35

−14.25 %

AirTime

Logarithm of total airborne time at airport level (in minutes)

0.0203

2.57

2.18 %

Eff911

Binary variables: 1 if the year is after 2001; 0 otherwise

−0.0157

−3.69

−1.70 %

GasPrc

1-year lag state level gasoline through company outlets price by all sellers (in dollar)

0.0053

1.73

0.55 %

rho0

Instantaneous spatial lag coefficient

0.9464

90.67

Other variables

rho1

1 time lag spatial lag coefficient

−0.7469

−32.04

phi1

1 time lag coefficient

0.6188

42.09

N

150

T

15

Spatial lag

1

Temporal lag

1

AIC

−6996

BIC

−6888

a

Explanatory variables have both direct and indirect effects in short and long terms. This table only reports short term direct effect

the largest negative direct effect, indicating that its increase on the airport market share would have the largest influence on airfare reduction. The average airfares are lowered by 0.52 % if the Virgin America increases the airport market share at airports by 1 %. AirTran and Spirit also have large negative estimated marginal effects, indicating airport airfares are lowered by 0.44 and 0.38 %, respectively, with a 1 % market share


Table 3 AIC and BIC values of different lag lengths

Temporal lags length

Spatio-temporal lags length

AIC

BIC

0

0

−1,886

−1,812

0

1

−1,913

−1,833

1

0

−5,712

−5,632

1

1

−6,996

−6,888

2

0

−5,476

−5,390

2

1

−6,994

−6,880

2

2

−6,992

−6,872

0

2

−1,842

−1,756

1

2

−6,994

−6,880

3

0

−6,088

−5,997

3

1

−6,756

−6,659

3

2

−6,774

−6,671

3

3

−6,774

−6,665

1

3

−6,739

−6,641

2

3

−6,765

−6,662

improvement. The largest low-cost airline company in the U.S., Southwest, has a negative but an insignificant marginal effect, indicating that its effect on airport airfare is not as definite as other companies. This finding is not surprising. McCartney (2011) claimed that the price of Southwest had gone up by 39 % since 2006, while the rest of industry was only up by 10 %. In fact, Southwest is experiencing an identity crisis as it rebrands the airlines as a purveyor of cheap travel to a more comprehensive travel experience (Tuttle 2013). On the other hand, the study confirms that low-cost airlines generally reduce airfare except Sun Country. Most of them are expanding their markets. Lower prices are the main strategies for low-cost airlines to compete with conventional airlines with larger service networks, higher brand awareness, and more effective frequent-flyers programs. Sun Country Airlines does not show a significant coefficient, which may be due to its limited domestic service network, thus limited data points and vague trends in the estimation. In terms of the market-related factors, population is related positively to airfare with an insignificant estimator. The variable is not removed from the final model, because the variable can still capture the demand of air market to some extents. Seat availability has a negative marginal effect of 8.17 %, indicating that 1 % additional seats is related to a 8.17 % drop in the average airfare, implying that the supply abundance can further help with airfare reduction. As for operation-related factors, the significant negative coefficient of load factors is consistent with the expectation as high utilization rate typically reduces the price of service. Airborne time has a significant positive coefficient, indicating that price is higher when the voyage is longer. Fuel price demonstrates a positively significant effect, suggesting that airfare increases when the fuel price is high. The 911 attack is found to have lowered airfare by 1.70 %, confirming findings of previous studies.

Albany International Airport

ALB

John F. Kennedy International Airport

JFK

FNT

VPS

a

Bellingham International Airport

Both direct and indirect effects are considered in this table

BLI

The most distant airport

Minneapolis-Saint Paul International Airport

Northwest Florida Regional Airport

MSP

Distance to ALB around 1000 miles

Detroit Metropolitan Wayne County Airport

Bishop International Airport

DTW


LaGuardia Airport

Burlington International Airport

BTV

Newark Liberty International Airport

Syracuse Hancock International Airport

SYR

EWR

Westchester County Airport

HPN

LGA

Bradley International Airport

BDL


Airport name

Airport Code

2551

1027

1012

512

494

146

143

136

127

121

117

81

Distance from ALB

269.15

515.59

443.64

378.41

410.87

403.93

484.03

359.62

437.64

427.11

366.12

412.35

424.36

Current

Airfare

268.98

515.45

442.96

377.43

409.73

400.75

480.62

356.64

436.31

425.56

363.72

410.38

269.17

515.72

443.67

378.41

410.87

403.48

483.49

359.18

437.07

426.55

365.6

411.47

396.22

1 year

Instant 405.72

Dynamic

Non-Dynamic

Table 4 Neighbor airports’ airfare change in response to 10 % market share improvement of AirTran at ALB

269.06

515.03

443.28

377.87

410.24

402.04

481.95

357.84

436.5

425.86

364.53

410.65

382.27

5 years

269.12

515.42

443.5

378.18

410.61

402.99

482.96

358.73

436.85

426.28

365.23

411.17

378.91

10 years

269.11

515.32

443.63

378.11

410.52

402.78

482.74

358.53

436.79

426.21

365.08

411.09

378.43

30 years



5.2 Dynamic Analysis The analysis with the consideration of dynamic effect of the model also reveals important findings of the airfare problem. The one-period spatio-temporal lag coefficient has a significant negative coefficient of −0.7469. The one-period pure temporal lag has a significantly positive coefficient of 0.6188. These two terms, along with the other estimated variables, can connect current airfare with explanatory variables changes in previous years. Mathematically, this dynamic effect can be written by the following partial derivatives. ∂Y t ∂xi;t−Δt;k

0 ¼@

Δt X

1 ðBGÞs Aðβk Bi Þwhere G ¼ ðρ1 W þ ϕ 1 I N Þ

ð4Þ

s¼0

One important condition of Eq. 4 is that the largest characteristic root of the matrix B·G should be smaller than 1 (Elhorst 2014). Otherwise, the model would not converge. The estimated coefficients fit this condition, which means the effect of an explanatory variable would become a negligible amount after years. Intuitively, Eq. 4 calculates the current airport airfare response with respect to an explanatory variable change at a certain airport at Δt years ago. In other words, this derivative illustrates the marginal effect with the consideration of time. Note that once an explanatory variable changes at Δt years ago, the change remains in the system forever. For example, the low-cost airlines’ market share improvement would remain at the airport. Together with the instantaneous spatial lag term, this dynamic spillover effect is able to assess the influence of low-cost airlines’ market share improvements on airport airfare in time and space. The following example shows the dynamic spillover effect of a 10 % market share improvement of AirTran Airway at ALB. Based on the non-dynamic effect, the airfare at ALB will drop 4.4 % immediately after AirTran increases 10 % market share at ALB (a $18.64 saving). Considering the dynamic effect, the airfare will keep decreasing by more than 2.3 % in the following years if other factors remain constant. The trend of the airfare change at ALB is shown in Fig. 1. After 10 years, the price stabilizes at around 10.8 % lower than current price (a $45.5 reduction). This figure intuitively shows the direct effect of low-cost airline over time. The airfares at other airports will also drop due to the spatial spillover specification, which is the indirect effect. The magnitude of these indirect effects is calculated according to Eq. 4. Table 4 lists the airfare trends at the neighboring airports in the next 30 years. The effects at other airports reduce with distance to ALB. This trend is essentially because passengers tend to use alternative airports when the closest airport has high airfares. They evaluate the trade-off between the utility gained from ticket savings and the utility lost from accessing the remote airport. Therefore, closer airports receive a larger impact from the price change at nearby airports. The Bradley International Airport (BDL), which is closest airport (81 miles away) from ALB, will be impacted most significantly by the price reduction at ALB. Farther airports receive lighter impacts. This finding confirms that airfares have to be competitive to avoid the


Fig. 1 Airfare response to AirTran taking 10 % market share at ALB

airport leakage, which refers to the phenomenon that passengers would use distant airports with lower ticket price (Suzuki et al. 2003; Hess and Polak 2006). Figure 1 also shows the price change at airports that are 100 miles and 500 miles away from ALB. In summary, this dynamic spillover analysis framework provides an innovative way to assess the impact of low-cost airlines on airport airfare. The impact of low-cost airlines’ market share can be quantitatively obtained by the derivatives, and thus evaluated over time and space.

6 Conclusions This paper develops a spatio-temporal regression model to examine the impact of lowcost airlines on the 150 mostly heavily used U.S. airports. Among the investigated lowcost airline companies, Virgin America is the most likely to reduce airfare to other lowcost airlines. The largest low-cost airline in the U.S., Southwest, also has an effective role in the airfare reduction but with an insignificant estimator. The consideration of spatio-temporal autoregressive terms in the regression is necessary, as supported by a series of statistical criteria. With the estimated model, spatio-temporal spillover effects are assessed to demonstrate the airfare evolution after the improvement of the low-cost airlines’ market share. A case study of AirTran at ALB is used for the illustrative purpose. It is estimated that the AirTran will drop the average airfare at ALB by $18.64 right after AirTran takes 10 % market share. AirTran will reduce the airfare until it is stabilized after 10 years. Other airports are also affected by the AirTran market share improvement at ALB, but the magnitudes decrease as time and the distance to ALB increase. With the insufficient data availability and research time, this study has some limitations. For example, the airport characteristics are not captured fully in the


regression model, such as airport accessibility, infrastructure, and airport categories, among other factors. In summary, this paper attempts to examine the impact of low-cost airlines from the perspective of airports. The spatio-temporal model is an innovative approach to investigate the impact of low-cost airlines in the literature of aviation. The dynamic spillover effect assessment framework provides an easily implementable tool to understand the evolution of airport airfare over time and space.

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