- Email: [email protected]
How the presence of low-cost carrier competition scheduling differentiation
Journal of Air Transport Management 16 (2010) 7–11
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
How the presence of low-cost carrier competition scheduling differentiation Emine Yetiskul a, *, Adib Kanafani b a b
Ministry of Public Works and Settlement, General Directorate of Technical Research and Implementation, Necatibey Caddesi, No: 63, 06100 Ankara, Turkey Institute of Transportation Studies, University of California, Berkeley, 112 Mc Laughlin Hall, Berkeley, CA 94720-1720, USA
a b s t r a c t Keywords: Airline competition Low-cost carriers Schedule differentiation
This study explores the relation between airline market structure and schedule differentiation. Using a location theory framework applied to product differentiation over the time scale, the analysis relates the level of competition, and the presence of low-cost carriers in non-stop US markets to schedule clustering. As expected from theory, it is found that schedule clustering increases with competition, resulting in reduction in product differentiation. It is also found that this tendency is lower in the presence of low-cost carriers and when there is a strong hub effect where dominant hub airline own, rather than compete with their feeder subsidiaries. Ó 2009 Published by Elsevier Ltd.
1. Introduction The emergence of low-cost airlines has had a major impact on air travel. They have captured an increasing share of the market carrying, for in 2003 27% of US domestic passengers (Bamberger and Carlton, 2006). There have been studies that explored the sources of competitive advantages of the entrants over major network carriers Theoretical and empirical studies highlight their low-cost business and management issues. Access to a market with heterogeneous consumers can be enhanced by offering a differentiated product. Operating a point-to-point network, providing frequent, direct flights from secondary airports of larger markets, low-cost airlines pursue a product differentiation strategy and focus on the markets where both variety and the number of the products are low. We focus on one element of product differentiation, the scheduling of flights. Using cross-sectional data of 2005 we test empirically the degree to which competition affects schedule differentiation and whether the localization of departures on the routes with low-cost carrier is different from those without. We also explore what other factors are effective in determining a carriers’ strategy to cluster or differentiate flight schedules. For this purpose we measure the clustering of flights during a day on the routes where low-cost and major carriers are observed as a monopoly or oligopoly. We examine strategic scheduling as a response by the major carriers to the increase in competition from low-cost carriers as well as a product differentiation strategy by low-cost carriers to accommodate in the market in addition to the
* Corresponding author. Tel.: þ90 312 4102455; fax: þ90 312 2303666. E-mail address: [email protected] (E. Yetiskul). 0969-6997/$ – see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.jairtraman.2009.06.003
methods such as charging lower fares, serving the less dominant airports of high traffic density markets, and avoiding hub-andspoke systems.
2. Differentiation in airline scheduling 2.1. Location theory Hotelling (1929) proposed a spatial framework in which the characteristics of the products arise from physical location and where transportation cost decreases the utility of consumers purchasing the product, and suggested that firms would tend to minimally differentiate in space. d’Aspremont et al. (1979) pointed out that equilibrium does exist if the transportation cost function is quadratic and demonstrate that firms tend to locate their products with maximum differentiation. DePalma et al. (1985) introduced consumer heterogeneity and showed that, at equilibrium, n firms locating at the center of the market charge prices higher than the marginal cost of production by exploiting the heterogeneity in consumers’ tastes. In fact, this is a contradiction with the original the Hotelling model, in which consumers always buy from the firm offering the lowest delivered price. Thus different equilibrium outcomes can be suggested. Most work on location choice simplifies; besides, the airline industry and other transport and network industries have some special characteristics that present challenges to the application of location theory. For example, the theoretical models generally assume that the demand is inelastic and the distribution of consumers in the space is uniform. However, in airline industry demand is elastic and the passengers are distributed non-uniformly in their preferred departure times. Additionally, consumer
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E. Yetiskul, A. Kanafani / Journal of Air Transport Management 16 (2010) 7–11
heterogeneity in airline industry arises not only from the intertemporal demand variations but also from the differences in the rescheduling time costs of passengers and their tastes. Therefore, airlines compete by offering different levels of qualities such as frequency, pricing, and scheduling as well as brand loyalty programs. Besides, each route is part of a network so airlines may face operational rigidities and prefer to decrease costs and increase the utilization of their hardware and crew instead of competing. Empirical investigation of location theory has not been as common as its theoretical quest. Borenstein and Netz (1999) and Salvanes et al. (2005) tested the predictions of location theory regarding scheduling of flight departures and estimated that competitors are driven closer to each other when market competition increases in air transport industry. On the other hand, Netz and Taylor (2002) empirically tested the location of Los Angelesarea gasoline stations in physical space and found considerable evidence that firms locate their stations in an attempt to spatially differentiate their product as market competition increases, which is opposite to scheduling differentiation in the airline industry. 2.2. Predictions We look at the relation between competition and schedule differentiation, and explore how this differs among low-cost carriers and major carriers. We do not attempt to analyze the complete business model of major and low-cost carriers and the factors that affect their decisions in management issues, instead we limit our investigation to a structural equation for departure-time differentiation with a given number of flights on a route. Similar to the previous studies, we expect to find a negative relation between degree of competition and differentiation in airline scheduling. We take a step forward by arguing that this negative relation is less on the routes where low-cost carriers are present. To test these hypotheses, we define indices for competition and schedule differentiation. As regards competition measures, the important factor that may contribute to decrease in competition is the integration between major and regional carriers providing services to feed the larger carriers and operating larger aircrafts on high demand routes. As there are markets in which some of the flights are offered by a major while some are operated by a feeder, we calculate the competition levels on the routes in several different ways in order to capture differences between the routes served by a major and its feeder carrier and characterized by both major carriers. Although we are primarily interested in the effect of competition on scheduling, it is important to control for other variables that may affect flight scheduling. As low-cost carriers do not necessarily compete with major carriers by using the same airport, and they often utilize secondary airports of multi-airport metropolitan areas, which generate another source of competition, we use multiairport indicator and expect that the presence of another airport in the same area contributes to decrease in flight schedule differentiation. To control the effect of airport service level in terms of density at primary and non-primary commercial service airports, rank order based on its share of passenger enplanements nationwide is used as a control variable. The increase in airport service level would tend to dampen the effect of strategic scheduling incentives of carriers. Operational rigidities can pose other constraints that affect airlines’ strategic responses through schedule differentiation. As almost all the major carriers offer connecting services via their hub cities on US domestic routes, network integration is an important factor for major them. The effect of this operational rigidity depends on the choice between overall and segment profitability for an airline, a route includes a hub restricts the flexibility of the
airline in scheduling. Strategic flight scheduling can also be constrained by demand-side consideration. As passengers prefer to take flights, departing and arriving during standard times, not in the middle of the night, demand for the flights which depart or arrive at times during early morning hours can be considerably less. Travel time on a route plus time zone change can affect the strategic scheduling negatively on long travel routes and the routes from western to eastern US, with the time zone change. Finally, average capacity constraints on routes are addressed in the analyses. As high load factors on a route cause both more intense competition in scheduling due to market share effects on the route and less intense competition due to market power effect, it is difficult to predict whether the relation is positive or negative. 2.3. Departure-time differentiation The departure times of flights in a day can be interpreted as locations on the circular time scale of a 24-h clock. To characterize departure-time differentiation, an index that measures how each flight competes with not only its most nearby neighbors but also with all of the scheduled flights of the day is used. The differentiation index, adapted from the one used by Borenstein and Netz (1999), DIFF1 takes a value in the interval [0, 1]. When this index is equal to 1, the flights are evenly distributed over a day and the headway in minutes is 1440/n where n shows the number of nonstop flights in a market. DIFF is calculated by using scheduled departure times of all flights in Airline Service Quality Performance (ASQP) database as of May 18, 2005.2 On some routes, airlines offer multi-stop direct flights so we also observe the departure times of one or more intermediary stop direct flights. Another source of competition comes from connecting flights, especially departing from the same airport. However, to avert data complexity issues we limit our sample to routes where a substantial proportion of passengers fly directly. According to the Department of Transportation’s Database 1B (DB1B)3 for the second quarter of 2005, markets in which at least 80% of all passengers fly direct are included in the sample. Additionally, markets with less than 36 passengers during the quarter are excluded. Each observation in the sample is a directional airport-pair (e.g., if St. Louis International (STL) to Minneapolis St. Paul International (MSP) is one observation, then MSP to STL is another). Finally, we stratify the markets according to the following hierarchy:
1 There are n non-stop flights, scheduled according to t(n) ¼ {t1,.,ti} that denotes the vector of departure times of the flights i. jt1 t2j is the measure of the departure-time difference in minutes between the first and second flight during a day. The average departure-time distances in minutes between each pair of flights on Pn1 Pn 2 each route are measured as:AVGDIFF ¼ nðn1Þ i¼1 j>1 ½minfjti tj j; 1440 j a a ti tj jg ; 0 < < 1; in which 1440 denotes the day length in minutes. AVGDIFF is minimized at zero when all flights are scheduled at the same time and maximized when flights are distributed evenly over a day. The power of a denotes the marginal effect of changes in time differences between flights. We choose a as 0.5 and then normalize the average differentiation by the maximum possible, MAXDIFF, which is the value of the index when the flights are equally spaced around the 24-h. circle. ( 2 Pn=21 1440a n n k n þ 2ð720a Þ if n is even nðn1Þ k¼1 MAXDIFF ¼ Pðn1Þ=2 1440a 2 n k if n is odd: n nðn1Þ k¼1
Finally, we call the resulting ratio:DIFF ¼ 2
AVGDIFF MAXDIFF
ASQP database maintained by the Bureau of Transportation Statistics, DOT compares the actual flight departures of individual flights of the reporting airlines with the Official Airline Guide (OAG). Twenty US top carriers were reported in 2005. The observations in this study are the routes, representing 90% of the seats served in the market, as defined by the 20 carriers covered in the ASQP database. 3 DB1B is a 10% sample of airline tickets from reporting carriers collected by the Bureau of Transportation Statistics, DOT and contains directional market characteristics of each domestic itinerary of the Origin and Destination Survey.
E. Yetiskul, A. Kanafani / Journal of Air Transport Management 16 (2010) 7–11
influence schedule differentiation, we estimate the following model of the differentiation index:
Table 1 Majors and regional partners in 2005. Majors
Regional partners
American Airlines Continental Airlines Delta Airlines
American Eagle Airlinesa Expressjet Airlines Atlantic Southeast Airlinesa Comaira Skywest Airlines Expressjet Airlines Skywest Airlines
Northwest Airlines United Airlines
Source: regional airline association (www.raa.org). a Regional carriers are fully owned by the major in May 2005.
- Market size: this is measured by the number of daily direct flights, with 3, 4, 5, or 6 flights. We ignore markets with less than 3 daily flights as there would be no point in looking at schedule differentiation. - Low-cost carriers: we characterize markets by the presence of low-cost carriers4 measured by their market share. - Degree of competition: the Herfindahl Index is used as an indicator for the degree of competitiveness in each market. The market size is fixed by the number of direct daily flights. The index, defined as the sum of squared flight shares of all carriers in the market, increases when the number of carriers decreases in the market and/or the shares of carriers in the market are less differentiated. i. Competition level 1 (HERF_1): we calculate Herfindahl Index from the flight shares, provided by ASQP. ii. Competition level 2 (HERF_2): to capture the effect of integration between a network carrier and its feeders on competition, we analyze organizational decisions of the major carriers (Table 1). If a major carrier and its regional partners fully owned by the major are operating in the same market, we consider that both are not competing with each other. Accordingly we calculate Herfindahl Index using flight shares of both operating under a single firm. For example, we consider Delta Airlines (DL) and Atlantic Southeast (EV) as a single firm, therefore the market therein as a monopoly. iii. Competition Level 3 (HERF_3): we measure the degree of competition by generating all regional carrier partners for network carriers in each observed market. We not only compose the regional partners owned by the major but also other cooperating partners. For example, the market served by Delta Airlines (DL), Atlantic Southeast (EV) and Skywest Airlines (OO) is considered as monopoly. If United Airlines (UA) offers flights in addition to these three carriers, the market is considered an oligopoly as Skywest Airlines is the partner of both major carriers. The following relationship between the three competition measures always holds: HERF_1 HERF_2 HERF_3. The true level of competition in each market is within these bounds. The data consist of a sample of 724 markets and each observation includes all non-stop flights on a 3, 4, 5 or 6 direct daily flight route.
3. Model of schedule differentiation 3.1. The model To provide an econometrically based quantification of the various effects, and to correct for the other factors that can
4
See Appendix.
9
LnTDIFF ¼ b0 þ b1 LnFLT þ b2 LnCOMP þ b3 LCRATIO þ b4 MPORT þ b5 SPORT þ b6 HUB þ b7 LONGHAUL þ b8 LnLOADFAC þ 3
ð1Þ
where, LnTDIFF is transform of the departure-time differentiation index, Ln{DIFF/(1 DIFF)}. This transformation gives the index an infinite positive range as compared to the (0,1) range of DIFF; LnFLT is logarithm of the number of daily flights on the route. LnCOMP is logarithm of the inverse of Herfindahl index calculated on the basis of non-stop flight market share; LCRATIO is the ratio of the low-cost carrier non-stop flights to all flights on the route; MPORT is a dummy, with a value of 1 if there exits an alternative airport for either endpoints of the route within 50 miles of the airport5; SPORT is the service (density) level of origin or destination airport based on the higher share of passenger enplanements6; HUB is a dummy, equal to 1 when the origin airport is a concentrated hub7; LONGHAUL is a dummy for long-haul routes. It is equal to 1 when average scheduled actual flight time with time zone difference on the route is longer than 210 min8 and LnLOADFAC is logarithm of the average passenger load factors on the route.9 3 is the normally distributed error term with zero mean. To capture the effects in a wider range and differentiate the monopoly routes with competitive ones, we assume a log–log relationship. We estimate three models (Models A1–A3) by varying competition variable, LnCOMP, to capture the effect of regional carriers on competition, thereby, schedule differentiation. Due to the virtual decrease in competition when the partners are considered as the competitors of the majors, competition variable in Model A1 tends to have a weaker (negative) association with differentiation than in Model A2 and both have lower association levels than in Model A3. The other independent variable, low-cost carrier flight ratio, LCRATIO is utilized to test whether there are differences in the localization of the flights between the routes with and without lowcost carriers. An increase in LCRATIO is expected to have a positive effect on departure-time differentiation, and consequently, on waiting costs of passengers as low-cost carriers have a common strategy to differentiate their products and compete with majors. The negative correlation between differentiation variable, LnTDIFF and competition, LnCOMP can be seen as evidence that competition on a route decreases differentiation. However, this does not guarantee that a firm offers flights scheduled closely to its rival’s flights. An alternative explanation for the reduced differentiation in competitive markets is that carriers schedule their own flights, closer to each other while moving away from the rivals’ flights due to the market effect. On the other hand, when measuring the effect of low-cost carrier presence on the route
5
http://www.alternateairports.com is used to create the alternative airport list. The rank of airports is taken from Primary and Non-primary Commercial Service Airports, CY 2005 Passenger Boarding and All-Cargo Data of US Federal Aviation Administration (FAA). The inverse of the rank is used. 7 According to US General Accounting Office (1993) definition, an airport where one airline handled at least 60% of enplaning passengers or two airlines handled at least 85% of enplaning passengers is considered as concentrated and if the airport is selected from among the 75 busiest airports (by number of enplaning passengers) in the US during 2005. This is calculated from Primary and Non-primary Commercial Service Airports, CY 2005 Passenger Boarding and All-Cargo Data of FAA. 8 This is calculated from the difference between the scheduled departure and arrival times of the flights, reported in ASQP Data on May 18, 2005. 9 The data source of available seats and non-stop segment passengers transported for May 2005 is T-100 Domestic Segment (US Carriers) Data from the US Bureau of Transportation Statistics. 6
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E. Yetiskul, A. Kanafani / Journal of Air Transport Management 16 (2010) 7–11
Table 2 WLS estimation results (dependent variable: LnTDIFF). Variable
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Coeff. Model A1
Model A2
Model A3
2.352*** 0.196** 0.272***
2.343*** 0.187**
2.360*** 0.212***
0.384*** 0.196*** 0.175*** 0.188*** 0.023 0.253*** 0.480*** 0.243 724
0.215*** 0.154*** 0.204*** 0.043* 0.233*** 0.414*** 0.259 724
0.420*** 0.244*** 0.162*** 0.223*** 0.060** 0.238*** 0.434*** 0.259 724
Weighted by: LnFLT. ***Significant at 1%. **Significant at 5%. *Significant at 10%.
flight, we are unable to detect whether low-cost carriers spread their flights out more even on the routes where competition takes place. More differentiation can be driven by locating their own flights, farther to each other, especially when they are monopoly on the route. To shed some light on this effect, we calculate the differentiation index for the flight clusters within airlines and between airlines separately, thus separating within-firm differentiation from between-firm differentiation. Using the same departure-time data and grouping flights by carrier, we calculate the average time distances within and between the flights of each carrier. These new indices are abbreviated as WDIFF (within-firm differentiation) and BDIFF (between-firm differentiation) and each can be a value greater or less than 1. Note that on monopoly routes, there is only WDIFF while on the routes where each carrier only schedules one flight; only BDIFF is calculated. We estimate other models when these within and between differentiation indices are dependent variables measured in logarithms.
4. Results In the estimation of the models, as the error term variance may increase as a function of the route size, i.e., the number of flights or load factor across routes; this might create heteroscedasticity, which pose a potential problem by inducing bias in the estimation results. Therefore, estimations are carried out using Weighted Least Squares with the number of flights (logarithm transformed) as the weight variable.10 The estimation results of the models (Models A1–A3 in Table 2) where the logarithm of the transformed differentiation index, i.e., LnTDIFF, is the dependent variable indicate that as the number of departures increases, the differentiation index decreases, implying that the waiting (or schedule delay) cost of passengers decreases. The coefficient estimates for competition variable are negative and statistically significant in all models. This finding indicates a tendency for competition towards reduced product differentiation, which means that a monopolist locates its products in a more differentiated pattern in order to extract maximum performance exploiting consumer heterogeneity for schedule times. Additionally, the
10 Prior to the model estimations, scatterplot analyses of each independent variable against dependent variable are carried out and the number of flights is found as the source of heteroskedasticity. Then, log-likelihood estimation to determine a suitable value of weight is done in SPSS using the regression weight estimation coupled with the WLS procedure in each model. All models are checked for VIF values that show no indication of multi-collinearity.
relationship between the coefficients of the three competition measures, i.e., jLnCOMP 1j jLnCOMP 2j jLnCOMP 3j, holds, which can be interpreted that the regional carriers decrease the intensity of competition resulting in less clustering so the lowest level of competition, LnCOMP_3 in this study comes in the highest level of negative effect on schedule differentiation. In Table 2, the impact of LCRATIO is positive in all models as expected and the coefficient estimates are statistically significant. This positive impact of low-cost carriers on schedule differentiation may be interpreted in two ways. First, low-cost carriers locate farther away from the majors while reducing the fares to increase product differentiation and entry into the market. Second, in response to intensive price competition from the low-cost carriers, it might be the case that the majors locate their flights farther away to reduce the effects of price competition. The effect of competition coming from neighboring airports is also accounted in the models. The negative coefficient estimates of multi-airport indicator, MPORT support another kind of decreased differentiation as competition increases due to the departures scheduled at the airports in the same metropolitan area. All coefficients of airport service level, SPORT are positive and statistically significant. The increase in service level is associated with a decrease in strategic scheduling incentives of carriers, implying that airlines are less constrained when airport service level is high. The other factor that constrains scheduling comes from operational rigidity. Coefficient estimates of dummy variable for concentrated hubs, HUB are all negative in the models, however only in two models, i.e., Models A2 and A3, coefficient estimates are statistically significant. Negative coefficients can be interpreted as a result of less flexibility in scheduling due to the hubbing effect. The coefficient estimates of long-haul routes, LONGHAUL are all negative, implying that the longer the travel time, the more carriers are restricted by passenger demand and tend to locate their flights with less differentiation and avoid inconvenient arrival and departure times for the travelers. Finally, Eq. (1) also accounts for the effect of average load factors, LnLOADFAC, on the routes. The negative signs indicate a decline in product differentiation when load factors are high. Another interpretation may be that the routes with higher loads more distinct demand peaks, which would also be associated with less product differentiation. To investigate the source of the decline in product differentiation with competition, we estimated another group of models (Models W1–W3) in which the logarithm of within-firm differentiation index, LnWDIFF is taken as the dependent variable while the independent variables are kept the same as in the previous group of models. The coefficient estimates in Table 3 parallels the ones
Table 3 WLS estimation results (dependent variable: LnWDIFF). Variable
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Coeff. Model W1
Model W2
Model W3
0.094*** 0.016 0.044***
0.078*** 0.028**
0.081*** 0.028***
0.060*** 0.034*** 0.031*** 0.022* 0.021*** 0.018** 0.033* 0.083 716
0.030*** 0.029*** 0.018 0.014** 0.020*** 0.037** 0.104 718
Weighted by: LnFLT. ***Significant at 1%. **Significant at 5%. *Significant at 10%.
0.076*** 0.026*** 0.023*** 0.019* 0.011** 0.024*** 0.036*** 0.130 722
E. Yetiskul, A. Kanafani / Journal of Air Transport Management 16 (2010) 7–11 Table 4 WLS estimation results (dependent variable: LnBDIFF). Variable
Coeff. Model B1
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO RCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Model B2
Model B3
Model B1 Model B2 Model B3
0.346*** 0.499*** 0.598*** 0.357*** 0.424*** 0.569*** 0.099** 0.194*** 0.213*** 0.082** 0.156*** 0.205*** 0.048 0.089** 0.020 0.019 0.004 0.091** 0.064** 0.067** 0.094** 0.153*** 0.182*** 0.173** 0.021 0.022 0.021 0.000 0.025 0.024 0.040 0.100 0.055 0.012 0.028 0.046 0.037** 0.042* 0.046 0.025 0.031* 0.044 0.074*** 0.077*** 0.064*** 0.033** 0.048*** 0.059** 0.074 0.019 0.001 0.038 0.082 0.034 0.125 0.135 0.174 0.219 0.262 0.169 247 180 143 247 180 143
Weighted by: LnFLT ***Significant at 1%. **Significant at 5%. *Significant at 10%.
reported in Table 2 in signs except for the variable COMP.11 The coefficient estimates for COMP are all positive and statistically significant, indicating that carriers do not cluster their own flights, thereby, the degree of within-firm differentiation is more than the average degree of differentiation. Finally, in Table 4 we present the estimation results of the models (Models B1–B3) where the logarithm of between-firm differentiation index, i.e., LnBDIFF, is the dependent variable. This is to see whether the driving force behind the previous results is the differentiation between the firms on the routes with low-cost carriers. The impact of LCRATIO is positive in Models B2 and B3, presented on the left hand side of Table 4 and but is significant only in Model B3, where the degree of competition is the lowest but the strongest. This result indicates that the positive effect of regional partners on differentiation due to the decrease in competition overweight the positive effect of low-cost carriers, resulting in a negative link between LCRATIO and LnTDIFF in Model B1. To examine carefully for the accuracy of this result, we also estimate another group of models where a new variable RCRATIO, the ratio of the regional carrier non-stop flights to all flights on a route is included. In the second group models, LCRATIO and RCRATIO come out as positive and statistically significant. The positive estimated impact of lowcost carriers on differentiation suggests that low-cost carriers
11 As the range of Ln(WDIFF) is different from Ln{DIFF/(1 DIFF)}, the variable magnitudes presented in 3 are lower than those in Table 2.
11
schedule their flights farther from their rivals as a way of differentiating their products. On the other hand, majors will tend to reduce the effects of price competition by locating their flights farther. The positive coefficients of regional carrier ratio might also result from the business strategy developed by majors in response to overall presence of low-cost carriers. To deter the entry of low-cost carriers into isolated markets connected to the hubs of majors, feeders operate under different schedules engaging in more spatial differentiation. Finally, by comparing the number of observations between the models, one can find the virtual competition in each model and the negative signs of competition variable confirm that competition is associated with less differentiation than monopoly. 5. Conclusions Econometric analysis shows that competition in a market leads to less departure-time differentiation when the determinants of flight scheduling are controlled for. This tendency of carriers towards reduced product differentiation can be inferred in Hotelling’s model, but these reductions are less in markets with low-cost carriers than those with only majors. This strategic decision to differentiate flight schedules is, on the one hand, pursued by lowcost airlines to access and position themselves in the market and, on the other, by majors to compete with low-cost airlines and respond to their potential entry into their markets. This implies maximal differentiation principle suggested by d’Aspremont et al. We find a positive relationship in feeder market shares, which can be explained by the reduction in competition. References Bamberger, G., Carlton, D., 2006. Predation and the entry and exit of low-fare carriers. In: Lee, D. (Ed.), Competition Policy and Antitrust. Elsevier, Amsterdam. Borenstein, S., Netz, J., 1999. Why do all the flights leave at 8 am?: competition and departure-time differentiation in airline markets. International Journal of Industrial Organization 17, 611–640. d’Aspremont, C., Gabszewics, J.J., Thisse, J.F., 1979. On Hotelling’s ‘stability in competition’. Econometrica 47, 1145–1150. DePalma, A., Ginsburg, V., Papageorgiou, Y., Thisse, J.-F., 1985. The principle of minimum differentiation holds under sufficient heterogeneity. Econometrica 53, 767–781. Hotelling, H., 1929. Stability in competition. The Economic Journal 39, 41–57. Netz, J., Taylor, B.A., 2002. Maximum or minimum differentiation? Location patterns of retail outlets. The Review of Economics and Statistics 84, 162–175. Salvanes, K.G., Steen, F., Sørgard, L., 2005. Hotelling in the air? Flight departures in Norway. Regional Science and Urban Economics 35, 193–213.
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
How the presence of low-cost carrier competition scheduling differentiation Emine Yetiskul a, *, Adib Kanafani b a b
Ministry of Public Works and Settlement, General Directorate of Technical Research and Implementation, Necatibey Caddesi, No: 63, 06100 Ankara, Turkey Institute of Transportation Studies, University of California, Berkeley, 112 Mc Laughlin Hall, Berkeley, CA 94720-1720, USA
a b s t r a c t Keywords: Airline competition Low-cost carriers Schedule differentiation
This study explores the relation between airline market structure and schedule differentiation. Using a location theory framework applied to product differentiation over the time scale, the analysis relates the level of competition, and the presence of low-cost carriers in non-stop US markets to schedule clustering. As expected from theory, it is found that schedule clustering increases with competition, resulting in reduction in product differentiation. It is also found that this tendency is lower in the presence of low-cost carriers and when there is a strong hub effect where dominant hub airline own, rather than compete with their feeder subsidiaries. Ó 2009 Published by Elsevier Ltd.
1. Introduction The emergence of low-cost airlines has had a major impact on air travel. They have captured an increasing share of the market carrying, for in 2003 27% of US domestic passengers (Bamberger and Carlton, 2006). There have been studies that explored the sources of competitive advantages of the entrants over major network carriers Theoretical and empirical studies highlight their low-cost business and management issues. Access to a market with heterogeneous consumers can be enhanced by offering a differentiated product. Operating a point-to-point network, providing frequent, direct flights from secondary airports of larger markets, low-cost airlines pursue a product differentiation strategy and focus on the markets where both variety and the number of the products are low. We focus on one element of product differentiation, the scheduling of flights. Using cross-sectional data of 2005 we test empirically the degree to which competition affects schedule differentiation and whether the localization of departures on the routes with low-cost carrier is different from those without. We also explore what other factors are effective in determining a carriers’ strategy to cluster or differentiate flight schedules. For this purpose we measure the clustering of flights during a day on the routes where low-cost and major carriers are observed as a monopoly or oligopoly. We examine strategic scheduling as a response by the major carriers to the increase in competition from low-cost carriers as well as a product differentiation strategy by low-cost carriers to accommodate in the market in addition to the
* Corresponding author. Tel.: þ90 312 4102455; fax: þ90 312 2303666. E-mail address: [email protected] (E. Yetiskul). 0969-6997/$ – see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.jairtraman.2009.06.003
methods such as charging lower fares, serving the less dominant airports of high traffic density markets, and avoiding hub-andspoke systems.
2. Differentiation in airline scheduling 2.1. Location theory Hotelling (1929) proposed a spatial framework in which the characteristics of the products arise from physical location and where transportation cost decreases the utility of consumers purchasing the product, and suggested that firms would tend to minimally differentiate in space. d’Aspremont et al. (1979) pointed out that equilibrium does exist if the transportation cost function is quadratic and demonstrate that firms tend to locate their products with maximum differentiation. DePalma et al. (1985) introduced consumer heterogeneity and showed that, at equilibrium, n firms locating at the center of the market charge prices higher than the marginal cost of production by exploiting the heterogeneity in consumers’ tastes. In fact, this is a contradiction with the original the Hotelling model, in which consumers always buy from the firm offering the lowest delivered price. Thus different equilibrium outcomes can be suggested. Most work on location choice simplifies; besides, the airline industry and other transport and network industries have some special characteristics that present challenges to the application of location theory. For example, the theoretical models generally assume that the demand is inelastic and the distribution of consumers in the space is uniform. However, in airline industry demand is elastic and the passengers are distributed non-uniformly in their preferred departure times. Additionally, consumer
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heterogeneity in airline industry arises not only from the intertemporal demand variations but also from the differences in the rescheduling time costs of passengers and their tastes. Therefore, airlines compete by offering different levels of qualities such as frequency, pricing, and scheduling as well as brand loyalty programs. Besides, each route is part of a network so airlines may face operational rigidities and prefer to decrease costs and increase the utilization of their hardware and crew instead of competing. Empirical investigation of location theory has not been as common as its theoretical quest. Borenstein and Netz (1999) and Salvanes et al. (2005) tested the predictions of location theory regarding scheduling of flight departures and estimated that competitors are driven closer to each other when market competition increases in air transport industry. On the other hand, Netz and Taylor (2002) empirically tested the location of Los Angelesarea gasoline stations in physical space and found considerable evidence that firms locate their stations in an attempt to spatially differentiate their product as market competition increases, which is opposite to scheduling differentiation in the airline industry. 2.2. Predictions We look at the relation between competition and schedule differentiation, and explore how this differs among low-cost carriers and major carriers. We do not attempt to analyze the complete business model of major and low-cost carriers and the factors that affect their decisions in management issues, instead we limit our investigation to a structural equation for departure-time differentiation with a given number of flights on a route. Similar to the previous studies, we expect to find a negative relation between degree of competition and differentiation in airline scheduling. We take a step forward by arguing that this negative relation is less on the routes where low-cost carriers are present. To test these hypotheses, we define indices for competition and schedule differentiation. As regards competition measures, the important factor that may contribute to decrease in competition is the integration between major and regional carriers providing services to feed the larger carriers and operating larger aircrafts on high demand routes. As there are markets in which some of the flights are offered by a major while some are operated by a feeder, we calculate the competition levels on the routes in several different ways in order to capture differences between the routes served by a major and its feeder carrier and characterized by both major carriers. Although we are primarily interested in the effect of competition on scheduling, it is important to control for other variables that may affect flight scheduling. As low-cost carriers do not necessarily compete with major carriers by using the same airport, and they often utilize secondary airports of multi-airport metropolitan areas, which generate another source of competition, we use multiairport indicator and expect that the presence of another airport in the same area contributes to decrease in flight schedule differentiation. To control the effect of airport service level in terms of density at primary and non-primary commercial service airports, rank order based on its share of passenger enplanements nationwide is used as a control variable. The increase in airport service level would tend to dampen the effect of strategic scheduling incentives of carriers. Operational rigidities can pose other constraints that affect airlines’ strategic responses through schedule differentiation. As almost all the major carriers offer connecting services via their hub cities on US domestic routes, network integration is an important factor for major them. The effect of this operational rigidity depends on the choice between overall and segment profitability for an airline, a route includes a hub restricts the flexibility of the
airline in scheduling. Strategic flight scheduling can also be constrained by demand-side consideration. As passengers prefer to take flights, departing and arriving during standard times, not in the middle of the night, demand for the flights which depart or arrive at times during early morning hours can be considerably less. Travel time on a route plus time zone change can affect the strategic scheduling negatively on long travel routes and the routes from western to eastern US, with the time zone change. Finally, average capacity constraints on routes are addressed in the analyses. As high load factors on a route cause both more intense competition in scheduling due to market share effects on the route and less intense competition due to market power effect, it is difficult to predict whether the relation is positive or negative. 2.3. Departure-time differentiation The departure times of flights in a day can be interpreted as locations on the circular time scale of a 24-h clock. To characterize departure-time differentiation, an index that measures how each flight competes with not only its most nearby neighbors but also with all of the scheduled flights of the day is used. The differentiation index, adapted from the one used by Borenstein and Netz (1999), DIFF1 takes a value in the interval [0, 1]. When this index is equal to 1, the flights are evenly distributed over a day and the headway in minutes is 1440/n where n shows the number of nonstop flights in a market. DIFF is calculated by using scheduled departure times of all flights in Airline Service Quality Performance (ASQP) database as of May 18, 2005.2 On some routes, airlines offer multi-stop direct flights so we also observe the departure times of one or more intermediary stop direct flights. Another source of competition comes from connecting flights, especially departing from the same airport. However, to avert data complexity issues we limit our sample to routes where a substantial proportion of passengers fly directly. According to the Department of Transportation’s Database 1B (DB1B)3 for the second quarter of 2005, markets in which at least 80% of all passengers fly direct are included in the sample. Additionally, markets with less than 36 passengers during the quarter are excluded. Each observation in the sample is a directional airport-pair (e.g., if St. Louis International (STL) to Minneapolis St. Paul International (MSP) is one observation, then MSP to STL is another). Finally, we stratify the markets according to the following hierarchy:
1 There are n non-stop flights, scheduled according to t(n) ¼ {t1,.,ti} that denotes the vector of departure times of the flights i. jt1 t2j is the measure of the departure-time difference in minutes between the first and second flight during a day. The average departure-time distances in minutes between each pair of flights on Pn1 Pn 2 each route are measured as:AVGDIFF ¼ nðn1Þ i¼1 j>1 ½minfjti tj j; 1440 j a a ti tj jg ; 0 < < 1; in which 1440 denotes the day length in minutes. AVGDIFF is minimized at zero when all flights are scheduled at the same time and maximized when flights are distributed evenly over a day. The power of a denotes the marginal effect of changes in time differences between flights. We choose a as 0.5 and then normalize the average differentiation by the maximum possible, MAXDIFF, which is the value of the index when the flights are equally spaced around the 24-h. circle. ( 2 Pn=21 1440a n n k n þ 2ð720a Þ if n is even nðn1Þ k¼1 MAXDIFF ¼ Pðn1Þ=2 1440a 2 n k if n is odd: n nðn1Þ k¼1
Finally, we call the resulting ratio:DIFF ¼ 2
AVGDIFF MAXDIFF
ASQP database maintained by the Bureau of Transportation Statistics, DOT compares the actual flight departures of individual flights of the reporting airlines with the Official Airline Guide (OAG). Twenty US top carriers were reported in 2005. The observations in this study are the routes, representing 90% of the seats served in the market, as defined by the 20 carriers covered in the ASQP database. 3 DB1B is a 10% sample of airline tickets from reporting carriers collected by the Bureau of Transportation Statistics, DOT and contains directional market characteristics of each domestic itinerary of the Origin and Destination Survey.
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influence schedule differentiation, we estimate the following model of the differentiation index:
Table 1 Majors and regional partners in 2005. Majors
Regional partners
American Airlines Continental Airlines Delta Airlines
American Eagle Airlinesa Expressjet Airlines Atlantic Southeast Airlinesa Comaira Skywest Airlines Expressjet Airlines Skywest Airlines
Northwest Airlines United Airlines
Source: regional airline association (www.raa.org). a Regional carriers are fully owned by the major in May 2005.
- Market size: this is measured by the number of daily direct flights, with 3, 4, 5, or 6 flights. We ignore markets with less than 3 daily flights as there would be no point in looking at schedule differentiation. - Low-cost carriers: we characterize markets by the presence of low-cost carriers4 measured by their market share. - Degree of competition: the Herfindahl Index is used as an indicator for the degree of competitiveness in each market. The market size is fixed by the number of direct daily flights. The index, defined as the sum of squared flight shares of all carriers in the market, increases when the number of carriers decreases in the market and/or the shares of carriers in the market are less differentiated. i. Competition level 1 (HERF_1): we calculate Herfindahl Index from the flight shares, provided by ASQP. ii. Competition level 2 (HERF_2): to capture the effect of integration between a network carrier and its feeders on competition, we analyze organizational decisions of the major carriers (Table 1). If a major carrier and its regional partners fully owned by the major are operating in the same market, we consider that both are not competing with each other. Accordingly we calculate Herfindahl Index using flight shares of both operating under a single firm. For example, we consider Delta Airlines (DL) and Atlantic Southeast (EV) as a single firm, therefore the market therein as a monopoly. iii. Competition Level 3 (HERF_3): we measure the degree of competition by generating all regional carrier partners for network carriers in each observed market. We not only compose the regional partners owned by the major but also other cooperating partners. For example, the market served by Delta Airlines (DL), Atlantic Southeast (EV) and Skywest Airlines (OO) is considered as monopoly. If United Airlines (UA) offers flights in addition to these three carriers, the market is considered an oligopoly as Skywest Airlines is the partner of both major carriers. The following relationship between the three competition measures always holds: HERF_1 HERF_2 HERF_3. The true level of competition in each market is within these bounds. The data consist of a sample of 724 markets and each observation includes all non-stop flights on a 3, 4, 5 or 6 direct daily flight route.
3. Model of schedule differentiation 3.1. The model To provide an econometrically based quantification of the various effects, and to correct for the other factors that can
4
See Appendix.
9
LnTDIFF ¼ b0 þ b1 LnFLT þ b2 LnCOMP þ b3 LCRATIO þ b4 MPORT þ b5 SPORT þ b6 HUB þ b7 LONGHAUL þ b8 LnLOADFAC þ 3
ð1Þ
where, LnTDIFF is transform of the departure-time differentiation index, Ln{DIFF/(1 DIFF)}. This transformation gives the index an infinite positive range as compared to the (0,1) range of DIFF; LnFLT is logarithm of the number of daily flights on the route. LnCOMP is logarithm of the inverse of Herfindahl index calculated on the basis of non-stop flight market share; LCRATIO is the ratio of the low-cost carrier non-stop flights to all flights on the route; MPORT is a dummy, with a value of 1 if there exits an alternative airport for either endpoints of the route within 50 miles of the airport5; SPORT is the service (density) level of origin or destination airport based on the higher share of passenger enplanements6; HUB is a dummy, equal to 1 when the origin airport is a concentrated hub7; LONGHAUL is a dummy for long-haul routes. It is equal to 1 when average scheduled actual flight time with time zone difference on the route is longer than 210 min8 and LnLOADFAC is logarithm of the average passenger load factors on the route.9 3 is the normally distributed error term with zero mean. To capture the effects in a wider range and differentiate the monopoly routes with competitive ones, we assume a log–log relationship. We estimate three models (Models A1–A3) by varying competition variable, LnCOMP, to capture the effect of regional carriers on competition, thereby, schedule differentiation. Due to the virtual decrease in competition when the partners are considered as the competitors of the majors, competition variable in Model A1 tends to have a weaker (negative) association with differentiation than in Model A2 and both have lower association levels than in Model A3. The other independent variable, low-cost carrier flight ratio, LCRATIO is utilized to test whether there are differences in the localization of the flights between the routes with and without lowcost carriers. An increase in LCRATIO is expected to have a positive effect on departure-time differentiation, and consequently, on waiting costs of passengers as low-cost carriers have a common strategy to differentiate their products and compete with majors. The negative correlation between differentiation variable, LnTDIFF and competition, LnCOMP can be seen as evidence that competition on a route decreases differentiation. However, this does not guarantee that a firm offers flights scheduled closely to its rival’s flights. An alternative explanation for the reduced differentiation in competitive markets is that carriers schedule their own flights, closer to each other while moving away from the rivals’ flights due to the market effect. On the other hand, when measuring the effect of low-cost carrier presence on the route
5
http://www.alternateairports.com is used to create the alternative airport list. The rank of airports is taken from Primary and Non-primary Commercial Service Airports, CY 2005 Passenger Boarding and All-Cargo Data of US Federal Aviation Administration (FAA). The inverse of the rank is used. 7 According to US General Accounting Office (1993) definition, an airport where one airline handled at least 60% of enplaning passengers or two airlines handled at least 85% of enplaning passengers is considered as concentrated and if the airport is selected from among the 75 busiest airports (by number of enplaning passengers) in the US during 2005. This is calculated from Primary and Non-primary Commercial Service Airports, CY 2005 Passenger Boarding and All-Cargo Data of FAA. 8 This is calculated from the difference between the scheduled departure and arrival times of the flights, reported in ASQP Data on May 18, 2005. 9 The data source of available seats and non-stop segment passengers transported for May 2005 is T-100 Domestic Segment (US Carriers) Data from the US Bureau of Transportation Statistics. 6
10
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Table 2 WLS estimation results (dependent variable: LnTDIFF). Variable
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Coeff. Model A1
Model A2
Model A3
2.352*** 0.196** 0.272***
2.343*** 0.187**
2.360*** 0.212***
0.384*** 0.196*** 0.175*** 0.188*** 0.023 0.253*** 0.480*** 0.243 724
0.215*** 0.154*** 0.204*** 0.043* 0.233*** 0.414*** 0.259 724
0.420*** 0.244*** 0.162*** 0.223*** 0.060** 0.238*** 0.434*** 0.259 724
Weighted by: LnFLT. ***Significant at 1%. **Significant at 5%. *Significant at 10%.
flight, we are unable to detect whether low-cost carriers spread their flights out more even on the routes where competition takes place. More differentiation can be driven by locating their own flights, farther to each other, especially when they are monopoly on the route. To shed some light on this effect, we calculate the differentiation index for the flight clusters within airlines and between airlines separately, thus separating within-firm differentiation from between-firm differentiation. Using the same departure-time data and grouping flights by carrier, we calculate the average time distances within and between the flights of each carrier. These new indices are abbreviated as WDIFF (within-firm differentiation) and BDIFF (between-firm differentiation) and each can be a value greater or less than 1. Note that on monopoly routes, there is only WDIFF while on the routes where each carrier only schedules one flight; only BDIFF is calculated. We estimate other models when these within and between differentiation indices are dependent variables measured in logarithms.
4. Results In the estimation of the models, as the error term variance may increase as a function of the route size, i.e., the number of flights or load factor across routes; this might create heteroscedasticity, which pose a potential problem by inducing bias in the estimation results. Therefore, estimations are carried out using Weighted Least Squares with the number of flights (logarithm transformed) as the weight variable.10 The estimation results of the models (Models A1–A3 in Table 2) where the logarithm of the transformed differentiation index, i.e., LnTDIFF, is the dependent variable indicate that as the number of departures increases, the differentiation index decreases, implying that the waiting (or schedule delay) cost of passengers decreases. The coefficient estimates for competition variable are negative and statistically significant in all models. This finding indicates a tendency for competition towards reduced product differentiation, which means that a monopolist locates its products in a more differentiated pattern in order to extract maximum performance exploiting consumer heterogeneity for schedule times. Additionally, the
10 Prior to the model estimations, scatterplot analyses of each independent variable against dependent variable are carried out and the number of flights is found as the source of heteroskedasticity. Then, log-likelihood estimation to determine a suitable value of weight is done in SPSS using the regression weight estimation coupled with the WLS procedure in each model. All models are checked for VIF values that show no indication of multi-collinearity.
relationship between the coefficients of the three competition measures, i.e., jLnCOMP 1j jLnCOMP 2j jLnCOMP 3j, holds, which can be interpreted that the regional carriers decrease the intensity of competition resulting in less clustering so the lowest level of competition, LnCOMP_3 in this study comes in the highest level of negative effect on schedule differentiation. In Table 2, the impact of LCRATIO is positive in all models as expected and the coefficient estimates are statistically significant. This positive impact of low-cost carriers on schedule differentiation may be interpreted in two ways. First, low-cost carriers locate farther away from the majors while reducing the fares to increase product differentiation and entry into the market. Second, in response to intensive price competition from the low-cost carriers, it might be the case that the majors locate their flights farther away to reduce the effects of price competition. The effect of competition coming from neighboring airports is also accounted in the models. The negative coefficient estimates of multi-airport indicator, MPORT support another kind of decreased differentiation as competition increases due to the departures scheduled at the airports in the same metropolitan area. All coefficients of airport service level, SPORT are positive and statistically significant. The increase in service level is associated with a decrease in strategic scheduling incentives of carriers, implying that airlines are less constrained when airport service level is high. The other factor that constrains scheduling comes from operational rigidity. Coefficient estimates of dummy variable for concentrated hubs, HUB are all negative in the models, however only in two models, i.e., Models A2 and A3, coefficient estimates are statistically significant. Negative coefficients can be interpreted as a result of less flexibility in scheduling due to the hubbing effect. The coefficient estimates of long-haul routes, LONGHAUL are all negative, implying that the longer the travel time, the more carriers are restricted by passenger demand and tend to locate their flights with less differentiation and avoid inconvenient arrival and departure times for the travelers. Finally, Eq. (1) also accounts for the effect of average load factors, LnLOADFAC, on the routes. The negative signs indicate a decline in product differentiation when load factors are high. Another interpretation may be that the routes with higher loads more distinct demand peaks, which would also be associated with less product differentiation. To investigate the source of the decline in product differentiation with competition, we estimated another group of models (Models W1–W3) in which the logarithm of within-firm differentiation index, LnWDIFF is taken as the dependent variable while the independent variables are kept the same as in the previous group of models. The coefficient estimates in Table 3 parallels the ones
Table 3 WLS estimation results (dependent variable: LnWDIFF). Variable
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Coeff. Model W1
Model W2
Model W3
0.094*** 0.016 0.044***
0.078*** 0.028**
0.081*** 0.028***
0.060*** 0.034*** 0.031*** 0.022* 0.021*** 0.018** 0.033* 0.083 716
0.030*** 0.029*** 0.018 0.014** 0.020*** 0.037** 0.104 718
Weighted by: LnFLT. ***Significant at 1%. **Significant at 5%. *Significant at 10%.
0.076*** 0.026*** 0.023*** 0.019* 0.011** 0.024*** 0.036*** 0.130 722
E. Yetiskul, A. Kanafani / Journal of Air Transport Management 16 (2010) 7–11 Table 4 WLS estimation results (dependent variable: LnBDIFF). Variable
Coeff. Model B1
CONSTANT LnFLT LnCOMP_1 LnCOMP_2 LnCOMP_3 LCRATIO RCRATIO MPORT SPORT HUB LONGHAUL LnLOADFAC Adjusted R2 Observations
Model B2
Model B3
Model B1 Model B2 Model B3
0.346*** 0.499*** 0.598*** 0.357*** 0.424*** 0.569*** 0.099** 0.194*** 0.213*** 0.082** 0.156*** 0.205*** 0.048 0.089** 0.020 0.019 0.004 0.091** 0.064** 0.067** 0.094** 0.153*** 0.182*** 0.173** 0.021 0.022 0.021 0.000 0.025 0.024 0.040 0.100 0.055 0.012 0.028 0.046 0.037** 0.042* 0.046 0.025 0.031* 0.044 0.074*** 0.077*** 0.064*** 0.033** 0.048*** 0.059** 0.074 0.019 0.001 0.038 0.082 0.034 0.125 0.135 0.174 0.219 0.262 0.169 247 180 143 247 180 143
Weighted by: LnFLT ***Significant at 1%. **Significant at 5%. *Significant at 10%.
reported in Table 2 in signs except for the variable COMP.11 The coefficient estimates for COMP are all positive and statistically significant, indicating that carriers do not cluster their own flights, thereby, the degree of within-firm differentiation is more than the average degree of differentiation. Finally, in Table 4 we present the estimation results of the models (Models B1–B3) where the logarithm of between-firm differentiation index, i.e., LnBDIFF, is the dependent variable. This is to see whether the driving force behind the previous results is the differentiation between the firms on the routes with low-cost carriers. The impact of LCRATIO is positive in Models B2 and B3, presented on the left hand side of Table 4 and but is significant only in Model B3, where the degree of competition is the lowest but the strongest. This result indicates that the positive effect of regional partners on differentiation due to the decrease in competition overweight the positive effect of low-cost carriers, resulting in a negative link between LCRATIO and LnTDIFF in Model B1. To examine carefully for the accuracy of this result, we also estimate another group of models where a new variable RCRATIO, the ratio of the regional carrier non-stop flights to all flights on a route is included. In the second group models, LCRATIO and RCRATIO come out as positive and statistically significant. The positive estimated impact of lowcost carriers on differentiation suggests that low-cost carriers
11 As the range of Ln(WDIFF) is different from Ln{DIFF/(1 DIFF)}, the variable magnitudes presented in 3 are lower than those in Table 2.
11
schedule their flights farther from their rivals as a way of differentiating their products. On the other hand, majors will tend to reduce the effects of price competition by locating their flights farther. The positive coefficients of regional carrier ratio might also result from the business strategy developed by majors in response to overall presence of low-cost carriers. To deter the entry of low-cost carriers into isolated markets connected to the hubs of majors, feeders operate under different schedules engaging in more spatial differentiation. Finally, by comparing the number of observations between the models, one can find the virtual competition in each model and the negative signs of competition variable confirm that competition is associated with less differentiation than monopoly. 5. Conclusions Econometric analysis shows that competition in a market leads to less departure-time differentiation when the determinants of flight scheduling are controlled for. This tendency of carriers towards reduced product differentiation can be inferred in Hotelling’s model, but these reductions are less in markets with low-cost carriers than those with only majors. This strategic decision to differentiate flight schedules is, on the one hand, pursued by lowcost airlines to access and position themselves in the market and, on the other, by majors to compete with low-cost airlines and respond to their potential entry into their markets. This implies maximal differentiation principle suggested by d’Aspremont et al. We find a positive relationship in feeder market shares, which can be explained by the reduction in competition. References Bamberger, G., Carlton, D., 2006. Predation and the entry and exit of low-fare carriers. In: Lee, D. (Ed.), Competition Policy and Antitrust. Elsevier, Amsterdam. Borenstein, S., Netz, J., 1999. Why do all the flights leave at 8 am?: competition and departure-time differentiation in airline markets. International Journal of Industrial Organization 17, 611–640. d’Aspremont, C., Gabszewics, J.J., Thisse, J.F., 1979. On Hotelling’s ‘stability in competition’. Econometrica 47, 1145–1150. DePalma, A., Ginsburg, V., Papageorgiou, Y., Thisse, J.-F., 1985. The principle of minimum differentiation holds under sufficient heterogeneity. Econometrica 53, 767–781. Hotelling, H., 1929. Stability in competition. The Economic Journal 39, 41–57. Netz, J., Taylor, B.A., 2002. Maximum or minimum differentiation? Location patterns of retail outlets. The Review of Economics and Statistics 84, 162–175. Salvanes, K.G., Steen, F., Sørgard, L., 2005. Hotelling in the air? Flight departures in Norway. Regional Science and Urban Economics 35, 193–213.