Journal of Aviation/Aerospace Education & Research

Volume 2 Number 1 JAAER Fall 1991 Article 5

Fall 1991

Competition for Hub Dominance: Some Implications to Airline Profitability and Enplanement Share

Atef Ghobrial

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Scholarly Commons Citation Ghobrial, A. (1991). Competition for Hub Dominance: Some Implications to Airline Profitability and Enplanement Share. Journal of Aviation/Aerospace Education & Research, 2(1). https://doi.org/10.15394/ jaaer.1991.1053

This Article is brought to you for free and open access by the Journals at Scholarly Commons. It has been accepted for inclusion in Journal of Aviation/Aerospace Education & Research by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. Ghobrial: Competition for Hub Dominance: Some Implications to Airline Profi COMPETITION FOR HUB DOMINANCE: SOME IMPLICATIONS TO AIRLINE PROFITABILITY AND ENPLANEMENT SHARE

Atef Ghobrial

ABSTRACT

The phenomenon ofairline competition for hub dominance in terms offrequency concentration has been on the rise since deregulation. Using cross-sectional time-series data, this study explores the impact of hub dominance on airline profitability and enplanement share at the hub airport. The regression analysis finds a significantpositive relationship between airline profitability and hub dominance. The relationship between an airline's share ofpassenger enplanements and its hub dominance seems to follow an S-curve indicating that high frequency shares are associated with even higher enplanement shares, and conversely.

INTRODUCTION exists a positive and strong and hub dominance using Deregulation has led to relationship between airline regression analysis. The second radical changes in airline hubbing and profitability. They phase of this paper assesses the network planning. A main focal developed two indices for airline impact of hub dominance on point for these changes has hubbing and profitability for 17 airline share of passenger been the greatly increased airlines in 1982. The regression enplanements at the hub air­ emphasis on Ihubbing.I All of the hub index on the profit­ port. The paper concludes with carriers have sought to build up ability index showed a very poor some implications to airline their previous hubs, and in many fit and the independent variable network planning, congestion at cases have created new ones. (hub index) seemed to be statis­ major hubs, and the public The hubbing phenomenon tically insignificant. Toh and policy towards airline mergers. has been the subject of many Higgins (1985) also commented TRENDS IN AIRLINE studies. Kanafani and Ghobrial that profitability is not NETWORK CENTRALITY (1982) studied the relationship guaranteed by hub netwrok AND HUB DOMINANCE between the economies of centrality, which is contrary to A comparison of airline aircraft size and airline hubbing. popular belief. networks of today with those of In 1985, they expanded their This paper is an attempt to the mid 1970s amply analysis to assess the impact of extend the work of Toh and demonstrates that hubbing has airline hubbing on airport Higgins to explore further the increased. The now familiar congestion and its implications impact of hub dominance on pattern of multiple links to airport economics. Kanafani airline profitability and its emanating from a handful of hub and Hansen (1985) showed that, passenger enplanement share. airports has replaced the when all other variables (such as The findings of Toh and Higgins seeming hodge-podge that stage length, aircraft capacity, together with the results of this characterized many route network size, etc.) are controlled study will help shed some light systems under regulation. airlines with strong hubbed route on the commonly held belief that One indication of a hubbed systems incur roughly the same hubbing leads to more network is the concentration of cost to provide a given amount successful and profitable operations at a few airports of transportation as those with operations (see Aviation Week which serve as transfer points for less hubbed systems. Ghobrial and Space Technology, connecting traffic. Measures of and Sousa (1987) investigated November 1981). The paper concentration of an airline's some airline strategies to begins by discussing the operations can therefore serve dominate their main connecting phenomenon of airline hubbing as measures of the degree of hubs. and hub dominance. This is hubbing of that airline's route Toh and Higgins (1985) followed by exploring the network. One such a measure is attempted to test whether there relationship between profitability the one-airport concentration

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Table 1 Operational Statistics for Selected Airlines In 1980

Airline Main Hubbing hub airline profit hub index * dominance ranking at index ** index * main hub %

AA ORO 0.146 26.80 2 -3.50 AL PIT 0.189 63.04 1 9.47 BN DFW 0.321 37.82 1 -5.70 CO DEN 0.252 26.30 3 -5.28 DL ATL 0.224 46.40 1 5.42 EA ATL 0.208 41.19 2 0.47 FL DEN 0.225 34.14 1 7.49 NW MSP 0.156 41.09 1 2.12 OZ STL 0.205 32.36 2 -0.34 PA MIA 0.141 21.99 2 -8.97 PI ROA 0.081 99.50 1 6.62 RC ORO 0.066 15.39 3 1.53 TW STL 0.140 41.94 1 -1.85 UA ORO 0.164 43.61 1 -1.55 WA LAX 0.170 25.07 2 -3.62

* Estimated using data from IAirport Activity Statistics of Certificated Route Air Carrier-, U.S. Department of Transportation, The Federal Aviation Administration, 1980. ** Estimated using data from lAir Carriers Financial Statisticsl, Civil Aeronautics Board and the U.S. Department of Transportation, 11980

ratio which is defined as the ratio years 1976, 1978, 1980, 1982, be estimated as the share of that of the airline's departures from 1984 and 1986 was estimated. airline's departures of the total its main hub to the total aircraft For the purpose of illustration, aircraft departures by all airlines departures from all airports Table 1 depicts this trend for the from that hub. This index can within the airline's domestic selected airlines in 1980. Overall, also serve as a proxy for hub system. This ratio will be referred the time-series data suggest an monopoly. The hub dominance to as the hubbing index. For a upward trend in the hubbing index was calculated for all air­ complete hub-and-spoke index. The few instances of lines under investigation at their network structure (i.e.; all spoke downward movement are likely main connecting hub over the cities are connected only to the due to large scale network period 1976-1984. Only domestic hub airport) the number of expansions, which naturally tend operations were considered for aircraft operations (i.e.; take-offs to reduce concentration. airlines offering both domestic and landings) at the hub airport A parallel phenomenon to and international services such is equal to the number of aircraft airline hubbing is hub as Pan Am, TWA, Northwest, etc. operations at all other airports. dominance. While hubbing Table 1 reports the hub The hubbing index is, therefore, measures network centrality dominance index (in percent­ equal to 0.5 for a complete within the airline's system, hub ages) for the airlines under in hub-and-spoke network dominance measures airline 1980. Also depicted in Table 1 is structure. The trend of the concentration relative to other the airline's ranking at its main hubbing index for some selected airlines operating at the same connecting hub in terms of the airlines' distributions of hub. An airline's dominance number of aircraft departures. scheduled departures for the index at a particular hub could An analysis of the time-series https://commons.erau.edu/jaaer/vol2/iss1/5 DOI: https://doi.org/10.15394/jaaer.1991.1053JAAER, Fall 1991 21 2 Ghobrial: Competition for Hub Dominance: Some Implications to Airline Profi Hub Dominance

data of airline ranking at major served by that hub. Since the positive and strong relationship hubs over 1976-1984 indicates monopolist can alter the supply between airline profitability and that airlines have exploited the of flights and set the price (i.e.; hub dominance. The profitability opportunities of free entry and airfare), monopoly profits in this index is estimated by dividing exit of deregulation and case are usually larger than operating income (i.e., operating attempted to dominate the hubs those attained· under highly revenue-operating expense) by which they use as funnel and competitive conditions. the operating revenue and transfer points for their HUB DOMINANCE AND multiplying it by 100 to be passengers. AIRLINE PROFITABILITY expressed in percentage terms. Hub dominance appears to be The analysis of airline A profitability index is calculated highly instrumental in profitability is a complex one that for all the airlines under consid­ establishing a strong regional involves a. number of factors eration. Table 1 shows the identification among passengers including network structure, hub estimated profitability index for which is important for marketing dominance, fleet type and these airlines in 1980. the airline. Examples are Delta at average aircraft size, stage The Model Atlanta, American at Dallas/Fort length, average yield, route The relationship between airline Worth, TWA at St. Louis, and monopoly, load factor, degree of profitability and hub dominance Continental at Houston. In unionization, and management is expressed as a linear addition, a dominant airline at a skills. For the purpose of this regression model which takes particular hub can reap the study, we will attempt to test the the following form: monopoly profits in the markets hypothesis that there exists a

Equation 1

PROFF u+PDOMr+tFIRMI+8(Y7~+ ~(Y8~+4>( Y8~ +3(Y84) + a(Y86)+e (1)

Where PROFi is the profitability model is intended to capture the order serial correlation cannot be index in percentage terms of effects of other variables rejected. No correction was, airline i, DOMi is its hub influencing the carrier's profit­ therefore, preformed on the dominance index in percentage ability. This is useful when results obtained. points at the main hub, and analyzing cross sectional data. Analysis FIRMi is a firm-specific variable The model in Equation 1 was The coefficient B in both model which takes on the value one for estimated twice (i.e.; with and estimations is positive and airline i and zero otherwise with without the firm-specific variable statistically significant at the 0.05 Pan Am (PA) as the control FIRM) using the cross sectional, level. The hypothesis that there carrier. Y78, Yao, Y82, Y84 and time series data over 1976-84. exists a positive and strong Y86 in Equation 1 are dummy The results of estimating both association between profitability variables for the year with 1976 models along with t-statistics are and hub dominance cannot, as the control year (e.g.; Y80 shown in Table 2. For the therefore, be rejected. The value takes on the value one for purpose of simplicity and of coefficient B in the second observations in 1980 and zero comparison, the variable FIRM is model estimation indicates that a otherwise.) CI, 8, ~, 9, II, ., ~, replaced by its corresponding one percent increase in an and a are the coefficients to be airline code in Table 2. Note that airline's hub dominance index estimated and e is the error the Durbin-Watson value in will likely be associated with an term. Note that the inclusion of a Table 2 indicates that the hypo­ increase of 0.178 percentage firm-specific variable FIRM in the thesis that there exists no first points in its profitability index.

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The results suggest that a multiple correlation coefficient industry. This pattern seems to 2 dominant airline at a particular (R ) improved from 0.326 to accord with the trends in fuel hub reaps the monopoly profits 0.543. The yearly dummy prices as well as the intense by charging higher fares. variables suggest increasing competition among airlines in the The inclusion of the firm­ losses between 1980 and 1982, first few years of deregulation specific variable increased the with 1984 marking a period of which may have led to destruct­ regression fit as the value of the financial recovery for the ive airfare competition.

Table 2: Results of Model Estimating Equation 1

MODEL 1 MODEL 2 VARIABLEI Estimated T- Estimated T- COEFFICIENT Value Statistics Value Statistics

CONSTANT -1.535 -0.60 -11.239 -3.23 DOM 0.205 4.74 0.178 2.58 Y78 -3.041 -1.04 -2.865 -1.06 Y80 -6.529 -2.28 -7.631 -2.79 Y82 -10.964 -3.86 -11.324 -2.70 Y84 -6.953 -2.36 -6.899 -2.39 Y86 -6.713 -2.22 -8.247 -2.86 AA 12.658 3.02 AL 11.882 2.34 BN 10.468 2.03 CO 14.351 3.43 DL 14.422 3.35 EA 12.435 2.85 FL 10.662 2.66 NW 12.888 2.91 OZ 13.662 3.04 PI 9.361 1.61 RC 18.112 3.99 TW 7.089 1.65 UA 10.593 2.76 WA 10.123 2.33

R-8quared = 0.33 R-8quared = 0.54 Durbin-Watson = 2.18 Durbin-Watson = 2.41

The above results coupled with further, we provide two the most profitable airline those obtained by Toh and examples. The first is the case of between 1980 and 1984. The Higgins suggest that increased USAir which, in 1980, established second example is Pride Air emphasis on hUbbing cannot, by a hub in Pittsburgh where which, in late 1985, adopted a itself, contribute to airline profit­ competition was generally complete hub-and-spoke system ability unless it is associated with absent. While most airlines with New Orleans as the main airline dominance of the main experienced losses in 1980 and hub (i.e., hubbing index equals hub (or the few hUbs) it uses for 1982, USAir posted impressive 0.5). Pride Air could not with­ its operations. To elaborate profits, and, overall, USAir was stand competition at New https://commons.erau.edu/jaaer/vol2/iss1/5 23 DOI: https://doi.org/10.15394/jaaer.1991.1053JAAER, Fa//1991 4 Ghobrial: Competition for Hub Dominance: Some Implications to Airline Profi Hub Dominance

Orleans from other dominant frequency shares are associated where Pim is the probability that airlines (such as Delta, Eastern, with even higher market shares a passenger at hub m will select Continental, Southwest, etc.) and and conversely. Following the an airline i, or simply the had to declare bankruptcy by the same approach, we will test the passenger enplanements share end of 1986. hypothesis that there exists a of airline i at hub m; and U{im) is In light of the above, one can positive and strong relationship the utility of airline i to conclude that the commonly between an airline's enplane­ passengers at hub m. The logit held belief that hubbing leads to ment share at a given airport model has widely been used in more successful operations is and its hub dominance. An similar applications, primarily attributed mainly to the fact that airline's enplanement share at a because of its advantages most airlines tend to dominate hub is estimated as the ratio relative to other probabilistic the main connecting hubs within between the airline's passenger choice model forms. For a their systems. This is apparent enplanements to the total discussion and derivation of the by examining individual airline's passenger enplanements by all logit model, the reader can refer ranking at the major hub as airlines at that hub. to Kanafani (1985, Chi 5). shown in Table 1. The Model The passenger utility's function HUB DOMINANCE AND In developing an enplanement consists of a vector (i.e.; array) AIRLINE ENPLANEMENT share model for an airline at a of the carrier's service attributes SHARE given hub, it will be assumed at a particular hub. These The second phase of this study that passengers exhibit utility attributes include flight is to assess the impact of hub maximization behavior in their frequency, aircraft size, airfare, dominance on airline choice of a carrier. It can be departure schedule, and airline enplanement share at the hub readily shown that by maximizing image. For the purpose of this airport. Route monopoly and the passenger's utility function, study, the utility function will market dominance have been the standard multinomial logit consist of the carrier's flight the subject of many studies probabilistic choice model can frequency at the hub. Other which attempted to relate an be obtained, such that: variables influencing an airline's airline's passenger share in a share of passenger enplane­ given market to its frequency Equation 2 ments will be accounted for by share of the total flights in the incorporating a firm-specific market. Renald (1970), Fruhan elJ(im) variable in the utility function. (1972), and Miller (1979) showed p1m .E e IJ{Jn1J (2) Based upon the above that the relationship between an J discussion, the utility function airline's market share and its UOm) is given as: frequency share follows an S-curve; meaning that high

Equation 3

U(jm) =a. LOG(FREQ) jm+P flRMr- e (3)

where FRE.Qjm is the flight term. A PRIORI sign of CI is performance, in-cabin service, frequency of airline j at hub m, expected to be positive since an and flight itinerary (e.g.; non-stop FIRMj is a firm-specific variable increase in flight frequency will flights versus multi-stop or which takes on the value one for yield better levels of service. connecting flights). Note that the carrier j and zero otherwise. CI Levels of service include such flight frequency variable (FREQ) and B are the coefficients to be measures as frequency of flights, is expressed in a logarithmic estimated, and c is the error aircraft size, on-time form in order to examine, in

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Hub Dominance

Table 3 Input Data for the Enplanement Share Model for 1980

Hub Airline Enplanements Aircraft (millions) Departures

ORO AA 3745.3 47344 DL 1975.2 25286 UA 6217.5 77356

PIT AL 3022.6 54291 TW 978.2 15068 UA 619.5 9651

DFW BN 3645.7 57352 AA 3079.5 45658 DL 1677.5 23257

ATL DL 10342.7 119472 EA 7620.1 105170 RC 789.1 21620

DEN FL 2230.6 41880 CO 2107.1 31711 UA 2670.3 39002

MSP NW 1787.1 25618 RC 1023.3 24060 WA 532.2 8481

MIA PA 1078.4 14030 EA 2289.9 27881 DL 819.7 12116

STL TW 2333.1 34127 OZ 1042.4 24705 AA 661.9 14637

LAX WA 1953.7 23380 AA 1673.3 17337 UA 3033.6 34989

Source: IAirport Activity Statistics of Certificated Route Air Carriers. 1 U.S. Department of Transportation. The Federal Aviation Administration. 1980

relative terms, the relationship frequency share as explained a multiple airport region, Harvey between an airline's enplane- earlier. In his investigation of (1986) showed that using a ments share and its flight passenger choice of an airport in nonlinear specification for https://commons.erau.edu/jaaer/vol2/iss1/5 DOI: https://doi.org/10.15394/jaaer.1991.1053JAAER, Fall 1991 25 6 Ghobrial: Competition for Hub Dominance: Some Implications to Airline Profi Hub Dominance

yielded a better estimate than a three airlines. The logit model time-series data for the selected linear one. was, therefore, estimated using airlines over 1976-84, passenger In order to test the data for the top three airlines a enplanements for each airline at hypothesisthat there exists a given hub. The hubs selected for a given hub were converted to positive and strong relationship estimating the model are those proportions and the logit model between airline enplanement listed in Table 1 over the period in Equation 2 was estimated share and its hub dominance, from 1976 to 1984. For the twice (i.e.; with and without the the logit model in Equation 2 is purpose of illustration, Table 3 firm-specific variable FIRM). estimated. In their survey of shows the 1980 data for aircraft Because of the logarithmic airline strategies to dominate departures and passenger specification of the model, two their main hubs, Ghobrial and enplanements for the top three observations were omitted from Sousa (1987)showed that most airlines dominating particular the data, that is, Piedmont's passenger enplanements at a hubs. operations at ROA in 1976 and given hub are carried by the top Using the cross sectional, 1978. The results of estimating

Table 4: ResuRs of Estimating Equation 3

MODEL 1 MODEL 2 VARIABLE! Estimated T­ Estimated T­ COEFFICIENT Value Statistics Value Statistics

FREQ 1.0505 57.79 1.0790 63.45 AA 0.0928 1.27 AL -0.3360 -3.33 BN 0.0022 -0.03 CO 0.0799 0.82 DL 0.0453 0.60 EA -0.1899 -2.88 FL -0.1928 -1.86 NW 0.0482 0.53 OZ -0.1812 -1.88 PA 0.2029 2.06 PI -0.20~ -2.22 RC 0.5561 -6.87 TW 0.1459 1.73 UA 0.1010 1.41 WA 0.1313 0.17

LOG LIKELIHOOD At Zero Slope: -263.62 -262.62 At Convergance: -44.74 13.59 R-Squared: 0.94 0.97

both models along with t-statis­ variable FIRM is replaced by its Analysis tics are shown in Table 4. For corresponding airline code in The sign of CI agrees with its A the purpose of comparison, the Table 4. PRIORI sign and the FREQ

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variable seems to be highly (i.e., hub dominance index) Atlanta area learn which carrier significant. Moreover, using the along with its share of pass·enger offers the greatest flight statistical hypothesis testing, one enplanements (in percentage frequency (Delta in this case) cannot reject the hypothesis that points). The observed pattern and given the existence of the coefficient u is greater than Delta's dominance of Atlanta information costs, passengers one in both model specifications. through frequency concentration tend to contact that carrier first. The relationship between is evident. Because Delta is the Information costs include the enplanement share and hub dominant airline at Atlanta its time and communication dominance seems, therefore, to share of passenger enplane­ expenses of calling several follow an S-curve meaning that ments is consistently higher than airlines and/or travel agencies. high frequency shares at a given its corresponding aircraft Passengers flying Delta via airport are associated with even departure share as depicted in Atlanta can enhance their higher enplanement shares, and Figure 1. chances of enjoying single-plane conversely. The disproportionality service. Furthermore, through For the purpose of illustration, phenomenon between an increased frequency concen­ we consider the case of Delta airline's enplanement shar.e and tration at Atlanta and a proper Airlines' operations at Atlanta its frequency share is attributed timing of flight arrivals and Hartsfield Airport between 1976 to passenger identification with departures, Delta Airlines can and 1986. Figure 1 depicts the the dominant carrier in the offer connecting passengers a trends in Delta's share of the region. For instance, local single-airline service (i.e., on-line total aircraft departures at Atlanta travelers originating in the connection,) thus reducing the

60

50

40

t • Delta's Aircraft c 30 B Departure Share is a. • Delta's Enplanement 20 Share

10

0 1976 1978 1980 1982 1984 1986 Year

Figure 1: Aircraft Departure Share and Enplanement Share for Delta Airlines at Atlanta Hartsfield Airport

passengers chances of missing line frequency competition as a bute to airline profitability unless a connection or losing baggage. means to dominate its major it is associated with airline CONCLUSIONS connecting hubs. It is concluded dominance of the main hub (or This paper attempts to shed that airline network centralization the few hubs) it uses for its light on the phenomenon of air- (hubbing) cannot by itself contri- operations. By dominating its

https://commons.erau.edu/jaaer/vol2/iss1/5JAAER, Fall 1991 27 DOI: https://doi.org/10.15394/jaaer.1991.1053 8 Ghobrial: Competition for Hub Dominance: Some Implications to Airline Profi Hub Dominance

main hubs, airlines can reap the in stochastic delay (i.e., changes impacts such as congestion, monopoly profits from the in aircraft size). Pacific South­ pollution, and noise. Congestion markets served by these hubs west Airlines appeared to have delay may outweigh some of the and realize higher enplanement adopted this strategy by benefits of aircraft concentration shares than departure shares. replacing many of its B-727 with by increasing flying costs for Several conclusions emerge the smaller Bae 146-100 and Bae airlines and travel time for from the above discussion. First, 146-200 aircraft in many routes passengers. in establishing a hub, airlines in the California corridor. Third, LIMITATIONS OF THE STUDY seem to follow a number of since there has been a trend in In attempting to assess the strategies including: 1) staying mergers between two airlines impact of hub dominance on away from competition by dominating the same hub. airline profitability and monopolizing the hub such as Examples are Northwest and enplanement share, it was USAir operations at Pittsburgh, Republic at Minneapolis, TWA assumed that flight frequency is and Piedmont at Charlotte; and and Ozark at St. Louis, and the only explanatory variable in 2) engaging in frequency Southwest and Transtar at the econometric models. Other competition to dominate the hub, Houston. One can view these variables contributing to airline and establishing regional identi­ mergers as a means to create a profitability and enplanement fication with passengers to monopoly power and charge share were accounted for by promote airline services. higher airfares. From the incorporating a firm-specific Examples are Delta at Atlanta, perspective of policy making, the variable in the models. While the American at Dallas/Fort Worth, issue of monopoly power ought results showed a positive strong and TWA at St. Louis. Second, to to be considered when relationship between profitability take advantage of the nonlinear approving mergers between and enplanement share on one relationship between passenger airlines hubbing at the same hand and hub dominance on the enplanements share and airport. other, further research is needed frequency share, airlines may Finally, while hub dominance to incorporate other hub elect to operate higher flight through frequency competition dominance strategies in the frequency with relatively smaller offers opportunities both for models. These include airfare size planes. Agarwal and Tally airlines and passengers, it may structure at the main hubs, (1985) showed that passengers pose problems as well. aircraft size, arrangements for are more sensitive to changes in Excessive frequency concentra­ traffic feeding through a subsid­ frequency delay (i.e., changes in tion at airports may result in iary, and frequency concentra­ flight frequency) than to changes some negative economic tion during peak periods.

Atef Ghobrial holds a Ph.D. in Transportation, a Master of Business Administration, and a Master of Science in Civil Engineering from the University of California at Berkeley. He is presently an Associate Professor at Georgia State University where he directs the aviation administration program. He has published several papers in the area of air transportation, and is a transportation consultant in the U.S. and abroad.

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Aviation Week and Space Technology, November 9, 1981, p. 49. Agarwal, V. and Talley, W. (1985) The Demand for International Air Passenger Services Provided by U.S. Carriers. 11 International Journal of Transport Economics, pp. 63-70. Douglas, G. and Miller III J. (1974). Economic Regulation of Domestic Air Transport: Theory and Practice, Brookings Institution, Washington, D.C.

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Fruhan Jr, W. (1972). The Fight for Competitive Advantage-A Study of the United States Domestic Trunk Carriers, Division of Research, Graduate School of Business Administration, Harvard University, Boston, Massachusetts. Ghobrial, A. and Sousa, J. (1987). Airline Strategies for Hub Dominance: A Statistical Analysis. Journal of Transportation Research Forum. Harvey G. (1986). Airport Choice in a Multiple Airport Region. Department of Civil Engineering, Stanford University. Kanafani, A. (1985). Transportation Demand Analysis, McGraw Hill, New York. Kanafani, A. and Ghobrial A. (1982). Aircraft Evaluation in Air Network Planning. 1I Transportation Engineering Journal ofthe American Society ofCivil Engineering, vol. 108, no. TE3, pp. 282-300. Kanafani, A. and Ghobrial, A. (1985). Airline Hubbing-Some Implications for Airport Economics. Transportation Research, Vol. 19A, No.1, pp. 15-27. Kanafani, A. and Hansen, M. (1985) Hubbing and Airline Costs. Research Report, Institute of Transportation Studies, Berkeley, UCB-ITS-RR-85-12. Miller, James. (1979). Airline Market Shares Versus Capacity Shares and The Possibility of Short-Run Loss Equilibria. Research in Law and Economics, Vol. 1, pp. 81-96. Renal, G. (1970). Competition in Air Transportation: An Econometric Approach. Unpublished MS thesis, Department of Aeronautics and Astronautics, MIT, Massachusetts. Toh, R. and Higgins, R. (1985). The Impact of Hub and Spoke Network Centralization and Route Monopoly on Domestic Airline Profitability. Transportation Journal. vol. 24, no. 4, pp. 16-27. U.S. Department of Transportation. The Federal Aviation Administration, Airport Activity Statistics of Certificated Route Air Carriers. Published Annually. U.S. Department of Transportation. Air Carriers Financial Statistics. Published Quarterly.

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