Transportation Research Part E 46 (2010) 109–119

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Transportation Research Part E

journal homepage: www.elsevier .com/locate / t re

Investigating the effects of economies of scope on firms’ pricingbehavior: Empirical evidence from the US domestic airline industry

Christian Hofer *, Cuneyt ErogluDept. of Marketing and Logistics, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR 72701, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 24 October 2008Received in revised form 26 May 2009Accepted 4 August 2009

Keywords:Economies of scopeBelly cargoAirfaresContingency frameworkUS airline industry

1366-5545/$ - see front matter � 2009 Elsevier Ltddoi:10.1016/j.tre.2009.08.010

* Corresponding author. Tel.: +1 479 575 6154.E-mail address: chofer@walton.uark.edu (C. Hofe

This study investigates to what extent cross-product (belly cargo) output affects (passengerticket) prices in the US domestic airline industry. The empirical analysis indicates thatgreater cargo volumes generally result in lower air fares, presumably as a result of the air-lines’ realization of economies of scope. This magnitude of this price effect, however,depends on certain firm and route market characteristics. The findings of this study addto extant research on economies of scope, multi-product yield management and airlinepricing and provide important insights for policy makers and airline managers alike.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

This paper empirically investigates the effects of passenger airlines’ air cargo operations on the pricing of passenger ser-vices using data from the US domestic airline industry. Air cargo services play an important role in the business models ofboth low-cost carriers and legacy carriers as they generate a second revenue stream in addition to that from passenger ser-vices. Air cargo services, besides supplementing revenues, also provide a relatively more stable growth opportunity for air-lines. Boyd (2006) notes that airlines expect that ‘‘cargo traffic [will continue] to grow more rapidly and reliably than thevolatile passenger sector” (p. 1). In a similar vein, Page (2007) notes that the ‘‘belly cargo trade [. . .] is showing improvedyield even as the passenger airlines tighten overall capacity.” Yet, the management of dual outputs—passenger and cargoservices—poses an array of challenges. While passengers and cargo compete for the limited capacity on an aircraft, airlinescurrently utilize separate revenue management systems for passengers and cargo (Graff, 2008), and thereby, risk suboptim-ization. However, the integration of revenue management systems for two distinctly different services remains a complexproblem for airlines (Becker and Dill, 2007). As such, the interaction of airlines’ freight and passenger operations has receivedmuch managerial interest.

The dual output nature of the airline industry has attracted substantial attention from academic researchers as well. Forexample, the interdependencies between passenger and cargo transportation services have been studied with respect tooperating costs (e.g. Zhang et al., 2004), capacity planning (Sandhu and Klabjan, 2006), flight scheduling (Tang et al.,2008), and revenue management decisions (e.g. Slager and Kapteijns, 2004). Yet, the effect of dual output operations inthe airline industry on the strategic (pricing) behavior of firms remains unexplored. This paper contributes to the extantliterature in three ways. First, while the existence of economies of scope in the airline industry has been well established,both theoretically and empirically (e.g. Gillen et al., 1990), the resulting impact on pricing mechanisms has not been

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110 C. Hofer, C. Eroglu / Transportation Research Part E 46 (2010) 109–119

addressed in the literature. Taking an industrial organization perspective, we propose and empirically test hypotheses on theeffects of cross-product output levels on the pricing behavior of airlines. Second, as airlines utilize separate revenue manage-ment systems for passenger and cargo segments (Graff, 2008) and explore ways to integrate these distinct systems into astreamlined revenue management system (e.g. Becker and Dill, 2007), our findings may provide important insights for suchinitiatives. Third, our findings add to the literature on airline pricing. Even though this mature stream of research has doc-umented the effects of many factors, such as market structure (e.g. Borenstein, 1989) and firm operating characteristics (e.g.Hofer et al., 2008), on pricing of air transportation services, the effect of cargo operations on passenger pricing remains unex-plored. As such, our findings contribute to a better understanding of airline pricing mechanisms.

The remainder of this article is organized as follows: Section 2 presents the research model. In this section, we provide abrief review of the relevant literature and propose formal hypotheses from an industrial organization perspective. In Section3, we present an empirical model to test the proposed hypotheses, describe data collection and model estimation issues. Sec-tion 4 presents the results. Section 5 contains a summary of findings, contributions to theory and practice, limitations, andsuggestions for future research.

2. Research model

The research model used in this paper is shown in Fig. 1. The first part of the research model describes the link betweendual output operations in the airline industry and the resulting economies of scope. This link has been widely acknowledgedby both practitioners and researchers. Gimeno and Woo (1999) note that ‘‘[i]n the airline industry, it appears that economiesof scope are strong and well understood by managers” (p. 255). From a theoretical perspective, an airline is a firm that sellstwo products (passenger and cargo service) which compete for the same finite capacity (Kasilingam, 1996). Zhang et al.(2004) demonstrate analytically that an airline’s marginal cost per passenger decreases as cargo volumes increase and econ-omies of scope are realized. This contention is supported by empirical evidence suggesting that an airline’s activity in multi-ple market segments (scheduled passenger, cargo, charter) results in lower operating costs (Leggette and Killingsworth,1983; Roy and Cofsky, 1985; Gillen et al., 1990). Antoniou (1992) finds that a passenger airline’s profitability increases withcargo volumes, suggesting that air carriers leverage at least some of the cost advantage arising from the realization of econ-omies of scope. The interested reader is referred to Antoniou (1991) for a comprehensive review of the literature on econ-omies of scope and scale in the airline industry. In this research, the existence of economies of scope and resulting costreductions is presumed and not explicitly modeled. Hence, these constructs are drawn with dotted lines in Fig. 1.

Despite ample theoretical and empirical evidence on the existence of economies of scope arising from the dual outputoperations in the airline industry, little is known about how they impact market outcomes. The second part of the researchmodel aims to fill this gap in the literature. Our main assertion is that the cost reduction attained through economies of scopeaffects the pricing behavior of airlines. Furthermore, we expect this effect to be moderated by market-specific and firm-spe-cific factors. More specifically, airlines may, in some instances, leverage cost reductions attained through economies of scopeto offer lower airfares in order to gain competitive advantage. This pricing effect should be observed to varying degreesdepending on market characteristics, such as competitive intensity, distance, and tourist orientation. Similarly firm charac-teristics like a carrier’s cost structure, market share, and load factors are expected to impact the effect of cross-product(cargo) output levels on (passenger) prices. The research model also allows for control variables that may affect a firm’spricing behavior. In sum, our research model proposes a contingency framework to explain the nature of the effect of dualoutput operations on an airline’s pricing behavior.

Domestic and international airlines markets have been greatly impacted by deregulation and globalization (Chen andChen, 2003). Intense competition has been a hallmark of the airline industry in the post-deregulation era (Kawasaki,

Economies of Scope

Passenger

Cargo

Cost Reduction

PricingBehavior

Firm-specific factors

Market-specific factors

Control Variables

H3, H4, H5

H6, H7

Part 1 Part 2

H1, H2

Fig. 1. Research model.

C. Hofer, C. Eroglu / Transportation Research Part E 46 (2010) 109–119 111

2007). Airlines have responded to increased competition with increased entry barriers such as alliances (Park, 1997; Lin,2008). Furthermore, airline markets tend be geographically restricted terms and defined as routes between two airports(Siegmund, 1990). As a result, most airline markets can be characterized as oligopolies (Shy, 2001). Moreover, prior researchhas modeled competition in the airline industry as various forms of oligopoly (e.g. Wei and Hansen, 2007). Given such a com-petitive environment, airlines may leverage any differential advantage in their cost structures. In other words, cost reduc-tions resulting from economies of scope may be passed onto passengers in the form of reduced air fares. We, therefore,propose Hypothesis 1 as follows:

H1. Greater cross-product output (cargo) volumes will result in lower prices (passenger fares), ceteris paribus.

Caves et al. (1980) have shown that the effect of cargo service output volume on costs is non-linear: their analysis of USrailroad data presents evidence of economies of scope, but also concludes that the magnitude of such economies generallydecreases as cargo volumes increase, thus suggesting that there is an ‘‘efficient scope of operations.” We may, therefore, ex-pect to find that the non-linearity of the cargo-cost relationship will also be reflected in airlines’ pricing behavior:

H2. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be decreasing in cargovolumes, ceteris paribus.

2.1. Firm-specific contingency factors

Firms in the airline industry have been differentiated with respect to their business models as legacy carriers and low-cost carriers.1 Oliveira and Huse (2009) list many operational tactics employed by low-cost carriers to achieve lower costs thanlegacy carriers (Boguslaski et al., 2004), which allows them to pursue aggressive pricing strategies (Tretheway, 2004). The costand pricing strategies of low-cost carriers also lead to different market outcomes from those of legacy carriers (Hofer et al.,2008). Thus, it is plausible that cargo output may differentially affect low-cost and legacy carriers’ pricing behaviors.

In addition, it is apparent, from US transportation statistics,2 that legacy carriers and low-cost airlines pursue differentgrowth strategies in the cargo segment. While the legacy carriers’ cargo volumes have generally decreased over the past fewyears, with relatively flat passenger numbers, the low-cost carriers’ freight numbers are on the rise, as are the passenger num-bers. At Southwest Airlines, for instance, the total cargo volume has increased by nearly 50% between 2000 and 2007, comparedto a 35% increase in the number of passengers. The differences in the legacy and low-cost carriers’ growth strategies may sug-gest that the airlines’ pricing behaviors are also differentially affected by cargo output, and hence cost reductions resulting fromthe realization of economies of scope.

H3. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be different for legacycarriers and low-cost carriers, ceteris paribus.

The following two hypotheses refer to two closely related constructs, namely market share and load factor, which areimportant performance measures in the airline industry. Airlines have an incentive to increase both their market shareand load factor. A dominant market share can give a carrier greater pricing power (e.g. Borenstein, 1989), while a higher loadfactor can enhance a carrier’s asset utilization, operational efficiency, and profitability (Siegmund, 1990; Chen and Chen,2003). The constructs of market share and load factor are tied together through the mechanism of capacity allocation. Asairlines decide flight schedules and assign aircraft to routes, they effectively allocate productive capacity to various routemarkets. Prior research has identified flight frequency in a given route market and aircraft capacity as significant determi-nants of market share and passenger demand (Wei and Hansen, 2005, 2007). In addition to affecting market share, the size ofaircraft serving a route market can impact load factors. As such, we presume an airline’s pricing decisions to have similareffects on market share and load factor, ceteris paribus.

As airlines realize economies of scope, they may pursue different pricing strategies depending on their market share in agiven route market. Airlines with low market shares may use cost savings arising from economies of scope to lower airfaresand to increase passenger demand. Conversely, airlines with dominant market shares may enjoy pricing power (Hofer et al.,2008) and may not feel compelled to leverage cargo revenues to lower passenger fares. Therefore, we hypothesize that:

H4. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be decreasing in routemarket shares, ceteris paribus.

A similar argument can be made for carriers’ passenger load factors which have been shown to be an important differ-entiator of airline operations (Dai et al., 2005). When load factors are high, there is little to no excess capacity, and the airlinemay have pricing power. Conversely, airlines may pass on cost savings arising from economies of scope to lower ticket pricesand increase load factors (Elliott, 2002). Hence, it is hypothesized that:

1 While there is no unique definition of the two types of carriers, a widespread assumption appears to be that all airlines that operated prior to the USdomestic airline industry’s deregulation in 1978 are considered legacy, or ‘‘high-cost”, carriers. All airlines that started operations after the US air travel marketwas deregulated are regarded as low-cost carriers. The only notable exception is Southwest Airlines which, due to its different business model, significantlylower operating costs, and typically lower fares, is considered a low-cost carrier.

2 Source: US Department of Transportation Bureau of Transportation Statistics, Form 41 Traffic data.

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H5. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be decreasing in loadfactors, ceteris paribus.

2.2. Market-specific contingency factors

Turning to the market-specific contingency factors, it is expected that the effect of air cargo on passenger fares may varywith the length of the route market. US DOT statistics (2003–2007) show that cargo loads were, on average, 37% higher inlong-haul route markets than in short-haul route markets. Moreover, many carriers use larger aircraft with greater cargocapacity in long-haul markets. There is, thus, greater potential for the realization of economies of scope in long-haul marketsthan in short-haul markets. We therefore expect the effect of cargo output on airfares to be greater in long-distance routemarkets.

H6. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be increasing in distance,ceteris paribus.

A second market-specific contingency factor that may affect airline pricing behavior is a route’s tourism orientation. Priorresearch commonly defines tourist routes as those route markets that either originate from or end in Florida or Nevada (e.g.Dresner et al., 1996). Price typically is the key competitive factor in route markets with higher shares of tourist travelers(Dresner et al., 1996). We therefore expect that airlines will leverage cost reductions arising from economies of scope to offercompetitive prices particularly in price sensitive tourist markets. The negative effect of cargo loads on ticket prices will, thus,be greater in tourist markets.

H7. The marginal effect of cross-product output (cargo) volumes on prices (passenger fares) will be greater in touristmarkets than in non-tourist markets, ceteris paribus.

3. Data and methodology

3.1. Data sources

We compile a panel data set where each observational unit represents a particular airline’s activities (e.g. passenger vol-ume, cargo volume, airfares) on a specific origin and destination (O&D) route market during a given quarter between 2003and 2006. The resulting data set includes about 65,000 quarterly O&D route market carrier level observations from the 2000largest US domestic route markets.3 The data were obtained from the US Department of Transportation’s DB1A database, whichcomprises detailed information about every tenth ticket sold in the US.4 In addition, airline-specific financial and operating datawere retrieved from other data sets compiled and published by the US Bureau of Transportation Statistics.5 Their richness interms of volume, detail, and quality explain the popularity of US domestic airline industry data in empirical research (seee.g. Borenstein, 1989; Morrison, 2001; Hofer et al., 2005).

3.2. Variables and descriptive statistics

The following data items were collected for analysis: unit price (fare), output level for passenger services (airlinepax), out-put level for cargo services (freight, mail), distance between an O&D airport pair (miles), tourist orientation of a route market(tourist), circuity factor of an airline’s routing (circuity), load factor of a carrier (loadfactor), an airline’s operating cost (airline-cost), a firm’s financial health (zscore), and competitive characteristics of a route market and the O&D airports (routeshare,maxairportshare, routehhi, maxairporthhi), and population and income levels in the metropolitan areas of the O&D airports(population, income). Table 1 displays some descriptive statistics for these variables.6 A more detailed definition of thesevariables is provided in Appendix A. The output variables (airlinepax, freight, mail) as well as the miles variable are clearlyskewed. Hence, we take the natural logarithm of these variables prior to the empirical estimation. The routehhi and max-airporthhi variables are also logged to facilitate the interpretation of the coefficient estimates.

Note that whereas transportation of freight and mail can have similar operational implications for an airline, there aresignificant differences between these services in terms of demand and pricing characteristics. Freight service is providedby many airlines to meet the demand of many individuals or firms. However, mail service demand is generated by a singleentity, namely, the US Postal Service. Therefore, we have included mail in our econometric model to account for any effect it

3 It is noted that freight volumes are reported on a segment rather than on an O&D basis only. The data set used here includes only those route-carrierobservations for which freight volumes (even if zero) are reported.

4 The data were purchased from Database Products Inc., a reseller of DB1A data. Database Products screens the data for potential data entry and reportingerrors and prepares customized data sets.

5 These data sets include, most notably, Form 41 Financial Data, Schedules B1 and P12, and Form 41 Traffic, Domestic Segment data.6 As noted by an anonymous referee, there are some observations with very low passenger counts (as low as 1) and low load factors (as low as 0.76%). We

investigated whether excluding such marginal observations (with fewer than five passengers and/or load factors lower than 20%) changes the estimationresults, and found that the coefficient estimates are robust.

Table 1Descriptive statistics (N = 64,845).

Variable Mean Standard deviation Minimum Maximum

Fare ($) 141.66 49.55 30.79 1251Airlinepax 1672 2712 1 35,203Freight (lbs) 95,610 536,456 0 20,200,000Mail (lbs) 55,798 202,953 0 7478,903Miles 1234 673 109 2724Tourist 0.27 0.45 0.00 1.00Circuity 1.10 0.18 0.93 5.01Loadfactor (%) 74.32 9.44 0.76 100.00Airlinecost ($) 0.11 0.02 0.04 0.28Zscore �0.71 5.43 �81.05 21.49Routeshare (%) 31.57 30.24 0.00 100.00Maxairportshare (%) 26.51 22.11 0.00 100.00Routehhi 4575 2079 1153 10,000Maxairporthhi 3077 1354 813.14 10,000

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may have on passenger fares. However, we exclusively rely on the freight variable to formally test the hypotheses proposedabove, even though the effect of mail on airfares mirrors that of freight according to our results.7

While we do not show a correlation table due to space constraints, we note that the output variables (airlinepax, freight,mail) are naturally highly correlated, with correlations of between 0.54 and 0.65. For the most part, however, the correlationcoefficients are relatively small (generally less than 0.5). Given the lack of excessive correlations between any two variables,we note that the only risk potentially resulting from the previously mentioned high correlations is some degree of varianceinflation, which may weaken the significance levels of the statistical results.

3.3. Model specification

We specify the following econometric model (Eqs. (1) and (2)) to test the proposed hypotheses by closely following somepreviously published research in the aviation economics field (e.g. Borenstein, 1989; Hofer et al., 2005). Microeconomic the-ory suggests that prices and output levels are determined simultaneously in a given route market at a particular point intime. More specifically, changes in ticket prices (fare) can influence passenger demand (airlinepax). Similarly, fluctuationsin passenger demand (airlinepax) can put upward or downward pressure on prices (fare). In line with prior research, addi-tional firm and market-specific control variables are also included in the model. Thus, we formulate the following model:

7 Det

lnðairlinepaxrktÞ ¼ a0 þ a1lnðfarerktÞ þ a2lnðmilesrÞ þ a3circuityrk þ a4touristr þ a5zscorekt þ a6loadfactorkt

þ a7airlinecostkt þ a8lnðpopulationrtÞ þ a9lnðincomertÞ þ a10lnðfreightrktÞ þ a11lnðmailrktÞ

þX

firm fixed effectsk þX

time fixed effectst þ urkt ð1Þ

lnðfarerktÞ ¼ b0 þ b1lnðairlinepaxrktÞ þ b2lnðmilesrÞ þ b3circuityrk þ b4touristr þ b5zscorekt þ b6loadfactorkt

þ b7airlinecostkt þ b8lnðroutehhirtÞ þ b9lnðmaxairporthhirtÞ þ b10routesharerkt

þ b11maxairportsharerkt þ b12lnðfreightrktÞ þ b13lnðmailrktÞ þ b14½lnðfreightrktÞ�2 þ b15½lnðmailrktÞ�2

þX

firm fixed effectsk þX

time fixed effectst þ v rkt ð2Þ

where subscripts r, k, and t denote a route market, a carrier, and a time period, respectively, and urkt and vrkt are error terms.Note that the squared terms at the end of Eq. (2) were added to capture any non-linear effects of cargo volumes on fares. Inline with previous research, we include firm and time fixed effects to account for unobserved heterogeneity (e.g. Oliveira andHuse, 2009).

3.4. Estimation methodology

As mentioned above, fare and airlinepax are endogenous variables whose values are determined simultaneously within asystem of linear equations. Estimating such a system of equations using the ordinary least squares (OLS) method wouldresult in biased estimates. Thus, we use two-stage least squares (2SLS) and three-stage least squares (3SLS) methods for esti-mation. Both 2SLS and 3SLS estimators are consistent but 3SLS is asymptotically more efficient than 2SLS while also moresensitive to misspecification of the model (Kennedy, 2003). Differences between the 2SLS and 3SLS results should be smallif the model is adequately specified. While we report the 3SLS estimates only, we note that these estimates are largely

ailed results are available from the authors upon request.

Table 23SLS estimation results (N = 64,845).

Panel A: passenger model Panel B: fare model

Variable Coefficient Variable Coefficient

Intercept �15.7563 Intercept 2.5198ln(fare) 6.9086 ln(airlinepax) 0.0576ln(miles) �2.1740 ln(miles) 0.2886Circuity �5.0311 Circuity 0.4942Tourist 1.3116 Tourist �0.1644Zscore �0.0089 Zscore 0.0020Loadfactor 0.0388 Loadfactor �0.0045Airlinecost �7.2598 Airlinecost 0.8990ln(population) 0.0045 ln(routehhi) �0.0209ln(income) 0.4333 ln(maxairporthhi) �0.0107ln(freight) 0.0886 Routeshare 0.0015ln(mail) 0.0410 Maxairportshare 0.0006

ln(freight) �0.0143ln(mail) �0.0027ln(freight)-squared 0.0008ln(mail)-squared 0.0001

Note: All coefficient estimates are significant at p < 0.0001 level.

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consistent with the 2SLS estimates in terms of their signs and statistical significance levels. In our analyses, hypothesis testresults are invariant to the estimation method used.

4. Empirical results and discussion

4.1. Results for the baseline econometric model

Table 2 shows the baseline estimation results. Panel A presents the estimation results for Eq. (1) where the dependentvariable is the number of passengers (airlinepax), and Panel B for Eq. (2) where the dependent variable is passenger fares(fare). Note that time fixed effects and firm fixed effects are not shown in the interest of space. The system weighted R-squared statistic is 0.57, suggesting that the models explain nearly 57% of the variability in the data.

Panel A in Table 2 reveals that many variables have the expected signs: on average, passenger numbers are lower for morecircuitous routings, higher in markets with larger shares of tourist traffic, lower when an airline’s operating costs are higher,and higher in route markets connecting more densely populated and wealthier metropolitan areas. In addition, we note thatpassenger numbers are also larger when freight and mail volumes are larger. This is likely a size effect. In route markets withgreater capacity, i.e. more frequent flights and larger aircraft, passenger, freight and mail volumes will be greater than inmarkets with smaller capacity. The only unexpected coefficient is that of the endogenous fare variable. This result, however,should not be read as indicating that passenger numbers increase with fares. Rather, the opposite seems to be the case, as canbe seen in the fare model which we discuss next.

In the fare model (Panel B, Table 2), fares are shown to increase as passenger demand increases.8 Similarly, fares tend tobe higher on longer routes and on more circuitous routes. As expected, fares in tourist markets are lower, and healthy car-riers with higher Z scores are able to charge higher prices than their distressed competitors. Higher load factors result in low-er ticket prices, and carriers with higher operating costs sell their tickets at higher prices, all else equal. The coefficients of theroute and airport concentration variables are negative, suggesting that fares in less competitive (i.e. more concentrated) mar-kets are actually slightly lower than fares in more competitive (i.e. less concentrated) markets. While this result is may seemcounterintuitive, we note that market shares are controlled for separately. Holding the latter constant, an increase in marketconcentration, thus, implies an increase in a competitor’s market power which may spark competitive pricing actions. Themarket share variables, however, carry the expected positive and significant coefficients, indicating that airlines can capital-ize on their power in route and airport markets by charging higher fares.

The coefficients of the freight and mail variables are of particular interest in this study. Both coefficients are negative andstatistically significant. This finding suggests that passenger fares tend to decrease when passenger airlines carry largerfreight or mail volumes. Thus, the results suggest that any economies of scope the carriers may realize are, at least in part,passed onto passengers by means of lower air fares. This finding is consistent with the theoretical contention of Zhang et al.(2004). Thus, Hypothesis 1 is supported.

The squared terms of the freight and mail variables are included in the fare model (Eq. (2)) to test the hypothesis that theeffect of cargo on fares is decreasing with cargo volumes (Hypothesis 2). This contention is confirmed by the positive and

8 While many researchers may expect a negative effect of passenger volumes on fares (suggesting returns to density), Dresner and Tretheway (1992)demonstrate both theoretically and empirically that a positive coefficient is not unexpected. The interested reader is referred to Dresner and Tretheway (1992)for a more detailed discussion.

Table 3Summary results for hypothesis tests.

Hypothesis Grouping N bfreight Conclusion

H3: Firm type Legacy carrier 47,564 �0.0132* SupportedLow-cost carrier 17,280 0.0224*

Low-cost carrier (short-haul) 4266 �0.0056*

Low-cost carrier (long-haul) 4391 0.0517*

H4: Route share 1st quartile (<5.34%) 16,206 �0.0102* Supported4th quartile (>53.41%) 16,207 0.0075*

H5: Load factor 1st quartile (<70.26%) 15,966 0.0011 Not supported4th quartile (>80.15%) 16,152 �0.0087*

H6: Distance 1st quartile (<689 miles) 16,188 �0.0007 Supported4th quartile (>1739 miles) 16,172 �0.0074*

H7: Route type Non-tourist 47,026 �0.0069* SupportedTourist 17,818 �0.0150*

* Significant at p < 0.01.

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significant coefficient of the squared terms. While the effect of cargo loads on passenger fares is always negative, the mag-nitude of this effect decreases as cargo volumes increase and economies of scope are exhausted. Thus, it appears that pas-senger fares are impacted to a greater extent when the airline carries moderate rather than large amounts of belly cargo. Thisfinding is intuitively appealing considering that for most passenger airlines cargo operations are merely an ancillary revenuesource (Kasilingam, 1996). Dealing with particularly large cargo volumes may, therefore, represent an operational burdenthat partially negates the economic benefit of dual output operations. Hence, Hypothesis 2 is supported.

4.2. Results for the contingency framework

In order to test Hypotheses 3–7, which are based on our contingency framework, we have estimated our baseline econo-metric model under different scenarios and compared the marginal effect of cargo output (freight) on prices (fare). In theinterest of clarity of exposition, we have dropped the square terms from the econometric model which would have compli-cated comparisons and only report coefficient estimates for the freight term in our fare model (Eq. (2)) in Table 3. The fullregression results are largely consistent with those reported in Table 2 and are available from the authors upon request.

Hypothesis 3 refers to the differential effect of cargo on fares, depending on an airline’s business model, i.e. legacy carriersversus low-cost carriers. After dividing the sample into legacy and low-cost carrier observations, each subsample was ana-lyzed separately, and the respective coefficients of the freight variable are reported in Table 3. The marginal effect of cargooutput is negative and significant (�0.0132) for legacy carriers and positive and significant (0.0224) for low-cost carriers,indicating a clear difference between legacy and low-cost carriers. Therefore, Hypothesis 3 is supported.

There may be at least two explanations for this observation. First, low-cost carriers may not see an economic need to passon any potential cost savings resulting from the realization of economies of scope. Higher-cost legacy carriers, in turn, mayleverage such economies of scope to lower fares and compete more effectively in those markets where higher cargo volumeshelp reduce the specific operating costs per available passenger seat-mile. This logic is consistent with Gimeno and Woo’s(1999) conclusion that the presence of economies of scope results in increased financial performance only when firms havea cost advantage relative to their competitors. By definition, low-cost carriers have this cost advantage and can, thus, in-crease profits by leveraging any additional cost savings resulting from the realization of economies of scope. Legacy(high-cost) airlines, in contrast, may have to pass on such economies to customers to remain competitive.

Second, all airlines classified as low-cost carriers in this research operate fleets of narrow-body aircraft only. Dependingon the stage length of a particular route, take-off weights and available passenger and cargo payload capacity may thereforebe constrained.9 This implies that passengers and cargo compete for a finite amount of capacity in at least some long-haul routemarkets. As such, an increase in freight capacity allocation could potentially limit the passenger seat inventory available for sale,resulting in higher passenger fares. We investigate this contention by separately analyzing low-cost carriers’ short-haul andlong-haul routes. Specifically, we rank all low-cost carrier observations by route length and perform estimations on the firstquartile (all routes shorter than 590 miles) and last quartile (all routes longer than 1492 miles) of all observations. For theshort-haul sample, the coefficient of the freight variable is statistically significant and negative (�0.0056). For the long-haulsample, in turn, the coefficient of the freight variable is significant and positive (0.0517). This finding supports the contentionthat larger cargo volumes will lead to higher passenger fares in capacity constrained long-haul route markets. As expected, thisphenomenon is not observable in (presumably unconstrained) short-haul markets.

Hypothesis 4 suggests that the marginal effect of cargo output on price will be smaller as a carrier’s route market shareincreases. To test this hypothesis, we split the sample into four quartiles with respect to the route market share and compare

9 On comparatively short routes (e.g. Dallas to Houston) there will be virtually no capacity constraints imposed on either passengers or belly cargo (otherthan physically available space). Conversely, there will most likely be binding capacity constraints when relatively small aircraft (e.g. an Airbus 320 or a Boeing737) are deployed on relatively long routes (e.g. New York to Los Angeles).

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the estimates for low (1st quartile) and high (4th quartile) route market share observations. The coefficient estimate of thefreight variable is negative and statistically significant (�0.0102) for low market share observations. When market shares arehigh, however, the positive coefficient (0.0075) indicates that airlines actually charge higher ticket prices as cargo volumesincrease. This finding confirms our contention that cargo volumes will differentially affect an airline’s pricing behaviordepending on the carrier’s route market shares and, thus, pricing power. When market shares are low, airlines leverage econ-omies of scope resulting from cargo operations to lower passenger fares in an attempt to increase market shares. When mar-ket shares are high, in turn, airlines have no incentive to lower prices and may even increase passenger fares. Consequently,Hypothesis 4 is supported.

A similar argument was proposed for load factor as a moderating variable (Hypothesis 5). The effect of cargo on fares ishypothesized to be greater when an airline’s load factor in a given route market is lower. This contention, however, is notconfirmed by the empirical results: freight appears to have no significant effect on fares when load factors are relativelylow (0.0011, statistically insignificant). When load factors are high, in turn, the results unexpectedly suggest that greaterfreight volumes result in lower air fares. Therefore, Hypothesis 5 is not supported. This finding may point toward a needto better integrate passenger and cargo yield management systems (Graff, 2008).

Market characteristics such as route length and tourist orientation were also hypothesized to moderate the relationshipbetween cargo output and prices. Hypothesis 6 suggests that the negative marginal effect of cargo on passenger fares will begreater on longer routes than on shorter routes since the potential for the realization of economies of scope is greater in long-haul route markets due to higher demand for cargo services and because larger aircraft (typically used on longer haul routes)have greater cargo capacity. The coefficient estimates shown in Table 3 provide support for this contention. While freightdoes not significantly impact fares in short-haul markets, this effect is statistically significant and negative in longer haulmarkets (�0.0074). Hence, Hypothesis 6 is supported.

Hypothesis 7 proposed that the effect of cargo volumes on prices should be greater in tourist markets where customersare more price sensitive and cost reductions resulting from economies of scope can be used to lower fares and compete moreeffectively. The empirical results confirm that the negative effect of freight on fares is about twice as large in tourist routemarkets (�0.0150) than in non-tourist markets (�0.0069). Thus, Hypothesis 7 is supported as well.

5. Concluding remarks

5.1. Summary of findings

Results from our analysis confirm some basic contentions about economies of scope in the airline industry arising fromthe joint provision of passenger and cargo services. Furthermore, our findings add to the extant literature by providing addi-tional insights into how the economies of scope affect airlines’ pricing behavior. First, we find evidence indicating that great-er belly cargo volumes generally result in lower air fares, presumably as a result of the airlines’ realization of economies ofscope. Second, as cargo volumes increase and economies of scope are exhausted, this effect decreases in magnitude. Thesefindings provide empirical evidence confirming prior research.

We further find that the effect of cargo output on passenger service prices is moderated by both firm-specific and market-specific characteristics. This effect is greater for legacy carriers than for low-cost carriers. Moreover, the negative effect ofcargo on prices is greater when airlines have low route market shares. We also find evidence that the impact of cargo vol-umes on passenger fares is greater in long-haul markets than in short-haul markets. Likewise, the impact of cargo loads onfares is larger in tourist markets than in non-tourist markets. Thus, we find strong empirical support for the theoreticalframework shown in Fig. 1. Only one hypothesis, that relating to load factor as a moderating variable is not supported.Yet, this finding is interesting, too, since it may be an indication of the insufficient integration of cargo and passenger yieldmanagement practices.

5.2. Contribution to theory

This study contributes to three streams of literature: (1) economies of scope in the airline industry, (2) multi-productyield management, and (3) airline passenger pricing. The extant literature on economies of scope in the airline industryhas established that carriers can achieve economies of scope by joint production of services in multiple segments such asscheduled passenger service, charter service, and freight service (Leggette and Killingsworth, 1983; Roy and Cofsky, 1985;Gillen et al., 1990; Antoniou, 1992; Zhang et al., 2004). Developing an analytical model, Zhang et al. (2004) posit that econ-omies of scope from the joint production of passenger and cargo services should lead to cost reductions for an airline. Thesecost savings could potentially be passed on passengers or be retained to boost firm profitability. Empirical evidence for thelatter was found by Antoniou (1992). However, there was a gap in the literature on the existence of the former. The findingsin this study fill this gap by providing empirical evidence that airlines pass on at least some of the cost saving from econo-mies of scope to consumers (passengers).

This paper further extends the literature on economies of scope in the airline industry by investigating a number of con-tingency factors that drive the pricing behavior of airlines in the presence of economies of scope. When faced with a decisionas to what portion of cost savings to pass onto passengers and what portion to retain for enhancing profitability, an airline

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may behave differently depending on market-specific and firm-specific factors. We show that an airline’s business model(low-cost or legacy carrier), market position, and load factor, as well as a particular route’s length and type (tourist ornot), affect the extent to which economies of scope are reflected in passenger prices.

Furthermore, our empirical analysis indicate that there may be an ‘‘efficient scope of activities” where an airline reapsmaximum cost benefits from joint production of multiple services. Cost savings realized through economies of scope seemto follow a non-linear and decreasing functional form, similar to the one found in the railroad industry (Caves et al., 1980).This finding points to an interaction between economies of scope and economies of scale. The extent and scope of this inter-action remains an interesting topic for future research.

The second stream of literature to which our study contributes is the multi-product operations and yield managementliterature. Kasilingam (1996) notes that airlines traditionally schedule passenger services first and then market cargo capac-ity as a by-product of passenger services. Researchers have, therefore, focused on developing multi-product scheduling andcontrol models (e.g. Tang et al., 2008; Sandhu and Klabjan, 2006) and integrated yield management systems (e.g. Billingset al., 2003; Slager and Kapteijns, 2004; Becker and Dill, 2007; Graff, 2008). None of the latter studies, however, specificallyconsider the possibility that increases in one output (e.g. belly cargo) may affect sales prices of the other output (e.g. pas-senger fares) and, hence, revenues. The lack of a holistic framework under which to study multi-product operations inthe airline industry may lead to suboptimal results. The findings of this study are, therefore, relevant to the theory andpractice of multi-product yield management. Specifically, as researchers build microeconomic models for the airlineindustry, our findings could serve as guidelines for an optimal approach. First, future research should consider economiesof scope and economies of scale simultaneously to capture their interaction. Second, market-specific and firm-specific factorsstudied in this paper must be taken into account as they drive the pricing behavior of airlines and affect major marketoutcomes.

The third literature stream to which this study contributes is concerned with investigating the factors impacting passen-ger fares. Specifically, prior studies have examined the effects of market structural variables such as market shares and mar-ket concentration levels (e.g. Dresner and Tretheway, 1992; Dresner and Windle, 1992; Evans and Kessides, 1993; Borensteinand Rose, 1994; Lin et al., 2001), operating characteristics such as network design (e.g. Brueckner et al., 1992, 2001; Kawa-saki, 2007) and other firm-specific factors such as firm financial condition and operating cost structure (e.g. Hofer et al.,2005) on ticket prices. Most of these studies aim at providing insights into airlines’ pricing strategies and assessing the allo-cative efficiency of air travel markets. While this research stream is well developed, it is noteworthy that no prior study hasconsidered carriers’ freight volumes as an explanatory factor of passenger ticket prices. This is rather surprising given thatthe importance of considering multiple outputs has long been recognized in the economics literature on (airline) cost func-tions (e.g. Caves et al., 1980). The effect of freight output on the supply function may, therefore, be expected to also affect themarket outcome in terms of passenger fares. This article, thus, proposes improvements to the specification of extant faremodels by quantifying how an airline’s cargo output will affect passenger prices.

5.3. Contribution to practice

This paper provides insights that are relevant to airline managers. A major challenge for airlines is the integration of rev-enue management systems for passenger and cargo operations (Becker and Dill, 2007; Graff, 2008). At present, airlines useseparate revenue management systems for pricing these two different services. Ignoring the important interdependenciesand potential synergies between these two services may lead to suboptimal pricing decisions. This could negatively affectan airline’s profitability as well as its market share. For example, our analysis reveals that market shares and load factorshave unexpectedly opposite effects on the extent to which cargo outputs impact an airline’s pricing behavior. This may sug-gest that passenger and cargo pricing mechanisms are, on average, not yet sufficiently integrated, although further studiesare needed for a definite conclusion. However, even by itself, this observation points to the need for streamlined manage-ment of revenue streams for multi-product airline operations.

Policy makers and researchers should also take interest in our findings: the 9/11 Commission Act of 2007 requires that byFebruary 2009, 50% of all belly freight carried on US passenger aircraft be subject to security screenings. By August 2010, allbelly cargo must be screened. The implications of this legislation for airlines may be substantial: these screening require-ments will most likely add to the cost10 and average air cargo transit times, thereby eroding some of this mode’s distinctadvantages. As a result, demand for air cargo transportation services and, thus, airlines’ cargo revenues, may be expected to de-cline. According to our results, this decrease in belly cargo volumes may also lead to an increase in passenger fares. This exampleillustrates the importance of considering the interactions between airlines’ passenger and freight operations. This study will,thus, help policy makers assess the implications of air cargo related policy decisions for the passenger segment.

5.4. Limitations and future research

Like any study, our study has limitations. First, we consider only US domestic air travel market. Thus, our results may notbe generalized to international passenger and cargo markets. Second, some relevant data were not available for our analysis,

10 According to Ott (2008), the cargo security screening requirement involves an estimated $4 billion cost over a 10 year time period.

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such as route market level costs, profitability and cargo rates. Such data would have complemented our findings on the effectof cargo volumes on passenger prices with findings on the effect of passenger volumes on cargo volumes.11

There are ample opportunities for future research in this area: for instance, the effect of passenger volumes and other firmand market characteristics on (belly) freight rates is unknown. Gaining a better understanding of the mutual effects andinteractions of these cargo and passenger services would be useful, especially in the integration of revenue management sys-tems for cargo and passenger operations.

Studying the effects of joint production of cargo and passenger services on the competitive landscape in the airline indus-try is another interesting future research opportunity. As a network industry, airline markets provide unique entry barriersfor new competitors. Pricing of cargo and passenger services not only maximizes a carrier’s profits but also serves as an entrybarrier for competitors. As such, studies of pricing strategies designed for providing a more competitive air travel industrymay be of interest to researchers and policy makers.

Acknowledgments

The authors would like to thank the editor and two anonymous referees for their constructive comments on earlier ver-sions of this paper. In addition, we thank seminar participants at the University of Maryland and Martin Dresner and BobWindle, in particular, for their insights and guidance. The support provided by the Supply Chain Management Research Cen-ter at the Sam M. Walton College of Business, University of Arkansas is also greatly appreciated.

Appendix A. Description of variables

Note that the subscript r denotes a particular route market, k a particular firm (carrier), and t a particular time period(quarter).

i. Farerkt is the average ticket price charged by a carrier on a given route market.ii. Airlinepaxrkt is the number of passengers carried by an airline in a particular route market in a given time period.

iii. Milesr is a measure of the distance between origin and destination airports in specific route market.iv. circuityrk is a variable that measures the degree to which an airline’s routing deviates from the optimal direct routing

in case passengers have to make an intermediate connection between the origin and destination airports.v. Touristr is a binary variable which identifies route markets as being likely to have a larger proportion of tourist trav-

elers. All route markets that either originate from or end in Florida or Nevada are designated as tourist routes (Dresneret al., 1996).

vi. Zscorekt is a composite measure of a firm’s financial condition (approximated by Altman’s Z score) and has been shownto impact fares (Hofer et al., 2008).

vii. Loadfactorkt is an indicator of the airline’s average passenger occupancy rates. Prior research has typically found thathigher load factors are associated with lower fares, ceteris paribus (see e.g. Hofer et al., 2008).

viii. Airlinecostkt is measured as an airline’s operating cost per available seat-mile. This variable is also used to identifyidentifies low-cost and legacy carriers using the procedure suggested by Hofer et al. (2005): we define low-cost car-riers based on the residuals of a regression of Airlinecost on a carrier’s average stage length. Doing so allows us toadjust operating costs for differences in the average route distance. Out of a total of 33 airlines included in our dataset,we define 16 as low-cost carriers. While the cutoff is, naturally, somewhat arbitrary we note that there is a clear costgap between the low-cost airline group and the group of legacy carriers12.

ix. Populationrt is the product of the metropolitan populations of the origin and destination airport areas.x. Incomert is the weighted average income of the origin and destination metropolitan areas.

xi. Routehhiijt is a measure of the degree of market concentration of a route market and is calculated as the sum of thesquared market shares of all airlines operating in the route market (Herfindahl–Hirschmann Index).

xii. Maxairporthhirt is the larger of the origin and destination airports’ enplanement HHIs which measures the competitivecharacteristics of the origin and destination airports. The underlying logic is that if one or both of the route’s endpointsare dominated by one or few carriers, then access to the route market may be difficult for potential new entrants andfares may be expected to be higher.

xiii. Routeshareijt is an airline’s market share in a given route market and is an estimate of the degree of market power anairline may have in a particular market.

xiv. Maxairportsharerkt indicates the larger of the focal airline’s enplanement shares at the origin and destination airports.

11 A referee recommended using IATA TACT rates as a reference. Another referee also suggested investigating how integrators impact legacy and low-costcarriers’ freight rates.

12 The following airlines are categorized as low-cost airlines: Frontier Airlines, Airtran Airways, Mesa Airlines, Southwest Airlines, Trans States Airlines,Midwest Airlines, Alaska Airlines, SkyWest Airlines, Atlantic Southeast Airlines, Comair, Spirit Air Lines, PSA Airlines, USA 3000, Express Jet, Sun CountryAirlines, JetBlue Airlines.

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xv. Freightrkt is the amount of cargo (other than mail) that is transported by a carried in a route market during a given timeperiod.

xvi. Mailrkt is the amount of mail that is transported by a carried in a route market during a given time period.

References

Antoniou, A., 1991. Economies of scale in the airline industry: the evidence revisited. Logistics and Transportation Review 27 (2), 159–184.Antoniou, A., 1992. The factors determining the profitability of international airlines: some econometric results. Managerial and Decision Economics 13 (6),

503–514.Becker, B., Dill, N., 2007. Managing the complexity of air cargo revenue management. Journal of Revenue and Pricing Management 6 (3), 175–187.Billings, J., Diener, A., Yuen, B., 2003. Cargo revenue optimization. Journal of Revenue and Pricing Management 2 (1), 69–79.Borenstein, S., 1989. Hubs and high fares: dominance and market power in the U.S. airline industry. RAND Journal of Economics 20 (3), 344–365.Borenstein, S., Rose, N., 1994. Competition and price dispersion in the U.S. airline industry. The Journal of Political Economy 102 (4), 653–683.Boguslaski, R., Ito, H., Lee, D., 2004. Entry patterns in the Southwest Airlines route system. Review of Industrial Organization 25 (3), 317–350.Boyd, J., 2006. United studies freighter market. Journal of Commerce (July 24), 40–41.Brueckner, J., Dyer, N., Spiller, P., 1992. Fare determination in airline hub-and-spoke networks. The Rand Journal of Economics 23 (3), 309–333.Brueckner, J., Dyer, N., Spiller, P., 2001. A model of scheduling in airline networks—how a hub-and-spoke system affects flight frequency, fares and welfare.

Journal of Transport Economics and Policy 35 (2), 195–222.Caves, D., Christensen, L., Tretheway, M., 1980. Flexible cost functions for multiproduct firms. The Review of Economics and Statistics 62 (3), 477–481.Chen, F., Chen, C., 2003. The effects of strategic alliances and risk pooling on the load factors of international airline operations. Transportation Research Part

E, Logistics and Transportation Review 39 (1), 19–34.Dai, Y., Raeside, R., Smyth, A., 2005. The use of load factors to segment airline operators. Journal of Revenue and Pricing Management 4 (2), 195–203.Dresner, M., Lin, J., Windle, R., 1996. The impact of low cost carriers on airport and route competition. Journal of Transport Economics and Policy 30 (3), 309–

328.Dresner, M., Tretheway, M., 1992. Modeling and testing the effect of market structure on price. The case of international air transport. Journal of Transport

Economics and Policy 26 (2), 171–184.Dresner, M., Windle, R., 1992. Airport dominance and yields in the U.S. airline industry. Logistics and Transportation Review 28 (4), 319–339.Elliott, T., 2002. Maximising revenue production while cutting costs: an airline industry mandate. Journal of Revenue and Pricing Management 1 (2), 355–

368.Evans, W., Kessides, I., 1993. Localized market power in the U.S. airline industry. The Review of Economics and Statistics 75 (1), 66–75.Gillen, D., Oum, T., Tretheway, M., 1990. Airline cost structure and policy implications: a multi-product approach for Canadian airlines. Journal of Transport

Economics and Policy 24 (1), 9–34.Gimeno, J., Woo, C., 1999. Multimarket contact, economies of scope, and firm performance. Academy of Management Journal 42 (3), 239–259.Graff, J., 2008. Revenue management for the whole aircraft: coordinating acceptance decisions for passenger and cargo transportation. Journal of Revenue

and Pricing Management 7 (4), 397–401.Hofer, C., Dresner, M., Windle, R., 2005. Financial distress and US airline fares. Journal of Transport Economics and Policy 39 (3), 323–340.Hofer, C., Windle, R., Dresner, M., 2008. Price premiums and low cost carrier competition. Transportation Research Part E, Logistics and Transportation

Review 44 (5), 864–882.Kasilingam, R.G., 1996. Air cargo revenue management: characteristics and complexities. European Journal of Operational Research 96 (1), 36–44.Kawasaki, A., 2007. Price competition and inefficiency of free network formation in the airline market. Transportation Research Part E, Logistics and

Transportation Review 43 (5), 479–494.Kennedy, P., 2003. A Guide to Econometrics, fifth ed. MIT Press, Cambridge, MA.Leggette, J., Killingsworth, B., 1983. An empirical study of economies of scope: the case of air carriers. Studies in Economic Analysis 7 (2), 27–33.Lin, M., 2008. Airline alliances and entry deterrence. Transportation Research Part E, Logistics and Transportation Review 44 (4), 637–652.Lin, J., Dresner, M., Windle, R., 2001. Determinants of price reactions to entry in the U.S. airline industry. Transportation Journal 41 (2/3), 5–22.Morrison, S., 2001. Actual, adjacent, and potential competition: estimating the full effect of Southwest Airlines. Journal of Transport Economics and Policy

35 (2), 239–256.Oliveira, A., Huse, C., 2009. Localized competitive advantage and price reactions to entry: full-service vs. low-cost airlines in recently liberalized emerging

markets. Transportation Research Part E 45 (2), 307–320.Ott, J., 2008. Deadline looms for cargo screening on passenger airplanes. Aviation Week, September 5, 2008.Page, P., 2007. Airlines flying profitably. Traffic World, January 22, 2007. .Park, J., 1997. The effects of airline alliances on markets and economic welfare. Transportation Research Part E, Logistics and Transportation Review 33 (3),

181–195.Roy, R., Cofsky, D., 1985. An empirical investigation of production technology of Canadian air services. Canadian Transport Commission, Research Branch,

Economic and Social Research Directorate. Report No. 1985, 03E, April.Sandhu, R., Klabjan, D., 2006. Fleeting with passenger and cargo origin–destination booking control. Transportation Science 40 (4), 517–528.Siegmund, F., 1990. Competition and performance in the airline industry. Policy Studies Review 9 (4), 649–663.Shy, O., 2001. The Economics of Network Industry. Cambridge University Press.Slager, B., Kapteijns, L., 2004. Implementation of cargo revenue management at KLM. Journal of Revenue and Pricing Management 3 (1), 80–90.Tang, C., Yan, S., Chen, Y., 2008. An integrated model and solution algorithms for passenger, cargo, and combi flight scheduling. Transportation Research Part

E, Logistics and Transportation Review 44 (6), 1004–1024.Tretheway, M., 2004. Distortions of airline revenues: why the network airline business model is broken. Journal of Air Transport Management 10 (1), 3–14.Wei, W., Hansen, M., 2005. Impact of aircraft size and seat availability on airlines’ demand and market share in duopoly markets. Transportation Research

Part E, Logistics and Transportation Review 41 (4), 315–327.Wei, W., Hansen, M., 2007. Airlines’ competition in aircraft size and service frequency in duopoly markets. Transportation Research Part E, Logistics and

Transportation Review 43 (4), 409–424.Zhang, A., Van Hui, Y., Leung, L., 2004. Air cargo alliances and competition in passenger markets. Transportation Research Part E, Logistics and

Transportation Review 40 (2), 83–100.