Transportation Research Part A 43 (2009) 779–789

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Transportation Research Part A

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Is it the labor unions’ fault? Dissecting the causes of the impairedtechnical efficiencies of the legacy carriers in the United States

Mark Greer *

Department of Economics, Dowling College, Oakdale, NY 11769, USA

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

Article history:Received 3 December 2007Received in revised form 20 July 2009Accepted 29 July 2009

Keywords:AirlinesUnionizationEfficiencyData envelopment analysis

0965-8564/$ - see front matter � 2009 Elsevier Ltddoi:10.1016/j.tra.2009.07.007

* Tel.: +1 631 244 3239; fax: +1 631 244 1085.E-mail address: greerm@dowling.edu

Data envelopment analysis is used to evaluate the technical efficiencies of a number ofmajor passenger airlines in the United States at transforming their inputs (labor, fueland fleet-wide seating capacity) into available seat-miles. A tobit regression model is thenused to identify the underlying drivers of airline efficiency, as measured by the data envel-opment analysis efficiency score. The impact of unionization on airline efficiency is foundto be statistically insignificant, controlling for the influences of other hypothesized deter-minants of airline efficiency: the average age of an airline’s fleet, the average size of its air-craft, its average stage length, the extent to which the airline relies of hubbing within itsroute structure, the percent of its passenger enplanements that are international, andwhether the airline is a legacy carrier. The statistically significant drivers of airline effi-ciency, at a ten percent level of significance, are average aircraft size, average stage lengthand the extent to which the airline relies on hubbing and connecting flights within its routestructure. The stage length variable is not significant at a five percent level of significance,however. An increase in average aircraft size or in average stage length enhances an air-line’s efficiency whereas an increase in hubbing reduces it.

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1. Introduction

Airline labor unions are often blamed, especially by airline management, for undermining airline efficiency, but do theyactually do this? This paper examines this question by assessing the impact of labor unions on the technical efficiency ofairlines. Technical efficiency, which is sometimes referred to as X-efficiency, has to do with minimizing the quantities ofphysical inputs used to produce a unit of physical output, given the existing state of production technology. The outputof an airline’s production process is available seat-miles (ASMs), where an ASM is one seat flown one mile, whether occupiedby a passenger or not. The conversion of an airline’s produced inventory of ASMs into revenue passenger-miles is a marketingfunction, not part of the airline’s production process. For the purposes of this analysis, an airline’s inputs are labor, fuel, andfleet-wide seating capacity. This study measures how efficiently each airline in the dataset transforms these three inputs intoASMs. Once this is done, the paper then seeks to determine whether the degree of unionization of an airline’s employeesaffects its efficiency.

This analysis adopts an entirely physical, as opposed to monetary, concept and measure of efficiency. In those cases wherea choice could be made about whether to measure a variable in its physical unit of measure or its monetary value, the phys-ical unit was chosen. This happened in the instances of the labor and fuel inputs, which are measured here in terms of full-time equivalent employees and millions of gallons, respectively, rather than as dollars of expense. (Obviously, the fleet-wide

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Table 1Pearson correlation coefficients (n = 85).

DEAefficiencyscore

Union density(percent employeesunionized)

Average aircraftsize (seats perplane)

Averageaircraftage

Averagestagelength

Degreeofhubbing

Legacy carrierdummyvariable

Percentpassengers flyinginternationally

DEA efficiency score 1 �0.451 0.502 �0.369 0.529 �0.446 �0.414 �0.179Union density

(percentemployeesunionized)

�0.451 1 �0.011 0.695 �0.325 0.574 0.519 0.270

Average aircraft size(seats per plane)

0.502 �0.011 1 0.099 0.802 0.147 0.022 0.138

Average aircraft age �0.369 0.695 0.099 1 �0.099 0.650 0.485 0.334Average stage

length0.529 �0.325 0.802 �0.099 1 0.082 0.099 0.406

Degree of hubbing �0.446 0.574 0.147 0.650 0.082 1 0.658 0.534Legacy carrier

dummy variable�0.414 0.519 0.022 0.485 0.099 0.658 1 0.805

Percent passengersflyinginternationally

�0.179 0.270 0.138 0.334 0.406 0.534 0.805 1

780 M. Greer / Transportation Research Part A 43 (2009) 779–789

seating capacity input and the ASM output can only be measured in their physical units.) One reason for avoiding monetaryunits of measure for these two input variables is that different carriers pay different prices for their inputs. Consequently,using monetarily defined input variables would entail using different units of measure for different airlines.

An efficient airline, as defined here, is one whose ASMs cannot be produced, under conditions of technology present at thetime, using less of each of the three inputs. An inefficient carrier is one whose output of ASMs can be produced using pro-portionally less of each input, the possible proportional reduction of its inputs serving as a measure of its inefficiency. Thestudy uses data envelopment analysis (DEA) to identify the efficient and inefficient carriers, and to measure the inefficiencyof the latter.

This study found that the more heavily unionized carriers are generally less efficient than the lesser unionized ones,where efficiency is measured in terms of the carrier’s DEA efficiency score (see Table 1). One should certainly not infer fromthis, though, that unionization causes inefficiency, for the more heavily unionized airlines also tend to share certain othercharacteristics in common that would, theoretically, impair their efficiencies. To begin with, as Table 1 reveals, the moreheavily unionized carriers also tend to be older legacy carriers with older fleets and a reliance on hubbing and connectingflights within their route structures, both of which should reduce their technical efficiencies, for reasons explained laterin this paper.1 In addition to measuring the airlines’ DEA efficiency scores, we will use a tobit regression analysis to controlfor these other potential influences on airline efficiency in order to isolate the impact of unionization on efficiency.

2. Relationship to existing literature

A number of articles using DEA and tobit regression models to evaluate the determinants of airline efficiency have re-cently appeared. Examples include Schefczyk (1993), Fethi et al. (2002), Scheraga (2004), Chiou and Chen (2006) and Sche-raga (2006). Additional studies have applied DEA to the airline industry without using tobit regression analysis to ascertainthe determinants of airline efficiency. Examples of these papers include Banker and Johnston (1994), Coelli et al. (2002), Sick-les et al. (2002), Capobianco and Fernandez (2004), Ray (2004b), and Greer (2006, 2008). One shortcoming of many of theaforementioned studies is that they tend to measure the input and output variables in their monetary values. The exceptionsare the article by Sickles, Good and Getachew and the ones by Greer. Unlike most of the previous literature, this study mea-sures all inputs an output in their physical quantities. Another shortcoming with much of the existing DEA literature on air-line efficiency is that a number of articles, e.g. Schefczyk (1993), Scheraga (2004) and Scheraga (2006), define output in termsof revenue passenger-miles and/or revenue cargo ton-miles, which conflates the marketing function of an airline with itsproduction process.

Kuhn (1998) and Metcalf (2002) have surveyed the literature on the effect of unions on firm productivity and have foundthat unions tend to raise productivity in some industrial contexts, but reduce it in others. Both of these studies focus primar-ily on how unions impact productivity, which is not the same concept as technical efficiency, although they are closely re-lated. Efficiency is evaluated with respect to a given production technology, whereas the concept of productivity can allowfor productivity-enhancing technological change over time.

1 The reader may be puzzled at the smallness of the correlation, shown in Table 1, between a carrier’s status as a legacy carrier and its average stage length,since legacy carriers exclusively engage in very long-haul intercontinental flights. This is because, with few exceptions, the data on legacy carriers used in thisstudy include, wherever complete data are available, data on their subsidiaries, which fly very short-haul, feeder operations for the mainline operation. Thisalso explains why the correlation between the legacy carrier dummy variable and average aircraft size is so small.

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Gittell et al. (2004), whose concerns are the most similar to the ones here, focus specifically on the airline industry andexamine whether unionization and the quality of labor–management relations influence certain service quality and produc-tivity variables. They find that, controlling for variables measuring workplace culture and the degree of conflict in labor–management relations, unionization has no statistically significant influence on labor productivity, but does substantiallyboost aircraft productivity, defined as aircraft utilization. The analysis undertaken here differs from that of Gitell, vonNor-denflycht and Kochan in that we examine the influence of unionization on technical efficiency rather than productivity,and we use the airlines’ DEA efficiency scores to measure their efficiency. We also use a somewhat different set of controlvariables. That is, we exclude capital intensity and employment growth from our set of control variables and include, unlikethe Gitell, vonNordenflycht and Kochan study, the average age of the airline’s fleet, the extent of hubbing within its routestructure, whether the carrier is one of the older legacy carriers, and the percent of its passengers who are flying internation-ally among our control variables. We will explain the reasons for using these control variables later.

This paper is the first to addresses whether the extent of unionization among an airline’s employees impacts the technicalefficiency of the airline, where technical efficiency is measured using DEA. It is also the first to address whether an airline’sDEA efficiency score is impacted by the average age of an airline’s fleet, the average size of its aircraft, the extent to which itrelies on hubbing and connecting flights, and whether or not the airline is one of the older legacy carriers, although the focuson these variables, which serve primarily as controls, is not the central concern of the paper.

3. Some hypothesized drivers of airline efficiency

A priori, it is not clear what impact unionization should have on airline efficiency. On the one hand, one of the primaryfunctions of a labor union is to protect the jobs of its members. As a result, airline unions negotiate for work rules and termsand conditions of employment, such as restrictive work rules, that make it difficult for airlines to reduce their labor input.This, of course, boosts the amount of labor input the airline uses per ASM, which places downward pressure on its efficiency.Work rules stipulated by collective bargaining agreements tend to increase the time it takes to service an aircraft at the ter-minal and thus reduce the number of flights the airline can generate from the aircraft during a typical day. This puts upwardpressure on the available fleet-wide seating capacity input used to produce the airline’s ASMs. The reader can probably thinkof additional ways that labor unions may curtail airline efficiency.

On the other hand, there exists reason to believe that unions may enhance the efficiency of an airline. Unionized employ-ees enjoy significantly better compensation packages than non-unionized employees (Card, 1998; Hirsch and Macpherson,2000; Hirsch, 2008). As a result, they have a considerable stake in the long-term financial viability of the organization forwhich they work. If the organization collapses, the likelihood that they will be able to maintain their current level of incomeand benefits is quite small. This gives unionized employees an incentive to make the workplace more rational and efficient.(Of course, this argument applies exclusively to unionized employees working for a firm in a competitive industry, whereinattentiveness to efficiency may lead to liquidation at the hands of competitors, and does not apply to unionized employeesat a monopoly or unionized public sector employees.) By making an effort to enhance the efficiency of the operation, union-ized staff can foster the long-term financial health and viability of the enterprise, thereby securing their own livelihoods. Anorganizational culture that values innovation and input by front line staff and where trust and cooperation are embedded inthe culture can be especially effective at channeling this incentive into operational efficiency improvements. Indeed, as Kuhn(1998) and Metcalf (2002) point out, the literature on unions and productivity indicates that unions tend to have a positiveimpact on productivity when the organizational culture of a firm is non-adversarial in character. In addition, Gittell et al.(2004) emphasize the importance of a positive workplace culture in promoting employee productivity. Southwest Airlines,one of the most heavily unionized and also most technically efficient operations in the industry, may attest to this (Gittell,2003). Chaison (2007) remarks how recent hardball tactics used by certain other carriers to pressure their unions into majorconcessions may raise operational problems for them over the long-term, by undermining staff morale, which is vital in aservice industry like the airlines.

Gittell et al. (2004) drawing upon previous literature in the field, note three additional avenues through which labor un-ions may improve the performance of an airline. One of the three operates through enhancing the quality of service, and theother two through heightening efficiency. The latter, which are the most relevant to the concerns of this paper, whose focusis not service quality, are that the union wage premium gives management a greater incentive to utilize efficiently the laborinput, and that the sense of participation and job security coming about through unionization can lead employees to exertmore discretionary effort.

Separate from unionization, a number of other variables would be expected to affect an airline’s efficiency, and it is importantthat the analysis control for these variables in assessing the impact of unionization on efficiency, since these variables are cor-related to one degree or another with the union density of an airline. One of them, average aircraft age, has already been men-tioned. Compared to newer aircraft, older aircraft burn more fuel per ASM and require more maintenance, which place upwardpressure on input usage per unit of output. In addition to the average age of its aircraft, the extent to which an airline relies onhubbing and connecting flights over direct point-to-point service should also negatively impact its technical efficiency. If usinga hub-and-spoke route structure, an airline tends to stack its arrivals and departures in order to minimize connection times in-curred by passengers. This means that the airline’s hub airport operations will be characterized each day by a few very busyperiods, each beginning with a stack of arrivals and ending with a stack of departures, with lulls of relative inactivity in between.

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However, the busy periods dictate its staffing needs at the airport, which means that many of its staff will be left at least some-what idle during the lulls, and it will need to use more labor for the terminal functions than it would if its flights were spread outmore evenly during the day. Consequently, airlines relying more heavily on direct service should exhibit higher levels of tech-nical efficiency than those more reliant on the hub-and-spoke system (although there are good reasons having to do with net-work economies and minimizing the cost per additional city-pair added to the network for using the latter type of routestructure.) Indeed, Lott and Taylor (2005) estimate that the hub-and-spoke network system adds 45% to the passenger handlingcosts of US carriers that employ it, compared to those that do not. This observation is certainly consistent with the hypothesisbeing made here about the effect of this network system on technical efficiency.

Theoretically, another influence on airline efficiency is the average size of an airline’s aircraft. Smaller craft tend to boostthe labor input needed to produce each ASM because the flight crew, cabin staff and terminal employees are spread over asmaller number of seat-miles, the smaller is the craft they service. In addition, smaller aircraft tend to burn more fuel perASM than larger aircraft. Bailey and Panzar (1981) discuss evidence of significant economies of aircraft size and decliningmarginal cost per seat-mile as aircraft size increases, which is consistent with the hypothesis being made in this paper. Itis noteworthy that, as Table 1 indicates, there is a small negative correlation between the average size of an airline’s aircraftand the percent of its employees who belong to unions.

One would expect an airline’s average stage length (distance per flight) to be positively associated with its technical effi-ciency. The longer is the stage length of a flight, the greater is the degree to which the labor used in the terminal function, e.g.ticketing and baggage handling, is spread out over ASMs. Also, the fuel consumed to lift the aircraft to cruising altitude isspread out over more ASMs, the longer is the distance of the flight. In effect, resources used as part of the terminal functionand getting the flight to cruising altitude are fixed, independently of the length of the flight, and the greater is the length ofthe flight, the more spread out are these resources over flight-miles. Gittell (2003) elaborates on additional reasons why anincrease in stage length has beneficial effects on productivity. As Table 1 shows, there is a negative correlation between anairline’s union density and the average stage length of its flights.

This paper also speculates that the older legacy carriers may be less efficient than the newer, non-legacy carriers for reasonscompletely separate from the differences in average aircraft age between the two types of carriers. (For the purposes of thisstudy, legacy carriers are defined as those that were classified by the United States Department of Transportation as majoror national carriers in 1978, the year that airline deregulation was signed into law.) These reasons have to do with organizationalculture and its persistence. Schein (1992) argues that, in the early stages of an organization’s history, its organizational culturebecomes imbued with certain belief systems and assumptions, often at the instigation of the organization’s founders, becausethese belief systems and assumptions are functional in the context of the environment that the young organization exists with-in. Over time, these aspects of organizational culture become deeply ingrained as the organization matures (Schein, 1992). If, ata later stage in its history, the environment facing the organization changes in a substantive way, its ingrained assumptions andbeliefs may become maladaptive, and yet the members of the organization may continue to hold them because the adoption ofother, more functional ways of thinking about the organization, its members and its relationship to its environment causes anx-iety and guilt and calls into question the organizational identities of its members (Schein, 1992). In fact, the embedded assump-tions and beliefs then act as psychological filters that blind the members of the organization to information pertaining to thethreats that the new environment poses (Schein, 1992).2

One should note that the legacy carriers came into existence and reached maturity when the airline industry was cartel-ized under the Civil Aeronautics Board. One would expect that, in a somewhat atypical environment such as this, a certainparticular type of organizational culture, along with its foundational assumptions and beliefs, would have taken root andbecome firmly engrained because they helped the organization survive and thrive in this context. For example, it is easyto imagine that antagonistic labor–management relations would not have been thought of as being dysfunctional since itwas virtually impossible for labor strife to lead to the demise of the organization at the time. Since deregulation, the envi-ronment in which airlines operate has changed and become considerably more competitive and less regulated, yet it seemsplausible, for the reasons Schein gives, that the organizational cultures that evolved during the previous era may have per-sisted, however ill-suited they may be for the new environment.

The newer, non-legacy carriers, having developed their organizational cultures in the relatively competitive environmentafter airline deregulation, may benefit from organizational histories and cultures better suited for the times, and thus may bemore efficient than the legacy carriers on these grounds. It is worth noting that, according to Metcalf (2002), in competitiveindustries, unions tend to raise productivity whereas in uncompetitive ones, they tend to do the opposite. Is it possible thatunionization may enhance efficiency in the context of a non-legacy carrier whose organizational culture and its underlyingassumptions and beliefs developed in a competitive environment, such as Southwest Airlines, while hampering it among thelegacy carriers? If ‘‘legacyness” is, because of the organizational culture associated with it, a hindrance to efficiency in today’senvironment, one cannot conclude that unionization causes inefficiency, for, as Table 1 shows, there is a strong positive cor-relation between an airline’s status as a legacy carrier and the union density of its workforce.

2 Interestingly, Schein emphasizes at various points the importance of psychological security on the part of the members of an organization as a necessarycondition for constructive change in organizational cultural and the maintenance of staff morale, especially in times of crisis (Schein, 1980). One can onlywonder what impact the repeated rounds of layoffs and downsizings among the legacy carriers since the 1980s have had on the psychological security of thepeople who work at these carriers and what this has done to their willingness to perceive their organizations in a different way – a way that would haveenabled them to adapt to the new environment better.

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Another striking difference between the heavily and lightly unionized carriers is that the former fly a disproportionatenumber of international flights, as shown in Table 1. Fethi et al. (2002) claim that flying international routes impairs tech-nical efficiency by raising the heterogeneity of the production process whereas Scheraga (2004) posits that it is not clearwhat impact flying international routes would have on technical, input–output efficiency. This paper takes the theoreticalposition that flying more international passengers than the industry average would hamper an airline’s technical efficiency,compared to other airlines. In addition to the reason given by Fethi et al., one should also note that international passengersrequire considerably more processing at check-in than domestic passengers, and aircraft turnaround times are far higher oninternational flights than domestic ones. These boost the labor and seating capacity input requirements for each ASM pro-duced on international flights.

In order to isolate the independent impact of each theoretical driver of airline efficiency, including union density, we willapply, later in this paper, a tobit regression analysis to the DEA efficiency scores. The independent variables in the regressionequation will be the percent of the airline’s employees who are unionized, the average size of the airline’s aircraft, and theother hypothesized drivers of efficiency described above.

4. Sources and compilation of data

A number of complex issues arose in the collection and compilation of the data used in this analysis. This warrants a discus-sion of the sources of data and how they were compiled. Such a discussion will also shed insight on the analysis that follows.

Data were collected for the years, 1999–2008, and are available upon request from the author. All data used in the anal-ysis were compiled in a consistent manner. For some years, due to a corporate reorganization or a change in the data dis-closed in an airline company’s Securities and Exchange Commission Form 10-K filing, certain data could not be compiledin a manner consistent with the rest of the data used in the study. In these instances, all data for the carrier for that yearwere omitted from the study.

Some of the legacy carriers included in the study use one or more regional affiliates to fly primarily shorter, feeder routesfrom small destinations to a major hub serving as a base for the carrier’s mainline operation. The regional affiliates used forthe commuter operations tend to operate turboprops and regional jets whereas the mainline operation of the carrier useslarger aircraft. In cases where the mainline carrier exerts significant operational control over the regional affiliate, the authorchose to include all data pertaining to the regional affiliate in the data for the mainline carrier. The rationale for doing this isthat, in these circumstances, the mainline carrier and its regional affiliates constitute a single overall operation that has uni-fied control over the productivity and efficiency of its elements. The ownership of a substantial equity position, meaninggreater than 10% of outstanding equity, by a mainline carrier in a regional affiliate was deemed evidence of significant oper-ational control of a regional affiliate.3 In cases where complete data on subsidiary regional affiliates were not available, only thedata for the mainline legacy carrier were included. Of course, in cases where the mainline legacy carrier did not have a substan-tial equity holding in a regional affiliate, the data for the affiliate were not included in the data for the mainline carrier.

The dataset includes data for Airtran Airways from 2003–2008; Alaska Airlines from 1999–2008; American Airlines for theyears 2000, and 2004–2006; American West Airlines from 1999–2005; ATA from 2003–2004; Continental Airlines for the years1999–2000 and 2004; Continental Airlines (mainline operation only, including Continental Micronesia) for 2001–2003 and2005–2008; Delta Airlines for 2004; Frontier Airlines for the years 1999–2000 and 2004–2008; JetBlue Airways for 2001–2008; Northwest Airlines for 2007; Northwest Airlines (mainline operation only) for the years 2003, 2004 and 2006; SouthwestAirlines for the years 1999, 2000, and 2003–2008; TWA for 1999; United Airlines for 1999–2008; US Airways for 2004; US Air-ways (mainline operation only) for the years 1999–2003 and 2005; and the merged US Airways–America West Airlines for 2008.

Data pertaining to the airlines’ labor, fuel and seating capacity inputs, and to their ASM output, were compiled usingessentially the same methodology as used by Greer (2006). This includes using the relative physical weights of the passen-gers and cargo hauled to allocate the labor and fuel inputs to the two respective outputs, then including only the labor andfuel inputs attributable to the passenger hauling function in the dataset. We also adjusted further the labor input to reflectthe fact that different airlines engage in the outsourcing of services to varying degrees, like Greer (2006) does.

It is important to note that the seating capacity input is not a measure of utilization of aircraft capacity; rather it is a measureof total aircraft capacity itself, defined as the year-round average fleet-wide seating capacity across all of the carrier’s aircraft.(The number was arrived at by calculating the total number of seats available on all the aircraft in the airline’s fleet at the end ofthe current and prior years, the data for which can be derived from the airline’s annual reports to shareholders and/or Securitiesand Exchange Commission Form 10-K filings, then averaging the two numbers.) ASMs relative to the seating capacity inputwould serve as a measure of aircraft utilization. However, there was no analytical need to capture that ratio here.

The aforementioned data pertaining to inputs and outputs were used in the DEA conducted in this study. We turn now tothe sources and compilation of data pertaining to hypothesized efficiency drivers used in the tobit multiple regression anal-ysis, which was used to isolate the impact of each driver on the airlines’ DEA efficiency scores.

3 Except in cases where complete data on the subsidiary commuter/feeder subsidiary are not available, data for Alaska Airways include data for HorizonAirways, data for American Airlines include data for American Eagle and Executive Airlines, data for Continental Airlines include data for Continental Expressand Continental Micronesia, data for Delta Airlines include data for Comair and Atlantic Southeast Airlines, data for Northwest Airlines include data for MesabaAirlines and Pinnacle Airlines until the latter was sold by the parent company, and data for US Airways include data for Allegheny Airlines, Piedmont Airlinesand PSA Airlines.

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With the exception of American Airlines, data pertaining to the fraction of the airlines’ employees who belonged to unionswere obtained from their annual reports and 10-K filings. A year-round average estimate of the fraction who were unionizedwas arrived at by taking the average of the fraction unionized at the end of the year and the end of the previous year. In caseswhere one of these two fractions was not reported in the annual report or 10-K filing, the number for the other yearend was usedin lieu of the year-round estimate. The Investor Relations Department of American Airlines directly supplied the author withdata on the fraction of that carrier’s employees who were unionized in at the beginning of 2005 and the beginning of 2006.

Data on the airlines’ average stage length were derived from their Form 10-K filings, their annual reports to shareholdersand Schedule T-2 of the United States Bureau of Transportation Statistics’ (BTS’s) on-line database. Where minor discrepan-cies existed between data supplied by the BTS and the data provided in the companies’ 10-K filings and annual reports, thelatter data were used.

The average number of seats per plane was used to measure the average size of an airline’s aircraft. The data on seats perplane were obtained from the airlines’ annual reports and 10-K filings. A weighted average number of seats per plane at eachyearend was calculated by multiplying the number of seats in each model in the airline’s fleet by the number of aircraft ofthat model the carrier owned or leased at yearend, summing the products, then dividing by the number of aircraft in thecompany’s fleet at yearend. The year-round average aircraft size was arrived at by taking the average of the average aircraftsize at the end of the year and at the beginning of the year.

Data were collected on the average age of the aircraft in the airlines’ fleets. These data were obtained from their annualreports and 10-K filings. A weighted average age for each airline’s fleet was calculated for each yearend by multiplying theaverage number of seats on each model of aircraft by the number of aircraft of that model in the airline’s fleet, then multi-plying by the average age of that model, summing the resulting products, and dividing the sum by the total number of seatsavailable across all models. An average fleet age for a given year was calculated as the average of the weighted average agesfor the end of that year and the end of the prior year.

Data pertaining to the scope of the airlines’ international operations in comparison to their domestic operations were col-lected. Data on passengers who were flying on international flight segments were obtained from Schedule T-100 of the BTS’son-line database. These numbers were divided by the total number of the airline’s passengers to arrive at the percent whowere flying international routes.

There are no readily available data directly measuring the degree of hubbing in each airline’s route structure for each yearof this study. We were able to indirectly assess this variable, however, using the BTS’s DB1B database, which is a 10% sampleof all tickets sold by reporting carriers. For each airline for each year, we summed the total flight coupons and divided it bythe number of tickets to arrive at an estimate of the number of coupons per ticket for that carrier for that year. Presumably,the greater is the number of flight coupons per ticket for a carrier, the greater is the extent to which it relies on hubbing andconnecting flights in order to transport its passengers.

As previously mentioned, the legacy carriers were members of the de facto regulated cartel coordinated by Civil Aeronau-tics Board prior to 1979. The organizational culture born in and fostered by such an environment may not be conducive toefficiency in the context of the new regulatory environment. For the purposes of the tobit multiple regression analysis, it wasnecessary to construct a dummy variable (‘‘legacy dummy”) indicating whether the carrier was a member of the cartel priorto deregulation. In cases where an airline was categorized as a ‘‘major” or ‘‘national” carrier by the United States Departmentof Transportation in 1978, the year that airline deregulation was signed into law, the dummy variable was assigned a value ofone for that carrier for all years in this study. If not, then a value of zero was assigned to the dummy variable for that carrierfor all years in this study. We acknowledge, however, that, in the case of Continental Airlines after 1994, when managementof the airline embarked on a concerted and thoroughgoing effort to improve labor–management relations (Gittell et al.,2004), it may be somewhat problematic to place the airline in the same category as the other legacy carriers in terms ofits organizational culture in the area of labor relations and employee morale.

One noteworthy characteristic of the dataset is that there exist considerable and systematic differences between the leg-acy and non-legacy carriers in terms of the hypothesized drivers of airline efficiency. Table 2 below reveals the differencesbetween these two types of airlines in terms of the mean values of the hypothesized efficiency driver variables.

Table 2Mean values of efficiency drivers (n = 85).

Variable Mean value legacies (Alaska Airways, AmericanAirlines, Continental Airlines, Delta Airlines,Northwest Airlines, TWA, United Airlines andUS Airways prior to merger with America WestAirlines)

Mean value non-legacies (Airtran Airways,America West Airlines, ATA, Frontier Airlines,JetBlue Airways, Southwest Airlines andmerged US Airways–America West Airlinescombination)

Probability value ofdifference between means(two-tailed t-test assumingunequal variances)

Union density 70.890 41.749 0.000Ave. plane size 139.652 138.710 0.832Ave. age fleet 9.807 6.321 0.000Ave. stage

Length935.447 879.316 0.358

Hubbing 2.571 2.117 0.000Percent

international14.687 2.064 0.000

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The merged US Airways–America West Airlines combination is treated as a non-legacy because American West Airlineswas the dominant partner in this merger. As the reader can see, the legacies and non-legacies differ markedly in terms ofunionization, the average age of their fleets and the extent to which they engage in hubbing. The difference in average planesize and average stage length may be less than the reader would expect, since the legacies exclusively fly very long-haulinternational routes using wide-body aircraft. One should recall, however, that in most of the data records for the legacy car-riers, we have included data on controlled subsidiaries that fly very short-haul feeder routes to hubs. This explains why thedifferences in means for these two variables is so small.

5. Analytical methods and results

5.1. Data envelopment analysis

The primary alternative to DEA as a means of measuring airline efficiency is stochastic frontier analysis. However, DEAoffers an important advantage over stochastic frontier analysis in that DEA makes no assumption about the functional formof the production function. Stochastic frontier analysis, by contrast, requires one to make an arbitrary assumption about thefunctional form of the production function. Nor does DEA, as a non-stochastic technique, make any assumptions about thedistribution or independence of error terms, or any of the other assumptions of classical regression analysis. The one highlyvisible restrictive assumption DEA makes is that the production technology is convex.

DEA originates in the works of Farrell (1957), Charnes et al. (1978, 1985). Charnes et al. (1994), Coelli et al. (1998), andRay (2004a) provide comprehensive surveys of DEA in all of its contemporary variants. This study employs the input-ori-ented, constant returns to scale version of DEA developed by Charnes et al. (1978). The reason for this is that there existsconsiderable evidence of constant returns to scale in the airline industry. One piece of evidence is that small and large car-riers coexist over extended periods of time in the industry, an observation consistent with constant returns to scale. Formalempirical studies of the airline industry indicating constant returns to scale include White (1979), Caves et al. (1984, 1985)and Sickles et al. (2002).

A radial, as opposed to additive, measure of efficiency is used to assure that the DEA results are invariant to the units of mea-sure chosen. The input-oriented DEA technique used here determines whether the quantity of output produced by a decision-making unit (DMU), in the present context an airline company, can be produced using proportionally less of each input than theDMU is using. Other DMUs in the industry, in particular the efficient DMUs, serve as benchmarks in conducting this assessment.

DEA identifies the efficient DMUs in the industry during a given period. Once the efficient DMUs have been identified, sois the boundary, or efficiency frontier, of the production possibilities set for that period, since the input–output vectors of theefficient DMUs are assumed to lie on the efficiency frontier.

DEA identifies the efficient DMUs during a period by ascertaining, in the case of each DMU, whether it is possible to producethe DMU’s output in that period using less of each input. If it is possible to do this, then the DMU is inefficient. If it is not possible,then the DMU is efficient. One way it may be possible to produce a DMU’s output while using proportionally less of each inputwould be by combining the production processes of the other DMUs in the industry, each scaled up or scaled down, to create a‘‘virtual” DMU that is more efficient, that is, produces the same output using less of each input, than the DMU in question.4 If it ispossible to do this, then the DMU is inefficient. If not, then the DMU is efficient, and its input–output combination lies on the effi-ciency frontier. The following linear programming problem is used to ascertain whether a DMU is efficient:

5.1.1. DEA linear programming problem

4 Of cDEA mamost si

Minimize ht ht; kt1; . . . ; kt

S

Subject to : 1:Xs

i¼1

kti ASMt

i P ASMto

2:Xs

i¼1

kti Fuelt

i 6 htFuelto

3:Xs

i¼1

kti Labort

i 6 htLaborto

4:Xs

i¼1

kti SeatCapt

i 6 htSeatCapto

5: kti P 0 for all i

6: ht P 0

ourse, this claim is premised on the assumption that the production possibilities set is convex, which is probably the most contentious assumption thatkes. There appear to be no glaring non-convexities owing to indivisibilities of an input in the production technology used to generate ASMs. Perhaps the

gnificant indivisibility can be found in the aircraft input, but even a wide-body aircraft constitutes a very small proportion of any major airline’s fleet.

786 M. Greer / Transportation Research Part A 43 (2009) 779–789

This linear program must be solved for each DMU in the industry for each year. ASMti refers to the ASM output of the ith

airline in year t. The number of DMUs in the industry is S. t is the time period indicator. (t = 1999, 2000, . . . , or 2008). Thesubscript, o, refers to the DMU whose efficiency is being evaluated. kt

i is the weight given to the ith DMU’s input–output vec-tor in constructing the virtual DMU which will be used to evaluate the efficiency of DMU o in year t.

Psi¼1k

ti ASMt

i is thus theoutput of the virtual DMU in year t. Note that it is a linear combination of the actual output levels of other firms in the indus-try during that year.

Psi¼1k

ti Fuelt

i , which is a linear combination of the quantities of the fuel input used by the other firms inthe industry, is the quantity of fuel used by the virtual DMU in year t.

Psi¼1k

ti Labort

i andPs

i¼1kti SeatCapt

i refer to the labor andseating capacity inputs used by the virtual DMU. ht, the variable to be minimized in the linear programming problem, is theuniform proportional reduction in DMU o’s inputs. Solving the linear programming problem amounts to finding the lowestpossible value for ht, which we call h�t, for which there exists a virtual DMU that uses no more than h�t times each of theinputs of DMU o (see constraints #2–#4) while at the same time producing at least as much of each output as that DMU(see constraint #1). h�t is also known as the DEA efficiency score for DMU o in year t.

If h�t equals one, then it is not possible, even by creating a virtual DMU whose input–output bundles are linear combina-tions of the input–output bundles of the other DMUs, to produce at least as much output as DMU o while using less of eachinput in that year. In this case the DMU is efficient in year t, and its input–output combination lies on the efficiency frontier,or boundary of the production possibilities set for that year. If the DMU’s efficiency score, or h�t, falls short of one, then it ispossible to come up with a linear combination of other DMUs’ production processes, i.e., create a virtual DMU, that producesthe same output as DMU o while using less of each input. In fact, the virtual DMU uses input quantities equal to h�t times thecorresponding input quantities used by DMU o. In the case where h�t < 1, the DMU is not efficient, and its input–output com-bination lies inside the production possibilities set. One can view h�t as the minimum quantities of inputs, expressed as aproportion of the DMU’s inputs, technically needed to produce the DMU’s output and 1 � h�t as the maximum proportionalreduction in inputs used by the DMU that could occur while maintaining the technological possibility of producing theDMU’s output. Of course, if a DMU is efficient, then it is already using 100% of the inputs necessary to produce its output,and there is no uniform proportional reduction in its inputs that could occur without reducing its output.

The boundary of the production possibilities set presumably expands outward each year due to technological improve-ments. In the case of the airline industry over the period examined here, the most obvious instances of productivity-enhanc-ing technical change are the adoption of Internet-based ticketing and reservations systems and the introduction of morefuel-efficient aircraft to the carriers’ fleets. Since the DEA efficiency scores are benchmarked relative to the efficiency frontierfor a given technology, it was necessary to run the foregoing DEA linear programming problem separately on each year’sdata.

Efficiency Measurement System, version 3.0, was used to solve the DEA linear programming problem for each airline ineach year. The DEA efficiency scores were then used in the next stage of the analysis, which was to identify the variables(fraction of the airline’s workforce that is unionized, the average age of its fleet, etc.) that determine an airline’s DEA effi-ciency score. A tobit regression analysis was undertaken for this purpose.

5.2. Tobit regression analysis

The DEA efficiency score is both a left- and right-censored variable (0 6 h�t 6 1). Therefore, the application of ordinaryleast squares would lead to biased and inconsistent estimates of the regression model’s parameters. As an alternative to or-dinary least squares, we use the tobit regression model, which is designed for left-censored dependent variables (Tobin,1958).

In order to convert h�t to a left-censored variable, we adopt the transformation first used by Chilingerian (1995) and sub-sequently adopted by, Fethi et al. (2002) and Scheraga (2004):

y ¼ ð1=h�tÞ � 1: ð1Þ

y is a left-censored variable in that the lowest value it can take is zero. One should also note that, given the definition of y, thegreater is the airline’s DEA efficiency score, the lower is y. The following regression equation is used in the tobit model:

yi ¼b0 þ b0Xiþ ui if yi > 0;0; otherwise

�ð2Þ

where b0 is the intercept term, b is the vector of parameters, Xi is the vector of independent variables (fraction of workforceunionized, average age of fleet, etc.), and ui is the error term, which is assumed to be normally and independently distributedwith a constant variance, r2.

Maximum likelihood estimation is used to estimate the parameters of the regression equation. The following likelihoodfunction is used to accomplish this:

L ¼Yyi¼0

ð1� FiÞYyi>0

expf�½1=ð2r2Þ�ðyi � b0 � b0XiÞ2gð2pr2Þ1=2 : ð3Þ

where

Table 3Tobit re

Regr

UnioAve.Ave.Ave.PercExteLegaLog lStanStan

M. Greer / Transportation Research Part A 43 (2009) 779–789 787

Fi ¼Z ðb0þb0XiÞ=r

�1

1

ð2pÞ1=2 e�t2=2dt; ð4Þ

Eviews, an econometric and statistical software, was used to solve Eqs. (2) and (3). Table 3 presents the results.Since, in the case of the unionization variable, the alternative hypothesis posits either a positive or negative influence on

an airline’s DEA efficiency score (and thus either a negative or positive influence on y) a two-tailed probability value is re-ported in the case of this variable. In the case of each other regressor, the alternative hypothesis posits a difference from zeroin one direction only; therefore, a one-tailed test of significance is reported in each other case.

In interpreting the results in Table 3, one should recall that, given the definition of the variable, y, a negative estimate for aparameter implies a positive relationship between the regressor and the DEA efficiency scores, and a positive parameter esti-mate implies a negative relationship between the regressor and the efficiency scores.

One should not interpret the parameter estimates in Table 3 as estimated measures of the marginal effects on y, the trans-formed DEA variable, of small changes in the explanatory variables, for regression parameters do not have the same meaningin the context of a tobit regression as they do in a standard multiple regression equation. In the case of a tobit model with aleft-censored dependent variable, a change in an independent variable has two effects: it changes the probability that thedependent variable will fall above the censoring limit, and it influences the value the dependent variable will take in thosecases where it is above the limit. The interpretation of regression coefficients is no straightforward matter in a context suchas this. The signs of the estimated coefficients and their probability values, though, have the same interpretations as found ina standard multiple regression model.

It is noteworthy that all of the postulated drivers of efficiency other than the unionization variable, which has no expectedsign, have the expected signs. However, the only ones that are statistically significant at five percent are the hubbing andaircraft size variables, the former impacting efficiency negatively and the latter positively. The stage length variable is sta-tistically significant at ten percent. Most noteworthy for the purposes of this study is that the parameter estimate for theunionization variable, while pointing toward an impairment of technical efficiency by unionization, does not deviate fromzero by a statistically significant margin.

In interpreting the regression results, and especially the estimated standard errors and probability values, the readershould be cautioned that, while the sample size is large (n = 85), there is considerable multicollinearity among the regressors,as reported in Table 4.

As one observes in Table 4, the variance inflation factor for the stage length variable is quite high. In fact, regressing thisvariable on the other independent variables yields an R-square of almost 91%. The high degree of multicollinearity betweenthe stage length variable and the other regressors may explain why the probability value for its estimated coefficient is sohigh. Of course, the multicollinearity between this independent variable and the others should also be kept in mind in inter-preting the probability values for the other coefficients. The parameter estimates remain unbiased, however.

6. Discussion of results

While the heavily unionized airlines tend to be less efficient than the less unionized ones, the data analyzed here do notpoint toward unionization as the cause of their impaired technical efficiency. Rather, the tendency for relatively heavily un-ionized airlines to also be the ones that rely heavily on hub-and-spoke route structures appears to account for the low levelsof technical efficiency among these carriers. For reasons explained previously, the hub-and-spoke route structure and theassociated stacking of flights that goes along with it diminish the technical efficiency of an airline. To be sure, there are soundeconomic reasons to operate a hub-and-spoke route structure. The network economies and miniscule cost of adding an addi-tional city-pair to the network stand out as two very good reasons for adopting this type of route structure. However, theanalysis of the data undertaken here points toward a considerable cost in terms of reduced technical efficiency – and con-sequent increase in cost per ASM – from doing this.

To a much lesser extent, the technical efficiencies of the airlines with higher union densities are being adversely impactedby their using slightly smaller aircraft, on average, than the airlines with lower union densities. One can observe from Table 1

gression results.

essor Parameter estimate Standard error Prob. > Chi-square

n density 0.0008 0.0005 0.1518age of fleet 0.0008 0.0033 0.3989size aircraft �0.0019 0.0010 0.0342stage length �0.0001 9.71E�05 0.0639

ent passengers international 0.0024 0.0027 0.1866nt of hubbing 0.1594 0.0385 0.0000cy dummy �0.0037 0.0356 0.4586ikelihood = 71.981dard error of regression = 0.060dard deviation of dependent variable = 0.093

Table 4Variance inflation factors.

Variable VIF

Union density 3.86Ave. age of fleet 2.39Ave. size aircraft 6.80Ave. stage length 10.87Percent of passengers flying internationally 6.13Extent of hubbing 2.46Legacy dummy 4.57

788 M. Greer / Transportation Research Part A 43 (2009) 779–789

that there is a small negative association between union density and average aircraft size, and from Table 3 that aircraft sizehas a statistically significant negative impact on the transformed dependent variable, and thus a positive effect on efficiency.However, considering the weakness of the association between average aircraft size variable and the union density variable,this influence cannot be a major one. The hubbing influence is a much more important reason why the relatively unionizedcarriers tend to exhibit lower levels of efficiency.

It is quite surprising that the parameter estimate for the aircraft age variable is not statistically significant, for the theo-retical reasons for expecting aircraft ageing to diminish efficiency seem to be ironclad. Multicollinearity may account for theweakness of this finding.

The findings here corroborate the results of Gittell et al. (2004), who report that, controlling for certain other influenceson airline productivity, the degree of unionization of an airline’s employees does not adversely impact airline operations; infact, they find that it influences aircraft productivity in a positive way. Although their concern varies slightly from the con-cerns here (service quality, productivity and operating margins as opposed to technical efficiency), and they use somewhatdifferent techniques of analysis, the results are roughly translatable in the sense that both studies indicate that unionizationper se has no discernable detrimental impact on the operation of an airline. One difference in findings between the two stud-ies actually has to do with one of the common control variables used: the Gittell, vonNordenflycht and Kochan paper findsthat an increase in an airline’s average stage length adversely impacts labor productivity whereas this study indicates that itenhances technical efficiency, although the finding is not strongly statistically significant.

If indeed an increase in an airline’s average stage length bolsters its technical efficiency, then we have found another rea-son why the relatively unionized carriers tend to have lower DEA efficiency scores than the lesser unionized ones: as Table 1shows, unionization and average stage length are negatively correlated among the airlines in our sample. This is because therelatively unionized carriers tend to be legacy carriers that run extensive very short-haul commuter–feeder operations intotheir hubs. One should bear in mind, though, that the stage length variable is not statistically significant at ten percent.

Another difference between the findings reported here and those in the Gittell, vonNordenflycht and Kochan article has todo with the influence of organizational culture in general and labor–management relations in particular on airline perfor-mance. Making the assumption that there are differences between the organizational cultures and labor–management rela-tions of the legacy carriers and those of the newer, non-legacies, this paper finds that such differences have no influence onairline efficiency. By contrast, the Gittell, vonNordenflycht and Kochan study indicates that organizational culture and labor–management relations influence certain quality of service and productivity variables. One should bear in mind, though, thatthe legacy dummy variable used in this paper is only an indirect measure of organizational culture whereas the Gittell, von-Nordenflycht and Kochan study uses a more direct proxy variable to capture this phenomenon. Furthermore, Gittell, vonNor-denflycht and Kochan provide reasons why Continental Airlines and Delta Airlines, at certain stages in their post-deregulation histories, did not share the same organizational cultural traits in the area of labor–management relationsand attitudes as the other legacy carriers. Therefore, in the present paper, the use of the legacy dummy variable may notbe fully up to the task of capturing these critical aspects of organizational culture, since Continental and Delta are bothcounted as legacy carriers throughout the entire period of this study.

One of the differences in the sets of control variables used in the Gittell, vonNordenflycht and Kochan article and in thispaper points to potential avenues for further research. In the former study, Southwest Airlines appears to account for a verysubstantial share of those observations in the dataset where the quality of labor–management relations is deemed to be po-sitive and the organizational culture is characterized by a high degree of trust. The authors find that these two characteris-tics, especially the latter, positively influence airline productivity. The present study, unlike the Gittell, vonNordenflycht andKochan one, uses hubbing, as measured by the average number of coupons per ticket issued by the airline, as a control var-iable and finds that hubbing strongly diminishes airline technical efficiency. Southwest happens to be one of the airlines inthe sample that relies relatively heavily on direct flights between city-pairs in its network, with an average number of cou-pons per ticket of 1.98 compared to an average of 2.41 for all the other airlines in the database. To what extent is Southwest’ssuperior operational performance, measured in terms of technical efficiency, labor productivity and/or aircraft productivity,due to its positive workplace culture and non-adversarial labor–management relations, and to what extent is it due to itsshunning the traditional hub-and-spoke network structure? Unfortunately, due to fundamental differences in the natureof the data used in the two studies, e.g. quarterly data used in one but annual in the other, it would not be an easy matterto sort this issue out using the existing databases.

M. Greer / Transportation Research Part A 43 (2009) 779–789 789

Acknowledgements

The author wishes to thank the Department of Economics at the State University of New York at Stony Brook and the En-ergy Centre at the University of Auckland for access to computer resources that were vital to the undertaking of this project.The author also wishes to thank the referees for comments and suggestions that greatly improved the quality of this paper.Of course, any errors or omissions are the author’s own. This project was supported through released time by DowlingCollege.

References

Bailey, E., Panzar, J., 1981. The contestability of airline markets during the transition to deregulation. Law and Contemporary Problems 44, 125–145.Banker, R.D., Johnston, H.H., 1994. Evaluating the impacts of operating strategies on efficiency in the US airline industry. In: Charnes, A., Cooper, W.W.,

Lewin, A.Y., Seiford, L.M. (Eds.), Data Envelopment Analysis: Theory, Methodology, and Application. Kluwer Academic Publishers, Boston, pp. 97–128.Capobianco, H.M.P., Fernandez, E., 2004. Capital structure in the world airline industry. Transportation Research Part A 38, 421–434.Card, D., 1998. Deregulation and labor earnings in the airline industry. In: Peoples, J. (Ed.), Regulatory Reform and Labor Markets. Kluwer Academic

Publishers, Boston, pp. 183–231.Caves, D.W., Christensen, L.R., Tretheway, M.W., 1984. Economies of density versus economies of scale: why trunk and local service airline costs differ. Rand

Journal of Economics 15, 471–489.Caves, D.W., Christensen, L.R., Tretheway, M.W., Windle, R.T., 1985. The effects of new entry on productivity growth in the US airline industry 1947–1981.

Logistics and Transportation Review 21, 299–335.Chaison, G., 2007. Airline negotiations and the new concessionary bargaining. The Journal of Labor Research 28, 642–657.Charnes, A., Cooper, W.W., Golany, B., Seiford, L., Stutz, J., 1985. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical

production functions. Journal of Econometrics 30, 91–107.Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M. (Eds.), 1994. Data Envelopment Analysis: Theory, Methodology, and Application. Kluwer Academic

Publishers, Boston.Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444.Chilingerian, J., 1995. Evaluating physician efficiency in hospitals: a multivariate analysis of best practices. European Journal of Operational Research 80,

548–574.Chiou, Y., Chen, Y., 2006. Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transportation Research

Part E 42, 116–127.Coelli, T., Grifell-Tatje, E., Perelman, S., 2002. Capacity utilization and profitability: a decomposition of short-run profit efficiency. International Journal of

Production Economics 79, 261–278.Coelli, T., Rao, D.S.P., Battese, G.E., 1998. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Boston.Farrell, M.J., 1957. The Measurement of productive efficiency. Journal of the Royal Statistical Society Series A, General 120, 253–281.Fethi, M.D., Jackson, P.M., Weyman-Jones, T.G., 2002. Measuring the efficiency of European airlines: an application of DEA and tobit analysis. Working Paper.

University of Leicester, Management Center, Leicester, United Kingdom.Gittell, J.H., 2003. The Southwest Airlines Way: Using the Power of Relationships to Achieve High Performance. McGraw-Hill, New York.Gittell, J.H., vonNordenflycht, A., Kochan, T.A., 2004. Mutual gains or zero sum? Labor relations and firm performance in the airline industry. Industrial and

Labor Relations Review 57, 163–180.Greer, M., 2006. Are the discount carriers actually more efficient than the legacy carriers? A data envelopment analysis. International Journal of Transport

Economics 33, 37–55.Greer, M., 2008. Nothing focuses the mind on productivity quite like the fear of liquidation: changes in airline productivity in the United States, 2000–2004.

Transportation Research Part A 42, 414–426.Hirsch, B.T., 2008. Sluggish institutions in a dynamic world: can unions and industrial competition coexist? The Journal of Economic Perspectives 22, 153–

176.Hirsch, B.T., Macpherson, D.A., 2000. Earnings, rents and competition in the airline labor market. Journal of Labor Economics 18, 125–155.Kuhn, P., 1998. Unions and the economy: what we know; what we should know. Canadian Journal of Economics 31, 1033–1056.Lott, S., Taylor, A., 2005. Hub networks hurt by handling capacity. Aviation Week and Space Technology 163 (18 (November 7)), 56.Metcalf, D., 2002. Unions and Productivity, Financial Performance and Investment: International Evidence. Centre for Economic Performance, The London

School of Economics and Political Science, London.Ray, S.D., 2004a. Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research. Cambridge University Press, Cambridge, UK.Ray, S.D., 2004b. A bootstrap-regression procedure to capture unit specific effects in data envelopment analysis. Indian Economic Review 39, 89–110.Schefczyk, M., 1993. Operational performance of airlines: an extension of traditional measurement paradigms. Strategic Management Journal 14, 301–317.Schein, E.H., 1980. Organizational Psychology, third ed. Prentice-Hall, Englewood Cliffs, New Jersey.Schein, E.H., 1992. Organizational Culture and Leadership, second ed. Jossey-Bass, San Francisco.Scheraga, C.A., 2004. Operational efficiency versus financial mobility in the global airline industry: a data envelopment and tobit analysis. Transportation

Research Part A 38, 383–404.Scheraga, C.A., 2006. The operational impacts of governmental restructuring of the airline industry in China. Journal of the Transportation Research Forum

45, 71–86.Sickles, R.C., Good, D.H., Getachew, L., 2002. Specification of distance functions using semi- and nonparametric methods with an application to the dynamic

performance of Eastern and Western European air carriers. Journal of Productivity Analysis 17, 133–155.Tobin, J., 1958. Estimation of relationships for limited dependent variables. Econometrica 26, 24–36.White, L.J., 1979. Economies of scale and the question of ‘‘natural monopoly” in the airline industry. Journal of Air Law and Commerce 44, 545–573.