Fleet Standardisation and Airline Performance

Journal of Transport Economics and Policy, Volume 49, Part 1, January 2015, pp. 149–166

Li Zou, Chunyan Yu, and Martin Dresner

Address for correspondence: Li Zou, College of Business, Embry-Riddle Aeronautical University, 600 S. Clyde Morris Blvd., Daytona Beach, FL 32114 ([email protected]). Chunyan Yu is also at the College of Business, Embry-Riddle Aeronautical University. Martin Dresner is at the Robert H. Smith School of Business, University of Maryland, College Park, MD 20742.

Abstract We develop three fleet standardisation measurements to estimate their impacts on airline costs and profitability. Using panel data for a group of US airlines from 1999 to 2009, we find that fleet standardisation, as expected, leads to lower unit costs. However, after controlling for its cost- reducing effects, fleet standardisation is negatively related to profit margin. Our findings provide quantitative evidence of the trade-off between the costs and benefits from fleet commonality. Although airlines can benefit from cost savings in flight operations and maintenance with a more standardised fleet, the potential negative revenue impacts from fleet standardisation have generally been overlooked.

Date of ﬁnal version: January 2014

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1.0 Introduction

Flight operations and maintenance normally account for nearly 50 per cent of passenger airlines’ operating expenses in the USA. It has been widely claimed that a more diverse fleet may result in higher costs associated with pilot training, scheduling, ground handling, maintenance, and the lack of simplicity in operations, while a standardised fleet could reduce both flight operations expenses and maintenance costs (West and Bradley, 2008). Moreover, a high degree of fleet standardisation enables an airline to benefit from additional cost savings related to aircraft purchasing (Bru¨ggen and Klose, 2010). Because of its distinct cost advantages, fleet commonality has been considered to be a key attribute for the low-cost carrier (LCC) model (Zhang et al., 2008; Bru¨ggen and Klose, 2010; Conrady et al., 2010). Despite the well-acknowledged cost savings from fleet standardisation, there may be some disadvantages on the revenue side, which have been overlooked in the previous literature. For example, a standardised fleet may lack flexibility in terms of payload and range to tailor to the demands of a complex network structure, and thus may become a hindrance for an airline to maximise its revenue potential. Even low-cost airlines may benefit from introducing new aircraft types to existing standardised fleets, since the increased diversification could generate additional revenue and help to increase profitability. In addition to the route selection constraint, airlines with a highly uniform fleet may lack the agility to react to changing market demand, especially on routes where flight frequency cannot be easily modified due to slot control limitations. Conversely, airlines with a diversified fleet are more capable of adjusting aircraft size, not only to accommodate demand fluctuation, but also to respond better to competitors’ actions. Airlines often trade aircraft size for flight frequency in order to gain market share in a competitive setting (Wei and Hansen, 2005) or in a market with growing traffic (Pitfield et al., 2010). Because of these offsetting effects on cost and revenue, the overall impact of fleet standardisation on profitability is uncertain. This paper tests the hypothesis that fleet standardisation has double-edged effects on airline profitability and the overall performance impacts from fleet standardisation may vary depending on the standardisation level. Our empirical results provide quantitative evidence of the trade-off between the costs and benefits from fleet commonality. We find that airlines can improve profitability by standardising their fleet at the family level, but standardisation at the aircraft model level may lead to lower profits. These results suggest that while maintaining fleet standardisation at the family level, an airline with its fleet consisting of more diversified aircraft models may achieve a higher profit margin. According to Kilpi (2007), fleet composition refers to the similarities and differences in the technical and operational characteristics among the aircraft in a particular fleet. Aircraft differ in terms of their payload capabilities at different ranges, fuel consumption, maintenance requirements, reliability, and so on. Major aircraft manufacturers generally offer multiple product lines termed as aircraft families, with each family consisting of a number of models.1 Aircraft from the same family tend to share similar characteristics,

1For example, the Boeing 777 family consists of ﬁve passenger models and one freighter model, including 777-200, 777-200ER, 777-200LR, 777-300, 777-300ER, and 777-Freighter. The passenger models range from 301 seats to 368 seats in a three-class conﬁguration, with a range capability of 5,240 nautical miles (9,700 km) to 9,395 nautical miles (17,395 km).

150 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner whereas aircraft by different manufacturers often have significantly different technical and operational characteristics. Therefore, we develop measures to distinguish fleet standardisation at three different levels: manufacturer, family, and model. Such a hierarchical classification approach is helpful in determining which level of standardisation is most associated with potential cost savings and profitability increases. In this paper, we extend previous research and address the following questions: 1. To what extent can fleet standardisation reduce an airline’s unit cost? 2. What is the impact of fleet standardisation on profitability? 3. How are the impacts of fleet standardisation at the aircraft family level different from the impacts at the aircraft model or manufacturer levels? Our results shed light on two important fleet composition decisions faced by an airline: whether to have a more diversified or standardised fleet; and at which level fleet standardisation maximises profitability. The rest of the paper is organised as follows. Section 2 conducts a brief review of the literature on fleet standardisation, presents how we measure the degree of fleet standardisation, and provides a summary on the overall trend in fleet standardisation for US airlines from 1999 to 2009 (the most recent data available at the time of writing). In Section 3, we describe our empirical model and provide a data summary. The estimation results are reported in Section 4, followed by simulation analyses. The final section summarises the paper, discussing managerial implications, limitations, and future research potential.

2.0 Fleet Standardisation — Measurement and Impact

In order to assess the impact of fleet standardisation on profitability, one must first establish valid measures of standardisation. De Borges Pan and Espirito Santo (2004) introduce the IPF,2 IPC,3 and IPM4 indices as measurements for fleet standardisation. They consider three categories of aircraft characteristics, including aircraft cell (or model), engine powerplant, and avionic parts, and develop two fleet standardisation measures: IPC for aircraft cell (or model) standardisation; and IPM for powerplant standardisation. Building on IPC and IPM, a composite index, namely IPF, is created to measure the overall degree of an airline’s fleet standardisation. Using the IPC standardisation measurement, Kilpi (2007) finds a positive correlation between fleet uniformity and airline operating profit margin for ten major international airlines during the period 1997–2005. Using a similar standardisation measurement approach, Bru¨ggen and Klose (2010) conclude that fleet standardisation has a positive impact on return on sales, and this positive impact increases as fleet size grows. Finally, basing their standardisation measure on a count of the different aircraft types in a fleet, West and Bradley (2008) find that for ten major US airlines during the period 2004–6, the operating profit margin from domestic services increases as fleet complexity decreases.

2IPF refers to Fleet Standardization Index, and is from the initials in Portuguese of Indice de Padronizac¸a˜ode Frotas. 3IPC refers to Cell Standardization Index; cell was used by the authors to refer to the hull of aircraft. 4IPM refers to Powerplant Standardization Index.

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A potential shortcoming of the IPC measurement is that all differences between aircraft are counted the same, whether they are distinctions between aircraft manufactured by different companies (Boeing 737 vs. Airbus A320), or differences between models within the same aircraft family (Boeing 737-300 vs. Boeing 737-800). Clearly, the training costs, maintenance costs, and staffing costs will be greater when the aircraft used by an airline are ‘more diverse’. In our paper, we propose three levels of fleet standardisation indices as follows: (1) aircraft manufacturer as the basic level (Boeing vs. Airbus); (2) aircraft family as the second level (Boeing 737 vs. Boeing 757); and (3) aircraft model as the third level (Boeing 737-300 vs. Boeing 737-800). Note that these measures form a hierarchy. Aircraft differences at the manufacturer level also reflect differences at the family level, while aircraft differences at the family level also reflect differences at the model level.5 Our calculation for fleet standardisation at all three levels is similar to the Herfindahl– Hirshman Index (HHI), which is widely used to measure the degree of market con- centration in industrial organisation economics. The formulae for these standardisation indices, HHI_mfr, HHI_family, and HHI_model, are as follows: N The number of aircraft made by Manufacturer i 2 HHI mfr = , (a) i = 1 The total number of aircraft in the fleet F The number of aircraft belonging to Aircraft Family j 2 HHI family = , (b) j = 1 The total number of aircraft in the fleet M The number of aircraft belonging to Aircraft Model k 2 HHI model = . (c) The total number of aircraft in the fleet k = 1 A comparison between our standardisation measurements and IPC is provided in Table 1,6 with four sample airlines. The table shows that whereas the IPC values indicate that all four airlines have the same degree of fleet standardisation, our measures show differences at the model, family, and manufacturer level, potential causes of cost, and profitability differences among the carriers. Thus, our HHI-based measures, when used in combination, can provide a closer look at standardisation differences among airlines than the single IPC developed by De Borges Pan and Espirito Santo (2004). In general, the US airlines7 have increased their fleet standardisation between 1999 and 2009. This result holds true for all three of our fleet standardisation measurements, as well

5The standardisation indices are based on a hierarchy of differences, with differences between manufacturers the most basic distinction, followed by family differences and then distinctions among models. As an example, if an airline owns one aircraft from Family A and another from Family B, then, by definition, the two aircraft must not only represent two different families but must also be distinct models. Likewise, if an airline owns one aircraft from Manufacturer X and another from Manufacturer Y, then, by definition, the aircraft must also be from different families and must be distinct models. 6We choose the IPC measure for comparison because it most closely associates with the standardisation indices we developed. 7The airlines in the sample include eight mainline airlines, eight low-cost airlines, and twelve regional and other airlines. In total, these twenty-eight airlines account for 93 per cent of passenger traffic (RPKs) in the US airline industry during the period concerned. Consistent with the latest classification by Bureau of Transportation Statistics (BTS), the eight network airlines are American, Alaska, Continental, Delta, Northwest, United, US

152 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner

Table 1 The Comparison of Fleet Standardisation Measurements

HHI_ HHI_ HHI_ Year Fleet model family mfr IPC

Southwest 2006 193 Boeing 25 Boeing 737-500 265 Boeing 0.46 1 1 0.33 737-300 737-700 Air Wisconsin 2003 17 BAE146 49 Canadair 7 Dornier 382 0.51 0.51 0.51 0.33 CL6002B19 100/200 Spirit Airlines 2006 18 A319 6 A321 12 MD DC9-80 0.38 0.55 0.55 0.33 Skywest 2005 69 EMB 120 125 Canadair 27 Canadair 0.43 0.43 0.57 0.33 Airlines CL6002B19 100/200 CL6002C10 as for the previously used IPC measurement.8 Such an industry-wide increase in standardisation occurred as low-cost airlines expanded their market share in the USA and network carriers sought to emulate some of the key low-cost strategies — for example, by simplifying fleet composition. Moreover, we find that low-cost airlines, on average, have a higher degree of fleet standardisation than do regional airlines, while the fleet for network airlines, in general, is the least standardised as compared with the other two types of airline. In the next section, we describe the model that we use to assess the impact of fleet standardisation on airline costs and profitability.

3.0 Empirical Analysis

In this section, we conduct empirical analyses to investigate the relationship between an airline’s ﬂeet standardisation and the airline’s unit operating cost (as measured by operating expenses per available seat mile (ASM)) and operating proﬁt margin.

3.1 Data description The data used in this study is primarily drawn from the US Department of Transportation’s Form 41 and T100, which we acquired from OAG Aviation Solutions. In addition, we compiled fleet composition and aircraft utilisation information from the ICAO and OAG Fleet iNet database. The initial fleet data obtained from ICAO covers seventy- three US airlines for the period 1999–2009. Our data filtering process is as follows. First, we excluded twenty pure cargo airlines and ten charter operators. Of the remaining forty-three airlines offering scheduled passenger services, we dropped fifteen airlines that

Airways, and Midwest Express; the eight low-cost airlines include AirTran Airways, Allegiant Air, ATA Airlines, Frontier Airlines, JetBlue Airways, Southwest, Spirit Airlines, and Sun Country; and the rest of the airlines in the sample include Air Wisconsin, American Eagle, Atlantic Southeast, COMAIR, ExpressJet, Horizon Air, Mesa Airlines, MESABA, Pinnacle, SkyWest, Trans States Airlines, and Hawaiian. 8The airlines in the sample are found to have a 32.43 per cent increase, on average, in ﬂeet standardisation measured by HHI_mfr, a 23.64 per cent increase by HHI_family, a 16.22 per cent increase by HHI_model, and a 44 per cent increase by IPC.

153 Journal of Transport Economics and Policy Volume 49, Part 1 had fewer than three years observations during the 11-year period studied. Following the suggestion by a reviewer, we focused our analysis on mainline and low-cost airlines, while excluding regional airlines as regional airlines operate distinctly from network and low-cost airlines in many aspects.9 After combining the OAG and ICAO data sets and eliminating a few cases with missing values and outliers, our final sample consists of 137 observations for eight mainline and eight low-cost airlines for the years 1999–2009. A total of fifty-six aircraft models were operated by the airlines during this period, and these models are categorised into twenty-three aircraft families from eleven aircraft manufacturers. To fit the different aircraft models into their appropriate families, we follow the 2000 version of aircraft type classification adopted by the Federal Aviation Administration (FAA) for pilot certification purposes.

3.2 Variable development Our unit of analysis is for airline i in year t. The main independent variables are the four measures for fleet standardisation, including IPCit, HHI_modelit, HHI_familyit, and HHI_mfrit. We use the average cost per available seat mile to represent unit cost, and profitability is measured by operating profit margin. Three sources of operating revenues are considered: passenger services, cargo services, and other services. Operating expenses include labour and related expenses (such as pension benefits and payroll taxes), facility expenses (such as aircraft rent and depreciation), fuel costs, and all other costs. The two main input factors considered are labour and fuel. Total RPMs is included to control for output quantities and Number of Points is used as a measure for market scope (or network size). Operating characteristics include stage length,10 stage length squared, and the percentage of capacity (as measured by ASMs) provided by regional affiliates, since the US mainline airlines rely on regional carriers to feed traffic from smaller cities to their main hubs.11 To control for productivity in labour, fuel, and aircraft usage, we develop RPMs per employee to represent labour productivity, ASMs per gallon to represent fuel efficiency, and revenue block-hours per aircraft to represent aircraft utilisation. Other fleet characteristics considered include fleet size, average age of aircraft, and average size of aircraft. The determinants of profit margin also include passenger yield and load factor. Table 2 presents the definition and descriptive statistics for all the variables used in our analysis.

9For example, regional airlines, unlike mainline and low-cost airlines, do not sell their own tickets, and the majority of their revenues are earned through contractual agreements signed with mainline airlines for providing feeder services. Mainline carriers pay their regional affiliates based on a determination of their flight operating costs and other factors intended to approximate market rates for those services. 10The average stage length for an airline in a given year reported on Form 41 is defined as the ratio of the airline’s annual revenue aircraft miles flown to its number of revenue aircraft departures performed. Therefore, it is a departure-weighted measurement. Following the suggestion from a reviewer, we also developed a seat-adjusted stage length measure, which is calculated by dividing total revenue passenger miles (RPMs) an airline flies in a year by the number of enplaned passengers it carries in the year. The correlation between these alternate stage length variables is 0.93. 11We include the percentage of consolidated ASMs that are operated by regional carriers as a control variable in the unit cost and profit margin estimations, since we cannot separate out the revenue that mainline airlines earn as ‘ticketing airlines’ from the flights operated by regional affiliates, or the payments to the regionals for capacity purchases.

154 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner

Table 2 Variable Description and Descriptive Statistics

Mean Variable Description (Std. Dev.)

Unit cost (cents) The total operating expenses per available seat mile (CASM) for 10.59 airline i in year t (2.69) Profit margin The operating profit margin (that is, EBIT/Operating revenue) for −0.01 airline i in year t (0.107) IPC The overall fleet standardisation index for airline i in year t 0.29 (0.24) HHI_model The degree of fleet standardisation measured by the diversity of 0.42 aircraft models in the fleet for airline i in year t (0.27) HHI_family The degree of fleet standardisation measured by the diversity of 0.64 aircraft families in the fleet for airline i in year t (0.31) HHI_mfr The degree of fleet standardisation measured by the diversity of 0.85 aircraft manufacturers in the fleet for airline i in year t (0.20) Labour price The total salaries and related fringe benefits per employee for airline 69,866.2 ($ per employee) i in year t (17,205.3) Fuel price The fuel cost per gallon for airline i in year t 1.45 ($ per gallon) (0.77) Total RPMs The total revenue passenger miles for all services, including 4,240 (million) scheduled and non-scheduled revenue passenger traffic for airline (4,300) i in year t Number of points The number of airport destinations, including domestic and 82.53 international points served by airline i in year t (57.26) Stage length (miles) The total revenue passenger miles for all services divided by the 1,133.99 number of enplaned passengers for airline i in year t (316.95) % of passenger The percentage of passenger revenue in the total operating revenue 0.87 service revenue for airline i in year t (0.10) Fuel efficiency The amount of available seat miles flown by airline i from 62.67 (ASMs per gallon) consuming one US gallon of jet fuel in year t (13.59) RPMs per employee The total revenue passenger miles for all services divided by the 1,823,629 number of employees for airline i in year t (534,035) Block hours per The average daily revenue block hours per aircraft for airline i in 10.26 aircraft year t (2.06) Fleet size The average number of aircraft for airline i at the beginning and end 244.94 of year t (233.42) Avg. age of aircraft The average age of various aircraft models weighted by their 11.00 percentage in the fleet for airline i in year t (5.79) Avg. size of aircraft The average number of seats for various aircraft models weighted by 151.28 their percentage in the fleet for airline i in year t (22.01) Yield (cents) The total passenger yield (cents/RPMs), including scheduled and 12.02 non-scheduled passenger services for airline i in year t (2.07) Load factor (%) The total RPMs divided by the total ASMs, including both 74.60 scheduled and non-scheduled revenue services operated by airline (6.15) i in year t ASM % by regional The percentage of available seat miles that are operated by regional 5.23 affiliates carriers for airline i in year t (5.56)

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3.3 Empirical models We develop two reduced-form equations, as presented in equations (1) and (2), to estimate the impacts of fleet standardisation on unit cost and profit margin, respectively. Since pilots and/or maintenance workers are able to operate or service aircraft of the same model, or within the same family, without incurring time and monetary expenses for retraining, it is expected that there is a positive association between fleet standardisation and labour productivity. To account for this endogeneity concern, we use a one-year lagged labour productivity variable as an instrument to substitute for labour productivity in the unit cost and profit margin estimations. In addition, as noted above, we use the percentage of available seat miles by regional affiliates as a proxy measure to represent the reliance that mainline airlines have on their regional partners for providing traffic from smaller cities to major hubs.12 Moreover, given that the operation of freighter aircraft does not impact ASMs, but adds costs to airlines, we use the percentage of passenger revenue in total operating revenue as a control variable in the unit cost estimation. Finally, due to the use of panel data, we include year dummy variables to control for unobserved, external factors (such as inflation, economic recession, and so on) that may impact unit cost and profit margin of an airline over the time period of analysis. The cost equation is presented as follows:

ln(Unit costit) = a0 + a1 ln(FSIit) + a2 ln(labour priceit) + a3 ln(fuel priceit)

+ a4 ln(total RPMsit) + a5 ln(number of pointsit) 2 + a6 ln(stage lengthit) + a7[ln(stage lengthit)]

+ a8 ln(revenue share of passenger servicesit) + a9 ln(fuel eﬃciencyit)

+ a10 ln(lagged RPMs per employeeit)

+ a11 ln(revenue block hours per aircraftit) + a12 ln(ﬂeet sizeit)

+ a13 ln(average age of aircraftit) + a14 ln(average size of aircraftit)

+ a15 ln(ASM% by regional affiliatesit) 10 + bt (year dummy variables) + 1it. (1) t = 1 Seristo and Vepsalainen (1997) develop a theoretical framework to analyse the primary drivers for airline profits. Based on their model, we specify the profit margin equation as the following:

Operating proﬁt marginit = g0 + g1 ln(FSIit) + g2 ln(passenger yieldit)

+ g3 ln(passenger load factorit) + g4 ln(labour priceit)

+ g5 ln(fuel priceit) + g6 ln(total RPMsit)

12Given that the value for this variable is always zero for low-cost airlines, we follow a common procedure, adding a very small number (that is, 10−6) to the original values for all the observations, including mainline and low-cost airlines, before taking their natural logarithms.

156 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner

+ g7 ln(number of pointsit) + g8 ln(stage lengthit) 2 + g9[ln(stage lengthit)] + g10 ln(fuel eﬃciencyit)

+ g11 ln(lagged RPMs per employeeit)

+ g12 ln(revenue block hours per aircraftit)

+ g13 ln(ﬂeet sizeit) + g14 ln(average age of aircraftit)

+ g15 ln(average size of aircraftit)

+ g16 ln(ASM% by regional aﬃliatesit) 10 + lt (year dummy variables) + uit. (2) t = 1

In equations (1) and (2), we use four measures to represent fleet standardisation indices (FSI), including IPCit, HHI_modelit, HHI_familyit, and HHI_mfrit. Because of the nature of the panel data, we cannot assume that the error terms, 1it in equation (1) and uit in equation (2), are independent and identically distributed. Instead, they may be serially correlated within the panel and/or heteroscedastic and contemporaneously correlated across panels (Judge et al., 1988). To address these concerns, we first conduct the Breusch–Pagan/Cook–Weisberg tests to check the null hypothesis that the error variances are equivalent across panels based on the ordinary least squares (OLS) estimation results. While the results for equation (1) indicate the absence of heteroscedasticity, the null hypothesis of homoscedasticity is strongly rejected in the profit margin estimations. Further, the results from the Wooldridge test (Wooldridge, 2002) strongly indicate the presence of the first-order autocorrelation, AR(1), in both equations. When the autocorrelation coefficient is unknown, the feasible generalised least squares (FGLS) approach is recommended for estimation (Judge et al., 1998). According to Beck and Katz (1995), the use of FGLS for variance–covariance estimates is especially suitable when there are ten to twenty panels and ten to forty time periods per panel (Stata, 2003). Since our data set has sixteen sample airlines over 11 years, we use the FGLS procedure, allowing for AR(1) in both equations (1) and (2). Moreover, a heteroscedastic error structure is specified in equation (2) only. The estimation results are presented and discussed in the following section.

4.0 Estimation Results and Robustness Check

4.1 Estimation results Models (1) to (4) in Table 3 report the estimation results for equation (1) using IPC, HHI_model, HHI_family, and HHI_mfr to measure the degree of fleet standardisation, respectively. As expected, the coefficients for all four FSI measures in Models (1) to (4) are negative and highly significant. These consistent results suggest that fleet standardisation reduces unit cost, ceteris paribus; that is, unit cost will be lower for an airline with a fleet consisting of fewer aircraft models, families, or manufacturers, holding other factors constant. However, after controlling for fleet standardisation at the aircraft model and

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Table 3 The FGLS Estimation Results for Unit Cost Equation

Independent variables Model (1) Model (2) Model (3) Model (4) Model (5)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Constant 5.155 5.218 5.244 5.448 5.270 (0.856) (0.838) (0.852) (0.858) (0.828) ∗∗∗ ln(IPC) −0.056 (0.021) ∗∗∗ ∗ ln(HHI_model) −0.109 −0.068 (0.030) (0.036) ∗∗∗ ∗∗ ln(HHI_family) −0.124 −0.093 (0.032) (0.044) ∗∗∗ ln(HHI_mfr) −0.107 0.011 (0.036) (0.052) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(labour price) 0.196 0.175 0.168 0.174 0.171 (0.048) (0.047) (0.047) (0.048) (0.047) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(fuel price) 0.332 0.327 0.300 0.316 0.309 (0.043) (0.042) (0.041) (0.043) (0.042) ∗∗ ∗∗ ∗∗ ∗ ∗∗ ln(total RPMs) −0.102 −0.093 −0.084 −0.127 −0.082 (0.043) (0.042) (0.041) (0.041) (0.041) ln(number of points) 0.015 0.021 0.004 0.036 0.002 (0.033) (0.032) (0.034) (0.032) (0.033) ln(stage length) −0.039 −0.020 −0.128 −0.069 −0.089 (0.089) (0.087) (0.096) (0.091) (0.090) [ln(stage length)]2 0.061 −0.002 0.085 0.082 0.041 (0.097) (0.096) (0.101) (0.098) (0.097) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(% of passenger services rev) −0.569 −0.525 −0.566 −0.470 −0.558 (0.129) (0.127) (0.129) (0.136) (0.137) ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ln(fuel efficiency) −0.193 −0.228 −0.155 −0.231 −0.180 (0.071) (0.068) (0.069) (0.069) (0.070) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(lagged labour productivity) −0.166 −0.168 −0.142 −0.167 −0.153 (0.043) (0.042) (0.041) (0.043) (0.042) ln(block hours per aircraft) −0.082 −0.064 −0.085 −0.025 −0.078 (0.058) (0.055) (0.055) (0.056) (0.057) ∗∗ ln(fleet size) 0.077 0.063 0.057 0.109 0.048 (0.051) (0.050) (0.048) (0.048) (0.051) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(avg. age of aircraft) −0.053 −0.057 −0.047 −0.051 −0.053 (0.016) (0.016) (0.016) (0.016) (0.015) ln(avg. size of aircraft) 0.014 0.039 −0.068 0.099 −0.043 (0.102) (0.097) (0.105) (0.098) (0.107) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(ASM% by regional affiliates) 0.016 0.013 0.020 0.021 0.015 (0.004) (0.004) (0.004) (0.004) (0.004) Number of observations 137 137 137 137 137 Wald x2 906.21 956.87 856.79 899.06 990.34 Prob . x2 0.0000 0.0000 0.0000 0.0000 0.0000 Adj. R2 0.89 0.90 0.89 0.89 0.90

Notes: The coefficients on year dummy variables are not reported. The numbers in parentheses are standard errors ∗∗∗ ∗∗ ∗ for coefficients. Significant at 0.01 level; significant at 0.05 level; significant at 0.1 level.

158 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner family levels, fleet standardisation at the manufacturer level (that is, HHI_mfr) does not have any significant effect on unit cost, as shown by Model (5). This finding is reasonable, as HHI_mfr has minimal variation after controlling for the degree of standardisation at both aircraft model and family levels. Further, Model (5) shows that the coefficient for 2 HHI_model is not statistically different from the coefficient for HHI_family (x(1) = 0.18, with its p-value as 0.6744), implying that an increase in fleet standardisation at either the aircraft model or aircraft family level reduces unit cost to a similar extent. The coefficients of the control variables have the expected sign and are consistent from Models (1) to (5). In summary, unit cost is found to increase with fuel price and labour price, while it is negatively associated with total RPMs, the revenue share of passenger services, fuel efficiency, and lagged labour productivity. Neither fleet size nor average size of aircraft are found to have any significant effect on unit cost. The variables including stage length and stage length squared both have expected signs, but are insignificant.13 As for the aircraft age, the negative and significant coefficient suggests that after controlling for fuel efficiency, aircraft utilisation, labour price, and fuel price, the cost savings in aircraft depreciation and leasing associated with an older fleet may be sufficient to offset the higher maintenance and servicing cost. The estimation results from Table 4 show that fleet standardisation at the family level contributes to greater profit margins, while the effects of standardisation at the model and manufacturer levels are not significant; this may be attributed to offsetting revenue and cost effects from fleet standardisation. For example, when an airline operates a single type of aircraft model, it will obtain the greatest cost savings from having a uniform aircraft model in its fleet. On the other hand, the airline’s revenue potential may be constrained from this standardisation, since the standardised aircraft model may be suitable for only a limited number of markets.14 In comparison, an airline with a highly standardised fleet in terms of aircraft family may still have a quite diversified composition of aircraft models, and is therefore less affected by the lack of route variety.

4.2 Robustness checks An analysis of our data set indicates high correlation between some explanatory variables. For example, the variable ln(total RPMs) is highly correlated with ln(number of points) at 0.89, and with ln(fleet size) at 0.97. To diagnose whether multi-collinearity among these variables causes a serious estimation problem, we computed variance inflation factor (VIF) scores for all predictors. It shows that both ln(total RPMs) and ln(fleet size) have very high VIF values (greater than thirty in the unit cost estimation; above 100 in the profit margin estimation). To address this situation, we removed ln(total RPMs) and

13Since ln(stage length) and [ln(stage length)]2 are highly correlated at 0.99, the variance inflation factor (VIF) scores for these two variables far exceed the acceptable level. To address the problem, we used the centred ln(stage length) by subtracting its mean from each observation, and then calculated the squared ln(stage length) based on the centred values. After this adjustment, the correlation between centred ln(stage length) and [ln(stage length)]2 dropped to −0.41. 14A preliminary analysis suggests that there is a negative association between fleet standardisation and stage length variation, traffic density variation, and the number of points served by airlines. This analysis is available upon request.

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Table 4 The FGLS Estimation for Proﬁt Margin Equation

Independent variables Model (1) Model (2) Model (3) Model (4) Model (5)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Constant −4.144 −4.136 −4.281 −4.099 −4.389 (0.988) (0.982) (0.967) (0.989) (0.983) ln(IPC) 0.009 (0.016) ln(HHI_model) 0.033 0.018 (0.022) (0.025) ∗∗∗ ∗∗ ln(HHI_family) 0.057 0.065 (0.021) (0.027) ln(HHI_mfr) 0.027 −0.029 (0.024) (0.033) ∗∗ ∗∗ ∗∗ ∗∗ ∗∗∗ ln(passenger yield) 0.183 0.162 0.205 0.170 0.210 (0.084) (0.082) (0.083) (0.083) (0.085) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(passenger load factor) 0.680 0.688 0.684 0.653 0.708 (0.162) (0.158) (0.152) (0.159) (0.152) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ln(labour price) −0.124 −0.109 −0.117 −0.111 −0.119 (0.038) (0.038) (0.038) (0.038) (0.039) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(fuel price) −0.257 −0.254 −0.238 −0.247 −0.240 (0.041) (0.041) (0.040) (0.041) (0.041) ln(total RPMs) 0.039 0.033 0.039 0.046 0.034 (0.039) (0.038) (0.037) (0.036) (0.039) ln(number of points) −0.012 −0.003 0.008 −0.011 0.012 (0.027) (0.026) (0.027) (0.027) (0.026) ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗ ln(stage length) −0.199 −0.214 −0.151 −0.196 −0.155 (0.064) (0.063) (0.065) (0.065) (0.065) ∗∗ ∗∗∗ ∗ ∗∗ ∗ [ln(stage length)]2 0.169 0.197 0.136 0.175 0.134 (0.074) (0.074) (0.075) (0.076) (0.075) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(fuel efficiency) 0.278 0.290 0.232 0.278 0.232 (0.061) (0.061) (0.063) (0.061) (0.065) ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ln(lagged labour productivity) 0.102 0.103 0.086 0.099 0.088 (0.036) (0.035) (0.035) (0.036) (0.035) ln(block hours per aircraft) −0.001 −0.003 0.008 −0.017 0.022 (0.046) (0.043) (0.044) (0.044) (0.048) ln(fleet size) −0.035 −0.029 −0.035 −0.046 −0.025 (0.043) (0.041) (0.040) (0.039) (0.043) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(avg. age of aircraft) 0.047 0.048 0.043 0.046 0.044 (0.014) (0.014) (0.014) (0.015) (0.014) ∗∗ ∗∗ ∗∗ ln(avg. size of aircraft) −0.194 −0.216 −0.137 −0.208 −0.127 (0.091) (0.085) (0.088) (0.089) (0.092) ln(ASM% by regional affiliates) 0.001 0.003 −0.001 −0.0001 0.001 (0.003) (0.003) (0.003) (0.003) (0.003) Number of observations 137 137 137 137 137 Wald x2 281.10 275.54 302.53 274.38 305.79 Prob . x2 0.0000 0.0000 0.0000 0.0000 0.0000 Adj. R2 0.51 0.54 0.53 0.51 0.54

160 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner ln(fleet size) from the unit cost and profit margin estimations. The results15 are consistent with those presented in Tables 3 and 4. After the modification, the VIF values for all predictors are less than the commonly accepted level of ten, indicating that multi- collinearity may not be a serious concern. Southwest Airlines is well known for its standardised fleet, low-cost structure, and consistent profit. Therefore, it is important to determine whether our result is dominated by Southwest to ensure the findings are applicable to US air carriers in general. To accom- plish this, we re-estimated the unit cost and profit margin equations, including a dummy variable for Southwest. After controlling for Southwest effects, we still find that unit cost is negatively related to fleet standardisation at the model and family levels, while profit margin is positively associated with fleet standardisation at the family level. To further investigate the impact of fleet standardisation on profit margin above and beyond its effect through unit cost, we estimate a two-equation system as follows:

Unit operating cost = g(ﬂeet standardisation, input factor prices, output quantities, operating characteristics,

productivities, year dummy variables) + dit, (3) Operating proﬁt margin = h (ﬂeet standardisation, unit operating cost, load factor, passenger yield, year dummy variables)

+ zit. (4) In the above system, unit cost appears in the profit margin equation, but profit margin does not appear in the estimation for unit cost. This is an example of a recursive, triangular system (Kmenta, 1997). According to Greene (2000), the use of iterated feasible generalised least squares (FGLS) procedure would generate consistent and efficient estimators for a triangular model with a non-diagonal variance–covariance matrix. The estimator from using the two-stage least squares (2SLS) method, however, will be consistent, but not efficient. To assure the robustness of the results, we estimated the model using both FGLS and 2SLS procedures,16 and found that the estimation results for the profit margin equation (as shown in Table 5) are quite consistent between the two procedures. As indicated by the results from Model (5), when all three HHI measures are included, the coefficient for HHI_model is negative and marginally significant (g =−0.04, p = 0.14) at the 15 per cent level, whereas the coefficient for HHI_family is positive and highly significant (g = 0.08, p = 0.003) at the 1 per cent level. The contrasting results suggest that fleet standardisation at the aircraft model level may have a negative effect on profit margin, offsetting some of the benefits from cost reduction. On the contrary, fleet standardisation at the family level has a direct, positive effect on profit margin, beyond its impact through cost savings. This benefit could be attributed to the fact that an airline with standardisation at the family level might still have sufficient capacity diversity to serve

15Due to page limitation, we do not report the results in this paper, but they are available upon request. 16Due to page limitation, we do not report the 2SLS estimation results in this paper, but they are available upon request.

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Table 5 The FGLS Estimation for the Proﬁt Margin Equation in the Triangular Model

Model (1) Model (2) Model (3) Model (4) Model (5)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Constant −3.29 −3.44 −3.63 −3.08 −3.36 (0.78) (0.75) (0.76) (0.78) (0.75) ln(IPC) 0.0002 (0.012) ∗ ln(HHI_model) 0.01 −0.04 (0.02) (0.03) ∗∗ ∗∗∗ ln(HHI_family) 0.04 0.08 (0.02) (0.03) ln(HHI_mfr) 0.009 −0.03 (0.03) (0.04) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(passenger yield) 0.31 0.32 0.32 0.30 0.32 (0.08) (0.08) (0.08) (0.08) (0.08) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ln(load factor) 0.75 0.80 0.84 0.74 0.81 (0.15) (0.15) (0.15) (0.15) (0.15) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Unit cost (ﬁtted) −0.31 −0.32 −0.31 −0.35 −0.38 (0.08) (0.09) (0.08) (0.09) (0.09) ln(ASM% by regional aﬃliates) −0.001 0.001 0.002 0.001 0.001 (0.003) (0.003) (0.003) (0.003) (0.003) Number of observations 137 137 137 137 137 Wald x2 227.37 231.58 251.09 225.30 283.71 Prob . x2 0.0000 0.0000 0.0000 0.0000 0.0000 Adj. R2 0.34 0.35 0.39 0.32 0.41

Notes: The coefficients on year dummy variables are not reported. The numbers in parentheses are standard errors. ∗∗∗ ∗∗ ∗ Significant at 0.01 level; significant at 0.05 level; significant at 0.15 level. various types of markets. For example, although there was a single aircraft family (that is, Boeing 737) in the fleet of Alaska Airlines in 2007, the airline operated five different models in the 737 family, consisting of 737-200, 400, 700, 800, and 900, with seat capacities ranging from 111 to 172. This enabled the airline to achieve cost savings associated with having a uniform fleet of 737 aircraft without greatly sacrificing its route network diversity. These findings imply that in order to increase profit margin, an airline may concentrate its fleet among fewer aircraft families as long as its fleet consists of a wide variety of aircraft models. Finally, the coefficient for HHI_mfr is not significant in Model (5).

5.0 Case Study

To further illustrate the impacts of fleet standardisation on airline unit cost, we conduct a scenario analysis based on the data for Southwest and American Airlines in the year 2007. Table 6 presents the comparison between these two airlines in their fleet characteristics and other key aspects of their operations. It is evident that Southwest Airlines has a more standardised fleet than American Airlines and also lower unit costs. Based on the HHI fleet standardisation indices and

162 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner

Table 6 The Comparison between Southwest and American Airlines in 2007

Southwest American Airlines

Fleet composition Boeing 737-300/500/700 Airbus 300-B4600; Boeing 737-800; Boeing 757-200; Boeing 767-200/300ER; DC9-80 IPC 0.33 0.12 HHI_model 0.46 0.29 HHI_family 1 0.33 HHI_mfrn 1 0.91 Unit cost (CASM) 9.09 cents per ASM 13.03 cents per ASM Profit margin 0.08 0.03 Passenger yield 12.69 cents per RPM 13.07 cents per RPM Load factor 72.58% 81.49% Labour price $100,455.2 $89,318.46 Fuel price $1.69 per gallon $2.05 per gallon Number of points 64 167 Total RPMs 7,241 million 13,800 million Seat-adjusted stage length 710 miles 1,405 miles Fuel efficiency 66.89 ASMs per gallon 59.98 ASMs per gallon RPMs per employee 2,149,947 1,921,524 Block hours per aircraft 11.17 9.97 Fleet size 484 689 Avg. age of aircraft 9.85 years 15.14 years Avg. size of aircraft 136 seats 177 seats the unit cost estimation results from Model (5) in Table 3, the predicted unit cost is 9.05 cents per available seat mile (ASM) for Southwest and 12.60 cents for American Airlines. We conduct a simulation analysis to address the following two questions:17 1. What would be the unit cost for Southwest Airlines if it had the same degree of fleet diversification as American Airlines? 2. What would be the unit cost for American Airlines if it had the same degree of fleet standardisation as Southwest Airlines? Results from the simulation show that the unit cost for Southwest would increase by 1.29 cents if the airline had exactly the same degree of fleet diversification as American Airlines (that is, HHI_model, HHI_family, and HHI_mfr). Conversely, American Airlines would reduce its unit cost by 1.58 cents if it had the same extent of fleet standardisation as Southwest. To illustrate the findings from our profit margin estimations, we use Southwest Airlines as an example and compare the following three scenarios for its fleet composition. In Scenario I, we consider the existing fleet composition for the airline in 2007, consisting of 193 Boeing 737-300, twenty-five Boeing 737-500, and 266 Boeing 737-700 aircraft.

17Note that we are not suggesting that American Airlines can easily be converted to a Southwest clone (nor Southwest to an American clone). We provide this example only as a way of illustrating the costs of operating a diversified fleet or, conversely, the benefits that can be derived from standardisation.

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Table 7 The Comparison among Scenarios I, II, and III

Scenario I Scenario II Scenario III

HHI_model 0.46 0.37 0.36 HHI_family 1 1 0.74 HHI_mfr 1 1 1 Unit cost (cents) 9.05 9.20 9.48 Proﬁt margin 10.83% 11.15% 7.59%

Scenario II simulates the recent fleet expansion plan of the airline. On 8 March 2012, South- west took the delivery of its first Boeing 737-800 aircraft and expects to receive a total of seventy-three such aircraft by the end of 2018 (Compart, 2012).18 The selection of the 737-800 model fits well with the airline’s LCC strategy of operating a highly standardised fleet. With the addition of the seventy-three 737-800 model aircraft, the airline’s fleet will become more diversified at the aircraft model level, but will still be standardised at the family and manufacturer levels. Our analysis in Scenario III is based on a hypothetical example. In this scenario, Southwest Airlines is assumed to fully integrate the eighty- eight Boeing 717s from its acquisition of AirTran Airways. The addition of the 717s would diversify the airline’s fleet not only at the aircraft model level, but also at the family level. Table 7 presents the simulation results for Scenarios I–III. As shown in Table 7, the projected profit margin for Southwest is the highest with Scenario II, where the airline diversifies its fleet at the model level, but maintains its standardisation at the family and manufacturer level. In Scenario III, when the airline adds 717s into its fleet, the profit margin is found to be the lowest among the three scenarios, because of the higher operating costs associated with a combined 737/717 fleet. This simulation result may explain why Southwest decided to lease all of AirTran’s 717s to Delta Airlines.

6.0 Conclusions, Implications, and Future Research

As evidenced by the number of airline bankruptcies in recent years, airlines operate in a volatile and very competitive market, and few carriers have been consistently profitable. Therefore, airlines constantly seek new ways to reduce costs and to increase revenues. Costs associated with operating and maintaining aircraft account for a significant portion of airline operating expenses. As a result, it is important for airlines to understand how fleet composition may impact their financial performance in order to remain competitive in a very challenging industry. Despite the important practical implications of fleet

18Mike Van de Ven, Chief Operating Officer, made the following comments (Transportation Business Journal, 2012): ‘Not only is this a beautiful aircraft, and one our customers are sure to love, but it will also play an important strategic role in our future. The -800 aircraft carries 175 passengers, close to a 30 per cent increase over our current fleet configuration, which will improve our unit cost per flight. Additionally, it complements our existing fleet with opportunities for longer-haul flying and schedule flexibility by allowing additional capacity in high-demand, slot-controlled, or gate-restricted airport.’

164 Fleet Standardisation and Airline Performance Zou, Yu, and Dresner composition, there are very few studies investigating the impacts of fleet standardisation on airline costs and profitability. This paper enriches the literature in two main aspects. First, we develop the HHI measurements as an alternative approach to assess the fleet standardisation at different levels. Second, we provide quantitative evidence showing the offsetting effects from fleet standardisation on airline costs and revenue. The potential negative revenue impacts from fleet standardisation have generally been overlooked. Our analysis, based on sixteen mainline and low-cost airlines in the USA from 1999 to 2009, reveals that fleet standardisation (as expected) leads to lower unit costs. Further, we find evidence in support of the argument that having fewer aircraft models may restrict an airline’s ability to serve different types of markets, thereby limiting its revenue potential. Nevertheless, the overall impact on profit margin from fleet standardisation at the aircraft family level is positive. This finding is consistent with previous studies (for example, Kilpi, 2007; West and Bradley, 2008; Bru¨ggen and Klose, 2010). These results shed light on an important decision faced by an airline; that is, at what level does it benefit most from fleet standardisation? In this paper, we focused on the type of aircraft, categorised into various models, families, and manufacturers, to assess the degree of fleet standardisation. However, as noted by De Borges Pan and Espirito Santo (2004) in their pioneering study on fleet standardisation, an in-depth understanding of the economic impact of fleet composition requires a more comprehensive measurement built upon a mix of aircraft cell, engine, and avionics equipment. Given the importance of engine selection and its distinct purchasing characteristics, future research may explore the impacts of engine standardisation on airline operational and financial performance, and provide further implications for airline management. In addition, there are several ways in which fleet standardisation may impact airline costs. For example, an airline with a more standardised fleet may benefit from labour cost savings or from lower inventories of spare parts. Future research may examine in greater depth the sources of cost savings with fleet standardisation. Finally, it is noted that our results provide only indirect evidence of the potential revenue loss from fleet standardisation. A more direct, rigorous investigation of the relationship between fleet standardisation and airline network structure would help specify how fleet standardisation may restrain airlines from serving more diverse, dynamic markets, and result in potential traffic loss or yield reduction.

References

‘Aerospace and defense companies; Southwest airlines celebrates the arrival of the carriers first Boeing 737- 800 aircraft’, Transportation Business Journal (8 April 2012), p. 84. Beck, N. and J. N. Katz (1995): ‘What to do (and not to do) with time-series cross-section data’, American Political Science Review, 89(3), 634–47. Bru¨ggen, A. and L. Klose (2010): ‘How fleet commonality influences low-cost airline operating performance: empirical evidence’, Journal of Air Transport Management, 16(6), 299–303. Compart, A. (2012): ‘Southwest shuffles 737 deliveries to keep fleet size flat’, Aviation Daily, 388(36), 1. Conrady, R., F. Fichert, and R. Klophaus (2010): ‘European LCCs going hybrid: an empirical survey’, Proceedings of 14th ATRS Conference, Porto, Portugal. De Borges Pan, George A. and R. A. Espirito Santo (2004): ‘Developing a fleet standardization index for airline pricing’, Journal of Air Transportation, 9(2), 97–110.

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Greene, W. H. (2000): Econometric Analysis (fourth edition), Prentice-Hall, Upper Saddle River, NJ. Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lutkepohl, and T. Ch. Lee (1988): The Theory and Practice of Econometrics (second edition), John Wiley & Sons, Singapore. Kilpi, J. (2007): ‘Fleet composition of commercial jet aircraft 1952–2005: developments in uniformity and scale’, Journal of Air Transport Management, 13(2), 81–9. Kmenta, J. (1997): Elements of Econometrics (second edition), University of Michigan Press, Ann Arbor, MI. Pitfield, D. E., R. E. Caves, and M. A. Quddus (2010): ‘Airline strategies for aircraft size and airline frequency with changing demand and competition: a simultaneous-equations approach for traffic on the north Atlantic’, Journal of Air Transport Management, 16(3), 151–8. Seristo, H. and A. Vepsalainen (1997): ‘Airline cost drivers: cost implications of fleet, routes, and personnel policies’, Journal of Air Transport Management, 3(1), 11–22. Stata Statistical Software: Release 8.0 (2003): College Station, TX: Stata Corp. Wei, W. and M. Hansen (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), 315–27. West, D. and J. Bradley (2008): ‘Airline flight networks, cycle times, and profitability: 2004–2006’, Oper- ational Management Research, 1, 129–40. Wooldridge, J. M. (2002): Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, MA. Zhang, A., S. Hanaoka, H. Inamura, and T. Ishikura (2008): ‘Low-cost carriers in Asia: deregulation, regional liberalization and secondary airports’, Research in Transportation Economics, 24(1), 36–50.

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