Size versus efficiency: a case study of US commercial airports

Journal of Air Transport Management 9 (2003) 187–193

Size versus efficiency: a case study of US commercial airports Massoud Bazargan*, Bijan Vasigh College of Business, Embry–Riddle Aeronautical University, Daytona Beach, FL 32114, USA

Abstract The paper presents a productivity analysis using data envelopment analysis (DEA) of 45 US commercial airports selected from the top 15 large, medium, and small hub airports. Financial and operational data, such as aircraft movements, number of airport gates, the annual number of enplaned passengers and runway capacity, is used. Initially, a DEA is deployed to analyze the efficiency and performance measures of airports within each group by comparing and cross-referencing them with each other. We then extend our analysis to identify those airports that are not efficient and are thus dominated by other airports that are more efficient. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Airports; Benchmarking; Data Envelopment Analysis (DEA)

1. Introduction Productivity measures are used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an organization in an industry. Performance can be assessed based on financial efficiency or operational efficiency. Efficiency has several dimensions, two of which are economic efficiency and technological efficiency: Economic efficiency means that the firm is using resources in such combinations that the cost per unit of output for that rate of output is the least. Technological efficiency means that it must not be possible to produce the same rate of output with less of any resource. Feng and Wang (2000) developed a performance evaluation model for airlines that included the consideration of indicators in production, marketing, and execution. The air transportation association (ATA) estimated that inefficiencies in the air traffic system cost the airlines in excess of $3 billion in 1995 (Jenkins, 1999). These inefficiencies may cause an air carrier to abandon an airport altogether. Sarkis (2000) argues that airlines choose airports that are more efficient. Jenkins states that a 1% increase in efficiency of the airports would equate savings of at least $200 million for the ten US major airlines. Vasigh and Hamzaee (1998) consider that *Corresponding author. E-mail address: [email protected] (M. Bazargan).

airports contribute to regional and national economic development as well as provide public goods and services in aiding national defense. Furthermore, the efficiency of an airport is vital for continued government supports. Gillen and Lall (1997) propose that the airports should be so efficient, which must be viewed as mature firms able to stand on their own. Despite its importance, few studies have focused on airport productivity or operational efficiency (Sarkis, 2000). Hansen and Weidner (1995) examine the efficiency of 14 multiple airport system (MAS) regions in the US using binary Logit model. Gillen and Lall (1997) adopt data envelopment analysis (DEA) and Tobit Models to measure and rank the productivity of 21 top US airports. They define airports to produce two separate classes of services namely terminal services and movements. In addition, they show the impact of various inputs on airport productivity. Hooper and Hensher (1997) adopt total factor productivity (TFP) to measure airport performance at six Australian airports over 3 years. Vasigh and Hamzaee (1998) assess the economic importance of seven US commercial airports using total factor productivity. DEA is becoming increasingly popular for productivity studies in diverse industries. In the aviation front, Martin and Roman (2001) applied DEA method to analyze the technical efficiency and performance of several Spanish airports. DEA is also practical in determining the efficiency of units that consume inputs or produce outputs which lack natural prices (Button

0969-6997/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0969-6997(02)00084-4

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M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

and Weyman-Jones, 1993a, b). A major attractiveness of DEA is its ability to handle multiple inputs and outputs to derive the relative efficiencies. This approach eliminates the difficult task of weight estimation, as needed by other multiple input/output models such as TFP. Parker (1999) uses DEA to identify the sources of technical relative efficiency applied to former British Airport Authority (BAA), before and after privatization. In a thorough analysis, Sarkis (2000) examines 44 airports over a 5-year period for operational efficiency by measuring their relative productivities as a ratio of outputs to inputs with performance measurement areas of infrastructure, environment, accessibility, capacity, and investment. Sarkis (2000) adopts DEA and variants of DEA to examine the productivity of these airports as well as testing proposed hypotheses. Adler and Berechman (2001) adopt DEA to rank and examine western European airports and in particular how attractive these airports are to airlines. Fernandes and Pacheco (2002) studied 33 Brazilian airports to identify and rank them according to the number of passengers processed using DEA. The success of DEA in evaluating relative productivities without the need for subjective estimation of weights as applied to diverse industries coupled with limited applications of DEA to airport benchmarking prompted the adoption of this approach.

2. Data envelopment analysis DEA is a non-parametric multiple input–output efficiency technique that measures the relative efficiency of decision making units (DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. Because of this property, DEA has gained increasing popularity in the last two decades. DEA was originally proposed by Charnes et al. (1978) and Cooper et al. (2000) and Levin and Seiford (1997) provide a comprehensive list of applications of DEA and its variants. Emorouznejad and Thanassolis (1997) provide a list of 1500 applications of DEA. This model, which is commonly referred to as a CCR model, assumes constant return to scales. Banker et al. (1984) presented a revised efficiency model that is commonly refereed to as a BCC model based on variable returns to scales. In these models, the DMUs are not penalized for operating at non-optimal scale (constant returns). These models yield the same results if achievement of efficiency or failure is the only topic of concern (Cooper et al., 2000). When the CCR model is as follows, we can assume there are m inputs, s outputs and n DMUs and then it is a matter of solving the linear programming

model: Max UY0 Subject to VX0 ¼ 1;  VX þ UY X0; V X0; UX0;

ð1Þ

where X is the m  n matrix of all DMU inputs, Y is the s  n matrix of all outputs. X0 and Y0 are the input and output column matrix for DMUs, V and U are the input and output weights to be determined by the linear programming model. In the optimal weights, ðV  ; U  Þ we may find numerous zeros indicating that the DMU has a weakness in the corresponding items compared with other DMUs. Cooper et al. (2000) consider that these specialized DMUs are mix-inefficient even if they are represented as efficient DMU. This difficulty happens frequently for DMUs that are exceptionally good in one input or output (no matter how poor they are on other inputs and/or outputs). To address this, Charnes et al. (1978) propose adding a non-Archimedean infinitesimal, e to the model: Max

UY0

Subject to

VX0 ¼ 1;  VX þ UY X0; V Xe1; UXe1;

ð2Þ

where 1 is a vector of all 1s. Ali and Seiford (1993) and Mehrabian et al. (2000) provide guidelines on determination of e: The DEA results are classified as efficient or inefficient. The DEA model is used to locate relative inefficiencies. DEA yields projections of inefficient DMUs onto an efficient piecewise linear frontier (Golany and Yu, 1995).

3. Research scope and data acquisition In examining airport system productivity, two performance measures could be used—financial and operational. While it is a useful source of information, financial performance of airports as the only assessment factor can be misleading in many cases. According to Hooper and Hensher (1997), economic or social inefficiencies could exist while there are measures of noticeable financial performance. Therefore, other input and output factors such as gates, runways, aircraft movements, and number of enplaned passengers, cargo, and delays are included. The FAA classifies airports based on the percentage of national total of passengers enplaned. A large hub airport is defined as the one with 1% or more of national enplaned passengers. The percentages for medium and small hub airports were 0.25–0.99% and

117650388.86 296,111,657 1 37.29 188449213.59 449,466,088 4 117.73 104734297.14 296,767,377 1 37.65 175847715.07 385,981,353 4 116.87 100199542.82 280,776,542 1 37.30 157872418.74 366,230,992 4 116.73 103166674.36 227,739,753 1 38.07 158210989.20 287,908,910 4 115.53 102551730.13 194,942,740 1 40.19

21,575,725 412,858 186,919 172,751,121 158,113,252 0.97 7,756,868 147,598 52,700 126,668,009 63,113,527 0.02 20,739,735 406,220 181,467 172,105,425 149,050,566 0.97 7,655,620 149,880 63,126 117,658,842 74,842,103 0.02 19,964,065 401,480 176,732 156,652,471 151,922,195 0.98

SD Mean Mean

SD

1999 1998

SD

7,236,296 145,732 54,542 109,558,270 70,774,253 0.02 19,406,375 398,386 175,925 149,047,667 133,784,173 0.98

151559701.53 270,936,180 4 113.33

According to Cooper et al. (2000), a good rule of thumb for the number of DMUs in applying

total operating expenses total non-operating expenses runways gates

*

(I) (I) (I) (I)

*

of of of of

*

Inputs Average Average Average Average

*

7,020,097 149,791 49,443 114,070,639 70,289,862 0.02

*

Numbers of passengers: passengers arriving or departing at an airport. Number of air carrier operations: the total number of air carrier movements (landings and take offs) following FAA definitions. Number of other operations: all the movements other than air carriers such as commuters, general aviation and military. Aeronautical revenue: all revenues that are generated by aviation activities such as landing fees, terminal fees, apron charges, fuel flowage, fixed base operators (FBOs), rentals and utilities. Non-aeronautical revenue: rent, concessions, parking, rental cars, catering, etc. Percentage of on time operations: the percentage of operations (air carriers and others operations) on time divided by annual number of operations at the specific airport.

18,699,395 392,332 175,035 148,908,326 120,872,747 0.98

*

189

Outputs Total passengers Air carriers annual operations Other annual operations Aeronautical operating revenues Non-aeronautical operating revenues % of operation in time

There are 6 output measures:

Mean

*

SD

*

Mean

*

Operating expenses: represent the financial resources needed to run an airport including personnel compensation and benefits, communications and utilities, supplies, materials, repairs and maintenance, services and other expenses. Non-operating expenses: debt services, capital expenditures and other non-operating expenses. Number of runways: the available number of runways at each airport. Number of gates: all the gates with jet ways and other non jet-way gates.

Table 1 Annual mean and standard deviation of the input–output data for large hub airports

*

Mean

2000

The top 15 airports from each hub category are identified together with input and output data from 1996 to 2000 (Federal Aviation Administration, 1997, 2002a, b, c, various years). The airports were selected in a way that consistently retained their positions in the three hubs grouping in all the study period. Input and output data are based on the total annual number of operations including air carriers, general aviation, air taxis and military (Federal Aviation Administration, 2002a). These data are common among all the airports and contain financial and operational figures. Four input measures were selected:

1997

*

1996

*

Is there any evidence that efficiencies of these three hubs are different? If so, what hubs tend to be more efficient? Are these efficiencies stable over time or they tend to fluctuate annually?

Year

*

SD

0.05–0.24%, respectively, in 1997. Based on this classification, we attempt to address the following questions:

7,857,988 148,875 53,802 132,251,387 66,406,998 0.02

M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

14397822.12 22,555,508 1 19.90

861,787 34,321 92,187 10,379,499 15,530,880 0.00

38309356.27 41,928,216 3 39.20

4,444,438 116,457 160,990 25,282,676 33,408,350 1.00

total operating expenses total non-operating expenses runways gates

11580719.87 19,594,845 3 19.07

(I) (I) (I) (I)

Inputs Average Average Average Average

of of of of

1,486,527 44,150 108,240 7,745,212 12,942,583 1.00

4190176.94 14,095,485 1 4.82

393,563 13,047 34,737 3,540,127 8,724,635 0.00

12958975.00 20,125,568 3 19.07

1,463,834 43,883 107,456 9,205,191 13,745,097 1.00

Mean

Mean

SD

1997

1996

Outputs Total passengers Air carriers annual operations Other annual operations Aeronautical operating revenues Non-aeronautical operating revenues % of operation in time

Year

Table 3 Annual mean and standard deviation of the input–output data for small hub airports

total operating expenses total non-operating expenses runways gates

37109298.73 38,923,086 3 39.20

(I) (I) (I) (I)

Inputs Average Average Average Average

of of of of

4,307,100 114,470 159,956 24,776,096 32,171,865 1.00

Mean

Mean

SD

1997

1996

Outputs Total passengers Air carriers annual operations Other annual operations Aeronautical operating revenues Non-aeronautical operating revenues % of operation in time

Year

Table 2 Annual mean and standard deviation of the input–output data for medium hub airports

6271306.98 10,093,994 1 4.82

394,440 12,782 36,113 6,797,090 8,664,484 0.00

SD

13422836.29 25,032,298 1 19.90

909,586 33,606 87,604 10,574,490 12,981,710 0.00

SD

13055843.93 24,639,267 3 19.07

1,384,332 39,029 110,233 9,368,944 14,512,005 1.00

Mean

1998

37766233.67 62,203,619 3 39.20

4,502,735 117,935 160,762 26,571,027 35,765,605 1.00

Mean

1998

7429973.04 22,987,991 1 4.82

221,940 11,474 37,138 5,549,071 9,369,147 0.00

SD

14187882.71 40,403,171 1 19.90

904,712 30,876 88,198 12,074,763 15,622,361 0.00

SD

14878082.87 23,372,937 3 19.07

1,414,234 39,757 114,816 9,548,259 15,183,173 1.00

Mean

1999

41539290.88 82,695,206 3 39.20

4,833,331 128,019 161,356 27,381,425 38,961,010 1.00

Mean

1999

8011342.50 10,644,033 1 4.82

195,980 11,966 39,553 4,592,565 8,810,886 0.00

SD

17140682.79 69,702,453 1 19.90

1,018,983 32,238 88,599 10,635,581 16,854,478 0.00

SD

16038278.73 26,692,454 3 18.93

1,446,675 41,626 113,301 9,869,746 16,725,323 1.00

Mean

2000

44652735.58 84,381,821 3 39.67

5,143,129 135,824 156,144 30,091,756 41,832,644 1.00

Mean

2000

8770747.70 12,315,711 1 4.67

201,945 11,855 39,684 4,588,746 9,360,378 0.00

SD

19444889.73 62,904,233 1 19.39

1,084,716 33,596 81,841 13,108,616 19,286,372 0.00

SD

190 M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

DEA is

Table 4 CCR relative scores for large hub airports

nXmaxfm  s; 3ðm þ sÞg; where n is the number of DMUs, m is the number of inputs and s is the number of outputs. The 45 airports (DMUs) used here far exceeds max (24, 30)=30. Tables 1–3 present the annual mean and standard deviation of this input–output data for all the 45 airports over the study period divided by group (large, medium and small).

4. Methodology Preliminary results of the CCR model show that at least 75% of all airports are represented as efficient in each year of the study. However, in order to evaluate the efficiency of hubs and statistically verify their relative efficiencies they need to be fully rank. This is a common drawback of DEA where DMUs are listed as efficient and therefore cannot be ranked. In many cases, it is necessary to fully rank the DMUs. For this purpose, others have discriminated among the efficient DMUs and achieved full ranking through a filtering process, e.g., Andersen and Petersen (1993), Golany and Roll (1993), Golany and Yu (1995), Cook et al. (1993), Sinuany-Stern and Friedman (1998) and Adler and Golany (2001). To achieve a full ranking of all airports, a virtual super efficient airport is introduced and included with the existing DMUs. This ensures that there is only one efficient airport (efficiency of 1) with others being inefficient. The efficient frontier, based on the CCR model, therefore consists of only this virtual super efficient airport. All other airports are penalized for not operating at the same scale of efficiency. This approach is adopted for ranking of the airports only. This ranking is justified as the CCR model with constant returns to scale and the same virtual airport is used for all airports as the reference set. The input and output for this virtual super efficient airport are: XVirtual ¼ minfXj g

8j ;

YVirtual ¼ maxfYj g

8j ;

191

ð3Þ

where XVirtual and YVirtual are the input and output vectors of the virtual super-efficient airport and Xj and Yj are the input–output vectors of the jth DMU. In other words, the virtual airport has the lowest input and the highest output among the 45 airports. The CCR model was run with the inclusion of the new virtual airport. As expected, in all years the virtual airport was the only efficient airport. The results of the CCR model for the airports grouped according to their hub sizes are shown in Tables 4–6. Fig. 1 shows the average relative efficiencies among all the three hubs

Airport

1996

1997

1998

1999

2000

ATL DEN DFW DTW EWR IAH JFK LAS LAX MIA MSP ORD PHX SFO STL Average

0.4489 0.3422 0.2879 0.3398 0.5290 0.4187 0.4640 0.4221 0.4298 0.5975 0.5478 0.2732 0.8259 0.4011 0.3308 0.4439

0.4784 0.3427 0.2530 0.3432 0.5377 0.4231 0.4634 0.4224 0.4366 0.5960 0.5547 0.2734 0.8210 0.4043 0.3339 0.4456

0.4803 0.3545 0.2676 0.3444 0.5342 0.4204 0.4612 0.4249 0.4454 0.5851 0.5566 0.2750 0.8224 0.3931 0.3327 0.4465

0.4798 0.3585 0.2523 0.3421 0.5355 0.4214 0.4599 0.4294 0.4479 0.6178 0.5550 0.2710 0.8088 0.3955 0.3387 0.4476

0.4809 0.3523 0.2529 0.3441 0.5285 0.4217 0.4590 0.4336 0.4353 0.5946 0.5578 0.2744 0.5644 0.4051 0.3412 0.4297

Table 5 CCR relative scores for medium hub airports Airport

1996

1997

1998

1999

2000

BNA CLE DAL IND MCI MDW MEM MSY OAK PDX RDU SJC SJU SMF SNA Average

0.4122 0.4144 0.5699 0.5508 0.5493 0.3300 0.5492 0.5441 0.4400 0.5508 0.5496 0.5538 0.8179 0.8191 0.9887 0.5760

0.4160 0.6072 0.5755 0.5550 0.5547 0.3341 0.4186 0.5512 0.4311 0.5564 0.5560 0.5604 0.8252 0.8263 0.9889 0.5838

0.4176 0.4214 0.5784 0.5576 0.5569 0.3350 0.4202 0.5550 0.4996 0.5572 0.7959 0.5604 0.8304 0.8274 0.9782 0.5927

0.4202 0.4215 0.7900 0.5616 0.5600 0.3352 0.4215 0.5552 0.4716 0.5555 0.5622 0.5628 0.8359 0.8363 0.9863 0.5917

0.4213 0.4227 0.5750 0.5636 0.5618 0.3387 0.4216 0.5569 0.5436 0.5636 0.5641 0.5628 0.8412 0.8377 0.9789 0.5836

Table 6 CCR relative scores for small hub airports Airport

1996

1997

1998

1999

2000

ALB BHM BOI COS DAY ELP GEG GSO GUM LIT OKC ORF RIC ROC TUL Average

0.8220 0.8197 0.8214 0.5520 0.5463 0.6022 0.8166 0.8232 0.8186 0.7462 0.5632 0.8173 0.5447 0.5489 0.5498 0.6928

0.8301 0.8266 0.8283 0.5561 0.5821 0.6077 0.8238 0.8300 0.8264 0.7533 0.5685 0.8261 0.5517 0.6027 0.5559 0.7046

0.8345 0.8305 0.8328 0.5571 0.5922 0.6105 0.8282 0.8344 0.8279 0.7573 0.5713 0.8350 0.5548 0.5567 0.5586 0.7054

0.8393 0.8346 0.8366 0.5614 0.5570 0.6138 0.8314 0.8385 0.8302 0.7612 0.5736 0.8336 0.5566 0.5587 0.5602 0.7058

0.8386 0.8382 0.8383 0.6316 0.7712 0.6686 0.8387 0.8397 0.8333 0.8296 0.6252 0.8328 0.5579 0.5598 0.5622 0.7377

M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

192

Historical development of the ER for every Gro 0.8000 Average Small Hubs

Average Medium Hubs

Average Large Hubs

0.7500

0.7000

0.6500

Efficiency Ratio

0.6000

0.5500

0.5000

0.4500

0.4000

0.3500

0.3000 1996

1997

1998

1999

2000

Year

Fig. 1. Average relative efficiency for every type of airport.

Table 7 Kruskal–Wallis test results Year

1996 1997 1998 1999 2000

Table 8 Mann–Whitney Test for pairwise comparisons

Mean rank Small

Medium

Large

33.200 33.470 33.130 32.670 33.800

23.530 23.070 23.200 23.600 23.070

13.270 12.470 12.670 12.730 12.130

Chi-square Asymptotic significance

Pairwise comparasions

Mann– Whitney U

Z

Asymptotic significance

Significance leveln

15.623 19.174 18.218 17.323 20.411

U(1,2)96 U(1,3)96nn U(2,3)96 U(1,2)97nn U(1,3)97nn U(2,3)97nn U(1,2)98 U(1,3)98nn U(2,3)98 U(1,2)99 U(1,3)99nn U(2,3)99 U(1,2)00 U(1,3)00nn U(2,3)00

64.000 23.000 56.000 54.000 14.000 53.000 59.000 14.000 56.000 65.000 15.000 56.000 57.000 6.000 56.000

2.012 3.712 2.344 2.426 4.086 2.468 2.219 4.086 2.344 1.970 4.044 2.344 2.302 4.417 2.344

0.044 0.000 0.019 0.015 0.000 0.014 0.026 0.000 0.019 0.049 0.000 0.019 0.210 0.000 0.019

0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017

0.000 0.000 0.000 0.000 0.000

over 5 years. This graph suggests that the relative efficiency of the airports is highest for small and lowest for large hubs.

5. Statistical validations The results of relative efficiency values are used to address the three questions posed. The first question asks if there is any statistical difference between the efficiencies of the three hubs. The non-parametric Kruskal–Wallis test is used for testing because DEA efficiency scores do not fit within a standard normal distribution. Table 7 presents the results for the three hubs. At a 5% significance level, the average efficiency scores among the three hubs are different for all years of the study. The non-parametric Mann–Whitney U test is used to make pairwise comparison between the three hubs to see

n A significance level of 0.05 was assumed, but was adjusted using the Bonferroni Method for Pairwaise Comparasions. nn A significant difference between the group was found.

which are the most efficient. Table 8 shows the results of these comparisons—U refers to means of Mann– Whitney U test, 1 refers to small, 2 to medium and 3 to large hub airports. A significant difference exists across all the years between the relative efficiency of large and small airports (at 5% level). Only in 1997 is there a significance difference between medium and large hubs. This test shows that the small airports consistently outperform the large hubs based on their

M. Bazargan, B. Vasigh / Journal of Air Transport Management 9 (2003) 187–193

relative efficiency scores in all 5 years. However, the difference between small and medium, or large and medium, is not high enough to conclude that small outperforms medium or medium outperforms large hubs (except in 1997).

6. Conclusion This paper presents a DEA of the efficiency of US commercial hub airports. Initially, the airports are grouped according to FAA classification of small, medium and large hubs. Relative efficiency scores generated through DEA are used for ranking the airports. Non-parametric statistical tests confirm that there exists a significant difference among the three types of hub. Further analysis shows that small hubs consistently outperform the larger hubs in terms of relative efficiency.

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