OPERATIONAL EFFICIENCY IN BRAZILIAN AIRPORTS: AN ANALYSIS THROUGH THE HIERARCHICAL LINEAR MODELING WITH REPEATED MEASURES

Belfiore, Patrícia; Fávero, Luiz Paulo; Serra, Ricardo Goulart; Tarantin Junior, Wilson; Costa, Igor Pinheiro de Araújo; Moreira, Miguel Ângelo Lellis; Terra, Adilson Vilarinho; Santos, Marcos dos

doi:10.1590/0101-7438.2023.043.00281311

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ARTICLES • Pesqui. Oper. 44 • 2024 • https://doi.org/10.1590/0101-7438.2023.043.00281311 copy

OPERATIONAL EFFICIENCY IN BRAZILIAN AIRPORTS: AN ANALYSIS THROUGH THE HIERARCHICAL LINEAR MODELING WITH REPEATED MEASURES

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study analyzes the operational efficiency of Brazil’s 30 largest airports from 2014 to 2018, using a two-stage model. Operational efficiency is defined as an airport’s capacity to effectively utilize resources, such as facilities, staff, and technology, to cater to air traffic and passenger needs. The first stage involved measuring operational efficiency through data envelopment analysis. The second stage used a three-level hierarchical linear model to identify influencing variables. Key findings reveal that location significantly impacts airport efficiency, which generally declined during the study period. The interest rate, the only notable economic factor, had a negative effect on efficiency. Factors like the number of aircraft parking positions, years of airport operation, and the number of airlines positively influenced efficiency. Conversely, governance structure, airport size, commercial establishment count, and vehicle parking lot numbers didn’t notably affect efficiency variation. This methodological approach provided more accurate predictions than traditional regression models.

Keywords:
data envelopment analysis; performance analysis; three-level hierarchical linear model with repeated measures; efficiency and productivity; airport

Determinant Factor	Variable	Unit	Label
			Public = 0
			Private = 1
Governance Structure	Property	Nominal	Mixed = 2
Airport Operational Characteristics	Size	m2	Size
	Number of commercial establishments		Commerce
	Number of aircraft parking positions		Positions
	Number of vehicle parking lots		Parking lots
	Airport years of experience		Experience
Service Strategy	Number of airlines		Airlines
Economic Factors	Average oil barrel price	US$/bbl^a FOB^b	Oil
	Foreign exchange	R$/US$	Exchange
	Interest rate	%	Interest
	GDP^c growth (t-1)	%	GDP
	Unemployment rate (t-1)	%	Unemployment
Location	Airport location	State	Location

Airport (Decision-making Unit)	Air Transport Movements^a	Paid Cargo and Mail (kg): Shipments and Receipts	Air Passenger Movements^b
Aracaju	10,511.40	2,893,534.00	1,221,695.80
Belém	33,554.80	29,280,421.00	3,428,490.80
Belo Horizonte Confins	101,554.80	32,483,399.00	10,205,399.60
Brasília	143,482.00	90,067,094.00	17,817,206.40
Campinas	114,229.40	233,051,174.60	9,217,247.40
Campo Grande	14,625.20	6,505,266.80	1,522,068.80
Cuiabá	30,828.40	11,420,587.60	2,980,598.60
Curitiba	65,415.00	26,904,066.20	6,639,258.20
Florianópolis	32,072.20	9,769,247.80	3,550,457.80
Fortaleza	46,312.20	45,596,343.20	6,087,102.80
Foz do Iguaçu	15,977.00	660,115.40	1,999,410.20
Goiânia	31,441.60	11,898,892.60	3,039,459.20
João Pessoa	10,987.40	4,284,924.20	1,374,182.60
Londrina	10,500.00	1,794,999.00	962,442.40
Maceió	15,133.80	2,965,716.00	1,977,262.40
Manaus	31,000.80	127,774,118.60	2,907,680.40
Natal	17,384.60	9,764,213.80	2,219,867.80
Navegantes	14,505.60	2,470,712.60	1,504,842.00
Porto Alegre	68,519.40	32,369,834.60	7,904,932.60
Porto Velho	9,062.40	5,735,882.80	840,006.60
Recife	61,295.00	55,433,938.80	7,254,243.20
Rio de Janeiro Galeão	117,770.00	119,819,456.00	15,981,285.40
Rio de Janeiro Santos Dummont	95,320.20	6,467,327.00	9,214,135.40
Salvador	69,681.40	52,960,646.20	8,201,773.40
São Luís	15,711.20	9,410,590.00	1,635,881.40
São Paulo Congonhas	170,321.40	47,345,630.60	19,798,121.40
São Paulo Guarulhos	267,549.40	577,000,267.40	37,984,034.40
Teresina	10,876.20	5,225,293.60	1,088,293.60
Uberlândia	12,474.20	1,811,140.60	1,058,368.40
Vitória	28,757.20	18,602,615.80	3,118,685.20

	Input			Output
	Aircraft Yard Area -m² (Input)	Takeoff & Landing Total Area - m² (Input)	Passenger Terminal Total Area - m² (Input)	Air Transport Movements (Output)	Paid Cargo & Mail (kg): Shipments and Receipts (Output)	Air Passenger Movements(Output)
Average	143,519	145,243.7	63,277.07	55,561.81	52,725,581.66	6,424,481.14
Standard Deviation	208,136.7	63,673.25	87,025.25	58,960.61	111,046,809.40	7,782,323
Minimum	14,633	76,545	4,414	7,728	614,492	767,851
Maximum	975,513	329,460	387,000	283,781	634,000,267.40	41,284,034.40

Decision-making Unit	2014	2015	2016	2017	2018	Average	Rank
Aracaju	0.4448	0.4234	0.4059	0.3832	0.3739	0.4062	25
Belém	0.7895	0.7451	0.6074	0.6022	0.5574	0.6603	16
Belo Horizonte - Confins	0.6693	0.6483	0.5583	0.5584	0.5683	0.6005	20
Brasília	0.7473	0.7903	0.7358	0.6562	0.6650	0.7189	12
Campinas	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1
Campo Grande	0.6989	0.8667	0.8718	0.7948	0.7915	0.8047	10
Cuiabá	0.7521	0.8710	0.7383	0.5826	0.5923	0.7073	13
Curitiba	0.3595	0.3201	0.2740	0.2725	0.2616	0.2975	27
Florianópolis	0.7799	0.8694	0.8911	0.9308	0.9201	0.8783	8
Fortaleza	1.0000	1.0000	0.9605	0.9126	0.9577	0.9662	7
Foz do Iguaçu	0.2296	0.2454	0.2141	0.2415	0.2584	0.2378	28
Goiânia	0.5058	0.4558	0.3905	0.3689	0.3888	0.4220	24
João Pessoa	0.4146	0.4836	0.5236	0.5335	0.5424	0.4995	22
Londrina	0.4296	0.4375	0.4685	0.4599	0.4726	0.4536	23
Maceió	0.2118	0.2249	0.2319	0.2290	0.2415	0.2278	29
Manaus	1.0000	1.0000	1.0000	0.9880	0.8981	0.9772	6
Natal	0.1463	0.2612	0.2489	0.2267	0.2455	0.2257	30
Navegantes	0.6216	0.6977	0.7034	0.7461	0.9635	0.7465	11
Porto Alegre	0.6842	0.6687	0.5849	0.5728	0.5925	0.6206	18
Porto Velho	0.8125	0.7940	0.6336	0.4256	0.4408	0.6213	17
Recife	0.9004	0.9430	0.8287	0.8013	0.8235	0.8594	9
Rio de Janeiro -Galeão	0.3988	0.3757	0.3503	0.3381	0.3179	0.3562	26
Rio de Janeiro - Santos Dummont	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1
Salvador	0.7158	0.6763	0.5965	0.5239	0.5220	0.6069	19
São Luís	0.8040	0.8144	0.6441	0.6537	0.5724	0.6977	15
São Paulo - Congonhas	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1
São Paulo - Guarulhos	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1
Teresina	1.0000	1.0000	1.0000	0.9877	0.9003	0.9776	5
Uberlândia	0.7507	0.7422	0.6397	0.6505	0.7097	0.6986	14
Vitória	0.4892	0.5202	0.5446	0.5842	0.6005	0.5477	21

Decision-making Unit	2014	2015	2016	2017	2018	Average	Rank
Aracaju	0.4045	0.3590	0.3511	0.3533	0.3541	0.3644	25
Belém	0.7741	0.6476	0.4917	0.5076	0.5139	0.5870	15
Belo Horizonte - Confins	0.5932	0.6284	0.5496	0.5514	0.5683	0.5782	16
Brasília	0.6830	0.6823	0.6286	0.6005	0.6474	0.6484	13
Campinas	1.0000	0.9364	0.8483	0.9074	1.0000	0.9384	4
Campo Grande	0.6989	0.7211	0.6371	0.6146	0.6508	0.6645	11
Cuiabá	0.7305	0.7705	0.6185	0.5087	0.5391	0.6335	14
Curitiba	0.3212	0.3048	0.2647	0.2674	0.2604	0.2837	27
Florianópolis	0.7521	0.7483	0.7109	0.7834	0.8030	0.7595	9
Fortaleza	1.0000	0.9034	0.7917	0.8068	0.9118	0.8827	5
Foz do Iguaçu	0.1990	0.2197	0.1977	0.2324	0.2527	0.2203	28
Goiânia	0.4302	0.4006	0.3513	0.3517	0.3875	0.3843	24
João Pessoa	0.3998	0.4171	0.4135	0.4376	0.4677	0.4271	22
Londrina	0.4296	0.4076	0.3828	0.3658	0.3919	0.3955	23
Maceió	0.1998	0.2021	0.2030	0.2114	0.2304	0.2094	29
Manaus	1.0000	0.8122	0.7536	0.8053	0.8377	0.8418	6
Natal	0.1450	0.2388	0.2105	0.2064	0.2350	0.2071	30
Navegantes	0.6028	0.6602	0.6155	0.6783	0.8790	0.6872	10
Porto Alegre	0.6123	0.5967	0.5360	0.5471	0.5856	0.5756	18
Porto Velho	0.7878	0.6512	0.5060	0.3622	0.3894	0.5393	20
Recife	0.8333	0.8064	0.7127	0.7333	0.8051	0.7782	8
Rio de Janeiro - Galeão	0.3472	0.3295	0.3154	0.3198	0.3174	0.3259	26
Rio de Janeiro - Santos Dummont	1.0000	1.0000	0.9096	0.9335	0.9616	0.9609	2
Salvador	0.7093	0.6085	0.4986	0.4710	0.5020	0.5579	19
São Luís	0.7982	0.6571	0.4703	0.4952	0.4701	0.5782	16
São Paulo - Congonhas	0.8911	0.9776	0.9909	0.9920	1.0000	0.9703	1
São Paulo - Guarulhos	0.9949	0.9526	0.8887	0.9131	1.0000	0.9499	3
Teresina	1.0000	0.8917	0.7538	0.7459	0.7406	0.8264	7
Uberlândia	0.7185	0.7176	0.5834	0.5951	0.6485	0.6526	12
Vitória	0.4460	0.4471	0.4544	0.5109	0.5632	0.4843	21

Year t	Airport j	Location k	Efficiency
(Level 1)	(Level 2)	(Level 3)	(Y _tjk )
1	1	1	0.4045
2	1	1	0.3590
3	1	1	0.3511
4	1	1	0.3533
5	1	1	0.3541
1	11	10	0.1990
2	11	10	0.2197
3	11	10	0.1977
4	11	10	0.2324
5	11	10	0.2527
1	30	23	0.4460
2	30	23	0.4471
3	30	23	0.4544
4	30	23	0.5109
5	30	23	0.5632

	OLS	HLM3
Fixed Effects	Coefficient	Coefficient
ϒ000	0.5970773 ***	0.5886218 ***
ϒ000	(0.0196281)	(0.0449662)
Random Effects
Location (τ _u000 )		0.0247316 *
Location (τ _u000 )		(0.0139129)
Airport (τ _r000 )		0.0248524 **
Airport (τ _r000 )		(0.011066)
Residual (σ ²)		0.0055248 ***
Residual (σ ²)		(0.0007133)
Log restricted-likelihood		119.041
LR test vs. OLS linear regression		chi2 (2) = 241.14
LR test vs. OLS linear regression		sig. chi2 = 0.000

	OLS	HLM3
Fixed Effects	Coefficient	Coefficient
year	omitted because of collinearity	-0.0304682 **
year	omitted because of collinearity	(0.0137552)
Property_pu	0.0005183	0.0636678
Property_pu	(0.0845324)	(0.1958613)
Property_pr	0.0253428	0.0403057
Property_pr	(0.0779822)	(0.1598442)
size	-7.56e-09 *	-5.01e-09
size	(4.21e-09)	(9.25e-09)
commerce	-0.002915 ***	-0.0011862
commerce	(0.0004977)	(0.0008209)
positions	-0.003262 ***	-0.0043217 ***
positions	(0.0007276)	(0.0006438)
parkinglots	0.0000941 ***	0.0000429
parkinglots	(0.0000178)	(0.0000464)
experience	0.0053331 ***	0.0052709 ***
experience	(0.0008272)	(0.0015987)
airlines	0.0178514 ***	0.014936 ***
airlines	(0.0036366)	(0.0035717)
oil	0.0003901	-0.0022386
oil	(0.0017475)	(0.0018278)
exchange	omitted because of collinearity	0.1175076 *
exchange	omitted because of collinearity	(0.0650074)
interest	-0.0060879	-0.0114815 ***
interest	(0.0215517)	(0.0018883)
gdp	2.47e-06	omitted because of collinearity
gdp	(0.0262107)	omitted because of collinearity
unemployment	-0.019004 *	omitted because of collinearity
unemployment	(0.0107965)	omitted because of collinearity
constant (ϒ₀₀₀)	0.550622 **	0.1969086
constant (ϒ₀₀₀)	(0.2689884)	(0.3244751)
Random Effects
Location
(τ _u000 year)		0.0009724 **
(τ _u000 year)		(0.0004136)
(τu000)		0.0564411 ***
(τu000)		(0.0194064)
Airport
(τ _r100 year)		0.0002049 ***
(τ _r100 year)		(0.0001765)
(τr000)		0.0020173
(τr000)		(.0037822)
Residual (σ ²)		0.0011933 ***
Residual (σ ²)		(0.0001814)
Log restricted-likelihood		115.081
LR test vs. OLS linear regression		chi2 (4) = 288.65
LR test vs. OLS linear regression		sig. chi2 = 0.000

	OLS	HLM3
Fixed Effects	Coefficient	Coefficient
year	-0.030452 **	-0.0352056 ***
year	(0.0162027)	(0.0075832)
positions	-0.0023563 ***	0.0039658 ***
positions	(0.0007067)	(0.0005659)
experience	0.0039631 ***	0.0036492 ***
experience	(0.0008674)	(0.0011499)
airlines	0.0185042 ***	0.012948 ***
airlines	(0.0038505)	(0.0029331)
interest	-0.0112002	-0.0112002 ***
interest	(0.0084779)	(0.001534)
constant	0.5182444 ***	0.623239 ***
constant	(0.1439958)	(0.0956811)
Random Effects
Location
(τ _u100 year)		0.0009993 **
(τ _u100 year)		(.000398)
(τu000)		.0529503 ***
(τu000)		(.0174719)
Airport
(τ _r100 year)		.0001285
(τ _r100 year)		(.0001394)
(τr000)		.0026943
(τr000)		(.0025413)
Residual (σ2)		.0015415 ***
Residual (σ2)		(.0002327)
Log restricted-likelihood		147.297
LR test vs. OLS linear regression		chi2 (4) = 311.96
LR test vs. OLS linear regression		sig. chi2 = 0.000

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			• Number of remote aircraft parking positions	• Aircraft movements
			• Number of check-in counters	iii - SFA (PAX model)
			• Number of baggage claim belts	• Number of passengers
			ii - DEA (ATM model)	iv - SFA (ATM model)
			• Airport area	• Aircraft movements
			• Number of runways
			• Runway length
			• Number of aircraft parking positions (terminal)
			• Number of remote aircraft parking positions
			iii - SFA (PAX model)
			• Number of baggage claim belts
			• Number of aircraft parking positions (terminal)
			• Number of remote aircraft parking positions
			iv - SFA (ATM model)
			• Number of runways
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			• Departure lounge
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			• Curb frontage
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			• Number of gates	• Aircrafts movements
			• Operating costs	• Other movements
			• Non-operating costs	• Aeronautical revenues
				• Non-aeronautical revenues
				• % of on-time operations
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			• Number of runways	• Number of passengers	• Ownership structure
			• Terminal area	• Cargo	• Airport size
			• Number of gates	• Non-aeronautical revenue	• Average aircraft size
			• Soft cost input		• % of international passengers
					Factors under managerial control
					• Business diversification strategy
					• Outsourcing
					• Service quality
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			• Payroll	• Cargo plus mail
			• Operating costs	• Operating revenue
				• Non-aeronautical revenues
				• Other revenues
Pels et al. (2003PELS E, NIJKAMP P & RIETVELD P. 2003. Inefficiencies and scale economies of European airport operations. Transportation Research Part E , 39: 341-361. Available at: https://doi.org/10.1016/S1366-5545(03)00016-4. https://doi.org/10.1016/S1366-5545(03)00... )	33 European airports, 1995-1997	DEA and SFA	i - ATM model	i - ATM model
			• Airport area	• Aircraft movements
			• Number of runways	ii - APM model
			• Number of terminal aircraft parking positions	• Number of passengers
			• Number of remote aircraft parking positions
			ii - APM model
			• Number of check-in counters
			• Number of baggage claim belts
			• Aircraft movements
Oum and Yu (2004OUM T & YU C. 2004. Measuring airport’s operating efficiency: a summary of the 2003 ATRS global airport benchmarking report. Transportation Research Part E , 40: 515-532. Available at: https://doi.org/10.1016/j.tre.2004.08.002. https://doi.org/10.1016/j.tre.2004.08.00... )	76 worldwide airports, 2000-2001	VFP and log-linear regression	• Number of employees	• Number of passengers	Factors beyond airport control
			• Soft cost input	• Cargo	• Airport size
				• Aircraft movements	• Average aircraft size
				• Non-aeronautical revenues	• % of international passengers
					• % of cargo in total traffic
					• Capacity constraints
					Factors within airport control
					• Passenger satisfaction
					• % of non-aeronautical revenue
					• Terminal operator
Sarkis and Talluri (2004SARKIS J & TALLURI S. 2004. Performance based clustering for benchmarking of US airports. Transportation Research Part A , 38: 329-346. Available at: https://doi.org/10.1016/j.tra.2003.11.001. https://doi.org/10.1016/j.tra.2003.11.00... )	44 US airports, 1990-1994	DEA, Multi-factor efficiency models and CA	• Operating costs	• Operating revenues
			• Number of employees	• Aircraft movements
			• Number of gates	• General aviation movements
			• Number of runways	• Number of passengers
				• Cargo
Yoshida and Fujimoto (2004YOSHIDA Y & FUJIMOTO H. 2004. Japanese-airport benchmarking with the DEA and endogenous-weight TFP methods: testing the criticism of overinvestment in Japanese regional airports. Transportation Research Part E , 40: 533-546. Available at: https://doi.org/10.1016/j.tre.2004.08.003. https://doi.org/10.1016/j.tre.2004.08.00... )	67 Japanese airports, 2000	Two-stage DEA model:	• Runway length	• Number of passengers	• Third-category regional airports
		1) DEA and TFP index;	• Terminal area	• Cargo	• Airports that started their operations in the 1990s
		2) OLS regression	• Access cost	• Aircraft movements
			• Number of employees
Lin and Hong (2006LIN L & HONG C. 2006. Operational performance evaluation of international major airports: an application of data envelopment analysis. Journal of Air Transport Management , 12: 342-351. Available at: https://doi.org/10.1016/j.jairtraman.2006.08.002. https://doi.org/10.1016/j.jairtraman.200... )	20 major worldwide airports, 2003	DEA models	• Number of employees	• Number of passengers
			• Number of check-in counters	• Cargo
			• Number of runways	• Aircraft movements
			• Number of parking positions
			• Number of baggage claim belts
			• Number of aprons
			• Number of boarding gates
			• Terminal area
Oum et al. (2006OUM T, ADLER N & YU C. 2006. Privatization, corporatization, ownership forms and their effects on the performance of the world’s major airports. Journal of Air Transport Management , 12: 109-121. Available at: https://doi.org/10.1016/j.jairtraman.2005.11.003. https://doi.org/10.1016/j.jairtraman.200... )	116 worldwide airports, 2001-2003	VFP and log-linear regression	• Number of employees	• Number of passengers	Airport characteristics
			• Soft cost input	• Aircraft movements	• Airport size
				• Non-aeronautical revenues	• Runway utilization
					• Average aircraft size
					• % of international passengers
					• % of cargo in total traffic
					Other factors
					• Ownership structure
					• Regional business environments
					• Business diversification (% of non-aeronautical revenue)
Barros and Dieke (2007BARROS C & DIEKE P. 2007. Performance evaluation of Italian airports: a data envelopment analysis. Journal of Air Transport Management , 13: 184-191. Available at: https://doi.org/10.1016/j.jairtraman.2007.03.001. https://doi.org/10.1016/j.jairtraman.200... )	31 Italian airports, 2001-2003	DEA models	• Labor	• Aircraft movements
			• Capital costs	• Number of passengers
			• Other operating costs	• Cargo
				• Handling receipts
				• Aeronautical sales
				• Non-aeronautical sales
Barros (2008aBARROS C & DIEKE P. 2008. Measuring the economic efficiency of airports: a Simar-Wilson methodology analysis. Transportation Research Part E, 44: 1039-1051. Available at: https://doi.org/10.1016/j.tre.2008.01.001. https://doi.org/10.1016/j.tre.2008.01.00... )	32 Argentine airports, 2003-2007	Two-stage DEA model:	• Number of employees	• Aircraft movements	• Time trend
		1) DEA	• Runway area	• Number of passengers	• Airport hub status
		2) SWBT regression	• Apron area	• Cargo	• Work load units (WLU)
			• Passenger terminal area
Barros (2008bBARROS C. 2008b. Technical efficiency of UK airports. Journal of Air Transport Management , 14: 175-178. Available at: https://doi.org/10.1016/j.jairtraman.2008.04.002. https://doi.org/10.1016/j.jairtraman.200... )	27 UK airports, 2000-2005	SCF (LR) estimated using ML	• Operating costs	• Number of passengers	• Time trend
			• Labor costs	• Aircraft movements	• Labor costs
			• Capital premises		• Capital premises
			• Capital investments		• Capital investments
					• Number of passengers
					• Aircraft movements
					• Owned by BAA
					• Owned by Manchester airports
					• Owned by TBI
Barros and Dieke (2008BARROS C & DIEKE P. 2008. Measuring the economic efficiency of airports: a Simar-Wilson methodology analysis. Transportation Research Part E, 44: 1039-1051. Available at: https://doi.org/10.1016/j.tre.2008.01.001. https://doi.org/10.1016/j.tre.2008.01.00... )	31 Italian airports, 2001-2003	Two-stage DEA model:	• Labor costs	• Aircraft movements	• Time trend
		1) DEA models	• Capital invested	• Number of passengers	• Airport hub status
		2) SWBT regression	• Operating costs excluding labor costs	• Cargo	• Work load units (WLU)
				• Handling receipts	• Ownership structure
				• Aeronautical revenues	• Location
				• Non-aeronautical revenues
Oum et al. (2008OUM T, YAN J & YU C. 2008. Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports. Journal of Urban Economics, 64: 422-435. Available at: https://doi.org/10.1016/j.jue.2008.03.001. https://doi.org/10.1016/j.jue.2008.03.00... )	109 Worldwide airports, 2001-2004	SCF (SR) estimated via Bayesian approach	Variable inputs	• Number of passengers	(i) Geographic distribution of airports (%)
			• Number of employees	• Aircraft movements	(ii) Ownership structure (%)
			• Non-labor variable cost	• Non-aeronautical revenue	(iii) Airport characteristics
			Fixed inputs		• % of international passengers
			• Number of runways		• % of cargo
			• Passenger terminal area
			Variable inputs’ prices
			• Wage rate
			• Non-labor input price
			Variable inputs’ share
			• Labor cost share
Pathomsiri et al. (2008PATHOMSIRI S, HAGHANI A, DRESNER M & WINDLE R. 2008. Impact of undesirable outputs on the productivity of US airports. Transportation Research Part E , 44: 235-259. Available at: https://doi.org/10.1016/j.tre.2007.07.002. https://doi.org/10.1016/j.tre.2007.07.00... )	56 US airports, 2000-2003	DDF and Luenberger productivity index	• Land area	Desirable outputs
			• Number of runways	• Non-delayed flights
			• Runway area	• Number of passengers
				• Cargo
				Undesirable outputs
				• Delayed flights
				• Time delays
Yu et al. (2008YU MM, HSU SH, CHANG CC & LEE DH. 2008. Productivity growth of Taiwan’s major domestic airports in the presence of aircraft noise. Transportation Research Part E , 44: 543-544. Available at: https://doi.org/10.1016/j.tre.2007.01.005. https://doi.org/10.1016/j.tre.2007.01.00... )	4 Taiwan’s airports, 1995-1999	• Traditional MPI	Inputs	Desirable output
		• Extended MPI	• Operating costs	• Aeronautical revenue
		• Extended MLPI with DDF	• Labor costs	• Non-aeronautical revenue
			• Capital costs	Undesirable output
			Environmental factors	• Aircraft noise
			• Aircraft movements
			• Number of passengers
Barros and Weber (2009BARROS C & WEBER W. 2009. Productivity growth and biased technological change in UK airports. Transportation Research Part E , 45: 642-653. Available at: https://doi.org/10.1016/j.tre.2009.01.004. https://doi.org/10.1016/j.tre.2009.01.00... )	27 UK airports, 2000-2005	DEA and MPI	• Labor costs	• Number of passengers
			• Capital costs	• Cargo
			• Other costs	• Aircraft movements
Chi-Lok and Zhang (2009CHI-LOK A & ZHANG A. 2009. Effects of competition and policy changes on Chinese airport productivity: an empirical investigation. Journal of Air Transport Management , 15: 166-174. Available at: https://doi.org/10.1016/j.jairtraman.2008.09.003. https://doi.org/10.1016/j.jairtraman.200... )	25 Chinese airport, 1995-2006	Two-stage DEA model:	• Runway length	• Number of passengers	(i) Airport localization program
		1) DEA and MPI	• Terminal size	• Cargo	(ii) Competition intensity
		2) OLS and Tobit regression		• Aircraft movements	(iii) Public listing
					(iv) Airport characteristics
					• Airport hub status
					• Local economy
					• Coastal city
					• Tourist city
					• Population
					• Demand and supply shocks
					(v) Event variables
					• Airline mergers
					• Open-skies agreements
					• Guangzhou new airport
Lam et al. (2009LAM S, LOW J & TANG L. 2009. Operational efficiencies across Asia Pacific airports. Transportation Research Part E , 45: 654-665. Available at: https://doi.org/10.1016/j.tre.2008.11.003. https://doi.org/10.1016/j.tre.2008.11.00... )	11 major Asian Pacific airports, 2001-2005	DEA models:	• Labor costs	• Aircraft movements
		a) CCR	• Capital costs	• Number of passengers
		b) BCC	• Soft cost input	• Cargo
		c) SBM	• Trade value
		d) Cost efficiency model
		e) Allocative efficiency model
Tovar and Martín-Cejas (2009TOVAR B & MARTIN-CEJAS R. 2009. Are outsourcing and non-aeronautical revenues important drivers in the efficiency of Spanish airports? Journal of Air Transport Management , 15: 217-220. Available at: https://doi.org/10.1016/j.jairtraman.2008.09.009. https://doi.org/10.1016/j.jairtraman.200... )	26 Spanish airports, 1993-1999	SFA	• Number of employees	• Aircraft movements	• Outsourcing
			• Land area	• Average aircraft size	• Non-aeronautical revenue
			• Number of gates	• % of non-aeronautical revenue	• Cargo
Assaf (2010ASSAF A. 2010. Bootstrapped scale efficiency measures of UK airports. Journal of Air Transport Management , 16: 42-44. Available at: https://doi.org/10.1016/j.jairtraman.2009.03.001. https://doi.org/10.1016/j.jairtraman.200... )	27 UK airports, 2007	DEA and Bootstrapped DEA	• Number of employees	• Number of passengers
			• Airport area	• Cargo
			• Number of runways	• Aircraft movements
Yang (2010YANG HH. 2010. Measuring the efficiencies of Asia-Pacific international airport - Parametric and non-parametric evidence. Computers & Industrial Engineering, 59: 697-702. Available at: https://doi.org/10.1016/j.cie.2010.07.023. https://doi.org/10.1016/j.cie.2010.07.02... )	12 international airports in Asia-Pacific region, 1998-2006	DEA and SFA (Cobb-Douglas production function) estimated using ML	• Number of employees	• Operating costs	• Number of employees
			• Number of runways	• Operating revenues	• Number of runways
					• Operating costs
					• Time trend
Tovar and Martín-Cejas (2010TOVAR B & MARTIN-CEJAS R. 2010. Technical Efficiency and Productivity Changes in Spanish Airports: A Parametric Distance Function Approach. Transportation Research Part E , 46: 249-60. Available at: https://doi.org/10.1016/j.tre.2009.08.007. https://doi.org/10.1016/j.tre.2009.08.00... )	26 Spanish airports, 1994-1999	SFA and MPI	• Number of employees	• Aircraft movements
			• Number of gates	• Average aircraft size
			• Airport area	• % of non-aeronautical revenue
Lozano and Gutiérrez (2011aLOZANO S & GUTIÉRREZ E. 2011a. Efficiency analysis and target setting of Spanish airports. Networks & Spatial Economics, 11: 139-157. Available at: https://doi.org/10.1007/s11067-008-9096-1. https://doi.org/10.1007/s11067-008-9096-... )	41 Spanish airports, 2006	Non-radial DEA models:	• Runway area	• Number of passengers
		a) RMOTE	• Apron capacity	• Aircraft movements
		b) CRS	• Passenger throughput capacity	• Cargo
		c) SE	• Number of baggage claim belts
		d) NIRS	• Number of check-in counters
		Target-setting DEA model	• Number of boarding gates
Lozano and Gutiérrez (2011bLOZANO S & GUTIÉRREZ E. 2011b. Slacks-based measure of efficiency of airports with airplanes delays as undesirable outputs. Computers & Operations Research, 38: 131-139. Available at: https://doi.org/10.1016/j.cor.2010.04.007. https://doi.org/10.1016/j.cor.2010.04.00... )	39 Spanish airports, 2006-2007	SBM model and DDF	• Runway area	Desirable outputs
			• Apron capacity	• Aircraft movements
			• Number of baggage claim belts	• Number of passengers
			• Number of check-in counters	• Cargo
			• Number of boarding gates	Undesirable outputs
				• % of delayed flights
				• Average delay time
Tsekeris (2011TSEKERIS T. 2011. Greek airports: Efficiency measurement and analysis of determinants. Journal of Air Transport Management , 17: 140-142. Available at: https://doi.org/10.1016/j.jairtraman.2010.06.002. https://doi.org/10.1016/j.jairtraman.201... )	39 Greek airports, 2007	Two-stage DEA model:	• Number of runways	• Number of passengers	• Location (island or mainland)
		1) DEA models;	• Terminal and airplane parking area	• Cargo	• Size of operations
		2) SWBT regression and Bootstrapped censored quantile regression	• Operating hours	• Aircraft movements	• Operating characteristics
Assaf and Gillet (2012ASSAF A & GILLEN D. 2012. Measuring the joint impact of governance form and economic regulation on airport efficiency. European Journal of Operational Research, 220: 187-198. Available at: https://doi.org/10.1016/j.ejor.2012.01.038. https://doi.org/10.1016/j.ejor.2012.01.0... )	73 International airports across Europe, North America and Australia, 2003-2008	Two-stage DEA model:	• Number of employees	• Number of passengers	• Ownership structure
		1) DEA and SFA;	• Other operating costs	• Aircraft movements	• Economic regulation
		2) SWBT regression	• Number of runways	• Non-aeronautical revenue
			• Passenger terminal area
Assaf et al. (2012ASSAF A & GILLEN D. 2012. Measuring the joint impact of governance form and economic regulation on airport efficiency. European Journal of Operational Research, 220: 187-198. Available at: https://doi.org/10.1016/j.ejor.2012.01.038. https://doi.org/10.1016/j.ejor.2012.01.0... )	27 UK airports, 1998-2008	SFA	• Labor costs	• Number of passengers
			• Capital costs	• Aircraft movements
			• Materials costs	• Cargo
				• Non-aeronautical revenues
Chow and Fung (2012CHOW C & FUNG M. 2012. Estimating indices of airport productivity in Greater China. Journal of Air Transport Management , 24: 12-17. Available at: https://doi.org/10.1016/j.jairtraman.2012.04.004. https://doi.org/10.1016/j.jairtraman.201... )	30 Chinese airports, 2000-2006	MPI and SFA	• Terminal area	• Number of passengers
			• Runway length	• Cargo
			• Time trend	• Aircraft movements
Gitto and Mancuso (2012GITTO S & MANCUSO P. 2012. Bootstrapping the Malmquist indexes for Italian airports. International Journal of Production Economics, 135: 403-411. Available at: https://doi.org/10.1016/j.ijpe.2011.08.014. https://doi.org/10.1016/j.ijpe.2011.08.0... )	28 Italian airports, 2000-2006	Bootstrapped MPI	• Labor costs	• Aircraft movements
			• Capital costs	• Number of passengers
			• Soft cost input	• Cargo
				• Aeronautical revenues
				• Non-aeronautical revenues
Perelman and Serebrisky (2012PERELMAN S & SEREBRISKY T. 2012. Measuring the technical efficiency of airports in Latin America. Utilities Policy, 22: 1-7. Available at: https://doi.org/10.1016/j.jup.2012.02.001. https://doi.org/10.1016/j.jup.2012.02.00... )	21 Latin America airports, 2000-2007	DEA models and MPI	• Number of employees	• Number of passengers
			• Number of runways	• Cargo
			• Terminal area	• Aircraft movements
Scotti et al. (2012SCOTTI D, MALIGHETTI P, MARTINI G & VOLTA N. 2012. The impact of airport competition on technical efficiency: a stochastic frontier analysis applied to Italian airport. Journal of Air Transport Management , 22: 9-15. Available at: https://doi.org/10.1016/j.jairtraman.2012.01.003. https://doi.org/10.1016/j.jairtraman.201... )	38 Italian airports, 2005-2008	SFA	• Runway capacity	• Aircraft movements	• Airport competition
			• Number of aircraft parking positions	• Number of passengers	• Ownership structure
			• Terminal area	• Cargo	• Degree of dominance of the main airline in an airport
			• Number of check-in counters
			• Number of baggage claim belts
			• Number of employees
Voltes-Dorta and Pagliari (2012VOLTES-DORTA A & PAGLIARI R. 2012. The impact of recession on airports’ cost efficiency. Transport Policy , 24: 211-222. Available at: https://doi.org/10.1016/j.tranpol.2012.08.012. https://doi.org/10.1016/j.tranpol.2012.0... )	194 Worldwide airports, 2007-2009	SCF (SR)	(i) Variable costs	• Domestic-Schengen passengers
			• Labor costs	• International passengers
			• Materials costs	• Aircraft movements
			(ii) Fixed factors	• Maximum take-off weight
			• Terminal area	• Cargo
			• Runway length	• Non-aeronautical revenue
			• Number of boarding gates
			• Number of check-in counters
			• Number of baggage claim belts
			(iii) Other
			• Time trend
			• Number of employees
			• % of dominant carrier
			• % of airline traffic
			• % of charter traffic
			• % of low-cost traffic
			• Ownership structure
Wanke (2012aWANKE P. 2012a. Capacity shortfall and efficiency determinants in Brazilian airports: Evidence from bootstrapped DEA estimates. Socio-Economic Planning Sciences, 46: 216-229. Available at: https://doi.org/10.1016/j.seps.2012.01.003. https://doi.org/10.1016/j.seps.2012.01.0... )	65 Brazilian airports, 2009	Bootstrapped DEA and FDH model	• Aircraft movements	• Number of passengers
				• Cargo
				• Mail
Wanke (2012bWANKE P. 2012b. Efficiency of Brazil’s airports: Evidences from bootstrapped DEA and FDH estimates. Journal of Air Transport Management , 23: 47-53. Available at: https://doi.org/10.1016/j.jairtraman.2012.01.014. https://doi.org/10.1016/j.jairtraman.201... )	63 Brazilian airports, 2009	DEA, Bootstrapped DEA, PCA, and CA	• Airport area	• Aircraft movements	(Cluster analysis)
			• Apron area	• Number of passengers	• Regular flights
			• Number of runways	• Cargo	• Location
			• Runway length		• International airport
			• Number of aircrafts parking positions		• Airport hub status
			• Terminal area
			• Number of vehicles parking lots
Adler et al. (2013ADLER N, LIEBERT V & YAZHEMSKY E. 2013. Benchmarking airports from a managerial perspective. Omega, 41: 442-458. Available at: https://doi.org/10.1016/j.omega.2012.02.004. https://doi.org/10.1016/j.omega.2012.02.... )	43 European airports (1998-2007)	Two-stage network DEA model:	• Staff costs	• International passengers
		1) CA;	• Other operating costs	• Domestic passengers
		2) DEA models and PCA	• Runway capacity	• Cargo
			• Terminal capacity	• Aircraft movements
		PCA	• International passengers	• Non-aeronautical revenues
			• Domestic passengers	• Aeronautical revenues
			• Cargo
			• Aircraft movements
Choo and Oum (2013CHOO Y & OUM T. 2013. Impacts of low cost carrier services on efficiency of the major U.S. airports. Journal of Air Transport Management , 33: 60-67. Available at: https://doi.org/10.1016/j.jairtraman.2013.06.010. https://doi.org/10.1016/j.jairtraman.201... )	63 US airports, 2007-2010	Two-stage model:	• Number of employees	• Number of passengers	• % of LCC passenger
		1) VFP and SFA; 2)	• Soft cost input	• Aircraft movements	• Airport output scale
		a) VFP regressions:		• Non-aeronautical revenues	• % of non-aeronautical revenue
		OLS, RE and FE; b)			• % of international passengers
		SFA: Tobit regression			• % of connecting passengers
					• % of cargo traffic
					• Runway utilization
					• Average aircraft size
De Nicola et al. (2013NICOLA A, GITTO S & MANCUSO P. 2013. Airport quality and productivity changes: a Malmquist index. Transportation Research Part E , 58: 67-75. Available at: https://doi.org/10.1016/j.tre.2013.07.001. https://doi.org/10.1016/j.tre.2013.07.00... )	20 Italian-airports, 2006-2008	Two-stage model:	• Labor costs	• Work load units (WLU)	Quality indicators
		1) MPI;	• Capital costs	• Aircraft movements	• % of delayed flights
		2) FA and Pooled-OLS regression	• Soft cost input		• Waiting time in queues at check-in
					• Baggage reclaim time
					• Mishandled bags
Martini et al. (2013MARTINI G, MANELLO A & SCOTTI D. 2013. The influence of fleet mix, ownership and LCCs on airport’s technical / environmental efficiency. Transportation Research Part E , 50: 37-52. Available at: https://doi.org/10.1016/j.tre.2012.10.005. https://doi.org/10.1016/j.tre.2012.10.00... )	33 Italian-airports, 2005-2008	Two-stage DEA model:	• Terminal area	Desirable outputs	Aeronautical factors
		1) DDF and DEA;	• Runway length	• Aircraft movements	• Fleet mix
		2) Adapted SWBT regression	• Number of baggage claim belts	• Work load units (WLU)	• Airport size
			• Number of aircraft parking positions	Undesirable outputs	• Presence of low-cost-carriers
				• Total costs of local air pollution	• Airline’s market power (degree of dominance of the main airline at each airport)
				• Noise levels	Non-aeronautical factors
					• Ownership structure
Chang et al. (2013CHANG YC, YU MM & CHEN P. 2013. Evaluating the performance of Chinese airports. Journal of Air Transport Management , 31: 19-21. Available at: https://doi.org/10.1016/j.jairtraman.2012.11.002. https://doi.org/10.1016/j.jairtraman.201... )	41 Chinese-airports in 2008	Two-stage DEA model:	• Business hour	• Aircraft movements	Airport service strategies
		1) DEA-imposed quasi-fixed input constraints models;	• Runway area	• Number of passengers	• Number of destinations
		2) SWBT regression	• Terminal area	• Mail/Cargo	• Number of airlines served
					• Number of international routes
					Airport geographical characteristics
					• City levels
					• Distance to Central Business District (CBD)
					• Flight area
Ha et al. (2013HA HK, WAN Y, YOSHIDA Y & ZHANG A. 2013. Airline market structure and airport efficiency: evidence from major Northeast Asian airports. Journal of Air Transport Management , 33: 32-42. Available at: https://doi.org/10.1016/j.jairtraman.2013.06.008. https://doi.org/10.1016/j.jairtraman.201... )	11 Northeast Asia airports, 1994-2011	Two-stage DEA model:	• Runway length	• Work load units (WLU)	Governance structure
		1) DEA models and SFA;	• Terminal size		• Ownership transition
		2) Tobit regression	• Number of employees		• Corporatization
					• Localization
					• State shares
					Competition User impacts
					• Customer power
					• Dominant airline market share
					• Airline concentration
					Airport characteristics
					• Input variable
					• Output variable
					• Open sky
					• New airport
					• Runway structure
					Hinterland characteristics
					• Per capita GPD
					• Population
					Traffic composition
					• International traffic
					• Cargo traffic
Martín et al. (2013MARTÍN J, RODRÍGUEZ-DÉNIZ H & VOLTES-DORTA A. 2013. Determinants of airport cost flexibility in a context of economic recession. Transportation Research Part E , 57: 70-84. Available at: https://doi.org/10.1016/j.tre.2013.01.007. https://doi.org/10.1016/j.tre.2013.01.00... )	194 Worldwide airports, 2007-2009	Two-stage model:	(i) Variable costs	• Domestic-Schengen passengers	Ownership structure Outsourcing
		1) SCF-SR;	• Labor costs	• International passengers	• % of materials costs
		2) Linear regression	• Materials costs	• Aircraft movements	Diversification
			(ii) Fixed factors	• Average landed maximum take-off weight	• % of non-aeronautical revenue
			• Check-in desks	• Cargo	Airline dominance and traffic mix
			• Number of boarding gates	• Non-aeronautical revenues	• Airline traffic shares
			• Warehouse area		• Share of charter traffic
			• Terminal area		• Share of low-cost traffic
			• Runway length		Other factors
			(iii) Other		• Airport size
			• Time trend		• Variation in passenger traffic between 2007 and 2009
			• Number of employees		• Pre-crisis efficiency level
			• Airline traffic shares		• Localization
			• Share of charter traffic
			• Share of low-cost traffic
			• Ownership structure
Wanke (2013WANKE P. 2013. Physical infrastructure and flight consolidation efficiency drivers in Brazilian airports: a two-stage network-DEA approach. Journal of Air Transport Management , 31: 1-5. Available at: https://doi.org/10.1016/j.jairtraman.2012.09.001. https://doi.org/10.1016/j.jairtraman.201... )	63 Brazilian airports, 2009	Two-stage network-DEA model and CA	• Terminal area	• Aircraft movements	(Cluster analysis)
			• Number of aircraft parking positions		• Location
			• Number of runways		• International airport
			• Aircraft movements	• Number of passengers	• Airport hub status
				• Cargo	• Regular flights
Adler and Liebert (2014ADLER N & LIEBERT V. 2014. Joint impact of competition, ownership form and economic regulation on airport performance and pricing. Transportation Research Part A, 64: 92-109. Available at: https://doi.org/10.1016/j.tra.2014.03.008. https://doi.org/10.1016/j.tra.2014.03.00... )	51 European and Australian airports, 1998-2007	Two-stage DEA model:	• Staff costs	• Number of passengers	Airport characteristics and management strategies
		1) DEA (WA-I);	• Other operating costs	• Cargo	• % of non-aeronautical revenue
		2) Robust cluster and RE regression	• Runway capacity	• Aircraft movements	• High levels of delay
				• Non-aeronautical revenues	• Runway capacity utilization
					• Aircraft movements
					• Average aircraft size
					Ownership, regulation and competition
					• Ownership structure
					• Economic regulation
					• Regional competition
					Time trend
					• Year 1999
					.
					.
					.
					• Year 2009
Ahn and Min (2014AHN YH & MIN H. 2014. Evaluating the multi-period operating efficiency of international airports using data envelopment analysis and the Malmquist productivity index. Journal of Air Transport Management, 39: 12-22. Available at: https://doi.org/10.1016/j.jairtraman.2014.03.005. https://doi.org/10.1016/j.jairtraman.201... )	23 major international airports, 2006-2011	DEA (CCR, BCC, SE, both input and output oriented) and MPI	• Land area	• Aircraft movements
			• Runway length	• Number of passengers
			• Passenger terminal area	• Cargo
			• Cargo terminal area
Coto-Millán et al. (2014COTO-MILLÁN P, CASARES-HORTANÓN P, INGLADA V & AGÜEROS M. 2014. Small is beautiful? The impact of economic crisis, low cost carriers and size on efficiency in Spanish airports (2009-2011. Journal of Air Transport Management , 40: 34-41. Available at: https://doi.org/10.1016/j.jairtraman.2014.05.006. https://doi.org/10.1016/j.jairtraman.201... )	35 Spanish airports, 2009-2011	Two-stage DEA approach:	• Labor costs	• Number of passengers	• Airport size
		1) DEA and MPI;	• Capital costs	• Cargo	• Share of LCC (low-cost carriers) passengers
		2) Tobit regression	• Other operating costs	• Aircraft movements
Li (2014LI SL. 2014. The cost allocation approach of airport service activities. Journal of Air Transport Management , 38: 48-53. Available at: https://doi.org/10.1016/j.jairtraman.2013.12.018. https://doi.org/10.1016/j.jairtraman.201... )	Magong airport, 1991-2000	Two-stage DEA model:	• Number of employees	• Airport Service Costs	• Number of employees
		1) DEA;	• Labor costs		• Labor costs
		2) Regression analysis	• Apron area		• Apron area
			• Cargo terminal area		• Cargo terminal area
			• Passenger terminal area		• Passenger terminal area
			• Scheduled flight numbers		• Scheduled flights numbers
			• Number of passengers
			• Arrival passenger numbers
			• Departure passenger numbers
			• Passenger capacity of peak hour
			• Cargo
Merkert and Mangia (2014MERKERT R & MANGIA L. 2014. Efficiency of Italian and Norwegian airports: a matter of management or of the level of competition in remote regions? Transportation Research Part A , 62: 30-48. Available at: https://doi.org/10.1016/j.tra.2014.02.007. https://doi.org/10.1016/j.tra.2014.02.00... )	35 Italian and 46 Norwegian airports, 2007-2009	Two-stage DEA model:	Technical inputs	• Aircraft movements	• Classification of the airports
		1) Bootstrapped DEA;	• Terminal area	• Number of passengers	• Military aviation
		2) Tobit regression	• Apron area	• Cargo	• Italy or Norway
			• Number of runways		• Population
			• Runway length		• Profitability
			• Runway area		• Competition
			• Airport area
			• Number of employees
			Financial inputs
			• Operating costs
			• Staff costs
			• Material costs
Scotti et al. (2014SCOTTI D, DRESNER M, MARTINI G & YU C. 2014. Incorporating negative externalities into productivity assessments of US airports. Transportation Research Part A , 62: 39-53. Available at: https://doi.org/10.1016/j.tra.2014.02.008. https://doi.org/10.1016/j.tra.2014.02.00... )	44 US airports, 2005-2009	Two-stage model:	• Land area	Desirable outputs	• Fleet mix
		1) DDF approach;	• Terminal area	• Number of passengers	• Airport size
		2) Tobit Regression	• Runway length	• Aircraft movements	• Percentage of night flights
			• Number of boarding gates	• Cargo	• Multiple airport system
			• Operating costs	Undesirable outputs	• % of international passengers
				• Flight delays
				• Noise
				• Local air pollution
Tsui et al. (2014aTSUI W, BALLI H, GILBEY A & GOW H. 2014a. Operational efficiency of Asia - Pacific airports. Journal of Air Transport Management , 40: 16-24. Available at: https://doi.org/10.1016/j.jairtraman.2014.05.003. https://doi.org/10.1016/j.jairtraman.201... )	11 New-Zealand airports, 2010-2012	Two-stage model:	• Operating costs	• Operating revenues	• Population around the airport
		1) SBM model and MPI;	• Number of runways	• Number of passengers	• Airport hub status
		2) SWBT regression		• Aircraft movements	• Airport operating hours
					• Airport ownership structure
					• Christchurch earthquakes
					• Rugby World Cup 2011
Tsui et al. (2014bTSUI W, GILBEY A & BALLI H. 2014b. Estimating airport efficiency of New Zealand airports. Journal of Air Transport Management , 35: 78-86. Available at: https://doi.org/10.1016/j.jairtraman.2013.11.011. https://doi.org/10.1016/j.jairtraman.201... )	21 Asia-Pacific airports, 2002-2011	Two-stage DEA approach:	• Number of employees	• Number of passengers	• Time trend
		1) DEA;	• Number of runways	• Cargo	• GPD per capita
		2) SWBT and RE	• Runway length	• Aircraft movements	• % of international passengers
		Tobit regression	• Passenger terminal area		• Airport hub status
					• Airport ownership structure
					• Airport operating hours
					• Airport hinterland population
					• Alliance membership of dominant airline
Lai et al. (2015LAI PL, POTTER A, BEYNON M & BERESFORD A. 2015. Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique. Transportation Policy, 42: 75-85. Available at: https://doi.org/10.1016/j.tranpol.2015.04.008. https://doi.org/10.1016/j.tranpol.2015.0... )	24 major international airports, 2010	DEA and AHP/DEA-AR	• Number of employees	• Number of passengers
			• Number of gates	• Cargo and mail
			• Number of runways	• Aircraft movements
			• Terminal area	• Aeronautical and non-aeronautical revenues
			• Runway length
			• Operating costs
Merkert and Assaf (2015MERKERT R & ASSAF A. 2015. Using DEA models to jointly estimate service quality perception and profitability - Evidence from international airports. Transportation Research Part A , 75: 42-50. Available at: https://doi.org/10.1016/j.tra.2015.03.008. https://doi.org/10.1016/j.tra.2015.03.00... )	30 international airports, 2013	Two-stage DEA model:	• Runway length	Profitability	• % of non-aeronautical revenue
		1) DEA and bootstrapped DEA;	• Terminal size	• Profit margin	• Ownership structure
		2) SWBT Regression	• Number of employees	Perceived service quality	• % of LCC airlines
				• Skytrax (ranking determined by industry body)	• Asia-Pacific localization
				• Pax reviews (ranking determined by costumers)	• % of international passengers
				Other common outputs	• Number of gates
				• Number of passengers
				• Cargo
				• Aircraft movements
Zou et al. (2015ZOU B, KAFLE N, CHANG YT & PARK K. 2015. US airport financial reform and its implications for airport efficiency: An exploratory investigation. Journal of Air Transport Management , 47: 66-78. Available at: https://doi.org/10.1016/j.jairtraman.2015.05.002. https://doi.org/10.1016/j.jairtraman.201... )	42 US airports, 2009-2012	Two-stage DEA model:	• Labor costs	Desirable outputs	Funding sources used by US airports
		1) DEA;	• Capital costs	• Number of passengers	• Passenger facility charges
		2) RE regression	• Material costs	• Aircraft movements	• Airport improvement program grants
				• Cargo	Runway utilization factors
				• Non-aeronautical revenue	• Passengers per runway
				Undesirable output	• Cargoes per runway
				• Total flight arrival delay	• Delay per runway
					Year
					• 2010
					• 2011
					• 2012
					Hub size
					• Medium
					• Small
					• Non-hub
See and Li (2015SEE K & LI F. 2015. Total factor productivity analysis of the UK airport industry: a HicksMoorsteen index method. Journal of Air Transport Management , 43: 1-10. Available at: https://doi.org/10.1016/j.jairtraman.2014.12.001. https://doi.org/10.1016/j.jairtraman.201... )	45 UK airports, 2001-2009	Two-stage model: 1)	• Labor costs	• Aeronautical revenue	• Ownership structure
		Hicks-Moorsteen	• Capital costs	• Non-aeronautical revenue	• Airport size (number of passengers)
		TFP index;	• Other operating costs		• First lag of TFP level
		2) FGLS and			• Economic regulation
		continuous updated
		GMM regression
					• Weekly opening hours
					• Ownership structure
					• % of international traffic
					• Airport size (WLU)
					• Population density around the airport
				• Number of passengers	• Level of seasonality
		Two-stage DEA model:	• Staff costs	• Aircraft movements	• Joint military-civil airport
		1) DEA;	• Other operating costs	• Cargo	• Spain or Turkey
Ülkü (2015ULKÜ T. 2015. A comparative efficiency analysis of Spanish and Turkish airports. Journal of Air Transport Management , 46: 56-68. Available at: https://doi.org/10.1016/j.jairtraman.2015.03.014. https://doi.org/10.1016/j.jairtraman.201... )	41 Spanish and 32 Turkish airports, 2009-2011	2) OLS and Tobit regression	• Runway area	• Non-aeronautical revenues	• Year (2009, 2010 or 2011)
Örkcü et al. (2016ORKCÜ H, BALIKÇI C, DOGAN M & GENÇ A. 2016. An evaluation of the operational efficiency of turkish airports using data envelopment analysis and the Malmquist productivity index: 2009-2014 case. Transport Policy, 48: 92-104.)	21 Turkey airports, 2009-2014	Two-stage DEA model:	• Number of runways	• Aircraft movements	• Population around the airport
		1) DEA and Malmquist productivity index;	• Runway units	• Number of passengers	• Airport hub status
		2) SWBT Regression	• Passenger terminal area	• Cargo	• Airport operating hours
					• Joint military-civil airport
					• Percentage of international traffic
Chaouk et al. (2020CHAOUK M, PAGLIARI R & MOXON. 2020. The impact of national macro-environment exogenous variables on airport efficiency. Journal of Air Transport Management , 82: 1-11. Available at: https://doi.org/10.1016/j.jairtraman.2019.101740. https://doi.org/10.1016/j.jairtraman.201... )	59 European and Asia-Pacific airports	Two-stage DEA model:	• Number of runways	• Number of passengers	• Air transport output
		1) DEA;	• Number of gates	• Aircraft movements	• Institutions
		2) SWBTRegression	• Terminal area	• Cargo	• Infrastructure
			• Number of employees	• Non-aeronautical revenues	• Macro-economic environment
					• Health and primary education
					• Higher education and training
					• Goods market efficiency
					• Labour market efficiency
					• Financial market development
					• Technological readiness
					• Market size
					• Business sophistication
					• Innovation
					• Safety and security
					• Corruption perception
					• Human development
					• Travel and tourism
Huynh et al. (2020HUYNH T, KIM G & HA HK. 2020. Comparative analysis of efficiency for major Southeast Asia airports: A two-stage approach. Journal of Air Transport Management , 89: 1-9. Available at: https://doi.org/10.1016/j.jairtraman.2020.101898. https://doi.org/10.1016/j.jairtraman.202... )	9 major Southeast Asia Airports	Two-stage DEA model:	• Runway length	• Passenger movement	• Airport characteristics
		1) DEA;	• Terminal area	• Cargo	• Governance structure
		2) Tobit Regression	• Apron capacity	• Aircraft movements	• Competition
					• User impact

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