# The Technical Efficiency of French Regional Airports and Low-Cost Carrier Terminals

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## Abstract

**:**

## 1. Introduction

## 2. Review of French Airports and Privatization

## 3. Research Methodology

#### 3.1. Data Envelopment Analysis-Principal Component Analysis (DEA-PCA) and Malmquist Productivity Index

_{1j}, x

_{2j}, …, x

_{mj}and y

_{1j}, y

_{2j}, …, y

_{sj}, respectively [37]. In the DEA result, ${R}_{ir}^{j}$ could be obtained using Max ${h}_{0}={{\displaystyle \sum}}_{r=1}^{s}{u}_{r}{y}_{r0}$, subject to ${{\displaystyle \sum}}_{r=1}^{s}{u}_{r}{y}_{rj}-{{\displaystyle \sum}}_{i=1}^{m}{v}_{i}{x}_{ij}\le 0$, $j=1,\dots ,n$ and ${{\displaystyle \sum}}_{i=1}^{m}{v}_{r}{x}_{i0}=1$ to avoid an infinite number of solutions in the case of constant return-to-scale (CRS, CCR model) and Max ${h}_{0}={{\displaystyle \sum}}_{r=1}^{s}{u}_{r}{y}_{r0}+{u}_{0}$, subject to ${{\displaystyle \sum}}_{r=1}^{s}{u}_{r}{y}_{rj}-{{\displaystyle \sum}}_{i=1}^{m}{v}_{i}{x}_{ij}+{u}_{0}\le 0$, $j=1,\dots ,n$ and ${{\displaystyle \sum}}_{i=1}^{m}{v}_{r}{x}_{i0}=1$ for a variable return-to-scale (VRS, BCC model) based on the output-oriented equation (B. The case of the input-oriented equation minimizes the θ subject to $-{y}_{i}+Y\lambda \ge 0$, $\theta {x}_{i}-X\lambda \ge 0$, $\lambda \ge 0$, where, θ is a scalar and $\lambda $ is an n × 1 vector. The DEA results always satisfy the condition that ${h}_{0}$ or θ of VRS is greater than or equal to ${h}_{0}$ or θ of CRS. For various reasons, the application of DEA in airport efficiency studies has focused on the input rather than the output-oriented equation [38]. One possible reason for this finding is that greater control is possible over input variables [6] than output variables, given restrictions on the maximum number of aircraft and passenger movements allowed by the government [10]. However, the rankings of DMUs are not the same when we use different DEA methods. Deregulation of the airport market has stimulated maximization of output with the given input. Therefore, we have 71 different DEA results from four DEA methods (input-oriented, output-oriented, CRS, and VRS) and 25 different combinations of one to five input and output variables.

_{1}, PC

_{2}, …, PC

_{p}, eigenvalues ${e}_{1},{e}_{2},\dots ,{e}_{M}$ (${e}_{1}\ge {e}_{2}\ge \dots \ge {e}_{M}\ge 0$) and normalized eigenvalues ${l}_{1},{l}_{2},\dots ,{l}_{M}$. Based on References [36,37], we have ${X}_{i}={{\displaystyle \sum}}_{k=1}^{M}{l}_{k}{d}_{jk}$, where,${d}_{jk}=\frac{{\displaystyle \sum}{R}_{ir}^{j}}{n}$, (j = 1, …, n). Thus, we can obtain ${X}_{i}={{\displaystyle \sum}}_{i=1}^{M}{\beta}_{i}{d}_{\left(jk\right)i}$, where, i = 1, …, m, where, ${\beta}_{i}\left({l}_{i}\right)=$ normalized eigenvalue (proportion) of each principal component (PC

_{i}) for DMU j of t from 2006 to 2012. The four different methods, namely, CRS and VRS with input- and output-orientation, give different DEA results (71${R}_{ir}^{j}$, i = 1, …,5, r = 1, …,5, j = 1, … n) varying from 0.0063 to 1.0000 for the pooled technical efficiency of NCE from 2006 to 2012. Additionally, we applied PCA to the 71 different DEA results to improve the discriminatory power, obtain more stable and objective technical efficiency of all the regional airports in France, and offer more results to measure efficiency. When we combine DEA and PCA, the data variation is narrower than in DEA alone, from 0.7542 to 0.8472 (see Table 5). In the third stage, based on the first and second steps, we applied the Malmquist Productivity Index (MPI) to compute the total factor productivity change (TFPC), which can be decomposed into technical change (TC) and TE change (TEC) of each airport. Productivity and technical change can be measured in several ways. The Malmquist index was first presented in a consumer theory context [39] and later for productivity analyses [40]. The index is as a geometric mean of two Malmquist productivity indexes expressed in distance functions.

_{t}, x

_{t}) and B (q

_{t+1}, x

_{t}

_{+1}) are the quantities of input and output variables in periods t and t+1, and the efficiencies are defined by the frontier of t and t+1, respectively. Using Equations (4) and (5), we obtain $\mathrm{TEC}=\frac{{q}_{t+1}}{{q}_{c}}$ (6) and $\mathrm{TC}={[\frac{{q}_{t+1}/{q}_{b}}{{q}_{t+1}/{q}_{c}}\times \frac{{q}_{t}/{q}_{b}}{{q}_{t}/{q}_{a}}]}^{0.5}$ (7). This research uses both CRS and VRS (see Figure 2). Therefore, the $\mathrm{PEC}=[\frac{{q}_{t+1}/{q}_{g}}{{q}_{t}/{q}_{a}}]$ (8), $\mathrm{SEC}=[\frac{{q}_{b}/{q}_{a}}{{q}_{h}/{q}_{g}}]$ (9), and $\mathrm{TC}={[\frac{{q}_{e}}{{q}_{b}}\frac{{q}_{h}}{{q}_{f}}]}^{0.5}$ (10).

#### 3.2. DEA-PCA and Malmquist Productivity Index Results

## 4. Interpretation and Implications: Focusing on Low-Cost Carriers (LCCs) and LCC-Dedicated Terminals (LCCTs)

#### 4.1. Interpretation of Pooled Technical Efficiency (TE) Results

#### 4.2. LCCT and French Regional Airports

#### 4.3. Viability of Regional Airports in France

## 5. Conclusions, Limitations, and Recommendations

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Malmquist productivity indices for CRS and variable return-to-scale (VRS). Source: Authors’ elaboration.

**Figure 3.**Pooled Technical efficiency changes from 2006 to 2012 for regional airport groups in France.

Category | Airports | Number | ||
---|---|---|---|---|

Paris airports | CDG Charles-de-Gaulle | ORY Orly | 2 | |

Large | BOD Bordeaux; BSL Basel-Mulhouse-Freiburg; LYS Lyon; MPL Montpellier; MRS Marseille | NCE Nice; NTE Nantes; SXB Strasbourg; TLS Toulouse | 9 | |

Dom-Tom | CAY Cayenne; DZA Mayotte-Dzaoudzi-Pamandzi; FDF Martinique; NOU Nouméa | PPT Tahiti; PTP Pointe-à-Pitre; RUN La Réunion | 7 | |

Middle | AJA Ajaccio; BES Brest; BIA Bastia; BIQ Biarritz; BVA Beauvais | CGF Carcasonne; FSC Figari; LDS Tarbes; LIL Lille; PGF Peripignan | PUF Pau; RNS Rennes; TLN Toulon | 13 |

Small | BZR Beziers; CFE Clermont-Ferrand; CFR Caen; CLY Calvi; CMF Chambery; DOL Deauville | EGC Bergerac; ETZ Metz; FNI Nimes; GNB Grenoble; LIG Limoges; LRH La Rochelle | LRT Lorient; PIS Poitiers; RDZ Rodez; UIP Quimper; XCR Chalons | 17 |

Others | AGF Agen; ANE Angers; ANG Angouleme; AUF Auxerre; AUR Aurillac; AVN Avignon; BOU Bourges; BVE Brive; BYF Albert; CER Cherbourg; CET Cholet; CHR Chateauroux; CMR Colmar; CQF Calais; CTT Le Castelet; CVH Courchevel; DCM Castres; DIJ Dijon | DLE Dole; DNR Dinard; EBU Saint-Etienne; ENC Nancy; EPL Epinal; GAT Gap; IDY Ile-d’yeu; LAI Lannion; LEH Le Havre; LFEA Belle-Ile; LFEC Ouessant; LME Le Mans; LPY Le Puy; LTQ Le Touquet; LTT La Mole; LVA Laval; NCE (*) Port Grimaud; NCY Annecy | NIT Niort; NVS Nevers; ORE Orleans; PGX Perigueux; QAM Amiens; QYR Troyes; RHE Reims; RNE Roanne; SBK Sanit-Brieuc; SYT Saint-Yan; TUF Tours; URO Rouen; VAF Valence; VNE Vannes; XCZ Charleville; XMF Montbeliard; XVS Valaciennes | 53 |

Total number of airports | 101 99 (**) |

Category | Avg WLU per Airport * | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 |
---|---|---|---|---|---|---|---|---|

Large | 539,901 (100%) | 9 | 9 | 9 | 9 | 9 | 9 | 9 |

DOM-TOM | 141,371 (26.2% **) | 6 | 6 | 6 | 6 | 6 | 7 | 7 |

Middle | 86,968 (16.1% **) | 14 | 14 | 14 | 14 | 13 | 9 | 11 |

Small | 20,741 (3.8% **) | 15 | 15 | 15 | 15 | 14 | 11 | 15 |

Others | 1595 (0.3% **) | 49 | 44 | 39 | 44 | 28 | 17 | 18 |

Total (535) | 94,261 | 93 | 88 | 83 | 88 | 70 | 53 | 60 |

Valid data (433, 81%) | 41 (44.1%) | 66 (75.0%) | 70 (84.3%) | 74 (84.1%) | 69 (98.6%) | 53 (100%) | 60 (100%) |

Region | 2011 | 2012 | 2013 | 2014 | CAGR (*) | ||||
---|---|---|---|---|---|---|---|---|---|

Regional airports | 64,808,646 | 39.6% | 68,783,291 | 41.0% | 71,049,915 | 41.3% | 72,108,646 | 41.1% | 3.6% |

Large | 48,032,204 | 29.4% (74.1%) | 51,398,907 | 30.6% (74.7%) | |||||

Paris airports | 88,109,627 | 53.9% | 88,788,465 | 52.9% | 90,327,071 | 52.6% | 92,676,342 | 52.8% | 1.7% |

Sub-total | 152,918,273 | 93.5% | 157,526,756 | 93.8% | 161,376,986 | 93.9% | 164,784,988 | 94.0% | 2.5% |

DOM-TOM | 10,677,378 | 6.5% | 10,426,005 | 6.2% | 10,482,787 | 6.1% | 10,596,227 | 6.0% | −0.3% |

Total | 163,595,651 | 100% | 167,953,254 | 100% | 171,859,773 | 100% | 175,381,215 | 100.0% | 2.3% |

In/Out | Variables | Obs. | Min. | Max. | Average | Std. Dev. | CV |
---|---|---|---|---|---|---|---|

Input variables | A. # of employees | 409 | 1 | 573 | 98 | 112 | 1.14 |

B. Labor cost (k€) | 397 | 2 | 84,212 | 7328 | 14,594 | 1.99 | |

C. Debt (k€) | 241 | 1 | 175,802 | 22,504 | 40,708 | 1.81 | |

D. Subsidization (k€) | 183 | 1 | 18,936 | 1381 | 2975 | 2.15 | |

E. Operational cost (k€) | 321 | 283 | 183,336 | 18,685 | 31,494 | 1.69 | |

Output variables | 1. Passenger | 534 | 133 | 11,197,734 | 891,911 | 1,901,211 | 2.13 |

2. Cargo (ton) | 134 | 2 | 142,253 | 20,909 | 25,913 | 1.24 | |

3. Movement | 527 | 41 | 184,901 | 13,634 | 28,472 | 2.09 | |

4. Revenue (k€) | 322 | 76 | 210,383 | 21,049 | 36,592 | 1.74 | |

5. Net Profit (%) | 321 | −73.9% | 42.1% | 1.4% | 10.8% | 7.91 |

**Note**: Obs.: Observation number; CV: Coefficient of Variation.

Input Variables | Output Variables | # of Observation/Total | Efficiency of the Nice (NCE) Airport (2012) |
---|---|---|---|

A, B, C, D, E | 1, 2, 3, 4, 5 | 154/535 | 0.9227/1.0000/1.0000; CCR_Out/BCC_Out/BCC_In |

A, D, E | 1, 2, 3 | 306/535 | 0.8427/1.0000/1.0000 |

A | 3 | 407/535 | 0.6192/N/A/0.8153 |

B | 3 | 396/535 | 0.0063 (CCR_In)/N/A/0.5258 |

… | … | … | … |

25 different combinations of variables | 433/535 | 0.4934/1.0000/0.9555 |

Airport Size | N | Mean | Std. Dev | Std. Error | 95% Confidence Interval for Mean | Min | Max | Mean Difference (Post Hoc Test) | ANOVA | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Lower Bound | Upper Bound | L | D | M | S | ||||||||

Large (L) | 63 | 0.7012 | 0.122 | 0.015 | 0.67 | 0.73 | 0.3449 | 0.8737 | F-value 68.567 ** | ||||

Dom-Tom (D) | 44 | 0.4902 | 0.167 | 0.025 | 0.44 | 0.54 | 0.1349 | 0.8033 | 0.2111 ** | ||||

Middle (M) | 88 | 0.4993 | 0.150 | 0.016 | 0.47 | 0.53 | 0.1600 | 0.8020 | 0.2019 ** | −0.0091 | |||

Small (S) | 94 | 0.3870 | 0.187 | 0.019 | 0.35 | 0.43 | 0.0074 | 0.7877 | 0.3142 ** | 0.1031 * | 0.1122 ** | ||

Others | 136 | 0.3156 | 0.161 | 0.014 | 0.29 | 0.34 | 0.0326 | 0.7850 | 0.3857 ** | 0.1746 ** | 0.1837 ** | 0.0714 * | |

Total | 425 | 0.4447 | 0.206 | 0.010 | 0.43 | 0.46 | 0.0074 | 0.8737 |

Airports | TFPC (M) | PEC | SEC | TC | Observation Years |
---|---|---|---|---|---|

NCE | 0.9865 | 1.0503 | 0.9470 | 0.9919 | 2008–12 |

LYS | 0.9794 | 1.0039 | 0.9878 | 0.9876 | 2008–12 |

MRS | 1.0215 | 1.0612 | 0.9503 | 1.0128 | 2008–12 |

TLS | 1.4251 | 1.1213 | 1.0276 | 1.2368 | 2008–12 |

BSL | 1.2278 | 1.1307 | 0.9601 | 1.1310 | 2008–12 |

BOD | 1.2533 | 1.0738 | 1.0193 | 1.1451 | 2008–12 |

NTE | 1.0600 | 1.0161 | 1.0074 | 1.0356 | 2008–12 |

MPL | 0.8620 | 0.9842 | 0.9574 | 0.9147 | 2008–12 |

SXB | 1.3852 | 1.0205 | 1.1163 | 1.2159 | 2008–12 |

Geometric Mean of Large airports | 1.1185 | 1.0502 | 0.9958 | 1.0695 | |

RUN | 0.6774 | 0.8897 | 0.9618 | 0.7916 | 2008–12 |

PTP | 0.6053 | 0.8425 | 0.9710 | 0.7399 | 2008–12 |

PPT | 0.7329 | 0.8307 | 1.0630 | 0.8299 | 2008–12 |

FDF | 0.9304 | 0.9882 | 0.9831 | 0.9576 | 2008–12 |

NOU | 1.9886 | 0.9287 | 1.4175 | 1.5105 | 2008–12 |

CAY | 0.9327 | 1.0376 | 0.9373 | 0.9591 | 2008–12 |

DZA | 0.0976 | 0.4249 | 0.9279 | 0.2475 | 2011–12 |

AJA | 1.3731 | 0.9822 | 0.9831 | 1.2095 | 2008–12 |

LIL | 1.2434 | 1.1098 | 1.0424 | 1.1397 | 2008–12 |

BIQ | 1.7933 | 1.2118 | 0.9871 | 1.4197 | 2008–12 |

BIA | 0.7900 | 0.9220 | 1.0012 | 0.8681 | 2008–12 |

BES | 3.0335 | 1.5568 | 0.9332 | 1.9461 | 2008–12 |

PUF | 0.2526 | 0.6180 | 1.0174 | 0.4380 | 2008–12 |

LDE | 0.6263 | 0.8151 | 1.2122 | 0.7552 | 2008–12 |

FSC | 3.1078 | 1.2984 | 0.9050 | 1.9746 | 2008–12 |

TLN | 0.4550 | 0.8063 | 1.0349 | 0.6234 | 2008–10, 12 |

RNS | 0.8288 | 0.8963 | 0.6856 | 0.8934 | 2008–10, 12 |

BVA | 1.3225 | 1.6310 | 0.9895 | 1.1826 | 2008–10, 12 |

PGF | 0.2296 | 0.5610 | 0.9826 | 0.4136 | 2008–10 |

CCF | 0.9406 | 0.9930 | 1.0046 | 0.9639 | 2008–09 |

Geometric Mean of Middle and Dom-TOM airports | 0.8216 | 0.9239 | 0.9936 | 0.888 | |

CFE | 7.4855 | 2.9384 | 0.7613 | 3.3460 | 2008–10, 12 |

GNB | 0.2476 | 0.6831 | 0.8375 | 0.4327 | 2008–12 |

LIG | 0.2130 | 0.6676 | 0.8069 | 0.3954 | 2008–10, 12 |

CLY | 0.7921 | 0.9002 | 1.0119 | 0.8695 | 2008–11 |

ETZ | 0.0907 | 0.4682 | 0.8178 | 0.2369 | 2008–10, 12 |

EGC | 2.3475 | 1.3519 | 1.0406 | 1.6686 | 2008–12 |

CMF | 14.0842 | 1.6460 | 1.7501 | 4.8892 | 2008–12 |

LRH | 3.5734 | 1.4480 | 1.1494 | 2.1471 | 2008–12 |

BZR | 3.0118 | 1.5464 | 1.0051 | 1.9377 | 2008–12 |

FNI | 2.7877 | 1.2808 | 1.1765 | 1.8499 | 2008–10 |

LRT | 0.7459 | 0.9213 | 0.9653 | 0.8387 | 2008–12 |

RDZ | 0.2109 | 0.6133 | 0.8748 | 0.3930 | 2008–12 |

DOL | 4.8213 | 1.9716 | 0.9516 | 2.5698 | 2008–12 |

UIP | 0.2371 | 0.5804 | 0.9689 | 0.4217 | 2008–12 |

CFR | 0.8725 | 0.8958 | 1.0570 | 0.9214 | 2008–12 |

PIS | 10.1827 | 2.0123 | 1.2574 | 4.0245 | 2008–12 |

XCR | 151.3291 | 5.5258 | 1.3476 | 20.3214 | 2009–12 |

Geometric Mean of Small airports (): excluding extreme outliers value exceed 10 of TFPC. | 1.6805 (0.9205) | 1.2032 (1.0171) | 1.0229 (0.9511) | 1.3654 (0.9515) | - |

DIJ | 6.5312 | 1.8291 | 1.1582 | 3.0831 | 2009–12 |

AGF | 2.7897 | 1.4156 | 1.0649 | 1.8507 | 2008–12 |

URO | 0.3739 | 1.0796 | 0.6249 | 0.5542 | 2009, 12 |

ENC | 0.1499 | 0.6143 | 0.7620 | 0.3203 | 2008–10, 12 |

AUR | 1.2258 | 1.0363 | 1.0468 | 1.1299 | 2008–12 |

CMR | 0.3693 | 0.6116 | 1.0976 | 0.5500 | 2008–10, 12 |

AUF | 0.3727 | 0.6943 | 0.9705 | 0.5531 | 2008–10, 12 |

LME | 0.5163 | 0.7948 | 0.9658 | 0.6726 | 2008–12 |

PGX | 3.6793 | 1.2622 | 1.3341 | 2.1850 | 2008–12 |

AVN | 0.4263 | 0.7214 | 0.9857 | 0.5996 | 2009–10, 12 |

CHR | 0.8175 | 0.9196 | 1.0032 | 0.8861 | 2009–12 |

XVS | 0.1314 | 0.5883 | 0.7550 | 0.2960 | 2008–12 |

LTQ | 0.5861 | 0.9941 | 0.8123 | 0.7257 | 2009–12 |

DCM | 0.2397 | 0.7047 | 0.8014 | 0.4244 | 2008–10, 12 |

VAF | 0.0732 | 0.3528 | 0.9959 | 0.2082 | 2008–12 |

ANG | 0.3243 | 1.3586 | 0.4691 | 0.5088 | 2008–12 |

VNE | 25.0322 | 1.8696 | 1.9394 | 6.9040 | 2010, 12 |

LEH | 1.2517 | 0.9358 | 1.1690 | 1.1442 | 2008, 10–11 |

be | 0.1537 | 0.5245 | 0.9014 | 0.3251 | 2008–11 |

LPY | 2.0984 | 1.4487 | 0.9285 | 1.5600 | 2008–11 |

CVF | 1.7257 | 1.0578 | 1.1760 | 1.3873 | 2010–11 |

DLE | 1.1330 | 0.9980 | 1.0533 | 1.0778 | 2008–11 |

XMF | 1.1642 | 1.0340 | 1.0278 | 1.0955 | 2008–11 |

DNR | 0.1285 | 0.4756 | 0.9252 | 0.2920 | 2008–10 |

NCY | 0.8341 | 0.9433 | 0.9859 | 0.8969 | 2008–10 |

ANE | 0.4334 | 1.1142 | 0.6424 | 0.6055 | 2008–10 |

QYR | 0.8257 | 1.1769 | 0.7870 | 0.8914 | 2008–10 |

BOU | 24.3695 | 2.2391 | 1.6020 | 6.7937 | 2008–09 |

NVS | 0.8857 | 1.5439 | 0.6170 | 0.9298 | 2008–09 |

ORE | 19.6040 | 9.6927 | 0.3392 | 5.9622 | 2008–09 |

EBU | 14.5241 | 2.1558 | 1.3528 | 4.9803 | 2008–09 |

Geometric Mean of Other airports (): excluding extreme outliers value exceed 10 of TFPC | 0.9588 (0.6094) | 1.0602 (0.9062) | 0.9274 (0.9052) | 0.9750 (0.7429) | - |

All French airports (): excluding extreme outliers value exceed 10 of TFPC. | 1.0345 (0.7799) | 1.0223 (0.9500) | 0.9916 (0.9510) | 1.0206 (0.8614) | 2008–12 |

1.2599 | 1.0731 | 1.0221 | 1.1487 | 2011–12 | |

0.9148 | 0.9849 | 0.9798 | 0.9479 | 2010–11 | |

0.9965 | 1.0043 | 0.9944 | 0.9979 | 2009–10 | |

7.0720 | 1.2080 | 1.8103 | 3.2339 | 2008–09 |

**Note**: Airports that do not have two consecutive years’ data after 2008 are omitted.

Dependent Variable | DEA-PCA | |||||
---|---|---|---|---|---|---|

Variables | Coeff. | Std. Coeff. | Std. Error | VIF | t-Value | Bootstrap ^{(1)} Sig. |

Constant | 3.005 | 0.258 | 11.661 | 0.000 ** | ||

ln_WLU_Emp | −0.328 | −0.556 | 0.037 | 2.030 | −8.859 ** | 0.000 ** |

ln_Op_WLU | 0.368 | 0.301 | 0.071 | 1.746 | 5.177 ** | 0.008 * |

ln_A_NA | 0.093 | 0.112 | 0.039 | 1.124 | 2.395 * | 0.062 |

ln_LCC | 0.038 | 0.067 | 0.027 | 1.179 | 1.411 | 0.196 |

LCCT | −0.178 | −0.101 | 0.084 | 1.190 | −2.105 * | 0.000 ** |

^{2}= 0.736 (F-test 76.011 **); ** Significant at α = 0.001; * Significant at α = 0.05; (1) 1,000 bootstrap samples.

Indices | Category | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | Average/Total |
---|---|---|---|---|---|---|---|---|---|

Operational Indices | WLU/Emp | - | 609 | 639 | 631 | 660 | 743 | 775 | 702 |

Large | - | 1972 | 1801 | 1766 | 1960 | 1944 | 2066 | 1921 | |

DOM-TOM | - | 863 | 899 | 907 | 926 | 942 | 847 | 891 | |

Middle | - | 773 | 899 | 801 | 810 | 846 | 905 | 829 | |

Small | - | 480 | 449 | 487 | 451 | 530 | 520 | 499 | |

Others | - | 99 | 115 | 110 | 134 | 97 | 78 | 106 | |

Op/WLU | - | - | 1.4061 | 2.1434 | 2.0737 | 2.0673 | 1,6504 | 1.8629 | |

Large | - | - | 0.1449 | 0.1521 | 0.1457 | 0.1424 | 0.1442 | 0.1459 | |

DOM-TOM | - | - | 0.2177 | 0.2295 | 0.2292 | 0.2222 | 0.2598 | 0.2323 | |

Middle | - | - | 0.1741 | 0.1805 | 0.1826 | 0.1914 | 0.1832 | 0.1816 | |

Small | - | - | 0.2450 | 0.4991 | 0.3058 | 0.3387 | 0.3274 | 0.3441 | |

Others | - | - | 3.3314 | 5.4055 | 4.7875 | 5.9579 | 4.9433 | 4.8174 | |

WLU change (yoy) | - | −10.32% | 7.63% | −16.33% | 0.98% | 6.91% | 1.64% | −2.97% | |

Large | - | 2.99% | −7.44% | −4.39% | 4.33% | 7.82% | 6.15% | 1.42% | |

DOM-TOM | - | 5.52% | 11.50% | −1.80% | 3.70% | 3.46% | −2.38% | 3.07% | |

Middle | - | 5.10% | 8.55% | −5.98% | −3.24% | 1.50% | 5.45% | 1.71% | |

Small | - | −11.75% | 0.87% | −20.63% | −4.11% | 10.63% | 9.95% | −1.27% | |

Others | - | −18.86% | 13.28% | −22.37% | 3.72% | 8.24% | −2.97% | −7.88% | |

Financial Indices | Revenue change (yoy) | - | - | - | −1.79% | 5.86% | 11.34% | 4.32% | 4.62% |

Large | - | - | - | −2.02% | 10.25% | 2.26% | 8.14% | 4.54% | |

DOM-TOM | - | - | - | 5.99% | 0.43% | 11.92% | 15.08% | 8.47% | |

Middle | - | - | - | −4.99% | −0.09% | 4.52% | 11.30% | 1.82% | |

Small | - | - | - | 1.86% | 6.77% | 26.55% | −8.62% | 3.85% | |

Others | - | - | - | −4.40% | 8.21% | 10.64% | 5.94% | 5.71% | |

Net Profit % | - | - | −2.51% | −1.65% | 2.91% | 4.28% | 1.33% | 0.64% | |

Large | - | - | 3.52% | 4.90% | 6.90% | 8.16% | 8.54% | 6.39% | |

DOM-TOM | - | - | 3.92% | 3.81% | 5.19% | 1.68% | −16.66% | −1.24% | |

Middle | - | - | −2.94% | −1.05% | −0.42% | 0.52% | 4.34% | −0.18% | |

Small | - | - | −6.30% | −5.05% | 1.54% | 3.08% | 0.08% | −1.43% | |

Others | - | - | −3.61% | −3.45% | 3.37% | 6.18% | 4.85% | 0.96% | |

A/NA ratio | 4.7473 | 1.5774 | 1.8790 | 2.0012 | 2.0725 | 2.2640 | 1.8367 | 2.27 | |

Large | 1.2601 | 1.2478 | 1.3140 | 1.3563 | 1.3457 | 1.3927 | 1.3736 | 1.33 | |

DOM-TOM | 2.4651 | 2.5064 | 2.8268 | 2.4780 | 2.6037 | 4.5135 | 4.0441 | 3.10 | |

Middle | 3.4188 | 1.5926 | 1.6792 | 1.7018 | 2.0741 | 2.4560 | 2.0788 | 2.13 | |

Small | 4.2344 | 1.9846 | 2.5185 | 2.5089 | 2.1215 | 1.7798 | 1.9646 | 2.45 | |

Others | 9.7354 | 1.3157 | 1.6286 | 1.9826 | 2.1654 | 1.9821 | 1.0780 | 2.39 | |

Subsidization (k€) | 42,840 | 19,767 | 22,563 | 44,215 | 44,569 | 27,411 | 51,329 | 252,694 (100%) | |

Large | 14,174 | 2513 | 9310 | 4934 | 2,274 | 6071 | 20,593 | 59,869 (23.7%) | |

DOM-TOM | 22,949 | 15,391 | 8808 | 15,301 | 15,975 | 4947 | 18,534 | 101,905 (40.3%) | |

Middle | 2354 | 1730 | 2947 | 20,815 | 5915 | 1843 | 4918 | 40,522 (16.0%) | |

Small | 3363 | 0 | 567 | 269 | 33 | 833 | 5243 | 10,567 (4.2%) | |

Others | 0 | 133 | 931 | 2896 | 20,372 | 13,717 | 2041 | 39,831 (13.8%) | |

Subsidization/WLU (€) | 6.22 | 2.73 | 3.12 | 6.34 | 6.25 | 3.91 | 6.45 | 5.01 | |

Large | 3.04 | 0.51 | 1.98 | 1.08 | 0.48 | 1.20 | 3.81 | 1.76 | |

DOM-TOM | 29.18 | 18.66 | 9.74 | 17.51 | 17.60 | 5.07 | 19.58 | 16.38 | |

Middle | 2.26 | 1.55 | 2.41 | 17.46 | 5.10 | 2.48 | 3.90 | 5.24 | |

Small | 10.45 | 0.00 | 1.71 | 0.90 | 0.12 | 4.13 | 16.27 | 5.09 | |

Others | 0.00 | 1.89 | 13.85 | 45.88 | 280.74 | 568.52 | 105.13 | 104.46 |

**Note**: Original data source: DGAC 2006 to 2012; author’s calculation.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hong, S.-J.; Jeon, M.
The Technical Efficiency of French Regional Airports and Low-Cost Carrier Terminals. *Sustainability* **2019**, *11*, 5107.
https://doi.org/10.3390/su11185107

**AMA Style**

Hong S-J, Jeon M.
The Technical Efficiency of French Regional Airports and Low-Cost Carrier Terminals. *Sustainability*. 2019; 11(18):5107.
https://doi.org/10.3390/su11185107

**Chicago/Turabian Style**

Hong, Seock-Jin, and Minjun Jeon.
2019. "The Technical Efficiency of French Regional Airports and Low-Cost Carrier Terminals" *Sustainability* 11, no. 18: 5107.
https://doi.org/10.3390/su11185107