INFLUENCE OF THE LIBERALIZATION OF THE AIR TRANSPORT INDUSTRY ON CONFIGURATION OF THE TRAFFIC IN THE AIRPORT NETWORK

Transcription

1 INFLUENCE OF THE LIBERALIZATION OF THE AIR TRANSPORT INDUSTRY ON CONFIGURATION OF THE TRAFFIC IN THE AIRPORT NETWORK Oscar Díaz Olariaga Facultad de Ingeniería Civil, Universidad Santo Tomás. Bogotá (Colombia) José F. Zea Facultad de Estadística. Universidad Santo Tomás. Bogotá (Colombia). ABSTRACT The present article analyzes the air transport behavior of an airport network from the point of view of concentration / dispersion (higher / lower hierarchical differentiation) in a context of post-liberalization of air transport. As a case study, for the application of the analysis methodology, the case of Colombia is used, where liberalization of the aviation industry began in During the study period, , the most important airports were privatized, there was a release of air fares, and new air operators entered the market. The most important results of the present investigation indicate a situation of concentration of the connectivity in a few nodes (airports) of the network. Keywords: airport, air passenger traffic, air transport network, liberalisation, Colombia 1. INTRODUCTION In the 1990s many countries in Latin America and the Caribbean (L & C) embarked on an unprecedented process of structural reform. Public policies developed at that time implied action in two distinct fields, deregulation and privatisation of infrastructure services. In the context of air transport, public policy foster deregulate the commercial aviation sector and allow entry or creation of more air operators in different countries. On the other hand, the airport sector suffered a transformation, which is still ongoing, giving rise to the entry of the private sector under different formulas for airport concessions in several Latin American countries. Colombia was no exception to this transformation. Colombian air transport is experiencing a fast and dynamic development that began about two decades ago. This period coincides with the establishment of continued implementation of public policy designed specifically for the air transport sector with the aim of promoting and strengthening it. 1

2 Air transport in Colombia went from being an alternative transportation to become an essential means to transport passengers across the country for the following reasons. First, the country's topography hinders connectivity, since Colombia is crossed south west north east by three Andes mountain ranges, and second, in the south lies the 500,000 square kilometre Amazon rainforest (about 44% of the country's land surface). This geographical situation has hindered the development of transport. On the one hand there is no passenger rail system, and on the other hand there is no network of high-capacity highways; however the Colombian government is now investing heavily to expand and modernize the medium capacity road network. Thus, the growth of air traffic in Colombia has been strengthened since the 1990s by a public policy oriented to liberalising air space for internal and external markets, and by refocusing public investment towards modernising and updating airport infrastructure, outsourcing the airports with greatest air traffic in the country (currently 18). The first generation of airport concessions is implemented in the mid-1990s, and since then three more generations have been developed; and this is where the role of geography was relevant as the airport concession followed a strongly decentralizing pattern and making it geographically as extended as possible in the vast Colombian geography. All the airports in concession are distributed throughout 13 administrative departments (or regions) along with the capital district (Bogota) which serves the department in which it is located (as it is the only airport in the department). And in May 2016 the completion of the current fourth generation of granting concessions to two other airports situated in two different administrative departments of the above is expected. This broad geographic coverage of the Colombian airport system has enhanced the movement of (domestic) air passengers, especially in the last decade, giving a solution of mobility due to the aforementioned circumstances. Then, this paper analyses the development of the spatial configuration of the network of Colombian domestic air transport after the industry deregulation. And for that, the analytical framework proposed by Derudder and Witlox (2009) will be used, based on a set of spatial interaction rates, which measures the degree of hierarchical differentiation (concentration and / or dispersion) in a network, but with a modification where statistical analytical methodology is proposed in order to improve the understanding and interpretation of the results. And finally we mention that the present work also helps fill a major gap in the scientific literature related to the processes of deregulation of air transport in Latin America in general and Colombia in particular. 2. LITERATURE REVIEW There are many studies that address various aspects of air transport deregulation. These studies address topics such as spatial effects of deregulation in connectivity and accessibility (O'Connor, 2003; Bowen, 2002), market competition and consolidation 2

3 (Goetz, 2002; Goetz and Sutton, 1998), network structures (O'Kelly, 1998; Shaw and Ivy, 1994), airfare prices (Vowles, 2006; Stavins, 2001), and airline alliances (Fan et al., 2001; Oum et al., 2001; Vowles, 2000). Other studies focus on the analysis of post-deregulation situations in the particular context of individual countries or regions; in addition to countless works in relation to the United States, we have, e.g. Europe (Fan, 2006; Goetz and Graham, 2004; Thompson, 2002; Graham, 1998, 1997), Canada (Oum 1991), for Australia (Hooper, 1998; Forsyth, 1991), Asia (Bowen, 2004, 2000; Bowen and Leinbach, 1995), Brazil (Oliveira et al., 2016; Koo and Lohmann, 2013), Central and Eastern Europe (Jankiewicz and Huderek-Glapska, 2016), and Africa (Njoya, 2015; Surovitskikh and Lubbe, 2015; Daramola and Jaja, 2011). From the public policy point of view, Koo and Lohmann (2013) examine the relationship between the volatility of public policy aeronautics and space evolution of air transport supply; these authors focus on the domestic aviation sector in two comparative cases, Australia and Brazil. They applied the generalized entropy indexes to measure the changing spatial capacity (seats) of air transport in a given period of time (25 years) and found evidence of a correlation between the volatility of public air transport policy and spatiality. Changes in the concentration of traffic at airports as a result of deregulation have also been analysed (Rodrigues et al., 2015; Halpern, 2011; Suau-Sánchez and Burghouwt, 2011; Derudder and Witlox, 2009; Dobruszkes, 2009; Papatheodorou and Arvanitis, 2009). Koo et al. (2016) provide an important methodological input regarding how the concentration can be measured in the context of airport dependence; the study focuses on the deregulation of air transport markets in Europe, the consequences of dependence on airports, and how this dependence can be measured. Oliveira et al. (2015) investigate the main drivers of the concentration of the air transport network in Brazil (air transport market subject to rapid growth) and in a post deregulation context, And in the research line for this paper, Daramola and Jaja (2011) analysed the change in the spatial configuration of the domestic air transport network in Nigeria in the post deregulation period. They conclude from their research that globalisation has led to a concentration of connectivity in a limited number of nodes and links instead of a dispersion throughout the network. The deregulation of air transport services in Nigeria led to a predominance by the main cities, counted as domestic network nodes, with respect to passenger traffic. And Derudder and Witlox (2009) analysed the impact of progressive deregulation in the spatiality of airline networks. They propose a new methodology for analysing airline networks and the methodology used to evaluate the spatial effects of airline deregulation in Europe. They develop an analytical framework based on a set of spatial interaction rates, which measures the degree of hierarchical differentiation (concentration and / or dispersion) in a network. 3. CASE STUDY: DEREGULATION OF AIR TRANSPORT IN COLOMBIA 3.1 Deregulation of the airport sector Thus, from the middle of the 1990s and in four temporal phases, or generations, the Colombian government gave a total of 18 airports in the country in concession, within 3

4 which are the largest and most important of the 75 administered by the public aeronautical authority with the aim of achieving a better administration, modernisation and expansion, operation, commercial exploitation and maintenance of the airports with greater traffic. Under the stated objective from the airport concessionaires in the country, the company, society, consortium or airport concessionaire functions only as an administrator thereof (Díaz Olariaga, 2017, 2016). 3.2 Deregulation of the airline sector Regarding air fare deregulation in Colombia, from 1992 until 2007 with the passing of Aerocivil Resolution 3299 (on national and international rates for scheduled passenger air transport), Aerocivil intervention in aeronautical rates was to approve the rates proposed by the airlines for a given route and not directly setting the same. The proposed rates and conditions must meet the technical criteria of equity and adequacy established by the Aviation Regulations of Colombia (public document that regulates all civil aviation activity in Colombia). Resolution 3299 of 2007 amends the rates system by removing the minimum rates for domestic and international air transport companies. The resolution states that companies are free to set rates, provided that such rates do not exceed those Aerocivil has approved for other airlines at the highest level (Díaz Olariaga and Ávila, 2015). The immediate effect of tariff deregulation was a substantial reduction in the charges set by companies for both domestic and international destinations. 3.3 Effects of deregulation Domestic passenger traffic grew 400% since the start of deregulation (1991) until 2014 (Aerocivil, 2017). The 1st (1996) and 2nd (2000) generation of airport concessions did not affect the growth of air transport significantly, which also was affected adversely by the great economic crisis in Colombia between 1999 and 2002 (Fedesarrollo, 2011). However, significant growth in the air transport sector only started in 2006 with the implementation of sector specific public policies included in the National Development Plan (DNP, 2006). This growth was further enhanced in 2007 with the privatization of the country's largest airport: El Dorado, in Bogotá, which handles 46% of total passenger traffic and 69% of all air cargo, and later by the 3rd generation of airport concessions in 2010 (Diaz Olariaga and Avila, 2015). 4. DATA For the analysis is focused exclusively on domestic passenger traffic (regular and non regular) along with the airports of the Colombian airport network, in the period , that is to say, for a 23 year period starting in the year following deregulation of the air transport industry in Colombia, the data was obtained from the Colombian Civil Aviation Authority statistical system (Aerocivil, 2017). For this reporting period, the structure of the Colombian airport network has remained unchanged (the same airports), but what has changed is the ownership model of several airports in the network, the most important of 4

5 the network being given in concession to private operators, and of course their successive expansions / upgrades and technological upgrading. For the analysis we considered data traffic (domestic traffic) of a group consisting of 34 airports, which handle 98.88% of total domestic passenger traffic in the network. The data from the 34 airports surveyed are shown in Table 1. City IATA airport code Type of airport Operation Network participation (Domestic PAX) (2014) Leticia LET International Public 0.52% Carepa APO Domestic Private 0.49% Medellín EOH Domestic Private 2.36% Rionegro MED International Private 11.92% Arauca AUC Domestic Pública 0.31% Barranquilla BAQ International Private 5.00% Cartagena CTG International Private 6.90% Manizales MZL Domestic Public 0.48% Florencia FLA Domestic Public 0.20% Yopal EYP Domestic Public 1.14% Popayan PPN Domestic Public 0.24% Valledupar VUP Domestic Private 0.87% Quibdó UIB Domestic Private 0.84% Montería MTR Domestic Private 1.90% Bogotá D.C. BOG International Private 36.28% Maicao MCJ Domestic Public 0.15% Riohacha RCH International Private 0.32% Neiva NVA Domestic Public 0.74% Santa Marta SMR International Private 2.88% Puerto Gaitán PGT Domestic Public 0.41% Villavicencio VVC Domestic Public 0.51% Pasto PSO Domestic Public 0.61% Tumaco TCO Domestic Public 0.21% Cúcuta CUC International Private 2.23% Puerto Asís PUU Domestic Public 0.20% Armenia AXM International Public 0.80% Pereira PEI International Public 3.10% Providencia PVA Domestic Private 0.17% San Andrés ADZ International Private 3.30% Barrancabermeja EJA Domestic Private 0.53% Bucaramanga BGA International Private 3.83% Corozal CZU Domestic Private 0.10% Ibagué IBE Domestic Public 0.40% Cali CLO International Private 8.94% Table 1. Information and characteristics of participating airports in the analysis. Source: Aerocivil,

6 5. METHODOLOGY The analytical technique used here is essentially based on the methodology proposed by Derudder and Witlox (2009) but with a modification where as an alternative a statistical analytical methodology is proposed in order to improve the understanding and interpretation of results obtained. Then the objective is to measure the hierarchical differentiation of Colombian domestic air transport network. The analysis focuses on two aspects of hierarchical differentiation: dominance (a nodal level) and connectivity (at the link level). However, given that the same degree of connectivity within a network can be associated with different domain levels, it is necessary to combine the indices for both dimensions; in conclusion, a total of four indexes, two were used for measuring the domain and two for measuring connectivity. One of the indexes of dominance is based on cities Overall Distribution Index (ODI C ) and measured at the global level in the network. A second index of dominance is the non- Directional Dominance Index (DIT i ) which is measured at the level of individual cities, the subscript i will refer to the city i. Similarly, for connectivity measurements, a first index is calculated globally for the network, and is called the Overall Distribution Index, based on links (ODI l ); and a second index is calculated at the individual connections or links level, it is the Relative Strength Index (RSI ij ), ij subscripts refer to one pair of cities (city i and city j). The Overall Distribution Index based on cities ( ) is a measurement of entropy measuring the extent to which interactions (travel) are evenly distributed over all cities (airports) in the network, and is calculated based on the number of passengers each city/airport has relative to the total number of passengers in the network (participation): ODI = ( ) ( ) (1) ( ) where is the participation of city i in total number of passengers on the network, and C is the number of cities (or airports) in the network. If all air traffic was uniformly distributed in all cities this index would take the value of 1 if a city instead focuses all air traffic this index takes the value of 0. The non-directional Dominance Index (DIT i ) is calculated as the ratio of the sum of interactions associated with city i and the total size of network interactions: DIT = (2) where is the number of passengers associated with city i. The index takes values between zero and one, and the cities with larger values are the dominant cities in the 6

7 network. Major differences between values for the different cities indicate a high degree of hierarchical differentiation; when a few cities take very large values in the index relative to the other there is a high dominance and low connectivity to global network; for a quantification of the degree of hierarchical differentiation, it is usually working with the standard deviation of values, but as a methodological alternative, this paper proposes to use the Coefficient of Variation of values. The following two indexes are based on links or city-pair, the first is the Overall Index Distribution links being based on an index of entropy which measures how evenly distributed interactions between pairs of cities (or links). The other index used is the Relative Strength Index which is simply the ratio of the interaction of a link between two cities on the total network interactions. Then the Overall Distribution Index based on links (ODI l ) is a measurement of entropy measuring the extent to which interactions (travels) are uniformly distributed along all links (city pairs) in the network, the subscript l refers to a specific link: = ( ) ( ) (3) ( ) where is the share of passengers travelling on the link l with respect to the total number of passengers, and L is the total number of links in the network. The interpretation of the index is similar to ODI air traffic for each city, if all air traffic was uniformly distributed in all the links it would take the index value of 1, if instead if a link concentrated all air traffic this index takes the value of 0. And finally we have the Relative Strength Index (RSI ij ): = (4) where is the total number of passengers travelling between city i and city j. And similar to the DIT, this index takes values between 0 and 1 and the links between two cities with the largest values are dominant in the network. Large differences between the values of the Relative Strength Index for each link indicate a high degree of hierarchical differentiation, i.e. key routes exist in the network and less global network connectivity. In this analysis two proposals arising from the Derudder and Witlox (2009) methodology are also incorporated. These proposals are justified by the relatively important dimension of the network discussed herein (large number of nodes), and with the aim of better interpreting the values obtained for DTI i and RSI ij levels, which will also help to better interpret the concept of hierarchical differentiation. First, Derudder and Witlox (2009) propose to calculate the standard deviations of the values of both indexes (DTI i and RSI ij ) as a general measure of the second hierarchical 7

8 differentiation, and which is useful for the interpretation of differences between the values of DTI i and RSI ij. A high standard deviation reflect significant differences in the values of the indexes and, therefore, entail more domain (in certain cities / airports) and less connectivity (in the network). A low standard deviation means a uniform distribution in travel (in cities/airports). However, and alternately, in this paper, the calculation of the standard deviations of DIT i and RSI ij values is replaced by the calculation of the Coefficient of Variation (CV) (Ahmed, 1995, 1994), which it is a statistic that quantifies the variability of a data set and allows comparability between datasets with different means and variance and it is an independent statistic of the units of measurement: = (5) where refers to the standard deviation of DIT i values in the network and refers to the average of the DIT i values. In a similar way it is defined the Coefficient of Variation of RSI ij values: = (6) where refers to the standard deviation of the RSI ij links values as previously defined and to the average RSI ij. CV interpretation is similar to the standard deviation for DTI i (for cities) or RSI ij (for links) values. High index values reflect large differences in the DTI i (or RSI ij ) values, which implies greater domination by a few cities (or a few links) and lower global network connectivity. Second, and following Derudder and Witlox (2009), calculating a normalization of indexes it is proposed by using the corresponding values for Rank Size Distribution (RSD) as a reference point. Rank Size Distribution is a mathematical framework commonly used in the study of networks, and can be defined as: =, (for cities) (7) =, (for links) (8) where ( ) is the rank size predicted number of passengers associated with the city c (link l) in a distribution range size distribution, ( ) is the rank order of the city (link) c in the distribution, and,, is the number of passengers associated with the largest city (link) in the network. The main advantage of using the Rank Size Distribution (RSD) as a benchmark is that it provides a balanced distribution between the two extremes of maximum and minimum hierarchical differentiation (Derudder and Witlox, 2009). 8

9 Then, using the Rank Size Distribution at the city level, the city level DIT and ODI indices are calculated. The notation and calculation of the indices based on Rank Size Distribution are described below:, = where is the range size associated with the city c. This index will depend only on the number of nodes in the network., = ( ) ( ) ( ) (9) (10) where is the city's participation with respect to the distribution range size of the network: = where C is the total number of cities or airports in the network. The, has the same interpretation to ODI c and ODI l but using Rank Size Distribution. It is defined in a similar way to the ODI (link level):, = ( ) ( ) ( ) (11) (12) where his participation that the link l with respect to the size range distribution, = and L refers to the number of links on the network. Finally mention that the computations developed in this study were performed with R, a open source statistical software (R Core Team, 2015). 6. RESULTS (13) In Figure 1, the domestic passenger traffic at airports analysed for the extreme years of the study period (1992 and 2014) is presented. 9

10 Fig. 1 - Comparative domestic passenger traffic by airport and Source: Aerocivil, Figure 2 shows the results of non-directional Dominance Index (DIT i ) (which is measured at the level of individual cities) and expressed as a percentage (%) for ease of understanding of the indicator. In the study period ( ) increased dominance in very few airports in the network, the more relevant the airport in the capital city of the country (Bogota-BOG) can be seen. In all other airports in the network dominance it has remained the same or even decreased slightly. 10

11 Fig. 2 - Results of the non-directional Dominance Index (DIT i ) (%). Table 2 presents the calculation of DIT based on the Ranks Size Distribution (, ) and the DIT c, which is based on the relationship of interactions of each city and the total number of interactions on the network, the indexes are presented for 1992 and The DIT c for 1992 and 2014 identify the dominant cities as: Bogota (BOG) (hub and capitalcity airport); Rionegro (MDE) (near the city of Medellín, second largest city and area of important industrial and economic development); Cali (CLO) (the country s third largest city); Cartagena (CTG) (the country s main tourist centre and an important port); Barranquilla (BAQ) (important port and logistics centre in addition to being a tourist centre); Bucaramanga (BGA) (airport serving an important oil and coal region in Colombia); and finally San Andres Island (ADZ) (an island in the Caribbean Sea, visited by large numbers of tourists, a city which is only accessible by air). In table 3 you can appreciate the increase in concentration (greater hierarchical difference) for the airport in the capital city of Bogotá (BOG) with respect to the Rank Size Distribution. There is a notable share loss in Cali Airport (CLO) and an increased participation in Rionegro (MDE), airport serving the city of Medellín, due to much greater economic growth in this city over the past three decades, much larger than the growth in Cali (Galvis, 2014; Sanchez-Jabba, 2012). And on the other hand, the loss of participation by Medellín Airport (EOH) is observed after Rionegro airport (MDE) becomes operational (1985) (expanded 11

12 and modernized in 2008), and absorbs a fraction of the domestic traffic, air cargo and all international traffic from Medellín city airport (EOH) (Diaz Olariaga and Avila, 2015). Airport Airport BOGOTA (BOG) 34.5 (24.28) 41 (24.28) PASTO (PSO) 1.1 (1.35) 0.5 (1.35) RIONEGRO (MDE) 10.1 (12.14) 11.7 (12.14) VILLAVICENCIO (VVC) 0.9 (1.28) 0.5 (1.28) CALI (CLO) 11.4 (8.09) 8.7 (8.09) BARRANCABERMEJA (EJA) 0.43 (1.21) 0.48 (1.21) CARTAGENA (CTG) 5.1 (6.07) 6.5 (6.07) LETICIA (LET) 0.3 (1.16) 0.47 (1.16) BARRANQUILLA (BAQ) 4.6 (4.86) 4.7 (4.86) CAREPA (APO) 0.46 (1.1) 0.45 (1.1) BUCARAMANGA (BGA) 3.2 (4.05) 3.6 (4.05) MANIZALES (MZL) 1.2 (1.06) 0.4 (1.06) SAN ANDRES (ADZ) 5 (3.47) 3.1 (3.47) PUERTO GAITAN (PGT) 0 (1.01) 0.37 (1.01) PEREIRA (PEI) 2.8 (3.04) 2.8 (3.04) IBAGUE (IBE) 0.75 (0.97) 0.36 (0.97) SANTA MARTA (SMR) 1.8 (2.70) 2.6 (2.70) ARAUCA (AUC) 0.9 (0.93) 0.3 (0.93) MEDELLIN (EOH) 4.1 (2.43) 2.1 (2.43) RIOHACHA (RCH) 0.42 (0.90) 0.29 (0.90) CUCUTA (CUC) 3.7 (2.21) 2.1 (2.21) POPAYAN (PPN) 0.25 (0.87) 0.22 (0.87) MONTERIA (MTR) 0.83 (2.02) 1.72 (2.02) TUMACO (TCO) 0.35 (0.84) 0.19 (0.84) EL YOPAL (EYP) 1.7 (1.87) 1 (1.87) FLORENCIA (FLA) 0.43 (0.81) 0.18 (0.81) VALLEDUPAR (VUP) 0.48 (1.73) 0.78 (1.73) PUERTO ASIS (PUU) 0.29 (0.78) 0.18 (0.78) QUIBDO (UIB) 0.63 (1.62) 0.76 (1.62) PROVIDENCIA (PVA) 0.61 (0.76) 0.15 (0.76) ARMENIA (AXM) 0.56 (1.52) 0.72 (1.52) MAICAO (MCJ) 0 (0.74) 0.14 (0.74) NEIVA (NVA) 1 (1.43) 0.7 (1.43) COROZAL (CZU) 0.12 (0.71) 0.09 (0.71) Table 2. Results of the DIT c (in parenthesis the results of, ). Figure 3a shows the Overall Distribution Index trend at the cities level (ODI C ). A contraction in ODI is verified when descending from approximately 0.7 to 0.65, showing an increase of hierarchical differentiation during the study period ( ). If distribution based on the network range size (ODI RSD,C ) is taken as a reference (0.83), it can conclude that the hierarchical difference in the network is not extreme (as it fluctuates between 0.65 and 0.70 approximately) but it is greater than the reference distribution (range size). This indicates that although Bogota (BOG) is the dominant city, Colombia has other important nodes in the airport network. Figure 3b indicates that the network has seen an increase in the dispersion, from 210% to 245% during the study period indicating increased hierarchical differentiation at the cities level. Figure 4 shows the behaviour of the Relative Strength Index (RSI) for main national routes between 1992 and There are four key routes involving the capital city of Bogota (BOG) airport observed leading to the conclusion that there is a high degree of hierarchical differentiation, at the link level, in the Colombian air transport network. 12

13 Fig. 3a - ODI c ( ). Fig. 3b - ( ). To quantify the hierarchical differentiation at the link level Overall Distribution Index at the links level (ODI l ) and the Coefficient of Variation of RSI ij ( ) are computed for main links existing in Colombia's domestic network and are referred to in this study. Thus, in Figure 5a we see how ODI l performance has increased from 0.84 to 0.85, remaining almost constant in the study period. The index fluctuation values are very close to the ODI l based on Rank Size Distribution (ODI RSD,l ), which take the value One can conclude then that the hierarchical differentiation at the routes level has remained fairly constant over the study period ( ). 13

14 Fig. 4 - RSI ij based on links (in %) ( ). Fig. 5a - ODI l ( ). Fig. 5b - ( ). 14

15 The results shown in Table 3 indicate that the RSI index based on links in both 1992 and 2014 have a similar behaviour based on the RSI index based on size-range distribution, therefore it can be concluded a moderate hierarchical concentration on the routes at the link level. Focused on the analysis of the main domestic routes over Colombia, the large differences in relative strength index found between the different routes of the network confirms the existence of a high concentration of connectivity in a few key routes, leading to a high hierarchical differentiation at the routes level. ROUTE RSI l 1992 RSI l 2014 RSI RSD,l ROUTE RSI l 1992 RSI l 2014 RSI RSD,l BOG-CLO-BOG BOG-MTR-BOG BOG-MDE-BOG BOG-AXM-BOG BOG-CTG-BOG BAQ-MDE-BAQ BOG-BAQ-BOG BOG-IBE-BOG BOG-BGA-BOG APO-EOH-APO BOG-CUC-BOG CLO-CTG-CLO BOG-PEI-BOG BOG-VUP-BOG BOG-ADZ-BOG EOH-UIB-EOH CLO-MDE-CLO MDE-SMR-MDE BOG-SMR-BOG CLO-BAQ-CLO ADZ-CLO-ADZ BOG-RCH-BOG BOG-MZL-BOG BOG-LET-BOG BOG-NVA-BOG CLO-SMR-CLO BOG-EYP-BOG BOG-EJA-BOG BOG-PSO-BOG EOH-MTR-EOH CTG-MDE-CTG CTG-PEI-CTG ADZ-MDE-ADZ BOG-EOH-BOG Table 3 -, and RSI l (1992 and 2014). 7. CONCLUSIONS Deregulation of air transport in Colombia that began in 1991, entailed no change in the structure of the network, i.e. since that time the present network consists of the same airports. However, public investment policies implemented starting a few years after the beginning of deregulation up to the present, have included all major airports, with which all relevant and geographically strategic airports (border areas, points of high tourism impact, island regions, etc.) have been substantially expanded and modernized over the past two decades. On the other hand, modernization of airport infrastructure was accompanied by the market entry of new aircraft operators, which boosted their offer mainly from tariff deregulation (2007) leading to a high level of demand, that, as is mentioned above, triggered domestic passenger traffic. The airlines had to meet this high demand and significantly increased their offer (seats) for both national and international segments (Diaz Olariaga and Lopez, 2016). 15

16 Based on this situation after industry deregulation analysing the behaviour of the network of Colombian domestic air transport was approached starting the standpoint of concentration / dispersion (higher / lower hierarchical differentiation) of the network. For this reason we opted for the method proposed by Derudder and Witlox (2009), as other authors have used it to study the effects of deregulation on domestic air networks (Daramola and Jaja, 2011). The results obtained here indicate that at both the nodes level (cities) and links level (routes) deregulation led to a sharp and pronounced concentration of connectivity, i.e. an increase of hierarchical differentiation, where the hub airport, BOG, in the country's capital city Bogota, has imposed its dominance on the network during the study period ( ), These results are in line with other similar research (situation of post deregulation of air transport network) in developing countries (Daramola and Jaja, 2011) and Latin America countries (Rodrigues et al., 2015), where they see the increased concentration of connectivity in a few network nodes. 8. REFERENCES AEROCIVIL (2017). Estadísticas de Transporte Aéreo. AHMED, S.E. (1995). A Pooling Methodology for Coefficient of Variation. Sankhyā: The Indian Journal of Statistics, Series B ( ), 57(1), AHMED, S.E. (1994). Improved estimation of the coefficient of variation. Journal of Applied Statistics, 21(6), BOSCH, A. & GARCÍA MONTALVO, J. (2003). Free and Nondiscriminatory Access to Airports: A Proposal for Latin America. Working paper, Washington D.C.: Inter-American Development Bank, BOWEN, J. (2002). Network change, deregulation, and access in the global airline industry. Economic Geography, 78(4), BOWEN, J. & LEINBACH, T. (1995). The state and liberalization: the airline industry in the East Asian NICs. Annals of the Association of American Geographers, 85(3), CARVALLO, C (2008). Experiencia chilena en concesiones aeroportuarias. Santiago de Chile : Ministerio de Obras Públicas.. DARAMOLA, A. & JAJA, C. (2011). Liberalization and changing spatial configurations in Nigeria s domestic air transport network. Journal of Transport Geography, 19, DERUDDER, B. & WITLOX, F. (2009). The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation. Journal of Transport Geography, 17, DÍAZ OLARIAGA, O. (2017). Políticas de privatización de aeropuertos. El caso de Colombia. Documentos y Aportes en Administración Pública y Gestión Estatal, 29,

17 DÍAZ OLARIAGA, O. (2016). Análisis de la evolución de las políticas públicas y de regulación en la industria aeroportuaria en Colombia. Documentos y Aportes en Administración Pública y Gestión Estatal, 26, DÍAZ OLARIAGA, O. & LÓPEZ, A. (2016). Comportamiento e interrelación del turismo y el transporte aéreo en Colombia. Estudios y Perspectivas en Turismo, 25(1), DÍAZ OLARIAGA, O. & ÁVILA, J. (2015). Evolution of the airport and air transport industry in Colombia and its impact on the economy. Journal of Airline and Airport Management, 5(1), DNP (2006). Plan Nacional de Desarrollo Bogotá: Departamento Nacional de Planeación. DNP (1994). Reordenamiento institucional y plan de expansión del sistema aeroportuario Documento CONPES Bogotá: Departamento Nacional de Planeación. DOBRUSZKES, F. (2009). Does liberalisation of air transport imply increasing competition? Lessons from the European case. Transport Policy, 16, FEDESARROLLO (2011). El impacto del transporte aéreo en la economía colombiana y las políticas públicas. Bogotá: Fedesarrollo. ESPIRITO SANTO, R. (2013). Airport privatization and Business Models: What Brazil has and what Brazil Needs. VI WALA Conference, Montreal, September 04-06, ESPIRITO SANTO, R.A.; CORREIA, F.C. & PALHARES, G.L. (2001). Airport Privatization in Brazil: Questions and Answers. Proceedings of the 36th Canadian Transportation Research Forum (CTRF), Annual Conference, 1, FAN, T. (2006). Improvements in intra-europe inter-city flight connectivity, Journal of Transport Geography, 14(4), FAN, T.; VIGEANT-LANGLOIS, L.; GEISSLER, C.; BOSLER, B. & WILMAKING, J. (2001). Evolution of global airline strategic alliance and consolidation in the twenty-first century. Journal of Air Transport Management, 7(6), FORSYTH, P. (1991). The regulation and deregulation of Australia s domestic airline industry. In: Button, K. (Ed.), Airline Deregulation: International Experiences. David Fulton Publishers, London, pp GALVIS, L.A. (2014). Economía de las grandes ciudades en Colombia: seis estudios de caso. Bogotá: Banco de la República de Colombia. GOETZ, A. & GRAHAM, B. (2004). Air transport globalization, liberalization and sustainability: post-2001 policy dynamics in the United States and Europe. Journal of Transport Geography, 12(4), GOETZ, A. (2002). Deregulation, competition, and antitrust implications in the US airline industry. Journal of Transport Geography, 10(1),

18 GOETZ, A. & SUTTON, C. (1998). The geography of deregulation in the U.S. airline industry. Annals of the Association of American Geographers, 87(2), GRAHAM, B. (1998). Liberalization, regional economic development and the geography of demand for air transport in the European Union. Journal of Transport Geography, 6(2), GRAHAM, B. (1997). Regional airline services in the liberalized European Union single aviation market. Journal of Air Transport Management, 3(4), HALPERN, N. (2011). Measuring seasonal demand for Spanish airports: implications for counter-seasonal marketing. Research in Transportation Business & Management, 1(1), HOOPER, P. (1998). Airline competition and deregulation in developed and developing country contexts Australia and India. Journal of Transport Geography, 6(2), JANKIEWICZ, J. & HUDEREK-GLAPSKA, S. (2016). The air transport market in Central and Eastern Europe after a decade of liberalisation Different paths of growth. Journal of Transport Geography, 50, KOO, T.; HALPERN, N.; PAPATHEODOROU, A.; GRAHAM, A. & ARVANITIS, P. (2016). Air transport liberalisation and airport dependency: developing a composite index. Journal of Transport Geography, 50, KOO, T. & LOHMANN, G. (2013). The spatial effects of domestic aviation deregulation: a comparative study of Australian and Brazilian seat capacity, Journal of Transport Geography, 29, LIPOVICH, G. (2008). The privatization of Argentine airports. Journal of Air Transport Management, 14, MENDIOLA, A.; ARÉVALO, G.; MARATUECH, P.; PÉREZ, J. & VALENCIA, J.C. (2011). Concesión del aeropuerto Jorge Chávez: evaluación del valor generado. Lima: ESAN Ediciones. NJOYA, E.T. (2015). Africa s single aviation market: The progress so far. Journal of Transport Geography, 50, O CONNOR, K. (2003). Global air travel: toward concentration or dispersal? Journal of Transport Geography, 11(2), O KELLY, M. (1998). A geographer s analysis of hub-and-spoke networks. Journal of Transport Geography, 6(3), OLIVEIRA, A.V.M.; LOHMANN, G. & COSTA, T.G. (2016). Network concentration and airport congestion in a post de-regulation context: A case study of Brazil Journal of Transport Geography, 50,

19 OUM, T.; YU, C. & ZHANG, A. (2001). Global airline alliances: international regulatory issues. Journal of Air Transport Management, 7(1), OUM, T. (1991). Airline deregulation in Canada. In: Button, K. (Ed.), Airline Deregulation: International Experiences. London: David Fulton Publishers, pp PAPATHEODOROU, A. & ARVANITIS, P. (2009). Spatial evolution of airport traffic and air transport liberalisation: the case of Greece. Journal of Transport Geography, 17, R CORE TEAM (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. RICO GALEANA, O. (2008). The privatization of Mexican airports. Journal of Air Transport Management, 14, RODRIGUES, R.; ESTRADA, M. & FERNANDES, E. (2015). Spatial concentration and connectivity of international passenger traffic at Brazilian airports. Journal of Air Transport Management, 46, SANCHEZ-JABBA, A. (2012). La reinvención de Medellín. Cartagena de Indias: Banco de la República de Colombia. SEREBRISKY, T. (2012). Airport Economics in Latin America and the Caribbean. Washington D.C.: The World Bank, SHAW, S.L. & IVY, R. (1994). Airline mergers and their effect on network structure. Journal of Transport Geography, 2(4), STAVINS, J. (2001). Price determination in the airline market: the effect of market concentration. The Review of Economics and Statistics, 83(1), SUAU-SANCHEZ, P. & BURGHOUWT, G. (2011). The geography of the Spanish airport system: spatial concentration and deconcentration patterns in seat capacity distribution, Journal of Transport Geography, 19(2), SUROVITSKIKH, S. & LUBBE, B. (2015). The Air Liberalisation Index as a tool in measuring the impact of South Africa's aviation policy in Africa on air passenger traffic flows. Journal of Air Transport Management, 42, THOMPSON, I. (2002). Air transport liberalization and the development of third level airports in France. Journal of Transport Geography, 10(4), VOWLES, T. (2006). Airfare pricing determinants in hub-to-hub markets. Journal of Transport Geography, 14(1), VOWLES, T. (2000). The geographic effects of US airline alliances. Journal of Transport Geography, 8(4),