sustainability
Article
A Regional View of Passenger Air Link Evolution in Brazil
Vicente Aprigliano Fernandes 1, * , Ricardo Rodrigues Pacheco 2 , Elton Fernandes 2 , Manoela Cabo 3
and Rodrigo V. Ventura 2,3
1
2
3
*
Citation: Aprigliano Fernandes, V.;
Pacheco, R.R.; Fernandes, E.; Cabo,
M.; Ventura, R.V. A Regional View of
Escuela de Ingeniería de Construcción y Transporte, Pontifícia Universidad Católica de Valparaíso,
Valparaíso 2362804, Chile
Production Engineering Program (COPPE), Federal University of Rio de Janeiro (UFRJ),
Rio de Janeiro 21941-450, Brazil; rpacheco48@gmail.com (R.R.P.); tglcoppe@gmail.com (E.F.);
rodrigo.ventura@ibge.gov.br (R.V.V.)
Brazilian Institute of Geography and Statistics (IBGE), Rio de Janeiro 20031-170, Brazil; mcabo@impa.br
Correspondence: vicente.aprigliano@pucv.cl
Abstract: Passenger flows among cities, at both the domestic and international levels and in different countries and regions, have been studied for forecasting purposes. This paper seeks not
a forecasting mechanism, but to understand, by analyzing passenger origins and destinations by
Brazilian sub-region, how Brazil’s domestic air passenger network links have evolved. Using income,
population, and fare price as inputs, and seats sold as output, air link performance is examined
by data envelopment analysis to discuss the regional link of domestic passenger traffic in Brazil
and its dynamics, considering two specific years. The findings indicate that, although the highest
passenger flow density is concentrated in Brazil’s Southeast region, performance by emerging origins
and destinations (O-Ds), such as those connecting the Northeast, display more substantial strength
indices and advances (Malmquist analysis). The analysis of specific links was also important, which
showed that important Brazilian airports are not necessarily more competent in generating trips.
The Catch-Up indicator for innovation reveals the weak point in Brazil’s air transport network.
Although some airports enjoy strong networkability, they do not correspond in passenger origin or
destination strength.
Keywords: domestic air traffic; O-D; DEA; Malmquist; Brazil
Passenger Air Link Evolution in
Brazil. Sustainability 2022, 14, 7284.
https://doi.org/10.3390/su14127284
Academic Editor: Lynnette Dray
Received: 6 May 2022
Accepted: 12 June 2022
Published: 14 June 2022
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This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Passenger origin–destination (O-D) flow and its pattern are key components in transport companies’ business planning, mainly for airlines and airports. Its analysis is strategic
for the best air route design, which can be organized through direct or connected flights.
The actual O-D statistics of air passenger transport are not widely held available in the public domain, and as competition hardens, air fares fluctuate considerably. Such constraints
make it difficult to formulate highly accurate models. Statistics identifying the reason for
travel are also generally not publicly available. Passengers travel from one city to another
on business, on holiday, to visit friends or relatives, and so on. Even without knowing the
travel purpose, the passenger movement between different destinations can be investigated
using a gravity model that examines the aggregate flow between any two cities connected
by some common interest. It is interesting how network studies have been evolving, for
example, by using mobile phone data [1] and through complex network analysis [2]. These
kinds of studies are essential to understanding the role of cities’ hierarchy and transport
hubs on the development of the territory from different scales of analysis: local, regional,
national, and international.
In conceptual terms, gravity modeling uses the flow variable as a function of explanatory variables representing the mass and friction between any two points, and is also
widely used to estimate price–income elasticities of demand for forecasting purposes [3].
Sustainability 2022, 14, 7284. https://doi.org/10.3390/su14127284
https://www.mdpi.com/journal/sustainability
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Considering that the economy is a key driver of air travel demand [4], and that there
is a strong correlation between economic development and air passenger demand [5,6],
particularly in Brazil [7,8], this paper explores regional passenger flows by considering
the O-Ds’ population and income, as well as the effectively commercialized fares for a
significant sample of air travels in 2011 and 2016. In this matter, this research seeks to
respond to the following question: How can we optimize the passengers flow on a given
air route, given the income of the cities involved (O-Ds), their populations, and fares? By
focusing on how air transport routes are able to induce passenger demand flows under an
intra- and inter-regional view, and by highlighting bottlenecks regarding this issue, this
research contributes to discussions on sustainable and territorially well-balanced economic
development driven by the civil aviation industry, which are elements of development
that are straightly related [9]. The analyzed period is justified by the fact that 2011 is
the year in which the main airports in Brazil were conceded to private administration,
whereas 2016 marks the consolidation of this concession process. This paper discusses the
strength among air links, using data envelopment analysis (DEA) and Malmquist indices
to show the dynamics of structural change. This kind of analysis is highly important to
new Brazilian air transport market entrants endeavoring to establish themselves in Latin
America’s largest market.
In addition, recently, there has been a radical shift away from a prosperity scenario,
where air transport was growing, to a scenario where the economy is seeking avenues
to recover from negative growth rates and disruptive phenomena, such as pandemics.
Whereas the Brazilian annual geometric gross domestic product growth rate from 2011 to
2014 was 1.80%, from 2014 to 2016, it was −3.42% [10]. In an analogy between world crisis
analysis [11] and the Brazilian case, the economic and political crisis observed in Brazil
may be categorized as of major proportions, and domestic air passenger transport levels
may take up to 36 months to recover. This could take even longer considering the outbreak
of the Covid pandemic.
This paper is organized into seven sections. The first is concerned with contextualization and the study object. The second offers a review of selected literature, comprising the
publications considered most relevant to the study. The third presents the methodology
underpinning the discussion of findings. The fourth describes the data used in the methodology. The fifth briefly describes the Brazilian case. The sixth presents the findings from the
analysis and discussion. The seventh and final section states the main conclusions from the
analysis, and offers suggestions for future research.
2. Literature Review
Discussions on the origin–destination of air passenger traffic have been attracting
researchers’ interest for some time now, including Derudder and Witlox [12], Grubesic
et al. [13], and O’Connor and Fuellhart [14]. Derudder and Witlox [12], through a review
of the literature, argue that future research should develop alternative ways of recording
airline data with the aim of building more meaningful analyses, such as identifying the
true origins and destinations of passengers, rather than just a specific segment of a trip.
Grubesic et al. [13] examined the emerging global hierarchy of the air transportation
network by using the information of around 900 airline carriers from 2006, which considered
4650 origins and destinations. This study identified an increase in the complexity of the air
networks in North America, Europe, and Asia. With a global perspective, O’Connor and
Fuellhart [14] evaluate the relationship between cities´ hierarchy according to the Global
and World City Project of Loughborough and air service characteristics. Results of this
research show apparent differences between aircraft, airline size, and mode of operation
depending on the city´s hierarchy within the global context.
Bhadra and Kee [15] analyzed the structure and dynamics of the main air transport
markets in the United States, using quarterly series for the period between 1995–2006. The
analysis focused on the origin–destination of commercial flights on the market segmented
by the number of passengers transported per day (pax/day). They discovered that on
Sustainability 2022, 14, 7284
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thick markets (pax/day > 100), demand was relatively elastic to mean fares, whereas other
markets displayed inelastic demand. Their results also showed that passenger trip O-Ds
are generally income-inelastic. Pitfield et al. [16] made an empirical analysis of the fit
between aircraft size and flight frequency on long-haul routes, considering traffic, airline
behavior, and degree of competition. This study used 1990 data for nine routes on the
North Atlantic market, where a considerable number of airlines operate in a competitive
environment. Pitfield et al. used origin and destination data between European and United
States airports, consisting of the number of passengers, flight frequency, and aircraft size.
They concluded that, with increased passenger numbers, flight frequency was more likely
to increase than aircraft size.
Puller and Taylor [17] examined individual flight ticket transactions for each itinerary
on trunk routes of the United States domestic market in 2004. They used detailed data on
ticket purchase dates and tariffs, origin and destination, flight number, and service class.
Puller and Taylor concluded that airlines charged lower fares for similar flights depending
on the day of the week, and that this phenomenon suggested a day-of-purchase price
discrimination mechanism. Fageda and Flores-Fillol [18] examined air service provision on
thin routes, comparing the United States market with those of the European Union, using
origin–destination data between airports and others. Their analyses focused on the impact
of two major innovations in the air transport industry: regional jet technology and low-cost
airlines. Fageda and Flores-Fillol concluded that regional services provided higher flight
frequency at higher fare prices, whereas low-cost services offered cheaper fares at the cost
of lower flight frequencies.
Mumbower et al. [19] estimated flight price elasticity using an on-line database and
the corresponding aircraft seat maps. These elasticities were estimated considering the
number of reservations on the flight and how many days prior to departure the booking
was made. Their findings indicated that price elasticity varied with the day of the week
when the reservation was made, the time of day of departure, and competitors’ promotional
sale dates. They showed that airlines could use these detailed price elasticity estimates,
among other things, to forecast promotions. Luttmann [20] analyzed the reasons that may
lead an airline to charge different one-way and return fares on the same route, depending
on the point of origin of the trip, which is a case of directional price discrimination. For
that purpose, he used data on airline passenger roundtrip ticket prices and origins and
destinations obtained from the US Department of Transportation, as well as regional
economic data, the reason for travel, and so on. He found significant evidence of incomerelated directional price discrimination, that is, airlines charged higher fares to passengers
originating in cities with higher mean incomes.
Mohammadian et al. [21] examined the Australian domestic aviation market, using
monthly data from 2004 to 2015 for 21 routes connecting eight cities. The aim was to
investigate the relationship between demand per flight and capacity variables, such as
frequency, aircraft size, load factor, etc. The results suggested that competition led to more
flights, smaller aircraft, lower load factors, and more available seats in the markets studied.
They also concluded that city socioeconomic parameters, such as population and employment rate, influenced flight demand more strongly on long-haul routes. Oliveira et al. [22]
evaluated the effects of a series of determinants on air traffic network concentration and its
possible decoupling relative to social and economic activity concentrations. They used air
passenger, gross domestic product, and population for defining a concentration decoupling
index. Their analysis discussed the relation of the decoupling and concentration indices
with air passengers, international tourism, oil cost, slot constraints, airline competition,
and socioeconomic events. Urban and Hornung [23], mapping causalities of airline dynamics, identified key elements for long-haul flight competition, which are the generation of
transatlantic air transport demand, passenger choice, and airline ticket price and fleet development. Oliveira and Oliveira [24] showed that a Brazilian airline focusing on the regional
flights segment conquered many monopoly positions across the country, strengthening
its profitability.
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The literature concerning airline passenger flows has focused on estimating forecasting
models for better operational planning and scheduling by airlines. The studies generally
consider the passengers’ behavior related to economic and situational variables, considering
only the travel segments (instead of the actual origin and destination). This kind of
simplification in the analysis opens a research gap, namely the potential profit earned by
airlines as the result of changing market conditions. Furthermore, it is important to develop
research related to intra- and inter-regional air traffic in order to understand, in terms of
performance, the internal dynamics of air traffic in countries with continental dimensions,
such as Brazil.
3. Materials and Methods
3.1. Methodology
In emerging economies with continental-sized domestic markets, such as Brazil, China,
Russia, and others, the internal passenger traffic between their sub-regions assumes more
complex characteristics, requiring a better understanding both of these flows and of the
different regional particularities. This study applied a methodology for analyzing regional
flows in terms of performance and structural change over a given period. This approach
considers a different perspective regarding the elements that make up the independent
variables of a forecast function, examining their explanatory power to determine the volume
of passengers on a given route. The regional analysis will make it possible to identify the
level of performance, in terms of passenger flows, for each region of the country, as well as
the interregional interactions.
The methodology comprised three stages. The first involved organizing, describing,
and cleaning the available data to suit the study purpose. At this stage, the sample for
analysis was defined. The second stage involved calculating and analyzing the efficiency
indices of the air links using DEA. In the third, Malmquist indices were calculated, and
their variations were examined to reveal a structural change in the links. The analyses
covered both the total movement and the intra- and interregional O-Ds.
DEA analysis considered each O-D individually, but overall efficiency indices for
the sample of O-Ds were used here, by Brazilian sub-region. That is, these indices were
calculated for each region. The overall performance index was expressed by weighted
means for individual O-Ds. This result gave cluster means in relation to the efficiency
frontier in each year. The result was a number from 0 to 1, where 1 meant that the O-D or
cluster was at the efficiency frontier.
Dynamic performance analysis used the means from the Malmquist analysis. These
represented the movement of efficiencies for specific characteristics and changes specific to
an O-D or cluster. Malmquist analysis yields indices of change that vary around 1, where
values of less than 1 mean the characteristic is deteriorating, a value of 1 means stagnation,
and values greater than 1 represent progress. The cluster means from both the DEA and
the Malmquist indices were calculated using means weighted by the number of passengers
for each O-D in that cluster. This weighting is important, because the O-Ds had differing
market shares. As the analysis covered a five-year period, it was important to take growth
in demand as a reference point, because small efficiency gains might be insufficient to
explain air transport growth. The distinction between intra- and inter-regional passenger
flows is strategic for the development of the airline sector, and for the formulation of
policies for strengthening and regulating the sector.
Considering the O-Ds’ managerial dimensions as inputs, and passengers processed
as output, DEA depicted the best performance at each O-D and the overall situation
of domestic air transport as a whole. The output maximization approach proved more
appropriate to the study problem, because one of airlines’ main objectives is to maximize
the passenger flow. Moreover, dynamic analysis was conducted using balanced panel data
for the two years, 2011 and 2016.
DEA is a non-parametric method designed to measure the performance of a firm,
organization, program, etc., i.e., whatever is produced by a decision-making unit (DMU).
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Charnes et al. [25] presented the various formulations developed for the DEA approach.
In this study, the main issue was the organizations’ output potential. Given that the
study object comprised O-Ds of differing sizes, the model chosen was the output-oriented
approach using a variable return to scale (VRS). The proposed model, known as outputoriented BCC, involved the primal of the linear programming problem in Equation (1).
Output-Oriented BCC Primal
(BCCp-O)
→
→
max Z0 = φ + ε. 1 s+ + ε. 1 s−
φ, λ, s+ , s−
s. t.
φY0 − Yλ + s+ = 0
Xλ + s− = X0
(1)
→
1λ = 1
λ, s+ , s− ≥ 0
where: X is the vector of inputs used by the DMUs; Y is the vector of quantities produced
(outputs) by the DMUs; ε is the infinitesimal non-Archimedean constant that assures that no
input or output is assigned zero weight; s+ and s− are the slack vectors, respectively, of the
outputs and inputs; φ is a scalar variable that represents the possible radial increase to be
applied to all outputs so as to obtain the values of the projected outputs; and λ is the vector
whose optimal values form a combination of units that make up the performance of the
DMU under study, thus setting a direction by which to identify the sources of inefficiency
in this DMU.
An O-D’s efficiency rating at any given moment is a static value. Malmquist analysis was used to reflect the O-D’s dynamic behavior in relation to the efficiency frontier
from 2011 to 2016. The Malmquist index was used because it allows efficiency change
to be decomposed into dimensions that are useful for understanding O-D performance.
Authors have used different nomenclatures for these dimensions. The original study by
Malmquist [26] decomposed efficiency change into components, as described by Coelli
et al. [27], Färe et al. [28], Ray and Desli [29], and others. This study used the Malmquist
total factor efficiency index (MI) decomposition proposed by Ray and Desli [29] for the VRS
output-oriented DEA model. Ray and Desli decomposed the MI into three components.
Equation (2) shows the MI for two years, 2011 and 2016. Its components are catch-up (CU)
and frontier shift (FS) for the VRS, and the scale component (SEC).
MI x2011 , y2011 , x2016 , y2016 = CU × FS × SEC
(2)
where:
δ2016 ( x0 , y0 )2016
CU = 2011
δ
(( x0 , y0 )2011 )
s
δ2011 (( x0 , y0 )2011 ) δ2011 (( x0 , y0 )2016 )
×
δ2016 (( x0 , y0 )2011 ) δ2016 (( x0 , y0 )2016 )
s
σ2011 (( x0 , y0 )2016 ) σ2016 (( x0 , y0 )2016 )
×
SEC =
σ2011 (( x0 , y0 )2011 ) σ2016 (( x0 , y0 )2011 )
FS =
(3)
(4)
(5)
where:
(x0, y0 )t represents the position of the O-D considered in period t, by way of its inputs
and outputs;
δt represents the O-D’s efficiency index in period t for the variable return to scale
frontier in period t; and
σt represents the O-D’s scale efficiency in relation to the frontier in period t.
The DEA and Malmquist parameters were weighted by the number of seats sold on
the O-D. This weighting was important to prevent trends in the simple means of the country
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sub-region efficiency indicators, which might return disproportional measurements when
small links had greater indicators than high passenger-density links, and vice versa. The
analyses were conducted using mean values at the intra- and interregional levels. Although
DEA was conceived to analyze production performance, this paper uses a formulation of a
demand function, seeking to show how best O-D socioeconomic conditions and link price
result in air transport link demand.
3.2. Case Study
Brazil occupies a geographical area of 8.5 million km2 , making it the country with the
fifth-largest territory in the world, after Russia, Canada, China, and the United States. In
2018, it also had the fifth largest population (209 million), according to World Bank figures.
Its economy ranked seventh, with a GDP of around 3.388 trillion Int $ (PPP $—international
dollar) in 2017 [30]. However, in 2015, Brazil ranked only 74th in per capita GDP (about
8670 Intl $), a long way from the group of countries classified as developed—as was also
the case with other countries with large territories and significant GDPs, such as China,
India, and Russia. Brazil is a federative republic comprising 26 states and the Federal
District (the country’s capital), and is organized into five administrative regions: North
(N), Southeast (SE), Midwest (MW), Northeast (NE), and South (S). Each Brazilian state
has one municipality where the state capital is located. Brazil has 5570 municipalities, of
which 309 had populations of more than 100,000 in 2016 [10]. This is a size that can be
considered attractive for airline operations, especially when the city is more than 100 km
from cities that already have airports. Despite this potential, only 90 cities were found
to have airports operating regular flights in both 2011 and 2016. That fact seems to be
connected with Brazil’s low per capita income combined with the concentration of the total
GDP in the Southeast region.
Table 1 shows the distribution of GDP, population (POP), and GDP per capita according
to the Brazil’s five regions. All monetary values, in Reais (Brazilian currency), presented
in this study are in constant 2016 values, corrected by the National Extended Consumer
Price Index (IPCA), the country’s official inflation rate. Note that, although the SE region’s
share in GDP declined by 2.24 percentage points (p.p.), the SE continued to predominate
quite clearly as the country’s economic center, accounting for more than 50% of all income
and about 42% of the total population. The MW region, meanwhile, comprising a smaller
percentage of the population, returned a higher per capita income than the SE. Two factors
account for this situation: the first is that Brazil’s export-oriented agricultural and livestock
production is concentrated in the MW, and the second is that the nation’s capital is located
in this region. The NE region performed best in an increased share of GDP (a variation of
more than 0.93 p.p.). Fernandes et al. [31] showed that this was the region that prospered
most in service GDP, meaning that this region’s airports performed best in networkability.
However, the NE is the region with the lowest per capita GDP in Brazil. Although the N
and MW regions account for large portions of Brazil’s total area, they hold a smaller part of
the population.
Table 1. GDP, POP, and per capita GDP of Brazil’s regions.
Region
GDP (Billion Reals)
Per Capita GDP
(Reals)
POP (Millions)
2011
%
2016
%
2011
%
2016
%
2011
2016
SE
S
NE
MW
N
3452
1011
835
596
336
55.41
16.22
13.4
9.57
5.4
3332
1067
898
633
337
53.17
17.02
14.33
10.1
5.38
80.98
27.56
53.49
14.24
16.1
42.09
14.33
27.81
7.4
8.37
86.36
29.44
56.91
15.66
17.71
41.91
14.29
27.62
7.6
8.59
41,149
38,711
16,789
44,431
20,951
38,585
36,243
15,781
40,412
19,043
Brazil
6230
32,579
30,413
6267
192.4
206.1
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In the nomenclature used in this study, “passengers” (PAX) is synonymous with seats
sold. Note, in Table 1, that the SE region’s share in all variables declined, although it
continued to predominate by a considerable margin as the country’s economic powerhouse.
The NE region performed best as regards growth in share of PAX.
Among the main economic activities in the NE region, tourism is particularly notable
for its recent performance, and for its relevance in terms of income and employment
generation. Silva et al. [32] present a hierarchy of 50 Brazilian airports associated with
tourism, 38% of which are in the NE region, 20% in the MW, 18% in the N, 12% in the S,
and 12% in the SE. Accordingly, the rising gross value of the service sector in the NE region
is closely related to the tourism industry, whereas in the MW, the service sector’s gain in
gross value correlates with growth in the region’s agribusiness sector. These data show that
tourism plays a significant, but not predominant, role in PAX movement in the MW. The
sample used in this study encompasses the main municipalities served by airports. The
results for this group of cities better reflect the behavior of air transport in Brazil than the
general statistics, since it represents the main economic centers of the country—a source of
demand flows for air travel, and historically more sensitive to changes in the economic or
political scenario.
3.3. Data
Air transport O-D databases are scarce in the field of air transport research [33–36]. It
is thus common practice to use samples in related analyses. The data used in this study originated in microdata from the National Civil Aviation Agency (ANAC) (https://www.anac.
gov.br/assuntos/dados-e-estatisticas/microdados-de-tarifas-aereas-comercializadas, accessed on 5 May 2022), which show the volume of tickets purchased on each trip, i.e.,
from origin to destination, by the price paid and by month. For the purpose of analysis,
these data were transformed into annual values of O-D movement and weighted means
of prices paid by O-D. According to the ANAC, these data represent about 50% of the
total movement, because the database does not include tickets purchased under mileage
programs or specific agreements between purchasers and airlines. The data on domestic
airfares apply to all tickets sold. The GDP and POP figures for the main municipalities
served by the trip origin and destination airports were obtained from Brazilian Institute of
Geography and Statistics (IBGE).
The database is directional, i.e., it considers outward and returns tickets separately.
Each direction has a specific price. The links considered are those existing in 2011 and
2016, so that Malmquist analysis indices can be calculated. Links with an annual demand
of at least 52 tickets were considered, so as not to include occasional ticket purchases as
links. The data relate to the 26 state capitals, the national capital, 63 regional cities, and
three of the state capitals (Belo Horizonte, Rio de Janeiro, and São Paulo, Brazil) with two
airports each, totaling 93 airports that were operating in both 2011 and 2016. Within the
specifications of the sample, 3133 O-Ds present in both years were validated. Appendix A
contains a map of Brazil’s regions (Figure A1), showing the location of the airports covered
by the study, and, in Table A1, the ICAO codes for the airports, the cities relating to each
airport, and the name and abbreviation of each state.
The analytical model had five inputs and one output. The output is the number of
seats purchased from one origin to a given destination. The inputs are the GDP and POP of
the municipality of origin and destination municipality, and average ticket price weighted
by tickets sold (TICK) in the years in question. Table 2 shows the correlation between
output and inputs of the DEA model considered for analysis. As ticket prices vary inversely
with passenger demand, this variable was transformed to its reciprocal, as suggested by
Bowlin [37], in order to implement the DEA in such a manner as to guarantee the isotonicity
of the variables.
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Table 2. Correlation of PAX with the variables.
PAX
Variables
GDP ORIG
GDP DEST
POP ORIG
POP DEST
TICK
2011
2016
0.293
0.291
0.300
0.298
−0.302
0.323
0.321
0.329
0.327
−0.332
Table 3 shows the descriptive statistics for the variables considered for analysis in
this study. From Table 3, it can be seen that the standard deviations of the variables are
quite large, revealing a significant variety of situations, ranging from low-density and/or
short-haul links to high-density and long-haul links. Distances between airports in Brazil
can be greater than 4000 km, as between Cruzeiro do Sul, in AC state, and João Pessoa, in PB
state. There are 16 towns in the sample with fewer than 100,000 inhabitants; these are small,
isolated localities, of tourist or strategic interest to the country. One example is Fernando
de Noronha, an island and protected environment in the east of the NE region, whose
economic activity is fundamentally tourism. Another example is Coari, in AM state in the
N region, an isolated locality in the Amazon Forest, where there is significant production
of natural gas. Data for GDP (in million Reais) and POP (in thousands of the population)
relate to the 90 cities in the sample. The statistics used for the remaining variables refer to
the 3133 O-Ds.
Table 3. Descriptive statistics by variable.
Average
Standard deviation
Upper value
Lower value
Average
Standard deviation
Upper value
Lower value
Observations
Year
PAX
GDP
POP
TICK
2011
15,440
52,872
1,108,434
52
29,904
84,294
705,722
89
674
1421
11,316
3
378.41
372.58
2272.62
95.31
2016
12,659
40,773
827,281
52
29,137
83,060
687,036
114
711
1487
11,896
3
339.19
273.16
1637.08
107.25
3133
90
90
3133
4. Results
Table 4 shows the results of the DEA and Malmquist analysis of the intra-regional
links. It can be seen that only a small number of O-Ds were at the efficiency frontier—39
and 23, respectively, in 2011 and 2016—of a total of 690. In all, 20 O-Ds were at the efficiency
frontier in both years. The regions returned CUs of 0.70 to 0.74, indicating a uniform decline
in performance as regards this indicator of innovation. The SE and NE regions displayed
FS improvements, the SE’s being the most significant (FS = 1.25). SEC remained relatively
stagnant in all regions, ranging from 0.97 to 1.01. Overall, intra-regional air transport
performance declined, with MI from 0.62 (MW) to 0.88 (SE). Table 4 also shows that from
690 O-Ds, 245 had MIs improved (35.51%); this happens mainly in the SE and NE regions,
where FS were 1.25 and 1.07, respectively.
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Table 4. Intra-regional DEA and Malmquist indices.
Intra-Region
SE
S
NE
MW
N
DEA = 100
DEA < 100
Index < 1
Index = 1
Index > 1
Total
DEA 2011
DEA 2016
=100
=100
<1
=1
>1
0.88
0.71
0.74
0.62
0.66
7
7
10
15
2
5
8
8
167
48
112
24
93
1
-
90
30
74
5
46
444
1
245
690
39
23
444
1
245
O-Ds
PAX 2011
PAX 2016
DEA 2011
DEA 2016
CU
FS
SEC MI
257
78
187
29
139
9991
1751
2817
545
1414
7147
1159
2246
344
1021
0.62
0.80
0.55
0.39
0.63
39
651
0.58
0.70
0.60
0.33
0.60
23
667
0.70
0.74
0.70
0.73
0.71
1.25
0.97
1.07
0.87
0.96
1.01
0.99
0.99
0.98
0.97
673
2
15
690
324
21
345
690
402
13
275
690
690
16,519
11,918
690
690
MI
Overall, the analysis showed that in the intra-regional dimension, the indicator for
innovation (CU) was most strongly connected with a diminished performance by O-Ds.
From 690, 673 O-Ds had CU < 1 (97.54%). Meanwhile, the indicator for scale (SEC) stagnated,
reflecting no variations in the regional structures. Although FS did advance in the SE and
NE regions, those advances were insufficient to overcome the decline indicated by CU in
these regions, leading to MI < 1. In the S and N regions, FS was nearly stagnant, and MI was
thus influenced basically by the deterioration in CU. The MW region showed deteriorating
FS, which led, in turn, to greater degradation indicated by CU, leaving the region with
lower MI. The SE region’s performance, because of a good FS result (1.25), was the least
affected in total efficiency, represented by MI.
Table 5 shows the results of the DEA and Malmquist analysis of the interregional O-Ds,
where regular domestic air passenger transport flows are largest in Brazil, about 66% of
the passenger sample. Although the O-Ds were further from the efficiency frontier than
in the intra-regional cases, the indicators of improvement from the Malmquist analysis
were much more promising. The considerable number of O-Ds involving the SE region
(55% of the total) attests to this region being the main origin or destination of inter-regional
passengers. The SE region O-Ds accounted for more than 50% of intra-regional passenger
movement (Table 4). As in the intra-regional dimension, only a small number of O-Ds
defined the efficiency frontier. Of the 52 O-Ds that were at the frontier in at least one of the
study years, 32 were there in 2011, and 35 in 2016. Of these, 15 were at the frontier in both
years. All regions had some O-D at the efficiency frontier. The inter-regional O-Ds that
moved away from the efficiency frontier least were SE–NE and S–SE, which also comprised
the links with the greatest number of passengers. However, total efficiency declined in
both, with MI indicators equal to 0.88. The O-Ds with greatest performance improvements
(MI > 1) were SE–N, S–NE, and S–N, among which the S region was conspicuous for
improvement in total efficiency (MI = 1.15, 1.26, and 1.42). Poor O-D performance, as in the
case of intra-regional O-Ds, can be associated primarily with the indicator for innovation,
CU, which varied from 0.66 to 0.77. The indicator that displayed most overall improvement
was FS. The indicator for scale (SEC) remained largely close to stagnant. Most (56%) of the
inter-regional O-Ds returned an MI greater than 1, indicating an endeavor to improve the
total efficiency in most of the O-Ds.
From Table 5, it can also be seen that the main improvements observed in the FS
indicator did not occur on the highest-density passenger links, which were, in decreasing
order of number of passengers, SE–NE, S–SE, and SE–MW.
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Table 5. Inter-regional DEA and Malmquist indices.
Inter-Region
MW–NE
N–MW
N–NE
SE–MW
SE–NE
SE–N
S–MW
S–NE
S–N
S–SE
DEA = 100
DEA < 100
Index < 1
Index = 1
Index > 1
Total
DEA 2011
DEA 2016
=100
=100
<1
=1
>1
0.85
0.76
0.90
0.86
0.88
1.10
0.95
1.26
1.42
0.88
1
12
2
1
4
2
10
1
2
2
18
2
2
2
6
53
73
116
103
206
160
55
80
75
160
1
-
86
40
132
87
273
176
64
205
122
176
2399 585 1245 1081
17
16
1
44
1841 1182 1361
2443 2443 2443 2443
32
35
1081 1
O-Ds
PAX 2011
PAX 2016
DEA 2011
DEA 2016
CU
FS
SEC MI
139
113
248
191
479
336
119
285
197
336
1927
858
765
6015
9424
1579
1270
976
257
8784
1787
635
713
4735
8261
1589
1186
1202
361
7272
0.38
0.27
0.23
0.51
0.60
0.20
0.33
0.16
0.06
0.63
32
2411
0.41
0.21
0.23
0.48
0.62
0.23
0.34
0.20
0.10
0.60
35
2408
0.66
0.70
0.66
0.73
0.68
0.68
0.72
0.68
0.69
0.77
1.29
1.11
1.36
1.19
1.30
1.41
1.28
1.76
1.88
1.15
0.99
0.98
1.00
0.99
1.00
1.15
1.03
1.06
1.10
1.00
2443
31,856
27,742
2443
2443
MI
1361
5. Discussion
The intra-regional and inter-regional results obtained seem consistent with the particularities, recent trends, and economic dynamics of each of the Brazilian regions. Taking the
discussion presented by Ter Wal and Boschma [38] about the influence of networks on the
development of regional economies, together with the intra-regional results given here, we
may infer that the SE region offers suitable social and economic conditions for increasing
O-D efficiencies, particularly in connections with state capital cities, such as São Paulo,
Rio de Janeiro, Belo Horizonte, and Vitoria. These cities have significant GDP, and greater
population density and diversity of economic activities, enabling the “social network” to
be more resilient [31]. As such, the connections with them, given the variety of economic
opportunities available in the region, are better able to withstand regional economic crises.
However, the industry does not seem to have made proper use of inter-regional potential.
The S region shares a characteristic with the SE region, in that it accounts for the secondlargest share of Brazil’s GDP [10]. As such, it offers suitable regional economic conditions
for improving the MI for certain air links. The opposite effect can be seen in relation to
the N region, which has the smallest share of national GDP, which is reflected in lesser
diversity and intensity of economic activities and dynamics within the region, as well as in
no improvement in the MI for intra-regional air links, whereas better prospects do exist at
the inter-regional level, primarily in links with the S and SE regions.
The intra-regional result for the MW region seems to be related to the importance of
agri-business to the region [39]. As was observed by Giuliani and Bell [40], with regard
to the knowledge and technology network connected with wine production in Chile,
which has little connectivity on a local or regional scale, i.e., it is an isolated network
concentrated within the wine-producing companies or those that connect with agents
outside the “natural” proximity zone (in other regions of Chile), this same process may
be occurring with air traffic in the MW region, reflecting the stagnation or degradation of
connections in the MW. At the inter-regional level, the O-Ds relating to this region returned
the lowest MI values of the set (0.76, 0.85, and 0.86).
After the SE, it is the NE region that has benefited most from domestic tourism [41]. It
should also be stressed that Bahia (BA) is the state with the largest GDP in the region [10],
especially in the tertiary sector, primarily tourism. Unlike what happens in the MW region,
the tourism-related service sector cuts across various other sectors and embodies more
significant relations of geographical proximity. This strengthens the production chain of
local and regional tourism, which influence factors from everyday leisure activities through
to tourism activities in other places within the area of influence of the region’s tourism
network, particularly the state of BA (where Salvador and Porto Seguro are located), as
can be observed in the NE region’s intra-regional results. However, although one of the
best-performing ODs in terms of total efficiency relates to the NE (S–NE), the set of higher
Sustainability 2022, 14, 7284
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inter-regional traffic density O-Ds (SE–NE) does not perform satisfactorily and deserves
attention from air transport operators.
6. Conclusions
The present research investigates the degree of robustness of the domestic passenger
air transport network in Brazil, using a regional analysis of the flow of travelers’ origin and
destination. The sample selected for analysis comprises the period when the important
economic changes experienced by the civil aviation industry in the country became more
evident. Furthermore, although not random, the sample analyzed allows us to infer a
general profile of the most relevant passenger flows for airline planning purposes.
The intra- and inter-regional analyses show that even though similarities exist, the various O-Ds differ in terms of performance, resilience, and potential. The weighted grouping
of intra- and inter-regional indicators for the O-Ds give a more synthetic view of the travel
generation efficiency in Brazil. Although the SE region returned better performance indicators at both intra- and inter-regional levels, other regions performed better in a number of
other dimensions. Better performance indicators can be connected with major economic
differences and population concentration, rather than with improvement in O-Ds. With
only three inter-regional exceptions (S–N, S–NE, and SE–N), the MI indicators showed that
airlines in Brazil have not managed to take advantage of the demand potential of domestic
passenger O-Ds. The domestic airlines’ weak point was revealed by the CU indicator, one
reason for which may be the lack of secondary airports in Brazilian state capitals, which
hinders supply flexibility. Another may be the entry barriers facing new competitors, which
are still rather high. Catching up and forging ahead calls for a more innovative attitude on
the part of domestic airlines in Brazil.
Brazil’s most recent family budget survey, covering the years 2017 and 2018 [10],
showed that the share of spending on domestic air transport had almost doubled in
Brazilian family budgets, as compared with the previous survey for 2008 and 2009 (from
0.32% to 0.57%). However small this percentage may be, this statistic suggests that Brazilian
households, in general, recognize the utility of air transport in their lives. Moreover, this
utility tends to be even greater in remote or isolated areas, such as those located in the
Brazilian Amazon, situated in the N region. However, the results point out that airlines
are still not showing themselves capable of taking advantage of this market opportunity
by offering products and services to expand the number of air travels in the country, so
that they return satisfactory efficiency indicators. In other words, the contribution of the
aviation industry in Brazil to the integration of markets, people, and economic development
is still below its potential level. Deepening this analysis and providing new elements for
this discussion will be the object of future research, which can be developed based on the
findings presented here.
Author Contributions: Conceptualization, V.A.F.; methodology, V.A.F., R.R.P. and E.F.; validation,
V.A.F., R.R.P. and E.F.; formal analysis, V.A.F., R.R.P. and E.F.; investigation, V.A.F., R.R.P. and E.F.;
data curation, R.R.P., E.F., M.C. and R.V.V.; writing—original draft preparation, V.A.F., R.R.P. and E.F.;
writing—review and editing, V.A.F., R.R.P., E.F., R.V.V. and M.C.; visualization, V.A.F., R.R.P. and E.F.;
supervision, V.A.F.; project administration, V.A.F.; funding acquisition, V.A.F. All authors have read
and agreed to the published version of the manuscript.
Funding: The APC was funded by Escuela de Ingeniería de Construcción y Transporte, Pontificia
Universidad Católica de Valparaíso.
Acknowledgments: The authors would like to thank the editor and anonymous reviewers for their
insightful comments and suggestions.
Conflicts of Interest: The authors declare no conflict of interest.
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Appendix A
Figure A1. Location of airports in Brazil, by state and region.
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Table A1. Airports in each city, state, and region (IDs relate to Figure A1).
ID
ICAO
CITY
STATE
Distrito Federal (DF)
85
SBBR
Brasília
47
SBGO
Goiânia
48
SWLC
Rio Verde
93
SBCN
Caldas Novas
82
SBCG
Campo Grande
83
SBCR
Corumbá
87
SBDO
Dourados
54
SBAT
Alta Floresta
84
SBCY
Várzea Grande
68
SBMO
Maceió
19
SNBR
Barreiras
Goiás (GO)
MIDWEST
(MW)
Mato Grosso do Sul (MS)
Mato Grosso (MT)
Alagoas (AL)
20
SBIL
Ilhéus
21
SBPS
Porto Seguro
22
SBQV
Vitória da Conquista
51
SBLE
Lencóis
69
SBSV
Salvador
50
SBJU
Juazeiro do Norte
65
SBFZ
Fortaleza
13
SBIZ
Imperatriz
64
SBSL
São Luís
15
SBKG
Campina Grande
66
SBJP
Santa Rita
16
SBFN
Fernando de Noronha
17
SBPL
Petrolina
67
SBRF
Recife
14
SBTE
Teresina
Piauí (PI)
88
SBSG
Natal
Rio Grande do Norte (RN)
18
SBAR
Aracaju
Sergipe (SE)
56
SBCZ
Cruzeiro do Sul
57
SBRB
Rio Branco
2
SWBC
Barcelos
3
SWKO
Coari
4
SWEI
Eirunepé
5
SWLB
Lábrea
6
SWPI
Parintins
7
SBUA
São Gabriel da Cachoeira
49
SBTF
Tefé
58
SBEG
Manaus
59
SBTT
Tabatinga
63
SBMQ
Macapá
REGION
Bahia (BA)
Ceará (CE)
Maranhão (MA)
NORTHEAST
(NE)
Paraíba (PB)
Pernambuco (PE)
Acre (AC)
Amazonas (AM)
Amapá (AP)
NORTH
(N)
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Table A1. Cont.
ID
ICAO
CITY
8
SBHT
Altamira
9
SBIH
Itaituba
10
SBMA
Marabá
61
SBBE
Belém
62
SBSN
Santarém
86
SBCJ
Parauapebas
1
SBVH
Vilhena
55
SBPV
Porto Velho
89
SBJI
Ji-Paraná
90
SSKW
Cacoal
60
SBBV
Boa Vista
11
SWGN
Araguaína
12
SBPJ
Palmas
29
SBVT
Vitória
23
SBAX
Araxá
24
SBBH
Belo Horizonte
25
SBMK
Montes Claros
26
SBUR
Uberaba
27
SBUL
Uberlândia
28
SBVG
Varginha
52
SBGV
Governador Valadares
53
SBIP
Santana do Paraíso
70
SBCF
Confins
91
SBZM
Juiz de Fora
30
SBCB
Cabo Frio
31
SBCP
Campos dos Goitacazes
32
SBRJ
Rio de Janeiro
71
SBGL
Rio de Janeiro
33
SBAU
Araçatuba
34
SBML
Marília
35
SBDN
Presidente Prudente
36
SBRP
Ribeirão Preto
37
SBSR
São José do Rio Preto
38
SBSJ
São José dos Campos
72
SBKP
Campinas
73
SBGR
Guarulhos
74
SBSP
São Paulo
92
SBAE
Bauru
STATE
REGION
Pará (PA)
Rondônia (RO)
Roraima (RR)
Tocantins (TO)
Espírito Santo (ES)
Minas Gerais (MG)
SOUTHEAST
(SE)
Rio de Janeiro (RJ)
São Paulo (SP)
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Table A1. Cont.
ID
ICAO
CITY
39
SBCA
Cascavel
40
SBLO
Londrina
41
SBMG
Maringá
75
SBFI
Foz do Iguaçu
76
SBCT
São José dos Pinhais
44
SBCX
Caxias do Sul
45
SBPF
Passo Fundo
46
SBSM
Santa Maria
79
SBPK
Pelotas
80
SBPA
Porto Alegre
81
SBUG
Uruguaiana
42
SBCH
Chapecó
43
SBJV
Joinville
77
SBFL
Florianópolis
78
SBNF
Navegantes
STATE
REGION
Paraná (PR)
Rio Grande do Sul (RS)
SOUTH
(S)
Santa Catarina (SC)
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