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Research Note
Tourism and regional
development in the Brazilian
Northeast
Luiz Carlos De Santana Ribeiro
Federal University of Sergipe, Brazil
Emerson Olivier Vieira Da Silva
Sa
˜o Luı
´s de Franc¸a Faculty, Brazil
Jose
´Roberto De Lima Andrade
Federal University of Sergipe, Brazil
Ke
ˆnia Barreiro De Souza
Center for Development and Regional Planning (CEDEPLAR-UFMG), Brazil
Abstract
This article aims to estimate the economic impacts of expenditure on tourism in the Brazilian
Northeast and its effects on the states’ productive structure and regional inequalities. We use an
interregional input–output matrix for the nine northeastern states and the rest of Brazil. The main
results show that tourist expenditure in the Northeast was responsible for a 3.9% increase in the
Northeast’s gross domestic product (GDP). Additionally, the sectorial analysis indicated significant
spillover effects to the rest of Brazil, especially from manufacturing industries. On the other hand,
tourist spending contributed to reducing regional inequalities.
Keywords
input–output, northeast region, regional inequality, tourist expenditure
Introduction
Tourism activities have specific characteristics which set their economic impacts apart relative to
other sectors (Palomo, 1990). These include (i) the importance of the local characteristics (climate,
landscape, historical, cultural attractions etc.), (ii) the type of tourist demand (local or foreign), and
(iii) the seasonality of demand.
Corresponding author:
Luiz Carlos De Santana Ribeiro, Economics Department, Federal University of Sergipe, Edson Ribeiro St, 64, Salgado Filho,
49020-370 Aracaju/SE, Brazil.
Email: ribeiro.luiz84@gmail.com
Tourism Economics
2017, Vol. 23(3) 717–727
ªThe Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/1354816616652752
journals.sagepub.com/home/teu
The Brazilian Northeast stands out in the first aspect, giving special attention to tourism
activities in the design of regional development strategies based on local comparative advantages.
For this reason, tourism has been used, or considered by governments in the Northeast, to be a
regional development strategy since the 1980s (Ribeiro et al., 2014).
Additionally, the regional distribution of domestic travel in Brazil identifies the Northeast as the
region with a predominantly receptive ratio (an outbound/inbound tourism ratio of 0.9). The region
represents 30%of total receptive tourism in Brazil and accounts for 30.8%of revenue from
domestic tourism (FIPE, 2012).
The economic effects of tourism can be measured using econometric models (Andrade, 2003;
Azzoni and Menezes, 2009), input–output (IO) models (Archer, 1995; Fletcher, 1989; Haddad
et al., 2013; Pratt, 2015; Ribeiro et al., 2014), and computable general equilibrium (CGE) models
(Blake et al, 2011; Taylor, 2010; Viana et al., 2014). CGE models are increasingly found in
international literature (Dwyer et al., 2004; Taylor, 2010; Zhou et al, 1997). However, the IO
model has the advantage of being transparent, making it possible to track derivation of the
results exactly.
This article aims to estimate the macroeconomic, regional, and sectorial importance of tourism
in the Brazilian Northeast in 2011. According to Song et al. (2012), the overall significance of the
industry within an economy can be called economic contribution of tourism or its economic
significance. The tourism economic impact refers only to changes caused after a specific event or
activity that did not exist previously. Nevertheless, the two analyses are directly related, since the
greater the significance of tourism for a region, the greater are the potential economic impact of
specific tourism events.
Using this terminology, we present here a significance analysis. Nevertheless, the term
‘‘impact’’ is used through the article, as it is common in traditional IO analysis, referring to the
economic effect of tourism activities within the region and its spillovers to other regions.
In addition, the main contribution of this article is to associate the IO results from tourism
expenditures with regional inequalities through the Gini coefficient. An interregional IO matrix
calibrated for 2004 was used (Guilhoto et al, 2010). It was and built for the nine northeastern states
and the rest of Brazil (RBR).
The rest of this article is organized as follows: The next section provides a brief description of
the IO model, the database, and the treatment of the variables. The following sections present main
results and conclusions, respectively.
Method and data
The IO matrix over time has become an important tool to measure and analyze the inter-
relationships between sectors and assist policy makers (Fletcher, 1989; Haddad et al., 2013;
Leontief, 1941, 1966). The basic model, its applications, and terminology can be found in
Miller and Blair (2009). In this article, we used tourism expenditure as exogenous shocks in
the final demand. As a result, we have variations (impacts) in the production as well as other
variables such as gross domestic product (GDP), employment, and taxes.
1
The results in
GDP were also used to calculate the variation in interregional inequality through the Gini
index.
The database covers 111 sectors and 10 regions, that is, 9 Northeastern states plus the
RBR. The sectors were aggregated in 52 for exposition purpose. The main tourist activities
2
718 Tourism Economics 23(3)
are road transportation,
3
air transportation, water transportation, lodging services, and food
services.
Activities related to travel agencies, culture, and leisure are grouped in the sector other services.
It is noteworthy that the former are used in the emissive state and not at the destination. Therefore,
travel agencies do not affect the economic impact of tourism much.
The average annual revenue generated by tourist expenditure was estimated for every state in
the Northeast using the method proposed by Ribeiro et al. (2014). Data on average revenue
(deflated to 2004 prices
4
) are consolidated in Table 1.
For the impact analysis, these data were broken down according to economic activity using the
share of jobs created by tourist activities
5
(Table 2).
Table 1. Average revenue from tourism by northeast state.
State
Average
permanence
(days) (a)
Average daily
expenditure per capita
(day) R$ (b)
Estimated
flux (thousand)
(c)
Average
revenue in 2011
(abc)
Average revenue
in 2004 (R$
million)
Alagoas 8.62 107.14 1946 1,797,311,774 1262
Bahia 10.50 65.70 6593 4,548,475,326 3193
Ceara
´8.44 78.81 4614 3,067,403,209 2154
Maranha
˜o 10.78 46.21 2049 1,020,708,357 717
Paraı
´ba 10.12 52.02 1409 741,638,750 521
Pernambuco 9.30 69.32 4558 2,938,915,228 2063
Piauı
´10.13 43.09 1032 450,294,153 316
Rio Grande
do Norte
9.44 74.95 2754 1,947,613,521 1367
Sergipe 8.80 59.40 747 390,490,465 274
Source: Author’s own from FIPE and Fundac¸a
˜o Integrated Tourism Commission/Northeast (CTI/NE) database.
Table 2. Average revenue per tourism sector 2004 (R$ million).
Activities
States
Road
transportation
Air
transportation
Water
transportation
Food
service
Lodging
service
Other
services
Alagoas 171 34 1.7 396 547 111
Bahia 459 97 19 987 1390 242
Ceara
´397 87 1 815 684 169
Maranha
˜o 142 30 13 206 276 49
Paraı
´ba 7 13 0.8 199 189 40
Pernambuco 356 74 3 723 732 176
Piauı
´60 14 0.3 101 123 18
Sergipe 48 7 0.8 97 95 27
Rio Grande do
Norte
171 35 6 402 673 81
Source: Author’s own from FIPE and IPEA database.
Ribeiro et al. 719
In Table 2, travel agencies, culture, and leisure activities are grouped in the sector other services
and rental was added to road transportation.
Results and discussion
The results presented from the simulations with the interregional IO matrix enable the identifi-
cation of the economic significance of tourism expenditure on economic variables: production,
GDP, employment, and Tax on Goods and Services (ICMS)
6
in regional and sectorial terms.
Table 3 summarizes the macroeconomic impact. The results show the increases in percentage
terms of these variables in relation to the base scenario, that is, 2004.
After a 4.2%increase in tourist expenditure in the Northeast between 2010 and 2011, a total of
80.3%was absorbed by the region itself, while 19.6%spilled over to the RBR, which represents a
0.2%increase in production in the RBR.
Moreover, the impact of tourism on employment as well as on GDP has little influence outside
the Northeast. This result might be expected as the characteristics of tourism activities are mostly
developed in the location itself and in labor-intense activities. Therefore, the total effect of tourism
expenditure on GDP is smaller than in employment and the same is valid for spillover effects. This
result can be broken by states as shown in Figure 1.
Table 3. Macroeconomic impact of tourist expenditure on selected variables 2004 (%).
States Production GDP Employment ICMS
Brazil 0.7 0.6 1.4 1.2
Rest of Brazil 0.2 0.1 0.1 0.13
Northeast 4.2 3.9 6.1 7.6
Source: Author’s own based on input–output simulations.
Note: GDP: gross domestic product.
0.0
2.0
4.0
6.0
8.0
10.0
12.0
AL BA CE MA PB PE PI SE RN
7.3
3.5
4.8
2.9 3.1 3.8
2.6 2.1
6.5
10.1
5.3
7.4
4.0 4.2
6.6
3.8 3.5
9.1
%
GDP Employment
Figure 1. Impacts on macroeconomic variables (%): employment and gross domestic product (GDP).
Source: Author’s own based on input–output simulations.
720 Tourism Economics 23(3)
Similar results for Brazil were reported by Ribeiro et al. (2013) and Viana et al. (2014).
The outstanding impact on employment and GDP in the states of Alagoas and Rio Grande do
Norte can be explained in part because the average daily expenditure per capita for the two states is
above the average. Additionally, these states have smaller economies and therefore they are more
sensitive to impacts. Excluding these states, Bahia, Pernambuco, and Ceara´ are the largest tourist
destinations in the region, receiving a large share of total tourism expenditures.
Figure 2 shows the impact on ICMS and production. Again, Alagoas and Rio Grande do Norte
stand out in relation to the Northeast; however, they are followed by Ceara´. One of the reasons for
this could be the variable average daily expenditure per capita, combined with the tourist flux,
considering that these variables are important components for the calculation of average tourism
revenue, which is taxed by ICMS and therefore directly influences the amount collected.
Piauı´ stands out as its ICMS/production ratio is well above the average. A possible explanation
for this might be the poor performance of domestic production to satisfy the new demand caused by
tourist activity. This could mean that Piauı´ has a dependence on imported products to satisfy the
local demand.
Regarding sectorial impacts, Table 4 shows how the impact is spread over the northeastern
states and RBR.
Except for the activities that make up the services segment, the sectorial impacts on GDP from
the RBR are higher in most activities compared to the Northeast. This can be explained by the
demand characteristics of tourism, where inputs are mostly provided in the region.
The result reveals a clear distinction between service sectors, where the absorption impact
occurs locally, and manufacturing industry with a large absorption in the RBR. This had been
expected because the South and Southeast regions are more industrialized than the Northeast,
which reinforces the structural weakness of the northeastern supply chain, as pointed out previ-
ously by Guilhoto et al (2010), Haddad (1999), Perobelli et al. (2013), and Ribeiro et al. (2013).
Among the nine northeastern states, in relative terms, we highlight Bahia, Ceara´, and Per-
nambuco for the impacts tourism has on certain sectors that are well above that of other states,
especially activities directly linked to tourism. These states have the most modern and dynamic
economies in the region (Arau
´jo, 2004; Guimara
˜es Neto, 1997; Perobelli et al., 2013).
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
AL BA CE MA PB PE PI SE RN
9.0
3.5
4.8
2.8 3.4
5.3
2.0 2.2
7.9
16.9
5.6
9.7
4.6
6.4
7.8 6.8
3.6
13.4
%
Production ICMS
Figure 2. Impacts on macroeconomic variables (%): production and ICMS: Tax on Goods and Services.
Source: Author’s own based on input–output simulations.
Ribeiro et al. 721
Table 4. Degree of absorption of sectorial impact on GDP and employment.
Sectors
GDP Employment
Northeast
Rest of
Brazil Northeast
Rest of
Brazil
Agriculture, silviculture, forest exploration 43.6 56.4 67.1 32.9
Livestock and fishing 47.4 52.6 66.9 33.1
Oil and natural gas 22.3 77.7 22.0 78.0
Iron ore 0.7 99.3 1.3 98.7
Others from extractivist industry 29.3 70.7 43.1 56.9
Food and beverages 64.4 35.6 67.2 32.8
Tobacco products 56.4 43.6 68.8 31.2
Textile 51.9 48.1 60.9 39.1
Clothing and accessories 49.0 51.0 64.4 35.6
Leather goods and footwear 49.8 50.2 45.1 54.9
Wood products, excluding furniture 7.4 92.6 11.7 88.3
Pulp and paper products 12.7 87.3 18.9 81.1
Newspapers, magazines, discs 23.2 76.8 32.9 67.1
Oil refining and coke 30.7 69.3 26.2 73.8
Alcohol 24.8 75.2 37.2 62.8
Chemicals 25.4 74.6 22.0 78.0
Rubber and plastic goods 8.4 91.6 7.7 92.3
Cement 36.2 63.8 38.2 61.8
Other nonmetallic mineral products 26.7 73.3 38.5 61.5
Manufacture of steel and derivatives 11.4 88.6 12.4 87.6
Metallurgy of nonferrous metals 16.7 83.3 19.6 80.4
Metal products, excluding machinery and equipment 22.6 77.4 19.6 80.4
Machineries and equipment 5.8 94.2 8.6 91.4
Home appliances 13.1 86.9 20.0 80.0
Machinery for office and computer equipment 24.1 75.9 20.8 79.2
Electrical machinery, equipment, and materials 22.5 77.5 21.3 78.7
Electronic materials and communication equipment 5.7 94.3 9.7 90.3
Medical and hospital equipment/instruments,
measurement, and optical
16.7 83.3 25.7 74.3
Automobiles, station wagons, and pickups 4.3 95.7 5.1 94.9
Other transport equipment 11.9 88.1 13.8 86.2
Other industries 39.0 61.0 34.2 65.8
SIUP 71.2 28.8 79.1 20.9
Construction 67.0 33.0 77.0 23.0
Trade 81.5 18.5 88.2 11.8
Cargo transport 65.9 34.1 76.2 23.8
Road transportation 99.7 0.3 99.8 0.2
Air transportation 98.2 1.8 99.0 1.0
Rail transportation 72.3 27.7 81.2 18.8
Water transportation 99.7 0.3 99.8 0.2
Transport auxiliary activities—passenger 85.3 14.7 91.1 8.9
Mail 70.3 29.7 80.2 19.8
(continued)
722 Tourism Economics 23(3)
Finally, the Gini index decreased by approximately 0.16%. This result is similar to that found by
Haddad et al. (2013), who using an interregional IO model, also reported that domestic tourism
plays a role in improving regional inequalities in Brazil.
The Gini result can also be broken down according to sector (Table 5). It can be seen that in
most sectors (55.8%) this indicator dropped, which means improvement (deconcentration) in the
distribution of wealth among the sectors.
Table 4. (continued)
Sectors
GDP Employment
Northeast
Rest of
Brazil Northeast
Rest of
Brazil
Information services 70.0 30.0 77.4 22.6
Financial intermediation and warranties 63.4 36.6 73.6 26.4
Real-estate services and rent 76.5 23.5 77.0 23.0
Maintenance and repair services 83.2 16.8 85.5 14.5
Lodging services 100.0 0.0 100.0 0.0
Food services 99.8 0.2 99.9 0.1
Services provided to companies 70.3 29.7 77.9 22.1
Mercantile education 72.8 27.2 83.6 16.4
Mercantile health 76.7 23.3 86.6 13.4
Other services 98.4 1.6 99.1 0.9
Public services 67.6 32.4 77.7 22.3
Source: Author’s own based on input–output simulations.
Note: GDP: gross domestic product; SIUP: Industrial Services of Public Utility.
Table 5. Sectorial Gini index variation before and after simulations (%).
Sectors
Gini ex
ante
Gini ex
post
GINI
variation
(%) Sectors
Gini ex
ante
Gini ex
post
Gini
variation
(%)
Agriculture,
silviculture,
forest
exploration
0.4718 0.4709 0.19 Livestock and fishing 0.3375 0.3386 0.31
Others from
extractivist
industry
0.4093 0.4088 0.12 Oil and natural gas 0.6854 0.6855 0.01
Leather goods and
footwear
0.7068 0.7068 0.00 Iron ore 0.5028 0.5031 0.05
Pulp and paper
products
0.6609 0.6599 0.14 Food and beverages 0.4052 0.4077 0.61
Chemicals 0.7311 0.7309 0.04 Tobacco products 0.5014 0.5014 0.00
(continued)
Ribeiro et al. 723
Table 5. (continued)
Sectors
Gini ex
ante
Gini ex
post
GINI
variation
(%) Sectors
Gini ex
ante
Gini ex
post
Gini
variation
(%)
Manufacture of
steel and
derivatives
0.7712 0.7710 0.03 Textiles 0.5373 0.5374 0.00
Metallurgy of
nonferrous
metals
0.7873 0.7873 0.00 Clothing and accessories 0.5985 0.5987 0.03
Machinery and
equipments
0.5842 0.5842 0.01 Wood products, excluding
furniture
0.6216 0.6216 0.00
Home appliances 0.6915 0.6915 0.00 Newspapers, magazines,
discs
0.5279 0.5282 0.06
Automobiles,
station wagons,
and pickups
0.8724 0.8723 0.01 Oil refining and coke 0.8348 0.8352 0.05
Other industries 0.4346 0.4346 0.00 Alcohol 0.5606 0.5608 0.04
SIUP 0.4112 0.4106 0.16 Rubber and plastic goods 0.6064 0.6064 0.00
Construction 0.3942 0.3941 0.01 Cement 0.3503 0.3503 0.00
Trade 0.3680 0.3680 0.00 Other nonmetallic mineral
products
0.4673 0.4676 0.07
Cargo transport 0.4921 0.4884 0.76 Metal products, excluding
machinery and
equipment
0.6790 0.6791 0.02
Air transportation 0.4803 0.4485 6.62 Machinery for office and
computer equipment
0.8684 0.8685 0.01
Rail transportation 0.6894 0.6880 0.20 Electrical machinery,
equipment, and
materials
0.7418 0.7419 0.01
Transport
auxiliary
activities—
passenger
0.4849 0.4802 0.98 Electronic materials and
communication
equipment
0.6899 0.6899 0.01
Mail 0.3105 0.3097 0.27 Medical and hospital
equipment/instruments,
measurement, and
optical
0.7241 0.7241 0.00
Information
services
0.3630 0.3628 0.07 Other transport
equipment
0.7669 0.7677 0.10
Financial
intermediation
and warranties
0.3956 0.3951 0.11 Road transportation 0.3104 0.3171 2.17
Real-estate
services and rent
0.3758 0.3756 0.07 Water transportation 0.5427 0.5690 4.85
Maintenance and
repair services
0.4574 0.4544 0.67 Public services 0.2945 0.2945 0.01
(continued)
724 Tourism Economics 23(3)
As expected, the tourism sector had the most significant changes since these sectors were those
that received the impacts in the simulations. Among these, only road and water transportation
contributed to the concentration of income. Despite this, most of the activities that had positive
variations in the Gini index are in the manufacturing segments.
Conclusion
This article attempted to analyze the importance of tourist expenditure in the Brazilian Northeast in
2011 and its effects on regional inequality using an interregional IO model combined with an
interregional Gini coefficient.
In general, tourism has shown positive impacts in the Brazilian Northeast. The magnitude of the
impact and its extent of absorption are heterogeneous among its states due in part to the productive
structure of each state, the amount of tourist spending there (reflecting the maturity of the tourism
product), and the relative importance of tourism for the whole of the economy.
The interstate heterogeneity of the Brazilian Northeastern productive structure tends to rise with the
trend of concentration of large investments in infrastructure (ports in Pernambuco and Ceara´), and the
automotive industry (Pernambuco), pharmachemical industry (Pernambuco), oil refineries (Per-
nambuco and Ceara´), cellulose and paper (Bahia and Maranha
˜o), and steel (Ceara´ and Maranha
˜o)
(Resende et al., 2015). In this sense, tourism appears to be capable of providing an alternative
development strategy in states thatare not benefited by this new dynamic of investments in the region.
It is worth mentioning that in four states in the Northeast (Bahia, Sergipe, Pernambuco, and
Ceara´) nowadays the National Tourism Development Program (Prodetur Nacional) is running
with support from the Inter-American Development Bank. According to Ribeiro et al. (2013),
among other goals, this program aims to strengthen the national tourism policy through a series of
investments. Therefore, this kind of policy can increase tourism in the region, which can further
improve the regional inequality. It is important to beware of the possibility of a superficial analysis
of the degree of spillover in the manufacturing sector as a whole, which can lead to suggestions of
political ‘‘import substitution’’ without any commitment to economic efficiency.
Further research includes updating the IO matrix and the development of CGE interregional
models that can clarify the restrictions from resource scarcity and price changes (Haddad et al.,
2013).
Table 5. (continued)
Sectors
Gini ex
ante
Gini ex
post
GINI
variation
(%) Sectors
Gini ex
ante
Gini ex
post
Gini
variation
(%)
Lodging services 0.5219 0.4126 20.93
Food services 0.4522 0.4159 8.02
Services provided
to companies
0.4726 0.4715 0.25
Mercantile
education
0.4344 0.4342 0.05
Mercantile health 0.5295 0.5293 0.03
Other services 0.3869 0.3809 1.57
Source: Author’s own based on input–output simulations.
Ribeiro et al. 725
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Notes
1. To see more details about the input–output impact analysis, see Ribeiro et al. (2014).
2. Following Brazilian Institute of Geography and Statistics (IBGE) survey classification—Tourism Eco-
nomics: A Macroeconomic Perspective 2000 to 2005. Available at: http://biblioteca.ibge.gov.br/visuali
zacao/livros/liv37902.pdf.
3. This sector also includes rental transportation service.
4. For this, General Price Index - Internal Availability (IGP-DI) stable index obtained from IPEADATA was
used.
5. Available at: http://extrator.ipea.gov.br/—technical cooperation between IPEA, Tourism’s Ministry, and
Federal District Planning Company (CODEPLAN/DF) dedicated to studies and research on the tourism
sector.
6. Tax on transactions on the circulation of goods and transportation services delivery. This tax was chosen
because it is a major source of state revenue generated by the tourism sector.
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