The Tertiary Sector and Regional Inequality in Brazil*
Carlos R. Azzoni1, Alexandre S. Andrade2
Department of Economics, University of Sao Paulo, Brazil
1. Introduction
The economic evolution of the world determined an increased importance for tertiary
activities. This is particularly important in the last decades, in which globalization, economic
restructuring and many other processes have accelerated the rhythm of tertiarization of
economic activities. For less developed countries, this process is even more intense, given
their late entrance into the globalization process, and the lower level of tertiarization they
started with.
In the case of Brazil, this can be seen in Figure 1, displaying sectoral shares in GDP from
1980 through 20023. Tertiary activities accounted for around 50% of GDP in the 80s; in the
first years of this decade this share jumped to 57%. Considering employment, the growing
importance of tertiary activities is even more impressive, as Figure 2 indicates. Starting with
51% of total employment in Brazil in 1980, the share for 2001 was an impressive 65%!
Similar situations were observed in Spain (Yserte, 2002), Italy (Evangelista and Sirilli, 1998),
*
Paper presented at the 50th North American Meetings of the Regional Science Association International,
Philadelphia, USA, Nov 19-22, 2003. Support from CNPq (Bolsa Produtividade em Pesquisa), Finep/PRONEX
(Grant 41.96.0405.00, Nemesis – Núcleo de Estudos Espaciais Sistêmicos) and Fipe is acknowledged.
1
Professor of Economics, [email protected]
2
Graduate student [email protected]
3
Source: IBGE www.ibge.gov.br, Contas Nacionais
Germany (Ellger, 1997; Kaiser, 2002), USA (Hammond and Thompson, 2001), and UK
(Marshall et al., 1987).
It seems, thus, that an important structural modification is taking place at a rapid pace,
especially in less developed countries, and certainly in the Brazilian case. Within the tertiary
sector, important technological and organizational changes have taken place. Even in
traditional activities within the sector, such as restaurants, drugstores, supermarkets, etc., a
better-qualified labor force is required. This brings an interesting spatial bias, for poor regions
within countries lack this kind of newly demanded factor. This process leads to increasing
interregional inequality. On the other hand, these are high income-elasticity sectors, and their
growth is expected to happen in rich regions first and in poorer regions latter. This is
exemplified in Rubalcaba and Cago (2003), who tested for different explanatory hypothesis
for service sectors performance. The authors show that the influence of locational factors
varies in great deal across type of sector, region and country, but that factors such as income,
density and labor qualification are always important.
Interestingly enough, the tertiary sector receives very little attention from regional scientists,
as compared to agriculture and manufacturing4. This might be due to its heterogeneity and to
the lack of data sources. The spatial bias mentioned above, however, makes this sector an
interesting one for study, and probably a necessary one. Silveira-Neto and Azzoni (2002), for
the Brazilian case, and Iserte (2002), for the Spanish case, indicate that tertiary activities have
important influence towards regional divergence. In this paper we deal with tertiary activities
in Brazil in a preliminary fashion. In the first part we present some competitiveness indicators
in commerce and services for different Brazilian macro regions, and analyze their evolution
over time. We then move to a more detailed sectoral disaggregation and consider the
evolution of these activities across Brazilian states over the last two decades. We finish by
calculating some convergence regressions, and analyzing the association of convergence to
growth, spatial concentration levels and variations in spatial concentration.
4
In the case of Brazil, Kon (1999), and Menezes and Carrera-Fernandes (1998) are the only excepctions.
Fi g ur e 1 - S e c t o r a l sh a r e s i n GD P
70
T er t i ar y
60
50
40
M a nuf a c t ur i ng
30
20
A gr i c ul t ur e
10
0
Figure 2 - Proportion of employment in tertiary activities
0.70
0.65
0.60
0.51
2001
1999
1998
1997
1996
1995
1993
1992
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
0.50
2. Regional indicators of competitiveness
In this section we present some indicators of regional competitiveness in tertiary activities for
the five official macro regions in Brazil, and for the state of São Paulo. As can be seen in
Table 1, regional concentration is very high in tertiary activities in Brazil. The state of São
Paulo alone hosted 29% of commercial establishments in the year 2000, and accounted for
30% of national employment, 38% of total wage payments and 33% of total net revenue. The
remaining of the Southeast region, to which São Paulo state belongs, accounted for 22% of
establishments, 23% of employment, and 22% of the wage bill and net revenue. In Services
the concentration is even higher for all variables.
Table 1 – Regional shares in tertiary activities, 2000
Commerce
Establish. Employment
Services
Wage
Bill
Revenue Establish. Employment
Wage
Bill
Revenue
North
0.01
0.02
0.02
0.03
0.01
0.02
0.02
0.02
Northeast
0.18
0.16
0.12
0.14
0.10
0.13
0.09
0.09
South
0.22
0.21
0.20
0.21
0.24
0.17
0.14
0.14
Center-West
0.07
0.08
0.06
0.08
0.06
0.06
0.05
0.06
Southeast
0.51
0.54
0.60
0.55
0.59
0.61
0.69
0.69
Sao Paulo state
0.29
0.30
0.38
0.33
0.36
0.37
0.45
0.45
Remaining
Southeast
0.22
0.23
0.22
0.22
0.23
0.25
0.24
0.24
Brazil
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
The competitiveness indicators will be calculated for two moments in time: late 70’s and late
90’s. In the first case, we will consider census data for 1975 and 1980 (Commerce and
Services censuses, Ibge); for the latter case, we will use information from surveys covering
the years 1998-2000 (Pesquisa Annual do Comércio and Pesquisa Annual de Serviços, Ibge).
These two moments were chosen mainly by data availability, but they are very interesting for
the analysis, for they cover a period starting before globalization and all structural
modifications that took place in the world’s economy recently. For the sake of simplicity, we
just call them “Late 70s” and “Late 90s”.
Three very simple indicators of competitiveness are calculated: a) revenue/wage bill; b)
revenue/employment, and c) revenue/establishment. Each one of these indicators is limited,
and all three should be used jointly in the analysis. The ratio revenue/wage bill indicates
profitability; the other two are straightforward. The idea is to compare these indicators across
space. In each and every case, the national average will be set equal to one and deviations
from this average will be considered.
Global results are presented in Table 2, involving wholesale, retail and services. Table 3
presents the same indicators for sub-sectors within services. The focus of analysis will be the
Southeast region and, within it, São Paulo state. For wholesale commerce, it can be seen that
these areas present lower-than-average, and declining, revenue/wage ratios. Although they
present above-average indicators of revenue/worker and revenue/establishment, the more
recent indicators clearly declined over time. The same holds for retail commerce, only that
the revenue/wage ratio for the Southeast region increases marginally, although still below
average in late 90s. Thus, it is clear that the most important centers of Brazilian economy are
either below average in terms of competitiveness or loosing competitiveness over time as the
commerce sector is concerned.
Table 2 - Competitiveness indicators
Commerce Wholesale
Late 70s
Late 90s
Commerce Retail
Late 70s Late 90s
Services
Late 70s Late 90s
Revenue/Wage Bill
North
Northeast
Southeast
South
Center-West
São Pauo State
1.03
1.13
0.99
1.01
0.85
0.95
1.36
1.27
0.89
1.09
1.37
0.83
1.20
1.39
0.92
0.99
1.15
0.94
1.27
1.16
0.93
1.05
1.19
0.88
1.04
1.19
0.97
0.92
1.24
0.86
1.09
0.94
1.01
0.98
0.98
1.02
Revenue/Worker
North
Northeast
Southeast
South
Center-West
São Pauo State
0.70
0.65
1.16
0.89
0.74
1.16
1.30
0.89
1.03
0.94
1.09
1.06
0.67
0.57
1.21
1.13
0.99
1.36
1.31
0.88
1.03
0.99
1.01
1.14
0.75
0.57
1.18
0.73
1.10
1.15
0.96
0.68
1.13
0.83
0.83
1.27
Revenue/Establishment
North
Northeast
Southeast
South
Center-West
São Pauo State
0.55
0.36
1.44
0.96
0.75
1.44
1.56
0.96
1.07
0.80
1.31
1.12
0.53
0.38
1.56
1.35
1.00
1.84
2.24
0.78
1.06
0.93
1.18
1.10
0.75
0.39
1.37
0.71
1.12
1.38
1.80
0.92
1.17
0.59
0.93
1.27
1.00
1.00
1.00
1.00
1.00
1.00
Brazil
The same does not hold for services in general. As for the ratio revenue/wage, these regions
were below average in late 70s and moved above average in late 90s, as the other regions
moved contrariwise, with the exception of the North. A free import zone was established in
this region in the early 70s, leading to a boom in commerce sector activities. As for the other
indicators, São Paulo state and the Southeast region present above average figures, with
declining values for revenue/establishment and for revenue/worker in the case of the
Northeast region. São Paulo state presented growth in this latter indicator.
As a preliminary conclusion, it is clear that a different situation holds for services, as
compared with commerce. Table 3 explores sub-sectors within this general sector. In general,
hotels and restaurants and real state present a similar behavior as commerce, except for
revenue/establishment, for which the rich areas are increasing competitiveness. For
transportation, São Paulo state presented improvements in revenue/worker and for
revenue/establishment, as did the Southeast region in this latter case. The really interesting
modifications occurred in services to firms and miscellaneous, in which the rich areas move
from below to above average in revenue/wages, and increased their advantage in the other
two indicators, with one exception for the Southeast region. These could be considered more
sophisticated sectors, for services to firms are related to outsourcing, consulting, etc., and
miscellaneous tends to include new activities not included in the previous classifications.
Finally, computing activities were only present in the surveys after the 1980 census, and only
data for the late 90s are available. The results indicate high competitiveness in all indicators
for the rich areas, especially so for the state of São Paulo. In the case of the center-west
region, results are biased by the presence of the federal government in Brasília, concentrating
all data processing for federal activities in Brazil. It seems clear, thus, that in the most
sophisticated sub-sectors within services the rich regions are not only above average, but are
increasing competitiveness over time.
Table 3 - Competitiveness indicators for services sub-sectors
Hotels and restaurants
Late 70s
Late 90s
Real state
Late 70s Late 90s
Transportation
Late 70s Late 90s
Services to firms
Late 70s Late 90s
Miscellaneous
Computing
Late 70s Late 90s Late 90s
Revenue/Wage Bill
North
Northeast
Southeast
South
Center-West
São Pauo State
1.16
1.49
0.93
0.97
1.16
0.92
0.91
0.99
1.02
0.92
1.04
0.94
0.85
1.01
1.01
1.11
0.83
0.99
1.06
0.96
1.02
0.98
1.07
0.84
0.80
0.93
0.99
0.82
1.34
0.83
1.34
0.98
0.98
1.02
1.19
0.96
1.11
1.64
0.84
1.54
1.34
0.93
0.94
0.95
1.03
0.96
0.83
1.08
2.26
1.48
0.94
0.89
1.27
0.84
0.78
0.84
1.09
0.84
0.85
1.21
0.65
0.70
1.04
0.97
1.06
1.18
Revenue/Worker
North
Northeast
Southeast
South
Center-West
São Pauo State
0.93
0.61
1.21
0.86
0.84
1.31
0.88
0.83
1.11
0.82
0.96
1.15
0.31
0.86
1.08
0.89
0.87
1.10
0.82
0.70
1.09
0.94
0.89
0.95
0.75
0.69
1.11
0.59
1.26
0.97
1.18
0.75
1.08
0.90
0.90
1.18
1.07
0.67
1.07
1.11
1.02
1.18
0.72
0.63
1.14
0.86
0.73
1.31
0.97
0.63
1.18
0.96
0.87
1.21
0.73
0.63
1.25
0.66
0.68
1.65
0.71
0.44
1.20
0.69
1.04
1.51
Revenue/Establishment
North
Northeast
Southeast
South
Center-West
São Pauo State
1.16
1.49
0.93
0.97
1.16
0.92
1.76
1.20
1.06
0.68
1.21
1.02
1.06
1.32
1.00
0.85
1.17
0.94
1.07
0.76
1.15
0.75
0.91
1.05
1.05
0.57
1.26
0.42
1.40
1.12
2.24
1.09
1.29
0.50
0.80
1.27
1.11
0.55
1.20
0.97
0.97
1.24
1.38
0.88
1.16
0.60
0.73
1.29
1.01
0.51
1.27
1.02
0.85
1.33
0.87
0.80
1.32
0.50
0.76
1.78
3.86
0.75
1.05
0.56
11.39
1.14
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Brazil
3. Spatial concentration: a finer analysis
Given the above results, it seems important to explore the available information in greater
detail. This will be based on the figures from a yearly survey of households PNAD developed
by IBGE, the Brazilian official statistics agency. Data for each state are available for the
period 1981-2001, allowing for a disaggregation of tertiary activities into 24 sub-sectors.
Figure 3 presents employment growth, the average of the last 3 years in relation to the
average of the first 3 years of the series. On average, employment grew 1.85 times in the
period, almost doubling. Hotels and restaurants, social clubs, associations and churches, and
commerce modern are the sub-sectors with the largest growth.
Based on Devereux et. al. (1999), the spatial concentration index (SCI) was calculated for
each year
SCI =
K
1

s j − K 

j =1 
∑
2
in which sj is the share of a state in the employment of sector j, and K is the number of states
(26). In case employment is evenly spread across states, sj = 1/K and SCI = 0. The larger the
value of ICI, the greater spatial concentration.
Figure 4 displays the results for two periods, the average of 1981/85 and the average of
1996/2001. In general, sectors highly concentrated presented lower growth rates, but there are
some exceptions, such as publicity, marketing and decoration. Over time, only six sectors
increased spatial concentration as measured by employment: miscellaneous; security;
communication; commercial representation, storage and agriculture; household services; and
travel agencies. The sectors for which increased concentration was the highest were
household services and commercial representation, storage and agriculture.
Figure 3 - Employment growth by sub-sector (Avg 99-2001/Avg 81-83)
Miscellaneous
Comm Repres. Storage, Agriculture
Security
Communication
Travel agencies
Public services
Legal
Public education
Finance old
Sports, Leisure, Artistic
Commerce old
Fitness centers, Apparel repair
Finance modern
Household services
Real state
Transportation
Health
Private education
Repair shops
Publ. Marketing, Decoration
Accounting, Architecture, Engineering
Commerce modern
Clubs, Associations, Churches
Hotels and restaurants
1
2
3
4
Figure 4 - Evolution of sectoral spatial concentration
0.25
0.20
1981/1985
1996/2001
0.15
0.10
0.05
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4. Convergence
The previous indicators reported information on concentration and absolute values. It is
interesting now to explore income obtained in those sectors and how it is evolving over time.
For that we use convergence regressions, in which income per worker growth over a period is
correlated to the initial level of income per worker. In case the coefficient on initial income is
significant, we find signs of absolute convergence, that is, all states are converging to the
same income level. For income we use hourly wage from PNAD.
Table 4 presents the results. Convergence is observed for 11 sectors only; four sectors
presented divergence, as can be seen in the summary below. Therefore, the results indicate
that spatial processes shaping tertiary sector spatial concentration are diverse and
heterogeneous. Some sub-sectors present convergence but the majority does not. It is
interesting thus to explore what happens with spatial concentration and employment growth
in relation to convergence.
Convergence results for sectors:
Convergence
Finance old; Finance modern; Fitness centers, apparel
repairs; Household services; Sports, leisure, artistic
Legal; Accounting, architecture, engineering; Travel
agencies; Public schools; Private schools;
Communication*
Non significant
Hotels and restaurants; Security; Real state selling and
administration; Transportation; Publicity, marketing,
decoration; Commercial representation, storage,
services to agriculture; Social clubs, associations,
churches; Health; Miscellaneous
Divergence
Repair shops; Commerce modern; Public services**;
Commerce old
* 10% significance
** 5% significance
Figure 5 displays the coefficient on initial income in the horizontal axis, and employment
growth in the vertical axis. A positive relationship is apparent: sectors for which convergence
is observed experienced lower employment growth rates; sectors with high employment
growth rates did not present convergence. It seems, thus, that labor income convergence is
taking place in less dynamic sectors.
The next step is to investigate whether employment growth is associated to spatial
concentration. Figure 6 portrays employment growth in the horizontal axis, and spatial
concentration at the end of the period in the vertical axis. It does not seem to be any
relationship between the two variables. That is, employment growth and end-of-period
concentration do not seem to be associated.
Figure 5 - Convergence versus absolute growth across sectors
5
Employment growth
4
Convergence
3
2
1
0
-0.05
0
Coefficient on initial income
0.05
Figure 6 - Spatial concentration and absolute growth
0.20
Spatial concentration 1996/2001
0.15
0.10
0.05
0
1
2
3
4
5
Employment growth
The last steps involve the association of convergence and spatial concentration. Figure 7
portrays the coefficient on initial income in the horizontal axis, and end-of-period spatial
concentration in the vertical axis. The results indicate a negative relationship between the two
variables, with sectors spatially concentrated presenting income convergence, which is not
observed for sectors less concentrated spatially. This leads to the question of whether or not
there is an association between convergence and changes in spatial concentration.
Figure 7 - Convergence and spatial concentration
Spatial concentratiion
0.20
0.10
-0.05
0
0.05
Initial income coefficient
Figure 8 displays convergence in the horizontal axis and the change spatial in concentration
in the vertical axis. It can be observed that, in general, convergence is observed in sectors for
which spatial concentration increased in the period.
Figure 8 - Convergence and concentration change
2
Concentration change
1.5
1
0.5
0
-0.05
0
Initial income coefficient
0.05
That is, some spatially concentrated sectors are experiencing increases in spatial
concentration and, at the same time, convergence in labor income levels. On the other hand,
sectors less spatially concentrated, and which are becoming more evenly distributed over
space, tended to present higher spatial inequalities in labor income. This is a very interesting
result indeed, showing dissociation between concentration and income inequality. That is,
convergence is taking place for sectors with low growth, spatial concentration levels, and
increasing spatial concentration. The high-growth sectors do not present convergence. This
indicates that spatial inequality tends to increase in the future, as the high-growth sectors
increase their shares in total employment.
5. Final remarks
In this paper we developed a preliminary analysis of tertiary sector activities in Brazil. This is
a very important sector to study, given its increasing importance in GDP and, especially, in
employment. Previous studies had indicated that these activities could be contributing to the
increase in spatial inequality in the country. The results observed in this study confirm that
idea.
We started with an analysis of competitiveness indicators for different areas in the country.
The results indicate that the rich areas are losing competitiveness in commerce and in
traditional services, but are becoming more competitive in the modern sub-sectors within the
tertiary, such as in services to firms, computing, etc.
The analysis of growth and concentration revealed a great variety across sub-sectors,
indicating that it is important to develop detailed analysis to come to relevant conclusions. As
for spatial concentration, the majority of sub-sectors presented decreasing concentration in
the period, although six sub-sectors presented increasing concentration.
We then moved to labor income convergence, and we have observed that only a sub-set of
sectors presented convergence. By correlating convergence with concentration and
concentration changes, we found that convergence is present for low-growth, spatially
concentrated sectors. That is, sectors more evenly spread across the territory and sectors that
tend to increase their shares in total employment are not converging. Combining these results
with the observed increasing share of tertiary activities in GDP and total employment, the
conclusions indicate increasing spatial income inequality in the country.
This was a preliminary exploratory analysis. Better data sources and more sophisticated
techniques should be applied in the future. The results achieved are interesting enough to
suggest that additional efforts should be devoted to a better understanding of the spatial
processes behind tertiary activities dynamics.
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Table 4 – Convergence results: Income growth = f(Constant + initial income level)
Sector
Commerce Old
Constant
Initial Income Adj. R2
F
-.0052668
.0036163
-.0141054**
.0067878
.0736989***
.023026
.0174654 ***
.0053704
.0268424**
.0108762
-.0334581***
.0120287
0.28
10.58
0.17
6.09
0.21
7.74
.0401537**
.0184376
.0113583
.0095874
-.0283242***
.0109968
.0000465
.0101935
0.19
6.63
0.04
0.00
.0050964
.0069203
.0171435
.0141131
.0110428***
.0028413
-.0027303
.0034582
.0241004***
.0030978
-.0053976
.006818
-.0166594*
.0089479
.0068388
.0075067
.0192185***
.0067926
-.0159897**
.0061657
-.01
0.63
0.09
3.47
-0.01
0.83
0.22
8.01
0.19
6.73
Household Services
.054072***
.0024252
-.0416637***
.0041528
0.80
100.65
Security
.0203096*** -.0025416
.0051816
.008025
-0.04
0.10
Sports, Leisure, Artistic
.0128995**
.0062678
.0445651***
.0073024
-.0067396
.0100662
0.22
8.20
0.23
8.08
-0.04
0.01
0.12
4.55
-0.02
0.59
0.48
21.95
-0.02
0.44
-0.02
0.93
0.76
81.09
18.71
6.75
0.14
5.04
0.04
1.94
Commerce Modern
Finance Old
Finance Modern
Real State Selling and Administration
Transportation
Communication
Hotels and Restaurants
Repair Shops
Fitness Centers, Apparel Repairs
Legal
Publicity, Marketing, Decoration
Accounting, Architecture, Engineering
-.014107***
.0049251
-.027246***
.0095844
-.0007981
.0072167
.022983***
.0068712
Commercial Representation, Storage, Services to Agriculture .0114978
.023788
Travel Agencies
.0561727***
.0122074
Social Clubs, Associations, Churches
.0022846
.0097614
Health
.0084895
.0058491
Public Schools
.0384604***
.0040287
-.0155142**
.0072744
-.0143592
.0186861
-.0420481***
.0089753
.0085606
.0128728
.0058494
.006068
-.0280124***
.0031108
Private Schools
-.0109483***
.004213
.0162431**
.0072351
-.0089209
.0064021
Public Services
Miscellaneous
.0230452***
.0054947
-.0162277*
.0094495
-.0025177
.0075534