Broadband economic impact in Brazil - Hildebrando Rodrigues (2011)

Macedo et al. Broadband Economic Impact in Brazil:
a Simultaneous Equations Analysis

Broadband Economic Impact in Brazil: a Simultaneous
Equations Analysis
Hildebrando Rodrigues Macedo Alexandre Xavier Ywata de Carvalho
ANATEL IPEA
hmacedo@anatel.gov.br alexandre.ywata@ipea.gov.br

Biographies
Hildebrando Rodrigues Macedo: Telecommunications Regulation Specialist at the National Telecommunications Agency
(Anatel), Brazil. Currently pursuing his master’s degree in Management at University of Brasilia.
Alexandre Xavier Ywata de Carvalho: Quantitative Methods Coordinator at the Directorate of Regional and Urban Studies,
Institute of Applied Economic Research (Ipea), Brazil. He received his PhD from Northwestern University.

ABSTRACT
This study evaluated the broadband economic impact in Brazil by using simultaneous equation analysis with endogenous
variables. It was found that each 1p.p. increase in the broadband penetration is related to between 0.038p.p and 0.18p.p. GDP
growth, and between 0.196p.p. and 0.362p.p. GDP per capita growth. Data regarding the number of broadband accesses,
disaggregated by each State as well as the investment in the telecommunications sector, consolidated nationwide, for the
period 2000 to 2008 came from ANATEL, the Brazilian telecommunications regulatory agency. Some of these data had to be
estimated as well the prices charged, which can be partially credited for the high economic impact found. The effort to
overcome the lack of reliable statistics in Brazil by estimating the missing data is an important part of the work and must be
seen as an incentive for doing such studies in countries dealing with the same lack of data problem.

KEYWORDS
Broadband, Economic Impact, Telecommunications, Technology Diffusion.

INTRODUCTION
Given the benefits brought by the availability of broadband internet access, some countries have started public policies
towards making this a universal service. This because the broadband networks became important development tools for the
countries, allowing to transform the existing economic activities as well to create new ones.
As examples, both US, FCC(2009) and Brazil, MC (2009), started to implement national broadband plans. The difference is
that in US the focus is to reach rural areas, whereas in Brazil the aim is to connect all localities with optical fiber links,
mainly the smaller cities, which suffer with deficiencies in the service provided, caused in part by the lack of proper backhaul
connections.
This study tried to answer the following questions, for the Brazilian case:
 If there is a positive link between the increase in the broadband penetration and the local economic development. This
positive relation is widely accepted, but the intention was to particularize it for Brazil.
 How much is the broadband economic impact in Brazil.
For the first question the study showed a positive relation between increase of the broadband penetration and GDP and GDP
per capita growth.
For the second one, the results indicated that each 1p.p increase in the broadband penetration is related to between 0.038 to
0.18p.p. GDP growth, and between 0,196 to 0,362p.p. GDP per capita growth.
The results show a high economic impact. The lack of reliable data for some of the variables and the need to estimate the
missing data brings additional imprecision to the results, which must be interpreted in a more cautious way.

Proceedings of the 5th ACORN-REDECOM Conference Lima,Peru, May 19-20th, 2010 211


Despite the risk of some imprecision, it is important that countries with similar situation of Brazil, lacking of good statistics
data, try to make an extra effort to do these estimates. Otherwise they would give up using essential information for the
planning of public policies for the sector in order to increase the digital inclusion of the local population.
Basing theses policies only on studies abroad, including a wide range of heterogeneous countries, may give mislead results,
because they do not take into account the particularities of each country. So it is essential that each country be able to analyze
its particular reality in order to be able to plan its digital inclusion policies.
The main purpose of this work is to replicate the study of Koutroumpis (2009), which studied the broadband economic
impact in 22 OECD countries, using data from 2002 to 2007, employing a system of simultaneous equations with endogenous
variables, but using data from Brazil

REFERENCES
The availability of broadband networks affects the economic development of the countries in several ways. Holt e Jamisson
(2008), for example mentioned that the economy becomes more competitive with the improvement of the companies
efficiency and its innovation capacity. Broadband networks act as a tool allowing them to incorporate new knowledge and
processes to its activities.
A World Bank study, from Qiang, Rossotto e Kimura (2009, p.49), analyzing data from about 120 countries, identified for
developing counties that each 1p.p. increase in the broadband penetration is related to 0.138p.p. increase in the GDP per
capita growing rate.
Crandall, Lehr e Litan (2007, p.2) analyzed United States employment level data from 2003 to 2005 finding a relation
between 1p.p. increase in the broadband penetration with 0.2 to 0.3p.p. increase in the employment level.
Datta e Agarwal (2004), using panel data analysis for the period from 1980 to 1992 of 22 countries found significant link
between telecommunications infrastructure investment and economic development.
Koutsky e Ford (2005) identified an increase of approximately 100% in the economic activity of Lake County, Florida, U.S.
after the local municipality deployed an extensive fiber optic network, when compared to other similar locations where such
telecommunications network was not deployed.
There is a simultaneous relationship between telecommunications investment and economic development, because at the
same time that telecommunications investment leads to economic growth, the country growth demands more
telecommunications network capacity meaning that more investment in the sector is required.
To take this into account, some authors use simultaneous equations systems with endogenous variables modeling the supply
and the demand, like in the study of Röller e Waverman (2001) which analyzed the economic impact of fixed line telephone
deployment in 22 OECD countries. This approach was later used in Koutroumpis (2009), focusing on the broadband impact
in 22 OECD countries from 2002 to 2007.

DATA USED
The data comprises the period from 2000 to 2008. The data regarding broadband networks investment, net operational
revenue of the broadband providers and the prices charged for the broadband prices are consolidated for the whole country.
The remaining data is disaggregated for each of the 27 Brazilian States.
The number of broadband accesses, disaggregated by municipality is available only from 2007 and on and before that only
available at a national consolidated level. These are collected by Anatel, the Brazilian telecommunications regulatory agency.
So for 2000 to 2006 the distribution of the number of accesses among the States had to be estimated, as detailed in the
Appendix B.
It was considered as a broadband access those connections with data transmission speed as classified by Anatel: up to
64kbps, from 64kbps to 512kbps, from 512kbps to 2Mbps, from 2Mbps to 34Mbps and above 34Mbps.
The price charged has great impact over the broadband demand in Brazil, as shown in studies of Wohlers, Abdala, Oliveira e
Kubota (2009), Ávila (2008) and Guedes, Pasqual, Pitoli and Oliva (2008). In Ávila (2008), the price-demand elasticity
found varied from -1 to -3.36.



Because of the lack of reliable data of the prices charged, allowing a composition of a historical series, this important variable
had to be estimated based on the maximum declared value for acquisition, obtained through surveys performed by Cetic, an
organization involved in the internet domain management in Brazil, between 2005 and 2008.
The methodology, with its limitations, is similar to that used by Oliveira (2008, p.14) and is detailed in the Appendix D.
Other data regarding the economy, as GDP, GDP Per Capita, education level of population, came from IBGE, the Brazilian
statistics bureau.

THE ECONOMETRIC MODEL
This work tried to replicate the study of Koutroumpis (2009), but using data from Brazil, using a simultaneous equations
system with endogenous variables. Some adaptations in the models were required to achieve compatibility with the data
available in Brazil.

Model Variables
Table Table 1 shows a description of the variables used in the models.

DENS_BBt: Broadband density. Number of accesses per 1000 inhabitants, for each State, between 2000 and 2008.
Source: ANATEL.
INVEST_BBt and REV_BBt: Broadband annual investments (2002 to 2008) and Gross annual operational revenue
(2000 to 2008) of the broadband providers. Data aggregated for the whole country. Source: ANATEL.
GDPt e GDPCt: GDP and GDP per capita for each state from 2000 to 2008. The 2008 GDP per state was estimated
distributing the national GDP following the same shares of each state on the 2007 GDP. Source: IBGE.
POP_50Kt: Share of the State population living in cities with at least 50.000 inhabitants. Proxy for population
concentration, replacing the variable used by Koutroumpis (2009) of the share of the population living in areas where
density ≥ 500 inhab./ km2. Source: IBGE.
POP_15_YR_8_YR_EDUt and PERCENT_EDUt: population and percentage of the State population at least 15 years
old and with 8 years or more of complete education. Source: IBGE.
PRICEt: Average price charged for the broadband access. Those are estimated values according the methodology
detailed in the Appendix D.

Table 1 – Variables used.

Model Description
Six models were used to evaluate the impact of the increase in the broadband density on the GDP and GDP per capita, being
3 for the GDP and 3 for the GDP per capita, with the use of GMM and 3SLS estimation methods. The results are shown on
Tables Table 3 and Table 4. The three types of models are listed on the Table Table 2:

Model Dependent Variable Description
1 GDP
Without the price variable
2 GDP per Capita
3 GDP
Including the price variable
4 GDP per Capita
5 GDP With the price variable but without the variables
regarding demographic density and education
6 GDP per Capita level.

Table 2 – Regression models.



Two variables were excluded in the third type of model, because the demographic concentration variable was not acting as
expected in the two previous models and the education level variable, had a different behavior than the expected when acting
jointly with the price variable in the second type model.

Model 1 – GDP – Without the price variable
ln(GDP )   P 0   P1  ln( INVEST _ BBt )   P 2  ln( POP _ 15 _ YR _ 8 _ YR _ EDU t )  (Eq. 1)
Aggregated Production Function – t

GDP   P 3  ln( DENS _ BBt )   t

ln ( DENS _ BBt )   D 0   D1  ln(GDPCt )  (Eq. 2)
Demand for Broadband
Infrastructure   D 2  ln( PERCENT _ EDU t )   D3  ln( POP _ 50 K t )   D

Supply of Broadband ln ( INVEST _ BBt )   O0   O1  ln( REV _ BBt )   O (Eq. 3)
Infrastructure
 DENS _ BBt  (Eq. 4)
Broadband Infrastructure ln 
 DENS _ BB    PBL 0   PBL1  ln( INVEST _ BB )   PBL

Production Function  t 1 

Model 3 – GDP – With the price variable
Aggregated Production Function ln(GDP )   P 0   P1  ln( INVEST _ BBt )   P 2  ln( POP _ 15 _ YR _ 8 _ YR _ EDU t ) 
t (Eq. 1)
– GDP   P 3  ln( DENS _ BBt )   t

Demand for Broadband ln ( DENS _ BBt )   D 0   D1  ln(GDPCt )   D 4  ln( PRICE t )  (Eq. 5)

Supply of Broadband
ln ( INVEST _ BBt )   O0   O1  ln( REV _ BBt )   O 2  ln( PRICEt )   O (Eq. 6)
Infrastructure

Broadband Infrastructure  DENS _ BBt  (Eq. 4)
ln 


Model 5 – GDP – With the price variable but without the demographic density and education level variables
Aggregated Production ln(GDP )   P 0   P1  ln( INVEST _ BBt )   P 2  ln( POP _ 15 _ YR _ 8 _ YR _ EDU t ) 
t (Eq. 1)
Function – GDP   P 3  ln( DENS _ BBt )   t

ln ( DENS _ BBt )   D0   D1  ln(GDPCt )   D 4  ln( PRICEt )   D (Eq. 7)
Infrastructure
Supply of Broadband
Infrastructure

ln 


Table 3 – Regression models having the GDP as main dependent variable.



Model 2 – GDP per capita – Without the price variable
Aggregated Production ln(GDPC )      ln( INVEST _ BB )    ln( POP _ 15 _ YR _ 8 _ YR _ EDU )  (Eq. 8)
t P0 P1 t P2 t
Function – GDP per
capita   P 3  ln( DENS _ BBt )   t

Demand for Broadband ln ( DENS _ BBt )   D 0   D1  ln(GDPCt )  (Eq. 9)

Supply of Broadband
ln ( INVEST _ BBt )   O0   O1  ln( REV _ BBt )   O (Eq. 10)
Infrastructure

ln 


Model 4 – GDP per capita – With the price variable
Aggregated Production ln(GDPCt )   P 0   P1  ln( INVEST _ BBt )   P 2  ln( POP _ 15 _ YR _ 8 _ YR _ EDU t )  (Eq.1)
Function – GDP per capita   P 3  ln( DENS _ BBt )   t

Demand for Broadband ln ( DENS _ BBt )   D 0   D1  ln(GDPCt )   D 4  ln( PRICE t )  (Eq. 5)

Supply of Broadband
Infrastructure

ln 
 DENS _ BB    PBL 0   PBL1  ln( INVEST _ BBt )   PBL


Model 6 – GDP per capita – With the price variable but without the demographic density and education level
variables
Aggregated Production ln(GDPCt )   P 0   P1  ln( INVEST _ BBt )   P 2  ln( POP _ 15 _ YR _ 8 _ YR _ EDU t )  (Eq.2)
Function – GDP per capita   P 3  ln( DENS _ BBt )   t

ln ( DENS _ BBt )   D0   D1  ln(GDPCt )   D 4  ln( PRICEt )   D (Eq.7)
Infrastructure
Supply of Broadband
Infrastructure

Broadband Infrastructure  DENS _ BBt  (Eq.4)
ln 
 DENS _ BB    PBL 0   PBL1  ln( INVEST _ BBt )   PBL


Table 4 – Regression models having the GDP per capita as main dependent variable.

Comments about the models
The original Koutroumpis (2009) model evaluating the broadband impact on the GDP was modified, resulting in the Models
1, 3 and 5, with the same purpose.
In order to use the same model structure of Koutroumpis (2009) to also evaluate the broadband impact on GDP per capita, in
the aggregated production function the GDP variable was replaced by GDP per capita, resulting in the Models 2, 4 and 6.
Other adaptations on the Koutroumpis (2009) model included the replacement of the some of the original variables by other
ones with data available for Brazil and some small modifications on the equations to improve the regression results.



Koutroumpis (2009) Reference Model
For comparison purposes, the Koutroumpis (2009) model is shown in the Table 5.

Equations

Aggregated Production ln(GDPt )   P 0   P1  ln(K t )   P 2  ln(LFt )   P3  ln(PEN t )   P (Eq. 12)
Function – GDP (PIB)
ln( PEN t )   D 0   D1  ln(GDPCt )   D 2  (BBPrt ) 
Demand for Broadband (Eq. 13)
Infrastructure   D3  (EDU t )   D 4  ( URBt )   D5  (R & D t )   D

ln( BBIt )   O 0   O1  ln( BBprt ) 
Supply of Broadband   O 2  ln( InterPlatform t )  (Eq. 14)
Infrastructure
  O 3  ln( Regulation t )   O

 PEN t 
 PEN    P 0   P1  ln( BBIt )   P
ln 
Broadband Infrastructure
 (Eq. 15)

Table 5 – Koutroumpis (2009) model equations.

Where

BBItI : Broadband annual investment.
Interplatformt: Herfindahl-Hirschman index, HIRSCHMAN (1964), for the competition among the several
available broadband technologies as DSL, WiFi, WiMAX, fiber optics, 3G cellular and others.
EDUt and R&Dt: Percentage of the GDP spent annually on education and research and development.
Kt: Stock of telecommunications investment.
LFt: Labor force. Population between 15 and 64 years old.
PENt: Broadband penetration. Number of broadband accesses per 100 inhabitants.
GDPt and GDPCt : GDP and GDP per capita.
BBPrt: Average price of a 1Mbps broadband connection.
URBt : Percentage of the population living in areas with demographic density ≥ 500 inhab./km2.
REGt: Percentage of broadband accesses offered through unbundling.

Table 6 – Koutroumpis (2009) model variables.

Some comments are made in order to better understand the differences between the models used in this work and the original
from Koutroumpis (2009):

a) Aggregated Production Function:
In Koutroumpis (2009) it was used the broadband infrastructure stock, instead of the investment in the sector. This because
according with the author the user demand is for the telecommunications infrastructure of the broadband companies that is



the mean by which the users will be able to benefit from the service provided, and not from the investments made. Due the
lack of equivalent data available in Brazil, it was used the investment made by the providers, as shown in Table C.1.
For the human capital (labor force) the results were better when using the population 15 years old and above and with at least
8 years of complete education.
The broadband density (penetration) was expressed as the number of accesses per 1000 inhabitants.

b) Demand for Broadband Infrastructure:
Initially the price was not included because of the lack of a reliable historical data series for Brazil. But given its importance
it was included in the later models, using estimated data following the methodology described in the Appendix D.
To replace the variable of the percentage of population living in areas with demographic density ≥ 500 inhabitants/km2, it
was used the percentage of population in each state living in localities with at least 50,000 inhabitants.
In Figures C.1 e C.2, using ANATEL data from 2007, one can see that most of the broadband accesses in Brazil
(approximately 90%) are concentrated in larger cities, with more than 50,000 inhabitants, despite that about 92% of the
localities have up to 50,000 inhabitants. Other disparity shown is that while 29% of the population lives cities with at least
500,000 inhabitants, these locations concentrate about 58% of all broadband accesses.
But how this variable had not the expected result, it was removed from the models 5 and 6.
It was not included variables regarding R&D investment because the low investment in the area in Brazil
The percentage of GDP spent on education was included initially, but showed poor results and was removed from the model.

c) Supply of Broadband Infrastructure:
It was used a simplified version to try explain the incentives that the broadband providers have to deploy or extend their
networks. In Koutroumpis (2009), it was used the price charged for the service and the percentage of all DSL accesses
offered using the networks of other providers, in the unbundling mechanism. This because the higher the price, more
attractive is the market for the companies. The more the possibility of using third part networks to reach the final user,
without having to built its own network, the more interested the new entrants are because the lower investments required.
In the model used here, it was used the gross operational revenue as a proxy of the profits of the broadband providers. So, the
more the revenue, the more the incentive to expand the network. The ideal would be to use data regarding the profits, but
those were not available, only the revenue, collected by ANATEL.
The HHI index for competitions among technologies was not included due the lack of data. ANATEL only started to collect
the number of access, speeds and technologies of the broadband access for each city only from 2007 and on.
The unbundling variable was not included because the lack of data in Brazil.

Analysis of the Results

GDP – Models 1, 3 and 5 and GDP per Capita – Models 2, 4 e 6
The results are shown in Appendix A on Tables A.1 (GDP) and A.2 (GDP per capita).
In the Aggregated Production Function for the GDP, (Eq. 1), P3 in Table A.1 gives the GDP/broadband penetration
elasticity. We see that P3 has values between 0.038 and 0.18 indicating that each 1p.p. increase in the broadband penetration
is related to GDP growth varying between 0.038 and 0.18p.p.
These are high impact values. If we take into account that between 2007 and 2008 the broadband penetration increased about
30%, from 45.8 to 59.1 accesses per 1000 inhabitants, according Table C.1, and applying P3 over these values one obtains a
GDP growth between 1.14 and 5.4p.p.



To compare, Table C.1 shows that the real GDP growth in 2008 was 5.08%. These values present higher broadband
economic impact in Brazil than compared with the results found in Koutroumpis (2009, p.478) which were 0.012, 0.023,
0.025 and 0.204 p.p., despite the difference between models.
Same high impact was observed on the GDP per capita, as shown in the results for  P3 in Table A.2, which was between
0.196 and 0.362. In the same way the model indicates that each 1p.p. increase in the broadband penetration is related to GDP
per capita growth varying between 0.196 and 0.362. Applying the same 30% broadband penetration growth leads to a GDP
per capita increase between 5.88 and 10.86p.p. The real GDP per capita growth in 2008 was 4%.
So the results of this model say basically that almost all economic growth can be credited only to the increase in the
broadband density, which of course is not true.
A possible explanation to the higher economic impact found for Brazil rely on the different historical moments where
broadband networks were introduced there and in the more developed countries. In richer countries the broadband internet
access networks became widely available when these had already reached a stable and high level of economic development.
So the broadband appeared only as an additional factor helping the development, having its impact being diluted among the
many others available that lead those countries to a high development level over the years.
For these nations the broadband networks acted as instruments to catch up with the developed countries, so that’s why it is
expected to have higher broadband economic impact on them.
Similar higher economic impacts are observed regarding the widespread introduction of mobile phone networks in some
developing countries. Analyzing data from some African countries, Waverman, Meschi e Fuss (2005, p. 11) mentioned that
mobile phone networks had in some cases the double of the economic impact in developing countries than in developed ones.
Even the World Bank Study performed by Qiang, Rossotto e Kimura (2009, p. 45 e 47) points out the higher economic
impacts of the increase of fixed line telephones and broadband networks in developing countries when compared to those
more developed.
Other fact that could help explain the higher results is that the broadband penetration in Brazil is low, 5.91 access per 100
inhabitants in 2008, compared to 26.7 accesses per 100 inhabitants in the United States and 32 accesses per 100 inhabitants
in South Korea, according data extracted from Katz (2009) shown in the Table C.2. So it is easier to have higher penetration
growing rates, starting from a small base of users.
Another factor is that 2000 to 2008 was a period of relative “good” economic growth in the Brazilian economy, so when the
model relates the steadily GDP growth in the period with the evolution of the penetration, as shown in Figure C.3, it may give
the impression of a higher broadband impact than the reality. Probably the impact would be lower if it was included the GDP
growth of 2009, which was near zero, caused by the international financial crisis. This was not done because the 2009 data
was not available when the making of this work. This reinforces the need of more data in order to obtain more reliable
results.
Making some comments about some of the variables:
 POP_50Kt: Its coefficient had not a positive sign as expected. It was expected that the higher the percentage of the State
population lived in larger cities ≥ 50,000 inhabitants, the higher the broadband penetration would be. This because for the
broadband providers the cost benefit is better to offer their services in larger locations with higher population concentration.
So the higher the proportion of the population living in these locations, theoretically the higher the broadband penetration.
The result was not within the expected because in some of the smaller States, great part of the population lives in the State
capital, larger than 50,000 inhabitants, resulting that the overall State population lives in cities larger than 50,000 inhabitants.
 PRICEt: Had a consistent behavior across all models, with negative sign for its coefficient, meaning that the higher the
price, the lower the broadband penetration. The coefficient D4 was between –2.16 and –1.79 (Tables A.1 and A.2), being the
factor with higher impact on the broadband penetration. On the supply side, O2 was between 0.19 e 0.25, with the positive
sign indicating that the higher the prices, the more willing to offer the service the providers are.
In this case D4 represents the price-demand elasticity and resulted in the same range of values found by other studies like
Wohlers, Abdala and Kubota (2009), Ávila (2008) and Guedes, Pasqual, Pitoli and Oliva (2008), situated between –3.36
and –1.



CONCLUSIONS
The results are consistent with other studies in the area, showing a positive relation between the increase of the penetration of
broadband internet access and GDP and GDP per capita growth.
The broadband economic impact found was higher than other studies, showing a relation of 1p.p. broadband penetration
increase with GDP growth between 0.038 and 0.18p.p. and with GDP per capita growth between 0.196 and 0,362p.p..
There are several limitations of the study, mainly related with the lack of important data as the number of broadband accesses
disaggregated by State between 2000 and 2006 as well the prices charged for the service. Because these missing data had to
be estimated, some additional imprecision was brought to the results.
Other aspect the study shows is the high price sensitivity that broadband penetration has in Brazil, with the price-elasticity
found situated between –1.79 and –2.16, confirming the results of other studies for the Brazilian market.

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16. Koutsky, T. M. and Ford, G. S. (2005) Broadband and Economic Development: A Municipal Case Study from
Florida, Review of Urban & Regional Development Studies, Journal of the Applied Regional Science Conference,
Vol. 17, No. 3, p. 219-229, Wiley-Blackwell.
17. MC (2009) Ministério das Comunicações (Ministry of Communications), Um Plano Nacional para Banda Larga,
(Broadband National Plan) <www.mc.gov.br/wp-content/uploads/2009/11/o-brasil-em-alta-velocidade1.pdf>.
18. Oliveira, A. R. de (2008) Análise dos Impactos Sociais do Art. 9º da Proposta de Revisão do Plano Geral de
Outorgas de Serviços de Telecomunicações Prestado no Regime Público – PGO, CGEE Centro de Gestão de



Estudos Estratégicos, 28p.
www.anatel.gov.br/Portal/documentos/sala_imprensa/21-10-2008--17h28min44s-NT_André Rossi.pdf
19. Qiang, C. Z. W., Rossotto, C. M. and Kimura, K. (2009) Economic Impacts of Broadband, in ICAD2009 -
Information and Communications for Development: Extending Reach and Increasing Impact, p.35 a 50, The World
Bank, Washington, DC.
20. Röller, L. H. and Waverman, L. (2001) Telecommunications Infrastructure and Economic Development: A
Simultaneous Approach, The American Economic Review, Vol. 91, No. 4, p. 909-923, American Economic
Association, september.
21. Waverman L.; Meschi, M. and Fuss, M., (2005) The Impact of Telecom on Economic Growth in Developing
Countries. In Africa: The Impact of Mobile Phones, Vodafone Policy Paper Series, No. 2, March, pp. 10–24.
http://www.vodafone.com/etc/medialib/public_policy_series.Par.77697.File.dat/public_policy_series_2.pdf.
22. Wohlers, M. de A., Abdala, R. F. de S., Kubota, L. C. and Oliveira, J. M. de (2009). Banda Larga no Brasil – por
que ainda não decolamos?, Radar – Tecnologia, Produção e Comércio Exterior, n.5, p. 9-15, IPEA, december.
www.ipea.gov.br/sites/000/2/pdf/091221_radar.pdf.



Appendix A – Regression Results
Period: 2000 to 2008 (9 samples). Observations included: 243. Methods: GMM and 3SLS.
Obs.: a) Between parenthesis: t-statistics; b) All coefficients bellow 1% significance, with exceptions: ** 5%; c) In the
Broadband Infrastructure Production Function the lack of the intercept affected the R2 characteristics, resulting in a negative
value.

Model 1 Model 3 Model 5
Dependent Variables Coefficients
GMM 3SLS GMM 3SLS GMM 3SLS
Aggregated Production Function – GDP
0.384152 0.381463 0.386214 0.391608 0.380838 0.396752
INVEST_BBt P1
(48.36476) (26.13861) (53.84365) (29.16251) (51.34634) (29.67189)
1.099994 1.095014 1.103447 1.092716 1.115865 1.084271
POP_15_YR_8_YR_EDU t P2
(86.52065) (47.89375) (96.44659) (51.88025) (91.91260) (51.74455)
0.132607 0.180419 0.096566 0.103947 0.037591 0.108581
DENS_BB t P3
(7.805992) (8.670279) (6.707684) (5.612564) (2.227868) ** (5.860035)
Demand for Broadband (Broadband Penetration)
1.088145 0.783770 1.713477 1.585110 1.453160 1.243869
GDPCt D1
(6.998335) (5.795702) (20.32760) (18.55224) (42.76642) (46.68460)
0.148596 0.149381 -0.028978 -0.021806
PERCENT_EDUt D2 – –
(17.00035) (16.46474) (-4.367984) (-2.471171)
-3.297796 -2.621738 -0.374489** -0.457876
POP_50K t D3 – –
(-11.34698) (-9.661079) (-2.324592) (-2.516569)
-2.110566 -1.871161 -2.128333 -1.788876
PRICE t D4 – –
(-29.58193) (-22.63430) (-38.05935) (-39.76378)
Supply of Broadband (Broadband Investment)
4.709844 3.377441
Constant (intercept) O0 – – – –
(9.125580) (6.924596)
0.738647 0.796967 0.889836 0.900437 0.888904 0.901550
REV_BBt O1
(33.03946) (37.70361) (309.8954) (211.1424) (278.3672) (211.3017)
0.245925 0.195672 0.247895 0.190876
PRICE t O2 – –
(19.62621) (10.18291) (17.77594) (9.925758)
Broadband Infrastructure Production Function
(Broadband Penetration Variation)
0.023311 0.021910 0.021655 0.021523 0.022169 0.021482
INVEST_BBt PBL1
(19.38364) (16.39933) (21.95414) (16.12071) (21.12353) (16.09012)
R2
Aggregated Production Function (GDP) 0.943602 0.939749 0.943867 0.944700 0.940207 0.944647
Demand for Broadband (Broadband Penetration) 0.641189 0.651794 0.854035 0.873159 0.812515 0.865199
Supply of Broadband (Broadband Investment) 0.853393 0.855476 0.838145 0.853177 0.839006 0.853862
-0.031278 -0.025090 -0.025248 -0.025483 -0.025335 -0.025580
(Broadband Penetration Variation)c

Table A.1– Coefficients of the Models 1, 3 and 5.



Model 2 Model 4 Model 6
Dependent Variables Coefficients
GMM 3SLS GMM 3SLS 3SLS GMM
Aggregated Production Function – GDP per capita
0.300596 0.320713 0.291462 0.313750 0.307291 0.326707
INVEST_BBt P1
(23.24792) (16.89092) (23.37458) (16.46235) (27.64092) (18.75077)
0.125845 0.088370 0.146340 0.117908 0.135701 0.104163
POP_15_YR_8_YR_EDU t P2
(6.225596) (2.988210) (7.505312) (3.951584) (7.662507) (3.808836)
0.321389 0.362174 0.259887 0.237915 0.195625 0.202685
DENS_BB t P3
(13.73108) (12.66426) (12.98031) (9.431967) (8.353454) (8.015462)
Demand for Broadband (Broadband Penetration)
1.002672 0.487666 2.109207 2.004954 1.319185 1.339643
GDPCt D1
(6.028245) (3.235005) (17.37478) (17.33658) (39.89024) (44.02001)
0.154057 0.163598 -0.055774 -0.044998
PERCENT_EDUt D2 – –
(17.62490) (17.73362) (-5.416182) (-4.154802)
-3.158045 -2.090083 -0.971635 -0.923631
POP_50K t D3 – –
(-9.919893) (-6.914956) (-4.275140) (-4.299299)
-2.155022 -2.070894 -1.927884 -1.950034
PRICE t D4 – –
(-22.46718) (-21.27334) (-35.36667) (-37.98579)
Supply of Broadband (Broadband Investment)
4.636146 2.961065
Constant (intercept) O0 – – – –
(8.835450) (5.984964)
0.741689 0.815054 0.896181 0.900912 0.898364 0.899547
REV_BBt O1
(32.63128) (38.01361) (275.6979) (209.5491) (245.7452) (208.7685)
0.216369 0.192051 0.207230 0.198550
PRICE t O2 – –
(15.27225) (9.908948) (12.83509) (10.22116)
(Broadband Penetration Variation)
0.023503 0.021899 0.020947 0.021942 0.022595 0.021857
INVEST_BBt PBL1
(19.37924) (16.39141) (20.65696) (16.42393) (22.06186) (16.36240)
R2
Aggregated Production Function (GDP per capita) 0.419730 0.389399 0.446801 0.465342 0.455947 0.463187
Demand for Broadband (Broadband Penetration) 0.641589 0.641744 0.846131 0.855566 0.851969 0.849668
Supply of Broadband (Broadband Investment) 0.853245 0.853967 0.847906 0.854098 0.849350 0.852951
-0.033052 -0.025089 -0.027753 -0.025098 -0.026625 -0.025090
(Broadband Penetration Variation) b

Table A.2 – Coefficients of the Models 2, 4 and 6.



Model Instruments
1 POP_15_YR_8_YR_EDU, GDPC, PERCENT_EDU, POP_50K and REV_BB.
2 POP_15_YR_8_YR_EDU, PERCENT_EDU, POP_50K and REV_BB.
3 POP_15_YR_8_YR_EDU, GDPC, PERCENT_EDU, POP_50K, REV_BB and PRICE.
4 POP_15_YR_8_YR_EDU, PERCENT_EDU, POP_50K, REV_BB and PRICE.
5 POP_15_YR_8_YR_EDU, REV_BB, GDPC and PRICE.
6 POP_15_YR_8_YR_EDU, REV_BB and PRICE.

Table A.3 – Instruments.

Appendix B – Estimation of the Distribution of the Broadband Accesses for Each State between 2000 and 2006
Because Anatel only has data of the broadband accesses numbers disaggregated by municipality from 2007 and on, it was
necessary estimate the distribution of the accesses among the States between 2000 and 2006.
Based on IBGE-PNAD (annually census surveys) data referent to the number of homes with internet access (dial-up or
broadband), it was presumed that the distribution of the proportion of all broadband accesses in the country, among the
different States, followed the distribution of the proportion of homes with internet access in each State.
It was used the Anatel data from 2007 and 2008 to validate the methodology and the results were R22007 = 0.86 for 2007 and
R22008 = 0.79 for 2008.
For illustration purposes only, the Figures B.1 allow a visual comparison of both set of data showing that they follow
approximately the same distribution.

2008 - Comparison: Shares of States on the country’s total broadband accesses X
Shares of States on the country total homes with internet access
40
38
36 Share of each State on the country total broadband accesses
34
32 Share of each State on the country total homes with internet
30
28
Participation (%)

26
24
22
20
18
16
14
12
10
8
6
4
2
0
SP RJ MG PR RS SC DF BA GO PE CE ES MT MS PA PB RN MA AM SE RO PI AL TO AC AP RR
State



2007 - Comparison: Shares of States on the country total broadband accesses X
Shares of States on the country total homes with internet access
40
38
36
Share of each State on the country total broadband accesses
34
32 Share of each State on the country total homes with internet
30
28

Participation (%)
26
24
22
20
18
16
14
12
10
8
6
4
2
0
SP RJ MG PR RS SC DF BA GO PE CE ES MT MS PA PB RN MA AM SE RO PI AL TO AC AP RR
State

Figure B.1 – Comparison between the distribution of the total of broadband accesses among the States with the same distribution
of the homes with internet Access in 2007 and 2008. Source: elaborated by the authors based on data from IBGE-PNAD and
Anatel-SICI.

Appendix C – Additional Data Used
These are additional data used in the work mentioned along the text. Some of the figures allow having some insight on the
broadband landscape in Brazil.

Distribution of the broadband accesses according to the range of population of the municipalities (4th Quarter 2008).
Distribution of the percentage of municipalities according to the range of its population.
70

Percentage of broadband accesses according to the population of the municipalities
58.41
60
Percentage of municipalities by size of its population

50
45.97
Percentage (%)

40

30
24.89

20
15.93
10.24 7.01 8.2
10 5.73 5.55
2.24 2.39 1.91 2.19 1.72 2.84 2.46 1.65 0.66
0
Up to 10000 From 10000 From 20000 From 30000 From 40000 From 50000 From 100000 From 20000 >500000
inhab. to 2000 To 30000 to 40000 to 50000 to 100000 to 200000 to 500000 inhab.
inhab. inhab. inhab. inhab. inhab. inhab. inhab.
Range of population of the municipalities

Figure C.1– Distribution of the percentage of the broadband accesses and the municipalities of the country according to its
population. Source: elaborated by the authors from data from ANATEL-SICI and IBGE.



Distribution of the broadband accesses according to the range of population of the municipalities (4 th Quarter 2008).
Distribution of the country´s of inhabitants living in cities according to its size of population.
70

58.41
60 Percentage of broadband accesses according to the population of the municipalities

Percentage of inhabitants according to the size of the municipalities population
Percentage (%)

50

40
29.16
30
15.93
20 14.89
10.49 11.83
7.29 5.59 7.01 9.89
7.12 8.2
10
2.39 3.71
2.24 1.91 2.19 1.72
0
Up to 10000 From 10000 From 20000 From 30000 From 40000 From 50000 From 100000 From 20000 >500000 inhab.
inhab. To 20000 to 30000 To 40000 to 50000 to 100000 to 200000 to 500000
Inhhab. inhab. inhab. inhab. inhab. inhab. inhab.
Range of population of the municipalities

Figure C.2 – Distribution of the percentage of the country´s broadband accesses and inhabitants according to the size of the
municipalities’ population. Source: elaborated by the authors from data from ANATEL-SICI and IBGE.

Proportion of Broadband providers
GDP Total Investment (billions of R$) operational revenue
GDP population Broadband
Annually. GDP per Number of (billions of R$)
(trillions ≥ 15 years old accesses per
Year Growth capita – broadband
R$) and ≥ 8 years 1000 Broadband Fixed Whole
(%) (R$) accesses
of complete inhabitants Telecom. Telecom Gross Net
study. Services sector
1994 0.349 5.33 2,227.42 3.30
1995 0.706 4.41 4,437.54 4.30
1996 0.844 2.15 5,233.99 7.40
1997 0.939 3.39 5,745.05 7.60
1998 0.979 0.04 5,910.38 12.30
1999 1.065 0.25 6,310.98 12.20
2000 1.179 4.31 6,886.28 0.28 0.7 122,504 16.20 3.61 2.86
2001 1.302 1.31 7,491.20 0.34 2.1 360,171 17.0 22.10 4.29 3.35
2002 1.478 2.66 8,378.10 0.36 3.4 587,185 1.8 6.0 10.10 5.21 4.13
2003 1.700 1.15 9,497.69 0.38 5.5 966,255 2.28 3.80 9.0 6.16 4.92
2004 1.941 5.71 10,692.19 0.39 17.6 3,157,470 1.65 3.90 13.90 7.56 5.91
2005 2.147 3.16 11,658.10 0.39 23.6 4,363,842 2.46 5.40 15.20 9.91 7.41
2006 2.369 3.96 12,686.60 0.41 31.6 5,921,917 3.66 5.90 12.50 13.66 10.43
2007 2.661 6.09 14,464.73 0.43 45.8 8,711,305 3.88 6.20 15.10 18.39 13.65
2008 2.890 5.08 15,240.10 0.44 59.1 11,401,901 5.92 8.90 25.70 21.85 16.32

Table C.1– Some of the economy and Telecom sector data used. Source: Elaborated by the authors from data from Anatel, IBGE
and IPEA. Obs.: 1USD  R$1.7 or R$1  0.6USD



Evolution of the number of broadband accesses per 1000 inhabitants in Brazil
60
59.1
50
45.8
per 1000 inhabitants
Broadband accesses
40
31.6
30
2.,6
20
17.6
10
3.4 5.5
0.7 2.1
0
94 95 96 97 98 99 00 01 02 03 04 05 06 07 08
Year

Figure C.3 – Evolution of the number of broadband accesses per 1000 inhabitants in Brazil. Source: Elaborated by the authors
from data from Anatel.

Number of broadband accesses Number of broadband accesses per 100
Country Region
per 100 inhabitants inhabitants (in the region)
Argentina 7.9
Brazil 5.91a
Chile 8.4 Latin America 5.5
Colombia 4.2
México 7.1
Canada 29.0
North America 27.8
USA 26.7
Spain 20.8
France 28.0
Europe 24.8
Portugal 16.0
UK 28.5
Australia 25.4
South Korea 32.0 Asia and Oceania 14.0
Malaysia 4.6
South Africa 0.8
Africa 1.6
Morocco 1.5
Table C.2 – Broadband densities in some countries at the end of 2008. Source: Elaborated by the authors from data from Katz
(2009) and Anatel. Obs.: a Replaced with data from Anatel. In Katz (2009) the value for Brazil is 5.3.



Appendix D – Broadband price-demand sensitivity estimation

The broadband price-demand elasticity in Brazil is high. In studies of Guedes, Pasqual, Pitoli e Oliva (2008, p.7) they found
value of –2.0 and in Ávila (2008, p.42 e 49), the elasticity was between –3.36 to –1.0.
Applying the same method as in Oliveira (2008, p.14), it was estimated how the broadband penetration is affected by its price
change. It was based on the maximum declared valued of acquisition according to surveys from Cetic (2005 a 2008). With
these data the graphs in Figure D.1 were elaborated. The price-demand curves, over the data from 2005 to 2008 were
obtained using the model as in equation 16.
.P
Q= S.e (Eq. 16);

Where: Q: quantity (broadband penetration).
S: saturation level of broadband penetration.

 : decay factor.
P: price.
A: total of broadband access in the locality (State).
D: total of homes in the locality (State).
Broadband price-demand sensitivity - 2005 to 2008
Broadband price-demand sensitivity - 2008
80 80
Percentage of homes where users were willing to

Percentage of homes where users were willing to

Demand curve x Price estimated by regression
2008 2007
Subscribe the internet access service (%)

70
Demand x Price (survey data) 70 2006 2005
General model
Subscribe the internet access service (%)

60
--0.018205.x 60
y = 90.62617.e General model
2
50 R = 0.992638 y = 66,483939.e-0.014821.x
50
R2 = 0,960405
40
40
30
30
20
20
10
10
0
0 50 100 150 200 250 300 0
Maximum declared value declared by user (home) willing to subscribe
0 50 100 150 200 250 300
to the internet access service (R$).
Maximum declared value declared by user (home) willing to subscribe
to the internet access service (R$).

Figure D.1 – Curves plotted over data from surveys performed by Cetic from 2005 to 2008 evaluating the price-sensitivity to
internet access subscription in Brazil. Source: elaborated by the authors on data from Cetic (2005 to 2008).
The models had R2 > 0.98. For each year between 2005 and 2008, where adjusted individual curves by regression, using the
Cetic survey data, and for 2000 to 2004 it was used a general model obtained by regression on the consolidated data from
2005 to 2008.


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