trade blocs and the global digital divide: a spatial panel
TRANSCRIPT
“Trade Blocs and the Global Digital Divide: A Spatial Panel Data
Approach”
TESIS PARA OPTAR AL GRADO DE
Economia Analytica
Alumno: Kasper Jeroen Mulder
Profesor Guía: Nicolas Grua
Santiago, Noviembre 2015
2
ABSTRACT: In order to get a better understanding of worldwide Internet usage differences,
spatial interaction effects are added to a model explaining cross-country growth in Internet
usage. The paper finds that ICT infrastructure growth has a positive and significant effect on
Internet usage growth in one’s own country as well as in other countries. The findings suggest
that the efficiency of policies aimed at decreasing the global digital divide can be increased if
they are initiated on a trade bloc level. Contrary to earlier papers no significant role for
income in explaining cross-country Internet usage differences is found.
KEYWORDS: Digital divide, Internet, Spatial modelling, ICT infrastructure
JEL: O21, O32, O33
3
There exists a growing concern about the worldwide digital divide. This divide is defined by
the OECD (2001, p.4) as “the gap between individuals, households, businesses and
geographic areas at different socio-economic levels with regard to their opportunities to
access information and to their ability to use the Internet for a wide variety of activities”.
In the literature most references to the digital divide are equated to the worldwide differences
in access to Internet. The literature mostly uses number of Internet users (Beilock and
Dimitrova, 2003; Chinn and Fairlie, 2006 and Guillén and Suárez, 2005), and number of
Internet hosts (Hargittai, 1999; Kiiski and Pohjola, 2002 and Oyelaran-Oyeyinka and Lal,
2005).
Figure 1 shows the average Internet usage over the period 2000-2009 for high, middle and
low income countries, as classified in the year 2000 by the World Bank (Soubbotina and
Sheram, 2000). It shows that Internet usage in both high income countries and middle income
countries has grown at a fast pace. However, the gap between the high income and middle
income countries is substantial and has stayed quite constant over the period. The gap
between the high income countries and low income countries is even more striking. Also, the
low income countries on average show no growth rates, further increasing this gap between
them and high and middle income countries. International agencies like the World Bank, UN
and OECD have expressed growing concern that the increase of the gap between developing
and developed countries may leave many nations economically behind (World Bank Group
strategy for ICT, 2012; International Telecommunication Union, 2012 and OECD, 2001)
Figure 1: Average Internet usage as percentage of total population for the period 2000-2009
Source: Data from the UN International Telecommunication Union
This concern is linked to the fact that the widespread access to Internet has become a key
driver of country competitiveness and economic growth (Röller and Waverman, 2001; Kenny,
2003 and Czernich, Falck, Kretschmer and Woessmann, 2009). The Internet has great
promises in increasing productivity and improving accountability and governance. It is
increasingly determining the ability of individuals, firms and territories to remain competitive
by producing and working more efficiently.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
High-income countries
Middle-income countries
Low-income countries
4
Furthermore, the Internet is recognized as being able to help reduce poverty directly and
indirectly by providing access to information, equalizing opportunities in rural areas and
contributing to pro-poor market developments such as microfinance and mobile money
(Madon, 2000; World Bank, 2012). Inequality in worldwide diffusion of Internet may
therefore have serious implications for worldwide differences in economic growth and human
development.
Following the recognition of the importance of the worldwide Internet diffusion for the
economy, governments and organizations initiate many programs to increase Internet access
of the people. The World Bank Group strategy for ICT (2012) acknowledges that worldwide
the focus of universal access policies and programs changed from voice telephony to
broadband for high-speed Internet. Current universal access programs however show mixed
results. Government IT spending is considered having a high risk-high reward nature. The
World Bank Group strategy for ICT finds that only 59% of the World Bank’s IT project
components have achieved or are likely to achieve their objectives fully or substantially.
In order to effectively deal with the worldwide digital divide the success rate of programs
aimed at increasing Internet usage in countries therefore must increase. For this it is essential
to know which factors are the main drivers behind the growth of a country’s Internet access.
Understanding the determinants of worldwide Internet diffusion is therefore of great interest.
To date, cross-country literature has mainly focused on national factors affecting the Internet
usage growth. However, given the highly inter-dependent nature of Internet as well as the
globalization of most economies, Internet usage in a country is likely to also be determined by
spatial interaction between countries. In this context, this paper contributes to the existing
literature by capturing this spatial component using spatial modelling.
Figure 2 shows worldwide Internet usage as percentage of total population in the years 2000
and 2009. The figure points to a regional development of growth in Internet usage in this
period. Where for all non-developed countries the percentage of Interent users of total
population was less than 10% in 2000, many reagional differnces can be seen in 2009. For the
non-developed countries, the highest growth toke place in South-America and East-Europe,
followed by Central Asia and the Arabic countries. Internet usage in Central American, Sub-
Sahara and South Asian countries stayed behind, still being used by less than 10% of the
population for almost all countries in those regions.
5
Figure 2: Worldwide Internet usage as percentage of total population by in the years 2000
and 2009
Year: 2000
Year: 2009 Source: Data from the UN International Telecommunication Union
To capture the spatial component in the model explaining growth in Internet usage two
different hypotheses are tested in this paper. The first hypothesis deals with spatial interaction
effects between countries based on geographical factors. The first hypothesis is tested using a
first-order binary contiguity matrix and states:
Internet usage in a country is affected by domestic effects as well as spillover effects from
neighboring countries
The motive behind extending the model with spatial interaction effects based on geographical
factors is that the possibilities of the Internet increase with the number of people using it.
Although distance does not affect the working of Internet itself, economic activity associated
with Internet usage is affected by distance.
If neighboring countries increase the usage of Internet, this increases the possibilities of
Internet usage for a country in various ways. Items can be sold via the Internet in neighboring
countries and vice versa, companies and organizations operating in both countries are able to
improve their processes using Internet and mutual problems like crime fighting and natural
disasters can be better coordinated, among many other possibilities.
50%-100%
30%-50%
10%-30%
0%-10%
50%-100%
30%-50%
10%-30%
0%-10%
6
The second hypothesis deals with spatial interaction effects between countries sharing a great
deal of economic activity. In this paper economic activities is measured as trade flows
between countries. The second hypothesis states:
Internet usage in a country is affected by domestic effects as well as spillover effects from its
trading partners
The motive behind this spatial extension is that if a country’s trading partners make little use
of Internet, the necessity of Internet usages for economic activities in that country is low. On
the other hand, if the trading partners’ economies are highly dependable on the Internet, a
country also needs to adopt an Internet based economy in order to efficiently trade with its
partners.
This hypothesis is tested using three different spatial matrices. One matrix is based on the
main export partners and main import partners over the sample period. The second matrix is
based on relative trade between all countries in the base-year 2000. The last matrix is a block-
diagonal matrix based on worldwide trade blocs.
The paper finds that including spatial interaction effects indeed improves the model, both for
models based on the first-order binary contiguity matrix and for models based on the block-
diagonal matrix. For the other two types of models the paper does not find a significant
improvement. The improved models find a strong and highly significant local indirect effect
for ICT infrastructure in explaining Internet usage. These results indicate that Internet usage
in a country is affected by both domestic variables and foreign variables.
The paper proceed as follows. Section 1 discusses the main literature on the determinants of
worldwide differences in Internet usage. Thereafter, section 2 discusses the theory on spatial
modelling. Section 3 discusses the data used in this paper. In section 4 first a benchmark
model is constructed on the basis of the variables discussed in the main literature. Second, the
spatial models are discussed. Furthermore, based on economic theory, the different W-
matrixes used for the spatial models are introduced. Hereafter, section 5 discusses the
methodology. Section 6 discusses the results. Section 7 elaborates on the policy implications
following the results. Finally, section 8 discusses the limitations and further research
opportunities, where after in section 9 the conclusion of this paper is stated.
1. LITERATURE REVIEW
Many different variables have been identified as determinants of Internet usage in a country.
This section discusses the main literature on cross-country Internet usage.
Hargittai (1999) is considered the first major research on explaining differences in Internet
connectivity. Focusing on OECD countries only, he finds significant positive effect from
GDP per capita and phone density. Furthermore, he finds a significant negative effect of the
existence of a monopoly in the telecom sector, suggesting an important role for
7
telecommunication policy. Education, pricing and income distribution, measured by the GINI
coefficient, do not appear to have a significant influence on Internet usage in his model.
Kiiski and Pohjola (2002) analyze both a small sample of OECD countries as well as a large
sample of both developing and industrial countries for the period 1995-2000. For the OECD
sample they find that GDP per capita and Internet access cost explain best the observed
growth in Internet host per capita. They do however not find a significant effect of
competition in the telecommunications market. Furthermore, they do not find significant
explanatory power for investment in education on Internet penetration growth, and the
variable measuring English proficiency enters the regression with the wrong sign. The lack of
the explanatory power of these variables however can be due to the low variation in the
sample of only OECD countries.
For the larger sample the results change so that investment in education does become
significant. However, data on Internet access cost is excluded as it is not available for the
developing countries. Instead telephone tariffs access cost is used, which is only significant if
controlled for technological infrastructure.
Beilock and Dimitrova (2003) also find that per capita income is the most important
determinant of Internet diffusion. Other important determinants they find are ICT
infrastructure and openness of a society, measured using a Freedom and non-Freedom
dummy. For the other non-economic factors than openness they do not find a significant
relationship.
Oyelaren-Oyeyinka and Lal (2003) use cross-country data on Africa alone. In accordance
with previous studies they find a significant positive influence of GDP per capita and ICT
infrastructure, measured using number of telephone lines and computers per capita.
Furthermore they find a significant and positive coefficient for education.
Guillen and Suarez (2005) research the determinants of cross-national differences in Internet
use using data of 118 countries between 1997-2001. They also find a significant effect of
GDP per capita and number of telephone lines. Contrary to Kiiski and Pohjola (2002), they do
find a significant positive effect for competition in the telecommunication sector.
Wunnava and Leiter (2009) employ cross-sectional data from 100 countries to analyze the
main determinants of inter-country Internet diffusion. They identify economic strength,
measured as GDP per capita, telecommunication and ICT infrastructure, English proficiency
and a country’s political and economic openness as the fundamental factors in determining
worldwide internet diffusion. In addition, they find tertiary school enrollment and income
equality to play a significant role.
Andrés, Cuberes, Diouf and Serebrisky (2010) analyze the process of Internet diffusion using
a panel of 214 countries during the period 1990-2004. The main determinants of Internet
diffusion they find are GDP per capita, real cost of Internet and computers per capita.
Furthermore, they find evidence for national network effects, measured as the lag of the
number of users per capita in a given country.
8
Chinn and Fairlie (2010) use panel data for 161 countries. Like many other papers they find
significant coefficients for GDP per capita and technological infrastructure, measured in their
paper by telephone density. However, for trade openness and cost they do not find a
significant effect. Contrary to other papers they also find the quality of legal institutions to
significantly affect Internet usage.
Table 1 summarizes the different explanatory variables proposed in the literature. It specifies
the significant and insignificant explanatory variables for each paper, where X stands for
significant and O stands for insignificant. Furthermore the dependent variable and countries
included in the model are specified.
Table 1: Overview of determinants of Internet usage in discussed literature
Note: X indicates a significant effect and O indicates non-significant effect on Internet usage
*only for OECD countries, **only countries with Internet hosts larger than 50 in 1995
Study
Andrés et
al. (2010)
Beilock and
Dimitrova
(2003)
Chinn and
Failie (2010)
Guillian
Suarez
(2005)
Hargittai
(1999)
Kiiski and
Pohjola (2002)
Oyelaren-
Oyeyinka
and Lal
(2003)
Wunnava
and Leiter
(2009)
Countries
214
developed
and
developing
countries
105
developed
and
developing
countries
161
developed
and
developing
countries
118
developed
and
developing
countries
Western
Europe
OECD
countries;
Countries with
Internet hosts
larger than 50
in 1995 Africa
100
developed
and
developing
countries
Dependent varibales
No. of
Internet
users per
capita
No. of
Internet
users per
capita
No. of
Internet
users per
capita
No. of
Internet
users per
capita
No. of
Internet
hosts per
capita
No. of Internet
hosts per
capita
Internet
Users Index
No. of
Internet
users per
capita
Explanatory
variables
GDP per capita X X X X X X X X
Countries openness X O X
Urban Population O
Income distribution O X
Trade O
Education O X** X X
Number of
telephone linesX X X X X X
Number of
computersX X X X X
Cost X O O O X*
Competition in the
telecommunication
sector
X X O
Legal institutions X
Democarcy O
Cosmopolitanism X
Non-economic
factors unrelated to
openess
O
9
2. SPATIAL MODELLING
This sector addresses some theory on spatial econometrics which are used to construct the
spatial models in section 4. The main understanding of spatial econometric modeling is
obtained from Elhorst (2014).
2.1 Spatial models
Spatial econometric models are based on the idea that cross-sectional units interact with each
other. Three different types of interaction effects in a spatial model can be distinguished. First
are the endogenous interaction effects among the dependent variable (Y), in which the value
of the dependent variable for one unit is jointly determined by the dependent variable of
another unit. Second are the exogenous interaction effects among the explanatory variables
(X), where the dependent variable of a particular unit depends on independent explanatory
variables of other units. Third are the interaction effects among the error terms (ε), which
occur when omitted determinants of the dependent variable are spatially autocorrelated across
units. Furthermore, they occur when unobserved shocks follow a spatial pattern.
The non-spatial regression model is introduced in the form:
𝑌 = 𝛼ɩ𝑁 + 𝑋𝛽 + 휀
where Y denotes an N*1 vector of the dependent variables, 𝛼 is the constant term estimator,
ɩ𝑁 is a N*1 vector of ones, X denotes an N*K matrix of explanatory variables, β represents a
K*1 vector of fixed but unknown parameters and 휀 is the error term.
From (1), the full model with all types of interaction effects takes the following form:
𝑌 = 𝛿𝑊𝑌 + 𝛼ɩ𝑁 + 𝑋𝛽 + 𝑊𝑋𝜃 + 𝑢
where 𝑢 = 𝜆𝑊𝑢 + 휀 and W represents a non-negative N*N spatial weights matrix describing
the spatial relationship between the units. The W-matrix is further discussed below.
WY denotes the endogenous interaction effects among the dependent variable, WX denotes
the exogenous interaction effects among the independent variable and Wu denotes the
interaction effects among the error terms. δ is the autoregressive coefficient, θ represent a K*1
vector of fixed but unknown parameters and λ the spatial autocorrelation coefficient.
These different types of interaction effects result in seven different spatial models. These
models including their interaction effects are summarized in table 21.
1 The acronyms used in this paper are based on the book by Elhorst (2014)
(1)
(2)
10
Table 2: Different spatial models
Spatial Models Interaction effects among
General nesting spatial model (GNS) Y, X and ɛ
Spatial autoregressive combined model (SAC) Y and ɛ
Spatial Durbin model (SDM) Y and X
Spatial Durbin error model (SDEM) X and ɛ
Spatial lag model (SAR) Y
Spatial lag of X model (SLX) X
Spatial error model (SEM) ɛ
It should be noted however that use of the GNS model including the full set of interaction
effects is unpopular due to two reasons. In the first place, no general conditions under which
the parameters of the GNS model are identified have been provided. Only Lee, Liu and Lee
(2010) find them, however only for a specific form of the spatial weights matrix which is
unpopular in applied research. Second, the parameters of the GNS model are only weakly
identified, which has the effect that the significance levels of all variables go down. Given
this, the GNS model is also not dealt with in the empirics of this paper.
Spatial econometric models differ from non-spatial models in the sense that the explanatory
variables cause both direct and indirect effects. The direct effect is the effect of a particular
explanatory variable of a unit on its own dependent variable. The indirect effect is the effect
of a particular explanatory variable of a unit on the dependent variable in other units. In their
book LeSage and Pace (2009) point out that interpreting these effects represents a better basis
for testing if spatial spillovers exists than only looking at δ, θ and/or λ of one or more spatial
regression models.
These direct and indirect effects are derived by first rewriting (2) as:
𝑌 = (𝐼 − 𝛿𝑊)−1(𝑋𝛽 + 𝑊𝑋𝜃) + 𝛼ɩ𝑁 + 𝑢
Taking the partial derivatives of the expected values of Y with respect to the kth explanatory
variable of X in unit 1 to N then gives:
[𝜕𝐸(𝑌)
𝜕𝑥1𝑘⋯
𝜕𝐸(𝑌)
𝜕𝑥𝑁𝑘] = (𝐼 − 𝛿𝑊)−1 [
𝛽𝑘 𝑤12𝜃𝑘 · 𝑤1𝑁𝜃𝑘
𝑤21𝜃𝑘 𝛽𝑘 · 𝑤2𝑁𝜃𝑘
· · · ·𝑤𝑁1𝜃𝑘 𝑤𝑁2𝜃𝑘 · 𝛽𝑘
]
The diagonal element of the last matrix represent a direct effect, whereas the off-diagonal
elements represent an indirect effect.
As the different spatial models include different set of interaction effects it follows from (4)
that they also have different direct and indirect effects. For example, if both δ=0 and θ=0 no
indirect effects occur. Table 3, obtained from Halleck Vega and Elhorst (2015), summarizes
the direct and indirect effects for the different models.
(3)
(4)
11
Table 3: Direct and indirect effects of different models
Direct effects Indirect effects
OLS/SEM 𝛽𝑘
0
SAR/SAC Diagonal elements of (𝐼 −𝛿𝑊)−1𝛽𝑘
Off-diagonal elements of (𝐼 −𝛿𝑊)−1𝛽𝑘
SLX/SDEM 𝛽𝑘
𝜃𝑘
SDM/GNS Diagonal elements of (𝐼 −𝛿𝑊)−1(𝛽𝑘 + 𝑊𝜃𝑘)
Off-diagonal elements of (𝐼 −𝛿𝑊)−1(𝛽𝑘 + 𝑊𝜃𝑘)
Source: Halleck Vega and Elhorst (2015)
In both the OLS model and the SEM model the direct effect of an explanatory variable are
equal to the coefficient estimate variable 𝛽𝑘. As δ=0 and θ=0 in these models, indirect effect
are zero. In the SLX and SDEM model the direct effects are also equal to 𝛽𝑘. These models
however do have an indirect effect which equals the coefficient estimate of its spatial lagged
value 𝜃𝑘.
For the SAR and SAC models the direct effect of the kth explanatory variable is not equal to
𝛽𝑘, but 𝛽𝑘 multiplied with a number equal or greater than one. 𝛽𝑘 also appears in the indirect
effect, having the effect that the ratio between the direct and indirect effect of all explanatory
variables is the same. For the SDM and GNS model the direct and indirect effects of the
explanatory variables depend on both 𝛽𝑘 and 𝜃𝑘. This means that no prior restrictions are
imposed on the magnitude of both effects and thus that the ratio between them can differ for
different explanatory variables.
Indirect effects can further be split into local and global effects. Local effects occur if θ≠0 and
δ=0. They are called local effects because, as follows from (4), the indirect effects only fall on
the spatial units for which the elements of W-matrix are non-zero. Global indirect effects
occur when θ=0 and δ≠0, as the effects fall on all units. The idea behind this is that although
many elements of the W-matrix can be zero, the elements of (𝐼 − 𝛿𝑊)−1 are not zero.
2.2 W-matrix
The next part of this section discusses the spatial weights matrix. A spatial weights matrix W
is generally a symmetric N*N matrix which describes the spatial arrangements between units.
There are exceptions where asymmetric matrices exist, these are however not included in this
paper and therefore not touched upon further. The matrix thus takes the following form:
𝑊 = [
𝑤11 ∙ 𝑤1𝑁
∙ ∙ ∙𝑤𝑁1 ∙ 𝑤𝑁𝑁
]
W is a nonnegative matrix with known constants where the diagonal elements are always set
to zero, as no unit can have a spatial relationship with itself. The row elements of a weights
matrix display the impact on a particular unit by all other units. Conversely, the column
elements of a weights matrix display the impact of a particular unit on all other units.
(5)
12
Usually W is row-normalized, ensuring that all weights are between zero and one. This has
the effect that the impact of each unit by all other units is equalized. If W is row-normalized,
X may not contain a constant as X and WX would become perfectly multicollinear.
Alternatively, W can also be matrix-normalized. This is done by dividing the elements of W
by its the largest characteristic root. The effect is that the largest characteristic root of W has a
value of 1, just as in the case W is row-normalized. The advantage of this type of
normalization is that the proportions between the elements of W do not change and thereby
keep their economic interpretation.
Finally, in order to limit the cross-sectional correlation to a manageable degree it is important
that one of the following two conditions holds. The first condition originates from Kelejian
and Prucha (1998, 1999) and states that the row and column sums of the matrices W, (𝐼𝑁 −
𝛿𝑊)−1 and (𝐼𝑁 − 𝜆𝑊)−1 before W is row-normalized should be uniformly bounded in
absolute values as N goes to infinity. The second condition originates from Lee (2004) and
states that the row and column sums of W before W is row-normalized should not diverge to
infinity at a rate equal to or faster than the rate of the sample size N.
In his paper, Leenders (2002) stresses the vital importance of the chosen specification of W.
The usefulness of the entire approach of spatial econometric models hinges upon this matrix
as the value and significance level of the interaction parameters depend on it.
The spatial weights matrix W however cannot be estimated. It has to be specified beforehand
and preferably follow from the economic theory at hand. This gives difficulties, as different
theories imply a different W and often theory does not have much to say about the right
specification of W. Therefore, empirical researchers often investigate whether the results are
robust to the specification of W. This is done by estimating the same spatial econometric
model several times using different spatial weights matrices and investigate if the results are
sensitive to the choice of W.
For each spatial model often many different matrices can be specified following economic
theory, however there exists four commonly used matrices in applied research. The first is the
p-order binary contiguity matrix, where if p = 1 only first-order neighbors are included, if p =
2 only first and second-order neighbors are included, etcetera. Second is the inverse distance
matrix, which is specified using the distances between units. Sometimes a cut-off point is
used, which specifies the maximum distance between units where they still have an impact on
one another. Third is q-nearest neighbor matrix, where q is a positive integer displaying the
number of nearest neighbors considered. Finally the block diagonal matrix is commonly used,
where each block represents a group of spatial units that interact with each other but not with
units in other groups.
Matrices commonly used in applied research are based on geographical factors. This however
is not a requirement. Increasing attention has been given to matrices based on non-
geographical factors. In a study of economic growth, Conley (1999) for example uses a
measure of the transportation costs for physical capital between countries. Simmons and
Elkins (2004) model the diffusion of economic liberalization using a matrix based partially
13
on the liberalization of one’s neighbors, which is defined by either trade or group membership
instead of geography. In a study of national identity formation in Taiwan, Lin, Wu, and Lee
(2005) use occupation between individuals to identify connectivity. Beck, Beardsley and
Gleditsch (2006) analyze the variation in democracy among countries around the world using
two non-geographic measures of connectivity, being trade and common dyadic membership,
in various spatial analyses.
This paper uses geographical as well as non-geographical matrices to test the hypotheses.
Based on the information discussed in this and previous sections, the next sections introduces
the data and models used in this paper.
3. DATA
This section introduces the panel data set which is used in this paper. The variables included
in the dataset are based on the literature discussed above. Data of 167 countries over the
period 2000-2009 is used. Because spatial models require a complete balanced dataset these
countries are chosen based on the availability of data. Appendix B reports a list of all the
countries included in the dataset. All large as well as semi-large economies from all regions in
the world are included.
3.1 Dependent variable
The dependent variable in this paper is the percentage of Internet users of total population.
Data on percentage of individuals using the Internet per country is taken from the ITU, which
is the UN specialized agency for ICTs. The data of the ITU are collected from an annual
questionnaire sent to official economy contacts, usually the regulatory authority or the
ministry in charge of telecommunication and ICT, and from reports provided by
telecommunication ministries, regulators and operators.
Figure 3 shows the development of the level of Internet usage per year as average of all
countries over the period 2000-2009. A clear upward sloping trend can be seen. On average,
Internet usage in a country has grown from 8.04% to 30.62% of the population. As already
shortly adressed in the introduction great differences between countries exsits however, and
these differences are increasing. In 2000 the differnce between the country with the highest
and the lowest usage was 52 percentage point, with 52% for Norway and around 0% for
Afghanistan. In 2009 the diffence grew to almost 93 percentage point, with 93% for Iceland
and less than 1% for Sierra Leone.
14
Figure 3: Average Internet usage in percentage of total population for the period 2000-2009
Source: Data from the UN International Telecommunication Union
To test for stationairity of panel data series several serveral unit root tests are available. The
size of the sample as well as the assumption of a common autoregressive parameter determine
which test is most appropriate in a given situation.
For this paper tests developed by Im, Pesaran and Shin (2003) are used. In contrast to various
other panel unit root tests these tests allow the autoregressive parameter to be panel specific.
Given the great diversity of countries in the sample the assumption that all panels share the
same autoregressive parameter might be to simplifying. Furthermore, these tests fit best when
having a sample with a relative large set of panels in comparison with time periods, as is the
case with the sample in this paper.
Im–Pesaran–Shin (IPS) tests have as null hypothesis that all the panels contain a unit root
against the alternative hypothesis that some panels are stationary. The test, including a
constant and trend, clearly shows that the panel series of Internet usage is non-stationary.
To deal with the non-stationarity of the series first differences are taken. Figure 4 shows the
first differnces of Internet usage per year as average of all countries. The unit root test
including a constant rejects the null hypothesis of all panels contaning a unit root at the 1%
level.
Figure 4: Average yearly growth in Internet usage in percentage of total population for the
period 2000-2008
Source: Data from the UN International Telecommunication Union
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
35,00%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
2000 2001 2002 2003 2004 2005 2006 2007 2008
15
3.2 Explanatory variables
From table 1 the major role of income differences in explaining Internet usage is evident. All
papers discussed find a significant positive result for the level of income. Data on income is
widely available for many countries over a large period of time. For this paper GDP per capita
in USD is used, which is collected from the World Bank database. GDP per capita is in
constant 2005 USD.
Figure 5 shows the level of GDP per capita per year as average of all countries. As with the
level of Internet usage here also a clear upward sloping trend can be seen. On average the
GDP per capita was $9218.02 in 2000 and rose to $13086.55 in 2009. The country with the
highest GDP per capita was in both cases Luxembourg, where it increased from $72865.06 in
2000 to $79002.74 in 2009. The lowest GDP per capita grew on a much slower pace, being
$137.50 for Ethopia in 2000 and $150.22 for Burundi in 2009.
Figure 5: Average GDP per Capita in constant 2005 USD for the period 2000-2009
Source: Data from the World Bank
To measure the relative effect of income on growth in Internet usage, log-levels of GDP per
capita are taken. The IPS unit root test including a constant and a trend does not rejects the
null hypothesis of all panels contaning a unit root. GDP per capita can thus be regarded as a
non-stationairy variable. To deal with the non-stationairity the first differences of the variable
are taken. Figure 6 shows the average of the first differences for the sample period. IPS unit
root test including a constant rejects the null hypothesis at the 5% level.
Figure 6: Average yearly growth in log GDP per Capita in constant 2005 USD for the period
2000-2008
Source: Data from the World Bank
7500
8500
9500
10500
11500
12500
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
-0,02
-0,01
0
0,01
0,02
0,03
0,04
0,05
2000 2001 2002 2003 2004 2005 2006 2007 2008
16
However, including first differences in the model instead of levels of GDP per capita is not
without consequences. When using first differences the effect of growth in income on growth
in Internet usage is measured, whereas using levels measures the effect of a higher level of
income on the level of Internet usage. From a theoretical point it is very well possible that the
level of income has a significant effect on Internet usage, whereas income growth does not.
Internet could for example be regarded as a non-essential good which is only used by people
having an income above a certain threshold. If a country’s average income is well below this
threshold, a high increase in income might have no or only a small effect on Internet usage.
Conversely, growth in Internet usage in a country with an average income level above this
threshold might be affected even by a limited growth in income.
The literature discussed in this paper finds a significant effect for income level, not income
growth. From these papers it is however unclear if the non-stationairity of GDP per capita is
being dealt with. The results in these studies can therefore also be the result of a spurious
relationship.
One alternative for addressing the different levels in income of the countries is to use a
different time-varying measure of level of income. Examples of such variables are GNI per
capita and the Human Development Index. However, these variables are greatly determined
by or move parallel to GDP. Unit-root tests for these variables indeed indicate that they are
also non-stationary.
A second alternative is to include the base-year level of GDP per capita as variable in the
model. This variable does not suffer from non-stationairity as it is a constant. With panel data
however, including a constant variable is only possible under random effects models. As
discussed in the next section, the use of random effects imposes restrictions which are
unrealistic for this data. Consequently the models in this paper only include growth in GDP
per capita and not in levels. Income differences between countries will therefore only be
controlled for by the fixed effects used in the model.
Given the importance of income level found by all other papers it is useful to assess if the
exclusion of income level has major implications for the obtained results. In section 6.3 the
robustness of the obtained results will therefore be tested by including a variable measuring
income level. Because the results of these models can be based on a spurious relationship they
are only used to cross-check the results found in the paper. These results support the
robustness of the results obtained by the other models in this paper.
Next to stationairity, GDP per capita also faces the problem of possible endogeneity. As noted
in the introduction the importance of Internet in many economies is growing. Therefore it is
possible that Internet usage in a country partly determines GDP per capita. This endogeneity
issue is a limitation of this paper. However, for the sample period of this paper the direct
influence of Internet usage growth on GDP per capita growth can be assumed to be limited,
therefore the possible endogeneity issue can be considered to not have a great influence on the
results obtained in this paper.
17
Besides income, a fast majority of the studies point out the importance of ICT infrastructure
in explaining Internet diffusion. ICT infrastructure is the physical hardware used to
interconnect computers and users.
ICT infrastructure is often measured as computers and fixed telephone subscriptions per
capita. The use of the number of personal computers as explanatory variable can however be
problematic, as it is difficult to preserve that this variable is truly exogenous. When
determining Internet usage in the past it can be argued that the number of personal computers
in a country was not or hardly determined by Internet usage, as the main purpose of those
computers was not Internet usage. However, in the light of the rapid increase in importance of
Internet since the beginning of this millennium, it is likely that the availability of Internet
determines the decision to acquire a personal computer, thereby reversing the causality.
Due to the rapid increase of mobile cellular use also the use of data on fixed telephone
subscriptions per capita as measurement is problematic. The use of this relatively new
technology has dramatically decreased the need for fixed telephone lines, both in developed
and developing countries. Therefore, a per capita decrease in fixed telephone subscriptions
does not have to indicate a decrease in ICT infrastructure, as it is very well possible that
people substitute their fixed telephone lines for mobile cellular.
Alternatively, this paper uses mobile cellular subscriptions as indicator for ICT infrastructure.
The use of these devises requires a comprehensive technological infrastructure. Furthermore,
in the period covered by this paper the use of Internet on the mobile cellular was not yet
widespread, dismissing reverse causality.
Data on mobile cellular subscriptions as percentage of total population is also taken from the
ITU. As figure 7 shows the level of mobile cellular usage as percentage of total population as
average of all countries shows a clear upward trend. The average number of subscribers of
total population was 16% in 2000 and rose to 83% in 2009. This rise occurred in all the
countries in the world. The highest amount of subscriptions per capita was both in 2000 and in
2009 in Hong-Kong, with respectively 80% and 180% of total population, implying more than
one mobile cellular subscriptions per person on average in 2009. The lowest amount of
subscriptions in both 2000 and in 2009 stayed far behind, being less than 1% for Iraq in 2000
and 5% for Ethiopia in 2009.
Figure 7: Average mobile cellular subscriptions as percentage of total population for the
period 2000-2009
Source: Data from the UN International Telecommunication Union
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
18
The IPS unit root test including a constant and a trend does not reject the null hypothesis of all
panels contaning a unit root. To deal with non-stationairity of the panel series again first
differences are taken. Figure 8 shows the first differnces of mobile cellular as percentage of
total population per year as average of all countries. The unit root test rejects the null
hypothesis of all panels contaning a unit root at the 1% level.
Figure 8: Average yearly growth in mobile cellular subscriptions as percentage of total
population for the period 2000-2008
Source: Data from the UN International Telecommunication Union
In line with standard consumer demand theory, several authors use the cost of Internet access
as a determinant of Internet usage in a country. However, data on the cost of Internet is very
limited, especially for non-developing countries. Therefore its effect on Internet usage has not
yet been tested robustly and neither is it touched upon in this paper.
Education attainment of the population has also been conjectured as playing a critical role in
some papers. In this paper, education attainment is measured by mean years of schooling of
adults. It is calculated as the average number of years of education received by people aged 25
and older, converted from education attainment levels using official durations of each level.
The data is collected from the United Nations Development Program (UNDP).
Figure 9 plots the movement of mean years of schooling of adults as average of all countries.
The figure shows an increase over the years 2000-2009, however this increase is only small.
The average mean years of schooling grew with less than 0.8 years over the whole period.
Differences in mean years of schooling between countries however are substantial, ranging
from 1.10 for Niger to 12.70 for the United States in 2000 and 1.30 for Burkina Faso to 12.90
for the United States in 2009.
Figure 9: Average mean years of schooling for the period 2000-2009
Source: Data from the UNDP
0%
5%
10%
15%
2000 2001 2002 2003 2004 2005 2006 2007 2008
6,5
7
7,5
8
8,5
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
19
Figure 10 plots the averages of first differences of mean years of schooling. The IPS unit root
test for stationairity including a constant rejects the null hypothesis of all panels contaning a
unit root at the 1% level. As with GDP per capita, log-levels are taken in order to measure the
effect of relative growth on Internet usage.
Figure 10: Average yearly growth in log mean years of schooling for the period 2000-2008
Source: Data from the UNDP
Also openness of a country is identified by several papers as having a significant effect on
Internet usage in a country. Following Beilock at al. (2003), data on openness is taken from
the Freedom House’s index. This index is based on a 14-item Civil Liberties Checklist
covering freedom of expression and belief, freedom of association and organizational rights
and personal autonomy and economic rights. The score of a country ranges for 0 to 100,
where a score lower than 31 refers to “Free” and a score higher than 50 to “Not Free”.
Figure 11 shows the Freedom House’s index per year as average of all countries. Between
2000 and 2001 the average index decreased, but afterwards the figure shows a clear upward
trend. In absolute value this upward trend is however limited, increasing from 46.28 to 48.63.
Many individual countries however did experience great changes over the period 2000-2009.
As an higher value represents less openess this indicates that on average countries became
less open. This is supported by the amount of countries considerd “Free”. In the first year of
the sample 62 out of the 167 counrties are considerd “Free”, whereas in the last year this
decreased to 51 countries.
Figure 11: Average Freedom House index for the period 2000-2009
Source: Data from the Freedom House Index
0
0,002
0,004
0,006
0,008
0,01
2000 2001 2002 2003 2004 2005 2006 2007 2008
45
46
47
48
49
50
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
20
Figure 12 shows the average of the first differences. No clear trend can be detected. The IPS
unit root test including a constant rejects the null hypothesis of all panels contaning unit root
at the 1% level.
Figure 12: Average yearly growth in Freedom House index for the period 2000-2008
Source: Data from the Freedom House Index
Finally, table 1 identifies several other significant variables. However only very limited
amount of papers find a significant effect for these variables. Following Andrés et al. (2010),
the inclusion of country effects in the model should be able to capture the cross-country
differences explained by these variables.
4. MODEL
This section introduces the models which are used in this paper. First the base-model is
discussed, thereafter the different spatial models are discussed.
4.1 Base model
Because the model is based on panel data a choice needs to be made between fixed and
random effects. The advantage of fixed effects is that it allows for arbitrary correlation
between the error term and the explanatory variables, whereas random effects require that the
explanatory variables are strictly exogenous and uncorrelated with the individual specific
effect. On the other hand, when using fixed effects key explanatory variables which are
constant over time are eliminated.
The restriction imposed by random effects is likely to be too restrictive, as it is unrealistic to
assume that the omitted heterogeneity is uncorrelated with the repressors. Considering this,
using fixed effects for the panel data is preferred. Besides country effects the model also
includes time-period effects. Omitted effects that are common across all countries that
occurred during the period 2000-2009 are therefore controlled for. The base model is as
follows:
∆𝐼𝑈𝑖,𝑡 = 𝛽1∆𝑙𝑛𝑌𝑖,𝑡 + 𝛽2∆𝑇𝐼𝑖,𝑡 + 𝛽3∆𝑙𝑛𝐸𝑖,𝑡 + 𝛽4∆𝐹𝑖,𝑡 + 𝜇𝑖 + 𝜏𝑡 + 휀𝑖,𝑡
where the subscript i denotes countries (i=1,…,167) and t denotes time (t=1,…,9).
-1,00
-0,50
0,00
0,50
1,00
2000 2001 2002 2003 2004 2005 2006 2007 2008
(6)
21
IU stands for Internet usage, Y for GDP per capita, TI for ICT infrastructure, E for the
education attainment and F for degree of openness. As noted Y and E are taken in logs to
capture the effects of a percentage growth of these variables on the growth of Internet usage.
Finally, the model includes the country fixed effects, 𝜇𝑖, and time-period fixed effects, 𝜏𝑡.
One would expect 𝛽1 to be positive, since a higher income level is naturally associated with a
higher use of technology and a higher possibility of purchasing services and goods for which
Internet is used. A growth in income thus provides more opportunities for Internet usage, and
thereby has a positive effect on the growth rate.
Also the measurement of ICT infrastructure is expected to have a positive effect on Internet
usage, as an increase of the physical hardware used to interconnect computers and users is
expected to increase efficient Internet usage in a country. The coefficients 𝛽2 is thus expected
to be positive.
The relationship between education attainment and Internet usage growth is also expected to
be positive. Many reasons support the positive relationship. The two most apparent are that
literacy is required as the world wide web and email is almost entirely text based and that
some level of education is required to ensure a person is actually able to use a computer.
Furthermore, academic institutions play an essential role in adopting new technologies like
the Internet, thus strengthening the positive relationship.
Finally, an increase in the openness of a country is expected to have a positive effect on
Internet usage. The motive behind it is that Internet facilitates access to very hard to control
quantities of information. Closed countries try to limit the spread of this information, whereas
open countries promote this spread. As an increase in openness is measured by a decrease in
the Freedom House index, this implies that 𝛽4 is expected to have a negative sign.
4.2 Spatial Models
To test the hypotheses five different spatial model are considered in this paper; SEM model,
SAR model, SLX model, SDM model and the SDEM model. The model including all spatial
interaction effects is as follows:
∆𝐼𝑈𝑖,𝑡 = 𝛿𝑊∆𝐼𝑈𝑖,𝑡+𝛽1∆𝑙𝑛𝑌𝑖,𝑡 + 𝛽2∆𝑇𝐼𝑖,𝑡 + 𝛽3∆𝑙𝑛𝐸𝑖,𝑡 + 𝛽4∆𝐹𝑖,𝑡 + 𝜃1𝑊∆𝑙𝑛𝑌𝑖,𝑡 +
𝜃2𝑊∆𝑇𝐼𝑖,𝑡 + 𝜃3𝑊∆𝑙𝑛𝐸𝑖,𝑡 + 𝜃4𝑊∆𝐹𝑖,𝑡 + 𝜇𝑖 + 𝜏𝑡 + 𝑢𝑖,𝑡
where 𝑢𝑖,𝑡 = 𝜆𝑊𝑢𝑖,𝑡 + 휀𝑖,𝑡. From the model with the complete set of interaction effects each
of the spatial models follow.
To test the hypotheses proposed in the introduction four different W-matrices are used. One
matrix is based on geographical factors, one is based on both geographical and non-
geographical factors whereas the other two matrices are based on non-geographical factors.
The geographical matrix is the first-order binary contiguity matrix, assigning a one to an
element if the country is a first-order neighbor and a zero otherwise. The second matrix is
based on regional trade blocs, whereas the non-geographical W-matrices are based on
(7)
22
economic activity between countries. All W-matrices are row-normalized so that the effect of
the impact of each unit by all other units is equalized.
The first-order binary contiguity W-matrix satisfies the required necessary conditions
discussed in section 2. As no country is a neighbor of more than a certain number of
countries, the first condition is satisfied.
Regarding the first-order binary contiguity W-matrix one would expect 𝛿 to be positive. This
positive value is supported by the diffusion effect often associated with new technologies. The
familiarity with and adoption of Internet in a country will increase if neighboring countries
increase their Internet usage. Furthermore, the possibilities of Internet usage increase with
number of users. A growth of Internet usage in a neighboring country is therefore likely to
increase the possibilities of Internet, thereby increasing the demand for Internet.
Also 𝜃1, 𝜃2 and 𝜃3 are expected to be positive for this W-matrix. As noted above, a higher
income level in a country is naturally associated with a higher use of technology and a higher
possibility of purchasing services and goods via the Internet. As economic activities
increasingly do not stop at borders, a growth in the level of income in a neighboring country
opens up possibilities for Internet usage in the focal country.
An increase in ICT infrastructure increases the efficient usage of Internet. The increased
efficiency is likely to open up possibilities for Internet usage in neighboring countries. The
same line of reasoning applies to the level of education. A higher level of education in a
country increases its possibilities of exploiting Internet activity in neighboring countries,
thereby increasing the demand there.
Lastly, regarding the first-order binary contiguity W-matrix, 𝜃4 is expected to have a negative
sign. If, due to an increase of openness, people in a country are more freely allowed to use
Internet for a large range of activities, the possibilities of Internet usage in a neighboring
country also increases as information can be more easily shared.
The second W-matrix is based on existing regional trade blocs. Each element wij in the W-
matrix is assigned a one if country i and j are in a trade bloc and a zero if not. Appendix C
lists the thirteen different regional trade blocs considered. The last table in the appendix lists
the countries who do not participate in a trade bloc. These countries are considered to have no
spatial relationship with any other country and therefore have a row with only zeros.
As trade blocs reduce or eliminate barriers to trade, economic activity between participating
countries is likely to flourish. Furthermore, countries within a trade bloc often have much in
common in the area of culture and institutions. Worldwide differences in these areas are also
present on Internet. As will be discussed below, the advantage of using this matrix is that in
contrast to the two non-geographical matrices this matrix does not suffer from the condition
that the impact of few large countries is great.
For the non-geographical matrices trade flows are taken as measurement for the economic
activity between countries. Data on trade flows are taken from the COMTRADE database of
23
the United Nations Statistical Division, cleaned by the BACI team using their own
methodology of harmonization2. Net total values in USD of the trade flows are used.
The first non-geographical W-matrix assigns an one to an element if it was the main export or
main import partner of the country in the period 2000-2009, and a zero otherwise. A country
is a main export partner of another country if in the period 2000-2009 it was most often the
largest export destination of that other country. Similar, it is a main import partner if it was
most often the largest import origin in the period.
For 89 countries in the sample both the main import partner and the main export partner is the
same country. The other 78 countries have a different main import partner than main export
partner. The main trading partners are far from equally divided between the countries. Only
43 of the 167 countries are a main import or export partner of another country.
As figure 13 shows also the impact of these 43 countries is very skewed. Of these 43
countries the great majority of the countries is the main import or export partner of only a few
countries. Contrary, 60% of the times one of the top-five countries USA, Germany, China,
Russia and France is the main import or export partner of a country.
Figure 13: Number of times main trading partner of other country in the period 2000-2009
Source: Data from the United Nations Statistical Division
In this paper the choice has been made to only assign an one to the W-matrix elements of the
main import and main export partner. As with the p-order binary contiguity matrix, basing the
W-matrix on higher order trading partners is also possible. However, the results discussed in
section 6 show no improvement of the non-spatial model for the models based on the main
import and main export partner W-matrix. Including a W-matrix based on higher order
trading partners is therefore also not likely to produce significant improvements to the non-
spatial model and will not be considered in this paper.
2 COMTRADE import values are reported CIF (cost, insurance and freight) while COMTRADE exports are
reported FOB (free on board). Estimations of transport and insurance rates are used to remove the data from
import values. Thereafter the observed CIF/FOB ratios are regressed for a given flow on gravity variables and a
product-specific world median unit value. In a second step the reliability of countries reporting are evaluated.
Measures of the reliability of reported data are used as weights in the reconciliation of each bilateral trade flow.
78
46
312422
181312
8 8 6 6 6 4 4 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
USA
GER
CH
NR
US
FRA
ZAF
JPN
ITA
BR
AG
BR
AU
SIN
DSG
PSA
USP
AB
ELP
OR
THA
TUR
AR
EC
AN
HR
VG
RC
KO
RM
YSM
LIN
LDN
GA
SEN
CH
EC
IVET
HG
TM IND
KEN NZL
PA
KSR
BSW
ETG
OU
GA
UK
RV
EN
24
The second non-geographical W-matrix is based on export flows of all countries in the base-
year 2000. Each element wij in the W-matrix shows which percentage of the total exports of
country i are exported to country j. Due to unavailability of data, Botswana, Lesotho,
Luxemburg, Namibia and Swaziland are excluded from the sample. Also, because not all
countries in the world are included in the sample not all the rows sum up to 100%. However,
as all major countries are included in the sample only small percentages are excluded.
Furthermore, due to row-normalization the effect of the impact of each unit by all other units
is equalized.
Contrary to the W-matrix based on main trading partners this W-matrix takes in consideration
the relative economic activity between countries. However, as is the case with the W-matrix
based on main trading partners, there are a few large countries whose impact on all countries
is great, whereas the impact of all other countries is limited.
The three W-matrices all satisfy the first condition of the necessary conditions discussed in
section 2. For the second matrix, condition one is satisfied as all trading blocs only hold a
limited amount of members. For the third W-matrix a country by definition has no more than
one main export partner and no more than one main import partner. Finally, in the case of the
forth W-matrix, as percentages of the total export flows are taken, the row sum is never more
than 100% and therefore finite.
For the these W-matrices, 𝛿 is also expected to be positive. If economic activity between
country j and country i is high, a higher level of Internet usage in country j is likely to result in
a higher level of Internet usage in country i. If a certain country uses Internet for a great deal
of its economic activities, a country having large economic activity with that country is
required to use Internet for certain procedures. Furthermore, increasing the usage of Internet
increases the efficiency of trading between the countries.
𝜃1 is also expected to have a positive sign for this group of W-matrices. A higher level of
income is naturally associated with a higher use of technology and therefore a higher level of
Internet usage. Thus if there is a high degree of economic activity between country i and j, a
high level of income in one of the countries is likely to increase the possibilities of Internet
usage for the other country. Thereby thus having a positive effect on Internet usage.
The same line of reasoning applies to ICT infrastructure and education attainment. If a high
degree of economic activity between country i and j exists, an increase in ICT infrastructure
in one of the countries is likely to increase the efficiency of Internet usage for the other
country. 𝜃2is thus also expected to have a positive sign.
As noted earlier, a higher level of education attainment in a country increases the capabilities
of the people in that country to use the Internet. This is likely to increase the demand for
Internet based activities in the other countries, resulting in a positive sign for 𝜃3.
Finally, similar to the models including the geographical W-matrix, 𝜃4 is expected to have a
negative sign. An decrease in restrictions on openness in a country is likely to result in an
25
increase in Internet usage in a countries with whom they have a high degree of economic
activities.
4.3 Model selection
Besides the sign and significance of the spatial effects, it is also of interest to see which
spatial model fits the data best. This depends on the chosen W-matrix as well as the type of
interaction effects included. Theory on how to compare models including different W-
matrices is addressed in the next section.
Turning to the type of interaction effects included, the distinction can be made between local
and global effects. As shown above, local indirect effects occur if 𝜃𝑖 ≠0 and global indirect
effects occur if 𝛿 ≠0. As discussed in section 2, global effects differ from local effects in the
sense that effects fall on all units and not only those for which the elements of W-matrix are
non-zero.
Of the models considered in this paper the SEM model does not imply indirect effects. The
SAR model does imply global indirect effects, however has the limitation that the ratio
between the direct and indirect effect of all explanatory variables is the same. The SLX model
and SDEM model are local spillover specifications, whereas the SDM model is a global
spillover specification.
In the case of Internet usage, it is not ambiguous if global or local effects are supported by
theory. The presence of local effects follows from the fact that the vast majority of Internet
activity and its possibilities are locally based. Product which can be bought via Internet are
likely produced close by, do to transportation costs as well as taste preference. Language,
culture and institutional differences are very present on the Internet, making much of the use
of Internet regionally based rather than globally. Furthermore, the majority of companies and
subsidiaries operate on a regional basis rather than a global basis. Thus an increase in Internet
usage by companies in neighboring countries increases their necessity for Internet, whereas an
increase of usage in companies operating in totally different regions does not. Also, ICT
technology increases are more likely to only spillover to countries close by than globally.
These among more examples support local spillover effects for the model. However, also a
case can be made for global effects. The use of Internet itself is not affected by distance. An
increase in the usage in one country can therefore have an impact on usage in all countries
around the world, independent of distance.
Furthermore, the existence of global effects is supported by research on the diffusion of
innovations. Rogers (1986), considered the originator of this field of research, identifies a
critical mass of adopters and regular and frequent usage necessary to ensure the success of the
adoption of interactive communications innovations like Internet. Due to network effects, the
possibilities and effectiveness of Internet usage increases with the amount of users. A change
in one of the variables in country i increases the possibilities of Internet usage in country j,
which then increases the possibilities in country k and so on, impacting countries all around
the world and setting a process in motion which can lead to a new steady-state equilibrium.
26
This points to the inclusion of a spatial lag of the depend variable in the model. Contrary to
the SDEM model, the SDM model includes this spatial lag.
Moreover, the choice for global indirect effects is motivated by an econometric-theoretical
argument provided by LeSage and Pace (2009). They show that the SDM model produces
inefficient but unbiased parameters under the following three circumstances: (1) a potential
important variable is omitted from the model, (2) this variable is likely to be correlated with
the explanatory variable and (3) the disturbance process is likely to be spatially dependent.
This in contrast to SLX, SEM and SDEM models, which produce biased parameters.
Concerning this model, both a variable measuring cost of Internet and a variable measuring
income level differences can be regarded as a potential important variables omitted from the
model.
Given the theoretical support for both global and local indirect effects theory alone is not
sufficient to conclude which spillover specification is preferred. Therefore this is empirically
tested in this paper. The next section elaborates on how this is done.
5. METHODOLOGY
5.1 Estimation method
The base model (7) is estimated by OLS including fixed effects. For the SLX model this is
also possible as it includes only exogenous interaction effects. However, the other spatial
models cannot be estimated by OLS. In this section the maximum likelihood (ML) estimator
is derived for both the spatial model including endogenous interaction effects, SAR model,
and including interaction effects among the error term, SEM model.
The estimation by OLS ignores the endogeneity of WY, which biases the estimation. To
account for this endogeneity the spatial models extended to include a spatial lag in this paper
is estimated by ML as proposed by Anselin (1988).
The SAR model can be specified as
∆𝐼𝑈𝑖,𝑡 = 𝛿 ∑ 𝑤𝑖,𝑗𝑁𝑗=1 ∆𝐼𝑈𝑖,𝑡 + 𝑥𝑖,𝑡𝛽 + 𝜇𝑖 + 𝜏𝑡 + 휀𝑖,𝑡
where 𝑤𝑖,𝑗 is an element of the spatial weights matrix W and for convenience all explanatory
variables are pooled into 𝑥𝑖,𝑡𝛽. The log-likelihood function can then be written as
𝐿𝑜𝑔𝐿 = −𝑁𝑇
2𝑙𝑜𝑔(2𝜋𝜎2) + 𝑇𝑙𝑜𝑔|𝐼𝑁 − 𝛿𝑊 | −
1
2𝜎2∑ ∑ (∆𝐼𝑈𝑖,𝑡 −𝑇
𝑡=1 𝛿 ∑ 𝑤𝑖,𝑗𝑁𝑗=1 ∆𝐼𝑈𝑖,𝑡 −𝑁
𝑖=1 𝑥𝑖,𝑡𝛽 − 𝜇𝑖 − 𝜏𝑡)2
The term 𝑇𝑙𝑜𝑔|𝐼𝑁 − 𝛿𝑊 | represents the Jacobian term. This term is ignored in the OLS
estimator and takes into account the endogeneity of WY.
Anselin and Hudank (1992) have spelled out how a spatial model extended to include a spatial
error term can be estimated by ML. In line with the specifications of the SAR model, the SEM
model can be specified as
(8)
(10)
(9)
27
∆𝐼𝑈𝑖,𝑡 = 𝑥𝑖,𝑡𝛽 + 𝜇𝑖 + 𝜏𝑡 + 𝑢𝑖,𝑡 where 𝑢𝑖,𝑡 = 𝜆 ∑ 𝑤𝑖,𝑗𝑁𝑗=1 𝑢𝑖,𝑡 + 휀𝑖,𝑡
The log-likelihood function can then be written as
𝐿𝑜𝑔𝐿 = −𝑁𝑇
2𝑙𝑜𝑔(2𝜋𝜎2) + 𝑇𝑙𝑜𝑔|𝐼𝑁 − 𝛿𝑊 | −
1
2𝜎2∑ ∑ (∆𝐼𝑈𝑖,𝑡 −𝑇
𝑡=1 𝜆 ∑ 𝑤𝑖,𝑗𝑁𝑗=1 ∆𝐼𝑈𝑖,𝑡 −𝑁
𝑖=1 (𝑥𝑖,𝑡 −
𝜆 ∑ 𝑤𝑖,𝑗𝑁𝑗=1 𝑥𝑖,𝑡)𝛽)2
From (9) and (11) the log-likelihood functions of the SDM and SDEM model follow. The
section now turns to the methodology used to compare the performance of the different
models.
5.2 Model testing
First the panel model including fixed effects is estimated. The model is estimated with
country fixed effects only, time-period fixed effects only and with both country and time-
period fixed effects. A Likelihood-ratio (LR) test is then performed to see whether the country
fixed effects as well as the time-period fixed effects are jointly significant. A LR-test
compares the goodness of fit of two models, comparing how much more likely the data are
under one model than under the other. The higher the value of the likelihood function, the
better the set of parameters estimates fits the data set. The test statistic is compared employing
a Chi-squared distribution with certain degrees of freedom.
Second the spatial models are estimated. Using the estimations of the SAR and the SEM
model it is tested if the spatial extensions are significant. To test if the extension of a spatial
lag is significant a t-test with the null-hypothesis that 𝛿=0 is tested. The significance of the
extension of a spatial error is tested by the null-hypothesis that 𝜆=0. Also the SLX model is
considered. The significance of the spatial extension of the SLX model is tested by the null-
hypothesis that all 𝜃𝑖=0, based on an F-test.
Next the models with more than one type of spatial interaction effect are considered. In order
to study if including two instead of one interaction effect leads to a better fit the SDM model
can be tested against the SAR, SEM and SLX model. This is tested by comparing the
likelihood of the models to assess their fit by performing a LR-test. The null hypothesis states
that removing the spatial extension from the model does not substantially harm the fit of that
model.
If the SDM model is the result of the test procedures, the performance of the model are
compared with the SDEM model. As tests for significant differences between log-likelihood
values require the models to be nested they cannot be used when comparing the SDM and
SDEM model. Therefore the Bayesian perspective on model comparison taken from LeSage
(2014) is used, as this approach does not require nested models.
The Bayesian posterior model probabilities of the SLX, SDM and SDEM model conditional
on the data is calculated. This is done by first calculating posterior model probabilities
associated with each model and then using these probabilities to produce a model
specification that averages over the set of models. As the probabilities sum up to unity, the
model with the highest probability is said to fit the data best. The SLX model is included as
(11)
28
additional test to see if more than one type of spatial interaction effect should be included in
the model.
5.3 Comparing W matrices
Finally, models which use different spatial W-matrices are compared to see which matrix fits
the data best. Again tests for significant differences between log-likelihood function values
cannot be used as models with different W-matrices are non-nested. Therefore, following
LeSage and Page (2009), Bayesian posterior model probabilities are used to compare the
models.
Before the estimation of the models each probability is set equal to 1/S, where S stands for the
number of different models, so that a priori each model is made equally likely. For each W-
matrix the SDEM model, which the results show fits the data best, is then estimated using
Bayesian methods, where after posterior probabilities based on the data and the estimation
results are computed. The posterior probabilities sum up to unity, where the model with the
highest probability is said to fit the data best.
6. RESULTS
This section discusses the results of the estimations. First the results of the non-spatial model
are discussed. Thereafter the discussion turns to the different spatial models.
6.1 Results base model
Table 4 reports the results for the non-spatial models. The non-spatial models are panel data
models containing fixed effects, which, as discussed above, are preferred above random
effects. The first model only contains country fixed effects, the second model only contains
time-period fixed effects and the third model contains both country and time-period fixed
effects. In order to assess whether the country fixed effects as well as the time-period fixed
effects are jointly significant two LR-tests are performed. The log-likelihood values are
reported below in table 4. The first test assumes the model with only country fixed effects is
nested in the model with both effects whereas the second test assumes the model with only
time-period fixed effects is nested in the model with both effects. As already indicated by the
differences in log-likelihood values, both tests clearly reject the null-hypothesis, thereby
concluding that the model containing both country and time-period fixed effects is preferred.
Autocorrelation and heteroskedastisity in panel data models biases the standard errors and
causes the results to be less efficient. To test for autocorrelation a test derived by Wooldridge
(2002) is performed, which can be applied under general conditions. The test has a null
hypothesis of no autocorrelation. The F-test results give a value of 3.903 for 1 and 166
degrees of freedom, which is close to but just above the 5% significance level of 3.898. The
null hypothesis of no autocorrelation is thus rejected at the 5% level.
29
To detect if the panel data suffers from heteroskedastisity the residuals of the model are
plotted against the dependent variable, which is shown in figure 14 in Appendix A. No clear
sign of heteroskedastisity is detected in this figure.
To address the issue of biased standard errors following autocorrelation, robust standard
errors are used to estimate the panel and spatial models. This affects the standard errors but
leave the parameter values unchanged. Model 4 in table 4 reports the estimation results for the
panel model with both fixed effects and robust standard errors. The country and time-period
fixed effects are not reported in the table as they are not of interest for this paper.
Table 4: Results panel data models without spatial interaction effects
Model 1 Model 2 Model 3 Model 4
Determinants Country fixed effects
Time-period fixed effects
Country and time-period fixed effects
Country and time-period fixed effects and robust se
D.Y -0.0463 -1.7568 1.4874 1.4874
(1.756) (1.798) (1.835) (1.488)
D.TI 0.0449*** 0.0869*** 0.0472*** 0.0472***
(0.011) (0.012) (0.012) (0.013)
D.E 0.09314 -0.8962 -0.1685 -0.1685
(0.831) (0.953) (0.828) (0.148)
D.F -0.0181 -0.0202 -0.0273 -0.0273**
(0.024) (0.026) (0.023) (0.014)
N 1503 1503 1503 1503
R^2 0.126 0.048 0.104 0.104
F 4.41 6.21 3.65 3.65
Log-likelihood -3614.77 -3918.53 -3600.22 -3600.22
Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
Where all previous papers find a major role for income in explaining Internet usage, this
paper does not find a significant role for growth of income. An explanation is that previous
papers used income level as independent variable instead of income growth. Provided that the
findings of the other papers are not based on spurious results, these results thus indicate that it
is not so much growth of income but reaching a certain level of income which affects growth
in Internet usage .
As expected, the measurement for ICT infrastructure is highly significant and positive. An
one percentage point increase in ICT infrastructure growth increases Internet usage growth by
0.05 percentage points. This seems like a marginal increase, however in the light of the rapid
increase of the technological infrastructure this effect has been substantial. As noted in section
3, mobile cellular usage per capita rose from 0.16 to 0.83 on average in the sample period.
30
Despite strong theoretical support this paper does not find a significant effect for growth in
education attainment. An explanation can be the low variation of the panel data. For most
countries education attainment only increased marginally in the sample period, making it
difficult to capture its effect on Internet usage.
For openness of a country, a decrease of one in the index increases Internet usage growth by
0.03 percentage point and is significant at the 5% level. As a decrease in the index represents
more openness this means a higher degree of openness has a positive effect on Internet usage.
Because on average the countries in the sample have become less open in the period 2000-
2009, this implies this decrease in openness has held back the growth of worldwide Internet
usage. On average the increase of the index was however only 2.35. Thus the effect on
worldwide Internet usage growth can be regarded as marginal.
Lastly the low R-squared value is noticeable. The R-squared reported in the table excludes the
contribution by the fixed effects. Furthermore, this low value is likely the result of exclusion
of important variables in this model. As discussed above, due to data limitations and fear of
spurious results the variables cost and income level are not included. As the aim of this paper
is to capture the significance of spatial components in the model the value of the R-squared is
however not of further interest.
6.2 Results spatial models
The focus of the paper now turns to the estimation results of the spatial models. First the
results of each model containing a different W-spatial matrix are discussed. These results are
compared with the results of the non-spatial model. Conclusions can then be drawn if the
spatial extensions significantly improve the model, and if so which of the spatial extensions
should be included. Also the direct and indirect effects are discussed. Thereafter the models
with different W-spatial matrix are compared by Bayesian posterior model probabilities. From
this conclusions can be drawn on which model fits the data best.
Table 5 reports the estimation results of the different spatial models calculated using the first-
order binary contiguity W-matrix. For all models the coefficient estimates of growth in ICT
infrastructure and openness are statistically significant and the signs are as expected. ICT
infrastructure is still highly significant at the 1% level for all models. The coefficient
measuring openness losses some of its significance when including spatial interaction effects,
however this loss is not great. Growth in income and education attainment remain
insignificant. For the SAR and SDM model 𝛿 is positive and significant, whereas for the SEM
and SDEM model’s 𝜆 this also applies. The table furthermore shows a clear improvement in
the log-likelihood values for the spatial models, indicating an improvement of the model.
As the null-hypotheses that 𝛿=0 and 𝜆=0 are rejected at the 1% level the extension from the
non-spatial model to the SAR and SEM model significantly improves the model. The null-
hypotheses that all 𝜃𝑖=0 is rejected at the 1% level, indicating that also this extension
significantly improves the model.
31
When comparing the log-likelihood values an extension to the SDM model seems to improve
the model further, which is confirmed by the LR-tests performed. The test of the SDM model
against the SAR model rejects the null-hypothesis at the 5% level. The tests of the SDM
model against the SEM and SLX model rejects both null-hypotheses at the 1% level.
Finally the SDM model is compared to the SDEM model to assess if the indirect effects are
global or local specifications. The log-likelihood values in table 5 show little difference. As
discussed the comparison is done by calculating Bayesian posterior model probabilities of the
SLX, SDM and SDEM model. The results are reported in table 6. It reports the log-marginal
likelihood values as well as the model probabilities of the three models calculated by
Bayesian method.
The log-marginal likelihood values show little difference, however the SDEM model clearly
has a higher probability than the SDM model. Moreover, as indicated by the LR-test earlier,
both models are preferred over the SLX model. The model comparison thus shows that the
SDEM model fits the data best. That is, including exogenous interaction effects and
interaction effects among the error term. These results thus do not find support for the theory
on diffusion of interactive communications addressed in section 4. Instead it supports the idea
of local spillovers affecting Internet usage.
Table 5: Model comparison of estimation results explaining Internet usage growth with first-
order binary contiguity W-matrix
Determinants Panel SAR SEM SLX SDM SDEM
D.Y 1.4874 1.1388 1.6770 1.5268 1.6036 1.4307 (1.488) (1.332) (1.358) (1.466) (1.322) (1.349) D.TI 0.0472*** 0.0395*** 0.0344*** 0.0393*** 0.0341*** 0.0391*** (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) D.E -0.1685 -0.1229 -0.1020 -0.1873 -0.1387 -0.1575 (0.148) (0.128) (0.138) (0.145) (0.128) (0.145) D.F -0.0273** -0.0257* -0.0270* -0.0276* -0.0267* -0.0269* (0.014) (0.014) (0.014) (0.014) (0.015) (0.015) WD.IU
0.2851***
0.2738***
(0.064)
(0.064) WD.Y
-2.5811 -3.7332 -3.6972
(3.341) (3.029) (3.297) WD.TI
0.0806*** 0.0618*** 0.0726***
(0.026) (0.023) (0.025) WD.E
-0.9675 -0.6546 -0.5685
(0.634) (0.627) (0.663) WD.F
-0.0013 -0.0084 -0.0026
(0.028) (0.026) (0.028) Wu
0.2815***
0.2721***
(0.065)
(0.064)
R² 0.104 0.137 0.088 0.207 0.228 0.212 Log-likelihood -3600.22 -3559.39 -3561.22 -3591.25 -3553.58 -3554.60
Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
32
Table 6: Comparison SDM and SDEM model with first-order binary contiguity W-matrix
Spatial model Log-marginal likelihood
Bayesian posterior model probability
SLX -4040.35 0.0000
SDM -3952.58 0.2055
SDEM -3951.23 0.7945
Table 7 reports the direct and indirect effects of the models. For the direct effects the
coefficient for growth in ICT infrastructure shows a drop for the spatial models in comparison
with the non-spatial model. Apart from this the coefficient estimates for the direct effects do
not differ greatly between the different models.
As supported by theory the indirect values however do differ substantially. From construction
the indirect values of the non-spatial and SEM model are zero. For the other models only the
indirect effect of ICT infrastructure is significant. The coefficient estimate of the SLX and
SDEM model are quite comparable. The estimate of SDM model is slightly higher whereas
the SAR estimate is substantially lower. However, as the magnitude of the indirect effects in
relation to the direct effects is always the same for all explanatory variables in the SAR model
the coefficient estimates of this model can be ignored. The difference between the SDM and
the SLX and SDEM models can be explained by the fact that the former model includes
global spillover specifications whereas the latter models includes local spillover
specifications.
As the model comparison shows that the SDEM model fits the data best only the coefficient
estimates of this model are elaborated on further. For the SDEM model the direct effects of
ICT infrastructure is lower than for the non-spatial model, however highly significant indirect
effects exist. The indirect effects of the SDEM model are local specifications. An one
percentage point increase in ICT infrastructure growth increase domestic Internet usage
growth by 0.04 percentage point and total foreign Internet usage growth by 0.07 percentage
point. The total effect is thus 0.11 percentage point, which is much larger than the effect
estimated for the non-spatial model.
The results thus show that the total effect of an improvement in ICT infrastructure is
underestimated when spatial interaction effects are not accounted for. The other variables
show no great changes for the direct effect and have no significant indirect effect. A change in
the growth in income, education attainment or openness in a particular country thus does not
have a significant impact on Internet usage growth in other countries.
33
Table 7: Model comparison of estimated direct and indirect effects on Internet usage growth
with first-order binary contiguity W-matrix
Panel SAR SEM SLX SDM SDEM
Direct effects
D.Y 1.4874 1.3978 1.6770 1.5268 1.3434 1.4307
(1.488) (1.153) (1.358) (1.466) (1.312) (1.349) D.TI 0.0472*** 0.0414*** 0.0344*** 0.0393*** 0.0389*** 0.0391***
(0.013) (0.014) (0.013) (0.013) (0.013) (0.013)
D.E -0.1685 -0.1189 -0.1020 -0.1873 -0.1792 -0.1575
(0.148) (0.139) (0.138) (0.145) (0.140) (0.145)
D.F -0.0273** -0.0261* -0.0270* -0.0276** -0.0261* -0.0269*
(0.014) (0.014) (0.014) (0.014) (0.015) (0.015)
Indirect effects
D.Y
0.5452
-2.5811 -4.2379 -3.6972
(0.465)
(3.341) (4.014) (3.297)
D.TI
0.0165**
0.0806*** 0.0928*** 0.0726***
(0.007)
(0.026) (0.032) (0.025)
D.E
-0.0500
-0.9675 -0.8662 -0.5685
(0.622)
(0.634) (0.8253) (0.663)
D.F
-0.0106
-0.0013 -0.0001 -0.0026
(0.007) (0.028) (0.036) (0.028) Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
Turning to the non-geographical matrices first the estimations of the block-diagonal W-matrix
based on trade blocs is discussed. Table 8 reports the results. The coefficient estimates are
quite similar to those based on the first-order binary contiguity W-matrix. Again extending
the model to the SAR, SEM or SLX model significantly improves the model. The null-
hypotheses that 𝛿=0, 𝜆=0 and all 𝜃𝑖=0 for respectively the SAR, SEM and SLX model are all
rejected at the 1% level. Moving to the SDM model, including more interaction effects again
improves the model. The LR-tests reject all the null hypothesis at the 1% level.
The differences between log-likelihood values of the SDM and the SDEM model reported in
table 8 are again very small. The calculation of the Bayesian posterior model probabilities to
compare the SDM with the SDEM are reported in table 9. Differences reported in this table
are great, and the model comparison clearly points to the SDEM model having a better fit.
Again this paper thus finds no support for the theory on diffusion but instead for local
spillover effects.
34
Table 8: Model comparison of estimation results explaining Internet usage growth with block
diagonal W-matrix
Determinants Panel SAR SEM SLX SDM SDEM
D.Y 1.4874 1.0137 0.8057 0.4890 0.3954 0.7004
(1.488) (1.396) (1.420) (1.466) (1.426) (1.384)
D.TI 0.0472*** 0.0367*** 0.0319*** 0.0311*** 0.0278** 0.0301***
(0.013) (0.012) (0.013) (0.013) (0.013) (0.013)
D.E -0.1685 -0.1187 -0.0842 -0.2530 -0.1886 -0.2045
(0.148) (0.131) (0.146) (0.149) (0.136) (0.161)
D.F -0.0273** -0.0272** -0.0286** -0.0205 -0.0228* -0.0215*
(0.014) (0.014) (0.014) (0.014) (0.014) (0.013)
WD.IU 0.3206*** 0.2565***
(0.063) (0.050)
WD.Y 10.9641 8.6260 11.720
(6.720) (6.167) (7.902)
WD.TI 0.1160*** 0.0781** 0.1005**
(0.033) (0.032) (0.042)
WD.E -9.4294 -6.8872 -7.2003
(6.289) (4.736) (5.975)
WD.F 0.0786 0.0629 0.0646
(0.062) (0.060) (0.077)
Wu 0.3216*** 0.2515***
(0.065) (0.065)
R² 0.104 0.120 0.104 0.055 0.059 0.054
Log-likelihood -3600.22 -3578.17 -3580.06 -3582.06 -3569.94 -3571.09 Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
Table 9: Comparison SDM and SDEM model with block diagonal W-matrix
Spatial model Log-marginal likelihood
Bayesian posterior model probability
SLX -4039.81 0.0000
SDM -3948.05 0.0000
SDEM -3932.98 1.0000
The coefficient estimates of the direct and indirect effects are reported in table 10. Concerning
the direct effects again the coefficient estimates of growth in income and education attainment
are insignificant. The estimate of ICT infrastructure is highly significant for all models and
little differences in effect exist. Growth in openness is significant at the 5% level for the SAR
and SEM model. For the SDM model it is only significant at the 10% level, whereas it is not
significant for the other models. Again little differences in the effect exist.
The different models also show quite similar indirect effects. Only the indirect effect of ICT
technology is significant. Contrary to the previous models discussed also the magnitudes of
the effect do not differ much. The magnitude of both local and global indirect effects is quite
similar. This can be explained by the nature of the block diagonal matrix. Local effects fall on
all units for which the elements of the matrix is non-zero, in this case all fellow trade bloc
35
members. The majority of the trade blocs consists of a substantial group of countries. Almost
50% of the countries in the sample are member of one of the two largest trade blocs. For these
models the local effects thus often fall on a large group of countries. This implies that local
effects often behave quite similar to global effects, explaining the small differences between
them.
Concentrating solely on the model that fits the data best, the SDEM model, the results are
very similar to those based on the previous matrix. An one percentage point increase in ICT
infrastructure growth increases total Internet usage growth by 0.13 percentage points, slightly
higher than for the other model. This higher effect is due to the higher indirect effect. The
conclusions are however similar. Total effect of an improvement in ICT infrastructure is
underestimated when spatial interaction effects are not accounted for, whereas all other
variables included have no indirect effect.
Table 10: Model comparison of estimated direct and indirect effects on Internet usage growth
with block diagonal W-matrix
Panel SAR SEM SLX SDM SDEM
Direct effects
D.Y 1.4874 1.0047 0.8057 0.4890 0.5930 0.7004 (1.488) (1.198) (1.420) (1.466) (1.360) (1.384) D.TI 0.0472*** 0.0381*** 0.0319*** 0.0311*** 0.0301*** 0.0301*** (0.013) (0.014) (0.013) (0.013) (0.013) (0.013) D.E -0.1685 -0.1128 -0.0842 -0.2530 -0.3907 -0.2045 (0.148) (0.141) (0.146) (0.149) (0.267) (0.161) D.F -0.0273** -0.0274** -0.0286** -0.0205 -0.0220* -0.0215 (0.014) (0.013) (0.014) (0.014) (0.013) (0.014)
Indirect effects
D.Y
0.4495 10.9641 11.0277 11.720
(0.547) (6.720) (7.520) (7.902)
D.TI
0.0177** 0.1160*** 0.1036*** 0.1005**
(0.009) (0.033) (0.040) (0.042)
D.E
-0.0569 -9.4294 -8.9172 -7.2003
(0.074) (6.289) (6.523) (5.975)
D.F
-0.0129 0.0786 0.0412 0.0646 (0.013) (0.062) (0.079) (0.077)
Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
The models based on the other two geographical matrices, of which the results are reported in
tables 11 and 12, show very different results. For the models based on the main trading
partners W-matrix the extension to the SAR models is only a significant improvement at the
10% level. All other spatial extensions do not significantly improve the model. The LR-tests
furthermore show that including more spatial interaction effects does not improve the model
significantly, which is also indicated by the small differences in log-likelihood values between
the SDM and SDEM model and other models.
The results of the models based on the export flows W-matrix are quite similar. An extension
to the SAR and SEM model is significant only at the 10% level, whereas the extension to the
36
SLX model is highly insignificant. Also the inclusion of more spatial interaction effects does
not improve the model significantly.
An explanation for the insignificance of spatial extensions for these models is the fact that
there are a few large countries whose impact on all countries is great, whereas the impact of
all other countries is limited, as discussed in section 4. Significant improvement of the non-
spatial model using these W-matrices would indicate that changes in a few large countries
would have a great worldwide effect on Internet usage, whereas changes in all other countries
would only marginally affect some countries. Given the results this is clearly not the case.
The insignificance of the extension also follows from the coefficient estimates of the direct
and indirect effects. The results for the models are reported in tables 15 and 16 in the
appendix. Both tables report only highly insignificant indirect effects and limited differences
between the direct effects of the different models. The small differences between the
coefficient estimates of the non-spatial models is due to the exclusion of five countries in the
sample for the model based the W-matrix consisting of export flows.
Table 11: Model comparison of estimation results explaining Internet usage growth with W-matrix based on main trading partners
Determinants Panel SAR SEM SLX SDM SDEM
D.Y 1.4874 1.382 1.4963 1.8174 1.7152 1.7409
(1.488) (1.494) (1.494) (1.493) (1.499) (1.490)
D.TI 0.0472*** 0.0441*** 0.0454*** 0.0457*** 0.0432*** 0.0438***
(0.013) (0.013) (0.013) (0.014) (0.013) (0.013)
D.E -0.1685 -0.1081 -0.1348 -0.1792 -0.1206 -0.1325
(0.148) (0.177) (0.161) (0.152) (0.179) (0.171)
D.F -0.0273** -0.0258* -0.0272* -0.0287** -0.0278* -0.0280*
(0.014) (0.015) (0.015) (0.014) (0.015) (0.015)
WD.IU 0.0891* 0.0877*
(0.050) (0.049)
WD.Y -6.6336 -6.1378 -6.1441
(5.491) (5.451) (5.453)
WD.TI -0.0047 -0.0099 -0.0059
(0.020) (0.020) (0.020)
WD.E 15.5270 12.8539 13.9471
(12.899) (12.109) (12.056)
WD.F 0.0264 0.0369 0.0361
(0.044) (0.042) (0.043)
Wu 0.0692 0.0683
(0.046) (0.045)
R² 0.104 0.105 0.102 0.145 0.142 0.141
Log-likelihood -3600.22 -3596.07 -3595.26 -3597.00 -3593.04 -3592.24 Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
37
Table 12: Model comparison of estimation results explaining Internet usage growth with W-
matrix based on export flows in the year 2000
Determinants Panel SAR SEM SLX SDM SDEM
D.Y 1.4374 1.4091 1.5044 1.7191 1.7696 1.7513
(1.504) (1.535) (1.534) (1.534) (1.561) (1.552)
D.TI 0.0497*** 0.0467*** 0.0482*** 0.0480*** 0.0458*** 0.0469***
(0.014) (0.014) (0.013) (0.014) (0.014) (0.014)
D.E -0.1791 -0.1300 -0.1490 -0.1665 -0.1101 -0.1282
(0.155) (0.187) (0.172) (0.157) (0.190) (0.177)
D.F -0.0278** -0.0272* -0.0278* -0.0280** -0.0278* -0.02789*
(0.014) (0.016) (0.015) (0.014) (0.016) (0.015)
WD.IU 0.1511* 0.1582*
(0.088) (0.090)
WD.Y -11.316 -12.1928 -11.3943
(10.840) (10.8909) (11.004)
WD.TI 0.0355 0.0196 0.0330
(0.049) (0.051) (0.050)
WD.E 30.4753 28.7879 30.2235
(19.885) (19.011) (19.667)
WD.F 0.0201 0.0281 0.0256
(0.067) (0.066) (0.057)
Wu 0.1168* 0.1167*
(0.068) (0.067)
R² 0.109 0.108 0.106 0.129 0.127 0.127
Log-likelihood -3500.22 -3496.96 -3495.46 -3498.21 -3495.24 -3493.49 Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
To summarize, the spatial models based on the first-order binary contiguity W-matrix and the
block diagonal W-matrix show a significant improvement compared to the non-spatial model,
whereas the models based on the other two W-matrices do not. Where theory was ambiguous,
the empirics clearly show that for both the improved models the SDEM model fits the data
best, pointing to the existence of local spillover effects. These indirect effects are quite similar
for both matrices and substantially increase the impact of ICT infrastructure. In both cases,
excluding spatial interaction effects underestimates the total effect of a change in growth of
ICT infrastructure on total growth of Internet usage. The other variables have no significant
indirect effects. Including spatial interaction effects thus shows that both direct and indirect
effects of ICT technology play a vital role in cross-country Internet usage differences.
Lastly this section compares the different spatial W-matrices to see which matrix fits the data
best. Bayesian posterior model probabilities are used to compare the models. Table 15
provides the probabilities for the SDEM models. The model based on the export flows W-
matrix is excluded from the comparison because of differences in data set due to the exclusion
of some countries. Given the absence of a significant improvement for this model when
including spatial interaction effects it can however be concluded in advance that this model
does not fit the data best.
38
The results clearly show that the SDEM model based on the block diagonal trade bloc W-
matrix has the highest log-marginal likelihood and the highest probability, and is therefore
said to fit the data best. This contrary to the log-likelihood values reported in the tables above,
where the SDEM model based on the first-order binary contiguity W-matrix has the highest
value. With -3554.60 for the model based on the first-order binary contiguity W-matrix
against -3571.09 for the model based on the block diagonal trade bloc W-matrix. These
results thus underscore the importance of comparing spatial models with different W-matrices
by Bayesian posterior model probability.
As all trade blocs considered are regional blocs, the comparison shows that the data is better
described by spillover effects which affect a whole region than only first-order neighbors or
main trading partners. This results are in line with the findings of figure 2 in the introduction,
which shows a regional development of growth in Internet usage.
Table 13: Comparison SDEM models based on different spatial Weights-matrix
Spatial W-matrix Log-marginal likelihood Bayesian posterior model probability
First-order binary contiguity W-matrix -3951.23 0.0000
Block-diagonal trade bloc W-matrix -3932.98 1.0000
Main trading partner W-matrix -4046.45 0.0000
6.3 Robustness results
Before turning to the policy implications first the robustness of the results are checked. All
literature discussed identifies income level as an essential variable in explaining Internet
usage in a country. A variable measuring income level is however excluded in this paper
because of non-stationarity and unrealistic restrictions imposed by a random effects model. In
this paper therefore differences in income are accounted for by the fixed effects. The last part
of this section however tests if the exclusion of this variable has major implications for the
obtained results.
For this the SDEM model based on the block diagonal matrix is measured again using two
different models, now including a variable measuring a country’s income level. The first
model includes fixed effects and the log of GDP per capita as additional explanatory variable.
The second model includes random effects and the GDP per capita in the year 2000 as
additional explanatory variable, which stays constant over the whole period. As noted the
outcome of these models can be based on a spurious relationship and are therefore only used
to cross-check the results described above.
Table 16 reports the results of these two models as well as the results of the model without a
variable measuring income level. In the appendix the results for the direct and indirect effects
can be found. For both models the effect of the variable included is highly significant. More
importantly however, the inclusion of the variable has little implications for the estimates of
the other variables. ICT infrastructure remains the only significant spatial variable. The
magnitude of the significant variables of both fixed effects-SDEM models are quite
39
comparable. The magnitude of the random effects model differ somewhat, however this can
be explained by the different implications a random effects model implies. Furthermore, the
log-likelihood value of the random effects model is much lower than that of both fixed-effects
models, implying the model can be rejected in favor of the others. Therefore, these results
support the robustness of results obtained by the models in this paper.
Table 14: Model comparison of estimation results different SDEM models based on block
diagonal W-matrix
Determinants
Model 1: SDEM without income level
Model 2: SDEM including income level and FE
Model 2: SDEM including income level and RE
Y
3.602***
(0.883)
Y-base year
0.7853***
(0.069)
D.Y 0.7004 -0.5253 1.0590
(1.384) (1.435) (1.453) D.TI 0.0301*** 0.0258*** 0.0298***
(0.013) (0.013) (0.011) D.E -0.2045 -0.2907 -0.2236
(0.161) (0.153) (0.150)
D.F -0.0215* -0.0252* -0.2120*
(0.013) (0.014) (0.013)
WY
-5.3058
(4.484)
WY-base year
-0.0396
(0.046)
WD.Y 11.720 10.645 -2.1716
(7.902) (7.506) (7.5826) WD.TI 0.1005** 0.1308*** 0.0701*** (0.042) (0.042) (0.028) WD.E -7.2003 -8.1791 -10.4848
(5.975) (6.046) (8.297)
WD.F 0.0646 0.0822 -0.0149
(0.077) (0.078) (0.071) Wu 0.2515*** 0.2526*** 0.4107***
(0.065) (0.068) (0.065)
R² 0.054 0.077 0.633
Log-likelihood -3571.09 -3559.30 -3688.22 Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
40
7. POLICY IMPLICATIONS
As noted in the introduction, increasingly economist and institutions worry that the global
digital divide leaves many developing countries economically behind as Internet has become
a key asset in economies nowadays. Effective policies aimed to reduce this divide are
therefore of great economic and social importance. The results from this paper can help
improve the effectiveness of these policies, and thereby successfully decrease the divide.
The results show a significant improvement of the model explaining cross-country Internet
usage growth when including spatial effects. In both a model based on a geographical and a
non-geographical W-matrix a significant local indirect effect of ICT infrastructure is present.
Improving this infrastructure thus not only impacts Internet usage in one’s own country, but
also in that of other countries.
In all regions of the world examples of government investment in ICT infrastructure are
present. In the US, President Obama committed to making high-speed wireless services
available to at least 98% of Americans by making more airwaves available and through a $7
billion investment in high speed Internet infrastructure. For Africa the International Finance
Corporation, part of the World Bank Group, identifies improving the broadband infrastructure
as the next major challenge in the ICT sector. South-Africa for example aims to achieve this
with the Broadband Infraco project, providing a compelling combination of
telecommunications transport and value added services in the marketplace. In Asia, Malaysia
aims to substantially extend ICT infrastructure by investing in HIG-Speed Broadband and
Broadband to General Population programs. Singapore goes even beyond by investing in a
nationwide ultra-high speed fiber access infrastructure.
These examples, among many more, show that investment in ICT infrastructure is at the core
of most policies aimed at increasing Internet usage. The spatial component of ICT
infrastructure in explaining Internet usage growth following from the results in this paper can
be a key in improving the success rate of these policies. This for two reasons.
Firstly, the results show that in order to improve the impact of these policies they should be
initiated on the same regional level as trade blocs are initiated. In this way the spillover effects
of improvements in ICT infrastructure can be coordinated and identified properly, and thereby
effectively help decreasing the global digital divide.
Secondly, if the success of the policies is merely measured in total increase of Internet usage
in one’s own country, the effects of policies can be underestimated. The results show that
growth in ICT infrastructure has a significant positive impact on the Internet usage growth of
neighboring- and fellow trade bloc countries. This local indirect effect is even larger than the
domestic direct effect. The success of these policies should therefore be measured on how
they improved Internet usage on a national and international basis, not only national. If not,
the effect of successful policies might be underestimated and these policies may be placed on
a hold or not followed up by new ones.
41
In line with these findings the European Commission (2015, page 3) proposed the EU should
move to one single digital market. It states that: “All Member States are wrestling with similar
problems (concerning Internet and digital technologies e.d.) but on a national basis, which is
too limited to allow them to seize all the opportunities and deal with all the challenges of this
transformational change”.
However, from many governments it cannot be expected that they invest in projects which
only have a limited direct impact on the improvement of Internet usage in their own country.
If partnerships like the EU are not feasible a central role should therefore be played by
organizations like the World Bank and the UN, which have the funds as well as the political
force to initiate certain regional projects. These policies als perfectly fit in their goals of
reducing worldwide inequality and poverty.
To conclude, the results in this paper indicate that regional cooperation on trade bloc level in
ICT infrastructure policies aiming to improve Internet usage increases the impact as well as
better measure the total effect of these policies. This increases the effectiveness of policies
aimed at decreasing the worldwide digital divide. International organizations like the World
Bank and UN can play a critical role in this process.
8. LIMITATIONS AND RECOMENDATIONS
Before turning to the conclusion, this section discusses the main limitations of the research
conducted in this paper as well as possible further research.
In the models estimated in this paper the presumed important variables income level and cost
are excluded. Although this does not have great consequences for the implications of this
paper, including these variables is likely to improve the knowledge of explaining worldwide
Internet usage differences and its spatial components.
Concerning income level, further research could use a different variable which does not suffer
from non-stationarity. Alternatively, GDP-per capita could be transformed to make it
stationary. Concerning cost of Internet, increasingly more data is available. For example data
on the different prices of Internet connection for home owners could be used. Therefore in
future research this variable could be included. For now a smaller sample of countries could
be taken for which cost of Internet is already available. Spatial models however require
balanced panel data, thus a complete data set is necessary. Also, to effectively measure the
spatial interaction effects abundant spatial relationships should exists between the countries in
the sample.
Besides the exclusion of these variables, the possible endogeneity of the variable measuring
income growth is a limitation. Addressing this issue is however difficult, as it is hard to find a
variable measuring income growth which does not suffer from possible endogeneity.
42
Turning to spatial modelling, further research should consider different W-matrices as the
value and significance level of the interaction parameters depend on the chosen W-matrix.
This will shed more light on the true spatial relationship underlying Internet usage.
Lastly, micro-level research could focus on understanding the dynamics behind the spatial
interaction effect. Research on how and where ICT infrastructure affects Internet usage in a
foreign country could further help increase the impact of these policies.
9. CONCLUSION
The aim of this paper is to improve the understanding of cross-country Internet usage
differences by capturing the spatial component using spatial modelling. It tests if Internet
usage in a country is affected by domestic variables as well as spatial interaction with other
countries. This is done by means of including spatial interaction effects into the model
explaining growth in Internet usage. This increased understanding helps improve the
efficiency of policies targeted at reducing the global digital divide.
For the spatial models in this paper four different W-matrices are constructed. For spatial
models based on two of the four matrices the paper finds a significant improvement in the
performance of the model when spatial interaction effects are included. The hypotheses that
Internet usage in a country is affected by both domestic effects and spillover effects from
neighboring countries or trading partners is thus confirmed.
In both cases the paper finds a highly significant direct and indirect effect for ICT
infrastructure growth. This implies that changes in the growth of this infrastructure not only
impacts Internet usage growth in one’s own country, but also in that of other countries.
Including spatial interaction effects increases the total effect of ICT infrastructure
substantially, indicating that its effect on Internet usage is underestimated in non-spatial
models.
Contrary to earlier papers the paper does not find an important role for income in explaining
cross-country Internet usage differences. No significant effect of growth in income on growth
in Internet usage is found. This indicates that it is not so much growth of income but reaching
a certain level of income which affects growth in Internet usage, or that previous findings may
be based on spurious results.
Concerning the spatial models, the paper finds that the SDEM model based on the block-
diagonal trade bloc W-matrix fits the data best. This implies that the indirect effects are local
specifications which fall on fellow trade bloc members.
The findings suggest that in order to increase the efficiency of policies aimed at decreasing
the global digital divide, they should be initiated and conducted on a regional level
corresponding to trade bloc agreements. This enables policymakers to better coordinated and
identified the spillover effects of improvements in ICT infrastructure and properly measure its
effect.
43
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Appendix A: Tables
Figure 14: Plot of residuals fixed effects models against dependent variable
Table 15: Model comparison of estimated direct and indirect effects on Internet usage growth
with W-matrix based on main trading partners
Panel SAR SEM SLX SDM SDEM
Direct effects
D.Y 1.4874 1.382 1.4963 1.8174 1.6511 1.3589
(1.488) (1.494) (1.494) (1.493) (1.472) (1.267)
D.TI 0.0472*** 0.0441*** 0.0454*** 0.0457*** 0.0432*** 0.0452***
(0.013) (0.013) (0.013) (0.014) (0.013) (0.014)
D.E -0.1685 -0.1081 -0.1348 -0.1792 -0.0693 -0.0978
(0.148) (0.177) (0.161) (0.152) (0.177) (0.188)
D.F -0.0273** -0.0258* -0.0272* -0.0287** -0.0281* -0.0257*
(0.014) (0.015) (0.015) (0.014) (0.015) (0.015)
Indirect effects
D.Y
-6.1378 -6.6336 -6.3355 0.1495
(5.451) (5.491) (6.094) (0.164)
D.TI
-0.0099 -0.0047 -0.0071 -0.0053
(0.020) (0.020) (0.022) (0.004)
D.E
12.8539 15.5270 14.4437 -0.0125
(12.109) (12.899) (13.189) (0.026)
D.F
0.0369 0.0264 0.0354 0.0029
(0.042) (0.044) (0.048) (0.002) Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
-15
-10
-5
0
5
10
15
20
25
30
-10 -5 0 5 10 15 20 25 30 35
RES
IDU
ALS
GROWTH IN INTERNET USAGE
47
Table 16: Model comparison of estimated direct and indirect effects on Internet usage growth
with W-matrix based on export flows in the year 2000
Panel SAR SEM SLX SDM SDEM
Direct effects
D.Y 1.4374 1.3859 1.5044 1.7191 1.6897 1.7513
(1.504) (1.302) (1.534) (1.534) (1.529) (1.552)
D.TI 0.0497*** 0.0478*** 0.0482*** 0.0480*** 0.0459*** 0.0469***
(0.014) (0.015) (0.013) (0.014) (0.014) (0.014)
D.E -0.1791 -0.1195 -0.1490 -0.1665 -0.0201 -0.1282
(0.155) (0.199) (0.172) (0.157) (0.195) (0.177)
D.F -0.0278** -0.0270* -0.0278* -0.0280** -0.0282* -0.02789*
(0.014) (0.015) (0.015) (0.014) (0.016) (0.015)
Indirect effects
D.Y 0.2959 -11.316 -13.6669 -11.3943
(0.335) (10.840) (13.260) (11.004)
D.IT 0.0106 0.0355 0.0293 0.0330
(0.007) (0.049) (0.061) (0.050)
D.E -0.0281 30.4753 34.6988 30.2235
(0.0534) (19.885) (23.064) (19.667)
D.F -0.0059 0.0201 -0.0238 0.0256
(0.005) (0.067) (0.080) (0.057) Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
48
Table 17: Model comparison of estimated direct and indirect effects on Internet usage growth
with different SDEM models based on block diagonal W-matrix
SDEM without income level
SDEM including income level and FE
SDEM including income level and RE
Direct effects
Y 3.602*** (0.883) Y-base year 0.7853*** (0.069) D.Y 0.7004 -0.5253 1.0590 (1.384) (1.435) (1.453) D.TI 0.0301*** 0.0258*** 0.0298*** (0.013) (0.013) (0.011) D.E -0.2045 -0.2907 -0.2236 (0.161) (0.153) (0.150) D.F -0.0215* -0.0252* -0.2120* (0.013) (0.014) (0.013)
Indirect effects
Y -5.3058 (4.484) Y-base year -0.0396 (0.046) D.Y 11.720 10.645 -2.1716 (7.902) (7.506) (7.5826) D.TI 0.1005** 0.1308*** 0.0701*** (0.042) (0.042) (0.028) D.E -7.2003 -8.1791 -10.4848 (5.975) (6.046) (8.297) D.F 0.0646 0.0822 -0.0149 (0.077) (0.078) (0.071)
Note: ***, **, * indicate significantly different from zero at the 0.01, 0.05, and 0.10 levels, respectively
Standard errors are in parentheses
49
Appendix B: List of countries included
Afghanistan Denmark Kyrgyzstan Russian Federation
Albania Djibouti Lao P.D.R. Rwanda
Algeria Dominica Latvia Samoa
Angola Dominican Rep. Lebanon Saudi Arabia
Argentina Ecuador Lesotho Senegal
Armenia Egypt Liberia Serbia
Australia El Salvador Libya Seychelles
Austria Equatorial Guinea Lithuania Sierra Leone
Azerbaijan Estonia Luxembourg Singapore
Bahamas Ethiopia Macedonia Slovakia
Bahrain Fiji Madagascar Slovenia
Bangladesh Finland Malawi Solomon Islands
Barbados France Malaysia South Africa
Belarus Gabon Maldives Spain
Belgium Gambia Mali Sri Lanka
Belize Georgia Malta Sudan
Benin Germany Mauritania Suriname
Bolivia Ghana Mauritius Swaziland
Bosnia and Herzegovina Greece Mexico Sweden
Botswana Guatemala Moldova Switzerland
Brazil Guinea Mongolia Syria
Brunei Darussalam Guinea-Bissau Morocco Tajikistan
Bulgaria Guyana Mozambique Tanzania
Burkina Faso Haiti Namibia Thailand
Burundi Honduras Nepal Togo
Cambodia Hong Kong, China Netherlands Tonga
Cameroon Hungary New Zealand Trinidad & Tobago
Canada Iceland Nicaragua Tunisia
Cape Verde India Niger Turkey
Central African Rep. Indonesia Nigeria Uganda
Chad Iran (I.R.) Norway Ukraine
Chile Iraq Oman United Arab Emirates
China Ireland Pakistan United Kingdom
Colombia Israel Panama United States
Congo (Dem. Rep.) Italy Papua New Guinea Uruguay
Congo (Rep.) Jamaica Paraguay Uzbekistan
Costa Rica Japan Peru Venezuela
Côte d'Ivoire Jordan Philippines Viet Nam
Croatia Kazakhstan Poland Yemen
Cuba Kenya Portugal Zambia
Cyprus Korea (Rep.) Qatar Zimbabwe
Czech Republic Kuwait Romania
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Appendix C: Trade blocs
European Free Trade Agreement (EFTA)
Austria France Lithuania Slovakia
Belgium Germany Luxembourg Slovenia
Bulgaria Greece Malta Spain
Cyprus Hungary Netherlands United Kingdom
Czech Republic Iceland Norway Sweden
Denmark Ireland Poland Switzerland
Estonia Italy Portugal
Finland Latvia Romania
North American Free Trade Agreement
(NAFTA)
Canada United States Mexico
South Asian Association for Regional Cooperation
(SAARC)
Afghanistan India Nepal Maldives
Pakistan Sri Lanka Bangladesh
ASEAN Free Trade Agreement
Cambodia Singapore Viet Nam
Brunei Darussalam Thailand Malaysia
Philippines Indonesia Lao P.D.R.
Pacific Island Forum (PIF)
Fiji Papua New Guinea Tonga
New Zealand Samoa
Australia Solomon Islands
Euroasian Economic Community
(EAEC)
Kyrgyzstan Tajikistan
Russian Federation Belarus
Kazakhstan
Central European Free Trade Agreement
(CEFTA)
Albania Macedonia
Bosnia and Herzegovina Moldova
Croatia Serbia
51
Caribbean Community (CARICOM)
Jamaica Bahamas Dominica
Trinidad & Tobago Barbados
Haiti Cuba
Union of South American Nations (USAN)
Argentina Colombia Peru
Bolivia Ecuador Suriname
Brazil Guyana Uruguay
Chile Paraguay Venezuela
Central American Integration System (SICA)
Belize El Salvador Nicaragua
Dominican Rep. Guatemala Panama
Costa Rica Honduras
Arab League (AL)
Mauritania* Jordan Qatar
Libya* United Arab Emirates Saudi Arabia
Egypt* Iraq Syria
Algeria* Bahrain Yemen
Tunisia* Kuwait Morocco
Lebanon Oman
*also participating in the African Union
African Union (AU)
Tunisia* Burundi Equatorial Guinea Liberia Rwanda
Mauritania* Cameroon Ethiopia Madagascar Senegal
Libya* Cape Verde Gabon Malawi Seychelles
Egypt* Central African Rep. Gambia Mali Sierra Leone
Algeria* Chad Ghana Mauritius South Africa
Angola Congo (Dem. Rep.) Guinea Mozambique Sudan
Benin Congo (Rep.) Kenya Namibia Swaziland
Botswana Côte d'Ivoire Guinea-Bissau Niger Tanzania
Burkina Faso Djibouti Lesotho Nigeria Togo
Uganda Zambia Zimbabwe
*also participating in the Arab League
Countries without a trading block
Armenia Israel Turkey
Azerbaijan Japan Ukraine
China Korea (Rep.) Uzbekistan
Georgia Mongolia Iran (I.R.)
Hong Kong, China