CALL ME MAYBE:
THE IMPACT OF TELECOMMUNICATIONS ON ECONOMIC
GROWTH IN THE ASEAN REGION
McRey Banderlipe II
School of Economics, De La Salle University
______________________________________________________________________________
ABSTRACT
The importance of telecommunications plays a very important role in achieving one of the
targets in the Millennium Development Goals of the United Nations, which is to increase the
access of people in both fixed-line and mobile phone telecommunications, measured in terms of
teledensity. This paper attempts to determine if the teledensity in both modes of
telecommunications, as well as the aggregate of these two modes, can contribute to economic
growth, particularly in the ASEAN region where there is a promising venue to conduct business
and to harness the benefits of investing in such facilities. Using a panel data analysis from seven
ASEAN countries from 1992 – 2010 employing the LSDV-1 process and penetration rates as a
proxy for teledensity, the findings revealed that fixed-line penetration rates are positively related
to economic growth measured in terms of GDP per capita, while mobile and total penetration
rates are not significantly associated with economic growth considering that mobile
telecommunications were introduced later than fixed line telephones. It will still need a
considerable length of time to fully realize the benefits of telecommunications. Furthermore, this
study recognizes the need for better and proper use of telecommunications facilities and to
ensure its accessibility in the remote areas of the region. Such policies are important to ensure
that the benefits of telecommunications access can be fully achieved.
Keywords: telecommunications, teledensity, economic growth, telephones
1
Introduction
There is no doubt about the need for people to communicate with one another. Having a
conversation over a cup of coffee or a meal, writing lectures in the blackboard, delivering a talk
or a speech, publishing a book or a newspaper, doing sales talk using the telephone, and sending
a letter via snail mail are just some of the ways where one can communicate to another person.
Perhaps it could be their friend, students, audience, readers, and receiver of the call or mail to
whom information is intended to be conveyed.
With the advent of contemporary modes of communication using mobile phones, computers,
laptops, PC tablets, the e-mail, and the Internet, the way people communicate has changed
abruptly. The blossoming of the dot.com generation elevated communication to a higher level
where there is stronger focus towards the transfer of real-time information between senders and
receivers. Such introduction of new modes of communication technology also changed the way
economic activities take place because of the availability of the needed information that
facilitates effective decision making.
For a country to sustain an economy where information is very important, it has to develop
and to maintain an information generation and transfer system that promotes the interaction
among the members of the domestic economy and of the international community.
Telecommunications would be one of the ways to support the need for this mechanism. Prior
research suggests that investment in telecommunications increases growth dividend (Roller and
Waverman, 2001), combats poverty (Calderon and Serven, 2004, as cited in Negash and Patala,
2006), and promotes economic expansion (World Bank, 1991, as cited in Negash and Patala,
2006). However, the validity of these arguments is still in question up to this day.
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In the 8th plenary meeting of the United Nations General Assembly last September 8, 2000,
the 189 countries, including the 147 heads of state and government, signed the Millennium
Declaration. This declaration signifies the pledge of support from both developed and developing
countries to forge a partnership towards the creation of “an environment – at the national and
global levels alike – which is conducive to development and to elimination of poverty” (United
Nations, 2000). To represent such partnership, the United Nations released the set of Millennium
Development Goals (MDG) and targets, the most recent version of which was published on
January 15, 2008. Indicators for monitoring progress are identified side-by-side with the MDGs,
all of which must be achieved by 2015, the year of deadline set by the United Nations.
The United Nations believes that the emergence of an information superhighway should
serve as a channel for economic growth to address the eighth Millennium Development Goal
(MDG). Following the resolution adopted by the United Nations General Assembly in 1997
declaring access to communication as a basic human right (Salazar, 2006/2007), it was no
surprise that one of the goals of the MDG, particularly Target 8.F, is to make available the
benefits of new technologies, especially in information and communications. In the Official List
of MDG Indicators, achieving target 8.F can be done by cooperating with the private sector to
ensure that the benefits arising from new technologies can be channeled to everyone to foster
economic development. Table 1 presents the details of target 8.F.
Inspired by the landmark papers of Roller and Waverman (2001) and Waverman, Meschi,
and Fuss (2005) and Sridhar and Sridhar (2009), this study aims to investigate how
telecommunications affect economic growth, particularly within the economies in the ASEAN
region. When the Millennium Declaration was signed, academic literature on achieving target
8.F became extensive by discussing the impact of mobile telecommunications on the economic
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growth of developing countries (see articles of Biancini, 2011; Gruber and Koutroumpis, 2011;
Lam and Shiu, 2010; Chakraborty and Nandi, 2009; Sridhar and Sridhar, 2009; Shiu and Lam,
2008; Melamed, 2007; Negash and Patala, 2006; Waverman, et al., 2005; Datta and Arwal, 2004;
and Brock and Sutherland, 2000).
Table 1
MDG indicators on the benefits of new technologies as targeted in Goal 8
Goals and Targets
(from the Millennium Declaration)
Indicators for Monitoring Progress
Goal 8: Develop a global partnership for development
Target 8.F: In cooperation with the private sector, make
available the benefits of new technologies, especially
information and communications.
8.1 Fixed telephone lines per 100
inhabitants
8.2 Mobile cellular subscriptions per 100
inhabitants
8.3 Internet users per 100 inhabitants
Note: This is an excerpt from the original document. For the official list of indicators, the reader is advised
to visit the UN Millennium Development Goals Official List of MDG Indicators link http://mdgs.un.org
/unsd /mdg /Resources/Attach/Indicators/OfficialList2008.pdf
Note, however, that the previously mentioned papers do not totally make reference to Target
8.F of the MDG, particularly in 8.1 and 8.2, as some of them were written prior to the revision of
the indicators in 2008. Our study aims to provide significant contribution by measuring the
impact of telecommunications in the ever-changing economic dynamics of the ASEAN member
nations towards meeting the Millennium Development Goals set by the United Nations. From the
speech of the Prime Minister Go Chok Tong of Singapore during the Third ASEAN
Telecommunications and IT Ministers meeting in 2003, he iterated the need for ASEAN
industries to re-direct the future of Information and Communications Technology (ICT) since
ICT development fosters the move towards achieving economic recovery and job creation for
many people. Moreover, such undertaking contributes to sustained growth while bringing more
closely the different economies in the region.
4
Statement of the Problem
The importance of telecommunications infrastructure on economic growth has been
indispensable. The products of telecommunication lead to the increase in the demand for goods
and services used to produce them. In addition, economic returns on telecommunications
infrastructure outweigh the cost of searching for and exchanging information since it fosters
direct communication despite increased distances. Such benefit transcends to other sectors that
causes spillovers and externalities that contributes to the growth of other sectors and of the
economy on a broader perspective.
In the ASEAN region, the need to embrace technology would support continuous
improvement in terms of productivity, efficiency, competitiveness, and the quality of the lives of
people. This is the only way, according to Prime Minister Go Chok Tong (2003), for all of us to
reap the benefits of a truly connected ASEAN region. In response to this call, this paper will seek
to answer the following research question: Does teledensity/penetration rate of mobile and fixed
telecommunications affect economic growth among countries in the ASEAN region?
Review of Related Literature
Towards attaining the ultimate goal of development and poverty eradication, economists
have started to find out the potential sources for economic growth. The neoclassical growth
model proposed by Solow (1956) attempted to answer the question of possible convergence over
time in the levels of per capita income and output. Such prognostication inspired economic
researchers to empirically test the determinants of per capita growth, including the article on
increasing returns and long-run growth (Romer, 1986). Several papers identified in the
introductory part of the study by Röller and Waverman (2001) were clustered in terms of how
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human and physical capital is accumulated, how spillovers affect external economies, and how
industrial innovation can serve as the locomotive that will steer economic growth.
The prevailing questions on what could be the major sources of economic growth have
plagued economic researchers over time. In cross-country analysis of economic growth, Datta
and Agarwal (2004) questioned the possible convergence of per capita incomes across countries
in the long run, coupled with identifying the possible sources of growth for different economies.
The presence of this “guiding hand” that links per capita income and per capita production
served as the inspiration behind the works of Barro (1991) and Barro and Sala-i-Martin (1991)
which determined the factors of long-term growth emanating from the expanded neoclassical
growth theory by Solow (1956).
According to Chakraborty and Nandi (2011), the advances in economic literature augmented
Solow’s growth theory by providing possible explanation on the sources of technological
change. Such progress resulted to observed faster economic growth especially when the
government undertakes investment in public infrastructure. Consistent with the Solow (1956)
convergence hypothesis expanded in Barro and Sala-i-Martin (1992), greater attention was given
to telecommunications investment as a component of public investment and its ability to
generate economic gains since it creates spillover effects through network externalities to impact
economic growth than investing in alternatives forms of infrastructure.
Prior to the signing of the Millennium Declaration in 2000, a number of studies have already
examined the potential impact of telecommunication facilities on economic growth. Snow (1989)
argued that the policies constructed to liberalize ASEAN-US trade and direct foreign investment
in services should be aimed towards export-led growth, allocative efficiency, and technology
transfer, since information is treated as a service and resource in the world economy.
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In addition, the recent advances in technological progress resulted in the fall in the prices of
telecommunication services pricing and increased consumption of these services (Cronnin,
Collerlan, and Gold, 1997). As a factor input, substituting other inputs using telecommunications
would generate substantial and cost-effective savings to the economy particularly in resource
requirements. Such economic phenomenon of technological progress is evident since there is an
increased bias towards skilled workers and the use of weightless technologies, as coined in the
paper of Cameron (1998).
Before telecommunications technology was introduced, searching for information takes an
arduous process, and its impact on growth is not yet visible. This could be probably explained in
Brock and Sutherland (2000) where no significant influence is exhibited between
telecommunications and output growth in the Former Soviet Union republics although there is
perceived output growth upon the dissolution of the union. This was explained by the difficulty
experienced by these new former USSR countries to identify sources of financing for its
telecommunications facilities. Telecommunications, back then, are still facing the hard times at
the time of its adoption.
Alleman, et al. (1994, as cited in Negash and Patala, 2006) added that, in developing
countries, economic growth is not totally experienced despite the adoption of
telecommunications technology because most telecommunication firms are identified to be
owned and controlled by state governments, and like any public institution, they must compete
for budget allocations to sustain their operations and constant development. Government
authorities, at that time, might have been unaware of the potential economic benefits of
allocating a large chunk of their budget to telecommunication investments.
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However, as telecommunication continuously evolves in the 1990s, a noticeable growth of
consumption took place that signified the advent of a new technological revolution. This
increased consumption delivered promising benefits not only for convenience of users but also
contributed to the advancement of developed and developing economies. The landmark paper of
Roller and Waverman (2001) estimated a micro model for telecommunication investment with a
macro production function. The results of their study revealed a positive causal link between
telecommunications infrastructure and aggregate output, measured by GDP, using a set of
simultaneous equations. This is attributed to the presence of network externalities and critical
mass that leads to increased returns on growth levels of 21 OECD and a sample of non-OECD
countries that were included in the study. It was concluded that higher growth effects are visible
in developed OECD states in contrast with the less-developed non-OECD states.
To expand the study, Waverman et al. (2005) (WMF) modeled the impact of
telecommunications in less-developed countries since during this period of study, the fixed-line
communications are now being supplemented, if not replaced, by the increased usage of mobile
phones. The series of simultaneous equations adopted was found to experience difficulty in terms
of estimating the growth impact and robustness to changes in the sample and its specifications. It
is hypothesized that less-developed countries will have slower penetration rates for mobile
telecommunications than those countries that are already traversing the path of growth.
Post-Waverman studies further provided substantial evidence on the impact of
telecommunications and economic growth. For one, Negash and Patala (2006) stated that the
GDP budget allocation for telecommunication investment is not enough to realize its full
benefits; hence they proposed a community-focused framework to encourage economic activities
in developing countries with the aid of telecommunications. In 2007, Melamed replicated the
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WMF study in Canada and revealed that the country’s GDP would have increased by 1% as a
result of the increased penetration rate of mobile telecommunications if the benefits were already
realized. The problem is that there is high cost of mobile telephone services that prevents them to
increase mobile telephony subscriptions.
Shiu and Lam (2008) noted the presence of unidirectional relationship between GDP and
telecommunications development in China, but subdividing into eastern and western regions, the
significance of such relationship maybe attributed to the affluence of the eastern region; thus
they posed an argument that there are other ways that could stimulate growth in the western
region. In a study of Sridhar and Sridhar (2009), a 3SLS system of simultaneous equations
highlighted the positive association between the mobile and fixed-line phones and national
output when control variables for capital and labor are instituted. This conclusion was made after
an analysis of 24 developing countries, majority of which are located in Africa. However, a bidirectional relationship between GDP and teledensity as the measure of telecommunications
development is expected to exist, unless when mobile telecommunications and economic growth
is measured separately (Lam and Shiu, 2010). The same unidirectional relationships are also
evident in the papers of Biancini (2011) and Gruber and Koutroumpis (2011).
From this literature review, it is important to direct our attention to the focal point of this
paper, the ASEAN Region. This paper found the importance of providing an overview on the
evolution of this vibrant and dynamic sector that is a promising avenue for economic growth
among the countries in Southeast Asia.
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Telecommunications in the ASEAN Region
The ASEAN Region underwent the process of taking bold movements towards working as an
integrated group that aims to be an outward and forward looking so that they would realize the
effects of dynamic development while living in a community of caring societies. This took place
when the Heads of States and Governments assembled in Kuala Lumpur, Malaysia in December
1997 to promulgate the ASEAN Vision 2020 that envisages an economic region that harnesses
stability, prosperity, and competitiveness through liberalized flow of goods, services, investment,
and capital. This vision was supported by the crafting of the Hanoi Plan of Action in 1998. Such
plan of action is a series of steps towards strengthening international cooperation, economic
integration, as well as establishing infrastructures for the promotion of science and technology,
information technology, and human capital development (Trong Vu, 2009).
Consistent with the ASEAN Vision 2020, former Singapore Prime Minister Go Chok Tong
(2003) stated that since the thrust for regional integration is not new given by this vision, there is
a need to create the ASEAN Economic Community that will push forth the economic integration
of the different ASEAN countries. Through a stronger information and communications
technology (ICT) pillar, having such mechanism facilitates the accessibility of faraway markets,
the growth of various industries, as well as the boost in the economic sector of the region.
Towards attaining this goal, the following courses of action are proposed by Prime Minister Go
Chok Tong (2003):
1. Focus on providing people access to pervasive and reliable information and
communications infrastructure;
2. Develop people’s literacy and comfort in using ICT services;
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3. Work towards harmonized regulations so that telecom operators can participate fully in a
liberalized ASEAN market;
4. Build linkages to countries outside of the ASEAN region;
5. Embrace technology to improve the productivity, efficiency, and competitiveness of the
economies and the quality of the people’s lives; and
6. Protect information and communication networks from intentional harm and degradation.
These courses of action are deemed to form the very important task of the ASEAN member
countries to work towards a liberalized and protected telecommunications sector that will chart
the new direction of the ASEAN region towards becoming one of the strongest economic regions
in the world.
Given the manifold objective of telecommunications development in the ASEAN region, this
paper found the importance of providing an overview of the telecommunications sector in some
members of this region, as extracted from the paper of Trong Vu (2009).
Indonesia
Two phases of telecommunications development took place in this country. When Indonesia
adopted Telecommunications Law No. 3 from 1989-1999, state-owned companies (Telkom and
Indosat) were partially privatized, and build operate transfer (BOT) investment agreements with
foreigners were forged. It was during this time when foreigners were allowed direct equity
investments in value-added services to raise network teledensity (measured as the number of
fixed line subscribers per 100 people in their country) of 2.5% in 1992. However, most of the
firms quit the market at the onset of the Asian Financial Crisis of 1997.
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The issuance of Telecommunications Blueprint and the adoption of the Telecommunications
Law No. 36 marked the second phase in Indonesia’s telecommunications sector. It became
witness to the return of foreign investors and the welcoming of KPN, Deutsche Telecom, and
Telekom Malaysia which pushed forth privatization and competition in the industry. At present,
the major players in Indonesia are PT Telkom (Telkom), Telkomcel, Indosat, and Excelcomindo.
Malaysia
Being one of the top economies in the world in terms of purchasing power parity, the country
positioned itself quietly in advancing technology, having established more progressive
telecommunication facilities resulted from the consolidation of networks due to competition.
Mobile penetration in Malaysia has surpassed the 85% mark in the first half of 2007 and has
already 21 million subscribers during that time. Presently, Telekom Malaysia, Maxis
Communications, Celcom, and DiGi Telecommunications are the competing giants in this
industry.
Philippines
Combined efforts of the government and the private sector were aimed towards expanding
the national fixed network in the country. However, there exists difficulty in extending its fixedline telephone networks resulting only to a 5% teledensity compared to the targeted 12% in 2002.
In contrast, mobile phone penetration rate is much higher, surpassing the 50% mark in 2007 and
grows at an annual rate of 26%. Investors believe that there is a promising ground for
telecommunications considering that this sector contributes 10% to the country’s GDP. The only
challenge that this country has to face is for the government to provide guidance on foreign
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direct investment of telecommunication companies and to manage competition rather than solely
issuing licenses prior to operation. Philippine Long Distance Telephone Company (PLDT),
Smart Communications, Globe Telecom, and Digital Telecommunications Philippines, Inc.
(Digitel) are the key players in the Philippine telecommunications industry.
Singapore
As a highly developed country, Singapore is known for its high quality and progressive
regulated telecommunications industry and is hailed as one of the world leader in
telecommunications, with 98% of the households having fixed-line telephone networks.
Singapore was one of the first countries in the world to adopt a fully digital telephone network
and has established its strong presence in the regional telecommunications market through
SingTel, StarHub, and MobileOne (M1). Liberalization has allowed the entry of new operators
even if SingTel dominated the entire industry in the country.
Thailand
Despite the presence of uncertainty regarding the economic prospects given the range of
events that took place in the country for last decade, Thailand’s telecommunication sector
displayed vitality and a lot of energy as it registered high growth rates in the mobile telecom
market. The National Telecommunications Commission (NTC) was established in 2004 to
implement reforms in the market that once showed signs of tardy improvements. Although the
momentum was lost during the military coup overthrow of Thaksin Shinawatra’s government in
2006, the mobile penetration rate was at 67% while the subscriber growth rates had an annual
increase of 35%. TOT (formerly the Telephone Organization of Thailand) Corporation, True
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Corporation, Advanced Info Services (AIS), and DTAC (Total Access Communication) are the
present-day competitors in the industry.
Vietnam
When Vietnam became a full member of the ASEAN in 1995, it began setting ambitious
targets in the telecommunications sector with some shortcomings. However, limiting the level of
competition as well as the continuous improvement in its economic climate, Vietnam saw the
growth of its telecommunications industry over the years. In addition, when Vietnam officially
became a member of the World Trade Organization, it opened more opportunities towards
deregulation and liberalization of the sector resulting to a more competitive telecommunications
market in the country up to this date. Vietnam Post & Telecommunications (VNPT), Viettel,
MobiFone, and VinaPhone are the leaders in the telecommunications industry of this country.
Efforts towards Economic Integration and Telecommunications Development
Trong Vu (2009) stated that the ASEAN members organized various initiatives to face the
challenges and to identify opportunities brought about by the increased penetration of
information and communications technologies. In the 33rd ASEAN Ministerial Meeting in 2000,
it was affirmed that although such efforts are not intended to undertake numerous researches on
ICT, it simply encouraged all members to embrace technology to remain competitive and to stay
running as a vibrant and dynamic economy while improving the lives of the people.
True to its mission, the ASEAN organized annually the ASEAN Telecommunications
Ministers Meeting (TELMIN) and the ASEAN Telecommunications Senior Officials Meeting.
These meetings were conducted: 1) to develop, to coordinate, and to implement work plans in
the ICT with emphasis on telecommunications; 2) to promote the participation of the
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public/private sectors, as well as regional/international and non-government organizations; and
3) to establish working groups that will carry out the intended work plans. Appendix 1 presents
the summary of the TELMIN meetings from 2001 to 2009.
In addition, the members of the ASEAN region signed: 1) The e-ASEAN Framework
Agreement; 2) The Ministerial Understanding on ASEAN Cooperation in Telecommunications
and Information Technology; 3) The Vientiane Action Program on Telecommunications and IT
Sector; 4) The ASEAN Sectoral Integration Protocol for e-ASEAN; 5) The Brunei Action Plan
on Enhancing ICT Competitiveness; 6) The Siem Reap Declaration on Enhancing Universal
Access of ICT Services in ASEAN; 7) The Bali Declaration in Forging Partnership to Advance
High Speed Connection to Bridge Digital Divide in ASEAN; and 8) The Vientiane Declaration
on Promoting the Realization of Broadband across ASEAN. These declarations manifested their
solidarity in working towards reaping the benefits from the development, usage, and
implementation of telecommunications technologies, notwithstanding the need fostering regional
integration to promote economic growth and development in the region. Appendix 2 summarizes
the main points of these agreements signed by the ASEAN member countries.
Given the presence of limited studies measuring the impact of telecommunications on
economic growth in this region, we found it imperative to perform an attempt to contribute to
this expanding field of study. This prompted the researcher to consider the ASEAN region as a
suitable locale for this paper given its promising future as it took preliminary steps towards
economic advancement through the initiatives and declarations that they have signed in the
recent years, harnessing the power of telecommunications infrastructure and recognizing its
capability to influence other growing sectors of their respective economies.
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Note. Data obtained from the World Bank database (data.worldbank.org)
Figure 1
Mobile phone teledensity in ASEAN countries and the world, 1990-2011
Figure 1 presents the teledensity of mobile phone subscriptions in the ASEAN Region,
Timor-Leste, and the World for the years 1990 – 2011. As can be seen, Singapore, Vietnam,
Malaysia, Thailand, Brunei Darussalam, Indonesia, and the Philippines exceeded the world
average teledensity, indicating a huge number of inhabitants who has mobile phones. Other
countries have yet to surpass the world’s batting average for mobile teledensity.
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Note. Data obtained from the World Bank database (data.worldbank.org)
Figure 2
Fixed-line telephone teledensity in ASEAN countries and the world, 1990-2011
Figure 2 presents the fixed-line teledensity of ASEAN countries, Timor-Leste, and the World
for the years 1990 – 2011. In contrast to Figure 1, only Singapore and Brunei Darussalam
exceeded the world teledensity for fixed-line telephones. We also note that for some time,
Vietnam surpassed the world teledensity from 1993 – 2003, while the Philippines exceeded the
world’s teledensity mark in 2009. Thus, it can be inferred that more inhabitants from Southeast
Asia are geared towards the use of mobile phones because of convenience and compactness in
terms of the services that it provides to subscribers.
One of the limitations of this paper is that it will not cover the “density” of Internet users.
Given the limited data furnished by reliable data sources, the researcher opted not to include
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Internet usage for the moment. However, the researcher believes that internet facilities also
provide mechanism to facilitate communication between distant parties; notwithstanding the
integration of the Internet in telecommunication services. It is hoped that future studies would
consider this variable to provide a comprehensive purview of economic growth using the
complete of indicators identified in Target 8.F.
Framework of the Study
This study aims to determine the impact of telecommunications on economic growth among
the ASEAN member countries. Prior to the conduct of this study, this paper identified the
theoretical underpinnings that will serve as the foundation in formulating our approaches to
answer our research problem.
Growth
Most studies on telecommunications and economic growth were rooted in the neoclassical
growth theory proposed by Solow (1956) who attempted to support the belief in the convergence
theory that over time, per capita production will move together with per capita income which is
an evidence of economic growth. In his model, Solow identified the determinants of long term
per capita growth such as labor, capital, and knowledge. Emphasis is placed on knowledge
because Romer (2006) argues that since knowledge comes in various forms, many of these types
of knowledge played important roles in economic growth. The numerous scientific discoveries,
the invention of applied technologies and new products, and the subsequent improvements in the
design of these technologies and products after invention would have different effects on growth
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at different lagged periods. Whatever knowledge accumulation mechanisms are undertaken, it is
understood that these endogenous factors would contribute to the economic growth.
Network Externalities and Spillover Effects
Several studies were geared towards identifying endogenous sources of technological
change, under which telecommunications fall into place, as identified in Chakraborty and Nandi
(2011). This supports the claim of Roller and Waverman (2001) that telecommunications
infrastructure, resulting to the creation of the information superhighway, is clearly different from
transportation and other superhighways due to the presence of network externalities. The
network externality theory states that more utility is derived by users when there is growth in the
number of users of a particular infrastructure. In the case of telecommunications, there exist
network externalities due to the presence of spillover effects.
When people use fixed-line or mobile telecommunication systems, the costs of doing
business falls, the more efficient firms will operate because people are able to communicate
despite increased distances. Thus, firms can engage in more productive activities since the
information intensity of the production process increases. The productive units of a firm can,
therefore, produce better goods and services towards increasing the output of the firm. When
taken collectively, the output of all the firms in the economy will increase, indicating positive
signs of economic growth.
Transaction Costs and Spillover Effects
This paper also rests on the premise of Madden and Savage (2000) regarding the reduction of
transaction costs while doing business. Transaction costs are costs incurred when economic
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agents find other agents to exchange goods and services, reach an agreed price for such
exchange, and fulfill the stipulations of the exchange agreement. Since agents are not fully aware
of the prices at all times, they must canvass for the most favorable price in which transactions
can finally take place.
Telecommunications fulfill this role of reducing transaction costs since such infrastructure
reduces costs of acquiring and transmitting information, making firms and factor markets
efficient. Cognizant with the spillover effects, telecommunications infrastructure can service
information-intensive sectors such as finance, trade, tourism, transportation, and other sectors
identified in Cronnin et al. (1993). This is because the increase in information flows assists in the
integration of domestic and international markets and enhances national income by increased
competition market efficiency, and increase in entrepreneurial talent for human capital
development and research. These benefits, according to Madden and Savage (2000), create
efficient production mechanisms that can flow through all sectors of the economy.
Social Overhead Capital
Lastly, this paper utilizes the social overhead capital (SOC) concept identified in Waverman
et al. (2005). SOCs are expenditures on education, health and social services, and public
infrastructure. Telecommunications forms a crucial part on infrastructure spending, for the
presence of networked infrastructures pave way for a speedy flow of information. Given the
faster rate of information flows and lower costs arising from inflation, it therefore leads to the
creation of what the Chairman of the US Federal Reserve Board Mr. Alan Greenspan (as
indicated in Waverman, et al., 2005) calls as the “New Economy.” This new economy is a result
of externalities associated with networked computers that allows competition and new means of
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organizing a more enhanced production process that increases output and that leads the way
towards the path of economic growth.
Methodology
Operational Framework and Model Specification
This study utilized one of the structural models in Sridhar and Sridhar (2009) that traverses
from the micro-level demand for and supply of telecommunication infrastructure to aggregate
changes in penetration rates to the macro production function in which GDP is determined by
traditional inputs including capital and labor stock, and teledensity. As a limitation, this study
does not attempt to provide a general explanation on the determinants of national output; thus,
measures such as openness to trade and deficits are not included. We used a macro-production
function consistent with the input-output in which, similar to the challenges posed in Roller and
Waverman (2001) and the application made in Waverman et al. (2005), telecom investment is
treated to be endogenous. The model is intended to demonstrate relationships between telecom
penetration and growth, to identify factors that determine the supply of and demand for
telecommunication services, as well as to determine factors that may possibly influence the
change of penetration rates for telecommunication facilities.
Panel data analysis will be conducted on the following with their corresponding
specifications:
ln GDPPCit   1   2 ln GNEPCit   3 LFPRit   4 PENRit   5 t   it
(1)
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where:
ln GDPPCit = natural logarithm of real GDP (at constant 2000 US$) of country i in year t;
ln GNEPCit = proxy for expenditure (natural logarithm of gross national expenditure at
constant 2000 US$) for country i in year t;
LFPRit = total labor force participation rate (expressed in decimal terms) of country i in
year t;
PENRit = penetration rate (PENR, measured as teledensity divided by 100) of mobile
phones (CPPENR), fixed-line telephones (FLPENR), and total of mobile and
fixed-line telephones (TPENR) using teledensity (TEL, measured as the number
of subscribers per 100 inhabitants) for mobile phones (CPTEL), fixed-line
telephones (FLTEL), and total of mobile and fixed-line telephones (TTEL) for
country i in year t, and
t i = a variable that captures the essence of time trend (1 = 1992, 2 = 1993,…, 19 = 2010).
The complete dataset, found in Appendix 3 of this paper includes Ccode, a variable that
identifies the country where a particular data originated. Hence, it has the following indexed
values: 1 = Brunei Darrusalam, 2 = Indonesia, 3 = Malaysia, 4 = Philippines, 5 = Singapore, 6 =
Thailand and 7 = Vietnam. This variable, however, will not be used for statistical testing since it
is only intended for identification purposes.
We used the per capita figures for GDP and Gross National Expenditure to even out the
disparity of output and expenditures per person. This is to eliminate the undue advantage of
people who can largely benefit from GDP and national expenditures. The paper also computed
the natural logarithm of the variables except t to control the peculiarities of observations among
22
different countries since the values of the variables differ largely depending on the country and
the time the data was observed.
This working equation was prepared to determine whether teledensity, measured in terms of
penetration rates, has greater power to influence GDP since prior literature utilized either of
these variables. It is similar to the Cobb-Douglas production function model since our model
deals with the elasticity of output as influenced by the labor, capital, and knowledge. Note that
the measure of knowledge in this model is through technological progress that includes the effect
of telecommunications. The study initially estimated six (6) regressions for teledensity and
penetration rates separately for mobile phones, fixed-line telephones, and the sum of mobile and
fixed-line telephones. Since the penetration rate utilizes teledensity, this paper focused on
penetration rates as the proxy for telecommunications since the initial testing for both variables
yielded the same results.
However, for descriptive statistics purposes, teledensity was used to provide a more realistic
description of the initial data. The descriptive statistics also used the actual Gross Domestic
Product per capita (GDPPC) and Gross National Expenditure (GNEPC) instead of natural
logarithms and Labor Force Participation Rate (LFPR) expressed in percentage (%) instead of
decimal terms. The statistics are presented in Appendix 3.
Our a-priori expectation for these variables is that they will exhibit a positive relationship
with GDP per capita as a measure of economic growth. It is expected that higher the expenditure,
the teledensity and penetration rates of telecommunications, labor force participation rate, and
the essence of time will contribute to the increase of GDP over the period since prior literature
have confirmed the association of these variables with economic output.
23
Data for the Study
All the relevant data for this study were extracted from the World Bank database
(data.worldbank.org) which was conducted last December 4, 2012. Initially, extraction was made
utilizing the International Technological Union (ITU) World Telecommunications Indicators for
2011 database. However, due to the absence of relevant data for some countries, it generated an
unbalanced panel of only 69 observations from 1990-2003. Since the construct of this study is to
provide insights for as many ASEAN countries as possible for a considerable longer period using
a balanced panel of observations, the dataset from ITU was dropped.
The researcher gathered the data for the ten (10) ASEAN member countries and Timor Leste
from 1992 to 2010. We started with the year 1992 because the World Bank database provided
complete observations for most countries and it was during this period when the popularity of
mobile telecommunications became visible. The refinements made from the raw data prompted
the researcher to eliminate four countries for this study: Cambodia, Lao PDR, Myanmar, and
Timor Leste. Cambodia and Lao PDR showed complete set of observations prior to the year
2000 while most of the data for Myanmar were not available even up to 2010. Timor Leste was
excluded because limited data was made available by the World Bank as this country established
its sovereignty only in 2002. The World Bank’s description of the variables of interest is shown
in Table 2. All other relevant variables were computed as presented in the legend preceding
equation (1) and in Appendix 3.
Because we have already identified our working model in estimate the function of economic
growth using penetration rates of fixed-line and mobile phone telecommunications using N = 7
cross sectional observations for ASEAN countries and T = 19 years (from 1992 – 2010), a
balanced panel was established since each cross-sectional unit has the same number of time
24
series observations. It is therefore imperative for us to perform the panel data analysis using the
methodology identified in Gujarati and Porter (2009) using Stata12 software.
Table 2
Description of variables from the World Bank database
Variable
Name
Indicator Name
from the World
Bank Database
GDPPC
GDP (Constant 2000
US$)
GNEPC
Gross National
Expenditure (constant
2000 US$)
Verbatim Description
GDP per capita is gross domestic product divided by midyear population.
GDP is the sum of gross value added by all resident producers in the
economy plus any product taxes and minus any subsidies not included in
the value of the products. It is calculated without making deductions for
depreciation of fabricated assets or for depletion and degradation of
natural resources. Data are in constant U.S. dollars.
Gross national expenditure (formerly domestic absorption) is the sum of
household final consumption expenditure (formerly private
consumption), general government final consumption expenditure
(formerly general government consumption), and gross capital formation
(formerly gross domestic investment). Data are in constant 2000 U.S.
dollars.
Note: GNE per capita is not available in the World Bank Database. To
obtain GNEPC, we divided the Gross national expenditure by the
population of the country during the year of observation.
LFPR
Labor Force
Participation Rate,
Total (% of total
population ages
15-64)
CPTEL
Mobile Cellular
Subscriptions (per
100 people)
FLTEL
Telephone Lines (per
100 people)
Labor force participation rate is the proportion of the population ages 1564 that is economically active: all people who supply labor for the
production of goods and services during a specified period.
Mobile cellular telephone subscriptions are subscriptions to a public
mobile telephone service using cellular technology, which provide access
to the public switched telephone network. Post-paid and prepaid
subscriptions are included.
Telephone lines are fixed telephone lines that connect a subscriber's
terminal equipment to the public switched telephone network and that
have a port on a telephone exchange. Integrated services digital network
channels and fixed wireless subscribers are included.
Results and Discussion
This paper aims to determine the impact of telecommunications on economic growth in the
ASEAN region. We first present the descriptive statistics for the dataset compiled for 7 ASEAN
countries from 1992 – 2010, followed by the results of the tests conducted to determine the ideal
25
panel data model for this study, after which we present a thorough and insightful discussion
about the reported results.
Descriptive Statistics
Appendix 4 presents the descriptive statistics for each country involved in this study
computed using MegaStat. Note that we used the actual figures for GDPPCi, GNEPCi, and
LFPRi as well as for teledensity (CPTELi, FLTELi, and TTELi) where i is the index of Country
Code (Ccode). As can be seen, Singapore has the highest mean GDP per capita of US$24,552
and has set the highest GDP per capita of US$32,641. In addition, Singapore also registered the
highest per capita Gross National Expenditure amounting toUS$23,536. This could be attributed
to the highly developed status of Singapore, being the economic hub of Southeast Asia. It was
followed closely by Brunei Darussalam, Malaysia, and Thailand. In terms of labor force,
Vietnam has the highest labor force participation rate at 82.8% on an average basis.
Singapore registered the highest teledensity in mobile and fixed line telephones, having an
average teledensity of 69 subscribers per 100 inhabitants for mobile phones and 43 subscribers
per 100 inhabitants for fixed line telephones, compared to Indonesia with only 17 mobile phone
subscribers and 8 fixed line telephone subscribers. The advanced telecom facilities of this
country and its intense economic and market activities would have contributed to such high
teledensity. Brunei Darussalam, a progressive country although small in terms of land area and
population, came second; while Malaysia placed third. The coefficient of variation for CPTEL
and TTEL are highest in Vietnam which can be explained by rapid economic growth of which
increased teledensity may be a contributing factor.
26
For our next set of analyses, we transformed GDPPC, GNEPC, and LFPR into natural
logarithm forms, while we transformed CPTEL, FLTEL, and TTEL into CPPENR, FLPENR,
and TPENR, respectively by dividing the teledensity figures by 100. We also expressed labor
force participation rate in decimal form instead of percentages. The succeeding sections
highlighted the results for determining the best econometric model using the Naïve, Fixed
Effects, and Random Effects processes.
Naïve Models
We ran three naïve models using equation (1). The naïve model assumes that all coefficients
for a given subject are constant over time. Thus, we pooled all 133 observations (7 countries for
19 years) and estimated a grand regression with lnGDPPC as the dependent variable, without
reference to the cross-section or time-series nature of data. The results of the naïve regressions
using Stata12 are presented in Appendix 5, where it was observed that in both the CPPENR and
TPENR models, lnGNEPC is identified to be significant at α = 0.001, while LFPR is significant
at α = 0.05. For the FLPENR model, lnGNEPC, LFPR, and FLPENR is significant at α = 0.001,
α = 0.01, and α = 0.05, respectively. Consistent with the prior expectations stated in Gujarati
(2009), the R-squared of all the models are high for these naïve regressions.
In addition, we performed tests of plausibility and robustness in each of the naïve PENR
models. As can be seen from Appendix 5, all models do not exhibit multicollinearity and
heteroscedasticity but they showed traces of autocorrelation since it fails to account for the
unobserved heterogeneity of the variables as well as possible specification errors. In this regard,
we need to resort to other estimation techniques: The Fixed Effects Model (FEM) using Least
Squares Dummy Variable (LSDV) process, and the Random Effects (REM) model.
27
LSDV-1
The LSDV-1 model utilizes the following specification:
N
ln GDPPCit   1    i Dit   2 ln GNEPCit   3 LFPRit   4 PENRit   5 t   it
(2)
i 2
where i = 1, 2, 3, …, 7 (N) and t = 1, 2, 3,…, 19.
This model assigns dummy variables for each of the cross-section units; that is, countries, to
account for the observed heterogeneity of these units. So as not to fall into the dummy variable
trap, we do not assign a dummy variable for the base country, i.e. Brunei Darussalam, being the
first identified country in our study. Furthermore, the model assumes that while intercepts differ
across cross-sectional units, they are identified to be time-invariant. For this study, we determine
whether the differences in descriptive characteristics of each ASEAN country involved in this
study (such as GNE, LFPR, CPTEL, FLTEL, and TTEL) would affect economic growth,
measured in terms of GDP per capita.
Appendix 6 shows the results of the LSDV-1 model tested for CPPENR, FLPENR, and
TPENR. In CPPENR and TPENR models, both lnGNEPC and t showed statistical significance
with a p-value less than α = 0.001 among the variables of interest, excluding the LSDV-1
country dummy variables introduced, and are consistent with our a-priori expectations. For the
fixed line model, FLPENR also exhibited statistical significance at α = 0.01. We cannot,
however, make the final interpretations until the best model is identified.
LSDV-2
The LSDV-2 model utilizes the following equation specification:
28
T
ln GDPPCit   1    i M it   2 ln GNEPCit   3 LFPRit   4 PENRit   5 t   it
(3)
t 2
where i = 1, 2, 3, …, 7 and t = 1, 2, 3,…, 19 (T).
This model accounts for structural change over time by considering the time factor in the
evolution of technological progress in the telecommunications industry which can affect
economic growth. Here, we accounted for the unobserved heterogeneity across time periods.
With the advancement of telecommunications technology, the old technologies are dropped out
and new technologies are welcomed, resulting to a more competitive market for fixed-line and
mobile telephone subscriptions as prices become more reasonable and affordable for businesses
and households. So as not to fall into the dummy variable trap, we do not assign a dummy
variable for the base year (i.e., 1992).
Appendix 7 shows the results of the LSDV-2 model tested for the mobile phone, fixed line
and total penetration rates. For the mobile phone model, all variables of interest showed
statistical significance with a p-value less than α = 0.001 among the variables of interest,
excluding the LSDV-2 time dummy variables introduced. It is interesting to note that lnLF is not
consistent with our a-priori expectations, as it exhibited a negative coefficient when regressed
with lnGDPPC.
For the fixed line model, only lnGNEPC and LFPR exhibited statistical significance at
different levels while TPENR is significant at α = 0.05, among the variables of interest and are
consistent with our a priori expectations. Again, we cannot make any conclusion as of this time
until we arrive at the final model for this study.
29
LSDV-3
To account for the unobserved heterogeneity across countries and time periods, we use the
LSDV-3 model in the following form:
N
T
i 2
t 2
ln GDPPCit   1    i Dit    i M it   2 ln GNEPCit   3 LFPRit   4 PENRit   5 t i   it (4)
where i = 1, 2, 3, …, 7 (N) and t = 1, 2, 3,…, 21 (T).
This model assigned dummy variables for both countries and years covered, excluding the
base country and year, to account for the unobserved heterogeneity across space (countries) and
time (years). Intercepts of these models are identified to be time- and space- invariant.
Appendix 8 shows the results of the LSDV-3 model tested for the mobile, fixed line, and
total penetration rates. In all models, only lnGNEPC exhibited statistical significance at α =
0.001, with the signs of their coefficients consistent to our a priori expectations. We also find
CPPENR, FLPENR, and TPENR to be significant at α = 0.05. In all LSDV models, the ANOVA
tests suggest that all the variables, when taken collectively, shows statistical significance with pvalues less than α = 0.001, thereby indicating its strength to determine the impact of
telecommunications variables on economic growth, measured in terms of GDP.
Determining the Best LSDV Model
To determine the best model among the previously-discussed models, we use the Wald’s Test
or the restricted F-test that assesses if the restrictions made on each of the preceding models are
valid. Reducing the dummy variables indicating the time- and space- variant restrictions of
30
LSDV models to zero, it then results to the original naïve model that was identified in equation
(1). We set the following hypothesis:
Ho: The restricted model is valid.
Ha: The restricted model is not valid; hence the unrestricted model is valid.
The Wald’s Test statistic is computed as follows:
F
RSS R  RSS UR / m
RSS UR / DFUR
(5)
where:
RSS R = residual sum of the squares of the restricted model;
RSS UR = residual sum of the squares of the unrestricted model; and
DFUR = degrees of freedom of the unrestricted model.
Since the null hypothesis sets m restrictions, the restricted model must have fewer variables
than the unrestricted model. Using the F-distribution table, we determine whether the computed
F-statistic and p-value by Stata12 lies in the critical region of rejecting the null hypothesis,
indicating that restrictions are not valid. Following is a discussion of the tests to be conducted, as
well as the summary results for all tests.
31
Naïve vs. LSDV-1
In this test, the Naïve model is the restricted model while the LSDV-1 model is the
unrestricted model arising from the restrictions of cross-sectional variables. The hypothesis for
this test is:
Ho: γi = 0, where i = 2, 3…, N
Ha: Restrictions are not valid.
with the following m restrictions and degrees of freedom from the Unrestricted LSDV-1 model:
m = N - 1 = 7 -1 = 6; and
DFUR = NT – (N – 1 + k) = 7(19) – (7 – 1 + 5) = 122.
Naïve vs. LSDV-2
In this test, the Naïve model is the restricted model while the LSDV-2 model is the
unrestricted model arising from the restrictions of time series variables. The hypothesis for this
test is:
Ho: δt = 0, where t = 2, 3…, T
Ha: Restrictions are not valid.
with the following m restrictions and degrees of freedom from the Unrestricted LSDV-2 model:
32
m = T - 1 = 19 -1 = 18 – 1 (dropped constraint) = 17; and
DFUR = NT – (T – 1 + k) = 7(19) – (19 – 1 + 5) = 110 + 1 (dropped constraint) = 111.
Naïve vs. LSDV-3
In this test, the Naïve model is the restricted model while the LSDV-3 model is the
unrestricted model arising from the restrictions of both cross-sectional and time series variables.
The hypothesis for this test is:
Ho: γi = 0 and δt = 0, where i = 2, 3…, N and t = 2, 3…, T
Ha: Restrictions are not valid.
with the following m restrictions and degrees of freedom from the Unrestricted LSDV-3 model:
m = (N – 1) + (T – 1) = (7 – 1) + (19 -1) = 26 – 1 (dropped constraint) = 23; and
DFUR = NT – (N + T – 2 + k) = 7(19) – (7 + 19 – 2 + 5) = 116 + 1 (dropped constraint) =
105.
LSDV-1 vs. LSDV-3
In this test, the LSDV-1 model is the restricted model while the LSDV-3 model is the
unrestricted model arising from the restrictions on time series variables. Since only the time
series dummy variables are involved, we are only running a single test instead of the usual three
for all penetration rate variables. The hypothesis for this test is:
33
Ho: δt = 0, where t = 2, 3…, T.
Ha: Restrictions are not valid.
with the following m restrictions and degrees of freedom from the Unrestricted LSDV-3 model:
m = (T – 1) = (19 -1) = 18 – 1 (dropped constraint) = 17; and
DFUR = NT – (N + T – 2 + k) = 7(19) – (7 + 19 – 2 + 5) = 116 + 1 (dropped constraint) =
105.
LSDV-2 vs. LSDV-3
In this test, the LSDV- 2 model is the restricted model while the LSDV-3 model is the
unrestricted model arising from the restrictions on cross-sectional variables. Since only the crosssectional dummy variables are involved, we are only running a single test instead of the usual
three for all penetration rate variables. The hypothesis for this test is:
Ho: γi = 0, where i = 2, 3,…, N
Ha: Restrictions are not valid.
with the following m restrictions and degrees of freedom from the Unrestricted LSDV-3 model:
m = N - 1 = 7 -1 = 6; and
DFUR = NT – (N + T – 2 + k) = 7(19) – (7 + 19 – 2 + 5) = 116 + 1 (dropped constraint) =
105.
34
Appendix 9 presents the critical F-statistics for each of the tests using Stata12. Summarizing
the results, we can determine from Table 3 which is the ideal model to employ for this research.
Given the critical F-statistics and the p-value of each test, it can be concluded that the null
hypothesis has to be rejected, except for the LSDV-1 vs. LSDV-3 test and the Naïve vs. LSDV-2
for FLPENR. However, we focus on the first confrontation because both LSDV-1 and LSDV-3
is significant in most instances. But since the time trend t variable is not significant in all cases,
there arises confusion as to which among LSDV-1 and LSDV-3 have to be used. Thus, we need
to perform additional procedures to determine the best model for this study. In the meantime, we
use both LSDV-1 and LSDV-3 in our analyses.
Table 3
Summary of Restricted and Unrestricted test results
Restricted
Naïve
Unrestricted
LSDV-1
Variable
CPPENR
FLPENR
TPENR
CPPENR
FLPENR
TPENR
CPPENR
FLPENR
TPENR
Critical F
P-Value
33.40
0.0000***
35.31
0.0000***
33.65
0.0000***
Naïve
LSDV-2
1.92
0.0227*
1.67
0.0600
2.08
0.0125*
Naïve
LSDV-3
9.48
0.0000***
9.07
0.0000***
9.85
0.0000***
LSDV-1
LSDV-3
1.17
0.3043
LSDV-2
LSDV-3
24.41
0.0000***
Note: *** - significant at α = 0.001;* - significant at α = 0.05.
Decision
LSDV-1
LSDV-1
LSDV-1
LSDV-2
Naïve
LSDV-2
LSDV-3
LSDV-3
LSDV-3
LSDV-1
LSDV-3
Random Effects and the Breusch-Pagan Poolability Test
To express the so-called “ignorance” on the (true) model to be employed on the disturbance
term, as stated in Gujarati and Porter (2009), we need to run another set of regression using the
Random Effects Model. This aims to account for the individual differences in the intercept
35
values of each country reflected in the stochastic disturbance term. Appendix 10 presents the
regression results using the Random Effects Model. The results are at its best use they are
compared with Naïve model to determine which among the two is better for analysis.
Such poolability test will determine the presence of variation in the stochastic disturbance
term, indicating the presence of unobserved heterogeneity in the model. Using the BreuschPagan Poolability Test (Breusch-Pagan Lagrangian Multiplier Test for Random Effects), we test
the following hypothesis:
Ho: Naïve model is better.
Ha: Random Effects Model (REM) is better.
Table 4
Result of the Breusch-Pagan Poolability Test
. xttest0
Breusch and Pagan Lagrangian multiplier test for random effects
lnGDPPC[Ccode,t] = Xb + u[Ccode] + e[Ccode,t]
Estimated results:
Var
lnGDPPC
e
u
Test:
sd = sqrt(Var)
2.075065
.0033075
.0033599
1.440509
.0575107
.0579649
Var(u) = 0
chibar2(01) =
Prob > chibar2 =
27.43
0.0000
The null hypothesis will be accepted when the results of the test reveal that there is no
unobserved heterogeneity in the model caused by the variation in the error term. Rejecting the
null hypothesis indicates the unobserved heterogeneity and hence, there is a need to incorporate
the random effects in the model. Given the results of the poolability test in Table 4, a p-value less
36
than α = 0.001 strongly indicates that REM is a more suitable model than the Naïve model.
Hence, we will employ the Hausman test to determine the better model between the Fixed
Effects Model, represented by LSDV-3, and the Random Effects Model.
Fixed Effects Model vs. Random Effects Model
To finally determine the ultimate model to be employed in this study, we used the Hausman
Specification Test to analyze whether there is a relation between the unique errors and the
regressors as well as to test whether there is a significant difference between the fixed and
random effects estimators. We use the following hypothesis:
Ho: Random Effects Model (REM) is better.
Ha: Fixed Effects Model (FEM) is better.
The null hypothesis is to be accepted when the test reveals that these unique, random errors
are not correlated with the regressors of the model. Hence, REM will be preferred for use instead
of the FEM. The results of the tests in Appendix 11 revealed that using LSDV-1, all three models
have exhibited statistical significance with p-values greater than α = 0.001. On the other hand,
for LSDV-3, CPPENR and TPENR models generated chi-square values that fail to meet the
asymptotic assumptions of the Hausman test. It, therefore, does not allow us to come up with
generalizations as to whether REM or FEM using LSDV-3 is better. From the results of this test,
it can be said that the Fixed Effects Model using the LSDV-1 process is the most appropriate
model to be employed in this study. Perhaps the time factor does not strongly influence
37
penetration rates of mobile and fixed telecommunications unlike the difference in the
characteristics of the countries in the ASEAN region.
Testing for Plausibility and Robustness
With the results of this test, we can now test for plausibility and robustness of the LSDV-1
model. This model is identified to be suitable because it accounts for the unobserved
heterogeneity across countries included in this study. We notice that differences in the
characteristics of the ASEAN countries would influence teledensity and penetration rates and are
more likely to be indicative of a country’s productivity and economic growth rather than the
influence of time trend.
Appendix 12 presents the results of the test for multicollinearity employing the Variance
Inflation Factor (test). As can be seen for all models, lnGNEPC has the highest VIF value
compared to other variables of interest. We also observed high VIF and tolerance values in
variables that define the country where a particular observation came from. This result suggests
the need for corrective measures to eliminate a high degree of correlation among the variables.
In terms of heteroscedasticity, we employed the Breusch-Pagan/Cook-Weisberg test with the
null hypothesis that there exists constant variance in the error term. However, the results for
CPPENR, FLPENR, and TPENR models (see Appendix 13) reveal otherwise. Thus, with pvalues less than α = 0.001, the results of the test indicates the commission of this violation of
non-constant variances in all the three models. It, therefore, suggests that the robust option be
employed by these models to correct the problem.
In terms of autocorrelation, we used the Wooldridge test for autocorrelation in the panel data
with a null hypothesis that there is no first-order autocorrelation among the error terms of the
38
dataset drawn for this study. But, with the p-values less than α = 0.001, the results in Appendix
14 indicates that strong existence of autocorrelation in all the three models and corrective
measures must be undertaken to make them robust and plausible.
Thus, having committed all these violations, it can be inferred that the estimation process
carried out by OLS are not efficient. Hence, the robust option for our models is to employ the
generalized least squares estimation process since it utilizes first-order difference transformation
that will correct the first-order autocorrelation, as well as reducing the standard errors of the
regression estimates.
Table 5
Corrected regression for CPPENR using GLS
. xtgls lnGDPPC lnGNEPC LFPR CPPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_cons
1.074407
-.2851512
-.0463243
.0060003
-.3378212
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
132.4166
Std. Err.
.0082609
.1378695
.0448773
.002995
.140515
z
130.06
-2.07
-1.03
2.00
-2.40
P>|z|
0.000
0.039
0.302
0.045
0.016
=
=
=
=
=
133
7
19
34132.64
0.0000
[95% Conf. Interval]
1.058216
-.5553704
-.1342821
.0001303
-.6132255
1.090598
-.014932
.0416336
.0118704
-.0624169
Table 5 presents the regression results for the CPPENR model using Generalized Least
Squares estimation process. As can be seen, lnEXP, LFPR, and t were identified to be the
39
significant variables, with p-values less than α = 0.001 for lnGNEPC and α = 0.05 for LFPR and
t. In addition, lnCPPENR did not manifest statistical significance. We interpret the model such
that a one percent increase in expenditures results to the 1.07 percent increase in GDP per capita,
whereas the passage of time results to an increase in GDP per capita by .006 percent. The result
for labor force participation rate is quite disturbing since a one percent increase in LFPR results
to a decrease in GDP by .30 percent while a one percent increase in lnCPPENR decreases GDP
per capita by 0.28 percent. Since we are expecting that increasing labor force should support
economic, a counterintuitive result warrants some explanation.
Table 6
Corrected regression for FLPENR using GLS
. xtgls lnGDPPC lnGNEPC LFPR FLPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_cons
1.044135
-.3948661
.2627731
.0028489
-.0466192
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
134.2401
Std. Err.
.0129355
.1422361
.1200319
.0014156
.1624281
z
80.72
-2.78
2.19
2.01
-0.29
P>|z|
0.000
0.006
0.029
0.044
0.774
=
=
=
=
=
133
7
19
35085.23
0.0000
[95% Conf. Interval]
1.018782
-.6736438
.027515
.0000743
-.3649725
1.069488
-.1160885
.4980313
.0056234
.2717341
Table 6 presents the regression results of the final lnFLPENR model using GLS. From this
table, lnGNEPC, LFPR, and t exhibited statistical significance at α = 0.001, α = 0.01, and α =
40
0.05, respectively. The variable of interest, FLPENR, was identified to be significant at α = 0.05,
indicating that fixed-line telephone penetration rate/teledensity contributes to economic growth
in a significant way. Interpreting the results, we can say that a one percent increase in
expenditures results to a 1.04 percent increase in GDP per capita. In addition, the passage of time
contributes to the GDP per capita by 0.003 percent. On the other hand, a one percent increase in
labor force participation results to a .20 percent decrease in GDP per capita, resulting in a
counter intuitive relationship, which has to be explained as well. Most importantly, a one percent
increase in fixed-line penetration rate increases GDP per capita by .263 percent.
Table 7
Corrected regression for lnTPENR using GLS
. xtgls lnGDPPC lnGNEPC LFPR TPENR t
Cross-sectional time-series FGLS regression
Coefficients:
Panels:
Correlation:
generalized least squares
homoskedastic
no autocorrelation
Estimated covariances
=
Estimated autocorrelations =
Estimated coefficients
=
Log likelihood
=
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_cons
1.07074
-.2934206
-.0085078
.0037928
-.2935006
1
0
5
Number of obs
Number of groups
Time periods
Wald chi2(4)
Prob > chi2
131.9081
Std. Err.
.010682
.1403286
.0403858
.0028252
.1557084
z
100.24
-2.09
-0.21
1.34
-1.88
P>|z|
0.000
0.037
0.833
0.179
0.059
=
=
=
=
=
133
7
19
33871.64
0.0000
[95% Conf. Interval]
1.049804
-.5684596
-.0876625
-.0017446
-.5986834
1.091677
-.0183817
.0706468
.0093301
.0116822
Table 7 presents the regression results of the final model for lnTPENR. From this table, it can
be seen that lnGNEPC and LFPR are significant at α = 0.001 and α = 0.05, respectively. TPENR
41
is identified to be insignificant; this could be explained by the effect of mobile phone penetration
rate when we combined them together with fixed line penetration rates. Thus, a one percent
increase in expenditure results to an increase in GDP per capita by 1.07 percent. However, a one
percent increase in labor force decreases GDP per capita by .0.293 percent. All of the models
have been corrected for possible violations and hence, considered to be plausible. However, the
inconsistency of the labor force participation rate to support economic growth with prior
literature reviewed in this study, warrant sufficient explanation. It is also noticeable that only
fixed line telecommunications contribute to the per capita GDP instead of mobile phone
telecommunications. Perhaps the countries the ASEAN region have not yet fully realized the
benefits of mobile telecommunications and high teledensity brought about by increased
subscription to mobile phones.
To cap off the tests conducted for panel data, we tested for the hypothesis that the residuals
of the FEM LSDV-3 model are contemporaneously correlated; hence we employed the BreuschPagan test for Contemporaneous Correlation of Residuals. We developed 7 equations for each
cross-sectional unit and performed the regression together with the variables of interest. Should
contemporaneous correlation exist among residuals, it is best to employ Seemingly Unrelated
Regression Equations for our model. This was validated by our study as evidenced by the results
in Appendix 15, indicating that the best policy is to use Seemingly Unrelated Regression to avoid
micronumerosity by achieving full complement in our cross-sectional and time series
observations.
For the control variables, empirical evidence suggests that increase in expenditure stimulates
economic growth. Such is expected to happen when the government invests on public
infrastructure, and economic and social services that will benefit people and their communities.
42
Surprisingly, the negative relationship between labor force and economic growth signifies the
presence of stiff competition on job hunting that there is a high incidence of unemployment
among developing countries. It suggests that the government should continue its work in
mitigating its problem on unemployment through the provision of decent and stable employment
to the labor force so that economic growth may finally be felt.
From the variables of interest, only the penetration of fixed line telecommunications has
significant relationship with economic growth. It is undeniable that businesses would still resort
to the use of fixed-line telephones because traditional economic factors dictate the prominence of
the use of fixed landlines, as indicated in Sridhar and Sridhar (2009). It is because basic services
provided by landlines have lower rental ceilings that make landline telephones more affordable
to the businessmen and households. Furthermore, since these landlines are installed within the
premises of the firm, people are restricted to manage their calls related only to business matters
to ensure their productivity in effectively managing communications. In this way, businesses can
manage their operations effectively due to the proper use of fixed-line telephone facilities.
What could probably explain the insignificance of mobile and total penetration rates on the
per capita GDP as our measure of economic growth? Even if we control for capital and labor in
the model, the results subscribe to the idea of Shiu and Lam (2008) that the continuous
improvement and development in mobile telecommunications alone may not be sufficient
enough to stimulate economic growth, considering that there are other “complementary” factors
like promoting a more stable economic climate conducive to conduct business, the creation and
maintenance of public infrastructure suitable for transportation superhighway, providing greater
access to quality education, and sufficient training of manpower to encourage people to make the
best and proper use of telecommunications in conducting business transactions.
43
Moreover, as most of the countries in South East Asia are identified as developing countries,
perhaps these countries are facing difficulties in allocating their resources to fund different
investments to support economic growth. This is the rationale behind the results of the study of
Negash and Patala (2006) that developing countries have yet to fully realize the benefits derived
from investment in telecommunications, because most developing countries have vast rural
communities that installing telecommunications facilities are way beyond its practicality. This is
consistent to the premise of Roller and Waverman (2001) that investment in telecommunications
must first reach a critical level where significant benefits of economic development can be fully
achieved. Recognizing the fact that if telecommunications can accelerate economic activities,
Negash and Patala (2006) stated that it must reach the rural areas as well. Thus, there must be a
better way that mobile telecommunications must be accessible by members of this particular
vicinity as they provide the sources of different factors of production that result to the generation
of economic output.
Furthermore, there must be a better way of exploiting network externalities. Shiu and Lam
(2008) posited that penetrations rates may be high in urban areas but not in remote rural areas,
where telecommunication services are still hard to reach. As countries continue to develop their
telecommunication infrastructures, they still have to recover those sizeable investments; hence,
there is a need to impose higher tariffs and fees that makes telecommunication services not
affordable in some places in the region. This paper believes that providing affordable
telecommunication services, both fixed-line and mobile, are important to achieve the so-called
“critical mass” in which the benefits of telecommunications can be fully achieved.
Technically speaking, we also cannot discount the fact that there is a need to produce a more
rigid statistical scrutiny for the model we employed in this study. Most of the literature cited in
44
this paper utilized various structural forms. For one, Waverman et al. (2005) resorted to a simpler
cross-section analysis of data after it was found out that the use of simultaneous equations would
not work because of endogeneity issues. In addition, Negash and Patala (2006) utilized a simple
trend analysis for the 87 countries studied. Moreover, Datta and Agarwal (2004) enhanced the
panel data analysis by using a more dynamic panel data to avoid omitted variable bias for single
equation cross-sectional regression, while Grouber and Koutroumpis (2011) adopted the 3SLS
generalized method of moments in the estimation process. Lastly, since most literature uses GDP
as a measure of growth, perhaps the use of the GDP growth rate itself could be a more viable of
measure since GDP actually measures the aggregate output of a particular economy in a given
time. Other endogenous variables may also be considered for inclusion in the model, which was
not accounted for by this paper given the limited time. Perhaps we could also use another
measure of economic growth. Considering that this discourse is a contribution to the still limited
compendium of studies about the impact of telecommunications and economic growth in the
ASEAN region, the use of more stringent and better statistical techniques must be carefully
considered in future research.
Summary, Conclusions and Policy Recommendations
This paper attempted to determine the impact of telecommunications on economic growth in
the ASEAN region. The idea behind this study emanated from the theories of network
externalities and the spillover effects that reduce transaction costs brought about by the use of
telecommunications as a channel to support economic growth. In response to Target 8.F set by
the United Nations in its Millennium Development Program, we tested the impact of the
continuous increase of penetration by fixed-line and mobile telecommunications, as a result of
45
increased teledensity over the number of inhabitants, on economic growth which is measured in
terms of the country’s GDP per capita. The Fixed Effects Model using LSDV-1 process is
determined to be the plausible model for this study to account for the unobserved heterogeneity
associated with differences in the characteristics of the countries and of the time periods brought
about by the continuous surge of GDP, national expenditure, labor force penetration, and
teledensity in both mobile and fixed-line communications.
Drawn from a panel data of 7 countries from 1992 – 2010, the results of the study revealed
that the penetration of fixed line telecommunications is positively related to economic growth for
the countries located in this region. Separately, we identified mobile telecommunications and
total penetration to be not significantly associated with economic growth. This is because most
of the countries in the ASEAN region are developing countries; thus, the benefits of increased
investment in telecommunications and teledensity have yet to be realized over time, particularly
in mobile telecommunications which were introduced only for more than twenty years. Such
investment in telecommunications must be complemented by other factors such as providing
quality education and training towards the better and proper use of these facilities, the continuous
investment on public infrastructure to support the mobility of the transfer of goods and services,
and the continuous upgrading and development of rural areas where the factors of production are
located.
To contribute to the thrust of the United Nations in achieving its Millennium Development
Goals in 2015, certain policies have to be implemented to fully achieve the benefits of the
investment and proper use of telecommunications especially in developing countries like those
located in the ASEAN region. These are consistent with the recommendations of Shiu and Lam
(2008), Negash and Patala (2006), and Waverman et al. (2005). For one, there must be a
46
reduction of tariffs and other charges to subscribers of telecommunication facilities to increase
the affordability and accessibility of telecommunication services to the rural and marginalized
sectors of the country. In addition, there is a need to further improve the business environment
where the benefits of telecommunications can be fully maximized. Moreover, continuous
training and development towards the better use of telecommunication facilities must be
enforced through the aid of educational institutions and the private sector.
The liberalization of the telecommunications industry must be fully enforced to stimulate
competition among the different investing networks, making telecommunications become
competitive in terms of pricing and providing better services to its users. In addition, there is a
need for telecommunications facilities to be set up at various community centers so that people
from remote areas may benefit from the use of such facilities. This is to shift our focus from the
individual perspective to the community perspective since increasing access to
telecommunications will reduce the costs incurred per person, and the accessibility of these
services can spur economic growth in all areas of the country.
The private and other affiliated sectors should be continuously tapped to support the
government in its endeavors to provide a more cost-efficient, more accessible fixed line and
mobile telecommunications services for everyone to realize substantial benefits from the use of
these services. Consistent with Waverman et al. (2005), continuous efforts to eradicate poverty in
other aspects should be seriously considered to maximize the benefits of increased competition
and speedy penetration of mobile telecommunications in developing countries. Educational
institutions must be involved in teaching students how to use telecommunication facilities
properly to help them realize the benefits of such services as they advance in age and as they
enter the world of work.
47
This study believes that it is never too late for the ASEAN region to achieve the Millennium
Development Goals three years from now. Having achieved economic growth realized from the
use of fixed-line communications, this is the best time for this region to better understand the
power of mobile telecommunications and the entirety of telecommunications and how it can
bring the different member economies closer, as well as the other nearby economies, in the
continuous response to the global effort of forging partnerships with the different sectors and
industries to eradicate poverty by making available the benefits of new technologies, particularly
in information and communications.
48
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Appendix 1
Summary of nine ASEAN Telecommunications Ministers (TELMIN) meetings from 2001-2009
Date
Venue
July 2001
Kuala
Lumpur,
Malaysia
August
2002
Manila,
Philippines
September
2003
Singapore,
Singapore
August
2004
Bangkok,
Thailand
September
2005
Hanoi,
Vietnam
September
2006
Bandar Seri
Begawan,
Brunei
Darussalam
August
2007
Siem Reap,
Cambodia
August
2008
Bali,
Indonesia
Verbatim Highlights
Focused on narrowing the digital divide among ASEAN nations; development
of skills and expertise, promotion and enhancement of use of information and
communications technology, development of local and regional content for the
ASEAN region; and ensuring a well-coordinated network security. The
members signed the Ministerial Understanding on ASEAN Cooperation in
Telecommunications and Information Technology. It also implemented the eASEAN Framework Agreement.
Stressed the vital role of telecommunications in the integration of regional
economy, in attracting investments into region, and in developing the economies
of the ASEAN members. It also established the ASEAN ICT Center to foster
ASEAN cooperation in ICT, as well as the agreement to engage China, Japan,
and South Korea in ICT cooperation individually and in the ASEAN+3
Framework.
The ASEAN ministers expressed their commitment of enhancing regional
cooperation in cybersecurity. It also endorsed initiatives to advance ICT
integration and trade facilitations, promote universal access to ICT infrastructure
and services, and develop ICT skills and competencies. The meeting launched
the ASEANconnect, a publicly accessible digital divide database that houses key
data statistics and measurement indicators, analysis of initiatives to bridge the
digital divide among ASEAN nations.
Promotion of e-learning culture towards a knowledge-based ASEAN to make
ASEAN competitive in the world economy. The ASEAN also established the
ASEAN ICT Fund to accelerate the implementation of the ASEAN ICT Work
Program.
Emphasized the importance of cooperation in telecommunication among
ASEAN members. It became witness to the Hanoi Agenda on Promoting Online
Services and Applications to Realize e-ASEAN and ICT Focus 2005-2010.
Implemented the Brunei Action Plan which outlined a program of action in
building ASEAN capacity to enhance the region’s competitiveness in the ICT
sector. The ministers agreed to revitalize the e-ASEAN Youth Forum and the eASEAN Business Council.
Stressed the importance of strong human and institutional capacity and private
sector participation to transform the telecommunications and IT sector into an
important enabler for economic growth and poverty reduction. The ministers
adopted the Siem Reap Declaration on Enhancing Universal Access of ICT
Services in ASEAN. It also highlighted the importance and possibility of
cooperation with the ASEAN Dialogue Partners such as China, India, Japan, and
Korea.
Emphasized the crucial role of a secure and well-established information
infrastructure among ASEAN countries for the region’s economic growth and
competitiveness. The ministers adopted the Bali Declaration in Gorging
Partnership to Advance High-Speed Connection to Bridge the ASEAN Digital
Divide.
October
2009
Vientiane,
Laos
Adopted the Vientiane Declaration on Promoting the Realization of Broadband
Across ASEAN. The ministers agreed that the vision of the ASEAN ICT Master
Plan will identified “Towards an Empowering and Transformational ICT:
Creating and Inclusive, Vibrant, and Integrated ASEAN” to being the ASEAN
ICT to a higher level and to reinforce the role of ICT for ASEAN integration.
Note. From Trong Vu, S. T. (2009). Telecommunications sector in ASEAN: ASEAN economic integration and its
implications for labour in the region. Retrieved November 30, 2012 from ASEAN Services Employees Trade Union
Council Library Website http://www2.asetuc.org/pages/library.php
Appendix 2
Summary of agreements and declarations of ASEAN members on telecommunications
Title
Date
The e-ASEAN Framework
Agreement
November
2000
The Ministerial Understanding
on ASEAN Cooperation in
Telecommunications and
Information Technology
July 2001
The Vientiane Action Program
on Telecommunications and
IT Sector
2004
The ASEAN Sectoral
Integration Protocol for eASEAN
2004
Verbatim Highlights
The agreement has the following objectives:
a. Promote cooperation to develop, strengthen and enhance the
competitiveness of the ICT sector in ASEAN;
b. Promote cooperation to reduce the digital divide within
ASEAN Member States and amongst ASEAN Member States;
c. Promote cooperation between the public and private sectors in
realizing e-ASEAN; and
d. Promote the liberalization of trade in ICT products, ICT
services, and investment to support the e-ASEAN initiative.
The agreement has the following objectives:
a. Develop the ASEAN telecommunications and IT sector as a
catalyst to foster regional economic integration;
b. Enhance the overall competitiveness of the ASEAN region
through a vibrant telecommunications and IT industry; and
c. Develop the ASEAN Information Society, where its citizens
are able to work, communicate and recreate in the knowledgebased economy.
The action program is to leverage on information and
communications technology via public-private sector partnerships
and strong external linkages, to build a connected, vibrant, and
secure ASEAN community by:
a. Striving for universal access to ICT infrastructure and service;
b. Encouraging the development of a pervasive, inter-connected,
and secure ASEAN information infrastructure;
c. Strengthening the cooperation and assistance on regulatory
policy and strategy issues;
d. Creating digital opportunities through e-government, ecommerce, and e-society initiatives;
e. Enhancing the competitiveness and dynamism of the ASEAN
ICT sector by promoting and facilitating trade and investment
in ICT services; and
f. Developing highly skilled ICT human resources.
In line with the progressive, expeditious, and systematic
implementation of the e-ASEAN, the protocol was signed with
the following objectives:
a. Building ASEAN capacity, which is a vital component in
enhancing ICT competitiveness;
b. Developing the ASEAN Information Infrastructure as the
foundation for the sustainable development of an information
society;
c. Achieving broader economic and social benefits through wider
access to ICT;
d. Facilitating ICT trade and electronic commerce by addressing
non-tariff barriers to trade, as well as to lay policy and legal
infrastructure for electronic commerce;
e. Exchanging information on, and where appropriate harmonize,
policies and regulations to increase ASEAN’s ICT
competitiveness and welcoming the implementation of the
ATRC 2006-2007 work plan;
f. Engaging the private sector and youths;
g. Forging links with partners and key ICT international
organizations to pool our resources and expertise; and
h. Strengthening institutional foundations to achieve the
programs elaborated above.
The Siem Reap Declaration on
Enhancing Universal Access
August
The declaration is the efforts of the ASEAN members to build a
to ICT Services in ASEAN:
2007
connected, vibrant, and secure ASEAN community.
“ICT Reaching Out to the
Rural”
The Bali Declaration in
The declaration aims to deepen and strengthen regional initiatives
Forging Partnership to
August
and activities towards enhancing the infrastructure of the ASEAN
Advance High Speed
2008
information society, and to establish the foundation for ICT
Connection to Bridge Digital
applications, services and solutions in the region.
Divide in ASEAn
The Vientiane Declaration on
The declaration is a guide to promote broadband initiatives to
October
Promoting the Realization of
enable ICT to become a major empowering and transformative
2009
Broadband across ASEAN
force in the ASEAN Information Society.
Note. From Trong Vu, S. T. (2009). Telecommunications sector in ASEAN: ASEAN economic integration and its
implications for labour in the region. Retrieved November 30, 2012 from ASEAN Services Employees Trade Union
Council Library Website http://www2.asetuc.org/pages/library.php
Appendix 3
Data for the study and descriptive information
Ccode
GDPPC
GNEPC
LFPR
CPTEL
FLTEL
TTEL
t
1
19478.10
16,338.65
67.2
1.54
18.03
19.57
1
1
18997.88
16,585.70
67.4
3.03
20.13
23.16
2
1
19065.32
15,735.20
67.6
5.54
21.85
27.39
3
1
19395.52
15,532.07
67.9
12.39
23.52
35.91
4
1
19445.25
18,063.39
68.3
14.65
26.51
41.16
5
1
18685.24
16,151.68
68.8
14.77
25.20
39.97
6
1
18135.13
15,601.04
69.3
15.74
24.90
40.64
7
1
18253.78
14,123.73
69.7
20.65
24.74
45.39
8
1
18350.13
12,563.04
70.1
29.05
24.62
53.66
9
1
18441.35
12,827.92
70.4
42.77
26.45
69.22
10
1
18749.58
13,429.67
70.3
44.98
23.79
68.78
11
1
18896.53
12,823.16
70.2
50.85
23.49
74.34
12
1
18609.15
12,991.12
70.0
56.88
23.35
80.22
13
1
18311.88
13,145.67
69.9
64.14
23.10
87.24
14
1
18745.80
13,522.18
69.7
81.39
21.65
103.04
15
1
18416.91
15,340.54
69.6
96.99
21.07
118.06
16
1
17722.66
16,189.52
69.4
103.68
21.00
124.68
17
1
17092.46
16,034.47
69.3
105.37
20.56
125.93
18
1
17225.32
16,870.95
69.2
109.07
20.03
129.10
19
2
669.14
615.83
66.6
0.02
0.87
0.89
1
2
706.51
647.00
67.5
0.03
0.96
0.99
2
2
748.32
704.87
68.2
0.04
1.25
1.29
3
2
799.31
784.95
68.9
0.11
1.65
1.76
4
2
848.25
831.02
69.5
0.28
2.07
2.35
5
2
875.96
878.53
67.8
0.45
2.43
2.88
6
2
750.81
697.33
67.2
0.51
2.68
3.19
7
2
746.79
682.46
69.6
1.05
2.89
3.94
8
2
773.31
691.98
69.4
1.72
3.12
4.84
9
2
791.08
718.50
69.2
3.02
3.34
6.36
10
2
816.02
738.00
69.0
5.34
3.54
8.88
11
2
844.18
752.85
69.2
8.34
3.63
11.97
12
2
875.73
803.07
69.4
13.51
4.62
18.13
13
2
914.60
834.20
69.7
20.64
5.94
26.58
14
2
953.94
864.25
69.7
27.75
6.45
34.20
15
2
1003.36
908.94
69.7
40.17
8.40
48.57
16
2
1052.43
951.90
69.6
59.83
12.93
72.76
17
2
1089.72
977.83
69.8
68.94
14.66
83.60
18
2
1145.39
1,026.38
69.7
88.08
17.06
105.15
19
Ccode
GDPPC
3
2932.02
3
GNEPC
LFPR
CPTEL
FLTEL
TTEL
t
2,769.75
64.3
1.04
10.89
11.94
1
3140.83
3,039.84
64.3
1.73
12.24
13.96
2
3
3344.57
3,314.88
64.3
2.83
14.17
17.00
3
3
3581.95
3,688.02
64.3
4.85
16.08
20.93
4
3
3842.64
3,800.13
64.3
7.16
17.75
24.91
5
3
4022.84
3,991.34
64.3
9.18
19.39
28.57
6
3
3636.47
2,877.51
64.3
9.86
19.64
29.50
7
3
3767.64
2,851.24
64.2
13.08
19.38
32.45
8
3
4005.56
3,235.98
65.4
21.87
19.77
41.64
9
3
3933.94
3,178.06
65.1
30.82
19.65
50.47
10
3
4052.88
3,300.67
64.9
36.93
19.05
55.98
11
3
4194.26
3,398.62
64.6
44.39
18.24
62.63
12
3
4385.97
3,616.07
64.4
57.10
17.37
74.47
13
3
4529.60
3,738.89
64.1
74.88
16.73
91.61
14
3
4706.88
3,952.24
63.8
73.21
16.33
89.54
15
3
4925.77
4,247.56
63.5
86.31
16.08
102.39
16
3
5077.94
4,421.00
63.2
100.77
16.41
117.18
17
3
4914.91
4,247.86
62.9
107.85
16.19
124.04
18
3
5184.71
4,697.26
62.8
119.22
16.10
135.32
19
4
942.64
956.39
67.2
0.09
1.02
1.11
1
4
940.56
972.96
66.8
0.15
1.30
1.45
2
4
959.65
977.80
66.8
0.25
1.64
1.89
3
4
982.03
1,018.58
67.9
0.71
2.04
2.75
4
4
1016.31
1,063.88
67.8
1.35
2.52
3.88
5
4
1045.46
1,080.11
67.7
1.86
2.87
4.72
6
4
1016.78
1,081.69
68.2
2.34
3.37
5.71
7
4
1025.55
1,052.97
68.0
3.77
3.82
7.59
8
4
1048.07
1,068.94
66.5
8.35
3.96
12.31
9
4
1055.81
1,120.11
69.3
15.40
4.20
19.60
10
4
1071.69
1,166.90
68.1
19.08
4.11
23.18
11
4
1102.22
1,188.72
68.7
27.35
4.06
31.41
12
4
1153.02
1,209.98
68.0
39.24
4.10
43.33
13
4
1185.38
1,233.63
66.3
40.66
3.94
44.59
14
4
1225.05
1,221.63
65.4
49.21
4.17
53.38
15
4
1283.47
1,248.10
64.8
64.68
4.44
69.13
16
4
1314.23
1,307.44
65.4
75.54
4.52
80.06
17
4
1307.14
1,299.32
65.8
82.43
7.40
89.82
18
4
1383.40
1,382.96
65.9
85.67
7.27
92.94
19
5
16970.76
14,818.63
70.5
3.76
36.64
40.40
1
5
18446.17
16,300.48
70.1
5.44
37.89
43.33
2
5
19767.48
16,770.40
70.3
6.96
39.34
46.30
3
5
20571.46
17,152.94
69.0
8.79
41.03
49.82
4
Ccode
GDPPC
GNEPC
LFPR
CPTEL
FLTEL
TTEL
t
5
21258.53
17,847.42
70.8
12.05
43.69
55.74
5
5
22305.68
19,052.57
70.1
23.12
45.90
69.02
6
5
21092.56
16,536.27
70.0
29.10
47.27
76.37
7
5
22221.38
17,742.93
70.6
42.42
48.81
91.23
8
5
23814.56
20,754.17
71.0
70.10
49.65
119.75
9
5
22913.32
19,052.70
71.0
75.15
48.92
124.07
10
5
23658.87
18,849.90
71.1
82.16
47.80
129.96
11
5
25110.50
17,649.49
71.2
87.54
46.24
133.78
12
5
27068.97
20,008.62
71.3
95.93
44.64
140.56
13
5
28388.87
20,025.82
71.5
102.78
43.23
146.02
14
5
29925.50
21,169.27
71.7
108.59
42.03
150.62
15
5
31247.00
21,522.48
72.0
129.21
40.61
169.82
16
5
30131.62
23,536.53
72.3
134.42
39.31
173.73
17
5
28949.86
20,835.69
72.3
139.11
38.94
178.04
18
5
32640.68
21,407.04
72.8
145.18
39.24
184.43
19
6
1599.21
1,739.67
81.4
0.43
3.07
3.50
1
6
1718.03
1,877.46
79.5
0.70
3.77
4.48
2
6
1858.00
2,040.14
77.6
1.25
4.65
5.90
3
6
2011.83
2,266.73
78.0
2.18
5.84
8.01
4
6
2109.05
2,407.49
78.5
3.06
6.90
9.97
5
6
2057.07
2,151.22
78.6
3.62
7.92
11.54
6
6
1819.18
1,605.57
77.8
3.21
8.17
11.38
7
6
1877.31
1,659.08
76.7
3.75
8.36
12.11
8
6
1943.24
1,775.48
77.4
4.84
8.85
13.69
9
6
1962.24
1,789.07
77.8
11.82
9.47
21.28
10
6
2042.80
1,868.89
77.8
15.73
10.14
25.88
11
6
2164.30
1,995.73
77.7
33.39
10.15
43.54
12
6
2277.56
2,142.35
77.9
41.44
10.31
51.76
13
6
2359.64
2,287.82
78.1
46.68
10.55
57.23
14
6
2458.52
2,291.95
77.5
60.53
10.51
71.04
15
6
2562.72
2,331.60
78.1
78.14
10.36
88.50
16
6
2608.25
2,428.29
78.2
90.58
10.83
101.41
17
6
2531.23
2,216.82
76.2
95.99
10.49
106.48
18
6
2712.51
2,447.56
77.1
103.62
10.02
113.64
19
7
252.58
246.31
84.6
0.00
0.22
0.22
1
7
268.29
280.34
84.5
0.01
0.36
0.37
2
7
287.14
313.27
84.3
0.02
0.61
0.62
3
7
309.41
336.40
84.1
0.03
1.05
1.08
4
7
332.94
362.67
83.8
0.09
1.58
1.67
5
7
354.51
379.48
83.6
0.21
1.75
1.96
6
7
369.24
397.57
83.4
0.29
2.26
2.55
7
7
381.10
396.81
83.3
0.42
2.70
3.13
8
Ccode
GDPPC
7
401.55
7
GNEPC
LFPR
CPTEL
FLTEL
TTEL
t
411.44
83.0
1.00
3.23
4.23
9
423.83
433.59
82.8
1.57
3.83
5.40
10
7
448.60
473.46
82.6
2.36
4.88
7.24
11
7
475.97
513.78
82.4
3.37
5.41
8.78
12
7
506.94
540.47
82.2
6.03
12.31
18.34
13
7
543.36
566.59
81.9
11.54
11.25
22.78
14
7
581.58
614.38
81.7
22.47
10.19
32.66
15
7
623.96
734.02
81.4
52.96
13.13
66.10
16
7
656.31
785.36
81.2
87.11
17.18
104.29
17
7
684.00
798.96
81.2
113.03
20.05
133.08
18
7
722.81
849.74
81.2
127.00
16.36
143.37
19
Legend:
Name
Description
Numerical code assigned to the country in alphabetical order (1 = Brunei
Ccode
Darussalam, 2 = Indonesia, 3 = Malaysia, 4 = Philippines, 5 = Singapore,
6 = Thailand, 7 = Vietnam)
GDPPC
GDP per capita (in constant 2000 US$)
Gross National Expenditure per capita (GNE in constant 2000 US$
GNEPC
divided by the population)
LFPR
Labor Force Participation Rate, Total (ages 15-64, % of total)
CPTEL
Mobile Phone Teledensity (No. of subscribers per 100 inhabitants)
FLTEL
Fixed Line Teledensity (No. of fixed-line telephones per 100 inhabitants)
TTEL
Total Mobile Phone and Fixed Line Teledensity
t
Time Trend; (1 = 1992, 2 = 1993…, 19 = 2010)
Note: Data for GDPPC, GNE, LFPR, CPTEL, and FLTEL were obtained from the
World Bank Database (data.worldbank.org). All other variables are computed by the
researcher.
Appendix 4
Descriptive statistics for the variables of interest per country
1 - Brunei Darussalam
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
GDPPC1
18,527.26
666.83
17,092.46
19,478.10
-0.66
0.28
3.60%
GNEPC1
14,940.51
1,675.37
12,563.04
18,063.39
-0.02
-1.27
11.21%
LFPR1
69.17
1.02
67.20
70.40
-0.79
-0.64
1.48%
CPTEL1
45.97
37.68
1.54
109.07
0.53
-1.17
81.97%
FLTEL1
22.84
2.33
18.03
26.51
-0.23
-0.60
10.20%
TTEL1
68.81
36.96
19.57
129.10
0.42
-1.18
53.71%
2 - Indonesia
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
GDPPC2
863.41
133.20
669.14
1,145.39
0.71
-0.28
15.43%
GNEPC2
795.26
116.71
615.83
1,026.38
0.42
-0.72
14.68%
LFPR2
68.93
0.98
66.60
69.80
-1.27
0.43
1.42%
CPTEL2
17.89
27.00
0.02
88.08
1.61
1.59
150.94%
GDPPC3
4,114.81
653.10
2,932.02
5,184.71
0.02
-0.79
15.87%
GNEPC3
3,598.26
561.21
2,769.75
4,697.26
0.29
-0.81
15.60%
LFPR3
64.16
0.68
62.80
65.40
-0.48
0.26
1.06%
CPTEL3
42.27
40.09
1.04
119.22
0.67
-0.99
94.86%
GDPPC4
1,108.34
137.21
940.56
1,383.40
0.66
-0.78
12.38%
GNEPC4
1,139.58
123.83
956.39
1,382.96
0.25
-0.87
10.87%
LFPR4
67.08
1.25
64.80
69.30
-0.18
-0.89
1.86%
CPTEL4
27.27
30.69
0.09
85.67
0.85
-0.74
112.54%
FLTEL2
5.18
4.77
0.87
17.06
1.52
1.37
92.03%
TTEL2
23.07
31.73
0.89
105.15
1.61
1.57
137.55%
3 - Malaysia
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
FLTEL3
16.92
2.49
10.89
19.77
-0.96
0.75
14.70%
TTEL3
59.19
40.19
11.94
135.32
0.58
-1.01
67.90%
4 - Philippines
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
FLTEL4
3.72
1.68
1.02
7.40
0.59
0.89
45.15%
TTEL4
30.99
32.11
1.11
92.94
0.85
-0.68
103.61%
5 - Singapore
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
GDPPC5
24,551.78
4,605.42
16,970.76
32,640.68
0.24
-1.07
18.76%
GNEPC5
19,001.75
2,235.37
14,818.63
23,536.53
0.09
-0.52
11.76%
LFPR5
71.03
0.94
69.00
72.80
-0.06
-0.04
1.33%
CPTEL5
68.52
50.57
3.76
145.18
0.06
-1.50
73.80%
FLTEL5
43.22
4.15
36.64
49.65
0.10
-1.38
9.61%
TTEL5
111.74
50.53
40.40
184.43
-0.12
-1.50
45.22%
GDPPC6
2,140.67
322.66
1,599.21
2,712.51
0.27
-0.96
15.07%
GNEPC6
2,069.63
275.41
1,605.57
2,447.56
-0.23
-1.35
13.31%
LFPR6
77.99
1.09
76.20
81.40
1.66
5.03
1.39%
CPTEL6
31.63
36.81
0.43
103.62
0.92
-0.69
116.39%
FLTEL6
8.44
2.47
3.07
10.83
-1.08
-0.03
29.25%
TTEL6
40.07
38.52
3.50
113.64
0.85
-0.80
96.13%
GDPPC7
453.90
146.64
252.58
722.81
0.43
-1.00
32.31%
GNEPC7
496.56
183.90
246.31
849.74
0.68
-0.66
37.03%
LFPR7
82.80
1.17
81.20
84.60
0.03
-1.32
1.41%
CPTEL7
22.61
41.02
0.00
127.00
1.84
2.09
181.44%
FLTEL7
6.76
6.46
0.22
20.05
0.80
-0.76
95.55%
TTEL7
29.36
46.70
0.22
143.37
1.73
1.72
159.04%
6 - Thailand
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
7 - Vietnam
Mean
Std. Deviation
Minimum
Maximum
Skewness
Kurtosis
Coef. Variation
Appendix 5
Regression results of the naïve models for CPPENR, FLPENR, and TPENR
Mobile Penetration Rates (CPPENR)
Regression
. reg lnGDPPC lnGNEPC LFPR CPPENR t
Source
SS
df
MS
Model
Residual
272.845422
1.06315963
4
128
68.2113554
.008305935
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_cons
1.074407
-.2851512
-.0463243
.0060003
-.3378212
Std. Err.
.0084207
.1405364
.0457454
.0030529
.1432331
t
127.59
-2.03
-1.01
1.97
-2.36
Number of obs
F( 4,
128)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.045
0.313
0.052
0.020
Test for Multicollinearity
. vif
Variable
VIF
1/VIF
CPPENR
t
lnGNEPC
LFPR
5.53
4.48
2.01
1.19
0.180908
0.223349
0.496656
0.843183
Mean VIF
3.30
Test for Heteroscedasticity
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
2.82
0.0930
=
133
= 8212.36
= 0.0000
= 0.9961
= 0.9960
= .09114
[95% Conf. Interval]
1.057745
-.5632266
-.1368394
-.0000404
-.6212324
1.091069
-.0070759
.0441908
.0120411
-.05441
Test for Autocorrelation
. xtserial lnGDPPC lnGNEPC LFPR CPPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
93.783
Prob > F =
0.0001
Fixed Line Penetration Rates (FLPENR)
Regression
. reg lnGDPPC lnGNEPC LFPR FLPENR t
Source
SS
df
MS
Model
Residual
272.874178
1.03440303
4
128
68.2185445
.008081274
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_cons
1.044135
-.3948661
.2627731
.0028489
-.0466192
Std. Err.
.0131858
.1449876
.1223538
.001443
.1655702
Test for Multicollinearity
. vif
Variable
VIF
1/VIF
lnGNEPC
FLPENR
LFPR
t
5.07
4.55
1.30
1.03
0.197077
0.219854
0.770778
0.972698
Mean VIF
2.99
t
79.19
-2.72
2.15
1.97
-0.28
Number of obs
F( 4,
128)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.007
0.034
0.051
0.779
=
133
= 8441.56
= 0.0000
= 0.9962
= 0.9961
=
.0899
[95% Conf. Interval]
1.018045
-.6817488
.0206752
-6.32e-06
-.3742281
1.070225
-.1079835
.504871
.0057041
.2809897
Test for Heteroscedasticity
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
2.67
0.1022
Test for Autocorrelation
. xtserial lnGDPPC lnGNEPC LFPR FLPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
107.833
Prob > F =
0.0000
Total Penetration Rates (TPENR)
Regression
. reg lnGDPPC lnGNEPC LFPR TPENR t
Source
SS
df
MS
Model
Residual
272.837262
1.07131962
4
128
68.2093154
.008369685
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_cons
1.07074
-.2934206
-.0085078
.0037928
-.2935006
Std. Err.
.0108887
.1430431
.041167
.0028799
.1587204
t
98.34
-2.05
-0.21
1.32
-1.85
Number of obs
F( 4,
128)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.042
0.837
0.190
0.067
=
133
= 8149.57
= 0.0000
= 0.9961
= 0.9960
= .09149
[95% Conf. Interval]
1.049195
-.5764559
-.0899638
-.0019056
-.6075561
1.092286
-.0103854
.0729481
.0094911
.0205549
Test for Multicollinearity
. vif
Variable
VIF
1/VIF
TPENR
t
lnGNEPC
LFPR
6.34
3.95
3.34
1.22
0.157707
0.252921
0.299313
0.820137
Mean VIF
3.71
Test for Heteroscedasticity
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
2.67
0.1022
Test for Autocorrelation
. xtserial lnGDPPC lnGNEPC LFPR TPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
95.302
Prob > F =
0.0001
Appendix 6
Regression results of the LSDV-1 models for CPPENR, FLPENR, and TPENR
Mobile Penetration Rates (CPPENR)
. xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
Source
SS
df
MS
Model
Residual
273.506237
.402344391
10
122
27.3506237
.003297905
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_cons
.6291471
-.5522549
-.0186616
.0137417
-1.234957
-.6455487
-1.219028
.1289703
-.876891
-1.509828
4.036087
Std. Err.
.0412927
.5500986
.0307748
.002141
.1222052
.0772055
.112434
.0255615
.0769216
.1304191
.6508046
t
15.24
-1.00
-0.61
6.42
-10.11
-8.36
-10.84
5.05
-11.40
-11.58
6.20
Number of obs
F( 10,
122)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.317
0.545
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
=
133
= 8293.33
= 0.0000
= 0.9985
= 0.9984
= .05743
[95% Conf. Interval]
.5474041
-1.64123
-.0795834
.0095033
-1.476875
-.7983847
-1.441602
.0783687
-1.029165
-1.768005
2.747755
.7108901
.5367202
.0422602
.01798
-.9930399
-.4927127
-.9964533
.1795719
-.7246171
-1.25165
5.32442
Fixed Line Penetration Rates (FLPENR)
. xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
Source
SS
df
MS
Model
Residual
273.530616
.377965283
10
122
27.3530616
.003098076
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_cons
.5795523
-.5157861
.4843647
.0119182
-1.289767
-.6852135
-1.249774
.0372773
-.9058401
-1.604177
4.386275
Std. Err.
.0425688
.5324537
.1685265
.0012274
.1191605
.0751004
.1086574
.0393534
.0748889
.1296088
.632901
t
13.61
-0.97
2.87
9.71
-10.82
-9.12
-11.50
0.95
-12.10
-12.38
6.93
Number of obs
F( 10,
122)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.335
0.005
0.000
0.000
0.000
0.000
0.345
0.000
0.000
0.000
=
133
= 8829.05
= 0.0000
= 0.9986
= 0.9985
= .05566
[95% Conf. Interval]
.4952832
-1.569831
.1507496
.0094884
-1.525657
-.8338823
-1.464872
-.0406267
-1.05409
-1.86075
3.133384
.6638215
.5382591
.8179798
.014348
-1.053877
-.5365448
-1.034676
.1151814
-.7575901
-1.347603
5.639166
Total Penetration Rates (TPENR)
. xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
Source
SS
df
MS
Model
Residual
273.505069
.403511953
10
122
27.3505069
.003307475
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_cons
.6262811
-.5698494
-.0037141
.0128985
-1.239876
-.6501931
-1.224684
.1273716
-.8794029
-1.514455
4.078197
Std. Err.
.0419855
.5507311
.0318014
.0022314
.1227452
.0776878
.1128189
.0273375
.0771899
.1312605
.6552452
t
14.92
-1.03
-0.12
5.78
-10.10
-8.37
-10.86
4.66
-11.39
-11.54
6.22
Number of obs
F( 10,
122)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.303
0.907
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
=
133
= 8269.30
= 0.0000
= 0.9985
= 0.9984
= .05751
[95% Conf. Interval]
.5431666
-1.660077
-.0666682
.0084812
-1.482862
-.8039838
-1.44802
.0732544
-1.032208
-1.774298
2.781073
.7093955
.5203777
.05924
.0173158
-.9968893
-.4964024
-1.001348
.1814888
-.7265979
-1.254611
5.37532
Appendix 7
Regression results of the LSDV-2 models for CPPENR, FLPENR, and TPENR
Mobile Penetration Rates (CPPENR)
. xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Year
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.08741
.821171564
21
111
13.0041624
.007397942
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
1.058373
-.3433372
.1012133
-.006144
-.0227279
-.0190655
-.0282116
-.0453731
-.0215265
.0667456
.1016901
.0953359
.0932392
.0851856
.1089067
.0884831
.0879091
.0893398
.0443264
-.0088768
.0118497
0
-.1407719
Std. Err.
.0090654
.1336616
.0572989
.0042001
.044882
.0441372
.0436768
.0436275
.04359
.0436719
.043826
.0433625
.0432561
.0438937
.0438936
.0439477
.0441991
.0443196
.0434462
.0437818
.0447807
(omitted)
.1428139
t
Number of obs
F( 21,
111)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 1757.81
= 0.0000
= 0.9970
= 0.9964
= .08601
[95% Conf. Interval]
116.75
-2.57
1.77
-1.46
-0.51
-0.43
-0.65
-1.04
-0.49
1.53
2.32
2.20
2.16
1.94
2.48
2.01
1.99
2.02
1.02
-0.20
0.26
0.000
0.012
0.080
0.146
0.614
0.667
0.520
0.301
0.622
0.129
0.022
0.030
0.033
0.055
0.015
0.046
0.049
0.046
0.310
0.840
0.792
1.04041
-.6081965
-.0123284
-.0144667
-.1116645
-.1065262
-.11476
-.1318238
-.107903
-.0197932
.0148461
.0094102
.0075244
-.0017928
.0219287
.0013977
.0003255
.0015176
-.0417651
-.0956334
-.0768863
1.076337
-.0784778
.214755
.0021788
.0662088
.0683952
.0583368
.0410777
.0648501
.1532844
.1885342
.1812616
.1789541
.172164
.1958847
.1755684
.1754926
.177162
.1304179
.0778797
.1005857
-0.99
0.326
-.4237672
.1422234
Fixed Line Penetration Rates (FLPENR)
. xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Year
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.084425
.824156127
21
111
13.0040202
.00742483
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
1.051645
-.3784809
.1964314
-.0006415
-.0289083
-.031551
-.0461254
-.0695758
-.0504691
.0316658
.0639562
.0592109
.0573695
.0484676
.0744148
.0552374
.0572341
.0631968
.0304657
-.0151687
.0068293
0
-.0885281
Std. Err.
.0128376
.1391944
.1193913
.0025739
.044835
.0437308
.0427528
.0419253
.0412359
.040809
.0404223
.0401464
.0400684
.0400657
.0402677
.0406745
.0411996
.0418906
.0427423
.0437253
.0448577
(omitted)
.1638568
t
Number of obs
F( 21,
111)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 1751.42
= 0.0000
= 0.9970
= 0.9964
= .08617
[95% Conf. Interval]
81.92
-2.72
1.65
-0.25
-0.64
-0.72
-1.08
-1.66
-1.22
0.78
1.58
1.47
1.43
1.21
1.85
1.36
1.39
1.51
0.71
-0.35
0.15
0.000
0.008
0.103
0.804
0.520
0.472
0.283
0.100
0.224
0.439
0.116
0.143
0.155
0.229
0.067
0.177
0.168
0.134
0.477
0.729
0.879
1.026206
-.6543039
-.0401504
-.0057419
-.1177518
-.1182066
-.130843
-.1526536
-.1321808
-.0492
-.0161433
-.0203419
-.0220286
-.0309251
-.0053783
-.0253618
-.0244058
-.0198121
-.0542309
-.1018133
-.0820592
1.077083
-.1026578
.4330132
.0044588
.0599352
.0551045
.0385922
.0135019
.0312426
.1125316
.1440557
.1387636
.1367677
.1278604
.1542078
.1358365
.1388739
.1462057
.1151624
.0714759
.0957177
-0.54
0.590
-.4132214
.2361652
Total Penetration Rates (TPENR)
. xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Year
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.095792
.812788732
21
111
13.0045615
.007322421
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
1.050066
-.3755887
.0952411
-.0059815
-.0231725
-.0201903
-.0298723
-.0477755
-.0246595
.0619383
.0966194
.0905919
.0885448
.0811382
.1055273
.085108
.0852526
.0877139
.0437834
-.0093344
.0105699
0
-.0639059
Std. Err.
.0114607
.1354749
.0459443
.0037546
.0446049
.0437163
.0430474
.0426636
.0423118
.0419483
.0418604
.0415225
.0414348
.0419736
.0421588
.0423277
.0427814
.0432233
.0429758
.0435074
.0445308
(omitted)
.1570168
t
Number of obs
F( 21,
111)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 1775.99
= 0.0000
= 0.9970
= 0.9965
= .08557
[95% Conf. Interval]
91.62
-2.77
2.07
-1.59
-0.52
-0.46
-0.69
-1.12
-0.58
1.48
2.31
2.18
2.14
1.93
2.50
2.01
1.99
2.03
1.02
-0.21
0.24
0.000
0.007
0.040
0.114
0.604
0.645
0.489
0.265
0.561
0.143
0.023
0.031
0.035
0.056
0.014
0.047
0.049
0.045
0.311
0.831
0.813
1.027356
-.6440412
.0041993
-.0134214
-.1115602
-.106817
-.1151736
-.1323162
-.1085031
-.0211851
.0136701
.0083123
.006439
-.0020352
.0219868
.0012327
.0004785
.002064
-.0413761
-.0955473
-.0776708
1.072776
-.1071361
.1862828
.0014585
.0652151
.0664365
.055429
.0367652
.0591841
.1450617
.1795686
.1728716
.1706507
.1643116
.1890678
.1689832
.1700268
.1733638
.1289428
.0768784
.0988106
-0.41
0.685
-.3750451
.2472333
Appendix 8
Regression results of the LSDV-3 models for CPPENR, FLPENR, and TPENR
Mobile Penetration Rates (CPPENR)
. xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode i.Year
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.562993
.3455879
27
105
10.1319627
.003291313
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
.6723132
-.892699
.09457
.0045066
-1.077193
-.5967416
-1.093901
.0994135
-.7451794
-1.287576
-.0084444
.0029861
.0084244
.0081632
.0201834
.0433335
.0662698
.0668035
.0600705
.0552201
.074593
.0657119
.0655715
.0690279
.0408054
-.0007199
-.0038751
0
3.863861
Std. Err.
.0460504
.5826222
.0442767
.0034057
.1372705
.0842229
.125256
.0270705
.0870113
.1481876
.0299871
.0295831
.0295401
.0302691
.0299046
.0299834
.0305147
.0301151
.03046
.0307564
.0308058
.0305286
.0304616
.0302655
.0291589
.0292501
.0299684
(omitted)
.7013539
t
Number of obs
F( 27,
105)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 3078.40
= 0.0000
= 0.9987
= 0.9984
= .05737
[95% Conf. Interval]
14.60
-1.53
2.14
1.32
-7.85
-7.09
-8.73
3.67
-8.56
-8.69
-0.28
0.10
0.29
0.27
0.67
1.45
2.17
2.22
1.97
1.80
2.42
2.15
2.15
2.28
1.40
-0.02
-0.13
0.000
0.128
0.035
0.189
0.000
0.000
0.000
0.000
0.000
0.000
0.779
0.920
0.776
0.788
0.501
0.151
0.032
0.029
0.051
0.075
0.017
0.034
0.034
0.025
0.165
0.980
0.897
.5810038
-2.047931
.0067775
-.0022463
-1.349375
-.76374
-1.34226
.0457376
-.9177067
-1.581405
-.0679034
-.0556716
-.0501481
-.0518549
-.0391119
-.0161181
.0057647
.0070907
-.0003261
-.0057641
.0135109
.0051793
.0051717
.009017
-.0170113
-.0587175
-.0632969
.7636226
.2625332
.1823625
.0112596
-.8050108
-.4297432
-.8455411
.1530894
-.572652
-.9937473
.0510145
.0616439
.0669969
.0681813
.0794787
.1027851
.1267748
.1265163
.1204671
.1162042
.1356752
.1262445
.1259713
.1290387
.0986221
.0572777
.0555467
5.51
0.000
2.473206
5.254516
Fixed Line Penetration Rates (FLPENR)
. xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode i.Year
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.562169
.346411803
27
105
10.1319322
.00329916
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
FLPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
.6232316
-.5511353
.409191
.0098993
-1.174833
-.6290182
-1.152501
.0427759
-.8270915
-1.460272
-.0125592
-.0068246
-.0070279
-.0153426
-.0082705
.0023715
.0204425
.0228771
.0141525
.0117077
.034105
.0272727
.0321448
.0432667
.0285344
-.0065347
-.0111281
0
4.019608
Std. Err.
.0524954
.572534
.1972965
.0020862
.1426141
.0869461
.1280734
.0436021
.0895534
.1589514
.0299406
.0292776
.0288181
.0288743
.0280541
.0280315
.0280996
.0277304
.0280245
.0274616
.0274865
.027583
.0276877
.0280138
.0284993
.0291674
.0300742
(omitted)
.716781
t
Number of obs
F( 27,
105)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 3071.06
= 0.0000
= 0.9987
= 0.9984
= .05744
[95% Conf. Interval]
11.87
-0.96
2.07
4.75
-8.24
-7.23
-9.00
0.98
-9.24
-9.19
-0.42
-0.23
-0.24
-0.53
-0.29
0.08
0.73
0.82
0.51
0.43
1.24
0.99
1.16
1.54
1.00
-0.22
-0.37
0.000
0.338
0.041
0.000
0.000
0.000
0.000
0.329
0.000
0.000
0.676
0.816
0.808
0.596
0.769
0.933
0.469
0.411
0.615
0.671
0.217
0.325
0.248
0.125
0.319
0.823
0.712
.5191429
-1.686365
.0179884
.0057628
-1.457611
-.8014163
-1.406447
-.043679
-1.004659
-1.775443
-.0719259
-.0648766
-.0641689
-.072595
-.0638967
-.0532099
-.0352739
-.0321072
-.041415
-.0427437
-.0203956
-.0274192
-.0227547
-.0122796
-.0279744
-.0643682
-.0707597
.7273203
.5840939
.8003935
.0140358
-.8920557
-.4566202
-.8985549
.1292308
-.6495237
-1.1451
.0468075
.0512274
.0501132
.0419099
.0473557
.0579528
.0761589
.0778614
.0697199
.0661591
.0886057
.0819646
.0870444
.098813
.0850432
.0512989
.0485036
5.61
0.000
2.598363
5.440852
Total Penetration Rates (TPENR)
. xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode i.Year
i.Ccode
_ICcode_1-7
(naturally coded; _ICcode_1 omitted)
i.Year
_IYear_1-19
(naturally coded; _IYear_1 omitted)
note: _IYear_19 omitted because of collinearity
Source
SS
df
MS
Model
Residual
273.569193
.339387966
27
105
10.1321923
.003232266
Total
273.908581
132
2.07506501
lnGDPPC
Coef.
lnGNEPC
LFPR
TPENR
t
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
_IYear_2
_IYear_3
_IYear_4
_IYear_5
_IYear_6
_IYear_7
_IYear_8
_IYear_9
_IYear_10
_IYear_11
_IYear_12
_IYear_13
_IYear_14
_IYear_15
_IYear_16
_IYear_17
_IYear_18
_IYear_19
_cons
.6578491
-.9099692
.109665
.0034859
-1.096114
-.6112232
-1.107687
.0774531
-.7543269
-1.314117
-.0074997
.004445
.0103723
.0104014
.022395
.0439135
.0669053
.0675867
.0607135
.0572398
.0771294
.0680439
.0685316
.0726716
.0433188
.0001152
-.0047931
0
3.991524
Std. Err.
.0461235
.5750391
.0428056
.0033543
.1358084
.0839444
.1241629
.0297227
.0854869
.1454836
.0297181
.0292964
.0292102
.0298175
.0293461
.0290129
.029376
.0290434
.0293168
.0297472
.0299247
.0297016
.0298139
.0298446
.0288922
.028979
.0296831
(omitted)
.7005941
t
Number of obs
F( 27,
105)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
133
= 3134.70
= 0.0000
= 0.9988
= 0.9984
= .05685
[95% Conf. Interval]
14.26
-1.58
2.56
1.04
-8.07
-7.28
-8.92
2.61
-8.82
-9.03
-0.25
0.15
0.36
0.35
0.76
1.51
2.28
2.33
2.07
1.92
2.58
2.29
2.30
2.44
1.50
0.00
-0.16
0.000
0.117
0.012
0.301
0.000
0.000
0.000
0.010
0.000
0.000
0.801
0.880
0.723
0.728
0.447
0.133
0.025
0.022
0.041
0.057
0.011
0.024
0.024
0.017
0.137
0.997
0.872
.5663947
-2.050165
.0247894
-.003165
-1.365397
-.7776695
-1.353879
.0185183
-.9238316
-1.602584
-.0664253
-.0536444
-.047546
-.0487212
-.0357929
-.0136137
.0086581
.0099991
.0025837
-.0017435
.0177942
.0091511
.0094161
.0134953
-.0139691
-.0573447
-.0636493
.7493035
.2302271
.1945405
.0101368
-.8268312
-.444777
-.8614946
.1363878
-.5848222
-1.02565
.0514259
.0625344
.0682907
.0695241
.080583
.1014407
.1251524
.1251743
.1188433
.1162231
.1364645
.1269367
.127647
.1318478
.1006068
.0575751
.054063
5.70
0.000
2.602376
5.380673
Appendix 9
Results of Wald’s Test
Naïve vs. LSDV-1
Mobile Penetration Rates (CPPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7
(
(
(
(
(
(
1)
2)
3)
4)
5)
6)
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
F(
=
=
=
=
=
=
0
0
0
0
0
0
6,
122) =
Prob > F =
33.40
0.0000
Fixed Line Penetration Rates (FLPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7
(
(
(
(
(
(
1)
2)
3)
4)
5)
6)
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
F(
=
=
=
=
=
=
0
0
0
0
0
0
6,
122) =
Prob > F =
35.31
0.0000
Total Penetration Rates (TPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7
(
(
(
(
(
(
1)
2)
3)
4)
5)
6)
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
F(
=
=
=
=
=
=
0
0
0
0
0
0
6,
122) =
Prob > F =
33.65
0.0000
Naïve vs. LSDV-2
Mobile Penetration Rates (CPPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Year
. test _IYear_2 _IYear_3 _IYear_4 _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYe
> ar_11 _IYear_12 _IYear_13 _IYear_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 18 dropped
F( 17,
111) =
Prob > F =
1.92
0.0227
Fixed Line Penetration Rates (FLPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Year
. test _IYear_2 _IYear_3 _IYear_4 _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYe
> ar_11 _IYear_12 _IYear_13 _IYear_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 18 dropped
111) =
F( 17,
Prob > F =
1.67
0.0600
Total Penetration Rates (TPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Year
. test _IYear_2 _IYear_3 _IYear_4 _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYe
> ar_11 _IYear_12 _IYear_13 _IYear_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 18 dropped
111) =
F( 17,
Prob > F =
2.08
0.0125
Naïve vs. LSDV-3
Mobile Penetration Rates (CPPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode i.Year
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7 _IYear_2 _IYear_3 _IYear_4
> _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYear_11 _IYear_12 _IYear_13 _IYea
> r_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
_ICcode_2 = 0
_ICcode_3 = 0
_ICcode_4 = 0
_ICcode_5 = 0
_ICcode_6 = 0
_ICcode_7 = 0
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 24 dropped
105) =
F( 23,
Prob > F =
9.48
0.0000
Fixed Line Penetration Rates (FLPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode i.Year
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7 _IYear_2 _IYear_3 _IYear_4
> _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYear_11 _IYear_12 _IYear_13 _IYea
> r_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
_ICcode_2 = 0
_ICcode_3 = 0
_ICcode_4 = 0
_ICcode_5 = 0
_ICcode_6 = 0
_ICcode_7 = 0
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 24 dropped
F( 23,
105) =
Prob > F =
9.07
0.0000
Total Penetration Rates (TPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode i.Year
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7 _IYear_2 _IYear_3 _IYear_4
> _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYear_11 _IYear_12 _IYear_13 _IYea
> r_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
_ICcode_2 = 0
_ICcode_3 = 0
_ICcode_4 = 0
_ICcode_5 = 0
_ICcode_6 = 0
_ICcode_7 = 0
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 24 dropped
F( 23,
105) =
Prob > F =
9.85
0.0000
LSDV-1 vs. LSDV-3
. test _IYear_2 _IYear_3 _IYear_4 _IYear_5 _IYear_6 _IYear_7 _IYear_8 _IYear_9 _IYear_10 _IYe
> ar_11 _IYear_12 _IYear_13 _IYear_14 _IYear_15 _IYear_16 _IYear_17 _IYear_18 _IYear_19
( 1)
( 2)
( 3)
( 4)
( 5)
( 6)
( 7)
( 8)
( 9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
_IYear_2 = 0
_IYear_3 = 0
_IYear_4 = 0
_IYear_5 = 0
_IYear_6 = 0
_IYear_7 = 0
_IYear_8 = 0
_IYear_9 = 0
_IYear_10 = 0
_IYear_11 = 0
_IYear_12 = 0
_IYear_13 = 0
_IYear_14 = 0
_IYear_15 = 0
_IYear_16 = 0
_IYear_17 = 0
_IYear_18 = 0
o._IYear_19 = 0
Constraint 18 dropped
F( 17,
105) =
Prob > F =
1.17
0.3043
LSDV-2 vs. LSDV-3
. test _ICcode_2 _ICcode_3 _ICcode_4 _ICcode_5 _ICcode_6 _ICcode_7
(
(
(
(
(
(
1)
2)
3)
4)
5)
6)
_ICcode_2
_ICcode_3
_ICcode_4
_ICcode_5
_ICcode_6
_ICcode_7
F(
=
=
=
=
=
=
0
0
0
0
0
0
6,
105) =
Prob > F =
24.41
0.0000
Appendix 10
Random Effects Model (REM) regression results for CPPENR, FLPENR, and TPENR
Mobile Penetration Rates (CPPENR)
. xtreg lnGDPPC lnGNEPC LFPR CPPENR t, re
Random-effects GLS regression
Group variable: Ccode
Number of obs
Number of groups
=
=
133
7
R-sq:
Obs per group: min =
avg =
max =
19
19.0
19
within = 0.8617
between = 0.9991
overall = 0.9959
corr(u_i, X)
Wald chi2(4)
Prob > chi2
= 0 (assumed)
lnGDPPC
Coef.
lnGNEPC
LFPR
CPPENR
t
_cons
1.007303
-.0087504
-.03801
.0069566
-.0201091
.0236003
.4422996
.041955
.0027897
.4244109
sigma_u
sigma_e
rho
.05775633
.05742739
.50285579
(fraction of variance due to u_i)
Std. Err.
z
42.68
-0.02
-0.91
2.49
-0.05
P>|z|
0.000
0.984
0.365
0.013
0.962
=
=
2688.93
0.0000
[95% Conf. Interval]
.9610475
-.8756416
-.1202403
.0014889
-.8519391
1.053559
.8581408
.0442203
.0124242
.811721
Fixed Line Penetration Rates (FLPENR)
. xtreg lnGDPPC lnGNEPC LFPR FLPENR t, re
Random-effects GLS regression
Group variable: Ccode
Number of obs
Number of groups
=
=
133
7
R-sq:
Obs per group: min =
avg =
max =
19
19.0
19
within = 0.8653
between = 0.9991
overall = 0.9960
corr(u_i, X)
Wald chi2(4)
Prob > chi2
= 0 (assumed)
lnGDPPC
Coef.
Std. Err.
z
lnGNEPC
LFPR
FLPENR
t
_cons
.9718127
-.1062537
.3573031
.004087
.2891921
.0299994
.4344379
.2144201
.0013667
.4390353
sigma_u
sigma_e
rho
.05450452
.05566036
.48950926
(fraction of variance due to u_i)
32.39
-0.24
1.67
2.99
0.66
P>|z|
0.000
0.807
0.096
0.003
0.510
=
=
2830.79
0.0000
[95% Conf. Interval]
.9130151
-.9577363
-.0629526
.0014083
-.5713013
1.03061
.745229
.7775588
.0067657
1.149686
Total Penetration Rates (TPENR)
. xtreg lnGDPPC lnGNEPC LFPR TPENR t, re
Random-effects GLS regression
Group variable: Ccode
Number of obs
Number of groups
=
=
133
7
R-sq:
Obs per group: min =
avg =
max =
19
19.0
19
within = 0.8613
between = 0.9991
overall = 0.9959
corr(u_i, X)
Wald chi2(4)
Prob > chi2
= 0 (assumed)
lnGDPPC
Coef.
Std. Err.
z
lnGNEPC
LFPR
TPENR
t
_cons
1.007785
-.0097186
-.0256078
.006279
-.0170519
.0249898
.4441185
.0429715
.0029053
.4323495
sigma_u
sigma_e
rho
.05796492
.05751065
.5039338
(fraction of variance due to u_i)
40.33
-0.02
-0.60
2.16
-0.04
P>|z|
0.000
0.983
0.551
0.031
0.969
=
=
2671.10
0.0000
[95% Conf. Interval]
.9588057
-.880175
-.1098305
.0005848
-.8644414
1.056764
.8607377
.0586148
.0119733
.8303376
Appendix 11
Results of the Hausman Test for CPPENR, FLPENR, and TPENR using LSDV-1 and LSDV-3
LSDV-1
Mobile Penetration Rates (CPPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR CPPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
CPPENR
t
.6291471
-.5522549
-.0186616
.0137417
1.007303
-.0087504
-.03801
.0069566
(b-B)
Difference
-.3781562
-.5435045
.0193485
.0067851
sqrt(diag(V_b-V_B))
S.E.
.0338838
.3270774
.
.
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
145.44
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
Fixed Line Penetration Rates (FLPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR FLPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
FLPENR
t
.5795523
-.5157861
.4843647
.0119182
.9718127
-.1062537
.3573031
.004087
(b-B)
Difference
-.3922604
-.4095325
.1270616
.0078312
sqrt(diag(V_b-V_B))
S.E.
.0302017
.3078484
.
.
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
165.33
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
Total Penetration Rates (TPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
TPENR
t
.6262811
-.5698494
-.0037141
.0128985
1.007785
-.0097186
-.0256078
.006279
(b-B)
Difference
-.3815038
-.5601308
.0218937
.0066195
sqrt(diag(V_b-V_B))
S.E.
.0337386
.3256738
.
.
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
145.39
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
LSDV-3
Mobile Penetration Rates (CPPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR CPPENR t i.Ccode i.Year
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR CPPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
CPPENR
t
.6723132
-.892699
.09457
.0045066
1.007303
-.0087504
-.03801
.0069566
(b-B)
Difference
-.3349901
-.8839486
.13258
-.00245
sqrt(diag(V_b-V_B))
S.E.
.0395431
.3792357
.0141493
.0019537
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -642.36
chi2<0 ==> model fitted on these
data fails to meet the asymptotic
assumptions of the Hausman test;
see suest for a generalized test
Fixed Line Penetration Rates (FLPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR FLPENR t i.Ccode i.Year
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR FLPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
FLPENR
t
.6232316
-.5511353
.409191
.0098993
.9718127
-.1062537
.3573031
.004087
(b-B)
Difference
-.3485811
-.4448817
.0518879
.0058123
sqrt(diag(V_b-V_B))
S.E.
.0430791
.3729061
.
.0015761
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
73.04
Prob>chi2 =
0.0000
(V_b-V_B is not positive definite)
Total Penetration Rates (TPENR)
. quietly xi: reg lnGDPPC lnGNEPC LFPR TPENR t i.Ccode i.Year
. est store fixed
. quietly xtreg lnGDPPC lnGNEPC LFPR TPENR t, re
. est store random
. hausman fixed random
Coefficients
(b)
(B)
fixed
random
lnGNEPC
LFPR
TPENR
t
.6578491
-.9099692
.109665
.0034859
1.007785
-.0097186
-.0256078
.006279
(b-B)
Difference
-.3499357
-.9002506
.1352728
-.0027931
sqrt(diag(V_b-V_B))
S.E.
.0387671
.3652789
.
.0016765
b = consistent under Ho and Ha; obtained from regress
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test:
Ho:
difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -127.96
chi2<0 ==> model fitted on these
data fails to meet the asymptotic
assumptions of the Hausman test;
see suest for a generalized test
Note: Because we cannot draw conclusions from LSDV-3 due to the failure to meet asymptotic assumptions in
getting the p-value of such test, we find LSDV-1 to be a more suitable model and thus, LSDV-1 will be employed in
this study.
Appendix 12
Tests for Multicollinearity of LSDV-1 models using Variance Inflation Factor and Tolerance tests
Mobile Penetration Rates (CPPENR)
. vif
Variable
VIF
1/VIF
lnGNEPC
_ICcode_7
_ICcode_2
_ICcode_4
LFPR
_ICcode_3
_ICcode_6
CPPENR
t
_ICcode_5
121.94
83.99
73.75
62.43
45.76
29.44
29.22
6.30
5.55
3.23
0.008201
0.011906
0.013560
0.016019
0.021851
0.033973
0.034224
0.158713
0.180313
0.309926
Mean VIF
46.16
Fixed Line Penetration Rates (FLPENR)
. vif
Variable
VIF
1/VIF
lnGNEPC
_ICcode_7
_ICcode_2
_ICcode_4
LFPR
_ICcode_3
_ICcode_6
FLPENR
_ICcode_5
t
137.95
88.30
74.64
62.06
45.64
29.65
29.48
22.51
8.14
1.94
0.007249
0.011324
0.013397
0.016113
0.021910
0.033729
0.033920
0.044427
0.122834
0.515390
Mean VIF
50.03
Total Penetration Rates (TPENR)
. vif
Variable
VIF
1/VIF
lnGNEPC
_ICcode_7
_ICcode_2
_ICcode_4
LFPR
_ICcode_3
_ICcode_6
TPENR
t
_ICcode_5
125.70
84.84
74.19
62.67
45.74
29.72
29.34
9.58
6.01
3.68
0.007955
0.011787
0.013480
0.015956
0.021864
0.033650
0.034085
0.104434
0.166480
0.271752
Mean VIF
47.14
Appendix 13
Tests for Heteroscedasticity of LSDV-1 models using Breusch-Pagan/Cook-Weisberg Test
Mobile Penetration Rates (CPPENR)
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
63.21
0.0000
Fixed Line Penetration Rates (FLPENR)
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
68.20
0.0000
Total Penetration Rates (TPENR)
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnGDPPC
chi2(1)
Prob > chi2
=
=
62.33
0.0000
Appendix 14
Tests for Autocorrelation of LSDV-1 models using the Wooldridge Test
Mobile Penetration Rates (CPPENR)
. xtserial lnGDPPC lnGNEPC LFPR CPPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
93.783
Prob > F =
0.0001
Fixed Line Penetration Rates (FLPENR)
. xtserial lnGDPPC lnGNEPC LFPR FLPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
107.833
Prob > F =
0.0000
Total Penetration Rates (TPENR)
. xtserial lnGDPPC lnGNEPC LFPR TPENR t
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1,
6) =
95.302
Prob > F =
0.0001
Appendix 15
Tests to determine whether to use Seemingly Unrelated Regression
Mobile Penetration Rates (CPPENR)
. sureg (lnGDPPC1 lnGNEPC1 LFPR1 CPPENR1 t1) (lnGDPPC2 lnGNEPC2 LFPR2 CPPENR2 t2) (lnGDPPC3 l
> nGNEPC3 LFPR3 CPPENR3 t3) (lnGDPPC4 lnGNEPC4 LFPR4 CPPENR4 t4) (lnGDPPC5 lnGNEPC5 LFPR5 CPP
> ENR5 t5) (lnGDPPC6 lnGNEPC6 LFPR6 CPPENR6 t6) (lnGDPPC7 lnGNEPC7 LFPR7 CPPENR7 t7)
Seemingly unrelated regression
Equation
Obs
Parms
RMSE
"R-sq"
chi2
P
lnGDPPC1
lnGDPPC2
lnGDPPC3
lnGDPPC4
lnGDPPC5
lnGDPPC6
lnGDPPC7
19
19
19
19
19
19
19
4
4
4
4
4
4
4
.0200254
.0068476
.0133207
.0123533
.0322709
.012421
.0091442
0.6810
0.9978
0.9929
0.9889
0.9691
0.9928
0.9992
47.95
9637.43
2850.90
2052.02
636.59
3086.87
24850.09
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
lnGDPPC1
lnGNEPC1
LFPR1
CPPENR1
t1
_cons
-.0668882
2.013805
.1839282
-.019839
9.189702
.0608842
1.194184
.0817527
.0064476
1.247472
-1.10
1.69
2.25
-3.08
7.37
0.272
0.092
0.024
0.002
0.000
-.186219
-.3267525
.0236957
-.0324761
6.744703
.0524425
4.354363
.3441606
-.0072019
11.6347
lnGDPPC2
lnGNEPC2
LFPR2
CPPENR2
t2
_cons
.6754864
.167196
.0824678
.0066052
2.049458
.0180571
.2189268
.0116824
.0006103
.1589352
37.41
0.76
7.06
10.82
12.89
0.000
0.445
0.000
0.000
0.000
.6400951
-.2618926
.0595707
.0054091
1.737951
.7108776
.5962847
.105365
.0078014
2.360965
lnGDPPC3
lnGNEPC3
LFPR3
CPPENR3
t3
_cons
.383654
-.050387
-.2226529
.0353624
4.945895
.0310022
.8308424
.0404534
.0023933
.6392409
12.38
-0.06
-5.50
14.78
7.74
0.000
0.952
0.000
0.000
0.000
.3228908
-1.678808
-.30194
.0306716
3.693005
.4444172
1.578034
-.1433658
.0400532
6.198784
lnGDPPC4
lnGNEPC4
LFPR4
CPPENR4
t4
_cons
.3612261
-.9125775
.1484501
.0054286
4.980588
.1116222
.2498118
.0280956
.0021314
.6938643
3.24
-3.65
5.28
2.55
7.18
0.001
0.000
0.000
0.011
0.000
.1424505
-1.4022
.0933837
.0012513
3.620639
.5800016
-.4229554
.2035165
.009606
6.340537
lnGDPPC5
lnGNEPC5
LFPR5
CPPENR5
t5
_cons
.3394706
-1.694226
-.0771557
.0357782
7.647989
.100525
1.605739
.0941024
.0076941
1.520085
3.38
-1.06
-0.82
4.65
5.03
0.001
0.291
0.412
0.000
0.000
.1424452
-4.841416
-.2615931
.020698
4.668678
.536496
1.452965
.1072816
.0508584
10.6273
lnGDPPC6
lnGNEPC6
LFPR6
CPPENR6
t6
_cons
.4738208
-1.014343
.0020003
.01742
4.660871
.0231538
.2788939
.0217166
.0014265
.2960375
20.46
-3.64
0.09
12.21
15.74
0.000
0.000
0.927
0.000
0.000
.4284401
-1.560965
-.0405634
.0146241
4.080648
.5192014
-.4677213
.0445639
.0202159
5.241094
lnGDPPC7
lnGNEPC7
LFPR7
CPPENR7
t7
_cons
.2263129
-3.876799
-.0081151
.0357532
7.531758
.0409104
2.091623
.0094561
.0043503
1.86605
5.53
-1.85
-0.86
8.22
4.04
0.000
0.064
0.391
0.000
0.000
.14613
-7.976306
-.0266487
.0272267
3.874368
.3064958
.2227072
.0104185
.0442797
11.18915
Fixed Line Penetration Rates (FLPENR)
. sureg (lnGDPPC1 lnGNEPC1 LFPR1 FLPENR1 t1) (lnGDPPC2 lnGNEPC2 LFPR2 FLPENR2 t2) (lnGDPPC3 l
> nGNEPC3 LFPR3 FLPENR3 t3) (lnGDPPC4 lnGNEPC4 LFPR4 FLPENR4 t4) (lnGDPPC5 lnGNEPC5 LFPR5 FLP
> ENR5 t5) (lnGDPPC6 lnGNEPC6 LFPR6 FLPENR6 t6) (lnGDPPC7 lnGNEPC7 LFPR7 FLPENR7 t7)
Seemingly unrelated regression
Equation
Obs
Parms
RMSE
"R-sq"
chi2
P
lnGDPPC1
lnGDPPC2
lnGDPPC3
lnGDPPC4
lnGDPPC5
lnGDPPC6
lnGDPPC7
19
19
19
19
19
19
19
4
4
4
4
4
4
4
.0210332
.0087158
.0146361
.0128154
.0333556
.0113136
.0090109
0.6481
0.9964
0.9914
0.9881
0.9670
0.9940
0.9992
37.89
6366.51
2456.44
1635.99
608.79
3478.82
24618.49
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
lnGDPPC1
lnGNEPC1
LFPR1
FLPENR1
t1
_cons
-.0758397
-.3220255
.1152247
-.0049268
10.80058
.0793151
1.691651
.3477301
.0021614
1.783601
-0.96
-0.19
0.33
-2.28
6.06
0.339
0.849
0.740
0.023
0.000
-.2312945
-3.6376
-.5663138
-.0091631
7.304788
.0796151
2.993549
.7967633
-.0006905
14.29638
lnGDPPC2
lnGNEPC2
LFPR2
FLPENR2
t2
_cons
.679207
-.0851739
.5573478
.0060386
2.190133
.0211915
.2495129
.0857377
.0008023
.1925082
32.05
-0.34
6.50
7.53
11.38
0.000
0.733
0.000
0.000
0.000
.6376724
-.5742103
.3893049
.004466
1.812823
.7207416
.4038624
.7253907
.0076112
2.567442
lnGDPPC3
lnGNEPC3
LFPR3
FLPENR3
t3
_cons
.3110107
.1989394
.9568292
.0206248
5.271299
.0309128
.7342091
.1568995
.0008978
.6152635
10.06
0.27
6.10
22.97
8.57
0.000
0.786
0.000
0.000
0.000
.2504228
-1.240084
.6493119
.0188651
4.065405
.3715987
1.637963
1.264347
.0223845
6.477194
lnGDPPC4
lnGNEPC4
LFPR4
FLPENR4
t4
_cons
.6882704
-2.15903
.5745924
.0038581
3.551501
.1405358
.2813355
.4175132
.0031753
.9228483
4.90
-7.67
1.38
1.22
3.85
0.000
0.000
0.169
0.224
0.000
.4128253
-2.710437
-.2437185
-.0023654
1.742752
.9637154
-1.607622
1.392903
.0100816
5.360251
lnGDPPC5
lnGNEPC5
LFPR5
FLPENR5
t5
_cons
.2794955
-2.297334
-.0950172
.0309329
8.703537
.1016401
1.446116
.1776378
.0030395
1.350331
2.75
-1.59
-0.53
10.18
6.45
0.006
0.112
0.593
0.000
0.000
.0802846
-5.131668
-.4431809
.0249756
6.056937
.4787063
.537001
.2531464
.0368902
11.35014
lnGDPPC6
lnGNEPC6
LFPR6
FLPENR6
t6
_cons
.4919631
-.9060629
.4511037
.0156455
4.41836
.0208498
.2665427
.2460826
.0011334
.2597353
23.60
-3.40
1.83
13.80
17.01
0.000
0.001
0.067
0.000
0.000
.4510982
-1.428477
-.0312093
.0134241
3.909288
.532828
-.3836489
.9334167
.0178669
4.927432
lnGDPPC7
lnGNEPC7
LFPR7
FLPENR7
t7
_cons
.2244011
-6.404376
-.1153602
.0314274
9.685557
.0364435
1.896138
.0832789
.0043401
1.634867
6.16
-3.38
-1.39
7.24
5.92
0.000
0.001
0.166
0.000
0.000
.1529731
-10.12074
-.2785838
.0229209
6.481276
.2958291
-2.688014
.0478633
.0399338
12.88984
Total Penetration Rates (TPENR)
. sureg (lnGDPPC1 lnGNEPC1 LFPR1 TPENR1 t1) (lnGDPPC2 lnGNEPC2 LFPR2 TPENR2 t2) (lnGDPPC3 lnG
> NEPC3 LFPR3 TPENR3 t3) (lnGDPPC4 lnGNEPC4 LFPR4 TPENR4 t4) (lnGDPPC5 lnGNEPC5 LFPR5 TPENR5
> t5) (lnGDPPC6 lnGNEPC6 LFPR6 TPENR6 t6) (lnGDPPC7 lnGNEPC7 LFPR7 TPENR7 t7)
Seemingly unrelated regression
Equation
Obs
Parms
RMSE
"R-sq"
chi2
P
lnGDPPC1
lnGDPPC2
lnGDPPC3
lnGDPPC4
lnGDPPC5
lnGDPPC6
lnGDPPC7
19
19
19
19
19
19
19
4
4
4
4
4
4
4
.0193109
.0070963
.0142561
.0123062
.0305652
.0125579
.0091505
0.7034
0.9976
0.9918
0.9890
0.9723
0.9927
0.9992
49.61
9101.41
2511.74
2091.05
705.89
3072.73
25008.99
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
lnGDPPC1
lnGNEPC1
LFPR1
TPENR1
t1
_cons
-.0830708
1.32237
.2030996
-.0201672
9.771516
.0570439
.9725151
.0793602
.0059333
1.071327
-1.46
1.36
2.56
-3.40
9.12
0.145
0.174
0.010
0.001
0.000
-.1948748
-.5837241
.0475566
-.0317963
7.671755
.0287333
3.228465
.3586426
-.0085381
11.87128
lnGDPPC2
lnGNEPC2
LFPR2
TPENR2
t2
_cons
.6804177
.1680521
.0704048
.006466
2.015883
.0184554
.2221524
.010351
.0006368
.1622206
36.87
0.76
6.80
10.15
12.43
0.000
0.449
0.000
0.000
0.000
.6442458
-.2673587
.0501172
.0052179
1.697937
.7165896
.6034628
.0906924
.0077141
2.33383
lnGDPPC3
lnGNEPC3
LFPR3
TPENR3
t3
_cons
.3958693
.1726696
-.2618044
.0382611
4.73476
.0328658
.8720774
.0542177
.0032982
.6603461
12.05
0.20
-4.83
11.60
7.17
0.000
0.843
0.000
0.000
0.000
.3314536
-1.536571
-.3680691
.0317969
3.440505
.4602851
1.88191
-.1555397
.0447254
6.029014
lnGDPPC4
lnGNEPC4
LFPR4
TPENR4
t4
_cons
.3764907
-.9395889
.1505673
.0045929
4.893529
.1096875
.2412258
.0275127
.0021349
.6839166
3.43
-3.90
5.47
2.15
7.16
0.001
0.000
0.000
0.031
0.000
.1615073
-1.412383
.0966435
.0004086
3.553077
.5914742
-.4667949
.2044912
.0087771
6.233981
lnGDPPC5
lnGNEPC5
LFPR5
TPENR5
t5
_cons
.3762085
-1.885866
-.1340725
.0404298
7.472833
.0956949
1.390968
.0816422
.0069152
1.369894
3.93
-1.36
-1.64
5.85
5.46
0.000
0.175
0.101
0.000
0.000
.1886499
-4.612113
-.2940882
.0268762
4.787889
.5637671
.8403816
.0259432
.0539834
10.15778
lnGDPPC6
lnGNEPC6
LFPR6
TPENR6
t6
_cons
.4702293
-1.075536
.0101014
.0168695
4.738078
.023315
.2777716
.0236706
.0016177
.2985018
20.17
-3.87
0.43
10.43
15.87
0.000
0.000
0.670
0.000
0.000
.4245328
-1.619958
-.036292
.013699
4.153025
.5159259
-.5311134
.0564949
.0200401
5.323131
lnGDPPC7
lnGNEPC7
LFPR7
TPENR7
t7
_cons
.2273135
-3.731554
-.0067496
.0359865
7.403161
.0405518
2.028204
.0088263
.0042918
1.806582
5.61
-1.84
-0.76
8.38
4.10
0.000
0.066
0.444
0.000
0.000
.1478335
-7.70676
-.0240488
.0275747
3.862325
.3067935
.2436528
.0105495
.0443982
10.944