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Diffusion of internet technology in low- and middle income countries
A panel data study of 15 African countries 2006-2015
Martin Sigurd Beyer
Master’s thesis at the Centre for Technology, Innvoation and Culture (TIK)
UNIVERSITETET I OSLO
Fall 2017
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III Martin Sigurd Beyer 2017
«Diffusion of internet technology in low- and middle income countries – A panel data study of 15 African countries 2006-2015»
ESST – Society, Science and Technology in Europe Supervisor: Henrik Schwabe and Jan Fagerberg Words: 15.036 Pages: 45
http://www.duo.uio.no
Print: Reprosentralen, Universitetet i Oslo
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This thesis’ aim was to explore the diffusion of the internet, and which capabilities were necessary to successfully diffuse the technologies in low- and middle income countries. The thesis is a panel data analysis of fifteen mainland African countries with a significant
number of internet users today, through a time series of ten years – 2006 - 2015. The study aims to give indications to implications for investors and policy makers where internet technology investments are concerned, and to contribute to the current academic research on the field of internet diffusion in low and middle income countries.
The literature review showed that some research has already been done in this field, but the research is mostly focused on other regions or different stages of economic development or even outdated – as studies quickly become in a field with as quick development as
information and communication technology. The literature review is the background for the hypothesis for this thesis, that foreign direct investment, income, technological infrastructure and adoptability – proxied through mobile phone subscriptions, access price and education were the most important variables to explain growth in internet users. These variables were all analyzed.
The panel data study showed that of these explanatory variables, mobile phone subscriptions, secondary education and access point were the significant variables in the study. While the other variables were insignificant, it is likely that some are insignificant due to other reasons than that they are insignificant themselves. Foreign direct investment has in other research been linked strongly to human capital, and because the countries in this study is varying highly in human capital, this could lead to an expected insignificance. Primary and tertiary education both has very variable data, and on primary school, some countries reduce their enrollment over the time period while others increase. This makes the regression pull in different directions.
The findings in this study may be generalized to countries of similar development, and to countries about to enter the same levels of economic development, even though they are now on the lower end, and can give some indication to the many initiatives working with internet infrastructure in low income countries in the region. However, it is important to not only look at what grows internet usage in the country, even though it is closely linked to economic growth, but also remember how internet access can help grow other parts of the society, and increase the value of health care, education etc.
V though the results in this analysis is quite clear, more research is needed on the area to give a clearer and more precise picture of the capabilities countries needs to successfully diffuse internet technologies.
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There are several people that deserve gratidude for help and support during the process with this thesis. First, I would like to thank my supervisors, Henrik Schwabe and Jan Fagerberg, for good discussions and for help in narrowing the search and research question. A special thanks to Stine Lise Hattestad Bratsberg and the rest of my former colleagues at Pure Consulting for good discussions in the initial stages and support throughout the process.
Without the great minds in the organization and the flexibility in work this thesis would probably never be finished. In addition, Josef Noll, at the Basic Internet Foundation, deserves thanks for giving the background for the thesis, and for good discussions in the initial stages of the thesis. Joar Kvamsås and Jørgen Tresse, TIK students, class of 2018, deserves
gratitude for help with methodology with their knowledge of statistics and quantitive
methods in general. Lastly, Susan Alexa Pusch, thank you for reading through the paper and giving feedback on structure and formulations.
Martin Beyer, Oslo, October 2017
VII
1. Introduction 1
1.1 Background for thesis 1
1.2 Problem definition 3
1.3 Scope 4
1.4 Purpose and intended contribution 5
1.5 Structure 6
2. Theoretical background and literature review 8
2.1 Innovation in development 8
2.2 Capabilities of nations 9
2.3 Information and communication technology and economic growth 10
2.4 Technology diffusion 12
3. Empirical investigation 18
3.1Data selection 19
3.1.1 Choice of countries and time series 19
3.1.2 Choice of variables 22
3.2 Choice of methodology 26
3.2.1 Panel data 26
3.2.2 Fixed or random effects 27
3.3 Data analysis 29
3.3.1 Panel data regression 31
4. Discussion 33
4.1 Foreign direct investments 33
4.2 Income and price 34
4.3 Mobile phone subscriptions 35
4.4 Education 36
4.5 Generalization 38
4.6 Implications 39
4.7 Reliability 40
4.8 Validity 40
4.8.1 External Validity 40
4.8.2 Internal validity 41
5. Conclusion 44
References 46
Appendix A: Raw data
1 1. Introduction
«The 2030 Agenda for Sustainable Development recognizes the great potential of global connectivity to spur human progress. It challenges us
to ensure universal and affordable Internet access for all»
(António Guterres, Secretary-General, UN, cited by International Telecommunication Union, 2016c) 1.1 Background for thesis
Information and communication technologies, and perhaps especially internet technology have spread quicker than many could believe the last years. Today, six of the world's seven billion people have access to a mobile phone – while only four and a half billion has a toilet (Worstall, 2013).
Differences and inequality amongst countries and parts of the world regarding technological and economic development has been part of the public debate and academic research for decades, and even though life improves for most people on most metrics (United Nations, 2015), there is still a long way to go to lift the poorest countries into industrialization and beyond. It may seem like there are as many solutions as there are issues, and it is certainly disagreement to what should be prioritized of the different solutions. Should we build more schools? Focus on free trade? Build infrastructure?
The Sustainable Development Goals, which are a continuation and development of the Millennium Goals, describes the path the world needs to take from 2015 to 2030 to ensure a sustainable world in all its dimensions, which are economic, environmental and social inclusion. Digital inclusion is claimed to be a central driver (Madon, Reinhard, Roode, &
Walsham, 2009) (Piazolo, 2001) (International Telecommunication Union, 2016d) to achieve these.
2 The socio-economic challenges related to poverty, health and development are substantial.
The challenges are not only closely tied to the development of the welfare of people, but they are also argued to be a difficult hinder to actually do the necessary work to address these challenges. This means that the fact that a country is left behind, development wise, they are likely to struggle with ever catching up. The countries in this situation needs a catalyst to propel economic development and start catching up.
In the Sustainable Development Goals, information and communication technologies are mentioned as key to development and is part of the how-to in many of the goals. For example, Sustainable Development Goal number nine, Industry, innovation and infrastructure, the third sub goal is to «significantly increase access to ICT and strive to provide universal and
affordable access to the internet in the LDCs (least-developed countries by 2020»
(International Telecommunication Union, 2016b) While the role of information and communication technologies on the goal regarding industry, innovation and infrastructure might be fairly obvious, it is important to understand what the goal is trying to achieve. The goal on Industry, innovation and infrastructure is about inclusive economic growth, creating jobs and including all regions in all countries in decent infrastructure – both regarding communication, and regarding more traditional infrastructure. This is mirrored in goal number two, zero hunger, is not quite so obvious at first glance. But the International Telecommunication Union states:
«To feed a growing population, agriculture is increasingly knowledge-intensive.
ICTs help farmers improve crop yields and business productivity through better access to market information, weather forecasts, training programmes
and other online content tailored to their needs.»
(International Telecommunication Union, 2016a)
In terms of practical implementation of these goals, and to handle these challenges, one of the many initiatives to help solve this issue, comes from the Basic Internet Foundation, a
foundation that works on building internet infrastructure in Tanzania. Their business model is to offer free information through so-called info-internet, where a number of information based internet services are provided in a hard compressed way (mostly text and low resolution images), and charging for access to the rest of the internet, and full speed access.
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«Digital inclusion is a key for health, education and meaningful work.
Connecting developing economies with free access to information through the alliance of IT, Telecom, Academia, and public sector partners will
create the partnership for achieving the Sustainability Development Goals (SDG 2030).
The unique alliance, addressing a common mission and best praxis for partnership in digital access, will build the basis for sustainable development, a catalyst for
achieving the SDGs and business acceleration in Africa.»
(Basic Internet Foundation, 2014)
During talks with co-founder Josef Noll, we agreed that this is an interesting topic for a master thesis, and that I would pursue the subject on information and communication technologies and development.
There are, as mentioned, a wide array of challenges to discuss and, following this, there are an even wider array of research questions to choose. But what interested me most was how it seems to be very different how well different countries seems to do regarding technological and economic development, and the link between the two. Of all the private, public and civil initiatives to build the infrastructure, invest in companies and trade, it seems to be very different how large the return on investments across the borders. And this is one of the many interesting questions I could choose to work with. Are the results as different as they seem to be at first glance? And in that case, why? What are the actual factors deciding whether an investment in technology infrastructure will give substantial return, for investors and for the nation in terms of growth and use.
1.2 Problem definition
What capabilities are necessary to successfully diffuse internet technology in low- and middle income countries?
4 1.3 Scope
The aim of this thesis is to explore the capabilities necessary to successfully diffuse internet technologies in low- and middle income countries, through a panel data analysis of fifteen mainland African countries with a significant number of internet users today, through a time series of ten years – 2006 - 2015.
This problem definition is somewhat wide and it is therefore necessary to narrow it down to make it researchable. First, I will define some of the terminology to make it clear what I am writing about. The last term in the research question is 'low- and middle income countries'.
This has a clear definition, given and discussed in chapter three. But with the background for the thesis, and the discussions with Josef Noll from Basic Internet Foundation, it is natural to narrow the scope down to similar countries as Tanzania. However, if we choose countries in the same region of the income scale, it seemed quite impossible to do a proper analysis, as the data material is low and there are few countries in this scope that has absorbed enough of the technology to analyze the difference in capabilities and diffusion, when most of the countries has not actually diffused the technology yet. This will be discussed thoroughly in the chapter on empirical investigation.
This discussion concludes a series of 15 countries in mainland Africa, which are all low- and middle income, and has at least 20% internet users by the end of the time series
(2005 – 2015).
The term ‘internet technology’ is a very wide term. And this is deliberate. For the sake of the analysis, whether the users access information on a smartphone, a pc or any other device is not relevant, and therefore the thesis should not restrict the study to certain devices. The risk of studying one device, and its diffusion, would be that if this is not the most common device, we could get skewed results.
5 1.4 Purpose and intended contribution
The purpose of this thesis is to contribute to the understanding of how information and communication technologies are absorbed and diffused in low- and middle income countries.
Within this, there are two different areas this thesis seeks to contribute to. The first is implications for investors and policy makers and the second is contribution to current research.
As mentioned, the background for this paper is the role internet technology can play in closing the gap between low- and middle income countries and high income countries. It is important to know more on how the resources that are being invested are unfolding, because the effects seem to be unclear and uneven (Bollou, 2014) (Baliamoune-Lutz, 2003) and even measuring the effects is not unproblematic (Sutz, 2012) (Minges, 2016). Growing our understanding on these areas is important for multiple reasons. First of all, it can tell us something about what lies behind the data already collected. It can tell us why some countries seem to experience higher growth rates per invested dollar than others. But perhaps more importantly, it can tell us something about what factors needs to be in place for new investments in new areas to be successful. This can either:
a) tell us which countries to focus on when choosing to invest or
b) tell us what should be done prior to an investment to increase their capabilities to increase the chance of a successful investment.
There are currently multiple initiatives both investing and building ICT infrastructure in developing countries. Facebook has, through their foundation info.internet.org, connected 25 million people, mainly in south-east Asia, with their service Free Basics. Already mentioned Basic Internet Foundation are preparing investments in Tanzania. This is in addition to initiatives like ICT4SDG, from the International Telecommunication Union and programs through the UN and world Bank. This means that private, public and civil sector all see opportunities in building this infrastructure and we might see a large increase in investments in the coming years. And if these investments are to succeed, they both need to have the desired impact in the communities, lead to economic growth and give a reasonable return on investment for the investors.
6 These possible implications for policy makers and investors should however, not necessarily be taken at face value. There might be multiple reasons for wanting to build internet
infrastructure or increase number of people with internet access. One could assume that access of information could act as a catalyst for growth in human capital and welfare, even though the immediate return on investment might not be as high in a certain area as in others.
As we will see in the second chapter, there are some related research on the field already. But most of this research is either focused on different parts of the world, often OECD or on a more macro or micro level – either analyzing a large group of countries from a wide array of economic development stages or case studies of a few countries. Some research is done on the same type of countries as this thesis will analyze, but with different scopes, mainly with the connection between information and communication technologies and economic growth. The few studies that has somewhat the same scope as this thesis are relatively old, and in such a new, and growing, field of technology, might be outdated. All of this research is important background for my thesis, but there is a gap in the research – to study, at a macro level, the capabilities needed to diffuse internet technology in this part of the world.
This thesis, therefore, seeks to contribute to this gap in research, while at the same time give some indication to investment policies to how, when and where it might be most successful.
1.5 Structure
The thesis is structured as follows; First I will present some background on why innovation is important for development and which role it plays in the economic growth for the world in general, but especially for lower income countries. Here I will discuss what type of innovation might be most important to focus on in these countries.
Secondly, I will discuss capabilities of nations and third, the role of information and
communication technologies in economic growth. In the fourth, and final part of the second chapter I will go through technology diffusion and a literature review on what research has already found on the topic of internet diffusion.
7 The third part of the thesis is devoted to the empirical investigation of the thesis. I will outline the choice of samples, time series and variables and why they are chosen, and explain some of the challenges related to them, with regards to the data itself, but also with regards to what I am actually measuring with these data. Onwards, I will discuss the chosen methodology, before I will briefly outline the results of the analysis.
In chapter four, we will dive into the findings and discuss what they can imply. Here, I will go through all the variables in turn and discuss the findings and how significant or not they are, or if they are not significant, why that might be. I will also discuss how generalization able the findings might be, and what implications, if any, the results give. Lastly, I will discuss the reliability and validity of the study as a whole, before giving concluding remarks.
8 2. Theoretical background and literature review
There has already been done a lot of research on the field of diffusion of internet technology in different types of academic fields. In this chapter I will outline the most important research as well as discuss the relevant framework for the analysis later in the thesis. I will discuss innovation and its role in development, capabilities of nations, information and
communications technology and its role in economic growth, and technology diffusion.
2.1 Innovation in development
«Innovation should be understood as something new to a local context» (Aubert, 2005, p. 11) There are a number of different definitions of innovation, which are all regarding more or less the same phenomenon, but have a different approach to what actually counts as innovation.
The report Promoting innovation in developing countries lists three common different forms of innovation; adoption, adaption and creation (ibid.). Where the first is to absorb innovation created elsewhere into a new market and make it accessible to new consumers or businesses, the second is to adapt it to local needs and to build activities around it and the third is to create new ways of working. While this report mainly speaks of technology, it is just as relevant in other aspects of innovation. For this thesis however, technology adoption is the most relevant.
The socio-economic challenges related to poverty, health and development is not only reducing the well-being of the inhabitants of the countries, it is also substantially influencing the opportunities to actually address the challenges (OECD, 2012). The report on Innovation for Develpoment (OECD, 2012) argues that the role of innovation in these questions is demonstrated to benefit the long term development of emerging countries, but is often neglected in policies due to the narrow understanding of technology as a high technology activity, and thus a costly activity. But there are of course relevant innovation opportunities in lower technology sectors as well. These limitations made by policy makers can be a difficulty.
While we have a lot of experience and research regarding innovation policies in high income countries, a lot of this is not necessarily applicable in lower income countries. It is argued that the lower and middle income countries are facing major challenges to innovate and to transfer knowledge and technology from the more developed nations (Aubert, 2005). These reasons are some of the causes why these countries stay behind. They need to consider the policy
9 implications that hinder innovation, namely the level of education, the business environment and information infrastructure (ibid.). Aubert (2015 explains how low educational levels is a strong barrier to diffusion of innovation, as the higher level of technology the higher level of education is required to successfully use it. The business environment, often through a non- transparent financial system, a bureaucratic climate and applied law are all common problems that stop innovation development in the private sector. His third point is how a lack of
infrastructure makes communication slow, without proper telephone infrastructure, which mobile phone technology has not quite fixed in all parts of the world. Together with the need to transport goods, many countries are still experiencing this crucial system to fail them.
The same report outlines two drivers for innovation in development: The first is the globalization itself, removing distance and time needed to communicate and spread
knowledge and technology. Later in the chapter, foreign direct investments and trade barriers, and their role in the transfer of technology across borders will be discussed. The second is the technological change coming from the scientific advances over the past decades. This leads to great opportunities, but also challenges. If done right, the emerging economies can absorb the technology and knowledge from the wealthier countries and speed up their growth
substantially. But at the same time, the increasing global competition and the increasing necessity of technical knowledge and general education to compete in the global market may stand in the way of countries that wants to reap the rewards from these changes (ibid.).
2.2 Capabilities of nations
«(t)he promotion of innovation should be considered in a gradual manner in building upon resources and capabilities available in countries at their level of development and in taking due account of their specificities including their
conditions of governance.»
(Aubert, 2005, p. 7)
Capabilities are most common to discuss when looking at firms and organizations. But capabilities are also useful when looking at how nations compare or develop in regards to economic growth or other development metrics because it is «not sufficient to have access to knowledge, you must also have the necessary capabilities to understand, absorb and exploit it» (Fagerberg & Srholec, 2017) The report Promoting innovation in developing countries
10 suggests policies to grow capabilities depending on what level of economic development the country is. For low income countries with weak institutional capabilities, policy makers should focus on basic investments, technology infrastructure and set priorities on easy, basic innovations. Furthermore, all policies should focus on the contribution to improving
education, agriculture and other important aspects of development (Aubert, 2005). Through this, Aubert argues that countries can build a technology sector and start developing the capabilities needed to get to a path of sustainable growth. Where the institutional capabilities are stronger, however, they can go a bit further and focus on more dynamic and
comprehensive policies.
What these capabilities might be can seem quite vague. What it really means, in this setting, is the ability a nation has to make use of technological advances made by other countries.
Capabilities means for example a country’s ability to be exposed to a new technology, i.e. to what degree the country lets foreign companies, individuals et cetera enter the internal market with the technology. It means the people's capability to make use of the technology in an efficient way. This could be general education level, or the general infrastructure in a country and how that may affect whether the entire country can make use of the new technology or if it is isolated in a small section of the country.
There is probably not just one set of capabilities that is necessary for all sorts of innovation or all types of countries, and that is part of the background for the thesis, namely to find out which capabilities are most important to diffuse internet technology. To dive deeper into the issue, we will start by looking at economic growth in general and the role of technology in general, and information and communication technology specifically.
2.3 Information and communication technology and economic growth
I have already discussed the role of innovation in development. But because this thesis mainly focuses on diffusion of internet technology, the role of innovation in development must be looked at through the role of internet technology, and more specifically on economic growth.
It is logical then, to start by looking at some of the most widely used models for growth in economics, and what they say on the subject of technology.
11 When trying to explain differences in economic growth between countries, macro models like the neoclassical growth theory, or exogenous growth theory, developed independently by Robert Solow and Trevor Swan around 1956, argues that the total production of a nation can be described to be driven by capital and labor and how they develop over time. Thus, these main drivers in the market give us the Solow-Swan model where output is a function of capital (K) and labor (L) over time (t) (Solow, 1956; Swan, 1956);
Q= F(K,L;t)
This, however, is argued to explain only part of the differences in collected data (Fagerberg, 1987) and a year after his initial publication, Solow added technology as a factor to his model (Solow, 1957).
Q = A(t) f(K,L)
The problem in their implementation of technology, however, is that technology is treated as an exogenous factor, instead of being fully included it in the model. The Solow-Swan model, and the further developed model, MRW, after Mankiw, Romer and Weil (Mankiw, Romer, &
Weil, 1992) has become so well used that it is being called an ’industry standard’ (Dowrick &
Rogers, 2002). Part of the criticism Dowrick and Rogers express regarding the MRW model is that it assumes technology is implemented in new countries at the same rate – which is, at best, an over simplification. Later in the chapter, it will become clear that technology is highly differntiated in how it is spread and absorbed by different countries based on their economic and technological development. The issue is that technology is, of course, not one thing that can be spread to all different areas and situation in the same way, and therefore, the next step will be to look at how technologies might spread across borders.
Let us look at information and communication technology as an example to investigate this assumption, as it is the wider technology group of the one being studied here. Colecchia and Schreyer examines the contribution of ICT capital on economic growth in 9 OECD countries in the 1990s to (Colecchia & Schreyer, 2002). Their evidence concludes that all countries, with their different starting points, experienced a positive effect on economic growth from ICT capital – between 0,2 and 0,9 percentage points increased growth rate. Other relevant conclusions they found were, that due to the short product life cycle of ICT products and software, more of the output will have to be spent on continuous investments to uphold the effect on net output. Although it was a modest effect, «this observation is interesting from a
12 welfare perspective» (Colecchia & Schreyer, 2002). Secondly, they found that it was neither necessary nor sufficient to have substantial ICT production in the country itself to
successfully make use of the technology. The study rather indicated that the right framework for technology diffusion is the key.
The hypothesis that ICT has a connection to economic growth was thoroughly investigated by Khuong (2011) when he researched the effects on 102 countries in the time period from 1996 to 2005. He presents three theoretical grounds supporting this hypothesis:
«First, ICT penetration affects growth through fostering knowledge diffusion (especially from developed to developing countries) and innovation. Second, ICT penetration enhances the
quality of decision-making of firms and households, which improves the efficiency and effectiveness of resource allocations. Third, ICT penetration reduces production costs and
fosters demand and investment; and hence raises the level of output and growth.»
(Khuong, 2011)
Onwards he points out that the large effect information and communication technologies has on economic growth makes promoting the diffusion of internet «both urgent and strategic»
(ibid.) and that applies especially for countries which have not yet adopted the technology, or at least not to a substantial degree. The marginal effect is not linear. It weakens at higher levels of adoption, meaning that countries that has not started will gain far more from focusing on diffusion.
2.4 Technology diffusion
So what would policy makers do to gain access to this technology? Anyone with access to capital could, with the right priorities, just build the necessary infrastructure to give internet access to everyone. But if it was a simple question of building infrastructure and provide the service, and that would lead people and businesses to actually use it, and through this, the country would gain economic growth, then everyone would just do it. The diffusion of technology is the essential part. But first, we must get a more accurate view on technology creation and why it is not a viable option for these countries.
13 Countries on a comparatively low economic and technological level may
realize higher growth rates than other countries by exploiting the potential for imitation.
But this is certainly no “law”. It depends both on their own efforts and the innovative efforts of the more advanced countries in increasing the “gap”.
(Fagerberg 1987)
Even though development of technology is increasing in low and middle income countries at a higher rate than in high-income countries and we see a convergence in the period from 1993 to 2008, most development still takes place in the upper end of the GDP scale (The World Bank, 2008, p. 3-4), as illustrated in the figure below:
Figure 1: Scientific innovation and invention is almost exclusively a high-income activity
(The World Bank, 2008, p. 3)
Given that technology is a key aspect of economic growth, as previously discussed, imitation and adaption of technology from other countries will be crucial to increase economic growth and catch up in terms of economic development. The diffusion of technology is considered to be responsible for most of the technological progress in low and middle income countries, and
«(g)iven the still wide technology gap, this is likely to remain the case for the vast majority of developing countries. (The World Bank, 2008, p. 7).
14 The technology gap approach, mentioned in the quote from The World Bank, refers to «the international economic system (…) characterized by marked differences in technological levels and trends, differences which can only be overcome trough radical changes in
technological, economic and social structures» (Fagerberg, 1987). Fagerberg lists four main hypotheses in the technology gap approach;
(1) There is a close relation between a country’s economic and technological level of development.
(2) The rate of economic growth of a country is positively influenced by the rate of growth in the technological level of the country.
(3) It is possible for a country facing a technological gap, i.e. a country on a lower technological level than the countries on “the world innovation frontier”, to increase its rate of economic growth through imitation (“catching up”).
(4) The rate at which a country exploits the possibilities offered by the
technological gap depends on its ability to mobilize resources for transforming social, institutional and economic structures.
(Fagerberg, 1987)
When discussing low and middle income countries, these hypotheses, will link closely with their ability to diffuse new technologies, as these countries does not necessarily need to
develop their own technology, but rather incorporate solutions, that are already developed into their own markets.
How technology is diffused into new markets is an important question and is subject to a wide array of research which has found a series of key factors. Some factors are related to the technology itself, regarding its usefulness or whether there is a viable alternative already on the market (Hall, 2005), while other factors are more related to the market itself, and its organizations, customers and others’ capability to absorb the technology.
It is also a matter of exposure. For low and middle income countries, the decrease in trade barriers and increase in foreign direct investments (FDI) has contributed to increased exposure to new technologies and has gotten significant credit for the technology growth in these areas (The World Bank, 2008, p. 9-10).
15 Technology diffusion can happen through different channels, in which the knowledge and technology is adopted into the new market. One of the major channels is considered to be foreign direct investments, giving access to advanced technologies (Borensztein, De Gregorio, & Lee, 1998). The data Borensztein and his colleagues collected, indicates a positive correlation between foreign direct investments and economic growth, but there is a significant condition for this correlation, which is that the effect is strongly dependent on the human capital available in the country in question. In countries where available human capital is at a very low level, the direct effects of foreign direct investments is actually negative (Borensztein et al., 1998). The variable used for human capital was the one constructed by Barro and Lee (1993), cited by (Borensztein et al. (1998). The variable was average years of male secondary schooling.
The Global Innovation Index sheds some insight on foreign direct investment and especially what they call south-south investment, in contrast to the more classical north-south invesment when discussing development (Chaminade & Gomez, 2016). They argue that investments comming from countries in similar economic situations – or countries that recently have been in similar situations – are more efficient when transferring technology than the classical foreign direct investment. Most of all, the difference in effect has to do with the distance, or difference, in capabilities between the countries and this should make facilitating the
assimilation of the technology easier. So far, the research on south-south foreign direct investment is limited, due to the fact that technology investments between these countries are fairly new, and that data is very limited. The report agrees with Borensztein et al. (1998) in the positive effects from foreign direct investments.
More specifically on information and communication technologies, Kiiski and Pohjola (2002) give an overview on the research conducted before their article Cross-country diffusion of the internet and what findings are already discovered. They cite two papers of importance. The first analyses internet connectivity in 18 OECD countries and finds that regulations and competition have the largest impact on connectivity, with GDP per capita being the other factor with significant explanatory power (Hargittai, 1999). The second paper looks at a larger group of countries – 179 – on economic, social and political variables, but finds only GDP per capita and share of R&D spending in GDP to be statistically significant (Norris 2000, cited in Kiiski and Pohjola 2002). Kiiski and Pohjola (2002) themselves investigates diffusion of the internet and finds that in OECD countries, GDP per capita and access cost is the best
explainer, and in contrast to Hargittai, competition has no significant effect by itself.
16 However, they argue that competition will likely be visible in market prices and therefore also be indirectly part of their findings, indirectly. They even argue that most of the effect will be seen in market prices, and that there are few other ways to see the effect. When looking beyond OECD, Kiiski and Pohjola reports that also education becomes important, and that tertiary education is more important than average year of schooling (Kiiski & Pohjola, 2002), giving a rather different angle than Barro and Lee’s variable for human capital.
These three articles are broad and the scope is closely related to this paper. However, they are fairly old and focuses on different economic areas of the world. This is a problem, because both technology and the world are changing at a rapid pace. This goes especially for internet technology and devices that can connect to the internet. Therefore, research older than a decade cannot include the latest technological advances in the field, which might be game changing.
Mina Baliamoune-Lutz (2003) has examined the diffusion of ICT and has found that income is, logically enough, a «major determinant of ICT diffusion» (Baliamoune-Lutz, 2003), and, as previously referenced by The World Bank, trade policies has an effect. In addition, especially for the use of mobile phones and the number of internet hosts, civil and political liberties are important factors for the diffusion, as for example censorship usually limits the use of communication in a country.
Daniel Piazolo (2001) argues that the importance of the digital economy makes the diffusion of internet technology even more important than many other technological advances, and fears a digital divide will make it even more difficult for the lower income countries to catch up. Piazolo admits, however, that compared to the short amounts of years between the internet was widely adopted by consumers and business to when the article was released, it is not certain that this is a worse case than other, major technological shifts, like the steam engine and electricity. He proposes some strategies to overcome this divide, and some are relevant for the scope of this thesis. The main strategies are the growth in human capital, both in general education level and more specifically on technical skills like ICT engineers etc, and affordability for the consumer (Piazolo, 2001).
17 To summarize the review of current literature, there are multiple capabilities presented as significant and necessary to successfully diffuse internet technologies. And it seems quite clear that it is important to diffuse this technology, from an economic development point of view. Foreign direct investment and spending on research and development are presented as important, but foreign direct investment is positively significant only if the area receiving the investment has a high enough level of human capital. It is also suggested that the effects are stronger if the investment comes from countries on a similar level of technological
development. Gross domestic product per capita, or income, is presented as important, as well as access cost. Access cost is also presented to be where we see the effects from regulations and competition in the market. Education level is presented to be significant, not only to make the foreign direct investment efficient, but in itself to make the public capable of absorbing and using the technology effective.
18 3. Empirical investigation
To be able to answer the research question to the best of my abilities, the research design is of the outmost importance. The research design is important, not only related to if I can answer the research question, but also whether the results are reliable and valid. There are many questions we need to consider when choosing the research design. As already revealed early in the thesis, this will be a quantitative study, due to the scale of the analysis. To study many countries on a macro level over a large time span is close to impossible with qualitative methods. The sample size and the reasoning behind it will be explained below.
The study has not been reported to the 'Norsk senter for forskningsdata', as the nature of the study makes it not applicable for their work. The study will not handle personal data at all, and therefore falls outside of the frame of the data protection guidelines. All data collected in the study is openly available online. The data is made available by The World Bank and The International Telecommunication union and it is therefore no reason to believe there should be any problem with analysing, comparing and publishing results based on these data sets.
The problem definition from earlier in the thesis; What capabilities are necessary to successfully diffuse internet technology in low- and middle income countries, needs to be made researchable. Given the discussions in the previous chapter, I will go through how this will be studied in this chapter, from building a hypothesis, to choosing samples in the population, choosing variables and methodology for studying them.
Based on the previous literature review, we have seen that there is a linkage between
technology and economic growth and that researchers see the importance of diffusing internet to countries in lower and middle income levels to help them close the technology gap.
Previous research on diffusion of the internet has been largely focused on OECD countries and the few that has a different perspective is often closer to case studies than macro level.
Given this, this thesis will seek to highlight some perspectives on important factors for diffusion of internet technology on precisely these countries, and in addition, from a macro point of view – by analyzing and comparing on country level rather than on specific cases.
19 In the previous chapter, I outlined some important factors previous research has shown to be of importance to counrties in diffusing internet technology. From these factors, I can build the hypothesis to test in the analysis of the thesis: Foreign direct investment, income, general technology infrastructure, access price and education are significant factors for successfully diffuse internet technology in low and middle income countries.
3.1 Data selection
3.1.1 Choice of countries and time series
To keep the research as up to date as possible, the time series is chosen to be 2006 – 2015.
2015 is the latest year with proper data when I started this thesis, as some of the data from 2016 was not available yet. Starting at 2006 is to make sure to get a alarge enough time series – ten years – to eliminate as much random fluctuations as possible. A longer time series than ten years would mean trouble technology wise, as the technology develops so quickly. But from 2006 and onwards, smart phones, tablets and computers are both the major connectivity device, and no new major devises has entered the global market since.
The choice of countries to study is a difficult decision, as it is not only a question of how to best answer the research question, but also a pragmatic question on what countries are possible to analyze in an efficient and methodological reliable way. I have chosen some parameters and limits countries needs to fulfill in order to be sampled in this study:
For a country to be part of this analysis, they will have to have a not insignificant number of internet users by the end of the time series. By that, I have removed all countries with less than 20%, by the definition internet users are individuals who have used the Internet (from any location) in the last 3 months. While it is interesting to look at countries that has not successfully adopted this technology during the time series, the scope of the thesis is to look at what makes a successful diffusion, and I have chosen to put the resources on the more successful economies, and leave the question of why the otheres have not succeeded for other projects.
20 Secondly, I have chosen a geographic area to analyze. While this thesis started out as a co- operation with Basic Internet Foundation and they are currently focusing on Tanzania, it makes sense to choose as many relatable countries as possible. Ideally by economic development, technological development, culture and so on. However, there are not many countries in this region with a significant number of internet users. This is a problem. Because if the scope of the thesis is to analyze what capabilities are significant to have to be able to diffuse internet technology, we need to analyze countries that have – at least to some extent – already diffused the technology. Even when widening the scope to all countries in the greater region – continental Africa – with similar economic development, the analysis will be very narrow. So the scope has been widened to analyze all countries in continental mainland Africa to make sure the number of units is high enough. While this ensures a wide array of subjects to analyze, it makes the selection too wide. So all countries classified as high income, by The World Bank definition GNI per capita of $12 236 (The World Bank, 2017j) or more, are removed. Lastly, two countries that fits the criterias above has been removed. Gabon and Zambia has too few reliable data points and would make the analysis very difficult.
21 Table 1: list of country observations
Countries Min Max Number of observations
Algeria 2006 2015 70
Botswana 2006 2015 66
Cameroon 2006 2015 71
Cote de Ivory 2006 2015 62
Egypt 2006 2015 71
Ghana 2006 2015 75
Kenya 2006 2015 61
Morocco 2006 2015 73
Namibia 2006 2015 59
Nigeria 2006 2015 63
Senegal 2006 2015 73
South Africa 2006 2015 69
Sudan 2006 2015 63
Swaziland 2006 2015 65
Tunisia 2006 2015 75
This list shows that the data set is relatively unbalanced. It should be 80 data points on each country, but the data sets from The World Bank are highly variable on how consequent the collections are. Some of the variables have a data point on every single time point in the series, for every single country. Others are lacking several points. This makes the test much less reliable than the ideal test. I have dealt with this issue by doing estimations in Excel by extrapolating values based on the trend lines in the time series. This is of course not as valuable as actual measurements, but it is necessary to complete the analysis, as panel data needs complete data sets to work with1. It is mainly on education that data points are missing,
1 In the appendix, the estimated values can be found easily. The estimated values are coloured red, while the measured data is coloured black.
22 but there are data points missing on other indicators as well. The effects this may have on the results will be discussed in the next chapter.
An important fact on the countries in the list that are important to note is that all of these countries has had significant growth in number of users in the time series, with an average factor of 6.2 and mean 5.2. Lowest growth factor of 2.8 and highest 13.8. In addition, using the same definition as previously for income country classification, eight of the countries on this list was, at the start of the time series, classified as a low income country, while seven of them are not by the end of the time series – the definition being GNI per capita at $ 1,005 or less (The World Bank, 2017j). Senegal, being the only one still classified as lower income, was for some years above the threshold, but has fallen down again, due to a drop in GNI per capita from 2014 to 2015. This is interesting given the correlation between economic growth and technology diffusion discussed previously in the paper. We will discuss this more during the analysis later in the thesis.
3.1.2 Choice of variables
Based on the hypothesis, foreign direct investment, income, general technology
infrastructure, access price and education are significant factors for successfully diffuse internet technology in low and middle income countries, are based in the literature review in chapter two. For each of these factors, an indicator that accurately measures the factor for a country must be found. The importance of the indicators is substantial. The validity of the analysis is based largely on whether the measurements measure what I am trying to measure.
And it is not always easy to know what the different indicators might measure in relation to the factor you actually want to use in the model.
Given the presentation of previous empirical findings in the literature review of this paper the the chosen variables are the most significant from other’s findings. These are intuitively understandable that are connected to diffusion, and have been found to be connected to the diffusion of ICT in either different geographical areas or different time series. Therefore it will be interesting to see whether these are also the factors that affects diffusion in these countries in this time period.
23 The first variable to discuss is the dependent variable. This must reflect the actual diffusion of internet. There are many ways to measure this, but I have landed on individuals using the internet. The reasoning behind this is that there are so many ways to be connected, that to analyse number of connected hosts, number of household with internet connection etc may be desieving. There are two arguments for this. One, there are few ways to really know how many are using one access point in a household, so it may not reflect how many are actually using the internet if we set the variable to hosts per household. The other argument is that many people may have internet access through work or internet cafés without being connected at home. In addition, today there are so many devices that can connect to the internet, that it is very difficult to find accurate data on how many owns an internet device and especially, the same type of data for all countries. The definition for the variable is: « Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.» (The World Bank, 2017c).
Of explanatory variables, the first is foreign direct investments. This will both show how much foreign direct investment will affect the dependent variable, but I will also argue that changes in the level of foreign direct investment might indicate changes in trade policies, and can, indirectly, show an effect in changes regarding trade policies as well. The definition of this variable is defined as: «Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital» (The World Bank, 2017a).
The second explanatory variable is income level. There are many ways to measure income, and many things to include or exclude from income. Many of these are not that interesting in this analysis, as it is mainly changes in the numbers we are looking for. The data we will be using is the GNI per capita, defined as:
«(...) the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes
(less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. (...) To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies
24 a conversion factor that averages the exchange rate for a given year and
the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan,
the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.»
(The World Bank, 2017b)
The third explanatory variable is number of mobile cellular subscriptions per 100 people. This variable might indicate effect through different things. For one, it shows how many people are connected through mobile communication devices and thus indicate the level of technology infrastructure and adoptability in the public. The other side is that mobile phones is one of the means to connect to the internet itself, but as the raw data shows (See Appendix A), there are plenty of mobile phones and subscriptions without them actually connecting to the internet, given that the number of subscriptions by far outnumber the number of internet users. The definition used is:
«Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been
used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions
via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.»
(The World Bank, 2017d)
The fourth explanatory variable is price. The price level is perhaps one of the most intuitively effective factors, as it makes it far more accessible. As already discussed, it has been argued that price also covers the factor of market competition versus monopoly in telecommunication services (Kiiski & Pohjola, 2002), because they assume that the effects of competition in the market will be visible in market prices, and therefore this variable will show these effects as well. These are the only data not collected from The World Bank’s data sets, but a series of reports from the International Telecommunication Union. It is defined as:
25
«The fixed-broadband sub-basket refers to the price of a monthly subscription to an entry-level fixed-broadband plan. It is calculated as a percentage of a country’s average monthly GNI p.c. (...) For comparability reasons, the fixed-broadband sub-basket is based on a monthly data usage of (a minimum of)
1 GB. For plans that limit the monthly amount of data transferred by including data volume caps below 1 GB, the cost for the additional bytes is added to the
sub-basket. The minimum speed is added to the sub-basket. The minimum speed of a broadband connection is 256 kbit/s.»
(International Telecommunication Union, 2016d)
The fifth explanatory variable is education. This is split into three variables – primary, secondary and tertiary schooling. There are, again, different ways of measuring this. How many starts or how many finish (gross or net) and so on. The main data in this analysis is for primary education: «(n)et enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age» (The World Bank, 2017e), for secondary: «(t)otal enrollment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition» (The World Bank, 2017g) and tertiary: «(g)ross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level» (The World Bank, 2017i). The reasoning behind this choice of data is simple. Different countries report on different indicators with different quality – meaning that some countries have good statistics for net enrollment, others for gross. The countries that differ from the above definitions are:
Kenya, Morocco and Swaziland which we will use net data: «(n)et enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the
corresponding official school age» (The World Bank, 2017h). In addition, South Africa uses gross data for primary school (The World Bank, 2017f).
26 A last note on the variables is that both foreign direct investments and GNI per capita are measured in a way that will not let the chosen methodology – accounted for below – analyze in an efficient way for my problem definition. These variables are therefore logged to fit the analyzing tools better. Net foreign direct investments have, on two accounts, been negative, which makes it impossible to log. The lowest recorded foreign direct investment is -4.83e+08, and therefore, a constant of +4.84e+08 has been added to the log to make all numbers
positive. As it is changes we are interested in seeing, this is assumed to give the most accurate results on relations between the variables.
3.2 Choice of methodology 3.2.1 Panel Data
Based on the problem definition and what this thesis is seeking to contribute to, a panel data analysis should be feasible to give answers. Panel data analysis is a common analysis
technique in econometrics which combines cross section analysis with time series analysis – giving us the possibility to control for variables that are either missing in some entities or that are constant over time. We can find dynamic relationships and get more accurate predictions when combining the two techniques, gaining the best of both worlds from cross section analysis and time series analysis (Wooldridge, 2009, p. 444).
In a panel data analysis, I will need to make an equation for the regression. The standard equation for the model can be written as:
!"# = &'("#+ *" + +"
Written with the variables to be used in this thesis it will look like this:
,"# = &'-"#+ &./"#+ &01"#+ &23"#+ &45"#+ &67"# + &89"#+ *" + +"
Where I is internet users, F is foreign direct investments, G is income, M is mobile
subscriptions, P is price, p is primary school, S is secondary school and T is tertiary school. : is the coefficient for the variables, ; is the intercept and < is the error term.
27 To be able to conduct a strong analysis using panel data, there are some choices and
challenges we need to address. Using panel data as a method is not a one, singular method – how it is to be done depends strongly on the data in question. Wooldridge (2009:481) describes two of the more common methods in depth in his chapter Advanced Panel Data Methods and gives the choice between fixed effects and random effects to estimate the panel data model. For all tests I will use a 95% confidence level.
3.2.2 Fixed or Random Effects
Fixed effects explore the relation between variables inside the units. Every unit has
characteristics that may affect the variables to some degree and when we use fixed effects, we assume that something in the unit gives an effect and we need to control for this. The
argument behind this assumption is the correlation between the unit and the error of the variable. The fixed effect estimation removes this effect, so we can see the net effect the explanatory variable gives to the dependent variable. In addition, these characteristics needs to be unique to the unit and should not be correlated with other units’ characteristics. If they are correlated, fixed effects is not suitable for the analysis and random effects may be more suitable (Torres-Reyna, 2007).
In opposition – if we have reason to believe that the unobserved effects are not correlated with the explanatory variables in the unit, random effects are more suitable. In random effects, you gain the advantage of including the parts that fixed effects is meant to remove, so that if you have reason to believe that these effects are influencing the dependent variable, then random effects is likely to be a better choice. The problematic side of this is that these individual characteristics must be specified to not get omitted variable bias in the model (ibid).
Whether to use fixed effects or random effects is not always easy to know by looking at the raw data, but luckily there is a test that is fairly common to use. By applying both random effects and fixed effects on the data, we can compare the results using the Hausman test and the test will give us the answer. In the Hausman test, the null hypothesis is that there is no correlation between the unobserved effects and the explanatory variables in the units, and the alternative hypothesis is that it is a correlation. Given a certain significant level, we discard the null hypothesis (Wooldridge, 2009, p. 493). As we can see in the table below, the Hausman test gives a p-value of 0.000.
28 The abbreviations for the following table, and onwards in the analysis are:
Foreign direct investments logged = fdilog Gross national income per capita logged = gnilog Mobile subscriptions per one hundred people = mob Primary school enrollment = first Secondary school enrollment = second Tertiary school enrollment = third Table 2: Hausman test
Coefficients (b)
random
(B) fixed
(b-B) Difference
Sqrt (diag (V_b-V_B)) S.E.
fdilog 0.0046548 -.1396575 .1443123 .
gnilog -1.105775 .7527703 -1.858545 .
mob .2355303 .2183441 .0171862 .
first -.2018029 -.190206 -.0115969 .
second .2269794 .2472538 .0202745 .
third .0791555 .1483255 -.06917 .
B = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic
Chi2 (7) = (b-B) ' [ ( V_b-V_B ) ^ ( -1 ) ] ( b-B )
= 47.38
Prob > chi2 = 0.0000
( V_b – V_B is not positive definite )
29 This p-value states that we can reject the null hypothesis with a 99.9% confidence – meaning that we can assume that there is correlation between the unobserved effects and the
explanatory variables, and we will use fixed effects.
3.3 Data analysis
Figure 2: Scatter plots of the data
30 Y-axis: Internet users per one hundred people
X-axis: From top, left: Foreign direct investment logged, Gross national income per capita logged, mobile phone subscriptions per one hundred people, Price as percentage of gross national income per capita, Primary school enrollment, Secondary school enrollment and Tertiary school enrollment.
As we can see in these scatterplots, and which will become apparent later, as we do the actual panel data analysis, some of these scatter plots indicate stronger correlations than others. For instance, we can already assume that the price of internet connection variable has a strong correlation with number of internet users. In addition, we should look for signs that mobile phone subscriptions are correlated. The logged foreign direct investment scatter plot however, is interesting. The plot does have a cluster, but not in any specific direction. It will be
interesting to see how it will look in the fixed effects model. Primary school and tertiary school does seem to have a trend line, but there may be too many outliers for the effect to be significant, while the gross national income per capita, logged, seems to be too divided.
These scatter plots do not take into account grouping of the cross sections, significance level or any other necessary statistical tests, but it can give an indication on correlation between the data.
31 3.3.1 Panel data regression
After the regression was run through the program, the following results came out:
Table 3: Fixed effects regression
First off all, we can see that the p value for the F test suggests that we can assume, with 99,9% confidence that the results in this test is not by coincidence, meaning that we can assume that the model is significantly improved by including the variables in this regression – that the model has explanatory power, given the 95% confidence level stated previously. We can therefore continue analyzing these results to see what we have found.
32 Next, we will look at the coefficient of determination – the R-squared. In this analysis we are interested in the ‘within R-squared’. Here it is calculated to be 0.6624, meaning that 66,24%
of the variation in the dependent variable, internet users can be explained by the explanatory variables. That leaves us with 33.76% which is not explained by the model. What a good R- squared value is, is hard to determine, but for this thesis, close to two thirds of the variance explained by the model, in such a complex system is acceptable.
Now we know that the full model is assumed to be significant, and that it can explain almost two thirds of the variation in internet users. From here, we go on to look at the explanatory variables and their significance. Again, we will work with a 95% confidence level. Factors with a p value for the t test under 0.05 is useful to work with. The null hypothesis here, is that the coefficient for the variable equals zero, or that the variable has zero effect on the
dependent variable. With a p value for the t test under 0.05, we can reject the null hypothesis and be at least 95% confident that there is a significant effect.
The significance p values are for mobile phone subscriptions 0.000, price as percent of gross national income per capita 0.002 and secondary school enrollment 0.013. This does not necessarily mean that all the other variables have no effect, if they for example would lie just above the confidence level set, but the next p value is at 0.128, for primary school enrollment, meaning that we can only be 87.2% confident that it has an effect. This is not a significant level. The other variables
Onwards we will look at the coefficients. These show the relation between the explanatory variables and the dependent variables – how much the dependent variable changes if the explanatory variable changes by one – all other things left equal. We see that the largest effect of the significant variables is secondary schooling with 0.2472538, mobile subscriptions per 100 people at 0.2183441 and last, price as percent of gross national income per capita with -0.124295. Other interesting coefficients to note, is that foreign direct investments seems to have a negative coefficient in this regression, which seems counter intuitive. However, with a significance of only 15.6%, it is not worth trusting too much anyway.
These coefficients mean that a one percent point increase in secondary schooling is estimated to give 0.2472538 more individual users per 100.
