thesis.eur.nl · web viewthis paper investigates how two macroeconomic factors affect a...

Stanimir Kostov (ID: 356596)

Erasmus University Rotterdam

Master’s thesis

Erasmus School of Economics

How macroeconomic factors explain the technological lag

Thesis supervisor: Julian Emami Namini

Author: Stanimir KostovStudent ID: 356596

06/12/2015

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INTRODUCTION

This paper investigates how two macroeconomic factors affect a constructed technological

lag. There are two technological lags that are constructed. One is based on the number of

internet users and the other on the number of mobile phone users. The two lags are regressed

on the independent macroeconomic factors and on other control variables. The results show

that the technological lag for internet users is decreasing by the independent variable FDI

inflows. However in the other cases the null hypotheses of no relationship between the

dependent and independent variables cannot be rejected.

The remainder of this paper is structured as following: First, I will give an introduction to the

existing literature and how it has investigated the topic. (Section I). Subsequently the

empirical model of this paper will be explained (Section II), starting with an overview of all

the variables (Section 1), with an emphasis on why the variables are chosen and why it is

expected to be important for this study and then the mechanism of building the technological

lag will be presented (Section 2). The following sections will provide information about the

data sources (Section 3) as well as technically introducing the estimation model that I will try

to employ to test the hypotheses (Section 4). This is followed by the main results (Section 5).

I will finish the paper with a conclusion and try to give an outlook on potential further

research options this paper has revealed (Section 6).

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I. LITERATURE REVIEW

This paper, which is investigating the effect of foreign direct investment on technology

diffusion, is based on empirical studies. The topic is researched in detail and in this section is

presented a review of some notable findings.

One of the first works on the topic is presented by Findlay (1978), who postulates that foreign

direct investment increases the rate of technical progress in the host country through a

‘contagion’ effect from the more advanced technology, management practices, etc. used by

the foreign firms.

After this paper, a scholar decides to add on to the literature by taking into account

technological diffusion. More specifically, Borensztein (1997) examines empirically the role

of FDI in the process of technology diffusion and economic growth in developing countries.

The idea of the paper is that technology diffusion can take place through a variety of channels

that involve the transmission of ideas and new technologies. He concludes that FDI is in fact

an important vehicle for the transfer of technology, contributing to growth in larger measure

than domestic investment. The results suggest that FDI is an important vehicle for the transfer

of technology.

Consequently, Blomstrom and Sjoholm (1999) examine the effects on technology transfer and

spillovers deriving from ownership sharing of foreign multinational. They find that

technology spillovers are more a result of the increased competition that follows FDI than

ownership sharing of the multinational affiliates.

Instead of focusing on OECD and non- OECD countries such as Blomstrom and Sjoholm

(1999), DeMello (1999) compares the FDI’s affects on productivity between OECD and non-

OECD host countries. These cross-country works show that technology spillovers occur only

in selected countries. This result suggests that there may be a threshold level of human capital

and/ or development in order for a country to benefit from FDI.

Tian (2001) discusses how Transnational Companies may deal with the challenge of

managing FDI technology spillovers in emerging markets. In order to exploit as well as

protect technology, the study argues, Transnational Companies can choose between different

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entry modes, between different technologies, and between different investment priorities

when they enter emerging markets. TNCs may use any of the three approaches or any

combination of them in consideration of the particular circumstances they face.

A different aspect is researched by Sadik and Bolbol (2001) who study the extent of FDI

activities in the Arab countries, and whether FDI and its technology spillover have had an

impact on Arab economic growth and total factor productivity. Their results indicate that FDI

has added advantage of generating technology spillovers, but such spillovers are yet to be

witnessed.

Dutt and Ros (2008) state that an important consequence of FDI is that shifting production to

a developing country can reduce technology adoption costs for indigenous local firms. Since

better process technologies tend to be difficult to deduce from inspection of the final good,

first hand experience with the technology may be required. Multinational firms bring

production to the host country, providing workers with experience using the new technology.

A more recent paper by Javorcik (2010) reviews the effect of FDI and technology transfer.

She concludes that the findings of the existing literature point to the existence of spillovers

from FDI, but also show that such spillovers are by no means automatic. This suggests that

while subsidizing information provision by investment promotion agencies may be warranted,

the case for general FDI subsidies is much weaker.

An opposite view is provided by the next recent paper. Lucas and Sylla (2003) argue that the

internet might be one reason for widening the technological lag between developed and

developing countries. They conclude that developing countries are being left behind as the

transformation to the ‘new economy’ takes place in wealthier countries. Moreover, they find

that if this trend continues, it may have dire consequences for world economic inequality and

political stability, as did great innovations of earlier eras.

Another paper by Bode and Nunnenkamp (2011) identifies that when FDI is divided into

employment intensive and capital intensive, the effects on growth are different depending on

the type of country. More specifically, employment-intensive FDI, concentrated in richer

states, has been conducive to income growth, while capita lintensive FDI, concentrated in

poorer states, has not.

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The topic is discussed not only by scholars, but also by intergovernmental bodies such as

United Nations Conference on Trade and Development (UNCTAD). They have published a

research on how the technological progress affects growth. More specifically, the study shows

that technological progress is critical to economic growth and welfare for any country,

regardless of level of development. Nevertheless, many developing countries are acquiring

new technologies, including information and communication technology (ICT) equipment

such as mobile phones and computers, at a more rapid pace than older technologies.

However, the extent to which new, valuable technologies are transferred to host economies

varies significantly between regions and countries.

Ha and le Giroud (2014), who analyze variance in the FDI spillover effect due to subsidiary

heterogeneity, focus in particular on the type of innovation activities in MNE subsidiaries on

FDI spillover effects on local firms. The study presents policy implications for a

technologically dynamic host country which has attracted R&D-intensive FDI. It is notable

that the host-country governments compete to attract R&D centres of MNE groups and this

trend is more conspicuous amongst technologically-dynamic host countries.

Another paper on which this work is based is written by Lebesmuehlbacher (2014), who

investigates to what degree different channels spread technologies across borders in a

cointegrated panel framework. Results show that technology diffusion to both developed and

developing countries depends critically on investment and human capital. In this framework,

technology diffusion refers to how fast a given country uses a new technology productively

relative to the technology leader. This paper finds that technology diffuses indeed differently

to developed and developing countries. Technology diffusion to both developed and

developing countries depends critically on investment and human capital

More specifically, results indicate that migration posits a significant channel of technology

diffusion with interesting idiosyncratic effects between developed and developing countries:

the migration of highly educated individuals harms technology diffusion in developed

countries while it benefits diffusion on developing countries.

FDI affects technology diffusion through investments and knowledge transfers to existing

companies. Investments in a company can increase diffusion as new shareholders try to

maximize profits and dividends through influencing production processes.

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II. EMPIRICAL MODEL

1. VARIABLES

1.1 DEPENDENT VARIABLES

When choosing the dependent variables which are to be used for constructing the

technological lags, in the CHAT dataset there are more than 150 possibilities. However not all

of them were suitable for my research. In order to narrow down the categories I collected

suitable research papers based on each technology and evaluated which technology to use.

After careful consideration, I chose to construct two technological lags, one using the variable

internet users and one using the variable mobile phone users. A collected literature review is

presented in the following section, explaining why these variables are suitable for this study.

Mottaleb, (2007) analyzes the determinants of FDI and its impact on economic growth in

developing countries. In the paper, the variables Internet users and Telephone users are both

included in the model and they have a significant effect on FDI. Both variables are explained

in detail in the following subsections.

1.1.1 Internet Users

The first variable is Internet users measured per 100 people. More precisely, internet users are

people with access to the worldwide network.

1.1.1.1 Digital Divide

One of the reasons to include Internet Users as one of the dependent variables used to

construct a technological lag is the highly debated issue between scholars and policy makers –

the digital divide. It is defined as the differing amount of information between those who have

access to the Internet (especially broadband access) and those who do not have access.

In order to account for this digital divide I decided to construct the first technological lag with

data from the World Bank on the number of internet users per 100 people.

One of the scholars who investigate the differences in Internet connectivity in 1999 is

Hargiattai. In his work, Hargittai (1999) explains the differences in Internet connectivity

among OECD countries. First of all, it is important to recognize that the current spread of the

Internet indicates that even among the richest countries of the world, general economic

strength does matter in predicting Internet connectivity. He concludes that if governments are

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interested in keeping an increasingly knowledge intensive economy with a large reliance on

information, they may need to consider the implications of their telecommunication policies

with respect to Internet connectivity in particular.

Another, more recent study is provided by Clarke in 2007 in whichis investigated the effect of

internet on the middle- income countries. Clarke (2007) assesses one aspect of the claim that

the internet is one of the forces driving globalization, looking at whether internet access

appears to affect the export performance using data from enterprises in low and middle-

income economies in Eastern Europe and Central Asia. The paper finds a strong correlation

between exporting and internet access at the enterprise level. Moreover, this correlation

remains after controlling factors that might affect both exports and internet connectivity and

self-selectivity. The results from this study are consistent with the idea that the internet has

contributed to the idea of globalization, at least in the transition economies of Eastern Europe

and Central Asia.

1.1.2 Mobile Phone Users

The next technology which I use for constructing a technology lag is mobile phone users

measured per 100 people. More precisely the mobile cellular telephone subscriptions are

subscriptions to a public mobile telephone service using cellular technology. The indicator

applies to all mobile cellular subscriptions that offer voice communications.

An important feature is that this variable excludes subscriptions via data cards or USB

modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint,

radio paging and telemetry services.

1.1.2.1 Why use Mobile phones instead of Fixed Landlines

When the construction of the second dependent variable for the technological lag started,

there were two options – whether to choose mobile phone users or fixed landline

subscriptions. In order to find the most appropriate variable for the study, I compared the both

options. Using data from the World Bank, I calculated the average value fixed landlines and

mobile phones subscriptions per each year starting from 1975 to 2014. The results are shown

in Graph 1.

It can be clearly seen that during the period 1975 to 1995, landlines are above the number of

mobile phones. The reason is that the mobile phones are not popular and not yet made for

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mass production. During the period from 1995 to 2000 the difference is decreased and in 2001

the values are identical. After 2001 the number of mobile users is steeply increasing, while the

number of fixed landline subscriptions is staying the same, even with a slight decline during

the period from 2010 to 2014, The reason is that most people prefer the ease of using mobile

phones.

Many scholars agree that developing countries such as India have skipped the fixed landline

phone in the development of the country. Some literature provides specific information about

development from the use of mobile phones. One example is provided by Abraham (2007),

who argues that mobile phones ought to lessen the information asymmetries in markets, thus

increasing the efficiency in less developed countries.

The belief that mobile phones are more appropriate in this case is backed up by the study of

Aker and Mbiti (2010), who show that mobile phones have the potential to benefit consumer

and producer welfare, and perhaps broader economic development.

Another study, which adds up to the previous paper is written by Valk, Rashid and Elder

(2010), who provide evidence that mobile phones impact education by facilitating increased

access. More specifically, mobiles can reduce barriers to education while attaining

educational outcomes that are, at minimum, comparable to those of traditional educational

methods.

Apart from education, a paper by Lum (2011) investigates the effect of cell phones on

economic development and growth by performing an econometric analysis. Using several

approaches and testing over 20 econometric models, she concludes that the mobile cellular

subscriptions rate was found to have a positive and significant impact on countries’ level of

real per capita GDP and GDP growth rate.

Moreover, the statement that mobile phones contribute to economic growth is also discussed

by a paper from Deloitte and Cisco in which is concluded that the contribution of mobile

telephony to promoting economic growth is strong and materializes across both developed

and developing countries. Moreover, there is evidence that mobile services increase

significantly productivity.

One of the latest researches confirms and supports the existing literature that mobile phones

affect trade. More precisely in the paper by Sahin, Can and Demirbas (2014) it is shown that

telephone lines (per 100 people) variable is significant and has a positive effect on trade. This

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finding clearly shows that telephone lines can be seen as a substantial communication tool for

trade.

1.2 INDEPENDENT VARIABLES

1.2.1 FDI inflows

In the model, the first independent variable is FDI inflows relative to GDP. In greater detail,

foreign direct investment are the net inflows of investment to acquire a lasting management

interest in an enterprise operating in an economy other than that of the investor. It is the sum

of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as

shown in the balance of payments. This series shows net inflows (new investment inflows less

disinvestment) in the reporting economy from foreign investors, and is divided by GDP.

Most scholars, from the earlier research from DeMello (1999) and Borensztein (1997) to the

latest scholars such as Ha and le Giroud (2014) and Lebesmuehlbacher (2014), identify that

FDI has an important effect on the rate of technology adoption. That’s why FDI is one of the

main independent variables that is expected to have an effect on the technological lag.

More specifically, as the above mentioned papers conclude, it is expected that FDI inflows

will decrease the technological lag, meaning that the introduction of FDI inflows will

decrease the lag between the United States and the rest of the world.

1.2.2 Education

The next independent variable is Education. It is a gross enrolment ratio and is the total

enrollment in primary education, regardless of age, expressed as a percentage of the

population of official primary education age.

Kiiskia and Pohjolab (2002) investigate the factors which determine the diffusion of the

Internet across countriesusing data on Internet hosts per capita for the years 1995–2000 for a

sample of the OECD countries. They conclude that GDP per capita and Internet access cost

explain best the observed growth in computer hosts per capita. More importantly for my

research topic is that for a larger sample of both industrial and developing countries, the

results change in such a way that also education becomes significant. I am using sample with

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both industrial and developing countries, so including education as an independent variable

could better explain the model.

Yokota (2010) concludes that in order to receive the benefits of technology spillovers, host-

country governments need to attract FDI into different targeted industries according to

industry-specific development levels. Moreover an important result is that improved

education will increase the benefits accruing to FDI.

It is notable that the variable School can exceed 100% due to the inclusion of over-aged and

under-aged students because of early or late school entrance and grade repetition. It is

expected that in developing countries often students are over aged and often they are

repeating grades.1

1.2.3 FDIxEducation

Similar to the technique used in the paper by Borensztein (1997), I have included an

interaction term, FDI x Education, which improves the overall regression. Technically if two

predictor variables affect the outcome variable in a way that is non-additive, there should be

included an interaction term in the model to capture this effect.2 Moreover, as specified by

Taylor (2007), adding interaction terms to a regression model can greatly expand

understanding of the relationships among the variables in the model and allows more

hypotheses to be tested.

1.3 CONTROL VARIABLES

Apart from the dependent and independent variables, I need to include also control variables.

In empirical data analysis, control variables are not of primary interest (i.e., neither the

exposure nor the outcome of interest) and thus constitute an extraneous or third factor whose

influence is to be controlled or eliminated. More specifically, the control variables are used

due to the need of obtaining results that are not biased from differences between exposure

groups in that third variable.3

1 UNESCO. (2012), Institute of Statistics, Opportunities lost: The impact of grade repetition and early school leaving 2 Southhampton University. (2013/2014), Transformations, polynomial fitting, and interaction terms3 Pole & Bondy(2010), Control Variables

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1.3.1 General government final consumption expenditure (% of GDP)

In this research paper, the used control variable is government consumption expenditure,

similar to the paper of Lebesmuehlbacher (2014).

General government final consumption expenditure or general government consumption

includes all government current expenditures for purchases of goods and services including

compensation of employees. Moreover, it also includes most expenditures on national defense

and security, but excludes government military expenditures that are part of government

capital formation. The variable is in percentage of GDP, so that it can be easily interpret and

compared.

A paper by Pavitt and Walker (1976) point out that government expenditures promote

industrial development and foster technological diffusion. Athough the paper is from 1976,

the result can still be implied to this study.

A more recent study confirms the findings of Pavitt and Walker (1976) – the authors Cozzi

and Impullitti (2008) argue that the increase in government spending is increasing

technological innovations, which means that there is an increase in technological progress.

2. TECHNOLOGICAL LAG

2.1 Measuring Technology Diffusion

The first step in the Model was to compute the technological lags for internet users and for the

mobile phone users.

The advantage of measuring technology diffusion this way is that all diffusion lags are now in

terms of years and are therefore easy to interpret and comparable across technologies: the

larger the diffusion lag, the more years a country is behind the United States’ technology

usage. If for a given technology in a given year the United States is not the technology leader,

that is, the U.S. has a lower usage intensity than another country, the diffusion lag is negative.

For example, the number of computers per capita in 2002 in the United States was already

observed in Switzerland in 2001. Hence, the diffusion lag is -1 year. These intensities provide

a direct measure of technology along the intensive margin.

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2.2 Building The Technology Diffusion Lag

Based on the paper by Lebesmuehlbacher (2014), who argues that FDI affects technology

diffusion through investments and knowledge transfers to existing companies, I constructed

the technological lags

I compute the technology lag for mobile phone users and for internet users using the CHAT

Dataset. The technological lag can be interpreted as the time difference between a country’s

technology usage intensity and the last time the technology leader had a similar technology

usage intensity. The technology leader for these technologies is assumed to be the United

States of America. (Comin, Hobijn, and Rovito (2008)).

I will use the approach by Lebesmuehlbacher (2014) to construct the technology diffusion

lags.

Technology lags are the difference between the observed year t, and the last time the USA had

the same usage intensity Xn,l as country n for a given technology i.

The t formula is:

li ,n , t=( Xn ,t−XUSA , Smin

XUSA , Smax−XUSA , Smin)× Smax+( XUSA , Smax−Xn ,t

XUSA ,Smax−XUSA , Smin)× Smin

The variable Smax is defined as the last time the USA had a technology usage intensity higher

than X n, t and Smin is the last time the USA had a technology usage intensity lower than X n, t.

The variables Smin and Smax are either 0 or 1, depending on whether country n is

technologically more advanced that USA or less advanced than USA in a specific year.

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3. DATA SOURCES

I use the same dataset which is used in the paper by Lebesmuehlbacher (2014), which is the

Cross-country Historical Adoption of Technology (CHAT) data set by Comin, Hobijn, and

Rovito (2006).

CHAT is an unbalanced panel dataset with information on the adoption of 115 technologies in

more than 150 countries since 1800. Out of the 115 technologies in 150 countries over time I

used two technologies which are relevant for my research question. Moreover, in order to

make average diffusion lags comparable across countries, I consider only technologies for

which I have data on a wide range of countries.

One limitation of the CHAT data set is that it does not have complete data about the two

dependent variables. That’s why I extended the dataset and merged it with a dataset from the

World Development Indicators.

The result was a dataset with little missing information, which will lead to more accurate

results. In the dataset I used 109 countries including developing and developed countries.

The variables in the CHAT dataset are split into extensive and intensive margins.

The extensive measures of technology adoption capture the fraction of potential adopters that

have adopted a given technology. Alternately, intensive measures of technology adoption

capture the number of units of the new technology that each adopter uses. One example is the

number of personal computers per capita.

More specifically, the CHAT dataset by Comin and Hobijn (2009) consists of two types of

intensive measures of technology adoption. They consist in counting either how many units of

capital embodying the technology there are in the economy (i.e. numberof cell phones) or

how many units of a given output have been produced with the technology. In this study I am

using the first type of intensive measures of technology adoption.

Both Internet users and Mobile phone users are of this type.

One drawback of the intensive measures of technology adoption is that it is more difficult to

interpret. I solved the problem of difficult interpretation by using the following technique. In

order to obtain more useful results, I divided the number of mobile phone users and Internet

users by the population in a given country for each year. In this way the results are in ratios

and each number can be directly compared to others. In this way the updated dataset can be

used to construct the technological lags for the variables Internet users and Mobile Phone

Users.

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4. ESTIMATION MODEL

4.1 Data Model

The models in the paper that were tested have the following specification:

TECH Lagi ,t=β j+β1× FDI i ,t−1+β2× Educationi , t−1+β3× FDI i , t−1 × Educationi ,t−1+β4 ×Government Expenditure i ,t−1 +δ t+e i ,t

,

where:

TECH Lagi ,t - Measures technology lag for either internet subscriptions or mobile phones for

country i and period t,

FDI i ,t−1 – Net foreign direct investment (as percent of GDP) for country i and period t-1,

Schooli, t−1 – Education enrollment for country i and period t-1.

FDI i ,t−1× Educationi ,t−1 - The model also includes interaction terms between FDI and

Education enrolment as we are interested whether the adoption of new technologies or the

transfer of knowledge is dependent on the level of education (proxied by Education

enrolment).

Government Expenditurei , t−1 – Represents the control variable government expenditure.

In the panel model above we include both country fixed effects,β j, and time fixed effects, δ t.

Country fixed effects provide a means to control for omitted variables/factors such as

geographical proximity trade, etc., that are specific for each country. The idea is that whatever

the effects of the omitted variables have on the technology lag at any time, they will also have

the same effect at a later time; thus their effects will be constant, or “fixed.” Other effects,

such as globalisation or global economic development, have a common effect to all countries

and this is accounted for by time fixed effects. Table 1 presents a list of the used variables, a

description of them, and the source of the data.

It is notable to mention that geographical proximity is important factor because it can be

considered on two stages. The first one is when taking into account different neighbouring

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countries that could transfer knowledge and collaborate. The next one is within country,

meaning that different regions could also benefit from interacting between each other.

Bouba-Olga and Ferru (2012), investigate whether the geographical proximity still maters.

They perform empirical analysis and find out that geographical proximity exists.

Geographical proximity is not included in the model as a separate control variable because as

Bouba-Olga and Ferru (2012) state, the lack of available data has made it impossible to

provide real answers up to now.

Trade is also notable to mention. It is not included as a separate control variable because there

are two main problems. The first is that the available data is in nominal terms, and not in a

percentage of GDP, meaning that interpretation is not possible. The second and more

important problem is that there is very limited data available. Moreover the data is collected

only for a few countries, which means that is not possible to provide accurate results if

included in the regression model.

4.2 Hypotheses

In the following, we are consequently deriving our testable hypotheses, representing our a

priori expectations, from the theoretical background presented in the previous sections.

4.2.1 Hypothesis I

The technological lag for internet users is decreased by FDI inflows and education.

The corresponding Null Hypothesis I:

There is no relationship between the technological lag for internet users and the

independent variables (FDI inflows and Education)

(H1)

Separating the two independent variables

It is also important to see whether the variables separately explain the technological lag.

That’s why there are two additional hypotheses:

a. The technological lag for internet users is decreased by FDI inflows

With null hypothesis respectively:

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independent variable (FDI inflows)

(H1a)

b. The technological lag for internet users is decreased by education.



independent variable (Education)

(H1b)

4.2.2 Hypothesis II

The technological lag for mobile phone users is decreased by FDI inflows and education

Corresponding Null Hypothesis II:

There is no relationship between the technological lag for mobile phone users and the

independent variables (FDI inflows and Education)

(H2)

Separating the two dependent variables

It is also important to see whether the variables separately explain the technological lag.

That’s why there are two additional hypotheses:

a. The technological lag for mobile phone users is decreased by FDI inflows



independent variable (FDI inflows)

(H2a)

b. The technological lag for mobile phone users is decreased by education



independent variable (Education)

(H2b)

4.3 Testing The Hypothesis/ Estimation

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After collecting the data and formulating the hypothesis as well as the model the next step in

the research process is to estimate the model and test the hypothesis.

4.4 Choosing The Right Estimation Model

The model in this paper is based on panel data. This means that it contains repeated

observations over the same units collected over a number of periods. The use of panel data

models is of great use if the aim of the research is to specify and estimate more complicated

and more realistic models.

Two main advantages of using panel data are that it reduces identification problems and that it

gives more accurate estimators than from other sources. Some notable disadvantages are more

of practical nature such as the issue of independence of different observations. This problem

may complicate the analysis in nonlinear and dynamic models. Another issue is the missing

data. Often panel data sets suffer from missing observations and the analysis has to be

adjusted. 4

The main reason to use panel data in this research is because this type of data has advantage

over time series or cross sectional data sets: panel data can allows identification of certain

parameters or questions without the need to make restrictive assumptions.

4.5 Fixed or random effects?

When dealing with panel data, it is very important to choose whether to use fixed effects

approach or random effects approach. In this section both effects are explained and is shown

why I have chosen to use model with fixed effects.

A random effect model is appropriate in situations when the researcher is accumulating data

from a series of studies that had been performed by researchers operating independently, it

would be unlikely that all the studies were functionally equivalent. In this case the random-

effects model is more easily justified than the fixed-effect model. 5

4 Verbeek Marrno. (2002), A Guide to Modern Econometrics 4th edition5 Borenstein Michael, Hedges, Higgins, Rothstein. (2009), Introduction to Meta-Analysis

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It makes sense to use the fixed-effect model if two conditions are met. First, we believe that

all the studies included in the analysis are functionally identical. Second, the goal is to

compute the common effect size for the identified population, and not to generalize to other

populations. In this study this is the more relevant the fixed effect model.

4.6 Why fixed effects model?

The fixed effects model is a linear regression model in which the intercept terms vary over the

individual units.

When using panel data, it is possible to exploit the particular nature of the data owing to the

availability of repeated observations on the same individuals. This issue is addressed by

including individual- specific intercept terms in the model.

In this situation the model is:

y¿=α i+x¿' β+u¿

Where α i(i=1,…,N)) are fixed unknown constants that are estimated along with β, and where

u¿ is assumed to be independent and identically distributed over individuals and time. In this

model, the intercept term β0 is omitted because it is subsumed by the individual intercepts α i,

which are essentially the fixed effects. These effects capture all unobservable time- invariant

differences across individuals. In this approach consistent estimation does not impose that α i

and x¿' are uncorrelated.

Lebesmuehlbacher (2014) uses a panel cointegration regression in the form of Dynamic

Ordinary Least Squares. In this way he includes leads and lags which leaves an effective

estimation period of 25 years. Moreover to account for the lags in the series he uses linear

interpolaration. This procedure adds 55 developing countries to the list of 26 developed

countries.

In this paper I am also using a Linear Model, with cross section fixed effects and time period

fixed effects. The time period is from 1991 to 2013, resulting in 23 included periods and 109

countries. This paper adds to the paper of Lebesmuehlbacher (2014) by providing a larger

sample of both developed and developing countries.

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5. RESULTS

5.1 Descriptive statistics

The first step after gathering the data and constructing the dataset which is used for the

estimation, I had to verify that there are no errors in the data.

After checking that there are no blank cells, I made a descriptive statistics table, which I used

for making sure that the data is properly gathered.

In Table 2 are shown the number of observations as well as the number of missing data. The

fact that the number of observations for each variable is equal to 2616 and that there are zero

missing values suggests that the data is properly collected and ready for analyzing the

descriptive statistics.

Table 3 shows five important parameters, which are used to summarize the data. The first

column after the name of the variables is “N”, which is just the number of observations. The

number is identical in all rows, and is equal to the number of Table 1.

The next two columns “Minimum” and “maximum” show the lowest and the highest value of

each variable.

The first variable is the technology lag for internet users. The minimum value shows that the

minimum technology lag is -10 years, which means that some countries are 10 years more

advanced than the technology leader, which is USA. The maximum value is 20 years, which

means that some countries are 20 years behind the leader.

The next variable is the technological lag for mobile phones users. The interpretation is

similar to the technological lag for internet users, since this variable is also in years.

Since the next variable is Country, the minimal number is 1 and the maximum is 109, since in

this dataset there are 109 countries. The full list of countries is presents in Table 4. The mean

and standard deviation for this variable are of no importance.

Following is the variable FDI inflows relative to GDP. Here it is notable to see that the lowest

value is -57 and the highest is 431. This means that in some countries there are negative GDP

inflow values and in some very high inflows.

The next variable is Education expressed in percentages of the population of official primary

education age, where the value is zero, because there cannot be a negative years of Education.

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The value is over 100 because this variables a gross enrolment ratio and is the total enrollment

in primary education.

The last variable in the table is Government expenditure, which is measured by the General

government final consumption expenditure and it includes all government current

expenditures for purchases of goods and services. Also, in order to be comparable, it is

measure as s percentage of GDP. From the minimum and maximum value can be concluded

that the values vary from 2.9% up to 76.2%.

After analyzing the descriptive summary, it is notable to see what is the relationship between

the two dependent variables.

5.2 Relationship between the dependent variables.

In this section, a scatter plot of the two variables of interest is presented. The variables

observed are Internet users and Mobile Phone Users. The scatter diagram in Graph 2 is used

to investigate the possible relationship between the two technological lags in this paper.

Graph 2 Scatter plot of the dependent variables

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Technological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet UsersTechnological Lag for Internet Users


From the graph can be seen that most of the observations are focused between -10 and 10

years of technological development. Although there is no clear positive or negative pattern,

there are signs of positive association. Also there are some observations which are separated

from the main group. Furthermore, there appears to be a positive pattern starting from the

beginning of the graph. However the trend is not very clear.

Also, on the x axis there are a few observations that form a straight line in the zero region. this

may be due to the fact that the technological lags have zero , because in every considered

country, there is a year in which the technological development is equal to the technology

leader, which is the USA.

In order to gain more information about the results, Table 5 represents the key characteristics

of the graph. For both lags the mean is positive. This means that overall the mathematical

average of all 2616 observations is greater than zero. The median the maximum and

minimum values are used to identify whether there are any errors, for example if there are

values that are not realistic. The skewness is positive for both lags, which means that the

distribution is slightly assymentrical and is skewed to the right. The kurtosis identifies

whether a given distribution matches the Gaussian distribution. In this case the value is

positive, which means that the distribution is more peaked than the Gaussian distribution.

5.3 Fixed Effects Testing

The last step prior to presenting the regression results it is useful to analyze the data and to

perform a redundant fixed effects test. The test results are presented in Table 6.

Table 6 Redundant Fixed Effects Tests

Effects Test Statistic d.f. Prob.

Cross-section F 15,46 -103,208 0,00

Cross-section Chi-square 1256,91 103 0,00

Period F 89,07 -22,2084 0,00

Period Chi-square 1467,46 22 0,00

Cross-Section/Period F 28,45 -125,208 0,00

Cross-Section/Period Chi-square 2204,28 125 0,00

Observations 2214

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In Table 6 there are three sets of tests. They are computed based on 2214 total panel

unbalanced observations, 23 periods and 104 cross- sections.

The first set consists of the tests “Cross- section F” and “Cross-section Chi-square”. These

tests evaluate the joint significance of the cross section effects using the sums of squares

corresponding to the F- test, and the likelihood function, corresponding to the Chi- square test.

The corresponding restricted specification is one in which there are period effects only. The

two statistic values (15.46 and 1256.91) and the associated p-values strongly reject the null

hypothesis that the cross-section effects are redundant.

The next two tests are “Period F” and “Period Chi- square”. Their aim is to evaluate the

significance of the period dummies in the unrestricted model against a restricted specification

in which there are cross-section effects only. Similar to the first set, the statistic is sufficiently

large and the Prob. is equal to zero. This means that the null hypothesis of no period effects is

rejected.

The last set of tests is “Cross-Section/Period F” and “Cross-Section/Period Chi-square”. They

evaluate the joint significance of all of the effects, respectively. Again, both of the test

statistics are high and the Prob. is equal to zero, which means that the null hypothesis which

states that in the restricted there is only a single intercept.

5.2 Regression

This section will present and interpret the main results of the paper. In order to test the

previously stated hypotheses, a statistical software Eviews8 is used. The method used is least

squares and the sample is from years 1990 to 2013.

In this estimation the data is balanced, meaning that the experimental units that are assigned

to each of the treatments to be evaluated are the same.

As mentioned earlier, the regression will be with fixed effects. Moreover, the cross- section as

well as the period will be estimated with fixed effects, meaning that there will be both country

fixed effects and time fixed effects as specified in previous sections.

First the estimation with only the main variables will be discussed (Section 5.2.1), then the

estimation with added interaction term will be discussed (Section 5.2.2).

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5.2.1 Estimation of lags without interaction term

The benchmark regression, which does not include the interaction term FDIxEducation” is:

TECH Lagi ,t=β j+β1× FDI i ,t−1+β2× Educationi , t−1+β3× Government Expenditurei ,t−1+δt+ei , t

Table 7 shows the results from the internet lag when the independent variables are “FDI” and

“Education”. Also the control variable “Government Expenditure” is included in the

regression. In this benchmark regression, which is based on 2214 observations, the results are

the following.

First, the variable “FDI” has a negative coefficient, which means that it affects negatively the

internet lag. This means that as FDI inflows increase, the technological lag of internet users

will decrease. Also this coefficient has a significant effect because the associated t- statistic is

sufficient and the Probability is low. As identified by Dutt and Ros (2008), this result is

expected and this result confirms hypothesis H1a

However, the variable Education has a very low and positive coefficient. Moreover the result

is not significant. This means that the effect of Education has no effect on the technological

lag for internet users. This statement disproves out expectations and hypothesis H1b is not

rejected. The control variable “Government Expenditure” has a negative impact and is

significant on the 10% level.

These results lead to the conclusion that hypothesis I is not rejected. The null hypothesis of

H1 cannot be rejected, which means that there is no relationship between the technological lag

for internet users and the independent variables. When taking into consideration of the

independent variable Education, it is worth noting that Kiiskia and Pohjolab (2002) who

investigate the internet diffusion, find that education becomes significant only when the

sample is very large. One option is that the result is not significant because the sample is not

large enough. This statement is also valid for the rest of the results, in all cases when

education is not significant.

Furthermore, in order to receive more information about the results, in the regression are

included the R squared, the Adjusted R squared as well as the F statistic.

The R-squared and Adjusted R squared are included, because they give information about the

goodness of fit. The values are between zero and one, where one is a perfect match. In this

regression the R-squared is 64.5%, which is relatively high. However this result might be

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misleading, because with the addition of an additional predictor the value increases. This

might lead to misleading results. That’s why I have included the Adjusted R square. It is

always smaller than R squared because, as Frost (2013) states, it compares the explanatory

power of regression models that contain different numbers of predictors. In this scenario the

Adjusted R squared is 62.3% which is again comparatively high. This is overall good result.

Moreover in this regression is included the F- statistic and its probability. The F- statistic

gives information about the joint significance of the variables. In this regression the F-

statistic is high and the probability is zero. This means that the null hypothesis of no joint

significance can be rejected.

Table 8 shows the results from the mobile phones lag when the independent variables are

“FDI” and “Education” and the control variable “Government Expenditure” is included in the

regression. In this second benchmark model , which is based on 2214 observations, the results

are different from the first case. While the “FDI” variable has again a negative coefficient,

now the variable is not significant because of the high probability and low t- statistic.

The independent variable “Education” remains insignificant and with positive coefficient. In

this estimation none of the null hypotheses are rejected. The control variable as well is not

significant and with positive sign.

This means that the hypothesis H2a and H2b are not rejected, following that hypothesis H2 is

not rejected. This result means that the null hypothesis of H2 cannot be rejected for any of the

two independent variables. This result is contrary to the expectations ant to the existing

literature. One explanation could be provided from Yokota (2010), who argues that although

for large samples education and FDI will increase the technology spillovers, the country

governments need to attract FDI into different targeted industries according to industry-

specific development levels.

Here the R squared and the Adjusted R squared are respectively 55.8% and 53.1%. This

means that around 53% of the observations are explained. The F statistics is significant,

similarly to the result in Table 7.

5.2.2 Estimation of lags with interaction term

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After performing the benchmark regressions, it is important to estimate a new model, this

time including the interaction term “FDIxEducation”.

In this situation the model is:

TECH Lagi ,t=β j+β1× FDI i ,t−1+β2× Educationi , t−1+β3× FDI i , t−1 × Educationi ,t−1+β4 ×Government Expenditure i ,t−1 +δ t+e i ,t

In this situation the results are as follows. Table 9 presents the results of estimating the effect

on the technological lag for internet users on the independent variables “FDI”, “Education”

and “FDIxEducation”. Firstly, this regression is based on 2214 observations. Secondly, the

coefficient of “FDI” inflows is negative, meaning that a unit increase in FDI inflows will

reduce the technological lag for internet users. Similarly to the benchmark model, this value is

significant because it is with low probability and sufficiently high t- statistic. This means that

H1a is rejected, so that FDI inflows are decreasing the technology lag between countries. This

result is expected from the literature. Findlay (1978) postulates that foreign direct investment

increases the rate of technical progress

However, the variable “Education” has negative and insignificant effect on the technological

lag for mobile phones. This result means that H1b is not rejected. Furthermore, the interaction

term “FDIxEducation” has extremely small coefficient, but the effect on the technological lag

is significant on the 5% confidence intervals. The control variable has a negative coefficient

and is significant on the 10% level. From there observations can be concluded that H1 cannot

be rejected because the null hypothesis, which states that there is no relationship between the

technological lag and the independent variables, is not rejected.

In this regression the R squared and the Adjusted R squared are respectively 64.6% and

62.4%. This means that around 63% of the observations are explained. The F statistics has a

large value and is significant, similarly to the result in the previous regressions

The next step is to estimate a model with the same independent variables as in Table 9 but for

the technological lag for mobile phone users. Moreover, the number of observations is also

2214. Table 10 shows that the coefficient of “FDI” has a small and negative effect. But this

effect is not significant. The variable “Education” is characterized by a very small positive

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coefficient and insignificant result with high probabilities and low t- statistics. Moreover the

interaction term is also insignificant. In this estimation the control variable has a negative

effect and significant result on the 5% level. In this scenario both H2a and H2b are not

rejected, meaning that the null hypothesis II cannot be rejected.

A paper which might partially explain the result is written by Sadik and Bolbol (2001) who

use data from the Arab countries to study the extent of FDI activities. Their results indicate

that FDI has added advantage of generating technology spillovers, but such spillovers are not

immediate and are yet to be witnessed.


53.1%. This means that around 53% of the observations are explained. The F statistics has a

large value, the associated probability is equal to zero, which means that the result is

significant.

From this result can be concluded that H1 and H2 are not rejected. Actually only H1a is

rejected in every scenario. While the overall result is not confirming the main hypothesis, it is

notable that the variable internet users is decreasing the technological lag for internet users.

Table 11 represents a summary for all hypotheses and identifies whether each one is rejected

or not. The graph is used to illustrate the results of the regression.

Hypothesis Internet users

(benchmark)

Mobile phone

users

(benchmark)

Internet users

(with interaction

term)

Mobile phone

users (with

interaction term)

H1a Rejected H1b not rejected H1a rejected H1b not rejected

H1 Not rejected

H2a not

rejected

H2b not rejected H2a not rejected H2b not rejected

H2 Not rejected

One possible explanation is that from the available data can be seen that after a time period,

the developed countries are beginning to implement technologies faster that the technology

leader which is the United States of America. An example in the existing literature for this

possible explanation is provided by DeMello (1999). He concludes that technology spillovers

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driven by FDI occur only in selected countries.Moreover there may be a minimum level of

development for a country to benefit from FDI.

In order to identify whether investigating only developing countries will change the results, I

constructed the following section.

5.2.3 Separating the countries: developing or developed

After obtaining the results in section 5.2.1 and 5.2.2, I decided to separate the countries into

developing and developed. In doing so I am able to split apart the developing countries and to

find whether there is a difference in the estimation results. The expectation from this

additional estimation is that the dependent variables will explain the decreasing of the

technological lags with more certainty.

Table 12 represents the technological lag for internet users when only developing countries

are considered. In this regression, the total panel observations are 1328 because now I am

only considering the developing countries in my sample.

As expected now the coefficient of the variable FDI inflows is larger than the coefficient for

FDI inflows when considering all available countries. The result is also significant. A relevant

study by Dutt and Ros (2008) state that an important consequence of FDI is that shifting

production to a developing country can reduce technology adoption costs for indigenous local

firms. This means that the technological lag is expected to decrease from FDI inflows.

However, the variable Education, on the other hand, is still insignificant and its coefficient is

very close to zero. This result is similar to the result in the previous sections. Only the

interaction term has a significant result, but the coefficient is very close to zero. Moreover the

control variable is also insignificant.


73.5%. This means that around 74% of the observations are explained. It is notable that this

value is very high. Moreover, the F statistics has a large and significant value.

It is notable to investigate whether the technological lag for mobile phones is also affected by

the exclusion of developed countries. Table 13 lists the results for the 1328 panel

observations. “FDI” has a negative coefficient, but it is not significant. The variables

“Education” and the interaction term are insignificant and have positive coefficients. The

control variable has a negative and significant effect on the 5% level. Again all variables have

insignificant effects due to the low t- statistics and high probabilities. This means that the

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dependent variables do not explain the technological lag for mobile phones. This result means

that the chosen independent variables do not explain the technological lag for mobile phone.

In this regression the R squared and the Adjusted R squared are respectively 54.2% and 51%.

This means that around 51% of the observations are explained. The F statistics has a

significant value, which means that the variables are jointly significant.

The result is contrary to the expectation set by Yokota (2010). However, Kiiskia and Pohjolab

(2002) argue that education is significant in affecting technological lag only for a large

number of countries.

Another paper is provided by Kim, Mims and Holmes (2006) who argue that many mobile

wireless technologies such as calculators or computers in some countries are still far from

being used in everyday life.

5.3 Outcomes and findings

Overall, results indicate that cross section and time fixed effects are both separately and

jointly significant in all regressions. The results from the panel regressions are summarized in

the table below. (Table 14 in the Appendix)

Table 14 Results from panel regressions

Internet subscriptions Mobile phonesDependent variables All

countriesDeveloping countries

All countries

Developing countriesDiffusion Lags (t)

FDIt-1 -0.1014* -0.1688* -0.0103 -0.0643(0.0352) (0.0344) (0.041) (0.0465)

Educationt-1 -0.0001 0.0007 0.0028 0.0047(0.0027) (0.0028) (0.0031) (0.0037)

FDI*Educationt-1 0.0008** 0.0013* 0.0001 0.0006(0.0004) (0.0004) (0.0004) (0.0005)

Government expendituret-1 -0.0433* 0.0145 0.0014 -0.0605**(0.0237) (0.0226) (0.0276) (0.0306)

Cross-section fixed effects yes yes yes yesPeriod fixed effects yes yes yes yesNote: Standard Deviations in parenthesis; *, ** and *** denote 10, 5 and 1 %significance levels.

It can be seen that the independent variable FDI is significant in the technology lag for

internet subscriptions. The variable education has no effect on both technology lags, both in

developed and developing countries.

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Results show that in terms of internet subscriptions FDI inflows have a statistically significant

impact on the diffusion of new technologies. On average an increase of FDI inflows (as a per

cent of GDP) with 10 percentage points reduces the diffusion lag with slightly less than 1

year. This effect is more pronounced for developing countries which is in line with the fact

that these countries usually lack the capacity to generate new technologies which makes them

more dependent on the adoption of such technologies through FDI. Contrary to what might be

expected, education does not seem to contribute to the decrease of the diffusion lag. A

possible explanation could be that due to data limitations I control in the panel only for

primary enrolment in schools. Higher levels of education are probably more crucial for the

transfer of new technologies but such data is unavailable. Similar conclusions (in terms of

coefficient signs) can be made for the diffusion lag of mobile phones although results from

the estimation of panel regressions are not statistically significant.

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6. CONCLUSIONS AND FURTHER RESEARCH

The paper investigates how macroeconomic factors affect the technological lag between

countries. There are two technological lags that are tested – internet users lag and mobile

phone users lag. The results conclude that the independent variable FDI inflows significantly

explain the technological lag of internet users. This is consistent and expected from the

available literature. Surprisingly the independent variable Education did not affect

significantly the technological lag. Moreover, the regressions which take into account the

technological lag for mobile phones fail to reject the null hypothesis of no relationship

between the independent variables and the technological lag. After this result, I separated the

countries into developing and developed. The results were similar to those for all countries.

After having analysed the effect of FDI inflows and Education on the technological lags, I can

suggest some further research topics, which have been opened up by my results. One of the

main topics that I will discuss is the absence of available data and information. For example a

larger data for education and including more types of education would improve the results and

identify which type of education is explaining the technological lags. Another limit of the

paper related to the lack of available data is migration. Migration could be added as another

control variable. However there is no suitable data. Including such a control variable could

improve the results.

Another potentially promising field of research would be to investigate whether brain drain

has an effect on the technological lag. Brain drain is widely discussed and its effects are

interesting in this topic. Lastly, separating the types of FDI inflows could also be helpful. In

this way a more clear causal relationship will be established. Moreover having the possibility

to separate the types of FDI will give the opportunity to conclude which types of FDi are most

important for decreasing the technological lags.

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7. APPENDIX

Table 1 List of Variables

Name Definition Source

Internet

Users

Internet users are people with access to the worldwide

network.

CHAT Dataset

Mobile

Phone

Users

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.

CHAT Dataset

FDI Inflows Foreign direct investment are the net inflows of investment

to acquire a lasting management interest (10 percent or more

of voting stock) in an enterprise operating in an economy

other than that of the investor. It is the sum of equity capital,

reinvestment of earnings, other long-term capital, and short-

term capital as shown in the balance of payments. This series

shows net inflows (new investment inflows less

disinvestment) in the reporting economy from foreign

investors, and is divided by GDP.

World Bank

Database

Education

Enrollment

Gross enrolment ratio. Primary. Total is the total enrollment

in primary education, regardless of age, expressed as a

percentage of the population of official

World Bank

Database

Government

expenditure

It is measured by the General government final consumption

expenditure and it includes all government current

expenditures for purchases of goods and services. It is

measure as s percentage of GDP.

World Bank

Database

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Graph 1 Plot of Landlines and Mobile Phone Users from 1975 to 2015

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 20200.00

20.00

40.00

60.00

80.00

100.00

120.00

LANDLINES MOBILE PHONES

Table 2 Statistics: Number of valid and missing data

Internet Users

Mobile Phone Users

Country FDI Education

Government

expenditureN Valid 2616 2616 2616 2616 2616 2616

Missing

0 0 0 0 0 0

Table 3 Descriptive statistics

N Minimum Maximum Mean Std. DeviationInternet 261

6-10 20 4,47 5,039

Mobile Phone Users 2616

-11 19 1,99 5,072

Country 2616

1 109 55 31,47

FDI 2616

-57 431 3,89 10,247

Education 261 0 140 81,21 43,32

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6Government expenditure

2616

2,975 76,222 16,949 5,917

Valid N (listwise) 2616

Table 4 List of countries used

Countries

Albania Cabo Verde GermanyMacao SAR, China Romania

Algeria Canada GreeceMacedonia, FYR Russian Federation

AndorraCaribbean small states Greenland Malaysia San Marino

Angola Chile Grenada Maldives Saudi Arabia

Antigua and Barbuda China Hungary Malta Seychelles

Argentina Colombia Iceland Mauritius Singapore

Armenia Costa Rica India Mexico Slovak Republic

Australia CroatiaIran, Islamic Rep. Moldova Slovenia

Azerbaijan Cuba Ireland Monaco South AfricaBahamas, The Cyprus Israel Morocco Spain

Bahrain Czech Republic Italy Netherlands Sweden

Bangladesh Denmark JamaicaNew Caledonia Switzerland

Barbados Dominica Japan New Zealand ThailandBelarus Ecuador Jordan Norway Tunisia

BelgiumEgypt, Arab Rep. Kazakhstan Panama Turkey

Belize Estonia Kenya Paraguay UkraineBermuda Faeroe Islands Korea, Rep. Peru United Arab Emirates

Bolivia Fiji Kuwait Philippines United KingdomBosnia and Herzegovina Finland Latvia Poland UruguayBrazil France Lebanon Portugal Venezuela, RBBrunei Darussalam

French Polynesia Lithuania Puerto Rico Vietnam

Bulgaria Georgia Luxembourg Qatar

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Graph 2 Scatter plot of the dependent variables

Table 5 Summary of results for Graph 2

Variable Technological lag for internet users

Technological lag for mobile phone users

Mean 4,469 1,989Median 4 1Maximum 20 19Minimum -10 -11Std. Dev. 5,039 5,072Skewness 0,327 0,392Kurtosis 2,135 2,887

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Technological Lag for Internet Users


Observations 2616 2616

Table 6 Redundant Fixed Effects Tests

Effects Test Statistic d.f. Prob. Cross-section F 15,46 -103,208 0,00Cross-section Chi-square 1256,91 103 0,00Period F 89,07 -22,2084 0,00Period Chi-square 1467,46 22 0,00Cross-Section/Period F 28,45 -125,208 0,00Cross-Section/Period Chi-square 2204,28 125 0,00Number of observations 2214

Table 7 Internet lag benchmark regression (no interaction term)

Variable Coefficient Std. Error t-Statistic Prob.C 5,427 0,450 12,057 0,000FDI infolws -0,024 0,007 -3,630 0,000Education 0,003 0,002 1,174 0,241Government expenditure

-0,043 0,024 -1,807 0,071

Effects specification: Cross-section fixed (dummy variables)Period fixed (dummy variables)

R-squared 0,645 F-statistic 29,558Adjusted R-squared 0,623 Prob(F-statistic) 0,000Number of observations 2214

Table 8 Mobile phone lag benchmark regression (no interaction term)

Variable Coefficient Std. Error t-Statistic Prob.C 1,722 0,524 3,289 0,001FDI infows -0,002 0,008 -0,199 0,843Education 0,003 0,003 1,140 0,254Government expenditure

0,001 0,028 0,053 0,958


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Table 9 Internet lag regression (including interaction term)

Variable Coefficient Std. Error t-Statistic Prob.C 5,716 0,468 12,215 0,000FDI infolws -0,101 0,035 -2,883 0,004Education 0,000 0,003 -0,042 0,966FDIxEducation 0,001 0,000 2,232 0,026Government expenditure

-0,043 0,024 -1,826 0,068



Table 10 Mobile phone lag regression (including interaction term)

Variable Coefficient Std. Error t-Statistic Prob.C 1,7552 0,5451 3,2200 0,0013FDI infolws -0,0103 0,0410 -0,2516 0,8014Education 0,0028 0,0031 0,8950 0,3709FDIxEducation 0,0001 0,0004 0,2178 0,8276Government expenditure 0,0014 0,0276 0,0514 0,9590Effects specification: Cross-section fixed (dummy variables)

Period fixed (dummy variables)R-squared 0,558 F-statistic 20,421Adjusted R-squared 0,531 Prob(F-statistic) 0Number of observations 2214

Table 11 Summary of results for all countries in the sample

Null

Hypothesis

Internet users

(benchmark)

Mobile phone

users

(benchmark)

Internet users

(with interaction

term)

Mobile phone

users (with

interaction term)

H1a rejected H1b not rejected H1a rejected H1b not rejected

H1 Not rejected

H2a not rejected H2b not rejected H2a not rejected H2b not rejected

H2 Not rejected

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Table 12 Internet lag regression for developing countries (including interaction term)

Variable Coefficient

Std. Error t-Statistic Prob.

C 5,8418 0,4288 13,6224 0,0000FDI infolws -0,1688 0,0344 -4,9018 0,0000Education 0,0007 0,0028 0,2497 0,8029FDIxEducation 0,0013 0,0004 3,6230 0,0003Government expenditure 0,0145 0,0226 0,6402 0,5222Effects specification: Cross-section fixed (dummy variables)


Table 13 Mobile phone lag regression for developing countries (including interaction term)

Variable Coefficient

Std. Error t-Statistic Prob.

C 4,0404 0,5794 6,9740 0,0000FDI infolws -0,0643 0,0465 -1,3823 0,1671Education 0,0047 0,0037 1,2680 0,2050FDIxEducation 0,0006 0,0005 1,2553 0,2096Government expenditure -0,0605 0,0306 -1,9790 0,0480Effects specification: Cross-section fixed (dummy variables)


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Table 14 Results from panel regressions

Internet subscriptions Mobile phonesDependent variables All

countriesDeveloping countries

All countries

Developing countriesDiffusion Lags (t)

FDIt-1 -0.1014* -0.1688* -0.0103 -0.0643(0.0352) (0.0344) (0.041) (0.0465)

Educationt-1 -0.0001 0.0007 0.0028 0.0047(0.0027) (0.0028) (0.0031) (0.0037)

FDI*Educationt-1 0.0008** 0.0013* 0.0001 0.0006(0.0004) (0.0004) (0.0004) (0.0005)

Government Expendituret-

1

-0.0433* 0.0145 0.0014 -0.0605**

(0.0237) (0.0226) (0.0276) (0.0306)Cross-section fixed effects yes yes yes yesPeriod fixed effects yes yes yes yesNote: Standard Deviations in parenthesis; *, ** and *** denote 10, 5 and 1 %significance levels.

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