Eindhoven University of Technology MASTER ... · use of communication technology in a country. The...

Eindhoven University of Technology

MASTER

Telecommunication and democracyan empirical analysis of the effect of connectivity on democracy

Dekker, A.

Award date:2007

Link to publication

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https://research.tue.nl/en/studentthesis/telecommunication-and-democracy(bd43aeed-20d3-4786-889b-3ace9ac23fcd).html

technische universiteit eindhoven

nietuitleenbaar

r'o r~.~Pl-

Telecommunication and Democracy

An empirical analysis of the effectof connectivity on democracy

Aaldert Dekker (s461530)

Eindhoven, August 2007

Supervised by:

Dr. S.M. Sadowski Eindhoven University of Technology - Faculty of Technology Management

Prof. mr . dr . J.M . Smits Eindhoven University of Technology - Faculty of Technology Management

Eindhoven University of Technology

Faculty of Technology Management

Department of Technology and Policy

Telecommunication anddemocracy Summary

SummaryThis research explores the relationship between the use of communication technology and political democracy. Not

much statistical research had been done in the past on this relationship and there is no formal theory available to be

used. When research is available, the results are mostly out-dated and may not necessarily be relevant for today's

situation.

The overall aim of this study is to determine whether connectivity can facilitate democracy in countries that experience

low levels of democracy . To be able to achieve this goal, the following research question was defined :

What is the relationship between connectivity and democracy?

In the first part of this report, the literature is reviewed to define democracy . Although the concept of democracy remains

ambiguous and subject to different interpretations, this study addresses political democracy and focuses on two main

elements: political rights and civil liberties . Furthermore, the literature was reviewed to find the most important

determinants of democracy . The level of income (as measured by GDP per capita), the size of the population and the

educational attainment were found to be important in predicting a country's level of democracy . These indicators are

included in the empirical analyses .

The use of communication technology, an independent variable in this study, is then addressed by reviewing several

indices found in the literature . Based on these indices an index of connectivity was constructed for measuring the overall

use of communication technology in a country . The index includes internet users, main fixed telephone lines and mobile

subscribers . As the economic situation of a country (measured by GDP per capita) is important for both democracy and

connectivity, its relationship to both variables is addressed as well .

A set of statistical models was constructed in which the influence of the most important determinants of democracy,

found in the first part of the report, was examined . These models combine the traditional determinants of democracy with

the newly constructed connectivity index . As several independent variables (GDP per capita and connectivity) are highly

correlated, problems of multicollinearity were further analysed . To deal with these problems a different model was

constructed in which regional differences were accounted for . The empirical analyses includes a group of 80 countries

consisting of both developed and developing countries . Data was collected for the year 2005 .

The empirical analyses resulted in the following outcomes . Connectivity was found to be a significant predicator of

democracy in all models that were reviewed, thereby confirming findings in the literature . Evidence was found to support

the notion that economic prosperity increases the propensity for democracy . A country's educational attainment was

found to be positively related to democracy, however the results are not statistically significant . Similarly, the results for

population size were not significant, but negatively related . In the last section of the report recommendations for future

research are provided .

Keywords :

democracy, telecommunication, connectivity, regression analyses

ii

Telecommunication and democracy Preface

PrefaceThis report is the final thesis for my study Technology and Innovation Policy at the department Technology and Society

of the Eindhoven University of Technology (TU/e) . Writing this report has been quite an effort. Although it has been a

long process, it was a valuable experience as well . Not only in an educational sense, but also on a personal level . I

would not have been able to finish this report without the help and support I received from the people around me .

First of all, I would like to express my gratitude to my supervisors Bert Sadowski and Jan Smits . The guidance and

support I received have been invaluable in doing this research . Their remarks and comments have greatly contributed to

the report. Because most of the report was delivered only days before the last deadline, a lot of flexibility was demanded

of their part. I would also like to thank Wim Wenselaar and les Biemond for persuading me (and some other students) to

write this report at the university in stead of trying to work at home . This has been an important turning point . In addition,

their motivational talks have been of great support . I would like to thank my parents and my sister for supporting me in

every way possible during my studies. I would also like to thank all of my friends for occasionally checking up on me,

preparing meals when I did not have the time to cook, and for all other support . Finally, special thanks go to my girlfriend,

Hind. Her support, in every way thinkable, has been invaluable . Without her help, support, belief, motivation and patience

I would not have been able to finish this report .

Aaldert Dekker

Eindhoven, August 2007

®

Telecommunication and democracy Preface

iv

Telecommunication and democracv Abbreviations

AbbreviationsADSL Asymmetric Digital Subscriber Line

CCITT Comité Consultatif International Téléphonique et Télégraphique

DA) Digital Access Index

DOI Digital Opportunity Index

GDP Gross Domestic Product

GER Gross Enrolment Rate

HDI Human Development Index

ICT Information and Communication Technology

ICTDI ICT Diffusion Index

IDG International Data Group

IMF International Monetary Fund

ITU International Telecommunication Union

ISP Internet Service Provider

MDG Millennium Development Goals

NGO Non-governmental Organization

NER Net Enrolment Rate

OECD Organisation for Economic Co-operation and Development

P2P Peer-to-peer

PPP Purchasing Power Parity

POTS Plain Old Telephony Service

PSTN Public Switched Telephony Network

UNCTAD United Nations Conference on Trade and Development

UNDID United Nations Development Programme

v

Telecommunication and democracy Table of contents

UNESCO United Nations Educational, Scientific and Cultural Organization

UNPAN United Nations Online Network in Public Administration and Finance

VOIP Voice over IP

WEF World Economic Forum

MIS World Summit on Information Society

vi


Table of ContentsSummary .. . . . . . . . . . . . . . . . . . .. .. .. .. .. . . .. . . . . . . . . .. .. . . . . . . . .. . . . . . . . . . . . . . .. . . . . . .. .. . . .. .. .. . . . . . . . . . . .. . . . . . . . . . . .. .. . . . . . . . . .. .. . . . . . . . . . . . . . . . . . .. .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . .ii

. . . . . . .. .. .. .. .. . . .. .. .. . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. .. . . . . . . . . .. .. .. .. .. .. .. . . .. .. .. . . .. .. . . . .. . . . . . . . . . . . . .Preface. .. .. .. .. .. .. .. . . . . . .. .. .. .. . . . . .. .. .. .. .. ... III

Abbreviations .. .. . . . . . . . .. .. .. . . . . .. .. .. .. . . .. .. . . . . . . . . . . . . . . . . . . . . .. .. . . . . . . . .. .. . . .. .. .. . . . . .. .. .. .. . . . . . . .. .. .. . . . . . . .. .. .. . . . . . . . . .. .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . ..v

1 . Introduction. .. . . . . . .. . . . . . . . . . . .. .. . . .. .. . . .. . . .. .. .. .. .. . . . . . . .. . . .. . . . . . . . . . .. . . . . .. .. .. . . .. .. .. .. .. .. .. .. .. .. .. .. . . . . .. .. . . .. .. .. .. .. . . . . . . . . . . .. . . . . .. .. . . . .. . . . . . . . . .. . . . ..9

1 .1 Oxfam Novib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

1 .2 Research objective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

1 .3 Research design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

1 .3.1 Research aim and demarcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

1 .3 .2 Problem definition and research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

1 .3.3 Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

1 .4 Structure of the report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

2. Theoretical framework . . . . . .. . . . . . . . .. .. . . . . . . .. . . .. .. . . . . .. .. .. .. .. .. . . . . . . . .. .. . . . . . . . . .. .. . . . . . . . . .. .. . . . . . . . . .. .. . . .. . . . . .. .. .. .. . . . . .. .. .. .. .. . . .. .. .. .. .. . . . . . . . . .17

2.1 Political theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

2 .1 .1 Definitions of democracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

2 .1 .2 Determinants of democracy. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

2.2 Economic development and democracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

2.2.1 Economic theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

2.2.2 The effect of economic development on democracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

2.3 Economic development and telecommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .22

2.3.1 Aggregate correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .22

2.3.2 Structural economic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

2.4 Democracy and telecommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

2.4.1 The "dictator's dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .252.4.2 Quantitative analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

2.4.3 Discussion on Kedzie's research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

2.5 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

3. Methodology. . . .. .. . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. .. . . . . . . . . .. .. .. . . . . .. .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . .. . . . . . . . . .. .. .. . . . . . . . . . . . . . . . . . . .. . . . . .33

3.1 Method of analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

3 .1 .1 Regression analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

3.2 Measuring democracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

3.3 Indicators of connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

3.3.1 Overview of ICT indicators . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

3.3.2 Methodological issues of using ICT indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

3.3.3 Measuring connectivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

vii


4. Empirical analysis .. .. . . .. .. .. . . . . . . . . . . . . . . . . . .. .. .. .. . . .. .. . . . . . . . . . .. . . . . . . . . .. .. . . .. . . .. .. . . .. .. .. .. . . . . . . . . . . . .. . . . . .. .. . . . . .. . . .. .. .. .. .. .. .. . . . . . . . .. .. . . . . . . . .. . .. .41

4.1 Empirical models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

4.2.1 Dependent variable . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

4.2.2 Explanatory variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

4.3 Descriptive statistics. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46

4.4 Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

4.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51

5. Conclusions and recommendations . . . .. . . . . .. . . . . . . .. . . . . . . . . . . .. . . . . . . . . . .. .. . . .. .. . . .. . . .. .. .. .. .. . . . . . . . . . . . . . . . .. .. . . . . .. . . . . .. . . .. .. .. . .. . .. . . . . . .. . . . . .53

5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53

5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54

Literature .. . . . . . . . . . . .. .. .. .. . . . . .. . . .. .. .. .. .. . . . . . . . . . . .. . . . . . .. . . . . .. .. .. .. . . . . . ... .. . .. . . .. .. .. . . .. .. . . .. . . . . . . . . . . . . . . . . . .. .. . . .. . . .. .. . . .. .. .. .. .. . . . . .. . . . . . . . . . . . .. .. .. . . . . . .57

List of appendices . . . . . .. .. .. . . . . . . .. .. .. . . . . . . . . . . . . . .. . . .. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. .. .. .. .. .. .. .. . . . . .. .. .. . . . . . .. .. . . .. .. .. .. .. .. . . .. . . .. . . .. . . .. .. .. . . . . . . . . . . . . . . . . .61

Appendix A Democracy regressions . . . . .. . . .. .. .. .. .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. .. .. .. .. .. .. . . . . .. . . . . .. . . . . . .. .. .. .. . . . . .. . . .. .. . . . . .. . . . . . . . . . .. . . . .. . . . ..62

Appendix B Correlation political rights and civil liberties .. .. . . . . . . . . . . . . . . . . . .. .. . . .. . . . . .. . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . .63

Appendix C Distribution of explanatory variables . .. .. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .. .. . . .. .. .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .. .. .. .. . . . . . . . . . . ..64

Appendix C.1 GDP per capita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64

Appendix C.2 Distribution of population size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65

Appendix C.3 Net enrolment rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66

Appendix D Country list . . .. .. .. . . . . . . .. . . .. .. . . .. .. . . . . . .. .. .. . . .. .. .. . . . . .. .. .. .. . . . . . . .. . . . . . . . . . .. . . . . . . . . . . .. .. . . .. .. .. . . . . . . . . . . .. .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . .67

Appendix E Regression analyses . . . . .. .. . . . . . . . .. .. .. .. .. . . .. . . . . . . .. .. .. . . . . . . . . . . .. . . . . . .. .. .. . . . . . . .. . . . . .. . . .. . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . .. .. . . . . .69

Appendix F Regional analysis . . .. .. . . . . . . . . . . . . . . . . . .. . . . . .. . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. .. .. . . .. .. .. . . . . . . .. . . . .. . . . . .. . . . . . . . . . . .. . . . . . . .. . .. . . . . . . . . .. . . . . .74

viii

Telecommunication and democracy 1. Introduction

1 . IntroductionThis chapter is a general introduction to this study and describes the overall aim of the study . It starts by describing the

agency for which this study was conducted . It then provides a brief introduction to democracy and to information and

communication technology . Following from this section, the problem definition is provided and the relevance of the

research is discussed . The research questions, derived from the problem definition, are then defined and discussed . The

last section provides an overview of the structure of the report .

1 .1 Oxfam Novib

This research was conducted for Oxfam Novib', the Dutch member of the Oxfam International group . Oxfam is a group of

thirteen independent non-governmental organizations (NGOs) that work together and support around 4000 counterparts

and alliances in more than 100 countries . They work together in an effort to find lasting solutions to poverty, suffering and

injustice . Novib was founded in 1956 as a direct result of the massive flood disaster that struck the Netherlands in 1953 .

In 1994, it joined the Oxfam International group . As cooperation with other Oxfam members became more intensive, the

name Novib was changed to Ofxam Novib, underlining the importance of the aspect of international cooperation . The

main objective remained unchanged :

"Oxfam Novib's goal is to promote a global society in which the social-economic contradictions between

poor and rich are overcome, in which the world's prosperity is divided justly and in which people and

population groups can get to know and respect each other's cultures, and co-operate on the basis of

shared responsibility and mutual solidarity to benefit their development."

Oxfam Novib uses a 'rights based' approach, wherein each individual has the four principal rights . These are : the right to

a sustainable livelihood, the right to basic social services (education and health care), the right to life and security and

the right to be heard within social and political processes . The right to be heard in social and political processes is most

relevant to this study, as it is a fundamental right that individuals enjoy in true democracies .

There is ongoing international discussion about the roll that information and communication technologies (ICTs) can play

in achieving development goals. Perhaps the most important contribution to this discussion was the World Summit on

Information Society (WSIS), organized by the United Nations in Geneva in 2003 and Tunis in 2005 . WSIS was an

attempt to address issues raised by opportunities created by information and communication technologies . One of the

most important outcomes was that ICTs were generally recognized 'as effective tools to promote peace, security and

stability, to enhance democracy, social cohesion, good governance and the rule of law, at national, regional and

international levels' .

Although there seems to be a certain level of consensus on whether ICTs can contribute to development strategies,

there is controversy as well and some fundamental questions remain unanswered . There has been a steady growth in

the provision of information services in developing countries . Some people of these countries question the relevance and

appropriateness of the services offered . The extent to which information services actually contribute to the empowerment

of people and accountability of institutions is still subject of debate .

1 More information can be found at : http ://www .oxfamnovib .nU

9


Development agencies, like Oxfam Novib, have to decide whether to invest in the traditional direct approach or to invest

in ICTs . Investing in ICTs simply means fewer resources are available for more direct approaches . NGOs can also play

an important role in monitoring whether the free flow on information is not obstructed . In this study, the relevance of

investing in ICTs will be addressed by examining their influence on political democracy .

Because the debate on the contribution of ICTs to development is not concluded yet, it is important to do more research

to this contribution . This study will do so in focusing on the relationship between communication technology and

democracy. This is relevant for Oxfam Novib's right for every individual to be heard in social and political processes . ICTs

are not a high priority on the Oxfam Novib's agenda, however the organization would like to have a better understanding

of the importance of communication technology in achieving its goals . To conclude this section and underline the role of

democracy to development, the following fitting quote of Amartya Sen is given :

"No famine has ever occurred in a democratic country with a free press and regularelections."

1 .2 Research objective

The telecommunication industry has experienced continuous growth over the years . The total revenue of the

telecommunication market increases every year (ITU, 2006) . Worldwide, the number of fixed telephone lines and mobile

subscribers increases, as well as the number of internet users . Modern communication technologies enable an

increasing number of people to communication with others at diminishing costs. This is especially true for the developed

world, however, the same trend can be discovered in developing countries . Although the diffusion of telecommunication

technologies in developing countries still lags significantly behind that of developed countries, there is some evidence

that the digital divide between developing and developed countries has been reduced . Figure 1 .1 shows the gap over a

period of 10 years. In 1994, the number of mobile telephony subscribers was 27 times greater in the developed world,

compared to developing countries. By 2004, the same gap2 had been reduced to 4 times .

8.7 10 .19

0i7Y4 4 199S 149b 1947 1448 14" 20WQ 2001 2002 2065 20OAC_. p~ k • WOM -!- psvdopmy

Figure 1 .1 : Mobile subscribers4inhabitants (source: ITU)

An even more dramatic reduction for the gap in internet users is shown in figure 1 .2. In the same period, the gap

between internet users in developed and developing countries has been reduced from 73 to 8 times .

2 Note that the scale for both indicators is logarithmic . The gap remains substantial.

10


100 1

Figure 1 .2 : Intemet users/100 inhabitants (source: ITU)

Modern society is often referred to as the 'information society' or the 'networked societ y (bron?). These are not new

concepts, but have existed for many years . The discussion on the importance of information started more than four

decades ago with information theorists like Machlup, who defined knowledge as a commodity . Since then, there has

been an ongoing discussion on the major transformations that are possible through harnessing electronic information

processing technologies to the social and economic objectives of industrialized societies . According to Machlup (1962),

these technologies are vitally important components in the new ' information economies' or 'information societies' .

Different sources of literature define the importance of technology differently . Initially, ICTs were viewed upon as 'drivers

of change'. More recently, the emphasis has shifted to a perspective where these technologies are regarded as tools that

may provide a new potential for combining the information embedded in ICT systems with the creative potential and

knowledge embodied in people" (Mansell and Wehn, 1997) . In this view, ICTs are best considered as tools or facilitators

that may substitute under certain conditions for other means of knowledge creation in innovative societies (OECD, 1996) .

However, these technologies do not create transformations themselves . They are designed and implemented by people

in their social, economic and technological context . In addition, Falch (2005) suggests that these technologies do not

only expand the technological space wherein a certain decision must be taken, they influence directly the pattern of

decision-making .

ICTs are being used in virtually every segment of the manufacturing, services and natural resources industries . It is

therefore evident that they continue to have far reaching consequences for the society . In the manufacturing and

resource extraction industries ICTs are used to control technologies leading to innovations in both products and

processes. They play an increasingly important role in computational activities supporting scientific and technological

research, in the networks of communication research and development and in global business activities . In addition, they

are utilized by millions of individual citizens around the world to build new local and global communities. The rapidly

diminishing costs of communication will dramatically affect political and economic activities, as these are relatively

communications-intensive (Ward, 1996) . As communication costs are rapidly diminishing, access becomes affordable to

a growing number of people. One of the consequences will be that information of more different sources will become

available. At the same time, more people will have access to this information . This will almost certainly impact concepts

that are important for democracy, like transparency, accountability and empowerment . Participation will be enhanced as

people will have access to information concerning their daily lives .

11

Telecommunication and democracy 1 . Introduction

Intuitively it can be argued that communication technology is important for democracy . There are several examples in the

literature of countries in which the free flow of information had a crucial effect on political activities . One of the reasons

that the Russian coup in 1991 failed was that people were constantly informed on of its (lack of) progress . Another

example is the Tianenman Square demonstration . A possible reason for the length of the demonstration was the political

pressure from Chinese dissidents, who continuously informed the people about events from overseas .

Since the 1970s, the global spread of democracy has been quite impressive (figure 1 .3) . According to Freedom Housedata, the number of 'free' countries has risen from 42 in 1976 to 90 in 2006 . In the same period, the number of 'not free'

countries dropped from 68 to 45. In 2005, 122 countries were labelled electoral democracies. Despite these figures, the

spread of democracy seems to have come to a halt . There are many examples. In the Middle East, imposed democracy

in Iraq does not get off the ground . The recent coups in Fiji and Thailand are another reminder of how fragile democracy

can be. The collapse of the Soviet Union did not only bring democracy . Russia today seems to slide back into anauthoritarian state. In Latin America, countries like Venezuela are becoming less democratic under socialist control .

These events will, however, not be taken into account in this study, as they occurred very recently .

4,75-

4,50

4,25

4,00

3,75

3,50 ~

1970 1975 1980 1985 1990 1995 2000 2005

Figure 1 .3: Democracy (source : Freedom House)

There has not been much research into the relationship between the use of communication technologies and democracy

in a country . Hence, there is no formal theory developed. An exception is the empirical analyses of the relationship

between networked communications and political democracy by Kedzie (1997). He found a strong correlation betweendemocracy and interconnectivity in 1983 and 1993 . The data used in this research is outdated today . The situation today,

both technological and political, might not still be comparable . Kedzie used data on several networks (BITNET, UUCP,FidoNet) that are no longer of relevance . The only network used in the analyses that is still important today is the

internet. However, the internet has grown and changed dramatically and is certainly not comparable to its early days .

Technologies that potentially have great impact on contemporary democracy, such as weblogs and internet forums, were

not available during the period in which Kedzie performed his research .

Like Kedzie, this research will address the relationship between communications technology and democracy . The degree

of communication in a country will be determined using different indicators of access . Not only the internet will beincluded, but also other forms of communications. Since democratic change occurs at a very slow rate, data will be

collected for a period of 10 to 15 years depending on the amount available . Using statistical regression analysis the use

of communication technologies and several other determinants of democracy will be analysed .

12


1 .3 Research design

1 .3.1 Research aim and demarcation

Oxfam Novib has long been interested in whether information and communication technology (ICT) can contribute to

their development goals . There are different perspectives within the organization on how ICT should be embedded in the

overall strategy for development . As it is Oxfam Novib's conviction that democracy contributes to development, it is

relevant to gain insight into whether the use of ICT can contribute to increasing the level of democracy in countries that

experience relatively low levels of democracy . The aim of this study is not to provide guidelines or recommendations on

how ICT should be applied by development agencies in developing countries, rather it addresses the underlying

theoretical relationship between use of ICT and democracy . The term 'use of ICT' is rather ambiguous and requires

further demarcation . An important aspect of democracy is the availability of information and unrestricted access by all

individuals of the community . This implies a need for individuals to communicate easily with other individuals or groups .

In this study the'use of ICT' is more narrowly defined as the ability of individuals to communicate with other individuals or

groups. For this, the term connectivity is introduced, which will be defined later. The overall research aim of this study is

therefore defined as:

To determine whether connectivity can facilitate democracy in countries that experience low levels of

democracy.

1 .3.2 Problem definition and research questions

To achieve the research aim, this study is divided into a theoretical and an empirical part . The theoretical part first

explores the concept of democracy and its main determinants . This will be done by reviewing the literature . The influence

of information and communication technology is also examined . When the theoretical framework is drawn up, an

empirical analysis will be used to find evidence to support the hypotheses derived from the literature . The following

problem definition has been formulated :

What is the relationship between connectivity and democracy?

Providing an answer to this problem definition requires several sub-question . First, in a literature review, generally

accepted determinants of democracy will have to be identified . These determinants may be more important in predicting

democracy that the connectivity variable that is of special interest for this study . As these determinants may also be

mutually dependent, their relationships have to be examined as well . The following sub-question is therefore defined :

i. What are, according to the literature, the most important determinants of democracy and what are their mutual

influences? (chapter 2)

Before being able to engage in the empirical analysis to gather evidence to support theory, measures for the concepts of

democracy and connectivity will have to be found or constructed . These measures then have to be used to construct an

empirical model . The corresponding sub-question :

ii. How can the concepts of democracy and connectivity be measured? (chapter 3)

Having answered both questions the final step is the empirical analysis . A model will have to be constructed, that permits

the relationship as found in the literature to be examined . Data need to be gathered and finally the analysis will provide

13


evidence to support (or falsify) the relationships found in the literature . The following sub-question is defined :

iii. To what extent does empirical evidence support the relationship between democracy and connectivity as found

in the literature? (chapter 4)

The outcome of these three sub-questions will consequently be used to address the problem definition . By testing the

hypotheses formulated in the theoretical part, the relationship between connectivity and democracy will be described in

detail .

1 .3.3 Relevance

As mentioned briefly above, the aim of this study is to gain insight in the influence that communication technology has on

democracy. The study is primarily an empirical analysis, and as such will provide evidence to support or falsify the

relationships offered by literature . It will not deliver straight answers to how Oxfam Novib should apply information and

communication technology to facilitate or strengthen democracy . It does, however, attempt to describe the influence of

these technologies on democracy . This information will be useful for Oxfam Novib when developing new or modifying

existing strategies .

Although the intended beneficiary for the outcome of this study is Oxfam Novib, other development agencies and

organizations that promote democratic development may benefit as well . As the relationship between connectivity and

democracy has not been subject of many studies and, in addition, theories that have been developed are often

contradictory, the outcome of this study will be relevant for future formulation of theory on democracy .

1 .4 Structure of the report

The first chapter opens with a brief introduction into the research topic and overall aim of the research . It is followed by

the definition of the main research question, which is then split up into several sub-questions . These questions form the

basic framework on which this report is build . Each of the following chapters addresses one or more research questions .

Finally, the last chapter contains an overview of the research results .

In chapter two a theoretical framework is drawn up . First, the literature on political science is reviewed, including several

definitions of democracy. The relation between democracy and economic development is then addressed . It is followed

by an analysis of the relationship between economic development and telecommunications development . Having

described the relation of economic development with both variables, their mutual relationship is discussed . The last

section of this chapter provides the formulation of several hypotheses that will be tested in the remainder of the report .

Methodological issues are discussed in chapter three . It is an operationalization of democracy and the use of

communications technology . Several indicators of both concepts are reviewed and their advantages and shortcomings

discussed. The chapter concludes with the choice of several indicators that will be used in the empirical analyses of the

following chapter .

Chapter four contains the actual empirical analyses . Before addressing the analyses, a detailed description of the

dataset that is used and its characteristics is given . Several regression models are then constructed and their results are

discussed. In addition, the problem of multicollinearity is addressed by a regional analysis . The relationship between all

variables will be analysed and compared to the theory .

14


The final chapter, chapter five, contains an overall discussion of the results of the analysed models and dataset . The

shortcomings of the present study are discussed and recommendations are provided to improve the result . Finally,

recommendations for future research are given .

15


16

Telecommunicationand democracy 2. Theoretical framework

2 . Theoretical frameworkIn this chapter a theoretical framework will be presented that will be used to examine the problem definition as defined in

the previous chapter. In a review of the available literature the relationship between telecommunications and democracy

will be described . The first section explores political theory and seeks to define the concept of democracy . This includes

an overview of the most important determinants of democracy found in the literature . A casual view of the literature

revealed the importance of including a country's economic situation into the analysis . The degree of democracy and the

availability of telecommunications were both found to correlate with a country's GDP. The following sections will

elaborate on these relationships . Having examined the influence of the economic situation, the next section addresses

the relationship between telecommunications and democracy . A study carried out in 1997 by Kedzie will be discussed

and criticized in detail . This research is the point of departure for the rest of the study. In the last section of this chapter

hypotheses are formulated that will tested in the empirical analyse in the following chapters .

2.1 Political theory

This section, as earlier mentioned, will elaborate on the definitions and the determinants of democracy . The section is

divided into two parts ; the first part summarizes literature that offers definitions of democracy, the second part explains

more about the determinants of democracy and is mostly based on the study of Barro (1999) .

2 .1 .1 Definitions of democracy

Democracy is a complicated multidimensional concept for which many different definitions have been formulated over

the years. It is not surprising that the term democracy continues to have different meanings for different people. The

Greek city-state of Athens is generally considered to have established the earliest form of democracy. Democracy in

Athens was based on direct participation of its citizens . They did not elect officials to represent them, rather the citizenry

as a whole formed the Assembly in which they voted on legislation and executive bills. Citizenship was however rather

exclusive. Only adult male Athenians who had completed their military training had the right to vote . This excluded the

majority of the population . Women, slaves, children and resident foreigners were excluded from political decision making

(Dahl, 1989) .

Early democracies were, like the city-state of Athens, based on direct participation . It was only in the seventeenth century

that the notion of a representative government gradually became accepted . Until then, theorist like Rousseau (1968)

believed in active and involved citizens that would enjoy political and economic equality . As city-states grew, it became

apparent that direct democracy imposed severe limits on the size of a democratic state . Representative democracy, in

which the people are being represented by elected government officials, slowly became accepted as a solution to the

earlier limits on size (Dahl, 1989) .

Schumpeter was one of the first to define democracy in terms of institutions and procedures . In his definition, the

democratic method is :

". . . an institutional arrangement for arriving at political decisions in which individuals acquire the power to

decide by means of a competitive struggle for the people's vote" (Schumpeter, 1987) .

17

Telecommunication and democracy 2. Theoretical framework

In his view, democracy is mainly a mechanism for choosing political leadership .

Dahl, who also was of major importance for the formulation of the modern theory of democracy, emphasized the

continuing responsiveness of the government to the preferences of its citizens . Citizens must have the opportunity to

formulate their preferences, be able to signify these preferences to other citizens and the government and have their

preferences weighted equally, with no discrimination (Dahl, 1971), Dahl applies the term democracy for the ideal

situation in which these criteria are fully met . He introduced the term polyarchy for real world systems that are close to

this ideal situation . In Dahl's polyarchy seven institutions need to be present ; elected officials, free and fair elections,

inclusive suffrage, the right to run for office, freedom of expression, access to alternative information and associational

autonomy .

The same focus on elections can be found in other definitions as well . Bollen (1980) however, defines political

democracy:

. . as the extent to which the political power of the elite is minimized and that of the non-elite is

maximized "

In addition, Schmitter and Karl (1996) note the importance of competition and cooperation between elected

representatives and the need to hold rulers accountable for their actions . Following Schumpeters institutional framework,

Weiner et al. (1987) specify four conditions that need to be met in a democracy . Governments must be chosen through

competitive elections in which opposing political parties take part as well . All parties have the right to seek public support

(e .g. freedom of speech, access to the press, the right of assembly and protection against unlawful arrest) . Defeated

governments step down and are not punished by the new government (unless unlawful actions were conducted). Elected

governments exercise power, make policies and are accountable only to the electors, not to the military, monarchy,

bureaucracy or oligarchy (Weiner et al ., 1987) .

A fitting summary of the above is provided by Balema . He argues that democracy can generally be described as :

. . . a method of organizing government through elections and people's participation, not only in its

organizational process, but also in the activities of the government." (Balema, 2003)

To this statement he adds that people enjoy a certain level of civil and political liberties (Balema, 2003) . There are many

other definitions of democracy, however most of them include, to a certain degree, the same issues as described in this

paragraph . There are many different measures and indicators of democracy . In chapter 3 (paragraph 3.2) several of

these measures will be discussed . Not only direct measures of democracy are reviewed, but also measures of

prerequisites of democracy like the corruption perception index (CPI) and a measure for press freedom .

2.1 .2 Determinants of democracy

In a statistical analysis, Barro (1999) studied several determinants of democracy . Over 100 countries were included in a

panel study from 1960 to 1995 . Barro used Gastil's (1973) political freedom as the dependent variable in a regression

analysis. When replacing the dependent variable with Gastil's civil liberties, similar results were obtained . The findings of

the analyses will be briefly discussed (the results of the regressions are included in appendix A) .

Barro (1999) found improvements in the standard of living, as measured by per capita GDP and several health

indicators, to be the best predictor of political freedom . Countries with a higher per capita GDP and good basic health

18

Telecommunicationand democracy 2. Theoretical framework

care were found more likely to be democratic . The same goes for primary education, which was found to be positively

(and significantly) correlated to political freedom as well . Barro also considered educational inequality . The gap between

male and female educational attainment was found to be negatively and significantly correlated with political freedom .

This indicates that inequality in male and female attainment might restrain the degree of democracy . Barro offers two

explanations; the gap might be the result of general inequality between the sexes, or an alternative explanation is that

societies with more opportunities for female education are generally more participatory in nature and therefore more

receptive to democracy.

Population size and urbanization rate were also included in the analysis . A weak but positive relation between country

size and political freedom was found . Although in the theory of political science (see Upset, 1959) the urbanization rate is

often addressed, it remains indecisive about its sign in the equation . A rural population might be limited in organizing

itself, making it easier to oppress . It is, on the other hand, argued that a rural population is less dense and therefore

harder to oppress . Barro found the rate of urbanization to be negatively correlated with political freedom, however

marginally significant.

Barro also studied the effects of GDP generated by oil revenues on democracy by introducing an 'oil-country dummy into

the equation. The dummy correlated negatively, indicating that the strong relation between GDP and political freedom

applies less in oil-producing countries . An effort to extend this finding to natural-resources in general did not result in

significant values .

Another important observation made by Barro was that his study found democracy to be 'highly persistent over time .' A

five-year lagged variable of democracy was included in the regression . This variable's high regression coefficient (0 .61)

proves that democratic change is a slow process .

In the same study a range of other possible determinants of democracy was examined . Most of these have been

proposed in the political-science literature . The variables were included in the previously described model (one set at a

time). In most cases, the results were not statistically significant, therefore no hard conclusions could be drawn . This

does, however, not mean that they are of no influence. It would not be thorough to exclude these findings and therefore

they will be briefly discussed below .

Two health indicators were examined ; life expectancy at birth was found to be positively correlated, although marginally

significant. Infant mortality was, not surprisingly, negatively correlated (yet not significant) . Measures for upper-level

schooling were found to be statistically insignificant, so it appears primary education is more important for democracy .

The influence of income inequality was examined by introducing Gini coefficients for income distribution . Some indication

was found that greater income inequality predicts less democracy . Political science literature often stresses the

importance of a the middle-class . Barro indeed found an increase in the middle-class share of income to go along with

more political freedom . Another important factor is the population's degree of heterogeneity . The underlying thought is

that democracy is more difficult to sustain when a population is more heterogeneous (with respect to ethnicity, culture

and language). Some indication was found to support this (although marginally significant) . In the literature the rule-of-

law is also often addressed . Indicators to measure rule-of-law are the quality of the bureaucracy, political corruption,

likelihood of government expropriation and overall maintenance of the rule of law . Although intuitively it can be argued

that these are all important for democracy, no evidence was found to support this .

The final two factors Barro examined were colonial history and religion . Lipset et al. (1993) argue that colonial heritage

provides a critical learning experience for former colonies . They would inherit tendencies for democracy from their

19

Telecommunication and democracv 2 . Theoretical framework

previous rulers . Using a simple correlation, the opposite was found . Non-colonies were found to be more likely to to be

democratic. It was also found that former British and Spanish colonies were more democratic than colonies of France,

Portugal or other countries . In a similar manner, the influence of religion was studied . It was found that in almost all

cases Protestant countries were highly democratic . On the other end of the spectrum, Muslim countries and non-

religious countries were found usually not to be democratic .

2.2 Economic development and democracy

The relationship between economic development and democracy has been addressed in many studies in the past. Most

of these studies are interested in the impact that democracy has on economic growth . There has been research

addressing the differences between autocratic and democratic regimes, while others focus on the impact of democracy

on economic freedom . Additionally, there has been research to the reverse relationship, that is, the impact of economic

development on the propensity of a country to become democratic . This relationship is most important for the present

study, as democracy is the independent variable . It is important to discuss both relationships as the regression analyses

performed in the empirical analysis does not provide evidence on the causality of the relationships .

2 .2.1 Economic theory

Economists have long been interested in factors that cause different economies to grow at different rates and achieve

various levels of wealth . One of these factors that has been addressed is democratic freedom . Several views have been

developed, often opposing to each other . Sirowy and Inkeles (1990) label the various theoretical positions as the

"conflict", "compatibility" and "sceptical" perspective. The conflict perspective claims that economic growth is hindered by

democratic freedom . Adherents of the compatibility perspective argue that democracy has a positive effect on economic

growth. In the sceptical perspective, there is considerable doubt as to whether any systematic relationship between

democracy and economic growth exists at all . Only the conflict and compatibility perspective will now briefly be

discussed, as the sceptical perspective does not contribute to understanding the relationship .

In the conflict perspective, democracy and economic growth are seen as competing concerns. De Haan and Siermann

(1996) discuss several arguments supporting this view . An argument that applies specifically to developing countries, is

that it is argued that political institutions in these countries are (often) weak and fragile . Due to the availability of many

channels through which pressure groups can express their demands, democratic regimes might be overburdened, they

argue. Politicians, according to the authors, have to cater to all of these demands in order to stay in office . As a result it

will be difficult to implement policy targeted at rapid economic growth. In addition, in a democracy conflicts as a result of

heterogeneity of religion, region, ethnicity and class will have to be addressed . Authoritarian regimes, on the other hand,

may be better in suppressing these conflicts and implement policies to stimulate economic growth more easily . After

surveying 98 countries in the period from 1950 to 1977, Marsh (1979) concluded that :

"Political competition/democracy does have a significant effect on later rates of economic development; its

influence is to retard the development rate, rather than to facilitate it. In short, among the poor nations, an

authoritarian political system increases the rate of economic development, while a democratic political

system does appear to be a luxury which hinders development." Marsh (1979)

The regime of Singapore is a fitting example . It belongs to the group of high income countries and according to Freedom

House it is only partly free .

20


Adherents of the compatibility perspective belief that democracy and the existence of civil liberties and political rights

generate the conditions most conducive to economic development . They argue that democratic institutions provide a

check on governmental power . It thereby limits the potential of public officials to amass personal wealth and to carry out

unpopular policies (De Haan and Siermann, 1996) . Since at least some policies that stimulate growth will also be

politically popular, more political rights tend to be growth enhancing (Barro, 1996) .

A distinction is sometimes made between influences in the short and long run . It is argued that under some conditions

authoritarian rule may generate a more rapid rate of economic growth in the short run, while in the long run democracy is

more conducive to sustain growth (Sirowy and Inkeles, 1990) . An authoritarian regime may be needed in the beginning

of liberalization, because the electorates often turn down economic reform even when it is known that in the end they

would benefit the majority of the voters (De Haan and Sturm, 2003) . Countries like Chile, South Korea and Taiwan are

often presented as evidence . These countries introduced democracy only after economic reform was successfully

implemented (Edwards, 1991) .

There is little compelling empirical evidence on the relationship between democracy and economic growth . De Haan and

Sturm (2003) provide an overview of empirical studies. Their most important findings will be discussed here . De Melo et

al. (1997), using panel data for 26 countries in transition, found a correlation between economic liberalization and

Freedom House's political freedom. They estimated different determinants of economic liberalization and found political

freedom to have a positive and highly significant coefficient in their model . A study by Dethier et al . (1999) confirms this

relationship in an analysis of 25 post-communist countries of Central and Eastern Europe and the former Soviet Union

between 1992 and 1997. These studies confirm the effect of political freedom on liberalization . It is through liberalization

that the economic performance is affected ultimately . So the effect of political freedom found in these studies is indirect .

Fidmwc (2000) partly confirms these findings. In this study, which focusses on the economic performance of countries in

transition, a U-shaped effect was discovered . In accordance with the previously described evidence, full democracy was

found to lead to better economic performance. Contrary to the what was found before, countries with no democracy at all

were found to have good economic performance as well .

De Haan and Sturm (2003) focussed on developing countries and examined the relationship between economic and

political freedom in the period of 1975 to 1995 . They found that political freedom was positively correlated with increases

economic freedom .

It seems there are substantially diverse views on the effect of democracy on economic development . On the one hand,

some theories argue that democracy and economic development are mutually reinforcing . On the other hand, other

theories argue that economic development may be severely hindered by democracy. Still, there are theorists that insist

there is no tangible relationship at all . On some subjects there seems to be (some kind of) consensus. Most theories

agree that democracy requires as a prerequisite some level of economic development (Huntington, 1991) . However, it

remains unclear what levels of economic development are sufficient .

2.2.2 The effect of economic development on democracy

Although in economic theory the effect of democracy on economic development has been the subject of many studies,

research that addresses the reverse relationship is more scarce . The research of Lipset (1959) has lead to the common

idea that the propensity for democracy increases with an increase in economic prosperity. This is often referred to as the

Lipset/Aristotle hypothesis, as Lipset credits Aristotle as the source of the idea :

21

Telecommunication and democracy 2 Theoretical framework

"From Aristotle down to the present, men have argued that only in a wealthy society in which relatively few

citizens lived in real poverty could a situation exist in which the mass of the population could intelligently

participate in politics and could develop the self-restraint necessary to avoid succumbing to the appeals of

irresponsible demagogues." (Lipset, 1959)

According to Barro (2000), theories on the effect of economic conditions on democracy are not well developed . Lipset

(1959) stresses the importance of increased education and an enlarged middle class as key elements . He also refers to

Alexis de Tocqueville's (1835) idea that private organizations and institutions are important as checks on centralized

government power.

Despite the lack of a good theoretical model to determine the effect of economic conditions on democracy, Barro (1999)

found strong empirical evidence that confirms the Lipset/Aristotle hypothesis . He found increases in various measures of

the standard of living to predict a gradual rise in democracy. In addition, he found that when democracy was imposed

upon a country (e .g. by a former colonial power or by international organizations) without prior economic development,

democracy did not tend to last. Examples of such countries are most of the newly independent African states in the early

1960's. Conversely, Barro (1999) observed several non-democratic countries that obtained substantial economic

development . Examples of these countries include Chile, Korea, Taiwan, Spain and Portugal . In accordance with these

findings, Schwartz (1992) found that most OECD countries first established good economic development and only much

later introduced more democratic freedom .

2.3 Economic development and telecommunication

This section reviews the relation between telecommunications and economic development at a macroeconomic level .

Saunders et al . (1984) were among the first to provide some general and descriptive insights into the benefits of

telecommunications. They reviewed empirical evidence of macroeconomic and country level research of two different

types. The first type of research consisted of aggregate correlation analyses . Research of this approach includes both

cross-sectional studies, comparing variables between different countries, and time-series studies, in which a variable in a

single country is traced over time . The second type of research they reviewed was referred to as structural economic

analysis. This research relies primarily on classic input-output analysis .

2.3.1 Aggregate correlation analysis

Saunders et al . (1984) observed CCITT' data for a group of thirty industrial and developing countries in 1955, 1960 and

1965. They found a very strong correlation between telephone density (the number of telephones per 100 persons) and

GDP per capita . In the figure below, this correlation is reproduced with data of over 100 countries for the year 2002

(source of the data is ITU's World Telecommunication Indicators 2003) .

As an alternative to examining cross section data of many countries, time series data of single countries were examined

as well. These analyses yielded in findings similar to those obtained by the cross section data : a strong relation between

teledensity2 and GDP per capita. Another method of analysing benefits of telecommunications which was reviewed, was

the use of CCITT's telecommunications utilization factor. It is defined as the number of telephones per 100,000 US$ of

1 Since 1993, the CCITT is known as the ITU-T (ITU Telecommunications Standardization Sector) . The function of the ITU-T is to

provide global telecommunications standards by studying technical, operating and tariff questions . It aims to be recognized as thepre-eminent worldwide telecommunication standards body .

2 Teledensity here is defined as the fixed telephones in use for every 100 individuals within an area .

22


Figure 2 .1 : Teledensity and GDP per capita (in 2002)

GDP. High utilization factors were obtained for industrial economies, typically 10 or more . Developing countries with

strong industrial sectors had lower factors (between 5 and 8) and the poorest developing countries scored no more than

1 to 3 . This implies that an economy becomes more intensive in its use of telecommunication as it grows .

The use of different proxies for telecommunication (other than telephone) was also reviewed . A strong correlation

between telex density and GDP per capita was found . Since telex is used extensively in international trade, telex also

correlated with the value of imports .

Saunders et al . (1984) also reviewed research that analysed telecommunications traffic . Several studies tried to explain

variations in telecommunications traffic across countries and through time . Correlations between telephone calling rates

and GDP, GDP per capita, value of imports, value of exports and other measures of economic activity were determined .

It could not be shown that economic development influenced local telephone call traffic. However, long distance

telephone traffic increased at roughly double the rate of increases in GDP . Similar to the telex, international telephone

call traffic was strongly correlated with changes in the volume of international trade .

Reviewing the previously described research, three main conclusions about aggregate analysis were drawn . First, the

complexity of the relation between telecommunications and economic activity is far too great to be represented in a

single equation, thus compromising validity . Second, due to the heterogeneous nature of the data used, countries at one

end of the spectrum might not easily be compared with others on the other end . Last, although high correlation

coefficients were obtained, there was no evidence for causation . For example, it could be expected that an increase in

GDP is likely to result in greater teledensity . On the other hand, greater teledensity might result in growth of GDP (since it

would provide new opportunity) .

23


2.3.2 Structural economic analysis

The second type of research reviewed by Saunders et al . (1984) was structural economic analysis, which focussed onthe influence of telecommunications on the structure of the economy . In this approach, which relies primarily on classicinput-output analysis, the output of a particular sector (e .g. agriculture, mining, transport, etc.) is related to itsrequirements for input . One of these inputs being telecommunications services .

Three types of indicators for both developed and developing countries were calculated . The 'communications outputdistribution coefficient' is the proportion of total output from the communications sector purchased by each other sector .Five sectors were compared : agriculture, mining and manufacturing, service, household consumption, and other . It wasapparent that most of the communications output was consumed by the service sector . Agriculture used relatively littlecommunications . A drawback of this indicator is that it does not take the size of a sector into account (i .e. a sector with

intensive use of communications but small in size compared to other sectors, will most likely not make a significantcontribution) .

This distortion can be reduced by using the 'communications input coefficient'. It is the amount of communicationservices purchased by each sector per unit of output of that sector . Several studies found that the use oftelecommunications services was not equally important for all sectors . The tertiary sector was found to be the mostintensive in its use. Least dependent on telecommunications was the primary sector and the secondary sector wassomewhere in between. The studies also found a difference in the class of subscribers between developing anddeveloped countries . In 1976, subscribers in industry, banking, transport, and government in developing countries

accounted for nearly 90 percent of the total expenditure on telephone services, leaving only 10% for residentialsubscribers. In developed countries, a much higher proportion of residential subscribers was found . In addition to thedirect purchases by each sector, indirect purchases were studied as well (i .e. a sector might use input from anothersector, which in turn may rely on the communications sector). Compared to the direct coefficients, a greater overalldependence on communications was found for the agricultural sector. This effect, however, is more apparent indeveloped countries . This is probably because in these countries the agricultural sector is more dependent on inputs'

form other sectors, which in turn rely on communications services .

Of special interest are the direct and indirect coefficients for the communications sector itself. They indicate the extent towhich growth in the communications sector will stimulate growth in other sectors . Saunders et al . (1984) found that themost important input to the communications sector were households (labour), followed by services and transport . Insome countries machinery, trade, utilities, and construction were important as well . This dependence" on the labourintensive service sector indicates that growth in the communications sector will primarily feedback into householdincome .

Input-output analyses reveal part of the role telecommunications play in an economy . If the coefficients described abovewould vary little between countries, it would indicate a stable production function with fixed coefficients (i .e. an economyrequires a minimal amount of telecommunications, if this amount is not met, other sectors outputs will drop) . However,this does not seem the case, since great differences among countries were observed . Possible explanations are:differences in quality of output of the communications sector in different countries (supply might be less timely or quality

poor), wasteful communications (particularly in high income countries), systematic differences in sector organization,

3 Such as fertilizers, machinery, and technical assistance .

4 It must be noted that at the time of the research data on the communications sector often comprised both the communicationssector and postal sector . Since the postal sector is much more labour intensive than the communications sector, figures might havebeen distorted .

24

Telecommunicationand democracv 2. Theoretical framework

management, technology, work organization, benefits, etc. and substitution of other production factors such as increased

use of labour or transport .

2.4 Democracy and telecommunication

This section contains a discussion of the article 'Communication and Democracy : Coincident Revolutions and the

Emergent Dictators' by Christopher Kedzie (1997) . Kedzie examined the role information played in the demise of the

Soviet Union. He developed a theory on the effect that communication technology has on totalitarian societies . According

to Kedzie, they face a dilemma : either they try to control and hold back information and communication technology and

thereby fall further behind in the new industrial revolution, or they permit these technologies and inevitable loose their

totalitarian control . This is referred to this as the "dictator's dilemma", which will be described in the following section .

Kedzie also provides an extensive empirical analysis of the relationship between "electronic network interconnectivity"

and democracy. The results of this empirical analysis and the methodology used will be addresses the last paragraph of

this section.

2.4.1 The "dictator's dilemma"

In an attempt to find an explanation for the demise of the Soviet Union, Kedzie (1997) first explored the relationship

between economic development and democracy. Lipset (1959) was the first to confirm a significant correlation between

democracy and economic development. However, this theory does not offer an explanation to the democratic aspirations

of the Soviet Union, since they appeared while Soviet economic performance was falling .

Kedzie (1997) then turns to anecdotal evidence describing the role of information in the Soviet Union. Politicians,

journalists and analysts have all pointed to information currents as being responsible for changing the political climate .

Shimon Perez (Israel's foreign minister at that time) observed that communism fell without the participation of the

Russian army . It fell without the intervention of the United States, Europe, China or anybody, but became weak when it

could no longer blind the people or control the flow of information . Similarly, the policy-analysis community, concluded

that communism did not fall because of its centrally controlled economic policies or its excessive military burdens, but

because "its closed societies were too long denied the fruits of the information revolution that was developing elsewhere

over the last 40 years" (Kedzie, 1997) . Journalists agreed : "information (. . .] had undermined ideology, exposed the

bureaucracy." People were better informed about the past consequences of totalitarianism and better understood what

was at stake .

Traditional communication technology, as instituted by Lenin and Stalin, were 'top-down vertical' as opposed to modern

'horizontal' communications systems, which are much more difficult to control . In 1989 Relcom, an entrepreneurial

network, came on-line . Its purpose was to allow and encourage information to be unbound for the purpose of revitalizing

the Soviet economy. During the attempted coup in 1991, Relcom played an imported role in gathering and dissemination

of information. During three days it transmitted 46000 pieces of news, while all other channels were closed (Kedzie,

1997)

Gorbachev's predicament is an example of the "dictator's dilemma" . In order to capitalize on economic growth, control on

information needed to be loosened . However, without control on information, it would be difficult (if not impossible) to

preserve social control. Saudi Arabia and Singapore faced the same problem . The Saudis always wanted the latest

technology, but the Saudi authorities worried they would lose their tight grip on political dialogue . Meanwhile, business

25


executives need access to the latest information in order to be competitive . In the city-state of Singapore new technology

speeds up economic transactions, but at the same time make the debate about the country's future harder to control .

telephone

demó~s.rat~ildea ~1

\\computernetworks

television

dictator'spreference

message recipients

influence -o-

Figure 2.2: Communications participants

In order to understand how the dictator's dilemma is a result of advances in communication technology, Kedzie uses the

graphic of figure 2 .2. It shows who is able to communicate with whom . Traditional broadcast technologies (like television)

are located at the horizontal axis. They have a large audience, but use is greatly limited by economic, political and

technological constraints . Increases parallel to the vertical axis indicate an expanding accessibility of interactivecommunication . Influence increase as more people receive the message and autonomy increases as when more people

are able to originate and share their ideas. From a dictator's perspective, the situation in the bottom right of the figure

would be ideal (maximizing influence, while limiting autonomy) . The top right of the figure represents the democraticideal, where many originator are able to communicate with many recipients .

Traditional broadcast technologies (e .g. radio and television) are located near the dictator's optimum . They are easy to

control and therefore almost certain to strengthen authoritative control. The telephone is located near the vertical axis .Although it can approach universal access, the number of recipients per message is rarely more than one, severely

limiting influence . International access is also easily controllable . All traffic passes through certain exchange points, withcan be controlled by the authorities. Hence it is hardly a threat to a dictator. Modem electronic networks, on the other

hand, are no longer bound to one of the axis and reside in the middle of the figure . They provide both influence andautonomy. In the case of the Soviet Union, electronic networks provided new economic opportunities and at the same

time they provided political opportunities for those seeking to oppose authoritarian control . Controlling electronic

networks will be technically difficult and very costly .

2 .4.2 Quantitative analyses

Kedzie (1997) provides an extensive empirical analysis of the relationship between interconnectivity and democracy as

well. The indicator for democracy, the dependent variable, is derived from Freedom House's Comparative Survey of

Freedom (1993/94) . Freedom House provides country scores' for 'political rights' and 'civil liberties' . Since both are

generally recognized as fundamental elements of democracy, Kedzie constructs a single indicator by calculating the

average of both scores . Interconnectivity, in the study, is defined specifically as the use of electronic mail . The four mostdominant electronic mail networks in 1994 were Internet, BITNET, UUCP and FidoNet . The Matrix Information Directory

Service (MIDS) maintained historical data on the size of these networks . The basic unit for interconnectivity used here, is

5 An overview of the methodology on how these indicators are constructed, can be found and :http ://www .freedomhouse .org/template.cfm?page=35&year-2005

26

Telecommunication and democracy 2 . Theoretical framework

the number of network nodes . Kedzie performed several analyses, which will be discussed below .

Before determining the correlation between interconnectivity and democracy, Kedzie (1997) first identified several other

commonly accepted determinants of democracy. These are : economic development and education, human development

and health, ethnicity and culture, population size . Economic development was measured by GDP per capita . The

measure of human development and health is the average life expectancy. The measure of ethnic homogeneity is

defined as the percentage of the population that constitutes the largest ethnic group in a nation . Population size is

measured by the number of inhabitants of a country .

Kedzie (1997) found a powerful correlation between interconnectivity and democracy . The correlation coefficient on

interconnectivity (0 .73) is larger than any other predictor . Even the coefficient on GDP, which is often considered the

most important predictor of democracy, was substantially smaller (0 .57). In order to develop a more comprehensive

understanding of the relationship between democracy and its various predictors, Kedzie developed six different

regression models. These will now be discussed briefly .

Kezie (1997) first constructed two ordinary least square regression models using a different number of predictors . The

first consists of six predictors : interconnectivity, GDP, population, schooling, life expectancy and ethnicity . In his second

model three variables were excluded, leaving : interconnectivity, GDP and population . In both models the significance of

interconnectivity was greater than 99 .9%. In addition, the coefficient of interconnectivity was considerable (one point

increase in interconnectivity results in a five point increase in democracy) . Excluding the three variables resulted in a

very small drop of the adjusted Rz, underlining the importance of interconnectivity .

Kedzie (1997) used a natural logarithmic function to transform population size to obtain a more normally distributed

function . The coefficient for GDP is small, but negative and significant. Kedzie explains that this could support arguments

of some scholars who argue that democracy is not costless (e.g. in the case of Pinochet and Lee Kuan Yew) .

To account for regional differences Kedzie (1997) used the same two models and added regional interaction terms for

six predefined regions : Africa, Asia, Eurasia, Latin America, the Middle East and West Europe . Dummy variables were

created for each region. An interconnectivity variable for all regions was calculated by multiplying a country's

interconnectivity score with the dummy . Then, both the dummies and the regional interconnectivity variables where

included in the regression (while dropping one of the dummies) . The models showed a consistently strong correlation

between interconnectivity and democracy across these regional boundaries . This was most appeared in Africa and least

appeared in West Europe, as there is more room for democratic improvement in Africa . Again, the coefficient of GDP

was negative, although not significant .

Kedzie (1997) also performed a longitudinal time analysis . The changes in democracy and interconnectivity between

1983 and 1993 were studied, again using data from Freedom House and MIDS. Countries were divided into three groups

according to the Freedom House classification as either 'Not Free', 'Partially Free' or 'Free' . The 'Not Free' category

contained the largest number of countries . In this group, democratic change was best predicted by change in

interconnectivity (significance of over 99,9%) . The analysis revealed that there was not a single case of even a moderate

increase in the level of interconnectivity that was not also accompanied by at least a moderate increase in the level of

democracy. This corresponds to the dictator's dilemma. No country has yet been successful in exploiting networked

communications and at the same time avoid political liberalization . Kedzie then analysed groups of countries within the

'Not Free' group with similar characteristics. In all cases, except for one, the correlation between interconnectivity and

democratic change was confirmed . In the one case in which it was not confirmed, it could not be rejected and there were

27

Telecommunication and democracy 2 . Theoretical framework

statistical and speculative reasons why the correlation might be stronger than observed .

The 'Partially Free' group was the smallest and most diverse group, consisting of 31 countries . Similar results were

obtained, although with lower levels of significance . Asia was they only region with sufficient variance of interconnectivity

for a meaningful analysis . In this region Singapore proved a dominant outlier . With Singapore included the statistical

significance of interconnectivity was 60% . When excluded a level of 95% was obtained . The goodness-of-fit quadrupled

when excluding Singapore . In the 'Free' countries, again, a large correlation between interconnectivity and democratic

change was found . The slope of the regression line is however lower, because these countries are relatively stable

democracies (and hence not much democratic change occurs) .

Although the correlations found in the previous models were strong, they do not prove any causality between democracy

and interconnectivity. One might infer that interconnectivity leads to democracy, on the other hand democracy might also

lead to interconnectivity (as democracies rely on an informed public and may therefore seek interconnectivity) . Kedzie

(1997) used a model of two equations with multiple endogenous variables solvable by a two-stage least squares

estimation. This model assumes that democracy influences interconnectivity and also the opposite ; interconnectivity

influences democracy. So both are the dependent variable . In order to obtain a unique solution at least one 'instrumental

variable' is required . The two additional variables included were the number of telephone lines per capita and the

percentage of literacy . The resulting regression coefficients showed that interconnectivity remained a strong indicator for

democracy. However, the democracy did not prove to have any significant effect on interconnectivity .

An alternative explanation for the strong correlation between interconnectivity and democracy is the existence of a third

variable that influences both simultaneously . Economic development is often considered a prerequisite for democracy . It

also has a strong correlation with interconnectivity . Kedzie (1997) therefore uses GDP as the third variable . These same

technique as the previous method was used . It assumes that: democracy and interconnectivity predict economic

development, democracy and economic development predict interconnectivity and last, interconnectivity and economic

development predict democracy. Interconnectivity utilizes the same 'instrumental variables' as before. For democracy

these same independent variables as before were used, except for schooling, which is used as an independent variable

for economic development. Using this method, Kedzie found that economic development is not the third variable

influencing both democracy and interconnectivity . The regression coefficients for interconnectivity on democracy and

GDP are substantial and statistically significant . Democracy and GDP both do not influence interconnectivity strongly .

Reviewing all models, it was concluded that interconnectivity correlates positively with democracy at high levels of

significance . The models showed interconnectivity to be a statistically significant predictor of democracy . Democracy, on

the other hand, was shown not to be a good predictor of interconnectivity . Although it has not much use for policy

considerations, the size of the population was negatively correlated to democracy . In most models GDP was correlated

negatively as well. This provides support for the notion that advances in democracy are not without economic costs .

2.4.3 Discussion on Kedzie's research

Kedzie's research provides valuable insights into the relationship between interconnectivity and democracy . His empirical

study is one of the first to quantify this relationship. Since 1997, there has not been much research available that is

comparable. Kedzie gathered data in a 10-year period between 1983 and 1993 . Of the four networks that were used to

construct the interconnectivity index, only the internet remains important today . However, the contemporary internet is

not the same as in 1993 . Many more countries are connected today and the number of internet users is much greater

than in 1993 . While the underlying internet protocol (IP) used to communicate may not have been changed, the functions

28


and services the intemet offers today are much more abundant than in 1993 .

Internet services like weblogs, discussion fora and instant messaging are available for only several years . Considering

Kedzie's dictator's dilemma, these two relatively new technologies may be greatly important for democracy . In figure 2 .2,

these technologies would be placed inside the 'democratic ideal' zone (top-right) . They have the potential to reach a lot

of recipients . At the same time many individuals can contribute to the contents, making it extremely difficult for a

totalitarian regime to control . Both autonomy and influence are maximized . It is evident that the internet today provides

more opportunities than it did when Kedzie's research was performed . Other important technologies like VOIP and peer-

to-peer (p2p) were also not available in the early days. Although the impact of VOIP technology might be similar to that

of normal telephony, it does enable cheaper communication to a greater number of recipients . P2p technology, on the

other hand, may have the potential of much greater impact . P2p networks consist of many users that there often

anonymous . As there is no central control, it is very difficult (if not impossible) for an authoritarian regime to exert control

over the entire network. Although individual users may be targeted and shutdown, this does not effect the overall

network. When considering the dictator's dilemma it becomes clear what the impact of this technology potentially is . Both

autonomy and influence are maximized .

In most of the models Kedzie (1997) constructed, a negative coefficient for GDP per capita was found . Kedzie explains

this by referring to literature in which democracy has a negative effect on economic development . The results are

presented as evidence for the theory that democracy might not be costless . Kedzie briefly addresses problems of

multicollinearity. Of the explanatory variables, GDP per capita, schooling and connectivity where found to be highly

correlated. He argues that when multicollinearity is present in model, it reduces the efficiency of the predictors, but does

not bias them . Therefore, the effects of multicollinearity were not further investigated . However, a possible effect of

multicollinearity that Kedzie does not mention is that the sign of some of the explanatory variables may be affected . For

example, the sign of GDP per capita might have become negative by including the interconnectivity variable.

To test this, Kedzie's original datasets was used and the regression analyses were repeated . The regression results

could be replicated. To get an indication of whether multicollinearity could cause problems, the variance of fit (VIF) was

first calculated for each of the explanatory variables. The highest VIF was found for GDP per capita, schooling and

connectivity (which is in accordance with the results of the correlation analysis) . When removing the connectivity variable

from the model, the sign for GDP per capita changed from negative to positive . The coefficient of GDP per capita

remained very small, however it was no longer significant . There is no conclusive evidence that the negative sign for

GDP per capita is caused by multicollinearity, however it it cannot be ruled out either . As a consequence it should be

interpreted carefully and Kedzie's conclusions may not have been accurate .

2 .5 Hypotheses

In this section several hypotheses will be formulated . Although more hypotheses could be derived from the previous

literature review, because of time constraints, the number of hypotheses is limited . The variables that are most important

according to the literature, like income level, are included . An additional argument was the availability of the data . For

example, it would be very interesting to examine the influence of religion and ethnicity on democracy and connectivity .

However, gathering data to address these relationships would be too time consuming for the present study .

The first hypothesis is based on the Lipset/Aristotle hypotheses . As previously described, Lipset (1959) suggests that

6 The complete dataset is available at : http ://www .rand .org/pubs/rgs_dissertations/RGSD127/appa .html

29


prosperity stimulates democracy . When this is true, countries with high levels of income would be more likely to be

democratic. Findings from Barro's (1999) research point in the same direction and therefore following hypothesis isformulated :

Hypothesis 1: Countries with high levels of income are more likely to be democratic.

The influence of a countries educational attainment was also hypothesized to affect democracy. Barro (1999) foundsome evidence . Moreover, intuitively it can be argued that education is important for democracy . Well informed citizensthat understand what is at stake, will be more likely to participate in the democratic process . Hence, the hypothesis 2 isformulated :

Hypothesis 2: Educational attainment increases the propensity for democracy.

The size of a country's population was also suggested to affect democracy . Although Barro (1999) found a positive

correlation between population size and democracy, the evidence was not very conclusive . The next hypothesis is basedon the argument that larger places may be more difficult to be democratically manageable .

Hypothesis 3: Large countries are less likely to be democratic .

The final hypothesis is based on Kedzie's (1997) findings that suggest that connectivity is very important for democracy .

The following hypothesis is formulated :

Hypothesis 4: Connectivity contributes to democracy.

The empirical analysis of chapter 4 provides evidence to support or deny these hypotheses . In chapter 5 they arediscussed in detail.

2.6 Conclusion

The purpose of this chapter was to provide an understanding of the theoretical relationship between democracy and its

most important determinants, including connectivity . It does this by answering the first sub-question, which was definedin the previous chapter:

What are, according to the literature, the most important determinants of democracy and what are their

mutual influences?

The concept of democracy was explored first . Modern democracies were found to be representative democracies . In therest of the report when the term democracy is used this refers to representative democracy . Several definitions have

been discussed and it was found that most of these definition have common elements . In Schumpeter's view democracy

is mainly a mechanism for choosing political leadership . Later theorist, expanded this perspective and definedpreconditions, including : free and fair elections, inclusive suffrage, the freedom of expression, press freedom . In short, ina democracy people enjoy certain political rights and civil liberties . This is what will be used to measure democracy in the

next chapter .

Several determinants of democracy were found and discussed . For most of these determinants there is little compelling

evidence that they are in fact good predictors of democracy . The determinants the literature agrees on as being

30


important are included in the empirical analysis . These are a country's income (measured by GDP per capita), population

size, educational attainment.

The relationship between economic development and democracy was further examined . The literature provides different

perspectives on the influence that democracy has on economic development. Some belief economic development is

hindered by democracy, while others feel that democracy stimulates economic development . Furthermore, there are

sceptics who doubt there is any systematic relationship at all . Not much research was found on the reverse relationship .

Increases in the standard of living were found to predict a gradual rise in democracy . It was also found that many

countries first established economic growth and only later became more democratic . The relationship between economic

development and telecommunication was also examined . Both variables were found to be very highly correlated . The

literature is not clear on the causal direction of this relationship . It seems they are mutually reinforcing . These findings

will be used in the empirical analysis in chapter 4 .

In the last section of this chapter, research similar to the present study was reviewed . It was shown why different

technologies have different potential to influence democracy, by Kedzie's "dictator's dilemma" . Finally, the quantitative

analysis of Kedzie is discussed and criticized . Problems like multicollinearity were found, that will have to be taken into

account in the empirical analysis .

31


32

Telecommunication and democrary 3 . Methodology

3. MethodologyThis chapter describes the method used to analyse the data . The first section describes the appropriateness of a

regression analyses. The advantages of using a regression analysis will be discussed . Shortcomings are identified, and

possible problems of using a regression analysis are discussed . Furthermore, the possibility of reverse causality will be

discussed. In the following section, a measure for democracy is selected . The last section provides an overview of

existing measures of ICT. Common elements in these measures are identified, which leads to a choice of three

indicators that will be used in the empirical analyses to measure a country's degree of connectivity .

3 .1 Method of analysis

3 .1 .1 Regression analyses

The main aim of this study is to gain insight into the relationship between connectivity and democracy . In the previous

chapter democracy was found to be dependent on several other factors . The analysis in this study is non-experimental

and therefore there is not always control over other factors that may also determine democracy . A regression analysis is

valuable for quantifying the impact of various simultaneous influences upon a single dependent variable . Because of the

'omitted variable bias' in a simple regression, multiple regression is often essential even when the investigator is only

interested in the effects of one of the independent variables . By using a multiple regression analysis, the relationship

between connectivity and democracy can be assessed while at the same time other determinants of democracy can be

included in the model . The goal of the regression analysis is to determine the values of parameters (estimates) for a

function that cause that function to best fit a set of data observations . These parameters provide information on the

relationship between a certain variable and the dependent variable . The regression analysis also provides a measure for

the statistical significance. This is the degree of confidence that the true relationships are close to the estimated

relationships .

In this study a linear regression model will be used . This mean that the model assumes a linear relationship between the

dependent and explanatory variables . In his research on the determinants of democracy, Barro (1999), as shown in the

previous chapter, also used a linear regression model . Kedzie (1997), who also used democracy as the dependent

variable, used a linear regression model as well . As there was no literature found that reports on non-linear relationships

between the variables used in this study and democracy, this study also uses linear regression .

In this study regional effects are also taken in the equation . This is done by constructing interaction variables . The

interaction variables were created by multiplying a regional dummy with the connectivity variables . The resulting

variables can be used to assess the influence of connectivity in a certain region .

A common problem with multiple regression analysis is that sometimes two or more of the explanatory variables are

dependent. This causes what is called multicollinearity. The multicollinearity problem does not result in biased coefficient

estimates, but does increase the standard error of the estimates and thus reduces the degree of confidence that can be

placed in them . The difficulty arises when two independent variables are closely correlated, creating a situation in which

their effects are difficult to separate . Kedzie (1997) report on multicollinearity problems in his research . As some of the

variables are the same in this study, there is also the possibility of multicollinearity in this study .

33

Telecommunication and democracy 3. Methodology

3.2 Measuring democracy

Democracy is a basic concept in political science that is inherently difficult to measure . As scholars have developed

different definitions of democracy, it is not surprising that this resulted in a number of different measures . Some include

dimensions such as political rights and civil liberties, others use socio-economic equality, uncertainty and the absence of

military influence. In a comparative study of different democracy indicators, Casper and Tufis (2003) identified the three

most widely used measures in democratization research . These are Polity IV, Polyarchy 1 .2 and Freedom House.

Polity IV was originally designed to measure the durability of states . Two indicators were constructed, measuring

autocracy and democracy . Both are rated on a 11-point scale. The most recent dataset (Polity lVd) includes 160

countries and data is available from 1800 to 2004 . It must be noted that although the latest year included is 2004, this

does not mean that for this year data is available for all countries . Furthermore, data does not seem to be available for

every year. Vanhanen (2000) constructed the Polyarchy index . It was originally intended to explain historical patterns of

democratization . Polyarchy is constructed of two main elements: political competition and participation. These are

averaged to create a democracy index. The dataset contains 187 countries and is available from 1810 to 1998 . Freedom

House measures freedoms in two dimensions: political rights and civil liberties . Both are rated on a scale from I to 7,

where countries coded 1 were most free and those coded 7 were least free . The Freedom House dataset includes 192

countries from 1973 to the present.

Although these indicators use different methods to measure democracy, they were found to be highly correlated . This

often results in scholars treating them as virtually interchangeable and base their choice of a particular measure on the

time period covered or the number of valid cases for the variables with which they are most concerned . It is often

assumed that it does not matter which of these measures is used, because of they are highly correlated . Casper and

Tufis (2003) constructed a model based on democratization literature and found that this assumption is often not true . In

their model, they found that despite the high correlation between the three measures, different results can be produced .

This explains in part why debates on measuring democracy have continued rather than having been resolved . As the

present study focusses on the most recent year, the Freedom House indicator is the most obvious choice .

The literature offers several other interesting indicators . Freedom House compiles an annual indicator for press freedom .

Reporters Without Borders' provide a similar indicator. Press freedom is generally considered as very important for

democracy. However, press freedom alone is not a sufficient measure of democracy . Similarly, Transparency

Intemationaiz, provides their Corruption Perceptions Index (CPI), which ranks 150 countries according to perceived levels

of corruption . These are determined by expert assessments and opinion surveys . Although it would be interesting to

examine whether corruption is affected by connectivity, this study chooses to focus on a broader definition of democracy.

3.3 Indicators of connectivity

This section provides an overview of different approaches to measuring the extent of connectivity in an economy . The

following section provides an overview of several indices, how they are constructed and what indicators were used . The

purpose of this section is to find indicators that are commonly used to measure the availability and use of communication

technology . In the last section the three most common indicators are chosen . These will be used in the empirical analysis

in the following chapter .

1 Reporters Sans Frontières : http ://www.rsf .org

2 More information on how the CPI is contructed can be found at : http://www.transparency .org

34


3.3.1 Overview of ICT indicators

ICT Diffusion Index

The ICT Diffusion Index (ICTDI) was first constructed in 2003 by the United Nations Conference on Trade and

Development (UNCTAD) . The original index was compiled from both qualitative and quantitative data and covered three

dimensions: connectivity, access and policy . In later revisions the policy component was omitted and it was based solely

on quantitative data. ICTDI scores are available for 180 countries in 2004. For the period 1997-2004 only rankings are

available. Table 3 .1 shows the indicators used to construct the ICTDI .

Digital Access Index

The Digital Access Index (DAI) was developed in 2003 by the International Telecommunications Union (ITU). It was

developed to provide a transparent measurement for monitoring access to ICTs as defined in one of the United Nations'

Millennium Development Goals (MDG). The DAI combines' a group of eight indicators, covering five areas: availability of

infrastructure, affordability of access, educational level, quality of ICT services, and Internet usage. Based on their

scores, countries are classified in one of four categories : high, upper, medium and low (ITU, 2003). The index covers

178 countries and is available from 2002 and later years .

Digital Opportunity Index

In 2005, the Digital Opportunity Index was announced at the World Summit on Information Society (WSIS)4 Thematic

Meeting on Measuring the Information Society . It is closely related to the DAI, however it is based on a set of

internationally-agreed indicators . The index was first constructed in 2001 and covered 40 leading economies . From the

year 2004 it covers a total of 180 economies . The DOI is structured around three categories : opportunity, infrastructure

and utilization . Table 3.1 show the indicators .

Network Readiness Index

The Network Readiness Index (NRI) was first introduced in 2001 by the World Economic Forum . Further refined in 2003,

the NRI is now defined as the degree of preparation of a country (or community) to participate and benefit from ICT

developments . The NRI is an aggregate index comprising of three main components : the environment for ICT offered by

a given country or community, the readiness of a community's key stakeholders (individuals, businesses and

governments) to use ICT, and the actual use of ICT among these stakeholders. The index uses 48 indicators divided into

two sets, referred to as 'hard' and 'so f data. Hard data consist of statistics collected by independent agencies . Soft data

are subjective data gathered from questionnaires . Although the latter might provide valuable insights, they are not

included in this research because it would be too time consuming to collect. The 'hard' indicators are listed in table 3.1 .

The index covers around 104 countries and is available from 2002 .

Information Society Index

The Information Society Index (ISI) was created in the mid-1990s by IDC5 . It combines 15 variables in four categories

(social, internet, computers and telecommunication) to calculate and rank nations in an overall index . It provides a

measurementó of the "ability of 53 nations to participate in the information revolution ." In the 2003 version of the ISI report

23 indicators were used . How the index was constructed is not known, as this information is not freely available . The free

3 Also see : http://www.itu .int/ITU-D/ict/publicationsAnrtdr 03/material/DAI .pdf

4 World Summit on Information Society - http ://www .itu .int/wsis

5 International Data Group - http://www.idc .com/

6 http://www.idc.com/groups/isi/DOCS/factsheets .pdf

35


information available on IDC's website does not go into detail on how the IS[ is constructed . It does however provide a

list of the previously mentioned 15 variables (shown in table 3 .1) .

Infostate (Orbicom)

Orbicom, following economic theory, makes a distinction between consumptive and productive functions of ICT . It treats

ICT as both productive assets and consumables and developed a conceptional framework with the notions infodensity

and info-use. Infodensity is the productive capacity of a country which is determined by the quantity and quality of its

factors of production (in short : the sum of all ICT stocks, both capital and labour) . Info-use refers to the consumption

flows of ICTs in a certain period . A distinction is made between ICT uptake (referring to goods) and ICT intensity

(referring to services) . These two aggregated form a country's infostate, which can be used to compare countries . Data is

available for 139 countries from 1996 until 2001 . Table 3 .1 lists the indicators that where used .

EIU E-readiness index

The Economist Intelligence Unit (EIU) e-readiness index evaluates the technological, economic, political and social

assets of 68 (primarily developed) countries . It allows governments to compare the success of their technology initiatives

with those of other countries' . A collection of nearly 100 quantitative and qualitative criteria is organised into six

categories : connectivity and technological infrastructure, business environment, consumer and business adoption, social

and cultural environment, legal and policy environment, and supporting e-services . The e-readiness reflects a country's

ICT infrastructure and the ability of its consumers, businesses and governments to use ICT to their benefit . Data is

collected since 2000 . The methodology and composition has changed over the years, which compromises the ability to

make historical comparisons .

UNPAN E-readiness index

The United Nations Division for Public Administration and Development Management (UNPAN) e-readiness index

assesses the public sector e-government initiatives of UN Member States according to a weighted average composite

index of e-readiness . This is based on website assessment, telecommunication infrastructure and human resource

endowment (UNPAN, 2005) . The website assessment measure is based on a methodological framework and is

subjective in nature . The infrastructure component comprises six basic infrastructure indicators (listed in table 3 .1) . The

human resource endowment index relies on UNDP's HDI education index . The 2005 report contains data on 191 UN

Member States.

7 http ://graphics .eiu .com/fles/ad_pdfs/2006Ereadiness_Ranking WP.pdf

36

Telecommunication and democracv 3 . Methodology

index dimension indicator

ICTDI Connectivity Internet hosts per capitaPCs per capitaTelephone mainlines per capitaMobile subscribers per capita

Access Number of Internet usersLiteracyCost of a local callGDP per capita

DAI Infrastructure Fixed telephone subscribers per 100 inhabitantsMobile telephone subscribers per 100 inhabitants

Affordability Internet access price as a percentage of per capita income

Education Adult literacySchool enrolment

Quality International Internet bandwidth per capitaBroadband subscribers per 100 inhabitants

Usage Internet users per 100 inhabitants

DOI Opportunity Percentage of population covered by mobile cellular telephonyInternet access tariffs as a percentage of per capita incomeMobile cellular tariffs as a percentage of per capita income

Infrastructure Proportion of households with a fixed line telephoneProportion of households with a computerProportion of households with Internet access at homeMobile cellular subscribers per 100 inhabitantsMobile Internet subscribers per 100 inhabitants

Utilization Proportion of individuals that used the InternetRatio of fixed broadband subscribers to total Internet subscribersRatio of mobile broadband subscribers to total mobile subscribers

NRI Network use Percentage of computers with Internet connectionInternet Users per hostEstimated Internet users per 100 inhabitantsCellular subscribers per 100 inhabitants

Enabling Factors TeledensityYears to first adopt cellular telephonyWaiting list for telephone linesTelecommunication staff per 1,000 mainlinesTelephone faults per 100 mainlines

Hardware, Software and PCs per 100 InhabitantsSupport Software piracy

ICT Policy Internet access cost

Economic Environment Income per capita (PPP)

Social Capita No schooling in the total populationAverage years of schooling in the total populationIlliteracy

General Infrastructure Electricity consumptionElectric power transmission and distribution losses Percentage of paved roadsTelevision penetration

Table 3.1 : Indicators used in varies indices (quantitative data only)

37

Telecommunication and democracv 3 . Methodology

ISI Social Secondary and tertiary enrolmentCivil libertiesGovernment corruption

Internet Internet usersHome Internet usersMobile Internet userse-Commerce spending

Computer PCs per householdIT spending / GDPIT services / GDPSoftware spending

Telecommunication Broadband householdsWireless subscribersHandset shipments

Infostate Networks Main telephone lines per 100 inhabitantsWaiting lines/mainlinesDigital lines/mainlinesCell phones per 100 inhabitantsCable TV subscription per 100 householdsInternet hosts per 1,000 inhabitantsSecure servers/Internet hostsInternational bandwidth (Kbps per inhabitant)

Skills Adult literacy ratesGross enrolment ratiosPrimary, secondary and tertiary education

Uptake (use) TV equipped households per 100 householdsResidential phone lines per 100 householdsPCs per 100 inhabitantsInternet users per 100 inhabitants

Intensity (use) Broadband users/Internet usersInternational outgoing telephone traffic minutes per capitaInternational incoming telephone traffic minutes per capita

E-readiness Infrastructure PC's per 1000 persons(UNPAN) Internet users per 1000 persons

Telephone lines per 1000 personsOnline populationMobile phones per 1000 personsTV's per 1000 persons

Website assessment not included (qualitative data)

Human capital HDI education index (UNDP)

Table 3.1 : Indicators used in varies indices (continued)

Other indices

Other indices that are more or less related to ICT development have been developed . They will be briefly examined, to

identify the problems that arise when using them in the present study . In 2005, the UN Department of Economic and

Social Affairs developed its Index of Knowledge Societies (IKS) . It consists of 2004 data and is available for only 45

countries. As none of these countries are developing countries, this index is not of interest for this study . The World Bank

created the Knowledge Economy Index (KEI) . The KEI indicates whether the environment is conducive for knowledge to

be used effectively for economic development. It consists of eighty quantitative and qualitative indicators divided in four

categories : economic incentive regime, information infrastructure, innovation and education . The KEI includes 130

economies, however data is only available for two distinct years, 1995 and 2004 . The UNDP published their Technology

Achievement Index (TAI) in the Human Development Report 2004 . The TAI focuses on four dimensions of technological

capability that are important for a country to benefit from the network age (UNDP, 2004) . These are: creation of

technology, diffusion of recent innovations, diffusion of old innovations and human skills. Diffusion of recent innovations

is measured by the number of internet hosts per capita . Perhaps several years ago this might have provided some

38

Telecommunicationand democracy 3 Methodology

indication of the innovative nature of a country, however if this is still accurate today remains uncertain . Data for recent

years is not available. The index was constructed between 1995 and 2000 for 72 economies .

3.3.2 Methodological issues of using ICT indices

Reviewing the described indices uncovered certain limitations to their use . In a study of different indices of e-readiness in

Latin America and the Caribbean, Minges (2005) described several of these limitations that are also relevant to the

present study. The first major obstacle he encountered was the inclusiveness of an index . While some indices include

more than 180 economies (ICTDI, DAI and DOI), others are limited to no more than 50 (ISI and NRI) . Although this is

partly because it is difficult to obtain data for some countries, it is often caused by lack of interest of the designers of the

index. Some indices limit their scope to OECD countries or high income countries. This makes them useless in tracking

ICT development in economies that are in development . Furthermore, indices with a large number of indicators are

unlikely to be available for many countries, as obtaining all data is increasingly difficult .

Another problem is the lack of time-series data . While some indices are provided for several years, most are available for

just one year (TAI and DAI) or only a few (DOI) . Since most of the indices described above were developed after 2000,

they cannot be used to examine ICT development during the 1990s . In addition, some indices are constructed using data

that is already several years old . For instance, UNPAN's e-readiness index for 2004 uses data collected in 2002 . This

may compromise analyses that track several indicators over time .

Related to inclusiveness, some indices rely on estimated data . In order to include as many countries as possible, missing

data is sometimes estimated . This can lead to unreliable results . Sometimes the relevance of an indicators is disputable

(an example is the TAI, as discussed before) . The number of Internet hosts is frequently used in several of the described

indices . However, it is well known that Internet host data does not necessarily reflect the physical location of a host .

In general, most indices provide detailed information about the methodology used . There are, however, not many indices

that actually provide the raw data of which they were constructed . This makes it impossible to recalculate and verify the

index, compromising the transparency of the index. Particularly indices that are commercially available suffer from lack of

transparency.

A different problem is subjectivity. Some of indices, like the DAI and DOI, are explicitly constructed avoiding the use of

subjective indicators. Others rely on qualitative data, that are subjective by definition . Both the NRI and EIU use

questionnaires to measure regulatory and policy issues . The outcome of these questionnaires is based on the subjective

interpretation of their respondents . An additional problem of using questionnaires is that the sample size might not be

representative .

Most indices are broken down into sub-categories to assess in more detail a country's strength and weaknesses . The

indicators used to in these categories often differ between indices . For example, the DAI, DOI and UNPAN's e-readiness

index all include an infrastructure categories . However, they all use different indicators . Similarly, the definition of

connectivity differs between the e-readiness index and the EIU . Another inconsistency is that some indicators are used in

different categories . For example, the number of internet users as a percentage of the total population is included in the

following categories: usage (DAI), network use (NRI), internet (ISI), uptake (Infostate) and infrastructure (UNPAN e-

readiness) . This makes it difficult to compare the same areas of a country across indices .

A problem concerning time-series data is that indices tend to progress throughout their existence . As more experience is

39

Telecommunication and democracy 3 Methodology

gained, often it becomes clear that the index methodology needs further refinement or simplification . As a consequenceit might not be possible to make historical comparisons .

3 .3.3 Measuring connectivity

An important issue in selecting indicators to measure the degree of connectivity in a country is the availability of the data .Since the index of connectivity will be used in a regression analysis in the empirical part of this study, it is necessary to

collect data on as many countries as possible . This is an important criterion, because results attained with a smallsample size may not be applicable to the entire population . The most important source of data in this study is the ITUWorld Telecommunication Database 2006 .

A common element that can be found in all of the indices described in the previous section is the inclusion of some sortof ICT infrastructure category. Indicators that are used to measure infrastructure are : internet usage, fixed and mobiletelephone penetration, the number of PCs per capita, internet host penetration and internet affordability. Although theWorld Telecommunication Database contains many indicators for a large number of years, data for the most recentyears is not always complete . As a consequence not all of the indicators listed above can be used in this study . For2005, the following indicators have the greatest number of observations . These are the number of internet users, thenumber of main fixed telephone lines (per 100 inhabitants) and the number of mobile telephony subscribers (per 100inhabitants) . Data on the number of personal computers is severely limited, therefore this indicator will not be used .Indicators like broadband penetration, mobile internet and the number of public hotspots are not included in this study .These technologies are only recently available in developed countries . As they are absent in developing countries, theyare not relevant for the present study . In the empirical part of this study (section 4 .2.2) an index for connectivity will beconstructed using the number of internet users, the number fixed telephone lines and the number of mobile subscribers .

3.4 Conclusion

The purpose of this chapter was to find an adequate measure for the two most important variables for this study . Thefollowing question was formulated :

How can the concepts of democracy and connectivity be measured?

Different measures of democracy were found in the literature, all of which have their specific shortcoming . In most casesdata availability was the most important reason not to use the variable . Freedom House data was found to be the mostup to date and furthermore it includes a great number of countries . For these reasons, Freedom House data was used .

In the search for an adequate measure for connectivity, several ICT indicators were examined. Most indicators includesome kind of infrastructure indicator. Some indicators include additional factors like the standard of living, opportunity,affordability and utilization to determine the opportunity that people have to benefit from ICT . These additional variablessometime influence democracy and therefore they cannot be used as an independent variable in this study. This studyrelies mainly on infrastructure and factors like opportunity, utilization, affordability are not taken into account . Threecommonly used indicators were selected : the number of fixed telephone lines, the number of mobile subscribers and thenumber of internet users .

40

Telecommunication and democracy 4 Empirical analysis

4. Empirical analysisThis chapter describes the empirical analysis . Several regression analyses were conducted to study the effect of

connectivity on democracy, including the effect of the economic situation, the educational attainment and a country's

population size . In the first section several regression models are presented . The following section describes the

characteristics of the data of the dependent and explanatory variables . Descriptive statistics are provided as well . Based

on these statistic the decision was made to transform some of the variables . In the final section the different models will

be compared and the estimation results discussed .

4.1 Empirical models

Different sets of regression models were used in this study to examine the effects of connectivity and the other

independent variables, derived from the literature, on democracy (FH2005) . In the first four models, the variables are

included step-wise in the regression to be able to assess the contribution of each of the individual variables to the overall

fit of the model . The underlying regression of the model that includes all variables has been defined as :

FH2005 =Pa+ PI logGDP+ P2logPOP + P3 enrollment +P4 connect - n + E

In this model the estimated coefficients are (3o to (3., where Po is the constant and £ is the normally distributed error term .

The characteristics of the variables and their descriptives are discussed in the following section .

The second set of models addresses regional differences in connectivity. Six different regions were used based on

categories defined by the World Bank . These regions are: South East Asia and Pacific (asia), Europe and Central Asia

(eurasia), Latin America and Caribbean (latin), Middle East and North Africa (arab), Sub-Saharan Africa (africa), and

Western Europe (western) . Dummy variables (between parentheses in the previous sentence) were included to

distinguish between the different regions. The Western Europe region is not provided by the World Bank . It consists of

western European countries and includes high income countries like Australia, New Zealand and Iceland . Although

geographically these countries are not part of western Europe, they are included in this region on the basis of their

history and religion . This is very similar to the regions defined by Kedzie (1997) . The list of countries and their regions

can be found in appendix D. Interaction variables were created by multiplying the dummies with the connectivity variable .

The regression model is defined as :

go+ 0, logGDP +02logPOP + 03 enrollment + 04 (asia * connect-

n ) +FH2005 = 05 (arab * connect _ n ) + 06 (eurasia* connect _ n ) + 0, (latin * connect _ n ) +

08(africa* connect _n) + E

4.2 Data

In this section the characteristics of the data will be described . The unit of analysis is the political nation-state. Although

an analysis at sub-regional level might be important and revealing for understanding national differences, it will be much

more difficult to obtain all necessary data . The data used in this study originates from different sources and is collected at

country level. As many countries as possible were included, depending on the amount of data available . This includes

41


both developed and developing countries . Data sources and characteristics for the dependent and the explanatory

variables will be discussed in the following sections .

4.2.1 Dependent variable

Data on the dependent variable, democracy, is derived from Freedom House's annual Freedom in the World survey. Thesurvey measures freedom in political rights and civil liberties . Political rights enable people to participate freely in the

political process, including the right to vote freely for distinct alternatives in legitimate elections, compete for public office,

join political parties and organizations, and elect representatives who have a decisive impact on public policies and areaccountable to the electorate . Civil liberties allow for the freedoms of expression and belief, associational and

organizational rights, rule of law, and personal autonomy without interference from the state (Freedom House, 2006) .

The dataset includes numerical data ratings for 193 countries in the period of 1972 to 2006 .

The rating process' is based on a check-list of ten questions on political rights and fifteen on civil liberties . The questionson political rights are grouped into three sub-categories : electoral process, political pluralism and participation, andfunctioning of government . Similarly, the questions on civil liberties are grouped into : freedom of expression and belief,

associational and organization rights, rule of law, and personal autonomy and individual rights . Each of these questionsis assigned 0 to 4 raw points, where 0 represents the smallest degree of rights or liberties . The maximum raw score is 40for political rights and 60 for civil liberties . These raw scores determine the overall rating of 1 though 7 . A rating of 1indicates the highest degree of freedom, whereas 7 indicates the least amount of freedom . The combined average of

political rights and civil liberties determines whether a country is classified as free (F), partially free (PF) or not free (NF) .

Countries that received a rating of 1 for political rights have free and fair elections. In these countries the rulers areelected, there are competitive parties or other political groupings, and the opposition has actual power . In addition,

minority groups can participate in the government though informal consensus . Conversely, a rating of 7 indicates theabsence of political rights as a result of the extremely oppressive nature of the regime . This often coincides with civil war,

extreme violence, and warlord rule in the absence of an authoritative, functioning government. Countries that received a

rating of 1 for civil liberties ensure freedom of expression, assembly, association, education, and religion . The rule of lawis established as well as economic freedom. On the other end of the spectrum, countries with a rating of 7 have virtually

no freedom and are characterized by an overwhelming and justified fear of repression .

Freedom House (2006) recognizes political rights and civil liberties as fundamental elements of democracy . Althoughboth elements are very different in their focus, they are highly correlated in practice . Appendix B shows the correlationbetween political rights and civil liberties for 1985, 1995 and 2005 . Coefficients of 0 .932, 0.928 and 0 .952 (respectively)were found. Similar to Kedzie's methodology, it is decided to average both elements into a single democracy variable .The resulting variable is then inverted, as it is more intuitive for a higher rating to correspond to a greater degree of

democracy. The formula below was used to recode the data .

(pr+ cl )democracy = 8 - 2

1 The methodology, including the questions of the survey, are described in detail in the annual Freedom in the World reports and canalso be found in the methodology section of the website (http :Uwww.freedomhouse .org/j .

42


4.2.2 Explanatory variables

Economic situationThe gross domestic product (GDP) per capita is used to measure a country's economic situation . GDP is measured per

capita to account for the country size . Comparing GDP between countries might result in a misleading picture, as market

exchanges rates are not taken into account . To overcome this problem, this study will rely on the GDP adjusted for

purchasing power parity (PPP) . This indicator equalizes the purchasing power of different countries for a given basket of

goods. The IMF publishes data on several indicators of economic development in its biannual World Economic Outlook

database z. This research will rely on IMF's gross domestic product based on purchasing power parity per capita GDP .

Detailed information on how this indicator is constructed and what the basket of goods was used can be found in the

World Economic Outlook Report (IMF, 2007) .

Connectivity

Some studies rely on the number of internet hosts to measure internet connectivity . The number of hosts is measured by

top-level domain names (e .g . nl, de, .fr) . Generic top-level domains (like com, edu, gov, org, net) are often assigned

to the United States or distributed among the countries using different estimated weights, thereby compromising

reliability . In addition, the physical location of a host is not determined by its top-level domain . In fact, some hosts with

top-level domains of developing countries are actually located in developed countries, because there is more bandwidth

available. Although the number of hosts might be a conservative measure of internet presence in a country, it does not

necessarily reflect the number of internet users .

The ITU World Telecommunication Indicators database 2006 contains two indicators for measuring internet usage that

are of interest for this study. The number of internet subscribers per 100 inhabitants and internet users per 100

inhabitants. Internet subscribers refers to the number of dial-up, leased line and broadband internet subscribers . It

implies a certain degree of usage i n terms of realized actual users . However it may not reflect full usage as it omits free

and shared internet access (UNCTAD, 2005) . Many internet users obtain access without paying directly, either as a

household member, from work or at school . This is particularly of interest when determining internet usage in developing

countries, where subscribers generally constitute a small 'elite' . As this indicator fails to include common types of access

that prevail in developing countries (e.g. shared access and access in internet cafés), it might underestimate the number

of actual users by a factor of 2 to 3 in these countries (ITU, 2007) .

The number of internet users is based on nationally reported data. In some cases, surveys have been carried out to give

a more precise figure of the number of internet users . These surveys differ across countries. They cover different age

groups and define frequency of use differently. The estimates for countries that did not have surveys, are generally

derived from internet subscriber counts, reported by internet service providers .

It is apparent that all of the indicators described above suffer from shortcomings . As this study tries to look at a country's

actual internet usage by its citizens, the number of hosts seems of less relevance . Of the two remaining indicators the

number of internet subscribers might be more precise than the number of internet users, although it might underestimate

the actual number of users in developing countries . As the number of internet users in some cases is derived from the

number of internet subscribers, it would be more consistent to use the latter. Taking into account the availability of the

data, it is chosen to rely on internet users. When analysing the results, it will have to be taken into account that this

indicator is an estimation .

2 World Economic Outlook database : http:l/www .imf.org/external/pubs/fUweo/2006/01/datafindex .htm

43


Data on fixed main telephone lines is derived from the ITU World Telecommunication Indicator database 2006 as well .The ITU refers to main lines as "telephone lines connecting a consumer's equipment (e.g., telephone set, facsimilemachine) to the Public Switched Telephony Network (PSTN) and which have a dedicated port on a telephoneexchange ." For most countries, this includes public payphones and many countries include the number of ISDNconnections as well. In this study the number of fixed main telephone lines per 100 inhabitants is used .

The ITU database provides data on mobile subscribers as well . Mobile cellular telephone subscribers refers to users ofportable telephones subscribing to an automatic public mobile telephone service using cellular technology that providesaccess to the PSTN . In table 4.3 the number of mobile subscribers per 100 inhabitants is included . It ranges from 0 to155. Apparently, in some countries people own more that one phone on average . Some people have a mobile telephonefor work in addition to one for personal use. The actual number might also be lower as there are pre-paid cards that arestill valid, but no longer in use .

These three indicators describe, to a certain degree, a country's achievement in communications technology . Countrieswith much dial-up access, depend on the availability of a public telephony infrastructure . This is also true when ISDN orASDL technologies are in use . These technologies both depend on an adequate fixed telephony network .

Developed countries that were quick to establish a widely diffused fixed telephony network, were generally also fast inadopting mobile technology. These countries now have relatively high numbers of users for both networks. Fixed andmobile networks are used complementary to each other . The situation in developing countries is generally different . Forvarious reasons (mostly economic), some countries were never able to develop an extensive fixed telephony network .The availability of mobile technology created new opportunities that eventually lead to the adoption of these

technologies. Mobile telephony in these countries is used as a substitute for fixed telephony that was not available oraffordable. Cambodia is an example of a country that lacks an extensive fixed telephony network, but which neverthelesshas a high mobile penetration rate (UNCTAD, 2005) .

Considering the above, it will not be surprising that the three indicators will be correlated . This is confirmed by the highcorrelation coefficients found in table 4 .1 . Internet users and fixed main lines are indeed very strongly correlated (0 .871) .Less obvious is the strong correlation between mobile subscribers and fixed lines (0 .807). Apparently, not many

countries in the current sample have a high mobile penetration rate and a small number of main lines at the same time .Mobile subscribers and internet users also have a very high correlation coefficient (0 .812). Because of these strongcorrelations, the three variables cannot simply be included into a regression model . This would lead to multicollinearity,which refers to explanatory variables being correlated themselves . Although multicollinearity does not bias theestimators, it does increase the standard error of the estimates . In other words, it reduces the degree of confidence thatcan be placed in them . As studying the individual effects of these variables is beyond the scope of this study, a singleindicator for measuring connectivity will be constructed . It is difficult to determine whether the indicators are equallyimportant when creating a connectivity index . To find a solution for this problem, the methodology of several otherindices was reviewed .

44

Telecommunication and democracy 4. Empirical analy.~i§

Internet usersper 100

inhabitants

Main telephonelines (fixed lines)

per 100inhabitants

Mobile cellulartelephone

subscribers per100 inhabitants

Internet users per 100 Pearson Correlation 1 ,871(**) ,795(*")inhabitants

Sig . (2-tailed) ,000 ,000

N 80 80 80

Main telephone lines (fixed Pearson Correlation 871(*") 1 840(**)lines) per 100 inhabitants

Sig . (2-tailed) 000 000

N 80 80 80

Mobile cellular telephone Pearson Correlationsubscribers per 100 795(*") 840(*"') 1inhabitants

Sig . (2-tailed) ,000 ,000N

80 80 80

** Correlation is significant at the 0 .01 level (2-tailed) .

Table 4 .1 : Correlation between internet users, fixed telephone lines and mobile subscribers

ITU's Digital Opportunity Index (DOI) is constructed using three different categories : opportunity, infrastructure and

utilization . The scores of these categories are simply averaged . The infrastructure score is acquired by calculating the

average of five equally weighted indicators: proportion of households with a fixed telephone line, mobile subscribers per

100 inhabitants, proportion of households with internet access, intemet subscribers per 100 inhabitants, and the

proportion of households with a computer . These indicators themselves were calculated following the same methodology

used by UNDP to calculate the Human Development Index (HDI) . The formula below is used to normalize the different

dimensions (see UNDP, 2006) . The Digital Access Index (DAI) uses a similar methodology . It distinguishes between five

categories : infrastructure, affordability, knowledge, quality, and usage . Infrastructure is measured using fixed telephone

subscribers per 100 inhabitants and mobile cellular subscribers per 100 inhabitants . These are normalized using the

formula below and then averaged with equal weights. UNCTAD's ICT Diffusion Index (ICTDI) is created using the same

methodology . It too uses equal weights.

dimension index = actual value- minimum valuemaximum value -minimum value

This study will rely on the same method by first normalizing the indicators and then calculating the average, based on

equal weights .

Education

In this study, the primary enrolment rate will be used to determine a country's educational attainment . Many scientific

research relies on the UNSECO as the main source of data on education . The UNESCO Institute of Statistics (the

statistical branch of UNESCO) publishes time-series data of this indicator . Although data is collected on an annual basis,

the data available on UNESCO's website3 covers only a selection of years . The World Bank maintains its own database

3 UNESCO Institute of Statistics : http://www.uis .unesco .org/

45

Telecommunication and democracv 4 . Empirical anaW

of education statistics", which also includes the primary enrolment rate . Although this data is derived from UNESCO, datais available for more years . The World Bank provides two indicators that are of interest : gross primary enrolment (GER)and net primary enrolment (NER) . The GER is defined as the number of pupils enrolled in primary, regardless of age,

expressed as a percentage of the total population in the theoretical age group for primary education (UNESCO, 2007) .

The indicator includes pupils that, due to their age, belong to secondary education, but who are still enrolled in primaryeducation. It therefore frequently exceeds 100 percent . Some scholars have suggested that it overestimates the actual

enrolment by 10 to 30 percent (see Szirmai, 2005) . The net enrolment rate is a better measure of primary education . Itindicates what percentage of each category is actually somewhere in the school system . However, many studies depend

on the gross primary enrolment rate, because there is more data available,

rank country GER

1 Sierra Leone 145.14

2 Cambodia 136.61

3 Madagascar 133.52

4 Sao Tome and Principe 132.94

5 Lesotho 131 .06

6 Gabon 129.55

7 Guyana 129.46

8 Uganda 125.39

9 Malawi 124.94

10 Belize 124.08

rank country NER

1 Japan 99.91

2 Korea, Rep . 99.58

3 Spain 99.44

4 Greece 99.35

5 Finland 99.32

6 New Zealand 99.26

7 France 98.94

8 Norway 98.90

9 Italy 98.84

10 Belgium 98.77

Table 4 .2: Country rankings for gross and net primaryenrolment rate (source: World Bank)

Table 4 .2 compares the top-10 countries when ranked according to their gross or net primary enrolment rate . It becomes

apparent that using the gross primary enrolment rate is not without problems . It is very unlikely that countries like SierraLeone, Cambodia and Madagascar have achieved a better educational attainment than Japan, Korea and Spain . The

gross primary enrolment rate is often used as a complementary indicator to the net enrolment rate by indicating the

extend of over-aged and under-aged enrolment . This is not relevant for this study and therefore it is decided to use thenet enrolment rate. As a consequence there will be less data available .

Population size

The ITU World Telecommunication Indicator database 2006 contains data on population size as well . The source of thisdata is the population division of the UN Department of Economic and Social Affairs . Part of its responsibilities is

monitoring and publishing data on population issues and trends.

4.3 Descriptive statistics

Table 4.3 provides the descriptive statistics of the variables used in the regression models . The skewness is provided toassess whether the variables are (more of less) normally distributed . Although skewness by itself is not enough proof, it

does provide an indication. The number of cases in the dataset is 80 .

4 World Bank Education Statistics Database : http ://devdata.worldbank.org/edstats/

46

Telecommunication and democracy 4. Empirical analysis

N Minimum Maximum Mean

Std .

Deviation SkewnessStatistic Statistic Statistic Statistic Statistic Statistic Std. Error

FH2005 80 1,5 7,0 5,094 1,809 -,504 ,269GDP PPP per capita(Current international 80 596,076 69799,557 13121,499 13100,728 1,537 ,269dollar)

IogGDP 80 2,775 4,844 3,869 514 -,274 ,269Population (ITU) 80 80654 1103371008 41191377 127763390 7,515 ,269logPOP 80 4,907 9,043 6,951 ,807 -,238 ,269Net enrollment rate (%),primary level, total 80 32,190 99,910 87,073 15,829 -1,960 269

Internet users per 100inhabitants 80 ,208 87,755 22,456 22,913 1,114 ,269

Main telephone lines (fixedlines) per 100 inhabitants 80 172 68,664 22,521 19,733 , 615 ,269

Mobile cellular telephonesubscribers per 100 80 530 154,828 53,412 39,138 , 328 ,269inhabitants

connect -n 80 ,005 , 877 ,309 ,252 ,511 ,269Valid N (listwise) 80

Table 4 .3: Descriptive statistics

FH2005 is the measure of democracy, the dependent variable . The value ranges from 1 .5 to 7, which means that the

dataset does not contain the most authoritative countries (average index of 1) . This is not surprising, as these countries

are very "closed", making it difficult or impossible to gather the necessary data . The distribution is only slightly negatively

skewed. This is an important property as the regression analyses assumes that the variables are normally distributed .

The variable for measuring GDP (PPP per capita) is IogGDP . Although GDP per capita takes the population size of a

country into account, it is not normally distributed . As can be seen in table 4.3, the distribution of GDP PPP is positively

skewed. It is therefore transformed with a logarithmic func6on . Appendix C1 shows the distribution of the resulting

variable (IogGDP) before and after the logarithmic transformation . The distribution of the resulting variable is more close

to a normal distribution .

The population size has a very large positive skewness of 7 .515. The traditional solution, also applied in this study, is to

transform population size with a logarithmic function . Appendix C2 shows that the acquired distribution is more close to a

normal distribution .

The skewness of the net enrolment rate is -1 .960. Appendix C3 provides a closer examination of the distribution . It

clearly shows the negatively skewed distribution . Transforming the variable with a logarithmic function is not an option,

as it would only affect the magnitude. Therefore, the net enrolment rate is used as it is . The distribution not being normal

will affect the results, which will have to be taken into account when assessing the results of the regression analyses .

The variables for the number of internet users (inet n), fixed telephone lines (mainlines_n) and mobile subscribers

(mobile n) were first normalized (hence the underscore suffix) . The resulting variable for connectivity, connect-n, is the

average of these three normalized variables .

47

Telecommunication and democracy

4.4 Estimation results

4. Empirical analysis

The regression analysis of this study relies on the following variables : GDP per capita, population size, net enrolment

rate, the number of fixed telephone lines, the number of mobile subscribers and the number of internet users . The use ofthese variables demands a non-experimental design of the analysis . Because of this non-experimental design, there is

no control over the explanatory variables and therefore there is a possibility that two or more of the explanatory variables

are mutually dependant . In a controlled experiment, collinearities would simply be avoided . As this study is non-experimental, it is first examined whether there are collinearities between the explanatory variables . This is done bycalculating a correlation matrix for all explanatory variables (see table 4 .4 below). When two or more of these variablesare found to be highly correlated, this might be an indication that the model will suffer from multicollinearity . It must benoted that these correlations are merely an indication for multicollinearity, it is not conclusive evidence .

Io GDP lo POP

Net enrollmentrate (%),

primary level connect nlogGDP Pearson Correlation 1 -,235(*) , 630(*") ,916(**)

Sig . (2-tailed) 036 ,000 ,000logPOP Pearson Correlation -,235(*) 1 - ,056 -,217

Sig . (2-tailed) ,036 621 053Net enrollment rate (%), Pearson Correlation 630(**) - 056 1 559(-)primary level, total ,

Sig. (2-tailed) ,000 621 ,000connect -n Pearson Correlation ,916(**) -,217 ,559(*") 1

Sig . (2-tailed) ,000 ,053 ,000* Correlation is significant at the 0 .05 level (2-tailed).** Correlation is significant at the 0 .01 level (2-tailed).

Table 4 .4: Correlation coefficients of the explanatory variables

From table 4 .4 it becomes immediately apparent that GDP per capita and connectivity are very highly correlated, with acorrelation coefficient of 0 .916. This is an indication that a model that includes both of these variables simultaneously will

suffer from a high degree of multicollinearity . When performing the regression analysis, the possibility for multicollinearityshould be taken into account . Other high correlations can be found between GDP per capita and the enrolment rate

(0.630) and between connectivity and the enrolment rate (0 .559). Although, these coefficients are smaller and will

therefore less likely result in multicollinearity, they will be examined as well .

Table 4.5 shows the results from a step-wise inclusion of the explanatory variables to the model . The first model onlyincludes GDP per capita to predict democracy. The resulting goodness-of-fit (adjusted R2) is 0 .213. GDP per capita ispositively related to democracy and is, furthermore, highly significant . Differently put, GDP per capita alone explains 21 .3percent of the variation in democracy for the sample of 80 countries . Model 2 includes, in addition to model 1, the size ofthe population. This does not contribute to the overall fit of the model, as the adjusted R2 drops to 0 .204. The populationsize is negatively related to democracy, it is however not significant . GDP per capita remains significant in this model . Inmodel 3, the enrolment rate is included as well. The adjusted Rz increases marginally to 0 .207, which means that the fitof the model only very slightly improved . The enrolment rate is positively related to democracy, however it is not

significant . Finally, model 4 includes all four variables . The fit of the model jumps to 0 .326, so apparently the includedvariable, connectivity, is very important in predicting democracy . This is confirmed by the regression coefficient for

connectivity being much higher than the other variables and highly significant . As in the previous models population size

48


and enrolment rate remain insignificant .

model 1 model 2 model 3 model 4 model 5constant -1.343 -0.700 -0.632 8.295- 3.441 *

-1.371 2381 Z377 3.218 1.789IogGDP 1.664- 1.636- 1 .296- -1.573 *

0.351 0.364 0.470 0.872IogPOP -0.077 -0.109 -0.111 -0.061

0.232 0.233 0.215 0.216enrollment 0.017 0.020 0.011

0.015 0.014 0.013connect_n 6.252- 3.671 **"

1.650 0.832

adjusted R^2 0.213 0.204 0.207 0.326 0.306

' Significant at 10%

•* Significant at 5%

"' Significant at 1%

Note: Regression coefficients are bold, standard errors italic

Table 4 .5 : OLS regression models for democracy (FH2005)

It should be noted that the sign of GDP per capita in model 4 has become negative and is still significant . As it was

positively related to democracy in all previous models, this is a good indication of multicollinearity . This was already

anticipated because of the high correlation coefficients found between GDP per capita and connectivity . The variance-of-

fit (VIF) was calculated to test for multicollinearity . GDP per capita and connectivity were found to have a high VIF of

7.193 and 6.209 respectively (see model 4 in appendix E) . A consequence of multicollinearity is that although the model

might still be adequate in predicting democracy, the individual contributions of the variables that are collinear may not be

accurate. Multicollinearity does not bias the predictors, however it does result in large standard errors and variances . It

thereby reduces the confidence that can be placed into the predictors .

In model 5, GDP per capita was removed from the model to assess the influence of connectivity without adding GDP per

capita. In this model there are no variables with a high VIF (see model5 in appendix E) . Because there is no

multicollinearity in this model, the standard error of connectivity is smaller than in model 4 . Connectivity remains highly

significant and still has a coefficient much larger than the other variables, however this is accompanied with a drop in the

adjusted R2 . Overall it is apparent that connectivity is the most important factor in predicting democracy . Throughout all

models it has the highest regression coefficient and remains significant .

The second set of models (models 6 to 12 in table 4 .5) were included to assess regional differences in connectivity . The

connectivity variable was removed and replaced by interaction variables that were constructed by multiplying a regional

dummy with connectivity. Models 6 to 11 assess the relationship of the individual interaction variables . The interaction

terms for Asia, Eurasia, Latin America and Africa are not significant . The interaction term for the Arab region is highly

significant and negatively related to democracy . According to this model, an increase in connectivity in this region would

have a negative effect on democracy . The interaction term for Western Europe is also highly significant and connectivity

and democracy in this region are positively related .

49

Telecommunication and democracy 4. Empirical analysis

model 6 model 7 model8 model 9 model 10 model 11 model 12constant -0.186 -0.670 -t1.565 -1 .190 -1.081 3.193 -0.466

2.483 1.779 2.395 2.413 2.555 2.479 2.082

IogGDP 1.255- 1.893- 1.278- 1 .401 *** 1 .333- 0.207 1 .912 **

0.476 0.360 0.474 0.476 0.478 0.539 0.382

logPOP -0.151 - 0.240 - 0.109 -0.044 -0.067 -0.182 -0.269

0.243 0.175 0.234 0.238 0.249 0.219 0.207

enrollment 0.017 0.006 0.017 0.012 0.017 0.023 0.0050.015 0.011 0.015 0.015 0.015 0.014 0.012

connect_asia 1.194 0.3111.840 1.419

connect_arab -10.319- -10.499-

1.324 1.432

connect_eurasia 0.517 -0.720

1.225 0.989

connect_latin 2.377 0.3661.948 1.598

connect_africa 1.998 -1.1954.024 3.215

connect_w estern 2.7720.798

adjusted R"2 0.201 0,556 0.199 0.212 0.199 0.308 0.537

* Significant at 10%

** Significant at 5%

*** Significant at 1%

Note: Regression coefficients are bold, standard errors italic

Table 4.6: OLS regression models for democracy (FH2005)

Closer examination of GDP per capita reveals that this variable is highly significant in models 6 through 10 . However,

when the interaction variable for Western Europe is included, GDP per capita in model 11 looses its significance and the

coefficient decreases . It seems that the high degree of multicollinearity was mainly caused by countries in the Western

Europe region . Model 12 was constructed to attempt to reduce the degree of multicollinearity, by excluding this region

from the model. This resulted in GDP per capita being positively related to democracy and again highly significant .

Unfortunately, all regional connectivity predictors, except for the Arab region, are not significant . The overall fit of the

model increases to 0.537, however this is mainly because of the Arab interaction term, which by itself (model 7)

produces an R2 of 0 .556. In this model the sign of the interaction term for Eurasia and Africa becomes negative . This

might again be a result of multicollinearity .

4.5 Discussion

In this paragraph the implications of the results from the previous paragraph for this study will be discussed . The results

clearly show the importance of connectivity in predicting democracy, as it has a high regression coefficient and is highly

significant in models 1 though 5. However, some critical notes have to be made . The connectivity index was constructed

by simply averaging three indicators . This does not take into account that countries that lack a well established fixed

telephony network can still be "connected" through a good mobile network. These countries would score relatively low on

50


the connectivity index, because of a low score in number of main fixed telephone lines . Countries with high numbers for

both fixed and mobile telephony are mainly developed, high income, countries . This partly explains the high coefficient

for connectivity in model 4 . It would be more accurate to construct an index that allows for fixed telephony to be

substituted by mobile telephony .

Although there are good theoretical grounds to belief that the level of education is important for democracy, no significant

results were found in this study . In all models the coefficient of enrolment was very small and not significant . This might

be a result of the distribution of the enrolment variable . A regression analysis assumes all variables to be normally

distributed. Because the enrolment variable has a rather sizeable negative skew, this might have resulted in a drop of the

significance .

For population size negative coefficients were obtained, however none of the coefficients was significant . The implication

of a negative coefficient would be that smaller countries would be more likely to be democratic than larger countries . The

findings are in accordance with Kedzie's (1997) research, but contrary to Barro's (1999) findings who reports a positive

coefficient (although very small) . As argued by Alesina and Spolaoro (1995), a problem with the interpretation of country

size is that it is endogenous . Countries that were too large to be manageable are likely to have been split up in the past .

The braking up of large countries, like the former Soviet Union, into smaller nations might provide an opportunity for

democracy. Unfortunately, country size is hardly something policy can be targeted at .

Another factor compromising the outcome of the regression analyses is the distribution of the countries . The availability

of the data differs between countries. Data on high income countries, which are mostly democratic, is more easily

attainable than data on low income countries. As a result the dataset contains relatively few countries with a low

democracy score. The lowest democracy score included in the dataset was 1 .5. There are no countries with the lowest

possible score of 1 . The enrolment variable is the main cause for this . A way of increasing the number of countries would

be to include data on enrolment of previous years .

According to model 7, connectivity in the Arab region would have a negative effect on democracy . This is contradictory to

the relationship found by Kedzie . This region includes some oil-producing countries that have high levels of income, but

are classified as not free or party free by Freedom House . This offers an explanation for the negative effect that

connectivity in this region seems to have, in the current dataset. As a final remark, it must be noted the results presented

here do not provide any evidence on the direction of the causality between the variables .

4.6 Conclusion

In this chapter the empirical analysis was discussed. The corresponding sub question that was formulated in the

introduction was :

To what extent does empirical evidence support the relationship between democracy and connectivity as

found in the literature?

Before this question could be answered two sets of models that contain the most important determinants (as found in the

literature review of chapter 2) were constructed . The first regression model contains GDP per capita, population size,

enrolment rate and connectivity. In the second set of models, regional differences were accounted for by introducing

interaction variables that determine connectivity in a specific region .

51

Telecommunication and democracy 4 . Empirical analysis

Data on all variables was discussed and descriptive statistics were provide to assess the distribution of the variables .

The connectivity index was constructed by normalizing the number of internet users, fixed telephone lines and mobile

subscriber using the same method used to construct the HDI . The three normalized variables were then averaged into a

single connectivity index . Education was measured by using the net enrolment rate. Although there is much more data

available for the gross enrolment rate, this indicators was found not to be reliable for this study . The GDP per capita

adjusted for purchasing power parity was used to account for market exchange rates .

The estimation results pointed to connectivity as being the most dominant predictor of democracy . Because connectivity

itself is highly correlated with GDP per capita, the model suffers from a high degree of multicollinearity . This caused the

sign of GDP per capita to become negative, contrary to what theory predicts . Although this does not affect the bias of the

predictors it does cause problems when determining the individual contribution of the variables that were collinear . In the

last set of models used, it was found that the multicollinearity problem was mainly caused by the western Europe

interaction variable . The final model removes this interaction variable from the model which causes the sign to become

positive again .

The outcome was less clear on the influence of population size and education . For both variables no significant results

were obtained .

52

Telecommunication and democracy 5. Conclusions and recommendations

5. Conclusions and recommendationsIn this chapter the overall conclusions and recommendations are presented . The results of the previous chapters will be

discussed in detail to provide an answer to the problem definition as defined in the first chapter . In order to do this, the

research questions that were defined in the introduction will be addressed first. Then, the hypotheses will be addressed

and evidence to support of deny them will be discussed . In the final paragraph, recommendations for improving future

research are presented .

5.1 Conclusions

In the introduction of this report, several sub questions were defined to address the overall problem definition . These will

be repeated first and then discussed . The first question defined was :

What are, according to the literature, the most important determinants of democracy and what are their

mutual influences?

The results of the literature review in chapter 2 are mainly based on Barro's (1999) empirical analysis of democracy . The

most important determinants that were found in the literature and used in this study are ; income (GDP per capita),

population size and education (net enrolment rate) . The relation of income and democracy was further examined .

Economic theory that studies the influence of democracy on several economic indicators is divided in three perspective .

While some belief democracy stimulates economic development, others feel that democracy hinders economic growth .

There are even those who belief there is no systematic relationship at all . The reverse relation, that is the influence of

economic development on democracy has not been subject of many studies . Upset (1959) suggests that prosperity

stimulates democracy .

How can the concepts of democracy and connectivity be measured?

Democracy in this study is measured by using Freedom House's political rights and civil liberties indices . Both are

important elements of democracy and therefore the scores are averaged into a single democracy index. Although other

indicators of democracy are available, none of these datasets are more up to date than the Freedom House dataset .

Furthermore, it was found that different indicators for democracy are often highly correlated, which suggests that using

these indicators will result in a similar outcome . Connectivity is measured by combining three indicators of ICT that are

commonly used in the available ICT indicators . These are: the number of fixed telephone lines, the number of mobile

subscribers and the number of internet users . These three indicators were normalized and then averaged into the

connectivity variable .

To what extent does empirical evidence support the relationship between democracy and connectivity as

found in the literature?

To answer this question, first the four hypotheses will be addressed . The last hypothesis provides an answer to this

question .

Hypothesis 1 : Countries with high levels of income are more likely to be democratic .

53

Telecommunication and democragy 5. Conclusions and recommendations

The evidence found in this study supports this hypotheses . The coefficient for GDP per capita was found to be positively

related to democracy . In one model the relationship was found to be negative . However, there were good indications that

this was caused by multicollinearity .

Hypothesis 2: Educational attainment increases the propensity for democracy.

In all models that were analysed the coefficient for education was positive . This evidence supports hypothesis 2,

however the results found were not significant and therefore there is no conclusive evidence to support this hypothesis .

Hypothesis 3: Large countries are less likely to be democratic.

The coefficient for population size was negative in all models that were analysed . The coefficient of population size was

negative in all models . This would support hypothesis 3, that large countries are less likely to be democratic . However,

similar to the results on education, the population size was not significant . Therefore, this study does not provide

conclusive evidence to support this hypothesis .

Hypothesis 4: Connectivity contributes to democracy.

The evidence presented in this study does not deny hypothesis 4, however it does not provide conclusive evidence to

support it either. In all models a positive and highly significant coefficient for connectivity was found . Although it might

seem plausible to suggest that this supports the hypothesis, this study does not present any evidence on the direction of

the relationship . Democracy might be influenced by connectivity, however it is also possible that connectivity influences

democracy. A mutual reinforcing relationship is another possibility . However, Kedzie studied the direction of the

relationship and found some evidence that the causal relationship points from connectivity to democracy . There are no

reasons found why the direction of the relationship in this study would be different then in Kedzie's study . Which is not to

say that these reasons do not exists .

5.2 Recommendations

This paragraph presents two different kinds of recommendations . The first set of recommendations target the quality of

the empirical analysis . The second set provides recommendations for future research to contribute to the development of

new theory on the relationship between connectivity and democracy.

Dataset

Because the present study is based on the most recent data available, as a consequence the dataset is limited to only

80 countries. This is mainly caused by the availability of data on the variable for a country's educational attainment (net

enrolment rate) . The number of cases in the 2005 dataset of the World Bank is severely limited . A possible solution for

this problem would be to calculate an estimation for the net enrolment rate by using data of previous years . As the

educational attainment of a country cannot change very rapidly (education takes time), including data from the previous

years will most likely only marginally affect the reliability of the results .

Although a sample size of 80 countries might be sufficient when considering the entire population consists of around 200

countries, for the regional analysis the number of countries for some regions are severely limited . The regions for Africa

and western Europe are the largest group and each contain 17 countries . The number of countries in the Asia region, on

the other hand, consists of only 8 countries. In a sample size that small, the outliers have the potential to greatly reduce

54

Telecommunication and democracy 5 . Conclusions and recommendations

the reliability . Another shortcoming of the current dataset is that it does not include countries with the lowest possible

score of 1 for democracy. As the greatest improvement of democracy can be made in these countries, they are of special

interest for development organizations like Oxfam Novib .

Connectivity index

A different point of concern is the validity of the connectivity index . The index is intended to provide a general overview of

the level of connectivity in a country . It does this by averaging three commonly used ICT indicators into a single index.

These indicators are the number of internet users, the number of fixed telephone lines and the number of mobile

subscribers . The first problem is the simultaneous use of fixed telephone lines and mobile subscribers . As previously

noted, some countries (predominantly developing countries) have established an extensive mobile telephone network,

without the presents of a well developed fixed network . Compared to countries that have high numbers for both fixed and

mobile networks, these countries would score relatively low on the connectivity index . Therefore the current index may

overestimate connectivity in developed countries . When constructing a new index, substitution of fixed telephone by

mobile telephony should be accounted for.

The number of internet users may also compromise the outcome of this study . The variable used is an estimation and

may therefore reduce reliability of the regression analysis . Furthermore, internet usage does not necessarily imply

connectivity. The internet is used in a variety of activities . Some of these activities imply a certain degree of connectivity,

for example email, instant messaging, voice-over-IP, weblogs and forums . However, other activities like file-sharing,

browsing, online purchases and online gaming may not contribute to connectivity at all . When developing a new index of

connectivity, it would be more accurate to differentiate between different activities or services that people use . Although

this will increase validity of the connectivity index, it may prove very difficult to obtain the necessary data (especially in

developing countries) . It would, on the other hand, provide valuable information for development agency, as it enables

them to target their strategies at specific technologies .

Causality

The study uses regression analysis to examine the relationship between connectivity and democracy. Although a

regression analysis provides information on how two or more variables are related, it is silent on the causal direction . The

results clearly indicate that connectivity is positively related to democracy and although there is literature that argues that

connectivity influences democracy, arguments to the reverse relation exist as well . In order to determine whether

democracy is really influenced by connectivity, future research should focus on causality . One way of doing this is by

examining time-series data . Changes in both variables can then be monitored . Another method would be to use

variables that lag in time. It is likely that changes in connectivity will not affect democracy immediately . Only after some

time the effects would gradually become visible .

Case studies

This study has examined the relationship between connectivity and democracy by comparing data on a group of 80

countries. The study was concerned with quantifying the relationship and then assessing it in a model . Although this

does provide valuable information on the relationship between the two variables on an abstract level, it does not provide

the opportunity study details of the relationship . As Hargittai (1999) argues in a study that tries to offer an explanation for

differences between internet connectivity in a group of 18 OECD countries, quantitative aspects need to be

supplemented with qualitative information about country-specific attributes that may also affect the dependant variable .

As a next step in exploring the relationship between democracy and connectivity, it would be interesting to examine

55

Telecommunication and democracy 5 Conclusions and recommendations

countries in individual case studies. Countries with a high mobile penetration rate, that lack an extensive fixed telephony

network are interesting cases. It would be interesting to see if these countries obtain the same level of actual

connectivity.

56

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59


60

Telecommunication and democracy Appendices

AppendicesAppendix A Democracy regressions . .. .. . . .. . . .. .. .. .. .. . . . . .. . . .. .. . . . . . . . . .. .. . . . . . . . . . . . . . . . . . .. .. .. . . .. .. .. . . . . . . .. .. .. .. .. . . .. .. .. .. . . . .. . .. .. . .. ... .. . .. . .58

Appendix B Correlation political rights and civil liberties .. .. . . . . . . . . .. .. . . . . . . .. .. . . .. .. . . . . . . . . . . . . . . . . . . . . . . . . .. .. .. . . . . . . .. .. .. . . . . . . .. . .. . . . .. . .59

Appendix C Distribution of explanatory variables. . . .. .. . . . . . . . . .. .. . . . . . . . . .. .. .. . . .. .. .. . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . .. .. . . . . . . . . . . .. . . . . . . . . . . . . . . .. . .60

Appendix C.1 GDP per capita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60

Appendix C.2 Distribution of population size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Appendix C.3 Net enrolment rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62

Appendix D Country list . .. .. . . .. .. .. .. .. .. .. . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .. .. . . . . . . . . .. .. . . . . .. .. .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. .. .. . . . . . . .. .. . . . . . . . . . . . . . . . . .. . .63

Appendix E Regression analyses .. . . .. . . . . . . . . . .. . . . . . . . . .. .. .. . . . . . . .. .. .. .. . . . . .. .. .. .. . . . . .. . . . . . . .. . . . . . .. . . .. .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. .. . . .. . . . . .. . .65

Appendix F Regional analysis .. .. . . . . . . . . . . . . . . . . . .. .. .. .. . . .. .. .. .. .. .. .. .. .. .. .. .. . . .. .. .. .. .. .. .. . . . . . .. .. . . .. .. . . .. .. .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . .. . . . . . .. . .70

61

Telecommunication and democracy An D ndicps

Appendix A Democracy regressions

Regressions for democracy and civil liberties (Barro, 1999) .

(1) democracy (2) civil libertiesIndependent variable :

5-year lag of dependent variable 0.608 (0.041) 0.536 (0 .041)

10-year lag of dependent variable 0.102 (0 .040) 0.148 (0 .039)Log (GDP) 0.058 (0 .016) 0.054 (0 .014)Years of primary schooling 0.0134 (0.0059) 0.0143 (0 .0051)Gap between male and female primary schooling -0 .047 (0.013) -0 .043 (0 .011)

Urbanization rate -0.095 (0.048) -0 .075 (0 .041)Log (population) 0.0080 (0 .0044) 0.0012 (0.0038)Oil-country dummy -0 .094 (0 .031) -0.096 (0.027)R2 .62,16-67

.76, .76, .56

.64, .81, .77

.82,75,10

Number of observations 76, 88, 102102, 103, 100

76, 88, 102

102, 103, 100

Equation used to assess the time-lag influence on democracy :

democ,t=ao+aidemoc, I_T+a2democ, .t_2T+a3Z,,r_T+u,t

a,>0,a2>Oand 0<a,+ a2<1

i country

t time period

T time lag (5 years)

democ democracy indicator

Z vector of variables (such as per capita GDP, education, etc .)

u error term

62

Telecommunication and democraa

Appendix B Correlation political rights and civil liberties

ADoendices

[DataSet2] C :\Documents and Settings\s461530\Desktop\Data\Freedom House 2006\FIWAllScores .sav

USE ALL .COMPUTE filter_$=(year=1985) .VARIABLE LABEL filter_$ 'year=1985 (FILTER)' .VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected' .FORMAT filter_$ (fl 0) .FILTER BY filter-$ .EXECUTE .CORRELATIONS

/VARIABLES=pr cl/PRINT=TWOTAIL NOSIG/MISSING=PAIRWISE .

Correlations (year = 1985)

political rights civil libertiespolitical rights Pearson Correlation 1 ,932(*")

Sig. (2-tailed) ,000N 166 166

civil liberties Pearson Correlation ,932(**) 1Sig. (2-tailed) ,000N 166 166,

`* Correlation is significant at the 0 .01 level (2-tailed) .


olitical ri hts civil libertiespolitical rights Pearson Correlation 1 ,928(**)

Sig. (2-tailed) ,000N 191 191

civil liberties Pearson Correlation ,92g(`*) 1Sig. (2-tailed) 000N 191 191

** Correlation is significant at the 0 .01 level (2-tailed) .


political rights civil libertiespolitical rights Pearson Correlation 1 ,952(*")

Sig. (2-tailed) ,000N 192 192

civil liberties Pearson Correlation ,952(*") 1Sig. (2-tailed) ,000N 192 192

"* Correlation is significant at the 0.01 level (2-tailed) .

63


Appendix C Distribution of explanatory variables

Appendix C.1 GDP per capita

GDP PPP per

capita (Current

international

dollar lo GDPN Valid 173 173

Missing 13 13Mean 10514,72676 3,7729Median 6444,27500 3,8092Skewness 1,803 -,055Std. Error of Skewness ,185 ,185

GDP_PPP per capita (Current international dollar)

e01

0.000 20000,000 40000,000 60000,000

GDP_PPP per capita (Current International dollar)

logGDP

Mean =10514,727Std. D4v c11259,245

N=173

u ..n=s.n8W. Dw. =0.402

N=173

Appendices

64


Appendix C.2 Distribution of population size

Population

ITU lo POPN Valid 177 177

Missing 9 9

Mean 35990562,40 6,8129Median 8000000,00 6,9031Skewness 8,218 -,404Std. Error of Skewness ,183 ,183

Population (ITU)

M..-35~,4SC Dev •1,34E8

1-177

1

04,00 5,00 6,00 7,00

IogPOP

8,08

Mem ~A1Old Dw =0.882

N=177

65

Telecommunication and democracv

Appendix C.3 Net enrolment rate

N ValidMissing

MeanMedianSkewnessStd. Error of Skewness

Histogram

Mem -57 .69Std Dev. •13,889

N •171

12264

87,591092,2150-2,069

,219

66

Appendices


Appendix D Country list

ADoendices

Country ISO Income Region1 Algeria DZA middle Middle East & North Africa *2 Australia AUS high Western Europe3 Azerbaijan AZE middle Europe & Central Asia "4 Bahrain BHR high Middle East & North Africa5 Bangladesh BGD low South Asia *6 Barbados BRB high Latin America & Caribbean7 Belarus BLR middle Europe & Central Asia *8 Belize BLZ middle Latin America & Caribbean *9 Benin BEN low Sub-Saharan Africa *10 Bolivia BOL middle Latin America & Caribbean *11 Botsw ana BWA middle Sub-Saharan Africa *12 Bulgaria BGR middle Europe & Central Asia *13 Burkina Faso BFA low Sub-Saharan Africa *14 Cape Verde CPV middle Sub-Saharan Africa15 Colombia COL middle Latin America & Caribbean16 Denmark DNK high Western Europe17 Djibouti DJI middle Middle East & North Africa *18 Dominican Republic DOM middle Latin America & Caribbean19 Ecuador ECU middle Latin America & Caribbean *20 Egypt EGY middle Middle East & North Africa *21 B Salvador SLV middle Latin America & Caribbean *22 Eritrea ERI low Sub-Saharan Africa *23 Estonia EST high Europe & Central Asia24 Ethiopia ETH low Sub-Saharan Africa25 Finland FIN high Western Europe26 France FRA high Western Europe27 Ghana GHA low Sub-Saharan Africa28 Greece GRC high Western Europe29 Guatemala GTM middle Latin America & Caribbean *30 Honduras HND middle Latin America & Caribbean *31 Hungary HUN middle Europe & Central Asia *32 Iceland ISL high Western Europe33 India lND low South Asia *34 Indonesia ION middle East Asia & Pacific *35 Iran IRN middle Middle East & North Africa *36 Ireland IRL high Western Europe37 Italy ITA high Western Europe38 Japan JPN high East Asia & Pacific39 Kenya KEN low Sub-Saharan Africa *40 Kuw ad KWT high Middle East & North Africa41 Kyrgyzstan KGZ low Europe & Central Asia *42 Laos LAO low East Asia & Pacific *43 Lebanon LBN middle Middle East & North Africa *44 Lithuania LTU middle Europe & Central Asia *45 Luxembourg LUX high Western Europe46 Macedonia MKD middle Europe & Central Asia *47 Madagascar MDG low Sub-Saharan Africa *48 Malawi MWI low Sub-Saharan Africa *

67

Telecommunication and democracy Aooendices

49 Mali MLI low Sub-Saharan Africa

50 Malta MLT high Western Europe

51 Mauritania MRT low Sub-Saharan Africa

52 Mexico MEX middle Latin America & Caribbean *

53 Mongolia MNG low East Asia & Pacific

54 Morocco MAR middle Middle East & North Africa *

55 Netherlands NLD high Western Europe

56 New Zealand NZL high Western Europe

57 Niger NER low Sub-Saharan Africa *

58 Nigeria NGA low Sub-Saharan Africa *

59 Norw ay NOR high Western Europe

60 Oman OMN middle Middle East & North Africa *

61 Pakistan PAK low South Asia *

62 Panama PAN middle Latin America & Caribbean *

63 Peru PER middle Latin America & Caribbean *

64 Poland POL middle Europe & Central Asia *

65 Portugal PRT high Western Europe

66 Qatar QAT high Middle East & North Africa

67 Russia RUS middle Europe & Central Asia `

68 Senegal SEN low Sub-Saharan Africa *

69 Seychelles SYC middle Sub-Saharan Africa

70 Slovenia SVN high Europe & Central Asia71 South Korea KOR high East Asia & Pacific72 Spain ESP high Western Europe

73 St. Vincent & Grenadine VCT middle Latin America & Caribbean *

74 Sw itzerland CHE high Western Europe

75 Togo TGO low Sub-Saharan Africa

76 Tunisia TUN middle Middle East & North Africa *

77 Turkey TUR middle Europe & Central Asia *

78 United Arab Emirates ARE high Middle East & North Africa79 United Kingdom GBR high Western Europe

80 Venezuela VEN middle Latin America & Caribbean *

* World Bank regions

68


Appendix E Regression analyses

Regression (model 1)

[DataSetl] G :\reduced dataset .sav

Variables Entered/Removed(b)

Adjusted R Std . Error of theModel R R Square

Square Estimate

I ,473(a) ,223 ,213 1,604

a Predictors: (Constant), IogGDP

Model Summary

ANOVA(b)

Model

Model Variables Entered Variables Removed Method

I logGDP(a) Enter

a All requested variables entered .

b Dependent Variable : FH2005

Sum of Squares df Mean Square F

57,747 1 57,747 22,431RegressionResidual

Total

a Predictors : (Constant), IogGDP

FH2005b Dependent Variable:

Sig.,000(a)

79

Coefficients(a)

ModelUnstandardized Coefficients Standardized Coefficients t Sig .

B Std. Error Beta

(Constant)IogGDP 1,664

FH2005a Dependent Variable :

200,800

I I I1,371 -,980

4,736

,330

,000

69

Telecommunication and democrag;y Appendices





I logPOP, IogGDP(a) Enter



Model Summary

Adjusted R Std. Error of theModel R R Square

Square Estimate

I ,474(a) ,224 ,204 1,614

a Predictors: (Constant), logPOP, logGDP

ANOVA(b)

Regression

Residual

Total

58,033

200,514

258,547

df

77

Mean Square

29,017

Sig .

,000(a)

2,604

79

a Predictors: (Constant), logPOP, IogGDP


Coefficients(a)

Unstandardized CoefficientsModel

B Std. Error

Standardized Coefficients t Sig .Beta

(Constant) -,700 2,381

I IogGDP 1,636 ,364

logPOP -,077 ,232

a Dependent Variable: FH2005

Sum of Squares

,465

-,294 ,770

-,034

4,499

-,332

,000

70

on a




Sig.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 ,487(a) ,237 ,207 1,611

a Predictors : ( Constant), Net enrollment rate (%), primary level, total, logPOP, IogGDP

ANOVA(b)

Model

Regression

Residual

Total

df

61,383

197,163 76

79

20,461

2,594

Appendices

,000(a)

a Predictors : (Constant), Net enrollment rate (%), primary level, total, logPOP, logGDP

b Dependent Variable: FH2005

Coefficients(a)

Model


1 Net enrollment rate (%), primary level, total, logPOP, logGDP(a) . Enter



Unstandardized

Coefficients

B Std. Error

(Constant)

logGDPlogPOPNet enrollment rate (%), primary

level, total


Sum of Squares

-,632

1,296

-,109

,017

Mean Square

,470

,015

Standardized

Coefficients

,368

-,266

2,759

Sig .

,791

,007

-,049 -,468

1,136

,641

,259

71





Collinearity

Statistics


I connect n, logPOP, Net enrollment rate (%), primary level, total, IogGDP(a) . Enter



Model Summary


1 ,600(a) ,360 ,326 1,485

a Predictors: (Constant), connect n, logPOP, Net enrollment rate (%), primary level, total, IogGDP

ANOVA(b)

Model

Regression

Residual

Total

df

93,069

165,478

79

23,267

2,206

10,545

Sig.

,000(a)

a Predictors : (Constant), connect n, logPOP, Net enrollment rate (%), primary level, total, logGDP


Coefficients(a)

Model

Unstandardized Standardizedt

Coefficients Coefficients

B Std. Error Beta

8,295 3,218 2,578

-1,573 872 -,447

-,111 215logPOP -,517

(Constant)

connect -n

IogGDP

FH2005

Net enrollment rate (%),

primary level, total

a Dependent Variable :

Sum of Squares

,295

,020

6,252

,014

1,650

72

Mean Square

-,050

-1,803

3,790

Sig .

ance,012

,075

,607

,000

Toler

anceVIF

Anuendices

,139

,931

,593

7,193

1,075

1,687

6,209





ADDendices


I connect n, logPOP, Net enrollment rate (%), primary level, total(a) Enter



Model Summary


1 ,576(a) ,332 ,306 1,507

a Predictors: (Constant), connect n, logPOP, Net enrollment rate (%), primary level, total

ANOVA(b)

Model

Regression

Residual

Total

Sum of Squares df

76

79

28,632 12,604

Sig.

,000(a)

a Predictors : (Constant), connect n, logPOP, Net enrollment rate (%), primary level, total


Coefficients(a)

Model

(Constant)

logPOPNet enrollment rate (%),


connect -n

a Dependent Variable : FH2005

Unstandardized

Coefficients

B

-,061

85,897

172,650

258,547

Std. Error

1,789

3,671

,216

,013

Standardized

Coefficients

Mean Square

Beta

-,027

,094

01,924

Sig.Collinearity

Statistics

Toler

ance,058

,780

,408

,000

nA,947

,683

,653

1,056

1,464

73

Telecommunication and demo"cv

Appendix F Regional analysis




Collinearity

Statistics


I connect asia, Net enrollment rate (%), primary level, total, logPOP, IogGDP(a) . Enter



Model Summary


1 ,492(a) ,242 ,201 1,617

a Predictors: (Constant), connect asia, Net enrollment rate (%), primary level, total, logPOP, IogGDP

ANOVA(b)

Model Sum of Squares df Mean Square F Sig .

Regression 62,484 4 15,621 5,975 ,000(a)

I Residual 196,063 75 2,614

Total 258,547, 79

a Predictors : (Constant), connect asia, Net enrollment rate (%), primary level, total, logPOP, IogGDP

b Dependent Variable: F H 2 0 0 5

Coefficients(a)

Model

(Constant)

logGDP



connect asia


Unstandardized

Coefficients

-,186

Std. Error

2,483

,476

Standardized

Coefficients

,356

-,067

,017

1,194

,015 ,146

1,840 ,068

-,075

2,636

-,622

,649

,940

,010

,536

Toler

ance

Appendices

1,808

,865

,594

,912

1,156

1,683

1,097

74





Anoendices


1 connect arab, Net enrollment rate (%), primary level, total, logPOP, logGDP(a) . Enter



Model Summary


1 761(a) 579 556 1,205

a Predictors: (Constant), connect arab, Net enrollment rate (%), primary level, total, logPOP, IogGDP

ANOVA(b)

Model

Regression

Residual

Total

149,634

108,913

258,547

I df

79

Coefficients(a)

Sig .

a Predictors: (Constant), connect arab, Net enrollment rate (%), primary level, total, logPOP, logGDP


(Constant)

IogGDP

logPOP



connect arab

[ a Dependent Variable: FH2005

Unstandardized

Coefficients

-,670

1,893

-,240

006

-10,319

Sum of Squares

Standardized

Coefficients

37,408 25,760

5,263

TolerStd. Error

1,779

360

.ea.aeveeve...eeee,a,_,ea.e..e.-,377

538

,175 -,107

,051

Mean Square

707

000

Sig .000(a)

Collinearity

Statistics

ance

538

-1,373 ,174

-,605 -7,796

604

000

922

932

1,859

1,085

1,709

1,073

75





Model Summary


I ,489(a) ,239 ,199 1,619

a Predictors: (Constant), connect eurasia, logPOP, Net enrollment rate (%), primary level, total, Iog GDP

ANOVA(b)

Model

RegressionResidual

Total

61,851

196,696

df

79

Sig .,000(a)

Appendices

a Predictors: (Constant), connect eurasia, logPOP, Net enrollment rate (%), primary level, total, IogGDP


Coefficients(a)

Model

(Constant)

IogGDP

logPOPNet enrollment rate (%a),


connect eurasia

[ a Dependent Variable: FH2005

Sum of Squares

Unstandardized

Coefficients

-,565

-,109

,017

Std. Error

2,395

,015

Standardized

Coefficients

Mean Square

-,049

15,463

2,623

,363

5,896

-,236

2,694

-,467

,145 1,111

,043

,009

,642

,270

,674

Collinearity

Statistics

Toler

anceVIF

,559

,931

,594

,974

1,790

1,075

1,685

1,027

76





AoDendices


1 connec11 atin, IogGDP, logPOP, Net enrollment rate (%), primary level, total(a) . Enter



Model Summary


1 502(a) 252 212 1,606

a Predictors: (Constant), connect_latin, IogGDP, logPOP, Net enrollment rate (%), primary level, total

ANOVA(b)

Model

Model Sum of Squares df Mean Square F Sig .

Regression 65,223 4 16,306 6,326 ,000(a)

I Residual 193,324 75 2,578

Total 258,547 79

a Predictors : (Constant), connect _latin, IogGDP, logPOP, Net enrollment rate (%), primary level, total


(Constant)

IogGDP

I logPOPNet enrollment rate (%),


connect latin


Coefficients(a)

Standardized

Coefficients

Unstandardized

Coefficients

-1,190

1,401

-,044

,012

Std. Error

,476

,238

,015

,398

-,020

,109

1,948 ,128

Sig .Collinearity

Statistics

VIFL- I

-,493

2,942

-,185

,815

1,220

,623

,004

,854

,226

1 ,545 1,834

,884

,561

,908

1,784

1,1011

Tolerance

77





Appendices


1 connect_africa, Net enrollment rate (%), primary level, total, logPOP,

logGDP(a)Enter



Model Summary


I 490(a) 240 199 1,619

a Predictors: (Constant), connect africa, Net enrollment rate (%), primary level, total, logPOP, logGDP

ANOVA(b)

Model

Regression

Residual

Total

Sum of Squares df

79

15,507

2,620

5,918

Sig.

a Predictors : (Constant), connect africa, Net enrollment rate (%), primary level, total, logPOP, logGDP


Coefficients(a)

Model

(Constant)IogGDP

logPOP



connect africa


Unstandardized

Coefficients

-1,081

-,067

017

Std. Error

62,029

196,518

249

015

4,0241,998

Standardized

Coefficients

Mean Square

Beta

379

-,030

146

053

2,789

-,268

1,119

,496

673

007

790

267

Sig .

000(a)

Collinearity

Statistics

Toler

anceVIF

550

594

1,8191

1,683

874 1,144621

78





Model Summary

ADDendices


I ,586(a) ,343 ,308 1,505

a Predictors : (Constant), connect western, logPOP, Net enrollment rate (%), primary level, total, IogGDP

ANOVA(b)

Model

Regression

Residual

Total

88,709

169,838

df

79

2,265

9,793

Sig .,000(a)

a Predictors : (Constant), connect western, logPOP, Net enrollment rate (%), primary level, total, logGDP


Coefficients(a)

Model

Sum of Squares

Unstandardized

Coefficients

Std. Error

(Constant)

IogGDP



connect western


3,193

,207

2,479

-,182

,023

2,772

,539

,219

,014

,798

Standardized

Coefficients

,059

Mean Square

-,081

,198

-,831

1,619

,409

,110

,001

Collinearity

Statistics

Toler I

,922

,586

2,680

1,085

1,706

79





connect_africa, connect asia, connect arab, Net enrollment rate (%), primary level,

total, connect eurasia, connect-latin, logPOP, IogGDP(a)

Model Summary

ApDendices

a Predictors : ( Constant), connect_africa, connect_asia, connect arab, Net enrollment rate (%), primary level, total,

connect eurasia, connect_latin, logPOP, IogGDP

ANOVA(b)

Model

Regression

Residual

Total

Sum of Squares

151,012

107,535

df

79

Mean Square

12,463

Sig.,000(a)

a Predictors: (Constant), connect africa, connect_asia, connect arab, Net enrollment rate (%), primary level, total,

connect eurasia, conne ct-latin, logPOP, IogGDP


80


[ Model

(Constant)

logGDPlogPOPNet enrollment rate (%),


connect asia

connect arab

connect eurasia

connect latin

connect africa


Coefficients(a)

Unstandardized

Coefficients

-,466

1,912

-,269

,005

Std. Error

2,082

,207

,012

1,419

-10,499

-,720

,366

,989

1,598

-1,195

Standardized

Coefficients

-,120

,047

,018

-,616

-,060

,020

-,032

5,009

-1,296

,219

-7,330

,229

Sig .

,000

,469

,819

ADOendices

Collinearity

Statistics

Toler

anceVIF

,498

,830

2,007

1,205

,862

,793

,792

1,161

1,261

1,263

81

technische universiteit eindhoveri

Technische Universiteit EindhovenDepartment of Technology ManagementTema i .3zP.O. Box 513560o MB EindhovenThe Netherlands

Telephone: +31 (0)40 247 26 35Fax: +31 (0)40 246 85 26E-mail : facQa tm .tue .nlwww.tue.nl/tm

Photograph :Norbert van Onna, Eindhoven

June 2001

/ department of technology management

T U /e te ch „ sc he u„,e5,,e t e ,n dhmen

Postbus 901595600 RIM EinhovenTel 040 - 247 22 24 E I ii u iuiirn

200711601a ll

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