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Measuring digital development for policy-making: Models, stages, characteristics and causes

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  • + Musicarraona Boris Mir 2 months ago
    Moltes felicitats, Ismael.
    La punta d’iceberg d’un gran treball i la punta de llança d’un camí intel·lectual que et desitjo extraordinari. Tots esperem gaudir-ne!
  • + ictlogist Ismael Peña-López 2 months ago
    Caramba con los comentarios!!! :)))
  • + francescgomez Francesc Gómez Morales 2 months ago
    Me ha parecido un gran trabajo y el hecho de ponerlo online me ha animado a volver a considerar en un futuro retomar mis estudios de doctorado. A un doctorado así sí que le veo sentido :)
  • + marquibeck marina 2 months ago
    This presentation opens the path to awesome learning chances and serious inquiry opportunities ... We’ll be reflecting on the implications of this in class - spreading the memes around. Thanks for rocking the boat with an exemplary action also in sharing this. Chapeau.
  • + calbaladejo calbaladejo 2 months ago
    Congratulations, Isma! Te lo mereces...
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Notes on slide 1

With leave of the committee, I will now defense my PhD thesis…

Welzel, C., Inglehart, R. & Klingemann, H. (2003). “The theory of human development: A cross-cultural analysis”. In European Journal of Political Research, 42 (3), 341-379. Oxford: Blackwell.

Castells, M. (2000). “Materials for an exploratory theory of the network society”. In British Journal of Sociology, Jan-Mar 2000, 51 (1), 5-24. London: Routledge.

The Digital Economy is the set of different processes of digitization that take place in the economic and social spheres. These processes are a set of digital enablers that shift the industrial society into new social structures like the Information Society, the Knowledge Based Society or the Network Society. Our approach stands in between theory and empiricism. It tries to match (mostly) theoretical approaches with the evidence brought by existing data. There have been several approaches, but empirical evidence shows that there does not seem to be a comprehensive and multidisciplinary model. From a strictly empirical point of view, we still cannot glance theoretical constructs like the Information Society or the Network Society. These theoretical approaches have not succeeded in empirically explaining the reality. Our approach, an empirical one, tries to fill in this gap. Our research stands in the very frontier of actual research.

We perform an academic approach before the proliferation of such models

H: Institutional interests and lack of data lead to fragmented models to measure digital development that distort policy design. A comprehensive framework would improve such models and indicate in what ways the adoption of public policies would lead to higher stages of digital development.

WH1: A lack of quality data leads to fragmented models of digital development that make it both difficult to measure policies that foster the Information Society and to measure the impact of those policies on digital development, an implication being that these policies could have a better design either by focusing on filling conceptual voids or including feedback from better measurement.

WH2: A 360º digital framework approach shows that Infrastructure – Availability and Affordability –, the ICT Sector – the Industry and the skilled Workforce –, Digital Literacy – the level of Digital Literacy and Digital Literacy Training –, the Policy and Regulatory Framework – Regulation and Policies – and Content and Services – Availability and Intensity of Usage – are the key components of digital development and such a comprehensive framework for analysis could be applied in policy design.

WH3: Higher levels of wealth and economic development, education and the existence of digital infrastructures almost always coincide with higher levels of digital development. Nevertheless, Governments can accelerate the process of digital development through the adoption of public policies that frame and foster the Information Society – such as Government prioritization of ICT and assigning a high importance to ICT in government vision of the future – and establishing an appropriate Economic Incentive Regime. This will raise the probability of a country of reaching higher stages of digital development.

Academic approach before the proliferation of different models

Why not analogue indicators Infrastructures: Information and Communication Technologies. Can be divided into three groups: hardware, software and connectivity. Infrastructures, Availability: the presumed existence of these infrastructures. Infrastructures, Affordability: the cost for the end user of the acquisition of such infrastructures in relationship with one individual or community’s economic power – hence, the price in real terms. ICT Sector: The economic sector responsible for the provision of ICT Infrastructures ICT Sector, Enterprises / Industry: The existence of firms whose activities fits within the definition of the ICT sector. ICT Sector, Workforce: Skilled employees that work directly in the ICT Sector or whose activities are closely related to it . Digital Skills: Skills related relevant both to the use of electronic devices and the use of information in digital format Digital Skills, Digital Literacy Level: The measured levels of such skills in an individual or a community, both in relation to the number of literate people and the degree of their literacy. Digital Skills, Digital Literacy Training: The existence of courses, curricula or other training plans to increase the Digital Literacy Level. Policy and Regulatory Framework: Whether there are explicit rules, laws, policies, etc. that directly affect and try to put in order the Digital Economy. Policy and Regulatory Framework, ICT (Sector) Regulation: Rules created by the Legislative branch or other regulatory bodies to regulate the Digital Economy, especially the ICT Sector and its activities. Policy and Regulatory Framework, Information Society Strategies and Policies: Policies, strategic plans, etc. created by the Executive branch or other functions of government to frame their Digital Economy related policies. Content and Services: Content and services in digital form. Content and Services, Availability: The existence of such contents and services, including those arising from the private sector (for or without profit) and the public sector. Content and Services, Intensity of Use: The use of such content, measured both quantitatively and qualitatively.

Analysis of variables to avoid problems of multicollinearity Year 2007 really means that they were collected during 2007, but published afterwards. Sometimes data come from previous years. On the other hand, 2007 is the last year of an economic cycle: taking 2008 would surely change things. It is also the year of social networking sites. Problem of not having time series. Aggregation per country blurs the details within countries. Ways to simplify information: Factor analysis (non conclusive) and cluster analysis We repeated some statistics using two different datasets: WITSA and OECD. The former gathers the 75 most digitally developed countries which, in some way, are all the digitally developed countries of the World, leaving outside almost 200 countries that are simply too far from being called digital. The later, the OECD data set, includes arguably the most developed countries in the World. By using this second dataset we want to zoom into wealth and see whether, at the macro aggregate level, the patterns found for the WITSA dataset (broad range of economies) still apply when finding differences within a narrower and richer range of economies.

How are these tables built – what do they stand for

How is this chart built Designs based on a specific and applied purpose that fits the general goals of the fostering organization Designs adapted to the availability of data Unbalance towards infrastructure indicators – and telecommunications in particular –versus other kinds of indicators Imbalance between infrastructures and usage data

How is this chart built The prevalence of supply-side indicators means, for instance, that we are giving priority to the existence of infrastructure but leaving aside whether it is affordable for the end user Failing to measure the reasons for usage may actually lead to some towards paths of exclusion The imbalance between infrastructure + usage vs. other data categories leaves aside, once again everything in between what is to be used and the use of it, which we can call (as we did before) causes, or which we can call enablers.

How is this chart built Raboy, M. (1998). “Global Communication policy and human rights”. In Noll, R. G. & Price, M. E. (Eds.), A communications cornucopia: Markle Foundation essays on information policy, 218-242. Washington, DC.: Brookings Istitution Press. Raboy, M. (1995). “Access to Policy, Policies of Access”. In Javnost—The Public, 2 (4), 51-61. Ljubljana: Euricom.

How is this chart built Raboy, M. (1998). “Global Communication policy and human rights”. In Noll, R. G. & Price, M. E. (Eds.), A communications cornucopia: Markle Foundation essays on information policy, 218-242. Washington, DC.: Brookings Istitution Press. Raboy, M. (1995). “Access to Policy, Policies of Access”. In Javnost—The Public, 2 (4), 51-61. Ljubljana: Euricom.

How is this graphic built Non-hierarchical K-means cluster analysis to segment groups of countries whose variables have statistically significant similar values in opposition to the other groups.

How is this graphic built

How is this graphic built Contingency tables to find means that are significantly different between clusters and significantly similar within clusters Hypothesis of independence between a chosen variable and the distribution amongst clusters of the country set. A significant score for Pearson Chi-Square and Fischer’s Exact test will reject the hypothesis of independence, meaning that a country’s allocation to a particular cluster depends on its value for that selected variable We measure the correlation of the distribution amongst clusters and that same selected variable by means of the Pearson and Spearman correlations. Again, significant results tell us that both variables (the cluster and the chosen one) are correlated Haberman typified adjusted residuals. These residuals have a normal distribution. Taking a confidence level of 0.95, we can look for adjusted residuals with absolute value over 1.96, noting that there are more (or less ) cases than expected in comparison with the case where the two compared variables (the cluster and the other variable in our case) were independent.

None of the independent variables of the regression were used to build the clusters Why use a probabilistic model, binary, pros and cons Why not strivers and/or leapfroggers The Chi-Square test confirms that the power of the effect of the independent variables taken jointly is statistically significant, and the Hosmer and Lemenshow test rejects the null hypothesis that there is no difference between the observed and predicted values of the dependent variable, thus confirming the goodness to fit of the overall model. Indeed, the model predicts a total of 95.7% of all cases (46 countries), 96.8% of digital leaders and 93.3% of the rest of countries. The high value of Nagelkerke’s R-square implies quite a good degree in the explanatory power of the model too. Life expectancy at birth, total (years) (GEN30): the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life (World Bank, World Development Indicators). Inequality-20 (GEN05): ratio of the richest 20% to the poorest 20% (UNDP, Human Development Report ). Urban Population (%) (GEN07): urban population is the midyear population of areas defined as urban in each country and reported to the United Nations. This indicator measures the proportion between urban and the total population in percent (World Bank, World Development Indicators). Economic Incentive Regime (GEN08): The Economic Incentive and Institutional Regime is the simple average of the normalized scores on three key variables: Tariff & Nontariff Barriers, Regulatory Quality, and Rule of Law (World Bank, Knowledge Assessment Methodology). Tariff & Nontariff Barriers : is a score assigned to each country based on the analysis of its tariff and non-tariff barriers to trade, such as import bans and quotas as well as strict labeling (sic) and licensing requirements (the score is based on the Heritage Foundation's Trade Freedom score and used the World Bank, Knowledge Assessment Methodology) Regulatory Quality : measures the incidence of market-unfriendly policies such as price controls or inadequate bank supervision, as well as perceptions of the burdens imposed by excessive regulation in areas such as foreign trade and business development (World Bank, Governance Indicators / Knowledge Assessment Methodology). Rule of Law : this indicator includes several indicators which measure the extent to which agents have confidence in and abide by the rules of society. These include perceptions of the incidence of both violent and non-violent crime, the effectiveness and predictability of the judiciary, and the enforceability of contracts (World Bank, Governance Indicators / Knowledge Assessment Methodology). Government prioritization of ICT (LEGAL_D_04): measures from 1 (strongly disagree) to 7 (strongly agree) the answer to the question “Information and communication technologies (computers Internet etc.) are an overall priority for the government” (World Economic Forum, Executive Opinion Survey / Global Information Technology Report).

None of the independent variables of the regression were used to build the clusters The Chi-Square test confirms that the power of the effect of the independent variables taken jointly is statistically significant, and the Hosmer and Lemenshow test rejects the null hypothesis that there is no difference between the observed and predicted values of the dependent variable, thus confirming the goodness to fit of the overall model. Indeed, the model predicts a total of 94.6% of all cases (47 countries) – slightly less than the digital leaders model –, 96.4% of digital laggards and 88.9% of the rest of countries. The high value of Nagelkerke’s R-square implies quite a good degree in the explanation power of the model too. Inequality-10 (GEN06): is the ratio of richest 10% to poorest 10% (UNDP, Human Development Report). Health Public Expenditure (% of total Health expenditure) (GEN14): Public Health Expenditure is recurrent and capital spending in Health from central and local governments, external borrowing and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds, here measured as percent of total Health Expenditure, which is the sum of public and private health expenditure and covers the provision of health services (preventive and curative), family planning and nutrition activities, and emergency aid for health but excludes provision of water and sanitation. (World Bank, World Development Indicators). Population covered by mobile telephony (%) (INF_S_06): is the percentage of people within range of a mobile cellular signal regardless of whether they are subscribers. (World Bank, World Development Indicators). Importance of ICT to the government vision of the future (LEGAL_D_01): measures from 1 (strongly disagree) to 7 (strongly agree) the answer to the question “The government has a clear implementation plan for utilizing information and communication technologies for improving the country's overall competitiveness” (World Economic Forum, Executive Opinion Survey / Global Information Technology Report).

Some of the things I’ve done and found have theoretical and political implications, but these are topics about which it is difficult to talk about without moving outside of the boundaries of this research.

WH1: A lack of quality data leads to fragmented models of digital development that make it both difficult to measure policies that foster the Information Society and to measure the impact of those policies on digital development, an implication being that these policies could have a better design either by focusing on filling conceptual voids or including feedback from better measurement While monitoring has generated a wide array of tools, explanation of the reality has not The measurement of the digital economy is focused more in quantitative monitoring than on qualitative impact The imbalance between infrastructure + usage vs. other data categories leaves aside, once again everything in between what is to be used and the use of it, which we can call (as we did before) causes, or which we can call enablers Too little concern about the affordability of infrastructure relative to the view of residential users Forgetting the enablers also implies letting aside the possibilities of the (underrepresented in the indicators too) ICT Sector as a driver of development Narrow institutional interests and a lack of appropriate data have led to a biased or fragmented models of digital development that make it both difficult to measure policies that foster the Information Society and measure the impact of such policies in digital development. The effect of these biased models is a fundamental distrust towards the design of policies that have not tried to fill the gaps with further data coming from other sources that made it possible to fill in conceptual voids or to include feedback about the impact of such policies.

WH2: A 360º digital framework approach shows that Infrastructure – Availability and Affordability –, the ICT Sector – the Industry and the skilled Workforce –, Digital Literacy – the level of Digital Literacy and Digital Literacy Training –, the Policy and Regulatory Framework – Regulation and Policies – and Content and Services – Availability and Intensity of Usage – are the key components of digital development and such a comprehensive framework for analysis could be applied in policy design. A 360º digital framework approach should include five categories and ten subcategories so that all factors of digital development are appropriately covered: Infrastructure – Availability and Affordability –, the ICT Sector – the Industry and the skilled Workforce –, Digital Literacy – the level of Digital Literacy and Digital Literacy Training –, the Policy and Regulatory Framework – Regulation and Policies – and Content and Services – Availability and Intensity of Usage –

WH3: Higher levels of wealth and economic development, education and the existence of digital infrastructures almost always coincide with higher levels of digital development. Nevertheless, Governments can accelerate the process of digital development through the adoption of public policies that frame and foster the Information Society – such as Government prioritization of ICT and assigning a high importance to ICT in government vision of the future – and establishing an appropriate Economic Incentive Regime. This will raise the probability of a country of reaching higher stages of digital development. Governments’ actions determine digital development. The probability of a country of reaching higher stages of digital development is highly increased by governments prioritizing ICTs, by assigning a high importance to ICT in their vision of the future, and by establishing an appropriate Economic Incentive Regime

H: Institutional interests and lack of data lead to fragmented models to measure digital development that distort policy design. A comprehensive framework would improve such models and indicate in what ways the adoption of public policies would lead to higher stages of digital development. Narrow institutional interests and a lack of data lead to fragmented models to measure digital development that distort policy design. A comprehensive framework that includes all the relevant categories (Infrastructure – Availability and Affordability –, the ICT Sector – the Industry and the skilled Workforce –, Digital Literacy – the level of Digital Literacy and Digital Literacy Training –, the Policy and Regulatory Framework – Regulation and Policies – and Content and Services – Availability and Intensity of Usage –) would improve such models. Within that framework, the adoption of public policies to foster the Information Society would lead to higher stages of digital development

This is an unfinished thesis There’s a need to end up linking empiricism and theory

Thank you: Tim Kelly, Pilar López, Ismael Peña Sr., Pere Fabra, Mercè Guillén

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Measuring digital development for policy-making: Models, stages, characteristics and causes - Presentation Transcript

  1. Measuring digital development for policy-making: Models, stages, characteristics and causes Ismael Peña - López Internet Interdisciplinary Institute Universitat Oberta de Catalunya PhD Thesis Defence Barcelona, September 8th, 2009
  2. So…
  3. Index
    • Why this research
    • Hypotheses
    • Methodology
    • Results
      • Qualitative analysis
      • Quantitative analysis
    • Conclusions
    • Future lines of research
    /43
  4. Why this research /43
  5. Components of human development Why this research Socioeconomic development Individual resources Objective choice Value change Emancipative values Subjective choice Democratization Freedom rights Effective choice Adapted from Welzel et al. (2003) /43
  6. Social structures (& Network Society) Why this research Culture Adapted from Castells, M. (2000) /43 Matter (nature) Relationships of Production Relationships of Power Relationships of Experience
  7. The Digital Economy as an enabler Why this research /43 Source: author Development The Digital Economy Network Society Socioeconomic Development (individual resources) INFRASTRUCTURES Matter (nature) ICT SECTOR Production Value Change (emancipative values) (DIGITAL) LITERACY Experience Democratization (freedom rights) LEGAL FRAMEWORK Power USES (CONTENT & SERVICES) Culture
  8. Fostering access to the Digital Economy
    • A Digital Revolution : Mokyr (1997, 2000), Greenwood (1999), Boas et al. (2005), Zysman, J. & Newman (2006)
    • The Concept of Access : Raboy (1995, 1998), ITU (1998-2009), WEF (2002-2009), Sciadas (2003), Gillwald and Stork (2007)
    • The Digital Divide : NTIA (1999), Hargittai (2001), Bridges.org (2001), Warschauer (2003), Gunkel (2003), DiMaggio et al. (2004), Barzilai-Nahon (2006), Tibben (2007)
    • Policies of (universal) Access : Hudson (1994), Albery (1995), Compaine & Weinraub (1997), OECD (2001b), Loader & Keeble (2004), ITU (2005e), Kenny and Keremane (2007)
    Why this research /43
  9. General hypothesis Institutional interests Lack of data Inaccurate measuring models Inaccurate policy design Comprehensive framework Government commitment to foster ICTs Higher stages of digital development Why this research /43
  10. Working hypothesis #1 Institutional interests Lack of data Measuring difficulties Inaccurate policy design Poor input for policy design Poor impact measurement Why this research /43
  11. Working hypothesis #2 Infrastructures ICT Sector Digital Literacy Policy and Reg. Framework Content and Services Availability Affordability Industry Workforce Level Training Regulation Policies Availability Usage Accurate policy design Comprehensive framework Why this research /43
  12. Working hypothesis #3 Higher stages of digital development Wealth & Economic Development Education Digital Infrastructures Why this research /43 Government commitment to foster ICTs Economic Incentive Regime
  13. Methodology
  14. Proposed model: 360º Digital Framework Assets Flows Supply Demand Infrastructures Availability Affordability ICT Sector Enterprises Economy Workforce Legal Framework ICT (Sector) Regulation Information Society Strategies and Policies Content and Services Availability Intensity of Use Digital Skills Digital Literacy Level Digital Literacy Training Methodology /43 Source: author
  15. Qualitative analysis
    • 55 models of the Digital Economy: descriptive and theoretical models, composite indices, sets of indicators
    • Count of different indicators used (1578) and number of time series
    • Identification of categories and iterative category reallocation of indicators
    • For each model:
    • Description: who, when, where, why, how
    • Main publications
    • Distribution of indicators by category
    • Fitness of model in 360º Digital Framework
    • Critique
    Methodology /43
  16. Quantitative analysis: statistics Original data Dichotomized variables Standardized variables 2 .Factor Analysis  3. Cluster Analysis Stages of digital development: Clusters (WITSA) Clusters (OECD) 4. Characterization
    • 4 stages of digital development and its characteristics:
    • WITSA countries
    • OECD countries
    5. Logistic regressions
    • Determinants of stages of digital development:
    • WITSA countries
    • 1. Analysis of Variables:
    • correlations
    • standardization
    • dichotomization
    Relationships between variables 14 databases, 157 variables 257 countries year ~2007 65 vars. (WITSA) 53 vars. (OECD) 49 countries, 22 vars. (WITSA) 28 countries, 17 vars. (OECD) Non conclusive 2 regressions Methodology /43
  17. Bridging theory and practice Indicators (vars.) used to characterize the stages of digital development (WITSA) Methodology Indicators (then variables) used to build the clusters (WITSA) /43 Infrastruct. ICT Sector Digital Skills Policy and Regulatory Framework Content and Services Nondigital Supply/Assets 8 2 2 3 5 27 Demand/Flows 5 4 1 2 6 Infrastructures ICT Sector Digital Skills Policy and Regulatory Framework Content and Services Supply/Assets 6 1 1 2 3 Demand/Flows 1 1 1 1 5
  18. Results
  19. The state of world indicators and indices (I) Distribution of the extended aggregate categories – including analogue indicators Distribution of the extended aggregate categories – excluding analogue indicators Results: qualitative analysis /43 Charts show the number of indicators (%) in all Digital Economy models within each category
  20. The state of world indicators and indices (II) Distribution of the aggregate categories – including analogue indicators Distribution of the aggregate categories – including analogue indicators Results: qualitative analysis /43 Charts show the number of indicators (%) in all Digital Economy models within each category
  21. The Telecom Approach Results: qualitative analysis /43 Charts show the number of indicators in selected Digital Economy models within each category
  22. The Broadcasting/e-Readiness approach Results: qualitative analysis /43 Charts show the number of indicators in selected Digital Economy models within each category
  23. Cluster centre values for WITSA countries 1 - Broadband subscribers (per 100 people) 2 - Personal computers (per 100 people) 3 - Telephone mainlines (per 100 people) 4 - Mobile phone subscribers (per 100 people) 5 - International Internet bandwidth (bits per person) 6 - Internet Hosts (per 10000 people) 7 - Price basket for residential fixed line (US$ per month) 8 - Telecommunications revenue (% GDP) 9 - GDP per Telecom Employee (US Dollars) 10 - Human Capital 11 - Internet Access in Schools 12 - Laws relating to ICT 13 - Intellectual property protection 14 - Gov't procurement of advanced tech products 15 - Secure Internet servers (per 1 million people) 16 - Total Domains (per 100 people) 17 - Availability of government online services 18 - Internet users (per 100 people) 19 - Total ICT Spending, Consumer (% of GDP) 20 - Firm-level technology absorption 21 - Extent of business Internet use 22 - ICT use and government efficiency Results: cluster analysis Non-hierarchical K-means cluster analysis. Significance of F in ANOVA for all variables: p<0.001 /43
  24. Stages of digital development (WITSA)
    • Digital leaders (clusters #1 & #2; n = 1+14) :
    • USA, Australia, Austria, Finland, France, Germany, Ireland, Japan, Rep. of Korea, New Zealand, Norway, Singapore, Sweden, Switzerland, UK
    • Digital strivers (cluster #3; n = 17) :
    • Brazil, Bulgaria, Chile, Greece, Hungary, Italy, Jamaica, Mexico, Panama, Portugal, Romania, Saudi Arabia, Spain, Thailand, Tunisia, Uruguay, United Arab Emirates
    • Digital laggards (cluster #4; n = 14) :
    • Argentina, Bolivia, Ecuador, Egypt, India, Indonesia, Pakistan, Peru, Philippines, Sri Lanka, Algeria, Cameroon, Vietnam, Zimbabwe
    • Digital leapfroggers (cluster #5; n = 3) :
    • Jordan, South Africa, Senegal
    Results: cluster analysis /43
  25. Cluster centre values for OECD countries 1 - Broadband subscribers (per 100 people) 2 - Personal computers (per 100 people) 3 - Telephone mainlines (per 100 people) 4 - International Internet bandwidth (bits per person) 5 - Internet Hosts (per 10000 people) 6 - GDP per Telecom Employee (US Dollars) 7 - Human Capital 8 - Internet Access in Schools 9 - Laws relating to ICT 10 - Intellectual property protection 11 - Gov't procurement of advanced tech products 12 - Secure Internet servers (per 1 million people) 13 - Total Domains (per 100 people) 14 - Availability of government online services 15 - Internet users (per 100 people) 16 - Firm-level technology absorption 17 - Extent of business Internet use Non-hierarchical K-means cluster analysis. Significance of F in ANOVA for all variables: p<0.001 Results: cluster analysis /43
  26. Stages of digital development (OECD)
    • Primary digital leaders (clusters #1 & #2; n = 1 + 8) :
    • USA, Australia, Canada, Denmark, Netherlands, Norway, Sweden, Switzerland, UK
    • Secondary digital leaders (cluster #3; n = 8) :
    • Austria, Finland, France, Germany, Ireland, Japan, Rep. of Korea, New Zealand
    • Primary digital strivers (cluster #4; n = 5) :
    • Greece, Hungary, Italy, Poland, Spain
    • Secondary digital strivers (cluster #5; n = 5) :
    • Czech Republic, Mexico, Portugal, Slovak Republic, Turkey
    Results: cluster analysis /43
  27. Infrastructures 1 - Broadband subscribers (per 100 people) (*) 2 - Personal computers (per 100 people) (*) 3 - Telephone mainlines (per 100 people) (*) 4 - Mobile phone subscribers (per 100 people) (*) 5 - Population covered by mobile telephony (%) (*) 6 - International Internet bandwidth (bits per person) (*) 7 - Internet Hosts (per 10000 people) (*) 8 - Internet subscribers (per 100 inhabitants) (*) 9 - Residential monthly telephone subscription (US$) (**) 10 - Price basket for Internet (US$ per month) (**) 11 - Price basket for mobile (US$ per month) (**) 12 - Price basket for residential fixed line (US$ per month) (*) 13 - Telephone average cost of call to US (US$ per three minutes) (***) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  28. ICT Sector 1 - Telecommunications revenue (% GDP) (*) 2 - High-technology exports (% of manufactured exports) (**) 3 - Telephone subscribers per employee (***) 4 - Telephone employees (per 100 people) (**) 5 - Total full-time telecommunications staff (per 100 people) (*) 6 - GDP per Telecom Employee (US Dollars) (*) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  29. Digital Literacy 1 - Enrolment in science. Tertiary. (per 100 people) (*) 2 - Human Capital (*) 3 - Internet Access in Schools (*) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  30. Policy and regulatory framework 1 - Laws relating to ICT (*) 2 - Intellectual property protection (*) 3 - Level of competition - DSL (**) 4 - Level of competition – Cable modem (**) 5 - Gov't procurement of advanced tech products (*) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  31. Usage 1 - Secure Internet servers (per 1 million people) (*) 2 - Total Domains (per 100 people) (*) 3 - Total ICT Spending, Retail Trade (% of GDP) (*) 4 - Web Measure (*) 5 - Availability of government online services (*) 6 - International outgoing telephone traffic (minutes) (per 100 people) (*) 7 - Internet users (per 100 people) (*) 8 - E-Participation (*) 9 - Total ICT Spending, Consumer (% of GDP) (*) 10 - Firm-level technology absorption (*) 11 - Extent of business Internet use (*) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  32. Analogue indicators 1 - GDP (***) 2 - GDP Capita (*) 3 - GDP per capita, PPP (current international $) (*) 4 - GNI per capita, Atlas method (current US$) (*) 5 - GNI per capita, PPP (current international $) (**) 6 - HDI (*) 7 - Life expectancy at birth, total (years) (*) 8 - Improved water source (% of population with access) (*) 9 - Health Public Expenditure (% of govt. expenditure) (*) 10 - Health Public Expenditure (% of total Health expend.) (*) 11 - School enrollment, primary (% net) (***) 12 - School enrollment, primary (% gross) (**) 13 - Education Public Expenditure (% of govt. expenditure) (***) 14 - Gross National Expenditure (% of GDP) (**) 15 - General Govt. final consumption expend. (% of GDP) (***) 16 - Economic Incentive Regime (*) 17 - Innovation (*) 18 - Population in urban agglom. > 1 million (% of total pop.) (*) 19 - Inequality-10 (**) 20 - Mortality rate, infant (per 1,000 live births) (*) 21 - Population growth (annual %) (***) 22 - Interest payments (% of GDP) (*) 23 - Present value of debt (% of GNI) (**) 24 - GDP deflator (base year varies by country) (*) 25 - Inflation, consumer prices (annual %) (*) 26 - Inflation, GDP deflator (annual %) (*) 27 - Tax revenue (% of GDP) (**) % of countries that scored “high” on indicator per cluster (*): p<0.01 (**): p<0.05 (***): p<0.1 Results: characterization of stages /43 Leaders Laggards
  33. Determinants: digital leaders Results: logit regressions /43 logit(ZCLUSTER54_CB) = β1 • GEN30 + β2 • GEN05 + β3 • GEN07 + β4 • GEN08 + β5 • LEGAL_D_04+ ε Binary logistic regression with digital leaders (1 is a digital leader, 0 is not a digital leader) as the dependent variable. B S.E. Wald df Sig. Exp(B) Life expectancy at birth, total (GEN30) -.399 .208 3.664 1 .056 .671 Inequality-20 (GEN05) -1.066 .578 3.403 1 .065 .344 Urban Population (%) (GEN07) .138 .079 3.030 1 .082 1.148 Economic Incentive Regime (GEN08) 1.671 .877 3.628 1 .057 5.317 Government prioritization of ICT (LEGAL_D_04) 2.869 1.737 2.727 1 .099 17.611 N 46 Correctly predicted cases 95.7% 96.8% (leaders) 93.3% (rest) -2 Log likelihood 15.970 Cox & Snell R-square .646 Nagelkerke R-square .862 Chi-Square (sig) 47.799 (.000) Hosmer and Lemeshow Test Chi-Square (sig) 1.546 (.981)
  34. Determinants: digital laggards Results: logit regressions /43 logit(ZCLUSTER54_CBL) = β0 + β1 • GEN06 + β2 • GEN14 + β3 • INF_S_06 + β4 • LEGAL_D_01 + ε Binary logistic regression with digital leaders (1 is a digital laggard, 0 is not a digital laggard) as the dependent variable. B S.E. Wald df Sig. Exp(B) Constant 38.214 16.958 5.078 1 .024 3.945·10 16 Inequality-10 (GEN06) -.235 .138 2.909 1 .088 .790 Health Public Expenditure (% of total Health expenditure) (GEN14) -.176 .081 4.665 1 .031 .839 Population covered by mobile telephony (%) (INF_S_06) -.100 .050 3.936 1 .047 .905 Importance of ICT to government vision of the future (LEGAL_D_01) -4.304 2.239 3.696 1 .055 .014 N 47 Correctly predicted cases 94.6% 96.4% (laggards) 88.9 % (rest) -2 Log likelihood 11.391 Cox & Snell R-square .551 Nagelkerke R-square .823 Chi-Square (sig) 29.663 (.000) Hosmer and Lemeshow Test Chi-Square (sig) 3.684 (.815)
  35. Conclusions
  36. Working hypothesis #1 Institutional interests Lack of data Measuring difficulties Inaccurate policy design Poor input for policy design Poor impact measurement Conclusions /43 Designs based on a specific and applied purpose Designs adapted to the availability of data Bias towards infrastructures Bias towards supply indicators
  37. Working hypothesis #2 /43 Conclusions Infrastructures ICT Sector Digital Literacy Policy and Reg. Framework Content and Services Availability Affordability Industry Workforce Level Training Regulation Policies Availability Usage Accurate policy design Comprehensive framework Enables ICT vs. Dev. comparisons Comprehensive: Gathers all approaches Assets Flows Supply Demand Infrastructures Availability Affordability ICT Sector Enterprises Economy Workforce Legal Framework ICT (Sector) Regulation Information Society Strategies and Policies Content and Services Availability Intensity of Use Digital Skills Digital Literacy Level Digital Literacy Training Meets theory and empiricism Measures policy impact
  38. Working hypothesis #3 Higher stages of digital development Wealth & Economic Development Education Digital Infrastructures /43 Conclusions Government commitment to foster ICTs Economic Incentive Regime Stages of digital development Income Equality Education Health Government prioritization of ICT Importance of ICT to government vision of the future Leapfroggers
  39. General hypothesis Institutional interests Lack of data Biased measuring models Distorted policy design 360º Digital Framework Government commitment to foster ICTs Higher stages of digital development /43 Conclusions Inaccurate measuring models Inaccurate policy design Comprehensive framework Assets Flows Supply Demand Infrastructures Availability Affordability ICT Sector Enterprises Economy Workforce Legal Framework ICT (Sector) Regulation Information Society Strategies and Policies Content and Services Availability Intensity of Use Digital Skills Digital Literacy Level Digital Literacy Training Government commitment to foster ICTs Eco. Incentive Regime Designs based on a specific and applied purpose Designs adapted to the availability of data
  40. Limitations of the research
    • Theoretical framework to be improved: pros and cons of multidisciplinary research, theory vs. practice
    • Quality of data: coverage, soft data, lack of data
    • Quantity of data: time series, geographic coverage, loss of detail due to aggregation
    • Significance of models
    Conclusions /43
  41. Future lines of research
  42. Future lines of research
    • Strengthen the links between theory and practice
      • Towards multidisciplinary frameworks
    • Übercomprehensive model: Structural equation modelling
      • Simultaneousness, complex systems, network effects
      • Dynamic (time) analysis
    • How and why Leapfrogging
    • Design of Public policies to foster the Information Society
    Future lines of research /43
  43. Barcelona, September 8th, 2009. PhD Thesis Defence To cite this work : Peña-López, Ismael. (200 9 ) Measuring digital development for policy-making: Models, stages, characteristics and causes. PhD Thesis Defence. Barcelona, September 8th, 2009. <http://ictlogy.net/presentations/20090908_ismael_pena-lopez_-_measuring_digital_development_for_policy-making.pdf> References used in this presentation: http://ictlogy.net/bibciter/reports/bibliographies.php?idb=2 Special thanks: Tim Kelly, Pilar López, Ismael Peña Sr., Pere Fabra, Mercè Guillén To contact the author : http:// ismael .ictlogy.net More information please visit http://creativecommons.org/licenses/by-nc-nd/2.5/ All the information in this document under a Creative Commons license: Attribution – Non Commercial – No Derivative Works

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