‘Digital Divide’ Reconsidered: A Country- and Individual-Level Typology of digital inequality in 26 European Countries Boris Kragelj, University of Ljubljana Elmar Schlüter, University of Bielefeld

DIGITAL DIVIDE (definition) First definition: difference between technology „haves“ and technology „have not´s” Inequalities between individuals, households, companies and regions regarding the access and use of new ICT resources (specially internet): Inequalities in access to ICT (first level d. divide) Inequality in skills of using the internet (second level d. divide) Inequalities in ICT penetration between courtiers: Inequalities in access, use and skills to use ICT between more and less developed countries (global digital divide)... is empirical concept that refers to new form of social inequality, regarding the access and/or use of ICT and/or skills of using ICT (specially internet) at various levels of society.

DIGITAL DIVIDE (existing studies) Studies on d. divide varies considering: Definition and level of d. divide under study (first level, second level d. divide) Unit and societal level of study (individual, global) Dependent variables for measuring d. divide (internet access, internet use, skill of internet use...) Independent variables for describing risk groups (gender, age, education, income....) Different methods of analysis applied or different indexes of inequality developed (absolute dif., relative dif., time distance, digital divide index...) Various approaches increased scholarly understanding of the ‘Digital Divide’ in many ways, but they all study only certain view of the whole Digital Divide phenomena even though they might be strongly interrelated.

Aim of the present study Extending previous work on d. divide with integration of different analytical perspectives on digital divide studies within one single (multilevel latent class) model to examine their interrelations linking individual and societal level of analysis on both (first and second) levels of digital divide to simultaneously provide for individual and country level typology of groups regarding the inequality in access, use and skills of internet use Explaining differences between these types of digital inequality according to relevant independent socio-demographic and agregate- level variables (on individual and societal level of analysis respectively) MODELINGIndividual level of analysisSocietal level of analysis First level dig. divide diff. in access and use of internet between individuals diff. in access and use of internet between countries Second level dig. divide diff. in skills of using internet between individuals diff. in skills of using internet between countries

Research questions: Which groups of digital inequality (regarding internet access, use and skills) can be observed on the individual level, and how this groups differ across different (groups) of countries? What are individual and country-level determinants for individual and country affiliations to the (individual and societal) groups of digital inequality?

Research model: Multilevel latent class model C country Country - level Individual - level C ind Access Int. use Int. skills Gender Age Educat. Income Gini coef. % GDP for ICT exp. Tel. Price. Step 1 Step 4 Step 2 Step 3

Data and variables Individual level variablesCountry level variables Source: SIBIS and SIBIS+ Statistical indicators benchamrking the information society (EU/FP5 project) Source: EUROSTAT – structural indicators

Results: individual level (3) latent class solution

individual level (3) latent class solution with covariates

group level (5) latent G-class solution

(5) latent G-class solution with G-level covariates

Summary & Conclusion Using standard digital divide variables (access, use, skills of internet use) within “multilevel latent class framework” we have identified three types of classes regarding digital (in)equality on individual level, whose distribution differ significantly among five group of EU countries:  On ind. level we identified classes of (1) heavy users and (2) out of home moderate users (this group is different than anticipated!!!), and (3) non-users, the last group is presenting dig. divide risk group (52% of population) mainly represented by female, older, low income and low education individuals.  On soc. level we identified five groups of countries: (1) ICT leaders, (2) above ICT average (both mainly north of Europe), (3) ICT average (south of Europe), (4) lagging behind (eastern European block + Greece) and special one country (EE) class, with high share of out of home users and equal distribution between all individual level classes. These groups of countries varies considerably regarding structural characteristics such as ICT expenditure as % of GDP, gini coefficient and telecom prices  Risk group of countries that are lagging behind (presenting 1/3 of European countries) are characterised by high telecom prices and high income inequality, showing that much of digital divide in Europe can be explained with traditional forms of social inequality and poor national telecomm. policies (blocking free market, low investment in ICT...)  Digital divide as a new form social inequality is not new at all. Once again it only reflects already well established traditional forms of social inequality in Europe through a new perspective: risk groups of individual and countries are staying the same.

Methodological Discussion We found ML-LC to be useful when simultaneously searching for typologies and groups on two different levels of analysis, and at the same time explaining and predicting these group membership with other relevant (individual and structural) variables... Still (as the method is not so well documented) we run into several problems:  non convergence: some multilevel models with less DF converge better than one with more DF! How to explain this? Are results valid at all?  Choosing the best model solution: -Why at some point (when you move from individual level to multilevel framework, or when you introduce covariates) the model fit is considerably worse than before, even though you catch more variance and reduce the classification error? -How can you test if one model fits significantly better that the other in multilevel framework where you don’t have chi square statistics to conduct chi square test? -Should one search for best multi level solution before or after introducing covariates on individual level, and with or without covariates on structural level?  Choosing non-parametric vs. parametric multilevel model: In our case we have modelled existence of groups of countries on aggregate level (non parametric ML-LC???), but our solution showed that these groups actually (almost) show an order from leaders to laggards in terms of ICT! Would it be better to model a continuum (factor) of countries on the aggregate level (parametric ML LC, and how to test for this?

Thank you for attention! ‘Digital Divide’ Reconsidered: A Country- and Individual-Level Typology of digital inequality in 26 European Countries Boris Kragelj, University of Ljubljana Elmar Schlüter, University of Bielefeld

group level (5) latent G-class solution with macro level covariates