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Digital Divide Reconsidered: A Country and IndividualLevel Typology of digital inequality in 26 Euro

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Title: Digital Divide Reconsidered: A Country and IndividualLevel Typology of digital inequality in 26 Euro


1
Digital Divide ReconsideredA Country- and
Individual-Level Typology of digital inequality
in 26 European Countries
  • Boris Kragelj, University of Ljubljana
  • boris.kragelj_at_fdv.uni-lj.si
  • Elmar Schlüter, University of Bielefeld
  • E_Schluet_at_gmx.de

2
DIGITAL DIVIDE (definition)
  • First definition difference between technology
    haves and technology have nots
  • 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.

3
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.

4
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)

5
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?

6
Research modelMultilevel latent class model

Country
-

level
Gini coef.
Step 4
Step 3
C
country
GDP for ICT exp.

Tel. Price.
Gender
Age
Access
C
Educat.
Step 1
Step 2
ind
Int. use

Income
Int. skills
Individual
-

level
7
Data and variables
Individual level variables
Country level variables
Source EUROSTAT structural indicators
Source SIBIS and SIBIS Statistical indicators
benchamrking the information society (EU/FP5
project)
8
Results individual level (3) latent class
solution
9
individual level (3) latent class solution with
covariates
10
group level (5) latent G-class solution
11
(5) latent G-class solution with G-level
covariates
12
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.

13
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 dont 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?

14
Thank you for attention!Digital Divide
ReconsideredA Country- and Individual-Level
Typology of digital inequality in 26 European
Countries
  • Boris Kragelj, University of Ljubljana
  • boris.kragelj_at_fdv.uni-lj.si
  • Elmar Schlüter, University of Bielefeld
  • E_Schluet_at_gmx.de

15
group level (5) latent G-class solution with
macro level covariates
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