a critical analysis of digital divide measurement
DESCRIPTION
This is a summery of the article "A Critical Analysis of Current Indexes for Digital Divide Measurement" by Bruno et al. (2011). It also comes with a crude comparative graph at the end.TRANSCRIPT
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Bruno et al. (2011) A Critical Analysis ofCurrent Indexes for Digital Divide Measurement
DIGITAL DIVIDE
The definitions of a digital divide
Focusing on techno-
logical resources
Individuals who use
computers and the Internet
and individuals who do not(Mehra et al. 2004)
Information haves and have-
nots(Dewan and Riggins 2005, Ida and
Horiguchi 2008, Belanger and Carter 2009)
Persons who have access to
digital ICTs and those do not(Dewan and Riggins 2005)
Emphasizing
determining factors
Per capita income,
telecommunications
infrastructure and the quality
of regulation(Chinn and Fairlie 2006)
Economic, regulatory, and
sociopolitical characteristics of
countries(Cuillen and Suarez 2005)
Comprehensive
definitions
“unequal patterns of material
access to, usage capabilities
of, and benefits from
computer-based information
and communication
technologies that are caused
by certain stratification
processes that produce
classes of winner and losers
of the information society, and
of participation in institutions
governing ICTs and society.”(Fuchs 2005: 46)
Techno-centric Multidimentional
1 2 3
The development of an assortment of indexes
2003
Infostate
Index
2005 2009
Digital Access
Index ICT
Opportunity
Index
ICT
Development
Index
8
21
Digital Divide Measurement
Composite indexes
•The aggregation of several
indicators into a single
figure
•Representing the relative
position of countries
overtime
Digital
Opportunity
Index
11
10
11
• Emphasize income, education, age, sex, and
ethnicity, while not fully addressing the deeper social,
cultural, and psychological causes behind access
inequalities. … a lack of conceptual elaboration and
definition of the indicators used in composite indexes
(e.g. computer literacy, Internet use)(Van Dijk 2006)
• Too many indicators make data collection difficult (Braithwaite 2007)
• Measuring at the national level ignores community
level inequalities(Barzilai-Nahon 2006)
• The aggregation methodology of individual indicators
is responsible for biases (e.g. the weight) (Barzilai-
Nahon 2006, James 2007)
Critiques of composite indexes
should be both
efficient and effective
(Jollands et al. 2004)
• Emphasize income, education, age, sex, and
ethnicity, while not fully addressing the deeper social,
cultural, and psychological causes behind access
inequalities. … a lack of conceptual elaboration and
definition of the indicators used in composite indexes
(e.g. computer literacy, Internet use)(Van Dijk 2006)
• Too many indicators make data collection difficult (Braithwaite 2007)
• Measuring at the national level ignores community
level inequalities(Barzilai-Nahon 2006)
• The aggregation methodology of individual indicators
is responsible for biases (e.g. the weight) (Barzilai-
Nahon 2006, James 2007)
Critiques of composite indexes
A GOOD INDEX?
I nvestigation on 2005 ICT-OI and 2007 IDI
To seek the possibility to increase their efficiency by reducing
the number of indicators and using the same technique of
aggregation.
To analytically validate the critiques by Van Dijk (2005, 2006)
and Fuchs (2009): current digital divide research is affected by a
“reductionistic” approach to measurement that does not
emphasize the role of factors other than technological access
and use.
?1
2
Main telephone lines per 100 inhabitants
Mobile cellular subscribers per 100
inhabitants
International internet bandwidth
Adult literacy rates
Gross enrolment rates
Internet users per 100 inhabitants
Proportion of households with a TV
Computers per 100 inhabitants
Total broadband internet subscribers per
100 inhabitants
International outgoing telephone traffic
per capita
Network
Skills
Uptake
Intensity
Info-
density
Info-use
Geometric average
ICT-OI
Geometric average
Geometric average
Fixed telephone lines per 100 inhabitants
Mobile cellular telephone subscriptions
per 100 inhabitants
International Internet bandwidth (bit/s) per
Internet user
Proportion of households with a computer
Proportion of households with Internet
access at home
Internet users per 100 inhabitants
Fixed broadband Internet subscribers per
100 inhabitants
Mobile broadband subscribers per 100
inhabitants
Adult literacy rate
Secondary gross enrolment ratio
ICT
access
ICT skills
ICT use
x 40%
x 20%
IDI
Tertiary gross enrolment ratio
x 40%
Arithmetic average
Weighted sum
Calculate and analyze
the correlation among
each pair of indicators
Detect a set of variables
able to significantly
represent the
phenomenon within a
data set
Correlate each indicator
and each of the p
selected principal
components, then
individuate the
indicators with the
highest values of
correlation for each
principal component
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
Confirmation of the possibility
to reduce variables
The number of significant
variables (p < n)
Specific indicators to retain
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
4 components explain 90%It suggests that we could have similar
results by using reduced number of
indicators with the original index
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
Correlation
Matrix
Principal Component
Analysis
Indicator
Selection
ICT-OI and ICT-OIreduced
0.946 (R2= 0.896)
IDI and IDIreduced
0.916 (R2= 0.839)
Linear Regression Results
Original vs. Reduced DD Indexes
11 4
10 4
ICT-OI and ICT-OIreduced
0.946 (R2= 0.896)
IDI and IDIreduced
0.916 (R2= 0.839)
Linear Regression Results
ICT-OI and GDP
0.942 (R2= 0.887)
IDI and GDP
0.921 (R2= 0.845)
Strong correlation
Original vs. Reduced DD Indexes DD Indexes vs. Income Index
11 4
10 4
“Reductionistic”
It is possible to increase efficiency
by eliminating less significant
indicators
!“Redundant”
There is a need to include more
variables to comprehensively
capture the phenomenon
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Individuals Internet Access
Fem
ale
to
Ma
le R
ati
o o
f In
tern
et
Ac
ce
ss
*
Internet Access and Gender Equality by Country2008-2009, ITU
Higher Rank
Hig
her
Ran
k
Corr=0.46
Female Internet
Access %
Male Internet
Access %
*
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Internet Access and Gender Equality by Country2008-2009, ITU
Higher Rank
Hig
her
Ran
k
Thailand
Switzerland
Korea
Colombia
Senegal
UK
Corr=0.46
Individuals Internet Access
Fem
ale
to
Ma
le R
ati
o o
f In
tern
et
Ac
ce
ss
*
Female Internet
Access %
Male Internet
Access %
*