Presented by EL NO

e.no@lse.ac.uk

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 %

*