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7/23/2019 Convergence Analysis on DESI index
http://slidepdf.com/reader/full/convergence-analysis-on-desi-index 1/15
LST T 3220 – Statistical Consulting
Eduardo Marín Nicolalde
Noma: 1749-13-00
Date: 21st December 2015
Pro ram: STAT2 MS G
Automatic methods to analyze
Digital Scoreboard Data
7/23/2019 Convergence Analysis on DESI index
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Contents1. Abstract ................................................................................................................................. 3
2. Objectives .............................................................................................................................. 3
3. Data ....................................................................................................................................... 3
a) Indicator selection ............................................................................................................. 3b) Imputation ......................................................................................................................... 4
4. A proxy index for DESI ........................................................................................................... 4
a) Renaming the indicators ................................................................................................... 4
b) Filtering indicators ............................................................................................................. 5
c) Proxy DESI.......................................................................................................................... 5
5. Methodology ......................................................................................................................... 5
a) Beta Convergence Model .................................................................................................. 5
b) Catch-up Convergence speed............................................................................................ 6
c) Sigma convergence ........................................................................................................... 7
6. Results ................................................................................................................................... 7
a) Human Capital ................................................................................................................... 7
b) Proxy DESI index .............................................................................................................. 10
7. Conclusions ......................................................................................................................... 12
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1.
bstract
The main objective of the present project is to build an automatized method to analyze
technology indicators and its trend in convergence, defined as the tendency for a similar
behavior over the time. Data for the analysis proceed from the Digital Scoreboard and has a
different temporal horizon for each indicator. After a process of indicator selection and
imputation, a dataset of 41 variables where obtained and are ready to be analyzed. With theaim of having a general view of the convergence process, a methodology to build a proxy DESI
index from 2008 to 2013 is proposed and analyzed.
Methods to assess convergence where adapted from econometric methodology analyzing GDP
convergence. Beta convergence, Sigma convergence, simulations on the speed of catch-up as
well as graphical representations are the main tools to examine the data.
Results for some indicators show the presence of club-convergence meaning that in the long
run, we expect a similar demeanor of some of the members of the European Union.
Quantification of the influence of the initial condition of a country on the expected indicator
growth rate provide a good measure to promote public policies that support less-performant
countries in order to acquire the desired convergence and increase the speed to achieve it.
2.
Objectives
The main objective of this study is to determine and quantify the factors that influence the
convergence process in the countries of the EU30 group. If evidence of convergence is found,
quantify the speed of the convergence process under certain assumptions is desired as well.
3. Data
Given the Digital Scoreboard database, criteria on data completeness where applied to
discriminate indicators to be analyzed. Countries on the study are restricted for the EU30 group.Further filters and imputation methods were also applied. Here we resume the criteria and steps
followed.
a) Indicator selection
The given dataset was organized by a hierarchical structure of variables, breakdowns and units
of measure. In the present project, we consider that each possible combination of the
mentioned structure is an indicator.
Having at least 27 countries with reported information for each year was the main criteria to
select indicators. Because of the nature of the data, in some cases we have manually narrowed
the temporal horizon to select only the periods where the condition is fulfilled.
A second step was global information on the behavior of a given country, meaning that only
indicators whose measures respond to totals, median values and, in general, giving a full-picture
representation where retained.
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The mentioned procedure gives as result plenty of indicators where the temporal horizon of
measures are extremely varied. A third step was then a filter to select only indicators with at
least 4 common time points for the given countries. A database with 41 indicators with different
temporal horizons was obtained.
b)
Imputation
We emphasize that even with the filters applied on the indicator selection part, there are some
missing values for certain countries. If a given country does not provide information for at least
the 75% of time points, the whole country will be set as missing and it will not be included on
the analysis of a given indicator.
For countries that fulfill the precedent condition, we have found two types of values to be
imputed depending on its position. First, missing values in the middle of two temporal points
with complete information where imputed with the mean value of the points. This methods
assumes that the missing point follows the trend of the country behavior.
Second, missing points on the beginning or at the end of the series where imputed with its future
and past values respectively. An interpretation of this imputation method could be given by the
hypothesis that a country stays in the same condition for two consecutive time periods. Another
imputation method that could have been applied is a one step ahead/back forecasting but given
the smallest total number of time periods, predictions will possibly not consider the dynamics
of the country over the time. This, the high number of missing values at the beginning/end of a
series and the lack of an automatized method to input values with forecasting methods have
make us support the future/past value imputation.
Imputed values of each indicator are highlighted and are easy to track when opening the
databases attached to this report.
4.
proxy index for DESI
Aware of the power of a unique index to measure the global performance of the countries, we
have proposed an alternative methodology to have a measure with the same properties of DESI
index.
a) Renaming the indicators
Definition of indicators as the combinations of the hierarchical structure variable, breakdown
and unit of measure give long length indicators names. To facilitate data managing, we have
classified each one in a super category given for the group of membership of each variable
extracted from the metadata database.
Names of the super categories are: Broadband, broadband quality, e-Business, e-Commerce, e-
Government. E-Health, internet service, internet usage, ict skills and mobile. After classification,
we have renamed the indicators with letters of the alphabet and given a small interpretation of
each one. Append 1 contains the table of correspondences for the old and new names.
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b)
Filtering indicators
As the main objective is to have a similar temporal horizon for all the variables, we have selected
from the 41 indicators only those that have a similar length of time. The result of this filtering
have left us with 25 indicators with full information from 2008 to 2013.
c)
Proxy DESI
Sub dimensions of DESI index has been built by the aggregation of indicators with the same
weight. A weighted sum of sub dimensions gives as result a dimension whose weighted sum
gives finally the aggregate DESI index.
Considering that all our data is measured in the same percentage scale, an aggregation of the
indicators to obtain the same sub dimensions of DESI index seems appropriate. As the indicator
measuring the percentage of persons that have never used internet is not desirable and because
of the positive sense of the DESI index, only its aggregation will have a negative weight.
Countries on the Connectivity dimension were reduced to 28 as IS and NO have not complete
information. The latter implies that analyzing the global proxy DESI will not include those
countries but a complete study is possible for the other dimensions.
We remark that the weights given for the proxy index aggregation are the same as in the original
index but in some cases it have not been possible to calculate all the sub dimensions because
no data was available. In those cases, the available sub dimensions have taken a proportional or
full weight for calculation. Append 2 shows how the indicators were assigned to each sub
dimensions and their weights.
Finally a complete, filtered and imputed data base was obtained for a proxy of DESI indicator.
5.
Methodology
An R function has been built in order to automatize the study of convergence. The onlyrequirement for being used is related to the structure of the database which serves as input for
calculation: The data base should have Countries on the rows and years on the columns. Column
names must be included. The first name is “Countries” and names of the countries must be of
the type “x-YYYY” where x is the letter of the alphabet assigned to a given indicator (as in Append
1) and YYYY is the year on four digits format.
The function automatically selects only rows with complete information for the analysis. The
outputs of the function as well as the methodology are explained in the following steps.
a) Beta Convergence Model
Beta convergence model is an econometric approach to assess convergence on economicgrowth and has been applied with great success on countries that belongs to a supranational
organization as is the case of this study.
The main hypothesis is related to the influence of the initial condition of a country on the time
over the expected growth in the next time period where countries with poor performance tend
to growth faster than those with good performance. This latter could be explained by the
technological changes where innovation process by the performant countries is harder than the
adoption of available technologies by the less performant ones.
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Adapting this approach to the Digital Economy and Society Index is then straightforward.
The applied model was suggested by Baumol (1986) by specifying the growth equation:
ln, − ln(,) = + ln, + (1)
Where T is the total number of time periods and , , are the values on the last and initial
time periods for the country respectively. If the hypothesis of the model is true, the influence
of the initial conditions on the expected growth quantified by should be negative.
In some cases, the relation between growth and initial conditions is not linear. This means that
the inverse relationship stands until a given point and from there, it will not be inverse. For those
possible cases, a Quadratic model to estimate the inflection point has been proposed by
modifying the growth equation:
ln, − ln(,) = + ln, + ln,
+ (2)
If estimations of have negative and positive values after estimation and model
validation, the inflexion point could be measured by: (see Introductory Econometrics: A Modern
Approach by Wooldridge)
∗ = 2⁄ (3)
The estimation of the parameters has been obtained by Ordinary Least Squares (OLS) for both
models.
Econometric theory on estimation shows that unobserved idiosyncratic factors of each country
could lead to invalidate the precedent models and/or biased estimations. A proposed method
to avoid this problems is the Fixed-Effects estimation method on panel data which also supplies
an estimation of the intercept term for each country (the idiosyncratic differences).
Tools for validation of each parameter and the global models are given as output by the R
function.
b) Catch-up Convergence speed
Under the assumptions of Beta convergence, we could simulate the number of periods needed
to catch-up a target value/country given a target growth rate by (Rajasalu 2001):
( 1 + ) = (1 + ) (4)
Where is the value of the indicator on the last time period, is the average growth rate on
the analyzed time horizon and the number of time periods. is the target value to converge
and could be chosen as the best performant country or a given value like the mean performance
of the countries in a given year. In the first case,
could be set as the average growth rate of
the best performant country and in the second, set to zero as we are interested only in
convergence over a value.
Solving the equation for n we have:
= ()()()()
(5)
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We can show that will take positive values only if the average growth rate of the analyzed
country is higher than the one chosen as target (Which are the assumptions for Beta
convergence). Having a negative implies that the analyzed country will not catch the target
up. The presence of negative is expected for countries whose start values are close to best
performant countries as they belongs to the same cluster of countries whose performance is
quite good. In economic theory, those countries are said on equilibrium as their performance is
close to its maximum possible then, their growth rate will be close to zero.
c) Sigma convergence
The weakness of the described approaches are related to the lack of measures for convergence
in a given year. Sigma convergence provides a measure of dispersion of the countries for each
studied year which could be useful in case we want to observe if a past event (crisis, economic
support, investment on technology, etc.) has have some influence on the behavior. Sigma
convergence is a simple calculation of the standard deviation of each year on the analysis. The
results are given by a plot of standard deviation evolution over the time, so it is easy to identify
a tendency on convergence or the year with less dispersion.
6.
Results
In order to provide interpretation on the results provided by the automatized function, we have
analyzed the global proxy DESI index and its Human Capital dimension.
a)
Human Capital
The first output corresponds to the Beta Convergence model (Figure 1) and refers to the graphic
representation of equation (1) . On the vertical axis we observe the total growth of the countries
and on the horizontal axis its initial value. For the Human Capital dimension, we clearly observe
that the less performant countries (RO, BG and EL) have growth faster than the other countries.
We remark that a linear model does not seem appropriate to explain their relationship so wecan try for a quadratic model (Output 1).
Figure 1: Human Capital. Growth rate and initial conditions
ATBE
BG
CY
CZ
DE DK
EE
EL
ES
FIFR
HR
HUIE
IS
IT
LT
LU
LV
MT
NLNO
PL
PT
RO
SE
SISK UK
-100
0
100
200
300
400
0.2 0.4 0.6 0.8
Y0
T o t a l G r o w t h %
Total Growth
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Output 1: Human Capital. OLS Quadratic Model output
Analyzing the p-values of the estimations, we observe that the estimated coefficients are
individually significant. The global model (F-statistic) shows that the global model is also
significant. With the given information we could calculate the inflexion point as defined by
equation (5) which is equal to -1.86. As we are working in logarithmic scale in the model, we
must take the inverse function (exponential) to have interpretable results. The value is equal to
0.15 which means that while a country has initial value on Human Capital below it will maintainthe inverse relation for the influence of the initial conditions. Over 0.15 the relation is not inverse
which means that the influence of initial conditions is almost null.
To quantify the effects of the initial conditions, we prefer the Fixed-Effects results as shown on
the Output 2:
Output 2: Human Capital. Fixed-Effects model
Again the model is significant and validated automatically by a Durbin-Watson test on the
residuals. We observe that a constant has been calculated for each country. An easy example of
the interpretation could be done for example for RO, with a constant value of 0.63 and an
estimate of -0.94. For two different initial conditions (Y0 = (0.3; 0.5)) we can estimate the
expected growth of the country:
Exp. Growth rate 1= 0.63+0.3*(-0.94) = 35% and Exp. Growth rate 2= 0.63+0.5*(-0.94) = 16%
Call:
lm(formula = Ydiff ~ Y0 + Y0_2)
Coefficients:
Estimate Std. Error t value Pr(>|t|)(Intercept) 0.009255 0.003231 2.865 0.00798 **
Y0 -0.059661 0.006206 -9.613 3.29e-10 ***
Y0_2 0.016011 0.002353 6.805 2.62e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Multiple R-squared: 0.9907, Adjusted R-squared: 0.9901F-statistic: 1445 on 2 and 27 DF, p-value: < 2.2e-16
Oneway (individual) effect Within Model
Call:
plm(formula = growth ~ Y0, data = panel, model = "within", index =
c("country", "years"))
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
Y0 -0.942621 0.092872 -10.15 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-Squared : 0.464F-statistic: 103.015 on 1 and 119 DF, p-value: < 2.22e-16
AT BE BG CY CZ DE DK0.6553933 0.6853956 0.5430895 0.5510028 0.5863483 0.7171333 0.8125130
EE EL ES FI FR HR HU0.6395698 0.5237783 0.5451021 0.7752261 0.6781229 0.5430165 0.5700423
IE IS IT LT LU LV MT0.6468309 0.8676754 0.5041103 0.5223082 0.8308352 0.5729277 0.6085656
NL NO PL PT RO SE SI0.8407127 0.8473188 0.5516148 0.4859570 0.6283596 0.8326868 0.5958987
SK UK
0.6436804 0.7576447
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We can observe that the interception term acts as a constant of the growth rate which is
penalized by the initial condition in a factor of (-0.94). In conclusion, while the initial condition
increases, the expected growth decreases (Beta convergence) which means that bigger efforts
will have to be done as the country approach to the best performant cluster.
For the speed of convergence simulation, we have selected the best performant country and its
average growth rate as target (FR). Results are sorted by the number of periods for convergencecolumn “n”. Most of the countries have tendency to converge but, as expected, Human Capital
changes will take effect on the long run explaining the higher values of n. With exception of PT,
all the countries with negative values are not so far from the target country but as their growth
rate is smaller, we consider that they are in an equilibrium state. The same happens for IE, ES,
HR and EE where their growth rate is not much bigger than the target one even though they are
not so far.
Output 3: Speed of convergence
Finally, Figure 2 shows the evolution of the dispersion between countries as well as the global
behavior of the EU30 group. Clearly, we observe that there is a clear trend to converge as the
distances on the performance of the countries is becoming narrow. This latter is supported with
a reduction of the dispersion.
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Figure 2: Human Capital. Standard deviation evolution.
b)
Proxy DESI index
Again, we obtain as output the graphical representation of the initial conditions and growth rate
(Figure 3).
Figure 3: Proxy-DESI. Growth rate and initial conditions
We observe that the less performant countries are, again, RO and BG while the behavior of the
other countries is more similar. In this case, the quadratic models has not been validated so the
function pass directly to the Fixed-Effects model on Output 4. The model as well as the
coefficient estimations has been validated. We remark that for the global proxy-DESI index, the
penalization is stronger than the Human Capital estimation. An example for RO (Y0 = (0.1; 0.3))
in this case gives:
Exp. Growth rate 1= 0.43+0.1*(-1.43) = 28.7% and Exp. Growth rate 2= 0.43+0.3*(-1.43) = 1%
AT
BE
BG
CY
CZ
DE DK
EE
EL
ES
FI
FR
HR
HU
IE
IT
LT
LU
LV
MT
N
PLPT
RO
SE
SISK UK
0
25
50
75
0.2 0.3 0.4 0.5 0.6
Y0
T o t
a l G r o w t h %
Total Growth
0.25
0.50
0.75
2 0 0 8
2 0 0 9
2 0 1 0
2 0 1 1
2 0 1 2
2 0 1 3
time
V a l u e
Country
AT
BE
BG
CY
CZ
DE
DK
EE
EL
ES
FI
FR
HR
HU
IE
IS
IT
LT
LU
LV
MT
NL
NO
PL
PT
RO
SE
SI
SK
UK
Time perspective
0.17
0.18
0.19
0.20
0.21
0.22
0.23
2008 2009 2010 2011 2012 2013
time
S t d_
D e v
Standard Deviation Evolution
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Output 4: Proxy-DESI. Fixed-Effects model
As the penalization is stronger, the expected growth decreases quicker which is logic as
improving performance on the global measures is much harder. Simulations on speed of
convergence are shown on Output 5 where the best performance is located on DK.
Output 5: Speed of convergence
Oneway (individual) effect Within Model
Call:
plm(formula = growth ~ Y0, data = panel, model = "within",
index = c("country", "years"))
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
Y0 -1.43187 0.11705 -12.232 < 2.2e-16 *** ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-Squared : 0.57411
F-statistic: 149.634 on 1 and 111 DF, p-value: < 2.22e-16
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We remark that almost all the countries have obtained positive values and that countries with
high initial values (e.g. DE and AT) are not so far from DK which could mean that they are on an
equilibrium state as well as the countries with negative values of n (NL, FI, SE). The other
countries seem to converge in periods that oscillate between 17 to 5 years, if everything stays
constant.
Finally, dispersion analysis shows a trend on convergence even though the process
deaccelerated on 2012.
Figure 2: Proxy-DESI. Standard deviation evolution.
7.
Conclusions
In general, we observe the presence of a trend for convergence even though the speed to
acquire it variates for each country. The latter is explained by the presence of Beta-convergence
which implies that a constant effort is not enough to improve the performance and that while
the countries approach to the best performant countries, a higher effort will be needed.
As for the R function, it provides a bunch of tools to analyze convergence. Familiarity with the
function and the employed methods are recommended before interpretations. Changes could
be made in order to set desired targets on the speed of convergence simulations.
Finally, some methodology has been proposed to obtain a uniform measure on the technological
progress of the countries from 2008 to 2013. A more deep study in order to standardize a
method to calculate the original DESI index on the past is recommended as it could improve the
estimations proposed in this project.
0.09
0.10
0.11
0.12
2008 2009 2010 2011 2012 2013
time
S t d_
D e v
Standard Deviation Evolution
0.2
0.3
0.4
0.5
0.6
0.7
2 0 0 8
2 0 0 9
2 0 1 0
2 0 1 1
2 0 1 2
2 0 1 3
time
V a l u e
Country
AT
BE
BG
CY
CZ
DE
DK
EE
EL
ES
FI
FR
HR
HU
IE
IT
LT
LU
LV
MT
NL
PL
PT
RO
SE
SI
SK
UK
Time perspective
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Append 1: Table of Correspondences for old and new names of indicators
Digital Scoreboard Name Membership New Interpretation
bb_speed10TOTAL_FBBpc_lines bbquality a % fixed bb >= 10 Mbps
bb_speed2TOTAL_FBBpc_lines bbquality b % fixed bb >= 2 Mbps
bb_speed30TOTAL_FBBpc_lines bbquality c % fixed bb >= 30 Mbps
Price_Internet_onlyoffer_12_30_Mbpsmedian_euro_PPP
bbquality d Monthly median internet price12.30Mbps
bb_dslTOTAL_FBBpc_lines broadband a % dsl in fixed bb
bb_linesTOTAL_FBBnbr_lines broadband b bb Total # subscriptions
bb_neTOTAL_FBBpc_lines broadband c % new entrants in BB
bb_penetTOTAL_FBBsubs_per_100_pop broadband d BB penetration rate
e_broadent_all_xfinpc_ent broadband e % enterprises with BB connection
h_broadHH_totalpc_hh broadband f % households with BB connection
E_ERP1ent_all_xfinpc_ent ebusiness a % enterprises w/ e internal integration
E_WEBent_all_xfinpc_ent ebusiness b % enterprises w/ website
i_bfeuIND_TOTALpc_ind ecommerce c % individuals cross-border commerce
i_bgoodoIND_TOTALpc_ind ecommerce d % individuals buying online
i_blt12IND_TOTALpc_ind ecommerce e% individuals ordering goods/service
online
i_iusellIND_TOTALpc_ind ecommerce f % individuals selling online
e_esellent_all_xfinpc_ent ecommerce g % enterprises selling online
i_igov12rtIND_TOTALpc_ind egovernment h % individuals filling forms
i_iugov12IND_TOTALpc_ind egovernment i % individuals egov services
e_igovent_all_xfinpc_ent egovernment j % enterprises use eservices
e_igovrtent_all_xfinpc_ent egovernment k % enterprises filling eforms
i_ihifIND_TOTALpc_ind ehealth l % individuals eHealth information
i_cprgIND_TOTALpc_ind ict-skills a % i programmers
i_cwebIND_TOTALpc_ind ict-skills b % i created websites
st_gradTOTALnb_x1000inh_20_29 ict-skills c % sci-tech graduates x 1000
i_igovrtIND_TOTALpc_ind internet-services a % i filled eforms 3months
i_iubkIND_TOTALpc_ind internet-services b % i online banking
i_iugmIND_TOTALpc_ind internet-services c % i multimedia internet
i_iugovIND_TOTALpc_ind internet-services d % i use egovt 3 months
i_iuifIND_TOTALpc_ind internet-services e % i info good services
i_iujobIND_TOTALpc_ind internet-services f % i look a job
i_iunwIND_TOTALpc_ind internet-services g % i reading online
I_IUPH1IND_TOTALpc_ind internet-services h % i ecalls
h_iaccHH_totalpc_hh internet-usage i % households internet access
i_idayIND_TOTALpc_ind internet-usage j % i frequent internet users
i_ilt12IND_TOTALpc_ind internet-usage k % i internet use 12 months
i_iu3IND_TOTALpc_ind internet-usage l % i internet use 3 months
i_iumcIND_TOTALpc_ind internet-usage m % i nomadic use portable device
i_iuseIND_TOTALpc_ind internet-usage n % i regular internet users
i_iuxIND_TOTALpc_ind internet-usage o % i never used internet
i_iu3gIND_TOTALpc_ind mobile a % i mobile access to internet
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Append 2: Weights for proxy DESI index and indicator assignation
Proxy DESI Weights
Dimension Subdimension Weight
Connectivity 0.25
Fixed bb 1
Human Capital 0.25
Basic Skills and Usage 1
Use of internet 0.15
Content 0.33
Communication 0.33
Transactions 0.33
Integration on Digital Technology 0.2
Business digitalization 0.6
ecommerce 0.4
Digital Public Service 0.15
eGovernment 0.67
eHealth 0.33
DESI Weights
Dimension Subdimension Weight
Connectivity 0.25
Fixed bb 0.33
Mobile bb 0.22
Speed 0.33
Affordability 0.11
Human Capital 0.25
Basic Skills and Usage 0.5
Advanced Skills 0.5
Use of internet 0.15
Content 0.33
Communication 0.33
Transactions 0.33
Digital Technology 0.2
Business digitalization 0.6
ecommerce 0.4
Digital Public Service 0.15
eGovernment 0.67
eHealth 0.33
Indicators Assignation
Connectivity
Fixed BB
a broadband % dsl in fixed bb bb_dslTOTAL_FBBpc_lines
b broadband BB Total # subscriptions bb_linesTOTAL_FBBnbr_lines
d broadband BB penetration rate
bb_penetTOTAL_FBBsubs_per_100_po
p
e broadband % enterprises with BB connection e_broadent_all_xfinpc_ent
f broadband % households with BB connection h_broadHH_totalpc_hh
Human Capital
Basic Skills and Usage
i internet-usage % households internet access h_iaccHH_totalpc_hh
n internet-usage % i regular internet users i_iuseIND_TOTALpc_indo- negative internet-usage % i never used internet i_iuxIND_TOTALpc_ind
Use of internet
Content
e internet-services % i info good services i_iuifIND_TOTALpc_ind
f internet-services % i look a job i_iujobIND_TOTALpc_ind
g internet-services % i reading online i_iunwIND_TOTALpc_ind
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Communication
h internet-services % i ecalls I_IUPH1IND_TOTALpc_ind
Transactions
b internet-services % i online banking i_iubkIND_TOTALpc_ind
Integration on Digital Technology
Bussines digitalization
a ebusiness % enterprises w/ e internal integration E_ERP1ent_all_xfinpc_ent
b ebusiness % enterprises w/ website E_WEBent_all_xfinpc_ent
eCommerce
c ecommerce % individuals cross-border commerce i_bfeuIND_TOTALpc_ind
d ecommerce % individuals buying online i_bgoodoIND_TOTALpc_ind
e ecommerce
% individuals ordering goods/service
online i_blt12IND_TOTALpc_ind
f ecommerce % individuals selling online i_iusellIND_TOTALpc_ind
g ecommerce % enterprises selling online e_esellent_all_xfinpc_ent
Digital Public Service
eGovernment
h egovernment % individuals filling forms i_igov12rtIND_TOTALpc_ind
i egovernment % individuals egov services i_iugov12IND_TOTALpc_ind
j egovernment % enterprises use eservices e_igovent_all_xfinpc_ent
k egovernment % enterprises filling eforms e_igovrtent_all_xfinpc_ent
eHealth
l ehealth % individuals ehealth information i_ihifIND_TOTALpc_ind