Convergence Analysis on DESI index

7/23/2019 Convergence Analysis on DESI index

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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



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



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.



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.



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.



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)



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



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



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.



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



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



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



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



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



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

Documents

Convergence Analysis on DESI index