USING DOMAIN NAMES AS INDICATORS OF THE DIFFUSION OF THE INTERNET

 

Maurizio Martinelli
Institute for Informatics and Telematica – CNR
Via Giuseppe Moruzzi 1, 56124 Pisa, Italy
Tel. +39.050315.2087
maurizio.martinelli@iit.cnr.it

 

Rita Rossi
Institute for Informatics and Telematica – CNR
Via Giuseppe Moruzzi 1, 56124 Pisa, Italy
Tel. +39.050315.2601
rita.rossi@iit.cnr.it 
 
Irma Serrecchia
Institute for Informatics and Telematica – CNR
Via Giuseppe Moruzzi 1, 56124 Pisa, Italy
Tel. +39.050315.2086
irma.serrecchia@iit.cnr.it
 
Andrea Bonaccorsi
Sant’Anna School of Advances Studies
P.zza Martiri della Libertà 33, 56124 Pisa, Italy
+39.050883323
bonaccorsi@sssup.it
 
Cristina Rossi
Sant’Anna School of Advances Studies
P.zza Martiri della Libertà 33, 56124 Pisa, Italy
Tel. +39.050315.3458
cristina.rossi@iit.cnr.it
 
Alessandro Scateni
Sant’Anna School of Advances Studies
P.zza Martiri della Libertà 33, 56124 Pisa, Italy
Tel. +39.050315.3458
scateni@sssup.it

ABSTRACT

The last 10 years witnessed an exponential growth of the Internet. According to Hobbes' Internet Timeline, the Internet hosts are about 93 million, while in 1989 they were 100,000. The same happens for second level domain names. In July 1989 the registered domains were about 3,900 while they were over 2 million in July 2000. The last Network Wizard survey stated what in January 2003, the Internet hosts around the world were over 170,000,000.

This poster reports about the construction of a database containing daily observations on registrations of domain names underneath the “it” ccTLD in order to analyse the diffusion of Internet among families and businesses.

The section of the database referring to domains registered by individuals is analysed. The penetration rate over the relevant population of potential adopters is computed at highly disaggregated geographical level (province). A concentration analysis is carried out to investigate whether the geographical distribution of Internet is less concentrated than population and income suggesting a diffusive effect. Regression analysis is carried out using social and economic indicators. At the present we are building up a database containing domains registered by firms together with data about the registrants; the idea is to use this new database and the previous one in order to deeply investigate the adoption and diffusion of Internet in Italy.

 

Keywords

Domain names, Internet diffusion, Digital Divide

 

INTRODUCTION

We report about the diffusion of Internet among households and firms based on daily observations of registrations of domain names under the “it” ccTLD.

The main difficulty in measuring Internet diffusion is its distributed nature: it has no central authority in control and no directory of users exists. Moreover, it is not possible to give an unambiguous definition of an Internet user. A lot of different definitions are present in literature dealing with the time spent on line (Federcomin, 2000), the age of the users, the kind of activity performed (e-mail, surfing the Web, ftp and so on). Several Internet metrics are available. The most suitable are the so-called endogenous metrics that are “obtained in an automatic or semiautomatic way from the Internet itself” (Diaz-Picazo, 1999). These metrics have the unquestionable advantage of the accuracy. Among them the most used in the literature are Internet hosts and domain names (Naldi, 1997; Zook, 1999; Bauer, Berneand and Maitland, 2002). The widespread utilization of Internet hosts is probably due to the easiness in obtaining data. The organizations that manage the different ccTLD and gTLD, perform the host count under their TLD on a regular basis and provide these data on the Web or by ftp. For instance every six months Network Wizard publishes the results about all the TLD on its web site, whereas RIPE publishes data about the ccTLD in its area (Europe, North Africa, Middle East) on a monthly basis.

Among endogenous metrics, domain names represent a valid alternative to Internet hosts. This metric underestimates Internet diffusion: not all the users register a domain, nevertheless domains identify a lower bound in diffusion capturing the proactive and interacting use of the network.

ANALYSIS OF INTERNET DIFFUSION

The analysis is based on the database of domain names registered under the “it” ccTLD in the period 1996 - 2001. A heavy work of data cleaning and classification was undertaken.

As far as domain names of individuals are concerned the regulatory framework allowed registration starting from January 15th 2000. Since then the pattern of registration has been linear (Figure 1).

With respect to firms, a change in the regulatory framework must be considered. In the period January 1st 1996 -  December  14th 1999 firms were allowed to register a single domain name. Starting from December 15th, no restriction was applied.

In the first period (Figure 2) the pattern of diffusion was approximated by a curve reflecting a classical diffusion dynamic in presence of network externality.  In the initial stage the curve lies below a classical logistic curve because of negative feedbacks provided by the low number of Internet users worldwide. At that stage, the dominant use of an Internet domain was represented by electronic mail. As it happened for fax technology, initial diffusion was hindered by the low number of other people using the e-mail. In the second period (Figure 3), the observed pattern is approximated by a quadratic function, suggesting a slowdown in the growth rate of Internet diffusion. A similar pattern is observed for Internet users in several countries irrespective of the date of initial diffusion.

 

                           


 

 

NATURAL PERSON AND DIGITAL DIVIDE

Diffusion of Internet among households has a clear regional pattern. All Southern regions lie in the lower part of the ranking, with the exception of Sardegna. In the upper part we find almost only Center - North regions. Among the top, we find a heavily tertiary region (Lazio), some industrial regions (Lombardia, Toscana, Emilia Romagna) and several small regions with a flourishing tourist economy (Trentino, Umbria, Val d’Aosta).

Casella di testo: Fig. 4 – Penetration Rates (per 10,000 inhabitants)

These results fit into the discussion about the socio-economic impact of Information and Communication Technologies (ICTs). Evidence of a digital divide at the level of countries is overwhelming. Even though ICTs are largely based on immaterial investment rather than heavy physical capital, evidence shows that low and middle income countries lag behind in their diffusion and use. Much less empirical evidence is available on infra-national disparities.

We find clear evidence of a regional digital divide, that cuts across regions within the same country.

 

ANALYSIS OF CONCENTRATION

                                                                                                Fig. 5 – Lorenz Curve

 

                                                                                           Concentration indexes show that the geographical concentration of Internet is higher than population or income. Far from being a                                                                                                 technology that reduces regional disparities, it seems that Internet enhances such disparities.


 

 

 

 

Exploratory regression model

As expected, penetration rates are positively correlated to income per capita and value added per employee. Even within regions of similar economic level, however, there are interesting variations. The best regression model to explain geographical variability is one that includes a proxy for innovative activities (patents per 1000 inhabitants) and two indexes of socio-cultural vitality (private expenditure for music and theatre, cultural and entertainment infrastructure).

Casella di testo: R2 = 0.571

 Casella di testo: Fig. 6 – Regression Model
The combination of technological indicators and indicators pointing to entertainment and cultural activities is interesting. According to Florida (2002), US cities that have experienced the highest rate of growth in the ‘90s are those that combine strong technological activity, exciting social environment, and tolerance to deviance. These factors are attractive for the so called creative class, i.e. the growing sector of the economy working to creative tasks, from research to design, from consultancy to advertising.

Although the evidence should be carefully controlled, preliminary results on the diffusion of Internet among persons seem to confirm this combination.

 

 

 

 

 

 

ACKNOWLEDGEMENTS

The authors would like to thank Professor Franco Denoth for his criticism and suggestions.

REFERENCES

  1. Maurizio Naldi. Size estimation and growth forecast of the Internet. Centro Volterra, Tor Vergata, 1997

  2. Gonzalo Figura Díez-Picazo. An analysis of international Internet diffusion, Ph.D. Thesis, MIT, 1999

  3. Federcomin. Il Mercato ICT - Osservatorio Federcomin n.2:   http://www.federcomin.it/sviluppo/Produzio.nsf/(FedercominTuttiStudi)/EF5B2F8C4E3A90F3C1256ADC0042F75B?opendocument - 2001

  4. Johannes M. Bauer, Michel Berne, Carleen Maitland Internet Access in the European Union and the United States Telematics and Informatics, 19(2) pp. 117-137, 2002

  5. Richard Florida R. The rise of the creative class. Basic Books,New York, NY, 2002