who live in rural communities—so,
specific policies to address not only
these countries but also particular
groups within them will be necessary
to speed up adoption.
This column’s model suggests we will
fall short of the 2020 and 2025 U.N. targets. As shown in our projections, low
and middle-income countries have yet
to enter a rapid growth phase and may
be at risk of moving into a slow growth
phase well before approaching a point
of universal access. This is worrying
since 50% of the world that is still offline is mostly from these countries. If
we adopt the U.N. position that the Internet is a human right, then this access
gap is profoundly significant.
Reaching a world of broad universal
access will require a combination of innovative policies, technologies, and new
business models. Policies will need to
address the specific reasons why some
groups (for instance, women) are less
likely to use the Internet. Service providers will need to adopt business models that can accommodate the needs
of low-income and rural populations.
And while innovative new technologies
(such as global low-orbit satellites) look
promising, we need to make sure that
the right regulations are in place to ensure the greatest benefit for those who
are not yet connected. Finally, we need
improved and more transparent data
sources and methodologies on Internet
access and use, which can help overcome today’s access gap and enable the
realization of policy goals.
1. Andrés, L. et al. The diffusion of the Internet: A cross-country analysis. Telecommunications Policy 34, 5-6
2. Hargittai, E. Weaving the Western Web: Explaining
differences in Internet connectivity among OECD
countries. Telecommunications Policy 23, 1999, 701–718.
3. Rogers, E.M. Diffusion of Innovations, 5th Edition. Free
Press, New York, 2003.
4. Wolcott, P. et al. A framework for assessing the global
diffusion of the internet. Journal of the Association for
Information Systems 2, 1 (2001).
Carlos Iglesias ( email@example.com) is
a Senior Research Manager, Web Foundation, in Oviedo,
Dhanaraj Thakur ( firstname.lastname@example.org) is
Research Director, Web Foundation, in Washington, DC, USA.
Michael L. Best ( email@example.com) is an Associate
Professor of interactive computing and international affairs
at the Georgia Institute of Technology, Atlanta, GA, USA.
Copyright held by author.
forecast by simply continuing the model dynamics into the future. This allows
us to get an estimation of how close (or
far) we are to achieving the aforementioned U.N. SDG 2020 and Broadband
Commission 2025 goals.
We focus on the three regions where
the next 50% of users are most likely
to come from: Latin America and the
Caribbean; Asia and the Pacific; and
Africa. Figure 1 provides the historical
data, between 1995 and 2017/2018, of
average Internet use rates for these regions, as well as globally. We also added projections for each region and the
world up to 2025; the table in this column includes our estimates of Internet
use rates for those target years.
Based on these models, we will
meet neither the U.N. SDG 9c 2020 nor
Broadband Commission 2025 targets
at current rates. The 2020 target calls
for universal access in LDCs. “
Universal access” does not necessarily imply
that 100% of the population is online.
Rather, we take an 85% adoption rate as
universal access.d Clearly, we are a long
way from even that milestone globally,
and particularly for LDCs. The 2025
target calls for a 75% global broadband
Internet use rate (and 35% in LDCs). Instead, by then we project 70% for global
Internet use (broadly defined as mentioned earlier and may not include actual broadband use) and 31% in LDCs.
In attempting to understand these
trends we draw on diffusion of innovation theory (for example, Rogers3) and
specifically its application to Internet
use (see for example Wolcott, et al. 4).
That research looks at many aspects of
technology adoption including types
of adopters and factors that drive overall adoption. This, in turn, has been
applied to studies on trends in global
and regional Internet use. 1 According
to the diffusion of innovation framework, adoption over time is typically de-
d Defining universal access is part of a complex
ongoing discussion that is out of the scope
of this column. For this analysis we take the
population of five years and above as potential
Internet users (based on UN population data;
see http://bit.ly/348ypBS). Moreover, the ITU
data suggests that even in high-income coun-
tries Internet penetration rates have plateaued
between 80%–90% (which is understandable as
some groups in the population may not be able
to or interested in access). Thus, we take 85%
as a reasonable and conservative reference for
scribed with an S-curve. That is, we will
observe slow growth at first (indicating
adoption among early users), followed
by rapid growth (as the technology be-
comes mainstream), and ending with
slow growth again (close to universal ac-
cess as late adopters trickle on board).
This S-curve is present in Figure 2
when we look at high-income countries
as defined by the World Bank and depict-
ed by the green line. 1 This curve shows
an initial slow-growth phase for the first
five years followed by 12 years of rapid
growth between 1995 and 2007, followed
again by slow growth from 2008 to pres-
ent. This final slow-growth period of late
adopters started with more than 65% of
the entire population already online.
Turning to middle and low-income
countries, we do not observe the same
S-curve pattern compared to the high-
income data. In particular, for middle
and low-income countries, we see a
slow-growth late adopter regime well
before we hit universal access. While
the final slow-growth phase in high-
income countries started at approxi-
mately 65% penetration and continues
above 85%, the projected slow-growth
phase in middle and low-income econo-
mies could start as soon as 42% and 13%
penetration rates respectively, without
being preceded by an extended period
of rapid exponential growth. In other
words, while Internet use rates in high-
income countries have begun to pla-
teau around the universal access level,
in low and middle-income countries
the plateau may occur much earlier. Al-
though this model, and its underlying
datasets, does not allow us to robustly
predict penetration rates decades into
the future (and in any case, technology
adoption prediction of this sort is error-
prone), based upon this model if these
regions are ever going to reach universal
access it will not be for a very long time.
We are already experiencing slowing
Internet access growth rates, particularly among low and middle-income
countries. This is particularly salient
given that it is in these countries that
the next 50% reside, and points to the
need for everyone—policymakers, engineers, companies, and civil-society—
to carefully assess current strategies
if we wish to reach universal access.
These offline groups are more likely to
be women or older adults, with lower
levels of education and employment,