Increasing Broadband Internet Penetration

In the OIC Member Countries

and a hedonic function for ICT capital stocks. Broadband impact on the productivity of the

more developed countries in the sample was found to be .0013 and was statistically significant

at the 5% level

. In other words, Waverman estimated that for every 1% increase in

broadband penetration in high and medium impact income countries, productivity grows by

0.13%. In another document, the authors commented upon the productivity effect in the

countries of their sample with relatively low ICT penetration (Greece, Italy, Portugal, Spain and

Belgium.). They found that broadband impact on productivity was nil, which indicated the high

adoption costs, and critical mass thresholds

. In other words, for broadband to have an impact

on productivity, the ICT eco-system has to be sufficiently developed

Broadband impact on household income

In recent years, the implementation of national household surveys that now include ICT

modules has allowed to research the impact of broadband based on

micro-

economic data. For

example, using information from Peruvian households between 2007 and 2009, De Los Rios

(2010) found that, during this time period, Internet adopters experienced significant income

growth relative to those households that did not have the service. The author of this report

recently conducted a study evaluating the impact of broadband on household income in

Ecuador (Katz and Callorda, 2015).

To estimate the impact of broadband on poverty reduction using microdata, the authors

calculated the impact of broadband deployment on average income at the country’s county

level. Ecuador is an appropriate case for this analysis because, while at the end of 2009 the

country had a limited offering of residential broadband services, between 2009 and 2011, CNT,

Ecuador’s telecommunications fixed broadband provider, greatly expanded its coverage. As a

result, the population in newly served townships could access fixed broadband service for the

first time. This expansion led to a significant increase in broadband penetration at the

provincial level in the country. Based on disaggregated data, a variable was built indicating the

counties that lacked broadband access in 2009 (due to a lack of coverage) but gained service

by late 2010 / early 2011 (thanks to the aforementioned extension of the state-owned

telecommunications operator’s network). Through this process, two groups were created: 1) a

treatment group, comprised of those individuals living in cantons where broadband was

introduced during the 2010-2011 period, and 2) a control group, comprised of those

individuals living in cantons that already had access to residential broadband services by the

fourth quarter of 2009. Using this identification strategy, and given that the treatment group

and the control group are statistically equal at the baseline of the observed variables, a

regression model that estimates the impact of treatment on individual income levels was built.

Controls were included for the variables that, at the individual level, can affect income (age,

The original regression yielded a coefficient of 0.0027 for the 2/3 more developed countries in the sample and negative

effect for the lower third. A negative effect did not make sense so the authors constrained the effect for the lower third to

zero. At that point the coefficient for the full sample moved to 0.0013.

Waverman, 2009

For example, Waverman et al. estimated that in the United States broadband penetration contributed approximately to

0.26% per annum to productivity growth, resulting in 11 additional cents per hour worked (or US $ 29 billion per year).