Information Technology Reference
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for households from 1970 to 2000 were ineffec-
tive, inefficient and generally counter-productive
(Garbacz and Thompson, 2005; Thompson, 2007).
In 2005, Waverman tested a version of the 2001
Roller and Waverman model on data from Africa
but was unable to rely on the results as the model
was not robust to either changes in sample size or
changes in model specification (Waverman, 2005).
The ambiguity in empirical evidence to demon-
strate the effect of ICT, in general, and government
policies, specifically on macroeconomic variables,
to date, have led several international agencies
to conclude that the casual link between ICT
and development have yet to be established. The
measurement of ICT impacts is challenging for
a number of reasons (CEPAL, 2008B) including:
There are various approaches to assessing the
impact of ICT on productivity at the firm level,
as well as a variety of data sources used. While
this diversity has some benefits, it also limits
cross-country comparison (CEPAL 2008B). Some
generalized findings from firm level studies are
that ICT affects productivity in a positive way-
for example, through hardware and software
investment, through firm level use of multiple
electronic business links, and through greater
employee engagement with computers and the
Internet. However, the extent of the benefits differs
according to the type of firm - for example across
industries (with some service industries showing
a particularly strong effect), between young and
established firms (with the former showing greater
gains from IT investment), and between firms with
different ownership and geographic scope. There
are also relationships with other firm attributes,
for instance, there is a greater positive impact
where firms have more fixed investment, a higher
number of skilled employees and a greater level
of innovative activity (Eurostat, 2007).
For example a study of Thai manufacturing
firms found that the use of ICT (computers, the
Internet and web presence) was associated with
significantly higher sales per employee. More
importantly, the study illustrates that the use of
even the most basic of these ICTs - computers
- accounts for large differences in labor produc-
tivity between firms. Furthermore, variations in
the intensity of computer use resulted in larger
productivity differentials, for example, a 10 per
cent increase in the share of employees using
computers was associated with a 3.8 per cent rise
in labor productivity (UNCTAD, 2007).
While the causal effects of government ICT
policies on macro-economic variables may be dif-
ficult to establish, there are some indications that
there is a high correlational relationship between
broadband/Internet penetrations rates and growth.
This is shown in Figure 2.
Absence of a clear definition of impacts,
and these have been variously character-
ized, for instance, as strong or weak influ-
ences, direct or indirect impacts, positive
and negative impacts, short or long term
impacts, intended and unintended impacts,
intermediate and final impacts;
The range of possible statistical approach-
es to impact measurement, combined with
a lack of both comparable measurement
models and data;
The general challenge in measuring effects
of any kind as demonstrating the impact of
one factor on another can be difficult be-
cause a positive correlation cannot readily
be attributed to a cause and-effect relation-
ship; and
The nature of ICT itself, as an enabling
technology, implies that its effects will be
indirect. As such, the use and impacts of
ICT are ubiquitous yet difficult to measure.
It is not ICTs per se that have an effect on
the economy and society but how they are
used to transform organization, processes
and behaviors. (OECD, 2007).
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