The Role of ICT in Economic Development – A Partial Survey

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For both years, the estimates show a positive and statistically significant effect of
computer use on wages. The point estimates indicate that there was a substantial computer
premium of around 18.5 percent in 1984, which increased to 20.6 percent in 1989. To provide
an idea of the type of computer use which is most productive, Krueger examines the impact of
specific, computer-related tasks on wage premiums. That is, the wage specification (12) is
augmented with 11 task-specific indicator variables.
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The coefficients for the specific tasks
should be interpreted as indicating the additional payoff associated with the specific task relative
to any computer use at all. The highest payoffs seem to be associated with the use of e-mail,
which probably reflects the concentration of high-ranking executives who use e-mail.
Interestingly, the payoff for playing computer games is negative, suggesting that the use of
computers for nonproductive purposes does not enhance earnings.
Although the computer-use wage premium is large and statistically significant, a critical
concern is whether these estimates really reflect the effect of computer use on wages or are a
spurious correlation reflecting the unobserved quality of workers who use computers.
Recognizing this problem, Krueger tries out several empirical strategies to discern whether his
results are sensitive to sample-selection bias. The results stand up to scrutiny and, regardless of
the empirical strategy used, a 10-15 percent computer-use wage premium remains, suggesting
that workers who use computers earn more as result of their computer skills.
Having established that the use of a computer at work is accompanied by an increase in
mean wages, an issue which naturally flows from these results is whether the impact of using a
computer is uniform across skill groups. To examine this, a specification that includes both a
computer dummy and an interaction between the computer dummy and years of education is
estimated:
,
.
ln
i
i
i
i
i
i
i
E
C
C
E
X
W
ε
γ
α
ρ
β
+
+
+
+
=
(13)
where
E
is years of education. Results from both the data waves show a positive coefficient on
the computer dummy-education interaction term, indicating that the computer premium is higher
for more highly educated workers. The inclusion of the computer dummy and interaction term
also decreases the increase in the rate of return on education, i.e., the coefficient of
E.
This
indicates
that a portion of the increase in educational returns may be attributed to computer use.
After conducting several sensitivity checks to assess the robustness of the results, Krueger
concludes that the proliferation of computers accounts for between one-third and one-half of the
increase in the rate of return on education between 1984 and 1989.
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These specific tasks include word processing, bookkeeping, computer-aided design (CAD), e-mail, inventory
control, programming, desktop publishing, spreadsheets, sales and computer games.
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