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The Role of ICT in Economic Development – A Partial Survey
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exist only for the first category of computer users (17.1 percent). The addition of the experience
variables suggests that a large proportion of this premium is due to experience with new
technologies (10 percent). The high returns to the first category of computer users suggest that
the results may be driven by selection bias. To examine this possibility, the authors now exploit
the panel nature of their data.
Results based on panel-data regressions differ sharply from the cross-sectional results.
The coefficients on the computer-use variable for all three categories are small and statistically
insignificant. Turning to the experience variables, at least for the first category of computer
users, there seems to be positive returns on computer experience. This return is approximately
one percent for each year of experience. However, due to the rapid obsolescence of knowledge,
these returns peak quite rapidly (around 6 years) and remain flat thereafter. Overall, the results
suggest that computer users are selected on the basis of skill levels and that a substantial portion
of the computer-use wage premium reflects returns on unobserved heterogeneity. However,
experience with these technologies also increases the productivity of these skilled workers and
leads to further wage increases. The authors note that this pattern of results is quite consistent
with the slow increase in wage inequality observed in France in recent years.
Evidence based on panel data, albeit restricted to the manufacturing sector, is also
provided by Doms, Dunne and Troske (1997). The authors combine individual and plant data
from a variety of sources to create a panel data set that matches 34,034 workers to 358
manufacturing plants. One of the aims of their paper is to examine how plant-level wages vary
with the adoption and use of factory automation technologies. In contrast to other papers
(Berman, Bound and Griliches, 1994; Autor, Katz and Krueger, 1997) that use measures of
technology that are primarily information-processing technologies, Doms
et al.
(1997) construct
technology measures reflecting the use of ICTs in manufacturing. Plants are classified as high-
or low-technology plants on the basis of the number of technologies they use. To examine the
effect of technology while controlling for other variables, the authors regress average plant
wages for three categories of workers on their respective measures of technology. Based on a
cross-sectional regression, the authors find that there is a wage premium for workers in high-
technology plants. Average wages in high-tech plants are between 8.7 and 13.5 percent higher
for production and technical workers.
To examine the robustness of these results, the authors now use the longitudinal element
of their data to examine whether increases in average plant wages between 1977 and 1992 are
correlated with the number of advanced technologies adopted between 1977 and 1993. Their
results show that there is little correlation between the adoption of such technologies and plant-
level changes in workers’ wages. Despite a number of other sensitivity checks, their basic
message remains unchanged, i.e., technology adoption is not correlated with workers’ wages.
Further investigations reveal that the most technologically advanced firms paid their workers
higher wages prior to the adoption of these technologies and had high-productivity plants, both
prior to and after adoption. This again suggests that the cross-sectional correlation between
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