Computers ; Productivity ; Labor productivity

Ronald H. Schmidt

English

Research Papers in Economics

FABSF WEEKLY LETTER
Number 93-19, May 14, 1993
Computers and Productivity
It would be hard to imagine walking into almost
any workplace-from a supermarket to a chem-
ical plant to a travel agency-and not finding
computers playing an important role. Therefore,
it would seem safe to say that information tech-
nologies must be having a strong, positive impact
on productivity. Indeed, research has found that
computer users receive a significant premium in
average wages relative to non-computer users,
and this premium has been retained despite
rapid growth in the number of computer-literate
workers.
At the same time, however, some empirical
work fails to find evidence that computer pur-
chases increase productivity significantly. This
Letter reviews these empirical studies briefly, and
then discusses a possible solution to this puzzle:
namely, the deviation between measured produc-
tivity effects and compensation patterns may be
caused by errors in measuring output, so that
analyses of productivity gains by computer
users-and their industries-may be signifi-
cantly underestimated.
Recent studies
Several studies have attempted to measure the
effect of computer purchases on productivity.
Morrison and Berndt (1991) looked at productiv-
ity gains attributed to investment in information
technologies in U.S. manufacturing industries
over the period 1952 to 1986. Their empirical
work-which relies on estimates of output by
industry-suggested that firms had over-invested
in those technologies, and that the costs of the
equipment exceeded their benefits.
Loveman (1990) found similar results using data
for manufacturing firms in the u.s. and Western
Europe. He found that investment in information
technologies between 1978 and 1984 had "little,
if any, marginal impact on output or labor pro-
ductivity, while all other inputs into production-
including [non-information technologies] capi-
tal-had significant positive impacts on output
and labor productivity" (p.5).
This paradox-heavy investment in technologies
that do not appear to have any benefit-has sev-
eral potential explanations. First, the period cov-
ered in the data may be anomalous, and a longer
period or more recent data might give different
results. In fact, more recent unpublished work by
Berndt and Morrison suggested that investments
in the last several years may now be increasing
productivity in manufacturing industries.
Second, organizational factors may have reduced
the apparent payoff of informational technolo-
gies. Loveman argues that firmswere making
huge investments in these technologies at the
same time that they were incurring large costs in
"right-sizing" their operations to meet increased
international competition. Thus, the returns to
the technologies are difficult to isolate from the
many changes in other factors of the firm's pro-
duction technology. Moreover, firms may have
engaged in poor ex ante capital budgeting, in
large part because they lacked good understand-
ing of the potential payoffs to those investments.
Several authors also have pointed out the impli-
cations of a learning curve for computer users.
New information technologies must be assim-
ilated into the system and workers must be
trained to use them before any productivity gains
can emerge. For example, while workers clearly
can produce revised documents faster using a
wordprocessor, the wordprocessing program
takes time to learn. Thus, it could be that the
large investments ofthe early 1980s did not bear
fruit until the latter part of the decade, a time of
rapid productivity growth in the manufacturing
sector.
Measurement problems
Another explanation for the lack of measured pro-
ductivity gains attributed to computer use is sim-
ply the output of computer-using industries is
underestimated. As noted in a previou)' Weekly
Letter (Schmidt, 2114/92), the official output
measurements generated by the u.s. Bureau of
Economic Analysis (BEA) may be fraught with
unavoidable conceptual and measurement
problems.
Specifically, measuring the effect of information
technologies on labor productivity of computerFRBSF
users is quite difficult. Most of the studies have
concentrated on manufacturing productivity, be-
cause it is thought that the outputs from those
industries are better measured. However, adjust-
ments in quality are difficult to embed in output
measures, and it may be that quality is most af-
fected by the use of increased computerization.
Consider the automobile industry. During the last
two decades, this industry has changed dramat-
ically, with domestic producers losing market
share to foreign producers, particularly to Japan.
One of the biggest factors underlying the loss of
market share was the perception that Japanese
cars were better-built than domestic cars. To com-
bat this perception, U.s. auto manufacturers have
spent billions of dollars on computersand robot-
ics to reduce defects and increase precision.
Now consider how this investment in computers
affects productivity measures. Productivity, as
measured by the number of cars produced per
worker, may not have been significantly in-
creased by the investment. The most important
impact of computerization is on quality and
competitiveness (reducing product development
time, responding to changing tastes and compe-
tition, and moving to lower-cost "just-in-time"
inventory practices). These effects may not be cap-
tured in the BEA measures of output, although
they do represent gains in the real output to the
consumer-a better car.
Outside of manufacturing, the problem of meas-
uring the effects of information technologies is
even harder, and many of those industries are
some of the heaviest users of information tech-
nologies. Problems estimating the output froni
service-producing industries are formidable.
While some services can be counted objectively,
often services embed a qualitative factor that
cannot be directly recorded. The output received
by a customer of a service often includes intan-
gible factors resulting from the interpersonal
relations involved in the transaction.
Moreover, in many of the service-producing in-
dustries, BEA has relied upon measures of inputs
or input prices and assumptions about produc-
tivity to get measures of output. Thus, in some
industries, such as banking, BEA counts changes
in the number of employees and uses that as a
proxy for output changes. Therefore, if these
industries have invested heavily in computers
over the last two decades, and have thereby
saved on expanding their work force, then, by
construction, BEA's measure will reveal little or
no productivity gains to their investment in infor-
mation technologies.
BEA's proxies for output and the implications for
productivity effects from information technolo-
gies are problematic for other industries as well.
For example, in the trucking industry, output is
measured by the number of ton-miles traveled.
Firms that have installed satellite communications
and computerized delivery services now can re-
route trucks while they are on the road. As a
result, trucking firms can reduce their total dis-
tance traveled and deliver their product more ef-
ficiently. With BEA's methodology, however, this
increase in efficiency is counted as a decrease in
output, since mileage declines. Thus, measured
productivity decreases in their calculation be-
cause of the efficiencies generated by the invest-
ment in information technologies, even though
the firms are providing more and better services.
Computers and compensation
These conceptual explanations for underesti~
mated productivity gains from computer use are
consistent with some recent empirical evidence
on compensation patterns. For example, Krueger
(1993) looked at compensation to workers over
the period 1984 to 1989, using a data set that
asked about computer use at work and at home.
His findings indicated that the salaries of com-
puter users reflected a significant premium,
roughly 10 to 15 percent more than comparable
workers who did not use computers. Moreover,
this premium persisted between 1985 and 1989.
Krueger concluded that a large portion of the
rising return to education could be explained by
the increasing use of computers by those with
more education.
This result directly challenges the proposition
that computer use has not addedto productivity.
As industries have increasingly invested in com-
puters, they have paid more to workers who use
those machines. Moreover, given that the supply
of computer-using individuals rose sharply dur-
ing Krueger's study period, the persistence of the
premium suggests that the higher wages reflected
greater productivity of those individuals, rather
than simply a shortage of those workers. (The
existence of a differential at the time that firms
were rapidly introducing computers into the
workplace also suggests that some computer-
related productivity gains were immediatelyrecognized by the firm, even ifthe firm faced a
learning curve in taking full advantage of com-
puterization.) If productivity did not increase as
a result, it would be expected that those firms
that invested in computers would fail, because of
higher costs attributed to both the computers and
the wages paid to the computer users.
Compensation patterns across industries provide
further evidence of a schism between measured
productivity gains and measured compensation
gains-particularly in computer-using industries.
As discussed in the earlier Weekly Letter (2/14/92),
changes in average compensation provide an al-
ternative proxy of labor productivity growth in
a given industry. While most industries have
invested heavily in computers, several service
industries have shown especially large increases
in computer investments in the last decade. Fi-
nance and insurance, business services, health
services, and legal services have had especially
large proportional increases in computer invest-
ments, as measured by the increase in comput-
ers' share of the total capital stock in those
industries during the 1980s.
Differences between measured productivity
gains and observed compensation gains in these
industries are striking. In finance and insurance,
where computers have been essential in creating
new tools to move capital quickly, SEA's measure
shows productivity growth over the period 1964
to 1986 to have been 12 percentage points below
the average for all industries. At the same time,
real compensation per worker in finance and
insurance (deflated by the overall GDP deflator)
has grown 12 percentage points faster than the
average.
In the business services sector, which depends
heavily on computers, productivity growth is
measured to have risen 28 percentage points less
than the average for all industries. Compensation
also has not kept pace with the average, but
growth in compensation has not been as weak,
rising 18 percentage points less than the average.
Health care and legal industries show the largest
deviations, with measured productivity gains of
47 and 74 percentage points, respectively, below
the average for all industries, while compensa-
tion gains were 37 and 69 percentage points,
respectively, above the average.
This evidence is consistent with Krueger's find-
ings. The industries that reported the strongest
increases in computers as a share of the total
capital stock also reported rapid growth in aver-
age compensation. If compensation is related to
actual, as opposed to measured, productivity,
then part of the explanation for low returns to
computerization simply may be poor measure-
ment of output from computer-using industries.
Conclusions
Data on compensation, particularly on the wil-
lingness of employers to pay for computer skills,
suggest that our current statistics are undermeas-
uring the output effects resulting from the appli-
cation of computing power in many industries. In
larg~ part because of difficulties associated with
quantifying the outputs of these sectors (and ad-
justing for quality and timeliness), productivity
growth tied to computers may be significantly
understated.
The implications of these findings are significant.
If the compensation data are reflecting unmeas-
ured output growth, total output growth in the
economy may be understated. Moreover, much
of the hand-wringing associated with analyses
pointing to the shift to a service economy may
be misplaced, if the productivity gains in these
growing service industries are actually much
higher than current measures suggest.
Ronald H. Schmidt
Senior Economist
References
Krueger, Alan B. 1993. "How Computers Have
Changed the Wage Structure: Evidence from
Microdata, 1984~89." Quarterly journal ofEco-
nomics (February) pp. 33-60.
Loveman, Gary W. 1990. An Assessment ofthe Pro-
ductivity Impact of Information Technologies.
Working Paper 90s: 88-054 (September). MIT
Sloan School of Management.
Morrison, Catherine J., and Ernst R. Berndt. 1991.
Assessing the Productivity of Information Technol-
ogy Equipment in U.S. Manufacturing Industries.
NBER Working Paper No. 3582 (January).
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11/20
11/27
12/4
12/11
12/25
1/1
1/8
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. 1/29
2/5
2/12
2/19
2/26
3/5
3/12
3/19
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92-41
92-42
92-43
92-44
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93-01
93-02
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An Assessment ofthe Productivity Impact of Information Technologies. Working Paper 90s:

How Computers Have Changed the Wage Structure: Evidence from Microdata, 1984~89.&quot; Quarterly journal ofEconomics

Computers and productivity

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