This research was sponsored by the MIT Center for Coordination Science, the MIT International Financial Services Research Center, and the Sloan Foundation. Portions of this paper are based on an article titled "The Productivity Paradox of Information Technology," which originally appeared in Communications of the ACM, in December, 1993. Special thanks to Lorin Hitt and Miriam Avins for numerous valuable comments.
In recent years, the relationship between information technology
(IT) and productivity has become a source of debate. In the 1980s
and early 1990s, empirical research generally did not significant
productivity improvements associated with IT investments. More
recently, as new data are identified and new methodologies are
applied, several researchers have found evidence that IT is associated
not only with improvements in productivity, but also in intermediate
measures, consumer surplus, and economic growth. Nonetheless,
new questions emerge even as old puzzles fade. This survey reviews
the literature, identifies remaining questions, and concludes
with recommendations for applications of traditional methodologies
to new data sources, as well as alternative, broader metrics of
welfare to assess and enhance the benefits of IT.
I. The "Productivity Paradox"-A Clash of Expectations and Statistics 1II. Research on Economy-wide Productivity and Information Worker Productivity 8III. Industry-Level Studies of Information Technology Productivity 13IV. Firm-Level Studies of Information Technology Productivity 17A. Service Sector Studies 17B. Studies of Manufacturing Sector and Cross-Sector Studies 21V. Contribution to Consumer Surplus and Economic Growth 25VI. Conclusion: Where Do We Go from Here? 30Figures 37Bibliography 44Index of TablesTable 1: Principal Empirical Studies of IT and Productivity. 7Table 2: Selected Investment Components in 1970 and 1993. 9Table 3: Industry-Level Studies. 16Table 4: Investment in Computers (OCAM) in the US economy. 17Table 5: Studies of Firms in the Service Sector 21Table 6: Studies of Manufacturing Firms and Cross-Sector Firms 24Table 7: Growth Rates of Aggregate Output and Contribution of Factors. 28
I. The "Productivity Paradox" - A Clash of Expectations and Statistics
Over the past decade, both academics and the business press have
periodically revisited the so-called "productivity paradox"
of computers: while delivered computing-power in the United States
has increased by more than two orders of magnitude since the early
1970s (figure 1), productivity, especially in the service sector,
seems to have stagnated (figure 2). Despite the enormous promise
of information technology (IT) to effect "the biggest technological
revolution men have known" [Snow, 1966], disillusionment
and frustration with the technology are evident in headlines like
"Computer Data Overload Limits Productivity Gains" [Zachary,
Interest in the "productivity paradox" has engendered
a significant amount of research. Although researchers analyzed
statistics extensively during the 1980s, they found little evidence
that information technology significantly increased productivity.
As Robert Solow quipped, "you can see the computer age everywhere
but in the productivity statistics."
Now, after some researchers found firm-level evidence that IT
investments earned hefty returns, the media pendulum has swung
in the opposite direction. Businessweek's proclamation
of "the productivity surge" due to "information
technology", and Fortune magazine's headline heralding
the arrival of "technology payoff" represent the latest
trend. A growing number of academic studies also report positive
effects of information technology on various measures of economic
Just as the business media's premature announcement of a
paradox" was out of proportion to the more carefully worded
academic research, the current cover stories on "productivity
payoff" are often overblown. A consensus on the relationship
between IT investment and economic performance is still elusive.
More than a decade ago, one of the earliest surveys concluded
that we still had much to learn about measuring the effects of
computers on organizations [Attewell and Rule, 1984]. A more
recent survey also reports a "sobering conclusion: our
of how information technology affects productivity either at the
level of the firm or for the economy as a whole is extremely
As more research is conducted, we are gradually developing a clearer
picture of the relationship between IT and productivity. However,
productivity measurement isn't an exact science; the tools are
blunt, and the conclusions are not definitive. Thus, while one
study shows a negative correlation between total factor productivity
and high share of high-tech capital formation during 1968-1986
period [Berndt and Morrison, 1995], another study suggests that
computer capital contributes to growth more than ordinary capital
[Jorgenson and Stiroh, 1995]. More recently, Brynjolfsson
and Hitt  report positive effects of IT based on firm-level
This paper seeks to summarize what we know; distinguish the central
issues from peripheral ones; and clarify the questions that future
research should explore. Results and implications of different
studies should be interpreted in the context of specific research
questions. The question of aggregate economic performance differs
from the question of firm-level economic performance. Data sources,
and performance measures may also depend on the level of aggregation.
Even within one level of aggregation, results may depend on the
measure of performance or research method. It is hoped that the
process of reviewing studies of the productivity mystery will
serve as a useful springboard for examining other methodologies
and the broader issues involved.
As a prelude to the literature survey, it is useful to define
some of the terms used and to highlight some of the basic trends
in the economics of IT.
"Information technology" can be defined in various
ways. In terms of capital, among the most common is the BEA's
(U.S. Bureau of Economic Analysis) category "Office, Computing
and Accounting Machinery (OCAM) which consists primarily of computers.
Some researchers look specifically at computer capital, while
others consider the BEA's broader category, "Information
Processing Equipment (IPE)." IPE includes communications
equipment, scientific and engineering instruments, photocopiers
and related equipment. Besides, software and related services
are sometimes included in the IT capital. Recent studies often
examine the productivity of information systems staff, or of
workers who use computers.
"Labor productivity" is calculated as the level of
output divided by a given level of labor input. "Multifactor
productivity" (sometimes more ambitiously called "total
factor productivity") is calculated as the level of output
for a given level of several inputs, typically labor, capital
and materials. In principle, multifactor productivity is a better
measure of a firm or industry's efficiency because it adjusts
for shifts among inputs, such as substituting capital equipment
for labor. However, the data needed to calculate multifactor
productivity are more complex.
In productivity calculations, "output" is defined
as the number of units produced times their unit value, proxied
by their "real" price. Determining the real price of
a good or service requires the calculation of individual price
"deflators" to eliminate the effects of inflation.
The price of computing has dropped by half every 2-3 years (figure
3a and figure 3b). If progress in the rest of the economy had
matched progress in the computer sector, a Cadillac would cost
$4.98, while ten minutes' labor would buy a year's worth of groceries.
There have been increasing levels of business investment
in information technology equipment. These investments now account
for over 10% of new investment in capital equipment by American
firms (figure 4, table 2).
Information processing continues to be the principal task undertaken
by America's work force. Over half the labor force is employed
in information-handling activities (figure 5).
Overall productivity growth appears to have slowed significantly
since the early 1970s and measured productivity growth has fallen
especially sharply in the service sectors, which account for 80%
of IT investment (figure 2, table 4).
White collar productivity statistics have been essentially stagnant
for 20 years (figure 6).
These trends suggest the two central questions which comprise the productivity paradox: 1) Why would companies invest so heavily in information technology if it didn't add to productivity? 2) If information technology does contribute to productivity, why is its contribution so difficult to measure?
This paper builds on a number of earlier literature surveys.
This review considers over 150 articles, but is not comprehensive.
Rather, we aim to clarify the principal issues surrounding IT
and productivity. We assimilate the results of a computerized
literature search of 30 leading journals in information systems
and economics. In addition, many of the leading researchers in
this area identified recent research that has not yet been published.
The productivity of IT can be measured using data on the whole
economy, on specified industries or on individual firms. In the
1980s and early 1990s, disappointment in information technology
was chronicled in articles disclosing broad negative correlations
with economy-wide productivity. Several econometric estimates
also indicated low IT capital productivity in a variety of manufacturing
and service industries. More recently, researchers began to find
positive relationships between IT investment and various measures
of economic performance at the level of individual firms. The
principal empirical research studies of IT and productivity are
listed in table 1.
Table 1: Principal Empirical Studies of IT and Productivity*
|Aggregate Level Studies||Jonscher ,
since the early 1970s and measured productivity |
|Morrison & Berndt ||Brand & Duke|
|(Economy- wide and Industry- level)||Baily [1986b], Baily & Chakrabarti ,
Baily & Gordon 
|Berndt et al. 
Berndt & Morrison 
|Roach , Roach , Roach [1989b]||Siegel & Griliches ||Roach , Roach [1989a], Roach |
|Brooke ||Siegel |
|Lau & Tokutsu |
|Oliner & Sichel |
|Jorgenson & Stiroh |
|Micro-Level||Osterman ||Loveman ||Cron & Sobol |
|Studies||Dos Santos ||Weill [1988, 1992]||Pulley & Braunstein |
|Krueger ||Dudley & Lasserre ||Bender |
|(Firm and Workers)||Brynjolfsson & Hitt ||Barua, Kriebel & Mukhopadhyay ||Bresnahan |
|Hitt & Brynjolfsson ||Brynjolfsson & Hitt
Brynjolfsson & Hitt 
|Lichtenberg ||Strassmann 
|Brynjolfsson & Hitt ||Harris & Katz [1987, 1991]|
|Parsons et al. |
|Diewert & Smith |
II. Research on Economy-wide Productivity and Information Worker
Economists have been unable to explain the slowdown in measured
productivity growth that began in the early 1970s. Labor productivity
grew about 2.5% per year from 1953 to 1968, but dropped to about
0.7% per year from 1973 to 1979. Multifactor productivity growth
declined from 1.75% a year to 0.32% [Baily, 1986b]. Even after
accounting for factors such as the oil price shocks, changes in
the quality of the labor force and potential measurement errors,
most researchers still find an unexplained residual drop in productivity
that roughly coincides with the rapid increase in the use of information
Jorgenson and Stiroh's  more recent growth accounting confirms
this correlation. They calculate that average multifactor productivity
growth dropped from 1.7% per year for the 1947-73 period to about
0.5% for the 1973-1992 period. At the same time, OCAM capital
as a percentage of all producers' durable equipment (PDE) investment
rose from about 0.5% in the 1960s to 12% in 1993. A broader category
of IT capital, information processing equipment (IPE), now constitutes
34.2% of all PDE investment (table 2). Although productivity
growth, especially in manufacturing, has rebounded somewhat recently,
the overall negative correlation between productivity and the
advent of computers underlies many of the arguments that information
technology has not helped the United States' productivity and
that information technology investments have been counterproductive
(See, for example Baily [1986b]).
Table 2. Selected Investment Components in 1970 and 1993*
|Item \ Year|
Sources: Survey of Current Business, July 1994; U.S. Bureau of Economic Analysis (1992, vol. 2. Tables 5.4 and 5.8); adapted from Oliner and Sichel .
Note: Information Processing Equipment: OCAM (office,
computing and accounting machinery), communication equipment,
and scientific and engineering equipment.
This argument was made more explicitly by Stephen Roach [1987,
1988] who focused on the productivity of information workers.
In the past, office work was not very capital intensive, but
recently the level of IT capital per "white collar"
information worker has approached that of production capital per
"blue collar" production worker. Concurrently, the
ranks of information workers have ballooned and the ranks of
production workers have shrunk. Roach shows that output per production
worker grew by 16.9% between the 1970s and 1986, while output
per information worker decreased by 6.6%. He concludes: "America's
productivity shortfall [is] concentrated in that portion of the
economy that is the largest employer of white-collar workers and
the most heavily endowed with high-tech capital." Roach's
analysis provided quantitative support for widespread reports
of low office productivity.
But the economy's productivity record in the 1970s and 1980s cannot be blamed on the investment in information technology; many other factors also affect productivity and, until recently, computers were not a major share of the economy. Consider an order of magnitude estimate. In 1992, IT capital stock (OCAM) was equal to about 10% of GNP (with a base year of 1987). If, hypothetically, IT were being used efficiently and its marginal product were 50% (exceeding the return to most other capital investments), then the level of GNP would be directly increased about 5% (10% x 50%) because of the current stock of IT. However, information technology capital stock did not jump to its current level in one year; rather, the increase must be spread over about 30 years, suggesting an average annual contribution to aggregate GNP growth of 0.15%. This contribution would be very difficult to isolate because so many other factors affected GNP, especially in the relatively turbulent 1970s and 1980s. Indeed, if the marginal product of IT capital were anywhere from 0% to +65%, it would still not have affected aggregate GNP growth by more than about 0.2% per year. More comprehensive growth accounting exercises confirm this estimate. (See section V.)
Thus, very large changes in capital stock are needed to measurably
change total output, although computers may have had significant
effects in specific activities, such as transaction processing,
and on other characteristics of the economy, such as employment
shares, organizational structure, and product variety. However,
as the information technology stock continues to grow and the
share of the total economy accounted for by computers becomes
substantial, we should begin to find changes in the level of aggregate
GNP. Indeed, some recent studies do report a high contribution
of computers to GDP growth. (See, for example, Oliner and Sichel
 and Jorgenson and Stiroh .)
Just as it is hard to isolate information technology's effect
on the economy, white collar productivity cannot be directly inferred
from the number of information workers per unit of output. For
instance, if a new delivery schedule optimizer allows a firm to
substitute one clerk for two truckers, the increase in the number
of white collar workers is evidence of an increase in their
relative productivity as well as the firm's productivity. Osterman
 suggests that such efficiency improvements can explain
why firms often hire more clerical workers after they introduce
computers, and Berndt et al.  confirm that information technology
capital is, on average, a complement for white collar labor and
is correlated with fewer blue collar workers. Berman, Bound and
Griliches  also find that the "increased use of non
production workers is strongly correlated with investment in computers
and in R&D." Unfortunately, it is exceedingly difficult
to directly measure the productivity of office workers.
Independent of its implications for productivity, growth in the
white collar work force cannot be attributed solely to information
technology. Although almost half of workers now use computers
in their jobs [Katz and Krueger, 1994], the ranks of information
workers began to surge even before the advent of computers [Porat,
1977 ]. In fact, Jonscher  argues that the increased demand
for information technology created economies of scale and learning
in the computer industry, thereby reducing the cost of computers.
In line with this argument, the unbalanced growth hypothesis may
provide a sensible economic explanation. Economic growth may
slow down because of intrinsically slow technical progress in
the white collar sector, since it is less subject to automation.
Then why is the white collar sector's share in the economy growing?
One possible answer is the higher income elasticity (and lower
price elasticity) of demand for services of this sector. As income
increases, people demand more services of white collar sectors.
Thus, even if information technology does not add to productivity,
companies in developed countries may be forced to invest in it.
Since it is difficult to measure white collar sectors' output,
the story becomes complicated. Companies invest in computers
to produce "unmeasurables", as argued in Griliches .
In short, the increased IT use may not be a source of the productivity
slowdown, but simply a response to the overall transformation
of the economy. Furthermore, the main benefits from using computers
appear to be in areas like improved quality, variety, timeliness,
and customization, which are not well-measured in official productivity
statistics [Brynjolfsson, 1994].
III. Industry-Level Studies of Information Technology Productivity
The last section has shown that contrasting the economy-wide productivity
slowdown with increasing IT investment is an obtuse approach,
because so many other factors may intervene. Going down to the
firm-level helps to control many problems that arise from aggregation,
but it is often difficult to find data representative for the
whole economy. Industry-level studies may provide a middle-of-the-road
alternative. Table 3 summarizes some of the important studies.
While earlier studies failed to identify positive effects of
IT, recent studies found more encouraging results. We start with
studies on service sectors.
It has been widely reported that most of the productivity slowdown is concentrated in the service sector [Schneider, 1987; Roach, 1987, 1991]. Before about 1970, service and manufacturing productivity growth rates were comparable, but since then the trends have diverged significantly. Meanwhile services have dramatically increased as a share of total employment and to a lesser extent, as a share of total output. Because services use up to 80% of computer capital (table 4), the slow growth of productivity in the service sector has been taken as indirect evidence of poor information technology productivity.
Roach's research on white collar productivity, discussed above,
focused principally on IT's performance in the service sector
[1987a, 1989a, 1989b, 1991]. He argued that IT is an effective
substitute for labor in most manufacturing industries, but has
been associated with bloating white-collar employment in services,
especially finance. He attributed this to relatively keener competitive
pressures in manufacturing and foresees a period of belt-tightening
and restructuring in services as they begin to face international
However, studies of manufacturing also found evidence that computers
may not increase productivity. Berndt and Morrison analyzed a
broader data set from the U.S. Bureau of Economic Analysis (BEA)
that encompasses the whole U.S. manufacturing sector. In the
first paper [Morrison and Berndt, 1991], they examined a series
of parameterized models of production, and found evidence that
every dollar spent on IT delivered, on average, only about $0.80
of value on the margin, indicating a general overinvestment in
IT. Their later paper [Berndt and Morrison, 1995] examined broad
correlations of IT investment with labor productivity and multifactor
productivity. This approach did not find a significant difference
between the productivity of IT capital and other types of capital
for a majority of the 20 industry categories examined. They did
find that investment in IT was correlated with increased demand
for skilled labor.
Siegel and Griliches  used industry and establishment data
from a variety of sources to examine several possible biases in
conventional productivity estimates. They found a positive simple
correlation between an industry's level of investment in computers
and its multifactor productivity growth in the 1980s. They did
not examine more structural approaches, in part because of troubling
concerns about the reliability of the data and government measurement
techniques. Their findings contrast with those of Berndt and
Morrison . However, Berndt and Morrison  also document
positive correlations between IT capital and some measures of
economic performance in the specifications where cross-sectional
effects were emphasized. In addition, Berndt and Morrison's level
of aggregation (two-digit SIC code) is broader than that of Siegel
and Griliches' (four-digit SIC code).
Many researchers working on industry-level data express concerns
about data problems, which are often caused by aggregation. For
example, the BEA data is mainly used for industry-level analysis,
but it is subject to subtle biases due to the techniques used
to aggregate and classify establishments. One of Siegel and
Griliches' principal conclusions was that "after auditing
the industry numbers, we found that a non-negligible number of
sectors were not consistently defined over time."
Siegel  attempts to tackle the data problems that arise
from two possible sources of measurement error. The first kind
of error occurs when computer price and quantity are measured
with error. The second source of error is more subtle: firms
invest in computers not only for cost reduction but also for quality
improvement. Since the quality improvement is not fully taken
into account in the traditional statistics, the errors in output
measurement are correlated with computer investment. These two
kinds of errors cause bias and inefficiency in estimation. After
attempting to control for these errors using a "multiple-indicators
and multiple-causes" model, Siegel found a significant positive
relationship between multifactor productivity growth and computer
investment. He also finds that computer investment is positively
correlated with both product quality and labor quality, a result
that is consistent with Brynjolfsson , Berndt and Morrison
, and Berman, Bound and Griliches .
Table 3: Industry-Level Studies*
|Brand ||Services||BLS*||Productivity growth of 1.3%/yr in banking|
|Roach , Roach [1989a], Roach ||Services||Principally BLS, BEA*||Vast increase in IT capital per information worker and a decrease in measured output per worker|
|Morrison & Berndt ||Manufacturing||BEA||IT marginal benefit is 80 cents per dollar invested|
|Berndt et al ,
Berndt & Morrison [1995 ]
|Manufacturing||BEA, BLS||IT not correlated with higher productivity in most of industries, but correlated with more labor|
|Siegel & Griliches ||Manufacturing||Multiple gov't sources||IT-using industries tend to be more productive; government data is unreliable|
|Siegel ||Manufacturing||Multiple gov't sources||A multiple-indicators and multiple-causes model captures significant MFP effects of computers|
BLS: U.S. Bureau of Labor Statistics
BEA: U.S. Bureau of Economic Analysis
Table 4. Investment in Computers (OCAM) in the U.S. economy.
(percentage of total in current dollars)*
|Plus consumer and|
|Unmeasurable sector output||63%||69%||70%|
Source: BEA, adapted from Griliches 
* Unmeasurable sectors: construction, trade, finance and other services; in these sectors outputs are
difficult to measure, relative to measurable sectors.
IV. Firm-Level Studies of Information Technology Productivity
Over the past ten years, many studies examined the relationship
between firms' IT investment and their performance. Interestingly,
studies that have used larger and more recent datasets have found
evidence that IT positively affects firm performance. Research
results in manufacturing often show stronger effects than studies
of services, probably because of better measurement.
A. Service Sector Studies
Strassmann  reports disappointing evidence in several studies.
In particular, he found that there was no correlation between
IT and return on investment in a sample of 38 service sector firms:
some top performers invest heavily in IT, while others do not.
In his later book , he concludes that "there is no
relation between spending for computers, profits and productivity".
Several studies have examined IT's impact on the performance of
financial services firms. Parsons, Gottlieb and Denny  estimated
a production function for banking services in Canada. They found
that the impact of IT on multifactor productivity was quite low
between 1974 and 1987. They speculated that IT has positioned
the industry for greater growth in the future. Similarly, Franke
 found that IT was associated with a sharp drop in capital
productivity and stagnation in labor productivity, but remained
optimistic about the future potential of IT, citing the long time
lags associated with previous "technological transformations"
such as the conversion to steam power. In contrast, Brand and
Duke  used BLS data and techniques, and found that moderate
productivity growth had already occurred in banking.
Harris and Katz  and Bender  examined data on the
insurance industry from the Life Office Management Association
Information Processing Database. They found positive but sometimes
weak relationships between IT expense ratios and various performance
ratios. Alpar and Kim's  studied 759 banks and found that
a 10% increase in IT capital is associated with a 1.9% decrease
in total costs. Several case studies of IT's impact on performance
have also been done. Weitzendorf and Wigand  developed
a model of information use in two service firms; and Pulley and
Braunstein  studied an information services firm, and found
an association between IT investment and increased economies of
Estimating a production function, Brynjolfsson and Hitt 
found that for the service firms in their sample, gross marginal
product averaged over 60 percent per year. Their 1995 study reports
that IT contributes as much output in the service sector as in
the manufacturing sector [Brynjolfsson and Hitt, 1995]. Because
they used firm-level data, this result suggests that the productivity
"slowdown" in the service sector may be an artifact
of the mismeasurement of output in aggregate datasets. Indeed,
even when firms were classified into "measurable" and
"unmeasurable" sectors as defined by Griliches ,
no noticeable difference in IT productivity between the sectors
was found using this firm-level data.
Diewert and Smith  provide an interesting case study of
a large Canadian retail distribution firm. They found that the
firm experienced an astounding 9.4% quarterly multifactor productivity
growth, for six consecutive quarters starting at the second quarter
of 1988. They argue that "these large productivity gains
are made possible by the computer revolution which allows a firm
to track accurately its purchase and sales of inventory items
and to use the latest computer software to minimize inventory
Measurement problems are more acute in services than in manufacturing,
partly because many service transactions are idiosyncratic, and
therefore not amenable to statistical aggregation. Even when
data are abundant, classifications sometimes seem arbitrary.
For instance, in accordance with one standard approach, Parsons,
Gottlieb and Denny  treat time deposits as inputs
into the banking production function and demand deposits
as outputs. The logic for such decisions is sometimes tenuous,
and subtle changes in deposit patterns or classification standards
can have disproportionate impacts.
The importance of variables other than IT is also particularly
apparent in some of the service sector studies. In particular,
researchers and consultants have increasingly emphasized the need
to reengineer work when introducing major IT investments. As
Wilson  suggests, it would be interesting to know whether
reengineering efforts are the main explanation for Brynjolfsson
and Hitt's [1993, 1995] findings that IT is correlated with increased
output. A recent survey found that, in fact, firms that had reengineered
were significantly more productive than their competitors [Brynjolfsson,
Table 5: Studies of Firms in the Service Sector*
|Pulley & Braunstein ||An info-service firm||Significant economies of scope|
|Clarke ||Case study||Major business process redesign needed to reap benefits in investment firm|
|Strassmann  Strassmann ||Computerworld survey of 38 companies||No correlation between various IT ratios and performance measures|
|Bender ||LOMA insurance data on 132 firms||Weak relationship between IT and various performance ratios|
|Franke ||Finance industry data||IT was associated with a sharp drop in capital productivity and stagnant labor productivity|
|Harris & Katz [19 91]||LOMA insurance data for 40||Weak positive relationship between IT and various performance ratios|
|Noyelle ||US and French industry||Severe measurement problems in services|
|Parsons et al. ||Internal operating data from 2 large banks||IT coefficient in translog production function small and often negative|
|Alpar and Kim ||Large number of banks||IT is cost saving, labor saving, and capital using|
|Weitzendorf & Wigand ||Interviews at 2 companies||Interactive model of information use|
|Diewert & Smith ||A large Canadian retail firm||Multi-factor productivity grows 9.4% per quarter over 6 quarters|
|Brynjolfsson & Hitt ||IDG, Compustat, BEA||Marginal products of IT do not differ much in services and in the manufacturing; Firm effects account for 50% of the marginal product differential|
B. Studies of Manufacturing Sector and Cross-Sector Studies
There have been several firm-level studies of IT productivity
in the manufacturing sector. Some of the important results are
summarized in table 6. A study by Loveman  provided some
of the first econometric evidence of an IT productivity shortfall,
when he examined data from 60 business units using the Management
Productivity and Information Technology (MPIT) subset of the
Profit Impact of Market Strategy (PIMS) database. As is common
in productivity literature, he used an ordinary least squares
regression and assumed that production functions could be approximated
by a Cobb-Douglas function. Loveman estimated that the contribution
of information technology capital to final output was approximately
zero over the five-year period he studied in almost every subsample.
His findings were fairly robust to a number of variations on
his basic formulation.
Barua, Kriebel and Mukhopadhyay  traced Loveman's results
back a step by looking at IT's effect on intermediate variables
such as capacity utilization, inventory turnover, quality, relative
price, and new product introduction. Using the same data set,
they found that IT was positively related to three of these five
intermediate measures, but that the effect was generally too small
to measurably affect final output. Dudley and Lasserre 
also found econometric support for the hypothesis that better
communication and information reduce the need for inventories,
without explicitly relating this to bottom-line performance measures.
Using a different data set, Weill  disaggregated IT by
use, and found that significant productivity could be attributed
to transactional types of information technology (e.g., data processing),
but was unable to identify gains associated with strategic systems
(e.g., sales support) or informational investments (e.g., email
In a series of studies utilizing large firm-level surveys by
Data Group (IDG), Brynjolfsson and Hitt report that IT improves
productivity. Their 1993 study found that while gross marginal
product of non-computer capital ranges from 4.14% to 6.86%, that
of computer capital averages 56% - 68%. The results of this and
their later study  imply that the following three null hypotheses
can be rejected:
H1: IT capital has a zero gross marginal product.
H2: IT capital has zero net marginal benefit, after all costs have been subtracted.
H3: IT capital's marginal product is not different from
that of other capital.
Their point estimates of gross marginal products indicate that
at the margin computer capital generates 10 times more output
than other capital of equal value. Brynjolfsson and Hitt 
show up to half of the excess returns imputed to IT could be attributed
to firm-specific effects.
If gross marginal product of information technology capital is
really so large, what friction or market failure prevents firms
from investing in more computers, until the marginal products
of all capital goods become equal? One reason is that computer
capital has a higher user cost. According to Oliner and Sichel
, from 1970 to 1992 the user cost of computer capital averaged
36.6 % per year, while that of other types of capital was 15.4%.
The remaining portion of the answer may come from adjustment
or hidden costs of information technology investment, such as
the complementary organizational investments required to realize
the benefits of IT.
Lichtenberg  confirms the results of Brynjolfsson and Hitt,
using similar data and methods. He also analyzes Informationweek
survey data and uncovers essentially the same results. His formal
tests reject the above null hypotheses. Importantly, Lichtenberg
extends his study to report the marginal rate of substitution
between IT and non-IT workers. At the sample mean, one IT worker
can apparently be substituted for six non-IT workers.
Table 6: Studies of Manufacturing Firms and Cross-Sector Firms*
|Loveman ||PIMS/MPIT||IT investments added nothing to output|
|Dudley & Lasserre ||US and Canadian Aggregate Data||IT and communication reduces inventories|
|Weill ||Valve manufacturers||Contextual variables affect IT performance Transaction processing IT produce positive results|
|Barua, Kriebel & Mukhopadhyay ||PIMS/MPIT||IT improved intermediate outputs, if not necessarily final output|
|Brynjolfsson & Hitt ||IDG; Compustat: BEA||The gross marginal product of IT capital is over 50% per year in manufacturing|
|Brynjolfsson & Hitt ||IDG; Compustat: BEA||Firm effects account for half of the productivity benefits of earlier study|
|IT has excess return; IT staff's substitution effect is large|
|Kwon & Stoneman ||UK survey||New technology adoption especially computer use has a positive impact on output and productivity|
Research in manufacturing generally finds higher returns to IT
investment than in the services, probably because of better measurement.
Yet the MPIT data, which both Loveman  and Barua et al.
 use, must be scrutinized. While the point estimates for
IT's contribution were quite low, the standard errors were very
high so that 95% confidence interval often exceeded 200% for many
of Loveman's estimates. These studies may also be unrepresentative,
since the period covered by the MPIT data, 1978- 83, was unusually
The IDG data set, which is among the largest data sets used in
this research area, substantially mitigates data problems, although
it contains data on large firms only, and so may not be a representative
random sample. Indeed, Brynjolfsson and Hitt  attribute
the statistical significance of their findings partly to the large
size of the IDG data set, which enables them to more precisely
estimate returns for all factors. Utilizing comprehensive surveys
of the UK engineering industry undertaken in 1981, 1986, and 1993,
Kwon and Stoneman  also find that the use of computers
and numerical control machines has increased output and productivity.
V. Contribution to Consumer Surplus and Economic Growth
Some researchers have identified sizable contributions of IT to
consumer surplus and to economic growth. Some important studies
are summarized in table 8. Growth accounting and consumer surplus
analysis are techniques to identify and measure "pecuniary
externalities", which Griliches [1992, 1994] distinguishes
from "non-pecuniary externalities or spill-overs."
Pecuniary externalities arise when the price of some input declines.
For example, when computer prices are declining exogenously,
profit-maximizing firms substitute computer systems for other
input factors, such as labor or warehouse space. Lowered prices
of computers and other inputs shift marginal cost curves downward.
These marginal cost curves result in higher output and lower
prices. The output increase is a measure of the pecuniary externality;
the benefits created by the computer sector are reflected in greater
output of computer-using industries. A second measure
of the pecuniary externality is consumer surplus. As computer
prices fall, many firms and customers that could not afford computers
become able to purchase them, while the customers who were willing
to pay higher prices enjoy a windfall price reduction.
Pecuniary externalities directly increase labor productivity,
yet they do not necessarily increase multifactor productivity.
Pecuniary externalities do not change the production function;
rather they change the input mix. In contrast, non-pecuniary
externalities, or spill-overs, arise from technical change; people
may have found smarter ways of making goods and services using
information technology. The production possibility frontier shifts
out; both labor productivity and multifactor productivity should
Bresnahan  estimated the benefits to consumers of declining
computer prices. Using the hedonic price index method, he calculates
that the consumer surplus was five or more times of computer expenditures
in the late 1960s financial sector. Adopting similar assumptions,
Brynjolfsson  estimates that, in 1987, between $69 billion
and $79 billion consumer surplus was generated by $25 billion
in expenditures on information technology capital.
Now we turn to several growth accounting results. Jorgenson and
Stiroh's  comprehensive growth accounting found that from
1979 to 1985 computers and peripherals contributed to output growth
by 0.52% per year, and that from 1985 to 1992, the contribution
is 0.38% per year (see Table 7). One of their study's main contributions
is the careful calculation of capital's service flow. Because
they assume that computers maintain their full ability until retirement,
their estimation of computer capital's contribution becomes larger
than that of Oliner and Sichel .
Oliner and Sichel  carefully examine how the various excess
return hypotheses of computer capital affect growth. As a baseline
they estimate that the contribution of computer capital to output
growth is 0.16% per year for the 1970-1992 period. Using Romer's
[1986, 1987] assumption that physical capital provides a positive
externality, the contribution goes up to 0.32%. Brynjolfsson
and Hitt's  higher estimate for the return on computer capital
raises the contribution to 0.35%. They also try to incorporate
Alan Krueger's  result of return on workers' computer use.
If the return is equal to the difference in the marginal product
between computer-using workers and non-using workers, the contribution
is 0.38%. Oliver and Sichel claim that an annual contribution
of up to 0.38% is not large enough to offset the approximately
1% drop in output growth since the 1970s.
Table 7. Growth Rates of Aggregate Output and Contribution
of Factors (1947-92)*
Source: Adapted from Jorgenson and Stiroh (1995)
The following rough calculation may provide some intuition about
the size of the computer's contribution to national output. From
Jorgenson and Stiroh , we take the simple average contribution
for the 1979-1992 period or 0.45%. We compare it with the 0.72%
contribution of other capital. The share of computers in total
capital stock was 1.6% in 1993, implying that one unit of computer
capital contributes as much to the growth of output as 98 units
of other forms of capital. In 1993, GDP grew by $173 billion.
Computers contributed $29 billion; other capital contributed
$46 billion. The unexplained residual (MFP) contribution is $40
billion. A rough estimate shows that the implicit marginal product
of computer capital in Jorgenson and Stiroh's study is also over
Using data from 367 large firms that together generated $1.8 trillion
in output per year from 1988 to 1992, Brynjolfsson and Hitt 
provide an interesting growth accounting result. For their sample
of firms, IT capital contributes about 1% per annum to output
growth - a larger growth contribution than that of ordinary capital
in absolute value. Lau and Tokutsu  calculate an even bigger
contribution to growth, attributing approximately half of the
real output growth (1.5% growth per annum) during the past three
decades to computer capital. They also argue that the annual
rate of inflation dropped by 1.2% per year because of the rapid
decline in computer prices. In line with these studies, Roy Radner
suggests that "productivity growth has slowed down for other
reasons, unrelated to the IT story. Without IT, things would
have been worse, and output growth would have been lower."
In summary, the weight of evidence from various studies suggests
that information technology capital generates billions of dollars
annually for the U.S. economy, both in terms of output growth
and consumer surplus. Meanwhile, the recent firm-level analyses
of Brynjolfsson and Hitt[1993, 95] and Lichtenberg  have
begun to remedy the shortfall of evidence regarding the productivity
contribution of IT.
Table 8: Studies on Contribution to Consumer Surplus and Economic
|Bresnahan ||Financial service firms||Large gains in imputed consumer welfare|
|Lau & Tokutsu ||Multiple Gov't sources||Computer capital contributes half of output growth|
|Brynjolfsson & Hitt ||IDG*, Compustat||Growth contribution of computers is 1% per year among 367 US large firms|
|Oliner & Sichel ||principally BEA*||Growth contribution of computers is 0.16% - 0.38% per year varying by different assumptions|
|Jorgenson & Stiroh ||principally BEA||Growth contribution of computers for the 1979-92 period is 0.38 - 0.52% per year|
|Brynjolfsson ||BEA||$70 billion consumer surplus is generated annually in the late 1980s.|
IDG: International Data Group
BEA: U.S. Bureau of Economic Analysis
VI. Conclusion: Where Do We Go from Here?
Sections II, III, IV, and V presented a review of the principal
empirical literature on the productivity of information technology.
Looking at the simple relationship between the productivity slowdown
of the whole US economy and the rapid growth of computer capital
is too general an approach. Poor data quality for IT outputs
and inputs has exacerbated this problem. Due to the application
of improved methodologies and the identification of more reliable
and larger datasets, researchers have made some progress with
industry-level and firm-level studies. Recently, some researchers
have found positive effects of IT. Careful growth accounting
exercises and estimation of production and cost functions for
specific sectors or industries can provide sharper insights.
Consumer surplus analyses are useful exercises for identifying
alternative ways to triangulate IT value. These studies suggest
that without IT, the US economy would probably be in a worse situation.
This section proposes further research questions and methodologies.
The first priority is to improve the data and the measurement
techniques. Government statistics, especially in services and
for information workers, have not kept up with the growing importance
and complexity of these sectors. Therefore, researchers may have
to perform their own corrections on the data, turn to private
sources of secondary data, or gather data themselves. Researchers
should make their data available to other researchers so that
a cumulative tradition can be maintained. The studies of Weill
, Dos Santos et al. , Berndt & Morrison 
and Brynjolfsson and Hitt [1993, 1995] are examples of new data
identification and development.
One effective way to identify possible gaps in the data is to
compare them with benefits that managers and customers expect
from IT, such as quality, timeliness, customer-service, flexibility,
innovation, customization and variety. In principal, many of
these benefits are quantifiable. In fact, some firms already
attempt such an analysis in their capital budgeting and justification
processes. In addition, many companies have developed elaborate
measurement programs, for example, as part of total quality management,
these programs augment or even supersede financial accounting
measures and can serve as a foundation for more refined metrics
[Kaplan and Norton, 1992].
Many economists also have tried various methods to overcome the
shortfall of government statistics, and to incorporate quality
changes when estimating price indices. The long history of hedonic
price index method is a good example, but some economists argue
even the hedonic method does not capture all the benefits associated
with product innovation and differentiation. Trajtenberg 
devises a new method of quality adjusted price index calculation,
adopting the discrete choice model. Fisher and Griliches 
argue that if new inexpensive (quality-adjusted) goods are introduced
and gain market share at the expense of existing goods, the official
statistics by the Bureau of Labor Statistics will seriously overestimate
inflation. Hausman  also reports a 20% to 25% overestimation
of the consumer price index for ready-to-eat cereals, based on
his analysis of Apple Cinnamon Cheerios.
Unfortunately, for many services, even basic output measures must
be created, because government and accounting data records only
inputs. Baily and Gordon  and Noyelle  among others,
have done much to improve measurement in areas such as banking
and retailing, while relatively good statistics can be compiled
from private sources in areas such as package delivery. Unfortunately,
the individualized nature of many services defies aggregation.
The output of a lawyer, manager or doctor cannot be extrapolated
from the number of meetings attended, memoranda written or medications
provided. The complexity of the "Diagnostic Related Group"
approach to valuing medical care is both a step in the right direction
and a testament to these difficulties. A researcher who seeks
to rigorously measure the productivity of service industries generally
must undertake this detailed work before jumping to conclusions
based on input-based statistics. Similarly, disaggregating heterogeneous
types of IT by use, as Weill  did in a manufacturing study,
can increase the resolution of standard statistical techniques.
Because so many factors affect firm performance, it is generally
impossible to distinguish the impact of IT using simple bivariate
correlations. It is essential to control for other factors such
as other inputs and their prices, the macro-economic environment,
demand schedules for output, and the nature of competition. Because
many unobservable factors affect either the whole industry or
one firm persistently, examining a panel consisting of both time
series and cross-sectional data is the best approach, where feasible.
Importantly, we must remember that our tools are still blunt.
Managers do not always recognize this and tend to rely too much
on any one study of IT and productivity. While the studies usually
state the limitations of the data and methods, sometimes only
the surprising conclusions are reported by the media. Because
significant investment decisions are based on these conclusions,
researchers must be doubly careful to communicate the limitations
of their work.
Researchers might also look to business for profitable research
questions. A recurrent theme in the business press is the idea
that information technology should not so much help us produce
more of the same things as allow us to do entirely new things
in new ways. For instance, Watts  finds that information
technology investments cannot be justified by cost reductions
alone, but that instead managers should look to increased flexibility
and responsiveness, while Brooke  writes that information
technology leads to greater variety but lower productivity as
traditionally measured. Diewert and Smith's  study makes
another interesting point in respect to variety. They show that
to rigorously measure the productivitwhile IT facilitates great
efficiency in inventory management,
aggregate inventory level of the U.S. economy did not shrink over
the past 40 years, as reported in Blinder and Maccini's .
Diewert and Smith argue that "a wide spread proliferation
of new products into the world economy" results in no macro-level
inventory change even when great micro-level improvements have
This literature highlights how difficult and perhaps inappropriate
it would be to translate the benefits of information technology
into quantifiable productivity measures of output. Intangibles
such as better responsiveness to customers and increased coordination
with suppliers do not always increase the amount or even intrinsic
quality of output, but they do help make sure it arrives at the
right time, at the right place, with the right attributes for
each customer. Berndt and Malone  suggest that: "we
need to spend more effort measuring new forms of value--such as
capabilities for knowledge creation--rather than refining measures
of productivity that are rooted in an Industrial Age mindset."
Just as managers look beyond "productivity" for some of the benefits of IT, so must researchers be prepared to look beyond conventional productivity measurement techniques. For instance, because consumers are generally assumed to be in the best position to assess the utility they gain from their purchases, so researchers might look to IT buyers for an estimate of IT value, as Bresnahan  and Brynjolfsson  did.
As another example, if rational investors value both the tangible
and intangible aspects of firms' revenue-generating capacity,
then changes in stock market value should approximate the true
contribution of IT to the firm, not only in cost reductions, but
also in increased variety, timeliness, and quality, and in principle,
even the effectiveness of the firm in foreseeing and rapidly adapting
to its changing environment. While relying on consumer or stockholder
valuations begs the question of actual IT productivity to some
extent, at a minimum these measures provide two additional benchmarks
that can help triangulate IT value [Hitt and Brynjolfsson, 1994].
While the value of IT remains controversial, it is clear that
the measurement problem is becoming more severe. Developed nations
are devoting increasing shares of their economies to service-
and information-intensive activities for which output measures
are poor. The emerging "information age" has prompted
a new approach to management accounting [Beniger, 1986; Kaplan,
1989]. Similarly, researchers should take the opportunity to
rethink how we measure productivity and output.
Source: Based on data from [BEA, National Income and Wealth Division], adapted from Jorgenson and Stiroh .
Note: Constant dollars (base year 1987) calculated by hedonic price method, see Dulberger .
Source: Based on data from [Bureau of Labor Statistics,
Productivity & Testing]
Source: Based on data from [U.S. Dept. of Commerce, Survey of Current Business]
Note: PDE, Producer's Durable Equipment
Source: Grove , and company data. Trend lines are by authors' estimation.
Note: P6, P7 microprocessors and 256M, 1G, 4G DRAMs
are estimated by Intel and Semiconductor Industry Association.
Source: Based on data from [BEA, National Income
and Wealth Division]
Source: Porat [ 1977]. The defining criterion for information workers is
whether the primary
activity is knowledge creation, warehousing, or dissemination. The
classification scheme includes people engaged in the following
Source: Porat [ 1977]. The defining criterion for information workers is whether the primary activity is knowledge creation, warehousing, or dissemination. The classification scheme includes people engaged in the following activities:
TYPOLOGY OF PRIMARY INFORMATION SECTOR INDUSTRIES USED IN FIGURE 5
KNOWLEDGE PRODUCTION AND COMMUNICATION INDUSTRIES
R&D and Inventive Industries
Private Information Services
INFORMATION DISTRIBUTION AND COMMUNICATION INDUSTRIES
Public Information Services
Regulated Communication Media
Unregulated Communicated Media
SEARCH AND COORDINATION INDUSTRIES
Search and Non-Speculative Brokerage Industries
Non-Market Coordination Institutions
INFORMATION PROCESSING AND TRANSMISSION SERVICES
Non-Electronic Based Processing
Electronic Based Processing
INFORMATION GOODS INDUSTRIES
Non-Electronic Consumption or Intermediate Goods
Non-Electronic Investment Goods
Electronic Consumption or Intermediate Goods
Electronic Investment Goods
SELECTED GOVERNMENT ACTIVITIES
Primary Information Services in the Federal Government
State and Local Education
Information Structure Construction and Rental
Source: Roach 
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