Many changes in the organization of work in the United States
since 1975 have been attributed in the literature to the large
technological improvements and the sharply higher levels of investment
in computers and related technologies over the same period. Few
empirical studies have attempted to empirically examine these
relationships. The primary goal of this paper is to assess the
hypothesis that the rapid growth of information technology (IT)
is at least partially responsible for one important organizational
change, the shift of economic activity to smaller firms. We examine
this hypothesis using industry-level data on IT capital and four
measures of firm size, including employees per firm. We find broad
evidence that investment in IT is significantly associated with
subsequent decreases in the average size of firms. We also find
that the effects of IT on organizations are most pronounced after
a lag of two to three years. Although the correlations we find
do not necessarily imply causality, they are consistent with the
theory that IT makes market-based coordination more attractive
relative to internal coordination.
1. Research problem
Industrialized economies have recently entered a period of substantial
organizational change. This transition has been likened to a "second
industrial divide" (Piore & Sabel, 1984) or the "coming
of the new organization" (Drucker, 1988) and has been widely
discussed in the both the business and academic literatures (see
(Huber, 1990); and the studies reviewed therein). Among the postulated
aspects of the transition are decreases in firm size, a shift
to externally provided services, and a shift from mass production
to more flexible arrangements. The growing attention to these
organizational changes has coincided with a rapid drop in the
price of computing power (Gordon, 1987), significant increases
in information technology (IT) usage, and one infers, decreases
in the costs of information processing in general.
Because both firms and markets have often been modeled as information
processing entities (Arrow, 1973; Galbraith, 1977; Hayek, 1945),
one might reasonably suspect that some of the recent organizational
changes are related to the massive deployment of IT. Indeed, the
theoretical links between the two have often been made in the
literature (see (Crowston & Malone, 1988) for a review.) Unfortunately,
empirical research on the relationship between IT and organizational
structure has produced few if any reliable generalizations, in
part because case studies have predominated. Indeed, when taken
as a whole, the literature on IT impacts presents many contradictory
results (Attewell & Rule, 1984) .
The principal aim of this paper is to empirically examine the
impact of IT on one important characteristic of economic organization:
firm size. To address this question, we have obtained economy-wide
data on investments in IT in the U.S., enabling the use of econometric
techniques to more broadly study its impact. To our knowledge,
this is the only public domain data on IT investments in the US
economy. It includes reasonably accurate hedonic price deflators
which take into account quality improvements of over 20% per year
in computing power. In order to understand the changes more fully,
we examined four different measures of firm size: 1) the number
of employees per establishment, 2) the number of employees per
firm, 3) the sales per firm and 4) the value-added per firm.
The paper consists of five sections. Section 2 provides background
on the recent trends in IT usage and average firm size, and discusses
the theoretical relationship between these two variables. In section
3, we explain the methodology and data used in this study. Section
4 presents the results of the regressions and explores some explanations
for the results. We conclude with some interpretations of the
results and suggestions for further research in section 5.
2. Background and hypotheses
2.1 Trends in the key variables
In this section, we summarize previous evidence of two significant
trends in the last 15 years: 1) The number of employees in the
average business establishment has decreased substantially, and
2) the real stock of IT has grown enormously. The previous work
does not examine whether these two trends are related, but the
evidence for each independently is strong.
2.1.1 Firm size
According to several sources, firm size, as measured by the number
of employees, has declined. For instance, Piore (1986) cites data
from County Business Patterns showing that the average establishment
size has been decreasing since the 1970s, reversing an earlier
trend towards ever-larger firms. The Bureau of Labor Statistics
reports that from 1980 to 1986 firms of under 100 employees created
six million new jobs while firms of over 1000 employees experienced
a net loss of 1.5 million jobs. The BLS data also showed that
intermediate size classes had employment gains inversely proportional
to their size.
The phenomenon is not unique to the U.S. but is also being experienced
by other major industrial countries. As shown in figure 1, Huppes
(1987) reports that data from the UK, Germany, and the Netherlands
also show a recent decrease in average firm size, despite increases
until the early 1970s .
Our examination of data from County Business Patterns and from
Compustat reveals the number of employees per firm has indeed
declined in the manufacturing sector, but there is no strong trend
in the service sector (figure 2). We also find that although firms
tend to be smaller in the service sector, employment has grown
more rapidly in services than in manufacturing (figure 3). In
addition, we examined two other measures of firm size, sales per
firm and value-added per firm, for manufacturing industries. We
did not find any overall declines in these measures of firm size.
(figure 4 and figure 5)
2.1.2 Information Technology
The data confirm the almost self-evident increase in power and
ubiquity of IT. We find that investment in computers has increased
steadily and dramatically since at least 1971. After taking into
account quality improvements (which allow each dollar to buy more
IT), there has been over a tenfold increase in IT investments
between 1971 and 1990 (see figure 6). Each of the major business
sectors shows the same accelerating trend toward increased use
of IT (figure 7). A category of equipment that was largely insignificant
two decades ago is now very important.
Driving much of this investment are exponential declines in the
price/performance of computers and related technology (Gurbaxani
& Mendelson, 1990). Furthermore, "Moore's Law",
which posits a doubling of transistor density every 18 months
is projected to hold into the next century; the one to two million
transistors per chip in 1990 may be 50-100 million by the year
2000 (Grove, 1990)(figure 8). Whatever effects of IT we detect
today, these numbers are of sufficient magnitude to augur even
greater effects in the near future.
2.2 The Relationship between IT and firm size
In order to understand the impact of IT on firm size, it is useful
to understand the economic rationale underlying the determination
of firm size in the first place. As Friedman (1955, p. 233) notes:
"The appropriate size of firm to produce, say, copper, may
be different for two different mines, and both can exist simultaneously
because it is impossible to duplicate either one precisely. .
. . The existing distribution [of firm sizes] reflects both 'mistakes'
and intended differences designed to take advantage of the particular
specialized resources under the control of different firms."
Ijiri and Simon (1977) provide more detailed models of the processes
that give rise to a distribution of firms of different sizes in
an industry. They note that the size distribution of firms in
an industry is a result of the successes and failures of numerous
individual firms over time. The success of each individual firm,
in turn, is determined by a number of factors, including how well
it takes advantage of its specialized resources and also how well
it adapts to changing conditions in the industry as a whole.
In this paper, we are especially concerned with how firms adapt
to one particular kind of change: the availability of increasingly
powerful and inexpensive IT. The overall effects of this technology
clearly depend on how individual firms use it and how this usage
affects their subsequent performance. If each firm's use of IT
leads to widely varying results, then there might be no overall
patterns of change in the economy as a whole. On the other hand,
if certain kinds of adaptation to IT are generally more successful
than others, then we may be able to observe the resulting changes
as overall trends in the economy. For instance, if the widespread
use of IT increases the viability of smaller firms relative to
larger ones, then this might be part of the reason for the decrease
in average firm size that we noted above.
A central theoretical question, then, is why the increasing use
of IT might change the relative viability of small and large firms.
We summarize the arguments that have been proposed to answer this
question in two basic categories: (a) labor substitution,
and (b) outsourcing.
2.2.1. Labor substitution
Perhaps the simplest explanation that has been proposed for why
firm size might be related to IT is that firms can sometimes use
IT to produce the same output with fewer people. By substituting
automated processing for human labor, the argument goes, these
firms can increase productivity and reduce costs.
Somewhat surprisingly, however, previous studies have not provided
broad support for the hypothesis that IT substitutes for labor
or even increases the productivity of labor. For example, a growing
literature on what has come to be known as the "IT productivity
paradox" finds that IT does not yet appear to have led to
significant increases in productivity (Brynjolfsson, 1993 in press)
provides a review of the literature. See also Loveman, 1988; Roach,
1987a). Robert Solow summarized the results of this literature
most succinctly when he said "We see computers everywhere
except in the productivity statistics." (Solow, 1990)
Furthermore, in direct studies of the relationship between IT
and employment, there is some evidence that IT may actually increase
employment. For instance, Osterman (1986) found that IT investment
ultimately resulted in a complementary increase in the number
of clerks and managers employed after a lag of several years.
Two recent papers by Morrison and Berndt (1991; 1990) also addressed
this question using essentially the same IT data set we are using.
By specifying a general production function, they found that IT
was on balance a complement, not a substitute for labor,
especially white collar labor. Specifically, they conclude: "...
rather than being aggregate labor-saving, increases in [IT] tend
to be labor-using" (Berndt & Morrison, 1991, p. 1).
Even though this previous work casts some doubt upon the labor
substitution hypothesis as an explanation for decreasing firm
size, our study will allow us to examine the hypothesis from another
perspective. If labor substitution due to IT is the primary explanation
for decreasing firm sizes, then we should expect to see a decrease
in the number of employees per firm associated with IT use, but
no decrease in the sales per firm. In fact, if this hypothesis
is correct, we might even see an increase in the sales per firm
associated with IT use.
Another possible explanation for why IT might lead to smaller
firms is that IT might allow firms to "outsource" more
of their activities. That is, the use of IT might lead firms to
"buy" rather than "make" more of the components
and services needed to make their primary products. For instance,
when a firm like Ford needs tires for the cars it produces, it
has two choices about how to obtain these tires: It can make them
internally, or it can buy them from a tire supplier. Which of
these choices is preferable in a given situation depends, in part,
on their respective costs.
We can divide these costs into two categories--production costs
and coordination costs. Production costs refer to the costs
of the physical production process itself--tasks like molding
and cutting the rubber for tires. Coordination costs, on the other
hand, refer to the costs of managing the dependencies between
production tasks. For example, coordination tasks include making
sure that the rights things and the right people are at the right
places at the right times.
We can further divide coordination costs into two subcategories--internal
coordination costs and external coordination costs.
When Ford produces its own tires, for instance, the internal coordination
costs include the costs of managers and others who decide when,
where, and how to produce the tires. When Ford buys tires from
an outside supplier, the external coordination costs include (a)
the supplier's costs for marketing, sales, and billing and (b)
Ford's costs for finding suppliers, negotiating contracts, and
In both cases, coordination costs include information intensive
activities such as gathering information, communicating, and making
decisions. Since IT is particularly useful in these kinds of information
intensive activities, several previous theories suggest how IT
might affect firm size by reducing these coordination costs. As
the following subsections describe, the theories make different
predictions, depending on which kinds of costs are affected most
(see Gurbaxani and Whang (1991) for a summary).
184.108.40.206. Reducing internal coordination costs more than external
If IT reduces the costs of internal coordination more
than external coordination, then we would expect firms
to make more things internally. This means we would expect firms
to grow in size. For example, if IT greatly reduced the costs
for managers to monitor and control what their subordinates were
doing throughout a large organization, then this might lead firms
to make more things internally where they could be more controlled
more effectively and at less cost than if they were purchased
from an outside supplier.
One kind of internal coordination cost simply involves moving information to the places where decisions are made and then informing others about the decisions. Another important kind of internal coordination cost arises because the interests of individual employees are often not the same as those of the firm as a whole. Research in agency theory has studied extensively how these conflicts of interest can be managed by approaches such as monitoring employees or providing them with performance-based pay (Eisenhardt, 1989; Jensen, 1983) . Since these approaches involve information-intensive activities, it seems plausible that IT might affect their costs. More generally, since many early applications of computers have focused on internal systems rather than interorganizational systems, we might expect that these systems would affect internal coordination costs more than external ones.
220.127.116.11. Reducing external coordination costs more than internal
If IT reduces the costs of external coordination more than
internal coordination, then we would expect to see firms buy more
things externally. In this case, the average size of firms should
decrease. For example, if it is easier and cheaper for a firm
to find an external supplier for new parts than to make them internally,
then the firm is more likely to buy the parts outside and less
likely to need as much internal manufacturing capacity.
The factors that lead to high "transaction costs" for
external coordination have been analyzed extensively by research
in transaction cost theory (Williamson, 1975; 1985) . In general,
the "opportunistic" behavior of firms negotiating contracts
with each other often leads to costs (such as legal and accounting
expenses) that would not be necessary if the same transactions
were coordinated internally. For example, when a supplier invests
in special machinery that is useful only for one customer, the
supplier is vulnerable if that customer threatens to buy somewhere
else. Similarly, when it is difficult for buyers to find out about
alternative sources of supply, they are vulnerable to excessive
charges from the supplier they customarily use.
While IT certainly cannot eliminate opportunistic human behavior,
its functions (such as increasing the availability of information)
can reduce the problems opportunism causes (Brynjolfsson, Malone
& Gurbaxani, 1988; Malone, Yates & Benjamin, 1987) . More
generally, by reducing the costs of many of the information searching
and accounting activities that are needed for coordination with
external suppliers, IT can make buying things externally more
attractive to firms.
A related effect of IT is that it might reduce market coordination
costs by changing the "specificity" of assets themselves.
For instance, Klein, Crawford and Alchian (1978) , and Grossman
and Hart (1986) have emphasized that when assets are specific
to one another, market coordination will be inefficient and this
may lead to common ownership of large, related sets of assets.
However, if IT facilitates techniques like flexible manufacturing,
it may decrease the specificity of assets, and thus transform
internal production to production organized through smaller units
coordinated by markets. Applying the Grossman-Hart paradigm, Brynjolfsson
(1991b) formally modeled the role of asset flexibility, and also
showed that under appropriate assumptions, a broader distribution
of production knowledge and coordination knowledge would favor
18.104.22.168. Reducing coordination costs more than production costs
The theories reviewed so far allow us to predict either kind of
change: if internal coordination costs decrease most, firms should
grow; if external coordination costs decrease most, firms should
shrink. Malone and colleagues argue that, in general, we should
expect both kinds of coordination costs to decrease relative
to production costs. They further argue that this would still
favor buying rather than making (Malone, 1987; Malone & Smith,
1988; Malone, Yates & Benjamin, 1987) .
The basis of this argument is summarized in Figure 1. First, as
noted above, the costs of finding suppliers, negotiating contracts,
and paying bills often make external coordination more expensive
than coordinating the same activities internally would be (Williamson,
1975; 1985) . However, when external suppliers pool the demands
of multiple customers, they can often realize economies of scale
or other production cost advantages that internal production could
not achieve. Thus, in general, buying rather than making leads
to higher coordination costs but lower production costs.
Figure 1. Relative costs of "buying" components
and services externally vs. "making" them internally.
Now, if IT reduces both internal and external coordination costs,
it will decrease the importance of the dimension on which buying
has a disadvantage. Thus, it should increase the number of situations
in which is more attractive than making.
If more outsourcing occurs because of this or any of the
other effects described in this subsection, we should expect to
see a decrease in the average number of employees per firm. Unlike
the labor substitution hypothesis, however, the outsourcing hypothesis
predicts that the activities required to produce a product will
be divided among more separate firms. This means that, if the
outsourcing hypothesis is correct, the average amount of "value
added" and the average sales per firm should decrease.
There is some evidence suggesting that IT has led to a decline
in firm size in certain industries. A detailed study of the metal-working
industry found that vertical integrated firms were "decoupling"
into smaller firms in 88 of 106 sectors between 1972 and 1982
and that this could be tied to increased use of IT (Carlsson,
1988). In an article on how "value-adding partnerships"
are supplanting vertically integrated companies, Johnston and
Lawrence (1988) also cite examples in which this phenomenon is
partly enabled by IT. However, to date there has been no economy-wide
study to determine whether these changes are part of a broader
In summary, the theoretical literature suggests that IT will reduce
the costs of coordination both within firms and between firms.
We cannot know, a priori, however, which effect predominates and
whether a resulting shift is of an economically significant magnitude.
Furthermore, these theoretical arguments do not allow us to determine
whether the decrease in average firm size noted above is related--either
positively or negatively--to the increasing use of IT.
Fortunately, the question of how IT affects firm size is subject
to empirical investigation, and as noted above, the different
theories make different predictions about what changes we should
see. In the remainder of this paper, we use econometric techniques
to analyze the relationship in the U.S. economy as a whole between
IT usage and firm size. We also interpret these results in light
of the theories just described.
3. Data and methodology
Our approach was to use available U.S. data to directly examine
the relationship between IT and average firm size. The data are
divided into six sectors: durable goods manufacturing; non-durable
goods manufacturing; transport and utilities; wholesale and retail
trade; finance, insurance and real estate; and services. These
six sectors represent substantially all manufacturing and services
industries in the U.S. A series of regressions were then run on
this data to identify the direction and magnitude of the relationship
between IT and firm size, while controlling for total capital
stock, foreign trade, industry, and capital costs. Several regression
models were examined and data from alternative sources was also
used to help validate the results.
3.1 The data
There are at least three basic ways to measure the size of firms:
number of employees, total sales, and total value added.
3.1.1 Two measures of employees per firm
Data on average establishment size is available from County Business
Patterns' (CPB) annual summaries. The number of business establishments
and number of employees is provided for each sector of the economy.
Average establishment size is derived by dividing the latter by
the former, generating the variable SIZE1. Consistent data on
establishment size were available from 1976 to 1989, so we used
those years as the primary period of our study.
Although the data from County Business Patterns is the most comprehensive
census of establishments in the United States, another measure
of firm size, SIZE2, can be created from an alternative source:
Compustat. Compustat maintains data on every publicly-traded company,
including number of employees and the SIC code of its principal
line of business. Using the SIC code, we grouped all the companies
into the same sectors of the economy used above and added up the
number of employees and number of firms in each sector. This provided
a second measure of firm size.
While the Compustat sample is presumably biased towards large
firms (small firms are not usually publicly traded), it still
includes over 2000 firms for each year. It also has a virtue:
to the extent that "firms" differ from "establishments",
the Compustat data measures firm size directly.
3.1.2 Sales per firm and value added per firm
We also explored the impact of IT on two other definitions of
firm size: value of sales per establishment (SIZE3), and value
added per establishment (SIZE4). These additional regressions
serve as an extra robustness check on the employment-based measures
of firm size. They also help provide insight into the mechanism
by which IT affects firm size: if IT leads to a decrease in shipments
and value-added per firm then substitution effects alone do not
underlie the shift to smaller firms.
The data required for these two series is available from the Census
of Manufacturers and the Annual Survey of Manufacturers for the
manufacturing sectors of the economy. Unfortunately, no comparable
data is available for other sectors of the economy. The data was
analyzed for the period 1976 to 1989 at the two digit SIC code
level of aggregation. This distinguishes 20 different industries
within the manufacturing sector.
The value of industry sales is defined as the amount received
on net receivable selling value, f.o.b plant, after discounts
and allowances, and excluding freight charges and excise taxes.
This quantity (which is sometimes called "shipments")
is then divided by the number of establishments, from County Business
Patterns, to derive the average sales per firm.
Value added by manufacture is defined as the value of the finished
goods minus the value of the raw materials and other supplies.
This number is derived by first converting the value of shipments
to the value of production by adding in the ending inventory in
finished goods and work in process, and subtracting the beginning
inventory. The costs of materials, supplies, containers, fuels,
purchased electricity and contract work are subtracted from the
value of production to obtain the value added of manufacture.
Value added per establishment is then calculated by dividing total
value added by the number of establishments.
3.1.3 IT capital and total capital
Data from the Bureau of Economic Analysis (BEA) was used to derive
figures for IT investments, and total capital investments by industry
for each year. The BEA data classifies all economic activity in
the United States into 61 industries. For each industry, total
annual investments are measured in 27 asset categories. We used
category 14 -- "office, computing and accounting machinery"
(OCAM) for our IT figures and the sum of all other categories
for our total capital figures. Over 90% of the OCAM category consisted
of computers and associated peripherals, electronic calculating
machines and accounting machines but it also included typewriters
and "other office machines". Although imperfect, we
considered the OCAM category to be the best proxy for the new
electronics-based information processing technologies that may
have an impact on organizational structure; the BEA data is the
most authoritative government dataset on computing investments
in the United States (see e.g. (Gorman, Silverstein, Gerald &
Comins, 1985) (Berndt & Morrison, 1991) ).
It should be noted that the OCAM data include capital equipment
only, and do not include spending on software development, maintainance
or related services. Furthermore, some computing power is embedded
in scientific instruments, robotic machinery and even appliances.
These "computers" are also excluded from our data. Instead,
we focus on a somewhat narrower definition of informaton technology
that stresses office and coordination uses. While such uses account
for the vast majority of computing power and are in any event
likely to be highly correlated with other uses, our data is far
from "ideal". Accordingly, we generalize our findings
about IT to other manifestations of computing power only with
According to the BEA, the OCAM and total capital investment data
were based on US National Income and Product Accounts (NIPA) annual
investment expenditures by type of asset and allocated proportionately
across industries by using data from the BEA's capital
flow tables, which were available at five year intervals (1967,
1972, 1977, 1982 and 1987). The BEA made interpolations and extrapolations
for other years, using control totals from numerous independent
sources (e.g. the Annual Survey of Manufactures, payroll data,
employment data, inventory data, etc.) and some technical modifications
were also made, for instance to account for ownership changes
and investments by non-profit institutions. The industry-level
data originate from a variety of sources and the overall methodology
is described by the Bureau of Economic Analysis (1987) and Gorman
et al. (1985).
Each asset category also has an associated quality-adjusted (hedonic)
price deflator. The deflators were the same as those used in deriving
constant dollar GNP estimates. Most were based on Producer Price
Indexes published by the Bureau of Labor Statistics. Those for
computers were based on industry data as described in Cartwright
(1986). By dividing each investment by its associated deflator,
nominal investments were converted into constant-dollar or "real"
investments. One advantage of this deflator is that allows comparison
of different types of computers on the basis of "computing
power", including processor speed, memory, storage capacity,
display and various specific features. As a result, if some types
of firms purchase one type of computers and other purchase another
type, our results will be based on the difference in computing
3.1.4 Foreign Trade and Interest Rates
A principal alternative explanation for the decline in firm size
is that increased competition from foreign companies has forced
American companies to "downsize" and become more efficient.
To explore this hypothesis, and to help control for the impact
of changes in foreign trade, we included it as an independent
variable. Trade was defined as the sum of US merchandise exports
and imports for each year, as reported by the Department of Commerce.
The data exclude military transactions and were converted from
nominal dollars into constant dollars using the GNP deflator.
Because high interest rates discourage business expansion and
could also lead to a decline in firm size, the yearly rate of
interest on AAA rated bonds was also included. This data was obtained
from the Economic Report of the President (Bush, 1991) .
3.1.5 Data grouping and dummy variables
The data for the dependent and explanatory variables was categorized
by the Standard Industrial Classification (SIC) codes. The 61
industries used by the BEA were identified by their SIC codes
and were then grouped into the six sectors identified by CPB for
the regressions on SIZE1 and SIZE2 and, as mentioned above, into
20 manufacturing industries for regressions on SIZE3 and SIZE4.
To control for effects specific to a particular sector that do
not change over time, we included dummy variables for each of
the industry sectors. The most natural interpretation of the interaction
among the dependent and explanatory variables is in terms of percentage-effect,
so in accordance with common practice, all the relevant variables
were expressed in term of the natural logarithm. Another feature
of this specification is that it can be interpreted as a multiplicative
structural model in which the parameters are exponents. The estimating
equation we use is then derived by taking the natural logarithm
of both sides.
The basic technique used for analyzing the data was a two stage
least-squares regression estimate of the correlation between IT
and various measures of firm size, while controlling for other
explanatory variables. The data for all sectors over the time
period were pooled and, as described below, corrections were made
for potential heteroskedasticity, serial correlation and simultaneity.
Because IT capital and other capital are long-lived, a distributed
lag structure for the investments is appropriate. This enables
us to assess the impact of several years of IT and other capital
investments and to determine the time-path of the effects. We
allow for general patterns of depreciation (or more accurately,
effectiveness) by examining a polynomial distributed lag for IT
investments. By fitting a second-order polynomial distributed
lag structure, we can include the current year and four previous
years of data on investments in IT without introducing excessive
collinearity or unduly reducing the degrees of freedom. A concave
pattern (peaking in an arbitrary year), as well as various other
patterns such as the conventional straightline or declining-balance
depreciation, can be approximated by second order polynomials.
By construction, only changes in firm size within a sector were
examined by our models. However, part of the decline in average
firm size was due to shifts among sectors. This effect
was expressly excluded from our specification, because so many
other factors may affect shifts among sectors, and because it
does not appear to account for most of the decline in firm size.
Nonetheless, to the extent that the value chain for any one product
involves production across several sectors, the outsourcing hypothesis
discussed above would also be consistent with an intersectoral
shift which is not captured in our specification. 
Finally, as discussed above, four different dependent variables
were fitted, to reduce the sensitivity of the results to particular
data-gathering biases and to further explore the mechanism(s)
by which IT affects firm size.
3.2.1 The model
The model measures the relationship between levels of IT stock
and the average size of firms for a given sector in a given year,
while controlling for total capital, foreign trade, interest rates
and industry specific effects.
The basic regression model was:
This specification constrains the lag weights to follow a second
degree polynomial for the first five lagged values. By substituting
into the initial equation, a coefficient on the each of the lagged
values of IT can be computed and the resulting equation can then
be directly estimated using ordinary least squares.
The sum of coefficient on IT, S wi , will indicate the extent to which a percentage change in IT capital is correlated with changes in firm size, after controlling for the other variables in the equation. Predicted values of all the coefficients are given in section 3.3 below.
3.2.3 Data pooling and correction procedures
To increase the efficiency of the estimation, the time series
and cross-sectional observations were pooled. This provided an
adequate sample size but introduced potential problems like heteroskedasticity
among sectors and serial correlation between successive years.
Furthermore, by pooling the data, we implicitly restricted the
effect of IT and the other explanatory variables to be the same
across sectors and across time, and indeed, by estimating a least
squares regression, assumed that the relationship is (log-)linear.
If the true values differ across sectors or the relationship is
non-linear, the resulting estimates will be a weighted average,
and should be interpreted in this light. For instance, a zero
coefficient could be the result of positive effects in one sector
being cancelled by negative effects elsewhere, or a positive relationship
over one range of values being cancelled by a negative relationship
for other values. Finally, it is possible that the explanatory
variables (IT and other capital) will be jointly determined with
firm size, at least in the long run, and thus may be correlated
with the error term leading to inconsistent estimates. Fortunately,
we were able to correct for these potential problems by modifying
the specification of the model as outlined in the remainder of
Heteroskedasticity was a potential problem because the size of
the six sectors varied significantly in the pooled data, suggesting
a violation of the assumption of constant error variance. Although
the exact relative error variances cannot be determined, it is
common practice in the economics literature to assume that they
vary inversely with the size of the sector. Accordingly, the weighted
least squares correction technique was applied: each observation
on a sector was weighted by the size of the sector, as proxied
by the share of gross domestic product (GDP) produced in that
sector that year. We first computed the mean of the weighting
series (GDP) and then multiplied all the variables by the ratio
of the weighting series to its mean (GDP/GDP) yielding a specification
that corrects for heteroskedasticity and provides efficient parameter
estimates (Pindyck & Rubinfeld, 1991) .
As might be expected in time series regressions, the initial regressions
also revealed the potential presence of serial correlation, as
manifested in generally low Durbin-Watson statistics. Accordingly,
the Hildreth-Lu correction procedure was used in all regressions:
generalized differencing was applied to all the variables and
results were reported for the value of the estimated correlation
coefficient, r, that resulted in the lowest sum-of-squared residuals
(Hildreth & Lu, 1960). The corrected regressions did not exhibit
significant serial correlation.
As mentioned above, there is the possibility that some of the
independent variables (IT and other capital stock) are correlated
with the error term. This could occur for a number of reasons.
First, according to standard production theory, the optimal levels
of the various factors of production: employment, IT capital,
and other capital are each functions of the other two (at least
in the long-run) as well as other exogenous variables which are
observable to managers, but not to us. Secondly, theory suggests
that managers minimizing coordination costs will choose both the
firm size and IT investment with regard to levels of the other.
Either of these complications would result in simultaneity: a
shock that affected firm size might also affect investment in
the same year. Thirdly, all of the variables are measured only
with some error. It is possible that the measurement errors for
the independent variables may be correlated with the error term
from the regression. The appropriate correction for these possible
biases is to use Instrument Variables (IV) estimation as outlined
below, instead of Ordinary Least Squares (Berndt, 1991; Pindyck
& Rubinfeld, 1991) .
To derive consistent parameter estimates, we ran the regressions
in two stages, using lagged values of IT and TOT as well as price
index for IT investments as instruments to minimize the simultaneity
problem. The first stage consisted of estimating a reduced form
equation for ITt, TOTt
and the other independent (and presumably exogenous) variables
using ordinary least squares by regressing them on a constant,
a polynomial distributed lag of ITt-1, a polynomial
distributed lag of TOTt-1, TRADEt,
BONDt, the industry dummies and polynomial
distributed lag of PRICEt (a price index for
computers, to overidentify the equation). By using these variables,
all of which are presumed to be predetermined, the fitted values
of IT and TOT will be asymptotically independent of the current
error term, by construction. The second stage regression simply
replaced the original variables in equation (1) with these fitted
variables, yielding consistent estimators, at the expense of somewhat
higher standard errors.
It should be noted that, although we modified the regression to
correct for potential sources of bias and to improve efficiency,
the interpretation of the sign and magnitude of the cofficients
is the same as for ordinary least squares (Pindyck & Rubinfeld,
3.3 Predicted signs of the coefficients
According to the hypothesis that IT leads to smaller firms by
reducing the costs of inter-firm coordination, we should expect
the sum of the coefficients on IT to be negative for the regressions
on all four measures of firm size.. Conversely, a positive sign
would indicate that IT facilitated internal coordination more
than market coordination. If IT is a substitute for labor, we
should also see negative coefficients for the regressions on SIZE1
and SIZE2, but SIZE3 and SIZE4 would not be affected. Thus, while
all four measures give indications of the significance of IT's
impact on firm size, the latter two can provide some insight into
the mechanism of the effect.
The magnitude of these coefficients will tell us how much a percentage change in IT will affect firm size, all else being equal. The sum of the coefficients can be interpreted as the percentage change in firm size associated with a 1% change IT capital. Because of lags due to learning and other adjustments to the technology, we expect the impact of the IT investments will be increasing for small lags and then returning close to zero in distant years, exhibiting a concave pattern. The rise results as the technology is more fully exploited and the fraction of purchased IT stock that is "effective" increases. As the IT capital begins to deteriorate, the coefficient would fall.
It is sometimes argued that the rapidly increasing globalization
of the economy increases competitive pressures on American firms
and forces them to "downsize" (Kanter, 1989) ; Roach,
1987a #59. The underlying assumption is that previously they were
inefficiently large or overstaffed. This effect would lead to
a negative coefficient on TRADE, at least for SIZE1 and SIZE2.
However, an opposite effect is at least equally plausible. Globalization
leads to larger markets and provides an opportunity to further
exploit scale economies (Caves & Bradburd, 1988) . This could
lead to a larger average firm size, and a thus positive coefficient
The control variables have relatively straightforward interpretations.
Growth in the output of the economy is indicative not only of
cyclical business expansion, but also increased opportunity to
exploit scale economies. This should lead to a positive coefficient
on GNP. According to a transaction cost framework, industries
which require large fixed capital investments will, ceteris paribus,
have larger average firm size. The coefficient on TOT should thus
be positive. The current costs of capital, as proxied by interest
rates on AAA bonds, should be negatively correlated with growth
of firms, leading to a negative coefficient on BOND. The sector
dummy variables control for systematic variations in firm size
between sectors. For instance, manufacturing firms are typically
much larger firms than firms in the service sector, and this should
be reflected here.
The overall results indicate that IT is correlated with a decline
in all measures of firm size and the TRADE is correlated with
an increase in average firm size. We also found evidence suggesting
the impact of IT is not immediate but rather that it is strongest
after about 2 years.
4.1 Results for SIZE1
We found that the current level of IT stock is strongly correlated
with a decline in average establishment size, SIZE1. The coefficients
indicates that each 1% increase in IT investment for five years
is associated with a 0.13% decrease in the number of employees
per establishment, after controlling for the other variables,
and is significant at the 99.9% level (see table A). A distinct
concave pattern of effects is evident in the individual years'
investments in IT. Declines in establishment size are correlated
with IT investments for all lags, but the strongest impact is
for investments that have been lagged for one to two years, with
a coefficients of 0.032 and 0.031 respectively and t-statistics
of 3.5 and 2.7.
Interestingly, TRADE was strongly and unambiguously correlated
with an increase in average firm size, as measured by SIZE1.
The coefficient was 0.32 and the t-statistic was 4.5
The other variables are consistent with our predictions. Total capital stock and GNP are positively correlated with establishment size. The coefficient on BOND was negative, but it was not significantly different than zero. Dummy variables for each sector show that manufacturing establishment tend to be larger than service firms. The unusually high R2 is encouraging but somewhat misleading because it is largely due to the dummy variables which "explain" the large intersectoral variations in firm size.
The results of this regression suggest that increased levels of
IT in a sector lead to a decrease in the average size of firms
in that sector. The null hypothesis that IT does not affect firm
size can be rejected at the 99.9% level of significance. Furthermore,
the impact of IT on establishment size is of a sufficient magnitude
to be economically important. Because of the large increase in
IT investment over the period, this effect could account for the
20% decline in establishment size between 1977 and 1985, reversing
its long term upward trend. The alternative hypothesis that increased
globalization lead to smaller firms is not supported. It is in
fact contradicted by the data.
While the data from County Business Patterns is the most comprehensive
available, we sought to confirm our findings by running the same
regressions on several different data sets to help guard against
the possibility that our findings were somehow an artifact of
the County Business Patterns data. These results are presented
in the next sections.
4.2 Results for SIZE2
This impact of IT was further examined by constructing a different
measure of firm size (SIZE2) from data available through Compustat
as described in section 2. The Compustat data is less susceptible
to the same data gathering biases as the CBP data used to construct
SIZE1 and furthermore, allows us to detect changes in the size
of multi-establishment firms. For instance, it is possible is
that IT reduces the size of individual establishments but allows
firms to grow by adding more establishments. The CBP data would
not detect such growth.
The results using this new source for the dependent variable also
show that increasing IT stock is associated with decreasing firm
size (table A). Thus sum of the coefficients on IT was -0.14,
almost identical to that in the SIZE1 regressions, and it was
significant at the 99% level of confidence. Furthermore, TRADE
again had a positive coefficient, and it was significant at the
95% level. Two differences from the previous regression were that
1) the fit did not seem to be quite as good, as indicated by the
lower F-statistic, R2 and t-statistics and
2) the coefficients on the individual lags for IT investment did
not follow the hypothesized concave pattern, although the once-lagged
value was still the most significant.
The coefficients on the other variables are also qualitatively
similar to those in the previous regressions with the notable
exception that the trade sector tends to have larger firms, ceteris
paribus, as measured by Compustat, presumably because chain stores
involving multiple establishment are counted as only one firm.
The combined evidence of the preceding analysis of CBP and Compustat
data strongly suggest that increases in IT capital stock are associated
with significant declines in the number of employees per firm.
This result is consistent with both the hypothesis that IT leads
to a reduction in the proportion of internal coordination as well
as the hypothesis that IT substitutes for labor within firms,
enabling firms to continue to provide roughly the same scope of
activities, but with fewer employees. While we cannot rule out
either of these explanations looking only at employment per firm,
the next set of regressions are particularly supportive of the
coordination cost hypothesis.
4.3 Results for SIZE3 and SIZE4
We next examined the relationship between IT and two alternative
measures of firm size: gross shipments per firm (SIZE3) and value-added
per firm (SIZE4). As discussed in section 3.1.3, data for these
variables were available at the two-digit SIC code level of aggregation
The results for both these alternative measures provide very strong
additional evidence that IT has lead to a decline in the average
size of firms (table B). They suggest that, ceteris paribus,
doubling IT stock in an industry leads to a decrease in average
sales per firm in the same industry of about 13% and a decrease
of about 12% in average value added per firm over a period of
five years. Both of these estimates are significantly different
from zero at a 99.9% level of confidence and are remarkably consistent
with the estimates from the SIZE1 and SIZE2 regressions on different
data. The distributed lag specification for both SIZE3 and SIZE4
show the expected concave shape, peaking after two years. The
coefficients on lags of from 1 to 3 years are all negative and
significant at the 99% level.
Because these alternative measures of firm size are unlikely to
be affected by shifts among factors of production, the combined
evidence of these regressions with other studies of the effect
of IT on labor intensity and productivity (Berndt & Morrison,
1991; Loveman, 1988; Osterman, 1986), suggest that the decline
in employees per firm was not caused solely by the substitution
of capital for labor.
As discussed earlier, the observed reduction in all measures of
equilibrium size of firms is consistent with the hypothesis that
IT is facilating the "decoupling" of existing vertically
integrated firms and the supplanting of existing firms by value-adding
networks of new, smaller firms.
4.4 Results on subsamples of the data
4.4.2 Different time periods
To assess whether the relationship between IT and firm size changed
over time, we ran the model on subsamples disaggregated by year.
There was sufficient data to examine the effect on each of the
four measures of firm size in three overlapping samples corresponding
respectively to the first 2/3 of the time period (i.e. 1976-1984),
the second 2/3 (1981-1989), and a long difference of the first
and last third of the period (excluding 1981-1984).
As shown in tables C and D, the net relationship between IT and
all four measures of (SIZE1, SIZE2, SIZE3 and SIZE4) was consistently
negative in all three time periods. The effect was statistically
significant at at least the 99% level in 9 of the 12 cases examined.
The coefficients on the sum of IT investments ranged from a low
of .08 to a high of .39. For all four measures of firm size, the
effect was strongest in the latter 2/3 years as compared to the
first 2/3 of the sample. In all twelve cases, IT had the statistically
most significant impact after a lag of 1-2 years and a concave
pattern was apparent in 6 of the twelve regressions.
4.4.1 Manufacturing versus services
For the SIZE1 and SIZE2 regressions, we were also able to disaggregate
the data into "manufacturing" and "services".
"Manufacturing" consisted of two of the six sectors
(Durable and Non-durable goods manufacturing) grouped together.
The other four sectors (Transport and utilities; Trade; Finance,
insurance and real estate; and, other Services) comprised "services".
Although the number of observations were limited for each subsample,
we were able to identify some potential differences and to further
assess the robustness of our overall results.
In manufacturing industries, IT investments were associated with
economically important declines in both SIZE1 and SIZE2 at the
99% level of significance when all five years of investments were
considered. (table E). For service industries, IT was correlated
with declines in SIZE1 at the 99% level of significance and at
the 95% level for SIZE2. Once again, IT investments were most
strongly correlated with declines in firms size when they were
lagged by either 1 or two years.
Undoubtedly, the impact of IT differs across sectors, industries
and even firms. In our sample, there was only a small difference
between manufacturing and services. The coefficients were slightly
smaller in the service industry regressions for both measures
of firm size, but a Chow test indicated that the difference was
not statistically significant.
International trade in both sectors was correlated with increases
in both measures of firm size. This increase was statistically
significant in all regressions except the manufacturing subsample
On balance, the results of this set of regressions and those on the different time periods suggest that the correlations between IT and declines in various measures of firm size are not unique to any one sector or any one time period. Although some differences were found, they were relatively small so we are hesitant draw any inferences from them. Instead, we conclude only that the relationship between IT and firm size appears to be fairly widespread in the economy.
There is substantial evidence of a relationship between increased
levels of IT usage and smaller firm size. The overall relationship
is robust to a variety of specifications and at least four measures
of firm size. However, our findings should not be interpreted
to apply to all industries and all time periods. Our data were
far from perfect and the relatively undeveloped state of the economic
theory of the firm and of its relationship to IT in particular
made the use of detailed functional specifications unrealistic.
Our purpose was primarily to develop generalizable models that
could be applied broadly across the economy. As we continue to
develop more detailed and industry-specific models, and secure
better data sets, we will be able to make sharper predictions.
Nevertheless, our findings are consistent with the hypothesis
that IT leads to a decline in the average size of firm, enabling
us to empirically distinguish among some of the predictions of
prior theoretical work.
The decline in firm size is greatest with a lag of one to two
years following investments in IT, suggesting that the impacts
of the new technology are not fully felt immediately. This finding
may shed light on previous studies which found little or no impact
of IT in the same year that the investments were made, and is
consistent with earlier work on the effective time-path of capital
depreciation (Pakes & Griliches, 1984).
The decline in two of our measures of firm size (SIZE1 and SIZE2)
could be explained by the substitution of IT capital for labor,
but the substitution hypothesis would not explain the decline
in sales per firm (SIZE3) or value added per firm (SIZE4). In
contrast, our findings on all four measures are completely consistent
with a relative increase in the reliance on markets for coordination
following IT investments. Such a shift is specifically predicted
by Malone and Smith (1988) and Malone, Yates and Benjamin (1987)
and also provides an empirical basis for evaluating the relative
importance of the various forces identified in the other models
of the relationship between IT and firm structure that are reviewed
in section 2.2.
It is worth noting that the principal alternative explanation
for the decline in firm size in this period, increased international
competition, apparently does not explain the strong correlation
we found with increased investments in IT. On the contrary, trade
is strongly correlated with increases in firm size in virtually
all of our regressions.
Although the null hypothesis that IT was not correlated with declines
in firm size is strongly rejected by the data, other factors may
also have played a role. Furthermore, we cannot rule out more
complex relationships. For instance, if some third trend, like
increased turbulence in the economy, is associated with both increased
IT usage and with younger, smaller firms after a brief lag, it
could produce the correlation we found in our regressions.
There is, however, strong reason to believe that most of the growth
in IT investment has an exogenous technological basis. Increased
investment in IT appears to be almost entirely explained by the
rapid drop in its price (Gurbaxani & Mendelson, 1990; Chow,
1967) , and these price declines are directly attributable to
improvements in the technology, especialy for chip production
(Grove, 1990) . Although causality can never be proven by statistical
regression, the data do support the hypothesis that improvements
in technology have enabled a shift toward smaller firms.
This finding may be useful to corporate practitioners. For instance,
one implication is that the current downsizing of firms, the popularity
of outsourcing, and the rise of value-adding partnerships is not
simply a management fad, but rather may have a technological and
theoretical basis. Companies evaluating success strategies in
an environment of increasingly inexpensive information technology
will benefit from considering alternative forms of organizing
which depend more heavily on market coordination. For instance,
the comment by the former chairman of GM that their historically
high levels of vertical integration were no longer a competitive
advantage but had instead become a "semi-disadvantage"
worth several hundred dollars per car is indicative of the way
in less-integrated smaller firms have benefitted. Our findings
also suggest that it may be no accident that IT is often the catalyst
for "reenginering" projects that result in greater outsourcing
and leaner internal staffing.
Government can help or hinder this process. Rather than seeking
to support large failing firms, government may wish to direct
more efforts toward understanding whether a coalition of smaller
firms would be more successful in the long run. It is also important
to recognize that initiatives like funding a fiber optic communications
infrastucture and setting standards for EDI can accelerate the
trend which we have identified and may require commensurate changes
in industrial organization to fulfill their potential. However,
since we find evidence that industries are already adapting to
the new technological environment, we cannot conclude that any
special taxes or subsidies based on firm size are required.
5.3 Future research
In this paper, we have focused on changes in firm size associated
with IT investments within the same sector or industry. However,
to the extent that IT enables the externalization of services,
it may be contributing to the shift in employment from the manufacturing
sector to the service sector. Because the service sector generally
has smaller firms, such a shift would tend to amplify the trend
we identified in this study. An examination of this possibility
would be a fruitful direction for future research.
Secondly, in interpreting our results we have largely followed
the standard approach of presuming a dichotomy between markets
and firms, and therefore clear firm boundaries (Williamson, 1975)
. Perhaps some of the most interesting effects of IT will be the
enabling of new organizational forms such as "networks"
(Antonelli, 1988; Piore, 1989), "ad-hocracies" (Toffler,
1982) or more complex forms. Future research should seek to identify
and where possible quantify these new forms in order to establish
whether, how, and why IT affects their implementation.
Thirdly, while the reduced form models analyzed in this paper
are provocative, a better understanding of the theory of the firm
and a more formal theory of the relationship between IT and firm
structure are needed for more definitive hypothesis testing. The
recent work in agency theory and the property rights approach
to the theory of the firm are promising avenues to explore and
should lend themselves to econometric analysis of specific functional
Fourthly, our finding that IT is related to the decline in firm
size may shed light on another aspect of the current restructuring
of the Western economies: the recently-discovered benefits of
"focus" by firms. The new conventional wisdom has
been bolstered by Comment and Jarrell (1991), who concluded "Whatever
the reasons, the dramatic shift in corporate policy to reduce
corporate diversification and to increase corporate focus, which
is empirically verified by our data for the 1980s, has been associated
with substantial gains to shareholders in the focus-increasing
firms." Similar findings are reported by Morck, Shleifer
and Vishny (1990) who found that diversification was worse for
shareholders in the 1980s than in the 1970s, and Lichtenberg (1990)
who confirmed that de-diversification became a profitable strategy
between 1985 and 1989. Because diversification is typically modeled
as a response to market failures, it would be interesting to assess
whether IT has helped enable the emerging strategy of increased
focus by increasing the relative efficiency of product and capital
Finally, this paper has examined the relationship between IT stock
and one key indicator of the restructuring of the American economy,
firm size. By examining more refined measures, it will be possible
to evaluate stronger conclusions about the size and form of these
changes. While IT constituted less than 10% of all capital stock
in most of the period we examined, net investment in IT amounts
to up to 30% of net increases in capital stock in 1990. This trend,
combined with the relationship between IT and firm size identified
above, portend a potentially more radical restructuring of the
American economy in the next decade.
|Coordination Mechanism||Coordination Costs||Production Costs
No other figures available.
|Dependent Variable is SIZE1||Dependent Variable is SIZE2|
|Variable||Coefficient||T Statistic||Coefficient||T Statistic|
|R - Squared||0.9910||0.9608|
|Number of Observations||84||84||84||84|
|Dependent Variable SIZE3||Dependent Variable is
||Variable||Coefficient||T Statistic||Coefficient||T Statistic
||C||-4.225 ||-9.019*** ||-4.728 ||-8.180***
||ITNV(0) ||-0.01185 ||-0.490 ||-0.01611 ||-0.540
||ITINV(-1) ||-0.04352 ||-4.865*** ||-0.03780 ||-3.425***
||ITINV(-2) ||-0.05094 ||-3.504*** ||-0.04125 ||-2.300**
||ITINV(-3) ||-0.03409 ||-2.668*** ||-0.02646 ||-1.678**
||ITINV(-4) ||0.0070 ||0.361 ||0.00657 ||0.275
||IT SUM ||-0.1334 ||-9.193*** ||-0.1150 ||-6.426***
||TTINV(0) ||0.05650 ||1.501* ||0.02419 ||0.521
||TTINV(-1) ||0.06273 ||4.224*** ||0.03339 ||1.822**
||TTINV(-2) ||0.06131 ||3.165*** ||0.03473 ||1.453*
||TTINV(-3) ||0.05224 ||3.001*** ||.02820 ||1.313*
||TTINV(-4) ||0.03553 ||1.281 ||0.01380 ||0.403
||TT SUM ||0.2683 ||5.096*** ||0.1343 ||2.067**
||GDP ||0.1492 ||3.200*** ||0.009204 ||0.1599
||BOND ||-0.009570 ||-2.600*** ||-0.01318 ||-2.902***
||TRADE ||0.5544 ||8.310*** ||0.6775 ||8.231***
||D20 ||0.7248 ||7.934*** ||0.7348 ||6.519***
||D21 ||1.967 ||18.44*** ||1.939 ||14.74***
||D22 ||0.6481 ||8.454*** ||0.5891 ||6.227***
||D23 ||0.07540 ||1.127 ||0.08673 ||1.051
||D24 ||-0.1478 ||-2.047** ||-0.1982 ||-2.224**
||D25 ||0.2749 ||3.190*** ||0.2580 ||2.426***
||D26 ||0.8301 ||9.888*** ||0.9123 ||8.807***
||D27 ||-0.09009 ||-1.195** ||0.1627 ||1.750**
||D28 ||0.7599 ||7.587*** ||.9980 ||8.076***
||D29 ||1.593 ||20.75*** ||1.102 ||11.64***
||D30 ||0.3695 ||4.866*** ||0.4593 ||4.902***
||D31 ||0.6330 ||3.158*** ||0.4147 ||1.677**
||D32 ||0.2838 ||3.903*** ||0.3743 ||4.171***
||D33 ||0.9182 ||10.81*** ||.9320 ||8.897***
||D34 ||0.1600 ||1.963** ||0.3311 ||3.292***
||D35 ||0.2257 ||2.381** ||0.4852 ||4.147***
||D36 ||0.6441 ||7.013*** ||.9150 ||8.075***
||D37 ||1.078 ||10.88*** ||1.222 ||9.991***
||D38 ||0.6423 ||8.336*** ||0.8713 ||9.164***
||R - Squared||0.9956 ||0.9874||Durbin-Watson ||1.464 ||1.342||F Statistic ||2040 ||706.0
||Number of Obs. ||280 ||280 ||280 ||280
||Variable||Coefficient||T Statistic||Coefficient||T Statistic||Coefficient||T Statistic
||C ||-4.577 ||-2.940*** ||-2.076
||-1.767** ||-5.359 ||-3.399***
||ITNV(0) ||-0.01275 ||-0.301 ||-0.08407
||-2.274*** ||0.01570 ||1.124
||ITINV(-1) ||-0.02970 ||-2.528*** ||-0.03780
||-3.930*** ||0.00561 ||0.107
||ITINV(-2) ||-0.03690 ||-2.866*** ||-0.00626
||-0.469 ||-0.08581 ||-4.450***
||ITINV(-3) ||-0.03435 ||-1.708** ||0.01055
||0.537 ||-0.1172 ||-2.990***
||ITINV(-4) ||-0.02205 ||-1.376* ||0.01264
||0.822 ||-0.08862 ||-2.556***
||IT SUM ||-0.1357 -||4.649*** ||-0.1049
||-3.760*** ||-0.1289 ||-0.982
||TTINV(0) ||0.03831 ||0.580 ||0.2140
||2.949*** ||-0.1310 ||-0.543
||TTINV(-1) ||0.02940 ||0.939 ||0.1166
||4.647*** ||0.05011 ||0.564
||TTINV(-2) ||0.02111 ||0.629 ||0.04656
||2.244** ||0.1537 ||3.875***
||TTINV(-3) ||0.01345 ||0.351 ||0.00374
||0.122 ||0.1799 ||2.429***
||TTINV(-4) ||0.00641 ||0.229 ||-0.01178
||-0.479 ||0.1287 ||2.013**
||TT SUM ||0.1086 ||0.913 ||0.3692
||5.221*** ||0.3815 ||1.720**
||GDP ||0.4683 ||2.517*** ||0.1564
||0.7557 ||0.3392 ||1.657*
||BOND ||0.006395 ||1.014 ||0.01153
||1.579* ||-0.003052 ||-0.2688
||TRADE ||0.3986 ||3.368*** ||0.1058
||0.9844 ||0.4240 ||2.524***
||DU ||0.8305 ||29.90*** ||0.07424
||38.64*** || 0.7270 ||33.97***
||ND ||0.8190 ||17.27*** ||0.7151
||13.47*** ||0.7269 ||9.151***
||TU ||0.3401 ||4.942*** ||0.1881
||3.362*** ||0.1761 ||1.454*
||TR ||-0.03902 ||-2.168** ||0.04409
||-2.544*** ||-0.06197 ||-2.353**
||FI ||0.01492 ||0.3924 ||-0.1001
||-3.341*** ||-0.1015 ||-2.022**
||R - Squared ||0.9884 ||0.9924
||.9920||Durbin-Watson Statistic ||2.719 ||2.190 ||1.756||F Statistic ||293.5 ||512.6
||425.8||Number of Observations ||54 ||54 ||60 ||60 ||54 ||54
||Variable||Coefficient||T Statistic||Coefficient||T Statistic||Coefficient||T Statistic
||C ||-4.812 ||-1.257 ||-0.4331 ||-0.1520 ||-6.304 ||-1.950**
||ITNV(0) ||-0.07456 ||-0.716 ||-0.2049
||-2.287** ||-0.2570 ||-0.898
||ITINV(-1) ||-0.05113 ||-1.770** ||-0.05842
||-2.506*** ||-0.1310 ||-1.213
||ITINV(-2) ||-0.03196 ||-1.010 ||0.03533
||1.093 ||-0.04225 ||-1.069
||ITINV(-3) ||-0.01705 ||-0.345 ||0.07632
||1.602* ||0.00916 ||0.114
||ITINV(-4) ||-0.00639 ||-0.162 ||0.06454
||1.732** ||0.02325 ||0.327
||IT SUM ||-0.1080 ||-2.523*** ||-0.08716
||-1.288 ||-0.3979 ||-1.478*
||TTINV(0) ||0.07521 ||0.463 ||0.2913
||1.656* ||0.3842 ||0.777
||TTINV(-1) ||0.05761 ||0.748 ||0.07673
||1.261 ||0.1829 ||1.004
||TTINV(-2) ||0.04128 ||0.500 ||-0.05969
||-1.187 ||0.0438 ||0.538
||TTINV(-3) ||0.02624 ||0.279 ||-0.1179
||-1.583* ||-0.03306 ||-0.218
||TTINV(-4) ||0.01248 ||0.181 ||-0.09805
||-1.646* ||-0.04766 ||-0.364
||TT SUM ||0.2128 ||0.727 ||0.09235
||0.539 ||0.5302 ||1.166*
||GDP ||0.3562 ||0.7790 ||0.04243
||0.08455 ||0.3221 ||0.7677
||BOND ||-0.001350 ||-0.08717 ||0.005526
||0.3123 ||-0.01511 ||-0.6490
||TRADE ||0.4582 ||1.574* ||0.1629
||0.6255 ||0.6176 ||1.792**
||DU ||0.5448 ||7.981*** ||0.4079
||8.760*** ||0.4283 ||9.762***
||ND ||0.4383 ||3.761*** ||0.3187
||2.478*** ||0.2902 ||1.7824**
||TU ||0.04644 ||0.2746 ||0.05392
||0.4129 ||-0.1149 ||-0.4627
||TR ||0.4098 ||9.264*** ||0.3987
||9.491*** ||0.3692 ||6.841***
||FI ||-0.1095 ||-1.172** ||-0.2957 ||-4.070*** ||-0.2983 ||-2.897***
||R - Squared ||0.9470 ||0.9641
||0.9773||Durbin-Watson Statistic ||3.120 ||2.209 ||1.466||F Statistic ||61.26 ||105.7
||146.7||Number of Observations ||54 ||54 ||60 ||60 ||54 ||54
||Variable||Coefficient||T Statistic||Coefficient||T Statistic||Coefficient||T Statistic
||C ||-4.299 ||-7.684*** ||-3.085
||-4.194*** ||-6.252 ||-7.748***
||ITNV(0) ||-0.02430 ||-0.599 ||-0.06862
||-1.830** ||-0.08125 ||-1.923**
||ITINV(-1) ||-0.04105 ||-3.614*** ||-0.02819 ||-5.013*** ||-0.08359 ||-4.685***
||ITINV(-2) ||-0.04258 ||-3.087*** ||-0.00328 ||-0.137 ||-0.07083 ||-4.411***
||ITINV(-3) ||-0.02890 ||-2.029** ||0.00612
||0.272 ||-0.04297 ||-3.047***
||IT SUM ||-0.1368 ||-3.614*** ||-0.09396
||-5.013*** ||-0.2786 ||-4.685***
||TTINV(0) ||-0.00078 ||-0.017 ||.1650
||4.449*** ||0.1790 ||2.606***
||TTINV(-1) ||0.03863 ||2.338** ||0.07562
||4.891*** ||0.1256 ||4.190***
||TTINV(-2) ||0.05190 ||2.184** ||0.01828
||0.792 ||0.07801 ||3.390***
||TTINV(-3) ||0.03903 ||1.815** ||-0.00692 ||-0.344 ||0.03612 ||1.823**
||TT SUM ||0.1287 ||2.338** ||.2520
||4.891*** ||0.4188 ||4.190***
||GDP ||0.4025 ||5.784*** ||0.09170
||1.913** ||0.1901 ||3.138***
||BOND ||-0.008448 ||-1.173** ||-0.007236 ||-0.8827 ||-0.01619 ||-2.334**
||TRADE ||0.5880 ||6.795*** ||0.3937
||3.733*** ||0.8201 ||7.517***
||D20 ||0.6692 ||6.215*** ||0.7392
||8.029*** ||0.6765 ||5.161***
||D21 ||1.933 ||18.08*** ||1.922
||16.50*** ||2.079 ||14.65***
||D22 ||0.6768 ||7.862*** ||0.6457
||8.254*** ||0.5949 ||5.887***
||D23 ||0.0527 ||0.6783 ||0.09028
||1.305* ||-0.04364 ||-0.4707
||D24 ||-0.1080 ||-1.278 ||-0.1171
||-1.536* ||-0.2519 ||-2.512***
||D25 ||0.2928 ||2.925** ||0.2468
||2.767*** ||0.3167 ||3.163***
||D26 ||0.8291 ||8.553*** ||0.8213
||9.734*** ||0.8251 ||7.164***
||D27 ||-0.1553 ||-1.720** ||-0.1119
||-1.473* ||-0.02285 ||-0.2260
||D28 ||0.7358 ||6.319*** ||-0.7827
||7.738*** ||0.6626 ||4.423***
||D29 ||1.747 ||17.97*** ||1.578
||21.23*** ||1.552 ||15.53***
||D30 ||0.3986 ||4.587*** ||0.3614
||4.684*** ||0.3255 ||3.201***
||D31 ||.6630 ||3.342*** ||0.5936
||2.768*** ||0.7323 ||2.681***
||D32 ||0.2889 ||3.112*** ||0.2555
||3.490*** ||0.4091 ||4.483***
||D33 ||0.8941 ||8.701*** ||0.9642
||11.34*** ||0.8538 ||7.697***
||D34 ||0.1124 ||1.158* ||0.1726
||2.082** ||0.1345 ||1.229
||D35 ||0.1589 ||1.297* ||.2200
||2.338** ||0.3103 ||2.328**
||D36 ||0.6232 ||5.518*** ||0.6294
||6.980*** ||0.6589 ||5.118***
||D37 ||1.005 ||8.532*** ||1.087
||10.90*** ||1.059 ||7.537***
||D38 ||0.6117 ||6.529*** ||0.6278
||8.165*** ||0.7218 ||7.167***
||R - Squared ||0.9961 ||0.9968
||0.9965||Durbin-Watson Statistic ||1.756 ||1.352 ||1.917||F Statistic ||1524 ||2108
||1692||Number of Observations ||180 ||180 ||200 ||200 ||180 ||180
||Variable||Coefficient||T Statistic||Coefficient||T Statistic||Coefficient||T Statistic
||C ||-4.870 ||-6.465*** ||-3.865
||-4.287*** ||-6.502 ||-6.954***
||ITNV(0) ||-0.01716 ||-0.314 ||-0.0836
||-1.820** ||-0.06880 ||-1.405*
||ITINV(-1) ||-0.03598 ||-2.353** ||-0.02411
||-3.499*** ||-0.07438 ||-3.598***
||ITINV(-2) ||-0.03940 ||-2.121** ||0.00968
||0.331 ||-0.06477 ||-3.482***
||ITINV(-3) ||-0.02740 ||-1.429* ||0.01772
||0.643 ||-0.03998 ||-2.447***
||IT SUM ||-0.1199 ||-2.353** ||-0.08036
||-3.499*** ||-0.2479 ||-3.598***
||TTINV(0) ||-0.00953 ||-0.155 ||0.1529
||3.364*** ||.1160 ||1.458*
||TTINV(-1) ||0.03017 ||1.356* ||0.05852
||3.088*** ||0.05400 ||1.554*
||TTINV(-2) ||0.04499 ||1.406* ||0.00153
||0.054 ||0.01396 ||0.523
||TTINV(-3) ||0.03493 ||1.207 ||-0.01797
||-0.728 ||-0.00404 ||-0.176
||TT SUM ||0.1005 ||1.356* ||.1950
||3.088*** ||0.1799 ||1.554*
||GDP ||0.2745 ||2.930*** ||-0.02960
-||0.5041 ||-0.01629 ||-0.2322
||BOND ||-0.01502 ||-1.550* ||-0.01377
||-1.371* ||-0.01710 ||-2.127**
||TRADE ||0.6683 ||5.735*** ||0.5194
||4.019*** ||0.9636 ||7.623***
||D20 ||0.5183 ||3.575*** ||0.6532
||5.790*** ||0.8798 ||5.7934***
||D21 ||1.909 ||13.26*** ||1.883
||13.19*** ||2.117 ||12.87***
||D22 ||0.5673 ||4.894*** ||0.5457
||5.692*** ||0.5916 ||5.053***
||D23 ||0.06183 ||0.5905 ||0.09413
||1.110 ||-0.008930 ||-0.0831
||D24 ||-0.2233 ||-1.962** ||-0.1991
||-2.131** ||-0.2415 -||2.079**
||D25 ||0.2785 ||2.066** ||0.2282
||2.088** ||0.3072 ||2.648**
||D26 ||0.7818 ||5.990*** ||0.8257
||7.986*** ||1.050 ||7.868***
||D27 ||0.01255 ||0.1032 ||0.07947
||0.8532 ||0.3509 ||2.996***
||D28 ||0.7963 ||5.079*** ||.9130
||7.367*** ||1.102 ||6.350***
||D29 ||1.047 ||8.001*** ||1.086
||11.93*** ||1.165 ||10.06***
||D30 ||0.4047 ||3.459*** ||0.3912
||4.137*** ||.5240 ||4.447***
||D31 ||0.5413 ||2.026** ||0.4355
||1.657* ||0.3723 ||1.176
||D32 ||0.3161 ||2.530*** ||0.3073
||3.426*** ||0.5428 ||5.133***
||D33 ||0.7983 ||5.770*** ||0.8958
||8.601*** ||0.9745 ||7.583***
||D34 ||.1890 ||1.446* ||0.2664
||2.622*** ||0.4319 ||3.405***
||D35 ||0.2989 ||1.812** ||0.3842
||3.332*** ||0.7384 ||4.782***
||D36 ||0.7785 ||5.119*** ||0.7951
||7.196*** ||1.107 ||7.426***
||D37 ||1.000 ||6.308*** ||1.120
||9.171*** ||1.401 ||8.609***
||D38 ||0.7615 ||6.036*** ||0.7895
||8.378*** ||1.063 ||9.116***
||R - Squared ||0.9868 ||0.9911
||0.9912||Durbin-Watson Statistic ||1.561 ||1.088 ||1.875||F Statistic ||411.5 ||742.2
||665.7||Number of Observations ||180 ||180 ||200 ||200 ||180 ||180
|R - Squared||0.9979||0.9864|
|Number of Observations||28||28||56||56|
|R - Squared||0.9946||0.9674|
|Number of Observations||28||28||56||56|
|Dependent Variable is SIZE1||Dependent Variable is SIZE2|
|Variable||Coefficient||T Statistic||Coefficient||T Statistic|
|R - Squared||0.9884||0.9613|
|Number of Observations||84||84|
|Dependent Variable SIZE3||Dependent Variable is
||Variable||Coefficient||T Statistic||Coefficient||T Statistic
||C||-3.486 ||-8.466*** ||-4.060 ||-8.255***
||IT2(0) ||-0.09613 ||-3.290*** ||-0.09912
||IT2(-1) ||-0.04768 ||-5.424*** ||-0.05127 ||-4.883***
||IT2(-2) ||-0.01059 ||-0.645 ||-0.01112
||IT2(-3) ||0.01515 ||1.150 ||0.02131
||IT2(-4) ||0.02953 ||1.723** ||0.04604
||IT2 SUM ||-0.1097 ||-8.343*** ||-0.09416 ||-5.996***
||TTINV(0) ||0.1362 ||2.882*** ||0.1165 ||2.063**
||TTINV(-1) ||0.06406 ||4.172*** ||0.05019 ||2.737***
||TTINV(-2) ||0.01508 ||0.703 ||0.00103 ||0.040
||TTINV(-3) ||-0.01070 ||-0.593 ||-0.03096 ||-1.438*
||TTINV(-4) ||-0.01326 ||-0.507 ||-0.04581 ||-1.468*
||TT SUM ||0.1914 ||3.598*** ||0.09094 ||1.431*
||GDP ||0.09238 ||1.971** ||-0.04250
||BOND ||0.003231 ||0.8953 ||-0.001731 ||-0.4017
||TRADE ||0.4814 ||7.871*** ||0.5994
||D20 ||0.8471 ||9.131*** ||0.8157
||D21 ||1.983 ||18.17*** ||1.954
||D22 ||0.7608 ||9.707*** ||0.6759
||D23 ||0.09531 ||1.395* ||0.09979
||D24 ||-0.02070 ||-0.2817 ||-0.09591
||D25 ||0.2692 ||3.047*** ||0.2524
||D26 ||0.9367 ||10.90*** ||0.9817
||D27 ||-0.03158 ||-0.4075 ||-0.1974
||D28 ||0.9711 ||9.570*** ||1.152
||D29 ||1.686 ||21.54*** ||1.166
||D30 ||0.4497 ||5.768*** ||0.5112
||D31 ||0.5410 ||2.639*** ||0.3472
||D32 ||0.3257 ||4.342*** ||0.4000
||D33 ||1.017 ||11.81*** ||0.9962
||D34 ||0.2685 ||3.245*** ||0.4074
||D35 ||0.3239 ||3.320*** ||0.5473
||D36 ||0.7869 ||8.282*** ||1.008
||D37 ||1.212 ||12.02*** ||1.310
||D38 ||0.7429 ||9.258*** ||0.9418
||R - Squared||0.9954||0.9876||Durbin-Watson ||1.482||1.446||F Statistic ||1942||717.6||Number of Obs. ||280 ||280
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 We will use the term "IT" as shorthand for the Bureau of Economic Analysis (BEA) category "Office, Computing and Accounting Machinery", which is comprised primarily of computers. Other authors have used slightly different definitions but the basic trends appear to be similar regardless of the exact definition used.
 Other interpretations of the term "firm size" -- for instance, in terms of sales, assets or market capitalization -- are less common in the empirical literature. As we show below, a decline in employees per firm does not necessarily imply a decline in these other characteristics.
 While this is a broader set of data than has been used before, it would arguably have been preferable to examine data on 100% of the US economy. We were unable to find any comparable data for the government or the "underground" economy and only relatively incomplete data for resource industries like agriculture. In any event, the theory of, and even the definition of, "the firm" does not readily translate to these sectors.
 Although often used synonymously, establishments are not, strictly speaking, equivalent to firms. Nonetheless, the vast majority of firms consist of a single establishment. A study by Carlsson (1988) found that the correlation between changes in the number of establishments and the number of firms in a sample of manufacturing industries was over 97%.
 We we also tested a model which included the communications equipment category with substantially identical results. See appendix A.
 See also Berndt and Morrison (1991) for additional detail and interpretation. We thank John Musgrave for useful conversations on the data gathering methodology.
 The deflator for IT was actually an "inflator"; a dollar bought more IT each year than it did the year before.
 We thank an anonymous referee for this suggestion, which simplifies the interpretation of our previous, more complex, specification. Two notes are in order. 1) This approach assumes that lag structure is constant over time. 2) A second degree polynomial allows for only one inflection point and so it cannot duplicate an S-shaped pattern of changes in effectiveness. If the true pattern were S-shaped, then the PDL might be either convex or concave, depending on which curve of the "S" is more closely fitted.
 For instance, a recent study of Canadian data concluded that "the increasing importance of service sector employment played a role in the growth of small firm jobs, but was generally less important than shifts in the size distribution within the major industrial sectors." (Wannell, 1990)
 Presumably, not all of the "new" firms created when a vertically integrated firm decouples will be classified in the same industry as their "parent". Consider for instance the outsourcing of design work from a manufacturer. However, the remaining manufacturing firm would be smaller than before, so the outsourcing would be reflected in average firm size within the sector as well.
 In any event, the OLS coefficients were very similar. On the recommendation of a referee, we report the IV estimates because statistical tests to choose between IV and OLS estimates do not always exhibit desirable statistical power properties and hence IV estimates, which are always consistent, are preferred to possibly biased OLS estimates (Nakamura & Nakamura, 1985)
 Formally, this is equivalent to the prediction that c1 will be positive and c2 will be negative in equation (2).
 An F-test rejected the restriction that the coefficients on IT were equal to zero at the 99.9% level, strongly supporting its explanatory power in the regression.
 A review of the residuals from both regressions revealed one possible outlier in the Transport and Utilities sector. Because this outlier could be due to an error in data coding, we re-ran the regression without this data point. The revised results, (table C), do not significantly affect findings discussed in the text except to further strengthen the negative relationship between IT and firm size.
 As cited in Lichtenberg, (1990).