Full Paper in pdf - MISRC - University of Minnesota

DO INFORMATION TECHNOLOGY INDUSTRIES IN INDIVIDUAL STATES
RESPOND TO NATIONAL SHOCKS?
Robert J. Kauffman
Director, MIS Research Center, and Professor and Chair
rkauffman@csom.umn.edu
Ajay Kumar
Doctoral Program
akumar2@csom.umn.edu
Information and Decision Sciences Department
Carlson School of Management, University of Minnesota
Minneapolis, MN 55455
Last revised: October 2, 2006
_____________________________________________________________________________
ABSTRACT
Different states in the United States have reacted differently to the boom and bust period in the
information technology (IT) sector. We analyze the state-wise trends of employment in two IT
manufacturing sectors (the computer and peripheral manufacturing industry, and the
semiconductor and other electronic components manufacturing industries), and one IT service
industry (the software publishing industry) over the period 1994 to 2003. We use near vector
autoregression (NVAR) analysis to study the co-movement of employment patterns in these
industries at the national and state levels, and estimate the related contagion effects based on the
extent to which a national shock influences the state growth pattern for the industry. We find that
the IT manufacturing industries have significantly higher contagion effects as compared to IT
services industries. We also find that the larger IT-producing states tend to have larger contagion
effects. We explore the factors that characterize this long-term relationship between the whole of
United States and its individual states, and find that per capita state gross product, export base of
the state, average firm size, new economy index, venture capital are among the factors which
influence the contagion effects between states. We also note that their influence varies for
different IT industries.
Keywords and phrases: Aggregate analysis, co-movement, contagion effects, empirical
analysis, IT industries, IT supply, macroeconomy, near vector autoregression, shock analysis.
______________________________________________________________________________
Acknowledgments. The authors wish to thank our business school librarian, Van Houlson, and
the Minnesota Center for Population Studies for assistance with the data for this project. Ajay
Kumar acknowledges the Graduate School of the University of Minnesota for ongoing
fellowship support, and Rob Kauffman wishes to thank the MIS Research Center of the
University of Minnesota for partial support, as well as Donna Sarppo, Assistant Director, for
assistance.

INTRODUCTION
Employment in the computer and peripheral equipment manufacturing industry fell by nearly
22% between 1994 and 2003 in the United States. The decline in this sector’s employment is
commonly attributed to the increasing offshoring of manufacturing and the dotcom bubble. Both
these shocks occurred at the national level and were experienced by all states within the U.S.
However, the extent to which the shocks influenced the different states is different. During the
past decade, some states experienced tremendous IT industry growth and while others had to
deal with staggering declines in growth. For example, from 1994 to 2003, employment in the
computer and peripheral equipment manufacturing industry fell by 46% in California, but grew
by over 93% in Texas— a ―m iracle,‖ in the view of some observers (e.g., Virmani and Brown,
2005). The prior literature has not looked at this interesting phenomenon of different responses
by states to national level IT shocks. The diagnosis of how different states responded to shocks
in IT sector during the last decade is interesting because of the variations in the responses. It is
also relevant as it may provide insights about how other states might be able to duplicate Texas’
experience.
Regional economists and macroeconomists generally believe that there is long-term
underlying relationship between the national and regional economies of a country (Sherwood-
Call, 1988; Brewer, 1985; Kort, 1981). IT is an important sector in the new economy and an
understanding of the regional and national relationships in this sector is of interest to policy
planners the world over. Among not very different metropolitan areas in the U.S., the growth in
the high-tech industry explains nearly 70% of the variation in relative economic growth (DeVol,
1999). Understanding how a regional IT industry is affected by external shocks also has
important implications for forecasting the future of the industry in the state and its overall impact
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on the economy. Such analysis also has the potential to provide an understanding of the inherent
strengths of a region to play host to the development of IT industries. So the exploration of
factors which may explain such strengths are likely to help policy makers to address concerns
relating to the success of IT industries.
High-level growth trends may not always reflect the long-term relationship between regional
and state economies because industries often are influenced by unexpected local and national
factors. Local shocks occur in different forms. One occurs when a major unit of a firm in the IT
industry comes onto the scene, like Microsoft Research in Beijing (Wang, 1998), which may
spur the growth of related IT industries in the region irrespective of any other influences. Arthur
(1990) also refers to such a possibility, arguing that locations that are inherently inferior may
emerge as winners if, by accident or path dependence, they accumulate an early critical mass of
firms in an industry. Subsidies by local governments also may spur an industry in a region to
grow.
To effectively study growth trends for IT industries, we must capture the overall effect of
various influences, both local and national. But merely looking at time trends may not convey
whether they are occurring due to local forces or national forces. For example, although the
computer and other peripheral manufacturing industry has grown in Texas in the past fifteen
years while the national trend has been on decline, nevertheless it would be hard to conclude that
Texas was less impacted by national factors, such as offshoring and the dotcom bubble, than
other states. It is possible that Texas actually was equally or even more influenced than
California was, but since the local factors were stronger the overall trend shows an incline.
Thus, time trend alone may not signify the extent of influence that a shock at the national level
has on a state.
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But how can we study this? What means are available to measure nation to state and state to
nation level influences? The co-movement of macroeconomic variables can be used to assess
their long term relationships without making any unrealistic identification assumptions (Sims,
1980). Christiano and Fitzgerald (1998) define co-movement of macroeconomic variables in
different sectors in the economy in the context of business cycles as the degree to which they
move up and down together. Co-movement of the state economy and the national economy
using vector autoregression (VAR) has been studied by Sherwood-Call (1988). We propose to
analyze the co-movement in the growth of IT industries at the state level and the national level.
Based on the differential co-movement of these industries in different states, we hope to pinpoint
factors which influence the contagion effects experienced by IT industries in a state.
Contagion effects among IT industries are important as a way of characterizing dynamic
regional growth patterns of these industries. Although there have been some studies conducted in
the past which have looked at various aspects of the computer industry and its growth, these
studies have not looked into the relationships that these industries exhibit between the states and
the national level economy in the U.S. In addition, even though these other studies have studied
relationships among macroeconomic variables, they have not concentrated on the IT sector. One
reason for this is that the rapid pace of innovation in technologies has limited a longitudinal
study of the relationships that are present. However, some of these industries are beginning to
show signs of maturity, including slower growth in revenue, reduced profitability and increased
producer concentration (Macher and Mowery, 2004), which will permit a longer-term study of
the key state and national relationships.
We construct a time-series of data for three IT industries, computer and peripheral equipment
manufacturing, semiconductor and other electronic components manufacturing, and software
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publishing. We use these to characterize the relationships between different states of the U.S.
and the country as a whole for these industries and the co-movement of several key IT industry
indicators over a ten-year period. We expect that the relationships for IT industries will be
different from other industries because of the high degree of concentration in industrial structure
(Bresnahan, 1998) and the high degree of geographical localization (McKendrick et al., 2000).
The structures of the IT industries reflect their innovation and growth, and are marked by
network effects, strategic barriers and increasing returns to scale. So they may exhibit somewhat
different relationships. Another feature of this study is the 1994 to 2003 period that we cover for
our analysis. Two major shocks hit the IT sector during these years, the dotcom bubble and
explosive growth in IT outsourcing. Our study captures the effect of these shocks on the
industries.
We address the following research questions:
How can the study of co-movement inform us about the contagion effects that exist
between the national level and the state level for IT industries?
Is there a contagion effect between the national and state levels for IT industries? Is it
different for different IT industries? And if so, how?
Why do states show varying degrees of contagion effects? Are there factors that explain
the differences? Are these factors the same or different for different IT industries?
We find that IT manufacturing industries have significantly higher contagion effects as compared
to IT services industries. We also find that the larger IT producing states tend to have larger
contagion effects. We provide explanations for the variations in contagion effects between states.
The findings of this research have important policy implications with regard to managing in the
presence of IT shocks.
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The remainder of the paper is organized as follows. We next review the growth of the IT
industries that we will study in greater detail. We also review some of the important studies on
co-movement and how prior research informs our approach for understanding the contagion
effects between national-level growth patterns and state-level growth patterns in IT industries. In
the third section, the first stage of analysis of this study, we explain the methodology and the
data used for our near vector autoregression (NVAR) analysis of the indicators of state and
national level co-movement. We also present the results relating to the estimation of contagion
effects and discuss the implications of these results. The fourth section presents the second-stage
of analysis, in which we propose a model for characterizing the contagion effects more formally,
as well as discuss the details of the data used to test this model. The section also includes the
estimation results. We conclude by summarizing the main contributions of this paper, reflect on
the effectiveness of our approach to build new theory, identify the implications of the model and
data used, and offer some thoughts about how to continue the development of this research
stream.
2. IT INDUSTRY TRENDS: BACKGROUND AND GROWTH CO-MOVEMENT
We next review the broad patterns of growth of employment in three IT industries: computer
and peripheral equipment manufacturing, semiconductor and other electronic component
manufacturing, and software publishing. We then discuss growth co-movement and our approach
towards modeling contagion effects via vector autoregression.
2.1. Background on the IT Industries
The IT industries have undergone a change from the vertical industry structure seen in the
1970s and the 1980s to the horizontal industry structure observed in the 1990s. The vertical
structure characterizes an era when players such as IBM and Digital Equipment Corporation
5

dominated the market. These computer companies were vertically-integrated and provided a full
spectrum of computing services to their customers. Customer choice was limited to players like
these, and once a company was chosen, this led to automatic selection of all peripheral hardware
and software as well. Vertical structure in industry services made way for horizontal structure by
the mid-1990s. Horizontal structure meant that the IT industry increasingly was segregated into
different horizontal layers. These included the semiconductor device manufacturers, computer
and peripheral equipment manufacturers, operating system providers, application software
providers, and retail and distribution providers. Most companies operated in one of these layers.
This should have led to greater competition with more firms within each layer vying for the
attention of the customers. However, as the IT industries evolved, new dominant players
emerged within these layers. Some examples are: Intel, AMD and Motorola within the
semiconductor devices layer; Compaq, Dell and IBM in the computer manufacturing level;
Microsoft and Apple in the operating system (OS) layer (Bresnahan, 1998). The domination of
Windows as the OS platform and Intel as the chip manufacturer has been so complete that it
prompted Lawson (2002) to call the 1980s and 1990s the ―Wintel (Windows-Intel) E ra.‖
In addition to the concentrated nature of its structure, the IT industry also shows high
geographical concentration. Many industrial capabilities in IT have evolved in localized clusters,
as technology clusters or as operational clusters (McKendrick et al., 2000). Technology clusters
are based around a large science park with a research-based university, a metro-high tech zone,
and a relatively high concentration of focused industrial research (e.g., Stanford and Palo Alto,
California; University of Texas in Austin, Texas; and MIT in Cambridge, Massachusetts).
Operational clusters support manufacturing, assembly and allied activities. States that have a
6

significant presence of IT industries (e.g., California, Massachusetts, Texas and New York) also
show a high degree of cluster formation in these industries.
Notwithstanding these high-level similarities, employment levels and average firm size
trends show that there are significant differences in the way these industries have grown or
declined during 1994 to 2003. We will empirically compare the four leading states of California,
Massachusetts, Texas and New York to bring out these differences.
Computer and Peripheral Equipment Industry. Employment in the national computer
and peripheral manufacturing industry remained at the 1994 level till around 1999 and dipped in
2000. The annual decline in employment between 1999 and 2002 was nearly 11.4% overall. The
declining trend seems to have reversed in 2003, with employment growing 20.2% over 2001.
During this time, California was the state with the highest employment in 1994 in this industry,
constituting nearly 31.1% of national employment. National employment in the computer and
peripheral manufacturing industry, meanwhile, declined to 21.8% in 2003. The overall decline
of employment over the period was 45.3% for California in comparison to 22% for the nation.
On the other hand, Texas’ share of em ployment in the industry grew from 5.1% in 1994 to about
12.7% in 2003. Texas showed nearly 93% growth in employment over the same period 1 .
Massachusetts experienced retrenchment in growth, with an annual decline of over 12% between
1994 and 1999. Industry employment growth then was steady there till 2001, but began to
grow again at an annual rate of 28.2% during 2002 and 2003. New York showed the most
dramatic decline in 1995, and its industry employment declined 82.5%, as shown in Figure 1.
1 The employment in the computer and peripheral equipment industry in Texas in 2003 is 42,608 in the Economic
Census database. The same figure for 2002 is 13,463 and for 2001 it is 18,691. Since the difference between the
2003 and 2002 employment level showed an unreasonable jump with no obvious explanation, we requested the
Census Bureau to confirm their figures. They have orally informed that the 2003 employment figure is wrong. So
we used an approximate figure of 14,500 for employment in Texas for 2003 in this industry. The approximation
does not substantially affect the analysis and results.
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An establishment is a single physical location of a firm at which business is conducted and
services are provided. The size of each establishment in our data, represented by number of
employees per establishment did not change uniformly across states. 2 Nationally though, the
average number of employees per establishment has remained largely the same, indicating that
firm size did not change significantly. However, average firm size has declined in California,
from 149.8 employees in 1994 to 107.5 employees in 2003. Average firm size has also declined
in Massachusetts, though the decline is much smaller.
Figure 1. Employment in Computer and Peripheral Equipment Manufacturing Industries
# Employees/Establishment a
(Base Year 1994)
2.5
2
1.5
1
0.5
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (147.9)
California (149.8)
Massachusetts (96.0)
Texas (137)
New York (104.7)
Note: 1994 employment normalized to 1. Figures in parentheses show base year employment levels.
Meanwhile Texas shows a significant increase in average firm size, with employees per
establishment increasing from 137 in 1994 to 259.7 in 2003. In New York, average firm size
declined from 124.7 in 1994 to 22 in 2003. These changes in the computer and peripheral
2 An establishment is not necessarily identical to a company or enterprise, which may consist of one establishment
or more. Economic census figures represent a summary of reports for individual establishments rather than
companies (U.S. Census Bureau, 2004).
8

equipment manufacturing industry are depicted in Figure 2.
Semiconductor and Other Electronic Components. The U.S. once enjoyed 85% of global
market share in this industry (Browning et al., 1995). In the 1980s though, it began to face stiff
challenges from nations in the Asia-Pacific region, such as Japan, Taiwan, Singapore and Korea,
causing its market share to decline. In 1987, with the help of the U.S. government, the industry
formed SEMATECH, the Semiconductor Manufacturing Technology Consortium, to counter this
threat. This arrested the decline of the industry in the U.S. during the period that we studied,
however, employment in the semiconductor and other electronic components industry continued
to show small cyclical changes between 1994 and 2001, while sustaining existing levels. Later,
the employment dipped in 2002 and this downward trend continued during 2003, with an annual
decline in employment in these two years of 20.9%.
Figure 2. Firm Size in Computer and Peripheral Equipment Manufacturing Industries
# Employees/Establishment a
(Base Year 1994)
6
5
4
3
2
1
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (147.9)
California (149.8)
Massachusetts (96.0)
Texas (137)
New York (104.7)
Note: 1994 average firm size normalized to 1. Figures in parentheses in the legend indicate the number
of employees per establishment in the base year of 1994.
9

The trends of employment in the states have varied from state to state. California, the biggest
employer, comprising nearly 25.2% of the total employment in the semiconductor industry in
1994, mirrors the national trend during this period. The share of California in the overall
employment in the industry also declined, although not as much as in the computer and
peripheral equipment industry. T he state’s share of employment in 2003 was 21.9%. For
Massachusetts, the decline in employment in the industry started in 1999, two years earlier than
the national trend but the overall decline between 1994 and 2003 was not very different from the
national trend. Texas again shows a different trend. The employment in the industry in Texas
grew between 1994 and 2001 at an annual simple growth rate of 6.8%. However, employment
declined at 18.8% during 2002 and 2003. T exas’s overall share of employment in this industry
increased from 10.6% to 13.5% during the period under review. New York experienced a sudden
dip in employment in 1997 but has remained steady at that level subsequently. The employment
trends for this industry are shown in Figure 3.
Figure 3. Employment in Semiconductor and Other Electronic Components
Manufacturing Industries
Employmnet (Base Year 1994)a
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (328806)
California (82882)
Massachusetts
(15607)
New York (23697)
Texas (34746)
10

Note: 1994 employment normalized to 1. Figures in parentheses show base year employment levels.
Average firm size in the industry increased from 108.8 employees per establishment in 1994
to 127.94 in 2001, but declined to 94.7 in the 2002 and 2003. The average firm size in California
was 92 in 1994 and it declined further to 63.3 employees per establishment in 2003. Average
firm size in Massachusetts also fell: from 90.2 in 1994 to 70.6 in 2003. The average firm size in
Texas, however, increased from 187.8 in 1994 to 225.7 in 2003. The average firm size in New
York was 179.5 in 1994, but it declined to 120.5 in 2000 and again increased to 154.8 in 2003.
The trend in average firm size in semiconductor and other electronic components manufacturing
industries is shown in Figure 4.
Figure 4. Average Firm Size in Semiconductor and Other Electronic Components
Manufacturing Industries
# Employees/Establishment
(Base Year 1994)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (108.8)
California (92.0)
Massachusetts
(90.2)
New York (179.5)
Texas (187.8)
Note: 1994 average firm size normalized to 1. Figures in parentheses in the legend indicate the number
of employees per establishment in the base year of 1994.
Software Publishing. Employment in the IT services industry has increased, counter to the
decline we have seen in the IT manufacturing sector. National employment in the software
publishing industry shows simple annual growth of 14%. California comprised nearly 30% of
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the industry in 1994 and this share decreased slightly to 27.3% in 2003. The overall trend in
California mirrors the national trend with a slightly lower annual growth rate of 12.1%.
Massachusetts, however, shows growth in employment between the years 1994 to 2000 but a
subsequent decline. The simple annual growth rate between 1994 and 2000 is 12%, similar to the
national average, but thereafter shows an annual decline of 3.5% between 2001 and 2003. Texas
showed a simple annual growth rate of 35% during the period 1994 to 2003. New York shows a
trend similar to the national trend, with a slight peak in 1997 and an average simple annual
growth rate of 13%. These employment trends in the software industries are shown in Figure 5.
Figure 5. Employment in Software Publishing Industries
Employment (Base Year 1994) a
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (150093)
California (45003)
Massachusetts (17445)
New York (5980)
Texas (6264)
Note: 1994 employment normalized to 1. Figures in parentheses show base year employment levels.
The national software publishing industry showed an increase in average firm size during the
period 1994 to 2003. The average firm size increased from 21.9 to 37.6 employees per
establishment. California had an average firm size of 34.6 in 1994 and that increased to 51.5 in
2003. In Massachusetts, the average firm size increased from 38.9 to 48.2 in the same period.
12

The Texas software publishing industry was marked by smaller firms: the average firm size was
only 11.3 employees per establishment in 1994. It increased to 39.2 in 2003, showing
consolidation and representing an annual growth in firm size of 27.3%. In New York, the
average firm size increased from 17.3 to 26 employees. The pattern of average firm size changes
in the industry is shown in Figure 6.
Figure 6. Average Firm Size in Software Publishing Industries
# Employees/Establishment
(Base Year 1994)
4
3.5
3
2.5
2
1.5
1
0.5
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
US (21.9)
California (34.6)
Massachusetts (34.9)
New York (17.3)
Texas (11.3)
Note: 1994 average firm size normalized to 1. Figures in parentheses in the legend indicate the number
of employees per establishment in the base year of 1994.
For both the IT manufacturing and IT service industries, we note that the employment trends
differ in the main IT states. These states apparently responded differently to shocks (e.g., the
dotcom bubble and offshoring), even though the shocks were nation-wide in scope. These trends
vary from one IT industry to another and for the same IT industry from one state to another. A
time-series analysis of these trends provides an opportunity to study the co-movement of
employment nationally and at the state level.
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2.2. Vector Autoregression and Its Application in IS Research
Having noted the variations in the growth patterns of IT industries across different states and
their relationship with the growth of the industries at the national level, we next identify an
appropriate method to enable the scientific study of these relationships. Also note that since the
study involves relationships between the state and the national levels, the method should be
applicable for the study of macroeconomic phenomena. Vector autoregression (VAR) is a
versatile econometric methodology which permits the estimation of the relationship between
endogenous variables without making any a priori theoretical modeling assumptions. VAR
models are applicable to any level of analysis and, thus, permit the study of the co-movement of
variables that occur at both the national and state level, where the former is an aggregate of many
of the latter observations. Sims (1980) argued that most macroeconomic models which study
cyclic fluctuations among variables suffer from ―incredible identification.‖ 3 His view is that
building large macroeconomic models by incrementally solving single partial-equilibrium
equations amounts to identification by putting unreasonable restrictions on the variables which
have some influence across equations. He proposed an alternative style of macroeconomic
econometrics which eliminates these restrictions, and does not require the use of any specific
theoretical perspective as a basis for model formulation. He also showed how VAR models can
be used to estimate large-scale macroeconomic models that treat all variables as being
endogenous.
VAR methods have since been used in different disciplinary contexts, especially in finance
and economics, to study the co-movement of macroeconomic variables. Sherwood-Call (1988)
used vector autoregression to develop a linkage measure between state and national economic
3 According to Sims (1980), the reduced form equations used for identification typically involve normalization in the
relevant parameter space. However, there may be different ways to accomplish these normalizations. This further
implies that there may be different estimations to match the different normalizations.
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growth rates, to determ ine w hich characteristics of states’ econom ies are associated w ith stronger
or weaker linkages to the national economy. Cromwell (1992) used a VAR model to measure
linkages between the California economy and its neighboring states, and to see what to extent
economic shocks that impact California also spillover to its neighbors. His results showed that
California economy exhibits important spillover effects on other western states, particularly
those in close geographic proximity to it. Shea (1995) studied inter-industry co-movements
within seven large cities in the United States and found evidence that local spillovers explained
approximately one-third of the manufacturing employment volatility at the city level.
In other research, Carlino and DeFina (1995) used structural VAR techniques to explore
interregional links in regional per capita income growth and found that spillovers existed in that
context. Carlino and Sill (2001) more recently studied the dynamics in per capita income for four
major U.S. regions and found considerable differences in the volatility of regional business
cycles. They also found evidence for co-movement in cyclical responses for the different
regions. Coulson (1999) also used a VAR model for identifying sectoral sources of metropolitan
employment growth and found that manufacturing, services and public sector employment
shocks account for a substantial portion of employment growth variation. Coulson and Rushen
(1993) further applied VAR models to study the effects of structural shocks, like defense
spending, on the Boston, Massachusetts economy. Finally, Rousseau and Wachtel (2000) used a
VAR model to study the economic impacts of equity markets on the economy.
Although extensive studies of co-movement of different macroeconomic variables have been
done in many other academic disciplines and industry contexts, especially in the financial
markets world, there has been limited use of this methodology in the IS literature to date. An
exception is Kauffman et al. (2006), who employed a quasi-VAR approach to gauge the linkage
15

etween the network growth of electronic banking networks at the state and national levels.
When some VAR equations have regressors that are not included in others, the system of
equations is called a near vector autoregression (NVAR). Seemingly unrelated regression
provides efficient estimates of the VAR coefficients (Enders, 2004). Kauffman et al. (2006)
proposed how the NVAR approach could be used for the development of a model to understand
state and national linkages in electronic banking network and automated teller machine (ATM)
network growth. In addition, Kauffman and Wood (2001 and 2006) have used VAR modeling
methods to explore the extent to which Internet-based sellers of books, software and consumer
electronics respond to one another’s price changes in ―follow -the-leader‖ fashion over eight
lagged periods (m easured in days since a firm ’s change occurred). Finally, Kauffman and
Techatassanasoontorn (2005) also have used an NVAR model to study the diffusion of digital
wireless phone technology in different regions of the world, and to formulate the basis for a
regional contagion theory of technology diffusion. However, to our knowledge, there is no other
prior literature that examines the co-movement of IT industry-related variables (e.g.,
profitability, employment or capital growth, firm size, and so on).
3. MODEL, DATA AND RESULTS
The studies that we have reviewed show that the co-movement of employment in IT
industries at the national level and the state level can be used to evaluate the extent of the
influence that national shocks have on state-level IT industries.
3.1. An NVAR Model for State-Level and National-Level Linkages in IT Industry Growth
We apply an NVAR model to study co-movement of employment growth at the state and
national level in different IT industries without attempting to model the reasons for such co-
movement. This approach is in line with many well-known and often-referenced studies in
16

macroeconomics, where policy research has taken a step-wise approach to understanding a
variety of phenomena, including regional inflation (Aiyagari et al., 1998; Mehra, 1991), changes
in interest rates (Friedman and Kuttner, 1992; Zapata and Fortenbery, 1996), growth in regional
domestic product (Kose et al., 2003; Hercowitz and Sampson, 1991; Barro and Sala-i-Martin,
1991), and other issues (Kehoe and Perri, 2002; Gali, 1999; Long and Plosser, 1987).
In this context, a NVAR model enables the study of the co-movement of macroeconomic
variables without any unnecessary a priori restrictions about how the firms in these industries
grow over the long-term period. One may expect co-movement of employment in IT industries at
the state and national levels for different reasons. First, the variables that describe various
elements of growth in a national industry are typically a summation (e.g., total output) or a
weighted average (e.g., growth rate for capital investment) of the state-level industries. So it is
natural to expect that the trends in such variables that are observed in the states will reflect the
trends that are observed in national industrial growth. Second, many different kinds of shocks
have affected the IT industries. These include: the dotcom bubble in the U.S.; the increasing
capabilities of the technology and engineering workforces of China, India and Eastern Europe,
and the offshoring of IT services; and changes brought on by new standards and emerging
technologies (e.g., adoption of Wi-Fi 802.1g and WiMax, and voice over Internet protocol,
VoIP). Such impacts are felt by industries at both the national and the state levels, so this should
result in co-movement of some key industry-related variables. For states like California, which
comprise a large fraction of the overall share of IT industries in the national ―technology kitty,‖
we also expect a larger degree of co-movement.
On the other hand, local shocks that are specific to a state or a region may not be sufficient in
their influence to cause key industry indicators in a state to track the national trends. Many
17

economic variables which affect the growth of industry, including the availability of venture
capital, interest rate levels, the tax regime and available human resources, tend to differ from
state to state, with the result that there are different growth and performance environments in
play for different industries locally. When a large firm grows to define an industry in a state—
like Microsoft has come to define the IT industry in the Seattle and Redmond, Washington area
or Google in Mountain View, California— it creates positive shocks which result in the indicators
for the state-level industry resulting in deviations from the national trend for the same industry.
An NVAR model for studying co-movement for some key industry indicator variables should
include lagged values of the national indicators and all state-level indicators as independent
variables. However, such an approach is not feasible, as there are 51 state-level units (including
the Washington, DC, the nation’s capital) that comprise the United States. Moreover, even if
just two lags are considered, then the related NVAR mode would have more than 100 variables
and more than 100 equations. Apart from being unwieldy to instantiate and estimate, such a
system of equations is likely to suffer from serious multicollinearity problems.
With estimation feasibility in mind, we set up a two-equation model which captures the
essence of the estimation problem for state and national-level indicator co-movement for the IT
industries. One equation is intended to represent the United States as a whole; the other is for a
given state (Sherwood-Call, 1988; Kauffman and Techatassanasoontorn, 2005). The state
equation includes two lagged variables for the state and the United States as a whole, while the
United States equation is constrained to ensure uniformity across the VAR models for the
different states. The U.S. equation does not include any specific coefficients from individual
states; instead, it only considers an AR(1) structure for the U.S.-level indicator value. This effect-
constrained version of the U.S. equation and two-lag length model for the state equation is
18

appropriate for analysis of our data across different states and different industries. Note that this
modeling formulation places greater emphasis on the impacts of national-level shocks on the
state-level than vice-versa; the State variable does not occur in the U.S. equation. Since loss of
degrees of freedom is a critical issue in any NVAR model estimation, by using a constrained
model we can control the loss of degrees of freedom somewhat. 4 Based on these preliminaries,
the NVAR model for each industry j in state k is:
US
t t1
US t1
t
(United States Equation) (1)
2
k , t k
k
, tiUSti
2
k
, ti
State k , ti
i1
i1
State
t
19
(State Equation) (2)
Thus, our model is specified for one state relative to the national economy, and is iteratively
estimated for all states, and for each of the industries included in the study.
3.2. Data
We use data from the North American Industrial Classification System (NAICS) of the
United States Bureau of Economic Analysis (BEA) and the United States Economic Census
(www.census.gov/epcd/www/naicsdev.htm) for the study. NAICS is an objective classification,
developed for comparative analysis and research relating to different industries and is the most
widely-accepted industry classification available at this time. The NAICS also supports effective
empirical analysis because the United States government collects annual Economic Census data
4 Some natural questions to ask are: Why only two lags? How many lags are appropriate? First, in this kind of
demonstrational analysis, it is appropriate to specify a model that is feasible to instantiate from the point of view of
available data. As the reader will soon see, we do not have as many degrees of freedom as we might like with the
data that are available. Therefore, we did not try to model a VAR with more than three lags. Second, consistent with
S im ’s (1980) suggestions, w e bring no specific theory for the tim e fram e in w hich lagged im pacts m ay occur. W e
tried different combinations of lags with maximum of three lags and found the model as proposed most appropriate
based on statistical indicators, the Schwartz criterion, the Akaike information criterion, and the adjusted-R 2 criterion
(Pindyck and Rubinfeld, 1998).

for each industry for different geographical units, which permits time-series analysis. (See the
Appendix for additional information.)
Industry Choices. Based on the NAICS classification adopted by the U.S. Bureau of the
Census after 1997, we selected the following industries: computer and peripheral equipment
manufacturing (NAICS Code 33411), semiconductor and other electronic components
manufacturing (NAICS Code 33441), and software publishing (NAICS Code 51121). These
three industries represent the IT manufacturing and IT services sector and, thus, enable us to
study of behaviors across a broad spectrum of the IT sector. The time period over which we
study the trends in these industries is the ten years between 1994 and 2003. This period covers
the IT boom-and-bust period and the beginning of the recovery in the IT sector, which most
observers believe began in late 2003 or early 2004 (Frauheim, 2003; Seabrook, 2003). This
enables us to study the impacts of the shocks on the states.
Issues with NAICS and SIC in Data Set Construction. Changes in the industry
classification scheme that occurred in 1997 complicated our data collection. Prior to 1997, the
industry classification used was the Standard Industry Code (SIC). The SIC and NAICS
classifications do not have a complete one-to-one correspondence for the NAICS 33411 and
33441 industries. Each five-figure NAICS classification is made up of a number of six-digit
industries. For example, NAICS 33411 (computer and peripheral equipment manufacturing)
includes NAICS 334411 (electronic computer manufacturing), NAICS 334412 (computer
storage device manufacturing), NAICS 334413 (computer terminal manufacturing) and NAICS
334419 (other computer peripheral equipment manufacturing). The NAICS and SIC
classifications provide one-to-one correspondences between NAICS 334411 (with SIC 3571),
NAICS 334412 (with SIC 3572) and NAICS 334413 (with SIC 3575). However, there is no
20

corresponding classification for NAICS 334419 in the SIC classification. Therefore, we used just
three component industries (i.e., NAICS 334411, 334412 and 334413) to develop the data for the
NAICS 33411 five-digit industry for the period between 1994 and 2003. Similarly, the data for
NAICS 33441 (semiconductor and other electronic components manufacturing) is built up from
the related six-digit industries (i.e., NAICS 334411, 334412, 334413, 334414, 334415, 334417),
for which one-to-one correspondences were available in the SIC classification. The data set so
constituted represents exactly the same set of industries for the time period under study. These
kinds of one-to-one correspondences between the SIC and NAICS classification are essential in
order to create a meaningful time-series analysis. (See the Appendix for additional details.)
3.3. NVAR Analysis
We next explain the role of forecast error variance decomposition, its connection to the
measurement of contagion effect between the states and the national economy, and the results of
its application in our NVAR analysis.
Forecast Error Variance Decomposition Estimation. The forecast error variance
decomposition (FEVD) can be estimated using the NVAR models that we discussed earlier for
all of the states in the United States. The forecast error is the difference in the predicted value
from the actual value, based on the model represented in the state equation (Equation 2). The
FEVD decomposes this difference to estimate the extent to which the difference in a state’s
forecast can be attributed to the occurrence of national-level shocks, as represented by εt in the
model (Sherwood-Call, 1988). In other words, the FEVD captures the extent to which a
national-level shock may make the state deviate from the trend determined by the state model.
The FEVD figure, therefore, gives a reading on indicator co-movement when there is a national
shock. It acts as a measure of contagion effects between the nation and the states.
21

Results. The forecast error variance decomposition (FEVD) figures for the three IT
industries under study from 1994 to 2004 are given in Table 1 below.
Table 1. FEVD Values by Industry and State in the United States, 1994-2003
STATE FEVD VALUES BY INDUSTRY STATE FEVD VALUES BY INDUSTRY
SOFT-
WARE
CPEM SEMICON-
DUCTORS
SOFT-
WARE
CPEM SEMICON-
DUCTORS
Alabama 23.9% 28.9% 99.3% Missouri 78.8% 76.8% 73.8%
Alaska 18.1% 98.7% NA Montana 69.0% 70.1% 79.0%
Arizona 94.1% 89.8% 95.4% Nebraska 13.7% 26.4% 91.3%
Arkansas 31.2% 28.1% 99.8% Nevada 30.8% 35.8% 97.0%
California 91.1% 94.8% 96.9% New Hampshire 33.8% 98.2% 99.0%
Colorado 86.0% 84.2% 93.0% New Jersey 68.0% 95.9% 99.9%
Connecticut 68.3% 47.0% 95.8% New Mexico 75.1% 99.7% 99.5%
Delaware 67.0% 40.7% 96.9% New York 90.8% 52.2% 97.2%
DC 82.9% NA 98.9% North Carolina 68.4% 48.5% 78.3%
Florida 85.0% 93.7% 98.1% North Dakota 29.7% NA 94.5%
Georgia 75.1% 96.5% 96.6% Ohio 83.5% 74.7% 98.0%
Hawaii 46.7% NA 99.3% Oklahoma 54.2% 77.4% 83.5%
Idaho 25.2% 62.8% 96.0% Oregon 97.9% 75.4% 97.5%
Illinois 91,8% 67.3% 95.5% Pennsylvania 81.1% 58.9% 94.4%
Indiana 75.1% 98.8% 97.2% Rhode Island 51.2% 94.8% 83.4%
Iowa 63.7% 67.3% 98.5% South Carolina 87.5% 88.7% 97.5%
Kansas 94.6% 62.5% 99.5% South Dakota 22.8% 99.6% 93.9%
Kentucky 91.0% 84.1% 44.5% Tennessee 64.8% 99.0% 77.5%
Louisiana 32.1% 82.7% 51.9% Texas 99.9% 72.2% 99.4%
Maine 52% NA 80.2% Utah 69.0% 99.3% 97.2%
Maryland 83.3% 62.8% 90.0% Vermont 49.6% 93.8% 99.8%
Massachusetts 56.4% 91.6% 99.1% Virginia 77.3% 89.8% 99.2%
Michigan 35.6% 75.0% 99.5% Washington 75.8% 25.0% 99.9%
Minnesota 62.4% 99.4% 97.2% W. Virginia 43.8% 74.4% 99.9%
Mississippi 36.4% 28.1% 92.1% Wisconsin 72.7% 79.2% 93.7%
Wyoming 78.3% 15.3% 90.0%
Notes: Data are for 50 states. (1) Forecast error variation decompositions are: FEVDSOFTWARE— software
publishing industry; FEVDCPEM— computer and peripheral equipment manufacturing industry;
FEVDSEMICONDUCTORS— semiconductor and other electronic components manufacturing industry. (2) For some
states the FEVD figure could not be estimated due to multicollinearity problems. These instances are indicated
w ith ―N A ‖ and shaded gray in the table.
The average FEVD for the computer and peripheral equipment manufacturing industry,
FEVDCPEM-AVG, is 72.7%, indicating that 72.7% of the variation in forecast error can be attributed
to national shocks. The corresponding figure for the semiconductor and other electronic
components manufacturing industry, FEVDSEMICONDUCTOR-AVG, is 92.5%, and for the software
22

publishing industry, FEVDSOFTWARE-AVG, it is 63.4%. 5 These estimates show that a national shock
to the IT manufacturing sector is more likely to get transmitted with the state level as compared
to the IT services sector. Within the IT manufacturing sector, meanwhile, a national shock in the
semiconductor and other electronic components manufacturing industry is transmitted more fully
than is a national shock in the computer and peripheral manufacturing industry.
California shows a fairly high level of co-movement with the national trends for all three
industries, with FEVD figures ranging between 91.1% and 96.9%. This makes intuitive sense:
California is the biggest contributor to the national tally for IT industries; we expected to observe
this co-m ovem ent betw een C alifornia’s trends and the national-level trends for the different five-
digit NAICS industries. Massachusetts also shows large contagion effects with respect to the
semiconductor industry (FEVDSEMICONDUCTORS, MA = 99.1%) and the computer and peripheral
industry (FEVDCPEM, MA = 91.6%), but a lower contagion effect for the software publishing
industry (FEVDSOFTWARE, MA = 56.4%). Texas, in contrast, exhibits higher contagion effects for
the semiconductor and other electronic component industry (FEVDSEMICONDUCTOR, TX = 99.4%)
and the software publishing industry (FEVDSOFTWARE, TX = 99.9%), but it shows a lower contagion
effect for computer and peripheral equipment manufacturing (FEVDCPEM, TX = 72.2%).
We found that New York also has a high degree of contagion effects for the semiconductor
and other electronic component industry (FEVDSEMICONDUCTORS, NY = 97.2%), as well as the
software publishing industry (FEVDSOFTWARE, NY = 90.2%), but fewer contagion effects are at
work in computer and peripheral manufacturing (FEVDCPEM, NY = 52.2%). (The contagion
effects for all the other states are noted in Table 1.)
We also observe that the contagion effects for the three IT industries are different and do not
5 The different industries are marked with five-digit NAICS codes earlier in the text of the paper. For readability,
we shift to using industry sector names here and in Table 1.
23

track each other. The FEVD-correlation for the software publishing industry and the computer
and peripheral equipment manufacturing industry is 18.99%, while the semiconductor and other
electronic component industry have an FEVD-correlation of just 1.40%. The FEVD-correlation
for the computer and peripheral equipment manufacturing is negative and the semiconductor and
other electronic components industry at -7.33%.
3.4. Sensitivity Analysis
We specified the state equations with two lag lengths. In addition, we constrained the U.S.
model (Equation 1) with both lags of the state and the second own lag to be zero. We chose this
based on the Akaike information criterion (AIC) and the Schwartz Bayesian information
criterion (SBIC). 6 The two evaluative criteria enable an analyst to identify the extent of the
tradeoff between the complexity of a model and its degree of fit with the data (Burham and
Anderson, 2001).
To test the robustness of our estimations, we estimated FEVD by including the second own
lag in the United States model. The FEVD figures obtained with this alteration showed a high
correlation of 99.68% for the software publishing industry, 92.54% for the computer and
peripheral equipment manufacturing and 70.37% for the semiconductor and other electronic
components manufacturing industries with the FEVD figures shown in Table 1 above. This
suggests that our results are robust to changes in the lag structure that we chose.
3.5. Discussion
Although this study brings out some distinct and interesting patterns in the structural
relationship of the three industries, we do not have definitive theoretical explanations for these
6 The Akaike information criterion is AIC(m) = n ln (sm 2 ) + 2m, with sm 2 equal to the estimated variances of the
residuals, m the number of model parameters, and n equal to the sample size. Typical comparison involves models
of the form A(Rm) and A(Rm+1). The Schwartz Bayesian information criterion is SBIC(m) = n ln (sm 2 )+ p ln(m), with
p equal to the number of free parameters in the model.
24

patterns. We next will interpret these results on the basis of existing views on high tech industry.
De Vol (1999) has argued that high technology regions face greater risks because of
technology’s inherent volatility and close relationship with the business cycle of U.S. economy.
Our results are in accordance with this. We find that the large IT-producing states tend to show
high FEVDs, and thus, they tend to be affected by national shocks.
The results also indicate a lower average FEVD for the software industry as compared to the
hardware industry. (See Table 1 again). A possible explanation is the greater degree of vertical
specialization in the IT manufacturing industries (i.e., computer and peripheral equipment
manufacturing and semiconductor and other electronic components manufacturing) (Macher and
Mowery, 2004). The semiconductor industry has become increasingly specialized in product
design and manufacturing, while the computer industry has become specialized in various
horizontal layers, including components, computer assembly, operating systems, and
applications software (Bresnahan, 1998). This may reflect inherent differences in the services
sector and the manufacturing processes. Consistent with the results, it may be the case that states
with more service economy-led growth factors tend to be more insulated to national shocks and,
thus, they may be in a better position to chart their own course as compared to manufacturing
economy-dominated states.
The FEVD for the semiconductor and other electronic component industry shows high values
for most states. This suggests a very high linkage between the states and the national industry
structure for this industry. A possible explanation may be that the semi-conductor industry in
U.S. is increasingly specialized into two different functions. One is the design part of
semiconductor manufacturing, and many activities in this area are still occurring in the U.S.
economy. The other is the manufacturing part, which now is mostly carried out offshore in
25

countries such as Taiwan, Japan and Korea. Since offshoring is a nation-wide phenomenon in
the semiconductor industry, the industry’s grow th obviously will be closely tied to national
shocks.
Though the impression created by the business press has been that the national tech sector
decline had a lesser impact on the computer and peripheral equipment manufacturing industry in
Texas, our NVAR analysis shows something different. Texas, based on the results shown from
our data, exhibited a considerable degree of contagion effects from the nation (FEVDCPEM_TX =
99.4%), such that the national shock was practically fully transmitted to the state economy.
However, the state had a strong growth pattern of its own which overwhelmed the national
shocks. If the national shocks had not been there, our estimates would indicate that even
stronger growth of the industry in Texas should have occurred. We also note that Texas
contributes nearly 11% of the computer and electronic production and 12% of the exports to the
national economy, nearly twice its 7.5% contribution to national GDP. This reflects a relatively
large IT contribution from Texas to the national economy.
The contagion effect for the large IT-producing states for the software publishing industry is
much higher than the national average. This indicates that a national shock or innovation in the
software publishing industry flows through to these IT-producing states almost completely, while
it has a lesser impact on the smaller IT-producing states. This result matches our intuition that
the dotcom bubble and offshoring of software development are likely to hurt these states more
than others. We next characterize possible factors which may influence differences in the extent
of the contagion effects.
4. CHARACTERIZING THE FACTORS INFLUENCING CONTAGION EFFECTS
The heterogeneity of business cycles within the United States has been recognized in
26

previous research (e.g., Coulson and Rushen, 1993; Stock and Watson, 1998; Cambell, 1998). In
this second stage of the analysis, we try to identify contextual factors which can have influence
on the variation in contagion effects among states. The variation may be due to differences in
responses to common shocks, like the dotcom bubble, which was felt all over the U.S., or due to
idiosyncratic factors for each state. Since there is no obvious theoretical basis to suggest which
factors may explain the differences in contagion effects, we will use prior research literature to
guide our discovery.
4.1. Factors Driving the Contagion Effects
As our econometric analysis suggests, different IT industries show different contagion effects
and these effects vary from state to state. Since IT is a general purpose technology used by all
other industries (David and Wright, 2003; Jovanovic and Rousseau, 2003), a general
recessionary trend in the economy may lead to reduced demand for IT products and services
(Cragg and King, 1993). This suggests that the contagion effects may be related to the gross
domestic product (GDP) of the state. Wong (1998) studied the growth of information and
communication technologies (ICTs) in Singapore and suggested that the growth of information
economy is related to four components, one of which is the production and distribution of ICT
goods and appliances. 7 This indicates that the size of the IT sectors in the economy may also
impact the degree to which a state may exhibit a contagion effect. A state-level economy with a
higher dependence on the IT sectors is likely to be affected more by national level shocks in the
IT sectors.
Another factor which may influence the degree of the contagion effects is the level of
investment of venture capital in the state. Venture capital investment represents assessment by
the venture capitalist of a new venture’s likelihood of survival. These decisions are based on
7 The other three components are content industry, network infrastructure, and informatization.
27

factors such as the nature of the markets, competition, and regulatory environment in which the
venture exists (Shepherd, 1999). A state with higher venture capital investments represents an
assessment that a given industry in the state has higher likelihood of survival, ceteris paribus.
Keuschnigg (2004) posits that venture capitalists not only finance but also advise start-up
entrepreneurs and thereby add value to new firms. He shows how a productive and active venture
capital industry boosts innovation-based growth. De Vol (1999) also argues that without venture
capital infrastructure, a region’s technology cluster m ay not flourish. These studies suggest that a
state with a higher level of venture capital investment is less likely to be affected by national
shocks.
The idea that an export base is essential for regional economic growth is widely accepted by
economists. Lewis (1972) says that the ability to develop an export base is a critical determinant
of regional growth. Moosa (1996) studied time-series data on exports and output in the United
Kingdom during the period from 1885 to 1993 and found a causal relationship between exports
and growth. Smirnov (2002) studied which factors affect self-sustainability of regional
economic development and found that export base is an important determinant. Further,
Cuaresma and Worz (2005) found that high-tech exports have greater externalities and, therefore,
have a greater impact on output growth. These studies suggest that states with well-developed
export bases can withstand national shocks better and will exhibit lesser contagion effects.
Another factor which may influence the extent of contagion effects from the nation to a state
is the size of the state’s firms. Prior research shows that smaller firms tend to be more innovative
and innovation leads to growth. For IT industries, both large and small firms have shown
innovation, albeit of different types. For example, in the computer industry, complex
28

architectural type innovations, 8 involving greater complexity and requiring greater capital
involvement, have occurred in larger firms like IBM, whereas smaller design-oriented innovation
has occurred in smaller firms (Bresnahan and Greenstein, 1999). Thore (1996) studied return of
scale in the U.S. computer industry for the period 1981 to 1991. He found that the computer
industry can be divided into two camps: a few large mature corporations with decreasing returns
to scale, and many small start-up companies with advanced technology exhibiting increasing
returns to scale. Brunner (1991) studied the role of small firms in India's computer industry and
found that successful innovator firms have grown larger. Wagner (1995) explored the
relationships between exports and firm size and how they affect firm growth for 7,000
manufacturing firms in Germany. He found that firm growth and export performance are
positively related. His study indicates that larger firm sizes support growth in exports which, in
turn, support economic growth. This large firm-led theory of export growth runs counter to the
perspective that smaller firms promote economic growth. Hence, although firm size has been
found to be significant in different studies, the results have been mixed. So the influence of
average firm size on the contagion effect could be in either direction.
A number of studies at the regional and international level suggest that a society’s ability to
make use of ICTs depends on its level of e-readiness. Various indices have been developed to
measure e-readiness of countries by the different international agencies. They include the
International T elecom m unication U nion’s ―D igital A ccess Index‖ (D A I), the United Nations
Development Programme’s ―T echnology A chievem ent Index‖ (T A I), and the World Bank’s
―K now ledge E conom y Index‖ (K E I), am ong others. Societies which exhibit higher e-readiness
are also more dependent on IT products and applications and, therefore, are more likely to be
8 Henderson and Clark (1990) distinguish between the product and components of the product and define
architectural innovation as the innovation that changes the architecture of a product without changing its
components.
29

affected by national shocks and aggregate-level innovations. Hence, we expect high e-readiness
to be positively correlated to the observation of contagion effects.
The related factors can be chosen to explain the variation across different states in their
response to national shocks. We have not considered those factors which we expect to be same
across different states; they may not have explanatory power with respect to variations among
states. For example, Bresnahan and Greenstein (1999) posit that the computer industry structure
is based on computer platforms. However, this is a factor which is common across all states, so it
is not possible for us to include it as one of the independent variables.
4.2. Variables and Data
We use gross product of the state (STATE-GDP) as a measure of economic activity in the
state. To account for population differences among the states, we also take state-level GDP per
capita (STATE-GDP/POP) into consideration. We used data from the Bureau of Economic
Analysis (BEA) of the U.S. Department of Commerce STATE-GDP and Census Bureau data is
used for population (POP) of the states for the per capita GDP adjustment. BEA data do not
capture the details of the contribution of computer and peripheral equipment manufacturing
industries, but provides information about computer and other electronic product manufacturing.
We will code this as COEP-GDP (in contrast to our earlier use of CPEM computer and
peripheral equipment manufacturing). COEP-GDP is a meaningful indicator of IT
manufacturing industries in the state. Similarly, BEA data do not provide information about
software publishing industry alone, but instead recognize the contribution of publishing
industries in general, including software publishing (PUBL-GDP). So we use PUBL-GDP as an
indicator of the activities of the software publishing industry relative to STATE-GDP. 9
9 Since the print media industry is very strong, the software publishing industry may only be a fraction of the PUBL-
GDP variable. Thus we conclude that the variable which captures the effect of software publishing industry may be
30

We constructed the average firm size data constructed based on U.S. Census Bureau data for
number of employees and number of firms in the industry for 1999, with similar logic for the
choice of base year that we discussed before. For state-level venture capital investments (VCAP-
INV), we use data from the Pricewaterhouse Coopers-Thomson Venture Economics-National
Venture Capital Association MoneyTree survey (available at www.ventureeconomics.com/vec/
statshome.htm). For state-level merchandise exports share of the national total (EXPORT-
SHARE), segregated for the different industries, we used state-level export data from the
Economic Census of the U.S. Census Bureau. 10 Finally, for a state-level measure of e-readiness,
we use the state-level ―N ew Economy Index‖ (NEI), as developed by the Progressive Policy
Institute and described in Atkinson (2002). In all these instances, the figures used are for the
baseline year of 1999, which is roughly at the midpoint of the overall period of this longitudinal
study— a reasonable starting point in our analysis. We also will do a sensitivity analysis to
determine if the choice of year will significantly affect the results.
4.3. Results
We conjecture that some of the factors discussed in the prior subsection may influence the
contagion effects for IT industries. We also ran a simple linear regression with these factors as
independent variables with the FEVD results, as estimated earlier, as the dependent variable for
each IT industry. We checked for the presence of known-source heteroskedasticity in our data
set w ith W hite’s (1980) test and the p-values for significance are shown in Table 2 below. We do
not find significant heterskedasticity for our data set. We also tested for normality using the
noisy.
10 The State Export Data Series of the Economic Census has observations that are self-filed by qualified exporters,
forwarders, and carriers. The data specify the state of origin of movement of the export goods. The data have two
limitations. First, as with all self-reported data, it is likely to be noisier than if a more formal data collection
methodology were used to capture the same data. Second, in certain cases, the origin of movement is not the state of
origin of transportation. For example, whenever shipments are consolidated, the state of origin of movement will
reflect the state of location at which the shipments are consolidated.
31

SKTEST procedure of STATA 8.0, which performs the Jarque-Bera test (Spanos, 1993), based
on skewness and kurtosis for each of the variables in the data set. Our results showed that the
assumption of a normal distribution cannot be rejected for the variables in the data set. The
corresponding p-values for each of the estimated models are presented in Table 2.
Instead of presupposing a theoretical model in which to instantiate the independent variables
that we have discussed to this point, we again used the Akaike information criterion (AIC) and
the Schwartz Bayesian information criterion (SBIC) via the ICOMP model selection function of
STATA 8.0 to provide a basis for efficient variable choices. This additional analysis permitted
us to reduce the number of independent variables and to obtain parsimonious models for each of
the industries. The results for the full model and most appropriate reduced model for each of the
industries are shown in Table 2.
Table 2. Factors Characterizing Contagion Effects in Three IT Industries
CANDIDATE
FACTORS
51121
FULL
MODEL
(1)
51121
REDUCED
MODEL
(2)
33411
FULL
MODEL
(3)
33411
REDUCED
MODEL
(4)
33441
FULL
MODEL
(5)
32
33441
REDUCED
MODEL
(6)
EXPORT-SHARE -0.0553 -0.0567 0.0359 0.025 * -0.02
PUBL-GDP -1.5e-07 -3.84e-05 *** -2.89e-05 *** 2.04e-06
COEP-GDP 3.02e-05 * 3.31e-05 ** -2.61e-05 1.02e-05
STATE-GDP 0.0013 *** 0.0013 *** 3.97e-04 2.57e-04
VCAP-INV -9.12e-05 *** -9.6e-05 *** 3.22e-05 -2.43e-05
STATE-GDP/POP -5.048 -24.71 *** -17.05 ** 2.79
NEI 0.0068 * 5.27e-03 ** 0.017 *** 0.0137 *** 0.0016 0.003 **
AVG-FIRM-SIZE -0.0022 1.15e-04 * 8.76e-05 8.58e-05
R 2 0.4682 0.4479 0.4044 0.3559 0.1771 0.0941
Adjusted-R 2 0.3562 0.3806 0.2682 0.2754 0.0748
Heterosced. (p-value) 0.4631 0.7059 0.429 0.1751 0.4995 0.1604
Skewness (p > 2) 0.8763 0.3171 0.1851 0.7392 0.0000 0.7126
Notes. Models: full and reduced models, determined on the basis of Akaike information criterion and Schwartz
Bayesian information criterion, using STATA 8.0. Gray cells in the table indicate instances where no estimate of a
parameter was made to achieve a parsimonious model. Industries: 51121 = software publishing; 33411 = computer
and peripheral equipment manufacturing; 33441 = semiconductor and other electronic components manufacturing.
The following variables reflect 1999 data: EXPORT-SHARE, PUBL-GDP, COEP-GDP, STATE-GDP, VCAP,
STATE-GDP/POP. New Economy Index (NEI) is for 2002. Significance: * = p < .10; ** = p

The results in Table 2 are generally consistent with our expectations. The R 2 values for the
models are 46.82% for software publishing and 40.44% for computer and peripheral
manufacturing industries, which suggest that the independent variables included have reasonable
explanatory power for the contagion effect. For the software publishing industry, the contagion
effects are positively correlated with state-level GDP (STATE-GDP), the size of state-level GDP
for the computer and other electronic products industry (COEP-GDP) in state GDP, and the
overall level of e-readiness as represented by the New Economy Index (NEI). It is negatively
correlated to the venture capital investment in the state. For the computer and electronic product
manufacturing industry (COEP-GDP), the contagion effects are positively correlated with the
New Economy Index (NEI) but negatively correlated with state GDP per capita (STATE-
GDP/POP) and the size of publishing industry including software publishing (PUBL-GDP). For
the semiconductor and other electronic components industry, the contagion effects also are
positively correlated with the New Economy Index (NEI).
4.4. Sensitivity Analysis
We ran a regression for 1999, the middle year of the period for which we estimated the
contagion effects. Cross-sectional analysis may suffer if there is a sudden shock which changes
one or more of the independent variables. In the period under study, the stock market boom and
the subsequent crash are events which may have affected the results of this cross-sectional
analysis. To test whether there were any shocks which significantly affected our estimates in the
second-stage regression, we carried out the same estimations for 1997 and 2003, the beginning
and the end of data in this longitudinal study. Though there still may be biases, this should
provide a reasonable check for the robustness of our variable selection. For both these years, it
turns out, the regression results are similar. The significant variables are consistent across the
33

different regressions and the R 2 values are very close to the values we previously reported in
Table 2. This suggests that the correlations of factors are robust, and consistent and not transient.
4.5. Discussion
The New Economy Index (NEI) was positively correlated with the FEVDs for all three
industries. This signifies that states which have more advanced ITs tend to exhibit a greater
degree of contagion effects relative to the national level for IT industries. This result supports
our earlier assertion that the relationships between IT industries may be unique from other
sectors of economy. The software industry shows a positive correlation between the size of
economy of the state and the size of computer and electronic equipment industry. This implies
that states which have bigger economies tend to move in the same direction as the nation when a
national shock occurs. The state also shows negative correlation with venture capital investment,
which indicates that the presence of strong venture capital contributes to isolating the state from
national shocks in the software industry. Venture capital seems to act as some kind of ―shock
absorber‖ for the software industry. We should note, however, that this story may not be true for
all the industries that we studied; for example, venture capital is not significant for the hardware
industries. The GDP variables indicate the influence of scale economies on contagion effects.
We found this variable to be consistently significant for all of our estimations.
Although our analysis provides some initial understanding of the factors which may
influence the relationships that IT industries exhibit, we must caution the reader about the
limitations of this estimation. In this two-stage estimation approach, our first stage involves
NVAR estimation of contagion effects. The second stage involves regression of contextual
variables with FEVD as the dependent variable. Econometric estimation of FEVD via the related
contextual factors assumes that the data-generating process for the dependent variable satisfies
34

the conditions for linear regression. This regression may not yield consistent estimators if the
assumptions for linear regressions are not fully satisfied. While a detailed exposition of the
issues is beyond the scope of this paper, we still should point out some areas of concern. For
example, we expect that the residuals of the estimated model for a given state may be correlated
with the model residuals for contiguous states due to spatial interdependencies. A related
possible issue is endogeneity. In particular, we cannot assert that the data-generating process for
the model is strictly exogenous with respect to the process that generates the residuals. Here
again, a possible source of bias may be spatial interdependencies. In addition, some other
unobserved variables may be correlated with our explanatory variables. The results also may be
biased due to measurement error in the independent variables. Macroeconomic variables are
often measured with error. The direction of the bias may depend on the nature of correlation
between the variables and the correlation between the variables and the measurement error.
5. CONCLUSIONS
We analyze the growth of IT industries in the U.S. and show the existence of co-movement
between national-level and state-level indicators with respect to IT industries in the United
States. We also provide evidence that the contagion effects vary from state to state and are also
different for different industries. We further identify some factors which influence the degree of
the linkage relationship between states and the nation. Our study finds that that IT manufacturing
industries are more susceptible to contagion effects than IT service industries. We also show that
states with higher IT production tend to exhibit stronger contagion effects. We also find that per
capita state gross product, the export share of the state relative to the nation, the average firm
size, the degree of a state’s e-readiness, and venture capital investments are among the factors
which influence the contagion effects between states and the nation, though their influence varies
35

etween IT manufacturing and service sector. This research reflects a new attempt towards
understanding the underlying relationships among IT industries. Given the limitations of data for
an aggregate analysis of this nature, we have performed various tests to ensure that the results are
consistent and robust.
Although our results show these trends, we recognize that more research is required to fully
understand the nature of the contagion effects that we report between the nation and the states for
growth in IT industries we have considered. We recognize that there may be a number of
limitations to this research in its present form. A larger number of observations over a longer
time period would be useful for strengthening the case that we make, for example. The IT sector
has been experiencing renewed growth since late 2003, so lengthening our time-series to include
2004 and 2005 makes sense to try to understand the new shocks that are at work via our NVAR
models. Similarly, other variables that might be helpful in explaining the contagion effects may
also be appropriate to add. These include such variables as the distance of a given state from
other large IT-producing states (where the knowledge spillovers may be greater, leading to a
greater tendency for co-movement), the export shares for each of the IT sectors, and so on. In
our future research, we will be considering the inclusion of these and other potentially relevant
variables.
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40

Appendix A. NAICS Industry Classifications
NAICS
CODE
6-DIGIT
CODE
SIC
CODE
33411 334411 3571 Computer and
334412 3572
334413 3575
334419
INDUSTRY DESCRIPTION
peripheral
equipment
manufacturing
33441 334411 3671 Semiconductor
334412 3672 and other
334413 3674 components
334414 3675 manufacturing
334415
334416
3676
334417
334418
334419
3678
51121 7372 Software
publishers
Manufacturing and/or assembling electronic
computers (mainframes, personal computers,
workstations, laptops, computer servers) and
computer peripheral equipment (storage devices,
printers, monitors, input/output devices, terminals).
Manufacturing semiconductors, capacitors, resistors,
microprocessors, bare and loaded printed circuit
boards, electron tubes, electronic connectors,
modems, etc.
Computer software publishing or publishing and
reproduction. Carry out operations necessary for
producing and distributing computer software
(design, documentation, installation, software
purchase help). May design/publish or publish only.
Note: Classification systems used are (1) the North American Industrial Classification System (NAICS) of the
United States Bureau of Economic Analysis (BEA), and (2) the Standard Industry Classification (SIC) of the same
agency. NAICS codes replaced SIC codes after 1997. Gray cells in the table indicate instances where the data
from the SIC and NAICS codes do not have a one-to-one correspondence for six-digit industry codes. This places
some restrictions on the comparisons that can be made with data for different industries across the two classification
systems. In this research, we made tactical decisions regarding our six-digit industry selections so as to ensure that
there were valid one-to-one correspondences between the earlier SIC and the later NAICS data. For another
discussion of the issue of cross-classification systems comparability of BEA data, the interested reader should see
Han et al. (2005).
41