MURP_2012_Master_Thesis_Lee

UNIVERSITY OF CALIFORNIA,
IRVINE
Dynamic Shift-Share of the Association of Regional Shift and Innovative Capacity in the United
States: California (Sunbelt) and New York (Northeast)
THESIS
submitted in partial satisfaction of the requirements
for the degree of
MASTER OF URBAN AND REGIONAL PLANNING
in Social Ecology
by
Phillip Lee
Thesis Committee:
Professor Luis Suarez-Villa, Chair
Associate Professor Victoria Basolo
Professor Rodolfo Torres
2012

ii
Table of Contents
Title
Page #
List of Figures, Tables and Maps iii
Acknowledgements iv
Abstract of the Thesis v
Introduction 1
Literature Review 2
Objectives 11
Overview of the Objectives 11
Methodology 12
Shift-Share Analysis 14
Sources of Data 18
Theoretical Framework 20
Potential Validity Contentions 21
Results and Discussion of Interval Data 25
Dynamic Interval Shift-Share Analysis 27
Annual Shift-Share Analysis (Share, IM, and CADVT) 32
Other State Data and Spatial Comparisons 38
Summary of Interval and Annual Findings 41
Summary of Other State CADVT Maps 44
Conclusion 46
Appendices 51
INNOCAP and Shift-Share Equations 51
Interval Dynamic Shift-Share Tables 52
Annual Dynamic Shift-Share Tables 54
Other State Maps: 1990, 1995, 2000, 2005, and 2010 55
References 65

iii
List of Figures, Tables and Maps
Page #
Circulation Sphere 5
Production Sphere 6
Shift-Share Components
Share 16
Invention Mix (IM) 16
Competitive Advantage (CADVT) 17
Figure 1.1 Innovative Capacity (INNOCAP) 51
Figure 1.2, 1,3, 1.4 Shift-Share (Restated) 51
Table 2.1 Interval Individual California 52
Table 2.2 Interval Corporate California 52
Table 2.3 Interval Individual New York 53
Table 2.4 Interval Corporate New York 53
Table 3.1 Annual 54
4.1a 1990 Map Shift-Effect 55
4.1b 1990 Map CADVT 60

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Acknowledgements
I would like to express my deepest thanks and acknowledgments to my chair, Professor
Luis Suarez-Villa; I am greatly honored to be graced by his mentorship and seemingly limitless
knowledge. Through his work, I found inspiration; in pursuing a future so vast as to be nearly
incomprehensible to my mind, yet so shining I could not help but to approach. Innovative
capacity is a variable with much potential, as do the very patents that it tracks. Professor Luis has
laid the foundations by which I now trod upon, by those foundations was this thesis made
possible.
I would like to thank my committee members, Professor Rodolfo Torres and Professor
Victoria Basolo for their wisdom and assistance in reminding me that there are many roads that
lead to the same destination. And upon reaching that destination, may we understand the journey
that we took and find new paths to follow. Thank you Rudy, for helping me rekindle my dreams.
To Victoria for your confidence in me, that I may reach that brilliant future.
A thank you to Professor Daniel Stokols, for introducing me to the concept of Social
Ecology, which allowed me to better conceptualize the pathways that connect human society and
the systems by which that society is organized, and for me to be able to view myself as a social
ecologist. To Professor Marlon Boarnet, for your recommendations and expertise that affirmed
my belief in a perpetually-shifting geographical market.
To Jim Hirabayashi and the United States Patent and Trademark Office, for their
assistance in acquiring the aggregate patent data required to calculate innovative capacity. I hope
those pre-1950 disaggregates of Modal data prove helpful.
To the faculty of Urban and Regional Planning and the department of Planning, Policy
and Design, Janet Gallagher and my fellow MURP peers, I greatly appreciate their support
throughout the process. I am also thankful to the University of California, Irvine, and the School
of Social Ecology for granting me this opportunity not only to better myself, but to conceive of a
more prosperous tomorrow.

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Abstract of the Thesis
Dynamic Shift-Share of the Association of Regional Shift and Innovative Capacity in the United
States: California (Sunbelt) and New York (Northeast)
By Phillip Lee
Master of Urban and Regional Planning in Social Ecology
University of California, Irvine, 2012
Professor Luis Suarez-Villa, Chair
This project considers how the state of California and New York’s innovative capacity
trajectories relate to regional inversion in the United States by employing a dynamic shift-share
analysis from 1938 to 2010. The analysis utilizes the indicator of innovative capacity
(INNOCAP), a measure of innovative potential that employs utility patent data as a benchmark.
This methodology produces three components, each of which offers interesting comparisons
between the innovative potential of California and New York. These components also provide a
foundation for future research on the impact of INNOCAP when analyzing de/reindustrialization
in the modern economy.
This research highlights the building blocks of an emerging shift in the American market,
and possibly an emerging global one based on a new economy favoring more innovative and
geographically fluid forms of capital. This study underlines the importance of maintaining a
diverse mix of inventions in development, and a location’s geographical advantages. The results
among other states strengthen the perception of a shift from the Northeast to the Sunbelt, and
highlight the relatively immobile nature of corporate forms of innovative capacity, in contrast to
individuals. Findings suggest that maintaining a high level of diversity and competition in
innovative patenting may assist in the retention of a region’s innovative potential over time,
despite the tendencies of that diversity to spill into neighboring regions.

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Introduction
Heraclitus: Nothing endures but change.1
Innovation (the introduction of something new) by definition necessitates change.
Aspects of the modern world that do not adapt, change or diversify weaken, while those that
succeed outcompete others to become even stronger. This is especially true for the spatial
market, which is perpetually engaged in a cycle of alternating excess supply and demand.
Exemplified by market theories such as the product and business cycles, prosperity and crisis
become rationalized into being seemingly inevitable (Vasko, Ayres, & Fontvieille, 1989). From
the conflict birthed between those advocating Darwinian chaos of the free market, and those who
opposed that “order”, the roots of the modern market society was formed. Jane Jacobs, an
influential urban reformer, in her Economy of Cities (Jacobs, 1969), understood the accumulating
and competitive tendency underlying American society. Many of her foes, such as John Moses,
whom she criticized throughout her work and in reference by others2
, held in high regard an
idealistic “simpler” past based on previous negative conceptions of the market (LeGates & Stout,
2007). Jacobs realized the importance of understanding these modern forms of urban society, as
avatars of Western society in the 21st
century (Jacobs, 1969).
Capitalism, the name of this encompassing system, operates on the level of the market; it is
only bound as far as the market extends. In modern times, that power is not contained within
specific geographies, but rather operates on a global scale (Goldsmith & Blakely, 2010). Upon
this global stage, the region becomes a primary actor, despite national and geographical borders
(Bluestone & Harrison, 1982). Remembering the words of Marshall McLuhan, who predicted the
1
(Diogenes & Yonge, 1853)
2
(LeGates & Stout, 2007)

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development of a “global village” in his book Gutenberg Galaxy (McLuhan, 1962), the world
has become a singular marketplace through globalization. Within this larger universe, new flows
emerge to fundamentally alter the balance of power between geographies.
Regional inversion is the shift of socioeconomic power from one predominant region to
undeveloped or lagging ones (Suarez-Villa, 1993). As a concept, regional inversion affects
multiple levels of societal phenomena and assumes the concentration of some sectors
geographically. This assumption of regional concentration, or clustered economies has been
supported through agglomeration economies/technology/industry cluster studies by Michael
Porter (Porter, 1990), Paul Krugman (Krugman, 1991), and Diego Puga (Puga, 2010). Clusters
share origins with core-periphery models of urban growth, exemplified by the 1924 concentric
circle models developed by Ernest Burgess. The prevailing opinion among many academics,
such as Krugman and Puga, acknowledge the validity of these clusters and their importance in
the national and global economy. This acknowledgement is validated by references to known
centers of industry, such as Silicon Valley and Route 128 (Saxenian, 1994).
Literature Review
The basis of this project revolves around the geography of economic agglomerations on
the state level (i.e. California and New York). It was in 1942, at the onset of World War II, that
the American federal government began considering the impacts that industries had on specific
localities during the organization of wartime production (National Resources Planning Board,
1943). Included among these impacts was the effect that wartime production plants, the largest of
which employed a significant number of individual workers, may have on the labor markets of

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that locality post-war. One of the prime concerns in the 1942 report filed by the National
Resources Planning Board regarding the post-war environment was the question of dealing with
large concentrations of now unemployed workers, including all the decommissioned and
unemployed soldiers returning home from the warfront. Due to wartime necessity, many of these
concentrations of industry and labor were located within centralized and defensible locations
(National Resources Planning Board, 1943). These concentrations of capital and labor were seen
as potentially too politically volatile to be left alone, and with its’ jurisdiction over the
decommissioning wartime plants, the federal government was in an ideal position to plan the
post-war transition (National Resources Planning Board, 1943). Daniel Creamer, a member of
the National Resources Planning Board, introduced the usage of methodology tracking locational
shift of industries between states and the rest of the nation (National Resources Planning Board,
1943). This methodology was adapted and improved upon by others, due to its simplistic yet
effective nature in tracking a variety of industrial shifts. Eventually, this simple analysis of
locational shift evolved into the modern form of Shift-Share (Markusen, Noponen, & Driessen,
1991).
By utilizing a locational method like Shift-Share, it has become easier to track regional
inversion and the effects innovative capacity (INNOCAP) has between geographies. Economic
regional inversion only occurs when something within the structure of the market changes to
invalidate the advantages of previously dominant locations. Another external effect may be that
the factors that encouraged inversion have also uplifted previously lagged locations due to
dominance. What is certain is that regional inversion involves the shift of innovative economic
and entrepreneurial capacity from one region to another (Suarez-Villa 1990). As a result, the
dominant area is no longer competitive when compared to the emerging zones, as the advantages

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that the established area had in the past no longer apply. Due to the inherent profit seeking in an
ideally growing market (Marx, 2010) industries and firms that exploited the predominant region
in the past either leave, close down, or adapt to the new conditions, at times reforming into
completely different entities. This effect is consistent with the shifts measured in the original
Creamer analysis, as barring significant locational advantages, industries have a higher
likelihood of shifting than if they were attached to a location by compatible labor, resources, or
markets (National Resources Planning Board, 1943).
On the other hand, one would not expect such locations, especially those with significant
and, as of yet, unaffected political power, to take such losses lightly. Through actions involving
economic incentives, local and state subsidies, and other spatially-based incentives such as
“enterprise zones,” individual geographies attempt to retain their economic power (Green, 1991).
Hence political power is maintained by enticing new firms or convincing older firms to stay by
providing local-based advantages to offset the loss of market-scale ones. Nevertheless, despite
their attempts, many localities fail as factors at the meso- or macro-scale override any micro-
level incentives individual geographies may attempt to provide. It was discovered that within
many of the more successful “enterprise zones,” firms located within the zones not to utilize the
advantages of the zone, but due to macro-scale factors such as positioning within a regional
market (Green, 1991). Some of these firms that were notified by the authorities of the zone were
entirely unaware of its existence, although they were typically quick in exploiting the advantages
of the “enterprise zone” once notified (Green, 1991). The entire purpose of these “enterprise”
zones is to essentially anchor industries within a specific geography to prevent locational shift.
Their rates of success however, vary from place to place, and ultimately may be determined by
macro-scale market factors, despite local attempts.

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In essence, the capacity of a region to maintain their locational advantages is highly
dependent on market factors commonly attributed to capital and its employment. Despite the
contentions that those advocating the materialistic dialectic hold against capitalist enterprise (and
vice versa), the former methodology has produced rather accurate analyses of capitalism’s
strengths and weaknesses. The viewpoint is derived from this base conflict between the two
ideologies, each side striving over decades to unearth the other’s weaknesses. Many have simply
written off Marx’s conclusions due in part to this polarizing conflict. However, it would be
misguided to completely ignore all of Marx’s critiques. All perspectives hold merit, and those
that are found between long-term opponents hold a special validity obtained through trial by fire.
This is especially true for the market debate that has lasted since the beginning of the last
century. Marx’s (1894) visualization of the capitalist process provides the measures by which the
accumulative tendencies of the capitalist system and regional inversion can be linked (Marx,
2010). Using references from Marx and other observations of the capitalist system, David
Harvey (2010) formulated an equation that represents the basic process of capitalism, also known
as the circulation sphere:
Circulation Sphere: M  C  𝑪 𝟏
 𝑴 𝟏
+ m*.
Where M is money capital, and C is a commodity or commodities. M  C means that
money capital is exchanged for commodities (Harvey, 2010). 𝐂 𝟏
represents new commodities
that are transformed from basic commodities, such as raw materials, or input commodities like
engines and parts. 𝑴 𝟏
is the new money capital created by the exchange between the consumer
and producer/distributor. In addition, m* is the surplus value or profit that is also generated

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which is either employed as personal surplus or to be reinvested as additional productive capital.
𝑪 𝟏
to 𝑴 𝟏
+ m* occurs because the commodity has an exchange value, which in turn has its own
exchange value with other commodities being used as a universal constant to simplify exchanges
between individuals or societies (Harvey, 2010). Considering the goals of this study, the
maintenance involving the transformation of C  𝑪 𝟏
, a transformation that involves both the
manufacturing and services production, is important for a region to prevent outward shift
(especially the competitive advantage of that location).
Ultimately, Marx (Marx, 1978) from whom Harvey (2010) generated his model, thought
that the purpose of the capitalist cycle is to create and replenish the commodities plus surplus and
to transform of monetary capital into commodity capital. Marx’s 1894 model operates very much
in the same way, with the only exception at the level of importance that he assigns the transition
from C 𝐶1
, or the area in which P (productive capital) takes place (Marx, 1978). This takes the
form of the Production Sphere:
Production Sphere: M  C …P… 𝐶1
 𝑀1
+ m* (Marx, 1978).
Productive capital is the primary engine by which the sphere of production is propelled,
by which locational advantages are applied. Within this sphere, both fixed (machinery) and
circulating capital (inputs) can operate. These capitals are important because of Marx’s assertion
that they do not exist outside of the sphere of Production (P) (Marx, 1978). Unlike commodity or
monetary capital, fixed or circulating capital as a concept exists only within the production
sphere, and cannot be attached to either of the former models. (Marx, 1978). It is in productive
capital where INNOCAP has its greatest impact. Patents form the blueprints of not only

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commodities, but the various machines and processes that allow for the production of those
commodities. It also affects the productive value of labor, each patent potentially containing a
machine or a process to heighten the productive potential of any individual (Rifkin, 2004). Thus,
productive capital is most heavily influenced by advantages offered by labor and locational
factors and market-level shifts, lacking the mobility of either monetary or commodity capital.
This model is applied to INNOCAP and regional inversion, where fixed or circulating
capital (i.e. production capital) is important if developed within a primary region. If factors arise
that shift commodity or monetary capital elsewhere, productive capital within that region will
decline in value unless that capital can adapt or find alternative sources of monetary or
commodity capital (Marx, 1978). In the modern market, the typical product cycle of a particular
product (especially technological-based products; i.e. smart phones, tablet computers) has
become relatively short. The circulation sphere progresses and extinguishes significantly faster
for industries that constantly are pursing the cutting edge of their fields. In order to remain
fiscally viable, firms are forced into perpetually generating new innovative capacity (i.e. new
patents and products) to remain competitive (Suarez-Villa, 2009). The other option available is
to focus on increasing the productive capacity of the outdated product, per older industrial
models of production. However with the current overcapacity and overproduction of the global
market, it has become increasingly difficult for mass production firms to generate sufficient
revenue or monetary capital (Bluestone & Harrison, 1982; Rifkin, 2004).
If there is no value to purchase or sustain the monetary capital used to pay the workers or
obtain resources, then the productive capital cannot operate (Marx, 1978). One could potentially
link this with infrastructure (such as educational institutions) that affect or promote variables,
which lead to an increased INNOCAP (Suarez-Villa, 1997). A viable relationship between

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INNOCAP and educational infrastructure development has been tested by Luis Suarez-Villa,
which affects the quality of the labor force (Suarez-Villa, 1997). Labor makes up a portion of
circulating capital; it is combined with the means of production to create commodities (Harvey,
2010). However, if there are insufficiently skilled workers available to create that commodity
within a region, then the industries involved would be at a disadvantage compared to the other
regions. Thus, the odds that regional inversion will cause that region to shift their innovative
advantage outward to other areas will increase.
When linking the capitalist model with urban economies, a point of interest is located in
the shift from an older commodity to a new commodity (Harvey, 2010). This is the point at
which the urban economy and its variables, such as INNOCAP, have its greatest impact. Due to
a variety of advantages caused by agglomerating, or generally being close to each other, an
industry cluster gives an advantage in the shift from a commodity like iron, to a new commodity
like a car. The information creation and usage signified by INNOCAP within the cluster is a
factor that plays a significant role in this shift. This can be more easily accomplished if that
capacity is shared or distributed throughout a network, even more if the nodes are in close spatial
proximity (Suarez-Villa & Hasnath, 1993). This is in stark contrast to more isolated firms, which
may be unable to shift quickly to keep up with the C  𝐶1
process, with the exception of
industries that naturally diffuse geographically (i.e. short-term services). By the time a firm
notices the decline in competitive advantage, it may be too late to move.
One specific issue facing this study is the identification of the initial burst of
entrepreneurial activity that caused INNOCAP to shift inward within a region. One possible
solution is to watch the movement of the share of an expected entrepreneurial industry in the
local market, according to that industry’s product cycle (Mandel, 1978). Another solution is to

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look at the point in which an older activity enters a sustained decline, in contrast with the normal
steady growth of such activity under healthy market conditions such as at the end of an economic
wave (Mandel, 1978). Another implication to consider is the recent movement towards favoring
monetary capital over productive or commodity capital (Bluestone & Harrison, 1982). This
recent trend sacrifices tangible commodity production for monetary manipulation and the
services related (Goldsmith & Blakely, 2010). Although short-term surplus may be achieved
through monetary manipulation, long-term surplus generation is threatened if productive
capacity for basic employment industries are not continuously updated (Bluestone & Harrison,
1982).
In the modern age of heightened capital mobility, dramatic shifts in capital have become
more common and unpredictable (Goldsmith & Blakely, 2010). Capital mobility is defined as the
capacity for firms to uproot and relocate their factories, and other forms of productive capital to
new geographical locations reducing the certainty of the ability of local governments to retain
industries (Bluestone & Harrison, 1982). As valuable commodities lose tangible form, capital
mobility increases, employment of parallel and multiple source production amplifies, commodity
fluidity heightens, and the value of intangible commodities increases (Goldsmith & Blakely,
2010). Before capital mobility heightened, regional inversion as a concept was relatively slow,
taking decades to show significant impact (Suarez-Villa, 1993). Nevertheless, since the 1970s,
increasing capital mobility has rapidly changed the American marketplace due to the American
industrial sector’s decline, and the reconstruction of economies like Japan and Germany. As
more developing nations modernize technologically and economically, more viable markets
emerge (e.g. China). This new and highly competitive environment pressures firms to orient
themselves and their products towards a fluid and shifting globalized framework; those who do

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not are left behind (Goldsmith & Blakely, 2010). This project focuses on more localized shifts in
the states of California and New York, as well as other states in the United States; asking to what
extent has innovative capacity maintained geographic stability, and how that capacity has
affected the inward or outward shifts of those geographies?
Although this opening up of international borders could be considered a boon for multi-
national corporations, this dispersal of resources has reduced the capacity of governments to
provide oversight and fund their own projects from tax revenue (Bluestone & Harrison, 1982).
With globalization, it has become easier for firms to rapidly shift between regions and markets to
maximize profit. This economic inversion effect has not only occurred internationally, but also
within the United States (Suarez-Villa 1990). Evidence of this shift is embodied in the decline of
the American Northeast, as previously noted by Bluestone and Harrison (1982). In contrast, the
Sunbelt (states and regions below the frost-line, such as California, Texas and Florida) have
apparently “prospered” when compared to the hardships facing many established industrial
regions in the Northeast (Saxenian, 1994).Harkening back to Creamer’s original locational shift
analysis, with the increasing mobility of the labor force, lessening transportation and
communication costs; there are fewer incentives to remain a non-shifting industry.
These shifts in capital to the regional, national, and transnational scale hint towards the
emergence of a new stage of capitalism based on creativity and intangible “technocapitalism,”
the term coined by Luis Suarez-Villa in 1990. In a highly dispersed and diffused global
economy, tangible commodities have also become increasingly fluid (Goldsmith & Blakely,
2010). Products purchased from online distributors like Amazon or Ebay rush from one region to
another to satisfy the apparently endlessly shifting demand. In an increasingly liquid, competitive

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and intangible environment, the most prized resource that a location may be able to provide to
the new production sphere is the quality of their labor force (Goldsmith & Blakely, 2010).
Objectives
This study aims to:
1) Further highlight the process of regional inversion in the United States from the Northeast
to the Sunbelt; more specifically, from New York to California, and the effects of
locational shifts on INNOCAP and inventive potential.
2) Note the changes that lead to higher or lower amounts of INNOCAP through utilizing
Shift-Share analysis. That is, the differences in Share, Invention Mix (IM), and
Competitive Advantage (CADVT), that may play an important role in regional shifts.
3) Link and understand the effect INNOCAP has on the inversion process by utilizing
CADVT, and the effect (positive or negative) this has on a region’s innovative
development.
Overview of the Objectives
The goal of this research is to highlight the association between geography and
innovative potential and how both impact the Shift-Effect of particular localities. Considering the
tactics of many localities to attract businesses through fiscal and monetary incentives, factors
maintaining innovative potential may provide more tangible benefits than creating a “good
business” environment (Bluestone & Harrison, 1982). Monetary incentives used by many
localities to attract firms are relatively limited due to a competitors’ ease of replication
(Bluestone & Harrison, 1982). Through analyzing the dynamics of INNOCAP with and between
New York and California, this study offers potential alternatives for taxation-oriented measures

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to promote sustainable economic growth. As a result, local governments must not only consider
the viability of their “business environment,” but also the quality of their labor and human capital
for the promotion of CADVT. It is through analyzing INNOCAP by utilizing Shift-Share
analysis, that regional inversion will be better understood.
Methodology
This study utilizes and retools the dynamic Shift-Share method to employ the indicator of
INNOCAP instead of Shift-Share’s conventional usage of employment. Therefore, it will utilize
innovative potential (i.e. patents) as the benchmark. This paper presents a study of two locations:
California and New York. Using a historical context, we employed dynamic interval and annual
Shift-Share analysis to consider and compare each location from 1938 to 2010. The variable
being employed was INNOCAP (stock of inventive knowledge). The primary measures through
which Shift-Share identifies the Shift-Effect were the components: Share, Invention Mix (IM),
and Competitive Advantage (CADVT).
As previously mentioned in this study, Shift-Share analysis was readapted to work with
INNOCAP. This was defined as the cumulative innovative potential of a specific entity.
INNOCAP employed patent data as the primary measure, operationalized as the rate and lifespan
of utility patents per state and modal category (Individual/Corporate). That data is then applied to
a comparison between geographical entities that employ human capital (on the level of the firm,
an industry, or a region).
The methodology employed was dynamic, both with 5-year intervals and annually, as it
considers multiple time periods between the starting and endpoints within the target locations:
New York and California. In comparison with the conventional static model, Dynamic Shift-

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Share analysis involves multiple comparisons between different time periods. Although the term
dynamic was employed by Richard Barff and Prentice Knight to describe specific annual
comparisons, they acknowledge that their definition is an extension of Anthony P. Thirlwall’s
discretization of the static model into two or more intervals (Barff & Knight III, Spring 1988).
The conventional static shift-share only investigates between a single starting and endpoint,
whereas interval and dynamic annual models use multiple pairs of starting and endpoints
(Markusen, Noponen, & Driessen, 1991). Therefore, this project initially considered using data
at ten year dynamic intervals from 1900 to 2010. However, despite the lack of viable data in the
early 1900s, the starting point was increased to 1938 for the annual dynamic interval, and 1945
for the 5-year interval.3
For total nationwide aggregates, the starting point of 1938 for the annual analysis was
chosen primarily due to the constraints for viable nationwide data. In addition, 2010 was chosen
as the endpoint because it is the most recent point from which non-estimated data could be
obtained. For the dynamic Shift-Share interval, intervals were set at 5 years to highlight long-
term changes. For future considerations of INNOCAP and the methods employed in this study,
some slight alterations were required. For instance, new patent legislation was approved by the
United States government in 2011, which changed how one can calculate INNOCAP. The
variable then required the sum of 20 years (i.e., 2010 – 2030), instead of 17 years to calculate
INNOCAP from 1938. As the bulk of the data for this project was pre-2010, the recent change
will have no impact.
The level of research undertaken was largely confirmatory, built-off existing research by
Luis Suarez-Villa on technocapitalism; one of the objectives was to further highlight the effects
3
The lack of data has always been one of the major restrictions of the dynamic analysis. (Barff & Knight III, Spring
1988)

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of regional inversion and shift within the United States. Nevertheless, it was also desirable that
the results may provide further insights into to maintaining a decent level of INNOCAP and the
effects those factors that contribute to INNOCAP may have on the region as a whole. The
research was primarily quantitative, with minor spatial and historical archival qualitative
research to act as additional support, which may provide an interesting perspective on the
regional inversion process and locational shift.
Shift-Share Analysis
To reiterate; Shift-Share analysis, in its conventional form, allows the growth
performance of an industry, firm, or local economy to be placed in a comparative perspective
(McLean & Voytek, 1992). Shift-Share formulas were employed as the baseline for this study
and are defined by Mary McLean and Kenneth Voytek in their 1992 article Understanding Your
Economy. Their Shift-Share methodology analyzes differences between growth in a local
geographic locality and relative growth in a reference locality, typically on a national scale. The
conventional Shift-Share method assists in identifying the impact of an area’s industrial structure
or mix on local economic growth. Shift-Share also assesses the influence of local factors on
industrial growth and an area’s fiscal health (McLean & Voytek, 1992).
In general, the conventional Shift-Share utilizes demographic and revenue data to
calculate “three components to explain the disparity between regional and national growth”
(Markusen, Noponen, & Driessen, 1991). The first component defines National Share (NS),
which identifies trends in the larger economy that the subject (area) is a part. In the original
report by the National Resources Planning Board, it was through the relationship in which Share

15
was contrasted with the original indicator, that inward (>) or outward (<) shift of that indicator
was determined (National Resources Planning Board, 1943). The second component is Industrial
Mix (Im), which identifies tendencies that reflect specific or an overall mix of products or firms.
The third component of the conventional model is local factors; this component identifies trends
reflecting local influences on industry or an area’s performance (McLean & Voytek, 1992).
Local factors represent the advantages provided by a specific geography through government,
community, societal support, or market factors. Thus by converting employment to INNOCAP,
the third component can play an integral role in considering the effects INNOCAP may have on
regional inversion.
While the original models focused on regional employment or output per industry, this
project attempts to assess the INNOCAP of the two target states in relation to each other,
California and New York. According to Markusen (1991), Shift-Share analysis seeks “to
demonstrate how industry structure affects regional and local economies, to review regional
economic trends, and to advise policy makers on industrial targeting” (Markusen, Noponen, &
Driessen, 1991). This altered model assists in demonstrating the differences in innovative
potential between the two states; more specifically, it demonstrates the impact of innovation
within the target states’ productive structure and reviews entrepreneurial trends in relation to
each respective state. It is desirable that the results presented in this study will serve
policymakers by highlighting the importance of maintaining INNOCAP and providing
foundations for further research in INNOCAP.
Although Shift-Share analysis may possess some faults, it is an indispensable tool for
comparing different geographies (McLean & Voytek, 1992). Shift-Share analysis can be
calculated and reported periodically. If used properly as a diagnostic tool, Shift-Share can detect

16
trouble in a local or regional economy and can detect trends. As a method, Shift-Share is very
flexible in terms of geographical scope or activity, and can use published data, which may reduce
the cost of research and analysis (McLean & Voytek, 1992). It is because of these attributes that
Shift-Share analysis was chosen as the method to utilize INNOCAP as a benchmark.
If the sum of the three components for the Shift-Share analysis matches up with the
target/end year benchmark, then the solutions are valid, as the SUM represents the total change
in INNOCAP (McLean & Voytek, 1992). Each of the three components represents an aspect of
events that have affected INNOCAP during the time period involved, which in this study varies
from annual to a 5-year interval.
Although the formulas of Shift-Share with regards to INNOCAP have remained exact, their
meaning has changed slightly to the following:
1.1 The first component was Share; 𝑒𝑖
𝑡0
�
𝐸 𝑡∗
𝐸 𝑡0
�
In other words, Share is Starting Year�
National Target Year
National Starting Year
�
Share for INNOCAP shares many similarities to NS, as both seek to identify trends in the
larger economy, even though Share for INNOCAP focuses on innovative potential.
1.2 For Invention Mix (IM); I used the equation 𝑒𝑖
𝑡0
�
𝐸𝑖
𝑡∗
𝐸𝑖
𝑡0
−
𝐸 𝑡∗
𝐸 𝑡0
�
IM is Starting Year �
US Modal Target Year
US Modal Starting Year
−
National Target Year
National Starting Year
�
IM for INNOCAP also shares similarities to Industry Mix (Im) of the original Shift-Share
analysis. IM for INNOCAP seeks to identify the mix of inventions within the INNOCAP
of the benchmark. MODAL represents the Individual and Corporate INNOCAP.
Individual INNOCAP represents the rate and lifespan of patenting by Individual

17
entrepreneurs, Corporate INNOCAP represents patent rate and lifespan by corporate
entities/firms.
(In comparison with the conventional model, to avoid confusion, the original Industry
Mix has been labeled Im with a lower-case m, while this project’s Invention Mix is
labeled IM with a capital M.)
1.3 For Competitive Advantage (CADVT); the equation 𝑒𝑖
𝑡0
�
𝑒𝑖
𝑡∗
𝑒𝑖
𝑡0
−
𝐸 𝑡∗
𝐸 𝑡0
� was used.
In other words, CADVT is the Starting Year�
State Target Year
State Starting Year
−
US Modal Target Year
US Modal Starting Year
�
CADVT, like the other two Shift-Share components, also possesses a similarity with its
conventional form (i.e. local factors). However, for the purposes of this study, the goals
of CADVT are more precise. Therefore, CADVT seeks to identify the state-level
advantage that can help shift INNOCAP inward or outward.
For shift-share data employing the MODAL disaggregated US Individual and Corporate
INNOCAP, implications may arise from the fact that the data being employed is not a specific
geological location. This creates complications because Shift-Share analysis was originally
created to compare between two geographical points (McLean & Voytek, 1992). However,
MODAL compares between disaggregates of US Individual and Corporate INNOCAP. Early
input of the data into the formula, however, has produced ~ 0% chance of error when comparing
the sum of the Shift-Share and the benchmark. In regards to Shift-Share, being near or close to
0% error means that the result was valid, that the cumulative IM and CADVT added to Share
precisely equals the original value. As Share is meant to represent the national trend, while IM
and CADVT together representing the Shift-Effect, having their SUM total equal the origin

18
validates the components and the analysis. This phenomenon requires further investigation and
provides a potential opportunity to test the limits of Shift-Share analysis.
Sources of Data
For INNOCAP, patent data was obtained from the U.S Patent and Trademark Office
(USPTO), the Department of Commerce, and Professor Suarez-Villa. It was difficult to find
information on patents before 1950, and was even more so difficult to find disaggregates of
Individual and Corporate patents on a US national-scale. The USPTO’s online database rarely
has data before 1963, due to budgetary related problems. Nevertheless, pre-1950 patent
information was found in the reference encyclopedia Historical Statistics of the United States:
Colonial Times to 1970, which was compiled by the U.S. Department of Commerce. Also of
importance are disaggregates of the US Individual and Corporate patent counts (Census, 1975).
It was unfortunate that the 1970 compilation of Historical Statistics of the United States was the
last edition officially published by the federal government. However, the compilation of
historical statistics started by Historical Statistics has been continued up to 2007 by a private
corporation in Datapedia of the United States: American History in Numbers (Kurian, 2004).
Returning to the data, initially the time period sought for this study was throughout 1900 to
2010. However, the lack of viable patent data before 1920 made calculating INNOCAP difficult.
In the Historical Statistics of the United States compilation, disaggregates of US Individual and
Corporate patents necessary to complete the Shift-Share analysis are only reliable from 1921
onwards (Census, 1975). Therefore, the starting point was moved to 1938. This shift is due to the
calculation of INNOCAP, which requires 17 years of utility patent data as previously mentioned.

19
Innovative capacity itself could be measured through a variety of variables, but is most
commonly measured through the amount of utility patents being filed within a specific region
and time frame (Suarez-Villa, 1993).
Due to the specific process that they undergo to become approved, utility patents are used to
calculate innovative capacity. Other types of patents, such as design or plant patents are
approved differently from utility patents. Design patents primarily consist of trademarks and
cosmetic designs. Although they do involve creative thought, their impact on innovative
potential (what INNOCAP attempts to measure) is debatable. Plant patents, although it could be
argued that they are conceptually similar to utility patents for biological constructs, are too few
to be of statistical significance. This conclusion regarding plant patents is based on preliminary
analysis of the INNOCAP and patent data acquired from the USPTO. The benchmarks for
INNOCAP include controls for design and plant patents in the calculations for isolating utility
patents. For data that does not have utility patents disaggregated from design and plant patents
on the state level, the national percentage of design patents is used to isolate design patents from
aggregate totals. To reiterate, plant patents are not subjected to control due to their statistical
non-significance (extremely low presence/numbers).
INNOCAP of 1986 to 2010 will also be calculated using dynamic Shift-Share analysis. This
time period was chosen to account for the respective individual and corporate innovative
capacity of California and New York. Early state-level patent data has been limited in scope, due
to the absence of official documentation on aggregate counts of patent data. It has been
significantly more difficult to disaggregate individual and corporate utility patents from
individual states, than disaggregating individual and corporate utility patents on a national level.
With the national comparison, viable state-level disaggregates of individual and corporate utility

20
patent data are only confidently available from 1969 onwards. Including the 17 year period that
is required to calculate innovative capacity, results in 1986 are the earliest point that individual
or corporate innovative capacity on the state-level can be confidently employed in the Shift-
Share analysis. To reiterate, the variables being measured will be Innovative Capacity
(INNOCAP), Share, Invention Mix (IM), and Competitive Advantage (CADVT).
Theoretical Framework
In utilizing the comparison between the two geographic areas, modal categories and the
nation in general, the association between innovative capacity and competitiveness may be
strengthened. Fiscal measures and economic vitality enables the growth of innovative capacity.
From The Theory of Economic Development by Joseph Schumpeter (1983), the primary source
of individual innovation is the entrepreneur. Innovative activity that produces the utility
patenting required to conceptualize INNOCAP is caused by the entrepreneur, a person who
provides the springboard for either new inventions or new innovations. An entrepreneur, either
by themself or in the employ of a corporation, creates innovative capacity by either investing or
directly introducing a new commodity (Schumpeter, 1983). Such commodities can either be a
physical product, such as a tablet computer, or an idea about a process, such as a method to
create extremely thin and durable glass. However, such innovative capacity can be reproduced or
plagiarized, which may dilute the willingness of entrepreneurs (or the corporations investing in
them) to innovate (Schumpeter, 1983). Such examples of industrial piracy and espionage have

21
implanted themselves within the public image of globalization, like that of Apple Inc. and its
constant struggle against patent and copyright infringement in Asia. For the purposes of this
project, the entrepreneur as defined by Schumpeter will be the exemplar of Individual innovative
capacity.
The United States and most other developed nations ensure an entrepreneur that his or her
work will receive the proper credit by either using a patent or trademark (Suarez-Villa, 2000). It
is this intellectual property instrument that also serves as a benefit for those seeking to analyze
such innovative capacity. A similar method of categorizing innovative capacity is employed by
Suarez-Villa in his book The Rise of Technocapitalism. Here, patent data is employed to measure
innovative capacity and how such variables had changed overtime. In that analysis, Suarez-Villa
noted a significant shift of innovative capacity from the Northeast of the United States to the
Sunbelt (Suarez-Villa, 2000). In particular, New York and California are considered the largest
economies within their respective regions (Suarez-Villa, 2000).
Potential Validity Contentions
Although the data is collected from patent tallies, implications of temporal validity may
emerge due to the gradual improvement of such data due to technological replacement and
upgrades to the theories involved in recording. The 2012 U.S Patent and Trademark Office is
quite distinct from what the agency was fifty years ago, in terms of accessibility and format.
Thankfully, much of the required patent data is online, but some errors may have occurred
during the transfer from paper files to electronic ones. As we discovered, some patent data may
not be included in the online databases due to their age. There is just not enough time or

22
resources to go through all of the patent data to verify or repair these minor errors. This was one
rationale behind why Shift-Share analysis was selected over other geographical measures
(Suarez-Villa, 2000). As the data required for INNOCAP is primarily aggregate patent counts,
the impact of any specific patent (given random error) should be insignificant for this particular
study.4
For the primary source of innovative capacity, the data has already been acquired by
Professor Suarez-Villa, who I am greatly indebted for providing such valuable assistance. These
were acquired throughout the 1990s from many of the same resources mentioned previously, and
have been substantiated through repeated publishing and research (Suarez-Villa, 1990). I have
also employed Suarez-Villa’s formulas to update the list of INNOCAP in the United States, New
York and California, to 2010 by utilizing the patent counts obtained from the USPTO and
external statistical sources (Kurian, 2004). The USPTO has been generous in providing and
locating relevant patent information because we have assisted them with the acquisition of pre-
1950 data. When INNOCAP is compared with the innovative potential of a geographical area
like a state, there may exist some abstractions in sectors that are unaffected by INNOCAP, such
as the financial services sector. Nevertheless, if net national income could be compared with
INNOCAP, then there may be correlations within sectors that do not employ innovative human
inputs (Suarez-Villa, 1990). Those sectors may not contribute directly to innovative potential,
but do benefit from any positive externalities created by those inputs.
For the validity of the content, INNOCAP employs patent data to track innovative behavior
in an economy due to the concept that patents are a traceable indicator of entrepreneurial activity
(Suarez-Villa, 2000). Patenting allows for the entrepreneur or their employers to take
4
However, future investigation of the impact of a specific patent on a particular geography’s cumulative innovative
capacity may prove interesting.

23
responsibility and benefit from their actions (Schumpeter, 1983). The patented invention or
innovation cements the entrepreneur into the capitalist process as a facilitator of new capital.
Their idea is what allows the specific market flows for that commodity to exist and influence the
market. Without the protection of patent laws, what fiscal incentive would exist for an
entrepreneur to invest? A potential implication inherent in an indicator like INNOCAP is
whether the utility patents that make up the indicator are a valid foundation to represent
innovative potential. INNOCAP attempts to avoid a post-hoc fallacy by underlining its
relationship to utility patents approved by the USPTO’s application process. The requirements
for approval of utility patents are strict. Of the many applications that are filed, only a few are
selected after considerable investment and investigation by both the inventor and the USPTO
(Suarez-Villa, 2000). In piggybacking off the USPTO’s approval process, INNOCAP maintains
its functional validity. This is the reason INNOCAP controls for Design and Plant patents, which
have a more lenient approval process in comparison with utility patents.
Within the data on innovative capacity, potential problems may develop from the method
employed to control for design patents. Unlike plant patents, design patents accounted for at
most 10% of the total patents approved by the USPTO per year during preliminary conversion of
patents to INNOCAP (USPTO). This is especially true on the state level, as the USPTO does
possess online disaggregates of US national data. To separate design patents from utility patents
on the state level, the national percentage of design patents for that year is multiplied by the total
state aggregate for patents. This results in an estimation of the number of design patents for the
years before utility and design was separated on the state-level. Although this result is at best an
estimate, there are few other ways to accurately discern specific state-level patent data.
Acquiring accurate data on the US national level pre-1950 was difficult. Even then some parts of

24
the US data were incomplete such as for disaggregated US Individual and Corporate patents
from 1900 to 1920 (Kurian, 2004).
In addition to the quantitative dynamic Shift-Share analysis and the data produced from it,
historical archival data should strengthen the quantitative foundation with qualitative supports.
The data would provide a foundation that will account and deal with interference from external
factors (i.e. errors resulting from quantitative variations or historical shifts) affecting the direct
data created from the dynamic Shift-Share analysis. When considering factors like innovative
capacity, the structure of the larger market inevitability plays an integral role in the analysis of
the components of Shift-Share. A proper analysis of Shift-Share analysis requires that the reader
considers the myriad forms of economic activity under which capitalism operates, from
circulation of commodities to their production and the application of that knowledge to
geography. These forms are identified by two of Shift-Share three two components. First, IM
includes not only the mix of inventions being developed, but also the industries that are
developing them. Second, CADVT is the regional advantage offered by a specific geography.
Further questions may arise due to the retooling of a methodology not originally intended
for use with INNOCAP. A potential counter fact is that Shift-Share analysis is not an accurate
method to make comparisons between New York and California, this is somewhat true. Whether
or not Shift-Share analysis was designed for usage with INNOCAP, its primary purpose was to
facilitate geographical comparisons between localities and economic entities. Although Shift-
Share is not a precise comparison between two precise geographies, the results of the Shift-Share
could be placed in contrast with not only the two target states, but other states as well. In regards
to the non-spatial MODAL indicators, it could be argued that they are incompatible with Shift-
Share. Although they do not employ a geographical location for its Shift-Share analysis, they still

25
have potential for comparisons between forms and origins of innovation. Forms as in
INNOCAP for individual or corporate, and origins as in the literal physical units of individual
entrepreneurs or corporate research divisions. As an indicator of innovative potential, INNOCAP
may be able to extend the capacity of Shift-Share methodology to a comparison between industry
and economic models. Another counterfactual is the potential that INNOCAP and its usage of
utility patents maybe irrelevant. Through studies conducted by Suarez-Villa throughout the
1990s and early 2000s, utility patent data employed by INNOCAP has been shown to have a
high relationship with factors that are commonly associated with improving human capital, such
as public educational infrastructure spending (Suarez-Villa, 1996; Suarez-Villa, 1997)
Results and Discussion of Interval Data
Over the course of the study, a wide range of data was compiled at differing scales. The
project eventually focused on the state modal scale, where the respective state (California and
New York) INNOCAP interacts with the National US INNOCAP. To complete the Shift-Share
analysis, a third component was required. Originally, the third component was national
employment or output, whereas the other two components were identified as the targeted
benchmark region and the total national employment or output in all industries. For this study,
the target benchmark region was identified as the respective states and the total national
INNOCAP was the total national employment/output in all industries; the national
employment/output and the national disaggregates of the total individual and corporate patenting
within the US.

26
The reasoning behind this state modal choice was to offer an interesting perspective at
not only the shift between the respective states in regards to INNOCAP, but also the shift
between methods of entrepreneurial-ship from individuals to corporations as documented by
Suarez-Villa, Bluestone and Goldsmith (Bluestone & Harrison, 1982). For the full annual Shift-
Share5
, the dynamic annual compilation runs from the years 1938 to 2010. However, for the
abridged dynamic interval versions displayed in the main body of this report, the Shift-Share
tables use five year intervals. Starting from 1945 to 2010, these five year intervals helped
analyze the geographical shift between California and New York. The dynamic interval charts
allow for comparisons between the starting and end year of each 5 year period, and account for
policy and market impacts enacted or that occurred during the starting year. The dynamic
interval model can over or underestimate the shift in INNOCAP, due to sampling errors derived
from having intervals of multiple years. The dynamic annual chart offers a more specific year to
year comparison. Unlike the interval table, this is more than flexible enough to adjust for change
between the years due to a variety of effects, such as recessions and booms.
Additionally once the dynamic interval and annual charts have been analyzed, brief
considerations will be given to surrounding states and other regions. These considerations are
represented by five maps that show the continental United States and the influence of CADVT
over-time. Although useful, these maps are ultimately restricted by their rather recent time-span
of twenty-five years. In addition, they suffer from a malady typical of conventional shift-share
methods, in that exceptional years or periods may be excluded (Barff & Knight III, Spring 1988).
Nevertheless, that time-span was sufficient to offer a glimpse at the impact of INNOCAP, and
the factors which make up it, on respective states besides the primary targets of California and
5
Appendices 2.0 pg. 52

27
New York. To further simplify the comparisons, the maps only identify CADVT, the component
most attached to a specific geography.
Dynamic Interval Shift-Share Analysis (Tables 2.1, 2.2, 2.3, and 2.4)
According to Table 2.16
and 2.27
, regarding Individual and Corporate INNOCAP for
California, starting from 1945 to 1970, Share has been found to be below the target year
California INNOCAP. This is an ideal relationship, as it indicates that California’s INNOCAP
was at least above the national rate. This expansive period of high INNOCAP also highlights the
effects created by the end of World War II, and the start of the Cold War. Starting around the late
1950s, the Space Race also contributed to inspiring a new generation of innovators. However,
around 1970 to 1975 California’s INNOCAP was lower than Share, which meant that the state
was now performing below the rate of the nation. A plausible explanation for this decline points
towards the 1973 Arab Oil Embargo enacted by OPEC; the embargo was retaliation against the
United States and its allies for involving themselves in the Middle East. Despite being only a
few months, the embargo exacerbated an already weakened economic system, and many have
linked it with the outbreak of the 1970s recession. From around 1975 to 1995, California’s
INNOCAP was less than Share, and this was kept low partly due to the aforementioned events.
Near the end of the 1975 to 1995 period from 2000 to 2010, Share once more became less
than California’s INNOCAP. Around this time, the dotcom boom (~1995 – 2000) amplified the
desires of the regions for technological entrepreneurial development. In all four tables produced
from this study, the intervals around 2000 are the years in which CADVT is positive throughout.
6
7

28
Despite the dotcom bubble bursts around the early 2000s, California’s INNOCAP continued at
levels above Share at the same rate. Despite the late-2000 recession, California possesses the
highest levels of INNOCAP when compared to any other individual state.
For the New York Individual and Corporate Share, unlike California with its three
differing time periods, the only period of shift in Share occurred after the year 1950. Considering
Table 2.38
and 2.49
before 1950, Share was less than New York’s INNOCAP, with heavy
investments in industrial capacity due to wartime efforts. Afterwards, Share was greater than the
target benchmark from 1955 to 2010. New York’s INNOCAP most likely suffered in the
aftermath of World War II, especially as the Sunbelt states became more dominant. In addition,
industry in New York became increasingly service-based around fiscal and managerial
"commodities," as represented by the declining IM throughout. As New York’s INNOCAP was
already in decline by the time of the oil embargo and the 1970s recession, the majority of change
that occurred has been in the other components for New York.
If you compare California and New York’s Individual and Corporate IM, although their
numerical value may be different, both are correlated with their respective opponent in the
dynamic interval charts displayed in (Tables 2.1, 2.2, 2.3, and 2.4). California’s Individual IM
follows similar patterns to New York’s Individual IM, as California’s Corporate IM is similar to
New York’s Corporate IM. Although they differ numerically, this tendency highlights the effects
of national and global issues within respective states and how much impact those issues may
have on the INNOCAP of that region.
For both California and New York Individual IM, the components produced have
repeatedly come up negative. From the starting interval of 1945 to 2010, Individual IM is
8
9

29
negative. This is expected given an increasing orientation towards corporatism, or corporate
centric control of research and its commodification (Suarez-Villa, 2009). In contrast to
corporations, many individual entrepreneurs lack resources, expertise, and have greater difficulty
obtaining government support. Despite California’s reputation as an innovative and
entrepreneurial-friendly region exemplified by California Individual CADVT being consistently
positive, many individuals there have found it easier to work under the cover of a corporate
entity. New York’s CADVT was far more varied overtime; however, a negative tendency
prevailed throughout. During the intervals of 1955 to 1970, and 1985 to 1995, both IM and
CADVT for New York Individual IM components expressed negative values. Unlike California,
New York and its government may have chosen to focus on fiscal and managerial markets rather
than commodity development, as seen by the dominance of fiscal services. The interval around
1955 to 1960 could be seen as an after-effect of World War Two. For the 1970 interval, the
negative instability within the data may be a precursor to the 1970 recession and the oil embargo,
and for the interval period from 1985 to 1995, the after-effects of Black Monday, a stock market
crash. A market crash may have had more impact in New York than in California due to the
former integration into the world financial markets.
Corporate IM for both California and New York (Table 2.2 and 2.4) was found to be
consistently more varied than Individual IM. In both Shift-Share data sets, throughout the
intervals around 1945 to 1955, IM was positive. This heightened amount of IM was most likely
an externality of World War II, as previously mentioned. Between the intervals of 1960 to 1965,
IM became negative, an effect created by a corporate tendency to focus on vertical integration
and specialization. Once the positive IM of the immediate post-World War II years has allowed
for corporations to uncover the ideal product or commodity to specialize upon to maximize

30
profits, firms will tend to focus on that commodity and its production sphere. As noted by Jane
Jacobs, large firms tend to specialize in a particular commodity (Jacobs, 1969). Although a firm
may absorb or merge with another firm with a whole different interest, the primary commodity
of the main firm in the integration will take precedence. Thus once a firm has found a
specialization, they tend to become risk-averse to other potential commodities (Jacobs, 1969).
In the intervals of 1970 to 1980, Corporate IM once more becomes positive, then
negative, and back to positive. This chaotic interlude was most likely an accumulation of market
and industrial instability as firms attempted to rebalance themselves fiscally around 1970, an
outlier of the 1973 oil embargo mentioned previously which occurred during the 1975 interval.
As well as all the other external and internal conditions which all lead to the recession of the
1970s. The positive IM component for Corporate IM in 1980 can be seen as an attempt by
corporations to discover new commodities to re-specialize in. From 1985 to the interval around
2000, Corporate IM maintained a negative component. In the aftermath of the chaos in the
preceding period, it is likely that firms found their specialization or gave up actual commodity
production altogether.
The latter case is of giving up the commodity production (or transition) especially
documented as the American economy changed from a formerly manufacturing economy to a
more service oriented economy (Suarez-Villa, 2000). In the final intervals of 2005 and 2010,
Corporate IM once more became positive. A trend appears that points towards a tendency for
Corporate IM to generally increase near or after an economic crisis. For the 2005 interval, the
dotcom burst and resulting market crash; and the late-2000 recession caused the increase in the
2010 interval.

31
As previously mentioned, California Individual CADVT has been quite simple; starting
with the 1945 interval to 2010, California Individual CADVT has been consistently positive.
California, despite the recent recession and various budget crises, remains one of the most
innovative states in the US. If an Individual should choose to become an entrepreneur in
California, they will find a variety of local advantages to support their risk-taking, from
acclaimed institutions of higher education to a technological legacy exemplified by Silicon
Valley. In comparison, New York Individual CADVT is much more varied. This is expected
given New York’s reorientation toward service based industries post-WWII. From 1945 to 1950,
New York Individual CADVT was positive, a probable effect of the federal investment into one
of the primary gateways for shipping to a war-torn Europe.
During the 5 year interval of both 1955 and 1960, CADVT became negative, perhaps
signaling the shift in focus towards New York becoming a center of finance. Around 1965,
Individual CADVT once more became positive during the Vietnam War, then relapsed into a
negative component for the 1970 interval, just before the economic crisis of the 1970s. From
1975 to 1980, New York Individual CADVT became positive. This was not coincidental as
mentioned earlier, but a result of the 1970s recession that led to governments enacting measures
finding new avenues of economic activity to replace those that floundered. In the period of five
year intervals from 1985 to 1995, New York Individual CADVT oriented towards a negative
component. This is in consideration of the aftermath of the economic crisis during the 1970s, and
in comparison with California’s constant positive CADVT component around this time span,
perhaps this is an indicator of the regional inversion effect on the individual level. However
during the intervals around 2000 to 2010, Individual CADVT for New York became positive

32
once more, starting around the time of the dotcom boom until the late-2000 recession around the
time of this study.
California Corporate CADVT differs from the universally positive orientation of
California Individual CADVT. However, the consistently negative Corporate New York
CADVT can be contrasted with the California Individual CADVT as being the exact opposite,
with a small exception during the 2000 interval when the dotcom boom occurred. The mostly
negative component of New York Corporate CADVT hints towards the states and firms’ within
the state focus on service industries. In contrast, although both components benefit from
California’s entrepreneurial local tendencies, Corporate Shift-Share component-based entities are
more exposed to external macro factors than individuals despite their superior resources.
During the 1945 to 1950 intervals, Corporate California CADVT was negative. A
possible implication of this orientation was that much capacity was still focused in the Northeast
and the supply routes to a devastated Europe. From the 1955 interval onward, California’s
reputation as an innovative region held till the crisis decade of the 1970s. Beginning in the 1970
interval until around 1985, the Corporate California CADVT component was negative. While the
Northeast was hit hard by the recession, the auto-centric West was also battered by the crisis and
the 1973 Arab Oil Embargo. Nevertheless, once 1995 was reached, California Corporate
CADVT became consistent with California’s Individual CADVT.
Annual Shift-Share Analysis (Share, IM, and CADVT)10
Consistent with the previous dynamic interval results (Tables 2.1, 2.2., 2.3, and 2.4), both
states’ Individual and Corporate Share exhibited similar temporal ranges and only some minor
10

33
differences in the dynamic annual charts from the interval Shift-Share tables. Even though some
of these effects have already been reported in the dynamic table, more specific year to year
comparisons and unique instances will be the focus of this section of the Shift-Share analysis.
This is because of its size and complexity. Also, the majority of the data in the annual analysis
are shown to reiterate more specific application of the dynamic interval tables (Tables 2.1, 2.2,
2.3, and 2.4).
For California Individual and Corporate Share from 1938 to 1943, Share was less than
the SUM. However, in the year 1944, Share became greater than the SUM; Share then reverted
to less than SUM from 1945 until 1971. In 1972, Share became greater than SUM, and became
less than SUM in 1973. This small fluctuation occurred around the time of the 1973 Arab Oil
Embargo. From 1974 to 1993, California Share was greater than the SUM; finally, after 1994,
Share reverted to less than the SUM. On the other hand, for New York Individual and Corporate
Share, Share is identical to the static interval chart. The only period of shift in Share occurred
following the year 1950. Before 1950, Share was less than the target benchmark; afterwards,
from 1955 to 2010, Share was greater than the target benchmark, which may in part be due to the
Cold War. When considering interval charts, this shift exemplifies the effect World War II and
its aftermath on the Northeastern coastal state as one of the export points for a rebuilding Europe.
Similar to the interval charts, results between the respective modal categories of
Individual and Corporate IM are identical. For California and New York Individual IM, much of
the time period maintains a constant negative component, which is similar to the interval version
and is consistent with the Individual favoritism towards non-risky second-mover research.
Second-mover research means the innovation or improvement of existing commodities, in
contrast to pure invention first-mover research (Suarez-Villa, 2000). However, when considering

34
the annual table, some deviations from the constant negative IM returns exist during the early
years. From 1938 to 1940 pre-World War II, Individual IM was positive as the country recovered
from the Great Depression. From 1941 through World War II to 1953, IM became negative as
potential innovators were drafted or redirected their creativity towards wartime purposes. From
1954 and 1955, IM once more became positive, which may have been due to post-negative
World War II G.I. bill. From 1956 onward, Individual IM maintained the same negative
component as the interval Shift-Share table, signifying a focus towards adding or improving
existing commodities.
For the earlier studied years from 1938 to 1949, California and New York Corporate IM
displayed positive input. This is most likely a result of wartime industrialization, as American
corporations retooled their facilities for war and shifted resources towards research and
development. From 1950 to 1953, IM became negative, as corporations cut back on wartime
production to focus on a particular product. During the two year period of 1954 to 1955, IM once
more became positive, then shifted to a negative value during the second period from 1956 to
1957. During 1958, IM became positive, after which the IM switched between negative and
positive for the next two years (1959 and 1960), exemplifying a time period of constant
fluctuations in the market. Throughout the year 1961 to 1962, the IM became negative, and then
became positive from 1963 to 1964. Invention Mix returned to a negative component throughout
1965 through 1966. In 1967 to 1970, the IM became positive at the beginning of the crisis of the
1970s, and then became negative during the period between 1971 and 1973, the latter year in
which the 1973 Arab Oil Embargo was in full swing.
Finally, from 1974 to 1977, the IM became positive (note: 1975 is the change point in the
interval Shift-Share charts of Share from less to greater than SUM.) From 1978 to 1979, when

35
many industries attempted to adapt to an increasingly depressed market by diversification, the
IM alternated between negative to positive. However, from 1980 to 1982, IM once more became
negative. In 1983, IM became positive; then from 1984 to 1999, IM became negative around the
time of the first Gulf War. Invention Mix became positive in 2000 around the time Microsoft
was found guilty of an anti-trust suit. On the other hand, in 2001, IM became negative, with 9/11
as a possible catalyst; from 2002 to 2009, IM reverted to a positive value as firms sought out new
products to deter fiscal ruin due to the late-2000 recession. Finally, in 2010, Corporate IM for
California and New York displayed a negative value, perhaps hinting towards specialization of
new products in the aftermath of the late-2000 recession.
Consistent with CADVT in the Shift-Share Interval model, for California and New York
Individual and Corporate CADVT, all charts display unique values. This is significant as
CADVT is the primary component used to note and contrast regional inversion between states,
as it represents localized advantages that promote or sustain innovation such as government
support or locational advantages in labor, supply, and/or demand.
In the California Individual CADVT from 1938 to 1940, the component expressed
negative responses. However, between the years 1941 to 1982, CADVT became consistently
positive, buffered by Cold War spending. Only in the years 1983 to 1984 did CADVT become
negative; this is perhaps due to the residual effects of the decline of the Northeast. After 1984
CADVT reverted to a positive input until 2010. For California Corporate CADVT from 1938 to
1948, CADVT was negative, as industries converted their focus towards wartime pursuits.
Between the years 1949 to 1966, CADVT responded positively, fueled by the post-war boom.
From 1967 to 1970, CADVT became negative, then positive from 1971 to 1973 during the 1973
Arab Oil Embargo as the state openly sought and encouraged alternative energy research. From

36
1974 to 1980, Individual New York CADVT became negative; it then became positive during
the years 1981 to 1982, and finally became negative for a similarly sized period between the
years 1983 to 1984. After 1985, CADVT became consistently positive until 2010.
New York Individual CADVT has a brief period between the years 1938 to 1940 in
which CADVT is negative as a result of wartime efforts. From 1941 to 1952, CADVT became
positive for a period of 12 years post-World War II. Around 1953 to the year 1964 (with 1956
and 1962 as a positive exception), CADVT became negative. The years 1965 to 1966 have a
positive CADVT response, and from 1967 to 1969, CADVT is negative, the latter part hinting
towards the future instability of the region. Throughout the time period between the years 1970
to 1982 (with an exception of the years 1975, 1977, and 1980 when CADVT is negative),
CADVT is positive. These exceptions hint towards the instability affecting the Northeast during
the intervals involved, and are exemplified by the mergers and corporate raiders made notorious
during this time. From 1983 to 1993, CADVT once more became negative as an economic crisis
hit the Steel Belt; finally, between the years of 1994 to 2010 (with 2003 and 2005 as exceptions
because it was negative), New York Individual CADVT displayed positive inputs as states
implemented policies that encouraged entrepreneurship to lessen the impact of the late-2000
recession.
New York Corporate CADVT maintains negative inputs from 1938 to 1949, after which
CADVT becomes positive for a brief two year period (1950 to 1951) which coincides with
World War II. From 1952 until 1993, CADVT becomes consistently negative, a legacy of the
vertical integration method of industrial production common to the area. However, from 1993 to
1998 (with a negative value in 1995 as an exception), CADVT displays a positive values as both

37
firms and the state respond to an industrial decline. From 1999 to 2010, CADVT reverts to
negative values due to contributing impacts of 9/11 and the late-2000 recession.
For California Individual, IM and CADVT are only similar in a few instances.
Specifically, between the years of 1954 to 1955, both IM and CADVT are positive, bolstered by
wartime development and former soldiers supported by the GI bill. The only other instance of
similarity is between the years 1983 to 1984, where both IM and CADVT are negative.
However, from 1983 to 1984 there is concern throughout these charts, as an explanation for this
negativity is unclear. Market trends during this time-span have been relatively stable, and few
major events may be connected with this brief time-period.
For California Corporate, more instances of similarity exist than in California Individual.
The years 1949, 1954 to 1955, 1958, 1960, 1963 to 1964, 2000, 2002 to 2007 and 2009 are the
years that possess both positive values for IM and CADVT for California Corporate. Also, the
years 1978, 1980 and 1984 have both negative components for IM and CADVT. For New York
Individual IM and CADVT, there are no instances where both IM and CADVT are positive.
However, the years of 1953, 1957 to 1961, 1963 to 1964, 1967 to 1969, 1975, 1977, 1980, 1983
to 1993, 2003, and 2005 have both negative values for IM and CADVT. This predominance of
negative correlations indicates a relatively hostile environment for individual entrepreneurs in
New York. This is a tendency only highlighted once the observer considers the increasing scale
from which monetary services are taking place in New York with contrast to production of
tangible commodities in the past. Similar to New York Individual, New York Corporate IM and
CADVT do not have any instances in which IM and CADVT are positive. For instances in which
IM and CADVT are negative, the years 1952 to 1953, 1956 to 1957, 1959, 1961 to 1962, 1965 to
1966, 1971 to 1973, 1978, 1980 to 1982, a span of 7 years between 1985 to 1992, 1995, 1999,

38
2001, and in 2010 have the same negative trend with a reemphasis on monetary services as a
contributor.
Other State Data and Spatial Comparisons
To reiterate, the initial analysis did not evaluate other states due to the primary objective
of a thorough analysis of California and New York’s INNOCAP from 1938 to 2010. In addition,
the other states that were utilized in this GIS project lack specific utility patent data before 1963.
This utility patent data was required to accurately compile INNOCAP as a valid quantitative
indicator. As such, the shift-share analysis for this GIS project was composed of more recent
data (i.e. 1985 to 2010) than the primary project (i.e. 1938 to 2010). This secondary project is
based off data placed onto national scale GIS maps and utilizes five-year intervals to distinguish
between the five intervals, whereas the primary project utilizes annual data. On display are the
Shift-Effect (difference between Share and the target year INNOCAP) and CADVT (local
advantages)11
.
Shift-Effect and CADVT Map 199012
: The first map involves the five year interval from
1985 to 1990. The exemptions displayed on the Shift-Effect and the CADVT maps are identified
as the primary targets of the initial analysis, California in the West, and New York in the
Northeast. For the Shift-Effect map; throughout much of the South and West, for both Individual
and Corporate, the general trend was an inward shift. In contrast, the Northeast and the Midwest
were shifting outward. Interestingly, Individual Shift-Effect was inward around Maine in the
Northeast, a stark contrast with the rest of the region. On both maps, especially around the
11
Appendices 4.0 Maps pg. 55 and 64
12
Appendices 4.0 Maps pg. 55 and 60

39
central plains area of the Midwest and the West’s border, CADVT stayed in the moderate range
for both Individual and Corporate. Given this area’s orientation towards agricultural and resource
extraction industries, the results were consistent with the expectations.
Regarding Individual CADVT, much of the results are located in the eastern half of the
United States. For Corporate CADVT, local advantages are much more spread out, but located
within predictable locations. For instance, Texas and Florida in the Sunbelt (states below the
frost-line) are known today for their relatively “healthy” economies and their access to
innovative and demanding markets. The lowest CADVT point at 0.7 (Individual) or -0.7
(Corporate) was Delaware13
. The highest CADVT point, excluding California and New York,
was in Michigan, at 106 to –106 CADVT. As the home to America’s automobile industry, it was
expected that Michigan would maintain a positive Individual CADVT, through federal and local
government policies enacted to stem the post-1970 decline in manufacturing. Localized policies,
such as worker training and re-education benefits, have been distributed mainly to individuals.
Corporate entities may have long since left for such policies to have any effect.
: For the 1995 interval, much of the Shift-Effect and
similar to the 1990 interval, for the 1995 interval, CADVT remains around the same region. The
Shift-Effect for both Individual and Corporate now went inward for the area around Maine in the
Northeast. However, the Individual Shift-Effect reverted outward in many states in most regions,
including the Sunbelt and Northwest. On the other hand, Corporate Shift-Effect in the other
states grew inward. Concentration was still maintained around the Northeastern region of the
United States for Individual CADVT, whereas Michigan has declined slightly for Individual
13
The numerical orientation of CADVT and IM represents the contribution that the component plays in the Shift-
Effect of a particular state, what determines the overall direction of the shift is whether or not the component is
positive or negative.
14
Appendices 4.0 CADVT Maps pg. 56 and 61

40
CADVT. 1995 Corporate CADVT has maintained its 1990 values, with an exception of Ohio,
which completely reversed to a higher Individual CADVT value. Also, Oregon experienced a
slight decline in Corporate CADVT. Within this interval cycle, the highest CADVT point is
located in Florida, at 185 or -185 CADVT.
: For the interval around 2000, Individual CADVT
became much more prevalent around the central part of the United States, although notably, it
increased in Louisana and around Pennsylvania. In contrast, the Shift-Effect for Individual
turned further outward for more states throughout the nation. For Corporate CADVT, other than
California and New York, only Texas expressed a large CADVT at 448 or -448. However, the
Corporate Shift-Effect became inward in numerous states across the Midwest. Note that for
Individual CADVT, Competitive Advantage seemingly spreads out into neightboring states.
Recalling the question asked earlier in this study, localized advantages for Individual CADVT
has a tendency to disperse around specific concentrations from interval to interval.
: For the interval around 2005, Individual CADVT
reemerged significantly in the Eastern part of the United States. Nevertheless, the Individual
Shift-Effect reverted outward for all states other than Nevada. In addition, for Corporate
CADVT, Texas and Oregon display a significant value in comparison with other states.
Corporate Shift-Effect shifted inward in more Midwestern states during this time as well. Given
the arrangement of Individual CADVT from earlier interval periods, many of the initial states
with high CADVT may have created conditions that facilitated positive shifts towards
surrounding states. Although this observation falls in line with the initial expectation of a
western flow of CADVT, the data suggests that the local advantages indicated by CADVT
15
16

41
mostly remain localized to a particular region, especially for Individuals. The highest point in
2005 CADVT was 473 or -473 for Texas, whereas the lowest was 0.3 in Alabama.
: Considering the most recent 2010 interval, the
extremes found in 2005 lessened for Individual CADVT over the course of the interval. For the
Individual Shift-Effect, it turned outward for every state, while Corporate Shift-Effect absorbed
more states in an inward shift towards corporate innovation. Florida once more joined the
positive extremes of the Corporate CADVT, whereas the central regions of the United States
experienced more equitable conditions for Individual CADVT. The contrast between the Shift-
Effect and CADVT indicates that despite the positive orientation of Individual CADVT,
Individual Invention Mix has had a drastic negative effect on Individual INNOCAP.
Summary of Interval and Annual Findings
Returning to the primary interval and annual Shift-Share analysis of California and New
York, the years of exceptionally good or bad periods for INNOCAP in both states are of interest
in this study. The exceptionally good periods are shown by years or intervals with an INNOCAP
greater than the Share and both positive IM and CADVT (an inward Shift-Effect). In contrast,
the exceptionally bad periods are indicated by years or intervals with an INNOCAP less than the
Share and both negative IM and CADVT (an outward Shift-Effect).
For California Individual, although the interval table detects no extreme intervals, within
the annual data, some extreme periods appear. For example, in the years 1954 to 1955,
INNOCAP experienced an outgrowth of both commodity mix and local advantages to promote
that mix. The fact that the Soviet Union first tested a nuclear weapon in 1954 hints towards
17

42
increased support for innovation by the federal and local governments in an effort to stay ahead
technologically. In California Corporate, an earlier year (1949) was also exceptional with both
positive components during the announcement of Truman’s Fair Deal. The periods around 1983
to 1984 for California Individual are innovatively restrictive time periods (only 1984 for
Corporate count), although the reason for this lack remains to be determined18
.
The thoroughly positive periods of instances are much more common for California
Corporate. During the span between the years of 1958 to 1964, numerous positive combinations
of IM and CADVT appear in the middle of the Space Race. Additionally, in 1964, racial
segregation was outlawed by the 1964 Civil Rights act. The final burst of positive combinations
occurred from the year 2000 to around 2009, with a few exceptions in 2001 and 2008. Despite
the tech bubble burst during the late 2000s, California Corporate maintained a positive outlook
until the start of the late-2000 recession.
Similarly, with California Individual, California Corporate experienced a brief period of
negative combinations in both IM and CADVT around the year 1980 to 1984; this was found to
be due to the early 1980 recession, which partly resulted from the US Federal Reserve’s attempt
to reduce inflation. Although the interval table for New York Individual possesses a single
positive combination interval, there is no comparable positive year in the annual table19
. For
negative combinations, those instances are among the most common in both New York
Individual and Corporate. As a state, New York is relatively hostile to the factors that are a part
of INNOCAP in comparison with the more technological friendly environment of California.
Although as one of the primary financial nodes of the global market, INNOCAP would have less
18
It was found that around this period, a short recession (1981-1982) occurred due in part to the policies of the
Reagan administration and the Federal Reserve, especially when the latter attempted to deflate the dollar.
19
Appendices 3.0 Pg. 54

43
of an importance within New York, as long as the said financial markets remain stable, as New
York found during the late-2000 recession.
Considering historical events, there are those which exist throughout the study that have
played an important role in orienting INNOCAP for both California and New York; the first of
which is World War II and the start of the Cold War, especially in New York, although
Corporate INNOCAP in California has also been affected. It was only around this period in that
New York INNOCAP was above Share. After 1950 and onward, New York continuously
performed below the rate of the nation. Another series of events which impacted both states
dramatically is the 1970s recession and the Arab Oil Embargo, exemplified by the decline in
California’s INNOCAP compared to the Share in 1973, and the period of both negative IM and
CADVT in Corporate New York. The latter event had less of an impact in New York due to the
mono-centric and public transit friendly layout of its urban structure, while California was more
impacted due to its auto-dependence. Although both Modal CADVT components for California
and the Individual component for New York were recovered, New York Corporate continued on
its negative trajectory until the dotcom boom, and then reverted back to a negative orientation
afterwards.
Of consideration is the dotcom boom of the late 1990s, which encouraged many states to
promote technological innovation, and is exemplified by the universally positive CADVT
component from 1996 to 1998 in the annual table, and 2000 in the interval tables. Despite this
nationwide surge towards technological development, New York’s INNOCAP was unable to
surpass its Share before the bubble burst around the early 2000s. Thus, a stark contrast is drawn
between California and New York. Although New York experienced many of the same events
that encouraged the development of INNOCAP through positive CADVT for New York

44
Individual or positive IM for New York Corporate, after 1950 the state was, unable to surpass the
national rate. In contrast, while California experienced a decline around 1973, California
eventually recovered around the year 1994. Despite the similar macro-level advantages in
INNOCAP for both states, only California was able to take advantage of them in the long term,
despite comparable advantages in educational infrastructure and innovative precedence (1941 to
1950 in CADVT for Individual, and 1938 to 1949 in IM for Corporate).
Summary of Other State CADVT Maps20
A possible flaw with this study is that it is rather broad in nature when compared with
other projects that employ spatial GIS projections. Perhaps in the future, INNOCAP could be
reduced to a level within the state, which would allow for more precise measurements at the
county if not city-level. In addition, although the extension of patent lifespans is relatively
recent, it would be highly advisable for future research to consider necessary alterations in the
data. Annual estimates must also be considered eventually to properly account for all potential
externalities. Interval data is sufficient in comparisons with changes due to market conditions
and policies over-time, but is inadequate when assessing year to year growth and decay.
Recalling the differences between the static and dynamic models of Shift-Share, this
deficiency regarding interval models in favor of annual data also applies to the conventional
shift-share method, as stated by Markusen21
who refers to interval and dynamic methods as a
possible solution. Initial expectations are oriented towards transition over-time from the
20
Appendices 4.0 CADVT Maps pg. 55
21
Markusen, Ann, Helzi Noponen, and Karl Driessen. 1991. International trade, productivity, and
U.S. regional job growth: A shift-share interpretation. International Regional Science
Review 14 (1): 15-39.

45
Northeast to the South and West along states in CADVT. However, this smooth flow did not
occur; rather, CADVT bypassed entire regions to land at a specific dominant area or state. In the
specific regions from which CADVT experienced extreme positive or negative values, those
areas tend to have more effect on neighboring states.
The CADVT Components are reflective of their counterparts. A region/state friendly to
Individual CADVT or local advantages favoring innovation is not as efficient towards
comparable Corporate CADVT. Although it is possible that the selected state was not hostile
toward the inverse of its dominant Modal category, current factors within the state may simply
favor the dominant Modal component. The current trend of geographic distributions primarily
focuses on Individual CADVT around the Midwest. The recent late-2000s recession may have
played an important role in encouraging this trend, through the self-defensive tendencies of state
and local governments. Although Corporate CADVT and the innovative entities that utilize those
advantages may not be as affected by local policies, Individual CADVT and those small-scale
entrepreneurs that live within the target area may benefit more extensively than a corporate
macro-scale entity.
Policymakers need to consider how their policies affect the local advantages of their
constituencies. Sufficient competitive advantage in innovation is integral for a state to remain
competitive in the global market. Based on the results of this study, local policies have a higher
chance of affecting Individual CADVT than Corporate. Nevertheless, of the two Modal
measures, those states with a focus on Corporate CADVT and innovation may be at an advantage
despite the difficulty in initially acquiring such a concentration. This perspective towards the
stability of corporate innovation is highlighted by the relative stability of states, which is

46
exemplified by positive Corporate CADVT, such as in Texas. Within the past 25 years,
INNOCAP has remained relatively stable geographically.
Expectations of a southern and western flow of innovative potential at the expense of
northern states did not materialize. In regards to the Shift-Effect, one could argue that an
opposite Eastward effect is shifting towards corporate forms of innovation. Perhaps the predicted
shift had already occurred prior to the time intervals involved, given that the time period from
which the study began was well within the aftermath of the 1970s crises. During the recent late-
2000s recession, a previously mentioned point of interest, there was an increase in Individual
CADVT among the harder hit states. However, due to the outward shift of the Individual Shift-
Effect as time went on, a negative IM would explain the disparities between the Shift-Effect and
CADVT. States with high Corporate CADVT like Texas remained relatively stable throughout
time, being affected primarily by more macro-scale problems and the global market. Spatially,
within the span of 25 years, the United States’ Midwest and Southern regions have shifted the
most, while states with high Corporate CADVT have remained stable throughout the country
with a few exceptions. For the Corporate Shift-Effect, an inward shift of INNOCAP has occurred
in increasingly more states at the expense of the Individual Shift-Effect.
Conclusion – Analysis
INNOCAP was a new term created in 1990, and is a viable indicator of inventive and
innovative potential. It has been used with net national income (some regional), infrastructure
(both Public Educational and aggregate), and population demographics that utilized time-series
analyses. Both the Interval and Annual Dynamic Shift-Share models employed in this research

47
share various similarities with the conventional model of Shift-Share, and in essence, their goals
are the same (Suarez-Villa & Hasnath, 1993). From this analysis, a clearer picture of the
comparison between two states (California and New York) was drawn. In particular, the impact
that the external forces had on the INNOCAP of each region was made clearer. Throughout the
study, a picture emerges that contrasts technologically friendly California with the outward shift
of New York. Although both California and New York have high rates of economic growth, only
California can dependably have its INNOCAP linked to its current condition. Share in New York
has been consistently outward since the 1950s, while, with the exception of the decade in the
aftermath of the 1973 Arab Oil Embargo, California has maintained a inward shift in innovative
capacity.
Taken in its entirety, aspects of INNOCAP, especially CADVT, which is geographically-
fixed, have proven vulnerable to surges in transportation and energy costs. More precisely, this
refers to not only to the 1973 Arab Oil Embargo, but also the Iranian Revolution of 1979. These
blows to the international (and especially the American) markets severely impacted the Share
and IM of California and New York for at least a decade. On the spatial geographic map
comparisons of other states, CADVT has a tendency of spilling into other states over time,
although this effect was most pronounced with Individuals. This is logical given that policies
enacted within one state will affect the others. In this case, neighboring states will take measures
to bring their local advantages in line with the origin state in an effort to remain competitive
within a regional labor market. For example, California maintains a strong impact on Corporate
CADVT for the surrounding states, Nevada and Oregon.
Future extensions of this research should involve the expansion of Shift-Share analysis to
other states for the same temporal period as the pre-1963 portions of the analysis for California

48
and New York. Regarding Shift-Share data on other states excluding California and New York
within this study, only data within the last 25 years is applicable. To maintain compatibility
between time intervals of the primary Shift-Share analysis and the other states, the Other State
component data has been separated into 5-year intervals that can be linked to the dynamic
interval data of the primary analysis for California and New York. Presently, the comparison can
only be dated back to five 5-year intervals, as this is the extent to which the Other State data and
maps have been drawn. In the future, further research and compilation of individual INNOCAP
data of other states from 1938, the same time-span in which the primary analysis had taken place,
will allow a more thorough evaluation of INNOCAP’s effect on the entire nation, especially in
the time intervals during and post-World War II. The possibility also exists for a more local level
analysis employing metro- and micro- politan level data, although precise Modal data may prove
more difficult to acquire at that scale.
This research can be improved by implementing correlational analysis while utilizing
regression models to compare state GDP or GSP (Gross State Product) with INNOCAP.
Although preliminary results of correlation and regression for California and New York have
been processed, it was ultimately decided that they were unnecessary for the purposes of this
particular study. As for the data itself, INNOCAP as a term has been developed relatively
recently (within the past two-decades) in comparison to other more established variables
employed in conventional Shift-Share models. INNOCAP works well with Shift-Share analysis
due to its geographic origins. If INNOCAP can be validly applied to other methodologies, then it
will be possible to offer additional comparisons to strengthen the findings of this study. We
expect that with time and further development, the variable INNOCAP will become more
compatible when applied to multiple methodologies.

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